Geospatial analysis approaches for drinking water source protection areas
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GEOSPATIAL ANALYSIS APPROACHES FOR DRINKING WATER SOURCE PROTECTION AREAS PowerPoint PPT Presentation


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GEOSPATIAL ANALYSIS APPROACHES FOR DRINKING WATER SOURCE PROTECTION AREAS. 25 th Annual ESRI International User Conference Drinking Water Supply Issues Thursday, July 28, 2005 San Diego, CA. Authors: Jamie Cajka, RTI International (presenter) William Cooter, RTI International

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GEOSPATIAL ANALYSIS APPROACHES FOR DRINKING WATER SOURCE PROTECTION AREAS

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Geospatial analysis approaches for drinking water source protection areas

GEOSPATIAL ANALYSIS APPROACHES FOR DRINKING WATER SOURCE PROTECTION AREAS

25th Annual ESRI International User Conference

Drinking Water Supply Issues

Thursday, July 28, 2005

San Diego, CA

Authors:

Jamie Cajka, RTI International (presenter)

William Cooter, RTI International

Jay Rineer, RTI International

James Sinnott, RTI International

Roger Anzzolin, US EPA, Office of Ground Water and Drinking Water


Acknowledgements

Acknowledgements

  • The work described in this presentation was funded by the U.S. Environmental Protection Agency under Contract 68-C-02-110 with Research Triangle Institute (RTI). RTI gratefully acknowledges this support.

  • Disclaimer: Although the research described has been funded wholly or in part by the U.S. Environmental Protection Agency Contract 68-C-02-110 to Research Triangle Institute, it has not been subject to the Agency's review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

  • Disclaimer: In the interest of protecting certain types of information, and in accordance with EPA protocol, the GIS data displayed in the following images were rendered for display purposes only and do not accurately represent actual data from EPA’s spatial databases.


Purpose

Purpose

  • To describe the way in which an impervious surface dataset was created using current ArcGIS tools and added to EPA’s Drinking Water Mapping Application (DWMA).

  • To summarize the utility of this data layer and its use in the DWMA.


Presentation outline

Overview of the Drinking Water Mapping Application (DWMA)

Description of data inputs

Spatial Analyst/Model Builder/Python

Creation of Impervious Surface Dataset

Conclusions

Presentation Outline


What is the dwma

A secure, ArcIMS-based geospatial application for internal use at US EPA Office of Ground Water and Drinking Water (OGWDW).

First released in 2003 (version 1.04).

“Staff and managers now have a geospatial tool for the first time within the OGWDW to run queries, obtain reports and maps vital to the efficient management of programs under the Safe Drinking Water Act (SDWA)”.

“enhances the OGWDW capabilities to identify major contaminant risks to public drinking water supplies”

For version 2, additional functionality added including the impervious surface dataset.

What is the DWMA?


Dwma overview

DWMA Overview


Creation of source protection area polygons

Creation of Source Protection Area Polygons

  • Source water areas are the land areas that contribute water and pollutants to the water supply for surface water intakes or groundwater wells that supply drinking water.

  • Only a small number had already been defined by EPA.

  • Source Protection Area polygons from 8,000 surface water intakes, as well as approximately 200,000 wells were created for inclusion in the DWMA.

  • These are the polygons over which land cover data were to be summarized, although this technique is applicable to any set of polygons.


Source protection area creation

Source Protection Area Creation

  • The polygons for the 8,000 surface water intakes were created by an algorithm that traced around the upstream reaches 15 miles from the intake.

  • This is the distance water travels in one day.


Source protection area creation1

Source Protection Area Creation

  • Polygon constructed around the reaches that fall within 15 miles of the intake.

  • Convex hulls created in Oracle Spatial.

  • Not a watershed, but a reasonable approximation.


Source protection area creation2

Source Protection Area Creation

  • Wells handled differently, since they extend vertically.

