[1]. Abbaszadeh, M., Bazrafshan, O., Mahdavi, R., Sardooi, E. R., & Jamshidi, S. (2023). Modeling Future Hydrological Characteristics Based on Land Use/Land Cover and Climate Changes Using the SWAT Model. Water Resources Management, 1-18.
[2]. Achite, M., Bazrafshan, O., Wał ˛ega, A., Azhdari, Z., Krakauer, N., & Caloiero, T. (2022). Meteorological and Hydrological Drought Risk Assessment Using Multi-Dimensional Copulas in the Wadi Ouahrane Basin in Algeria. Water, 14(4), 653. doi: 10.3390/w14040653.
[3]. AghaKouchak, A., Bárdossy, A., & Habib, E. (2010). Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula. Advances in Water Resources, 33, 624-634. doi: 10. 1016/j.advwatres.2010.02.010
[4]. Avşaroğlu, Y., & Gumus, V. (2022). Assessment of hydrological drought return periods with bivariate copulas in the Tigris river basin. Meteorology and Atmospheric Physics, 134, 95. doi: 10.1007/ s00703-022-00933-2
[5]. Ayantobo, 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. doi: 10.1007/s00704-018-2724-x
[6]. Azhdari, Z., bazrafshan, O., Bazrafshan, J., Shekari, M., & Zamani, H. (2021). Meteorological drought monitoring based on multivariate statistical and probability indices in Hormozgan province. Journal of Arid Biome, 10(2), 1-17. doi: 10.29252/aridbiom.2021.15258.1821 [in Farsi]
[7]. Azhdari, Z., Bazrafshan, O., Zamani, H., Shekari, M., Psingh, V. (2021). Hydro-meteorological drought risk assessment using linear and nonlinear multivariate, Physics and Chemistry of the Earth, 123, doi: 10.1016/j.pce.2021.103046 [in Farsi]
[8]. Bazrafshan, O., Zamani, H., Mozaffari, E., Azhdari, Z., & Shekari, M. (2023). Trivariate risk analysis of meteorological drought in Iran under climate change scenarios. Meteorology and Atmospheric Physics, 135(6), 52. doi: 10.1007/s00703-023-00988-9
[9]. Bhuiyan, C. (2004). Various drought indices for monitoring drought condition in Aravalli terrain of India. In Proceedings of the XXth ISPRS Congress, Istanbul, Turkey.
[10]. Doesken, N. J., & Garen, D. (1991). Drought monitoring in the Western United States using a surface water supply index. In: Proceedings of the 7th Conference on Applied Climatology, Salt Lake City, UT, USA.
[11]. Dracup, J. A., Lee, K. S., & Paulson Jr, E. G. (1980). On the definition of droughts. Water Resource Research, 16, 297-302. doi: 10.1029/WR016i002p00297
[12]. Esit, M., & Yuce, M. I. (2023). Copula-based bivariate drought severity and duration frequency analysis considering spatial-temporal variability in the Ceyhan Basin, Turkey. Theoretical and Applied Climatology 151, 1113-1131. doi: 10.1007/s00704-022-04317-9
[13]. EskandariPour, M., & Soltaninia, S. (2021). Analyzing the duration frequency and severity of drought using copula function in the Yazd city. Journal of Water Climate Change, 13(1), 67-82. doi: 10.2166/wcc.2021.366
[14]. Goodarzi, M., Fatehifar, A., & Avazpoor, F. (2019). Bivariate Analysis of the Impact of Climate Change on Drought with SPEI Index and Coppola Functions (Case Study: Dugonbadan). Iran-Water Resources Research, 15(4), 352-365. [in Farsi]
[15]. Gusyev, M., Hasegawa, A., & Magome, J. (2015). Drought assessment in the Pampanga River basin, the Philippines – Part 1: Characterizing a role of dams in historical droughts with standardized indices. In Proceedings of the 21st international congress on modelling and simulation (MODSIM 2015), Queensland, Australia.
[16]. Joe, H. (1997). Multivariate Models and Dependence Concepts, Chapman & Hall.
[17]. Mirabbasi, R., Fakheri-Fard, A., & Dinpashoh, Y. (2012). Bivariate drought frequency analysis using the copula method. Theoretical Applied Climatology, 108, 191-206. doi: 10.1007/s00704-011-0524-7
[18]. Nafii, A., Lamane, H., Taleb, A., & El Bilali, A. (2023). An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment. MethodsX, 10, 102034.
[19]. Nalbantis, I., & Tsakiris, G. (2009). Assessment of hydrological drought revisited. Water Resources Management, 23, 881-897. doi: 10.1007/s11269-008-9305-1
[20]. Nelsen, R. B. (2006). An Introduction to Copulas, Springer.
[21]. Niemeyer, S. (2008). New drought indices. Options Méditerranéennes, Série A, 80, 267-274.
[22]. Palmer, W. C. (1965). Meteorological drought. US. Weather Bureau Res. Paper, 45, 1-58.
[23]. Shiau, J. T. (2006). Fitting drought duration and severity with two-dimensional copulas. Water Resources Management, 20, 795-815. doi: 10.1007/s11269-005-9008-9
[24]. Shiau, J. T., & Modarres, R. (2009). Copula-based drought severity-duration-frequency analysis in Iran. Meteorological applications, 16, 481-489. doi: 10.1002/met.145
[25]. Sklar, M. (1959). Functions de repartition a n dimensions et leurs marges. Publications de l’Institut Statistique de l’Université de Paris, 8, 229-231.
[26]. Teimouri, M., Asadi Nalivan, O., & Elahi, S. (2023). The Probabilistic Analysis of Drought Severity- Duration in North Khorasan Province using Copula Functions. Watershed Management Research Journal, 36(2), 36-52. doi: 10.22092/wmrj.2022.359052.1479 [in Farsi]
[27]. Tosunoğlu, F., & Onof, C. (2017). Joint modelling of drought characteristics derived from historical and synthetic rainfalls: Application of Generalized Linear Models and Copulas. Journal of Hydrology: Regional Studies, 14, 167-181. doi: 10.1016/j.ejrh.2017.11.001
[28]. Tsakiris, G., Kordalis, N., & Tsakiris, V. (2015). Flood double frequency analysis: 2D-archimedean copulas vs bivariate probability distributions. Environmental Process, 2, 705-716. doi: 10.1007/ s40710-015-0078-2
[29]. Zhang, L., & Singh, V. P. (2006). Bivariate flood frequency analysis using the copula method. Journal of Hydrologic Engineering, 11(2), 150–164. doi: 10.1061/(ASCE)1084-0699(2006)11:2(150)
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