Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China

Abstract

Waterlogging disasters are one of the most destructive meteorological disasters, which lead to crop yield reduction and cause a great threat to humanity and economic structure. This study presents the methodology and procedure for dynamic risk assessment of waterlogging disasters for maize in Midwest of Jilin Province, China. We took the representative waterlogging disaster years of 1994, 2005, and 2010 as examples, the growth-stage waterlogging index was established to assess the waterlogging disaster hazard by using standard antecedent precipitation index and the relative humidity index. Maize growing data and maize planting area data were combined to assess the waterlogging disaster vulnerability of maize, in which the CERES-Maize model was used to simulate the growth of maize at a daily time step for each grid. Based on the theory of natural disaster risk, the dynamic risk assessment model of waterlogging disaster for maize was built. In this study, the risk indexes were divided into five classes by using an optimal partition method. The grid GIS technology was used to map the spatial distribution of data and the grade of waterlogging disaster risk at a resolution of 5000 × 5000 m. The results show that areas with very low waterlogging disaster risk are mainly located in western and northeastern regions; in contrast, very high and high waterlogging disaster risk levels are mainly located in southern and central regions. Meanwhile, high risk areas at different growth stages gradually spread from the southwestern to the Midwestern and southeastern regions. This study could help the government when they make strategic decisions regarding food security in China, and the method of dynamic waterlogging risk disaster assessment could also be applied for other crops to control and prevent the occurrence and development of waterlogging disasters and reduce their adverse influence.