Flood modelling and flood decision making: a scoping review of the progress of flood technologies and applications

Abstract

Flood intensity and frequency are expected to continue rising due to climate change, necessitating improved prevention measures. Despite the growing body of research, communities and economies remain severely affected by the consequences of floods, underscoring the need to examine how flood models are understood and translated into disaster preparedness and mitigation strategies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR), a total of 824 articles published between 1980 and March 2026 were selected from the Web of Science, Scopus, and IEEEXplore. The review maps advance across several technologies, including hydrological modelling, geospatial analysis, multi-criteria decision analysis (MCDA), and artificial intelligence (AI), and explores how heuristic and metaheuristic approaches couple flood management and analysis. An exponential increase in the adoption of AI and MCDA was identified, driving an exponential increase in flood susceptibility and flood risk mapping across the reviewed literature. This review indicates that modern technologies play a decisive role in advancing flood applications; however, key methodological limitations persist. These limitations include a limited integration of urban infrastructure, stormwater drainage, and groundwater variability into flood prediction and assessment. A lack of coherent methodologies connecting discharge, surface runoff, urban flash floods, and prevention measures remains a significant gap. The translation of rainfall-runoff and discharge prediction into flood analysis is an ongoing challenge. Future research should therefore prioritise integrating runoff potential, discharge modelling, surface water drainage, and groundwater variability into flood applications. A decentralised methodological approach is recommended to empower state and local governments to implement adaptive, context-specific flood solutions, thereby enhancing early warning systems, urban planning, and emergency response. Furthermore, this review surfaces critical questions warranting focused investigation by global research communities: (i) relative importance of surface runoff as a flood-generating mechanism across different hydrological settings, and it weighting in flood susceptibility and risk mapping; (ii) hydrological distinctions and predictive possibilities between rainfall-runoff and observed streamflow modelling, and under what conditions do they best explain observed flooding, and (iii) do runoff calibration reliably explain flood extents and severity in urban settings and what methodological framework can make this operationally viable in data scarce environment?. Addressing these questions can advance the integration of hydrological processes and practical flood prediction and management.