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

Nguyen-Huy, Thong ORCID: https://orcid.org/0000-0002-2201-6666 and Kath, Jarrod ORCID: https://orcid.org/0000-0003-2391-1264 and Nagler, Thomas and Khaung, Ye and Aung, Thee Su Su and Mushtaq, Shahbaz and Marcussen, Torben and Stone, Roger (2022) A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar. Remote Sensing Applications: Society and Environment, 26:100733. pp. 1-19.

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Abstract

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


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Future Drought Fund Hub (1 Jun 2021 - )
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 26 Apr 2022 00:16
Last Modified: 06 May 2022 02:32
Uncontrolled Keywords: Climate characteristics; Flood monitoring; Joint distribution; Multivariate modelling; Risk management; Satellite-based precipitation; Tail dependences; Vine copulas
Fields of Research (2020): 37 EARTH SCIENCES > 3707 Hydrology > 370799 Hydrology not elsewhere classified
37 EARTH SCIENCES > 3701 Atmospheric sciences > 370101 Adverse weather events
37 EARTH SCIENCES > 3709 Physical geography and environmental geoscience > 370903 Natural hazards
Socio-Economic Objectives (2020): 19 ENVIRONMENTAL POLICY, CLIMATE CHANGE AND NATURAL HAZARDS > 1904 Natural hazards > 190404 Hydrological hazards (e.g. avalanches and floods)
18 ENVIRONMENTAL MANAGEMENT > 1801 Air quality, atmosphere and weather > 180104 Weather
Funding Details:
Identification Number or DOI: https://doi.org/10.1016/j.rsase.2022.100733
URI: http://eprints.usq.edu.au/id/eprint/47854

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