Aligning stormwater quality datasets with priority management objectives
Tahoe Science Program Round 12 Request for Proposals
Subtheme 2b: Quantifying the benefits of urban storm water management (primary)
Subtheme 4b: Identifying environmental indicators and development of approaches for monitoring and evaluation
November 14, 2011
Principal Investigator: Nicole Beck, PhD, 2NDNATURE LLC, nbeck@2ndnaturellc.com
Team Members: Brent Wolfe (nhc), Jeremy Sokulsky (Environmental Incentives)
Agency Collaborators: Jason Kuchnicki (NDEP), Shane Romsos (TRPA)
Grant Contact: Krista McDonald, 2NDNATURE LLC, krista@2ndnaturellc.com
2NDNATURE LLC, 500 Seabright Ave. #205, Santa Cruz, CA 95062, 831.426.9119, fax 831.426.7092
2009
2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 1 II. PROPOSAL NARRATIVE ABSTRACT Over the past decade, the Lake Tahoe Basin community (resource managers, scientific researchers, project implementers, etc.) has come together to identify and implement sustained and effective actions to reduce pollutant loads to the Lake from urban areas. There is now an opportunity to improve alignment of the stormwater data collection and analysis with the information needs of water quality improvement programs and management decision making. This research will collaborate with local agencies and regulators via the Storm Water Quality Improvement Committee (SWQIC) to develop the framework and technical guidance to achieve the following: •
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Compare and interpret predicted pollutant load reductions modeled by the Pollutant Load Reduction Model (PLRM; NHC et al. 2009) to measured stormwater data. Integrate stormwater quality data from multiple monitoring sites to determine stormwater quality trends over time and evaluate progress towards TMDL load reduction targets. Inform potential stormwater tool modifications and refinements through stormwater monitoring data analyses. Address additional priority management needs of future stormwater datasets. A Technical Applications Guide will be created to provide the practical details to ensure stormwater datasets can be synthesized, analyzed and reported in a standardized manner to inform critical programmatic management questions. The products will provide the necessary vision to disseminate stormwater quality obtained from a number of locations over a range of temporal intervals into formats usable by managers to make informed decisions. JUSTIFICATION STATEMENT The primary sources of the critical pollutants impairing lake clarity, fine sediment particles (FSP < 16μm) and nutrient species (nitrogen and phosphorous), have been linked to urban land use activities (LRWQCB and NDEP 2010). Significant resources are being expended to implement sustained and effective actions to reduce pollutant loads to the Lake from urban areas. A suite of models and tools has been developed and used to prioritize actions, define load reduction targets, support an accounting and tracking system, and report pollutant load reduction accomplishments over time (aka Lake Clarity Crediting Program; LRWQCB and NDEP 2011). All stakeholders agree that a focused and effective stormwater quality monitoring program is necessary to generate and provide quantitative feedback on the effectiveness of planned water quality improvement actions. The ideal scenario for the Lake Tahoe TMDL program would be for a stormwater monitoring effort to generate defensible long‐term stormwater quality datasets that: 1) demonstrate a decreasing trend in pollutant loading to Lake Tahoe resulting from water quality improvement actions ‐ followed by a measured increase in lake clarity; and 2) inform and improve the stormwater tools used by the TMDL program. Collecting stormwater data to meet this goal requires a strong and consistent technical approach to monitoring and data analysis that is comparable across selected monitoring locations in the Tahoe Basin. Local Tahoe Basin researchers have extensive expertise in site instrumentation and data collection, and the Regional Stormwater Quality Monitoring Program (RSWMP) quality assurance project plan (QAPP) defines how, what, and when stormwater quality data should be collected. An RSWMP process to install and manage data collection at stormwater sites is forthcoming, which in part will be driven by the Lahontan RWQCB NPDES permit requirements on the California side of the Tahoe Basin (LRWQCB 2011). On the Nevada side of the Basin, it is expected that a Memorandum of Agreement (MOA) between NDEP and local jurisdictions will include stormwater 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 2 monitoring goals