A two-stage robust decision-making framework (2S-RDM) for flood risk adaptation under deep uncertainty

Abstract

Flood is one of the major challenges facing human societies. Adapting to future flood risks involves deep uncertainty, especially when long-term projections of climate change are considered. This study proposed a Two-stage Robust Decision Making (2S-RDM) framework to help devise flexible and robust strategies capable of addressing the inherent deep uncertainty associated with managing flood risks. Taking the Yangtze River Basin in China as a case study, we simulated flood risks across ∼0.6 million scenarios until 2050. This analysis considered four types of uncertain factors, i.e., future climate change, socio-economic growth, industrial structure transformation, and population aging. We then examined the effectiveness of four adaptation measures and their combinations, i.e. building elevation, tunnel construction, people relocation, and river basin conservation. Our projections show that without immediate adaptation, an estimated 0.9 to 27.3 million people will be impacted by floods until 2050, accompanied with $33.8 to $198.5 billion economic losses in the entire basin. When defining the goal as limiting the affected population <0.05% and ensuring economic losses <0.02%, we identified 24 global robust strategies capable of meeting this criterion in >80% of scenarios. Then, we compared the 24 global robust strategies regarding their relative costs and performances in each of the future scenario pools. The final recommended solutions are hybrid strategies that integrate engineering-based measures with ‘soft’ adaptation options (e.g. Elevation++, Tunnel++, and Relocation). This study provides tools to design flood adaptation strategies not only robust across diverse scenarios but also flexible for decision-makers to customize and refine their strategies based on specific needs.