Robust hydrological simulation is key for sustainable development, water management strategies, and climate change adaptation. In recent years, deep learning methods have been demonstrated to outperform mechanistic models at the task of hydrological discharge simulation. Adoption of these methods has been catalysed by the proliferation of large sample hydrology datasets, consisting of the observed discharge and meteorological drivers, along with geological and topographical catchment descriptors. Deep learning methods infer rainfall-runoff characteristics that have been shown to generalise across catchments, benefitting from the data diversity in large datasets. Despite this, application to catchments in Africa has been limited. The lack of adoption of deep learning methodologies is primarily due to sparsity or lack of the spatiotemporal observational data required to enable downstream model training. We therefore investigate the application of deep learning models, including LSTMs, for hydrological discharge simulation in the transboundary Limpopo River basin, emphasising application to data scarce regions. We conduct a number of computational experiments primarily focused on assessing the impact of varying the LSTM model input data on performance. Results confirm that data constraints remain the largest obstacle to deep learning applications across African river basins. We further outline the impact of human influence on data-driven modelling which is a commonly overlooked aspect of data-driven large-sample hydrology approaches and investigate solutions for model adaptation under smaller datasets. Additionally, we include recommendations for future efforts towards seasonal hydrological discharge prediction and direct comparison or inclusion of SWAT model outputs, as well as architectural improvements.

翻译：稳健的水文模拟对于可持续发展、水资源管理策略和气候变化适应至关重要。近年来，深度学习模型在水文径流模拟任务中已被证明优于机理模型。大规模样本水文数据集的普及推动了这些方法的采用，这些数据集包含观测径流与气象驱动因子，以及地质和地形流域描述因子。深度学习方法能够推导出被证明具有跨流域泛化能力的降雨-径流特征，这得益于大型数据集中的数据多样性。尽管如此，其在非洲流域的应用仍然有限。深度学习方法采用不足的主要原因是缺乏支撑下游模型训练所需的时空观测数据。因此，本研究探讨了包括长短期记忆网络在内的深度学习模型在跨境Limpopo河流域水文径流模拟中的应用，重点关注数据稀缺区域的应用场景。我们开展了多项计算实验，主要评估改变长短期记忆网络模型输入数据对性能的影响。结果证实，数据约束仍然是深度学习在非洲河流流域应用的最大障碍。我们进一步阐述了人类活动对数据驱动建模的影响——这是数据驱动大样本水文方法中常被忽视的方面，并研究了小数据集下的模型适应解决方案。此外，我们还对未来季节性水文径流预测工作、SWAT模型输出的直接比较或整合，以及架构改进提出了建议。