Modeling of Temperature and Rainfall of Tabriz Using Copulas

Document Type : Original Article

Authors

1 Ph.D Student of Water Engineering, Young Researcher club, Tabriz Branch, Islamic Azad University, Tabriz, Iran

2 Assistant Professor, Water Eng. Dep., Agriculture Faculty, Shahrekord University

3 Associate Professor, Water Eng. Dep., Agriculture Faculty, University of Tabriz

Abstract

Because of nonlinear dependence between the random variables, probability distributions of multivariate random variables are generally more complex compared to their univariate counterparts. One approach to solve this problem is the use of copulas, which have more considered by researchers over recent years. In this study, the dependence of rainfall and temperature in Tabriz station was modeled using copulas. In this regard, monthly data of rainfall and temperature of Tabriz for a period of 1971-2008 was used. At the first step, by application of Kolmogorov–Smirnov test, exponential and generalized extreme value (GEV) distributions are selected as appropriate univariate distributions for rainfall and temperature, respectively. Then three type of copulas functions namely, Clayton, Gumbel and Frank are used for creating bivariate distribution of rainfall and temperature in Tabriz. The results indicated that the performances of all types of copulas are close together. However, an only Frank copula has capability to modeling the negative dependence between variables. Therefore, Frank function is selected as appropriate copulas for modeling dependence between rainfall and temperature in Tabriz station.

Keywords


1. Eckmann, J.P. and D. Ruelle. 1985. Ergodic theory of chaos and strange attractors, Reviews of modern physics, 57(3), 617.
2. Tanarhte, M., P. Hadjinicolaou and J. Lelieveld. 2012. Intercomparison of temperature and precipitation data sets based on observations in the Mediterranean and the Middle East, Journal of Geophysical Research: Atmospheres, 117.
3. Rajeevan, M., D.S. Pai and V. Thapliyal. 1998. Spatial and temporal relationships between global land surface air temperature anomalies and Indian summer monsoon rainfall,  Meteorology and Atmospheric Physics, 66(3-4), 157-171.
4. Huang, Y., J. Cai, H. Yin and M. Cai. 2009. Correlation of precipitation to temperature variation in the Huanghe River (Yellow River) basin during 1957–2006, Journal of hydrology, 372(1), 1-8.
5. Aldrian, E. and R. DwiSusanto. 2003. Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature, International Journal of Climatology, 23(12), 1435-1452.
6. Wilks, D.S. 2011. Statistical methods in the atmospheric sciences, Access Online via Elsevier, Vol. 100.
7. AghaKouchak, A., A. Bárdossy and E. Habib. 2010. Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula, Advances in Water Resources, 33(6), 624-634.
8. De Michele, C. and G. Salvadori. 2003. A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-copulas, Journal of Geophysical Research, 108(D2): 4067.
9. Salvadori, G. and C. De Michele. 2006. Statistical characterization of temporal structure of storms, Advances in Water Resources, 29(6): 827–842.
10. Grimaldi, S. and F. Serinaldi. 2006. Design hyetographs analysis with 3-copula function, Hydrological Sciences Journal, 51(2): 223−238.
11. Zhang, L. and V.P. Singh. 2007a. Bivariate rainfall frequency distributions using Archimedean copulas, Journal of Hydrology, 332: 93-109.
12. Zhang, L. and V.P. Singh. 2007b. Gumbel-Hougaard copula for trivariate rainfall frequency analysis, Journal of Hydrologic Engineering, 12(4): 409-419.
13. Serinaldi, F. 2008. Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields,  Stochastic Environmental Research and Risk Assessment, 22(6), 671-688.
14. Favre, A.C., S. El Adlouni, L. Perreault, N. Thiemonge and B. Bobee. 2004. Multivariate hydrological frequency analysis using copulas, Water Resources Research, 40:W01101, doi:10.1029/2003WR002456.
 
15. De Michele, C., G. Salvadori, M. Canossi, A. Petaccia and R. Rosso. 2005. Bivariate statistical approach to check adequacy of dam spillway, Journal of Hydrologic Engineering, 10(1): 50–57.
16. Shiau, J.T. 2006. Fitting drought duration and severity with two-dimensional copulas, Water Resources Management, 20: 795–815.
17. Shiau, J.T., S. Feng and S. Nadarajah. 2007. Assessment of hydrological droughts for the Yellow River, China, using copulas, Hydrological Processes, 21(16): 2157-2163.
18. Song, S. and V.P. Singh. 2010. Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data, Stochastic Environmental Research and Risk Assessment, 24: 425-444.
19. Laux, P., S. Vogl, W. Qiu, H.R. Knoche and H. Kunstmann. 2011. Copula-based statistical refinement of precipitation in RCM simulations over complex terrain, Hydrology and Earth System Sciences, 15(7), 2401-2419.
20. Mirabbasi, R., A. Fakheri-Fard and Y. Dinpashoh. 2012. Bivariate drought frequency analysis using the copula method, Theoretical and Applied Climatology, 108(1-2), 191-206.
21. Schoelzel, C. and P. Friederichs. 2008. Multivariate non-normally distributed random variables in climate research–introduction to the copula approach, Nonlin. Processes Geophys., 15(5), 761-772.
22. Huang, J. and H.M. Van Den Dool. 1993. Monthly precipitation-temperature relations and temperature prediction over the United States, Journal of Climate, 6(2), 1111–1132
23. Nelsen, R.B. 1999. An introduction to copulas, Springer.
24. Sklar, A. 1959. Fonctions de répartition à n dimensions et leursmarges, PublInst Statist Univ Paris 8:229–231.
25. Salvadori, G., C. De Michele, N.T. Kottegoda and R. Rosso. 2007. Extremes in nature. An approach using copulas, Springer, Dordrecht.