Assessment of Flood Catastrophe Risk for Grain Production at the Provincial Scale in China Based on the BMM Method

Abstract

Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change, and meanwhile, it is an urgent scientific issue need to be solved in agricultural risk assessment discipline. This paper developed the methodology of flood catastrophe risk assessment, which can be shown as the standard process of crop loss calculation, Monte Carlo simulation, the generalized extreme value distribution (GEV) fitting, and risk evaluation. Data on crop loss were collected based on hectares covered by natural disasters, hectares affected by natural disasters, and hectares destroyed by natural disasters using the standard equation. Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data. Block maxima model (BMM) approach based on the extreme value theory was for modeling the generalized extreme value distribution (GEV) of flood catastrophe loss, and then flood catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling flood catastrophe risk for grain production. The impact of flood catastrophe to grain production in China was significantly serious, and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China. Given the scenario of suffering once-in-a-century flood disaster, for majority of the major-producing provinces, the probability of 10% reduction of grain output is more than 90%. Especially, the probabilities of more than 15% decline in grain production reach up to 99.99, 99.86, 99.69, and 91.60% respectively in Anhui, Jilin, Liaoning, and Heilongjiang. Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.