Predicting Water Quality Impaired
Stream Segments using
Landscape-scale Data and a
Regional Geostatistical Model
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 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. EPA does not endorse any products or commercial services
mentioned in this presentation.
This research is funded by
U.S.EPA 凡Science To Achieve
Results (STAR) Program
Cooperative
Agreement # CR - 829095
Overview
Introduction
~
Background
~
Patterns of spatial
autocorrelation in stream
water chemistry
~
Predicting water quality
impaired stream segments
using landscape-scale data
and a regional geostatistical
model: A case study in
Maryland
The Clean Water Act (CWA) 1972
Section 303(d)
• Requires states and tribes to ID water quality
impaired stream segments
Section 305(b)
• Create a biannual water quality inventory
– Characterizes regional water quality
– Based on attainment of designated-use standards
assigned to individual stream segments
Probability-based Random Survey Designs
Used to meet section 305(b) requirements
• Derive a regional estimate of stream condition
– Assign a weight based on stream order
– Provides representative sample of streams by order
– Statistical inference about population of streams,
within stream order, over large area
– Reported in stream miles based on inference of
attainment
Disadvantages
• Does not take watershed influence into account
• Does not ID spatial location of impaired stream segments
• Fails to meet requirements of CWA Section 303(d)
Purpose
Develop a geostatistical methodology based on coarse-scale
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)
Terrestrial
COARSE
SCALE: Grain
Aquatic
Landscape
Climate
Atmospheric deposition
Geology
River Network
Topography
Soil Type
Network Connectivity
Nested Watersheds
Stream Network
Land Use
Vegetation Type
Topography Basin Shape/Size
Drainage Density
Connectivity
Confluence Density
Flow Direction
Network Configuration
Segment
Contributing Area
Segment
Tributary Size Differences
Network Geometry
Localized Disturbances
Land Use/ Land Cover
Riparian Zone
Reach
Riparian Vegetation Type
& Condition
Floodplain / Valley Floor Width
Microhabitat
FINE
Shading
Detritus Inputs
Cross Sectional Area
Channel Slope, Bed Materials
Large Woody Debris
Substrate
Overhanging
Vegetation
Biotic
Condition
Microhabitat
Biotic Condition, Substrate Type,
Overlapping Vegetation
Detritus, Macrophytes
Geostatistical Modeling
a.k.a. Kriging
• Interpolation method
• Allows spatial autocorrelation in error term
• More accurate predictions
Fit an autocovariance function to data
• Describes relationship between observations based on
separation distance
3 Autocovariance Parameters
2) Sill: delineated where
semivariance asymptotes
3) Range: distance within which
spatial autocorrelation occurs
Sill
Semivariance
1) Nugget: variation between sites as
separation distance approaches
zero
10
Nugget
0
0
Range
Separation Distance
1000
Distance Measures &
Spatial Relationships
Distances and relationships are
represented differently depending on
the distance measure
B
A
C
Straight-line Distance (SLD)
Geostatistical models typically based on SLD
Distance Measures &
Spatial Relationships
Distances and relationships are
represented differently depending on
the distance measure
B
A
C
Symmetric Hydrologic Distance (SHD)
Hydrologic connectivity: Fish movement
Distance Measures &
Spatial Relationships
Distances and relationships are
represented differently depending on
the distance measure
B
A
C
Asymmetric Hydrologic Distance
Longitudinal transport of material
Distance Measures &
Spatial Relationships
Distances and relationships are
represented differently depending on
the distance measure
B
A
C
Challenge:
• Spatial autocovariance models developed for SLD may
not be valid for hydrologic distances
– Covariance matrix is not positive definite
Asymmetric Autocovariance Models for Stream
Networks
• Weighted asymmetric hydrologic
distance (WAHD)
• Developed by Jay Ver Hoef, National
Marine Mammal Laboratory, Seattle
• Moving average models
• Incorporate flow volume, flow direction,
and use hydrologic distance
• Positive definite covariance matrices
Ver 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.
