Climate change and human activity impacts on future flood risk in the Pearl River Delta based on the MaxEnt model

Abstract

In the background of global climate change and rapid urbanization, extreme climate events are frequent, and highly urbanized areas flooding problems are becoming increasingly prominent. It becomes important to develop qualitative scenario storylines to assess future flooding risk in a changing environment over the highly urbanized areas. In this study, the future (2030–2050) flood risk in the Pearl River Delta (PRD) region of China was assessed based on the MaxEnt model. We have developed four future scenarios under different emission conditions based on the representative concentration pathways (RCPs) and the shared socio-economic pathways (SSPs). The MaxEnt model was trained by using flood hazard sample point data and flood risk indicators, meanwhile, food risk prediction with high accuracy was obtained. We analyzed the influencing factors of flood risk and predicted the flood risk of the PRD under four future scenarios. According to the results, there is a significant increase in the size and proportion of high flood risk areas in most scenarios. Flood risk under scenario SSP5-RCP8.5 are expected to be the most serious in the future. Population density (POPD) and Gross domestic product density (GDPD) have the highest explanatory power for flood risk. This study predict the changes of flood risk under the combined influence of climate change and human activities, and hoped to provide a reference for future planning and disaster mitigation construction in PRD cities.

Estimating the Amount of the Wild Artemisia annua in China Based on the MaxEnt Model and Spatio-Temporal Kriging Interpolation

“…In addition, the influencing factors on the amount o nua were studied using GeoDetector (Beijing, China; Available from: http://www tector.cn/ accessed on 20 September 2023). The MaxEnt model [13][14][15][16][17][18] takes existin graphical distribution information of a species and its environmental variables straints and employs specific algorithms to quantify the non-random relations tween the species' distribution area and the environmental variables. The analytica reflects the influencing factors with regard to species distribution and the degree to the habitat is suitable for its growth.…”

Spatial patterns of short‐term extreme precipitation and their causes

“…Guangdong is affected by monsoons, midlatitude westerly wind systems and typhoons, which lead to frequent HPEs, and these mainly occur from April to September. The flood risk in the Guangdong region is becoming increasingly severe owing to its high‐population density, well‐developed economy and frequent occurrence of extreme precipitation (Chen et al, 2020; Li et al, 2020; Zuo et al, 2023). In order to assess natural hazard risks and formulate strategies for mitigation and response, it is crucial to understand the atmospheric mechanisms governing extreme precipitation.…”