Flexible adaptation strategies against urban runoff uncertainty through integration of a probabilistic climate model and GBI

Abstract

Urban areas are becoming increasingly vulnerable to climate change due to shifting rainfall patterns. This study investigates adaptive strategies for stormwater management by integrating green and blue infrastructures (GBIs) under extreme climate uncertainty. A novel framework based on the analysis of reality in options (RIO) and GBIs, called (RIO-GBI), is introduced, which uses probabilistic climate modeling and dynamic decision-making to develop flexible adaptation strategies. A case study in Gorgan, Iran, applied the SSP245 climate scenario and the MRI-ESM2 model to project rainfall changes across three future periods (2025–2050, 2051–2075, and 2076–2100). The results indicate a decline of 8.48% in average precipitation during 2025–2050 compared with 2001–2024, followed by increases of 13.10% in 2051–2075 and 20.72% in 2076–2100. Using a geometric Brownian motion process, 27 plausible climate scenarios were generated and ranked by probability, with the three most likely pathways identified at 26.67, 19.81, and 10.20%, respectively. The findings highlight the effectiveness of combined GBIs, such as infiltration trenches, retention ponds. These strategies enhance urban resilience by improving stormwater management, reducing flood risks, and supporting sustainable urban development. Nevertheless, uncertainties in climate projections underscore the need for integrated uncertainty analysis in urban planning to ensure long-term adaptability.