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.

中文翻译：

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利用土壤水分指数反映径流特征，利用机器学习模型提高降雨径流模型精度

摘要数据驱动模型的进步因其在数据需求和高性能方面的优势而有助于降雨径流估算模型的改进。然而，仅依赖降雨数据的数据驱动模型在响应土壤湿度变化和径流特征的影响方面存在局限性。为了解决这些局限性，开发了一种选择预测变量的方法，该方法利用不同时间间隔的降雨积累来表示土壤湿度、径流系数的变化和径流特征。此外，本研究还调查了降雨产品 [例如气候灾害组红外降水与站数据 (CHIRPS) 和全球降水测量 (GPM)] 代表降雨数据的效用，同时还使用土壤水分指数 (SWI) 来增强径流估计。为了评估这些方法，利用随机森林（RF）和人工神经网络（ANN）模型来模拟每日径流。结合降雨量和 SWI 数据可以改善结果。与 ANN 和概念模型相比，RF 表现出优越的性能，无需基流分离或先行径流。此外，累积降雨量被证明是模型的宝贵输入。这些发现应有助于利用遥感数据估算降雨和土壤湿度测量数据有限的地区的径流。