Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression

Tao Fang,Huangyuan Tan,Jiping Cao,Liheng Huang,Jiaxin Liao,Yumin Chen

doi:10.3390/w11101969

Abstract

A Poisson regression based on eigenvector spatial filtering (ESF) is proposed to evaluate the flood risk in the middle reaches of the Yangtze River in China. Regression analysis is employed to model the relationship between the frequency of flood alarming events observed by hydrological stations and hazard-causing factors from 2005 to 2012. Eight factors, including elevation (ELE), slope (SLO), elevation standard deviation (ESD), river density (DEN), distance to mainstream (DIST), NDVI, annual mean rainfall (RAIN), mean annual maximum of three-day accumulated precipitation (ACC) and frequency of extreme rainfall (EXE) are selected and integrated into a GIS environment for the identification of flood-prone basins. ESF-based Poisson regression (ESFPS) can filter out the spatial autocorrelation. The methodology includes construction of a spatial weight matrix, testing of spatial autocorrelation, decomposition of eigenvectors, stepwise selection of eigenvectors and calculation of regression coefficients. Compared with the pseudo R squared obtained by PS (0.56), ESFPS exhibits better fitness with a value of 0.78, which increases by approximately 39.3%. ESFPS identifies six significant factors including ELE, DEN, EXE, DIST, ACC and NDVI, in which ACC and NDVI are the first two main factors. The method can provide decision support for flood risk relief and hydrologic station planning.

Highlights

Floods are the most common and destructive natural disaster around the world
Annual mean rainfall cannot pass the significance test of negative binomial regression (NB) and ESF-based Poisson regression (ESFPS) while river density cannot pass the test of Poisson Regression (PS) and NB, and accumulated precipitation (ACC), distance to mainstream (DIST) and ELE
Among the eight factors, according to their estimated coefficients and significance, elevation, elevation standard deviation, distance to the mainstream and normalized difference vegetation index (NDVI) are negatively correlated with the frequency of flood alarming events while frequency extreme rainfall, elevation and river density is positively related with it

Summary

Introduction

Floods are the most common and destructive natural disaster around the world. When the water level measured in large parts of a river becomes too high and exceeds its maximum capacity, flooding could occur. There are many types of floods such as river floods, flash floods, urban floods, sewer flooding and coastal flooding in China. Affected by the monsoon climate and geographical conditions, China is seriously impacted by flood disasters, especially flash floods and river floods [3]. Floods in the region has a wide range of impacts and can be sudden and strong, occur for long periods, frequent or seasonal. These obvious characteristics make floods one of the most important factors restricting China’s economic and social development

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Discussion

Conclusion