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Selection of the Best Copula Function in Bivariate Analysis of Water Resources Components (Case study: Siminehrood River Basin, Iran) | ||
| Water Harvesting Research | ||
| دوره 7، شماره 2، 2024، صفحه 289-300 اصل مقاله (678.99 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2024.8508.1160 | ||
| نویسندگان | ||
| Fahimeh Sharifan1؛ Yousef Ramezani* 2؛ Mahdi Amirabadizadeh2؛ Carlo De Michele3 | ||
| 1Ph.D. Student, Department of Water Engineering, University of Birjand, Birjand, Iran. | ||
| 2Associate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran. | ||
| 3Professor, Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy. | ||
| چکیده | ||
| The copula function is a joint distribution of correlated random variables that are defined based on univariate marginal distributions. The aim of the present study is to select the best copula function to create joint probability distributions between the pair of parameters of precipitation- river discharge, river discharge- river salinity and river discharge- groundwater level in the Siminehrood River Basin. The necessity of using copula functions is the existence of correlation between the desired pair of parameters. For review the correlation between the pair of parameters, Kendall's tau statistic was used. Correlation between the precipitation- river discharge, river discharge- river salinity and river discharge- groundwater level were obtained 0.43, 0.64 and 0.44, respectively. After correlation evaluation, the marginal distribution of the parameters was investigated. Using the Kolmogorov-Smirnov and Anderson-Darling tests, statistical distribution functions for precipitation, river discharge, river salinity and groundwater level were obtained Lognormal, Gamma, Burr and Lognormal distributions, respectively. Then, by examining the dependence structure and the structure of copulas and using NSE, RMSE and BIAS evaluation criteria, Clayton's copula function was selected for all three pair of parameters, which was used to create a joint probability distribution between the pair of parameters in the Siminehrood River Basin. | ||
| کلیدواژهها | ||
| Copula function؛ Joint distribution؛ Marginal distribution؛ Clayton؛ Correlation | ||
| مراجع | ||
|
Ayantobo, O. O., Li, Y., & Song, S. (2019). Copula-based trivariate drought frequency analysis approach in seven climatic sub-regions of mainland China over 1961–2013. Theoretical and Applied Climatology, 137, 2217-2237.
Chen, L., Guo, S., Chen, L., & Guo, S. (2019). Copula-based flood frequency analysis. Copulas and Its Application in Hydrology and Water Resources, 39-71.
Chen, L., Singh, V. P., Guo, S., Zhou, J., & Zhang, J. (2015). Copula-based method for multisite monthly and daily streamflow simulation. Journal of Hydrology, 528, 369-384.
Czado, C. (2010, May). Pair-copula constructions of multivariate copulas. In Copula Theory and Its Applications: Proceedings of the Workshop Held in Warsaw, 25-26 September 2009 (pp. 93-109). Berlin, Heidelberg: Springer Berlin Heidelberg.
De Michele, C., & Salvadori, G. (2003). A generalized Pareto intensity‐duration model of storm rainfall exploiting 2‐copulas. Journal of Geophysical Research: Atmospheres, 108(D2).
De Michele, C., Salvadori, G., Passoni, G., & Vezzoli, R. (2007). A multivariate model of sea storms using copulas. Coastal Engineering, 54(10), 734-751.
Ellouze-Gargouri, E., & Bargaoui, Z. (2012). Runoff estimation for an ungauged catchment using geomorphological instantaneous unit hydrograph (GIUH) and copulas. Water resources management, 26(6), 1615-1638.
Favre, A. C., El Adlouni, S., Perreault, L., Thiémonge, N., & Bobée, B. (2004). Multivariate hydrological frequency analysis using copulas. Water resources research, 40(1).
Genest, C., & Favre, A. C. (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of hydrologic engineering, 12(4), 347-368.
Grimaldi, S., & Serinaldi, F. (2006b). Asymmetric copula in multivariate flood frequency analysis. Advances in Water Resources, 29(8), 1155-1167.
Guo, E., Zhang, J., Si, H., Dong, Z., Cao, T., & Lan, W. (2017). Temporal and spatial characteristics of extreme precipitation events in the Midwest of Jilin Province based on multifractal detrended fluctuation analysis method and copula functions. Theoretical and Applied Climatology, 130, 597-607.
Hangshing, L., & Dabral, P. P. (2018). Multivariate frequency analysis of meteorological drought using copula. Water resources management, 32, 1741-1758.
Hao, C., Zhang, J., & Yao, F. (2017). Multivariate drought frequency estimation using copula method in Southwest China. Theoretical and Applied Climatology, 127, 977-991.
Janga Reddy, M., & Ganguli, P. (2012). Risk assessment of hydroclimatic variability on groundwater levels in the Manjara basin aquifer in India using Archimedean copulas. Journal of Hydrologic Engineering, 17(12), 1345-1357.
Kao, S. C., & Govindaraju, R. S. (2007). A bivariate frequency analysis of extreme rainfall with implications for design. Journal of Geophysical Research: Atmospheres, 112(D13).
Kao, S. C., & Govindaraju, R. S. (2008). Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas. Water Resources Research, 44(2).
Kao, S. C., & Govindaraju, R. S. (2010). A copula-based joint deficit index for droughts. Journal of Hydrology, 380(1-2), 121-134.
Keef, C., Svensson, C., & Tawn, J. A. (2009). Spatial dependence in extreme river flows and precipitation for Great Britain. Journal of Hydrology, 378(3-4), 240-252.
Kuhn, G., Khan, S., Ganguly, A. R., & Branstetter, M. L. (2007). Geospatial–temporal dependence among weekly precipitation extremes with applications to observations and climate model simulations in South America. Advances in Water Resources, 30(12), 2401-2423.
