Enhancing rainfall–runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics

Abstract

The advancement of data-driven models contributes to the improvement of estimating rainfall–runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have limitations in responding to the impact of soil moisture changes and runoff characteristics. To address these limitations, a method was developed for selecting predictor variables that utilize the accumulation of rainfall at various time intervals to represent soil moisture, the changes in the runoff coefficient, and runoff characteristics. Furthermore, this study investigated the utility of rainfall products [such as climate hazards group infrared precipitation with station data (CHIRPS) and global precipitation measurement (GPM)] for representing rainfall data, while also using the soil water index (SWI) to enhance runoff estimation. To assess these methods, the random forest (RF) and artificial neural network (ANN) models were utilized to simulate daily runoff. Incorporating both the rainfall and SWI data led to improved outcomes. The RF demonstrated superior performance compared with the ANN and the conceptual model, without the need for baseflow separation or antecedent runoff. Furthermore, accumulated rainfall was shown to be a valuable input for the models. These findings should facilitate the estimation of runoff in locations with limited measurement data on rainfall and soil moisture by utilizing remote sensing data.