Evaluating Agricultural BMP Effectiveness in Improving Freshwater Provisioning Under Changing Climate
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Freshwater provisioning (FWP) is a critical ecosystem service that is highly affected by climate change/variability as well as land use/land management. Agricultural best management practices (BMPs) are implemented to mitigate the adverse impacts of intensive agricultural production on flow and water quality, thus can potentially protect and improve FWP services. Many studies have assessed BMP effectiveness for improving hydrology/water quality, however the impact of climate changes on BMP effectiveness for protecting FWP is poorly understood. In this study, changes in FWP under 5 BMPs and 6 projected climate change/variability scenarios, were quantified. The Soil and Water Assessment Tool was used to quantify FWP services for baseline (1975–2004) and future climates (2021–2050). We then assessed the climate change impacts on BMP effectiveness for 13 watersheds in the Upper Mississippi River Basin. The results indicated that all 5 BMP scenarios behaved similarly under the historical and future climates, generally resulting in improved FWP services compared to the baseline agricultural management. The combined BMPs was the most effective way to enhance FWP. No-tillage and cover crops performed well in improving FWP in agriculturally-dominated watersheds, while filter strips and grassed waterways had high effectiveness in non-agriculturally dominated watersheds. Results for the climate scenarios indicate that 5 BMPs under future climate were still effective compared to baseline. The increased precipitation and rising temperatures generally improved BMP effectiveness in maintaining and improving FWP services, due to increased freshwater availability under the projected future climate.
KeywordsEcosystem service Streamflow Water quality SWAT
This study was funded by China Postdoctoral Science Foundation (Award No. 043206018), the U.S. Department of Energy (Award No. DE-EE0004396) and the USDA-NIFA (Award No. S1063). Great appreciation to Hendrik Rathjens for his technical help on bias correction of climate projection data. The authors also would like to thank anonymous reviewers for their comments and suggestions to improve the manuscript.
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Conflict of Interest
The authors declare that they have no conflict of interest.
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