QUANTIFICATION + CHARACTERIZATION OF
TROUT CREEK RESTORATION EFFECTIVENESS
TECHNICAL DOCUMENT/USER GUIDANCE
FINAL REPORT—JULY 2013
Quantification + Characterization of Trout Creek Restoration Effectiveness and Stream Load Reduction Tool (SLRTv1) Methodology User Guidance and Beta Spreadsheet Tool Prepared by: www.2ndnaturellc.com With technical contributions from: Andrew Simon (Cardno Entrix) This research was supported through a series of grants with the USDA Forest Service Pacific Southwest Research Station and using funds provided by the Bureau of Land Management through the sale of public lands as authorized by the Southern Nevada Public Land Management Act. http://www.fs.fed.us/psw/partnerships/tahoescience/. The views in this report are those of the authors and do not necessary reflect those of the USDA Forest Service Pacific Southwest Research Station or the USDA Bureau of Land Management. Final July 2013
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | i TABLE OF CONTENTS Executive Summary ................................................................................................................................................. ES.1 1 2 Introduction ........................................................................................................................................................ 1.1 1.1 Document structure ...................................................................................................................................... 1.1 1.2 Acknowledgements ..................................................................................................................................... 1.2 Trout Creek – A Case Study ............................................................................................................................... 2.1 2.1 Data collection/analysis methods ................................................................................................................ 2.3 2.1.1 Existing datasets .................................................................................................................................. 2.3 2.1.2 WY10‐WY11 data collection summary ................................................................................................. 2.6 2.2 WY10‐11 snowmelt results .......................................................................................................................... 2.14 2.2.1 Flow frequency analysis .................................................................................................................... 2.14 2.2.2 Channel capacity circa 2010 ............................................................................................................... 2.16 2.2.3 Event hydrology ..................................................................................................................................2.17 2.2.4 Floodplain retention .......................................................................................................................... 2.23 2.2.5 Event FSP loads .................................................................................................................................. 2.27 2.2.6 Key Findings from trout creek research ........................................................................................... 2.32 2.3 3 Trout Creek restoration benefits – Upper Reach ................................................................................. 2.33 Stream Load Reduction Tool (SLRT) Methodology ......................................................................................... 3.1 3.1 SLRT pollutant of concern ........................................................................................................................... 3.2 3.2 Computational approach ......................................................................................................................... 3.2 3.3 Catchment hydrology .............................................................................................................................. 3.5 3.3.1 Hydrology datasets ............................................................................................................................. 3.6 3.3.2 Flow frequency distribution ................................................................................................................ 3.7 3.3.3 Annual probability hydrographs ....................................................................................................... 3.14 3.3.4 Validation ........................................................................................................................................... 3.14 3.3.5 Limitations ......................................................................................................................................... 3.19 3.4 3.4.1 FSP datasets ....................................................................................................................................... 3.19 3.4.2 FSP concentrations ............................................................................................................................ 3.20 3.4.3 FSP loads ............................................................................................................................................ 3.23 3.4.4 Limitations ......................................................................................................................................... 3.25 3.5 Floodplain retention .............................................................................................................................. 3.25 3.5.1 FSP loads delivered to floodplain ..................................................................................................... 3.26 3.5.2 FSP loads retained on floodplain ...................................................................................................... 3.26 3.5.3 Verification using WY11 water quality data ....................................................................................... 3.29 3.5.4 Limitations ......................................................................................................................................... 3.31 3.6 3.6.1 4 Catchment FSP ....................................................................................................................................... 3.19 Channel erosion ...................................................................................................................................... 3.32 Channel erosion processes ................................................................................................................ 3.32 3.6.2 SLRTv1 inputs ..................................................................................................................................... 3.36 3.6.3 SLRT channel erosion approach ....................................................................................................... 3.40 3.6.4 Limitations ......................................................................................................................................... 3.41 SLRTv1 User Guidance ....................................................................................................................................... 4.1 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 ii | July 2013 4.1 SLRTv1_template.xlsx .................................................................................................................................. 4.1 4.2 SLRT input data needs ............................................................................................................................. 4.2 4.2.1 Meta data ............................................................................................................................................. 4.2 4.2.2 Catchment characteristics ................................................................................................................... 4.2 4.2.3 SEZ attributes ....................................................................................................................................... 4.7 4.2.4 BSTEM‐Dynamic outputs ................................................................................................................... 4.13 4.3 Catchment hydrology ............................................................................................................................ 4.22 4.4 Catchment FSP loading .......................................................................................................................... 4.24 4.5 Floodplain retention .............................................................................................................................. 4.24 4.6 Channel erosion ...................................................................................................................................... 4.24 4.7 FSP load reductions .............................................................................................................................. 4.26 4.8 SLRT summary ....................................................................................................................................... 4.26 5 Application of SLRT ........................................................................................................................................... 5.1 5.1 Upper Reach of Trout Creek ........................................................................................................................ 5.1 5.1.1 5.2 5.2.1 5.3 Validation ............................................................................................................................................ 5.8 Bristlecone SEZ ....................................................................................................................................... 5.10 Validation ........................................................................................................................................... 5.17 Basin context of SEZ load reduction estimates .................................................................................... 5.17 6 Limitations and Next Steps ............................................................................................................................... 6.1 7 References ......................................................................................................................................................... 7.1 LIST OF TABLES 1.1 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 Technical advisory committee members Summary of existing data compiled for Trout Creek Available USGS stream data relevant to Trout Creek restored reach Trout Creek data collection summary, WY10‐WY11 Cross section survey data collection summary Passive sampler installation summary for Upper Reach at Trout Creek, WY11 Floodplain passive sampler replicates and measurement precision Annual peak flood flow frequency analysis for TCPT and TCMA Channel capacity estimates based on cross section analysis between 2001 and 2010 Peak instantaneous discharge for the spring snowmelt events Overbank duration and magnitudes for Trout Creek restored reach during WY10 and WY11 snowmelt events Summary of hydrologic metrics during WY10 and WY11 snowmelt events WY11 spring snowmelt floodplain passive sampler water quality results of Trout Creek Upper Reach FSP retention estimates for 10 flood events sampled on the Upper Reach of Trout Creek and Upper Truckee River, WY09‐WY11 WY10 spring snowmelt event‐based FSP load calculations by site for each pre, post and overbank event WY11 spring snowmelt event‐based FSP load calculations by site for each pre, post and overbank event 1.2 2.4 2.6 2.6 2.9 2.12 2.12 2.14 2.16 2.17 2.20 2.20 2.24 2.25 2.29 2.32 Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT Summary of reach scale FSP loading mass balance results for Upper Reach Trout Creek, WY10‐
WY11 Key attributes quantified for the pre and post restoration conditions of the Upper Reach Trout 2.17 Creek 3.1 Hydrology calibration dataset Comparison of observed versus predicted flows for urban and non‐urban catchments using SLRT 3.2 flow frequency analysis methods Comparison of observed versus predicted flows for urban and non‐urban catchments using SLRT 3.3 annual probability hydrograph analysis methods Comparison of 1.5‐year recurrence interval flow to the calculated deviations between observed 3.4 and predicted percentile mean daily flows for urban and non‐urban catchments 3.5 FSP calibration dataset 3.6 Details of frequency distribution of geotechnical parameters Details of frequency distributions for composition and hydraulic resistance of bank materials in 3.7 the Tahoe Basin 3.8 Details of frequency distributions of percent silt/clay in the bank materials by region 4.1 Definitions of SLRT catchment types 4.2 Catchment characteristic inputs for each SLRT catchment type 4.3 Urban catchment condition considerations 4.4 SLRT geomorphic attributes 4.5 Recommended Manning’s n values 4.6 Floodplain condition considerations 4.7 Bank material percent fines by region 4.8 Required BSTEM‐Dynamic model runs for SLRTv1 4.9 Required number of stage to discharge relations for SLRTv1 4.10 Default bank and toe model inputs 4.11 Estimated roof‐reinforcement values for typical Tahoe Basin species 5.1 Summary of methods and data sources for Upper Reach Trout Creek SEZ SLRT inputs 5.2 Summary of methods and data sources for Bristlecone SEZ SLRT inputs 2.16 | iii 2.32 2.34 3.6 3.17 3.18 3.18 3.20 3.37 3.39 3.40 4.2 4.4 4.4 4.8 4.11 4.12 4.13 4.13 4.14 4.16 4.19 5.8 5.10 LIST OF FIGURES 2.1 Restored reach of Trout Creek completed in 2001 2.2 2.2 Hydrology & water quality instrument locations along Trout Creek 2.5 2.3 Location of cross section alignments within Trout Creek restored reach 2.8 2.4 Floodplain passive sampler collection approach 2.11 2.5 Floodplain passive sampler locations deployed for WY11 within Upper Reach Trout Creek 2.13 2.6 Turbidity v. FSP rating curve 2.15 2.7 Upper and Lower Reach Trout Creek – June 22, 2010 2.18 2.8 Event Hydrology Summary for WY10 and WY11 2.19 2.9 Approximate aerial extent of peak water surface elevation for spring snowmelt WY10 at Trout Creek 2.21 2.10 Approximate aerial extent of peak water surface elevation for spring snowmelt WY11 at Trout Creek 2.22 2.11 Retention as a function of channel capacity 2.26 2.12 FSP discharge time series – WY11 2.28 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 iv | 2.13 July 2013 Trout Creek representative cross sections 2.30 3.1 SLRT conceptual model approach 3.4 3.2 Hydrology and meteorology data used to develop SLRT 3.8 3.3 Representative flow frequency distribution histograms 3.9 3.4 Urban: Flow frequency development 3.11 3.5 Non‐urban: Flow frequency development, bin intervals 3.12 3.6 Non‐urban: Flow frequency development, maximum mean daily and bin 50 3.13 3.7 Urban: Percentile annual hydrographs 3.15 3.8 Non‐urban: Percentile annual hydrographs 3.16 3.9 FSP data used to develop SLRT 3.21 3.10 Discharge v. FSP concentration 3.22 3.11 Urban: FSP loading 3.24 3.12 Stage v. discharge relationship as a function of channel capacity 3.28 3.13 Retention as a function of channel capacity and floodplain condition 3.30 3.14 Streambank failure mechanisms 3.34 3.15 Frequency distribution of key channel parameters 3.38 4.1 SLRTv1 user input screenshot 4.3 4.2 Urban and non‐urban regions 4.5 4.3 NDA Screenshot 4.15 4.4 BSTEM input geometry screenshot 4.17 4.5 BSTEM bank material screenshot 4.18 4.6 BSTEM calculation screenshot 4.20 4.7 BSTEM run model screenshot 4.21 4.8 SLRTv1 inflowing hydrology & FSP loading screenshot 4.23 4.9 SLRTv1 floodplain retention screenshot 4.25 4.10 SLRTv1 stream channel erosion screenshot 4.27 4.11 SLRTv1 results summary screenshot 4.28 5.1 Upper Reach Trout Creek location map 5.2 5.2 SLRTv1 inputs and outputs for Upper Reach Trout Creek 5.3 5.3 Bristlecone SEZ location map 5.11 5.4 SLRTv1 inputs and outputs for Bristlecone SEZ 5.12 LIST OF APPENDICES A. Quantification and Characterization of Trout Creek Restoration Effectiveness (2NDNATURE et al., 2010b) B. Trout Creek WY10 Data Collection Summary (2NDNATURE, 2010) Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | v LIST OF ACRONYMS BSTEM Bank Stability and Toe Erosion Model BSTEM‐Dynamic Dynamic version of BSTEM FSP Fine sediment particles (TSS <16µm) NSE Nash‐Sutcliffe efficiency, a normalized statistic that determines the relative magnitude of the residual variance compared to the measure data variance PLRM SEZ Pollutant Load Reduction Model (nhc et al 2009) Stream environment zone LIST OF VARIABLES Variable Definition A SEZ catchment area Ai SEZ catchment impervious area Units sq‐miles; acres acres Ai % % impervious of SEZ catchment area % aob Representative bank angle of outside bends o astr Representative bank angle of straight reaches o Axs Channel cross‐sectional area at channel capacity bn Bin #n of the 50 bins used to create an 18 year flow frequency distribution c’ Effective cohesion ft2 unitless DFPb‐fsp Daily pollutant (FSP) load delivered to the floodplain for each discharge bin interval; calculated as FSPb – FSPb‐cc kg/day DFPfsp Average annual pollutant (FSP) mass delivered to the floodplain MT/yr eob‐p Unit bulk sediment erosion rate per length of the outer meander bend lengths for a given percentile flow year (p) m3/m/yr estr‐p Unit bulk sediment erosion rate per length of the straight reach lengths for a given percentile flow year (p) m3/m/yr FP [FSP] Average floodplain water FSP concentration mg/L FPC Floodplain condition score 1,3,5 FPC Floodplain condition score 1,3,5 Fs Factor of safety %F Percent fines of SEZ bank material [FSP] unitless 0‐1 FSP concentration mg/L FSP CRC Pollutant (FSP) characteristic runoff concentration; defined as an average annual expected runoff concentration from a specific land use type or contributing area. SLRT uses catchment scale CRCs to estimate the daily and annual pollutant loads generated from urban catchments. mg/L FSP(Q) Pollutant (FSP) daily load to discharge rating curve. Used for non‐urban stream 2NDNATURE, LLC | ecosystem science + design equation www.2ndnaturellc.com | 831.426.9119 vi | Variable July 2013 Definition Units catchments. FSP:BS FSPb FSPb‐an Daily pollutant (FSP) load for each discharge bin % kg/day Average annual pollutant (FSP) load for each discharge bin over the 18 years of interest FSPcc Pollutant (FSP) daily loading rate for the channel capacity discharge FSPQ Pollutant (FSP) loading rate as a function of discharge kg/yr MT/day mg/s hob Representative bank height of outside bends m hstr Representative bank height of straight reaches m INfsp Average annual pollutant (FSP) yield from the contributing catchment MT/yr lc Channel length measured along thalweg m lfp Floodplain longitudinal length m lob Contribution of channel length that are bends, measured along thalweg m lstr Contribution of channel length that is straight, measured along thalweg m n Manning’s value, which represents the relative resistance of the channel bed to the flow of water in it unitless Average annual pollutant (FSP) load estimated at downstream extent of SEZ reach MT/yr OUTfsp‐post Average annual pollutant (FSP) load, post‐restoration MT/yr OUTfsp‐pre Average annual pollutant (FSP) load, pre‐restoration MT/yr Mean annual precipitation in/yr OUTfsp P probp Probability of occurrence for a percentile flow, p unitless Q Instantaneous discharge cfs Qb The representative discharge value used to represent the mean daily flow of a specific bin (bn) within an SEZs flow frequency distribution cfs Qbi The incremental bin discharge interval for 49 of the 50 bins used to distribute 18 years of daily mean discharge data into a flow frequency distribution cfs Qb‐n The representative discharge value used to represent the flow of a specific bin (n) within an SEZs flow frequency distribution cfs Qcc Channel capacity discharge cfs Qmax Maximum mean daily discharge measured over the 18 year (WY89‐WY06) time interval cfs Qmd Mean daily flow, derived from USGS datasets (non‐urban) and SWMM post‐
processing of PLRM outputs (urban) cfs Qmd‐p Mean daily flow value for a given percentile (p) using the 18 year time series. cms Qsum Total discharge volume over the 18 year (WY89‐WY06) time interval cfs R Fraction of bulk sediment contained in SEZ bank material that are fines Regional coefficient used in hydrologic estimates of non‐urban catchments unitless Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT Variable Rb‐fsp Definition | vii Units Pollutant (FSP) retention coefficient as a function of discharge to channel capacity ratio. Three rating curves are provided to provide a range of retention efficiencies for floodplains of varying conditions. % RFPb‐fsp Average annual pollutant (FSP) mass retained on the floodplain for each discharge interval kg/yr RFPfsp Average annual pollutant (FSP) mass retained on the floodplain MT/yr Rfsp s Pollutant (FSP) retention coefficient expressing the fraction of the pollutant that is delivered to the floodplain that is retained; expressed as a percentage of the total DFPfsp. Reach slope % unitless SCEbs‐p Average annual bulk sediment mass generated from stream channel erosion for a probability flow year, p MT/yr SCEfsp Average annual pollutant (FSP) mass generated from channel erosion within the reach MT/yr SCEfsp‐p Average annual FSP mass generated from stream channel erosion for a probability flow year, p MT/yr SEZfsp Average annual pollutant (FSP) load reduction as a result of SEZ modifications, calculated as the OUTfsp‐pre‐ OUTfsp‐post tb‐day The average annual number of days a specific discharge interval occurs based on the 18 year hydrologic dataset (WY89‐WY06) days tob The average annual number of days overbank flow occurs based on the 18 year hydrologic dataset (WY89‐WY06) days V Velocity at channel capacity ft/sec Vin Average annual discharge volume WP Channel wetted perimeter at channel capacity  MT/year ac‐ft/yr ft kN/m3 Bulk unit weight of bank sediment Zcc Representative water depth at channel capacity ’ Angle of internal friction o
 Matric suction parameter o

Erodibility coefficient b
2NDNATURE, LLC | ecosystem science + design m cm3/Ns www.2ndnaturellc.com | 831.426.9119 Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | ES.1 EXECUTIVE SUMMARY This report and associated digital Stream Load Reduction Tool (SLRTv1) calculation templates are the final products for two complementary research efforts funded by the USDA Forest Service Pacific Southwest Research Station using funds from South Nevada Public Lands Act (SNPLMA) research grants. The combined research goals were to obtain and leverage SEZ specific data to develop a methodology that estimates the average annual pollutant load reduction associated with SEZ restoration efforts. SEZ restoration efforts that include functional geomorphic improvements have, and continue to be, conducted in the Tahoe Basin with a common goal and assumption that they provide a long‐term downstream water quality benefit (2NDNATURE et al., 2010a). A successful restoration of self‐sustaining fluvial processes is expected to reduce pollutant inputs from chronic bank and bed erosion and increase pollutant retention on the floodplain as a result of increasing the frequency and duration of overbank flows. Significant temporal and financial requirements make the quantification of the actual long‐term water quality benefit of a restored SEZ extremely challenging. The episodic nature of elevated flow conditions that cause erosion and/or inundate the floodplain means these events are unpredictable, infrequent and costly to monitor. In order to adequately capture and constrain the long‐term variability of the hydrology conditions that drive the water quality signal, consistent monitoring would need to be conducted for decades. This research built upon previous SNPLMA funded efforts by the 2NDNATURE team to sample overbank flows and inform the development of a reasonable floodplain retention coefficient as a function of discharge specifically for fine sediment (< 16m) for SLRTv1 (see Figures 2.11 and 3.13). Reach scale water quality monitoring was conducted during two consecutive overbank snowmelt events (WY10 and WY11) to quantify the FSP load differences introduced to and exported from the restored Upper Reach of Trout Creek over the duration of the events. Table 2.16 summarizes key characteristics of the WY10 and WY11 reach scale monitoring, including the significant FSP load reductions measured across the reach for each overbank event, 4.9 and 9.4 MT respectively. These measured reductions in the FSP load from the upstream to downstream boundary of the Upper Reach of Trout Creek are the net difference, where the FSP load at the downstream boundary (OUTfsp) is the sum of the FSP load delivered to the reach (INfsp), minus the mass retained on the floodplain (RFPfsp), plus the mass of FSP generated from the channel due to erosion (SCEfsp), or: OUTfsp (MT/yr) = INfsp – RFPfsp + SCEfsp (EQ Ex.1) The measured reach scale load reductions and the floodplain specific sampling conducted on both Trout Creek and the Upper Truckee River provide undeniable evidence that long term FSP load reductions can be achieved as a result of successful SEZ restoration efforts. The SLRTv1 was developed to provide a consistent and relatively simple estimation approach to quantify the average annual pollutant load at the downstream boundary of an SEZ by modeling the critical processes influencing water quality over decadal time scales. Specific objectives of SLRTv1 were: 
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Reliable, repeatable and cost‐effective estimation method, Applicable to range of SEZ scales, Incorporation of best available data and hypotheses of system function, Consistent with accepted stormwater tools and programs within Lake Tahoe Basin, and Improvable and adaptable over time 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 ES.2 | July 2013 Building upon the research findings, the SLRTv1 uses a mass balance approach to quantify how pollutant generation from bank erosion and pollutant removal from floodplain retention change given critical geomorphic modifications, using the fundamental equation Ex.1 above. Available stream (USGS gages) and stormwater (UCD, DRI, 2NDNATURE, etc.) hydrologic datasets were used to provide a simple method for the SLRTv1 user to generate site appropriate 18‐year catchment hydrologic and water quality inputs to any SEZ in the Tahoe Basin. The SLRT approach to estimate floodplain retention relied heavily on this and previous 2NDNATURE floodplain research, and stream channel erosion is estimated using an existing modeling tool that well represents the critical processes driving streambank instability: Bank Stability and Toe Erosion Model (BSTEM‐Dynamic 1.0; Simon et al., 1999; 2011). The SLRTv1 is reasonably compatible with the Pollutant Load Reduction Model (PLRM; nhc et al., 2009) outputs, such that the SLRTv1 estimates could potentially be used to support the tracking of pollutant load reductions in conjunction with the implementation of Lake Tahoe TMDL in the future. Given the best available data, SLRTv1 was applied to the Upper Reach of Trout Creek to represent pre‐ and post‐restoration conditions and yielded an estimated average annual FSP load reduction of 13.1 MT/yr as a result of the restoration actions. The preliminary baseline FSP load estimate for the City of South Lake Tahoe (CSLT) TMDL planning is 177 MT/yr, equating to the first milestone load reduction target of 10% to be approximately 17 MT/yr for CSLT (CSLT and nhc, 2013). SLRTv1 estimates on a small (1 acre) intervening SEZ in Placer County suggests an average annual FSP load reduction of 0.35 MT/yr, comparable to the expected load reductions achieved from a well‐maintained urban stormwater treatment BMP such as a dry or wet basin. SLRTv1 provides a theoretically sound methodology framework and general calculation process that undoubtedly can be improved with additional focused research across a greater range of SEZ setting and conditions over time. The primary research conclusions as well as limitations and improvement opportunities to optimize future SLRT estimates are provided in Section 6 and not repeated here, but it is expected that SLRTv1 can provide a valuable tool to support the water quality goals of fluvial restoration with reasonable quantitative estimates. DOCUMENT CONTENT This report contains the methods, results and interpretations of the datasets obtained under this research from the restored reach of Trout Creek in South Lake Tahoe (Section 2). Data collection focused on informing estimates of the delivery and retention of the pollutant impairing the clarity of Lake Tahoe, fine inorganic particles (FSP; < m) (LRWQCB and NDEP, 2010), on SEZ floodplains during overbank flow events. The data were invaluable contributions to the approach, development and preliminary calibration of the Stream Load Reduction Tool (SLRT v1) methodology. Section 3 contains the technical approach and justification for the SLRTv1, followed by a simple User Guidance (Section 4) and the demonstration of SLRTv1 on two SEZs in the Tahoe Basin (Section 5). Primary conclusions, limitations and recommended next steps are provided in Section 6. This report is accompanied by a series of digital templates (http://www.2ndnaturellc.com/client‐
access/slrttrout‐creek/) to simplify the step‐wise calculations required to obtain an average annual pollutant load reduction estimate from a specific SEZ.
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 1
| 1.1 INTRODUCTION This final report is the culmination of findings from two complementary research efforts supported by USDA Forest Service Pacific Southwest Research Station using funds from South Nevada Public Lands Act (SNPLMA) research grants. The 2NDNATURE team’s proposal entitled Quantification and Characterization of Trout Creek Restoration Effectiveness received USFS SNPLMA Round 9 grant funds in August 2009. Research was initiated with the production, review and finalization of a detailed Characterization Plan with the technical advisory committee (TAC) that outlined and refined the research goals, objectives, priorities, and proposed approach (Appendix A). Based on TAC input the research title for the Round 9 efforts was revised to the Stream Load Reduction Tool (SLRT) Methodology; Case Study: Trout Creek Restored Reach with the following revised goals: 1.
2.
Provide detailed guidance on the recommended methods to quantify the water quality benefit of stream restoration in the Lake Tahoe Basin, and Characterize the “desired condition” analog of a restored stream morphology and condition in the Lake Tahoe Basin (Trout Creek) by directly applying techniques developed by 2NDNATURE and others for Lake Tahoe streams. Given the prioritization on obtaining site‐specific data to inform the water quality benefit estimation approach, the “characterization” of the desired analog of Trout Creek restored reach (Goal #2) was limited to physical and chemical parameters that were collected with the intent of informing the stream load reduction method development. A supplemental SNPLMA grant was awarded to 2NDNATURE from SNPLMA Round 11 research funds in September 2011 to expand the development and application of a recommended methodology of the stream load reduction tool (SLRT) to include a representative range of stream environment zones (SEZs). The goal of the Round 11 resources explicitly built upon the Round 9 goal #1: 1A. Provide a load reduction estimation approach to quantify load reductions from specific SEZ restoration projects of all potential sizes in the Tahoe Basin given available data and models. Following the Round 11 award, 2NDNATURE and the USFS aligned the Round 9 and Round 11 schedules, process and final deliverables to allow leveraging of resources, TAC meetings and other expenditures. This report is the final deliverable for both USFS SNPLMA contracts and provides the culmination of data, methods, user guidance, and recommendations as a result of these and other complementary research efforts. The final version of this report included TAC review and comment incorporation from the draft. 1.1
DOCUMENT STRUCTURE The document is organized to clearly meet the critical goals of this research. Section 2 includes a detailed summary of the approach, methods, findings and summary of the evaluation of the Trout Creek restoration project. A culmination of information and data gathering from available sources, plus site‐specific evaluations by the 2NDNATURE team, are used to provide a quantitative summary of geomorphic, hydrologic and water quality benefits of Trout Creek restoration. A significant amount of the water quality data obtained during the WY10 and WY11 spring snowmelt events on Trout Creek were used to inform specific components of the load reduction estimation method (SLRT) 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 1.2 | July 25, 2013 developed under this research effort. Section 3 provides the technical approach to developing the load reduction tool, including inputs, calculations, assumptions and limitations. Section 4 is SLRT user guidance to estimate the water quality benefit of a SEZ restoration project, including generation of user input needs, running simulations, and comparing the pre‐ and post‐restoration scenarios. Section 5 summarizes the application of SLRTv1 to two SEZs, Upper Reach of Trout Creek and Bristlecone SEZ. Recommendations, limitations and next steps are provided in Section 6. 1.2
ACKNOWLEDGEMENTS The research and recommended methodology herein is the product of extensive data collection, information sharing, discussions, meetings and other contributions from the Lake Tahoe stream restoration community. Technical advisory members listed in the table below provided exceptional support throughout these efforts, contributing datasets and critical guidance to the research team. Technical contributions from Catherine Riihimaki (Princeton University), Matt Kiesse (River Run Consulting), Chad Praul (Environmental Incentives) and Andrew Simon and Natasha Bankhead (Cardno Entrix) and technical review of the draft report by Ed Wallace (nhc) were invaluable to the 2NDNATURE team and guided the theoretical approach, project management process and the technical data collection to meet the goals of this research. A special thank you to the California Tahoe Conservancy interns that assisted with snowmelt data collection and monitoring. Table 1.1. Technical advisory committee members. TAC Member Affiliation Scott Carroll California Tahoe Conservancy Cyndie Walck California State Parks Craig Oehrli USFS LTBMU Hannah Schembri Robert Larson Lahontan RWQCB Tiff van Huysen Jonathan Long USFS SNPLMA Science Jason Kuchnicki NDEP Shane Romsos Shannon Friedman TRPA Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 2
| 2.1 TROUT CREEK – A CASE STUDY Water was first introduced to 3 km of constructed new channel in the Trout Creek restored reach (Figure 2.1) in October 2001, resulting in a series of functional riparian improvements that have a myriad of physical, chemical, ecological and social benefits. Restoration goals included increasing overbank flow and floodplain sediment deposition, and creating wetland habitat adjacent to the channel (Haen Engineering, 1998). The new channel was designed to have overbank flow at discharge levels exceeding 70 cfs upstream of the Cold Creek confluence (see Upper Reach in Figure 2.1; Watershed Restoration Associates, 2000). Observations prior to and during this research indicate the intended constructed channel capacity has generally been maintained in the Upper Reach. The Lower Reach, located between the confluence with Cold Creek and Martin Avenue crossing (see Figure 2.1) has experienced a series of channel adjustments since 2001, including significant bank erosion and channel incision that were not intended by the restoration actions. Physical and chemical evaluations conducted in WY10 included the entire Trout Creek restored reach extending from Pioneer Trail to Martin Avenue, and the results are presented herein. However, given the focus on documenting the physical and water quality function of a relatively geomorphically stable restored stream system, the majority of the research and method application of this case study is focused on the Upper Reach of Trout Creek. Successful restoration of the Upper Reach of Trout Creek into a functional and self‐sustaining fluvial system can be quantified by a series of modified system attributes. Following the Riparian Ecosystem Restoration Effectiveness Framework process (Framework; 2NDNATURE et al., 2010a), the effectiveness of fluvial restoration efforts can be quantified if the approach is implemented during the pre‐restoration design and planning stage. However, for Trout Creek the lack of pre‐restoration monitoring datasets limits our ability to quantify changes in a number of specific attributes in a manner where we have high confidence that the differences measured are a result of restoration and not sampling error or natural variability. Therefore, the application of the Framework to the Trout Creek restoration is limited to directional process discussions and quantified changes in physical attributes using readily obtained data from design reports, aerial photographs, existing HEC‐RAS models, etc. to document the pre‐restored geomorphic form. While the direct comparison of the Trout Creek attribute values to other streams in the Tahoe Basin may not appropriate, the concept of demonstrating fluvial function as a collective series of attributes that have been modified to more desired conditions should provide valuable guidance to document restoration effectiveness on other systems in the future. Successful actions to restore a riparian system require the re‐establishment of the dynamic processes that collectively interact to support a balanced and self‐sustaining system, including continued erosion and deposition. The Upper Reach of Trout Creek has been touted as a desired analog of a Tahoe stream restoration effort due to the resulting balance between the incoming sediment and water and the valley morphology. Post restoration, with the addition of meander bends, the channel sinuosity has increased and the slope has decreased. Reduced channel capacity has increased the frequency, duration and extent of overbank flow events and reduced the shear stresses acting on the channel bed and banks during elevated flow conditions. Floodplain vegetation was established by salvaging pre‐project sod and plants, as well as scattering seeds of native riparian plants. The geomorphic improvements have successfully increased the adjacent shallow groundwater table in the meadow, increasing the dry season soil moisture conditions as indicated by the sustained, vigorous, and complex floodplain vegetation community. It is suspected that the restored channel and adjacent riparian system have resulted in improved ecological habitat for critical species, increased the 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 Martin
Ave
Trout Creek
Lower Reach
Cold Creek
Confluence
Cold Creek
Pio
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Tra
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Trout Creek
Upper Reach
FIGURE 2.1: Restored reach of Trout Creek completed in 2001.
Feet
FIGURE 1: RESTORED REACH OF TROUT CREEK COMPLETED IN 2001.
0
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Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 2.3 abundance and diversity of resident biological communities and have resulted in a sustained long term water quality benefit to downstream resources. One of the main objectives in evaluating Trout Creek under this research was to obtain data to inform quantification of the expected water quality benefits of SEZ restoration. Trout Creek and other low gradient meadow streams likely provide the greatest potential for the intersection of successful geomorphic restoration actions and water quality benefit in the Tahoe Basin. Since resources were limited, research focused on the Upper Truckee River and Trout Creek where the potential for sustained pollutant load reductions as a result of geomorphic restoration actions are the greatest. However, the potentially significant water quality benefits quantified herein may not be directly applicable to other SEZ restoration efforts, and the appropriateness of the application is highly dependent upon the contributing catchment and internal pollutant sources to the SEZ of interest. The application of the Framework (2NDNATURE et al., 2010a) requires consistent and focused pre‐ and post‐
restoration observations and metrics that collectively quantify changes as a result of restoration actions. Given that the restoration actions on the subject reach of Trout Creek (see Figure 2.1) were completed in 2001, quantification of the pre‐restoration conditions required the identification and compilation of a breadth of disparate datasets and information. In many instances, the quality of available data required reasonable estimates and assumptions to be made to recreate pre‐project attribute values. Regardless of the ability to recreate accurate and spatially detailed measures of pre‐project metric values, this research does provide a series of insightful and focused attributes that we believe collectively demonstrate the benefits and effectiveness of restoration actions on the Upper Reach of Trout Creek to improve the processes that support a functional riparian system. 2.1
DATA COLLECTION/ANALYSIS METHODS A significant data compilation and collection effort was undertaken to identify and obtain any relevant existing data generated by other researchers. In addition, 2NDNATURE conducted extensive field data collection during WY10 and WY11 to inform the quantification of a number of post‐restoration attributes, as well as provide invaluable data to inform the development of the Stream Load Reduction Tool methods while using Trout Creek Upper Reach as a case example. Below summarizes the existing datasets compiled with additional details contained in previous products from this research, including the Trout Creek Characterization Plan (2NDNATURE, 2010; Appendix A); Trout Creek WY10 Data Collection Summary (2NDNATURE, 2010; Appendix B); and Methodology to Predict Fine Sediment Load Reductions as a Result of Floodplain Inundation in Lake Tahoe Streams: Upper Truckee River (2NDNATURE, 2011a). 2.1.1
EXISTING DATASETS Table 2.1 summarizes the existing data compiled by the 2NDNATURE team for the restored reach of Trout Creek (see Figure 2.1). The existing data was leveraged as much as possible to guide efficient data collection as well as inform estimates of the site conditions prior to 2001 (pre‐restoration). Additionally, the U.S. Geological Survey (USGS) has long‐term stream gages upstream and downstream of the study site (Figure 2.2, Table 2.2.) that provided invaluable hydrologic and water quality datasets to this research. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 2.4 | July 2013 Table 2.1. Summary of existing data compiled for Trout Creek. Data type Geomorphic Data Report Source Data available Collection dates No report available at this time Scott Carroll /California Tahoe Conservancy (CTC) XS data 2006‐2009 X 2001‐2003 2001‐2003 X X 2001‐2003 X X X X Trout Creek Meadow Restoration, 2001‐2003 Geomorphic Monitoring (SH+G, 2004) No report available at this time Groundwater Data Stephen Andrews (UC Davis) Ed Langendoen National Sediment Laboratory Desert Research Institute (DRI) Soil Data Surface Water Quality Data Pebble counts XS data Longitudinal Swanson Hydrology + profiles Geomorphology Pool/riffle info (SH+G) Stream stage data Bankfull survey (wetted perimeter) Trout Creek Restoration and Wildlife Habitat Enhancement Project ‐ Water Quality Monitoring Report (Smollen, 2004) DRI Scott Carroll/CTC, City of South Lake Tahoe (CSLT) DRI Effect of geomorphic channel restoration on Streamflow and streamflow and Groundwater groundwater in Data snowmelt dominated watershed (Tague et al., 2008) Trout Creek Restoration and Wildlife Habitat Enhancement Vegetation Data Project ‐Baseline Vegetation Report, 2001 (Western Botanicals, 2001) 2001‐2003 2001‐2003 1997 Attribute class Channel/Floodplain Relationship Channel Stability Pre‐
Post‐
project project XS data 2008 X PSD, physical data 2008 X PSD, grain size data SW nutrient data (TSS, turbidity, TKN, PO4, OPO4, NO3, Ec) at 3 stations. Spot measurements, elevations, and graphs of MW at transects 1‐6. GW nutrient data (EC, TKN, PO4, TPsol) for monitoring wells (transects 1‐6) 8/4/00‐
7/30/02 (grab samples) X X X X X Christina Tague (UC Santa Barbara) Scott Valentine (Lake Tahoe Community College) No raw data Downstream Water Quality 1999‐2003 Channel/Floodplain Relationship 2001‐2002 1999‐2000, 2001‐2004 Channel/Floodplain Relationship X X Julie Etra/Western Botanicals Plant species, average cover %, frequency %, relative vegetation cover % used in reporting baseline data in 2001 2001 Floodplain Vegetation Community Condition Streambank Vegetation Community Condition X Topography Data None SH+G, Stephen Andrews (UC Davis) GPS point data 1997, (2008) Channel/Floodplain Relationship X X Aerial Images NAIP, IKONOS United States Forest Service (USFS) Images 1989, 2002 Floodplain Vegetation Community Condition Streambank Vegetation Community Condition X X TCMA
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FIGURE 2.2: Hydrology & water quality instrument locations along
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2.6 | July 2013 Table 2.2. Available USGS stream gage data relevant to Trout Creek restored reach. USGS stream gage ID# Location Water discharge Daily average 6/1/90‐4/25/13; 10336775 (TCPT Upstream at in Figure 2.2) Pioneer Trail 10336780 (CCPT Downstream in Figure 2.2) at Martin Ave. 10336790 Downstream Daily average 10/1/71‐9/30/92; (not shown in at Lake Tahoe Spot measurements 3/4/72‐9/3/08 Blvd. (n=476) Figure 2.2) 10336778 (CCPT Cold Creek at in Figure 2.2) Pioneer Trail 2.1.2
Suspended sediment % fines concentration (<63 µm) Grab samples 8/24/89‐
Grab samples 9/7/13 (n=486) 3/4/91‐5/14/88 (n=9) Daily average 10/1/60‐4/25/13; Daily average 10/1/73‐
Grab samples Spot measurements 11/9/73‐6/28/02 9/30/88; Grab samples 11/9/73‐5/14/88 (n=129) 11/9/73‐6/28/02 (n=113) (n=109) Spot measurements 8/24/89‐9/7/11 (n=554) Daily average 6/26/01‐9/30/03; Grab samples 3/4/72‐
9/3/08 (n=459) Grab samples 8/24/89‐
Spot measurements 8/24/89‐
6/4/03 (n=2) 10/7/04 (n=8) Grab samples 3/4/72‐5/21/08 (n=49) No grab samples WY10‐WY11 DATA COLLECTION SUMMARY To build upon the datasets listed above in Tables 2.1 and 2.2, 2NDNATURE focused WY10 and WY11 data collection on obtaining data to quantify geomorphic form, the channel/floodplain relationship and, to a lesser extent, the riparian and meadow vegetation community. Of particular interest was the physical and chemical system dynamics during overbank flow events, particularly related to floodplain inundation, floodplain deposition and channel morphologic changes. Field data collection and sampling efforts conducted by the 2NDNATURE team over WY10 and WY11 is summarized in Table 2.3. Table 2.3. Trout Creek data collection summary during WY10 and WY11. With the exception of the cross section surveys, all data was collected during the respective snowmelt runoff event for each water year. WY10 – Upper and Lower Reach Type Instrument Location Geomorphic Form Cross section Reaches 2‐6 (Figure 2.3) Continued channel geometry monitoring with support from CTC. Baro Troll EI Office Used to correct LevelTroll data. EI office located at South Y. Level Troll Reaches 1, 3 and 5 (Figure 2.2) 15‐min water surface elevations at 3 locations within restored reach. USGS gage TCPT, CCPT TCMA (Figure 2.2) USGS flow data used as input to (TCPT, CCPT) and output from (TCMA) Trout Creek restored reach. Flow data at site extrapolated using relationship with archive data between CCPT and TCPT. YSI TCPT, CCPT TCMA (Figure 2.2) 15‐min turbidity and stage recorders installed at existing or previous USGS flow stations. Stream grab WQ samples TCPT, CCPT TCMA (Figure 2.2) 13 samples at each site to calibrate YSI turbidity measurements. Floodplain transects Reaches 1‐6 (see Appendix B) 8 floodplain‐wide transects to monitor sediment deposition, vegetation characteristics, peak flow and/or sediment indicator height, and water retention depth at discrete locations. Hydrology Water Quality Floodplain Conditions Purpose Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 2.7 WY11 – Upper Reach Type Instrument Cross section Geomorphic Form Aerial photography LiDAR Baro Troll Hydrology Location Reaches 1‐6 (Figure 2.3) Trout Creek Restored Reach (Figure 2.1) A high resolution aerial photo was collected near the peak snowmelt event to document aerial extent of flood waters. Watershed Sciences produced a high resolution LiDAR dataset Lake Tahoe Basin for the Lake Tahoe Basin that was incorporated into topographic analysis. Used to correct LevelTroll data. EI office located on Lake Tahoe EI Office Boulevard. Level Troll USGS gage YSI Water Quality Stream grab WQ samples Floodplain Deposition Purpose Continued channel geometry monitoring with support from CTC. Floodplain passive WQ samples Reaches 1, 3 (Figure 2.2) 15‐min water surface elevations at 3 locations within restored Upper Reach. TCPT (Figure 2.2) TCPT, Reaches 1 & 3 (Figure 2.2) TCPT, Reach 1 & 3 (Figure 2.2) USGS flow data used as input to Trout Creek restored Upper Reach. Between Reaches 1 & 3 (Figure 2.5) 15‐min turbidity and stage recorders installed within restored Upper Reach. 14 samples at each site to calibrate YSI turbidity measurements with support from CTC interns. 53 samples total; 17 samples at each site (19 at Reach 3) to quantify floodplain retention of FSP particles during over bank flow. Notice that the locations of data collection during WY10 included both the Upper and Lower Reaches, followed by a focus on the Upper Reach in WY11 (see Table 2.3). The methods and results of the WY10 sampling efforts were extensively analyzed and reported in our WY10 Data Collection summary, included herein as Appendix B. One of the main intentions of the snowmelt monitoring was an FSP mass balance analysis of the reach with an attempt to quantify the difference in FSP input to and removed within the subject reach during an overbank snowmelt event. As the Appendix B indicates, our ability to measure FSP load reductions with WY10 data was limited due to the uncertainty with Cold Creek FSP loading and the fraction that actually reaches and/or is generated from the Trout Creek itself. In addition, the Lower Reach of Trout Creek has a channel capacity much larger than that of the Upper Reach, which limited the frequency of floodplain inundation while showing significant evidence of active channel and bank erosion. These variations in morphology led to the TAC supported decision to focus WY11 resources on constraining the Upper Reach in an attempt to better isolate the main factors assumed to influence downstream FSP loads: floodplain retention and channel erosion. Significant field support from CTC interns made the intensive snowmelt data collection efforts possible. 2.1.2.1
Geomorphic Form Long‐term geomorphic change can be monitored through repeated cross sections, and the cross section monitoring program on Trout Creek was in existence well before the 2NDNATURE work was initiated. A number of agencies and consultants have contributed to this valuable dataset over the last decade. A total of 30 cross sections were established and have been regularly surveyed since 2001. 2NDNATURE assisted the CTC with resurveying a number of these cross sections in late summer 2010 and 2011 (Figure 2.3 and Table 2.4). 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 Martin
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FIGURE 2.3: Location of cross section alignments established & incrementally surveyed by a variety of researchers since 2001.
