Analyzing the spatiotemporal dynamics of flood risk and its driving factors in a coastal watershed of southeastern China. (February 2021)

• Record Type: Journal Article
• Authors: Tang, Jianxiong; Li, Yanmin; Cui, Shenghui; Xu, Lilai; Hu, Yuanchao; Ding, Shengping; Nitivattananon, Vilas
• Is Part Of: Ecological indicators. Volume 121(2021)
• Publication Date: 2021-02
• Subjects: Flood risk assessment -- Spatial multi-criteria analysis -- GIS -- Spatiotemporal variation -- Contribution analysis; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.107134
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: A comprehensive framework for flood risk assessment is developed. Spatiotemporal variability of flood risk and its drivers are dynamically analyzed. Peak discharge, rainfall, age structure, reservoir, wetland, are main driving factors. Built-up area, dike, financial investment are obvious drivers in some urbanized areas. Spatially heterogeneous strategies should be fully considered in decision-making. Abstract: Rapid urbanization and climate change can cause more extensive flood risk, in the absence of urgent and efficient adaptation measures. As the occurrence of floods varies with time and space, comprehensive and dynamical assessment of the spatiotemporal variability of flood risk and understanding of its drivers is vital for flood risk management. In this study, we developed a spatial multi-criteria analysis (SMCA) framework for quantifying the spatiotemporal dynamics of flood risk through a case study in a coastal watershed of southeastern China from 1990 to 2015. A comprehensive framework for flood risk assessment was constructed from the hazard, exposure, sensitivity and adaptive capacity components with 23 indicators. The results showed that the highest risk happened in the stage of 2006–2010, while the lowest risk stage was 2011–2015, with higher flood risk in the downstream areas of Jiulong River watershed (JRW). The contribution of each indicator reflects the difference in temporal, spatial and quantity aspects. The top 5 driving factors for JRW included: peak discharge, maximum daily rainfall, age structure, wetland, and reservoir. The risk perception showed a continuous growing impact on flood risk. However, some indicators only showed obvious contributions in the specified area: for example, the built-up expansion in Zhangzhou city; the increase of dike length and the improvement of dike standard in Xinluo district; and the increase of government financial investment in Zhangping and Liancheng district. This study demonstrates the well-performance of our proposed novel approach for flood risk assessment. Our results and conclusions are also of significance for policymakers to understand and point out the deficiencies in the current actions of flood adaption, and consequently develop more targeted and spatially-specific strategies for flood adaptation, in the context of climate change and rapid urbanization.