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
Table 1: Potential data sources for driving variables of interest.
Why is this important?
Data Source
 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 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
 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)
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
Model Building Process:



Catchment
Reach
Driving Variable of Interest
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
unit
particle size, soil pH, cation exchange capacity, calcium carbonate, permeability, organic matter, erosion factor, geologic
SSURGO
unit
USGS NHD Data
drainage density in catchment, Strahler stream order, sinuosity, network relationships and distances
PRISM Data
mean annual or monthly precipiation and air temperature
EPA Geology
geologic unit
USGS NLCD
landcover, landuse types, riparian vegetation
NDIS Riparian Vegetation
riparian vegetation
National Inventory of Dams
1652 Dam locations within Colorado
USGS MASMILS
abandoned hardrock mines
Figure 1: Hierarchical stream structure and
processes found in natural watersheds.
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
Figure 2: Study area and R-EMAP sample site locations.
BMI
B
CO Regional Environmental Monitoring and Assessment Program
(R-EMAP):
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
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.
A
C

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)

Electrofishing during EMAP
sampling (Dave Theobald,
2003)

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
Model development, spatial analysis, model building, and landscape evaluation:
Expected Results:
Identify Important
Reach Scale
Habitat Conditions
Develop
Conceptual
Model
Spatial
Analysis
Develop
Statistical
Models
Model
Evaluation
Landscape
Evaluation
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
A typical stream in the Southern Rocky Mountains.
Source: http://water.usgs.gov/pubs/FS/fs122-97/html/photo2.htm
Evaluate model results and make changes to the conceptual model if necessary
Model and Landscape Evaluation
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
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.
available GIS datasets (Table 1)
 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
References:
1.
2.
3.
4.
Clean Water Act. 303(d). P.L. 92-500. 72.
Colorado Division of Minerals and Geology. Inactive Mine Reclamation Program. 2003.
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.
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.
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