Advertisement

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
Article
  • 58 Downloads

Abstract

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.

Keywords

Spatial targeting Climate change Mathematical programming Economic optimization SWAT-VSA 

Notes

Acknowledgements

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

References

  1. Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, van Griensven A, Van Liew MW, Kannan N, Jha MK (2012) SWAT: Model use, calibration, and validation. Transactions of the ASABE 55(4):1491–1508CrossRefGoogle Scholar
  2. Azzaino Z, Conrad JM, Ferraro PJ (2002) Optimizing the riparian buffer: Harold Brook in the Skaneateles Lake Watershed, New York. Land Econ 78(4):501–514CrossRefGoogle Scholar
  3. Bosch DJ et al. (2018) Meeting water quality goals under climate change in Chesapeake Bay watershed, USA. Journal of the American Water Resources Association 54(6):1239–1257CrossRefGoogle Scholar
  4. Bryant RB et al. (2011) US Department of Agriculture Agricultural Research Service Mahantango Creek Watershed, Pennsylvania, United States: Physiography and history. Water Resour Res 47(8):1–5.  https://doi.org/10.1029/2010WR010056 CrossRefGoogle Scholar
  5. Chesapeake Bay Foundation (2015) Best management practices. http://www.cbf.org/about-the-bay/issues/agriculture/best-management-practices
  6. Chesapeake Bay Program (2015) Chesapeake Statistics, United States Environmental Protection Agency. https://stat.chesapeakebay.net/?q=node/130&quicktabs_10=1. Accessed Apr 2015
  7. Collick AS et al. (2015) Predicting phosphorus dynamics in complex terrains using a variable source area hydrology model. Hydrol Process 29(4):588–601CrossRefGoogle Scholar
  8. Cools J et al. (2011) Coupling a hydrological water quality model and an economic optimization model to set up a cost-effective emission reduction scenario for nitrogen. Environ Model & Softw 26(1):44–51CrossRefGoogle Scholar
  9. Cooper SR (1995) Chesapeake Bay watershed historical land use: impact on water quality and diatom communities. Ecol Appl 5(3):703–723CrossRefGoogle Scholar
  10. Devereux OH, Rigelman JR (2014) CAST: An Online Tool for facilitating local involvement in watershed implementation plans for the Chesapeake Bay Total Maximum Daily Load. Journal of Water Management Modeling. https://www.chijournal.org/C364.  https://doi.org/10.14796/JWMM.C364
  11. Dillaha TA, Simpson TW, Weammert SE (2009) Offstream watering with fencing and offstream watering without fencing practices. Final Report December 2009, p 414Google Scholar
  12. Easton ZM et al. (2008) Re-conceptualizing the soil and water assessment tool (SWAT) model to predict runoff from variable source areas. J Hydrol 348(3):279–291CrossRefGoogle Scholar
  13. FAO (2007) State of the World’s Forests 2007. Food & Agriculture OrgGoogle Scholar
  14. GAMS Development Corporation (2018) User’s Guide. GAMS Development Corporation, Washington D.C. https://www.gams.com/latest/docs/UG_MAIN.html
  15. Giri S, Nejadhashemi AP, Woznicki SA (2012) Evaluation of targeting methods for implementation of best management practices in the Saginaw River Watershed. J. Environ. Manag. 103:24–40CrossRefGoogle Scholar
  16. Giri S et al. (2014) Analysis of best management practice effectiveness and spatiotemporal variability based on different targeting strategies. Hydrol Process 28(3):431–445CrossRefGoogle Scholar
  17. Jeppesen E et al. (2011) Climate change effects on nitrogen loading from cultivated catchments in Europe: implications for nitrogen retention, ecological state of lakes and adaptation. Hydrobiologia 663(1):1–21CrossRefGoogle Scholar
  18. Jha MK et al. (2010) Targeting land-use change for nitrate-nitrogen load reductions in an agricultural watershed. J Soil Water Conserv 65(6):342–352CrossRefGoogle Scholar
  19. Khanna M, Yang W, Farnsworth R, Önal H (2003) Cost-effective targeting of land retirement to improve water quality with endogenous sediment deposition coefficients. Am J Agric Econ 85(3):538–553CrossRefGoogle Scholar
  20. Kosten S et al. (2012) Warmer climates boost cyanobacterial dominance in shallow lakes. Glob Change Biol 18(1):118–126CrossRefGoogle Scholar
  21. McCarl BA, Spreen TH (1997) Applied Mathematical Programming Using Algebraic Systems.. Texas A&M, College Station, Texas. http://agecon2.tamu.edu/people/faculty/mccarl-bruce/mccspr/thebook.pdf, Accessed 12 Feb 2018
  22. Mearns LO et al. (2009) A regional climate change assessment program for North America. EOS, Trans Am Geophys Union 90(36):311–311CrossRefGoogle Scholar
