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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Relating landscape characteristics to non-point source pollution in mine waste-located watersheds using geospatial
1Laboratory for GIS and Remote Sensing, Department of Geosciences, University of Missouri--Kansas City, 5110 Rockhill Road, Kansas City, MO 64110, USA. hx502@umkc.edu
Journal of Environmental Management
|March 23, 2006
Summary
Watershed landscape characteristics significantly impact water quality, especially in areas with historical mining. Understanding these landscape-water quality links is key for effective pollution management and risk reduction.
Area of Science:
- Environmental Science
- Hydrology
- Remote Sensing
Background:
- Watershed landscape characteristics are crucial for surface water quality.
- Predicting pollution potential and developing management strategies require understanding landscape-water quality relationships.
- The Tri-State Mining District faces severe heavy metal pollution due to historical mining activities.
Purpose of the Study:
- To investigate the impact of landscape characteristics on water quality in mine waste-affected watersheds.
- To quantify the relationship between landscape metrics and in-stream water quality indicators.
- To assess the predictive power of landscape characteristics for surface water quality.
Main Methods:
- Classified multi-temporal Landsat imagery over three decades to characterize land use/land cover.
- Calculated landscape metrics (proportion, edge density, contagion) from classified imagery.
- Collected and analyzed in-stream water quality data (lead, zinc, iron, cadmium, aluminum, conductivity) over three decades.
- Performed statistical analyses to correlate landscape metrics with water quality indicators.
Main Results:
- Landscape characteristics explained up to 77% of the variation in water quality indicators within mine waste-located watersheds.
- Individual landscape metrics, like the proportion of mine waste area, showed predictive power for water quality.
- The predictive power of single landscape metrics was limited, typically explaining less than 60% of the variance in water quality indicators.
Conclusions:
- Landscape characteristics are significant drivers of surface water quality in mining-affected watersheds.
- While single metrics offer some predictive capability, a comprehensive understanding of landscape-water quality dynamics is essential for effective watershed management.
- The findings highlight the importance of considering landscape factors in mitigating pollution risks in post-mining environments.
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