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Updated: Apr 19, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Prioritizing watersheds for conservation actions in the southeastern coastal plain ecoregion
Taeil Jang1, George Vellidis, Lyubov A Kurkalova
1Rural Construction Engineering Department, Institute of Agricultural Science and Technology, Chonbuk National University, Jeonju-si, Jeollabuk-do, 561-756, Republic of Korea, tjang@jbnu.ac.kr.
Abstract:
The aim of this study was to apply and evaluate a recently developed prioritization model which uses the synoptic approach to geographically prioritize watersheds in which Best Management Practices (BMPs) can be implemented to reduce water quality problems resulting from erosion and sedimentation. The model uses a benefit-cost framework to rank candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most water quality improvement per conservation dollar invested. The model was developed to prioritize BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS Conservation Effects Assessment Project (CEAP). We applied the model to HUC-8 watersheds within the southeastern Coastal Plain ecoregion (USA) because not only is it an important agricultural area but also because it contains a well-studied medium-sized CEAP watershed which is thought to be representative of the ecoregion. The results showed that the three HUC-8 watersheds with the highest rankings (most water quality improvement expected per conservation dollar invested) were located in the southern Alabama, northern Florida, and eastern Virginia. Within these watersheds, measures of community attitudes toward conservation practices were highly ranked, and these indicators seemed to push the watersheds to the top of the rankings above other similar watersheds. The results, visualized as maps, can be used to screen and reduce the number of watersheds that need further assessment by managers and decision-makers within the study area. We anticipate that this model will allow agencies like USDA-NRCS to geographically prioritize BMP implementation efforts.
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