Estimation of surface runoff using satellite data in arid regions: case study of Jalajil Dam

The measurement of surface runoff holds pivotal significance in the prediction of rainfall and runoff patterns, with the outcomes of these predictive models playing a crucial role in sustainable water resource management. The accuracy and availability of surface runoff measurements considerably influence the level of uncertainty inherent in the model outcomes. Particularly in developing nations, continuous and reliable surface runoff data is often lacking. However, the advantage of satellite imagery lies in its constant and cost-free accessibility, offering a potential avenue for comprehensive measurements of the hydrological cycle. It is noteworthy that previous research has illuminated biases in satellite-based measurements, emphasizing the need for bias removal. This paper introduces an uncomplicated approach for calculating runoff measurements, segmented into three distinct stages. The initial phase involves the mapping of surface water using the Normalized Difference Water Index (NDWI). The subsequent stage entails the adjustment of estimated surface areas through a linear regression mechanism, based on average monthly surface area data. The final stage encompasses the computation of water volumes, facilitated by a storage elevation curve. To rigorously examine the efficacy of the proposed methodology, the study applies the correlation coefficient (CC), mean errors (ME), root-mean-square error (RMSE), and Nash Sutcliffe efficiency coefficient (NSE) to assess the performance at the Jalajil Dam in Saudi Arabia, spanning from March 2017 to September 2021. This timeline is divided into calibration (March 2017–August 2020) and validation (September 2020–September 2021) phases to ascertain the constant and slope of the linear regression. Overall, the methodology demonstrates promising results: Monthly calibrated runoff volumes exhibit a CC of approximately 0.98, the mean of ME settles around − 0.01 MCM, RMSE stands at 0.07 MCM, and the NSE attains a value of 0.91. The outcomes underscore that the proposed approach is well-suited for estimating runoff volumes at the dam, offering a viable option when ground observations are limited or unattainable.