Bayesian Parameter Estimation to Bivariate Drought Regional Frequency Analysis Model: Application to Han-River Watershed
- 1Sejong University, Civil & environmental Engineering, Seoul, Korea, Republic of (ghwns0215@gmail.com)
- 2Sejong University, Civil & environmental Engineering, Seoul, Korea, Republic of (gkghgmgn@gmail.com)
- 3Sejong University, Civil & environmental Engineering, Seoul, Korea, Republic of (hemiecho@gmail.com)
- 4Corresponding Author, Sejong University, Civil & environmental Engineering, Seoul, Korea, Republic of (hkwon@sejong.ac.kr)
Copula-based bivariate drought frequency analysis has been widely employed to evaluate drought risk in the context of point frequency analysis. However, the relatively significant uncertainties in the parameters are problematic when available data are limited. This study developed a bivariate regional frequency analysis model using Copula function within the Bayesian modeling framework. An experimental study is first performed to assure ourselves whether the proposed model can accurately reproduce drought characteristics. The proposed model is capable of effectively representing the recent drought events and can provide drought risk information along with its uncertainty. The results confirm that the proposed model is not only effectively representing correlation with regional dependencies of drought, but also providing the uncertainty of parameters.
KEYWORDS: Copula, Bayesian, Bivariate drought regional frequency analysis, Uncertainty
Acknowledgement
This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI 2018-07010.
How to cite: Kim, H. J., Kim, J.-G., Cho, H. M., and Kwon, H.-H.: Bayesian Parameter Estimation to Bivariate Drought Regional Frequency Analysis Model: Application to Han-River Watershed, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20713, https://doi.org/10.5194/egusphere-egu2020-20713, 2020