Flow
Patterns of Spatial Autocorrelation in Stream Water
Chemistry
Objectives
Evaluate 8 chemical response variables
1.
2.
3.
4.
5.
6.
7.
8.
pH measured in the lab (PHLAB)
Conductivity (COND) measured in the lab μmho/cm
Dissolved oxygen (DO) mg/l
Dissolved organic carbon (DOC) mg/l
Nitrate-nitrogen (NO3) mg/l
Sulfate (SO4) mg/l
Acid neutralizing capacity (ANC) μeq/l
Temperature (TEMP) °C
Determine which distance measure is most appropriate
•
•
•
•
SLD
SHD
WAHD
More than one?
Find the range of spatial autocorrelation
Dataset
Maryland Biological Stream Survey (MBSS) Data
• Maryland Department of Natural Resources
– 1995, 1996, 1997
• Stratified probability-based random survey design
• 881 sites in 17 interbasins
Maryland
Baltimore
Annapolis
Northeastern U.S.
Study
Area
Washington D.C.
Chesapeake Bay
Spatial Distribution of MBSS Data
N
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:
1. Calculate watershed covariates for each stream segment
• Functional Linkage of Watersheds and Streams (FLoWS)
2. Calculate separation distances between sites
• SLD, SHD, Asymmetric hydrologic distance (AHD)
3. Calculate the spatial weights for the WAHD
4. Convert GIS data to a format compatible with statistics software
FLoWS tools will be available on the STARMAP website:
http://nrel.colostate.edu/projects/starmap
1
2
2
1
3
SLD
3
SHD
1
2
3
AHD
Spatial Weights for WAHD
Proportional influence (PI): influence of each neighboring survey site
on a downstream survey site
• Weighted by catchment area: Surrogate for flow volume
1. Calculate the PI of each upstream
segment on segment directly
downstream
Watershed
Segment B
Watershed
Segment A
A
2. Calculate the PI of one survey site on
another site
• Flow-connected sites
• Multiply the segment PIs
B
C
Segment PI
of A
=
Watershed Area A
Watershed Area B
Spatial Weights for WAHD
Proportional influence (PI): influence of each neighboring survey site
on a downstream survey site
• Weighted by catchment area: Surrogate for flow volume
1. Calculate the PI of each upstream
segment on segment directly
downstream
A
C
B
E
2. Calculate the PI of one survey site on
another site
• Flow-connected sites
• Multiply the segment PIs
D
F
G
H
survey sites
stream segment
Spatial Weights for WAHD
Proportional influence (PI): influence of each neighboring survey site
on a downstream survey site
• Weighted by catchment area: Surrogate for flow volume
1. Calculate the PI of each upstream
segment on segment directly
downstream
A
C
B
E
2. Calculate the PI of one survey site on
another site
• Flow-connected sites
• Multiply the segment PIs
D
F
G
H
Site PI = B * D * F * G
Data for Geostatistical Modeling
1. Distance matrices
•
SLD, SHD, AHD
2. Spatial weights matrix
•
Contains flow dependent weights
for WAHD
3. Watershed covariates
•
Lumped watershed covariates
– Mean elevation, % Urban
4. Observations
•
MBSS survey sites
Geostatistical Modeling Methods
Validation Set
•
•
Unique for each chemical response variable
100 sites
Initial Covariate Selection
•
Reduce covariates to 5
Model Development
•
Restricted model space to all possible linear models
– Model set = 32 models (25 models)
•
One model set for:
– General linear model (GLM), SLD, SHD, and WAHD models
Response
ANC (μeq/l)
COND (μmho/cm)
DOC (mg/l)
DO (mg/l)
NO3 (mg/l)
pH Lab
SO4 (mg/l)
TEMP (°C)
Significant Covariates
PASTUR, LOWURB, WOODYWET, YR96, YR97
HIGHURB, LOWURB, COALMINE, YR96, NORTHING
WOODYWET, CONIFER, MIXEDFOR, LOWURB, NORTHING
DECIDFOR, HIGHURB, WOODYWET, YR96, YR97
