To capture the complexity of hydrological systems across regions, multidimensional domain knowledge (e.g. climate, soils, geology and topography) can be incorporated into deep learning models of streamflow behaviour. Such integration has demonstrated notable improvements in streamflow predictions, thereby enhancing accuracy and offering valuable insights for sustainable water resource management. However, this catchment-specific domain information also holds potential for assessing the suitability of deep learning models for runoff predictions under varied conditions. This study explores the wide-ranging performance of deep learning streamflow predictions across the diverse landscape of Australian catchments through the leveraging of newly-available, comprehensive hydrological and hydrogeological datasets. Data from CAMELS-AUS (the Australian adaptation of CAMELS [Catchment Attributes and MEteorology for Large-sample Studies]) and a nationwide set of hydrogeological catchment attributes are integrated at a continental scale to probe associations between deep learning prediction performance and catchment attributes. The study encompasses three steps: 1) unsupervised learning to identify common patterns of catchment attributes; 2) a continent-wide, deep learning time series model (long short-term memory [LSTM]) incorporating catchment attributes into concurrent predictions across hundreds of basins; and 3) visualising and investigating associations between high (or low) runoff prediction performance and various catchment attributes. The resulting visual analytical tool provides insights into continent-wide differences in performance and also facilitates analysis at the individual catchment level. Key findings reveal a) enhanced LSTM performance in catchments characterised by frequent or variable rainfall, hilly terrain, and low permeability; and b) challenges encountered by the LSTM in flat catchments with slow, infrequent flows, high permeability, and in predicting runoff peaks in regions of substantial summer rainfall. Understanding these performance patterns can help inform the application of global LSTMs in water resource management and hydrological forecasting. Future work may involve assessing how such domain knowledge could improve the extrapolation of predictions to ungauged catchments within each attribute cluster. This multi-catchment study highlights the scalability advantages of machine learning techniques for gaining hydrological insights at a continental scale.

中文翻译：

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澳大利亚深度学习径流预测与水文地质条件之间的关联


为了捕捉跨地区水文系统的复杂性，可以将多维领域知识（例如气候、土壤、地质和地形）纳入溪流行为的深度学习模型中。这种整合在溪流预测方面取得了显著的改进，从而提高了准确性，并为可持续的水资源管理提供了有价值的见解。然而，这种特定于流域的域信息也具有评估深度学习模型在不同条件下用于径流预测的适用性的潜力。本研究通过利用新提供的综合水文和水文地质数据集，探讨了深度学习径流预测在澳大利亚流域不同景观中的广泛性能。来自 CAMELS-AUS（澳大利亚对 CAMELS [大样本研究的流域属性和计量学] 的改编）和全国范围内的一组水文地质流域属性的数据在大陆范围内进行了整合，以探测深度学习预测性能和流域属性之间的关联。该研究包括三个步骤：1） 无监督学习，以识别集水区属性的常见模式;2） 一个大陆范围的深度学习时间序列模型（长短期记忆 [LSTM]），将流域属性纳入数百个流域的并发预测中;3） 可视化和调查高（或低）径流预测性能与各种流域属性之间的关联。由此产生的可视化分析工具提供了对整个大陆性能差异的见解，还有助于在单个流域层面进行分析。 主要研究结果显示 a） 在以频繁或多变的降雨、丘陵地形和低渗透性为特征的集水区中，LSTM 性能增强;b） LSTM 在水流缓慢、不频繁、渗透率高的平坦集水区以及预测夏季大量降雨地区的径流峰值时遇到的挑战。了解这些性能模式有助于为全球 LSTM 在水资源管理和水文预报中的应用提供信息。未来的工作可能涉及评估此类领域知识如何改进对每个属性集群内未测量集水区的预测外推。这项多流域研究强调了机器学习技术在大陆尺度上获得水文见解的可扩展性优势。