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Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 2.9 The extensive cross section dataset provides important data to 2NDNATURE team, allowing the quantification of a number of the critical post‐restoration attributes for Trout Creek (Section 2.3). Many of these attributes are used as inputs to SLRT presented later in this document (Section 4). Table 2.4. Cross section survey data collection summary. See Figure 2.3 for locations. XS ID 1A 1B 1C 1D 2A 2B 2C 2D 3A 3B 3C 3D 4A 4B 4C 4D CTC1 CTC2 CTC3 CTC4 CTC5 CTC6 5A 5B 5C 5D 6A 6B 6C 6D 2.1.2.2
Reach type Straight Bend Bend Straight Straight Bend Bend Bend Bend Straight Bend Straight Straight Bend Straight Bend Bend Bend Bend Bend Bend Bend Straight Bend Straight Bend Bend Straight Straight Straight 2010 2011 X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Hydrology Three In‐Situ Level Troll 500 instruments collected continuous (15‐minute interval) stage data at each reach location to monitor site hydrology. Staff plates were installed concurrently and water levels were measured during the deployment for QA/QC. All instrument elevations were referenced into existing cross section benchmarks as established by SH+G in 2000 to provide continuous absolute water surface elevation at each stage recorder. An In‐Situ BaroTroll instrument was also placed nearby in South Lake Tahoe at the main office of Environmental Incentives to correct for natural variation in atmospheric pressure of the Level Troll level readings. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 2.10 | July 2013 In WY10 2NDNATURE deployed water level recorders at Reaches 1, 3 and 5 in both Upper and Lower Reaches, see Figure 2.2) but in WY11 the project area was constrained above Cold Creek (Upper Reach) and water level recorders were deployed in Reaches 1 and 3 only. Continuous stream flow data obtained by the USGS gaging stations at TCPT and TCMA (see Figure 2.2) were downloaded from the USGS website for WY10 and WY11 snowmelts. A previous USGS gage was temporarily operated at CCPT from 2001‐2003 and incorporated into the WY10 loading analysis using a linear regression model created from simultaneous discharge measurements from CCPT and TCPT gage sites to generate WY10 discharge (see Appendix B for more details). 2.1.2.3
Trout Creek Snowmelt Water Quality Three YSI 600OMS water quality monitoring instruments (two 2NDNATURE owned, one borrowed from CTC) collected continuous (15‐minute interval) turbidity and depth at each USGS stream gage location within the extents of the project area for WY10 and constrained above Cold Creek in WY11 (see Table 2.3 and Figure 2.2). Surface water grab samples were collected at each location throughout the spring melt in 2010 in 10 cfs increments above 40 cfs. In WY11, surface water grab samples were focused at the higher discharge values above 90 cfs to increase data density at the upper range of discharge. All water samples were separated and one aliquot was analyzed in the field for turbidity using a Hach 2100P portable turbidimeter to QA/QC instrument turbidity. Samples were processed and submitted to WETLab for TSS and % of TSS <16µm by mass, allowing a calculation of FSP concentrations (mg/L) for each sample. The water samples were used to create empirical equations to convert field turbidity to FSP (by mass) concentrations (see Figure 2.6). 2.1.2.4
Floodplain Characteristics In order to cost‐effectively and systematically evaluate evidence of sediment retention as a result of floodplain inundation, 2NDNATURE installed and surveyed 8 floodplain‐wide transects using 3 foot rebar stakes in March 2010, before spring snowmelt (see Appendix B). After peak flows receded, floodplain transects were resurveyed to measure changes in surface elevation at the pin (i.e., sediment deposition), evidence of fine sediment film on floodplain vegetation, peak flow and/or sediment indicators, and water retention depth at discrete locations along the restored reach. See Appendix B for more details regarding methods, results and challenges faced in WY10 with respect to the floodplain surveys. These surveys were not repeated in WY11. 2.1.2.5
Floodplain Deposition Sampling techniques to quantify floodplain FSP retention were developed and employed by the 2NDNATURE team on the Upper Truckee River (2NDNATURE, 2011a). The approach to monitoring and quantifying the FSP load reductions associated with floodplain retention is illustrated in Figure 2.4. Passive samplers are installed as a series of transects that comprise a sampling grid. Each sampler collects a sample of floodplain water at a known location and discharge. The samples are collected by field personnel, analyzed for turbidity in the field using a Hach 2100P portable turbidimeter, and then processed and submitted to WETLab for TSS and % of TSS <16 µm by mass, allowing a calculation of FSP concentrations (mg/L) for each sample. These methods were deployed on the Trout Creek Upper Reach floodplain in early spring WY11 in hopes of sampling any floodplain inundation during the snowmelt event. 1
t #
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Passive Sample Collection
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Q cc = 8 8 cfs
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FLOODPLAIN PASSIVE SAMPLE COLLECTION APPROACH
Figure 2.4
2.12 | July 2013 Prior to installation on Trout Creek, the floodplain transects were surveyed and the elevation of each sampler was set to strategically establish the sampler network to target specific discharge conditions of Trout Creek (Table 2.5). At‐grade passive samplers were used on the Upper Truckee River effort (2NDNATURE, 2011a; 2011b) but not installed at Trout due to concerns of wind transported FSP contamination. A total of 15 perched passive samplers were distributed on the floodplain of the Upper Reach of Trout Creek (Figure 2.5). By instrumenting the Upper Reach floodplain in a “grid” with 3 evenly distributed transects each containing 5 passive samplers at 4 locations (1 duplicate per transect), we were able to document changes in FSP concentrations both horizontally and laterally as the water interacts with the floodplain. Refer to Figure 2.5 for locations of passive sampler sites (A, B, C, D) along each transect (PS1, PS2, PS3). A total of 53 floodplain passive samples were collected during WY11 spring snowmelt and the water quality results are summarized below in Table 2.5. Since the TCPT discharge during the WY11 snowmelt exceeded the maximum discharge captured by the passive samplers (180 cfs), manual samples were obtained from the surface water for 200, 215 and 250 cfs at each sampler location. Table 2.5. Passive sampler installation summary for Upper Reach at Trout Creek for WY11. Passive Sampler ID Transect 1 PS1AA PS1AB PS1B PS1C PS1D Target discharge (cfs) 90/100 90/100 170/180 170/180 Horizontal distance from stream (ft) Longitudinal Distance to Transect 2 ‐ 630 ft Passive Sampler ID 50 94 128 156 PS2CA PS2CB PS2D PS2A PS2B Target discharge (cfs) 90/100 90/100 170/180 170/180 Horizontal distance from stream (ft) Longitudinal Distance to Transect 3‐ 330 ft Passive Sampler ID 52 68 146 196 PS3A PS3BA Transect 2 Transect 3 Target discharge (cfs) Horizontal distance from stream (ft) PS3BB PS3C PS3D 90/100 90/100 170/180 170/180 56 101 171 265 One field replicate was installed per transect, resulting in 3 replicate pair samples to estimate field sampling precision of the passive samplers (Table 2.6). Ideally precision is measured in triplicate, but resource limitations prevented the expenditure of the necessary additional costs. Table 2.6. Floodplain passive sampler replicates and measurement precision. Site ID Date Turbidity (NTU) Flow (cfs) PS1AA 6/6/11 7.64 115 PS1AB 6/6/11 6.49 115 PS2CA 6/19/11 2.97 180 PS2CB 6/19/11 3.35 180 PS3BA 6/6/11 2.51 115 PS3BB 6/6/11 2.06 115 Average Field precision 16% 12% 20% 16% LEGEND
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FIGURE 2.5: Floodplain passive sampler locations deployed for WY11
within Upper Reach Trout Creek.
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2.14 | 2.1.2.6
July 2013 Turbidity to FSP rating curve Ongoing water quality monitoring efforts have identified strong correlations between turbidity and the mass of fine sediment particles (FSP <16µm) in storm water runoff (Kayhanian et al., 2005; 2NDNATURE and nhc, 2010; 2NDNATURE and nhc, 2012a), land use specific samples (2NDNATURE et al., 2010c; Heyvaert et al., 2010) and streams (2NDNATURE, 2011a). A total of 61 in‐stream and 53 floodplain samples were collected from Trout Creek, with streamflow ranging from 24 to 247 cfs. These data were combined with previous turbidity v. FSP data points obtained by 2NDNATURE using the same collection and analytical techniques to create and updated turbidity to FSP rating curve that consists of 192 water samples from Upper Truckee River and Trout Creek (Figure 2.6). The datasets rarely exceed 50 NTU turbidity or 40 mg/L FSP regardless of sampling a range of discharge conditions, including higher flows. An R2 value of 0.91 in Figure 2.6 provides additional evidence that turbidity can be used as a cost‐effective proxy for FSP concentrations in Tahoe surface waters. 2.2
WY10‐11 SNOWMELT RESULTS The hydrologic and water quality monitoring efforts conducted on Trout Creek provides critical information and data to inform FSP loading to, and potential retention within, an SEZ. The researchers were incredibly fortunate to prepare for and monitor two overbank snowmelt events, resulting in a very effective use of resources and the generation of an invaluable dataset. The long‐term cross section dataset established on the reach, and with contributions from many, provides the necessary geomorphic form information used to design, and interpret the snowmelt monitoring efforts. Below we summarize the key findings and implications of the snowmelt monitoring conducted on Trout Creek, with particular emphasis on the WY11 results. Details of the WY10 data, results and interpretations can be reviewed in Appendix B. 2.2.1
FLOW FREQUENCY ANALYSIS Two USGS stream gages within close proximity to the project boundaries were relied upon to analyze the recurrence intervals of the events sampled during spring snowmelts WY10 and WY11. Annual peak instantaneous discharge data obtained from the USGS for TCPT at the upstream boundary (see Figure 2.2, gage#10336775) and TCMA at the downstream boundary (see Figure 2.2, gage#10336780) were used to conduct a probability distribution and flood frequency curve for the subject reach (Table 2.7). While the contributing catchment area for TCMA is larger than that of TCPT (36.7 and 23.7 sq miles, respectively), Table 2.7 indicates similar discharge values for these sites at the higher recurrence intervals. This is likely a remnant of the available data; TCPT has nearly 23 years of discharge data, while TCMA has a discharge record over twice as long (>52 years) that includes a number of very dry water years in the late 1960s and 1970s. Table 2.7. Annual peak flood flow frequency analysis for TCPT (USGS gage #10336775) and TCMA (USGS gage #10336780) noting the discharge (cfs) for a series of standard recurrence intervals. USGS site TCPT #10336775 TCMA #10337780 Years in record 1.5‐yr (cfs) 2‐yr (cfs) 5‐yr (cfs) 10‐yr (cfs) 50‐yr (cfs) 100‐yr (cfs) 23 65 94 206 321 746 1025 51 99 137 268 387 756 966 90
Trout Creek
Upper Truckee River
80
y = 0.76x
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M easu red F S P C o n cen t rat io n (m g/ L )
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Linear regression generated based on 192 stream and floodplain water quality
samples collected by 2NDNATURE from the Upper Truckee River (2NDNATURE
2011) & Trout Creek (this research) between 2008 and 2011. Dotted lines
represent 95% confidence intervals for the slope of regression line, essentially
illustrating our confidence in the “tightness” of regression line.
TURBIDITY V FSP RATING CURVE
Figure 2.6
2.16 | 2.2.2
July 2013 CHANNEL CAPACITY CIRCA 2010 In order to quantify overbank flow volumes and pollutant loads during the snowmelt events monitored, estimates of the channel capacity are needed. Channel capacity is defined as the cross‐sectional capacity of the stream channel in a specific location and/or over a subject stream reach. Expressing channel capacity as unit discharge (cfs) allows a simple analysis of the incoming hydrologic time series to identify times and volumes that inundate the adjacent floodplain. Using the channel geometry analysis results produced by Northwest Hydraulic Consultants (nhc) from survey data between 2001 and 2010, channel capacity estimates for each cross section within the Upper and Lower Reaches were calculated using Manning’s equation (Table 2.8). These channel capacity estimates were verified using stage to discharge rating curves for the sites where 2NDNATURE installed 15‐min stage recorders (see Figure 2.1). As expected the channel capacity varies spatially, typically with a relatively larger cross‐sectional area (and therefore channel capacity) in meanders and pools compared to straight reaches and riffles. Given a relatively even distribution of cross‐sections across these different morphologic features within both reaches, the channel capacity estimates are averaged for the Upper and Lower Reaches yielding 88 cfs and 156 cfs, respectively (see Table 2.8). Table 2.8. Channel capacity estimates based on cross section analysis between 2001 and 2010 conducted by nhc using Manning’s equation. Given the variability in channel capacity values between cross sections, an average channel capacity is used to represent each reach and incorporate this variability. Upper Reach Reach 1
2
3
4
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6
XS ID Location XS area (ft2) Wetted perimeter (ft) Slope Manning’s n Vcc (ft/s) 1A Straight 25.7 17.9 0.0013 0.04 1.70 44 1B Bend 49.2 23.3 0.0013 0.04 2.19 108 1C Bend 25.8 17.0 0.0013 0.04 1.76 46 1D Straight 59.8 24.0 0.0013 0.04 2.45 146 2A Straight 26.0 19.0 0.0013 0.04 1.65 43 2B Bend 40.8 21.6 0.0013 0.04 2.04 83 2C Bend 16.5 15.1 0.0013 0.04 1.42 23 2D Bend 46.9 24.6 0.0013 0.04 2.05 96 3A Bend 33.4 17.5 0.0013 0.04 2.05 68 3B Straight 16.2 12.9 0.0013 0.04 1.56 25 3C Bend 45.8 20.7 0.0013 0.04 2.26 103 3D Straight 35.0 21.7 0.0013 0.04 1.84 64 4A Straight 30.9 18.9 0.0013 0.04 1.85 57 4B Bend 47.5 22.7 0.0013 0.04 2.18 104 4C Straight 43.0 23.8 0.0013 0.04 1.98 85 4D Bend 61.3 26.3 0.0013 0.04 2.34 144 CTC 1 Bend 48.8 21.2 0.0013 0.04 2.32 113 CTC 2 Bend 80.2 30.5 0.0013 0.04 2.53 203 CTC 3 Bend 51.3 22.6 0.0013 0.04 2.30 118 Qcc (cfs)
88 Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT Lower Reach Reach 1
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| 2.17 6
XS ID Location XS area (ft2) Wetted perimeter (ft) Slope Manning’s n Vcc (ft/s) CTC 4 Bend 68.1 25.0 0.0013 0.04 2.59 177 CTC 5 Bend 66.9 25.2 0.0013 0.04 2.55 171 CTC 6 Bend 114.8 33.9 0.0013 0.04 3.00 344 5A Straight 70.2 26.3 0.0013 0.04 2.56 180 5B Bend 60.7 24.8 0.0013 0.04 2.42 147 5C Straight 54.9 23.0 0.0013 0.04 2.38 130 5D Bend 58.3 23.1 0.0013 0.04 2.47 144 156 6A Bend 52.7 23.6 0.0013 0.04 2.28 120 6B Straight 52.6 26.1 0.0013 0.04 2.13 112 6C Straight 45.3 19.5 0.0013 0.04 2.34 106 6D Straight 43.5 22.5 0.0013 0.04 2.07 90 Qcc (cfs)
1 Averaged cross sectional area across post‐restoration survey data record. Output from nhc cross section analysis. 2 Averaged wetted perimeter across post‐restoration survey data record. Output from nhc cross section analysis. 3 Slope calculated using elevation difference between start and end of reach divided by the stream length of reach using GIS. 4 Manning’s n value estimated reach‐wide as 0.04, described as “clean, winding, some pools and shoals” for main channels (Chow 1959). 5 Velocity calculated using Manning’s equation for open channel flow velocity. 6 Channel capacity calculated by multiplying channel flow velocity (Vcc) and XS Area. Photos representing the Upper and Lower Reach floodplain were taken in June 22, 2010 at approximately 60 cfs at TCPT and 80 cfs at TCMA (Figure 2.7). The photos at the top illustrate the Upper Reach is at capacity and slight overbank flooding occurs at 60 cfs. Bottom photos indicate that significant channel capacity still remains within the Lower Reach at approximately 80 cfs, generally supporting the channel capacity estimates in Table 2.8. The photos also provide evidence of active bank erosion and sediment inputs into the Lower Reach during the WY10 snowmelt event. 2.2.3
EVENT HYDROLOGY The annual hydrographs for WY10 and WY11 as measured by the USGS TCPT gage are presented in Figure 2.8A, indicating the relative magnitude and duration of the two consecutive spring snowmelts monitored. Also included in Figure 2.8 are insets of the WY10 and WY11 snowmelt events, indicating the Upper and Lower Reach channel capacities (see Table 2.8) and highlighting the timing of the overbank flows as measured by TCPT (Figure 2.8B&D) and TCMA (Figure 2.8C). Table 2.9 indicates the instantaneous peak flood flow discharges for each of the snowmelt events monitored at TCPT, corresponding to recurrence intervals of 2.7 years and 6.3 years (see Table 2.7) for WY10 and WY11, respectively. A great deal of luck was needed to allow this research to correspond with two above average snowmelt runoff events. Table 2.9. Peak instantaneous discharge for the spring snowmelt events monitored. 6/8/2010 Spring snowmelt peak flow 127 cfs 2.7 yrs 6/29/2011 248 cfs 6.3 yrs WY Date WY10 WY11 RI Using the time series data shown in Figure 2.8, a series of hydrology metrics were generated for WY10 and WY11 snowmelt events to document the channel/floodplain interactions including the duration, volume and extent of overbank flow within Trout Creek between April 1st and August 31st (Table 2.10). Instantaneous 15‐min 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 A
B
Inundation
Edge of Bank
Edge of Bank
Inundation
Upper Reach. Q= 60 cfs; Qcc = 88 cfs
C
D
Bank Failure
~2 ft
~2 ft
Lower Reach. Q = 80cfs; Qcc = 156 cfs
Photos A & B from representative locations on Upper Reach indicate 60 cfs, as
measured at TCPT gage, is at or near channel capacity. Onset of overbank flow is
evident in both photos.
Photos C & D from representative locations on Lower Reach indicate 80 cfs, as
measured at TCMA gage, is well contained within the channel & active bank
erosion is evident.
Photos taken by CTC on June 22, 2010
UPPER AND LOWER REACH JUNE 22, 2010
Figure 2.7
300
A
Peak 127 cfs
6/8/2010
W Y 1 0
Peak 248 cfs
6/29/2011
W Y 1 1
250
T C P T U S G S
G age # 1 0 3 3 6 7 7 5
T C P T Discharge (cfs)
200
150
100
50
0
100
Upper Reach Qcc = 88 cfs
50
0
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(cfs) (cfs)
T C T M C M A A Discharge
Discharge
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Pre Event
17.8 days
Post Event
7.25 days
37.9 days
150
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T C P T
W Y 1 1
Overbank Event
46.9 days
0.8 days
Post Event
27.8 days
D
1.1 days
Pre Event
30.0 days
200
250
3.4 days
B
3.7 days
T C P T TC P T Discharge
Discharge (cfs)(cfs)
250
300
T C P T
W Y 1 0
Overbank Event
4.8 days
T C P T T C P Discharge
T Discharge (cfs)(cfs)
300
100
Upper Reach Qcc = 88 cfs
C
50
0
T C M A
W Y 1 0
200
150
100
50
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Lower Reach Qcc = 156 cfs
A . TCPT 15-min discharge record for WY10 and
WY11 with timing and magnitude of spring
snowmelt peaks noted.
B & C . WY10 snowmelt record for TCPT and
TCMA, respectively, with duration of pre, post and
overbank events noted. Channel capacity for Upper
and Lower Reaches are also noted.
D. WY11 snowmelt record for TCPT with duration
of pre, post and overbank events noted. Small
overbank events (red boxes) occur before the main
overbank event, each of which were accounted for
in the overbank event analysis. Channel capacity for
Upper Reach is also noted.
EVENT HYDROLOGY SUMMARY FOR WY10 AND WY11
Figure 2.8
2.20 | July 2013 data collected at the TCPT gage was assumed to represent flow within the Upper Reach, while the TCMA gage was assumed to represent flow downstream of Cold Creek in the Lower Reach. Duration included any time the discharge value (representing 15‐min) was equal to, or exceeded, channel capacity. The total volume of overbank flow was calculated as the sum of the total volume during each overbank event minus the total volume contained within the channel over the same time period. The Upper and Lower floodplain inundation volumes were estimated separately and summarized in Table 2.10 below. Table 2.10. Overbank duration and magnitudes for Trout Creek restored reach (see Figure 2.1) during WY10 and WY11 snowmelt events using 15‐min discharge data from USGS gages TCPT (Upper Reach) and TCMA (Lower Reach) between April 1st and August 31st (Total duration = 153 days). WY10 WY11 Duration of overbank flow (days) Volume of overbank flow (AF) Area of overbank flow (acres) Figure 2.9 Duration of overbank flow (days) Volume of overbank flow (AF) Area of overbank flow (acres) Figure 2.10 Upper Reach 4.8 124 23 46.9 5,640 30 Lower Reach 1.0 13 9 37.7 7,534 39 Figures 2.9 and 2.10 map the estimated aerial extent of inundation for each spring snowmelt peak flood event for WY10 and WY11, respectively. For WY10, flood inundation extents were created using the floodplain characteristics data and interpolated using the topography between measurements. For WY11, the research team funded an aerial photography firm, GeoCadd Surveys, to obtain a high resolution aerial photo on 6/17/2011 at 10:00 am when TCPT discharge was 161 cfs. This aerial was integrated with other flow indicators observed during post overbank assessments to create Figure 2.10. Additional metrics were summarized to inform the hydrologic conditions that drive stream channel erosion and bed incision. Channel erosion processes are sensitive to the frequency and duration of elevated flows relative to the channel capacity. Elevated flows contained within oversized channels can cause significant erosion, particularly during receding flow conditions when banks susceptible to erosion are saturated. Table 2.11 summarizes the duration of 4 critical discharge conditions (low flow, flow contained within the channel, bankfull, and exceeding bankfull) for the Upper and Lower Reach channels, based on the capacities defined in Table 2.11. Instantaneous 15‐min discharge records from USGS between April 1st and August 31st for each water year were used to produce the metric values in Table 2.11. Table 2.11. Summary of hydrologic metrics during WY10 and WY11 snowmelt events using 15‐min discharge data from USGS gages TCPT (Upper Reach) and TCMA (Lower Reach) between April 1st and August 31st. (Total duration = 153 days). Duration (days) in Upper Reach (Qcc =88 cfs) WY10 Over bankfull: >1.1 Qcc 2.8 WY11 40.2 Year 4.0 50% capacity to bankfull: 0.9‐0.5 Qcc 25.8 20.0 52.1 Bankfull: 1.1‐0.9 Qcc Duration (days) in Lower Reach (Qcc =156 cfs) 120.4 Over bankfull: >1.1 Qcc 0.1 40.8 32.8 Low flow: <0.5 Qcc 2.4 50% capacity to bankfull: 0.9‐0.5 Qcc 20.4 13.7 65.2 Bankfull: 1.1‐0.9 Qcc Low flow: <0.5 Qcc 130.1 41.2 LEGEND
Legend
WY10 Spring Snowmelt Peak
Trout Creek
Lower Reach
Trout Creek
Upper Reach
FIGURE 2.9: Approximate aerial extent of peak water surface elevation for spring snowmelt WY10 at Trout Creek.
Total Area of Innundation = 32 acres
Feet
0
250
500
1,000
LEGEND
Legend
WY11 Spring Snowmelt Peak
Trout Creek
Lower Reach
Trout Creek
Upper Reach
FIGURE 2.10: Approximate aerial extent of peak water surface elevation for spring snowmelt WY11 at Trout Creek.
Total Area of Innundation = 69 acres
Feet
0
250
500
1,000
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 2.2.4
| 2.23 FLOODPLAIN RETENTION Floodplain deposition monitoring was conducted on Trout Creek during the WY10 and WY11 snowmelt events using two different techniques (see Section 2.1.2.5 for methods). Due to limited resources, WY10 floodplain monitoring consisted of cost‐effective survey techniques including sediment pins and systematic visual surveys to document the spatial and vertical extent of overbank flow conditions and potential sediment and FSP retention within days of the peak. Details of the W10 floodplain monitoring are provided in Appendix B. While evidence of FSP film accumulation on the floodplain vegetation and sporadic bulk sediment deposits distributed throughout transects were observed and mapped, the ability to quantify the amount retained on the floodplain in WY10 using these techniques was limited. Supplemental SNPLMA Round 11 funding was awarded in the spring of WY11 and used to implement the floodplain sampling techniques described above in Section 2.1.2.5. The intended purpose of the floodplain deposition monitoring was to obtain additional FSP floodplain inundation data from the Upper Reach of Trout Creek using data collection and analysis methods consistent with those employed on the Upper Truckee River in WY09, WY10, and WY11 (2NDNATURE, 2011a; 2011b). Given the challenges associated with sampling floodplain deposition and the infrequent occurrence of overbank events, the available FSP floodplain retention datasets from both sites over a series of overbank discharge conditions are integrated to provide a reasonable estimation of floodplain FSP deposition as a function of discharge. Based on available information, these data are used to recommend an approach to estimating the FSP floodplain retention coefficient for the SLRT methodology presented in Section 3.5. Table 2.12 presents the FSP concentration data from 53 samples obtained during five discrete discharge conditions during the WY11 snowmelt. The grid sampling configuration (see Figure 2.4) allows estimates of the longitudinal and horizontal differences in FSP concentration as a result of floodplain interactions (see Figure 2.5 for locations of passive sampler sites (A, B, C, D) along each transect (PS1, PS2, PS3)). If we assume that the upstream passive samplers are inundated as water enters the floodplain and the downstream samplers collect samples from the same water volumes after they have travelled across the floodplain we can compare the data to infer longitudinal FSP retention, with a similar concept applying to horizontal differences. For each event discharge, the percent reduction is calculated between corresponding samples with increasing distance from the thalweg (horizontal) and downstream over the floodplain (longitudinal). The population of differences is averaged to estimate the retention coefficient for the specific discharge event and assumed to represent the average fraction of the FSP retained on the floodplain relative to the amount delivered. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 2.24 | July 2013 Table 2.12. WY11 spring snowmelt floodplain passive sampler water quality results for Trout Creek Upper Reach. Calculated FSP concentration (mg/L) Collection Q (cfs)1 115 ID A B PS1 5.37 1.91 PS2 21.05 10.64 PS3 PS1 180 PS2 215 250 1.74 D 2.17 1.66 2.40 11.10 4.48 2.17 2.18 PS1 5.05 3.50 3.37 PS2 4.83 3.76 4.40 6.54 PS3 3.50 2.49 5.40 3.34 PS3 200 2.93 C PS1 4.29 4.59 3.00 1.91 PS2 2.63 2.60 2.80 3.14 PS3 2.76 2.16 2.43 2.59 PS1 6.81 6.69 4.86 2.70 PS2 3.58 3.40 4.32 5.78 PS3 3.97 3.03 2.96 4.25 Similar floodplain sampling collection and analysis techniques were implemented on the Upper Truckee River in WY09, WY10 and WY11, and the results of the WY09 and WY10 SNPLMA Round 7 research efforts are summarized in 2NDNATURE (2011a). WY11 sampling efforts on the Upper Truckee were funded by CA State Parks and the data analysis details and results are summarized in 2NDNATURE (2011b). Using the available datasets a single estimated retention coefficient (Ri) was calculated for each of the 10 overbank events measured by 2NDNATURE between WY09‐WY11 (Table 2.13). In order to integrate and apply the retention coefficients obtained from two different systems, the relative magnitude of the overbank event in the context of each stream was defined. Normalizing each discharge sampled (Qs) by the respective channel capacity of the stream channel adjacent to the sampled floodplain (Qcc) yields a ratio, where Qs/Qcc = 1 at channel capacity and increases as a function of increasing discharge (and therefore greater magnitude of overbank flow). The FSP retention results suggest that the percent retention of FSP on the floodplain Trout Creek floodplain with evidence of extensive sediment can be reasonably estimated using retention post WY11 snowmelt event. 1
Reference Figure 2.4 for sample collections displayed on hydrograph. Grab samples were collected above 200 cfs at passive sampler instrument site in order to capture the larger than expected overbank event, rather than adjusting instrument elevations to sample passively. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 2.25 relatively cost‐effective techniques, though there is the risk that overbank flow will not occur and the costs of instrumentation and site preparation will not yield results. While a significant variability exists within each pair of samplers, the average estimated retention coefficients do suggest some FSP removal as a result of floodplain interactions. Figure 2.11 presents the data in Table 2.13 graphically and the best fit curve suggests an exponential decline in FSP retention on the floodplain as a function of discharge. Theoretically, this exponential relationship follows a general expectation that smaller flood flows possess lower velocities, shear stresses, and shallower depths on the floodplain, which all contribute to higher FSP retention. As discharge and water depth on the floodplain increase, the FSP retention decreases due to the elevated shear stress that exceeds the ability of the floodplain to retain sediment. Table 2.13. FSP retention estimates for 10 flood events sampled on the Upper Reach of Trout Creek and Upper Truckee River between WY09 and WY11. At the location of sample collection average Qcc for Trout Creek and the Upper Truckee River are 88 cfs and 290 cfs, respectively. TROUT CREEK (Qcc=88 cfs) UPPER TRUCKEE RIVER (Qcc=290cfs) Collection Q (cfs) Rfsp Qs/Qcc Collection Q (cfs) Rfsp Qs/Qcc 115 0.68 1.3 375 0.46 1.3 180 0.28 2.0 425 0.64 1.5 200 0.24 2.3 950 0.45 3.3 215 0.23 2.4 1100 0.19 3.8 245 0.23 2.8 1400 0.08 4.8 A number of limitations are associated with these sampling techniques and findings: 


Passive samplers sample at a discrete vertical location in the water column and not at the floodplain surface, likely skewing the actual amount of FSP retained on the floodplain surface following flood receedance. It is difficult to predict if this approach over or under estimates the mass of FSP per unit volume actually retained. The logistical challenges associated with quantifying the mass or volume actually retained using cost effective techniques are significant and could not be adequately resolved by this research given the available resources. The concentrations of FSP in the floodwaters sampled are typically <10mg/L. The TSS analytical reporting limit is 1 mg/L with a 5% analytical precision as reported by WetLab. The particle size distribution analytical precision is approximately 1%, yielding an FSP concentration analytical precision on the order of ±6%. In most instances the sample specific and event average differences in floodplain FSP concentrations exceed the analytical error. The dataset is limited to 10 events from 2 floodplains, and the condition of these two floodplains is assumed to be relatively similar with respect to the attributes assumed to enhance FSP retention. These attributes include: a laterally extensive floodplain, as indicated by a stage to discharge empirical relationship that has a significant and sustained slope reduction once channel capacity is exceeded; significant topographic complexity to enhance ponding and particle stranding; well‐distributed wood and other debris to reduce flow velocities and enhance flocculation and settling; and dense meadow vegetation distribution to provide surfaces for FSP to adhere. Based on these attributes we assume that the integration of the event data as a function of overbank discharge is reasonable to yield an estimated retention coefficient curve for relatively good or desired floodplain conditions. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 1.00
Trout Creek
0.90
Upper Truckee River
Floodplain retention coefficient (R
Floodplain
retention coefficient (Rfsp) fsp) 0.80
0.70
y = 0.80x‐1.2 R² = 0.69 0.60
No Overbank
Flow
0.50
0.40
0.30
0.20
0.10
0.00
0.0
0.5
1.0
1.5
2.0
2.5
3.0
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4.0
4.5
5.0
5.5
6.0
:Q
QQ
s cc cc
s:Q
Floodplain sediment retention results based on passive sample collection during 10 overbank events
monitored from WY09 - WY11 on Trout Creek and Upper Truckee River. To make results from the two
systems comparable, the sampled discharge (Qs) is normalized by the channel capacity (Qcc) to define the
relative magnitude of the event. The Trout Creek and Upper Truckee River floodplains are assumed to
represent relatively good floodplain condition, with topographic complexity to allow particle stranding,
wood and other debris to enhance deposition, and a dense meadow vegetation distribution. Even with
these characteristics, an exponential decay in FSP retention on the floodplain as a function of discharge
is expected. As water depth on the floodplain increases, the elevated shear stress will exceed the
floodplain’s ability to retain sediment. RETENTION AS A FUNCTION OF CHANNEL CAPACITY
Figure 2.11
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 

2.2.5
| 2.27 The non‐uniform flow across a floodplain of variable topography and preferential flow paths add additional complexity to these analyses and calculations of relative FSP retention. The exponential retention coefficient curves are assumed to be reasonable and support the theoretical concepts of processes that influence particle and, specifically, FSP retention on floodplains by others (Dunne and Leopold, 1978; Mount, 1995; Andrews et al., 2011); however continued effort to implement comparable sampling efforts on other floodplains across a range of discharges and floodplain conditions will greatly improve FSP retention coefficient estimates. EVENT FSP LOADS The YSI water quality instruments (see Table 2.3) were used to obtain continuous 15‐min turbidity datasets at strategic locations during each of the WY10 and WY11 snowmelt events to quantify FSP loading into and out of the restored Trout Creek reach. The intent was to provide comparable data to verify that a net FSP load reduction on a reach scale can be measured as result of floodplain inundation. For WY10, instrumentation was placed at TCPT, CCPT and TCMA to monitor the entire project reach (see Figure 2.2). Due to limited overbank flow within the Lower Reach in WY10, observed channel erosion in the Lower Reach, and the difficulties associated with constraining the Cold Creek discharge and FSP load inputs (see Appendix B), WY11 turbidity monitoring focused on the Upper Reach only. The highlights of the snowmelt FSP loading analysis are summarized below, but the extensive analysis of the WY10 data can be reviewed in Appendix B. Using the same data QA/QC and analysis techniques outlined in Appendix B, the WY11 continuous turbidity datasets from TCPT, Reach 1 and Reach 3 were converted into FSP concentration (using the equation in Figure 2.6) and multiplied by the respective TCPT discharge (see Figure 2.8D) to present FSP mass discharge (mg/s) time series for each location (Figure 2.12; the comparable data for WY10 is Figure 7 in Appendix B). To fill data gaps associated with filtering from QA/QC process, all discharge and FSP loading data collected in WY10 and WY11 were condensed into bihourly average measurements for analysis. Based on the hydrology time series shown in Figure 2.8, 3 types of flow events were defined for the period of April 1 to August 31 for each water year: pre‐event, overbank event, and post‐event. Pre‐event is defined as durations prior to elevated flow conditions when we would expect upstream and downstream loads to be similar and thus any differences can be used to assess measurement error. The overbank durations were identified when the stream discharge exceeded the defined channel capacity. The WY10 overbank and WY11 pre‐event durations were not continuous time periods due to use of discharge thresholds to identify conditions that met the intent of the event data analysis. The post‐event is when the discharge recedes to below channel capacity and extends to the end of the defined time period (August 31), when instruments were removed. For each of duration (pre‐event, overbank event, post‐event) noted in Figures 2.8B‐D, the total FSP mass load measured at each monitoring site was calculated. The change in FSP loads for each pre, post and during overbank event between sites and over the complete reach are calculated and presented in Tables 2.14 and 2.15. Change in FSP storage is a result of a mass balance of inputs and outputs between the respective monitoring locations. It is assumed that floodplain deposition is the primary sink and channel erosion is the primary source driving the measured changes in FSP along the subject reach and other factors are negligible. The change in load (storage) measured across the reach is the difference between the load at the upstream boundary minus the load measured at the downstream location. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 U p p er R each T ro u t C reek
0.090
T ro u t C reek at P io n eer T rail (T C P T )
IN
0.080
0.070
F S P Discharge (k g/ s)
0.060
0.050
0.040
0.030
0.020
0.010
0.000
3/18/11
0.090
4/7/11
4/27/11
5/17/11
6/6/11
6/26/11
7/16/11
8/5/11
8/25/11
M ID
T ro u t C reek at R each 1
0.080
F S P Discharge
(k g/ s)
F S P Discharge (k g/ s)
0.070
0.060
0.050
0.040
0.030
0.020
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0.000
3/18/11
0.090
4/7/11
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T ro u t C reek at R each 3
8/25/11
O U T
0.080
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F S P Discharge (k g/ s)
0.060
0.050
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0.030
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0.010
0.000
3/18/11
4/7/11
4/27/11
5/17/11
6/6/11
6/26/11
7/16/11
8/5/11
8/25/11
Data Obtained by 2NDNATURE - WY11, See figure TC1 for locations
FSP DISCHARGE TIME SERIES - WY11
Figure 2.12
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT storage = INfsp – OUTfsp | 2.29 (EQ 2.1) It is assumed that the primary processes influencing the change in storage within the subject reach are the catchment load delivered to the SEZ (INfsp) minus the load retained on the floodplain (RFPfsp) plus the load input from stream channel erosion (SCEfsp), where: OUTfsp = INfsp – RFPfsp+ SCEfsp (EQ 2.2) Load increases (i.e., negative change in storage) indicate a greater mass was measured at the downstream location (net gain), while positive values indicate a load reduction across the reach (net loss). The pre‐event loading comparisons are used to estimate the error of FSP load measurements between sites based on the assumption that pre‐event channel erosion is insignificant before flood flows occur (SCEfsp =0) and no deposition has occurred (RFPfsp=0). Thus the change in storage (Δ storage) loads between sites and across the reach should approximate 0. Any differences in the post‐event loads would suggest measurement error if the values are positive (ΔStorage > 0), because we know flows are not overbank. If post‐event values are negative (ΔStorage <0), it may be attributable to error and/or suggest sediment generation associated with stream channel erosion after flood flows recede. The FSP load values for WY10 in Table 2.14 differ from those in the WY10 data summary (Appendix B) due to the additional data obtained in WY11 that resulted in an updated turbidity to FSP rating curve (see Figure 2.6) and a revised estimate of the Upper Reach channel capacity (Qcc; see Table 2.8). While the values slightly differ, the interpretation of the WY10 reach scale FSP data is consistent with those in Appendix B. The overbank event duration suggests a net reduction in FSP between Pioneer Trail and Martin Avenue of approximately 4.9 MT, implying a greater mass of FSP was retained on the floodplain than generated from channel erosion. Given that very limited overbank flow and floodplain deposition occurred in the Lower Reach during the WY10 snowmelt, we assume the majority of FSP deposition occurred in the Upper Reach (see Table 2.10). The post‐event loads suggest a significant sediment load increase (‐5.1 MT) occurred as flows receded from bankfull to approximately 40% of channel capacity in the Upper Reach. Visual observations post‐event confirmed that active and significant bank erosion was present throughout the Lower Reach (see Figure 2.7). Cross section comparisons between the WY10 and WY11 snowmelt events also support that cross sectional increases via bank slumping and active erosion occurred along the Lower Reach, and this was likely the primary source of the estimated contribution of 5.1 MT of FSP (Figure 2.13). Table 2.14. WY10 spring snowmelt event‐based FSP load calculations by site for each pre, post and overbank event duration using processed bihourly averaged FSP load (mg/s) data at each site. SITE TCPT CCPT TCMA Source INfsp OUTfsp PRE EVENT FSP load NET (MT) (MT) 11.9 15.4 3.5 14.2 14.2 OVERBANK EVENT FSP load NET (MT) (MT) 13.5 18.7 5.1 13.8 13.8 POST EVENT FSP load NET (MT) (MT) 8.6 12.9 4.3 18.0 18.0 Δ storage: Δ storage: Δ storage: Duration (days): Daily rate (MT/day): 1.2 30.0 0.04 2NDNATURE, LLC | ecosystem science + design Duration (days): Daily rate (MT/day): 4.9 4.8 1.0 Duration (days): Daily rate (MT/day): ‐5.1 27.8 ‐0.2 www.2ndnaturellc.com | 831.426.9119 A
C ro ss S ect io n 3 C
C ro ss S ect io n 3 B
263
262
2010
2011
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2011
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260
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E l ev at io n (6 , X X X ft )
E l ev at io n (6 , X X X ft )
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Dist an ce fro m L eft B en chm ark (ft )
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Upper Reach of Trout Creek
B
C ro ss S ect io n 5 B
C ro ss S ect io n C T C 6
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2011
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E l ev at io n (6 , X X X ft )
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Lower Reach of Trout Creek
A . Representative cross sections for the Upper Reach illustrating
relatively stable cross sectional geometry (3B) and channel aggradation
(3C) on an outerbend between the fall 2010 and 2011 surveys.