PASTUR, PROBCROP, ROWCROP, LOWURB, WATER
PROBCROP, DECIDFOR, WOODYWET, ACREAGE, CONIFER
LOWURB, COALMINE, NORTHING, ER67, ER69
PROBCROP, LOWURB, WATER, YR96, YR97
Geostatistical Modeling Methods
Geostatistical model parameter estimation
• Maximize the profile log-likelihood function
Log-likelihood function of the parameters ( ,  ,  2 ) given the observed data Z is:
( ,  ,  2 ; Z )  
n
1
1
log( 2 )  log  2  
( Z  X )'  1 ( Z  X )
2
2
2
2
Maximizing the log-likelihood with respect to B and sigma2 yields:
ˆ  ( X '  1 X ) 1 X '  1Z
and
( Z  X ˆ ) '  1 ( Z  X ˆ )
ˆ 
n
2
Both maximum likelihood estimators can be written as functions of  alone
Derive the profile log-likelihood function by substituting the MLEs ( ˆ , ˆ ) back into the
log-likelihood function
2
n
n
1
n
 profile( ; ˆ , ˆ 2 , Z )   log( 2 )  log( ˆ 2 )  log  
2
2
2
2
Geostatistical Modeling Methods
Fit exponential autocorrelation function
if D  0
1
  C1 ( D;1 ,2 )  
(1  1 ) exp( D /  2 ) if D  0
where  is the covariance based on the distance between two sites, D, given the
covariance parameter estimates: nugget (0), sill ( 1), and range ( 2).
Model selection within model set
•
•
GLM: Akaike Information Corrected Criterion (AICC)
Geostatistical models: Spatial AICC (Hoeting et al., in press)
AICC  2 profile( ;  ,  2 , Z )  2n
p  k 1
n pk 2
where n is the number of observations, p-1 is the number of covariates,
and k is the number of autocorrelation parameters.
http://www.stat.colostate.edu/~jah/papers/spavarsel.pdf
Geostatistical Modeling Methods
Model selection between model types
•
•
•
100 Predictions: Universal kriging algorithm
Mean square prediction error (MSPE)
Cannot use AICC to compare models based on different distance
measures
Model comparison: r2 for observed vs. predicted values
Results
Summary statistics for distance measures
• Spatial neighborhood differs
• Affects number of neighboring sites
• Affects median, mean, and maximum separation distance
Summary statistics for distance measures in kilometers using DO (n=826).
Distance Measure
N Pairs
Min
Median
Mean
Max
Straight Line
Distance
340725
0.05
101.02
118.16
385.53
Symmetric
Hydrologic Distance
62625
0.05
156.29
187.10
611.74
Pure Asymmetric *
Hydrologic Distance
1117
0.05
4.49
5.83
27.44
* Asymmetric hydrologic distance is not weighted here
Results
Range of spatial autocorrelation differs:
•
•
•
Shortest for SLD
TEMP = shortest range values
DO = largest range values
180.79
100.00
Mean Range Values
SLD = 28.2 km
SHD = 88.03 km
WAHD = 57.8 km
301.76
90.00
Range (km)
80.00
70.00
SLD
60.00
SHD
50.00
40.00
WAHD
30.00
20.00
10.00
0.00
ANC
COND
DOC
DO
NO3
PHLAB
SO4
TEMP
Results
Distance Measures:
•
•
GLM always has less predictive ability
More than one distance measure usually performed well
• SLD, SHD, WAHD: PHLAB & DOC
• SLD and SHD : ANC, DO, NO3
• WAHD & SHD: COND, TEMP
SLD distance: SO4
•
ANC
DOC
COND
350000.00
40000.00
300000.00
35000.00
9.00
2.50
GLM
8.00
2.00
7.00
30000.00
250000.00
6.00
25000.00
1. 5 0
200000.00
5.00
20000.00
15 0 0 0 0 . 0 0
4.00
15 0 0 0 . 0 0
10 0 0 0 0 . 0 0
5000.00
0.00
0.00
GLM
SL
SH
1. 0 0
3.00
10 0 0 0 . 0 0
50000.00
MSPE
DO
2.00
0.50
1. 0 0
0.00
0.00
GLM
WAH
SL
SH
WAH
GLM
PHLAB
NO3
1. 2 0
SL
SH
GLM
WAH
SO4
0 . 18
400.00
0 . 16
350.00
1. 0 0
SL
SH
WAH
TEMP
SLD
SHD
9.00
8.50
0 . 14
300.00
0 . 12
0.80
250.00
8.00
0 . 10
0.60
0.40
0.20
0.06
15 0 . 0 0
0.04
10 0 . 0 0
0.02
50.00
GLM
SL
SH
WAH
7.50
7.00
0.00
0.00
WAHD
200.00
0.08
0.00
GLM
SL
SH
WAH
6.50
GLM
SL
SH
WAH
GLM
SL
SH
WAH
Results
