SWAT-Driven Exploration of Runoff Dynamics in Hyper-Arid Region, Saudi Arabia: Implications for Hydrological Understanding

Abstract

Hydrological modeling plays a vital role in water-resource management and climate-change studies in hyper-arid regions. In the present investigation, surface runoff was estimated by a Soil and Water Assessment Tool (SWAT) model for Wadi Al-Aqul, Saudi Arabia. The Sequential Uncertainty Fitting version 2 (SUFI-2) technique in SWAT-CUP was adopted for the sensitivity analysis, calibration, and validation of the SWAT model’s components. The observational runoff data were scarce and only available from 1979 to 1984; such data scarcity is a common problem in hyper-arid regions. The results show good agreement with the observed daily runoff, as indicated by a Pearson Correlation Coefficient (r) of 0.86, a regression (R2) of 0.76, and a Nash–Sutcliffe coefficient (NSE) of 0.61. Error metrics, including the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), were notably low at 0.05 and 0.58, respectively. In the daily validation, the model continued to perform well, with a correlation of 0.76 and regression of 0.58. As a new approach, fitted parameters of daily calibration were incorporated into the monthly simulation, and they demonstrated an even better performance. The correlation coefficient (regression) and Nash–Sutcliffe were found to be extremely high during the calibration period of the monthly simulation, reaching 0.97 (0.95) and 0.73, respectively; meanwhile, they reached 0.99 (0.98) and 0.63 in the validation period, respectively. The sensitivity analysis using the SUFI-2 algorithm highlighted that, in the streamflow estimation, the Curve Number (CN) was found to be the most responsive parameter, followed by Soil Bulk Density (SOL_BD). Notably, the monthly results showed a higher performance than the daily results, indicating the inherent capability of the model in regard to data aggregation and reducing the impact of random fluctuations. These findings highlight the applicability of the SWAT model in predicting runoff and its implication for climate-change studies in hyper-arid regions.