Title: Explainable artificial intelligence (XAI) for scaling: An application for deducing hydrologic connectivity at watershed scale Show Chinese title

Title: 可解释的人工智能（XAI）用于扩展：一种用于推断流域尺度水文连通性的应用

Abstract: Explainable artificial intelligence (XAI) methods have been applied to interpret deep learning model results. However, applications that integrate XAI with established hydrologic knowledge for process understanding remain limited. Here we show that XAI method, applied at point-scale, could be used for cross-scale aggregation of hydrologic responses, a fundamental question in scaling problems, using hydrologic connectivity as a demonstration. Soil moisture and its movement generated by physically based hydrologic model were used to train a long short-term memory (LSTM) network, whose impacts of inputs were evaluated by XAI methods. Our results suggest that XAI-based classification can effectively identify the differences in the functional roles of various sub-regions at watershed scale. The aggregated XAI results could be considered as an explicit and quantitative indicator of hydrologic connectivity development, offering insights to hydrological organization. This framework could be used to facilitate aggregation of other geophysical responses to advance process understandings. Show Chinese abstract

Abstract: 可解释的人工智能（XAI）方法已被用于解释深度学习模型的结果。 然而，将XAI与已建立的水文知识相结合以进行过程理解的应用仍然有限。 在这里，我们展示了在点尺度上应用的XAI方法可用于水文响应的跨尺度聚合，这是尺度问题中的一个基本问题，以水文连通性为例进行说明。 使用基于物理的水文模型生成的土壤湿度及其运动来训练长短期记忆（LSTM）网络，其输入的影响通过XAI方法进行评估。 我们的结果表明，基于XAI的分类可以有效识别流域尺度上不同子区域的功能角色差异。 聚合的XAI结果可以被视为水文连通性发展的明确和定量指标，为水文组织提供见解。 该框架可用于促进其他地球物理响应的聚合，以推进过程理解。