Predictive ability of models:
r2

Strong: ANC, COND, DOC, NO3, PHLAB
Weak: DO, TEMP, SO4
1.00
0.90
0.80
GLM
0.70
0.60
SLD
R2
r2 0.50
0.40
SHD
0.30
WAHD
0.20
0.10
0.00
ANC
COND
DOC
DO
NO3
PHLAB
SO4
TEMP
Discussion
Distance measure influences how spatial relationships are
represented in a stream network
•
•
Site’s relative influence on other sites
Dictates form and size of spatial neighborhood
Important because…
•
Impacts accuracy of the geostatistical model predictions
SLD
SHD
WAHD
Patterns of spatial autocorrelation found at
relatively coarse scale
• Geostatistical models describe more
variability than GLM
SLD, SHD, and WAHD represent spatial
autocorrelation in continuous coarse-scale
variables
SLD
• > 1 distance measure performed well
• SLD never substantially inferior
• Do not represent movement through network
Different range of spatial autocorrelation?
• Larger SHD and WAHD range values
• Separation distance larger when restricted to
network
SHD
Discussion
Probability-based random survey design (-) affected WAHD
• Maximize spatial independence of sites
• Does not represent spatial relationships in networks
• Validation sites randomly selected
275
244
244 sites did not have neighbors
Sample Size = 881
Number of sites with ≤1 neighbor: 393
Mean number of neighbors per site: 2.81
Frequency
149
133
109
66
38
35
32
12
19
7
15
13
6
1
0
0
2
13
14
15
16
17
0
0
1
2
3
4
5
6
7
8
9
10
11
Number of Neighboring Sites
12
Discussion
WAHD models explained more variability as neighboring sites
increased
Not when neighbors had:
Similar watershed conditions
Significantly different chemical response values
4500
4500
WAHD
GLM
Difference
•
•
00
0
1
2
3
4 5 6 7 8 9 10 11 12 13 14 15 16 17
Number of Neighboring Sites
Discussion
GLM predictions improved as number of neighbors increased
•
Clusters of sites in space have similar watershed conditions
– Statistical regression pulled towards the cluster
•
GLM contained hidden spatial information
– Explained additional variability in data with > neighbors
4500
4500
Difference
WAHD
GLM
00
0
1
2
3
4 5 6 7 8 9 10 11 12 13 14 15 16 17
Number of Neighboring Sites
Predictive Ability of Geostatistical Models
Coarse
COND
Scale of dominant
ecological processes
SO4
ANC
PH
NO3
DOC
TEMP
DO
Fine
0
0.5
r2
1.0
Conclusions
1) Spatial autocorrelation exists in stream chemistry data at a
relatively coarse scale
2) Geostatistical models improve the accuracy of water
chemistry predictions
3) Patterns of spatial autocorrelation differ between chemical
response variables
• Ecological processes acting at different spatial scales
4) SLD is the most suitable distance measure at regional
scale at this time
• Unsuitable survey designs
• SHD: GIS processing time is prohibitive
Conclusions
5) Results are scale specific
• Spatial patterns change with survey scale
• Other patterns may emerge at shorter separation
distances
6) Further research is needed at finer scales
• Watershed or small stream network
7) Need new survey designs for stream networks
• Capture both coarse and fine scale variation
• Ensure that hydrologic neighborhoods are represented
Predicting Water Quality Impaired
Stream Segments using
Landscape-scale Data and a Regional
Geostatistical Model: A Case Study In
Maryland
Objective
Demonstrate how a geostatistical methodology can be
used to meet the requirements of the Clean Water Act
1) Predict regional water quality
conditions
2) ID the spatial location of
potentially impaired stream
segments
1996 MBSS DOC Data
Kilometers
0
N
n
312
Min
0.6
1st Qu.