similar to the requirements within the draft CA NPDES permit (J. Kuchnicki, NDEP pers comm). RSWMP station establishment and data collection led by local Tahoe Basin jurisdictions will be supported through initial funding from the US Forest Service Lake Tahoe Basin Management Unit (LTBMU). The process for how stormwater quality data should be managed, synthesized, analyzed, and reported such to meet an array of short‐ and long‐term management objectives has yet to be outlined and defined. The proposed research will develop a Technical Applications Guide to: (1) Define the technical approach for compiling stormwater monitoring results to assess and improve TMDL stormwater tools to better represent observed land use condition, BMP function, or catchment seasonal and annual hydrology and water quality; (2) Develop guidance for comparing pollutant load reductions predicted by PLRM (NHC et al. 2009), which estimates performance on an average annual basis using long‐term continuous simulations, to measured estimates of load reductions developed by analyzing short‐term stormwater monitoring data; and (3) Integrate measured stormwater quality data from multiple catchment monitoring sites to determine stormwater quality trends over time and evaluate the effectiveness of specific water quality improvement activities. The research team provides the intimate familiarity with the Tahoe water quality improvement programs, historic and future stormwater datasets, sampling and analysis strategies, stormwater tool relationships, and stormwater quality data objectives necessary to develop feasible and relevant guidance. 2NDNATURE, LLC (2N) and Northwest Hydraulic Consultants (NHC) are the lead technical developers of the stormwater tools (PLRM and RAM tools) that support the Lake Clarity Crediting Program (Crediting Program), which was developed by Environmental Incentives, LLC (EI). The research conducted herein is necessary to transfer knowledge and process definition to stormwater tool users to ensure stormwater monitoring data are used and applied efficiently and effectively to improve the accuracy with which these tools project pollutant loading and represent actual water quality conditions. BACKGROUND/PROBLEM STATEMENT Stormwater monitoring data collection, management, analysis, and reporting is expensive and requires various levels of technical expertise. Clarity in the explicit data reporting formats and application of the data to address specific objectives would greatly reduce costs and increase future dataset comparability and consistency. With significant investment forthcoming to initiate the RSWMP program, the process and standardization for how collected data will be used to identify trends in pollutant loading, test hypotheses, improve modeling assumptions and inputs, and determine BMP effectiveness does not currently exist. The RSWMP program has been initiated and draft documents rich with detailed direction on data collection options, instrumentation, frequency, etc. are included in the Data Quality Objectives (DRI and UCD 2011a), Sampling and Analysis Plan (DRI and UCD 2011b) and Quality Assurance Project Plan (DRI and UCD 2011c). It is expected that the jurisdictions will create a collaborative RSWMP aimed at meeting their respective CA and NV stormwater goals (NPDES permit or MOA, respectively). What has yet to be defined is how the site‐specific datasets of urban hydrology and water quality from distinct locations at certain intervals for a collection of parameters will be used to answer specific management questions. Without an understanding of what will be done with the data once obtained, how can an informed data collection strategy be developed? This research will collaborate with the local agency and regulatory personnel through SWQIC to ensure the end goals of the datasets are understood and used to guide data collection strategies. While this research will not develop the detailed Monitoring and Evaluation Plan for RSWMP implementation, the researchers will collaborate with the RSWMP sub‐committee and provide the necessary technical framework and vision for how the future stormwater datasets will be analyzed and reported to inform the priority management questions. 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 3 GOALS, OBJECTIVES, HYPOTHESES GOAL Develop technical guidance on how to synthesize, analyze and report collected stormwater volume and water quality data to meet Tahoe Basin urban stormwater program objectives (i.e., Lake Tahoe TMDL, TRPA Monitoring and Evaluation Program (M&E Program), NPDES permits, and MOAs) and address critical management questions. OBJECTIVES 1.
2.
