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

Nassir Alamri, Kazir Afolabi (), Hatem Ewea and Amro Elfeki
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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

Sustainability, 2023, vol. 15, issue 3, 1-15

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.

Keywords: time of concentration; peak flood; flood hydrograph; uncertainty; PDF; arid regions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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