1.2
20
Median
1.7
Mean
1.9
3rd Qu.
2.7
Max
15.9
σ2
1.8
Methods
Potential covariates
Covariate
AREA
URBAN
BARREN
WATER
CONIFER
DECIDFOR
MIXEDFOR
EMERGWET
WOODYWET
COALMINE
EASTING
NORTHING
ER63-ER69
MEANELEV
SLOPE
ARGPERC
CARPERC
FELPERC
MAFPERC
SILPERC
MEANK
MAXTEMP
MINTEMP
PRECIP
ANPRECIP
Description
Catchment area (ha)
% Urban
% Barren
% Open Water
% Conifer or evergreen forest type
% Deciduous forest type
% Mixed forest type
% Emergent Herbacious Wetlands
% Woody or shrubby wetlands
% Coalmine
Easting - Albers Equal Area Conic
Northing - Albers Equal Area Conic
Omernik's Level III Ecoregion
Mean elevation in the watershed
Mean slope in the watershed
% Argillaceous rock type in watershed
% Carbonic rock type in watershed
% Felsic rock type in watershed
% Mafic rock type in watershed
% Siliceous rock type in watershed
Mean soil erodability factor in watershed
(adjusted for rock fragments)
Mean annual maximum temperature (°C)
Mean minimum temperature for January - April (°C)
Mean precipitation for January - April (mm)
Mean annual precipitation
Spatial
Resolution
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
1 foot
1 foot
1:7,500,000
30 meter
30 meter
1:250,000
1:250,000
1:250,000
1:250,000
1:250,000
1
4
4
4
4
kilometer
kilometer
kilometer
kilometer
kilometer
Methods
Potential covariates after initial model selection (10)
Covariate
AREA
URBAN
BARREN
WATER
CONIFER
DECIDFOR
MIXEDFOR
EMERGWET
WOODYWET
COALMINE
EASTING
NORTHING
ER63-ER69
MEANELEV
SLOPE
ARGPERC
CARPERC
FELPERC
MAFPERC
SILPERC
MEANK
MAXTEMP
MINTEMP
PRECIP
ANPRECIP
Description
Catchment area (ha)
% Urban
% Barren
% Open Water
% Conifer or evergreen forest type
% Deciduous forest type
% Mixed forest type
% Emergent Herbacious Wetlands
% Woody or shrubby wetlands
% Coalmine
Easting - Albers Equal Area Conic
Northing - Albers Equal Area Conic
Omernik's Level III Ecoregion
Mean elevation in the watershed
Mean slope in the watershed
% Argillaceous rock type in watershed
% Carbonic rock type in watershed
% Felsic rock type in watershed
% Mafic rock type in watershed
% Siliceous rock type in watershed
Mean soil erodability factor in watershed
(adjusted for rock fragments)
Mean annual maximum temperature (°C)
Mean minimum temperature for January - April (°C)
Mean precipitation for January - April (mm)
Mean annual precipitation
Spatial
Resolution
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
30 meter
1 foot
1 foot
1:7,500,000
30 meter
30 meter
1:250,000
1:250,000
1:250,000
1:250,000
1:250,000
1
4
4
4
4
kilometer
kilometer
kilometer
kilometer
kilometer
Methods
Fit geostatistical models
• Two distance measures: SLD and
WAHD
Autocorrelation
Function
Exponential
Restricted model space to all
possible linear models
• 1024 models per set (210 models)
Spherical
Mariah
Hole Effect
Linear with Sill
Rational Quadratic
Parameter Estimation
• Maximized the profile log-likelihood
function
SLD
WAHD
Methods
Model selection within distance measure & autocorrelation
function
• Spatial AICC (Hoeting et al., in press)
Model selection between distance measure & autocorrelation
function
• Cross-validation method using Universal kriging algorithm
– 312 predictions
• MSPE
Model comparison: r2 for the observed vs. predicted values
