Adapting water resources systems to climate change requires identifying hydroclimatic signals that reliably indicate long-term transitions to vulnerable system states. While recent studies have classified the conditions under which vulnerability occurs (i.e., scenario discovery), there remains an opportunity to extend such methods into a dynamic planning context to design and assess early warning signals. This study contributes a machine learning approach to classifying the occurrence of long-term water supply vulnerability over lead times ranging from 0 to 20 years, using a case study of the northern California reservoir system. Results indicate that this approach predicts the occurrence of future vulnerabilities in validation significantly better than a random classifier, given a balanced set of training data. Accuracy decreases at longer lead times, and the most influential predictors include long-term monthly averages of reservoir storage. Dynamic early warning signals can be used to inform monitoring and detection of vulnerabilities under a changing climate.

使水资源系统适应气候变化需要确定水文气候信号，这些信号可靠地表明向脆弱的系统状态的长期过渡。尽管最近的研究对发生漏洞的条件（即场景发现）进行了分类，但仍有机会将此类方法扩展到动态规划环境中，以设计和评估预警信号。这项研究使用北加州水库系统的案例研究，提供了一种机器学习方法，可以对交货期为0至20年的长期供水脆弱性进行分类。结果表明，在给定平衡的训练数据集的情况下，这种方法比随机分类器更好地预测了验证中未来漏洞的发生。随着交货时间的延长，精度会下降，最有影响力的预测因素包括储层长期每月平均储量。动态预警信号可用于通知监视和检测气候变化情况下的漏洞。