Create a data analysis framework to evaluate stormwater quality status and trends over time and across monitoring locations to: ƒ Assess TMDL program effectiveness; ƒ Assess effectiveness of specific water quality actions and projects in urban areas; and ƒ Characterize spatial patterns of stormwater loading. Develop the approach to compare catchment scale pollutant loads calculated from field measurements to the PLRM modeled estimates and inform future stormwater tool refinements. HYPOTHESIS The development of a Technical Applications Guide that outlines RSWMP data applicability to management questions and decision making in parallel to initial RSWMP implementation will result in an efficient and cost effective monitoring design for understanding pollution control effectiveness. APPROACH, METHODOLOGY AND LOCATION LOCATION OF RESEARCH The proposed research is applicable to all urban catchments within the Tahoe Basin. The extent of the Tahoe Basin urban area and a hypothetical urban catchment site location map is provided in Figure 1 for reference. APPROACH AND METHODOLOGY The draft California NPDES stormwater permit includes explicit stormwater monitoring reporting requirements for the permitees (i.e., municipal jurisdictions) to provide seasonal volumes, average seasonal pollutant concentrations, and total seasonal pollutant loads measured at the outlet of a catchment (LRWQCB 2011). The draft permit also requires the inclusion of quantitative metrics to identify the water year type (i.e., dry, average, wet) to provide a hydro‐climatic context for annual pollutant load observations. It is expected that the Nevada MOA will include stormwater monitoring goals similar to the requirements within the draft CA NPDES permit (J. Kuchnicki, NDEP pers comm). The technical approach and methodology below aligns with the draft NPDES permit requirements, while also providing relevant datasets to explore how stormwater data would be used to evaluate TMDL effectiveness, provide stormwater quality status and trend indicator tracking (per the M&E Program), and inform load reduction progress under the Crediting Program. Method details and technical considerations are provided below: 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 4 ESTIMATED STORMWATER TIME SERIES (WY1989‐WY2011) Stormwater quality data is extremely costly to obtain, manage and analyze. The research team believes that most effective and useful Technical Applications Guide will include a clear step‐by‐step data management, analysis and reporting procedure using a long‐term water quality dataset; however, at this time, such a dataset does not exist. One alternative is to produce a synthetic dataset for which various data analysis and reporting approaches can be demonstrated. However, the research team believes leveraging existing data and tools to estimate what the past stormwater water quality time series was for actual urban catchments may have value, beyond illustrating data analysis techniques. The research team therefore will estimate 22‐year urban stormwater quality datasets for 4 priority urban catchments, extending from WY1989 to WY2011 using the best available meteorology, hydrology and water quality data and the process outlined in Figure 2. The research team will coordinate with SWQIC and RSWMP to analyze catchments that local jurisdictions will target for stormwater monitoring to meet their NPDES permit requirements or MOA goals. Seasonal urban stormwater volumes and pollutant concentrations are assumed to respond to land use type, distribution, condition, and implementation of water quality improvement actions. Known changes in catchment condition during key time periods between WY1989‐WY2011 will be explicitly incorporated into PLRM accordingly based on land‐use and implementation action chronologies compiled by the research team for each catchment. The PLRM hydrology module, with SnoTel station meteorology as inputs, will be used to predict past seasonal catchment volumes from WY1989‐WY2011 for each of the selected catchments. PLRM catchment characteristic runoff concentrations (CRCs) for each time period will be compared to available existing stormwater quality data to estimate the seasonal pollutant concentrations for the four catchment sites from WY1989‐2011. Since the study catchments will not necessarily have adequate existing stormwater quality data available, we will assess the feasibility of regionalizing existing water quality datasets to estimate pollutant loads in the study catchments for key time periods. Catchment characteristics relevant to the regionalization analysis include proximity, size, land use distribution, and catchment morphology. These characteristics will be combined using a data‐based multivariate modeling approach such as Spatial Regressions on Watershed Attributes (SPARROW) (http://water.usgs.gov/nawqa/sparrow/). The seasonal catchment volumes and estimated average seasonal pollutant concentrations will be used to create estimated pollutant seasonal load time series from WY1989‐
WY2011 for each of the 4 urban catchments. The estimated stormwater quality dataset will be used to evaluate various data analysis alternatives, a process that will inform which data formats enable managers to best use stormwater datasets to draw meaningful conclusions. The preferred approaches will be documented in the Technical Applications Guide using the estimated time series examples to improve communication of the recommended procedures. Given the potential uncertainties associated with the estimated historic time series (WY1989‐2011), the results of these analyses will not be used to infer changes in water quality. However, future application to compare the estimated stormwater quality pre‐
