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Assessing the Anthropogenic and Climatic Components in Runoff Changes of the São Francisco River Catchment

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  • Larissa S. Melo

    (Federal University of Minas Gerais)

  • Veber A. F. Costa

    (Federal University of Minas Gerais)

  • Wilson S. Fernandes

    (Federal University of Minas Gerais)

Abstract

Quantifying the impacts caused by climate change and anthropogenic intervention on runoff is necessary for defining different adaptation strategies to climate change and understanding the future pattern of water and soil use for different human activities. Based on this rationale, this study proposes to associate the dynamics of flow variability in the São Francisco River catchment with that of climatic and anthropic covariates, by utilizing the Budyko framework and the decomposition method. Our results suggested that streamflow decreased in about 183 mm, 56 mm and 26 mm in the upper, middle and lower portions of the catchment, respectively. Climate change is the most prominent factor in the upstream region (75%, 43% and 42%), but the influence of human activities becomes more pronounced in the downstream areas (27%, 57% and 58%). However, as the drainage areas increase, model performance deteriorates, which introduces higher levels of bias and uncertainty to the attribution results.

Suggested Citation

  • Larissa S. Melo & Veber A. F. Costa & Wilson S. Fernandes, 2023. "Assessing the Anthropogenic and Climatic Components in Runoff Changes of the São Francisco River Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3615-3629, July.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03516-x
    DOI: 10.1007/s11269-023-03516-x
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    References listed on IDEAS

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    1. Adam Krajewski & Anna E. Sikorska-Senoner & Leszek Hejduk & Kazimierz Banasik, 2021. "An Attempt to Decompose the Impact of Land Use and Climate Change on Annual Runoff in a Small Agricultural Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 881-896, February.
    2. Hsin-Fu Yeh & Jyun Tsao, 2020. "Hydrological Response to Natural and Anthropogenic Factors in Southern Taiwan," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    3. Cheng Zhang & Chuansen Wu & Zedong Peng & Shengyang Kuai & Shanghong Zhang, 2022. "Synergistic Effects of Changes in Climate and Vegetation on Basin Runoff," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3265-3281, July.
    4. Shuai Li & Tao Du & Christopher James Gippel, 2022. "A Modified Fu (1981) Equation with a Time-varying Parameter that Improves Estimates of Inter-annual Variability in Catchment Water Balance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1645-1659, March.
    5. Denwood, Matthew J., 2016. "runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i09).
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