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
Space-Time Aquatic Resources
Modeling and Analysis Program
The work reported here was developed under STAR Research
Assistance Agreements CR-829095 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.
This research is funded by
U.S.EPA ・Science To Achieve
Results (STAR) Program
Cooperative
Agreement # CR - 829095
Overview
~
Introduction
Background
Objective
Methodology
Expected Results
Products
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. 92-500, 1972)
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
• Too many streams
– ~ 35,000 miles of streams in
Colorado
The Problem
• 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
Electrofishing during EMAP sampling
(Theobald, 2003)
Hierarchical Stream Structure
Figure taken directly from Frissell and others (1986).
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
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
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)
Methodology
1. Select dataset
2. Conceptual model development
3. Spatial analysis to derive explanatory
variables, hydrologic distances, and kriging
weights
4. Exploratory analysis to determine whether
spatial autocorrelation exists in the data
5. Model Development & Selection
Dataset Selection
Colorado Regional Environmental Monitoring and
Assessment Program:
- Distance between sample points is too great
- Mean = 26.16 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!
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?
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
Evaluate each chemical variable
1. Determine which distance measure is most appropriate
• Euclidean distance
• Hydrologic distance
• Asymmetric hydrologic distance
2. Determine within what distance spatial autocorrelation
occurs
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
Red Dog Mine Data
Conceptual Model Development
1. Compile potential explanatory variables for reach scale habitat conditions
• Based on ecological knowledge and the literature
2. 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
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
B
A
Distances and relationships are
represented differently depending on
the distance measure
C
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
B
A
Distances and relationships are
represented differently depending on
the distance measure
C
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
B
A
Distances and relationships are
represented differently depending on
the distance measure
C
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
B
A
Distances and relationships are
represented differently depending on
the distance measure
C
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
B
A
Distances and relationships are
represented differently depending on
the distance measure
C
Challenge:
Spatial Linear Model
• Spatial autocovariance models developed for Euclidean
distance may not be valid for stream distances
GIS Data for Asymmetric Kriging
1. Hydrologic distance matrix
• Hydrologic distance between sample points
2. Incidence matrix
• Weighted by catchment area
• Surrogate for flow volume
• Maintains flow dependent neighborhoods
3. Covariate representing distance from
basin outlet
4. Observations
GIS Tools
Automated tools needed to extract data about hydrologic
relationships between sample points did not exist!
1. Calculate hydrologic distance from point to point
2. Calculate hydrologic contributing areas (HCAs) for each
stream segment
3. Accumulating HCAs: Calculate digitally derived
catchment attributes from HCAs
4. Calculate proportional influence of one point
on another
•
Represent flow dependent relationships
5. Convert GIS data to a format compatible with statistics
software
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
Progress to Date
1. Select dataset
2. Conceptual model development
3. Spatial analysis to derive explanatory
variables, hydrologic distances, and
kriging weights
4. Exploratory analysis to determine
whether spatial autocorrelation exists in
the data
5. Model Development & Selection
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
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
Products
Map of the study area
•
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
Questions?
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
Proportional influence of one point on another
=
Product of edge proportional Influences in
downstream path
0.4312
0.5612
C
0.8018
A
AC = 0.3251 * 0.8018 * 1.0
BC = 0.6749 * 0.8018 * 1.0
•
Output: weighted incidence
matrix
0.1982
1.0
0.3251
1.0
0.6749
1.0
B
Edge proportional influence
Sample point
Stream network
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