TMDL may prove to be valuable once many years of post‐TMDL implementation data is available (circa 2022). TECHNICAL APPLICATIONS GUIDE DEVELOPMENT Data management: Stormwater data obtained from RSWMP or other future research efforts should conform to standardized data formats (such those maintained by the California Surface Water Ambient Monitoring Program [SWAMP]) and data management structure, such that disparate data can be efficiently integrated, shared, and understood by data users. Recommendations to guide the management of raw stormwater hydrology and water quality data may include data formats, data processing and storage protocols, and appropriate calculations to ensure consistency of the QA/QC’d datasets. 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 5 Data outputs to inform management questions: Data output formats identified in the Technical Applications Guide will be expressly developed for efficient use in resource management decision making. The priority management questions will be drafted and vetted with Basin managers, jurisdictional representatives, and regulators via SWQIC to ensure the researchers gain feedback on the management priorities, but some potential applications of this research are provided below. Status and trend analysis and reporting: Stormwater data analysis will include status and trends reporting that will be applicable throughout the Tahoe Basin. Reporting will consider typical statistical metrics of concentration and loads (mean/median seasonal values, upper 25th percentile values, water quality standard exceedance frequencies, and standard deviations) with a particular focus on stormwater quality changes over time. Surface water pollutant loading patterns are typically strongly dependent on rainfall and related streamflow patterns. This hydrologic variability can often overwhelm the signal of a pollutant loading or concentration change over time that is due to water quality improvement actions. By creating statistical models to quantify sources of variability (e.g., streamflow or urban snowmelt patterns) that may mask the signal of changes that are of most interest , we can remove it from a data time series and improve our ability to detect pollutant loading changes over time that are a direct result of water quality improvement actions. Two basic types of trends that can be tested are step‐trends and monotonic (continuous, unidirectional) trends. The step‐trend (Figure 3) tests the hypothesis that data collected before a specific time are significantly different from those collected at another time, such as before or after specific management action milestones provided in the TMDL (LRWQCB and NDEP 2010), where we have an a‐priori hypothesis about the time that a change occurred (Hirsch et al., 1982).When no a‐priori hypothesis exists, such as when several management actions have been implemented within a catchment over a period of years, testing for a monotonic trend is more appropriate. Water quality data are commonly skewed resulting in non‐normal distributions of residuals (Hirsch et al. 1982), and non‐
parametric procedures, such as the Mann‐Kendall test, are a powerful technique to detect a continuous water quality trend (Helsel and Hirsch 1988; Hirsch et al. 1991). This research would employ the seasonal Mann‐Kendall test (Helsel and Hirsch 2002), which adjusts to account for seasonal variability and concentrations below the analytical detection limits. Figure 4 illustrates components of the trend detection approach using a stormwater quality time series. The NDPES permit and MOA monitoring requirements can facilitate generation of a consistent and long‐term dataset by jurisdictions (RSWMP) to confirm water quality improvements are measurable over time to evaluate TMDL progress. Similarly, the M&E Program requires stormwater quality indicators and metrics to report status and trends (www.tahoemonitoring.org). While these programmatic objectives are slightly different, the approach and considerations of data synthesis, reporting and analysis are complementary. Meeting these objectives will provide managers with a succinct view of the presence, directionality, magnitude, and confidence of water quality changes over time and form the foundation for testing hypotheses about water quality improvement effectiveness. PLRM water year predictions: The Crediting Program (LRWQCB and NDEP 2011) includes agreements, called Catchment Credit Schedules, between regulators and jurisdictions that define the amount of load reduction credit awarded for implementation of specific water quality improvement actions in defined urban catchments. The annual credit amount is based upon the average annual pollutant (FSP) load reduction associated with the implemented and planned actions estimated using PLRM. With an appropriate method, RSWMP data collected in catchments included in the Crediting Program can be compared with PLRM predicted seasonal volumes and loads at same location. The PLRM uses historical meteorological data from 1989‐2006 to conduct long‐term