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Updated: Jul 2, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

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Published on: July 24, 2016

Watershed vulnerability predictions for the Ozarks using landscape models.

Ricardo D Lopez1, Maliha S Nash, Daniel T Heggem

  • 1U.S. Environmental Protection Agency, Office of Research and Development, 944 East Harmon Avenue, Las Vegas, NV 89119, USA. lopez.ricardo@epa.gov

Journal of Environmental Quality
|August 12, 2008
PubMed
Summary
This summary is machine-generated.

Landscape metrics predict water quality in the Ozark Mountains. Forested areas and reduced human use correlate with lower total phosphorus (TP) and total ammonia (TA), benefiting water health.

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Area of Science:

  • Environmental Science
  • Ecology
  • Hydrology

Background:

  • Assessing watershed health is crucial for managing water resources.
  • Identifying landscape indicators can predict water quality parameters.
  • The Upper White River watershed faces challenges related to nutrient and bacterial pollution.

Purpose of the Study:

  • To develop landscape-based indicators for predicting total phosphorus (TP), total ammonia (TA), and Escherichia coli (E. coli) concentrations.
  • To correlate remote-sensing derived landscape metrics with field-based water quality data.
  • To provide a tool for watershed managers in the Ozark Mountains for monitoring and restoration efforts.

Main Methods:

  • Utilized 46 broad-scale landscape metrics from remote sensing data.
  • Applied partial least squares (PLS) analyses to correlate landscape metrics with water quality measurements (TP, TA, E. coli).
  • Analyzed 244 sub-watersheds in the Upper White River basin.

Main Results:

  • A single significant factor explained 91% of TP variability, with barren and human-use proportions as key indicators.
  • Forested area presence and reduced fragmentation, especially near water bodies, negatively correlated with TP.
  • TA and E. coli models also showed significant relationships with landscape metrics, particularly forest, riparian, and urban attributes.

Conclusions:

  • Landscape attributes, including forest cover, riparian zones, and urban development, significantly influence water quality.
  • The developed models serve as a broad-scale vulnerability prediction tool for watershed managers.
  • Results aid in prioritizing and evaluating monitoring and restoration initiatives in the White River watershed.