Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 and 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1–6 h of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available.

由于各种因素（如降雨特征，土壤特性，土地利用和时间滞后）之间复杂的非线性关系，因此准确的降雨径流建模尤其具有挑战性。最近，随着计算系统和资源的改进，数据驱动的模型在径流预报中表现出良好的性能。但是，尚不清楚使用小时间分辨率的常见数据驱动模型的相对性能。这项研究提出了使用人工神经网络，支持向量回归和长期短期记忆方法以及分布式强迫数据进行数据驱动模型的应用，这些数据用于美国加利福尼亚州俄罗斯河流域2010年至2019年的径流预测。这些模型用于预测降雨，土壤水分，北美土地数据同化系统提供的基本流量和土地表面温度数据集。使用各种统计指标，根据季节性和基于事件的绩效对预测结果进行了评估。结果表明，长期-短期记忆和支持向量回归模型在每小时径流预报方面优于人工神经网络模型，并且在湿季相比干季，该模型的预测性能更高。此外，将数据驱动模型的结果与完全分布式的基于物理的水文模型国家水模型进行的比较表明，长期短期记忆和支持向量回归模型可提供可比的性能。