Preprints
https://doi.org/10.5194/nhess-2017-163
https://doi.org/10.5194/nhess-2017-163
19 Jun 2017
 | 19 Jun 2017
Status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

The Probabilistic Drought Forecast Based on the Ensemble Technique Using the Korean Surface Water Supply Index

Suk Hwan Jang, Jae-Kyoung Lee, Ji Hwan Oh, Jun Won Jo, and 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Suk Hwan Jang, Jae-Kyoung Lee, Ji Hwan Oh, Jun Won Jo, and Younghyun Cho
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Suk Hwan Jang, Jae-Kyoung Lee, Ji Hwan Oh, Jun Won Jo, and Younghyun Cho
Suk Hwan Jang, Jae-Kyoung Lee, Ji Hwan Oh, Jun Won Jo, and Younghyun Cho

Viewed

Total article views: 1,660 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,027 573 60 1,660 68 67
  • HTML: 1,027
  • PDF: 573
  • XML: 60
  • Total: 1,660
  • BibTeX: 68
  • EndNote: 67
Views and downloads (calculated since 19 Jun 2017)
Cumulative views and downloads (calculated since 19 Jun 2017)

Viewed (geographical distribution)

Total article views: 1,615 (including HTML, PDF, and XML) Thereof 1,606 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Nov 2024
Download
Short summary
In this paper, we focus on 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. The paper should be of interest to readers in the areas of the drought index and drought forecasts.
Altmetrics