Browse > Article
http://dx.doi.org/10.3741/JKWRA.2019.52.8.545

A development of multivariate drought index using the simulated soil moisture from a GM-NHMM model  

Park, Jong-Hyeon (Airfield Infra-Facilities Team, Incheon International Airport Corporation)
Lee, Joo-Heon (Department of Civil Engineering, Joongbu University)
Kim, Tae-Woong (Department of Civil and Environmental Engineering, Hanyang University)
Kwon, Hyun Han (Department of Civil and Environmental Engineering, Sejong University)
Publication Information
Journal of Korea Water Resources Association / v.52, no.8, 2019 , pp. 545-554 More about this Journal
Abstract
The most drought assessments are based on a drought index, which depends on univariate variables such as precipitation and soil moisture. However, there is a limitation in representing the drought conditions with single variables due to their complexity. It has been acknowledged that a multivariate drought index can more effectively describe the complex drought state. In this context, this study propose a Copula-based drought index that can jointly consider precipitation and soil moisture. Unlike precipitation data, long-term soil moisture data is not readily available so that this study utilized a Gaussian Mixture Non-Homogeneous Hidden Markov chain Model (GM-NHMM) model to simulate the soil moisture using the observed precipitation and temperature ranging from 1973 to 2014. The GM-NHMM model showed a better performance in terms of reproducing key statistics of soil moisture, compared to a multiple regression model. Finally, a bivariate frequency analysis was performed for the drought duration and severity, and it was confirmed that the recent droughts over Jeollabuk-do in 2015 have a 20-year return period.
Keywords
Soil Moisture; Copula; Drought Index; NHMM;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Baun, L. E., Petrie, T., Soules, G., and Weiss, N. (1970). "A maximization technique occurring in the statistical analysis of probabilistic functions of markov chain." The Annals of Mathematical Statistics, Vol. 41, pp. 164-174.   DOI
2 Cioffi, F., Conticello, F., Lall, U., Marotta, L., and Telesca, V. (2017). "Large scale climate and rainfall seasonality in a mediterranean area: insights from a non-homogeneous markov model applied to the agro-pontino plain." Hydrological Processes, Vol. 31, No. 3, pp. 668-686.   DOI
3 Farahmand, A., and AghaKouchak, A. (2015). "A generalized framework for deriving nonparametric standardized drought drought indicators." Advances in Water Resource, Vol. 76, pp. 140-145.   DOI
4 Hao, Z., and AghaKouchak, A. (2013). "Multivariate standardized drought index: a parametric multi-index model." Advances in Water Resource, Vol. 57, pp. 12-18.   DOI
5 Jang, N.-J., Jo, H.-J., and Lee, E.-J. (2016). "A study on the reuse of water reuse in Jeollabuk-do in response to climate change." Jeonbuk Institute, Vol. 16, No. 24.
6 Khalil, A. F., Kwon, H.-H., Lall, U., and Kaheil, Y. H. (2010). "Predictive downscaling based on non-homogeneous hidden markov models." Hydrological Sciences Journal, Vol. 55, No. 3, pp. 333-350.   DOI
7 Kim, G.-G., and Lee, J.-W. (2011). "Evalutation of drought indices using the drought records." Journal of Korea Water Resources Association, Vol. 44, No. 8, pp. 639-652.   DOI
8 Kim, T.-W., Valdes, J. B., and Yoo, C. S. (2006). "nonparametric approach for bivariate drought characterization using palmer drought index." Journal of Hydrologic Engineering, Vol. 11, No. 2, pp. 134-143.   DOI
9 Kwon, H.-H., and Lall, U. (2016). "A copula-based nonstationary frequency analysis for the 2012-2015 drought in California." Water Resources Research, Vol. 52, No. 7, pp. 5662-5675.   DOI
10 Kwak, J. W., Kim, Y. S., Lee, J. S., and Kim, H. S. (2012). "Analysis of drought characteristics using copula theory." Journal of the Korean Civil Engineering, Vol. 32, No. 3B, pp.161-168.
11 McKee, T. B. (1995). "Drought monitoring with multiple time scales." In Proceedings of 9th Conference on Applied Climatology, Boston.
12 McKee, T. B., Nolan J. D., and John, K. (1993). "The relationship of drought frequency and duration to time scales." Proceedings of the 8th Conference on Applied Climatology. Vol. 17. No. 22. Boston, MA: American Meteorological Society.
13 Mehrotra, R., and Sharma, A. (2005). "A nonparametric nonhomogeneous hidden Markov model for downscaling of multisite daily rainfall occurrences." Journal of Geophysical Research: Atmospheres, Vol. 110, No. D16.
14 Park, S. Y, Park, S. M, Im, J. H, Rhee, J. Y, Shin J. H., and Park, D. J. (2017). "Downscaling GLDAS soil moisture data in east asia through fusion of multi-sensors by optimizing modified regression trees." Water, Vol. 9, No. 5, pp. 332, doi:10.3390/w9050332.   DOI
15 Srivastava, P. K., Han, D., Ramirez, M. R., and Islam, T. (2013). "Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS Land surface temperature for hydrological application." Water Resources Management, Vol. 27, No. 8, pp. 3127-3144.   DOI
16 Xing, C., Chen, N., Zhang, X., and Gong, J. (2017). "A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations." Remote Sensing, Vol. 9, No. 5, doi:10.3390/rs9050484.   DOI