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A Multi-Scale and Multi-Threshold Approach

Regional Scale Ground-Water Vulnerability Assessments in the Mid-Atlantic Region Based on Statistical Probability Models. A Multi-Scale and Multi-Threshold Approach. Investigators and Cooperators. Earl Greene, Hydrologist, USGS, Baltimore, MD Andrew LaMotte, Geographer, USGS, Baltimore, MD

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A Multi-Scale and Multi-Threshold Approach

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  1. Regional Scale Ground-Water Vulnerability Assessments in the Mid-Atlantic Region Based on Statistical Probability Models A Multi-Scale and Multi-Threshold Approach

  2. Investigators and Cooperators • Earl Greene, Hydrologist, USGS, Baltimore, MD • Andrew LaMotte, Geographer, USGS, Baltimore, MD • Nagaraj Neerchal, Professor Statistics, UMBC • Kerry Ann-Kelly, Statistician, UMBC • Minglei Lui, Statistician, UMBC • Betsy Smith, EPA, ORD, ReVA

  3. Presentation Outline • Background and Research Objectives • Model Development • Some Results at Multiple -Scales and Multiple-Management Thresholds

  4. Research Overview ReVA Goal … to develop and demonstrate an approach to comprehensive, regional-scale assessment that effectively informs decision-makers as to the magnitude, extent, distribution, and uncertainty of current and anticipated environmental risks. USGS Goal … 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.

  5. Spatial Statistics • Subset of Statistics • Area of Statistics Concerned with Making Inferences on Areas Between Data Collected at Various Points in Space • Dependency of Point Data on Geographic Factors and can be Spatially Modeled

  6. (1,5) (4,5) (3,4) (1,3) (1,4) (5,1) Defining The Problem Values: (1,5) = 100 (3,4) = 105 (1,3) = 105 (4,5) = 100 (5,1) = 115 Jay Ver Hoef, 2002

  7. Output Maps • Probabilty Maps • Standard Error Maps • Confidence Interval Maps • Quantile Maps • Prediction Maps based on a Variable (Land-Use) Change

  8. Presentation Outline • Background and Research Objectives • Model Development • Some Results at Multiple -Scales and Multiple-Management Thresholds

  9. The Regional Model • Uses only a Selected Database and Regional Spatial Statistical Method • Nitrates are used as a Surrogate for Ground-Water Vulnerability • Regional Ground-Water Vulnerability Maps • Regional Error Maps

  10. Map of the Mid-Atlantic region showing well locations

  11. DEVELOPED AGRICULTURAL FOREST, WETLANDS Statistically Grouped Land Use/Cover

  12. Spatial Dependency of Well Nitrate Concentration on Land Use Radius is Maximized at 1500 m

  13. Generalized geology of the Mid-Atlantic region

  14. Explored Variables in the Regional 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

  15. Understand Thresholds Jay Ver Hoef, 2002

  16. 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.

  17. Presentation Outline • Background and Research Objectives • Model Development • Some Results at Multiple -Scales and Multiple-Management Thresholds

  18. Regional Ground Water Vulnerability – Detail (1500m x 1500m) Scale

  19. Regional Ground Water Vulnerability – Detail (1500m x 1500m) Scale

  20. Predictive Power of Model Using the Press Statistic

  21. Ground Water Vulnerability –Watershed Scale

  22. Summary and Benefits to Resource Managers • Improve our Knowledge of Ground-Water Quality and Vulnerability • Develop Grid Scale (Detail Scale) Model • Develop Watershed or County Based Specific Models • Many Applications • Future Scenarios • Human Health • Monitoring Locations • Management

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