# Presentation: 100769

## Regional Scale Ground-Water Vulnerability Assessments in the Mid-Atlantic Region Based on Statistical Probability Models

**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

**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

## Presentation Outline

**Presentation Outline**

- Background and Research Objectives
- Model Development
- Some Results at Multiple -Scales and Multiple-Management Thresholds

## 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.

## Spatial Statistics

**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

## Defining The Problem

**Values:**
-   **(1,5) = 100**
**  ****(3,4) = 105**
**  ****(1,3) = 105**
**  ****(4,5) = 100**
**  ****(5,1) = 115**

**Defining The Problem**

- Jay Ver Hoef, 2002

## Probabilty Maps
Standard Error Maps
Confidence Interval Maps
Quantile Maps
Prediction Maps based on a Variable (Land-Use) Change

**Standard Error Maps**
**Confidence Interval Maps**
**Quantile Maps**
**Prediction Maps based on a Variable (Land-Use) Change**

**Output  Maps**

## Presentation Outline

**Presentation Outline**

- Background and Research Objectives
- Model Development
- Some Results at Multiple -Scales and Multiple-Management Thresholds

## 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

## Map of the Mid-Atlantic region showing well locations

**Map of the Mid-Atlantic region showing well locations**

## Statistically Grouped Land Use/Cover

## Spatial Dependency of Well Nitrate Concentration on Land Use

- Radius is Maximized   at 1500 m

## Generalized geology of the Mid-Atlantic region

**Generalized geology of the Mid-Atlantic region**

## 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

## Understand Thresholds

**Understand Thresholds**

- Jay Ver Hoef, 2002

## 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.

## Presentation Outline

**Presentation Outline**

- Background and Research Objectives
- Model Development
- Some Results at Multiple -Scales and Multiple-Management Thresholds

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

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

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

## Predictive Power of Model Using the Press Statistic

**Predictive Power of Model Using the Press Statistic**

## Ground Water Vulnerability –Watershed Scale

## 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