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Improving Runoff Prediction Accuracy in a Mountainous Watershed Using a Remote Sensing-Based Approach

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  • Solmaz Fathololoumi

    (Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan 45371-38791, Iran
    School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Ali Reza Vaezi

    (Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan 45371-38791, Iran)

  • Seyed Kazem Alavipanah

    (Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Tehran 14155-6465, Iran)

  • Ardavan Ghorbani

    (Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran)

  • Mohammad Karimi Firozjaei

    (Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Tehran 14155-6465, Iran)

  • Asim Biswas

    (School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada)

Abstract

Due to the limited number and sparse distribution of meteorological and hydrometric stations in most watersheds, the runoff estimation based on these stations may not be accurate. However, the accurate determination of the Antecedent Soil Moisture (ASM) in watersheds can improve the accuracy of runoff forecasting. The objective of this study is to utilize the ASM derived from satellite imagery to enhance the accuracy of runoff estimation in a mountainous watershed. In this study, a range of Remote Sensing (RS) data, including surface biophysical and topographic features, climate data, hydrometric station flow data, and a ground-based measured SM database for the Balikhli-Chay watershed in Iran, were utilized. The Soil Conservation Service Curve Number (SCS-CN) method was employed to estimate runoff. Two approaches were used for estimating the ASM: (1) using the precipitation data recorded in ground stations, and (2) using the SM data obtained from satellite images. The accuracy of runoff estimation was then calculated for these two scenarios and compared. The mean Nash–Sutcliffe statistic was found to be 0.63 in the first scenario and 0.74 in the second scenario. The inclusion of ASM derived from the satellite imagery in the precipitation–runoff model resulted in a 51% increase in the accuracy of runoff estimation compared to using precipitation data recorded in ground stations. These findings have significant implications for improving the accuracy of ASM and runoff modeling in various applications.

Suggested Citation

  • Solmaz Fathololoumi & Ali Reza Vaezi & Seyed Kazem Alavipanah & Ardavan Ghorbani & Mohammad Karimi Firozjaei & Asim Biswas, 2023. "Improving Runoff Prediction Accuracy in a Mountainous Watershed Using a Remote Sensing-Based Approach," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12754-:d:1223183
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    References listed on IDEAS

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    1. S. K. Mishra & M. K. Jain & V. P. Singh, 2004. "Evaluation of the SCS-CN-Based Model Incorporating Antecedent Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(6), pages 567-589, December.
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    Keywords

    runoff; ASM; MODIS; HEC-HMS; SCS-CN method;
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