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Uncertainty Evaluation of Best Management Practice Effectiveness Based on the AnnAGNPS Model

Author

Listed:
  • Ying Chen

    (Fujian Normal University
    Fujian Normal University)

  • Binbin Lu

    (Fujian Normal University
    Fujian Normal University)

  • Chongyu Xu

    (University of Oslo)

  • Xingwei Chen

    (Fujian Normal University
    Fujian Normal University)

  • Meibing Liu

    (Fujian Normal University
    Fujian Normal University)

  • Lu Gao

    (Fujian Normal University
    Fujian Normal University)

  • Haijun Deng

    (Fujian Normal University
    Fujian Normal University)

Abstract

Uncertainty of best management practice (BMP) effectiveness is an important factor in the development of watershed management plans. This study explored the uncertainty of BMP effectiveness in reducing total nitrogen (TN) load owing to the uncertainty in hydrological parameters, thereby improving their reliability. A watershed model, annualized agricultural non-point source pollution (AnnAGNPS), was employed to evaluate the effectiveness of the four potentially feasible BMPs (i.e., riparian buffer, fertilization reduction, no-tillage, and parallel terraces) in the Shanmei Reservoir watershed, located in the southeastern coastal region of China. Annual and seasonal uncertainty variations in BMP effectiveness were evaluated based on ten parameter sets selected from 1000 parameter groups using Latin hypercube sampling. The results showed that the uncertainty of BMP effectiveness in reducing the TN load was larger than the uncertainty of TN load simulation at annual and seasonal time scales. The BMP effectiveness tended to be higher in summer than in the other seasons. The uncertainty of BMP effectiveness varied seasonally, and it was always lower in summer for most BMPs. This indicated that the impact of BMPs on reducing TN load was more effective, with a higher reduction rate and lower uncertainty in summer. Among the BMPs, the parallel terrace was the most effective measure for reducing TN load since it had the highest reduction rate and relatively low uncertainty. Although this study is a case study, it can provide a scientific reference for decision-making in uncertain situations when AnnAGNPS is applied for water quality simulations.

Suggested Citation

  • Ying Chen & Binbin Lu & Chongyu Xu & Xingwei Chen & Meibing Liu & Lu Gao & Haijun Deng, 2022. "Uncertainty Evaluation of Best Management Practice Effectiveness Based on the AnnAGNPS Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1307-1321, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03082-8
    DOI: 10.1007/s11269-022-03082-8
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    References listed on IDEAS

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    1. Xiaoyan Bai & Wen Shen & Peng Wang & Xiaohong Chen & Yanhu He, 2020. "Response of Non-point Source Pollution Loads to Land Use Change under Different Precipitation Scenarios from a Future Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 3987-4002, October.
    2. Schuwirth, Nele & Borgwardt, Florian & Domisch, Sami & Friedrichs, Martin & Kattwinkel, Mira & Kneis, David & Kuemmerlen, Mathias & Langhans, Simone D. & Martínez-López, Javier & Vermeiren, Peter, 2019. "How to make ecological models useful for environmental management," Ecological Modelling, Elsevier, vol. 411(C).
    3. Honghai Qi & Mustafa Altinakar, 2011. "Vegetation Buffer Strips Design Using an Optimization Approach for Non-Point Source Pollutant Control of an Agricultural Watershed," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 565-578, January.
    4. Chahor, Y. & Casalí, J. & Giménez, R. & Bingner, R.L. & Campo, M.A. & Goñi, M., 2014. "Evaluation of the AnnAGNPS model for predicting runoff and sediment yield in a small Mediterranean agricultural watershed in Navarre (Spain)," Agricultural Water Management, Elsevier, vol. 134(C), pages 24-37.
    5. Villamizar, Martha L. & Brown, Colin D., 2016. "Modelling triazines in the valley of the River Cauca, Colombia, using the annualized agricultural non-point source pollution model," Agricultural Water Management, Elsevier, vol. 177(C), pages 24-36.
    6. Xiaojing Ni & Prem B. Parajuli & Ying Ouyang, 2020. "Assessing Agriculture Conservation Practice Impacts on Groundwater Levels at Watershed Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1553-1566, March.
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    1. Linlin Gao & Yong Wu & Ling Li & Chi Sun & Donghao Li & Xueke Liu, 2024. "A Risk Assessment Method for Phosphorus Loss in Intensive Agricultural Areas—A Case Study in Henan Province, China," Agriculture, MDPI, vol. 14(10), pages 1-16, September.
    2. Jinfeng Yang & Xuelei Wang & Xinrong Li & Zhuang Tian & Guoyuan Zou & Lianfeng Du & Xuan Guo, 2023. "Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province," Sustainability, MDPI, vol. 15(23), pages 1-14, November.

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