Concurrent floods and concurrent droughts in nearby catchments pose challenges to risk assessment and water management. Climate change is affecting extremely high and low discharge, but the complex interplay between changes in individual catchments and in the dependence across catchments make it difficult to provide accurate assessments of the occurrence probabilities of concurrent extremes. In this work, we use a contemporary statistical deep learning model (the deep SPAR framework) to capture concurrent river floods and droughts in four catchments in the Upper Danube basin, based on discharge simulated by a hydrological model driven with large ensemble climate model output. The statistical model is able to accurately capture the multivariate extremes of the simulated discharge, which we assess by making use of the large available sample size. We subsequently use our statistical model to study changes in joint tail behaviour of discharge over time, finding that both compound flooding and drought-like conditions are becoming increasingly likely towards the end of the 21st century under a high-emission scenario. In particular, our results highlight that changes in the dependence structure of extremes strongly contribute to the detected changes, an aspect that would be difficult to capture with traditional approaches. This work paves the way for highly flexible, general inference on compound extremes in hydrological applications, and demonstrates key advantages of using statistical deep learning in this setting.

翻译：邻近流域的并发洪水和并发干旱给风险评估和水资源管理带来了挑战。气候变化正显著影响极端高流量和低流量事件，但单个流域变化与跨流域依赖性之间的复杂相互作用，使得准确评估并发极端事件的发生概率变得困难。本研究采用一种当代统计深度学习模型（深度SPAR框架），基于由大型集合气候模式输出驱动的水文模型模拟的流量数据，捕捉上多瑙河流域四个流域的并发河流洪水和干旱事件。该统计模型能够准确捕捉模拟流量的多元极端特征，我们利用大样本量对此进行了评估。随后，我们使用该统计模型研究流域流量随时间变化的联合尾部行为，发现在高排放情景下，到21世纪末，复合洪水事件和类似干旱的条件发生的可能性均呈现增加趋势。特别地，我们的结果强调，极端事件依赖结构的变化对检测到的变化具有显著贡献，而这一方面传统方法难以捕捉。本研究为水文应用中复合极端事件的高灵活性、通用推断铺平了道路，并展示了在此背景下使用统计深度学习的关键优势。