Climate Change Research

Climate Change Research

Evaluation of MSDI bivariate drought index based on precipitation and runoff (Arazkouse and Galikesh stations of Golestan province)

Document Type : Original Article

Authors
Khalil Ghorbani 1
Meysam Salarijazi 1
Laleh Rezaei Ghaleh 2
1 Associate Professor, Department of Water Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resource, Gorgan, Iran
2 3Ph.D. Candidate of Water Resources, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran.
10.30488/ccr.2023.426470.1179
Abstract
A comprehensive index definition that provides a more complete interpretation of meteorological and hydrological drought is essential. Based on the multivariate drought index (MSDI) was defined in this study based on rainfall and runoff to diagnose meteorological and hydrological droughts. In this regard, two indices, SPI and SSI, were calculated seasonally in the Galikesh and Arazkouse regions in Golestan province. 50-year rainfall and runoff statistics were used for the calculations. The MSDI index was also calculated using joint functions based on flow and precipitation variables. To test the compatibility of the MSDI index with SPI and SSI, the degree of correlation, trend, and change points were checked using Mann-Kendall and Pettit tests. The results showed that while there was no perfect correlation between the SPI and SSI indices, the MSDI index had a high correlation with both of them. The degree of correlation varied in different seasons and stations. Even in the worst case where SPI and SSI had no significant correlation, the MSDI index created a 62% correlation with SPI in Galikesh. The trend and mutation results were in good agreement in the two stations. At Arazkouse station, SPI had no trend, SSI only in spring, and MSDI had a trend in spring and summer. At Galikesh station, MSDI didn’t have a trend in any season, SPI had a trend in autumn, and SSI in spring. The MSDI index was similar to SPI and SSI indices in cases where they showed the same dry conditions. Otherwise, it was consistent with the index that showed a drier condition. Additionally, the MSDI index had a tendency to show drier seasons. In conclusion, the MSDI index can be considered a suitable index for the simultaneous analysis of meteorology and hydrology drought.
Keywords
Meteorological drought
Hydrological drought
Multivariate drought
SPI
SSI
MSDI

Subjects

  1. AghaKouchak A., Bárdossy A., & Habib E. 2010. Copula-based uncertainty modelling: application to multisensor precipitation estimates. Hydrol. Process, 24(15):2111–24.
  2. Aghakouchak, A., Ciach G., & Habib, E. 2010. Estimation of tail dependence coefficient in rainfall accumulation fields. Adv. Water Res. 33(9):1142–9.
  3. AghaKouchak A, Easterling D., Hsu K., Schubert S., & Sorooshian, S. 2012. Extremes in a changing climate. Netherlands, Dordrecht. Springer.
  4. Bardossy A. 2006. Copula-based geostatistical models for groundwater quality parameters. Water Resour Res. 42(11): W11416.
  5. Byun, H.R. & Wilhite D.A. 1999. Objective quantification of drought severity and duration. In: Journal of Climate. 12:2747-2756.
  6. Favre, A.C., E.L., Adlouni, S., Perreault, L., Thiémonge N., & Bobée, B. 2004. Multivariate hydrological frequency analysis using copulas. Water Resour Res. 40(1):W01101.
  7. Genest, C., & Favre, AC. 2007. Everything you always wanted to know about copula modeling but were afraid to ask. J. Hydrol. Eng. 12(4):347–68.
  8. Gibbs, W.J. and Maher, J.V. 1967. Rainfall deciles as drought indicators. Bureau of Meteorology. Bulletin, 48p.
  9. Hao, Z. and AghaKouchak, A. 2013. Multivariate standardized drought index: a parametric multi-index model. Advances in Water Resources, 57: 12-18.
  10. Hao, Z. & AghaKouchak, A. 2014. A Nonparametric Multivariate Multi-Index Drought Monitoring Framework. Journal of Hydrometerology, 15: 89-101. 
  11. Kao, S.C., & Govindaraju, R.S. 2010. A copula-based joint deficit index for droughts. J Hydrol. 380(1–2):121–34.
  12. Kendall, M.G. 1975. Rank Correlation Methods, Charles Griffin, London. 272.
  13. Mann, H. B. 1945. Nonparametric tests against trend. Econometrica. 13: 245-259.
  14. McKee, T.B.N., Doesken, J. & Kleist, J. 1993. The relationship of drought frequency and duration to time scales. Eight Conf. On Applied Climatology. Anaheim, CA, Amer. Meteor. Soc. 179-184.
  15. Mishra, Ashok K., & Vijay Singh, P. 2010. A review of drought concepts. Journal of Hydrology 391(1): 202-216.
  16. Mo KC. 2008. Model-based drought indices over the United States. Journal of Hydrometeorol. 9(6):1212–30.
  17. Nadi, M., & Shiukhy Soqanloo, S. 2023. Modification of standardized precipitation index in different climates of Iran. Meteorological Applications, 30(5), e2155.
  18. Nelsen R.B. 2006. An introduction to copulas. New York, Springer.
  19. Salvadori, G., De Michele, C., Kottegoda, N., & Rosso, R. 2007. Extremes in nature: an approach using copulas. New York: Springer.
  20. Salvadori, G., & De Michele, C. 2010. Multivariate multipara meter extreme value models and return periods: a copula approach. Water Res. Res. 46:W10501.
  21. Shiau, J. 2006. Fitting drought duration and severity with two-dimensional copulas. Water Resource Manage. 20 (5):795–815.
  22. Shukla, S., Steinemann, A.C., & Lettenmaier, DP. 2011. Drought monitoring for Washington State: indicators and applications. J. Hydrometeorol. 12(1):66–83.
  23. Sklar A. Fonctions de répartition à n dimensions et leurs marges. 1959. Publ Inst Statist Univ Paris.8:229–31.
  24. Trivedi, P.K., & Zimmer, D.M. 2005. Copula modeling: an introduction for practitioners. Found Trends Economet. 1(1): 1–111.
  25. Vicente-Serrano, Sergio, Beguería, Santiago & López-Moreno, J.I. 2010. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate. 23: 1696-1718.
  26. Zhang, X., Vincent, L. A., Hogg, W.D. and Niitsoo, A. 2000. Temperature and rainfall trends in Canada during the 20th century. Atmospheric Ocean. 38:395-429.