Predicting the likelihood of water quality impaired stream reaches using landscape scale data and a hierarchical methodology:
A case study in the Southern Rocky Mountains.
Authors: Erin E. Poston 1, David M. Theobald 1, Melinda J. Laituri 2, N. Scott Urquhart 3
1 Natural Resource Ecology Laboratory
2 Department of Forest, Rangeland, and Watershed Stewardship
3 Statistics Department
Colorado State University
Fort Collins, Colorado
Why is this important?
 The Clean Water Act (CWA) (1972) requires states and tribes to identify water quality impaired stream segments, to create a priority
ranking of those segments, and to calculate the Total Maximum Daily Load (TMDL) for each impaired segment based upon chemical and
physical water quality standards (P.L. 92-500, 1972)
 Biological data, such as benthic macroinvertebrates (BMI) and fish, are used in conjunction with reach scale habitat data to evaluate stream
condition
Catchment
The Problem:
 It is impossible to physically sample every stream within a large area
 Too many stream segments
 Limited personnel
 Cost associated with sampling (USEPA, 2001)
Model development, spatial analysis, model building, and landscape evaluation
Identify Important
Reach Scale
Habitat Conditions
Develop
Conceptual
Model
Spatial
Analysis
Model
Development
Model
Evaluation
Landscape
Evaluation
Reach
BMI
Figure 1: Hierarchical stream structure and
processes found in natural watersheds.
A New Approach:
Conceptual Model Development:


 Stream segments can be measured at a variety of scales and are hierarchical in nature (Frissell et al., 1986)(Fig. 1)
 Each of the coarser scales is believed to constrain the finer scales to some degree
 Reach scale habitat is believed to act as a functional link (Lanka et al., 1987) between the catchment and BMI (Richards et al., 1996)
 We believe that it is possible to predict reach scale characteristics using landscape variables derived from geographical information system
(GIS) data for use as input into hierarchical classification systems
Create a list of reach scale habitat conditions shown to be important to BMI in the Southern Rockies Ecoregion
Develop the conceptual model
 Compile potential explanatory variables for each of the reach scale habitat conditions, which are based upon ecological
knowledge and the literature
 Evaluate explanatory variables to determine whether information can be extracted or calculated using readily available GIS
datasets (Table 1)
Table 1: Potential data sources for driving variables of interest.
The Advantages:
Data Source
 Focus field sampling efforts on potentially impaired sites, making additional resources available for the
TMDL calculation for a specific segment
 Derive an estimate of regional stream condition using a hierarchical classification system that includes
landscape, reach, and BMI data housed in a GIS
 Every stream segment and catchment within the state could be sampled
USGS NHD Data
Driving Variable of Interest
network relationships and hydrologic distances, drainage density in catchment, Strahler stream order, sinuosity
elevation, channel gradient, slope, aspect, catchment area, stream gradient, standard deviation of elevation w/in
Digital Elevation Model (DEM) catchment, landform index, weighted distances from landuse, solar radiation inputs
particle size, soil pH, cation exchange capacity, calcium carbonate, permeability, organic matter, erosion factor, geologic
STATSGO or SSURGO
unit
PRISM Data
mean annual or monthly precipiation and air temperature
Objectives:
EPA Geology
geologic unit
USGS NLCD
landcover, landuse types, riparian vegetation
A GIS-based model will be used to mimic the hierarchical stream structure and processes found in natural watersheds. Specifically, the
relationship between landscape variables and reach scale habitat conditions most influential to BMI found in the in the southern Rocky
Mountains of Colorado will be explored. The hypotheses are the following:
NDIS Riparian Vegetation
riparian vegetation
National Inventory of Dams
1652 dam locations within Colorado
USGS MASMILS
abandoned hardrock mines
 Coarse-scale landscape variables such as catchment area, landuse, and geology can be used to predict the hydrologic, chemical, and
physical habitat conditions of stream reaches.
 Finer scale data will increase the precision of predicted reach scale habitat conditions.
 A model developed to predict specific reach scale habitat conditions can be used to test management alternatives within the catchment
to determine where remedial action will have the most effect.
Reach Conditions of Interest



Heavy metal concentration
Water hardness
Water temperature?



