Assessing uncertainty in best management practice effectiveness under future climate scenarios

Authors

  • Sean A. Woznicki,

    1. Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI, USA
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  • A. Pouyan Nejadhashemi

    Corresponding author
    1. Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI, USA
    • Correspondence to: A. Pouyan Nejadhashemi, Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI 48824, USA.

      E-mail: pouyan@msu.edu

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Abstract

Uncertainty of best management practice (BMP) performance in future climates is an important consideration for water resources managers. The objective of this study was to quantify the level of uncertainty in performance of seven agricultural BMPs due to climate change in reducing sediment, total nitrogen, and total phosphorus loads. The Soil and Water Assessment Tool coupled with mid-21st century climate data from the Community Climate System Model were used to develop climate change scenarios for the Tuttle Creek Lake Watershed of Kansas and Nebraska. Uncertainty level of each BMP was determined using Latin Hypercube Sampling, a constrained Monte Carlo sampling technique. Samples were taken from distributions of several variables (monthly precipitation, temperature, CO2, and BMP implementation parameters). Cumulative distribution functions were constructed for each BMP, pollutant, and climate scenario combination. Results demonstrated that BMP performance uncertainty is amplified in the extreme climate scenario. Among BMPs, native grass replacement generally had higher uncertainty level but also had the greatest reductions. This study highlights the importance of incorporating uncertainty analysis into mitigation strategies aiming to reduce negative impacts of climate change on water resources. Copyright © 2013 John Wiley & Sons, Ltd.

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