Results
SLD models performed better
than WAHD
1.6
1.4
Exponential
Rational Quadratic
Mariah
Exponential
1
Rational
Quadratic
0.997
1
SLD
3
4
WAHD
Rational
Quadratic
0.2
Linear with Sill
0.4
Hole Effect
0.6
Mariah
0.8
Spherical
Best models:
• SLD Exponential, Mariah,
and Rational Quadratic
models
1
Exponential
Exception: Spherical model
MSPE
1.2
0
1
Mariah
0.990
0.993
1
2
5
Autocorrelation Function
6
r2 for SLD model predictions
• Almost identical
• Further analysis restricted
to SLD Mariah model
Results
Covariates for SLD Mariah model:
WATER, EMERGWET, WOODYWET, FELPERC, & MINTEMP
Nugget
0.15
Sill
0.28
Range
7.02
Intercept
0.28
WATER
0.05
EMERGWET
0.04
WOODYWET
0.02
Positive relationship with DOC:
•
WATER, EMERGWET, WOODYWET, MINTEMP
Negative relationship with DOC
•
FELPERC
FELPERC
-0.0005
MINTEMP
0.07
Cross-validation intervals for
Mariah model regression coefficients
Cross-validation interval: 95% of regression coefficients
produced by leave-one-out cross validation procedure
Narrow intervals
•
Few extreme regression coefficient values
– Not produced by common sites
– Covariate values for the site are represented in observed data
– Not clustered in space
Model coefficients represent change in log10 DOC per unit of X
Statistic
Minimum
Maximum
Mean
Standard Dev
95% Lower Limit
95% Upper Limit
WATER (%)
0.0469
0.0537
0.0501
0.0007
0.0485
0.0522
EMERGWET (%) WOODYWET (%) FELPERC (%)
0.0306
0.0156
-0.0006
0.0425
0.0187
-0.0004
0.0344
0.0176
-0.0005
0.0009
0.0002
0.00005
0.0322
0.017
-0.0006
0.0366
0.0179
-0.0005
MINT (°C)
0.0616
0.071
0.0655
0.0007
0.0643
0.0669
r2 Observed vs. Predicted Values
18
Predicted DOC mg/l
rR2 2==0.7221
0.7221
0
0
5
n = 312 sites
r2 = 0.72
10
Observed DOC mg/l
15
1 influential site
r2 without site = 0.66
Model Fit
Squared Prediction Error (SPE)
Discussion
• SLD models more accurate than
WAHD models
• Landscape-scale covariates were
not restricted to watershed
boundaries
– Geology type
– Temperature
– Wetlands & water
Discussion
Regression Coefficients
Narrow cross-validation intervals
• Spatial location of the sites not as important as watershed
characteristics
Extreme regression coefficient values
• Not produced by common sites
• Not clustered in space
Local-scale factor may have affected stream DOC
• Point source of organic waste
Spatial Patterns in Model Fit
North and east of Chesapeake Bay - large SPE values
•
Naturally acidic blackwater streams with elevated DOC
•
Not well represented in observed dataset
– 2 blackwater sites
•
Geostatistical model unable to account for natural variability
– Large square prediction errors
– Large prediction variances
SPE values
Spatial Patterns in Model Fit
West of Chesapeake Bay - low SPE values
•
Due to statistical and spatial distribution of observed data
– Regression equation fit to the mean in the data
– Most observed sites = low DOC values
•
Less variation in western and central Maryland
– Neighboring sites tend to be similar
•
Separation distances shorter in the west
– Short separation distances = stronger covariances
SPE values
Model Performance
Unable to account for abrupt differences in DOC values between
neighboring sites with similar watershed conditions
What caused abrupt differences?