B . Representative cross sections for the Lower Reach illustrating bank
sloughing, bank failure, and bed scour between fall 2010 and 2011
surveys.
Data obtained by 2NDNATURE and CTC, See Figure XSloc for locations
TROUT CREEK REPRESENTATIVE CROSS SECTIONS
Figure 2.13
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 2.31 Recall that the OUTfsp value measured at the downstream boundary is the sum of the incoming FSP load (INfsp) plus the mass contribution from stream channel erosion along the subject reach (SCEfsp) minus the load retained on the floodplain (RFPfsp), as expressed in EQ 2.2 above. If we assume the WY10 post‐event load (‐5.1 MT) is due primarily to channel erosion, we can use the post‐event daily rate of FSP inputs (channel erosion) to estimate the FSP load retained on the floodplain by combining and rearranging EQs 2.1 and 2.2, and solving for the floodplain deposition value: RFPfsp = storage + SCEfsp (EQ 2.3) We assume stream channel erosion occurs at a constant rate during the post event and divide the post event change in storage (‐5.1 MT) by the event duration (27.8 days) to get an estimated daily channel erosion rate of 0.2 MT/day. For simplicity, we assume this is the daily average channel erosion rate during the overbank event as well, and we apply the daily FSP input rate from channel erosion of 0.2 MT/day to the overbank event duration of 4.8 days to estimate a total FSP input from channel erosion to be 0.96 MT. Based on processes that drive channel erosion, we expect a greater rate of channel erosion during the post‐event than the overbank event, as the saturated banks become exposed (Section 3.6) thus 0.96 MT may be a slight overestimate for the SCEfsp. Adding the input mass from channel erosion (0.96 MT; SCEfsp) to the measured downstream net FSP load (4.9 MT; storage), we estimate the FSP mass retained on the floodplain (RFPfsp) during the WY10 event to be 5.86 MT (RFPfsp) using EQ 2.3. For a simple verification of these estimates, we can divide this FSP mass by the estimated 137 AF of water that inundated the floodplain during this event (see Table 2.10) and obtain an average FSP concentration of floodplain waters on the order of 35 mg/L. While we did not sample the floodplain waters directly with passive samplers in WY10, this average concentration is within the range measured at the site during this research (see Figure 2.6). The pre‐event FSP load comparisons are used to estimate a potential sampling error of +/‐ 0.04 MT per day during WY10 overbank event, which is well below the signals measured during the overbank and post‐event durations. While these results are within reason, the excessive signal of channel erosion from the Lower Reach and the difficulty to constrain the FSP inputs from Cold Creek into the FSP mass balance estimate in WY10 led to the redesign of the WY11 FSP reach‐scale load monitoring to isolate the Upper Reach. FSP reach‐scale loading comparisons for WY11 are presented in Table 2.15. The turbidity sensors were installed on April 5, 2011 and discharge at TCPT exceeded 50 cfs shortly following installation. In order to define the pre‐
event as a duration with relatively lower flow conditions, such that the data could be used to assess sampling error, a data query for low flow conditions was required. The FSP instantaneous load time series over 30 days following installation was queried for conditions when TCPT discharge was below 60cfs and integrated to quantify the FSP load over 17.8 days. Even with these controls, both the pre and post event net change in storage are negative, which is assumed to be related to sampling error since deposition via overbank flow (RFPfsp; see Equation 2.1) is not occurring during these times. A number of challenges with calibrating and obtaining accurate values from the YSI turbidity meter at R3 were encountered during the monitoring, and, unfortunately, this instrument error was expected. Regardless the daily rate measured during the pre‐ and post‐events (assumed to be error) are lower than the daily rate measured during the overbank event. Subtracting out the average daily error (0.22 MT/day), we can estimate an error‐adjusted net difference during the WY11 overbank event of 0.2 MT/day, equating to an estimated net storage of 9.38 MT over the 46.9 days. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 2.32 | July 2013 This estimate assumes the FSP mass input from channel erosion in the Upper Reach during the event was negligible, as supported by visual surveys following the event. The average FSP concentration in floodplain waters for WY11 is estimated by dividing 9.38 MT by 5,640 AF (see Table 2.10) to yield 1.4 mg/L for the WY11 overbank event. After accounting for the estimated field measurement error, a measurable reduction of the FSP load delivered to the Upper Reach of Trout Creek due to floodplain deposition during a relatively large overbank event is still identified. Table 2.15. WY11 spring snowmelt event‐based FSP load calculations by site for each pre, post and overbank event duration using processed bihourly averaged FSP load (mg/s) data at each site. Site Source TCPT IN TCR1 MID Reach TCR3 OUT TCPT‐R1 Δ storage: R1‐R3 Δ storage: NET Δ storage: Duration (days): Daily rate (MT/day): PRE EVENT FSP load 9.5 6.0 4.6 3.5 1.4 4.9 17.8 0.27 OVERBANK EVENT FSP load 71.4 66.9 51.5 4.5 15.4 19.9 46.9 0.42 POST EVENT FSP load 3.4 2.8 2.1 0.6 0.7 1.3 7.3 0.17 A comparison of the estimated FSP load reductions for the WY10 and WY11 events aligns with expectations. Table 2.16 summarizes the error‐adjusted estimates of the floodplain retention FSP load (RFPfsp; MT) for the event; the daily average FSP retention rate (RFPfsp; MT/day); the average floodplain water FSP concentration (Ave FP [FSP]); and other interesting metrics. The analysis indicates the relatively smaller, short duration WY10 event resulted in less total FSP deposition but a much higher FSP concentration and rate of retention. These findings support the theoretical concept that shallower, lower flows on the floodplain will result in higher retention ratios. The relatively higher average FSP concentration of the floodplain waters also follows the concept of pollutant mass dilution as a result of orders of magnitude more runoff volumes produced by W11 snowmelt. While the magnitudes vary, the data and subsequent analysis suggest that FSP loads were removed from Trout Creek surface waters as a result of floodplain deposition during both WY10 and WY11 overbank events. Table 2.16. Summary of reach scale FSP loading mass balance results for WY10 and WY11 for the Upper Reach of Trout Creek. OVERBANK EVENT Snow
melt Peak RI (yr) Field error +/‐ (MT/d) WY10 2.7 WY11 6.3 2.2.6
Duration (d) Reach FSP change (MT) RFPfsp (MT) RFPfsp (MT/d) 0.04 4.5 4.9 5.9 1.2 Ave FP [FSP] mg/L 35 0.2 47.7 9.4 9.4 0.2 1.4 SCEfsp (MT) Post event SCEfsp (MT/d) 0.96 0.2 <0.001 <0.001 KEY FINDINGS FROM TROUT CREEK RESEARCH A common objective of SEZ restoration efforts is to improve downstream water quality as result of successful implementation. The research and data presented above demonstrates the challenges and complexity in documenting the pollutant load reduction for only two snowmelt events at one site for one pollutant. Theoretically, restoration of the physical processes that support a self‐sustaining and balanced riparian system Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 2.33 or SEZ should increase average annual floodplain retention of pollutants (particularly particulates) and reduce the pollutant generation from the system itself. We believe the sampling design and analysis approach presented herein could be slightly refined in the future to better quantify the key pollutant mass balance variables: change in pollutant load across a SEZ of interest (storage), pollutant load retained on the floodplain (RFPfsp) and pollutant load generated by the subject channel (SCEfsp). However, the sampling design and data collection needs of this effort were not trivial and require trained personnel, complex datasets and advanced data management techniques. Despite these challenges FSP load reductions were measured for two consecutive overbank events on the restored reach of Trout Creek. While the absolute accuracy of the actual FSP load reduction estimates could be improved, the signal of the load reductions far exceeds the noise due to sampling error. We argue this research demonstrates that total and fine sediment particle load reductions do occur during overbank events as quantified by both reach‐scale FSP loading analyses on Trout Creek and the floodplain retention sampling conducted on active floodplains on Trout Creek and the Upper Truckee River. 2.3
TROUT CREEK RESTORATION BENEFITS – UPPER REACH One of the initial objectives of this research was to quantify the restoration benefits of the Trout Creek project. Extensive time and resources were expended to identify and compile the breadth of existing data representing both pre‐ and post‐restoration site conditions. As documented in the process and products presented in the Riparian Ecosystem Restoration Effectiveness Framework (2NDNATURE et al., 2010a), quantification of restoration effectiveness is most relevant when a series of system attributes are identified and quantified during the restoration design phase. Critical consideration should be given to sampling design and data interpretation, so that pre‐ and post‐project comparisons have high confidence that the measured changes are due to implemented actions and not sampling error or natural variability. While a breadth of existing pre‐
restoration data was compiled by others on Trout Creek (see Table 2.1), the use of many of these data to confidently quantify the effectiveness of actions conducted in 2011 on Trout Creek is not possible, particularly with respect to vegetation community, habitat and biological community attributes. However, a series of assumed desired geomorphic form responses have been quantified using existing datasets as well as data obtained by the 2NDNATURE team during this research effort. The following is our additional rationale for why resources were not expended to quantify the ecological responses to restoration actions: 1) TAC discussions during this research prioritized the development of reasonable method to estimate the water quality benefits of SEZ restoration. This focus required extensive physical/chemical data collection and analyses that precluded the availability of resources to address vegetation, habitat and biological community improvements with the recommended rigor and considerations outlined in the Framework (2NDNATURE et al., 2010a). 2) The concurrent development of the Stream Load Reduction Tool (SLRT) allowed the 2NDNATURE team to focus the evaluation and quantification of potential input parameters to SLRT and assist in quantifying the long‐term water quality benefits of the restoration of Trout Creek. Many of the geomorphic form attributes presented below are direct inputs to SLRT. 3) Minimal pre‐restoration habitat and biological data was collected and documented in a manner that would allow direct pre‐ and post‐condition comparisons and provide a high confidence that 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 2.34 | July 2013 differences in the metric values were attributable to restoration actions and not natural variability or sampling error. 4) When implemented appropriately, the Framework (2NDNATURE et al., 2010a) attribute list is created during the restoration planning phase, when specific measurable attributes can be developed and quantified for the SEZ prior to restoration. The iterative attribute development process requires identification of specific and focused hypothesized cause and effect linkages as result of future restoration. The identification and development of Trout Creek attributes a decade post‐restoration inherently lacks the specific insight and intentions of the restoration team with respect to ecological communities that were hypothesized to greatly benefit from successful restoration actions. The physical and chemical intentions are typically those that support a self‐sustaining balanced fluvial system and thus are simpler to characterize than specific species or ecological communities. A series of geomorphic attributes were quantified using the best available data for both pre‐ and post‐
restoration conditions for the Upper Reach of the Trout Creek (Table 2.17). Many of these metrics are either required SLRT inputs (see Section 3), critical geomorphic form attributes, or can be quantified using data obtained during this research effort. The % and direction (increase or decrease) of change of each metric is provided, with nearly all attributes demonstrating a change in the likely direction intended by the restoration team. The data sources and analysis methods are summarized below. The units vary between English and metric due to the required format for specific SLRT inputs that are used to estimate the average annual FSP load reduction achieved by this restoration project in Section 5. The most significant change to Trout Creek morphology was the 20% increase in overall channel length and 62% reduction in the average reach channel capacity. The construction of meander bends throughout the reach increased total length and decreased channel slope and subsequently reduced the shear stresses acting on the channel and banks during elevated flow conditions. The substantial decrease in channel capacity has likely increased the adjacent meadow shallow groundwater elevations and soil moisture during the dry season, as evidenced by the prolific meadow vegetation community. The frequency, duration and magnitude of overbank flow have been significantly increased, leading to greater opportunity for pollutant retention on the floodplain. Table 2.17. Key attributes quantified for pre‐ and post‐restoration conditions of the Trout Creek Upper Reach. Ecosystem Attribute category class Pre‐
Post % restore (2011) change 0.0016 0.0013 ‐19% Total stream length (m) 1530 1829 +20% Stream sinuosity 1.54 1.85 +20% Average top width (m) 7.9 4.0 ‐49% 1001 +89% Attribute Channel slope Geomorphic Channel Outside bend length (m) 530 Form Stability Outside bend bank height (m) 1.34 1.1 ‐18% 37 53 +43% 1000 828 ‐17% Straight sections bank height (m) 1.0 0.76 ‐24% Bank angle of straight sections (deg) 34 39 +15% Bank angle of outside bends (deg) Straight length (m) Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT Ecosystem Attribute category class Pre‐
Post % restore (2011) change Channel capacity (cfs) 200 88 ‐56% Water depth at channel capacity (m) 1.10 0.93 ‐15% 5.0 1.9 ‐62% 1.2 17.8 +1,383% 96 1,380 +1,337% 1.09 0.65 ‐40% Attribute Reoccurrence interval discharge at TCPT Geomorphic Form Channel/ Floodplain | 2.35 needed to exceed channel capacity (yrs) Duration of overbank flow (days) Relationship (average annual using USGS data) Volume of overbank flow (ac‐ft) (average annual using USGS data) Average depth to groundwater (m) Beyond the general statements above, the interpretation of the magnitude of change and the post‐restoration attribute values is limited with respect to evaluating the “effectiveness” of the restoration with respect to the intentions at the time of restoration. Recommended applications of the Framework (2NDNATURE et al., 2010a) by restoration practitioners includes documentation of future post‐restoration targets for identified attributes, allowing a much more valuable interpretation of the absolute values such as a 20% increase in linear stream length or a channel capacity of 88 cfs 10‐years following restoration actions. Channel slope was calculated in GIS to determine the elevation change between start/end landmarks of stream reach using high resolution LiDAR data (Watershed Sciences, 2010) divided by the total stream length derived from SH&G shapefiles of channel alignment (SH&G, 2001). These shapefiles were also used to calculate stream sinuosity using valley length divided by stream length distances. Reaches were separated into outside bends and straight sections using aerial photography, where an outside bend was determined to be any section of stream length with a continuous arc representing a meander bend. All other sections were considered straight and cross section data were fleshed out to populate the metrics in Table 2.17. The lengths associated with each category were summed to produce outside bend length and straight length. Pre‐project HEC‐RAS cross sections (surveyed by SH+G in 2000) were used for channel geometry measurements for pre‐restoration scenario, while 2011 cross section data collected by 2NDNATURE/CTC were used for the post‐restoration scenario. Average bank angle was calculated using respective cross section data and averaging the angle that represented top of bank to bottom of bank for each cross section. Average top width was determined using respective cross section data and averaging the distance between left and right top of bank at each cross section. Pre‐restoration channel capacity was calculated using pre‐project HEC‐RAS model runs (by SH&G) to estimate the discharge value at which the water level reaches bank elevation. These model runs were also used to measure the water depth at channel capacity measured as the distance between water surface elevation and thalweg. Post‐restoration channel capacity was generated using Manning’s equation with 2001‐2010 cross section analysis outputs provided by CTC (see Table 2.8). Post‐restoration water depth at channel capacity was measured by vertical difference between top of bank and thalweg using 2011 cross section data from 2NDNATURE/CTC. Average bank heights were measured at each cross section using the elevation difference between top of bank and toe of bank along Upper Reach cross sections. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 2.36 | July 2013 Frequency of overbank flow was generated using the flow duration curve produced with daily discharge for TCPT and is described as the daily exceedence probability of the channel capacity value. Duration and volume of overbank bank flow comparisons rely upon the monitored WY11 snowmelt overbank event for the post‐
project conditions. The estimated duration and magnitude for pre‐restoration was determined by applying the WY11 snowmelt hydrology to the channel morphology that existed pre‐restoration. Groundwater transects data from Tague et al. (2008) were used to generate average depth to groundwater for both pre‐ and post‐
restoration scenarios and all measurements were averaged to generate depth to groundwater values. The SLRT results for the Upper Reach of Trout Creek are presented in Section 5, and include average annual estimates of floodplain inundation frequency and volumes, mass of FSP delivered to and retained upon the floodplain, and the mass of FSP generated from the channel for both pre‐ and post‐restoration conditions. These metrics in addition to an estimate of the average annual FSP load reduction as a result of restoration actions provide additional information to support the effectiveness of the Trout Creek restoration. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 3
| 3.1 STREAM LOAD REDUCTION TOOL (SLRT) METHODOLOGY The Stream Load Reduction Tool (SLRT) recommended methodology allows for a relatively simple and consistent estimate of the average annual fine sediment load reduced as a result of specific morphologic modifications to an open channel system, either a stream reach or smaller scale stream environment zone (SEZ), that accepts urban stormwater runoff in the Tahoe Basin. The development of the recommended methods was guided by a list of specific objectives that were refined with the TAC (see Table 1.1) to ensure the tool met the needs of stream restoration practitioners while providing valuable outputs that could potentially be incorporated into the Lake Tahoe Clarity Crediting Program (Crediting Program; LRWQCB and NDEP, 2011). These design objectives for the tool include: •
•
•
•
•
Reliable, repeatable and cost‐effective method, Applicable to range of SEZ sizes, Incorporation of best available data and hypotheses of system function, Consistent with accepted stormwater tools and programs that support the Lake Tahoe TMDL, and Improvable and adaptable over time. The SLRT is not intended to inform SEZ restoration design components, but rather provide SEZ restoration practitioners with a reasonable and consistent method to estimate and communicate the water quality benefit of restoration actions in a format acceptable and comparable to other water quality benefit estimation approaches used to support the Lake Tahoe TMDL. The proposed methodology provides a computational process to estimate the water quality benefit of SEZ restoration, macro‐enabled beta tools and user guidance on how to obtain data to generate the necessary SLRT inputs. The field of environmental improvement desires quantification and demonstration of the water quality benefits as a result of implementation to justify funding and document benefits. However, many funders or implementation programs throughout the country lack specific guidance on the quantitative estimation and verification methods to be used to report the potential benefits of improvement actions. In many instances, the Lake Tahoe TMDL and supporting Crediting Program have made great progress by clarifying an approach to estimating and tracking both project and regional scale water quality benefits by the Tahoe Basin implementers. The significant focus on upland and urban land management to achieve the TMDL goals under the initial version of the Crediting Program has deemphasized the potentially significant basin‐wide opportunity to reduce pollutant loading to Lake Tahoe as a result of SEZ restoration. SEZs appear to provide significant opportunity to supplement load reductions from urban source control and treatment systems. Typical centralized stormwater treatment BMPs (SWTs; e.g., dry basins, wet basins, cartridge filters, bed filters, etc.) that are constructed to accept and treat stormwater from public and private lands usually do not exceed 1 acre in size, nor do treatment volume capacities typically exceed 1.5 acre‐feet of water. Additionally, land availability to construct more SWTs in urban areas is limited, thus increasing the cost and decreasing the opportunities of future SWT implementation. In contrast, SEZs can be a few to hundreds of acres in size, require less continued maintenance to maintain the processes that improve water quality, and result in improvements to a number of beneficial uses and TRPA thresholds, including habitat, recreational, aquatic and wildlife resources. The Crediting Program provides a regulatory framework and supporting tools to estimate, track and verify the pollutant load reduction benefit of water quality improvement actions conducted in urban catchments, and the 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.2 | July 2013 Pollutant Load Reduction Model (PLRM; nhc et al., 2009) is the customized desktop tool recommended to estimate these benefits. The PLRM does not model open channels or streams, thus a critical objective of the SLRT is to provide an SEZ pollutant load estimation tool that reasonably aligns with PLRM outputs. The SLRT was developed as a potential load reduction estimation tool that could be incorporated into future versions of the Crediting Program if desired. Restoration of riparian systems in the Tahoe Basin has included significant geomorphic modifications to better align the existing geomorphic form with the contributing hydrology and sediment load, valley characteristics and other controlling factors. In nearly all instances, a stated goal of SEZ restoration actions in the Tahoe Basin includes improved downstream water quality, yet the ability to measure and quantify the achievement of this goal is extremely challenging. The daily, seasonal and annual hydrologic and pollutant generation variability requires years of high resolution data collected both pre and post improvements to measure the pollutant load reductions achieved above and beyond natural variability. While it is generally accepted that effective SEZ restoration efforts that restore an incised, eroding open channel into a functional, self‐sustaining feature do result in reduction in the average annual loads of sediment and nutrient constituents delivered downstream, methods available to quantify this benefit currently do not exist. The SLRT methodology has been developed to incorporate existing datasets, key geomorphic principles and pollutant generation and transport processes to provide a reasonable approach to estimate the water quality benefit of SEZ modifications. The recommended methodology is the result of a number of attempts at computational methods that iteratively resulted in the approach that would best meet each of the stated SLRT objectives. The team had a strong desire to provide a reasonable and repeatable method that did not require extensive resources or modeling expertise by the user to implement. The spatial morphologic complexity of a SEZ and the temporal hydrologic variability on daily, seasonal and annual time scale results in highly complex modeling opportunities, and these methods would involve extensive computational and processing requirements. Therefore, the recommended methodology estimates the average annual load reduction by reducing 18 years of historic hydrology data and available pollutant loading data into more manageable formats, and utilizing critical reach scale geomorphic characteristics. Opportunities to leverage site‐specific hydrologic or water quality data are identified, but the critical data needed by the SLRT user are specific pre‐ and post‐restoration channel, riparian and floodplain morphologic attributes. The user guidance (Section 4) includes key considerations and approaches to identify potential data sources, quantify each attribute, and select appropriate spatial resolution to represent the open channel reach of interest. 3.1
SLRT POLLUTANT OF CONCERN The initial version of SLRT methodology focuses on estimation of FSP (< 16µm) only, the Lake Tahoe TMDL primary pollutant of concern. This allowed focus of limited available resources, while providing a framework and methodology that can be efficiently expanded to other TMDL pollutants (i.e., total suspended sediment (TSS), dissolved phosphorous (DP), total phosphorous (TP), dissolved nitrogen (DN) and total nitrogen (TN)) in the future. 3.2
COMPUTATIONAL APPROACH The area of SEZ improvement is defined as a reach with definitive upstream and downstream boundaries. Consistent with PLRM, SLRT computes the average annual pollutant load reduction (SEZfsp) as the difference Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.3 between the pollutant load generated at the downstream extent (outlet) of a specific SEZ given conditions before (OUTfsp‐pre) and after (OUTfsp‐post) improvements, or: SEZfsp (MT/yr) = OUTfsp ‐pre – OUTfsp ‐post (EQ3.1) Figure 3.1 conceptually illustrates the pollutant mass balance approach employed by SLRT to estimate the average annual pollutant load at the downstream boundary (OUTfsp) for any SEZ configuration. SEZ geomorphic impairments are typically characterized by oversized or entrenched channels that have evolved from a series of previous impairments, modifications or system imbalances. Water quality effectiveness of a stream restoration project (defined as the expected pollutant load reduction as a result of restoration actions) can be assessed as a function of: 1.
2.
the storage of pollutants that would otherwise have reached downstream areas (floodplain retention), and the reduction in pollutant generation via improved stability of the stream banks that would otherwise have eroded. Computationally, the pollutant load at the downstream boundary (OUTfsp) equals the load delivered to the reach (INfsp) minus the mass retained on the floodplain (RFPfsp) plus the contribution from stream channel erosion (SCEfsp): OUTfsp (MT/yr) = INfsp – RFPfsp + SCEfsp (EQ3.2) An appropriate estimation of the average annual pollutant load reduction realized as a result of SEZ restoration actions requires a consistent and comparable calculation of the mass of pollutant generated and/or stored within the restored reach, pre‐ and post‐restoration. The difference between the scenarios should be, to the best approximation, attributable to the effectiveness of the restoration actions and not hydrology, pollutant supply variations from the contributing catchment, or other sources of variability. In order to ensure SLRT outputs are sensitive to restoration actions and not the inherent natural variability of these systems, both the pre‐ and post‐restoration scenarios use the same incoming average annual catchment hydrology and FSP yields (INfsp). The recommended use of a consistent time series of hydrology and pollutant loading for all SEZs evaluated using SLRT allows direct comparison of the pollutant load reduction estimates across SEZs, in addition to the opportunity to aggregate estimates to inform overall Basin‐wide SEZ water quality improvements. The estimation of a representative average annual pollutant load reduction requires an extended hydrologic and pollutant loading time series that collectively represent a reasonable range of hydrologic and pollutant loading conditions introduced to, and transported through, the subject SEZ reach. Remaining consistent with PLRM (nhc et al 2009), the SLRT hydrologic inputs are based on WY89‐WY06 hydrology. With the exception of SEZ restoration sites that are located in close proximity to long‐term USGS gages, site‐specific hydrology datasets from the time period of interest (WY89‐WY06) likely do not exist. Using historic hydrologic data from the Tahoe Basin, the necessary hydrology into any SEZ in the Tahoe Basin are estimated in SLRT based on simple catchment characteristics determined by the user. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 The Stream Load Reduction Tool (SLRT) computes the average annual pollutant load reduction (SEZfsp) as the difference
between the pollutant load generated at the downstream boundary of a specific SEZ during pre-restoration (OUTfsp-pre)
and post-restoration (OUTfsp-post) conditions.
S E Z
fsp
(M T / y r) = O U T
fsp - p re
- O U T
fsp - p o st
IN
Load Retention
Load Retention
RFP
RFP
Stream
Floodplain
Load Generation
Floodplain
SCE
Load Generation
SCE
OUT
For both pre- and post-restoration scenarios, SLRT employs a pollutant mass balance approach to estimate the average
annual pollutant loads at the downstream boundary of an SEZ (OUTfsp). The downstream load is equal to the inflowing
load at the upstream boundary (INfsp) less any sediment retained on the floodplain during overbank flow (RFPfsp) plus any
sediment generated by instream channel erosion during critical flows (SCEfsp).
O U T
fsp
(M T / y r) = I N
fsp
- R F P
fsp
+ S C E
fsp
SLRT CONCEPTUAL MODELING APPROACH
Figure 3.1
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.5 Different hydrologic and morphologic conditions drive the critical processes of floodplain retention (RFPfsp) and stream channel erosion (SCEfsp), requiring SLRT to model these two processes separately: 1.
2.
Floodplain retention modeling requires overbank flows only, rendering the frequency of occurrence and magnitude of flows below channel capacity irrelevant. An average annual flow frequency distribution focusing on the less frequent, higher discharge flows is used to model floodplain retention (RFPfsp) in SLRT using a simple mass balance approach and spreadsheet tool. In contrast, flows that range between low flow and channel capacity can generate a significant fraction of the average annual sediment load from the channel over decadal time steps. Also, the hydrograph structure is critical to estimating channel erosion processes, as the sequence of flows and associated groundwater levels affect the bank stability over time. Given the complexity of modeling stream channel erosion processes, SLRT relies upon a series of annual probability hydrographs input to the dynamic version of the Bank Stability Erosion Model (BSTEM‐Dynamic; Simon et al. 1999; 2011a) to estimate average annual pollutant loads generated from stream channel erosion (SCEfsp). It is assumed that any other potential sources or sinks of FSP (or total suspended sediment) within a defined SEZ on an average annual basis are negligible relative to channel erosion and floodplain deposition. This assumption is likely not true for nutrient species, and additional sources or sinks will need to be considered and potentially incorporated if nutrient modules are incorporated into SLRT in the future. 3.3
CATCHMENT HYDROLOGY SLRT requires average annual catchment hydrology in two different formats: annual flow frequency distribution and probability hydrographs. These catchment hydrologic inputs to the SEZ are required for 3 critical computations in SLRT (see Equation 3.2): 1.
2.
3.
INfsp: The catchment average annual flow frequency distribution is integrated with representative FSP concentrations to estimate the average annual daily (MT/day) and annual (MT/yr) FSP load delivered to the subject SEZ. RFPfsp: The catchment average annual flow frequency distribution is used to isolate flow conditions that exceed the channel capacity and deliver FSP loads to the floodplain. Incremental fractions of the loads delivered (DFPfsp) to the floodplain are retained (RFPfsp) as a function of the magnitude of each overbank flow interval and the floodplain condition. SCEfsp: A series of catchment probability hydrographs are used to estimate stream channel erosion volumes in the BSTEM‐Dynamic. It is intended that SLRT can be applied to SEZs that vary in catchment size and land use distribution, requiring the hydrology guidance to be appropriate for relatively small (<0.015 mi2) to large (>50 mi2) catchments with varying amount of impervious surfaces. Extensive hydrologic research and data analysis conducted on the Tahoe Basin watersheds have identified significant regional differences that are driven primarily by geology and meteorology (USDA, 2000; nhc et al., 2009; LRWQCB and NDEP, 2010). Utilizing the fundamental concepts of the rational method and the known regional hydrologic variations, available USGS streamflow data and PLRM urban hydrology data was used to identify a recommended approach to determine catchment hydrology. The following criteria were used to guide the selection of the SLRT hydrologic approach: 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.6 | 




3.3.1
July 2013 Create a method to allow the user to obtain reasonable average annual hydrology data in the necessary SLRT formats with minimal effort. Preserve regional hydrologic patterns that have been documented by others. Follow the fundamental concept that discharge increases proportionally with catchment area and % imperviousness. Provide adequate hydrologic resolution to capture theorized differences in the processes that control the relative sources or sinks of particulate pollutants within an open channel on an average annual basis; and Include all 6574 days of the defined discharge interval (WY89‐WY06) while significantly reducing data processing needs. HYDROLOGY DATASETS Historic hydrologic datasets were obtained or modeled for a series of Tahoe catchments that collectively are assumed to represent the potential range modeled by future SLRT users (Table 3.1), from stream riparian zones to small urban SEZs. The data were analyzed to identify a simple yet defensible method to provide the user with a reasonable incoming hydrology in the format required for SLRT using a series of readily obtained catchment characteristics. There are two catchment types, non‐urban and urban. The catchment of a typical LTIMP stream is termed non‐
urban and generally defined as a catchment area >1 mi2 (~640 acres) with <10% impervious surfaces. Urban catchments are smaller than 1 mi2 (~640 acres) and generally greater than 10% impervious. For non‐urban catchments in Table 3.1, the October 1, 1988 to September 31, 2006 mean daily streamflow datasets were obtained for 11 USGS regional gages that collectively represent all four regions within the Tahoe Basin (Figure 3.2). A range of 8 urban catchments were selected from catchments that had existing PLRM baseline models developed by either 2NDNATURE or northwest hydraulic consultants (nhc) (see Figure 3.2). For the selected urban catchments, PLRM outputs were post‐processed in SWMM to estimate the 18 year time series of mean daily flow. The contributing catchment area was obtained from the USGS site or exported from the respective PLRM models. Each of the daily flow time series were reduced into formats applicable for both stream channel erosion and floodplain deposition estimates and a series of analyses were conducted as described below. Type Region Catchment name Area (acres) % Impervious Urban catchments Table 3.1. Hydrology calibration dataset. Note the unit of measurement for total area is different depending upon the drainage type (non‐urban vs. urban). See Figure 3.2 for locations. South South South South South North North North Rocky Point Park Avenue Wildwood Osgood Blue Lakes Tahoe Estates Dollar Point Tahoe Pines C 169 225 108 341 17.3 195 216 34.5 18% 30% 43% 23% 35% 21% 32% 21% PLRM met grid/ Ave annual precip (in/yr) 891 – 20.69 892 – 18.56 891 – 20.69 890 – 21.60 779 – 22.91 462 – 33.43 342 – 35.06 150 – 37.92 Type Region Watershed (USGS gage ID) Non‐urban catchments Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT South South UTR UTR West West West North North East East Trout Creek (#10336775) Trout Creek (#10336780) Upper Truckee River (#103366092) Upper Truckee River (#10336610) Ward Creek (#10336676) Blackwood Creek (#10336660) General Creek (#10336645) Third Creek (#10336698) Incline Creek (#10336700) Glenbrook Creek (#10336730) Logan House Creek (#10336740) 3.3.2
Area (mi2) 23.7 36.7 34.3 54.9 9.70 11.2 7.44 6.05 6.74 4.11 2.09 | 3.7 Snowtel station ID/ Ave annual precip (in/yr) CSLT South Y (672) – 29.91 CSLT South Y (672) – 29.91 CSLT South Y (672) – 29.91 CSLT South Y (672) – 29.91 West Shore (238) – 34.96 West Shore (238) – 34.96 West Shore (238) – 34.96 Incline Village (817) – 26.95 Incline Village (817) – 26.95 East Shore (903) – 21.57 East Shore (903) – 21.57 FLOW FREQUENCY DISTRIBUTION An average annual flow frequency distribution is used to estimate the average annual FSP yield of the contributing catchment (INfsp) and to model the floodplain FSP retention (RFPfsp). Using a flow frequency distribution greatly simplifies the 18‐year hydrology record, while preserving the incremental discharge intervals relevant to pollutant loading and floodplain retention. For urban catchments, the flow frequency distribution is determined using catchment area and % impervious. For larger stream (non‐urban) catchments, the flow frequency distributions are based on the region, catchment area and mean annual precipitation. Below describes the approach used to generate and estimate catchment flow frequency distribution for any SEZ of interest. The 18 years of mean daily flow values were binned into 50 intervals to create flow frequency distribution for each of the calibration sites (see Table 3.1). Forty nine of the 50 bins have a standard discharge interval (Qbi). The incremental discharge interval for each site was determined such that 70% of all flows were contained within bins 1‐3, in order to standardize the frequency that lowflow conditions occur. There is a significant amount of time (~60% of all daily data) when urban sites are dry, and therefore this analysis was conducted only for mean daily flows > 0 cfs. Each of the 50 bins (bn) is represented by the median flow value (Qb), where: Qb = (Qbi * bn) – (0.5 * Qbi) (EQ 3.3) In order to capture the full range of mean daily flows, the discharge range of the final bin (bin #50; b50) will vary across sites and can be up to 150 times greater the bin interval discharge (Qbi). To reduce the influence of an extremely infrequent high discharge outlier, b50 is represented by the average of all flows within the bin instead of the median value. Using the calculated bin intervals for each site, flow frequency distributions were plotted as histograms and compared graphically. While the hydrology data for the 8 urban catchments were similar in shape and distribution, the non‐urban datasets varied considerably based on the regional location of the stream (see Table 3.1 and Figure 3.2). For each catchment type, the most representative flow frequency shape was selected to represent the regional flow frequency distribution of 50 discharge bin intervals for any SEZ of that type and region (Figure 3.3). Patterns across the catchment types and regions are used to determine the bin interval (Qbi) for bins 1‐49 and as well as the discharge for bin #50 (Qb‐50). 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 LEGEND
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FIGURE
Fi g u r e 3.2:
X . X Hydrology
: H y d r o l o and
g y meteorology
a n d m eteo data
r o l o g used
y d a tota develop
u s ed SLRT
to
approach
d ev el o p and
th e methodlogy.
S L R T a p p r o See
a c h Table
a n d 3.1
m ethforo details.
d o l o g y . S ee T a b l e
3 . 1 f o r d eta i l s .
M i l es
0
1. 25
2. 5
5
R E P R E S E N T A T IV E F L O W
F R E Q U E N C Y DI S T R I B U T I O N H I S T O G R A M S
Average annual flow frequency distributions by catchment type and region are presented below. These
histograms are based on the mean daily flow values for 18 years of data (WY89-WY06) for the catchment types
and regions presented in Table 3.1.
Frequency of occurrence (%of time)
100
90
80
0
60
50
0
30
20
10
0
Urban
100
90
80
0
60
50
0
30
20
10
0
North South Stream
100
90
80
0
60
50
0
30
20
10
0
UTR Stream
100
90
80
0
60
50
0
30
20
10
0
West Stream
100
90
80
0
60
50
0
30
20
10
0
East Stream
1
3
5
9
11 13 15 1
19 21 23 25 2
29 31 33 35 3
39
1
3
5
9
Bin Number bn
REPRESENTATIVE FLOW FREQUENCY DISTRIBUTION HISTORGRAMS
Figure 3.3
3.10 | July 2013 A range of catchment characteristics were tested to identify the best predictors of both the bin flow interval (Qbi) and maximum mean daily flow (Qmax) using the available hydrology datasets. In urban catchments, the total impervious area (Ai; in acres) showed the best relationship to both metrics, with R2 values of 0.95 and 0.65, respectively (Figures 3.4A&B). These empirical equations are used in SLRT where: URBAN Qbi = 9.0E‐04 * Ai URBAN Qmax = 0.13 * Ai (EQ 3.4) (EQ 3.5) For non‐urban stream sites, regional precipitation variations and unit annual runoff are used to predict the discharge bin interval (Qbi). The total discharge volume over the 18‐year time period (Qsum) is strongly correlated to the assigned Qbi (Figure 3.5A) and the mean annual precipitation (P) (see Table 3.1 for catchment average annual precipitation) appears to be a strong predictor of the total discharge per catchment area (A) (Figure 3.5B), where: NON‐URBAN Qbi = 4.0E‐05 * Qsum NON‐URBAN Qsum/A = 0.01 * P4.06 (EQ 3.6) (EQ 3.7) We combine and rearrange EQ 3.6 and EQ 3.7 to estimate Qbi for bins 1‐49 in a non‐urban catchment as a function of mean annual precipitation and catchment area: NON‐URBAN Qbi = 4.0E‐07 * P4.06 * A (EQ 3.8) The maximum mean daily discharge as a function of precipitation and catchment area yields distinct regional patterns with different slopes for the 11 stream (non‐urban) sites (Figure 3.6A). The slopes noted in Figure 3.6A are termed regional coefficients (R) and used to estimate the maximum mean daily flow for the 18‐year hydrology record using precipitation, catchment area and the regional coefficient (Figure 3.6B): NON‐URBAN Qmax = 640 * R * P * A (EQ 3.9) The final bin (b50) must contain all flows between the upper value of b49 and the maximum mean daily flow (Qmax). In bins 1‐49, the median discharge is used for all SLRT calculations (see EQ 3.3), but for the final bin (b50) the median appears to overestimate the magnitude of the greatest 0.3% of discharges. In the 8 urban catchments, the predicted median value for Qb‐50 was much higher than the observed average flow values, and therefore the 25th percentile value (Figure 3.4C) is used: URBAN Qb‐50 = Qmax – (0.75 * (Qmax – (Qbi *49))) (EQ 3.10) The observed average flow for bin 50 was compared to the predicted median value for the 11 non‐urban sites. In the stream catchments, the predicted mean value more closely aligned to the observed average flow values (Figure 3.6C), yielding the equation to calculate Qb‐50 as: NON‐URBAN Qb‐50 = Qmax – (0.5 * (Qmax – (Qbi *49))) (EQ 3.11) U R B A N C A T C H M E N T S (sit es l ist ed in T ab l e 3 . 1 )
0.08
0.0
A . I m p erv io u s area v . b in in t erv al
fl o w v al u e
0.06
Q (cfs)
bi
Bin Interval (cfs) Both the bin interval discharge value (A)
and maximum mean daily flow (B) are
highly correlated to the total impervious
area in urban catchments.
Qbi = 9.0E-0.4Ai
R2 = 0.95
0.05
0.0
0.03
0.02
0.01
0.00
0
10
20
30
0
50
60
0
80
90
Ai (acres)
Impervious Area (acres) y = 0.0009x
R = 0.95
1
B . I m p erv io u s area v . m ax im u m
m ean d ail y fl o w fro m W Y 8 9 - W Y 0 6
Qmax (cfs)
Max Mean Daily Flow (cfs) 12
Qmax = 0.13Ai
R2 = 0.65
10
8
6
2
0
0
10
20
30
0
50
60
0
80
90
A (acres)
i
Impervious Area (acres) 6
th
Qb-50 = Qmax - (0.75 * (Qmax - (Qbi *49))
Predicted Qb-50 (cfs)
For urban catchments, Qb50 is
calculated as the 25th percentile value
between Qb-49 and Qmax:
y = 0.13 x
R = 0.65
5
Bin 50 Predicted 25th Percentile (cfs) C . O b serv ed av erage m ean d ail y
fl o w o f b in # 5 0 v . t he p red ict ed 2 5
p ercen t il e fl o w o f b in # 5 0
3
y = 0.97x
R2 = 0.93
2
1
0
0
1
Data Source: PLRM outputs and SWMM
post-processing results as modeled by 2N
and nhc (2012).
2
3
Observed Q
5
6
(cfs)
Bin 50 Observed Average Flow (cfs) b-50
URBAN: FLOW FREQUENCY DEVELOPMENT
Figure 3.4
N O N - U R B A N C A T C H M E N T S (sit es l ist ed in T ab l e 3 . 1 )
A . T o t al 1 8 - y ear d ischarge v o l u m e (W Y 8 9 W Y 0 6 ) v . b in in t erv al fl o w v al u e
35
30
20
Qbi (cfs)
Total 18-year discharge volume (Qsum)
shows a strong correlation to the bin
interval discharge (Qbi) in non-urban
catchments.
25
15
Qbi = 4.0E-05Qsum
R2 = 0.99
10
5
0
0
100,000
200,000
300,000
00,000
Qsum (cfs)
500,000
600,000
00,000
y = E-05x
R = 0.99
25
20
Ratio: Total Flow to Area Regional mean annual precipitation is
strongly correlated to the total 18-year
discharge volume per catchment area for
non-urban catchments.
