Environmental Management

, Volume 63, Issue 2, pp 173–184 | Cite as

Meeting Water Quality Goals by Spatial Targeting of Best Management Practices under Climate Change

  • Yuelu Xu
  • Darrell J. BoschEmail author
  • Moges B. Wagena
  • Amy S. Collick
  • Zachary M. Easton


Agricultural production is a major source of nonpoint source pollution contributing 44% of total nitrogen (N) discharged to the Chesapeake Bay. The United States Environmental Protection Agency (US EPA) established the Total Maximum Daily Load (TMDL) program to control this problem. For the Chesapeake Bay watershed, the TMDL program requires that nitrogen loadings be reduced by 25% by 2025. Climate change may affect the cost of achieving such reductions. Thus, it is necessary to develop cost-effective strategies to meet water quality goals under climate change. We investigate landscape targeting of best management practices (BMPs) based on topographic index (TI) to determine how targeting would affect costs of meeting N loading goals for Mahantango watershed, PA. We use the results from two climate models, CRCM and WRFG, and the mean of the ensemble of seven climate models (Ensemble Mean) to estimate expected climate changes and the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model to predict crop yields and N export. Costs of targeting and uniform placement of BMPs across the entire study area (423 ha) were compared under historical and future climate scenarios. Targeting BMP placement based on TI classes reduces costs for achieving water quality goals relative to uniform placement strategies under historical and future conditions. Compared with uniform placement, targeting methods reduce costs by 30, 34, and 27% under historical climate as estimated by the Ensemble Mean, CRCM and WRFG, respectively, and by 37, 43, and 33% under the corresponding estimates of future climate scenarios.


Spatial targeting Climate change Mathematical programming Economic optimization SWAT-VSA 



This work was supported by the National Science Foundation, Water, Sustainability, and Climate program [grant number CBET-1360280] and by the United States Department of Agriculture, National Institute of Food and Agriculture, Hatch project VA-135911, accession number 227572. The authors express appreciation to Ray Najjar and Andrew Ross for assistance in generating climate data.

Supplementary material

267_2018_1133_MOESM1_ESM.docx (30 kb)
Supplementary Information


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Agricultural and Applied EconomicsVirginia TechBlacksburgUSA
  2. 2.Department of Biological Systems EngineeringVirginia TechBlacksburgUSA
  3. 3.Department of Agriculture, Food and Resource SciencesUniversity of Maryland Eastern ShorePrincess AnneUSA

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