An Integrated Multidimensional Resilience Index for urban areas prone to flash floods: Development and validation

Estefanía Aroca-Jiménez; José María Bodoque; Juan Antonio García

doi:10.1016/j.scitotenv.2023.164935

An Integrated Multidimensional Resilience Index for urban areas prone to flash floods: Development and validation

Sci Total Environ. 2023 Oct 10:894:164935. doi: 10.1016/j.scitotenv.2023.164935. Epub 2023 Jun 19.

Authors

Estefanía Aroca-Jiménez¹, José María Bodoque², Juan Antonio García³

Affiliations

¹ Department of Mining and Geological Engineering, University of Castilla-La Mancha, Avda. Carlos III, Toledo 45071, Spain. Electronic address: estefania.aroca@uclm.es.
² Department of Mining and Geological Engineering, University of Castilla-La Mancha, Avda. Carlos III, Toledo 45071, Spain.
³ Department of Business Administration, University of Castilla-La Mancha, Avda. Real Fábrica de Sedas, Talavera de la Reina 45600, Spain.

PMID: 37343874
DOI: 10.1016/j.scitotenv.2023.164935

Abstract

Resilience analysis is critical in developing flash flood risk reduction strategies in the context of global change and sustainable development. The most common method for assessing resilience is index-based. Nevertheless, the resulting indices typically fail to represent resilience's multidimensional character since they frequently disregard all involved dimensions (i.e., social, economic, environmental, physical, institutional, and cultural). Furthermore, regional resilience indices are rarely externally validated in urban areas prone to flash flooding because the required data are limited and flash flooding does not occur concurrently throughout the study region. This research developed and validated a regional Integrated Multidimensional Resilience Index (IMRI) in urban flash flood-prone areas to address the aforementioned knowledge gaps. The Monte Carlo method enabled internal validation of the IMRI following uncertainty and sensitivity analyses. Latent Class Cluster Analysis (LCCA) was used to characterize resilience, leveraging resulting regional spatial patterns. The findings obtained revealed that the most resilient urban areas have greater social and cultural resilience, while the least resilient urban areas should strengthen their social and institutional resilience. Validation results demonstrated a low bias between the IMRI scores and the control statistics derived from the Monte Carlo analysis as well as a higher than 80 % probability of not getting variations in the resilience categories, confirming the robustness of the IMRI. Through LCCA, five distinct regional spatial patterns of resilience were identified. The methodological approach deployed here enabled the identification of the underlying characteristics that determine the urban system's resilience to flash flooding, thereby supporting the formulation of resilience-building strategies for each dimension and urban area under consideration.

Keywords: Flash flooding; Flood risk management; Multidimensional approach; Resilience; Sensitivity analysis; Uncertainty.