Qsum/A = 0.01P4.06
R2 = 0.84
Qsum : A (in thousands)
B . M ean an n u al p recip it at io n v . t he rat io
o f t he t o t al 1 8 - y ear d ischarge v o l u m e t o
cat chm en t area
15
10
5
0
0
5
10
15
20
25
30
35
0
P (in)
Mean Annual Precip (in) Data Source: USGS mean daily
discharge for 11 stream gage sites
listed in Table 3.1.
y = 1E-05x
R = 0.8
.06
For non-urban (stream) catchments, the two equations above are
combined to generate an equation to estimate the site bin interval
discharge (Qbi) as a function of the regional mean annual precipitation
and total catchment area (A), where:
Qbi = 4.0E-07 * P4.06 * A
NON-URBAN: FLOW FREQUENCY DEVELOPMENT,
BIN INTERVALS
Figure 3.5
N O N - U R B A N C A T C H M E N T S (sit es l ist ed in T ab l e 3 . 1 )
3500
3000
West Shore
Qmax = 4.2P*A
R2 = 0.62
2500
Qmax (cfs)
Maximum Mean Daily Flow (cfs) Mean annual precipitation and catchment area
show regional correlations to the maximum mean
daily flow over the 18-year time frame.
UTR
Qmax = 1.9P*A
R2 = 0.99
A
A . R egio n al d ifferen ces in m ean an n u al
p recip it at io n an d t o t al cat chm en t area v .
m ax im u m m ean d ail y fl o w fro m W Y 8 9 W Y 0 6
2000
1500
North/South Shore
Qmax = 0.52P*A
R2 = 0.91
1000
500
West Shore
East Shore
Upper Truckee River
North South Shore
0
East Shore 0
Qmax = 0.81P*A
R2 = 0.67
200
00
600
800
1,000
1,200
1, 00
1,600
1,800
P*A
Annual Precip (in) x Area (sq miles) 3500
2500
Qmax (cfs)
Regional coefficients (R) are the slopes of each
regional curve shown in Panel A. The product
of R, precipitation and catchment area are
used to estimate the maximum mean daily
flow.
B
3000
Max Mean Daily Flow (cfs) B . R egio n al co efficien t , m ean an n u al
p recip it at io n an d t o t al cat chm en t area v .
m ax im u m m ean d ail y fl o w fro m W Y 8 9 W Y 0 6
2000
Qmax = 640 R*P*A
R2 = 0.96
1500
1000
500
0
0
1
2
3
5
6
R*P*A
R * P (in) * A (sq miles) y = 639.99x
R = 0.96 6
3000
Qb-50 = Qmax - (0.5 * (Qmax - (Qbi *49)))
Predicted Qb-50 (cfs)
For non-urban catchments, the bin 50 discharge
value is calculated as the median value between
Qb-49 and Qmax:
C
2500
Bin 50 Predicted Midpoint Flow (cfs) C . O b serv ed av erage m ean d ail y fl o w o f b in
# 5 0 v . t he p red ict ed m ed ian p ercen t il e fl o w
o f b in # 5 0
2000
1500
1000
y = 0.91x
R2 = 0.92
500
0
0
Data Source: USGS mean daily discharge for
11 stream gage sites 3.1
500
1000
1500
2000
2500
3000
Observed Qb-50 (cfs)
Bin 50 Observed Average Flow (cfs) NON-URBAN: FLOW FREQUENCY DEVELOPMENT,
MAXIMUM MEAN DAILY AND BIN 50
Figure 3.6
y = 0.91x
R = 0.92
3.14 | 3.3.3
July 2013 ANNUAL PROBABILITY HYDROGRAPHS A series of annual probability hydrographs are used as the hydrologic inputs to BSTEM‐Dynamic to estimate the FSP load generated from stream channel erosion (SCEfsp). The 18‐year mean daily flow record from PLRM or USGS is reduced to 4 annual hydrographs representing the 99th, 75th, 50th, and 25th percentile mean daily flow (Qmd‐99, Qmd‐75, Qmd‐50, Qmd‐25) from WY89‐WY06 at the upstream boundary for the SEZ of interest. The annual probability hydrographs are reported in cubic meters per second (cms) to match the hydrologic unit input requirements for BSTEM‐Dynamic. These four annual hydrographs are generated based on catchment type (urban or non‐urban), Tahoe Basin region (see Table 3.1), and contributing catchment area. This approach reduces the processing needs and focuses the estimates on the flow conditions that are expected to have the greatest impact on channel erosion on decadal time scales. Below describes the approach used to generate 4 annual probability hydrographs based on the 18‐year record for any SEZ of interest. For each catchment listed in Table 3.1, the percentile annual hydrographs were calculated using the 18 years of mean daily flows and compared graphically. Similar to the flow frequency distribution analysis, differences in the annual hydrographs were related to catchment type (urban vs non‐urban), regional location, catchment area, and % impervious area (urban only). The four percentile annual urban hydrographs were divided by the impervious catchment area (Ai; acres) to create unit runoff hydrographs. Each daily percentile flow (Qmd‐99, Qmd‐
75, Qmd‐50, Qmd‐25) is then averaged by region (north, south) to create the regional percentile annual hydrographs shown in Figure 3.7. In non‐urban catchments, Qmd‐p was normalized by the total catchment area (A; sq‐miles) and averaged by region, as shown in Figure 3.8. These representative annual hydrographs allow the SLRT user to select the correct regional unit hydrograph series based on catchment type (urban or non‐urban) and multiply the normalized percentile daily flows (Qmd‐p/A) by the subject SEZ catchment area (or impervious area in urban catchments) to obtain a series of representative percentile annual hydrographs. 3.3.4 VALIDATION Flow Frequency Predictions: Given the limited number of complete WY89‐WY06 hydrologic datasets available, a traditional validation of the methods using sites not included in the analysis was not possible. As an alternative, 3 urban and 5 non‐urban sites were used to compare the observed and predicted frequency of flows between the 1.5 and 20‐year recurrence intervals (Table 3.2). Flows in this range are expected to be the most relevant to the processes that drive FSP generation and retention within a defined SEZ. For both the urban and non‐urban catchment hydrology datasets, a flow frequency analysis of the annual peak mean daily flows for the 18 years of data was used to determine recurrence interval flow values. Given the high frequency of low flows in urban catchments, a partial duration flow frequency analysis was used and included all flows greater than the smallest value from the annual maximum series. Flow frequency comparisons are presented as the frequency of occurrence over the 18 year dataset and the absolute difference in the average number of days per year for the range between the 1.5‐year and 20‐year flows (see Table 3.2). In most instances, the difference between the observed and predicted frequency of occurrence (as average number of days per year) is less than 10 days (or 3% annually) for the range of critical flows (1.5 yr to 20 yr RI). The two sites with a greater discrepancy are Third Creek (16 days) and Trout Creek (31 days). Our ability to predict the number of days annually that the critical flows with respect to floodplain deposition (1.5 yr to 20 yr RI) occur is typically within 3% (or 10 days per year). Given that the same hydrology is used in both pre‐ and post‐restoration load reduction estimates in SLRT, these deviations are consistently U R B A N A N N U A L P R O B A B I L I T Y H Y DR O G R A P H S
Annual percentile unit hydrographs for urban catchments by region are presented below. These regional
unit hydrographs are based on the average percentile daily flow for the regions relevent urban catchments
presenteed in Table 3.1.
S O U T H R E G IO N
2.5E-03
25th
2.0E-03
50th
75th
1.5E-03
99th
Qmd-p/Ai (cms/acre)
1.0E-03
5.0E-04
0.0E+00
2.0E-04
(excludes 99th percentile)
1.5E-04
1.0E-04
5.0E-05
0.0E+00
N O R T H R E G IO N
4.5E-03
25th
4.0E-03
50th
3.5E-03
75th
3.0E-03
99th
2.5E-03
2.0E-03
1.5E-03
Qmd-p/Ai (cms/acre)
1.0E-03
5.0E-04
0.0E+00
2.5E-04
(excludes 99th percentile)
2.0E-04
1.5E-04
1.0E-04
5.0E-05
0.0E+00
1-Oct
1-Nov
1-Dec
1-Jan
1-Feb
1-Mar
1-Apr
1-May
1-Jun
1-Jul
1-Aug
1-Sep
URBAN: PERCENTILE ANNUAL HYDROGRAPHS
1-Oct
Figure 3.7
N O N - U R B A N A N N U A L P R O B A B I L I T Y H Y DR O G R A P H S
Regional unit runoff hydrographs for non-urban catchments are input into a Dynamic BSTEM estimate of the FSP
load generated from stream channel erosion in SLRT. For 11 non-urban catchments (see Table 3.1) the 99th, 75th,
50th, and 99th percentile mean daily flows were calculated based on 18 years of USGS data (WY89 - WY06). The
percentile flows are divided by the catchment area and averaged regionally to create the representative annual
probability hydrographs shown.
0.5
S O U T H R E G IO N
0.4
0.3
0.2
0.1
0.0
1.5
U P P E R T R U C K E E R IV E R
1.0
0.5
Qmd-p/A (cms/sq-mile)
0.0
3.5
W E S T R E G IO N
3.0
25th
2.5
50th
2.0
75th
99th
1.5
1.0
0.5
0.0
0.4
N O R T H R E G IO N
0.3
0.2
0.1
0.0
0.4
E A S T R E G IO N
0.3
0.2
0.1
0.0
1-Oct
1-Nov
1-Dec
1-Jan
1-Feb
1-Mar
1-Apr
1-May
1-Jun
1-Jul
1-Aug
Data Source: USGS mean daily discharge for 11 stream gage sites
listed in Table 3.1.
NON-URBAN: PERCENTILE ANNUAL HYDROGRAPHS
1-Sep
1-Oct
Figure 3.8
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.17 applied to both conditions. Given the resources available to develop a standardized method to estimate site hydrology, the team believes this approach is a reasonable yet simple method to generate the necessary SLRT hydrologic inputs for any SEZ in the Tahoe Basin. Table 3.2. Comparison of observed versus predicted flows for urban and non‐urban catchments using the SLRT flow frequency analysis methods described above in Section 3.3.2. Type Recurrence Interval (RI) Flows (cfs) Location Frequency of flow between RI=1.5 and 20 years 1.5 year RI 20 year RI Observed (%) Predicted (%) Deviation (# d/yr) Urban Wildwood 0.50 1.5 1.2% 1.5% +0.9 Urban Rocky Point 0.27 0.81 1.1% 2.0% +3.2 Urban Dollar Point 1.38 4.1 0.8% 0.4% ‐1.6 Non‐
Urban Third Creek 30 101 5.8% 1.5% ‐16 Non‐
Urban Logan House Creek 2.0 8.7 7.1% 6.9% ‐0.6 Non‐
Urban Blackwood Creek 172 2000 4.2% 3.6% ‐2.3 Non‐
Urban Trout Creek 53 501 22% 31% +31 Non‐
Urban Upper Truckee River 340 3150 4.1% 6.2% +7.7 Annual Hydrograph Predictions: A selection of sites was used to compare the observed percentile (25th, 50th, 75th, 99th) annual hydrographs to the hydrographs predicted using the methodology described above in Section 3.3.3. The absolute deviation between the observed and predicted daily flows was calculated, and the coefficient of determination (R‐squared) and Nash‐Sutcliffe efficiency (NSE) were calculated to evaluate the appropriateness of the use of the predicted values. R‐squared describes the degree of co‐linearity between the observed and predicted values, but R2 values are highly sensitive to outliers (i.e., the infrequent high flow events). NSE is a normalized statistic that determines the relative magnitude of the residual variance (i.e., noise) compared to the measured data variance (i.e., information) and has been found “to be the best objective function for reflecting the overall fit of a hydrograph” (Morias et al., 2007). NSE values range between ‐∞ and 1.0, where, similar to R2, 1.0 is the optimal value. Table 3.3 summarizes the R2 and NSE values for the 50th and 75th percentile annual hydrographs. The 50th and 75th percentile flows are assumed to result in the bulk of stream channel erosion on an average annual basis, and therefore are the focus of the validation. In all cases, we have high confidence that the predicted percentile flows are an appropriate approximation for SLRT users based on the values contained in Table 3.3. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.18 | July 2013 Table 3.3. R2 and NSE results for comparisons of observed versus predicted flows for urban and non‐
urban catchments using the SLRT annual probability hydrograph analysis methods described above in Section 3.3.3. R2 NSE Type Location 50 percentile 75th percentile 50th percentile 75th percentile Urban Wildwood 0.93 0.95 0.82 0.93 Urban Rocky Point 0.96 0.97 0.81 0.63 Urban Dollar Point 0.98 0.97 0.95 0.96 Non‐Urban Third Creek 0.97 0.87 0.92 0.81 Non‐Urban Logan House Creek 0.88 0.98 0.64 0.78 Non‐Urban Blackwood Creek 0.99 0.99 0.95 0.96 Non‐Urban Trout Creek 1.00 0.99 0.99 0.99 Upper Truckee River 0.99 0.99 0.93 0.93 Non‐Urban th
In addition, we compare the predicted and observed Qmd‐p deviation relative to the bankfull (1.5‐year) flow, which is critical to the geomorphic processes that drive stream channel erosion. Table 3.4 presents the 1.5‐yr flow for each site based on the 18‐year dataset. The site‐specific and predicted 50th and 75th percentile flow values (Qmd‐50 and Qmd‐75, respectively) were compared for each of the 365 days and the average of the deviation and standard deviation (SD) were calculated. The average and standard deviation were added together to define the envelope of expected deviation between the observed and predicted flows. This envelope of deviation was compared to the 1.5‐year discharge, expressed as a relative % error. In most cases, the deviation envelope is less than 10% of the 1.5‐year flow value, which is deemed acceptable for the purposes of SLRT. Table 3.4. Comparison of 1.5‐year recurrence interval flow to the calculated deviations between the observed and predicted percentile mean daily flows for urban and non‐urban catchments. The average and standard deviation for the range of deviations between the observed and predicted values were calculated, and then summed to define the envelope of deviation. The envelope is compared to the 1.5‐year recurrence interval flow as % relative error. Type Location 1.5 yr RI (cms) Urban Wildwood 0.013 Urban Rocky Point 0.008 Urban Dollar Point 0.040 Non‐Urban Third Creek 0.85 Non‐Urban Logan House Creek 0.057 Non‐Urban Blackwood Creek 4.9 Non‐Urban Trout Creek 1.5 Non‐Urban Upper Truckee River 9.6 50th percentile (Qmd‐50) Ave dev (cms) ± SD 2.7x10‐05 ± 2.0x10‐04 1.9x10‐05 ± 1.3x10‐04 5.4x10‐05 ± 2.5x10‐04 0.005 ± 0.043 0.002 ± 0.004 0.015 ± 0.19 0.019 ± 0.035 0.049 ± 0.55 75th percentile (Qmd‐75) Envelope (cms) Relative error 2.3x10‐04 1.6% 1.5x10‐04 1.9% 3.1x10‐04 0.8% 0.048 5.7% 0.007 11.6% 0.20 4.2% 0.054 3.6% 0.60 6.2% Ave dev (cms) ± SD 1.3x10‐04 ± 3.2x10‐04 1.6x10‐04 ± 3.6x10‐04 1.4x10‐04 ± 4.6x10‐04 0.019 ± 0.101 0.004 ± 0.006 0.014 ± 0.25 0.034 ± 0.076 0.077 ± 0.92 Envelope (cms) Relative error 4.4x10‐04 3.2% 5.2x10‐04 6.8% 6.1x10‐04 1.5% 0.12 14.1% 0.011 18.7% 0.26 5.4% 0.11 7.3% 1.0 10.3% Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.19 The above analyses suggest these simple methods generate reasonable hydrologic estimates for a range of SEZs types and sizes. The predictions appear reasonable for the range of flow magnitudes and frequencies that are critical to FSP sources and sinks given minimal effort required by the SLRT user. The SLRT user is welcome to use site‐specific hydrology datasets as they desire, with the caveat that the same hydrology is used for the both pre‐ and post‐restoration SLRT scenarios to minimize concerns that load reductions are influenced by hydrology differences between scenarios. 3.3.5
LIMITATIONS The hydrologic approach is not without limitations, including: 




3.4
Limited availability of complete daily flow records from WY89‐WY06, resulting in the use of only 8 urban catchments and 11 non‐urban (stream) catchments to define approach. Use of mean daily flows, instead of peak daily discharge, mutes the flashy hydrology that is prevalent in Tahoe systems and critical to processes driving erosion and deposition in Tahoe SEZs. The lack of available comparable long‐term hydrology data posed a significant challenge to the hydrology development. PLRM is not intended to be used to estimate specific event hydrographs or daily flow records and sufficient regional data is not available to fully develop explanatory variables, resulting in a simplified non‐urban catchment analysis. The lack of available data also limited the opportunities for validation that should use sites not included in the method development. The recommended size range for catchments modeled in PLRM is 10‐100 acres; however, several of the urban catchments used in this analysis are larger than this range (see Table 3.1). PLRM identifies two limitations in modeling larger than recommended catchments (nhc et al., 2009): o The simplified hydrologic routing used in PLRM may not adequately represent the catchment. o The meteorological grids used in PLRM are 800m2 or 160 acres. Only 1 grid may be applied to a catchment, and therefore may not fully represent catchments greater than the grid in size. For non‐urban catchments, we assume there is a linear relationship between area and discharge. However, we are limited by the data available and therefore the relationship is most reliable for catchments between 2.0 and 55.0 sq‐miles. For any non‐urban catchments outside this range, the area to discharge relationship may not be appropriate. CATCHMENT FSP The product of discharge and pollutant concentration equates to an instantaneous pollutant load, expressed in units of mass per time. The SLRT method requires a translation of the catchment flow frequency distribution into a catchment pollutant load to estimate both incoming FSP loading from catchment (INfsp) and the fraction retained on the floodplain (RFPfsp). Available stream and stormwater FSP datasets were compiled and used to provide a reasonable approach to estimate FSP loading into any SEZ in the Tahoe Basin. 3.4.1
FSP DATASETS While decades of hydrologic and water quality data exist throughout the Tahoe Basin, there is a limited amount of long‐term FSP stream and stormwater data available, with nearly all of the data obtained post 2006. Previous research has demonstrated strong correlations between turbidity and FSP concentration (mg/L) (2NDNATURE et al., 2010c; 2NDNATURE and nhc, 2012a; Heyvaert et al., 2010; Kayhanian et al., 2005) and 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.20 | July 2013 where appropriate, FSP concentrations were calculated using available turbidity to FSP rating curves to expand the data population. The SLRT compiled all readily available FSP data collected in the Tahoe Basin, resulting in over 144,000 data points collected from stormwater, streams and land use samples collected by 2NDNATURE, UCD, DRI and USGS (Table 3.5; Figure 3.9). Each data point includes a discharge at time of FSP sample collection. Table 3.5. FSP calibration dataset. Non‐Urban Urban Catchment 3.4.2
Monitoring Location Data Source n Incline Village 2NDNATURE and nhc (in progress) 1754 Park Avenue 2NDNATURE and nhc (2012a) 8 Pasadena 2NDNATURE and nhc (in progress) 1243 Osgood 2N and nhc (2012a, 2012b, in progress) 7404 Eloise 2NDNATURE and nhc (2012a) 9 Blackwood DRI 201 Eagle Rock DRI 122 General DRI 197 Glenbrook DRI 140 Incline DRI 190 Logan House DRI 127 Third DRI 171 Trout Creek 2NDNATURE (see Section 2) 33,489 Upper Truckee River 2NDNATURE (2006A, 2010) 319 Ward Creek DRI 214 FSP CONCENTRATIONS Integration of all FSP and discharge data from urban and non‐urban catchments did not yield a strong correlation (Figure 3.10A), resulting in a separation of the FSP concentration and discharge data by catchment type. The nearly 134,000 discharge‐FSP data points obtained from streams were plotted, and similar to the hydrology analyses, there is a regional component to the discharge to FSP relationship (Figure 3.10B). Figure 3.10C presents the regional curves for daily FSP load (kg/day) as a function of discharge used by SLRT for non‐
urban catchments. In contrast, Figure 3.10D illustrates stormwater FSP concentration is not strongly correlated to hydrology in the urban environment with a significant range of concentrations being observed during low discharge values typical of snowmelt runoff. In urban catchments, the FSP measured in stormwater has been documented to more closely correspond to seasonal land use condition, particularly road condition rather than discharge (nhc et al., 2009; 2NDNATURE et al., 2010c; 2NDNATURE and nhc, 2010, 2012a). These findings required a different approach in SLRT to estimate FSP loads in urban catchments. Similar to PLRM, SLRT predicts the FSP characteristic runoff concentration (CRC) as a function of catchment % impervious area and land use condition. T h ir d
LEGEND
Urban Sites
In c lin e
Non-urban Sites
In c lin e
V illa g e
W a rd
B la c k w o o d
G l en b r o o k
E a g leR o c k
L o g a n
H o u s e
California
Nevada
G en er a l
P a r k A v e.
O s g o o d
P a s a d en a
T r o u t C r eek
E lo is e
U p p er T r u c k ee R i v er
Fi g u r e 3 . 9 : FS P d a ta u s ed to d ev el o p S L R T a p p r o a c h a n d
m eth o d o l o g y . S ee T a b l e 3 . 5 f o r d eta i l s .
DI S C H A R G E T O F S P C O N C E N T R A T I O N R E L A T I O N S H I P S
A
No strong correlation between discharge and FSP
concentration is shown using all Tahoe Basin data
(>144,000 data points).
[FSP] (mg/L)
A . Discharge v . F S P co n cen t rat io n fo r al l
T aho e B asin d at a
Data Sources: 2NDNATURE, UC Davis,
Tahoe Environmental Research Center,
Desert Research Institute, and USGS
Q (cfs)
B . Discharge v . F S P co n cen t rat io n an d C . Discharge v . Dail y F S P l o ad fo r n o n - u rb an cat chm en t s
C
West Shore
FSP = 0.55 Q1.44
R2 = 0.66
South Shore
FSP = 4.7 Q1.24
R2 = 0.79
FSP (kg/d)
[FSP] (mg/L)
B
All Data
FSP = 4.4 Q1.25
R2 = 0.79
East Shore
FSP = 1.5 Q1.67
R2 = 0.83
Q (cfs)
North Shore
FSP = 0.80 Q1.84
R2 = 0.70
Q (cfs)
Non-urban discharge to FSP concentration relationship of 134,000 data points shows regional differences, and four
regional discharge to daily FSP load curves are identified for SLRT.
2500
D
D. Discharge v . F S P co n cen t rat io n fo r u rb an
cat chm en t s o n l y (1 0 , 4 5 0 d at a p o in t s)
[FSP]
(mg/L)
Measured FSP concentrations in urban catchments
are NOT a function of discharge.
2000
1500
1000
Q = 26.5
FSP
y = 26.5x
= -0.58
R2 = - nR0.58
= 10, 50
500
0
0.0
2.0
.0
6.0
8.0
Q (cfs)
10.0
12.0
DISCHARGE V FSP CONCENTRATION
Figure 3.10
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.23 The Placer County TMDL Pollutant Load Reduction Strategy development (2NDNATURE and nhc, 2011) included the development of numerous PLRM models and extensive analysis of the simulation results on urban catchment FSP loading. The integration of results across catchments identified that catchment % impervious (Ai%) was strongly correlated to unit runoff and the FSP mass generated per unit area of an urban catchment (Figures 3.11A&B; 2NDNATURE and nhc, 2011). URBAN unit runoff (in/yr/acre) = 20.4 * Ai%1.21 (EQ 3.12) URBAN unit FSP (lb/yr/acre) = 1592 * Ai%1.88 (EQ 3.13) Since both unit runoff and FSP mass for any urban catchment can be predicted using the % impervious, an estimation of the catchment FSP CRC can be obtained by dividing the FSP lbs/yr by the runoff volume (ft3/yr) and converting the units to mg/L. URBAN FSP CRC (mg/L) = 344 * Ai%0.67 (EQ 3.14) Assuming that Placer County urban catchments represent an average condition (e.g., condition = 3; plotted in bold in Figure 3.11C) relative to other Tahoe areas, the Placer FSP CRC as a function of catchment imperviousness is used to fabricate two other rating curves (see Figure 3.11C). Curves representing condition 1 is a 50% increase from the Placer County curve (3) and the condition 5 curve is a 50% reduction. The range of concentrations for typical Tahoe urban catchments predicted by the curves shown in Figure 3.11C are lower than the average concentrations measured in a range of Tahoe catchments over the past decade by a number of researchers (2NDNATURE, DRI, TERC, etc.): 37 ‐ 367 mg/L predicted vs. 43 ‐ 1200 mg/L measured. The conservative approach is justified, however, because the measured data is likely an overestimate. The data was collected primarily during storm events and not with the purpose of calculating an annual average. The equations contained in Figure 3.11C allow an estimate of the average annual urban catchment FSP CRC using an estimate of condition and the catchment % impervious (Ai%). 3.4.3 FSP LOADS For non‐urban (stream) catchments, the regionally‐appropriate discharge to FSP rating curve (FSP(Q)) in Figure 3.10C is used to calculate the daily FSP load (FSPb; kg/d) for the 50 flow frequency bins using Qb (cfs), where: NON‐URBAN FSPb = FSP(Q) * Qb (EQ 3.15) For urban catchments the appropriate unit FSP CRC is determined based on catchment % impervious area and average land use condition (see Figure 3.11C) and used to calculate the daily FSP load (FSPb; kg/d) for each of the 50 flow frequency bins using Qb (cfs), where: URBAN FSPb = FSP CRC * Qb (EQ 3.16) For all catchments, the FSPb for each discharge bin is multiplied by the average annual number of days (tb‐day) the respective flow conditions are expected to occur to calculate the average annual FSP load delivered (FSPb‐
an) for each Qb. The bin interval average annual FSP loads (FSPb‐an) can be summed to calculate the total average annual estimated FSP load delivered to the subject reach (INfsp; MT/yr) from the catchment, where: IN
2NDNATURE, LLC | ecosystem science + design ∑
FSP ∗ (EQ 3.17) www.2ndnaturellc.com | 831.426.9119 100
U 0R 0B A N C A 0.1T C H M E 0.2N T F S P L0.3O A DS
0.4
0.5
0.6
% Impervious Area
A
12
A . C at chm en t % im p erv io u s v .
av erage an n u al su rface ru n o ff p er
u n it area
Surface Runoff = 20.4 Ai%1.21
R2 = 0.88
yy=20.4x
20.4x1.21
R²=0.88
(B) Regression relationship
of % Impervious Area of
UPC versus average annual
Surface Runoff per unit area.
Surface
Runoff
(in/yr/acre)
Surface
Runoff (in/yr/acre)
Data Source: Placer County Stormwater
TMDL Strategy (2N and nhc 2011).
Figures A and B are taken directly from
Figure 2.3 of the report.
(B) Surface Runoff as a function of % Impervious Area of UPC
10
8
6
4
2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Regression relationships were developed to extrapolate the PLRM modeling results of the selected Placer
% Impervious Area
Ai%(%)UPC % impervious area was selected because it is appl
to the Placer County UPCs that were not modeled.
the UPCs with the densest (see Table 2.1) and less dense urban areas (see Table 2.2). GIS analysis was p
unmodeled UPCs and the regression equations below were used to estimate the FSP pollutant loading an
for each Placer County UPC. The results of this extrapolation, as well as the PLRM modeling, were used to
FIGURE 2.3 REGRESSION RELATIONSHIPS
OF PERCENT IMPERVIOUS AREA OF UPCS
County
baseline FSP loading from the urban area.
2ndnaturellc.com nhcweb.com
ph 831.426.9119
FSP (lb/yr/acre)
FSP mass per unit area (lb/yr/acre)
B . C at chm en t % im p erv io u s v . F S P l o ad in g
p er u n it area
Figures A and B are taken directly from Figure 2.3
of the Placer County Stormwater TMDL Strategy
report (2N and nhc 2011)
ph 530.544.3788
B(A) FSP Loading as a function of % Impervious Area of UPC
600
FSPy=1529x
mass = 1592 Ai%1.88
y 1529x
R²=0.85 2
R = 0.85
1 88
1.88
500
400
(A) Regressio
of % Impervio
versus avera
Loading per u
300
200
100
0
0
0.1
0.2
0.3
0.4
0.5
0.6
% Impervious Area
Ai%(%)
C . C at chm en t % im p ev io u s an d l an d u se
co n d it io n v . F S P co n cen t rat io n p er u n it area
12
3
350
FSP(Ai%) = 517 Ai%0.67
yy=20.4x
20.4x1.21
R²=0.88
1
10
5
300
250
(B) Regression relationship
of % Impervious Area of
UPC versus average annual
Surface Runoff per unit area.
200
150
Surface Runoff (in/yr/acre)
FSP(Ai%FSP CRC (mg/L/acre) ) (mg/L/acre)
Land use condition is ranked on a 1-5 scale, with 1
= worst condition and 5 = best. The Placer County
data is used to create the average condition
curve (score =3), and the other curves are scaled
based on the Placer data (25% higher for poorer
conditions; 50% lower for better conditions).
(B) Surface Runoff as a function of % Impervious A
C
00
8
344 Ai%0.67
6
4
172 Ai%0.67
2
100
50
0
0
0.1
0.2
0.3
0.4
% Impervious Area
0
0
FIGURE 2.3
10
20
30
0
50
60
0
Ai%(%)
Catchment % Impervious Area REGRESSION RELATIONSHIPS OF PERCENT IMPERVIOUS AREA OF UPCS
y = 516.5x0.6
y=3
2ndnaturellc.com
ph 831.426.9119
.33x0.6
y = 172.17x0.67 URBAN: FSP LOADING
Figure 3.11
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.25 3.4.4 LIMITATIONS The catchment FSP loading approach is not without limitations, including: 
While FSP is the primary pollutant impairing Lake Tahoe clarity, a relatively small amount of existing data and subsequent regional analyses on the sources, fate and transport is available. This research compiled, integrated and leveraged as much data and information as available to provide reasonable and consistent guidance to estimate long‐term FSP loading from an urban or non‐urban catchment using regional groupings. Tahoe Basin applied water quality research could greatly benefit from a more extensive and detailed assessment of existing datasets that provides a more robust and statistically defensible approach to estimating long‐term FSP loading to SEZs. 
The majority of the non‐urban FSP water quality data has been obtained from a small subset of watersheds in the basin. The desire to provide regionally representative rating curves required integration of data across watersheds, reducing the predictive capability for specific sites where FSP data does exist. The predictive capacity of the applied rating curves can be greatly improved by a more rigorous analysis of watershed‐specific FSP data and identification of discharge thresholds to create multiple rating curves with higher correlation coefficients, as illustrated by Simon et al. (2003). The average annual SEZ load reductions are highly sensitive to the incoming FSP yield delivered from the catchment. Applications of SLRTv1 to estimate the FSP load reductions in a watershed that contains a rich FSP to discharge dataset (see Table 3.5) could be better represented by a watershed specific FSP to discharge rating curve. 
The recommended approach to identifying an FSP CRC for an urban catchment is consistent with PLRM and other urban stormwater tools that define a range of urban land use conditions that bracket observed concentrations. The FSP CRCs provided for SLRT are within the range of measured Tahoe stormwater concentrations and average annual estimates. However, additional analyses of urban stormwater FSP datasets to identify causal factors and critical catchment characteristics on a regional scale would greatly improve the confidence and guidance in these values as wells as guidance to the user regarding selection of the appropriate catchment condition. 3.5
FLOODPLAIN RETENTION Collecting data to inform estimates of pollutant floodplain retention is extremely challenging and costly due to the infrequent and unpredictable timing and magnitude of floodplain inundation events. Rather than instrumenting each floodplain to measure the retention coefficient for a range of discharge conditions, SLRT provides the user with an approach to estimate the retention coefficient as a function of discharge based on the presence of floodplain conditions theorized to increase retention. In order to estimate the amount of floodplain pollutant retention we must know: 1.
Pollutant (FSP) load inputs to the upper boundary of the SEZ in a useable format to model expected delivery and retention of pollutants on the floodplain. The site‐specific determination of the flow frequency FSP loading to the SEZ from the contributing catchment is outlined in Section 3.4 above. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.26 | July 2013 2.
The frequency of discharge conditions that inundate the floodplain. The concurrent floodplain retention sampling and the reach‐scale FSP load comparisons from WY11 on the Upper Reach of Trout Creek are used to verify the assumption that floodplain retention can be reasonably estimated using a flow frequency distribution in place of a continuous simulation. 3.
The fraction of sediment deposited on the floodplain for different discharge conditions, termed a retention coefficient (Rfsp). The data obtained over years of floodplain sampling (see Section 2.2.4) is used to create a reasonable approach to estimate FSP floodplain retention for each discharge interval that inundates the floodplain for the range of potential floodplain conditions. 4.
A relative assessment of the characteristics of the associated floodplain that enhance pollutant retention including effective depths, vegetation distribution and density, floodplain complexity and other attributes. The concept of relative floodplain condition (FPC) is use to estimate the presence of these characteristics in SLRT. 3.5.1
FSP LOADS DELIVERED TO FLOODPLAIN The frequency of floodplain inundation and the associated sediment load delivered to the floodplain requires estimates of channel capacity (Qcc; cfs), expressed as discharge above which the floodplain becomes inundated. As presented in detail in Sections 3.3 and 3.4, the SLRT methods provide the user with incoming hydrology and associated FSP daily loads delivered to the site in a frequency distribution format. This format allows a very simple application of the estimated channel capacity provided by the user to quantify the volumes and associated FSP loads per bin interval (Qb and FSPb, respectively) delivered to the floodplain if Qb > Qcc. For all bin intervals that exceed the channel capacity, the mass retained in the channel is subtracted from the incoming FSP daily load where: DFPb‐fsp = FSPb – FSPb‐cc (EQ 3.18) The number of days each bin interval Qb occurs on an average annual basis (tb‐day) times the daily rate of floodplain FSP delivery (DFPb‐fsp) generates a total FSP mass delivered for each discharge interval. The integration of DFPb‐fsp for all discharge intervals is an estimate of the total average annual FSP mass delivered to the floodplain for any SEZ. The SLRT user guidance includes the details to simply compare the difference in the daily and average annual volumes and FSP loads estimated to be delivered to the floodplain given pre‐ and post‐restoration morphology. In the example sites used to demonstrate the SLRT results, the increase in the FSP mass delivered to the floodplain as a result of restoration is profound (see Section 5). 3.5.2
FSP LOADS RETAINED ON FLOODPLAIN In order to estimate the fraction of FSP mass retained for each discharge bin interval, the existing floodplain retention data is applied (see Figure 2.11). Normalizing the floodplain retention estimates by the event discharge relative to the site‐specific channel capacity as shown in Figure 2.11 supports the hypothesis that FSP retention on the floodplain varies as function of discharge and floodplain characteristics. Low discharge events are irrelevant because they do not exceed the channel capacity and thus the floodplain will not be inundated, and the extremely high discharge events appear to overwhelm the floodplain’s ability to retain FSP and likely other pollutants. In addition, the relatively infrequent nature of larger events makes them relatively Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.27 insignificant when estimating the average annual mass retained on the floodplain. These events are likely to transport large sediment and pollutant loads to downstream areas regardless of the restoration strategy that has been implemented. Floodplain complexity, vegetation distribution and effective velocity on the floodplain, particularly during the smaller overbank events, also affect the relative magnitude of the retention coefficient (Rfsp). It is assumed that the two floodplains sampled (Upper Truckee River and Trout Creek Upper Reach, see Section 2.2.4) represent desired floodplain conditions, including high vegetation distribution and vigor, high floodplain roughness, low effective velocities, and relatively shallow water depths. The floodplain slopes are both approximately 0.001 and average depths of water on the floodplain do not exceed 1.5 ft when the discharge is 3 times the channel capacity (Q/Qcc = 3). Snowmelt stage monitoring data during overbank flows are used to Floodplains of the Upper Truckee River (top) and Trout Creek (bottom) displaying good floodplain condition graphically illustrate the stage to discharge relationships (well vegetated, topographic complexity, etc.). at both monitored floodplains (Figure 3.12). Discharge is normalized to the respective channel capacity for each site for simple comparisons to Figure 2.11. The slopes of both stage to discharge curves decrease and remain dramatically lower when Q > Qcc, up to Q:Qcc ratios over 3.5, which were the maximum sampled during this research. The shape of the stage to discharge empirical relationship (Figure 3.12) is a representative proxy to document the expected inundation area as a function of discharge. An inset floodplain morphology that limits horizontal flow spreading would be reflected in a relatively high slope of the stage to discharge rating curve (potentially similar to slope at discharge values below bankfull) at discharges greater than 2 or 3 time channel capacity. In addition, both floodplains sampled for FSP retention were relatively well vegetated and had a high distribution of low lying wetland plants, grasses and shrubs (see photos at right), providing a high density of vegetation surfaces for FSP to interact with and adhere during lower flow conditions on the floodplain. UCD researchers conducted both field and laboratory experiments on the factors driving floodplain retention of FSP and documented effective biofilm accumulation of FSP on surfaces perpendicular to flows (Andrews et al., 2011). Over the years, 2NDNATURE field personnel have observed an accumulated FSP film on recently inundated vegetation in both vegetated stormwater treatment systems (i.e., wet basin) and floodplains. A well‐vegetated floodplain with a mixed distribution of grasses, wetland vegetation (such as sedges) and woody species (such as willow) increases roughness that reduces flow velocities and residence times, as well provides surface area perpendicular to the floodplain flow path to which fine sediment particles can adhere. The application of these findings to SLRT guidance suggests that the maximum FSP retention coefficient at relatively low floodplain flow conditions (discharge/channel capacity ratio < 3) will be achieved on floodplains that possess characteristics similar to the two floodplains that were sampled to generate Figure 2.11. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 S T A G E V . DI S C H A R G E R E L A T I O N S H I P
U p p er T ru ck ee R iv er (M O D1 )
5.00
.50
.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
Flow in channel only
Flow in channel & on floodplain
0.00
0
0.5
1
.50
1.5
2
2.5
T ro u t C reek (R each 1 )
3
3.5
3.50
.00
3.00
2.50
2.00
0
0.5
1
1.5
2
2.5
3
3.5
Stage to discharge relationships presented for the MOD1 site on Upper Truckee River and Reach
1 on Trout Creek, using 2NDNATURE water level data collection and USGS streamflow data from
WY09-WY11. The x-axis presents discharge (Qb) normalized by the site channel capacity (Qcc; 290 cfs
and 80 cfs, respectively) to improve site comparisons. For values <1.0, the slope of the relationship
is driven by the channel morphology (yellow lines). For values >1.0 (overbank flows), the relationship
slope is dependent on floodplain morphology (red lines). At some point, the extent of the valley
floor is reached, and the slope would increase again as the water is constrained by hillslopes.
For both sampled sites, there is no break in slope of the relationship when 1<Qb:Qcc<3.5, illustrating
continual flow spreading and nominal effective depth increases on the floodplain. A poor to
moderate floodplain condition would be represented by discernable increases in the stage to
discharge relationship when Qb:Qcc< 3.5. Such an increase in slope would provide evidence of
floodplain lateral and/or longitudinal constriction of the critical flows.
STAGE V. DISCHARGE RELATIONSHIP AS A FUNCTION OF
CHANNEL CAPACITY
Figure 3.12
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.29 To simplify the existing data, it is assumed that the retention coefficient as a function of discharge at overbank flows below 3 times channel capacity (Q≤ Qcc *3) is highly sensitive to floodplain conditions that influence the overbank flow velocity, roughness and density of surfaces that retain FSP, as presented in Figure 3.13. The selection of Qb:Qcc =3 as an anchor point in the Rfsp equations is based on visual inspection of the data available. A range of three floodplain conditions (1, 3, and 5) were created using best professional judgment with the data obtained from Trout and the Upper Truckee River representing the best possible condition (5). A poor condition floodplain would possess sparse upland vegetation, exposed soil, and/or include a morphology where depth and effective velocity on the floodplain surface increase sharply with increasing discharge. For example, an inset floodplain configuration where there is a lack of surface area available for water spreading either horizontally from the thalweg or longitudinally along the flow path would be characterized as poor floodplain condition. The retention of fine sediment on a floodplain as overbank flows exceed 3 times the channel capacity discharge appears to exponentially decline from 25% to 10% of the delivered load (see Figure 2.11). Given that flow conditions above 3 times the channel capacity will fully inundate and flatten grasses and other low lying vegetation, it is reasonable to assume that a modest FSP retention occurs during larger overbank events, regardless of floodplain condition. The fraction of FSP mass retained as a function of the discharge to channel capacity ratio when Qb < Qcc *3 for good, fair and poor condition floodplains are below and represented graphically in Figure 3.13. FPC (poor; 1) Rb‐fsp = 0.30*Qb:Qcc ‐0.166 (EQ 3.19) FPC (fair; 3) Rb‐fsp = 0.45*Qb:Qcc ‐0.535 (EQ 3.20) FPC (good; 5) Rb‐fsp = 0.60*Qb:Qcc ‐0.797 (EQ 3.21) (EQ 3.22) For all floodplains, regardless of condition, when Qb > Qcc * 3 then Rb‐fsp = 1.8*Qb:Qcc‐1.8 Guidance assisting the SLRT user to estimate relative floodplain condition (FPC) for each pre‐ and post‐
restoration floodplain is based on three general factors: topographic complexity, vegetation characteristics and the stage to discharge relationship of the floodplain surface that is controlled by both the longitudinal and horizontal extent of the floodplain surface. 3.5.3
VERIFICATION USING WY11 WATER QUALITY DATA One critical assumption of the floodplain retention approach outlined above is that estimating the mass retained per discharge interval and frequency of occurrence is reasonable, rather than an approach that preserves the continuous hydrograph structure. The WY11 Trout Creek FSP event loads measured along the Upper Reach and floodplain retention data are used to validate this critical assumption. The reach‐scale FSP load monitoring suggest that during the WY11 overbank event 71.4 MT of FSP were delivered to the upstream boundary of the subject Trout Creek reach (see Table 2.15), and approximately 9.4 MT of that were retained on the floodplain (see Table 2.16). During this event, we measured and estimated the floodplain FSP retention within the reach with the passive samplers. Therefore we used the WY11 floodplain retention measurements for 4 discrete discharge conditions (see Table 2.13) to estimate the event FSP mass retained on the floodplain (RFPfsp; MT) and compare that value to the measured amount of 9.4 MT. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 F L O O DP L A I N R E T E N T I O N A S A F U N C T I O N O F C H A N N E L C A P A C I T Y & F L O O DP L A I N C O N DI T I O N
Flooplain retention coefficient (Rfsp) for
length
1,000ft floodplain
1.0
0.9
FPC= 1
Rfsp= 0.30 Qb:Qcc -0.166
0.8
FPC= 3
Rfsp= 0. 5 Qb:Qcc-0.535
0.
