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

The uncertainties in the time of concentration (Tc) model estimate from contrasting environments constitute a setback, as errors in Tc 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 Tc model estimates. Further, the probability distribution that best fits observed Tc is determined. Lastly, a new Tc 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 Tc by 1%. The Kirpich model demonstrated the least overestimation of Tc by 4%. Log-normal distribution best fits the observed Tc 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 Tc estimation in the Saudi arid environment and improves peak flood forecasting

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

The uncertainties in the time of concentration (Tc) model estimate from contrasting environments constitute a setback, as errors in Tc 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 Tc model estimates. Further, the probability distribution that best fits observed Tc is determined. Lastly, a new Tc 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 Tc by 1%. The Kirpich model demonstrated the least overestimation of Tc by 4%. Log-normal distribution best fits the observed Tc 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 Tc estimation in the Saudi arid environment and improves peak flood forecasting

Flash flood risk assessment in urban arid environment:case study of Taibah and Islamic universities’ campuses, Medina, Kingdom of Saudi Arabia

Abstract Flooding impacts can be reduced through application of suitable hydrological and hydraulic tools to define flood zones in a specific area. This article proposes a risk matrix technique which is applied on a case study of Taibah and Islamic universities catchment in Medina, Kingdom of Saudi Arabia (KSA). The analysis is based on integration of the hydrologic model hydraulic models to delineate the flood inundation zones. A flood risk matrix is developed based on the flood occurrence probability and the associated inundation depth. The risk matrix criterion is classified according to the degree of risks as high, moderate and low. The case study has indicted low to moderate risk for flood frequencies of 5 years return periods and moderate to high risk may exist for flood with rerun period of 50 and 100 years. The results are projected on a two-dimensional satellite images that shows the geographical locations exposed to flooding. A quantitative summary of the results have been presented graphically to estimate the magnitude of the inundation areas that can assess the degree of damage and its economic aspects. The developed flood risk matrix tool is a quantitative tool to assess the damage which is crucial for decision makers

The impact of rainfall distribution patterns on hydrological and hydraulic response in arid regions:case study Medina, Saudi Arabia

Abstract Rainfall distribution patterns (RDPs) are crucial for hydrologic design. Hydrologic modeling is based on Soil Conservation Services (SCS) type RDPs (SCS type I, IA, II, and III). SCS type II method is widely used by hydrologists in arid regions. These RDPs were designed for the USA and similar temperate regions. There is no scientific justification for using SCS type II method in arid regions. The consequences of using SCS type II have impacts on the hydrologic and hydraulic modeling studies. The current paper investigates the validity of the SCS type II and in arid regions. New temporal RDPs were applied and compared with SCS type II RDPs. The produced peak discharges, volumes, maximum inundation depths, top widths, and velocities from both approaches were analyzed. An application is made on the protection channel in Taibah and Islamic Universities campuses in Medina, Saudi Arabia. A methodology was followed which included frequency analysis, catchment modeling, hydrological modeling, and hydraulic modeling. Results indicated that there are considerable consequences on infrastructural design, and hydrologic and hydraulic parameters if inappropriate RDPs are used. The investigation confirmed that the SCS type RDPs do not reflect the actual flood features in arid regions