continuous simulations with output summarized as average annual performance estimates. Figure 5A illustrates the inherent differences in 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 6 temporal resolution between typical PLRM average annual outputs and measured seasonal loads that create a technical limitation for the stormwater community hoping to compare stormwater measurements with PLRM outputs. A section of the Technical Applications Guide will provide standardized guidance for others (with appropriate hydrologic modeling experience) to generate PLRM results on event, seasonal and annual time steps using meteorologic input data corresponding to stormwater sampling periods. This will allow seasonal comparisons between the measured data and modeled outputs, as presented in Figure 5B. Once a number of years of stormwater and PLRM water year estimates are available, performance summary metrics can be calculated to alert managers of different types of deviation of PLRM from measured data. These metric may include high flow representativeness (Nash‐Suttcliffe Efficiency), low flow representativeness (root mean squared error), or systematic offsets (bias). Such comparisons will help to identify catchment‐specific deficiencies of PLRM relative to various components of the hydrograph. Figure 5B illustrates an example of a substantial systematic offset throughout the pollutant loading profile, such that the overall positive bias results from consistent under‐
prediction of pollutant loads by PLRM. Other hypothetical comparison scenarios may include seasonal deficiencies where PLRM may estimate low flow seasons well but display poorer performance during spring runoff, over‐
estimations, etc. Guidelines for how to compare and interpret graphical plots, along with statistical performance summary metrics, will inform managers as to the differences between measured and modeled loads. This information can be used to guide future decisions regarding clarity credits, load reduction targets or other programmatic issues. Strategies for tool modifications: Measured stormwater quality data can be used to identify improvements and modifications to the stormwater tools supporting the Crediting Program, namely PLRM, Road RAM (2N et al. 2010) and BMP RAM (2N et al. 2009). However, currently there is a lack of guidance as to what datasets are necessary, at what resolution verification of the tools is adequate and how to demonstrate the need for modifications. The research team has just initiated a PLRM calibration study for one water year on two urban catchments (SNPLMA 2011), and is also conducting other detailed verification of PLRM and Road RAM using other sources of funding. The research team will summarize the lessons learned from these studies to provide instructions that align PLRM predictions and measured data to illustrate the utility and deficiencies of PLRM for testing hypotheses at temporal/spatial scales relevant to management needs. F. RELATIONSHIP OF RESEARCH TO CURRENT EFFORTS The proposed research will inform and/or build upon a variety of programmatic, supporting tools and data collection efforts within the Tahoe Basin that extend from the TMDL, Crediting Program, Tahoe Status and Trend M&E Program and TMDL Management System to the PLRM, BMP RAM, Road RAM, and RSWMP. G. ENGAGING MANAGERS The team will engage managers via SWQIC to ensure collaboration with regulators, stormwater managers, and agency personnel. Engagement will include a number of meetings with smaller technical groups to solicit suggestions, management needs and technical direction. Three (3) formal meetings will be conducted at SWQIC to 1) inform the prioritization of management needs from the future stormwater quality datasets, 2) gain feedback on how well alternative analysis and reporting techniques may meet management objectives, and 3) discuss comments generated from the review of draft products. 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 7 H. DELIVERABLES/PRODUCTS TASK 1. SELECT CATCHMENTS AND DEVELOP WATER QUALITY DATASETS The team will coordinate with the jurisdictional representatives designing RSWMP data collection to select four priority catchments that will be monitored into the future. Available meteorological, land use, EIP project implementation, and stormwater quality datasets from 1989‐2011 will be summarized to determine applicability to the selected catchments, pollutants and time periods. PLRM simulations, using the SWMM interface and post processing, will be run to estimate seasonal volumes at the catchment outlet and compared to existing water quality datasets to estimate seasonal concentrations for each pollutant of concern. All assumptions, calculations and justification of the final data values will be documented. Deliverables: Seasonal stormwater quality and hydrology values for 4 priority urban catchments TASK 2. CONDUCT ALTERNATIVES ANALYSIS SWQIC members will be solicited to guide the prioritization of management questions and desired applications of stormwater datasets on both short (monthly) and long (multiple years) time scales. The technical team will utilize the available stormwater time series to illustrate the process and outputs of a number of analysis alternatives. Each alternative will include a technical description of the approach; temporal and spatial data requirements; relative complexity of guidance required; ability