Dissolved oxygen concentration
Nitrogen
Phosphorus
Methodology
Study Area (Fig. 2):




Located within Omernik’s Southern Rockies Ecoregion
Approximately 48,550 km2 in size (Jones et al., 2002)
Elevation: 1600 to 4400 meters (Jones et al., 2002)
Elevational banding in temperature and precipitation (USEPA, 2002)
 Patterns of microclimate resulting from aspect
Figure 2: Study area and R-EMAP sample site locations.
 Vegetative patterns due to differences in elevation, latitude,
direction of prevailing winds, and slope exposure
(USDAFS, 2002)
 Predominant landuses: grazing and mining
• 12 active mines exist in Colorado today
• Approximately 22,000 abandoned mines (Colorado Division
of Minerals and Geology)
Programmatically calculate hydrologic distance
• Written in Visual Basic for Applications (VBA) using
ArcObjects and ArcGIS version 8.3
• Input Data: National Hydrography Dataset (NHD) and
R-EMAP sample sites
• Set flow direction  NHD segments digitized
against flow
• Geometric network tracing functions
• Find Path and Upstream Trace
• Automation = more efficient for large datasets
• Flexible output: contains upstream, downstream, and
total hydrologic distance between sample sites
• Allows user to determine functional relationship
between sample points and then use appropriate
distance measure (Fig. 3)
• Output:
• NxN distance matrix used in spatial interpolation
• Format: comma delimited text file
• Compatible with statistics software



 Biological, chemical, and physical data collected in 1994 and 1995 by the
Environmental Protection Agency (EPA)
 86 second, third, and fourth order streams sampled during low flow periods
between late July and early September
 73 sites were randomly selected
 Additional 13 non-randomly selected sites were located either
upstream or downstream from mines
Reaches (segments)
HCA boundaries
Figure 4: HCA boundaries and NHD stream segments.
0.5612
C
0.8018
A
1.0
0.1982
1.0
0.3251
0.6749
B
1.0
Edge proportional influence
Sample point
Stream network

Input data: Cumulative catchment attributes and R-EMAP sample points
10-fold cross validation will be used to resample the data
• Iterative process used to resample small data sets (Reich et al., 2004)
Model Selection
• The Spatial Corrected Akaike Information Criterion (AICC) will be used to evaluate models (Hoeting et al., In Press)
• Simultaneously selects explanatory variables and fits a covariance function to the residuals
• Advantage: does not ignore spatial correlation in the selection of explanatory variables
• AICC measures the Kullback-Leibler distance (Hilborn and Mangel, 1997)
• Amount of information that is lost by using the candidate model to approximate the truth
• Optimization criterion: measures goodness of fit in the data and penalizes the model for each additional parameter
(Hilborn and Mangel, 1997)
• The Universal Kriging algorithm will be replaced with a directional kriging algorithm (Ver Hoef et al., In Press)
• Moving average model for stream networks
• Based on hydrologic distance and weighted by flow
Make changes to the conceptual model if necessary
B
A
C
Figure 3: Network and distance relationships. In a flow
dependent example, points A and B are neighbors to C, but C is
not a neighbor to either A or B. In addition, points A and B are
not neighbors to each other. Although the Euclidean distance
between points A and C is shorter than that of B and C, the
network distance between B and C is actually much shorter.
Model and Landscape Evaluation
 Predict specific reach scale conditions at points that were not sampled
 Mean square prediction error (MSPE) will be used to evaluate the predictive performance of the model (Hoeting et al., In Press)
• Measure of average difference between model predictions and observed values
 Explanatory variables of significance will be changed within the catchment to determine where remedial action will have the most effect
Expected Results:




Colorado Regional Environmental Monitoring and Assessment Program (R-EMAP):
0.4312
Figure 5: Calculating proportional influences.
Purpose: produce digitally derived data for input into spatial models
 Data Requirements:
• Easily accessible
• National coverage
• Low cost or FREE!