FPC= 5
No
overbank
flow
0.6
0.5
Rfsp= 0.60 Qb:Qcc -0.
9
Qb:Qcc 3
Rfsp= 1.8 Qb:Qcc -1.8
0.
0.3
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0
1
2
3
5
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channel
capacity
ratio
Discharge to
(Qb:Q
)
cc
Floodplain sediment (FSP) retention as a function of discharge (Q) and floodplain condition (FPC).
Discharge (Qb) is normalized by the channel capacity (Qcc) to define the relative magnitude of the
overbank event. For flows where Qb<Qcc, no overbank flow occurs. During overbank flows, the condition
of the floodplain is expected to play a significant role in the amount of FSP retained on the floodplain,
with floodplains with high topographic complexity, dense meadow vegetation, and a large surface area
expected to retain the most sediment (FPC=5). Poor floodplain condition (FPC=1) include small, inset
floodplains with bare soil and sparse vegetation. The good floodplain curve is based on data collected on
restored reach of Trout Creek and Upper Truckee River (see Figure 2.11).
RETENTION AS A FUNCTION OF CHANNEL CAPACITY &
FLOODPLAIN CONDITION
Figure 3.13
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.31 For each 2 hour time step in the WY2011 hydrology dataset, the FSP load remaining in the channel (FSPcc) was calculated as the product of the TCPT FSP concentration and the channel discharge capacity (Qcc). Using EQ3.18 above, the difference between the TCPT FSP load and the FSP load contained in the channel is assumed to be the FSP load delivered to the floodplain (DFPfsp) for each 2‐hour time step: DFPfsp = FSPQ – FSPcc (EQ 3.28) The measured Rfsp values from Trout Creek in WY11 (see Table 2.13) were used estimate the mass of FSP retained (RFPfsp) for each time interval given the following rules: 1.
2.
3.
4.
Q < Qcc, overbank did not occur Q < 115 cfs, then Rfsp = 0.68 * DFPfsp Q > 200 cfs, then Rfsp = 0.23 * DFPfsp If 200 cfs <Q >115 cfs, then Rfsp was calculated using a linear decline from 0.68 to 0.23, where Rfsp = ‐0.00455*Qmd + 1.20273 RFPfsp = DFPfsp *Rfsp (EQ 3.29) The incremental FSP loads retained on the floodplain over the 47.7 days of overbank flow yield a total event load of 11.2 MT, compared to the 9.4 MT measured (see Section 2.2.5 and Table 2.16). The 11.2 MT estimate using the measured Rfsp values is within 14% of the measured FSP load reduction between TCPT and R3 over the same time interval (see Figure 2.2). While these data are limited, the close alignment of measured and predicted load reductions suggests that the use of a flow frequency approach to estimate FSP retention is reasonable and the sequence of flows may not be necessary to model these processes in SLRT. The higher estimate relative to measured values supports the selection of the slightly lower Rb‐fsp curve to represent a good condition floodplain (see Figure 3.13) in SLRT rather than the curve derived from the passive sampling datasets (Figure 2.11). 3.5.4 LIMITATIONS The FSP floodplain retention approach is not without limitations, including: 
Basic geomorphic principles suggest that the stage to discharge relationship of the floodplain may be critical to determining applicable FSP retention curves. The SLRT provides guidance on the desired shape of the stage to discharge relationship for a sustainable fluvial system (see Figure 3.12). However, the limited data available prohibits a detailed analysis of the retention rates as a result of different floodplain morphologies and stage to discharge relationships in different catchment settings. Additional data collection to empirically test these assumptions is recommended. 
The floodplain retention coefficient values (see Figure 3.13) are based on limited sampling of 2 floodplains over 2 water years. The Trout Creek and Upper Truckee River floodplains are assumed to represent good floodplain condition (FPC=5), with high topographic complexity, dense meadow vegetation, and desired stage to discharge relationships during critical overbank discharge conditions. The average (FPC=3) and poor (FPC=1) condition curves are scaled based on best professional judgment and would be greatly improved with floodplain sampling similar to the methods described in Section 2 on a diverse range of floodplains. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.32 | 3.6
July 2013 CHANNEL EROSION Streambank erosion by mass‐failure processes represents an important form of channel adjustment and a significant source of sediment in disturbed streams. Simon et al. (2003; Simon, 2008) estimated that about 25% of the median annual, fine‐grained loading of sediment (<62 m) to the lake was derived from streambank erosion. About 20% of the median annual fine sediment delivered to Lake Tahoe was determined to come from the banks of the Upper Truckee River and Blackwood Creek. The addition of the Ward Creek watershed contribution increased the cumulative load to 22%, with the remainder emanating from the other 60 watersheds around the lake. To estimate the average annual fine sediment generated from streambank erosion processes, a mechanistic streambank stability model is required. In Lake Tahoe streams, virtually all fine sediment is located in the streambanks (Simon, 2008), thereby negating the need for predictions of sediment contributions from the channel bed. This section describes the processes represented in the Bank Stability and Toe Erosion Model (BSTEM‐Dynamic 1.0) and why BSTEM is recommended to estimate the potential average annual FSP load reductions from open channels as a result of restoration actions in SLRTv1. 3.6.1
CHANNEL EROSION PROCESSES Conceptual models of bank retreat and erosion emphasize the importance of interactions between hydraulic forces acting at the bed and bank toe and gravitational forces acting on in situ bank materials (Carson and Kirkby, 1972; Thorne, 1982; Simon et al., 1991). Failure occurs when erosion of the bank toe, and possibly the channel bed adjacent to the bank, increase the height and angle of the bank to the point that gravitational forces exceed the shear strength of the bank material. After failure, failed bank materials may be delivered directly to the flow and deposited as bed material, dispersed as wash load, or deposited along the toe of the bank as intact blocks, or as smaller, dispersed aggregates (Simon et al., 1991). Bank materials do not maintain constant shear strength (resistance to failure) throughout the year. Strength varies with the moisture content of the bank and the elevation of the saturated zone in the bank mass. The wetter the bank and the higher the water table, the weaker the bank mass becomes and the more prone it is to failure. Bank failures, however, do not occur frequently during high flows because the water in the channel is providing a buttressing, or confining force to the bank mass. This is true even though it is during high‐flow events that the bank may be undercut by hydraulic forces. It is upon recession of the flow, when the bank loses the confining force but still maintains a high degree of saturation that it is most likely to fail. Thus the most representative approach to modeling channel erosion is to employ a sequential hydrologic time series that preserve the sequence, duration and magnitude of flows experienced by a channel over time. Analyzing streambank stability is a matter of characterizing the gravitational forces acting on the bank (the driving forces) and the geotechnical strength of the in situ bank material (the resisting force). A factor of safety (Fs) for a streambank is expressed as the ratio between these geotechnical resisting and driving forces. A value of unity (or the critical case) indicates the driving forces are equal to the resisting forces and that failure is imminent. Driving forces include the weight of the bank material, water in the bank, surcharge from vegetation, and gravity. Resisting force comes from the resistance (shear strength) of the in situ bank material. Shear strength is a combination of frictional forces represented by the angle of internal friction (’), and effective cohesion (c’). Pore‐water pressures in the bank reduce the frictional component of shear strength. Meanwhile, roots growing in the bank act to increase the resisting force of the bank material, because roots Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.33 are strong in tension, whereas soil is strong in compression. Roots growing in a soil provide a reinforced matrix akin to rebar in reinforced concrete. Vegetation can also reduce soil pore‐water pressures via evapo‐
transpiration, thereby reducing one of the driving forces acting on the streambank. Hydraulic erosion of streambanks can also be considered in terms of driving and resisting forces. Applied shear stresses from the force of flowing water act as the driving force for hydraulic erosion and are controlled by channel slope and flow depth. Forces that resist hydraulic erosion are controlled by the material properties of the bank material, including whether the material is cohesionless (sands and gravels), or cohesive (silts and clays). The particle sizes and strength of the frictional and cohesive bonds between those particles control the critical shear stress of the material. When applied shear stresses exceed critical shear stresses, particles can be hydraulically eroded and entrained by flowing water. Larger particle sizes and/or stronger cohesive bonds increase resistance to hydraulic erosion, whereas greater flow depths and steeper channel slopes increase the driving forces acting on the bank. In addition, vegetation can act to modify the balance between hydraulic driving and resisting forces in several ways. Above ground biomass increases channel roughness and can deflect and absorb stresses imposed by flow, thereby reducing the applied stresses (driving force) acting on the bank materials. Below ground biomass has also been shown to increase the hydraulic resistance of a soil to erosion by up to an order of magnitude, because the roots both bind the soil particles together more tightly, and also protect soil particles from the force of the flowing water (Bankhead et al., 2010; Simon et al., 2011a). The Bank Stability and Toe‐Erosion Model (BSTEM; Simon et al. 1999; 2011a) is an existing and well‐tested modeling tool that represents the critical processes driving streambank instability. The magnitude of bank‐face and bank‐toe erosion and associated bank steepening by hydraulic forces are calculated using an algorithm that computes the hydraulic forces acting on either the left or right near‐bank zone during a particular flow event. The boundary shear stress exerted by the flow on each node is estimated by dividing the flow area at a cross‐section into segments that are affected only by the roughness of the bank or the bed, and then further subdividing to determine the flow area affected by the roughness on each node (e.g., Einstein, 1942). BSTEM combines three limit‐equilibrium methods that calculate the Factor of Safety (Fs) of multi‐layer streambanks. The methods employed within BSTEM are horizontal layers (Simon et al., 1999), vertical slices with tension crack (Morgenstern and Price, 1965) and cantilever failures (Thorne and Tovey, 1981) (Figure 3.14). Within BSTEM, all three methods can account for the strength of up to five soil layers, the effect of pore‐water pressure (both positive and negative (matric suction)), confining pressure due to streamflow and soil reinforcement due to vegetation using the RipRoot root‐reinforcement algorithm (Pollen and Simon, 2005; Pollen, 2007; Thomas and Bankhead, 2009). BSTEM is available in two versions. The first is a static version (BSTEM Static 5.4) that predicts stream bank Factor of Safety over a single hydrograph. This version of BSTEM allows the user to isolate variables such as groundwater level, flow stage, bank height, bank angle, bank materials, and vegetation types, so that the bank Factor of Safety can be predicted over a range of conditions. This version of the model is commonly used to predict bank stability under a worst case scenario for geotechnical stability, where the water table in the bank is high, but flow in the channel is low. This is known as a drawdown condition, and it is generally seen during the receding limb of a hydrograph, where the bank is still wet from infiltration from precipitation and the flow in the channel, but the flow level has receded leaving the banks without confining pressure from the flow. The user can also modify BSTEM inputs to investigate, for example, the effect on Factor of Safety of reducing the bank angle, the effect of planting vegetation of different types, and the time it takes for vegetation to stabilize a given stream bank. In addition, the user can test the effect of riprap and other bank protection measures on 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 M E C H A N IS M S O F S T R E A M B A N K F A IL U R E
Source: BSTEM
STREAMBANK FAILURE MECHANISMS
Figure 3.14
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.35 hydraulic erosion and resulting geotechnical stability (e.g., Simon et al., 2006; 2009; 2011a; Bankhead and Simon, 2009). The second version of BSTEM is a dynamic model (BSTEM‐Dynamic 1.0) that allows for the input of time‐series discharge data. This version can be used to assess geotechnical and hydraulic stability of an existing or designed bank over any time range required by the client, from one hour to decades. The dynamic version of the model has been used in a range of projects, where the user needs to know the potential lateral retreat of a bank over time under “existing” or various channel‐design or bank‐mitigation alternatives (e.g., Bankhead et al., 2010; Simon et al., 2011b). Both versions of the model have been tested and used in a number of environments, and continue to be improved as necessary. The Static version of the model is available for free download at: http://www.ars.usda.gov/Research/docs.htm?docid=5044. The application of current public version of BSTEM Static 5.4 for incorporation into SLRT was considered, but the limitation of static flow conditions to estimate average annual sediment generation was a critical concern. While the BSTEM‐Dynamic v1 version is current in beta form (and not publically available at this time), it has been identified as the most appropriate tool to meet the SLRTv1 needs as discussed below. This dynamic version of the model has been tested for sites along the Upper Truckee River (Bankhead et al., 2011) and has shown to provide good estimates of long‐term bank retreat and sediment delivery. 3.6.1.1
BSTEM‐DYNAMIC Version 1.0 A critical SLRT objective is to minimize the input requirements for the user, while providing a scientifically defensible method to estimate the average annual mass of FSP generated from an SEZ channel. As discussed previously, streambank stability is a function of the driving and resisting forces acting on a bank, which include a complex interplay between hydraulic, hydrologic and geotechnical processes. There has long been a debate in geomorphology about the effectiveness of fluvial processes across the range of flows that occur. What flow or range of flows does the most geomorphic “work” in terms of entraining and transporting sediment? Is it the high magnitude, low frequency events; the low magnitude, high frequency events; or some combination thereof? This question is even more relevant for streambank processes where bank failure is a combination of (1) hydraulic forces operating at the bank toe and along the bank face and (2) gravitational forces acting on the bank mass. These processes tend to be most effective at different flow magnitudes. For banks consisting of sand‐sized material, low flows can entrain sediment and, although erosion rates might be low, the occurrence of these flows can be quite frequent and sustained over long durations, making them effective at steepening bank toes. In contrast, bank saturation, with the associated loss of frictional strength and increased mass, occurs during and after prolonged high‐flow events as water infiltrates laterally into the bank, leading to collapse. The spatial and temporal variability in the timing, magnitude, duration, and sequence of flow events, therefore, tends to make streambank erosion a highly non‐linear process. Furthermore, changes in relative bank stability are partially controlled by changes in the elevation of the near‐bank groundwater surface as compared to the elevation of the water in the channel. The most critical condition in this case is where groundwater levels are high (perhaps from lateral infiltration of prolonged high flows) and the recession of stage is rapid. This is referred to as the rapid drawdown condition. Although this may not be common in the Lake Tahoe Basin due to the highly permeable nature of most bank sediments, it is important to have the ability to simulate the movement of water both flowing within the channel as well as the water draining from 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.36 | July 2013 the adjacent groundwater table through the channel banks. BSTEM‐Dynamic allows the representation of all of the critical processes driving bank‐erosion rates. 3.6.2 SLRTV1 INPUTS SLRTv1 incorporates the use of BSTEM‐Dynamic to generate unit, average‐annual erosion rates for both straight and meandering sections of the subject channel. To the extent practical, the input requirements for the required BSTEM‐Dynamic runs are identified and clear user guidance provided. Below summarizes these inputs and the supporting rationale behind their selection. 3.6.2.1
Hydrology Because the primary processes driving bank failure and erosion are highly dependent on the sequence of hydrographs, the best approximation of average annual load generation requires that the hydrologic inputs represent expected annual conditions and that these conditions include long periods of low and moderate base flow periods, episodic large runoff events (i.e., summer thunderstorms), and sustained elevated flow conditions during spring snowmelt. To account for temporal variability in flow and therefore bank erosion processes, SLRTv1 uses annual hydrographs for different percentile flow years (see Section 3.3.3). By using a reasonably long‐term flow record (WY89‐WY06) to create these annual hydrographs, each flow year contains appropriately sized flow peaks, magnitudes and durations of spring snowmelt, and timing and durations of low flows that represent the Tahoe Basin for typical wet, average and dry years. By modeling several percentile flow years, SLRTv1 captures the full range of potential annual sediment loadings for a given channel. The probability of a percentile flow year occurring can be considered in terms of percent time exceeded, such that the annual loads (MT/yr) can be weighted based on the frequency of occurrence to estimate an average annual sediment/FSP load. Weighting these flow years can be done in several ways, and the sensitivity of the average annual loadings to methods of weighting and the percentile flow years modeled is discussed in Section 3.6.4. The “Static” (single storm event) version of BSTEM (i.e., BSTEM 5.4) does not continuously simulate hydraulic and geotechnical processes but only a single flow event. BSTEM‐Dynamic is, therefore, the preferred version for SLRT because it simulates daily (or shorter) flow conditions over a period specified by the user. 3.6.2.2
Channel morphology To minimize user input requirements while still preserving the critical features of spatial variability of open channel morphology over an extended reach, SLRT required the user to define attributes of a representative straight channel and outer meander bend cross‐sections for the entire subject reach. SLRT focuses on modeling straight and outer meander bend morphology because the hydraulic forces acting at the channel boundary vary spatially according to channel sinuosity, and straight reaches and meander bends represent the spatial locations where the applied hydraulic forces would likely be the most different. By modeling erosion rates for these two cross section types we can capture the full range of representative erosion rates and sediment load generation potential. The representative erosion rates are applied to the total length of straight and outer meander bends, which collectively comprise the total length of the channel modeled. The inside meander bends were not included because they generally do not contribute fine‐grained sediment. Detailed guidance and potential data sources to define two representative cross sections are provided in Section 4. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 3.6.2.3
| 3.37 Geotechnical and hydraulic resistance parameters In order to simplify BSTEM user input parameters, Tahoe specific bank‐material datasets from previous studies (Simon et al., 2003) were compiled and analyzed to provide a series of recommended inputs for hydraulic‐ and geotechnical‐resistance parameters. Geotechnical‐resistance data focus on those attributes of the bank materials that control failure due to gravity. Hydraulic‐resistance data focus on those parameters that quantify resistance to particle‐by‐particle (hydraulic) erosion.
Geotechnical BSTEM inputs include: effective cohesion (c’); effective friction angle (‘); bulk unit weight (); b
and the angle representing rate of increasing cohesion with increasing matric suction ( ). With the exception of b, these data were collected by USDA, ARS‐National Sedimentation Laboratory (NSL) in several watersheds across the basin as part of previous studies (Simon et al., 2003; 2006; 2009). A total of 43 tests were available to create a frequency distribution in Figure 3.15A. The inter‐quartile range for each parameter is highlighted in Table 3.6 and represents the range of Basin values. It is expected that the median values adequately represent Tahoe SEZs for the purposes of SLRT, and these values are recommended for all SLRTv1 BSTEM runs. There is little data on the matric‐suction parameter b for alluvial materials. Two studies were conducted by the authors while with NSL on Goodwin Creek, Mississippi (Simon et al., 1999), and another study was conducted in Italy. In general a default value of 10o is used, although this variable can theoretically vary from about 5o to a maximum value equal to the friction angle (’) of the material. Table 3.6. Details of frequency distributions of geotechnical parameters: effective cohesion, friction angle and bulk unit weight for the Lake Tahoe Basin. Raw sample data from Simon et al. (2003). Percentile Effective cohesion (c’; kPa) Friction angle (ɸ’; degrees) Bulk unit weight (γ; kN/m3) 99.99 99.9 99 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 1 0.1 22.3 22.2 21.1 19.2 13.5 8.0 6.5 5.5 4.6 4.4 4.2 3.9 3.8 3.2 1.6 1.3 0.8 0.5 0.1 0.0 0.0 0.0 0.0 0.0 39.8 39.7 38.9 37.4 36.4 35.1 35.0 34.2 33.0 32.8 31.9 31.2 30.9 30.2 29.2 27.1 25.0 24.1 22.6 21.8 19.8 14.1 7.5 6.1 19.3 19.3 19.1 18.1 17.8 17.8 17.7 17.7 17.7 17.6 17.4 17.3 17.1 17.0 16.9 16.8 16.6 16.6 16.6 16.4 16.3 15.8 15.6 15.6 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 100
A
90
A . F req u en cy d ist rib u t io n o f
geo t echn ial p aram et ers, T aho e
B asin
80
0
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Effective cohesion (kPa)
10
Friction angle (degrees)
Saturated bulk unit weight (kN m3)
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o f b an k m at erial % fin es b y
regio n , T aho e B asin
0
F req u en cy d ist rib u t io n o f B . M ed ian p art icl e siz e an d C . C rit ical shear st ress fo r b an k face an d b an k
t o e m at erial s, T aho e B asin
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in Bank
Materials
% Silt and
FREQUENCY DISTRIBUTION OF KEY CHANNEL PARAMETERS
Figure 3.15
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.39 Input data defining the hydraulic resistance of the bank materials include: critical shear stress (τc, in Pa) and the erodibility coefficient (k, in cm3/N‐s). Selection of the appropriate values for these parameters can also be determined from either (1) the frequency distributions developed for the Lake Tahoe Basin or (2) from default values within BSTEM based on material type. For the purposes of determining hydraulic resistance, the frequency distributions are separated into two distinct groups: bank toe (up to 77 samples) and bank face (up to 164 samples). The distributions of median particle sizes are shown in Figure 3.15B along with the distribution for internal bank material. These grain‐size distributions were then used to develop a frequency distribution for critical shear stress (τc) for the bank toe and bank‐face materials (Figure 3.15C). For typical SLRT users in the Tahoe Basin, the median value for both the bank face (0.3 Pa) and bank toe (21.4 Pa) should be used. In most cases, the bank materials are permeated with a network of roots that provide enhanced hydraulic resistance to the surface materials. Based on research in alluvial materials in the Tahoe Basin and elsewhere, dense mattings of grass or meadow roots increase τc by a factor of about 10. Therefore, the median value of 0.3 Pa for the bank face would be increased to 3.0 Pa for input into the model. However, the bank toe value should not change. Table 3.7. Details of the frequency distributions for composition and hydraulic resistance of bank materials in the Tahoe Basin. Note: ML‐CL refers to silt‐clay. Raw sample data from Simon et al. (2003). Percentile 99.99 99.9 99 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 1 3.6.2.4
D50 (mm) 235 235 173 46.7 31.0 11.0 4.2 1.9 1.3 0.8 0.7 0.6 0.5 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.1 Bank face ML‐CL (%) 24.0 24.0 22.6 16.3 14.0 12.2 11.5 10.9 9.2 8.2 7.1 6.1 5.5 4.6 3.9 3.1 2.4 1.6 1.0 0.4 0 0 0 τc (Pa) 228 228 168 45.4 30.1 10.7 4.1 1.3 1.0 0.6 0.5 0.4 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 Bank toe ML‐CL (%) 41.4 40.3 29.4 23.6 17.9 15.0 11.0 6.4 5.6 4.4 2.9 1.2 0 0 0 0 0 0 0 0 0 0 0 D50 (mm) 235 231 190 136 114 100 79.2 64.8 53.2 45.0 34.4 26.3 22.0 1.71 0.56 0.42 0.32 0.26 0.21 0.20 0.18 0.13 0.11 τc (Pa) 228 224 185 132 111 97.2 77.0 62.9 51.7 43.7 33.4 25.6 21.4 1.2 0.4 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.1 D50 (mm) 30.8 29.4 15.5 2.2 1.1 0.91 0.81 0.77 0.68 0.60 0.41 0.4 0.35 0.31 0.29 0.25 0.22 0.2 0.18 0.16 0.13 0.12 0.10 Internal bank ML‐CL (%) τc (Pa) 33.0 30.0 32.9 28.6 32.9 15.0 25.4 2.1 22.4 0.8 19.9 0.6 18.5 0.6 15.0 0.5 13.1 0.5 11.6 0.4 10.1 0.3 8.7 0.3 7.4 0.2 6.8 0.2 5.5 0.2 4.5 0.2 4.0 0.2 3.4 0.1 2.8 0.1 2.5 0.1 1.8 0.1 0.5 0.1 0.2 0.1 Bank material fines As the objective of the sediment load reduction tool is to evaluate the potential reduction in fine‐sediment loads to the lake, the amount of fine‐grained materials in the banks is a critical input parameter to complete the SCEfsp estimates. The frequency distribution of Tahoe streambank materials previously obtained from 63 streams are presented by region and for the entire basin in Table 3.8, and displayed graphically in Figure 3.15D. The samples obtained are a combination of bank face and internal bank materials (200 samples across the 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.40 | July 2013 basin; see Figure 3.15B) since these are locations of the bank susceptible to erosion, failure and subsequent transport. The median values vary by region with the southern streams containing the greatest fraction of fines at 8.7% and the basin median of 5.7%. If the SLRT user choses to use the existing bank‐material data, the median value for the respective regions is recommended, or east, north, south and west values are 4.0%, 3.3% 8.7% and 8.1% respectively. Table 3.8. Details of frequency distributions of percent silt/clay in the bank materials for the four quadrants and for the entire Lake Tahoe Basin. Raw data from Simon et al. (2003). Percentile 99.99 99.9 99 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 1 Average East 8.08 8.07 8.01 7.73 7.23 6.61 5.97 5.38 4.95 4.84 4.70 4.43 4.02 3.11 2.59 2.30 1.60 0.99 0.55 0.49 0.19 0.00 0.00 3.61 Percent Silt/Clay in Bank Materials North South West 11.8 19.0 27.4 11.7 19.0 27.3 11.1 18.5 27.1 10.0 17.5 23.6 8.63 17.2 17.5 7.82 15.9 15.6 7.17 15.3 14.0 6.23 14.9 13.3 5.45 13.6 12.2 4.80 11.5 10.8 4.19 11.1 9.81 3.68 10.3 9.06 3.34 8.67 8.13 3.07 7.53 7.06 2.84 6.23 6.10 2.80 6.03 4.52 2.70 5.30 3.86 2.21 4.35 3.40 2.06 3.78 1.44 1.82 2.85 0.49 1.59 0.55 0.00 0.96 0.26 0.00 0.82 0.07 0.00 4.38 9.10 8.78 All 27.4 27.3 25.2 17.5 15.6 13.9 11.9 10.4 9.25 8.12 7.43 6.34 5.67 4.76 4.08 3.60 3.03 2.71 1.99 1.25 0.54 0.00 0.00 7.09 3.6.3 SLRT CHANNEL EROSION APPROACH The driving forces acting on a channel over a given reach vary spatially and temporally, and must be accounted for in SLRT to accurately provide average annual FSP generated from streambank erosion. To account for the spatial variations in hydraulic forces applied to the channel, two bank profiles are modeled: a representative straight reach and a representative outside meander bend. Inside bends are not considered an important source of FSP. BSTEM‐Dynamic provides an annual unit erosion rate per length of channel/bank (m3/m/yr) that is assumed to be representative of the straight and outside bend reaches within the subject SEZ modeled for a given annual hydrograph. The temporal variability in applied hydraulic forces is modeled by a series of annual probability hydrographs (see Section 3.3.3) representing a range of percentile flow years (25th, 50th, 75th and 99th percentiles) for both the representative straight reach and outer bend morphology (if applicable). The user is required to model the 4 annual hydrographs for straight and bend configurations for both the pre and post morphology. In many instances, the lower flow years will result in negligible erosion, so smaller flow years may not need to be modeled. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 3.41 The unit erosion rates obtained from BSTEM‐Dynamic are used to estimate the average annual sediment and FSP generated from the SEZ channel using the following calculations. The annual bulk sediment generation rate (estr‐p, eob‐p) for the straight and outer meander are multiplied by the respective total contributing lengths (l str and lout), bulk unit sediment weight () and unit conversion factors to calculate the annual bulk sediment mass (MT/yr) generated for each probability flow year, where: SCEbs‐p = (estr‐p * lstr *  ) + (eob‐p * lob *  )) EQ 3.30 Straight reaches represent both sides of the channel requiring the total straight reach length to be multiplied by two. Bank‐sample data taken from around the Tahoe Basin (Simon et al., 2003) are used to estimate the fraction of bank material that is fine grained and used to convert each bulk sediment load to a FSP load (MT/yr) for each annual hydrograph where: SCEfsp‐p = SCEbs‐p * FSP:BS EQ 3.31 The annual bulk sediment and FSP loads for each flow year must be integrated to estimate the average annual sediment and FSP loads while accounting for the frequency of occurrence of each annual probability hydrograph. Undoubtedly, the 99th percentile flow year generates the greatest annual loads yet only has the probability of occurring less than 1 out of 100 years. Therefore, the probability of exceedance of each percentile flow year is plotted against the annual bulk sediment and FSP loading rates as a cumulative distribution function. This allows appropriate scaling of the average load predictions based on the frequency of the annual hydrograph occurrence. A cumulative distribution function is used to calculate the area under each of load curve of the probability density function for the desired probability intervals, using the method to calculate the area of a trapezoid. This appropriately weights each of the annual loads based on the probability of occurrence. The mean of the probability weighted annual loads is assumed to represent the average annual bulk sediment (SCEbs) and FSP (SCEfsp) load generated from channel erosion for an SEZ. These same calculations are conducted for both the pre and post SEZ morphology. 3.6.4 LIMITATIONS Limitations of BSTEM‐Dynamic: 


The bed is fixed in BSTEM‐Dynamic 1.0, so degradation and increased instability of the banks through this process is not modeled. BSTEM is not a sediment transport model, so sediment routing and deposition is not modeled. The SLRT approach assumes all sediment and FSP generated from bank erosion is delivered to the flow and is exported from the reach. While this lag in transport of eroded material from the banks to the eventual export from the reach may not occur during the simulated year, it is reasonable to assume on decadal time scales the majority of material generated from bank erosion will be transported downstream. This approach is reasonable for a method aimed to model the long‐term average fine‐
grained annual load reductions associated with morphologic changes. BSTEM assumes that when a bank fails, the failed material is immediately transported away from the bank toe by the water flowing in the channel. In reality some of the failed material may remain at the toe of the bank, thereby protecting the bank from hydraulic erosion for a period of time. This 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 3.42 | July 2013 assumption can lead to overestimation of bank retreat and sediment loadings, but for the annual hydrographs modeled here, this overestimate should be of limited concern. Limitations of overall approach: 


Time required by user. The requirement for potentially 16 independent BSTEM‐Dynamic runs is based on the spatial and temporal resolution of data used to develop the average annual sediment loading. If only outer bend sites were run, overestimation of loadings would occur, and conversely, if only banks along straight reaches were modeled, underestimation of loadings would occur. Similarly, if only the 99th percentile flow year were modeled, this would far exceed the average annual loading value sought in this tool. The average annual loading is calculated by weighting the percentile flow years according to percent of time their resulting sediment loadings are exceeded. For example, the 99th percentile flow year is equaled or exceeded 1 % of the time. The trapezoid method for calculating the area under the curve of percentile flow year versus sediment loading is used to appropriately weight the sediment delivered in different flow years, with the average of the trapezoid areas representing the average annual loading value. Average annual loadings are dependent on the percentile flow years selected, as these affect the trapezoid areas and resulting average annual load calculation. The research team tested the sensitivity of the load calculations to the selection of the modeled flows ‐ removing one or more of the percentile flow years changes the estimated average annual loading. When more flow years are included, the trapezoid widths are smaller and estimations are likely more representative of the series of long term flows. It is particularly important that the higher percentile flow years (e.g., 99th percentile) are appropriately adjusted based on their infrequent occurrence or their higher sediment loadings will incorrectly increase the average annual value calculated. Future research should include a more comprehensive sensitivity testing on other sites with differing geometry and bank material characteristics. Where possible, BSTEM results should be validated against annual suspended sediment measurements across a range of wet and dry years. In addition, if historical bank geometry or lateral retreat data are available, BSTEM results can be calibrated over the period bounded by the repeat surveys and then run within the SLRT. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: DRAFT REPORT 4
| 4.1 SLRTV1 USER GUIDANCE Below is the step‐by‐step user guidance to calculate the average annual mass of fine sediment particles (FSP; < 16 µm) reduced as a result of morphologic modifications to an SEZ. A series of tools are provided to simplify the data input and calculations required by the user. A digital spreadsheet tool (SLRTv1_Template.xlsx) is provided for direct input by the user. SLRT users are also required to utilize a beta dynamic version of the Bank Stability Toe Erosion Model (BSTEM) to obtain a series bank erosion estimates for input into the SLRTv1_Template.xlsx. Once all necessary input data are entered, the SLRTv1_Template.xlsx uses preloaded equations and algorithms to simplify the calculations for the user and provide the average annual load reduction estimates. Example BSTEM‐Dynamic and Excel files for two test sites (Upper Reach of Trout Creek and Bristlecone SEZ) are provided as tangible examples for the user’s reference (see Section 5). All relevant digital files can be downloaded from http://www.2ndnaturellc.com/client‐access/slrttrout‐creek/. While these products are available for use, they are initial beta versions and may contain bugs, particularly if not used in the exact manner outlined below. SLRT users must QA/QC all calculations, as changes to cell references or equations may result in erroneous and unintended outputs. All variables are defined in List of Variables provided at the beginning of this report (see page vii) and, using the SLRT nomenclature, FSP is noted as the pollutant for each variable. The intent is that future versions could expand the SLRT methodology to other pollutants such as total suspended sediment (TSS), total nitrogen (TN) or dissolved nitrogen (DN), for example. 4.1
SLRTV1_TEMPLATE.XLSX SLRTv1_Template.xlsx is a blank macro‐enabled spreadsheet that auto‐generates the SLRT calculations using required data inputs to estimate an average annual FSP load reduction as a result of SEZ physical modifications. The calculation template contains 5 active worksheets described below. Cell Codes BLUE CELLS = USER INPUT REQUIRED GREY CELLS = SLRT CALCULATED VALUE WHITE CELLS = INFORMATION, INTERMEDIATE CALCULATION, UNIT CONVERSION, ETC. Worksheets 



USER INPUTS: Any and all user input values are entered here for both pre‐ and post‐restoration scenarios. Once all user data inputs are completed, all of the calculations in the remaining 4 worksheets below are auto‐generated. HYD FSP IN: SLRT estimates of contributing average annual catchment hydrology and FSP loading in required formats. RFPfsp: SLRT estimates of average annual FSP loads delivered to and retained upon the SEZ floodplain for both pre‐ and post‐restoration scenarios. SCEfsp: SLRT estimates of average annual FSP loads generated from channel erosion for both pre‐ and post‐restoration scenarios. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 4.2 | 
July 25, 2013 SLRT Results: SLRT estimate of average annual FSP load reduction as a result of restoration. A series of additional physical differences between pre‐ and post‐restoration conditions are provided as additional support for restoration effectiveness. Each worksheet has a pre‐defined Print view that will generate a single page report summary. Collectively these five (5) pages provide the critical inputs and outputs of the SLRT estimates for the SEZ of interest. 4.2
SLRT INPUT DATA NEEDS SLRTv1_Template.xlsx worksheet: USER INPUTS The SLRT method requires the use of available data to quantify a number of catchment and morphologic attributes that are necessary inputs to estimate the average annual pollutant load reduction. The SLRTv1_Template.xlsx USER INPUTS worksheet contains all required input parameters and associated units to complete SLRT calculations (Figure 4.1). Potential data sources and considerations for determination of each value are provided below. Once the user has populated the required fields noted in Figure 4.1 the SLRT calculation process can be efficiently completed. 4.2.1
META DATA In order to identify the site and user the SLRT input table includes a collection of meta data including user, SEZ catchment, and reach names and date of estimate (see Figure 4.1). It is recommended the user generate a SEZ location map that identifies the region, contributing catchment boundary and clear delineation of the areal extent of restoration actions (see Figures 5.1 and 5.3 as examples). 4.2.2
CATCHMENT CHARACTERISTICS The catchment characteristic inputs (see Figure 4.1) are used to estimate the incoming site hydrology and FSP yield from the catchment based on the methods outlined in Sections 3.3 and 3.4. The user must determine if contributing catchment is non‐urban or urban using criteria provided in Table 4.1. Table 4.1. Definitions of SLRT catchment types. Catchment Type Urban Non‐urban Characteristics 
Large amount of development and impervious surfaces (> 10% impervious) 
Typically smaller than 1 square mile (or 640 acres) 
SEZ is not located on an LTIMP stream 
Can be modeled by PLRM 
Large amount of forest and undeveloped lands (<10% impervious) 
Size can vary but typically larger than 1 square mile (or 640 acres) 
SEZ located on LTIMP stream or tributary 
Can NOT be modeled by PLRM STREAM LOAD REDUCTION TOOL (SLRTv1)
User Inputs
META DATA
USER NAME
WATERSHED/CATCHMENT
REACH NAME
Date of Estimate
CATCHMENT TYPE
REGION
SUB-REGION
CATCHMENT AREA
AREA UNITS
CATCHMENT % IMPERVIOUS
CATCHMENT LAND USE CONDITION
2NDNATURE
Trout Creek
Upper Reach (TCPT)
5/1/2013
CATCHMENT CHARACTERISTICS
Non Urban
Southshore
Southwest
23.7
Sq-miles
Urban Only
3
Urban Only
SEZ ATTRIBUTES
PRE RESTORATION
1530
0.0016
530
1.34
lc
s
lob
hob
POST RESTORATION
1829
0.0013
1001
1.1
37
aob
53
1000
1
lstr
hstr
828
0.76
Bank angle of straight reaches (degrees)
34
astr
39
Manning’s roughness value of channel
Fines to bulk sediment ratio (0-1 value)
0.03
0.087
n
FSP:BS
0.03
0.087
Channel capacity (cfs)
Floodplain length (m)
Floodplain condition score
200
458
3
Qcc
lfp
FPC
88
458
5
Effective cohesion (kPa)
Angle of internal friction (degrees)
30.9
3.8
17.1
10.0
c'
'
30.9
3.8
17.1
10.0
Channel length (m)
Channel slope (m/m)
Outside bend length (m)
Bank height of outside bends (m)
Bank angle of outside bends (degrees)
Straight length (m)
Bank height of straight reaches (m)
3
Bulk unit weight (kN/m )
Matric suction parameter (degrees)
Bank - Critical shear stress (Pa)
3
Bank - Erodibility coefficient (cm /Ns)
Toe - Critical shear stress (Pa)
3
Toe - Erodibility coefficient (cm /Ns)
3.00
0.645
21.4
0.127
b
c
k
c
k
3.00
0.645
21.4
0.127
BSTEM Dynamic OUTPUT
POST RESTORATION Qmd-p
PRE RESTORATION
0.136
3
Outside bend unit erosion rate (m /m/yr)
0.06
0.02
0.0043
3
Straight reach unit erosion rate (m /m/yr)
0.17
0.065
0.028
0.0076
e ob-99
e ob-75
e ob-50
e ob-25
0.036
e str-99
e str-75
e str-50
e str-25
99th
0.005
75th
0
50th
0
25th
0.075
0.018
0
99th
75th
50th
0
25th
SLRTv1 created by 2NDNATURE LLC 2013
USER INPUT [7/24/2013]
SLRTV1 USER INPUT WORKSHEET
Figure 4.1
4.4 | July 25, 2013 Once the catchment type is determined, obtain the information/data shown in Table 4.2 using GIS tools and available land use layers (recommended: TMDL Land Use GIS Layer, available at http://www.waterboards.ca.gov/lahontan/water_issues/programs/tmdl/lake_tahoe/index.shtml). It is critical that the user designate an urban catchment area in acres and a non‐urban catchment in square miles to properly estimate catchment hydrology. The catchment percent impervious and land use condition determinations are only required for urban catchments. Table 4.2. Catchment characteristic inputs for each SLRT catchment type. Catchment Type Urban Variable Units Region Sub‐region n/a n/a Catchment area acres % Impervious % Urban catchment land use condition Region n/a Region where non‐urban SEZ is located; Figure 4.2B Sub‐region n/a Sub‐region where non‐urban SEZ is located: see Figure 4.2B Catchment area mi2 Contributing catchment area to upstream boundary of SEZ, reported in square miles. Non‐urban Description Region where urban SEZ is located; Figure 4.2A Sub‐region where urban SEZ is located: see Figure 4.2A Contributing urban catchment area to upstream boundary of SEZ, reported in acres. Percent of catchment area with impervious surfaces Estimated catchment condition with respect to average annual generation and transport of fine sediment particles to SEZ. The FSP catchment load for an urban catchment is generated using an estimate of the catchment characteristic runoff concentration (FSP CRC) based on the % impervious and user‐defined relative catchment condition (see Section 3.4). Given that SLRT is focused on providing the estimated pollutant load reduced as a result of SEZ morphologic changes, the same FSP CRC must be used for both pre‐ and post‐restoration scenarios. PLRM is the recommended platform to estimate the urban catchment water quality benefits of source control and treatment actions conducted upstream of an urban SEZ. The default urban catchment condition is 3 in the SLRTv1_Template.xlsx, but Table 4.3 provides considerations to assist the user in determining if the catchment condition may deviate from the assumed average. Table 4.3. Urban catchment condition considerations to assist user with determination that the subject catchment deviates from the assumed Tahoe Basin average. Urban catchment condition Considerations 5 Above average: Significant water quality improvements have been implemented within the catchment to minimize FSP source generation (particularly on roads) and significantly reduce the hydrologic connectivity of the catchment; the majority of stormwater volumes generated within the catchment are routed to an effective and maintained WQIP; annual inspections and maintenance are conducted regularly on WQIP and private parcel BMPs. 3 Average: Urban catchment land use practices typical of Tahoe Basin and include winter road abrasive application, implementation of WQIP and erosion control projects over past decade, and reasonable private parcel BMP implementation. Catchment hydrologic connectivity is moderate; annual maintenance and inspections are conducted but are not high priority due to funding limitations; average catchment stormwater FSP concentrations are typically below 300 mg/L. 1 Poor: Source control and water quality improvement actions are below Basin average; catchment may be highly connected; maintenance and inspections are minimal; relatively elevated FSP concentrations in urban stormwater (> 300 mg/L) are expected. U rb an R egio n s
LEGEND
U rb an S u b - regio n s
N o r th
N o rt hsho re
Northeast
North
Northwest
W a s h o e C o u nt y
W a s h o e C o u nt y
C a r s o nC it y
C o u nt y
C a r s o nC it y
C o u nt y
D o u gl a s
C o u nt y
D o u gl a s
C o u nt y
P l a c er C o u nt y
P l a c er C o u nt y
E l D o r a do C o u nt y
E l D o r a do C o u nt y
East
West
S o u t S hsho
re
o u th
Southwest
Figure .2A: Urban regions (2 shown on left in red) and sub-regions ( shown
Fi g u r e 4 . 2: U r b a n a n d N o n - u r b a n R eg i o n D el i n ea ti o n f o r S L R T i n p u ts .
on right in yellow) delineations for SLRT inputs.
