Generation of rainfall scenarios based on rainfall transition probability to determine temporal distribution of independent rainstorms.
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| Title: | Generation of rainfall scenarios based on rainfall transition probability to determine temporal distribution of independent rainstorms. |
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| Authors: | Cha, Hoyoung1 (AUTHOR), Baik, Jongjin2 (AUTHOR), Lee, Jinwook3 (AUTHOR), Na, Wooyoung4 (AUTHOR), Bateni, Sayed M.5,6 (AUTHOR), Jun, Changhyun2 (AUTHOR) cjun@cau.ac.kr |
| Source: | Stochastic Environmental Research & Risk Assessment. Dec2024, Vol. 38 Issue 12, p4959-4977. 19p. |
| Subjects: | Rainfall, Rainfall probabilities, Markov processes, Climate change, Rainstorms |
| Abstract: | Given the increasing frequency of short-duration heavy rainfall events in certain regions attributable to climate change, this study was aimed at establishing a stochastic rainfall generator based on the rainfall transition probability (RTP). The generator used the temporal distribution with short time intervals to simulate such events. Rainfall observations in Seoul and Gyeonggi province were considered. Using rainfall data derived in 1-min intervals from rainfall observations, 1-min RTPs were computed based on the Markov chain concept. This computation, accounting for both first- and second-order RTPs, differentiated by rainfall intensity, revealed various characteristics. The first- and second-order RTPs, corresponded to 0.0 mm, varied from 13 to 32% depending on the rainfall intensity. Subsequently, the 1-min RTP was used to generate 10-min rainfall scenarios tailored to independent rainstorm events (IREs). The parameters included rainfall amounts at the start and end points, rainfall duration, total rainfall amount, and features of the first and second peaks. These parameters were divided into three stages, and the resulting outcomes were examined. It was consistent that the introduction of conditions enhanced the similarity performance. Moreover, the performance of rainfall scenarios varied depending on the first- and second-order RTPs as well as the temporal distribution of IREs. The results indicate that by setting conditions appropriate for IREs and RTPs, rainfall scenarios with reasonable temporal distribution similarity can be obtained. The proposed method can consider the complexity of rainfall models and resulting uncertainties associated with rainfall occurrences. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Given the increasing frequency of short-duration heavy rainfall events in certain regions attributable to climate change, this study was aimed at establishing a stochastic rainfall generator based on the rainfall transition probability (RTP). The generator used the temporal distribution with short time intervals to simulate such events. Rainfall observations in Seoul and Gyeonggi province were considered. Using rainfall data derived in 1-min intervals from rainfall observations, 1-min RTPs were computed based on the Markov chain concept. This computation, accounting for both first- and second-order RTPs, differentiated by rainfall intensity, revealed various characteristics. The first- and second-order RTPs, corresponded to 0.0 mm, varied from 13 to 32% depending on the rainfall intensity. Subsequently, the 1-min RTP was used to generate 10-min rainfall scenarios tailored to independent rainstorm events (IREs). The parameters included rainfall amounts at the start and end points, rainfall duration, total rainfall amount, and features of the first and second peaks. These parameters were divided into three stages, and the resulting outcomes were examined. It was consistent that the introduction of conditions enhanced the similarity performance. Moreover, the performance of rainfall scenarios varied depending on the first- and second-order RTPs as well as the temporal distribution of IREs. The results indicate that by setting conditions appropriate for IREs and RTPs, rainfall scenarios with reasonable temporal distribution similarity can be obtained. The proposed method can consider the complexity of rainfall models and resulting uncertainties associated with rainfall occurrences. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 14363240 |
| DOI: | 10.1007/s00477-024-02844-7 |