of others to consistently replicate the analysis; advantages; limitations; remaining data gaps; and the power and confidence to meet specific management questions. The technical team and SWQIC will collaborate to identify the priority stormwater indicators and reporting outputs to ensure stormwater datasets are accessible to inform priority management objectives. Deliverables: Data analysis and reporting alternatives analysis TASK 3. DEVELOP STORMWATER TECHNICAL APPLICATIONS GUIDE The research team will provide the necessary information to standardize the data synthesis, analysis and reporting methods using the 4 estimated datasets. The two‐tiered guidance approach will include (1) a detailed technical approach section that explains the theoretical background, key assumptions, and considerations of the statistical procedures involved, and (2) the step by step Technical Applications Guide for efficient stormwater data analysis and PLRM applications. The Technical Applications Guide will allow analysts and managers to produce the relevant reporting metrics and graphical displays to communicate regional stormwater trends, address management objectives, and inform adaptive management. The document will contain descriptions of all methods in detail adequate for others to integrate future datasets and reproduce analyses. The data reporting information and interpretation approaches necessary to meet each management objective will be clearly documented. Deliverables: Technical Applications Guide and supporting technical approach summary TASK 4. PROJECT ADMINISTRATION AND MEETINGS The team will engage SWQIC on three occasions at critical milestones outlined in the research schedule below. Each engagement will include a presentation of information and solicitation of feedback to direct the subsequent research and development of the technical guidance document. Numerous informal interactions and feedback solicitation will be sought from key regulators, funders, jurisdictions, and technical reviewers to ensure the recommendations meet priority needs. Project administration, invoicing and quarterly progress reports will be ongoing. 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 8 III. SCHEDULE OF MAJOR MILESTONES/DELIVERABLES Project schedule provided below is based on contract award by August 2012. Task Task 4. SWQIC Meeting #1: Present overview of research, coordinate collaboration process and contacts, select catchments Task 1. Identify, compile, and analyze existing stormwater datasets, land use and project implementation data, etc for selected catchments Task 1. Create PLRM models, estimate historic hydrology and water quality and create seasonal time‐series datasets (1989‐2011) Task 4. SWQIC Meeting #2: Review Task 1 results, identify priority management objectives of stormwater datasets Start Month Task 2. Alternative analysis development March 2013 Task 4. SWQIC Meeting #3: Alternatives analysis workshop. Determine priority data management, analysis and reporting formats to achieve management objectives. Task 3. Technical Guidance Document Task 4. Quarterly SNPLMA progress reports End Month
August 2012 Aug 2012 Dec 2012 Dec 2012 March 2013 March 2013 June 2013 June 2013 October 2013 Completed and submitted to USFS quarterly: Jan 1, April 1 July 1, Oct 1. 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 9 IV. LITERATURE CITED 2NDNATURE (2N). 2006a. Detention Basin Treatment of Hydrocarbon Compounds in Urban Stormwater. Final Report. Prepared for South Tahoe Public Utility District. March 17, 2006. 2N. 2006b. Lake Tahoe BMP Monitoring Evaluation Process: Synthesis of Existing Research. Final Report. Prepared for USFS Lake Tahoe Basin Management Unit. October 2006. 2N. 2007. Water Quality Evaluation of a Fertilized Turf Surface in the Lake Tahoe Basin (2002‐2006). Final Report. Prepared for Nevada Tahoe Conservation District. April 2007. 2N. 2008. Water Quality Performance Evaluation of Park Avenue Detention Basins; South Lake Tahoe, CA. Final Technical Report. Prepared for City of South Lake Tahoe Engineering Division. August 2008. 2N and Northwest Hydraulic Consultants (NHC). 2010. Focused Stormwater Monitoring to Validate Water Quality Source Control and Treatment Assumptions. Final Technical Report. Prepared for US Army Corps of Engineers, Sacramento District. March 2010. 2N, NHC, and Environmental Incentives (EI). 2009. Best Management Practice Maintenance Rapid Assessment Methodology (BMP RAM) Technical Document, Tahoe Basin. Final Document. Prepared for US Army Corps of Engineers, Sacramento District. September 2009. Documentation, users manual and tool available for download at http://www.swrcb.ca.gov/rwqcb6/water_issues/programs/tmdl/lake_tahoe/index.shtml 2N, NHC, and EI. 2010. Road Rapid Assessment Methodology (Road RAM) Technical Document, Tahoe Basin. Final Technical Report. Prepared for California Tahoe Conservancy and Nevada Division of Environmental Protection. November 2010. Documentation and users manual available for download at http://ndep.nv.gov/bwqp/tahoe8.htm Desert Research Institute (DRI) and UC Davis. 2011a. Data Quality Objectives (DQO) Version 1.4 Tahoe Regional Stormwater Monitoring Program (RSWMP). May 10, 2011. DRI and UC Davis. 2011b. Sampling and Analysis Plan (SAP) Version 1.4 Tahoe Regional Stormwater Monitoring Program (RSWMP). May 10, 2011. DRI and UC Davis. 