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Evaluation of the Time of Concentration Models for Enhanced Peak Flood Estimation in Arid Regions

Author

Listed:
  • Nassir Alamri

    (Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia)

  • Kazir Afolabi

    (Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia)

  • Hatem Ewea

    (Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia)

  • Amro Elfeki

    (Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, P.O. Box 80208, Jeddah 21589, Saudi Arabia
    Irrigation and Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

Abstract

The uncertainties in the time of concentration ( T c ) model estimate from contrasting environments constitute a setback, as errors in T c lead to errors in peak discharge. Analysis of such uncertainties in model prediction in arid watersheds is unavailable. This study tests the performance and variability of T c model estimates. Further, the probability distribution that best fits observed T c is determined. Lastly, a new T c model is proposed, relying on data from arid watersheds. A total of 161 storm events from 19 gauged watersheds in Southwest Saudi Arabia were studied. Several indicators of model performance were applied. The Dooge model showed the best correlation, with r equal to 0.60. The Jung model exhibited the best predictive capability, with normalized Nash–Sutcliffe efficiency ( NNSE ) of 0.60, the lowest root mean square error ( RMSE ) of 4.72 h, and the least underestimation of T c by 1%. The Kirpich model demonstrated the least overestimation of T c by 4%. Log-normal distribution best fits the observed T c variability. The proposed model shows improved performance with r and NNSE of 0.62, RMSE of 4.53 h, and percent bias ( PBIAS ) of 0.9%. This model offers a useful alternative for T c estimation in the Saudi arid environment and improves peak flood forecasting.

Suggested Citation

  • Nassir Alamri & Kazir Afolabi & Hatem Ewea & Amro Elfeki, 2023. "Evaluation of the Time of Concentration Models for Enhanced Peak Flood Estimation in Arid Regions," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1987-:d:1042531
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    Cited by:

    1. Mariusz Starzec & Sabina Kordana-Obuch & Daniel Słyś, 2023. "Assessment of the Feasibility of Implementing a Flash Flood Early Warning System in a Small Catchment Area," Sustainability, MDPI, vol. 15(10), pages 1-43, May.

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