Global Rainstorm Disaster Risk Monitoring Based on Satellite Remote Sensing

Methods of rainstorm disaster risk monitoring (RDRM) based on retrieved satellite rainfall data are studied. Due to significant regional differences, the global rainstorm disasters are not only affected by geography (such as topography and surface properties), but also by climate events. It is necessary to study rainstorm disaster-causing factors, hazard-formative environments, and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide. According to a global flood disaster dataset for the last 20 years, the top four flood disaster causes (accounting for 96.8% in total) related to rainstorms, from most to least influential, are heavy rain (accounting for 61.6%), brief torrential rain (16.7%), monsoonal rain (9.4%), and tropical cyclone/storm rain (9.1%). A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years. Taking the 7-day accumulated rainfall, 3- and 12-h maximum rainfall, 24-h rainfall, rainstorm threshold, and others as the main parameters, a rainstorm intensity index is constructed. Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data, population data, and economic data. Finally, a satellite remote sensing RDRM model is developed, incorporating the above three factors (rainstorm intensity index, hazard-formative environment factor, and hazard-affected body factor). The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.