A Laboratory Watershed Model to Study the Effect of Rainfall Intensity and Soil Surface Slope on Surface Runoff Rate of Karbala Desert Soil

Abstract

Precipitation generating surface runoff is very important in various water resources development and management activities such as flood control and management, irrigation scheduling, irrigation design, and drainage networks. The aim of this study is to indicate the effect of rainfall intensity and soil surface slope on the surface runoff rate of Karbala desert soil. This study was conducted on the soil of the Karbala desert in Iraq, located between the provinces of Karbala and Najaf, extended from the longitude 32o44'186'' E and latitude 44o100'960''N. In this study, a laboratory watershed model was designed and constructed to be used to examine surface runoff of the Karbala desert soil and other hydrological properties, under the influence of different precipitation intensities (1.83 cm/min, 1.67 cm/min, 0.9 cm/min, and 0.64 cm/min) and different slopes of the soil surface (0.0 %, 2.0 %, 3.3 %, and 6.7 %). A Gene Expression Programming model was used to develop an equation of the surface runoff rate for the Karbala desert soil. The results showed that the rate of surface runoff increased with increasing rainfall intensity. The highest surface runoff rate of 101.53 cm/hr was obtained when the rainfall intensity was 1.83 cm/min, while at the rainfall intensity of 0.64 cm/min, the highest value of surface runoff rate of 31.17 cm/hr was obtained. The results also showed that the slope of the soil surface has a clear effect on the surface runoff rates. The highest value of surface runoff rate of 96.82 cm/hr was obtained when the slope of the soil surface was 0.0%, while at the slope of the soil surface of 6.7%, the highest value of surface runoff rate of 101.53 cm/hr was obtained. According to the simulation results, the surface runoff rate equation created using the Gene Expression Programming model did a great job of predicting the Karbala desert soil surface runoff, with a coefficient of determination R2 equal to 0.8812 for training and 0.8155 for testing.