Kwon, H. H., & Lall, U. (2016). A copula‐based nonstationary frequency analysis for the 2012–2015 drought in California. Water Resources Research, 52(7), 5662-5675.
Latif, S., & Mustafa, F. (2020). Copula-based multivariate flood probability construction: a review. Arabian Journal of Geosciences, 13(3), 132.
Lazoglou, G., & Anagnostopoulou, C. (2019). Joint distribution of temperature and precipitation in the Mediterranean, using the Copula method. Theoretical and applied climatology, 135, 1399-1411.
Mirabbasi, R., Fakheri-Fard, A., & Dinpashoh, Y. (2012). Bivariate drought frequency analysis using the copula method. Theoretical and applied climatology, 108, 191-206.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2020). A new method for joint frequency analysis of modified precipitation anomaly percentage and streamflow drought index based on the conditional density of copula functions. Water Resources Management, 34, 4217-4231.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2022). Application of copula functions for bivariate analysis of rainfall and river flow deficiencies in the siminehrood River Basin, Iran. Journal of Hydrologic Engineering, 27(11), 05022015.
Nazeri Tahroudi, M., Shahidi, A., Khashei Seyuki, A., & Ramezani, Y. (2019). Designing the monitoring network of rain gauge stations using chaos theory (case study: Lake Urmia basin). Iran Irrigation and Drainage Journal, 13(2), 296-308. [InPersian]
Nelsen, R. B. (2006). An introduction to copulas. Springer.
Ozga-Zielinski, B., Ciupak, M., Adamowski, J., Khalil, B., & Malard, J. (2016). Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland. Journal of Hydrology: Regional Studies, 6, 26-51.
Qian, L., Wang, H., Dang, S., Wang, C., Jiao, Z., & Zhao, Y. (2018). Modelling bivariate extreme precipitation distribution for data‐scarce regions using Gumbel–Hougaard copula with maximum entropy estimation. Hydrological Processes, 32(2), 212-227.
Ramezani, Y., Nazeri Tahroudi, M., & Ahmadi, F. (2019). Analyzing the droughts in Iran and its eastern neighboring countries using copula functions. IDŐJÁRÁS/Quarterly Journal of the Hungarian Meteorological Service, 123(4), 435-453.
Renard, B., & Lang, M. (2007). Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology. Advances in Water Resources, 30(4), 897-912.
Salleh, N., Yusof, F., & Yusop, Z. (2016, June). Bivariate copulas functions for flood frequency analysis. In AIP conference proceedings (Vol. 1750, No. 1). AIP Publishing.
Salvadori, G., & De Michele, C. (2007). On the use of copulas in hydrology: theory and practice. Journal of Hydrologic Engineering, 12(4), 369-380.
Salvadori, G., & De Michele, C. (2010). Multivariate multiparameter extreme value models and return periods: A copula approach. Water resources research, 46(10).
Schoelzel, C., & Friederichs, P. (2008). Multivariate non-normally distributed random variables in climate research–introduction to the copula approach. Nonlinear Processes in Geophysics, 15(5), 761-772.
Serinaldi, F., Bonaccorso, B., Cancelliere, A., & Grimaldi, S. (2009). Probabilistic characterization of drought properties through copulas. Physics and Chemistry of the Earth, 34(10-12), 596-605.
Shiau, J. T. (2006). Fitting drought duration and severity with two-dimensional copulas. Water resources management, 20, 795-815.
Shiau, J. T., Wang, H. Y., & Tsai, C. T. (2006). Bivariate frequency analysis of floods using copulas1. JAWRA Journal of the American Water Resources Association, 42(6), 1549-1564.
Sklar, M. (1959). Fonctions de répartition à n dimensions et leurs marges. In Annales de l'ISUP, 8(3), 229-231.
Sobkowiak, L., Perz, A., Wrzesiński, D., & Faiz, M. A. (2020). Estimation of the river flow synchronicity in the upper indus river basin using copula functions. Sustainability, 12(12), 5122.
Tahroudi, M. N., Ramezani, Y., De Michele, C., & Mirabbasi, R. (2020). Analyzing the conditional behavior of rainfall deficiency and groundwater level deficiency signatures by using copula functions. Hydrology Research, 51(6), 1332-1348.
Wei, T., & Song, S. (2018). Copula-based composite likelihood approach for frequency analysis of short annual precipitation records. Hydrology Research, 49(5), 1498-1512.
Wen, Y., Yang, A., Kong, X., & Su, Y. (2022). A Bayesian-model-averaging copula method for bivariate hydrologic correlation analysis. Frontiers in Environmental Science, 9, 744462.
Xiao, Y., Guo, S., Liu, P., Yan, B., & Chen, L. (2009). Design flood hydrograph based on multicharacteristic synthesis index method. Journal of Hydrologic Engineering, 14(12), 1359-1364.
Yin, J., Guo, S., He, S., Guo, J., Hong, X., & Liu, Z. (2018). A copula-based analysis of projected climate changes to bivariate flood quantiles. Journal of hydrology, 566, 23-42.
Yoo, C., & Cho, E. (2019). Effect of multicollinearity on the bivariate frequency analysis of annual maximum rainfall events. Water, 11(5), 905.
Yue, S., Ouarda, T. B., & Bobée, B. (2001). A review of bivariate gamma distributions for hydrological application. Journal of Hydrology, 246(1-4), 1-18.
Zavareh, M. M., Mahjouri, N., Rahimzadegan, M., & Rahimpour, M. (2023). A drought index based on groundwater quantity and quality: Application of multivariate copula analysis. Journal of Cleaner Production, 417, 137959.
Zhang, L., & Singh, V. P. (2007a). Bivariate rainfall frequency distributions using Archimedean copulas. Journal of Hydrology, 332(1-2), 93-109. | ||
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