Cargando…
Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction
Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watershed...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer-Verlag
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3529872/ https://www.ncbi.nlm.nih.gov/pubmed/23142919 http://dx.doi.org/10.1007/s00267-012-9977-4 |
_version_ | 1782253956316004352 |
---|---|
author | Jang, Taeil Vellidis, George Hyman, Jeffrey B. Brooks, Erin Kurkalova, Lyubov A. Boll, Jan Cho, Jaepil |
author_facet | Jang, Taeil Vellidis, George Hyman, Jeffrey B. Brooks, Erin Kurkalova, Lyubov A. Boll, Jan Cho, Jaepil |
author_sort | Jang, Taeil |
collection | PubMed |
description | Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested. |
format | Online Article Text |
id | pubmed-3529872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Springer-Verlag |
record_format | MEDLINE/PubMed |
spelling | pubmed-35298722013-01-03 Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction Jang, Taeil Vellidis, George Hyman, Jeffrey B. Brooks, Erin Kurkalova, Lyubov A. Boll, Jan Cho, Jaepil Environ Manage Article Understanding the best way to allocate limited resources is a constant challenge for water quality improvement efforts. The synoptic approach is a tool for geographic prioritization of these efforts. It uses a benefit-cost framework to calculate indices for functional criteria in subunits (watersheds, counties) of a region and then rank the subunits. The synoptic approach was specifically designed to incorporate best professional judgment in cases where information and resources are limited. To date, the synoptic approach has been applied primarily to local or regional wetland restoration prioritization projects. The goal of this work was to develop a synoptic model for prioritizing watersheds within which suites of agricultural best management practices (BMPs) can be implemented to reduce sediment load at the watershed outlets. The model ranks candidate watersheds within an ecoregion or river basin so that BMP implementation within the highest ranked watersheds will result in the most sediment load reduction per conservation dollar invested. The model can be applied anywhere and at many scales provided that the selected suite of BMPs is appropriate for the evaluation area’s biophysical and climatic conditions. The model was specifically developed as a tool for prioritizing BMP implementation efforts in ecoregions containing watersheds associated with the USDA-NRCS conservation effects assessment project (CEAP). This paper presents the testing of the model in the little river experimental watershed (LREW) which is located near Tifton, Georgia, USA and is the CEAP watershed representing the southeastern coastal plain. The application of the model to the LREW demonstrated that the model represents the physical drivers of erosion and sediment loading well. The application also showed that the model is quite responsive to social and economic drivers and is, therefore, best applied at a scale large enough to ensure differences in social and economic drivers across the candidate watersheds. The prioritization model will be used for planning purposes. Its results are visualized as maps which enable resource managers to identify watersheds within which BMP implementation would result in the most water quality improvement per conservation dollar invested. Springer-Verlag 2012-11-11 2013 /pmc/articles/PMC3529872/ /pubmed/23142919 http://dx.doi.org/10.1007/s00267-012-9977-4 Text en © The Author(s) 2012 https://creativecommons.org/licenses/by/2.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Article Jang, Taeil Vellidis, George Hyman, Jeffrey B. Brooks, Erin Kurkalova, Lyubov A. Boll, Jan Cho, Jaepil Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction |
title | Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction |
title_full | Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction |
title_fullStr | Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction |
title_full_unstemmed | Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction |
title_short | Model for Prioritizing Best Management Practice Implementation: Sediment Load Reduction |
title_sort | model for prioritizing best management practice implementation: sediment load reduction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3529872/ https://www.ncbi.nlm.nih.gov/pubmed/23142919 http://dx.doi.org/10.1007/s00267-012-9977-4 |
work_keys_str_mv | AT jangtaeil modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction AT vellidisgeorge modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction AT hymanjeffreyb modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction AT brookserin modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction AT kurkalovalyubova modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction AT bolljan modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction AT chojaepil modelforprioritizingbestmanagementpracticeimplementationsedimentloadreduction |