Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state-of-the-art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end-to-end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short-term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning-based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

中文翻译：

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使用图神经网络探索大样本水文学的时空学习

对测量和未测量流域的流量预测在水资源规划中发挥着至关重要的作用，尤其是在气候变化的情况下。大样本水文数据集的可用性增加，加上深度学习技术的最新进展，为探索水文特征的时空模式以改进流量预测提供了新的机会。本研究的目的是适应和基准测试几种最先进的图神经网络 (GNN) 架构，包括 ChebNet、图卷积网络 (GCN) 和 GraphWaveNet，用于端到端图学习。我们将河流流域明确表示为图中的节点，学习时空节点依赖性，然后使用学习到的关系同时预测图中所有节点的流量。使用大样本研究的流域属性和气象学 (CAMELS) 数据集在固定图拓扑（转导学习）和可变图拓扑（归纳学习）两种设置下研究开发的 GNN 模型的功效，后者适用预测未测量盆地 (PUB)。结果表明，GNN 通常稳健且计算效率高，与使用长短期记忆 (LSTM) 网络训练的基线模型相比，其性能相似或更好。进行进一步的分析以解释边缘和节点级别的图学习过程，并研究不同模型配置的影响。