  23. Nakicenovic N et al. (2000) Special report on emissions scenarios (SRES), a special report of Working Group III of the intergovernmental panel on climate change. Cambridge, UK. Cambridge University PressGoogle Scholar
  24. Nutrient Management Expert Panel (2015) Nutrient management: Recommendations for approval by the Water Quality Goal Implementation Team’s Watershed Technical and Agricultural Workgroups. Phase 5.3.2. Chesapeake Bay Program, Annapolis, MarylandGoogle Scholar
  25. Penn State (2015) The Agronomy Guide 2015–2016. College of Agricultural Science. State College, PennsylvaniaGoogle Scholar
  26. Rao NS et al. (2009) Modeling watershed-scale effectiveness of agricultural best management practices to reduce phosphorus loading. J Environ Manag 90(3):1385–1395CrossRefGoogle Scholar
  27. Rhodes JL et al. (2011) Broiler production management for potential and existing growers. University of Maryland Extension Bulletin, College Park, Maryland. >https://extension.umd.edu//sites/extension.umd.edu/files/_docs/POULTRY_BroilerProductionManagement_final1.pdfGoogle Scholar
  28. Ritter WF, Scarborough RW, Chirnside AEM (1998) Winter cover crops as a best management practice for reducing nitrogen leaching. J Contam Hydrol 34(1):1–15CrossRefGoogle Scholar
  29. Rummukainen M (2010) State-of-the-art with regional climate models. Wiley Interdiscip Rev: Clim Change 1(1):82–96.  https://doi.org/10.1002/wcc.8 Google Scholar
  30. Stubbs M (2014) Conservation Reserve Program (CRP): Status and issues. Congressional Research Service Report, 42783, 24pGoogle Scholar
  31. United States Department of Agriculture (USDA) (2015) Crop progress and condition. National Agricultural Statistics Service, Northeastern Regional Field Office. http://www.nass.usda.gov/Statistics_by_State/Pennsylvania/Publications/Crop_Progress_and_Condition/
  32. United States Department of Agriculture (USDA) (2016) Conservation reserve program monthly summary - November 2016. https://www.fsa.usda.gov/Assets/USDA-FSA-Public/usdafiles/Conservation/PDF/NOV2016%20CRP%20Summary.pdf
  33. United States Environmental Protection Agency (USEPA) (2010a) Chesapeake Bay Total Maximum Daily Load (TMDL) Fact Sheet. Washington, DC. USEPA. http://www.epa.gov/reg3wapd/pdf/pdf_chesbay/BayTMDLFactSheet8_26_13.pdf. Accessed 1 July 2016
  34. United States Environmental Protection Agency (USEPA) (2010b) Chesapeake Bay Total Maximum Daily Load for nitrogen, phosphorus, and sediment. USEPA Region III. Philadelphia, PA. Phase 1 document: Commonwealth of Virginia Chesapeake Bay TMDL Phase I Watershed Implementation Plan Revision of the Chesapeake Bay Nutrient and Sediment Reduction Tributary Strategy November 29. USEPAGoogle Scholar
  35. Van Houtven G et al. (2012) Nutrient credit trading for the Chesapeake Bay: An economic study. RTI International, Research Triangle Park, NCGoogle Scholar
  36. Wagena MB et al. (2018) Impact of climate change and climate anomalies on hydrologic and biogeochemical processes in an agricultural catchment of the Chesapeake Bay watershed, USA. Sci Total Environ 637:1443–1454CrossRefGoogle Scholar
  37. Wagena MB, Easton ZM (2018) Agricultural conservation practices can help mitigate the impact of climate change. Sci Total Environ 635:132–143CrossRefGoogle Scholar
  38. Walthall CL et al. (2013) Climate change and agriculture in the United States: Effects and adaptation. USDA Technical Bulletin 1935, Washington DC, p 186Google Scholar
  39. Willis DB, Privette C (2017) A cost effective modeling approach for targeting the location of best management practices within a rapidly growing urban watershed to achieve regional water quality standards. In 2017 Annual Meeting, 4–7 February 2017, Mobile, Alabama (No. 252820). Southern Agricultural Economics AssociationGoogle Scholar
  40. Wu J et al. (2006) A water quality based approach for watershed wide BMP strategies. J Am Water Resour Assoc 42(5):1193–1204CrossRefGoogle Scholar
  41. Yang W, Weersink A (2004) Cost-effective targeting of riparian buffers. Can J Agric Econ/Rev Can d’agroeconomie 52(1):17–34CrossRefGoogle Scholar
  42. Yang W et al. (2005) Spatial targeting of conservation tillage to improve water quality and carbon retention benefits. Can J Agric Econ/Rev Can d’agroeconomie 53(4):477–500CrossRefGoogle Scholar
  43. Yang W, Liu W, Liu Y, Corry RC, Kreutzwiser RD (2014) Cost-effective targeting ofriparian buffers to achieve water quality and wildlife habitat benefits. International Journal of River Basin Management 12(1):43–55CrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations