1. Predicting the likelihood of water quality impaired stream reaches using landscape scale data and a hierarchical methodology
Erin Peterson
Geosciences Department
Colorado State University Fort Collins, Colorado

2. Space-Time Aquatic Resources Modeling and Analysis Program
The work reported here was developed under STAR Research Assistance Agreements CR awarded by the U.S. Environmental Protection Agency (EPA) to Colorado State University. This presentation has not been formally reviewed by EPA. EPA does not endorse any products or commercial services mentioned in this presentation.

3. Overview Introduction Background Objective Methodology~
Introduction
Background
Objective
Methodology
Expected Results
Products

4. The Clean Water Act (CWA) 1972
Requires states and tribes to identify water quality impaired stream segments
Create a priority ranking of those segments
Calculate the Total Maximum Daily Load (TMDL) for each impaired segment based upon chemical and physical water quality standards (P.L , 1972)

5. Bioassessments using Benthic Macroinvertebrates (BMI)
Identify water quality impaired segments using BMI data
BMI are sensitive to hydrologic, chemical, and physical degradation at all life stages
Integrative approach:
Species structure, species abundance, or functional group composition reflects background, point, and non-point source pollution
Episodic events
Measure spatial and temporal changes in the hydrologic, chemical, and physical properties of the stream segment using one sample

6. Electrofishing during EMAP sampling (Theobald, 2003)
Too many streams
~ 35,000 miles of streams in Colorado
Limited personnel
CO WQ Control Division has ~ 100 employees
Cost of sampling is high
Rapid assessment is difficult
Bioassessments do not provide information about the specific source of degradation
Difficult to extrapolate local data to derive a regional estimate of watershed health
Too much natural variability found in streams
Lawsuits filed by environmental groups demanding that the requirements of the CWA be met
The Problem
Electrofishing during EMAP sampling (Theobald, 2003)

7. Hierarchical Stream Structure
Figure taken directly from Frissell and others (1986).

8. Hierarchical Processes and Models
Complex biological processes
Hierarchical in nature
Need to be modeled and analyzed at multiple scales to more fully comprehend the causal factors associated with stream degradation
Complex hierarchical models
Need reach scale inputs for the models
Usually collected as field samples
Need to know them everywhere
Landscape
Reach
BMI

9. Objective
Develop a spatial model that can be used to predict reach scale characteristics
Potential Uses:
Input into a hierarchical classification model
Substrate type
Used to identify water quality impaired stream segments
Heavy metal concentration

10. How is this different than other spatial models?
How is this different than other models used to predict reach scale characteristics?
Spatially explicit: Represents the hydrologic relationships between sample points
Hydrologic connectivity, flow direction, and flow volume
GIS and remotely sensed data, rather than field samples, as inputs
Free data, national coverage, easily accessible
How is this different than other spatial models?
Flow dependent hydrologic distance
Asymmetric kriging method for stream networks
Developed by Jay Ver Hoef and others (In Progress)
Spatial AICC Method
Developed by Hoeting and others (In Press)

11. Methodology Select dataset Conceptual model development
Spatial analysis to derive explanatory variables, hydrologic distances, and kriging weights
Exploratory analysis to determine whether spatial autocorrelation exists in the data
Model Development & Selection

12. Dataset Selection
Colorado Regional Environmental Monitoring and Assessment Program:
Distance between sample points is too great
Mean = km
4 pairs had distance < 7 km
Collected during different years
Spatial neighbors, but not temporal neighbors
Sample sites are not connected by flow
16 sites had upstream neighbors
1 site had > 1 upstream neighbor
Water chemistry datasets collected from stream networks with spatially dependent sample points are difficult to find!

13. What is a reasonable neighborhood?
How close do sample points need to be?
Does distance differ for different reach attributes?
Heavy Metals vs. Substrate vs. Bug IBI?
What is the correct distance measure for each reach attribute?
Euclidean distance?
Symmetrical hydrologic distance?
Asymmetrical hydrologic distance?
Something else?

14. Maryland Biological Stream Survey (MBSS) Data
Collected by the Maryland Department of Natural Resources in 1995, 1996, 1997
955 sites in 17 basins
All sites within a basin collected in same year
Stratified random sampling design
Variety of data collected
Chemical, BMI, fish, aquatic plants, amphibians and reptiles, and physical habitat data
6 chemical variables collected
pH, acid neutralizing capacity, water temperature, conductance, sulfate, nitrate-nitrogen, and dissolved organic carbon

15. Evaluate each chemical variable
405 of the 898 sites had upstream neighbors
1396 neighboring pairs
10.06 km, the minimum was 0.05 km, and the maximum was km
431 sites had hydrologic distance < 3 km
Evaluate each chemical variable
Determine which distance measure is most appropriate
Euclidean distance
Hydrologic distance
Asymmetric hydrologic distance
Determine within what distance spatial autocorrelation
occurs

16. Teck Cominco Alaska Incorporated Red Dog Mine Data
54 sample points collected in 1979
Pre-mining zinc concentrations (ppm) in stream sediments
Most points within 500 meters of neighboring points
Zinc values range from 5ppm to 1150 ppm

17. Red Dog Mine Data

18. Conceptual Model Development
Compile potential explanatory variables for reach scale habitat conditions
Based on ecological knowledge and the literature
Evaluate explanatory variables to determine whether information could be extracted or calculated using a GIS
Data Source
Explanatory Variable
USGS NHD
hydrologic distances, catchment areas, flow direction, Strahler stream order
DEM
catchment area, slope and aspect of catchment, σ of elevation in catchment
STATSGO or SSURGO
permeability, erosion factor, geologic unit, particle size, calcium carbonate
PRISM
mean annual or monthly precipitation
NLCD or LANDSAT
vegetative cover, landuse

19. Applying Spatial Statistical Models to Stream Networks
Distance measures for kriging along stream networks
Must represent the biological or ecological nature of the
dependent variable A B C
Distances and relationships are represented differently depending on the distance measure

20. Applying Spatial Statistical Models to Stream Networks
Distance measures for kriging along stream networks
Must represent the biological or ecological nature of the
dependent variable A B C
Distances and relationships are represented differently depending on the distance measure

21. Applying Spatial Statistical Models to Stream Networks
Distance measures for kriging along stream networks
Must represent the biological or ecological nature of the
dependent variable A B C
Distances and relationships are represented differently depending on the distance measure

22. Applying Spatial Statistical Models to Stream Networks
Distance measures for kriging along stream networks
Must represent the biological or ecological nature of the
dependent variable A B C
Distances and relationships are represented differently depending on the distance measure

23. Applying Spatial Statistical Models to Stream Networks
Distance measures for kriging along stream networks
Must represent the biological or ecological nature of the
dependent variable A B C
Distances and relationships are represented differently depending on the distance measure
Challenge:
Spatial Linear Model
Spatial autocovariance models developed for Euclidean distance may not be valid for stream distances

24. GIS Data for Asymmetric Kriging
Hydrologic distance matrix
Hydrologic distance between sample points
Incidence matrix
Weighted by catchment area
Surrogate for flow volume
Maintains flow dependent neighborhoods
Covariate representing distance from basin outlet
Observations

25. GIS Tools
Automated tools needed to extract data about hydrologic relationships between sample points did not exist!
Calculate hydrologic distance from point to point
Calculate hydrologic contributing areas (HCAs) for each stream segment
Accumulating HCAs: Calculate digitally derived catchment attributes from HCAs
Calculate proportional influence of one point on another
Represent flow dependent relationships
Convert GIS data to a format compatible with statistics software

26. Asymmetric Kriging for Stream Networks
Developed by Jay Ver Hoef, Alaska Department of Fish and Game (Ver Hoef et al., In Progress)
Spatial statistical models for stream networks
Moving average models
Incorporate flow and use hydrologic distance
Represents discontinuity at confluences
Important for pollution monitoring
Flow

27. Progress to Date Select dataset Conceptual model development
Spatial analysis to derive explanatory variables, hydrologic distances, and kriging weights
Exploratory analysis to determine whether spatial autocorrelation exists in the data
Model Development & Selection

28. Model Development & Selection
Input data: explanatory variables and sample points
We will explore methods of cross validation
Model Selection
The Spatial Corrected Akaike Information Criterion (AICC) will be used to evaluate models (Hoeting et al., In Press)
Advantage: does not ignore spatial correlation in the selection of explanatory variables
Euclidean distance in the spatial linear model will be replaced with a covariance based on flow and stream distance (Ver Hoef et al., In Progress)
Make changes to the conceptual model if necessary

29. Expected Results A geostatistical model :
Predict a specific reach scale condition at points that were not sampled
Provide a better understanding of the relationship between the landscape and reach scale conditions
Give insight into potential sources of water quality degradation
Develop landscape indicators
Crucial for the rapid and cost efficient monitoring of large areas
Better understanding of spatial autocorrelation in stream networks:
What is the distance within which it occurs
How does that differ between chemical variables

30. Products Map of the study area Methodology
Shows the likelihood of water quality impairment for each stream segment
Based on water quality standards or relative condition (low, medium, high)
Future sampling efforts can be concentrated in areas with a higher probability of impairment
Methodology
Illustrates how States and Tribes can complete spatial analysis using GIS data and field data
GIS tools will be available

31. Questions?

32. Proportional Influence
Proportional influence: influence of each neighboring sample site on a downstream sample site
Weighted by cumulative catchment area: Surrogate for flow
Calculate influence of each upstream segment on segment directly downstream A C B
1.0
0.6749
0.3251
0.5612
0.4312
0.1982
0.8018
Edge proportional influence
Sample point
Stream network
Proportional influence of one point on another =
Product of edge proportional Influences in downstream path
AC = * * 1.0
BC = * * 1.0
Output: weighted incidence matrix

33. Applying Spatial Statistical Models to Stream Networks
Distance measures for kriging along stream networks
Must represent the biological or ecological nature of the dependent variable
Euclidean distance
Is this an appropriate measure of distance?
Continuous landscape variables: geology
Symmetrical hydrologic distance
Hydrologic connectivity
Flow direction is not important
Fish movement?
Flow dependent hydrologic distance
Hydrologic distance between two points
Takes flow direction into consideration
Downstream: Longitudinal transport of sediment in the stream
Upstream: Spawning