  • Source protection Area defined as simply a 1 mile radius buffer around each of approximately 200,000 wellheads.


National land cover dataset

National Land Cover Dataset

  • The NLCD was chosen since it is the most recent, nationally available, high resolution land cover dataset available.

  • The entire country is divided into 21 land cover classifications.

  • Permeability is a function of land cover.


Extraction of land cover counts

Extraction of Land Cover Counts

  • The goal of this step was to generate a cell count, by land cover category, for each of the approximately 200,000 SPA polygons.


Zonal statistics model builder

Zonal Statistics/Model Builder

  • The only ArcGIS tool that could work quickly on 200,000+ polygons was the zonal statistics as table tool found in the Spatial Analyst toolbox.

  • Unfortunately, it does not do a count by value, only a total count of cells that fall within each polygon.

  • The solution was to break each state’s NLCD raster layer into each of the 21 land cover classifications and run them separately.


Zonal statistics model builder1

Zonal Statistics/Model Builder


Export to script

Export to Script

  • Model Builder allows the user to export the model into three different scripting languages: Python, JScript, and VBScript.

  • The export results in a text file with the course objects already created and the parameters filled in.

  • For this analysis, the script needed to run for each state, and for each land cover code within the state.

  • This meant creating two nested loops around the code exported by Model Builder and replacing the hard coded parameters with variables.


Export to script1

Export to Script


Post processing

Post Processing

  • The resulting files were appended in Access.

  • Since some source protection areas span more than one state, the number of cells in each land cover code were summed.

  • The final tables contained counts by land cover type, by source protection area id.

  • Since each grid cell is 30 m X 30 m, the area consisting of each land cover could be calculated (900 sq. meter X number of cells).


Creation of impervious surface dataset

Creation of Impervious Surface Dataset

  • Data input 1 = NLCD counts by source protection area

  • Data input 2 = Impervious Cover coefficients taken from the default values of two EPA software products (ATtILA and BASINS).

  • NLCD raster information converted into a lumped parameter dataset to make estimates of percent impervious covers for all source protection areas (both intakes and wellheads).

  • Note: special analysis steps were taken for intakes in open water areas (e.g., Great Lakes intake structures) where contaminants from watershed runoff is of less concern than spills from boats and other vessels.


Dwma query

DWMA Query


Geospatial analysis approaches for drinking water source protection areas

DWMA Query


Geospatial analysis approaches for drinking water source protection areas

DWMA Query


Use of impervious surface dataset

Use of Impervious Surface Dataset

  • Examples

    • Single variable query – Source protection areas with impervious cover levels higher than 15-20% suggest significant alterations in natural runoff and infiltration patterns.

    • Multiple variable query - impervious cover can be combined with other vector data related to other potential point source and nonpoint source contaminants to establish risk in specific areas and to make comparisons between different regions.

    • Trend Analysis - As the National Land Cover Data for the 2001 baseline period (NLCD 2001) becomes available, time trend analyses can screen for source protection areas where imperviousness is changing over time.


Conclusions

Conclusions

  • In addition to updates for a wide range of useful vector data related to point source and nonpoint source contaminant risks, DWMA (Version 2) prototype provides the ability to:

    • analyze based on new raster information derived from the NLCD (the impervious surface dataset).

    • Display and analyze extensive cross-EPA program information on contaminant susceptibility for both surface water and ground water-dependent drinking water source waters.

    • Summarize vector and raster information over source protection areas to describe characteristics of the source waters serving public water systems.

  • The combination of model builder and scripting used for the impervious surface dataset creation was the right solution to summarizing NLCD over polygons.


Contact information

Contact Information

  • Jamie Cajka (presenter)

    RTI International, RTP, NC [email protected]

  • William Cooter

    RTI International, RTP, NC [email protected]

  • James Rineer

    RTI International, RTP, NC [email protected]

  • Roger Anzzolin

    US EPA, OGWDW, Washington, DC [email protected]


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