ABSTRACT
ABSTRACT: Drought 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 risk assessment discipline. This paper developed the methodology of drought catastrophe risk assessment, which can be shown as the process of crop loss collection, Monte Carlo simulation, the Generalized Extreme Value distribution (GEV) fitting, and risk calculation. Data on crop loss were collected based on hectares covered by drought disaster, hectares affected by drought disaster, and hectares destroyed by drought disaster 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 drought catastrophe loss, and then drought catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling drought catastrophe risk for grain production. The impact of drought catastrophe to grain production in China was serious, and very high risk of drought catastrophe mainly occurs in the northeast regions of China. Given the scenario of suffering once-in-a-century drought disaster, for majority of the major-producing provinces in China, the probability of 15% reduction of grain output is more than 90%. Our findings can provide multifaceted information about drought catastrophe risk that can help to guide management of drought catastrophe.
