We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous United States, with 25 years of meteorological and discharge data. Nine years were reserved for independent validation; two years were used as a warm-up period, one year for each of the calibration and validation periods, respectively; from the remaining 14 years, we sampled increasing amounts of data for model calibration, and found pronounced differences in model performance. While GR4H required less data to converge, LSTM and GRU caught up at a remarkable rate, considering their number of parameters. Also, LSTM and GRU exhibited the higher calibration instability in comparison to GR4H. These findings confirm the potential of modern deep-learning architectures in rainfall-runoff modelling, but also highlight the noticeable differences between them in regard to the effect of calibration data length.

与两种人工神经网络（ANN）架构（长短期记忆网络（LSTM）和门控循环单元（GRU））相比，我们系统地探索了校准数据长度对概念性水文模型GR4H的性能的影响。最近才被引入水文学领域。我们对美国连续6个流域进行了案例研究，获得了25年的气象和流量数据。保留了九年的时间用于独立验证；预热期为两年，每个校准和验证期分别为一年；在接下来的14年中，我们对越来越多的数据进行了采样以进行模型校准，并发现模型性能存在明显差异。尽管GR4H所需的数据较少，但是 考虑到参数数量，LSTM和GRU的发展速度非常快。而且，与GR4H相比，LSTM和GRU表现出更高的校准不稳定性。这些发现证实了现代深度学习架构在降雨径流建模中的潜力，但同时也突出了它们之间在校准数据长度影响方面的明显差异。