Southeast
Feet
I ta l i c i z ed tex t i n n o n - u r b a n c a tc h m en ts i n d i c a te s u g r eg i o n s .
0
7 ,00014 ,000
28 ,000
N o n - U rb an R egio n s
LEGEND
N o n - U rb an S u b - regio n s
N o r th
N o rt hsho re
Northeast
North
Northwest
W a s h o e C o u nt y
W a s h o e C o u nt y
C a r s o nC it y
C o u nt y
C a r s o nC it y
C o u nt y
D o u gl a s
C o u nt y
P l a c er C o u nt y
W est sho re
E l D o r a do C o u nt y
E ast sho re
D o u gl a s
C o u nt y
West
P l a c er C o u nt y
E l D o r a do C o u nt y
East
S o u t hsho re
S o u th
M ain st em
Southwest
Southeast
U T R *
Figure
Fi g u r e .2B:
4 . 2: Non-urban
U r b a n a n d regions
N o n - u (5,
r b a shown
n R eg on
i o n left
D elini n red)
ea ti o and
n f o sub-regions
r S L R T i n (p u ts .
shown
on
right
in
yellow)
delineations
for
SLRT
inputs.
Note:
The Upper
I ta l i c i z ed tex t i n n o n - u r b a n c a tc h m en ts i n d i c a te s u g r eg i o n s .
Truckee River mainstem is treated as its own region, separate from Southshore, due to di erences in hydrology.
Feet
0
7 ,00014 ,000
28 ,000
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: DRAFT REPORT 4.2.3
| 4.7 SEZ ATTRIBUTES A series of SEZ attributes are required as inputs to SLRT (see Figure 4.1). The user should utilize available field data to quantify each of the values which may include, but are not limited to, topographic surveys, cross‐
sections, geomorphic surveys, aerial photos, ground photos, and other information that would allow reasonable quantification of the required representative attributes for both pre‐ and post‐restoration conditions. Table 4.4 describes each attribute in detail and provides a number of potential data sources and considerations for generating values. There inherently will be challenges faced by the user in generating these values. For instance, pre‐restoration data is extremely limited from an SEZ that was restored many years ago, or if restoration was recently completed, the current post‐restoration condition may be expected to be temporary as the morphology and supporting vegetation tend toward a future equilibrium that will better represent average annual post‐restoration condition. Given that SLRT may be used at varying stages of restoration including planning and design, immediately following, or many years post‐restoration, a general recommendation is to reasonably estimate and represent the SEZ attributes 10‐years post restoration. While potentially challenging, the representation of a future desired condition provides a better estimate of the expected average annual load reduction once the restored site has reached a new equilibrium. In all instances the user is encouraged to compile and leverage available data, document the data sources, clearly articulate any assumptions in generating the required inputs, and critically evaluate the expected implications of using different input values prior to final selection. Guidance is provided to assist the user with the application of best professional judgment when selecting each SLRT input. In order to meet a number of the SLRT objectives of providing a reliable yet relatively simple approach to estimating the average annual pollutant load reductions, the method requires a simplification of the inherent spatial complexity of any SEZ into a series of representative or average attribute values. For each of the attributes in Table 4.4 the user should rely upon the best available and relevant datasets. At a minimum, field geomorphic surveys with a stadia rod, inclinometer, survey tape and camera can be used to obtain relevant site specific data to generate the morphologic values required. Regardless of the data source, when estimating a single representative values for an attribute, consider generating a population of values that represents the spatial distribution of each value within the overall reach. Use data integration techniques that either average a representative population of values or generate a reasonable spatial weighting scheme to integrate a series of values to generate the value that, on average, represents the subject reach. The user should critically evaluate if each value generated is reasonable and QA/QC each estimate with knowledge of the reach. It is recommended the user clearly document the data sources used and the assumptions associated with data integration to generate the SLRT inputs. Additional guidance for specific attributes is provided below. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 4.8 | July 25, 2013 Table 4.4. Descriptions of, and potential data sources to generate, SLRT geomorphic attributes. Note the order provided below differs from the order of data input in the SLRTv1_Template.xlsx USER INPUTS worksheet. Variable lc s lob lstr Attribute Channel length Channel slope Outside bend length Straight reach length Units of outside m m/m m m m m Bank angle of outside deg Bank angle of straight reaches GIS calculation Longitudinal survey deg Considerations Select a length of channel over which channel geometry and material types are relatively consistent. Distinct changes in bank height, vegetation characteristics, etc. should be modeled as separate reaches. Elevation difference of the upstream Topographic survey Ensure channel slope is measured at the thalweg boundary thalweg and downstream DEM and field survey depth from and extended over a length equivalent to at least 6‐
boundary thalweg divided by reach top of bank to thalweg in each 20 bankfull channel widths, a complete meander length. boundary wavelength or two‐pool riffle sequences. Length of channel (measured at the Aerial photograph Be consistent with determination of start and finish thalweg) located along a meander Topographic survey of each bend, such that angles are consistent when bend. Field surveys using survey tape designating each start/finish boundary. Length of channel (measured at the thalweg) that is a straight reach. Calculate lstr= lc ‐ lout thalweg at midpoint of curvature of Topographic surveys outside bends. Cross‐sections Average height from top of bank to survey tape thalweg of straight reaches. Average angle from top of bank to bends astr USGS map Field surveys using stadia rod and reaches aob Google Earth Average height from top of bank to Bank height of straight thalweg between upstream and downstream SEZ boundary. bends hstr Potential data source (s) Length of channel as measured at Bank height hob Description bank toe at midpoint of curvature of Topographic surveys outside bends Cross‐sections Average angle from top of bank to bank toe of straight reaches Field surveys using stadia rod, level and inclinometer (or iPhone app) Can be verified using: aerial photograph; topographic survey; field surveys using survey tape Obtain and average multiple measurements that represent range of heights to generate a reasonable average bank height of reach. Consider aligning number of measurements included in average with spatial distribution of each height value within reach. Obtain and average multiple measurements that represent range of angles to generate a reasonable average bank angle of reach. Consider aligning number of measurements included in average with spatial distribution of each angle value within reach. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT Variable Attribute Units Mannings n roughness of Description A coefficient that represents the n/a channel value relative resistance of the bed of a channel to the flow of water in it. | 4.9 Potential data source (s) Considerations Recommended n values for SLRT inputs based on channel substrate and riparian vegetation density are provided below (Table 4.5). Table 4.7 data is < 62m, which will overestimate the fraction that is < 16m, but uses the best datasets % fine FSP:BS sediment in banks 0‐1 fraction Fraction of SEZ bank/bed material that is silt/clay. Bank material data from Tahoe available. (see Table 4.7 provided below) If bank material is sampled, ensure samples are Sample bank material and submit spatially representative of locations susceptible to to laboratory erosion within SEZ, and obtain at least one field triplicate. Analyze for % < 16 m. Analytical costs will be higher, but more accurate than use of Table 4.7. HEC‐RAS model Qcc Channel capacity cfs Average channel capacity of reach Cross‐section surveys expressed as discharge measured at Field geomorphic surveys upstream boundary. Manning’s equation Longitudinal linear distance from lfp Floodplain length m upstream to downstream boundary of floodplain adjacent to subject reach. Presence and distribution of floodplain attributes that increase Floodplain FPC condition score 1, 3, 5 the relative particulate retention efficiency during overbank conditions. Expressed as a score of 1,3 or 5. 2NDNATURE, LLC | ecosystem science + design Complete and average multiple measurements that represent a range to generate a reasonable average channel capacity of the reach. Consider aligning number of measurements included in average with spatial distribution of each channel capacity within reach. Google Earth Measured the distance between the upstream and USGS map downstream boundaries of the subject SEZ. Unless GIS calculation the channel is straight, lfp should be shorter than lc. Visual surveys Ground photos Aerial photograph HEC‐RAS model Floodplain topographic surveys FPC is expressed as a relative score (1‐5) with 5 representing a floodplain that is highly complex and possesses many of the attributes theorized to retain a higher fraction of FSP during relatively small overbank flows. Additional guidance is provided below (Table 4.6). www.2ndnaturellc.com | 831.426.9119 4.10 | Variable Attribute Units July 25, 2013 Description Potential data source (s) Effective Default values are reasonable estimates for Tahoe cohesion, friction kPa, c', ɸ’, γ, angle, bulk o, b
ɸ unit weight, kN/m3
matric o
, on those attributes of the bank Default values are recommended materials that control failure due to based on available Tahoe datasets gravity. (see Table 4.10) Bank material data from Tahoe parameter Τc, k stress, erodibility coefficient SEZs based on existing datasets (Simon et al., 2003; Geotechnical‐resistance data focus suction Critical shear Considerations (see Section 3.6.3.3) Pa, cm3/N
s Hydraulic‐resistance data focus on those parameters that quantify Sample bank material and submit to laboratory. 2006; 2009) to reduce the effort of the user. If SEZ specific data is used, spatially representative observations of bank material composition are needed. Data collection and analysis may not improve accuracy of model given the level of effort needed to obtain site specific values. If bank material is sampled, ensure samples are spatially representative of locations susceptible to resistance to particle‐by‐particle erosion within SEZ, and obtain at least one field erosion. triplicate. Analyze for % < 16 m. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 4.2.3.1
| 4.11 Mannings n Table 4.5 provides a collection of recommended Manning’s n values for the SLRT user. While technically the roughness values will differ depending upon discharge, with higher flow perhaps experiencing higher resistance due to greater interaction with vegetated banks. Because relative roughness varies with depth, it is recommended that the user estimate n values at bankfull conditions for the reach of interest. Pre‐restoration condition should be represented based on bed substrate and the vegetation density to reflect the conditions of the decade prior to restoration. Similarly the SLRT user should determine the post‐restoration Manning’s n value as the reasonable expectation of vegetation density 10 years post‐restoration. All assumptions by the user to justify these determinations should be documented. Table 4.5. Recommended Manning’s n values for input to SLRT. Recommended values based on information provided in http://www.fsl.orst.edu/geowater/FX3/help/8_Hydraulic_Reference/Mannings_n_Tables.htm, http://www.fhwa.dot.gov/bridge/wsp2339.pdf and A. Simon (2013 pers. comm). To be consistent with BSTEM inputs, Manning’s values should only be provided to the nearest hundredth. Vegetation Density Channel dominant substrate type Firm soil Coarse sand Gravel (i.e. clay bed) Cobble None 0.03 0.03 0.04 0.05 Low 0.03 0.04 0.05 0.06 Medium 0.04 0.05 0.05 0.06 High 0.04 0.06 0.06 0.08 4.2.3.2
Vegetation density comments A channel with no vegetation Turf grass/weeds; young flexible tree seedlings; no vegetation on channel bed Brushy, moderately dense vegetation (e.g., 1‐to‐2‐year‐old willow trees in the dormant season) growing along the banks, with no significant vegetation evident on channel bed. E.g., 8‐to‐10‐years‐old willow or cottonwood trees with some weeds and brush. Channel Capacity (Q cc ) Channel capacity will vary throughout a subject reach so it recommended that the user obtains a series of Qcc estimates and integrates the values in a manner that will generate a representative average. Various hydraulic models will provide a Qcc estimates based user inputs and where models are available this approach is recommended to estimate the channel capacity. Below we provide guidance to manually estimate Qcc using available cross sections and the Manning’s equation. Calculate cross sectional area (Axs) at channel capacity (top of banks) (ft2). Calculate wetted perimeter (WP) at channel capacity (top of banks) (ft) using distance formula along cross section where distance = sqrt ((x2‐x1)2+(y2‐y1)2). 3. Calculate slope (s) of reach within which cross section exists by measuring elevation difference between start/end of reach and divide by measured channel length (aerial photo or GIS or survey). 4. Using Table 4.5, estimate Manning’s n value for stream channel within reach (dimensionless). 5. Calculate velocity (v) at channel capacity (top of banks) using Manning’s equation (ft/sec): (1.486/n) x (A/WP)^0.66 * s0.5 6. Qcc = v *A 1.
2.
2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 4.12 | 4.2.3.3
July 2013 Floodplain condition Determining the relative floodplain condition is based on three characteristics: topographic complexity of the floodplain; vegetation density, distribution and vertical variability; and average stage to discharge relationship on the floodplain. Topographic complexity, such as surface irregularities, wood and other features, increase roughness of the floodplain surface and reduce the effective velocity of flood waters, and as flows recede the low spots and flow barriers can strand and store sediment‐laden waters. Vegetation density, as well as spatial and vertical distribution, also reduce flow velocities and provide surfaces to which FSP can adhere. The stage to discharge relationship of the floodplain surface is controlled by the morphology of the floodplain, where a functional floodplain results in incremental increases in effective depth as discharge increases. See Figure 3.12, which represents the stage to discharge relationship of two good condition channel/floodplain relationships. In contrast, an inset or constrained floodplain surface will have a much greater rate of increasing floodplain depth as a function of discharge. It must be noted that the measured retention coefficients were developed by evaluating two low gradient stream systems. The user is encouraged to incorporate the vision of the potential desired and achievable floodplain characteristics given the hydrologic and geologic setting of the subject SEZ. Given the expected improvement of floodplain condition over time following restoration, if SLRT is applied when the subject SEZ has been restored but is less than a decade post‐restoration it is suggested the user reasonably estimates the expected floodplain condition 10 years post‐restoration, since SLRT is intended to be a long‐term average annual estimate. The user will need to document cause and effect assumptions associated with how the specific restoration configuration and actions reasonably support the future FPC vision. Documentation of restoration actions, such as placement of woody debris on the floodplain or channel/floodplain revegetation efforts that are assumed to lead to future assigned floodplain condition, should be included. Table 4.6 provides guidance to select the appropriate floodplain condition (FPC) score. As a general rule, if the floodplain topographic complexity, vegetation community and stage to discharge do not all align with a single condition, assign the condition best represented by two of the three factors. Table 4.6. SLRT user considerations of floodplain characteristics representative of floodplain condition (FPC). FPC Score Condition Topographic complexity Vegetation community Stage to discharge relationship Significant increase in floodplain depth as a function of discharge. Typical of an inset floodplain or other configuration where lateral migration of overbank flow is constrained. 1 Poor Minimal: simplified floodplain surface. Sparse: typical of a meadow with low soil moisture and drought tolerant plant species. Bare dry soil present in summer. 3 Fair Moderate Intermediate Moderate lateral or longitudinal floodplain confinement. High: characterized by presence of large wood, surface irregularities and other features that promote water and particulate stranding. Dense: well vegetated with meadow species such as grasses, sedge, willows, etc. Coverage is extensive with little to no unvegetated areas. Incremental increase in floodplain depth as discharge increases due to little lateral confinement of overbank flows. See Figure 3.12 for examples. 5 Good/ desired Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 4.2.3.4
| 4.13 Bank material fines As the objective of the sediment load reduction tool is to evaluate the potential reduction in fine‐sediment loads to the lake, the amount of fine‐grained materials in the banks is an important input parameter. The SLRT user can either: 1.
2.
Utilize the frequency distributions derived from previously sampled Tahoe bank materials by USDA, ARS‐National Sedimentation Laboratory (NSL) (Simon et al., 2003) or, Directly sample and analyze a collection of representative bank material samples from the SEZ. The frequency distribution of Tahoe stream bank materials previously obtained from 63 streams are presented by region and for the entire basin in Table 3.8 and displayed graphically in Figure 3.15D. The median FSP:BS values by sub‐ region (see Figures 4.2A&B) are recommended for SLRT input (Table 4.7), noting that these % values represent the <62 um fraction. Should the user choose to sample the SEZ directly the user should obtain a distribution of samples from both the bank face and internal bank material from a series of locations that are susceptible to erosion. Both outer meander bend and straight reaches should be sampled. The samples should be submitted to a geotechnical laboratory and analyzed for % of material < 16 m. Table 4.7. Bank material percent fines (silt + clay) by sub‐region (see Figures 4.2A&B). Region East Northwest, North & Northeast Southwest & Southeast West Basin Average FSP:BS (%) 4.0 3.3 8.7 8.1 5.7 4.2.4 BSTEM‐DYNAMIC OUTPUTS The final SLRT input requirements in the USER INPUT worksheet (SLRTv1_Template.xlsx) are the unit erosion rates (variable) generated from a series of BSTEM‐Dynamic model runs conducted by the user (see Figure 4.1). The detailed guidance below describes the methods to obtain the necessary BSTEM outputs to complete the SLRT average annual FSP loads generated from channel erosion For SLRTv1, the user will need to run BSTEM‐Dynamic a maximum of 16 times per Table 4.8. It is recommended to begin with the higher flow probabilities (i.e., 99th percentile) because, depending on model results, the lower flow data may not be necessary. Using the values in Figure 4.1 as an example, neither the outer bend nor straight reach post‐restoration morphologies were modeled for the 25th percentile flows because of the negligible unit erosion rates (i.e., <0.0000) obtained for the 50th percentile flow conditions. Table 4.8 Required BSTEM‐Dynamic model runs for SLRTv1, however sequential progression from high to low flows may eliminate the need for lower percentile analyses. Percentile 99th 75th 50th 25th Pre‐Restoration Straight Reach Outer Bend x x x x x x x x Post‐Restoration Straight Reach Outer Bend x x x x x x x x 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 4.14 | 4.2.4.1
July 2013 Stage to discharge relationships The user will need to determine the stage to discharge relationships for the pre‐ and post‐restoration conditions for each probability hydrograph as shown in Table 4.9. As noted above, depending upon the results of the higher percentile flow conditions, there is a possibility that not all flow conditions will need to be simulated and therefore not all relationships are necessary. Table 4.9. Required number of stage to discharge relations for SLRTv1, however sequential progression from high to low flows may eliminate the need for lower percentile analyses. Percentile 99th 75th 50th 25th Pre‐Restoration x x x x Post‐Restoration x x x x Data requirements:  Full channel cross section, longitudinal slope (dimensionless; m/m) of the channel, thalweg elevation and estimate of Manning’s roughness coefficient (n). This will be required for both the “existing” and “restored” conditions. See Table 4.4 and discussion above for guidance on determining these values.  Percentile (25th, 50th, 75th, 99th) annual hydrographs of mean daily flow from HYD FSP IN worksheet (columns S‐V, see Section 4.3 and Figure 4.3A). Method:  Open NDA_SLRTTemplate. xlsx and enable macros. INSTRUCTION worksheet provides instructions.  In INSTRUCTION worksheet (Figure 4.3B):  Enter the top bank elevation for the left and right bank in cells E14, E15 (flow cannot exceed the lower of these two values without going out of bank).  In X‐SEC worksheet (Figure 4.3C):  Enter cross‐section data (as x‐y coordinates in meters) into columns A and B, starting in A3, B3. This will provide stage values over the range of elevations provided and for a range of Manning’s roughness coefficients (n‐values). The cross section data is automatically graphed in rows 104‐125.  Enter channel slope (m/m) in cell C1.  Enter daily discharge values from the annual percentile hydrographs (in cms) in Column R (highlighted in blue).  Run the two workbook macros. This is done by selecting View Macros in this workbook. The two macros are “FlowArea and “NDA_SLRTTemplate.xlsm!WettedPerimeter” and can be run in either order. The calculated values will be used later as inputs into the calculations tab of BSTEM_Dynamic. 4.2.4.2
Setup BSTEM‐Dynamic For each model run, the user will setup the BSTEM‐Dynamic file as follows. Data requirements:  Bank geometry (x‐y coordinates or bank height and angle; bank‐toe length and angle), channel slope. See Table 4.4 and discussion above for guidance on determining these values.  Time series of channel depth for each percentile flow (see Section 4.2.4.1).  Hydraulic and geotechnical resistance data (see Table 4.4 and discussion below). NDA TEMPLATE WORKSHEETS
Figure 4.3
A
B
1 Bristlecone Pre SLOPE
2
x
y
3
0
0.3048
4
0.3048
0.3048
5
0.6096
0
6
1.524
0
7
1.8288
0.3048
8
2.1336
0.3048
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
C
0.008
0.14
0.14
9-Oct
10-Oct
14
15
G
0.15
8-Oct
13
F
0.14
0.15
0.15
5-Oct
6-Oct
7-Oct
Stage (increment Area
0.003048 0.002796
0.006096 0.005611
0.009144 0.008445
0.012192 0.011297
0.01524 0.014168
0.018288 0.017057
0.021336 0.019965
0.024384 0.022891
0.027432 0.025836
0.03048
0.0288
0.033528 0.031782
0.036576 0.034783
0.039624 0.037802
0.042672 0.04084
0.04572 0.043897
0.048768 0.046972
0.051816 0.050065
0.054864 0.053178
0.057912 0.056309
0.06096 0.059458
0.064008 0.062626
0.067056 0.065813
0.070104 0.069018
0.073152 0.072241
0.0762 0.075484
0.079248 0.078745
0.082296 0.082024
0.085344 0.085322
0.088392 0.088639
0.09144 0.091974
0.094488 0.095328
0.097536
0.0987
0.100584 0.102091
0.103632 0.105501
0.10668 0.108929
0.109728 0.112376
R
0.003034
0.006039
0.009017
0.011968
0.014893
0.017791
0.020664
0.023512
0.026336
0.029136
0.031912
0.034665
0.037395
0.040103
0.04279
0.045455
0.048099
0.050722
0.053326
0.055909
0.058473
0.061018
0.063544
0.066052
0.068541
0.071013
0.073467
0.075904
0.078325
0.080728
0.083116
0.085487
0.087843
0.090183
0.092508
0.094818
0.03
0.000175
0.000555
0.001091
0.001762
0.002557
0.003466
0.004482
0.005601
0.006818
0.00813
0.009533
0.011025
0.012603
0.014266
0.016011
0.017837
0.019742
0.021725
0.023784
0.025919
0.028129
0.030411
0.032767
0.035194
0.037692
0.04026
0.042897
0.045603
0.048378
0.05122
0.054129
0.057105
0.060147
0.063255
0.066428
0.069666
H
17
W.P.
0.921764
0.929137
0.936521
0.943914
0.951318
0.95873
0.966153
0.973585
0.981026
0.988477
0.995937
1.003405
1.010883
1.01837
1.025866
1.03337
1.040883
1.048405
1.055935
1.063474
1.07102
1.078576
1.086139
1.09371
1.10129
1.108877
1.116472
1.124075
1.131685
1.139303
1.146929
1.154562
1.162203
1.169851
1.177506
1.185168
0.14
12-Oct
16
E
0.14
11-Oct
12
I
0.04
0.000131
0.000416
0.000818
0.001322
0.001918
0.002599
0.003362
0.004201
0.005114
0.006098
0.00715
0.008269
0.009453
0.010699
0.012008
0.013378
0.014806
0.016294
0.017838
0.019439
0.021096
0.022809
0.024575
0.026395
0.028269
0.030195
0.032173
0.034203
0.036283
0.038415
0.040597
0.042829
0.04511
0.047441
0.049821
0.05225
0.05
0.000105
0.000333
0.000654
0.001057
0.001534
0.002079
0.002689
0.003361
0.004091
0.004878
0.00572
0.006615
0.007562
0.00856
0.009607
0.010702
0.011845
0.013035
0.014271
0.015552
0.016877
0.018247
0.01966
0.021116
0.022615
0.024156
0.025738
0.027362
0.029027
0.030732
0.032478
0.034263
0.036088
0.037953
0.039857
0.0418
J
0.22
0.23
0.23
0.22
0.22
0.23
0.23
0.23
0.23
0.23
0.24
0.23
U
Qmd-75
0.33
0.33
0.33
0.33
0.33
0.36
0.36
0.34
0.34
0.34
0.35
0.32
M
0.08
6.55E-05
0.000208
0.000409
0.000661
0.000959
0.0013
0.001681
0.002101
0.002557
0.003049
0.003575
0.004134
0.004726
0.00535
0.006004
0.006689
0.007403
0.008147
0.008919
0.00972
0.010548
0.011404
0.012288
0.013198
0.014134
0.015097
0.016086
0.017101
0.018142
0.019207
0.020298
0.021414
0.022555
0.023721
0.024911
0.026125
(m3/s)
K
L
Q (for given n)
0.06
0.07
8.74E-05 7.49E-05
0.000277 0.000238
0.000545 0.000467
0.000881 0.000755
0.001278 0.001096
0.001733 0.001485
0.002241 0.001921
0.002801 0.002401
0.003409 0.002922
0.004065 0.003484
0.004767 0.004086
0.005513 0.004725
0.006302 0.005401
0.007133 0.006114
0.008005 0.006862
0.008918 0.007644
0.009871 0.008461
0.010862 0.009311
0.011892 0.010193
0.01296 0.011108
0.014064 0.012055
0.015206 0.013033
0.016383 0.014043
0.017597 0.015083
0.018846 0.016154
0.02013 0.017254
0.021449 0.018384
0.022802 0.019544
0.024189 0.020733
0.02561 0.021951
0.027065 0.023198
0.028553 0.024474
0.030074 0.025777
0.031628 0.027109
0.033214 0.028469
0.034833 0.029857
Qmd-50
(m3/s)
Qmd-25
(m3/s)
10
11
D
DAY OF YEAR
0.14
0.14
0.14
0.14
9
T
ANNUAL PERCENTILE HYDROGRAPHS
S
1-Oct
2-Oct
3-Oct
4-Oct
5
6
7
8
3
4
1
2
R
0.09
5.82E-05
0.000185
0.000364
0.000587
0.000852
0.001155
0.001494
0.001867
0.002273
0.00271
0.003178
0.003675
0.004201
0.004755
0.005337
0.005946
0.006581
0.007242
0.007928
0.00864
0.009376
0.010137
0.010922
0.011731
0.012564
0.01342
0.014299
0.015201
0.016126
0.017073
0.018043
0.019035
0.020049
0.021085
0.022143
0.023222
N
O
0.1
5.24E-05
0.000166
0.000327
0.000529
0.000767
0.00104
0.001345
0.00168
0.002046
0.002439
0.00286
0.003308
0.003781
0.00428
0.004803
0.005351
0.005923
0.006517
0.007135
0.007776
0.008439
0.009123
0.00983
0.010558
0.011307
0.012078
0.012869
0.013681
0.014513
0.015366
0.016239
0.017132
0.018044
0.018977
0.019928
0.0209
0.46
0.48
0.46
0.48
0.49
0.50
0.50
0.50
0.52
0.52
0.52
0.51
(m3/s)
Qmd-99
V
A. ANNUAL HYDROGRAPH INPUTS FROM SLRT HYD
FSP IN WORKSHEET
P
R
Date
Q (cms)
10/1/1999
0.00
10/2/1999
0.03
10/3/1999
0.00
10/4/1999
0.02
10/5/1999
0.03
10/6/1999
0.00
10/7/1999
0.00
10/8/1999
0.01
10/9/1999
0.02
10/10/1999
0.00
10/11/1999
0.00
10/12/1999
0.01
10/13/1999
0.00
10/14/1999
0.00
10/15/1999
0.02
10/16/1999
0.00
10/17/1999
0.04
10/18/1999
0.00
10/19/1999
0.04
10/20/1999
0.00
10/21/1999
0.00
10/22/1999
0.00
10/23/1999
0.07
10/24/1999
0.02
10/25/1999
0.02
10/26/1999
0.02
10/27/1999
0.03
10/28/1999
0.03
10/29/1999
0.04
10/30/1999
0.04
10/31/1999
0.02
11/1/1999
0.02
11/2/1999
0.01
11/3/1999
0.01
11/4/1999
0.01
11/5/1999
0.07
Q
0.03
0.000
0.067
0.021
0.052
0.058
0.018
0.018
0.037
0.046
0.000
0.000
0.021
0.000
0.000
0.058
0.021
0.082
0.021
0.082
0.018
0.018
0.021
0.104
0.052
0.046
0.055
0.064
0.070
0.079
0.076
0.046
0.049
0.030
0.024
0.027
0.113
S
T
U
V
W
X
STAGE (use if lowest value in crossection >0)
0.04
0.05
0.06
0.07
0.08
0.000
0.000
0.000
0.000
0.000
0.079
0.091
0.101
0.110
0.119
0.024
0.027
0.030
0.034
0.037
0.061
0.070
0.076
0.085
0.091
0.070
0.079
0.088
0.098
0.107
0.024
0.027
0.030
0.034
0.037
0.021
0.024
0.027
0.030
0.034
0.046
0.052
0.058
0.064
0.067
0.055
0.064
0.070
0.076
0.082
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.027
0.030
0.034
0.037
0.040
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.067
0.076
0.085
0.094
0.104
0.024
0.027
0.030
0.034
0.037
0.098
0.113
0.125
0.137
0.146
0.024
0.027
0.030
0.034
0.037
0.098
0.110
0.125
0.134
0.146
0.024
0.027
0.030
0.034
0.037
0.021
0.027
0.030
0.034
0.034
0.024
0.027
0.030
0.034
0.037
0.122
0.140
0.158
0.171
0.186
0.064
0.073
0.079
0.088
0.094
0.055
0.064
0.070
0.079
0.085
0.064
0.073
0.082
0.088
0.098
0.076
0.088
0.098
0.107
0.116
0.082
0.094
0.107
0.116
0.128
0.094
0.110
0.122
0.131
0.143
0.091
0.104
0.116
0.128
0.137
0.055
0.064
0.070
0.076
0.085
0.058
0.067
0.076
0.082
0.088
0.037
0.040
0.046
0.049
0.055
0.027
0.030
0.037
0.040
0.043
0.030
0.037
0.040
0.046
0.049
0.131
0.152
0.168
0.186
0.198
C. X-SEC WORKSHEET
0.09
0.000
0.128
0.040
0.098
0.113
0.040
0.034
0.073
0.088
0.000
0.000
0.043
0.000
0.000
0.110
0.040
0.158
0.040
0.158
0.040
0.037
0.040
0.198
0.104
0.091
0.104
0.125
0.137
0.152
0.149
0.091
0.098
0.058
0.046
0.052
0.213
Y
0.1
0.000
0.137
0.043
0.107
0.122
0.043
0.037
0.079
0.094
0.000
0.000
0.046
0.000
0.000
0.119
0.043
0.168
0.043
0.168
0.040
0.040
0.043
0.213
0.110
0.098
0.110
0.134
0.143
0.165
0.158
0.098
0.104
0.061
0.049
0.055
0.229
Z
AA
Q (cms)
0.002
0.031
0.005
0.020
0.025
0.004
0.004
0.012
0.017
0.003
0.001
0.005
0.001
0.001
0.024
0.005
0.044
0.005
0.044
0.004
0.004
0.005
0.065
0.021
0.017
0.022
0.030
0.034
0.042
0.039
0.017
0.019
0.008
0.006
0.007
0.073
AB
0.03
0.000
0.067
0.021
0.052
0.058
0.018
0.018
0.037
0.046
0.000
0.000
0.021
0.000
0.000
0.058
0.021
0.082
0.021
0.082
0.018
0.018
0.021
0.104
0.052
0.046
0.055
0.064
0.070
0.079
0.076
0.046
0.049
0.030
0.024
0.027
0.113
AC
AD
AE
AF
AG
AH
DEPTH (use if lowest values in crossection = 0)
0.04
0.05
0.06
0.07
0.08
0.000
0.000
0.000
0.000
0.000
0.079
0.091
0.101
0.110
0.119
0.024
0.027
0.030
0.034
0.037
0.061
0.070
0.076
0.085
0.091
0.070
0.079
0.088
0.098
0.107
0.024
0.027
0.030
0.034
0.037
0.021
0.024
0.027
0.030
0.034
0.046
0.052
0.058
0.064
0.067
0.055
0.064
0.070
0.076
0.082
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.027
0.030
0.034
0.037
0.040
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.067
0.076
0.085
0.094
0.104
0.024
0.027
0.030
0.034
0.037
0.098
0.113
0.125
0.137
0.146
0.024
0.027
0.030
0.034
0.037
0.098
0.110
0.125
0.134
0.146
0.024
0.027
0.030
0.034
0.037
0.021
0.027
0.030
0.034
0.034
0.024
0.027
0.030
0.034
0.037
0.122
0.140
0.158
0.171
0.186
0.064
0.073
0.079
0.088
0.094
0.055
0.064
0.070
0.079
0.085
0.064
0.073
0.082
0.088
0.098
0.076
0.088
0.098
0.107
0.116
0.082
0.094
0.107
0.116
0.128
0.094
0.110
0.122
0.131
0.143
0.091
0.104
0.116
0.128
0.137
0.055
0.064
0.070
0.076
0.085
0.058
0.067
0.076
0.082
0.088
0.037
0.040
0.046
0.049
0.055
0.027
0.030
0.037
0.040
0.043
0.030
0.037
0.040
0.046
0.049
0.131
0.152
0.168
0.186
0.198
A
B
C
D
E
F
1 STEPS:
2 1. Enter Site name in Cell A1
2. Enter FULL cross-section in cells A3, B3 onwards. The data must be listed in
3 ascending order by x (column A)
4 3. Enter top bank elevation for left and right banks in cells E14 and E15 below.
5 4. Edit Cell C1 so that the slope is that of your study reach.
6 5. Copy and paste daily discharge values for your flow year into column R
6. Execute the FlowArea macro and then the WettedPerimeter Macro (the order
7 doesn't actually matter).
8
9
10
11
12
13
MINIMUM ELVATION
0.000
14
TOP BANK ELEVATION LEFT BANK
0.3048
15
TOP BANK ELEVATION RIGHT BANK
0.3048
INCREMENTS OF STAGE INCREASE
0.003
16
17
B. INSTRUCTIONS WORKSHEET
N O R M A L DE P T H A P P R O X I M A T I O N (N DA ) T E M P L A T E
AI
0.09
0.000
0.128
0.040
0.098
0.113
0.040
0.034
0.073
0.088
0.000
0.000
0.043
0.000
0.000
0.110
0.040
0.158
0.040
0.158
0.040
0.037
0.040
0.198
0.104
0.091
0.104
0.125
0.137
0.152
0.149
0.091
0.098
0.058
0.046
0.052
0.213
AJ
0.1
0.000
0.137
0.043
0.107
0.122
0.043
0.037
0.079
0.094
0.000
0.000
0.046
0.000
0.000
0.119
0.043
0.168
0.043
0.168
0.040
0.040
0.043
0.213
0.110
0.098
0.110
0.134
0.143
0.165
0.158
0.098
0.104
0.061
0.049
0.055
0.229
G
H
4.16 | July 2013 Method:  Open BSTEM_Dynamic_Template. xlsx and enable macros.  In INPUT GEOMETRY worksheet (Figure 4.4):  Select option B and input enter bank geometry data from SLRTv1 USER INPUTS worksheet.  Under bank layer thickness, enter the bank height value for layer 1 thickness.  In the “Channel and flow parameters” enter 1 for input reach length (this number is not utilized in BSTEM calculations) and input the reach slope (m/m) from SLRTv1 USER INPUTS.  Once all the information is entered select “Run Bank Geometry”. A dialogue box pops up with a preset of 2 as the initial index of the top bank point. Select “OK” and the macro automatically moves the user to the next tab.  Return to the “Input Geometry” tab. This time “Option A” is selected. Click on the View Bank Geometry macro to display the bank that will be modeled. There will be multiple points at the lowest elevation. These should be removed by moving the station value point W to point R. Delete the station and elevation values in point S through W. Select “View Bank Geometry” again and the redundant points at the bottom of the channel will be gone. Select a value to best represent the Top of Toe for the cross section used.  Select the Run Bank Geometry Macro a second time accepting the preset of 2.  In BANK MATERIAL worksheet (Figure 4.5):  Data can be provided for multiple layers and surfaces but for use here, probably only one bank layer and one bank toe will be used. Data on the hydraulic resistance (for surface sediments) and geotechnical resistance (for in situ materials) resistance of the banks can be obtained in three ways (see Table 4.4) and are listed in order of preference (1) direct field measurement; (2) default values based on tests conducted in the Tahoe Basin (Table 4.10); and (3) published default values based on tests conducted throughout the United States. Values in Table 4.10 are based on analysis presented in Sections 3.6.3.3. Table 4.10. Default bank and toe model inputs, based on analysis of Tahoe‐specific data presented in Sections 3.6.3.3. Bank Model Input Data Material Descriptors (BSTEM Friction Row #) angle ɸ' (degrees) Cohesion c' (kPa) Saturated unit weight (kN/m3) Toe Model Input Data ɸb (degrees) τc (Pa) k (cm3/Ns) Own data layer 1 (row 25) 30.9 3.80 17.1 10.0 3.00 0.645 Own data Bank Toe (row 35) ‐ ‐ 17.1 ‐ 21.40 0.127  For most SLRTv1 users, no data will be input into GRAIN SIZE DISTRIBUTION worksheet.  For most SLRTv1 users, no data will be input into BANK VEGETATION worksheet. However, :  If the user wishes to account for post‐restoration root‐reinforcement, Table 4.11 provides the estimated values for a typical Tahoe Basin species/vegetation types. The root‐reinforcement value for the selected species is entered into cell I29. B S T E M
I np u t b a nk geo m et r y a nd f l o w
IN P U T G E O M E T R Y
c o ndi t i o ns
W o r k t h r o u gh a l l 4 s ec t i o ns t h en h i t t h e " R u n B a nk G eo m et r y M a c r o " b u t t o n.
O p t i o n A - Draw a detailed bank
p r o f i l e u s i n g th e b o x es b el o w
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
n t b o x - c el l s i n th e
E l ev a t i o n
(m )
0. 00
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
0. 6 0
1. 6 0
0. 6 0
0. 6 0
0. 56
0. 52
0. 4 8
0. 4 4
0. 4 0
0. 3 6
0. 3 2
0. 28
0. 24
0. 20
0. 16
0. 12
0. 08
0. 04
0. 00
0. 00
0. 6
T o p o f
t o e?
C -P
s h ea r s u r f a c e
em er g en c e
u tto n .
0. 01
p a r a m et er s
I n p u t r ea c h l en g th ( m )
I n p u t r ea c h s l o p e ( m / m )
W
V
9 0. 0
b ) In p u tb a n k a n g le( )
0. 0
c ) I n p u t b a n k to e l en g th ( m )
0. 0
d ) I n p u t b a n k to e a n g l e ( o )
-
B a nk
m a t er i a l
Layer 2
B a nk l a y er t h i c k nes s ( m )
E l ev a ti o n o f
l a y er b a s e ( m )
a
T o p L a y er
L a y er 1
0. 6 0
-
n k to p : p l a c e b ey o n d s ta r t
s h ea r s u r f a c e
n k ed g e
b r ea k s o f s l o p e o n b a n k
( i f n o b r ea k s o f s l o p e p l a c e
a s i n ter m ed i a r y p o i n ts )
to p o f b a n k to e
- b r ea k s o f s l o p e o n b a n k to e
( i f n o b r ea k s o f s l o p e th en
i n s er t a s i n ter m ed i a r y
p o i n ts )
b a s e o f b a n k to e
en d p o i n t ( ty p i c a l l y m i d p o i n t
o f c h a n n el )
S ta ti o n ( m )
Layer 1
N o tes :
B a n k p r o f i l e m a y o v er h a n g .
I f th e b a n k p r o f i l e i s f u l l y p o p u l a ted ,
th e s h ea r s u r f a c e em er g en c e p o i n t
s h o u l d b e a n y w h er e b etw een p o i n ts
B a n d Q .
T h e s h ea r s u r f a c e em er g en c e p o i n t
m u s t n o t b e o n a h o r i z o n ta l s ec ti o n th e el ev a ti o n o f th i s p o i n t m u s t b e
u n i q u e o r a n er r o r m es s a g e w i l l
d is p la y .
