Effect and uncertainty analysis according to input components and their applicable probability distributions of the Modified Surface Water Supply Index

2018 ◽  
Vol 50 (1) ◽  
pp. 393-415 ◽  
Author(s):  
Suk Hwan Jang ◽  
Jae-Kyoung Lee ◽  
Ji Hwan Oh ◽  
Jun Won Jo ◽  
Younghyun Cho

Abstract 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.


Author(s):  
Suk Hwan Jang ◽  
Jae-Kyoung Lee ◽  
Ji Hwan Oh ◽  
Jun Won Jo ◽  
Younghyun Cho

Abstract. This study proposes the new hydrological drought index, Korean Surface Water Supply Index (KSWSI), which overcomes some of the limitations in the calculation of previous SWSI applied in Korea and conducts the probabilistic drought forecasts using KSWSI. In this study, all hydrometeorological variables in the Geum River basin were investigated and appropriate variables were selected as KSWSI components for each sub-basin. And whereby only the normal distributions are applied to all drought components, probability distributions suitable for each KSWSI component were estimated. As a result of verifying KSWSIs, the accuracy of KSWSIs showed better drought phenomenon in drought events. The monthly probabilistic drought forecasts were also calculated based on ensemble technique using KSWSI. In 2006 and 2014 drought events, the accuracy of the drought forecasts using KSWSIs were higher than those using previous SWSI, demonstrating that KSWSI is able to enhance the accuracy of drought forecasts. The influence of expanding hydrometeorological components and selecting appropriate probability distributions for each KSWSI component were analyzed. It is confirmed that the accuracy of KSWSIs may be affected by the choice of hydrometerological components, the station data obtained, the length of used data for each station, and the probability distributions selected. Furthermore, the uncertainty quantification of the KSWSI calculation procedure was also carried out using the Maximum Entropy (ME) theory. The large MEs and standard deviations of KSWSIs in the flood season cause uncertainties, implying that the selection of the appropriate probability distributions for selected drought components in the flood season is very important.


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