Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

降雨-径流模型是洪水预报、水资源管理和干旱预警的重要工具。随着空间技术的进步，大量的卫星降水产品 (SPP) 可以公开获得。然而，卫星数据在数据驱动的降雨径流模型中的应用正在兴起，需要仔细研究。在这项工作中，评估了两个卫星降雨数据集，即全球降水测量综合多卫星反演产品 V6 (GPM-IMERG) 和气候灾害组红外降水与站 (CHIRPS)，用于开发降雨径流模型和提前 1 天流量的预测。将 SPP 数据的准确性与印度气象局 (IMD) 网格化的降水数据集进行了比较。探测指标表明，对于小雨（1-10 mm），探测概率（POD）值在0.67和0.75之间，并且随着降雨范围的增加，即中强降雨（10-50 mm和>50 mm） , POD 值在 0.24 到 0.45 之间。这些结果表明，参考 IMD 网格数据集，卫星降水表现令人满意。利用印度东部两个流域近二十年（2000-2018）的日降水量数据，开发了人工神经网络、极限学习机（ELM）和长短时记忆（LSTM）模型，用于降雨-径流造型。使用已开发的降雨-径流模型进行的前一天径流预测证实，两种 SPP 都足以驱动降雨-径流模型，并使用 Nash-Sutcliffe 效率系数、相关系数和均方根估算出合理的准确度错误。特别是，使用 LSTM 和 GPM-IMERG 输入的 Vamsadhara 河流域 (VRB) 的 1 天流量预测导致 Nash-Sutcliffe 效率系数 (NSC) 值为 0.68 和 0.67，而 Mahanadhi 河流域 (MRB) 的 ELM 模型在训练和验证阶段，使用相同的输入导致 NSC 值分别为 0.86 和 0.87。同时，对于 VRB 具有 CHIRPS 输入的 LSTM 模型导致 NSC 值为 0.68 和 0.65，对于 MRB 具有 CHIRPS 输入的 ELM 模型导致 NSC 值为 0.89 和 0.88，. 这些结果表明，这两种 SPP 都可以可靠地与 LSTM 和 ELM 模型一起用于降雨径流建模和流量预测。本文强调使用 GPM-IMERG 产品的深度学习模型（例如 ELM 和 LSTM）可以为洪水易发流域的洪水预报开辟新的视野。