Layer 3
0. 00
L a y er 2
0. 00
L a y er 3
0. 00
L a y er 4
0. 00
L a y er 5
0. 00
B o tto m
L a y er
1
V
W
W
C h a nnel a nd f l o w
R -U
R -U
o
U
V
Q
Q
s h ea r
s u rfa c e
a n g le
a ) I n p u t b a n k h ei g h t ( m )
S
T
A - b a
o f
B - b a
C -P -
to s ev er a l l a y er s ) .
n f i n i n g p r es s u r e
Option B
S ta tio n
(m )
A
D ef i ni t i o n o f p o i nt s u s ed i n b a nk
p r o file
B
p o in t B
A
th e r el ev a
i r ed .
d iv id eitin
in c lu d ec o
k to e.
eo m et r y b
O p t i o n B - Enter a bank height and angle,
th e m o d el w i l l g en er a te a b a n k p r o f i l e
Option A
P o i nt
in
es
to
to
a n
G
E l ev a ti o n ( m )
t E I T H E R O p ti o n A o r O p ti o n B f o r B a n k P r o f i l e a n d en ter th e d a ta
a ti v e o p ti o n a r e i g n o r ed i n th e s i m u l a ti o n a n d m a y b e l ef t b l a n k i f d
b a n k m a ter i a l l a y er th i c k n es s es ( i f b a n k i s a l l o n e m a ter i a l i t h el p s
k i s s u b m er g ed th en s el ec t th e a p p r o p r i a te c h a n n el f l o w el ev a ti o n
a l c u l a te er o s i o n a m o u n t; o th er w i s e s et to a n el ev a ti o n b el o w th e b
r e b a n k p r o f i l e i s c o r r ec t y o u c a n v i ew i t b y c l i c k i n g th e V i ew B a nk
P a r a l l el l a y er s , s ta r ti n g f r o m
1) S el ec
a l ter n
2) E n ter
3 ) If b a n
a n d c
T o en s u
Layer 4
c T o e
m a t er i a l
Layer 5
b
d
B ed m a t er i a l
V i ew B a nk
G eo m et r y
R u n B a nk
G eo m et r y M a c r o
BSTEM “INPUT GEOMETRY” WORKSHEET
Figure 4.4
BSTEM “BANK MATERIAL” WORKSHEET
Figure 4.5
L a y er 2
B a nk M a t er i a l
L a y er 3
L a y er 4
L a y er 5
0. 7 1
3 .8 0
3 0. 9
10. 0
15. 0
15. 0
15. 0
15. 0
15. 0
15. 0
15. 0
15. 0
(degrees)
t h e er o di b i l i t y c o ef f i c i ent ( k ) ?
17 . 1
17 . 1
18 . 0
18 . 0
18 . 0
18 . 0
18 . 0
E r o d i b i l i ty C o ef f i c i en t ( c m
3
/N s )
I n p u t c r i ti c a l s h ea r s tr es s c ( P a )
N eed t o k no w
3 .0
10. 0
15. 0
0. 0
0. 0
0. 0
0. 0
0. 0
B a nk M o del I np u t D a t a
Saturated
unit weight
b
(kN/m 3 )
20. 0
20. 0
20. 0
Cohesion c'
(kPa)
3 0. 0
25. 0
20. 0
1. 000
-
3 6 .0
27 . 0
4 2. 0
4 2. 0
3 6 .0
Friction angle
 ' (degrees)
I n p u t n o n - c o h es i v e p a r ti c l e d i a m eter ( m m )
t h e c r i t i c a l s h ea r s t r es s ( c ) ?
O w n d a ta B a n k T o e
O w n d a ta l a y er 5
O w n d a ta l a y er 4
O w n d a ta l a y er 3
O w n d a ta l a y er 2
-
-
0. 0003 5
0. 0003 5
0. 512
0. 128
0. 0113
O w n d a ta l a y er 1
S ilt
S o ftc la y
S ti f f c l a y
A n g u la r s a n d
R o u n d ed s a n d
B o u l d er s
C o b b l es
G r a v el
Description
Mean grain
size, D 50 (m)
C r i ti c a l S h ea r S tr es s c ( P a )
N eed t o k no w
9
6 a ,6 b a n d 6 c
7 a ,7 b a n d 7 c
8 a ,8 b a n d 8 c
4 a a n d 4 b
5a a n d 5b
3
1
2
Bank material
type
M a t er i a l D es c r i p t o r s
T h es e a r e th e d ef a u l t p a r a m eter s u s ed i n th e m o d el . C h a n g i n g th e v a l u es o r d es c r i p ti o n s w i l l c h a n g e th e
v a l u es u s ed w h en s el ec ti n g s o i l ty p es f r o m th e l i s t b o x es a b o v e. A d d y o u r o w n d a ta u s i n g th e w h i te b o x es .
B a nk a nd b a nk - t o e m a t er i a l da t a t a b l es .
L a y er 1
-
-
0. 6 4 5
3 . 000
-
Chemical
concentration
(kg/kg)
-
B a nk T o e M a t er i a l
B A N K M A T E R IA L
S el ec t m a t er i a l t y p es ( o r s el ec t " o w n da t a " a nd a dd v a l u es b el o w )
B S T E M
0. 03 7 529 3 3 1
5. 06 4 E - 06
5. 06 4 E - 06
9 . 4 7 3 E - 07
1. 7 08 E - 06
7 . 4 3 9 E - 05
1. 13 0E - 06
0. 6 57 7
0. 6 57 7
1. 58 12
1. 4 9 6 2
3 . 523 7
4 . 056 3
1. 6 7 8 8
1. 6 7 8 8
1. 4 158
1. 253 1
3 . 17 6 9
2. 3 28 6
G r o u ndw a t er M o del I np u t D a t a
Hydraulic
van
van
Conductivity
Genuchten 
Genuchten n
k sat (m/s)
(1/m )
1. 7 4 5E - 03
3 . 523 7
2. 3 28 6
1. 7 4 5E - 03
3 . 523 7
2. 3 28 6
3 . 16 0E - 03
3 . 523 7
2. 3 28 6
k
3
/N s )
0. 004
0. 009
0. 03 0
(c m
2 1 .4 0
3 . 000
0. 127
0. 6 4 5
E r o d i b l e ( 0. 100 P a ) ,
M o d er a te ( 5. 00 P a ) , o r
R es i s ta n t ( 50. 0 P a )
C o a r s e ( 0. 7 1 m m ) o r
Fi n e ( 0. 18 m m )
4 9 8
124
11. 0
c ( P a )
T o e M o del I np u t D a t a
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 4.19 Table 4.11. Estimated root‐reinforcement values for typical Tahoe Basin species, 10‐years post‐channel restoration. Values obtained using the RipRoot algorithm, assuming a silt bank material, and a 1m rooting depth. Species/ Vegetation type Root‐reinforcement (kPa) Dry meadow grasses 1.36 Wet meadow grasses 1.90 Lodgepole pine 2.74 Geyer’s willow 3.75 Lemmon’s willow 2.22 Alder 2.09  In CALCULATION worksheet (Figure 4.6A):  Dates and values for the one‐year depth series (from the NDA_SLRTTemplate.xlsx) will be input in columns A and B, rows 66 onwards (these cells are highlighted in blue). Rows 66 downward should be empty for all columns in this worksheet prior to data being added. Delete values in all rows below row 65 as necessary.  Copy the date values starting in column Q3 X‐SEC worksheet of NDA_SLRTTemplate.xlsx and paste into cell A66 downward.  Select stage or depth data. If the lowest value in the cross section (column B) in the X‐SEC worksheet is greater than 0, the stage values (columns S‐Z in X‐SEC worksheet) should be used instead of the depth (columns AC‐AJ in X‐SEC worksheet).  Select appropriate column based on the appropriate Manning’s n value for the study reach (see Table 4.5 for guidance; enter value in SLRTv1 USER INPUTS). Note: The user may choose to select a different Manning’s n for different percentile‐flow years (i.e., a higher value for near bankfull flows due to the influence of vegetation). For either stage or depth, the columns correspond to various Mannings n values as shown in row 2. Select the data column associated the appropriate Manning value and copy from row 3 downward and paste into cell B66 downward. 4.2.4.3
Run BSTEM‐Dynamic Now that the BSTEM files are populated with the correct input data, follow these steps to run the model. Data requirements:  Manning’s n (from SLRTv1 USER INPUTS)  Location of bank edge (from cross section data) Method:  In RUN MODEL worksheet:  Check the effective‐stress box to activate the Manning’s n component of the model (cell L19).  Click on the “RUN MODEL” button and follow prompts (Figure 4.7), including:  which x,y point represents bank top‐edge (in most cases, accept default value of “2”),  Manning’s n value (should match the value selected from the “NDA_SLRTTemplate”),  the range of dates to be analyzed (CALCULATIONS cell A66 downward), and  starting groundwater depth (in most cases, equal to the initial surface‐water depth). 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 B S T E M
C A L C U L A T IO N S
A. DEPTH TIME SERIES INPUTS & FINAL EROSION OUTPUT
A
B
58
59 U ni t E r o s i o n R a t es ( m 3 / m / y r )
60
0 .0 1 1 3 8 0 8
61
M
62 F o r c e E q u i l i b r i u m C a l c u l a t i o ns
63
D a t e a nd T i m e
S t a ge G
64
(m a .s .l.)
65
66
10/1/1999
0.000426
67
10/2/1999
0.0793
68
10/3/1999
0.000426
69
10/4/1999
0.061
B.
HYDRAULIC,
GEOTECHNICAL
&
TOTAL
EROSION
OUTPUTS
70
10/5/1999
0.0732
71BE
10/6/1999
0.000426
BF
BG
BH
BI
BJ
BK
72
10/7/1999
0.000426
BD
5
58
59
60
61
B a nk M o del
62
63 Fa i l u r e b a s e Fa i l u r e a n g l e Fa i l u r e w i d th
m
d eg r ees
cm
6
0 .0 1 1 3 8 0 8
- 4 . 27 203 4 6 7 5
C
a n n in g 's n =
r o u ndw a t er t a
(m a .s .l.)
0.000425598
0.0793
0.000425598
0.061
0.0732
0.000425598
BL
0.000425598
2. 9 8
0. 4 7 7 9 7 26 03
0. 0000000
BM
0. 0113 8 08
T o e M o del
M a x i m u m L a ter a l R etr ea t E r o d ed A r ea - B a n k
cm
m 2
Fa i l u r e v o l u m e S ed i m en t l o a d i n g A v er a ge b o u nda r y s h ea r s t r es s
m 3
kg
P a
E r o d ed A r ea - T o e E r o d ed A r ea - T o ta l
m 2
m 2
C. INITIAL & SIMULATED BANK GEOMETRIES
Z
AA
AB
AC
AD
AE
AF
AG
AH
AI
AJ
AK
AL
AM
AN
AO
AP
AQ
AR
AS
AT
AU
AV
AW
AX
AY
AZ
BA
BB
14
Post-restoration initial geometry: outside
1.4
Post-restoration outside :75th-0.030
1.2
1.0
Pre-restoration outiside: 75th-0.030
0.8
Pre-restoration initial geometry: outside
0.6
0.4
0.2
0.0
-0.2
0.0
0.5
1.0
1.5
2.0
2.5
3.0
DISTANCE FROM START OF SURVEY, IN METERS
3.5
4.0
4.5
ARBITRARY ELEVATION, IN METERS
1.6
ARBITRARY ELEVATION, IN METERS
Y
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Initial Geometry
12
Scenario 2
Scenario 6
10
8
6
4
2
0
-2
0
5
10
15
20
DISTANCE FROM START OF SURVEY, IN METERS
BSTEM “CALCULATIONS” WORKSHEET
25
30
Figure 4.6
BC
B S T E M
R U N M O DE L
A. RUN MODEL WORKSHEET
B a nk m o del o u t p u t
V er i f y th e b a n k m a ter i a l a n d b a n k a n d b a n k - to e p r o tec ti o n i n f o r m a ti o n en ter ed i n th e " B a n k M a ter i a l " a n d " B a n k V eg eta ti o n a n d P r o tec ti o n "
w o r k s h eets . O n c e y o u a r e s a ti s f i ed th a t y o u h a v e c o m p l eted a l l n ec es s a r y i n p u ts , h i t th e " R u n B a n k - S ta b i l i ty M o d el " b u tto n .
L a y er 1
O w n D a ta
L a y er 2
S ilt
B a nk M a t er i a l P r o p er t i es
L a y er 3
S ilt
L a y er 4
S ilt
L a y er 5
S ilt
T o e M o del O u t p u t
V er i f y th e b a n k m a ter i a l a n d b a n k a n d b a n k - to e p r o tec ti o n i n f o r m a ti o n en ter ed i n th e " B a n k M a ter i a l " a n d " B a n k V eg eta ti o n a n d P r o tec ti o n "
w o r k s h eets . O n c e y o u a r e s a ti s f i ed th a t y o u h a v e c o m p l eted a l l n ec es s a r y i n p u ts , h i t th e " R u n T o e- E r o s i o n M o d el " b u tto n ( C en ter R i g h t
o f th i s p a g e) .
L a y er 1
O w n d a ta
B a nk M a t er i a l P r o p er t i es
L a y er 2
L a y er 3
L a y er 4
R es i s ta n t c o h es i v e R es i s ta n t c o h es i v e R es i s ta n t c o h es i v e
A c c o u nt f o r :
B a nk T o e M a t er i a l
L a y er 5
R es i s ta n t c o h es i v e
O w n d a ta
3 . 00
50. 00
50. 00
50. 00
50. 00
21. 4 0
0. 6 4 5
0. 014
0. 014
0. 014
0. 014
0. 127
M a t er i a l
C r itic a l s h
(P
E r o di b i l i t y
(c m 3
ea r s t r es s
a )
C o ef f i c i ent
/N s )
Effective stress
acting on each grain
R u n M o del !
B.TOP BANK POINT
D. DATE RANGE
C. MANNINGS N
E. STARTING GROUNDWATER ELEVATION
BSTEM “INPUT GEOMETRY” WORKSHEET
Figure 4.7
4.22 | 4.2.4.4
July 2013 Outputs Average annual unit‐loading results (per unit meter of bank) for hydraulic, geotechnical and total erosion will be output on the CALCULATIONS page in Cell BJ58 (see Figure 4.6B), along with the initial and simulated bank geometries (see Figure 4.6C). The final unit erosion rates are also displayed in Cell A60 for simplicity (see Figure 4.6A). These values are entered into the User Input worksheet (SLRTv1_Template.xlsx) for the appropriate scenario (pre‐ or post‐restoration) and percentile flow (i.e., 99th, 75th, 50th, or 25th). Note: if the model results indicate that unit erosion rates are negligible (<0.0000), then no additional runs are necessary for the lower percentile flows. For example, if the post‐restoration straight reach model for the 75th percentile flow yields a unit erosion rate (estr‐75) <0.0000, then the 50th and 25th flows do not need to be modeled. However, the post‐
restoration outer bend simulations may still be necessary. 4.3
CATCHMENT HYDROLOGY SLRTv1_Template.xlsx worksheet: HYD FSP IN Using available data, SLRT provides default flow frequency datasets and annual hydrographs by region and catchment type that are adjusted based on catchment characteristics for the specific SEZ of interest. Once the SLRT user completes the required “catchment characteristics” user inputs (see Figure 4.1) in the SLRTv1_Template.xlsx, estimates of catchment hydrology in the appropriate formats necessary for SLRT are automatically generated and presented in HYD FSP IN worksheet. The average annual hydrologic estimates provided from SLRT are estimates based on limited datasets and clearly documented assumptions (see Section 3.3) and there are known deviations from the predicted and observed hydrology. The SLRT user is welcome to generate estimated average annual flow frequency distributions and the appropriate percentile annual hydrographs independently, but the SLRT method requires the use of same incoming hydrology for both pre‐
and post‐restoration scenarios to estimate the average annual pollutant load reductions. In addition, it is recommended that the catchment hydrology used is based on, or comparable to, the hydrologic conditions that occurred between WY89–WY 06 to maintain consistency with the Pollutant Load Reduction Model (PLRM; nhc et al. 2009) estimates to the extent possible. The SLRTv1_Template.xlsx will auto‐generate estimates of the required hydrologic datasets based on the user catchment characteristic inputs and the methods outlined in Section 3.3. The discharge frequency distribution outputs includes the frequency of occurrence (number of days per year) and the median mean daily discharge (cfs) for each of the standardized 50 bin intervals of the predicted average annual hydrograph for the upstream boundary of the subject SEZ. The average annual frequency distribution hydrograph is used to estimate incoming average annual FSP loads (INfsp) and estimates of the average annual pollutant floodplain retention (RFPfsp). Four annual percentile hydrographs are generated for direct input into the BSTEM‐Dynamic platform to estimate a series of unit erosion rates given an expected range of annual discharge conditions (see Section 4.2.4 for BSTEM user guidance). The discharge frequency distribution and annual hydrograph estimates are presented in both graphical (Figure 4.8) and tabular formats. The user is encouraged to compare the estimates to any measured discharge values or other records to verify the hydrology estimates are reasonable for the SEZ of interest. STREAM LOAD REDUCTION TOOL (SLRTv1)
Predicted catchment hydrology and FSP loads
SEZ NAME: Upper Reach (TCPT)
CALCULATIONS
NAME
Mean Annual Precip (in)
Total Area (sq mi or acres)
Total Impervious Area (acres)- urban only
VALUE
29.91
23.7
0.0
VARIABLE
P
A
Ai
Bin Interval (cfs)
Regional Coefficient
Max Mean Daily Q (cfs)
9.303
0.0008
501.43
Qbi
R
Qmax
Bin 50 Value (cfs)
478.64
Qb-50
FSP CRC (mg/L) - Urban only
n/a
Vin
Bin Interval (cfs)
9.30
Qbi
n/a
FSPC
FSP CRC (mg/L) - Urban only
Average annual discharge volume (ac-ft/yr)
23101.3
Vin
141.5
FSPin
Average annual FSP load into SEZ (MT/yr)
Predicted incoming mean daily discharge frequency distribution 140
120
t (d/yr) 100
80
60
40
20
0
FSPb an (kg/yr) Qb (cfs) 18000
16000
14000
12000
10000
8000
6000
4000
2000
0
Predicted incoming average annual FSP load as a function of discharge Qb (cfs) Predicted Annual Hydrographs 12.00
Qmd p (m3/s) 10.00
99th
75th
50th
25th
8.00
6.00
4.00
2.00
0.00
Oct
SLRTv1 created by 2NDNATURE LLC 2013
Nov
Dec
Jan
Feb
Mar
MONTH Apr
May
Jun
Jul
Aug
Sep
Catchment hydrology and FSP loading [7/24/2013]
SLRTV1 “H Y D F S P I N ” WORKSHEET
Figure 4.8
4.24 | 4.4
July 2013 CATCHMENT FSP LOADING SLRTv1_Template.xlsx worksheet: HYD FSP IN Available FSP sample datasets for both urban and non‐urban sites were compiled and used to create a reasonable approach to estimate FSP catchment loading in the appropriate format necessary for SLRT calculations (see Section 3.4). The SLRT approach requires an estimated daily and annual FSP load delivered to the SEZ for each discharge bin interval contained within the flow frequency distribution. Given available data, SLRT estimates FSP daily load for non‐urban catchment as a function of discharge, and the rating curves vary by region in the Tahoe Basin (see Figure 3.10B). For urban catchments, a catchment FSP characteristic runoff concentration (CRC) is estimated based on the catchment area, percent impervious and estimated land use condition (see Figure 3.11C). Based on the user inputs, the SLRTv1_Template.xlsx predicts the daily FSP load (kg/d) and the average annual FSP load (kg/yr) for each discharge bin interval, presented in the HYD FSP IN worksheet. The results are presented in both graphical (see Figure 4.8) and tabular format. The bin interval annual loads are summed to estimate the average annual FSP load delivered to the SEZ. The representative FSP CRC (mg/L) is presented for urban catchments only. The same catchment hydrology and FSP loading datasets are used for both the pre‐ and post‐restoration scenarios. 4.5
FLOODPLAIN RETENTION SLRTv1_Template.xlsx worksheet: RFPfsp The SLRT method estimates the fraction of the average annual FSP mass delivered to the floodplain as function of discharge. For each bin where overbank flow is expected to occur, the tool calculates the daily FSP load less the load contained within the channel and multiplies the difference by the frequency of occurrence for that flow (see Section 3.5). The sum of the loads for all overbank flows is the estimated average annual mass of FSP delivered to the SEZ floodplain (DFPfsp; MT/yr). The worksheet calculates the average annual FSP mass retained on the floodplain based on the estimated DFPfsp and user inputs of floodplain condition (FPC). The retention coefficient is determined based on floodplain condition and the corresponding Rfsp(Q:Qcc) rating curve (see Figure 3.13). For each overbank bin, the appropriate retention coefficient is applied to the FSP mass delivered to the floodplain to determine the fraction of the mass retained. SLRT sums the masses for all overbank flows to estimate the average annual mass of FSP retained on the SEZ floodplain (RFPfsp; MT/yr). The floodplain delivery and retention metrics are generated automatically and displayed in both graphical (Figure 4.9) and tabular formats for both pre and post restoration scenarios in the RFPfsp worksheet. 4.6
CHANNEL EROSION SLRTv1_Template.xlsx worksheet: SCEfsp SLRT auto‐generates the average annual FSP generated from streambank erosion for pre‐ and post‐restoration scenarios based on the BSTEM‐Dynamic outputs for up to 16 spatial and temporal conditions. Two channel bank profiles are modeled to account for spatial variations in hydraulic forces: a representative straight reach STREAM LOAD REDUCTION TOOL (SLRTv1) FLOODPLAIN RETENTION ESTIMATES
REACH NAME
Date of estimate
Channel length (m)
Channel slope (m/m)
Channel capacity (cfs)
Floodplain condition score
Average days overbank (d/yr)
Channel FSP load (kg/d)
Upper Reach (TCPT)
PRE RESTORE
1530
0.0016
200
3
5/1/2013
POST RESTORE
1829
0.0013
88
5
Qcc
FPC
3.5
29.3
tob
3161
1061
FSPcc
Catchment FSP load (MT/yr)
FSPin
141.45
Delivered to floodplain (MT/yr)
4.43
34.47
Retained on floodplain (MT/yr)
1.61
12.12
3500.00
VARIABLES
DFPfsp
RFPfsp
FSP mass delivered to floodplain PRE RESTORE
3000.00
POST RESTORE
DFP fsp (kg/yr) 2500.00
2000.00
1500.00
1000.00
500.00
0.00
Qmd (cfs) FSP mass retained on floodplain 1200.00
PRE RESTORE
RFP fsp (kg/yr) 1000.00
POST RESTORE
800.00
600.00
400.00
200.00
0.00
Qmd (cfs) SLRTv1 created by 2NDNATURE LLC (2013)
Average annual floodplain retention estimates [7/25/2013]
SLRTV1 “R F P F S P ” WORKSHEET
Figure 4.9
4.26 | July 2013 and a representative outside meander bend. Four annual probability hydrographs (see Section 3.3.3) are modeled to represent a range of percentile flow years (25th, 50th, 75th and 99th percentiles). BSTEM‐Dynamic provides an annual unit bulk sediment erosion rate per length of feature (m3/m/yr) that is assumed to be representative of the straight and outside bend reaches within the subject SEZ modeled for a given annual hydrograph. For each hydrograph, the annual bulk sediment generation rates are multiplied by the respective total contributing lengths (e.g., straight reach, outer bend) and bulk unit sediment weight to calculate the annual bulk sediment mass (MT/yr) generated for each probability flow year. These values are converted to FSP load based on the regional FSP to bulk sediment ratios (see Figure 3.15D).To estimate the average annual sediment and FSP loads, each annual probability hydrograph is weighted based on its probability of occurrence (see Section 3.6), and these load values are averaged to calculate the average annual bulk sediment and FSP. The pre‐ and post‐restoration values are compared to estimate the average annual FSP load reduction as a result of decreased instream erosion. The stream channel erosion metrics values are generated automatically and displayed in both graphical and tabular formats (Figure 4.10) for both pre and post restoration scenarios in the SCEfsp worksheet. 4.7
FSP LOAD REDUCTIONS SLRTv1_Template.xlsx worksheet: SLRT RESULTS The SLRT outputs an average annual pollutant load reduction that is calculated as the difference between the pre‐ and post‐restoration average annual FSP load as estimated at the downstream boundary of the SEZ. SEZ‐LRfsp (MT/yr) = OUTfsp ‐pre – OUTfsp ‐post (EQ4.1) OUTfsp (MT/yr) = INfsp – RFPfsp + SCEfsp (EQ4.2) where: The SLRTv1_Template.xlsx SLRT RESULTS worksheet is a summary table that includes the SEZ average annual pollutant (SEZ‐LRfsp) load reduction, in addition to a series of simple quantitative comparisons between the pre‐ and post‐restoration scenarios to collectively document the effectiveness of the restoration actions as represented in SLRT (Figure 4.11). 4.8
SLRT SUMMARY Once the inputs and calculations within SLRT are acceptable to the user, the user can systematically print pre‐
defined summary reports for each of the 5 worksheets. Collectively, these 5 pages contain all of the relevant inputs, calculations and outputs from SLRT necessary for communication to others. It is recommended that (1) a site location map that clearly denotes the upstream and downstream boundary of the SEZ, the pre‐ and post‐
restoration channel alignment, and contributing catchment and (2) a table summary of the data sources and assumptions used to determine the input values accompany the SLRT summary pages. Section 5 provides explicit examples of SLRTv1 application, results and products for two Tahoe Basin SEZs. SLRTV1 “SCEFSP” WORKSHEET
Figure 4.10
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
0.00
POST RESTORE
10.24
0.891
2.575
PRE RESTORE
39.84
3.466
POST RESTORE
0.0360
0.0050
0.0000
0.0000
0.0750
0.0180
0.0000
0.0000
PRE RESTORE
0.1360
0.0600
0.0200
0.0043
0.1700
0.0650
0.0280
0.0076
0.20
Post-restoration
Pre-Restoration
0.60
Annual probability hydrograph (Qmd p) 0.40
0.80
99th
75th
50th
25th
99th
75th
50th
25th
Qmd-p
74%
% reduction
1.00
Annual fine sediment channel load per annual hydrograph Average annual bulk sediment generated (MT/yr)
Average annual FSP load generated (MT/yr)
Average annual FSP load reduction (MT/yr)
Bulk sediment
Straight reach unit erosion rate (m3/m/yr)
Bulk sediment
Outside bend unit erosion rate (m3/m/yr)
Channel length (m)
Outside bend length (m)
Straight length (m)
Fines to bulk sediment ratio (0-1 value)
5/1/2013
POST RESTORE
1829
1001
828
0.087
PRE RESTORE
1530
530
1000
0.087
Upper Reach (TCPT)
1.20
STREAM LOAD REDUCTION TOOL (SLRTv1)
SEZ CHANNEL EROSION ESTIMATES
REACH NAME
Date of estimate
SLRTv1 created by 2NDNATURE LLC and A. Simon 2013
Annual fine sediment load (MT/yr) 24.31
5.28
0.00
0.00
62.51
24.55
10.10
2.65
SEZ Total
FSP
0.99
0.75
0.50
0.25
0.99
0.75
0.50
0.25
Average annual channel erosion estimates [7/24/2013]
279.41
60.70
0.00
0.00
718.55
282.13
116.13
30.48
SEZ Total
Bulk sediment
PRE RESTORE POST RESTORE
Qmd-p
MT/yr
MT/yr
125.69
62.84
0.99
55.45
8.73
0.75
Outside Bends
18.48
0.00
0.50
3.97
0.00
0.25
592.86
216.57
0.99
226.68
51.98
0.75
Straight Reach
97.65
0.00
0.50
26.50
0.00
0.25
Annual Pollutant Loads per Annual Hydrograph
CATCHMENT TYPE
REGION
SUB-REGION
CATCHMENT AREA
AREA UNITS
CATCHMENT % IMPERVIOUS
CATCHMENT LAND USE CONDITION
Non Urban
Southshore
Southwest
23.7
Sq-miles
3
USER INPUTS
STREAM LOAD REDUCTION TOOL (SLRTv1)
PRE RESTORATION
POST RESTORATION
Results Summary
Channel length (m)
1530
1829
Channel slope (m/m)
0.0016
USER length
NAME (m) 2NDNATURE
Outside bend
530
WATERSHED/CATCHMENT
Bank height of outside bends (m) Trout Creek
1.34
REACH NAME Upper Reach (TCPT)
Bank angle of outside bends (degrees)
37
Date of Estimate
5/1/2013
Straight length (m)
1000
CATCHMENT TYPE
Non Urban
Bank height of straight reaches (m)
1
REGION
Southshore
Bank angle of straight reaches (degrees)
34
SUB-REGION
Southwest
Manning’s roughness value of channel
0.03
CATCHMENT AREA
23.7
Fines to bulk sediment ratio (0-1 value)
0.087
AREA UNITS
Sq-miles
Channel capacity (cfs)
200
CATCHMENT % IMPERVIOUS
Floodplain length (m)
458
CATCHMENT LAND USE CONDITION
3
Floodplain condition score
3
0.0013
1001
1.1
53
828
0.76
39
0.03
0.087
88
458
5
USER INPUTS
CHANGE
299
-0.0003
471
-0.24
16
-172
-0.24
5
0
0
-112
0
2
% CHANGE
20%
-19%
89%
-18%
43%
-17%
-24%
15%
0%
0%
-56%
0%
2
PRE RESTORATION
POST RESTORATION
CHANGE
% CHANGE
SLRT OUTPUTS
Channel length (m)
1530
1829
299
20%
AVERAGE ANNUAL ESTIMATES
POST RESTORATION
PRE RESTORATION
CHANGE
% CHANGE
Channel slope (m/m)
0.0016
0.0013
-0.0003
-19%
Predicted FSP catchment load (MT/yr)
141.45
141.45
0
0%
Outside bend length (m)
530
1001
471
89%
Predicted FSP load delivered to floodplain (MT/yr)
4.43
34.47
30.0
678%
Bank height of outside bends (m)
1.34
1.1
-0.24
-18%
Predicted
FSPangle
load of
retained
floodplain
(MT/yr)
12.12
652%
Bank
outsideonbends
(degrees)
371.61
53
1610.5
43%
Predicted FSP load from channel
erosion
(MT/yr)
3.47
0.89
-2.58
-74%
Straight length (m)
1000
828
-172
-17%
Predicted FSPBank
load at
downstream
boundary
(MT/yr)
143.30
130.22
-13.08
-9%
height of straight reaches (m)
1
0.76
-0.24
-24%
Bank angle of straight reaches (degrees)
34
39
5
15%
Manning’s roughness value of channel
0.03
0.03
0
0%
Fines to bulk sediment ratio (0-1 value)
0.087
0.087
0
0%
Average annual FSP Load Reduction (MT/yr)
13.08
Channel capacity (cfs)
200
88
-112
-56%
Floodplain length (m)
458
458
0
0%
Floodplain condition score
3
5
2
2
IN fsp (MT/yr)
DFPfsp (MT/yr)
RFPfsp (MT/yr)
SCEfsp (MT/yr)
OUTfsp (MT/yr)
SEZ LRfsp (MT/yr)
SLRT OUTPUTS
AVERAGE ANNUAL ESTIMATES
PRE RESTORATION
Predicted FSP catchment load (MT/yr)
141.45
POST RESTORATION
141.45
CHANGE
0
% CHANGE
0%
IN fsp (MT/yr)
Predicted FSP load delivered to floodplain (MT/yr)
4.43
34.47
30.0
678%
DFPfsp (MT/yr)
Predicted FSP load retained on floodplain (MT/yr)
1.61
12.12
10.5
652%
RFPfsp (MT/yr)
Predicted FSP load from channel erosion (MT/yr)
3.47
0.89
-2.58
-74%
SCEfsp (MT/yr)
143.30
130.22
-13.08
-9%
OUTfsp (MT/yr)
Predicted FSP load at downstream boundary (MT/yr)
13.08
SLRTv1 created by 2NDNATURE LLC (2013)
SLRTv1 created by 2NDNATURE LLC (2013)
Average annual FSP Load Reduction (MT/yr)
SEZ LRfsp (MT/yr)
SLRT RESULTS SUMMARY [7/24/2013]
SLRTV1 “RESULTS” WORKSHEET
Figure 4.11
SLRT RESULTS SUMMARY [7/24/2013]
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 5
| 5.1 APPLICATION OF SLRT The methodology and algorithms within the SLRTv1 were informed by the iterative implementation of the methods to estimate the average annual FSP load reductions provided by two SEZ restoration test sites: Upper Reach of Trout Creek and Bristlecone SEZ. An extensive amount of research, sampling and analysis was conducted during this research on the Upper Reach of Trout Creek as presented in Section 2. Bristlecone SEZ has a 287 acre urban catchment in Placer County at Dollar Point, and currently a restoration effort is in the planning phase. Bristlecone was selected as an urban SEZ example to ensure the SLRT methods were applicable to a small‐scale SEZ with a highly urbanized catchment. Below summarizes the inputs and outputs for each SEZ and compares the results to relevant hydrologic and pollutant loading estimates from other researchers to verify the reasonableness of various components of SLRTv1. 5.1
UPPER REACH OF TROUT CREEK The populated SLRTv1_ Trout Creek.xlsx is available for download from http://www.2ndnaturellc.com/client‐
access/slrttrout‐creek/. The SLRT inputs and results are best summarized by a site location map (Figure 5.1) and the SLRT summary report (Figure 5.2A‐E). Figure 5.1 clearly indicates the pre‐ and post‐restoration channel alignment, upstream and downstream boundaries of the analysis, and the contributing catchment. Figure 5.2A are the SLRT inputs values for both configurations of the Upper Reach of Trout Creek. The majority of these input values were generated by the methods documented in Section 2 of this report, and are summarized in Table 5.1. Figure 5.2B illustrates the incoming hydrology and FSP loading from the contributing catchment used in the analysis, with an estimated average annual yield of 141 MT/yr to the upstream boundary of the SEZ. Figure 5.2C presents the floodplain delivery and retention estimates in graphical format for each discharge interval. With restoration, frequency of overbank flows increased and SLRTv1 estimates the restoration resulted in an increase of 30 MT/yr of FSP delivered to the floodplain and an increased retention of 10.5 MT/yr, equating to a of 678% and 652% change, respectively, from pre‐restored conditions. Figure 5.2D summarizes the spatial extrapolation of the BSTEM‐Dynamic unit erosion results, presented as the SEZ total bulk and FSP loads for each of the 4 annual hydrographs. The integration of the annual channel erosion loads by the frequency of expected occurrence resulted in an estimated 74% reduction in FSP mass derived from bank material and a reduction of 2.58 MT/yr in the average annual FSP load generated from channel erosion. Figure 5.2E summarizes the a series of collective physical and chemical effective metrics for the Upper Reach of Trout Creek, including an estimated average annual FSP load reduction of 13.1 MT/yr as a result of the restoration actions. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 L o w er P ro j ect B o u n d ary
LEGEND
PRE-RESTORATION ALIGNMENT
POST-RESTORATION ALIGNMENT
Project
Location
C o n t rib u t in g C at chm en t
U p p er P ro j ect B o u n d ary
FIGURE 5.1: Map depicting Upper Reach Trout Creek project site
geography
restoration design
Figure X.X:and
Approximated
Water topography.
Surface Extents (WSE) for peak
flow conditions during WY2009.
