Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water...
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![Page 1: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/1.jpg)
Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground-
Water Vulnerability at Multiple Scales
Earl A. Greene and Andrew E. LaMotteUSGS
NWQMC, May 7-11, 2006, San Jose, CA
![Page 2: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/2.jpg)
Research Overview
… to characterize the statistical relation between ground-water quality and geographic factors to generate shallow ground-water vulnerability maps at multiple-scales and at multiple management thresholds.
![Page 3: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/3.jpg)
Ground-Water Vulnerability Strategies
• Need to access ground-water vulnerability at multiple scales (national, regional, watershed, local)
• Integration with other “stressors” and “resource” variables
- stressors (divers of change, nitrate, land use change, mining, pesticides)
- resource (wetlands, water quality, human health, water availability)
• Integration methods and techniques
![Page 4: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/4.jpg)
Understand Thresholds
Jay Ver Hoef, 2002
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Statistical Model Development
• Logistic-Regression method to predict the presence of nitrate concentrations above a specified management threshold value.
• Equations are developed using explanatory variables (land use, geology, soils, and other geographic datasets).
• Resulting equations are transformed to predict the probability of exceeding a specified management thresholds.
![Page 6: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/6.jpg)
Significant Variables in Ground-Water Vulnerability Model
• Land Use/Cover• Geology Type• Nitrate Input Function: Manure, Fertilizer,
Atmospheric Deposition• Soil Data: Hydrologic group, Organic Matter,
Depth to Bedrock, Depth to Water Table, and Percent Silt and Clay
• Population Density
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National, Regional, Local Scales of Ground-Water Vulnerability
NationalNolan
RegionalGreene and others, 2004
LocalLaMotte and Greene, in press
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Regional Vulnerability Analysis (ReVA)
• 1. EPA's Regional Vulnerability Assessment (ReVA) program is an approach to regional scale assessments
• 2. The ReVA program is responsible for the collection, management, and analysis of multiple data sources to evaluate environmental conditions and known stressors within the Mid-Atlantic region.
• 3. Goal - evaluate environmental condition and known stressors within the Mid-Atlantic region, but predicting future environmental risk to prioritize efforts to protect and restore environmental quality.
![Page 9: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/9.jpg)
Integration of Spatial Data: Methods for Evaluating Ground-Water Vulnerability
![Page 10: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/10.jpg)
Spatial Data Information for Decision Makers
•Data diagnostics and preparation
• Integration of data in selectable reporting units
• Statistical methods to support decision making
• Data access (summarized by reporting unit)
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Ground Water Vulnerability – Integration at Ground Water Vulnerability – Integration at a Watershed Scalea Watershed Scale
Overdispersion Statistical Method
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Multiple Decision-Criteria Require Multiple Integration Methods
•Ranking Methods (Condition)
Quantiles, Sum of Ranks, AHP
• Distance from Reference Point (Sustainability)
PCA, State Space, Criticality
• Overlay of stressors/resources (Value)
• Grouping of Like Units (Feasibility)
Cluster Analysis, Self-Organizing Maps
![Page 13: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/13.jpg)
Integration Methods that Rank Condition
Quantiles – numeric range of variables divided into equal subdivisions
Sum of Ranks – regional ranks of individual variables are summed
Analytical Hierarchy Process (AHP) – variables clustered by Principal Components Analysis and weighted by eigenvector scores
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Pristine Ground Water
Current Status
Future Status
Degraded
Driver of environmental change (nitrate, land use)
Integration Methods that Measure
Distance From Reference
PCA – Euclidean distance from
PCA-transformed watershed (ground water) scores
State Space – Mahalanobis
distance from watershed
(ground water) scores
(good, bad, middle)
Criticality – Fuzzy distance
between current and
“natural” state
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Integration Method that Ranks Vulnerability
Stressor / Resource Overlay – number of stressors overlayed with number of resources. Highest vulnerability is where there are high numbers of resources and high numbers of stressors.
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Integration Methods that Group Like-Units
Cluster Analysis – robust partitioning method
Self-Organizing Maps – neural networks
*Allows risk management of groups of units
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Data Issues Associated with Integration
Discontinuity – sensitivity to variables that only have integer values – may need to leave out
Skewness – some variables have highly skewed distributions – log transform or drop outliers
Imbalance – variables not equally distributed across families (e.g. terrestrial biodiversity, human population variables) – only report scores within families, or average within families and sum averages
Interdependency – variables correlated – some integration methods account for interdependency, otherwise must be resolved individually
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Which Integration Method is Appropriate for Specific Assessment Questions?
• What is the over ground-water condition of the region?
Quintiles (worst, best)
• What is the relative condition of units (e.g. watersheds, ground-water quality) across a region?
Sum, weighted sum, state space
• What / where are the most vulnerable ground-water resources given future stressor distributions?
Overlay
• Where is there a risk of major change?
Criticality method
• Where are the priorities for regional risk reduction activities?
Cluster analysis, self-organizing maps
![Page 19: Development of Spatial Probability Models to Estimate, Integrate, and Assess Ground- Water Vulnerability at Multiple Scales Earl A. Greene and Andrew E.](https://reader036.fdocuments.in/reader036/viewer/2022062519/5697bfb71a28abf838c9ef5a/html5/thumbnails/19.jpg)
Summary and Benefits to Resource Managers
• Improve our Knowledge of Ground-Water Quality and Vulnerability
•Develop Grid Scale (Detail Scale) Models and Up-scaled Watershed Models for Integration
•Develop Watershed or County Based Specific Models
•Many Applications
• Future Scenarios
• Human Health
• Monitoring Locations
• Management