The probabilistic drought prediction using the improved surface water supply index in the Korean peninsula
| Autor: | Ji Hwan Oh, Jae-Kyoung Lee, Jun Won Jo, Younghyun Cho, Suk Hwan Jang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Hydrology
geography geography.geographical_feature_category Index (economics) 010504 meteorology & atmospheric sciences 0208 environmental biotechnology Probabilistic logic 02 engineering and technology 01 natural sciences 020801 environmental engineering Peninsula Environmental science Surface water 0105 earth and related environmental sciences Water Science and Technology |
| Zdroj: | Hydrology Research. 50:393-415 |
| ISSN: | 2224-7955 0029-1277 |
| Popis: | This research proposes the Korean surface water supply index (KSWSI) which overcomes some limitations of the modified SWSI (MSWSI) applied in Korea and conducts probabilistic drought prediction using KSWSI. In this research, all hydrometeorological variables were investigated and four to six appropriate variables were selected for each sub-basin and probability distributions applicable for each variable were estimated. As a result of verifying KSWSI results, the accuracy of KSWSI showed better drought phenomenon in drought events than MSWSI. Moreover, the uncertainty quantification of KSWSI calculation procedure was also carried out using the maximum entropy (ME) theory. Estimating appropriate probability distributions for each drought component in the flood season is crucial because ME values and standard deviations of KSWSI are huge, implying that large uncertainty occurs in the flood season. It is confirmed that the accuracy of KSWSI may be affected by the hydrometerological variables selection, station data obtained, used data length, and probability distributions. Furthermore, monthly probabilistic drought predictions were calculated based on the ensemble technique using KSWSI. In 2006 and 2014 drought events, the accuracy of drought predictions using KSWSI was higher than those using MSWSI, demonstrating that KSWSI is able to enhance the accuracy of drought prediction. |
| Databáze: | OpenAIRE |
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