This study introduces a framework that strategically applies a Long Short-Term Memory (LSTM)-based approach for monthly runoff prediction in South Africa and Central Asia. The framework is distinct in its utilization of newly developed Global Land Data Assimilation System (GLDAS)-derived climate dynamics variables and Global Streamflow Indices and Metadata Archives (GSIM)-derived static descriptors, ensuring a consistent evaluation of Deep Learning (DL) performance across multiple continents, including North and South America, and Western Europe. Seven LSTM models were trained using seven different datasets, each representing a combination of these data-rich regions. We assessed the sensitivity of these seven training data sets to LSTM models by testing these trained models in predicting monthly basin-scale runoff across 214 test catchments located in South Africa and Central Asia. Our results show that runoff predictions generated by LSTM within the test domain could exhibit better prediction skills compared to those derived from GLDAS datasets. The performance of the trained LSTMs appears to be linked to hydrological similarities between the data-rich regions and the test basins. Also, our results indicate the importance of selecting the appropriate input sources for the LSTM models to achieve accurate runoff predictions at the test region. We emphasize the possibility of utilizing LSTM models that are leveraging on either North American catchments or a combination of South American and Western European catchments to predict basin-scale runoff in the test regions. To this end, this study harnesses the burgeoning availability of publicly stream-gauge datasets and DL to enhance water information prediction in ungauged regions, responding to the challenge of geographically unbalanced stream gauge instruments.

中文翻译：

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利用公开可用的流量计数据集和深度学习来估计未计量区域的月流域规模径流的框架


本研究引入了一个框架，战略性地应用基于长短期记忆（LSTM）的方法来预测南非和中亚的月径流。该框架的独特之处在于利用了新开发的全球土地数据同化系统（GLDAS）衍生的气候动态变量和全球水流指数和元数据档案（GSIM）衍生的静态描述符，确保对深度学习（DL）性能的一致评估多个大陆，包括北美、南美以及西欧。七个 LSTM 模型使用七个不同的数据集进行训练，每个数据集代表这些数据丰富区域的组合。我们通过测试这些经过训练的模型来预测位于南非和中亚的 214 个测试集水区的月流域规模径流，从而评估了这七个训练数据集对 LSTM 模型的敏感性。我们的结果表明，与 GLDAS 数据集生成的径流预测相比，测试域内 LSTM 生成的径流预测可以表现出更好的预测技能。经过训练的 LSTM 的性能似乎与数据丰富的区域和测试流域之间的水文相似性有关。此外，我们的结果表明为 LSTM 模型选择适当的输入源以在测试区域实现准确的径流预测的重要性。我们强调利用 LSTM 模型的可能性，这些模型利用北美流域或南美和西欧流域的组合来预测测试区域的流域规模径流。 为此，本研究利用不断涌现的公共流量计数据集和深度学习来增强未计量地区的水信息预测，应对地理上不平衡的流量计仪器的挑战。