Erin Peterson

Geosciences Department

Colorado State UniversityFort Collins, Colorado

Predicting the likelihood of water quality impaired stream

reaches using landscape scale data and a hierarchical

methodology

This research is funded by

U.S.EPA �Science To AchieveResults (STAR) ProgramCooperativeAgreement # CR - 829095

This research is funded by

U.S.EPA �Science To AchieveResults (STAR) ProgramCooperativeAgreement # CR - 829095

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.

Space-Time Aquatic Resources Modeling and Analysis Program

Overview~

Introduction Background

ObjectiveMethodology

Expected ResultsProducts

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)

• 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

Identify water quality impaired segments using BMI data

• 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)

Hierarchical Stream Structure

Figure taken directly from Frissell and others (1986).

Landscape

Reach

BMI

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

Hierarchical Processes and Models

Potential Uses:• Input into a hierarchical

classification model– Substrate type

• Used to identify water quality impaired stream segments

– Heavy metal concentration

ObjectiveDevelop a spatial model that can be 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 models used to predict reach scale characteristics?

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 SelectionColorado 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!

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?

What is a reasonable neighborhood?

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 variable1. 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

DEMcatchment 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

A

B

C

Distances and relationships are represented differently depending on

the distance measure

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

Distances and relationships are represented differently depending on

the distance measure

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

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

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

A

B

C

Distances and relationships are represented differently depending on

the distance measure

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

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

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

Automated tools needed to extract data about hydrologic relationships between sample points did not exist!

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 dataset2. Conceptual model development3. 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 ResultsA 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: 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

AC

B1.0

0.6749

0.3251

0.5612

0.4312

0.1982

0.80181.0

1.0

Edge proportional influenceSample pointStream network

AC = 0.3251 * 0.8018 * 1.0BC = 0.6749 * 0.8018 * 1.0

Proportional Influence

Proportional influence of one point on another =

Product of edge proportional Influences in downstream path

• Output: weighted incidence matrix

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