Feet
0
13 0
26 0
520
STREAM LOAD REDUCTION TOOL (SLRTv1)
User Inputs
META DATA
USER NAME
WATERSHED/CATCHMENT
REACH NAME
Date of Estimate
CATCHMENT TYPE
REGION
SUB-REGION
CATCHMENT AREA
AREA UNITS
CATCHMENT % IMPERVIOUS
CATCHMENT LAND USE CONDITION
2NDNATURE
Trout Creek
Upper Reach (TCPT)
5/1/2013
CATCHMENT CHARACTERISTICS
Non Urban
Southshore
Southwest
23.7
Sq-miles
Urban Only
3
Urban Only
SEZ ATTRIBUTES
PRE RESTORATION
1530
0.0016
530
1.34
lc
s
lob
hob
POST RESTORATION
1829
0.0013
1001
1.1
37
aob
53
1000
1
lstr
hstr
828
0.76
Bank angle of straight reaches (degrees)
34
astr
39
Manning’s roughness value of channel
Fines to bulk sediment ratio (0-1 value)
0.03
0.087
n
FSP:BS
0.03
0.087
Channel capacity (cfs)
Floodplain length (m)
Floodplain condition score
200
458
3
Qcc
lfp
FPC
88
458
5
Effective cohesion (kPa)
Angle of internal friction (degrees)
30.9
3.8
17.1
10.0
c'
'
30.9
3.8
17.1
10.0
Channel length (m)
Channel slope (m/m)
Outside bend length (m)
Bank height of outside bends (m)
Bank angle of outside bends (degrees)
Straight length (m)
Bank height of straight reaches (m)
3
Bulk unit weight (kN/m )
Matric suction parameter (degrees)
Bank - Critical shear stress (Pa)
3
Bank - Erodibility coefficient (cm /Ns)
Toe - Critical shear stress (Pa)
3
Toe - Erodibility coefficient (cm /Ns)
3.00
0.645
21.4
0.127
b
c
k
c
k
3.00
0.645
21.4
0.127
BSTEM Dynamic OUTPUT
POST RESTORATION Qmd-p
PRE RESTORATION
0.136
3
Outside bend unit erosion rate (m /m/yr)
0.06
0.02
0.0043
3
Straight reach unit erosion rate (m /m/yr)
0.17
0.065
0.028
0.0076
e ob-99
e ob-75
e ob-50
e ob-25
0.036
e str-99
e str-75
e str-50
e str-25
99th
0.005
75th
0
50th
0
25th
0.075
0.018
0
99th
75th
50th
0
25th
SLRTv1 created by 2NDNATURE LLC 2013
USER INPUT [7/24/2013]
UPPER REACH TROUT CREEK - USER INPUTS
Figure 5.2A
STREAM LOAD REDUCTION TOOL (SLRTv1)
Predicted catchment hydrology and FSP loads
SEZ NAME: Upper Reach (TCPT)
CALCULATIONS
NAME
Mean Annual Precip (in)
Total Area (sq mi or acres)
Total Impervious Area (acres)- urban only
VALUE
29.91
23.7
0.0
VARIABLE
P
A
Ai
Bin Interval (cfs)
Regional Coefficient
Max Mean Daily Q (cfs)
9.303
0.0008
501.43
Qbi
R
Qmax
Bin 50 Value (cfs)
478.64
Qb-50
FSP CRC (mg/L) - Urban only
n/a
Vin
Bin Interval (cfs)
9.30
Qbi
n/a
FSPC
FSP CRC (mg/L) - Urban only
Average annual discharge volume (ac-ft/yr)
23101.3
Vin
141.5
FSPin
Average annual FSP load into SEZ (MT/yr)
Predicted incoming mean daily discharge frequency distribution 140
120
t (d/yr) 100
80
60
40
20
0
FSPb an (kg/yr) Qb (cfs) 18000
16000
14000
12000
10000
8000
6000
4000
2000
0
Predicted incoming average annual FSP load as a function of discharge Qb (cfs) Predicted Annual Hydrographs 12.00
Qmd p (m3/s) 10.00
99th
75th
50th
25th
8.00
6.00
4.00
2.00
0.00
Oct
SLRTv1 created by 2NDNATURE LLC 2013
Nov
Dec
Jan
Feb
Mar
MONTH Apr
May
Jun
Jul
Aug
Sep
Catchment hydrology and FSP loading [7/24/2013]
UPPER REACH TROUT CREEK - HYD FSP IN
Figure 5.2B
STREAM LOAD REDUCTION TOOL (SLRTv1) FLOODPLAIN RETENTION ESTIMATES
REACH NAME
Date of estimate
Channel length (m)
Channel slope (m/m)
Channel capacity (cfs)
Floodplain condition score
Average days overbank (d/yr)
Channel FSP load (kg/d)
Upper Reach (TCPT)
PRE RESTORE
1530
0.0016
200
3
5/1/2013
POST RESTORE
1829
0.0013
88
5
Qcc
FPC
3.5
29.3
tob
3161
1061
FSPcc
Catchment FSP load (MT/yr)
FSPin
141.45
Delivered to floodplain (MT/yr)
4.43
34.47
Retained on floodplain (MT/yr)
1.61
12.12
3500.00
VARIABLES
DFPfsp
RFPfsp
FSP mass delivered to floodplain PRE RESTORE
3000.00
POST RESTORE
DFP fsp (kg/yr) 2500.00
2000.00
1500.00
1000.00
500.00
0.00
Qmd (cfs) FSP mass retained on floodplain 1200.00
PRE RESTORE
RFP fsp (kg/yr) 1000.00
POST RESTORE
800.00
600.00
400.00
200.00
0.00
Qmd (cfs) SLRTv1 created by 2NDNATURE LLC (2013)
Average annual floodplain retention estimates [7/25/2013]
UPPER REACH TROUT CREEK - RFPFSP
Figure 5.2C
UPPER REACH TROUT CREEK - SCEFSP
Figure5.2D
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
0.00
POST RESTORE
10.24
0.891
2.575
PRE RESTORE
39.84
3.466
POST RESTORE
0.0360
0.0050
0.0000
0.0000
0.0750
0.0180
0.0000
0.0000
PRE RESTORE
0.1360
0.0600
0.0200
0.0043
0.1700
0.0650
0.0280
0.0076
0.20
Post-restoration
Pre-Restoration
0.60
Annual probability hydrograph (Qmd p) 0.40
0.80
99th
75th
50th
25th
99th
75th
50th
25th
Qmd-p
74%
% reduction
1.00
Annual fine sediment channel load per annual hydrograph Average annual bulk sediment generated (MT/yr)
Average annual FSP load generated (MT/yr)
Average annual FSP load reduction (MT/yr)
Bulk sediment
Straight reach unit erosion rate (m3/m/yr)
Bulk sediment
Outside bend unit erosion rate (m3/m/yr)
Channel length (m)
Outside bend length (m)
Straight length (m)
Fines to bulk sediment ratio (0-1 value)
5/1/2013
POST RESTORE
1829
1001
828
0.087
PRE RESTORE
1530
530
1000
0.087
Upper Reach (TCPT)
1.20
STREAM LOAD REDUCTION TOOL (SLRTv1)
SEZ CHANNEL EROSION ESTIMATES
REACH NAME
Date of estimate
SLRTv1 created by 2NDNATURE LLC and A. Simon 2013
Annual fine sediment load (MT/yr) SEZ Total
FSP
SEZ Total
Bulk sediment
24.31
5.28
0.00
0.00
279.41
60.70
0.00
0.00
0.99
0.75
0.50
0.25
0.99
0.75
0.50
0.25
Average annual channel erosion estimates [7/24/2013]
62.51
24.55
10.10
2.65
718.55
282.13
116.13
30.48
PRE RESTORE POST RESTORE
Qmd-p
MT/yr
MT/yr
125.69
62.84
0.99
55.45
8.73
0.75
Outside Bends
18.48
0.00
0.50
3.97
0.00
0.25
592.86
216.57
0.99
226.68
51.98
0.75
Straight Reach
97.65
0.00
0.50
26.50
0.00
0.25
Annual Pollutant Loads per Annual Hydrograph
SUB-REGION
CATCHMENT AREA
AREA UNITS
CATCHMENT % IMPERVIOUS
CATCHMENT LAND USE CONDITION
Southwest
23.7
Sq-miles
3
USER INPUTS
PRE RESTORATION
POST RESTORATION
ChannelSTREAM LOAD REDUCTION TOOL (SLRTv1)
length (m)
1530
1829
Channel slope (m/m)
0.0016
0.0013
Results Summary
Outside bend length (m)
530
1001
Bank height of outside bends (m)
1.34
1.1
USER NAME
2NDNATURE
Bank angle of outside bends (degrees)
37
53
WATERSHED/CATCHMENT
Trout Creek
Straight length (m)
1000
828
REACH NAME Upper Reach (TCPT)
Bank height of straight reaches (m)
1
0.76
Date of Estimate
5/1/2013
Bank angle of straight reaches (degrees)
34
39
CATCHMENT TYPE
Non Urban
Manning’s roughness value of channel
0.03
0.03
REGION
Southshore
Fines to bulk sediment ratio (0-1 value)
0.087
0.087
SUB-REGION
Southwest
Channel capacity (cfs)
200
88
CATCHMENT AREA
23.7
Floodplain length (m)
458
458
AREA UNITS
Sq-miles
Floodplain condition score
3
5
CATCHMENT % IMPERVIOUS
CATCHMENT LAND USE CONDITION
3
CHANGE
299
-0.0003
471
-0.24
16
-172
-0.24
5
0
0
-112
0
2
% CHANGE
20%
-19%
89%
-18%
43%
-17%
-24%
15%
0%
0%
-56%
0%
2
SLRT OUTPUTS
USER INPUTS
AVERAGE ANNUAL ESTIMATES
POST RESTORATION
PRE RESTORATION
CHANGE
% CHANGE
PRE RESTORATION
POST RESTORATION
CHANGE
% CHANGE
Predicted FSP catchment load (MT/yr)
141.45
141.45
0
0%
Channel length (m)
1530
1829
299
20%
Predicted FSP load delivered
to
floodplain
(MT/yr)
4.43
34.47
30.0
678%
Channel slope (m/m)
0.0016
0.0013
-0.0003
-19%
Predicted FSP load retained
floodplain
12.12
10.5
652%
Outsideonbend
length(MT/yr)
(m)
5301.61
1001
471
89%
Predicted FSP
load
from of
channel
erosion
-2.58
-74%
Bank
height
outside
bends(MT/yr)
(m)
1.343.47
1.10.89
-0.24
-18%
Bank
angle
of outside bends
(degrees)
37
53
16
43%
Predicted FSP
load
at downstream
boundary
(MT/yr)
143.30
130.22
-13.08
-9%
Straight length (m)
1000
828
-172
-17%
Bank height of straight reaches (m)
1
0.76
-0.24
-24%
Bank angle of straight reaches (degrees)
34
39
5
15%
Average annual FSP Load Reduction (MT/yr)
13.08
Manning’s roughness value of channel
0.03
0.03
0
0%
Fines to bulk sediment ratio (0-1 value)
0.087
0.087
0
0%
Channel capacity (cfs)
200
88
-112
-56%
Floodplain length (m)
458
458
0
0%
Floodplain condition score
3
5
2
2
IN fsp (MT/yr)
DFPfsp (MT/yr)
RFPfsp (MT/yr)
SCEfsp (MT/yr)
OUTfsp (MT/yr)
SEZ LRfsp (MT/yr)
SLRT OUTPUTS
AVERAGE ANNUAL ESTIMATES
PRE RESTORATION
Predicted FSP catchment load (MT/yr)
141.45
POST RESTORATION
141.45
CHANGE
0
% CHANGE
0%
IN fsp (MT/yr)
Predicted FSP load delivered to floodplain (MT/yr)
4.43
34.47
30.0
678%
DFPfsp (MT/yr)
Predicted FSP load retained on floodplain (MT/yr)
1.61
12.12
10.5
652%
RFPfsp (MT/yr)
Predicted FSP load from channel erosion (MT/yr)
3.47
0.89
-2.58
-74%
SCEfsp (MT/yr)
143.30
130.22
-13.08
-9%
OUTfsp (MT/yr)
Predicted FSP load at downstream boundary (MT/yr)
13.08
SLRTv1 created by 2NDNATURE LLC (2013)
SLRTv1 created by 2NDNATURE LLC (2013)
Average annual FSP Load Reduction (MT/yr)
SEZ LRfsp (MT/yr)
SLRT RESULTS SUMMARY [7/24/2013]
UPPER REACH TROUT CREEK - SLRTV1 RESULTS
Figure 5.2E
SLRT RESULTS SUMMARY [7/24/2013]
5.8 | July 2013 Table 5.1. Summary of methods and data sources for Upper Reach Trout Creek SLRT inputs. Attribute Catchment type Data source & assumptions Criteria in Table 4.1 Region / Sub‐region Figure 4.2B Catchment area GIS analysis Channel length (lc) Channel slope (s) Reach length (lob; lstr) See Table 2.17; GIS analysis of existing shapefiles and LiDAR data Bank height (hob; hstr) See Table 2.17; long‐term cross section monitoring and data Bank angle (aob; astr) collection Channel capacity (Qcc) Water depth at channel capacity (Zcc) See Section 2.2.2; nhc data and Manning’s equation Manning’s value (n) Andrew Simon existing Tahoe and Trout Creek data. Floodplain length (lfp) See Table 2.17; floodplain monitoring and GIS analysis Assume pre‐restoration condition is the Basin average (3) Floodplain condition (FPC) from historic aerials Post‐restoration Trout Creek floodplain condition is desired condition (5) from field surveys and sampling. % fines Geotechnical inputs 5.1.1
Table 4.7; Southern region median Tahoe basin median values; Table 4.10 VALIDATION The SLRTv1 estimates a 13.1 MT/yr reduction as a result of a nearly 2,000 linear meter channel restoration effort. Given that the Upper Reach of Trout Creek restoration is considered one of the most successful restoration efforts conducted in the Tahoe Basin, where 30 acres of floodplain treated over 5,000 acre feet of water during the WY11 snowmelt (see Table 2.15) and minimal channel erosion evidence is observed, this annual load reduction estimate seems reasonable. Preliminary baseline FSP load estimate for the City of South Lake Tahoe (CSLT) TMDL planning is 176 MT/yr, equating to a first milestone TMDL load reduction target of 10% to be in the range of 17 MT/yr for CSLT (CSLT and nhc, 2013). The breadth of available data and research conducted on Trout Creek allows a series of comparisons between SLRT estimates and those generated by others. These comparisons were used to inform the final SLRTv1 algorithms and provide confidence that the estimates are reasonable. 5.1.1.1
Catchment FSP Yield Figure 5.2B indicates that the average annual FSP yield delivered to the Upper Reach at Trout Creek is 141 MT/yr. This annual FSP yield can be compared to estimates made by Simon et al. (2003) at the USGS site at Pioneer Trail, where they estimated 331 MT/yr of total sediment and approximately 130 MT/yr of fine sediment (defined as <62 µm) using the fraction of fines to total sediment used for the USGS gage as Martin Avenue Trout Creek, suggesting 141 MT/yr as estimated by SLRTv1 aligns with other estimates. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 5.1.1.2
| 5.9 Floodplain Retention Previous estimates of floodplain retention on an average annual basis in the Tahoe Basin are not available. However, the Upper Reach of Trout Creek was used to obtain and develop the floodplain FSP retention algorithms in SLRT. Section 3.2.4 details a validation using the floodplain retention measurements during WY11 to predict the measured FSP load differences upstream and downstream of the subject floodplain on Trout Creek. The predicted FSP load difference was within 14% of the measured difference, providing confidence that the SLRT retention estimates, at least for good floodplain conditions, are reasonable. The only overbank flows during WY10 and WY11 at Trout Creek were the snowmelt events, thus accounting for 100% of the floodplain inundation and retention contribution for each water year. Comparisons of the SLRT average annual FSP load retained on the floodplain (12.1 MT/yr; see Figure 5.2C) and the measured WY10 and WY11 snowmelt floodplain retention loads (5.9 and 9.4 MT/yr, respectively; see Table 2.16) show the estimated and measured values are of similar magnitude. While these data are extremely limited, the general alignment of measured and predicted floodplain retention loads suggests the SLRTv1 approach to estimate floodplain retention is within reason. A primary recommendation to inform and improve SLRTv1 is the establishment of consistent reach‐scale water quality monitoring in a manner necessary to obtain the required dataset to validate average annual FSP load reduction estimates made by SLRT. 5.1.1.3
Channel Erosion Simulated unit‐loading rates (in m3/m) for the Trout Creek test case are relatively low (0.05 to 0.2 m3/m) compared to those of other modeled banks in the Tahoe Basin and elsewhere. This is not surprising given the generally low bank heights (less than 1.5 m) in the reach. Direct comparisons are, however, difficult in that unit‐
loading rates for other studies are based on an annual hydrograph. For a typical high‐flow year Simon et al. (2009) reported annual, unit‐loading rates ranging from 4.7 to 37.9 m3/m for sites along Blackwood and Ward Creeks and the Upper Truckee River. Rates for the 90th percentile flow year along the Big Sioux River, SD ranged from 1.7 to 13.6 m3/m (Bankhead and Simon, 2009), while along the much larger Tombigbee River, unit‐
loading rates for a high‐flow year were simulated to be 550 m3/m (Bankhead et al., 2008). The time series of 19 stream cross section data obtained by the CTC, 2NDNATURE and others from 2001 to 2010 (see Tables 2.1 and 2.3; Figure 2.3) was used to estimate the annual average bulk and fine sediment mass derived from stream channel erosion on the Upper Reach of Trout Creek. A unit volumetric change per year for each cross section (m3/m/yr) was estimated by calculating the net cross sectional area (fill‐scour) over the longest duration between surveys. The net change is assumed to be representative of 1 longitudinal meter of channel. The reach average was calculated (0.00024) and multiplied by the post restoration channel length (1829 m), yielding an estimated net of 0.78 MT/yr of bulk sediment and 0.068 MT/yr of fines using the channel material composition dataset presented in Figure 3.15. The FSP MT/yr of 0.068 is an order of magnitude less than the 0.89 MT/yr estimated using BSTEM dynamic, but the hydrology and duration of flows experienced between 2001‐2010 are expected to trend below average annual conditions. Future refinements and calibration of the SLRT stream channel erosion approach using available cross section chronologies is a priority. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 5.10 | 5.2
July 2013 BRISTLECONE SEZ The populated SLRTv1_Bristlecone.xlsx is available for download from www.2ndnaturellc.com/SLRT. Figure 5.3 clearly indicates the pre‐ and post‐restoration channel alignment, upstream and downstream boundaries of the analysis and the contributing catchment. At the time of this analysis, Bristlecone SEZ exists in the pre‐ restoration conditions. Preliminary restoration design plans developed by Woods Rogers were provided to 2NDNATURE by the CTC to determine the post‐restoration condition for this effort. The SLRT input values are presented in Figure 5.4A and the data, methods and assumptions used to generate these values are provided in Table 5.2. The remaining SLRT summary report pages are included as Figure 5.4B‐E. Table 5.2. Summary of methods and data sources for Bristlecone SEZ SLRT inputs. Attribute Data source and assumptions Catchment type Criteria in Table 4.1 Region / Sub‐region Figure 4.2A GIS data generated during Placer County TMDL Strategy (2NDNATURE and nhc, 2011); Includes West Highlands, East Catchment area, % impervious Highlands, Industrial Circle, and Aspen Bristlecone catchments used in PLRM development. Catchment land use condition Table 4.3; Assumed basin default value Pre: field survey and available shape files. Post: AutoCAD restoration Channel length (lc) design plans by Woods Rogers. Same boundaries used for pre‐ and Floodplain length (lfp) post‐restoration scenarios. Upper boundary consistent with post‐
project alignment and lower boundary at intersection with lake. Elevation difference over the channel length interpreted using Channel slope (s) LiDAR data. Pre: field survey and available shape files. Post: AutoCAD restoration Reach length design plans by Woods Rogers. Straight reaches and outer bends (lob; lstr) interpreted from available data. Bank height (hob; hstr) Pre: field survey and available shape files. Bank angle (aob; astr) Post: AutoCAD restoration design plans by Woods Rogers. Using calculations presented in Section 4.2.3.2, channel capacity was Channel capacity (Qcc) estimated for pre‐ and post‐restoration scenarios. Water depth at channel capacity (Zcc) Manning’s value (n) Table 4.5; based on site visits and photographs Floodplain condition (FPC) % fines Table 4.6; Assume pre‐restoration condition is the Basin average and post‐restoration condition will be desired condition. Table 4.7; Northern region median Geotechnical inputs Estimated based on bank height calculations Table 4.10; Tahoe basin median values LEGEND
PRE-RESTORATION ALIGNMENT
POST-RESTORATION ALIGNMENT
U p p er
P ro j ec
t B o u n
d ary
C o n t rib u t in g C at chm en t s
W es t H i g h l a n d s
E a s tH ig h la n d s
I n d u s tr i a l C i r c l e
A s p en B r i s tl ec o n e
Project
Location
Bristlecone Street
Lo w
er P
ro j e
ct B
o u n
d ary
FIGURE 5.3: Map depicting Bristlecone SEZ project site geography and
Fi g u r e 5. 3 :
M a p
d ep i c ti n g
B r i s tl ec o n e S E Z
p r o j ec t
restoration
design topography.
g eo g r a p h y .
Feet
0
15
3 0
6 0
STREAM LOAD REDUCTION TOOL (SLRTv1)
User Inputs
META DATA
USER NAME
WATERSHED/CATCHMENT
REACH NAME
Date of Estimate
CATCHMENT TYPE
REGION
SUB-REGION
CATCHMENT AREA
AREA UNITS
CATCHMENT % IMPERVIOUS
CATCHMENT LAND USE CONDITION
2NDNATURE
Dollar Point
Bristlecone SEZ
5/9/2013
CATCHMENT CHARACTERISTICS
Urban
Northshore
North
287
Acres
30%
3
Urban Only
Urban Only
SEZ ATTRIBUTES
PRE RESTORATION
POST RESTORATION
68.54
0.01
17.5
0.6
lc
s
lob
hob
83.56
0.008
77
0.31
Bank angle of outside bends (degrees)
90
aob
45
Straight length (m)
Bank height of straight reaches (m)
51
0.6
lstr
hstr
6.6
0.3
Bank angle of straight reaches (degrees)
90
astr
45
Manning’s roughness value of channel
Fines to bulk sediment ratio (0-1 value)
0.04
0.033
n
FSP:BS
0.04
0.033
Channel capacity (cfs)
Floodplain length (m)
Floodplain condition score
0.51
458
3
Qcc
lfp
FPC
0.28
458
5
Effective cohesion (kPa)
Angle of internal friction (degrees)
30.9
3.8
17.1
10.0
c'
'
30.9
3.8
17.1
10.0
Channel length (m)
Channel slope (m/m)
Outside bend length (m)
Bank height of outside bends (m)
3
Bulk unit weight (kN/m )
Matric suction parameter (degrees)
Bank - Critical shear stress (Pa)
3
Bank - Erodibility coefficient (cm /Ns)
Toe - Critical shear stress (Pa)
3
Toe - Erodibility coefficient (cm /Ns)
3.00
0.645
21.4
0.127
b
c
k
c
k
3.00
0.645
21.4
0.127
BSTEM Dynamic OUTPUT
POST RESTORATION Qmd-p
PRE RESTORATION
0.011381
Outside bend unit erosion rate (m3/m/yr)
0.000000
0.000000
0.000000
3
Straight reach unit erosion rate (m /m/yr)
0.011381
0.000000
0.000000
0.000000
e ob-99
e ob-75
e ob-50
e ob-25
0.001892
99th
0.000000
75th
0.000000
50th
0.000000
25th
e str-99
e str-75
e str-50
e str-25
0.001892
0.000000
0.000000
99th
75th
50th
0.000000
25th
SLRTv1 created by 2NDNATURE LLC 2013
USER INPUT [7/24/2013]
BRISTLECONE SEZ - USER INPUTS
Figure 5.4A
STREAM LOAD REDUCTION TOOL (SLRTv1)
Predicted catchment hydrology and FSP loads
SEZ NAME: Bristlecone SEZ
CALCULATIONS
NAME
VALUE
VARIABLE
Mean Annual Precip (in)
Total Area (sq mi or acres)
Total Impervious Area (acres)- urban only
30.25
287
86.1
P
A
Ai
Bin Interval (cfs)
Regional Coefficient
Max Mean Daily Q (cfs)
0.077
n/a
11.19
Qbi
R
Qmax
Qb-50
Bin 50 Value (cfs)
5.65
FSP CRC (mg/L) - Urban only
153.5
Vin
Bin Interval (cfs)
0.08
Qbi
154
FSPC
FSP CRC (mg/L) - Urban only
Average annual discharge volume (ac-ft/yr)
195.7
Vin
Average annual FSP load into SEZ (MT/yr)
17.4
FSPin
Predicted incoming mean daily discharge frequency distribution 300.0
t (d/yr) 250.0
200.0
150.0
100.0
50.0
0.0
Qb (cfs) Predicted incoming average annual FSP load as a function of discharge 4000
FSPb an (kg/yr) 3500
3000
2500
2000
1500
1000
500
0
Qb (cfs) Predicted Annual Hydrographs 0.40
0.35
99th
75th
50th
25th
Qmd p (m3/s) 0.30
0.25
0.20
0.15
0.10
0.05
0.00
Oct
SLRTv1 created by 2NDNATURE LLC 2013
Nov
Dec
Jan
Feb
Mar
MONTH Apr
May
Jun
Jul
Aug
Sep
Catchment hydrology and FSP loading [7/24/2013]
BRISTLECONE SEZ - HYD FSP IN
Figure 5.4B
STREAM LOAD REDUCTION TOOL (SLRTv1) FLOODPLAIN RETENTION ESTIMATES
Bristlecone SEZ
REACH NAME
Date of estimate
Channel length (m)
Channel slope (m/m)
Channel capacity (cfs)
Floodplain condition score
PRE RESTORE
68.54
0.01
0.51
3
5/9/2013
POST RESTORE
83.56
0.008
0.28
5
Qcc
FPC
Average days overbank (d/yr)
14.8
32.4
tob
Channel FSP load (kg/d)
189
102
FSPcc
Catchment FSP load (MT/yr)
FSPin
17.39
Delivered to floodplain (MT/yr)
3.46
5.63
Retained on floodplain (MT/yr)
0.67
1.02
800
DFPfsp
RFPfsp
FSP mass delivered to floodplain PRE RESTORE
700
POST RESTORE
600
DFP fsp (kg/yr) VARIABLES
500
400
300
200
100
0
Qmd (cfs) FSP mass retained on floodplain 160
PRE RESTORE
POST RESTORE
140
RFP fsp (kg/yr) 120
100
80
60
40
20
0
Qmd (cfs) SLRTv1 created by 2NDNATURE LLC (2013)
Average annual floodplain retention estimates [7/25/2013]
BRISTLECONE SEZ - RFPFSP
Figure 5.4C
BRISTLECONE SEZ - SCEFSP
Figure 5.4D
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.00
POST RESTORE
0.01
0.000
0.002
PRE RESTORE
0.06
0.002
POST RESTORE
0.00189
0.00000
0.00000
0.00000
0.00189
0.00000
0.00000
0.00000
PRE RESTORE
0.01138
0.00000
0.00000
0.00000
0.01138
0.00000
0.00000
0.00000
0.20
Post-restoration
Pre-Restoration
0.60
Annual probability hydrograph (Qmd p) 0.40
0.80
99th
75th
50th
25th
99th
75th
50th
25th
Qmd-p
87%
% reduction
1.00
Annual fine sediment channel load per annual hydrograph Average annual bulk sediment generated (MT/yr)
Average annual FSP load generated (MT/yr)
Average annual FSP load reduction (MT/yr)
Bulk sediment
Straight reach unit erosion rate (m3/m/yr)
Bulk sediment
Outside bend unit erosion rate (m3/m/yr)
Channel length (m)
Outside bend length (m)
Straight length (m)
Fines to bulk sediment ratio (0-1 value)
5/9/2013
POST RESTORE
83.56
77
6.6
0.033
PRE RESTORE
68.54
17.5
51
0.033
Bristlecone SEZ
1.20
STREAM LOAD REDUCTION TOOL (SLRTv1)
SEZ CHANNEL EROSION ESTIMATES
REACH NAME
Date of estimate
SLRTv1 created by 2NDNATURE LLC and A. Simon 2013
Annual fine sediment load (MT/yr) 0.01
0.00
0.00
0.00
0.08
0.00
0.00
0.00
SEZ Total
FSP
0.99
0.75
0.50
0.25
0.99
0.75
0.50
0.25
Average annual channel erosion estimates [7/24/2013]
0.30
0.00
0.00
0.00
2.37
0.00
0.00
0.00
SEZ Total
Bulk sediment
PRE RESTORE POST RESTORE
Qmd-p
MT/yr
MT/yr
0.35
0.25
0.99
0.00
0.00
0.75
Outside Bends
0.00
0.00
0.50
0.00
0.00
0.25
2.02
0.04
0.99
0.00
0.00
0.75
Straight Reach
0.00
0.00
0.50
0.00
0.00
0.25
Annual Pollutant Loads per Annual Hydrograph
WATERSHED/CATCHMENT
Dollar Point
REACH NAME
Bristlecone SEZ
Date of Estimate
5/9/2013
CATCHMENT TYPE
Urban
REGION
Northshore
SUB-REGION
North
CATCHMENT AREA
287
AREA UNITS
Acres
CATCHMENT % IMPERVIOUS
0.3
CATCHMENT LAND USESTREAM LOAD REDUCTION TOOL (SLRTv1)
CONDITION
3
Results Summary
USER INPUTS
POST RESTORATION
PRE RESTORATION
CHANGE
% CHANGE
Channel length (m)
68.54
83.56
15.02
22%
USER NAME
2NDNATURE
Channel slope (m/m)
0.01
0.008
-0.002
-20%
WATERSHED/CATCHMENT
Dollar Point
Outside bend length (m)
17.5
77
59.5
340%
REACH NAME
Bristlecone SEZ
Bank height of outside bends (m)
0.6
0.31
-0.29
-48%
Date of Estimate
5/9/2013
Bank angle of outside bends (degrees)
90
45
-45
-50%
CATCHMENT TYPE
Urban
Straight length (m)
51
6.6
-44.4
-87%
REGION
Northshore
Bank height of straight reaches (m)
0.6
0.3
-0.3
-50%
SUB-REGION
North
Bank angle of straight reaches (degrees)
90
45
-45
-50%
CATCHMENT AREA
287
Manning’s roughness value of channel
0.04
0.04
0
0%
AREA UNITS
Acres
Fines to bulk sediment ratio (0-1 value)
0.033
0.033
0
0%
CATCHMENT % IMPERVIOUS
0.3
Channel capacity (cfs)
0.51
0.28
-0.23
-45%
CATCHMENT LAND USE CONDITION
3
Floodplain length (m)
458
458
0
0%
USER INPUTS
Floodplain condition score
3
5
2
2
POST RESTORATION
PRE RESTORATION
CHANGE
% CHANGE
Channel length (m)
68.54
83.56
22%
SLRT OUTPUTS 15.02
Channel
slope
(m/m)
0.01
0.008
-0.002
-20%
AVERAGE ANNUAL ESTIMATES
POST RESTORATION
PRE RESTORATION
CHANGE
% CHANGE
Outside
bend length
17.5
77
59.5
340%
Predicted
FSP catchment
load (m)
(MT/yr)
17.39
17.39
0
0%
Bank
height
of outside
bends (m)
0.63.46
0.31
-0.29
-48%
Predicted FSP
load
delivered
to floodplain
(MT/yr)
5.63
2.2
63%
Bank angle of outside bends (degrees)
90
45
-45
-50%
Predicted FSP load retained on floodplain (MT/yr)
0.673
1.017
0.345
51%
Straight length (m)
51
6.6
-44.4
-87%
Predicted FSP load from channel erosion (MT/yr)
0.002
0.000
-0.002
-87%
Bank height of straight reaches (m)
0.6
0.3
-0.3
-50%
Predicted FSP load at downstream boundary (MT/yr)
16.72
16.37
-0.35
-2%
Bank angle of straight reaches (degrees)
90
45
-45
-50%
Manning’s roughness value of channel
0.04
0.04
0
0%
Fines to bulk sediment ratio (0-1 value)
0.033
0.033
0
0%
Average annual FSP Load Reduction (MT/yr)
0.35
Channel capacity (cfs)
0.51
0.28
-0.23
-45%
Floodplain length (m)
458
458
0
0%
Floodplain condition score
3
5
2
2
IN fsp (MT/yr)
DFPfsp (MT/yr)
RFPfsp (MT/yr)
SCEfsp (MT/yr)
OUTfsp (MT/yr)
SEZ LRfsp (MT/yr)
SLRT OUTPUTS
AVERAGE ANNUAL ESTIMATES
PRE RESTORATION
Predicted FSP catchment load (MT/yr)
17.39
POST RESTORATION
17.39
CHANGE
0
% CHANGE
0%
IN fsp (MT/yr)
DFPfsp (MT/yr)
Predicted FSP load delivered to floodplain (MT/yr)
3.46
5.63
2.2
63%
Predicted FSP load retained on floodplain (MT/yr)
0.673
1.017
0.345
51%
RFPfsp (MT/yr)
Predicted FSP load from channel erosion (MT/yr)
0.002
0.000
-0.002
-87%
SCEfsp (MT/yr)
Predicted FSP load at downstream boundary (MT/yr)
16.72
16.37
-0.35
-2%
OUTfsp (MT/yr)
0.35
SLRTv1 created by 2NDNATURE LLC (2013)
Average annual FSP Load Reduction (MT/yr)
SLRT RESULTS SUMMARY [7/24/2013]
BRISTLECONE SEZ - SLRTV1 RESULTS
SLRTv1 created by 2NDNATURE LLC (2013)
SEZ LRfsp (MT/yr)
Figure 5.4E
SLRT RESULTS SUMMARY [7/24/2013]
Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 5.2.1
| 5.17 VALIDATION The hydrologic and FSP loading estimates can be compared to PLRM catchment outputs conducted by 2NDNATURE and nhc on other Placer County urban catchments. The contributing catchment to Bristlecone SEZ is a fraction of the Dollar Point (PC20) catchment modeled in 2011, consisting of 287 acres versus 378 acres respectively (2NDNATURE and nhc, 2011). PC20 was estimated to have an average annual runoff volume of 4.5 in/yr, and SLRT estimates the annual runoff volume of 8.1 in/yr. 2NDNATURE and nhc (2011) estimated an FSP loading rate of 88 lbs/yr/acre (2NDNATURE and nhc 2011; Table 2.4) and SLRT estimates Bristlecone FSP loading to be 133 lbs/yr/acre. For context, the FSP loading rate estimated for the Upper Reach of Trout Creek catchment is 20 lbs/yr/acre. These comparisons of catchment runoff and FSP loading are considered reasonable given that the primary source of FSP to Lake Tahoe is assumed to be urban impervious surfaces (LRWQCB and NDEP 2010) and urban stormwater concentrations are typically 4 to 5 times lower than those measured in streams given comparable discharge conditions. Since there is no known comparable data or estimate of the potential FSP load reductions provided by a small scale urban SEZ, initial comparisons between Bristlecone and Trout Creek SLRTv1 load reduction estimates are made and appear reasonable. The floodplain delivery and floodplain retention estimates suggest the very small channel capacity results in only a 63% increase in the FSP delivered to the floodplain as a result of morphologic changes, compared to an increase of 678% estimated in Trout Creek, with an order of magnitude more FSP mass per year delivered to the floodplain. As anticipated, small scale SEZ restoration does not appear to provide a measurable FSP load reduction benefit as a result of reduced channel erosion, with 99% of the 0.35 MT/yr due to increased floodplain retention. 5.3
BASIN CONTEXT OF SEZ LOAD REDUCTION ESTIMATES The above application of SLRTv1 has provided two independent estimates of the FSP average annual load reductions provided as a result of two very different SEZ scales: 2000 linear meters of stream channel and < 100 meters of an urban SEZ. The average annual load reductions of FSP are dominated by the changes in floodplain retention, rather than the reductions in channel generation of FSP as a result of reduced erosion, primarily due to very small contribution of channel material that is silt or smaller. These measurements support the underlying hypotheses of the TMDL: the relatively recent human activities (particular winter road abrasive applications; 2NDNATURE et al., 2010c) have resulted in a significant increase in the generation and transport of FSP loads to the lake, and thus the presence and supply of fines in Tahoe watersheds prior to urban development (pre 1960’s) during SEZ soil formation was negligible. As expected and desired, the annual FSP load reductions estimated for these two sites vary by nearly two orders of magnitude. We use the City of South Lake Tahoe’s TMDL Pollutant Load Reduction Strategy (CSLT and nhc, 2013) to put these estimates into TMDL load reduction context. The City of South Lake Tahoe’s baseline average annual FSP load is estimated at 177 MT/yr. The TMDL milestones require incremental load reductions of 10%, 21% and 34% in the baselines loads, translating to 17 MT/yr by 2016, 37 MT/yr by 2021 and 60 MT/yr by 2026, respectively. The SLRTv1 estimate suggests that a restoration of this scale could contribute up to 75% of the required load reduction to meet the City’s first milestone. Bristlecone is located in Placer County, where the baseline FSP load is estimated to be 234 MT/yr. An SEZ restoration of the Bristlecone scale (approximately 3 acres of SEZ) is estimated to provide an average annual 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 5.18 | July 2013 FSP load reduction of 0.35 MT/yr or a 1.5% contribution toward Placer County’s 10% initial milestone load reduction target of 23 MT/yr. 2NDNATURE and nhc (2011) estimated that all 11 WQIPs completed between 2004 and 2010 by the County provided an average FSP load reduction of 0.61 MT/yr per WQIP with an estimated average project delivery cost of $2.5 million (see Section 4 of that report). The complete cost to plan, design and implement an SEZ restoration of the Bristlecone scale (1 acre) is approximately $500,000. In addition to the cost to load reduction comparisons, the on‐going maintenance requirements to ensure long‐
term FSP load reductions by a typical WQIP structure, such as a wet or dry basin, are not necessary for a functional SEZ. Continued adaptive management of both SEZ restoration projects and WQIPs are required to ensure systems function as intended but, due to differences in the processes by which FSP load reductions are achieved, the maintenance needs for the two systems are very different. Treatment BMPs, particularly Treatment BMPs that rely upon infiltration, are often critical components of the load reductions WQIP provide. Numerous recent stormwater studies have noted a general lack of maintenance efforts at these Treatment BMPs (2NDNATURE, 2006B; 2NDNATURE and nhc, 2010) and FSP accumulation at the infiltration surface of these BMPs rapidly and effectively reduces infiltration performance due to the gradual formation of an impermeable layer (2NDNATURE and nhc, 2013). Regular maintenance is needed to manually remove the FSP and restore the infiltration capacity of a stormwater treatment BMP that relies upon infiltration to reduce pollutant loads. On the other hand, floodplain retention in an SEZ can occur year after year and actually benefits the restoration of the system, further increasing the long‐term cost effectiveness of SEZ restoration actions. Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 6
| 6.1 LIMITATIONS AND NEXT STEPS With limited resources, the SLRTv1 was developed using extensive site‐specific data collection and subsequent analyses to inform the approach and assumptions within the methodology. While the site‐specific water quality field sampling and data compilation from other sources have been extensive, the application of these data to reasonably estimate the average annual FSP load reduction from an SEZ restoration effort in the Tahoe Basin was extremely challenging. The development team relied on numerous assumptions and existing data, models and literature to define the specific hydrology, pollutant loading, floodplain retention and stream channel erosion algorithms that reasonably represent average annual values. Validation of the SLRTv1 estimates would be very resource‐intensive. In order to measure and isolate the change in pollutant loads as a result of the SEZ modifications and validate the SLRTv1 estimates, researchers would require decades of a consistent water quality dataset pre‐ and post‐restoration while the contributing catchment remains in a relatively static condition. Regardless of the challenges of generating and validating accurate average annual values, we believe the SLRTv1 meets the initial research objectives and can be used to generate relatively accurate and repeatable estimates with a minimal amount of computational complexity and effort required by the user. The great majority of the user time is focused on characterizing a series of SEZ attributes to represent pre‐ and post‐restoration site conditions. The creation of the SLRT structure, format and process allows clear identification of how future data can be obtained to improve the algorithms, test the outputs and calibrate the model. A number of limitations and priority recommendations are provided below to guide future efforts to generate data and/or conduct analyses that could improve confidence in SLRT outputs. The relative priority and level of effort required to complete each recommendation is also provided. 

SLRTv1 Application: The 2NDNATURE team has been awarded SNPLMA research funds (Round 12) to apply the SLRTv1 method to nearly a dozen SEZ restoration projects that have been completed in the Upper Truckee River watershed. A number of the recommendations regarding SLRT assumptions and calculation approaches contained herein will be explored during the next research effort. SLRTv1 was tested and refined based on the application of the method to only two SEZ restoration projects. The SLRTv1 application on the Upper Truckee River projects will generate a population of invaluable data to compare and potentially recommend refinements to SLRTv1. Of particular interest is how the cumulative SLRT load reduction estimates from a series of restoration actions within the same watershed compare to previous calculations of overall catchment loading by other researchers. An appropriate method to integrate the cumulative impacts of these SEZ restoration projects on a watershed scale may be necessary. Additionally, a number of validation efforts of SLRTv1 estimates can be completed with a much larger dataset of SEZs modeled. The SNPLMA Round 12 research will be complete in spring 2014 and will incorporate and/or elaborate on the SLRTv1 recommendations below as appropriate. Catchment hydrology and FSP loading: SLRTv1 provides users with a simple method to auto‐generate the necessary catchment hydrology and FSP data to any SEZ, ensuring consistency of the estimates across sites and users. However, the resources were not available to analyze existing datasets with the level of rigor required to provide the best possible hydrologic and FSP loading predictions for any SEZ in the Tahoe Basin. Watershed‐specific datasets were regionally integrated, thereby losing some of the potentially valuable spatial resolution of the data. The development team believes the process, approach and outputs of the SLRT hydrology and FSP loading are robust, but additional analysis using more spatially explicit modeling could like improve our ability to predict these inputs using the existing datasets. Subsequent 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 6.2 | 
July 2013 analyses may result in watershed specific hydrology and FSP rating curves consisting of a series of sequential segments with improved predictive power and finer meteorology resolution. However, the priority to complete these improvements using research funds was relatively low given that, regardless of the accuracy, the same catchment inputs are used for both the pre‐ and post‐restoration scenarios in SLRT. Future SLRT refinements should include guidance to allow users to incorporate site specific hydrology and pollutant loading data into creating and verifying the catchment datasets used to estimate the average annual load reductions. Floodplain retention: The fundamental concepts of fluvial geomorphology, floodplain retention sampling efforts, and the WY10 and WY11 water quality data and resulting loading calculations from Trout Creek (Section 2.2.5), all suggest effective fluvial restoration will result in a significant increase in the average annual pollutant load delivered to, and likely retained, on a functional floodplain. Compared to the SLRTv1 estimated FSP load reductions associated with less erosion from the native channel material following restoration, floodplain deposition is the process with significantly more influence on downstream water quality improvements. This is consistent with the Lake Tahoe TMDL (LRWQCB and NDEP, 2010), where a fundamental hypothesis is that the majority of FSP loads delivered to the lake are derived from human activities on urban surfaces, and not from SEZ channel erosion. This reduces the significance of FSP loads contributed from SEZs, which were formed by natural fluvial processes over the last hundreds or thousands of years. It is likely that, with regards to total sediment, the relative signal of channel erosion to the average annual estimated bulk sediment load reductions achieved as a result of restoration actions is much greater. However, total sediment is not a pollutant included in SLRTv1. The significant role of floodplain retention in achieving FSP load reductions in SEZs makes the accuracy of the SLRT algorithms used to predict FSP retention of upmost importance. The existing retention datasets have been obtained from two low gradient floodplains in relatively good conditions with the collective attributes assumed to maximize FSP retention. However, no data has been obtained from high gradient systems, from those with differing geology and associated “natural” floodplain configurations, nor from moderate or poor condition floodplains. Thus the SLRT algorithms (see Figure 3.13) were generated using best professional judgment. Using the data collection and analysis protocols developed and employed by the 2NDNATURE team, continued floodplain retention sampling across a range of floodplain and overbank event conditions would be extremely valuable to inform future improvements to the moderate and poor FPC curves represented in Figure 3.13. Focused sampling on a range of floodplain morphologies that control differences in the stage to discharge relationships above channel capacity would greatly improve the floodplain retention estimates for non‐optimal floodplains. Future research that informed pollutant retention relative to floodplain hydraulic conditions would also be valuable. Additional sampling should build upon the attributes of floodplain condition developed from SLRT (see Table 4.6) and incorporate quantitative thresholds to more definitively define the characteristics of each floodplain condition category. Given the potential that turbidity can be used as a reasonable proxy for FSP, large datasets on floodplain retention dynamics can be obtained relatively cost‐effectively in the future. This is a high priority recommendation that requires a modest annual, but continued long‐term, investment. Should this future field data collection on SEZ floodplains be conducted, it is recommended samples are additionally analyzed for TSS at a minimum, and perhaps key nutrient compounds as well. Data to develop and incorporate Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT | 6.3 floodplain retention for total sediment fate and transport is the critical data gap necessary to expand SLRT to include total sediment load reduction estimates. 

Channel erosion: Due to the complexity and intermittent nature of the interacting processes driving channel erosion, the use of BSTEM‐Dynamic was required to confidently predict average annual sediment and FSP loads generated from an SEZ channel. Limitations associated with BSTEM‐Dynamic include: (1) the currently available model is a beta version that may be revised following testing and (2) the SLRT requires the user to complete 16 independent BSTEM model simulations. Opportunities exist to improve the reliability and user guidance for BSTEM‐Dynamic by the BSTEM development team, should resources become available. This is a high priority recommendation that would require minimal cost and a short time frame for completion. The current SLRT approach to generating a reasonable average annual FSP load generation estimate required significant resources and has only been refined and tested on 2 SEZs. The application of BSTEM‐
Dynamic and SLRTv1 on a range of SEZs would allow the tool developers to investigate the potential to reduce the number of annual hydrograph simulations necessary to obtain a representative average annual. Following the upcoming effort to apply SLRT to the completed SEZ restoration efforts on the Upper Truckee River (SNPLMA Round 12 research funding), this analysis will be conducted. This is a high priority recommendation that can be incorporated into a currently funded effort by the 2NDNATURE team. Use and Feedback: The SLRTv1 is a beta version ready for testing by the Tahoe community and any future versions will greatly benefit from informed user feedback and suggestions. Please direct questions or recommendations to info@2ndnaturellc.com or contact us at the number below. 2NDNATURE, LLC | ecosystem science + design www.2ndnaturellc.com | 831.426.9119 Quantifying the Benefits of Stream Restoration Efforts in the Lake Tahoe Basin: FINAL REPORT 7
| 7.1 REFERENCES 2NDNATURE, LLC. 2006A. CSLT Upper Truckee River Sediment Monitoring: Middle Reach (2002‐2005). Final Report. Prepared for City of South Lake Tahoe. March 2006. http://www.2ndnaturellc.com/wp‐
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