Predicting Water Quality Impaired Stream Segments using Landscapescale Data and a Regional Geostatistical Model . Erin Peterson Environmental Risk Technologies CSIRO Mathematical & Information Sciences St Lucia, Queensland. This research is funded by. This research is funded by. U.S.EPA.
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Erin Peterson
Environmental Risk Technologies
CSIRO Mathematical & Information Sciences
St Lucia, Queensland
This research is funded by
U.S.EPA
U.S.EPA
凡
Science To Achieve
Science To Achieve
Results (STAR) Program
Results (STAR) Program
Cooperative
Cooperative
CR
CR


829095
829095
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#
Agreement
Agreement
SpaceTime Aquatic Resources Modeling and Analysis Program
The work reported here was developed under STAR Research Assistance Agreement CR829095 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.
Dr. David M. Theobald
Natural Resource Ecology Lab
Department of Recreation & Tourism
Colorado State University, USA
Dr. N. Scott Urquhart
Department of Statistics
Colorado State University, USA
Dr. Jay M. Ver Hoef
National Marine Mammal Laboratory, Seattle, USA
Andrew A. Merton
Department of Statistics
Colorado State University, USA
Introduction
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Background
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Patterns of spatial autocorrelation in stream water chemistry
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Predicting water quality impaired stream segments using landscapescale data and a regional geostatistical model: A case study in Maryland, USA
Probabilitybased Random Survey Designs
Develop a geostatistical methodology based on coarsescale GIS data and field surveys that can be used to predict water quality characteristics about stream segments found throughout a large geographic area (e.g., state)
Aquatic
Terrestrial
Landscape
River Network
COARSE
Climate
Atmospheric deposition
Geology
Topography
Soil Type
Network Connectivity
Stream Network
Nested Watersheds
Drainage Density
Confluence Density
Connectivity
Flow Direction
Network Configuration
Vegetation Type
Basin Shape/Size
Land Use
Topography
Segment Contributing Area
Segment
Tributary Size Differences
Network Geometry
Localized Disturbances
Land Use/ Land Cover
Reach
Riparian Zone
Riparian Vegetation Type
& Condition
Floodplain / Valley Floor Width
Cross Sectional Area
Channel Slope, Bed Materials
Large Woody Debris
Overhanging
Vegetation
Substrate
Microhabitat
Microhabitat
FINE
Biotic Condition, Substrate Type,
Overlapping Vegetation
Detritus, Macrophytes
Shading
Detritus Inputs
Biotic
Condition
Sill
Semivariance
Nugget
Range
0
1000
0
Separation Distance
Geostatistical Modeling
Distances and relationships are represented differently depending on the distance measure
A
C
Distance Measures & Spatial RelationshipsStraightline Distance (SLD)
Geostatistical models typically based on SLD
A
C
Distance Measures & Spatial RelationshipsSymmetric Hydrologic Distance (SHD)
Hydrologic connectivity: Fish movement
A
C
Distance Measures & Spatial RelationshipsAsymmetric Hydrologic Distance
Longitudinal transport of material
A
C
Distance Measures & Spatial RelationshipsVer Hoef, J.M., Peterson, E.E., and Theobald, D.M., Spatial Statistical Models that Use Flow and Stream Distance, Environmental and Ecological Statistics. In Press.
Objectives Chemistry
Evaluate 8 chemical response variables
Determine which distance measure is most appropriate
Find the range of spatial autocorrelation
Maryland Biological Stream Survey (MBSS) Data
N Chemistry
Spatial Distribution of MBSS Data
2 Chemistry
1
3
1
2
3
1
2
3
SHD
AHD
SLD
GIS Tools
Automated tools needed to extract data about hydrologic relationships between survey sites did not exist!
