A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar

In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding event preparedness plans mitigation measures. Introducing an initial framework of spatially probabilistic research, this study highlights integrated statistical copula satellite data-based approach to modelling the complex dependence structures between characteristics, i.e., duration (D), volume (V) peak (Q). The uses Global daily satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (spatial resolution ∼5 km) during 1981–2019 derive a Standardized Antecedence Index (SAPI) its characteristics through time-dependent reduction function Myanmar. An advanced vine model was applied joint distributions each grid cell. southwest (Rakhine, Bago, Yangon, Ayeyarwady) south (Kayin, Mon, Tanintharyi) regions are found be at high risk, probability up 40% occurrence August September Ayeyarwady). results indicate strong correlation among characteristics; however, their mean standard deviation different. findings reveal significant differences spatial patterns exceedance different combined scenarios. that duration, volume, concurrently exceed 50th-quantile (median) values about 60–70% along administrative borders Chin, Sagaing, Mandalay, Shan, Nay Pyi Taw, Keyan. worst case highest areas, extreme values, 90th-quantile, 10–15% southeast Mon areas around these states 30% Dekkhinathiri township (Nay Taw). proposed could improve evaluation probabilities used early warning assessment management. is also applicable larger scales (e.g., regions, continents globally) hydrological design events assessments insurance).