1. 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 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) A New Approach:  Stream segments can be measured at a variety of scales and are hierarchical in nature (Frissell et al., 1986) (Figure 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 benthic macroinvertebrates (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 Methodology: Study Area (Figure 2):  Located within Omernik’s Southern Rockies Ecoregion  Approximately 48,550 km 2 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  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) Objectives: 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:  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. Model Development:  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 driving variables for each of the reach scale habitat conditions, which are based upon ecological knowledge and the literature  Evaluate driving variables to determine whether information can be extracted or calculated using readily available GIS datasets (Table 1) Catchment Reach BMI Figure 1: Hierarchical stream structure and processes found in natural watersheds. Reach Conditions of Interest:  Heavy metal concentration and water hardness  Substrate composition  Water temperature  Percent pool and riffle  Dissolved oxygen concentration  Width to depth ratio  Water velocity Model Building Process:  10-fold cross validation will be used to resample the data and to estimate the error in the models  Spatial interpolation, such as kriging, will be used to predict the error in each model  A distance measure that captures the unique relationship between two points in a stream network will be developed for use in the spatial interpolation (personal communication Theobald, 2003) (Figure 3)  uses the network distance between two points rather than Euclidean distance across the landscape and takes direction into consideration The Advantages:  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 A B C Figure 3: Network and distance relationships. In this 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. Expected Results:  A statistical model will be produced using readily available GIS datasets and the CO 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 Spatial Analysis:  Perform spatial analysis on GIS datasets to produce digitally derived driving variables  Statistical: standard deviation of elevation within the catchment  Topological: slope and aspect of catchment, stream gradient  Proximity: sample points within a specified distance of a road  Overlay: extract landcover, landuse, and elevation data for catchments  Weighted distance of landuse to sample point  Calculate hydrologic distance along stream network  Evaluate model results and make changes to the conceptual model if necessary Model and Landscape Evaluation  If the model produces satisfactory results  it will be used to predict specific reach scale conditions at points that were not sampled  driving variables of significance will be changed within the catchment to determine where remedial action will have the most effect Table 1: Potential data sources for driving variables of interest. Model development, spatial analysis, model building, and landscape evaluation: Spatial Analysis Develop Statistical Models Identify Important Reach Scale Habitat Conditions Develop Conceptual Model Landscape Evaluation Model Evaluation 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. Figure 2: Study area and R-EMAP sample site locations. CO Regional Environmental Monitoring and Assessment Program (R-EMAP):  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  Goal: to determine whether increased metal concentrations were causing a decline in the biological integrity of the stream (USEPA, 1993) A typical stream in the Southern Rocky Mountains. Source: http://water.usgs.gov/pubs/FS/fs122-97/html/photo2.htm Electrofishing during EMAP sampling (Dave Theobald, 2003) 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. 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. 5. 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. 6. 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. 7. U.S. Environmental Protection Agency. Biological Indicators of Watershed Health: Design a Sampling Effort. 2002. 3. 8. 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. 9. 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. 10. USDA Forest Service. Southern Rocky Mountain Steppe--Open Woodland--Coniferous Forest--Alpine Meadow Province 2002.