2011c. Quality Assurance Project Plan (QAPP) Version 1.4 Tahoe Regional Stormwater Monitoring Program (RSWMP). May 10, 2011. Gunther, M.K. 2005. Characterization of nutrient and suspended sediment concentrations in stormwater runoff in the Lake Tahoe basin. MS Thesis, University of Nevada at Reno. Helsel, R. D. and R.M. Hirsch. 1988. “Applicability of the t‐test for detecting trends in water quality” by Robert H. Montgomery and Jim C. Loftis. Journal of American Water Resources Association 24 (1), 201–204. Helsel, D.R. and R.M. Hirsch. 2002. Statistical Methods in Water Resources Techniques of Water Resources Investigations, Book 4, Chapter A3. U.S. Geological Survey. 522 pp. Heyvaert, A., J. Reuter, J. Thomas, and J. Kuchnicki. 2007. Tahoe TMDL Stormwater Monitoring, 2007 Assessment. Desert Research Institute and University of California‐Davis Tahoe Environmental Research Center. Hirsch, R.M., J.R. Slack, and R.A. Smith. 1982. Techniques or trend analysis for monthly water quality data. Water Resources Research 18:107‐121. Hirsch, R.M., R.B. Alexander, and R.A Smith. 1991. Selection of methods for detection and estimation of trends in water quality. Water Resources Research 27: 803‐813. 2NDNATURE, LLC
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2NDNATURE Proposal: Tahoe Research Supported by SNPLMA Round 12 Theme 2b: Quantifying the benefits of urban storm water management p. 10 Lahontan Regional Water Quality Control Board (LRWQCB) and Nevada Division of Environmental Protection (NDEP). 2010. Lake Tahoe Total Maximum Daily Load, Technical Report. California and Nevada. June 2010. Available at http://www.swrcb.ca.gov/rwqcb6/water_issues/programs/tmdl/lake_tahoe/index.shtml LRWQCB and NDEP. 2011. Lake Clarity Crediting Program Handbook for Tahoe Basin TMDL Implementation v1.0. Prepared by Environmental Incentives, LLC. South Tahoe Basin, CA. September 2011. Available at http://www.swrcb.ca.gov/rwqcb6/water_issues/programs/tmdl/lake_tahoe/index.shtml LRWQCB. 2011. Updated waste discharge requirements and National Pollutant Discharge Elimination System (NPDES) permit for stormwater/urban runoff dischargers from El Dorado County, Placer County and the City of South Lake Tahoe within the Lake Tahoe hydrologic unit. http://www.waterboards.ca.gov/lahontan/water_issues/programs/tmdl/lake_tahoe/docs/prpsd_drft.pdf NHC, Geosyntec Consultants, and 2N. 2010. PLRM Model Development Document. Prepared for Tahoe Basin Storm Water Quality Improvement Committee. South Tahoe Basin, CA. October 2009. Complete documentation, users manual, and tool available for download from https://www.tiims.org. Swanson Hydrology and Geomorphology (SH+G). 2003. Assessment of Seasonal Pollutant Loading and Removal Efficiency in Detention Basins. Final Technical Report. Prepared for Tahoe Regional Planning Agency and the US Environmental Protection Agency. February 2003. 2NDNATURE, LLC
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Tahoe Basin urban areas denoted in red.
Area of
interest
Lake
Tahoe
Catchment Outfall
LEGEND
LEGEND
Hypothetical Catchment
Tahoe Basin Urban Area
Figure 1: Tahoe Basin Urban Area and Hypothetical Urban
Catchment Site Location Map
Feet
0
300 600
1,200
PROCESS TO ESTIMATE HISTORIC SEASONAL STORMWATER DATA FROM 4 URBAN CATCHMENTS
Diagram summarizes the process used to generate estimated stormwater quality datasets for 4 urban catchments where
RSWMP monitoring is planned in the future. The primary objective of these datasets is to provide reasonable stormwater
data to illustrate potential data analysis alternatives with respect to site-specific and basin-wide stormwater assessments.
Data Management Process Flow
A. Continuous
Meteorology Data
(WY1989-2011)
DATA SOURCE: SnoTel, pre-processed for PLRM
DATA: Precipitation (in), Temperature (oC)
APPLICATION TO INFORM FUTURE RSWMP MONITORING & ANALYSIS:
• Weather Station Instrumentation (proximity to catchment, temporal resolution, parameters,
etc.);
• Water Year Type Determination (dry, average, wet, etc.).
B. Chronology of
Catchment Land-use
Changes
DATA SOURCES: Jurisdiction WQIP Records, Aerial Photographs, GIS
DATA: Changes in % Impervious Coverage, Land Use Distribution, Infiltration, Stormwater Routing
(i.e., Curb and Gutter), etc.
POTENTIAL ISSUES:
• Recreating timeline accurately given potential for incomplete data records;
• Historic data likely not recorded in manner consistent with PLRM inputs.
C. Catchment
Hydrologic Simulation
(WY1989-2011)
D. Applicable Measured
WQ Data & Timeline of
Catchment Changes
Affecting Water Quality
E. Catchment WY and
Seasonal Loads (kg)
(WY1989-2011)
DATA SOURCE: PLRM Continuous Hydrologic Simulations using SWMM
DATA: Seasonal Volumes (cf), WY1989-2011.
DATA SOURCES: LTIMP; Previous WQ Studies1
DATA: Representative Seasonal Concentrations (mg/L) for TSS, FSP, turbidity, TN, TKN, DIN, NO3, TP,
PP, DP, SRP, etc. as appropriate to align with management objectives
POTENTIAL DATA MANAGEMENT ISSUES:
• Lack of historic FSP data2;
• Need to apply best available data.
DATA SOURCES: Product of Seasonal Volumes and Pollutant Concentrations determined above
DATA: Seasonal Pollutant Loads (kg), WY1989-2011.
1
Potential Sources: 2N 2006A, 2006B, 2007, 2008; 2N and NHC 2010, 2011; Gunther 2005; SH+G 2003;
Heyvaert et al. 2007
2
Based on previous research (2N et al. 2010; Gunther 2005; LRWQCB and NDEP 2010) there is potential to
relate FSP to existing TSS and turbidity datasets.
