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A satellite-based analysis of tropical cyclone rainfall for improved flood hazard assessment, case study in Dominica

Nabukulu, Catherine (2021) A satellite-based analysis of tropical cyclone rainfall for improved flood hazard assessment, case study in Dominica.

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Abstract:The severity of weather events accompanying tropical cyclones (TC), such as torrential precipitation and strong winds, is changing because of increased global anthropogenic warming. The last 5-10 years have witnessed overwhelming flooding from tropical cyclone extreme rainfall causing damages that have burdened the economy, especially for developing countries in TC-prone zones. Due to the scarcity of long-term TC rainfall records, flood modelers in these countries use regional design storms for TC-related flood hazard assessment. However, design storms might fail to represent the intricate patterns of TC precipitation due to the few observational recordings of TCs by rain gauges, giving unrealistic estimations of the TC-related flooding hazard. This research’s solution is a new approach that attempts to categorize the structure of TC associate rainfall by utilizing satellite precipitation estimates from GPM-IMERG V06 data to improve TC-related flood hazard assessment. The method was tested on Tropical storm Erika (2015), which brought torrential rainfall to the study area in the vicinity of Dominica. The TC rainfall’s distinct spatial-temporal behaviours were revealed using K-means in a time series clustering analysis. The research focused on the differences in the temporal distribution of the rainfall, also emphasized by the distinct flood responses modeled in openLISEM. First, Tropical storm Erika’s rainfall temporal distribution was analyzed for three values of optimal clusters (K), i.e., 5, 4, and 3. For each K value, one cluster is excluded from further analyses as its location away from TC, the precipitation amount, and intensity were significantly lower than the other clusters. The second step in the developed approach involved setting a 10mm/hr starting threshold to align the pixel times series. The third step was to derive cluster representative signals used as the precipitation input in the flood model. Rainfall signals resulting from K=5 had similar quantified responses in flood extent, depth, volume, duration, and runoff ratio between the cluster signals. A final step in the form of an optimization approach was implemented to address these similarities and improve the generalization of the TC rainfall. At a reduced K value, the TC precipitation was divided into three (for K= 4) and two (for K=3) levels of magnitude with distinct quantified flood responses. Concluding, rainfall signals resulting from K=4 were selected as the TC associate rainfall dataset since they were associated with higher magnitudes in flood response. We observed that different temporal behaviours of varying magnitude exist for precipitation accompanying a given tropical cyclone. Since flood characteristics change with intricate rainfall patterns, the consequences suffered in an area depend on which part of the TC passes that location. This study showed that flood hazard modelers and risk planners can utilize the developed approach to generate a reliable TC associate rainfall dataset to make better-informed decisions related to TC-induced flooding.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
Link to this item:https://purl.utwente.nl/essays/88722
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