Unravelling compound risks of hydrological extremes in a changing climate: Typology, methods and futures

• Published in: arXiv.org 2024-09
• Main Authors: Chun, Kwok P, Octavianti, Thanti, Papacharalampous, Georgia, Tyralis, Hristos, Sutanto, Samuel J, Terskii, Pavel, Mazzoglio, Paola, Treppiedi, Dario, Rivera, Juan, Dogulu, Nilay, Adeyemi Olusola, Dieppois, Bastien, Dembélé, Moctar, Moulds, Simon, Cheng, Li, Morales-Marin, Luis Alejandro, Macdonald, Neil, Toundji, Olivier Amoussou, Yonaba, Roland, Salomon Obahoundje, Massei, Nicolas, Hannah, David M, Sivarama Krishna Reddy Chidepudi, Byman Hamududu
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Climate action; Climate change; Climate science; Conditional probability; Disaster management; Disaster risk; Emergency preparedness; Extreme values; Generative artificial intelligence; Hydrology; Risk management; Risk reduction; Socioeconomic factors; Socioeconomics
• ISSN: 2331-8422

We have witnessed and experienced increasing compound extreme events resulting from simultaneous or sequential occurrence of multiple events in a changing climate. In addition to a growing demand for a clearer explanation of compound risks from a hydrological perspective, there has been a lack of attention paid to socioeconomic factors driving and impacted by these risks. Through a critical review and co-production approaches, we identified four types of compound hydrological events based on autocorrelated, multivariate, and spatiotemporal patterns. A framework to quantify compound risks based on conditional probability is offered, including an argument on the potential use of generative Artificial Intelligence (AI) algorithms for identifying emerging trends and patterns for climate change. Insights for practices are discussed, highlighting the implications for disaster risk reduction and knowledge co-production. Our argument centres on the importance of meaningfully considering the socioeconomic contexts in which compound risks may have impacts, and the need for interdisciplinary collaboration to effectively translate climate science to climate actions.