Abstract
Abstract The emergy theory provides a new approach for flood risk assessment from an ecological perspective. By employing the emergy method, we used five indicators (rainfall runoff, medical workers and students per 10,000 people, social fixed assets investment, unit land GDP, and land-use types) from three dimensions (natural environment, population, and social economy) and the GIS technique to assess the potential impact and risk of a flood disaster on different regions in Ya'an City. Our findings revealed regional differences in the distribution of flood risks in Ya'an City: Lushan County and Yucheng District face the highest risks, followed by Tianquan County and Mingshan District, and Shimian County has the lowest risk. The index method was employed to analyze the regional differences. By training a back-propagation neural network with data on flood disasters in the study area, we produced a flood risk distribution map. We found that Mingshan District, Lushan County and Yucheng District have higher risks than other regions. The results largely agree with what we obtained using the emergy method. Our study shows that flood risk assessment based on the emergy theory can provide a scientific basis for flood control and disaster relief initiatives.
Highlights
Floods are one of the most common hazards worldwide, have a wide-ranging impact and present a serious threat to socio-economic development and people’s lives and properties (Foudi Osés-Eraso & Tamayo 2015)
Wu et al (2018), based on the emergy theory, calculated the rainstorm intensity, susceptibility, and adaptive capacity to assess the vulnerability of each district of Zhengzhou in Henan province to flood disasters, and the results showed that different districts have different demand for rainfall flood control, and the districts of higher vulnerability demand higher
Compared with some previous research efforts, we have found both indicate that a region, more developed in the economy with more accumulative assets, sees higher potential impact and boasts higher adaptive capacity (Wu et al 2018)
Summary
Floods are one of the most common hazards worldwide, have a wide-ranging impact and present a serious threat to socio-economic development and people’s lives and properties (Foudi Osés-Eraso & Tamayo 2015). In July 2020, most parts of Sichuan, Chongqing, Hubei, Anhui and Zhejiang around China experienced multiple rounds of heavy rainfall, resulting in severe flood disasters around many regions, leading to some major losses to the sustainable development of our national economy, jeopardizing the security of people’s lives and properties, and affecting social stability. Most efforts for flood risk assessment in China and abroad have been made with historical flood data, hydrological and hydrodynamic models, and index methods (Tanoue et al 2016; Abdulrazzak et al 2019; Yongzhi et al 2021). Bhuiyan & Baky (2014) employed ArcGIS to map the flood hazard distribution around the low-lying areas of Bangladesh, and used digital elevation model (DEM) data to extract index factors, to work out the flood risk analysis diagram for different scales of floods. Most efforts for flood risk assessment in China and abroad have been made with historical flood data, hydrological and hydrodynamic models, and index methods (Tanoue et al 2016; Abdulrazzak et al 2019; Yongzhi et al 2021). Bhuiyan & Baky (2014) employed ArcGIS to map the flood hazard distribution around the low-lying areas of Bangladesh, and used digital elevation model (DEM) data to extract index factors, to work out the flood risk analysis diagram for different scales of floods. Kazakis et al (2015) employed the analytic hierarchy process to determine the weight of total flow, altitude, land-use type and other attributes, and superposed different parameter information following their weighted values, and completed flood risk assessment. Chengguang et al (2015) selected 10 assessment indexes based on disaster-inducing factors, hazardinducting environment, and hazard-affected bodies, built up a random woodland intelligence algorithm-based flood risk assessment model, and used the GIS technology to assess the flood risks in the Dongjiang
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