Proportional influence calculations
AC = 0.3251 * 0.8018 * 1.0
BC = 0.6749 * 0.8018 * 1.0
Model Development and Evaluation:
 Create hydrologic contributing areas (HCAs) for each
stream segment
• Methods and VBA program developed by
David M. Theobald and Mary Kneeland
• Input Data: NHD waterbodies and reaches, DEM,
and flowdirection grid
• “Grows” contributing areas away from each stream
segment using the WATERSHED command
• “Bumps” HCA boundary at each iteration
• Prevents boundary delineation at slight
depression in DEM
• Output: overland hydrologic contributing area for
each NHD segment
Electrofishing during EMAP sampling
(Theobald, 2003)
 Proportional Influence
• Written in VBA using ArcObjects and ArcGIS version 8.3
• Proportional influence = influence of neighboring sample sites
on another sample site
• Weighted by cumulative catchment area
• Surrogate for flow
• Flow dependent
• Calculate proportional influence for every edge
• Influence of each upstream segment on segment directly
downstream
• Find path between sample points
• Calculate proportional influence (Fig. 5)
• Product of edge proportional influences
• Output: weighted incidence matrix for spatial interpolation
Spatial Analysis:
Dataset:
 Goal of R-EMAP study : to determine whether increased metal concentrations were
causing a decline in the biological integrity of the stream (USEPA, 1993)
 Calculating digitally derived landscape metrics: Accumulating HCAs
• Written in VBA using ArcObjects and ArcGIS version 8.3
• Input Data
• Geometric network
• Created using R-EMAP sample sites and NHD data with HCA attributes contained as edge weights
• Accumulate HCA attributes downstream using geometric network
• IForwardStar and INetTopology provide access to logical network
• Cumulative catchment attribute = sum of upstream HCA attributes
• Output: cumulative catchment attributes stored in R-EMAP sample sites attribute table
• Used for input into spatial models
A statistical model will be produced using readily available GIS datasets and the Colorado R-EMAP dataset, which will predict a specific
reach scale condition at points which were not sampled
A map of the study area that shows the likelihood of water quality impairment for each stream segment
 Can be based on water quality standards or relative condition (low, medium, high)
A methodology will be developed, which illustrates how state agencies can accomplish spatial analysis using GIS data and CO R-EMAP
data
The model will also be used to test management alternatives within the catchment to determine where remedial action will have the most
effect
References:
1. Clean Water Act. 303(d). P.L. 92-500. 72.
2. Colorado Division of Minerals and Geology. Inactive Mine Reclamation Program. 2003.
3. Frissell, C.A., Liss, W.J., Warren, C.E., Hurley, M.D. (1986) A Hierarchical Framework for Stream Habitat Classification: Viewing Streams in a Watershed Context. Environmental Management, 10,
199-214.
4. Hilborn, R. and Mangel, M. (1997) The Ecological Detective: Confronting Models with Data. Princeton University Press, Princeton, New Jersey.
5. Hoeting, J.A., Davis, R.A., Merton, A.A. (In Press) Model Selection for Geostatistical Models. Ecological Applications
6. Jones, K.B., Heggem, D.T., Wade, T.G., Neale, A.C., Ebert, D.W., Nash, M.S., Mehaffey, M.H., Hermann, K.A., Selle, A.R., Sugustine, S., Goodman, I.A., Pedersen, J., Bolgrien, D., Viger, J.M.,
Chiang, D., Lin, C.J., Zhong, Y., Baker, J., Remortel, R.D. (2000) Assessing landscape condition relative to water resources in the Western United States: A strategic approach. Environmental
Monitoring and Assessment, 64, 227-245.
7. Lanka, R.P., Hurbert, W.A., Wesche, T.A. (1987) Relations of Geomorphology to Stream Habitat and Trout Standing Stock in Small Rocky Mountain Streams. Transactions of the American
Fisheries Society, 116, 21-28.
8. Reich, R.M., Lundquist, J.E., Bravo, V.A. International Journal of Wildland Fire 13(1): 119-129 2004. Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA
9. Richards, C., Johnson, L.B., Host, G.E. (1996) Landscape-scale influences on stream habitats and biota. Canadian Journal of Fisheries and Aquatic Science, 53, 295-311.
10. U.S. Environmental Protection Agency. Biological Indicators of Watershed Health: Design a Sampling Effort. 2002. 3.
11. U.S. Environmental Protection Agency. Regional Environmental Monitoring and Assessment Program. 93. Washington, D.C., U.S. Environmental Protection Agency, Office of Research and
Development.
12. U.S. Environmental Protection Agency. Survey Designs for Sampling Surface Water Condition in the West. Survey Designs for Sampling Surface Water Condition in the West. 2001. Washington,
DC , United States Environmental Protection Agency, Office of Research and Development. EMAP-West Communications.
13. USDA Forest Service. Southern Rocky Mountain Steppe--Open Woodland--Coniferous Forest--Alpine Meadow Province, 2002.
14. Ver Hoef, J.M., Poston, E.E., Theobald, D.M. Some new spatial statistical models for stream networks. In Press.
The work reported here was developed under the STAR Research Assistance Agreement CR-829095 awarded by the U.S. Environmental Protection Agency (EPA) to Colorado State University. This presentation has not been
formally reviewed by EPA. The views expressed here are solely those of the presenter and the STARMAP, the Program (s)he represents. EPA does not endorse any products or commercial services mentioned in this
presentation.
This research is funded by
U.S.EPA ・Science To Achieve
Results (STAR) Program
Cooperative
Agreement # CR - 829095