Wrote Visual Basic for Applications (VBA) programs to:
Watershed
Segment B
Watershed
Segment A
A
B
C
Watershed Area A
Segment PI
of A
=
Watershed Area B
Spatial Weights for WAHD
A
C
B
E
D
F
G
H
Spatial Weights for WAHD
survey sites
stream segment
Site PI = B * D * F * G
Spatial Weights for WAHD
A
C
B
E
D
F
G
H
Geostatistical Modeling Methods Chemistry
Geostatistical Modeling Methods Chemistry
Loglikelihood function of the parameters ( ) given the observed data Z is:
Maximizing the loglikelihood with respect to B and sigma2 yields:
and
Both maximum likelihood estimators can be written as functions of alone
Derive the profile loglikelihood function by substituting the MLEs ( ) back into the loglikelihood function
where ChemistryC1 is the covariance based on the distance between two sites, h, given the autocorrelationparameter estimates: nugget ( ), sill ( ), and range ( ).
Geostatistical Modeling Methods
Geostatistical Modeling Methods Chemistry
where n is the number of observations, p1 is the number of covariates, and k is the number of autocorrelation parameters.
http://www.stat.colostate.edu/~jah/papers/spavarsel.pdf
Summary statistics for distance measures in kilometers using DO (n=826).
* Asymmetric hydrologic distance is not weighted here
Results
180.79 DO (n=826).
301.76
SLD
SHD
WAHD
Results
Mean Range Values
SLD = 28.2 km
SHD = 88.03 km
WAHD = 57.8 km
GLM DO (n=826).
SLD
MSPE
SHD
WAHD
Results
r DO (n=826).2
GLM
SLD
SHD
WAHD
Results
Predictive ability of models:
Strong: ANC, COND, DOC, NO3, PHLAB
Weak: DO, TEMP, SO4
r2
SHD DO (n=826).
WAHD
SLD
Discussion
Distance measure influences how spatial relationships are represented in a stream network
Patterns of spatial autocorrelation found at relatively coarse scale
SLD, SHD, and WAHD represent spatial autocorrelation in continuous coarsescale variables
SLD
SHD
244 sites did not have neighbors coarse scale
Sample Size = 881
Number of sites with ≤1 neighbor: 393
Mean number of neighbors per site: 2.81
Frequency
Number of Neighboring Sites
Discussion
4500 coarse scale
WAHD
GLM
Difference (O – E)
0
0
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
8
Number of Neighboring Sites
Discussion
WAHD models explained more variability as neighboring sites increased
4500 coarse scale
WAHD
GLM
Difference (O – E)
0
0
1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
8
Number of Neighboring Sites
Discussion
Coarse coarse scale
COND
SO4
ANC
PH
NO3
DOC
Scale of influential
ecological processes
TEMP
DO
Fine
0.5
0
1.0
Predictive Ability of Geostatistical Models
r2
Conclusions coarse scale
Conclusions coarse scale
Predicting Water Quality Impaired Stream Segments using coarse scale
Landscapescale Data and a Regional Geostatistical Model: A Case Study In Maryland
Objective coarse scale
Demonstrate how a geostatistical methodology can be used to compliment regional water quality monitoring efforts
Methods coarse scale
Potential covariates
Methods coarse scale
Potential covariates after initial model selection (10)
Methods coarse scale
Model selection coarse scalewithin distance measure & autocorrelation function
Model selection between distance measure & autocorrelation function
Methods
MSPE coarse scale
Mariah
Linear with Sill
Rational
Quadratic
Spherical
Exponential
Hole Effect
Autocorrelation Function
Results
Results coarse scale
Negative relationship with DOC
Model coefficients represent change in log10 DOC per unit of X
Crossvalidation intervals for
Mariah model regression coefficients
r X2 Observed vs. Predicted Values
1 influential site
r2 without site = 0.66
n = 312 sites
r2 = 0.72
Squared Prediction Error (SPE) X
Model Fit
Spatial Patterns in Model Fit
Spatial Patterns in Model Fit
Unable to account for abrupt differences in DOC values between neighboring sites with similar watershed conditions
Water Quality Attainment by Stream Kilometers X
Implications for Water Quality Monitoring
Implications for Water Quality Monitoring stream segments throughout a large area