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

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.