PROCESS FOR CREATING ESTIMATED HISTORIC CATCHMENT DATASET
FIGURE 2
DETECTING STEP TRENDS
The Technical Applications Guide will provide examples using the historic estimated dataset to illustrate status and trend
analysis and reporting. One potential type of analysis is step trends, which is used when there is a hypothesis that the
implementation of a specific action at a known time has had a statistically significant effect on the water quality conditions.
The pre- and post- action implementation data sample distributions can be compared using a number of statistical tests to
determine whether or not a change has occurred.
Comparison of Pre- and Post-Action Implementation Load Distributions
Pre Action
Implementation
% of Observations
Post Action
Implementation
Increasing Catchment FSP Load (lb)
The example above provides a hypothetical comparison of FSP load distributions measured at
a catchment outfall prior to the implementation of pollutant recovery actions (green line) and
following implementation (olive line). The graph shows a shift of the sample data distribution for
the post-implementation samples, suggesting that the implementation of those actions within the
catchment led to a significant decrease in overall FSP loads leaving the catchment.
EXAMPLE ILLUSTRATING STEP TREND ANALYSIS
FIGURE 3
DETECTING CONTINUOUS TRENDS
Another potential type of analysis is monotonic trends, which can be used to evaluate the long-term trends in the continuous
dataset. This analysis method can be useful when examining the cumulative effect of a range of water quality improvement
actions that have been implemented over an extended period of time.
A. Time series of Catchment Pollutant Loads and Climatic Variability
Time period of action implementation
ClimaticVariability
Catchment FSP Load (lb)
Climate driven source of variability
(i.e., precipitation, snowpack, etc.)
FSPLoads
Time
Hypothetical time series plot of catchment pollutant loading along with a generic source of climatic variability. There are no
discernible trends in FSP loads following the implementation of pollutant reduction actions in the catchment.
B. Dataset with unwanted sources of variability removed to detect a continuous trend
Catchment FSP Load (lb)
Time period of action implementation
Detectable decreasing trend in catchment
pollutant load following initiation of water
quality improvement actions
Time
Hypothetical catchment pollutant load time series after applying a statistical model (e.g., LOWESS smoothing) to remove the
variability associated with unwanted inter-annual and seasonal effects (e.g., precipitation variations, snowpack depths, etc.) for
detection of a continuous trend. A clear decreasing trend is visible following the implementation of pollutant reduction actions.
Trend reporting metrics include:
• Confidence level (95%)
• Directionality of Change (↓)
• Rate of Change (3.0 lb/year)
• Total Change Over 25 years (75 lb)
EXAMPLE OF MONOTONIC TREND ANALYSIS
FIGURE 4
USING FUTURE RSWMP MEASURED CATCHMENT DATASET TO INFORM MANAGEMENT DECISIONS
A. Current Issue Comparing Measured Data to PLRM Predictions
180
Catchment FSP Load (lb)
160
PLRMexpectedaverageannual(lb/year)
MeasuredSeasonalLoad(lb/season)
140
120
100
80
60
40
20
0
2014
2024
Time
PLRM predicts an expected average annual catchment FSP load (grey line above) based on user inputs that include stormwater
quality improvement actions to reduce pollutant loads. RSWMP monitoring data will result in FSP loads calculated from field
measurements (green triangles).
DATA ANALYSIS NEED: Given the differing resolutions between PLRM and measured data, how can we compare the two?
B. Potential Future Technical Guidance for Comparisons of Measured Data to Predicted Values
300
PLRMPredictedWY
MeasuredWY
Catchment FSP Load (lb)
250
n
Bias =
200
1-
∑Pi
i
n
∑Mi
i
150
where P is the PLRM predicted
value, and M is the value calculated
from field measurements and i is
the time step used for both.
100
50
0
Time
Using measured meteorologic data, PLRM can be used to estimate predicted seasonal or annual catchment FSP loads (red line)
which can be compared to measured data (green line) on an annual or seasonal basis.
DATA ANALYSIS NEED: How can we evaluate differences between PLRM predicted loads and those based on field
measurements to improve urban stormwater tools and inform the Crediting Program, Tahoe TMDL, etc?
Statistical performance summary metrics can be calculated to indicate the nature of the deviation of measured observations
from PLRM outputs. Metrics include quantifying the magnitude of systematic offsets (e.g., bias) or the performance in different
flow conditions. In the example above, consistent under prediction by PLRM is concisely summarized as a positive bias (equation
provided above) to express an important deviation of PLRM from loads calculated using field based data.
HYPOTHETICAL APPLICATION OF RSWMP DATA TO
INFORM MANAGEMENT DECISIONS
FIGURE 5