Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale

Yuntao Wang; Chi Zhang; Albert S Chen; Guoqiang Wang; Guangtao Fu

doi:10.1016/j.scitotenv.2023.164852

Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale

Sci Total Environ. 2023 Oct 1:893:164852. doi: 10.1016/j.scitotenv.2023.164852. Epub 2023 Jun 17.

Authors

Yuntao Wang¹, Chi Zhang², Albert S Chen³, Guoqiang Wang¹, Guangtao Fu⁴

Affiliations

¹ Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
² School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China. Electronic address: czhang@dlut.edu.cn.
³ Centre for Water Systems, Faculty of Environment, Science and Economy, University of Exeter, North Park Road, Harrison Building, Exeter EX4 4QF, UK.
⁴ Centre for Water Systems, Faculty of Environment, Science and Economy, University of Exeter, North Park Road, Harrison Building, Exeter EX4 4QF, UK. Electronic address: g.fu@exeter.ac.uk.

PMID: 37331395
DOI: 10.1016/j.scitotenv.2023.164852

Abstract

The assessment of flood risk and resilience has become increasingly important in recent years for effective urban flood management. While flood resilience and risk are two distinct concepts with unique assessment metrics, there is lack of quantitative analysis and understanding of the relationship between them. This study aims to investigate this relationship at the grid cell level in urban areas. To assess flood resilience for high-resolution grid cells, this study proposes a performance-based flood resilience metric, which is calculated using the system performance curve based on flood duration and magnitude. Flood risk is calculated as the product of maximum flood depth and probability, considering multiple storm events. The case study of Waterloo in London, UK is analyzed using a two-dimensional cellular automata-based model CADDIES, which consists of 2.7 million grid cells (5 m × 5 m). The results indicate that over 2 % of grid cells have risk values exceeding 1. Furthermore, there is a 5 % difference in resilience values below 0.8 between the 200-year and 2000-year design rainfall events, specifically 4 % for the former and 9 % for the latter. Additionally, the results reveal a complex relationship between flood risk and resilience, though decreasing flood resilience generally leads to increasing flood risk. However, this relationship varies depending on the land cover type, with building, green land, and water body cells showing higher resilience for the same level of flood risk compared to other land uses such as roads and railways. Classifying urban areas into four categories, including high risk vs. low resilience, high risk vs. high resilience, low risk vs. low resilience, and low risk vs. high resilience, is crucial in identifying flood hotspots for intervention development. In conclusion, this study provides an in-depth understanding of the relationship between risk and resilience in urban flooding, which could help improve urban flood management. The proposed performance-based flood resilience metric and the findings from the case study of Waterloo in London could be valuable for decision-makers in developing effective flood management strategies in urban areas.

Keywords: Flood hotspots; Risk-resilience interplay; System performance; Urban surface water flooding.