• Point sources of organic pollution
– Not represented in the model
• Non-point sources of pollution
– Lumped watershed attributes are non-spatial
– Differences due to spatial location of landuse are not
represented
– Challenging to represent ecological processes using coarsescale lumped attributes
– i.e. Flow path of water
Generate Model Predictions
Prediction sites
• Study area
– 1st, 2nd, and 3rd order non-tidal streams
– 3083 segments = 5973 stream km
• ID downstream node of each segment
– Create prediction site
• More than one site at each confluence
Generate predictions and prediction variances
• SLD Mariah model
• Universal kriging algorithm
• Assigned predictions and prediction variances back to
stream segments in GIS
DOC Predictions (mg/l)
Weak Model Fit
Strong Model Fit
Water Quality Attainment by Stream Kilometers
Threshold values for DOC
• Set by Maryland Department of Natural Resources
• High DOC values may indicate biological or ecological stress
Theshold
Low
Medium
High
DOC (mg/l)
< 5.0
5.0 - 8.0
> 8.0
Stream
Kilometers
5387.67
400.19
185.16
Percent
90.2
6.7
3.1
Implications for Water Quality Monitoring
1) One geostatistical model can be used to predict DOC in
stream segments throughout a large area
•
•
Can be used to provide an estimate of regional stream DOC values
Cannot ID point sources of organic pollution
2) Tradeoff between cost-efficiency and model accuracy
Western Maryland
• Can be described using a single geostatistical model
Eastern and northeastern Maryland
• Accept poor model fit
• Collect additional survey data for regional geostatistical model
• Develop a separate geostatistical model for eastern Maryland
Implications for Water Quality Monitoring
3) Apply this methodology to other regulated constituents
•
•
•
•
Technical and Regulatory Services Administration within the MDE
modifying the NHD
– Include water quality standards & stream-use designations by
NHD segment
Use water quality standards instead of thresholds
Categorize predictions into potentially impaired or unimpaired status
Report on attainment in stream miles/kilometers
Conclusions
1)
Geostatistical models generated more accurate DOC
predictions than previous non-spatial models based on
coarse-scale landscape data
2)
SLD is more appropriate than WAHD for regional
geostatistical modeling of DOC at this time
3)
Adds value to existing water quality monitoring efforts
•
•
•
•
Used to comply with the CWA more easily
Additional field sampling is not necessary
Inferences about regional stream condition can be
generated
It can be used to identify the spatial location of potentially
impaired stream segments
Conclusions
4)
Model predictions and prediction variances
•
5)
Allow additional field efforts to be concentrated in
– Areas with large amounts of uncertainty
– Areas with a greater potential for water quality
impairment
Model results can be displayed visually
•
Allows professionals to communicate results to a wide
variety of audiences
Thank You!
Advisors: Dave Theobald and Melinda Laituri
Committee Members: Will Clements and Brian Bledsoe
Collaborators: N. Scott Urquhart, Jay M. Ver Hoef, and
Andrew A. Merton
Team Theobald: Grant Wilcox, John Norman, Nate
Peterson, and Melissa Sherburne
Dennis Ojima and Keith Paustian
Family and friends
My husband Nate
Questions?