Modeling and Bivariate Analysis of Meteorological Drought Using Data Generation with Climate Change Approach (Case Study: Lake Urmia)

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Farzad  Khezri ¹ (Ph.D. Candidate, Department of Water Engineering, Kerman Unit, Islamic Azad University, Kerman, Iran.)
Mohsen  Irandost ² (Assistant Professor, Department of Water Engineering, Kerman Unit, Islamic Azad University, Kerman,)
Navid  Jalalkamali ³ (Assistant Professor, Department of Water Engineering, Kerman Unit, Islamic Azad University, Kerman, Iran.)
Najme  Yazdanpanah ⁴ (Associate Professor, Department of Water Engineering, Kerman Unit, Islamic Azad University, Kerman, Iran.)

Received: 2021-06-24 Accepted : 2021-08-28 Published : 2021-12-22

Keywords: Climate Change, Copula Functions, Multivariate analysis, Pathway Scenarios, Drought,

Abstract :

Background and Aim: Climate Climate change is one of the important factors that will affect different parts of human life on the planet and will have detrimental effects on the environment, socio-economic, and especially water resources. Knowledge of climate change can provide comprehensive plans in various areas of management regarding the monitoring of droughts and their potential risks. Drought can occur in any area, even wetlands. This phenomenon depends on various factors and parameters and one of the most important symbols of this phenomenon is the occurrence of drought is a decrease in rainfall and therefore the analysis of precipitation data is of special importance to study drought. The purpose of this study is to analyze drought variables using SPI and SPImod indices and detailed functions.Method:  In this study, to model the multivariate analysis of drought in Lake Urmia basin using RCP8.5 and RCP4.5 representative concentration pathway scenarios, data and models of atmospheric circulation of historical data (1991-2010) for three near horizons (2030- 2011), medium (2065-2046) and round (2099-2080) were simulated and produced. Then, using SPImod index and copula functions, drought multivariate analysis was performed in MATLAB software environment. In general, first, using the mentioned indicators (two indicators, SPI and SPImod), the characteristics of drought intensity and duration were extracted, then, using coding in MATLAB software environment, eight families of Archimedean detailed functions were used.Results: The results of multivariate analysis showed that the Joe copula function is the best copula function for drought multivariate analysis (For analysis of both severity and duration of drought for the study area). Also, the results of probability and the joint return period showed that in the coming periods, at least droughts of the same level as historical droughts and even more severe will occur. Thus, by studying the period of combined and conditional returns and Kendall, the results showed that at a certain critical probability level, the amount of Kendall return period is much more than the standard return period, so that this difference increases with increasing that certain amount.Conclusion: The results obtained with the climate change approach on the meteorological drought of Lake Urmia showed that in the coming periods we will see an increase in temperature, which will affect the rate of trade in the region and water resources, on the other hand, because the data Meteorology and hydrology are used to calculate the types of droughts, so droughts affected by climate change will be so that in future periods 46% to 48% of the months will be dry in different horizons. Finally, the results of the time series of indicators showed that during the statistical period at least 40% of the months were dry and this intensity of droughts in the Urmia station is much higher than others. The modified SPI largely eliminates the disadvantages of conventional SPIs and takes into account seasonal variations in precipitation in the calculation of the SPI index.

References:

Reference:

Ayantobo, O.O., Li, Y. and Song, S. 2019 Multivariate Drought Frequency Analysis using Four-Variate Symmetric and Asymmetric Archimedean Copula Functions. Water Resour Manage 33, 103–127 2019. https://doi.org/10.1007/s11269-018-2090-6.

Bahri, M. Command, M.T. and Goodarzi, M. 2015. Study of droughts in the decade 2030-2011 due to climate change, Case study: Eskandari watershed, Isfahan province. Watershed engineering and management. 7 (2), 171-157. [in Persian]

Bouabdelli, S., Meddi, M., Zeroual, A., and Alkama, R. 2020. Hydrological drought risk recurrence under climate change in the karst area of Northwestern Algeria. Journal of Water and Climate Change.

Danandeh Mehr, A., Sorman, A. U., Kahya, E., and Hesami Afshar, M. 2020. Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, 65(2), 254-268.

Durante, F.and Salvadori, G., 2010. On the construction of multivariate extreme value models via copulas. Environmetrics, 21: 143-161.

Golmohammadi,,M. and Masah Bovani, A. 2011. Investigation of changes in the severity and return period of drought in Qarasu Basin in future periods affected by climate change. Journal of Soil 25 (2), 326-315.

Hesami Afshar.M, Unal Sorman.A and Tugrul Yilmaz. M, 2016. Conditional Copula-Based Spatial–Temporal Drought Characteristics Analysis—A Case Study over Turkey. Water 2016, 8, 426; doi:10.3390/w8100426.

Ildermi, A., Nouri, H. and Karami, M. 2016. Assessment of Drought and Climate Change in the Future Period Using General Atmospheric Circulation Models (Case Study: Gorgan-Qarahsu Watershed - Iran). Journal of Geographical Studies of Arid Areas. 7 (26), 124-111.

Khani Tamaleh, Z. Rezaei, H. and Mir Abbasi Najafabadi, R. 2020. Frequency analysis of three variables of drought characteristics in eastern Iran using nested joint functions. Iranian Water Resources Research, 16 (2), 213-202. [in Persian]

Khani Tamaleh, Z. Rezaei, H. and Mir Abbasi Najafabadi, R. 2020. Application of nested joint functions for frequency analysis of four variables of meteorological droughts (Case study: West of Iran). Journal of Soil Resources Conservation, 10 (1), 112-93. [in Persian]

Kao SC, and Govindaraju RS, 2010. A copula-based joint deficit index for droughts. Journal of Hydrology 380(1-2): 121-134.

Moafi Madani, S., Mousavi Baigi, S., and Ansari, H. 2012. Prediction of drought situation in Khorasan Razavi province during 2030-2011 using statistical exponential microscale of LARS-WG model output. Geography and Environmental Hazards, 1 (3), 21-37.

McKee, T.B., Doesken, N.J., and Kleist, J. 1993. The relationship of drought frequency and duration to time scales, paper presented at Eighth Conference on Applied Climatology. Am. Meteorol. Soc., Anaheim, CA.

Mesbahzadeh, T., and Sardoo, F. S. 2019. Assessment and Prediction of Droughts Using Climate Change Scenarios (The Case Study: Southeastern Iran). Russian Meteorology and Hydrology, 44(8), 548-554.

Mousavi, S-F. 2005. Agricultural drought management in Iran. Proc. Water Conservation, Reuse, and Recycling: Proceedings of an Iranian-American Workshop. National Academies Press, pp.106-13.

Nodeh Farahani, M. Rasekhi, A. Permas, B and Keshvari, A. 2018. Investigation of the effects of climate change on temperature, precipitation and droughts of the future period of Shadegan Basin, Iranian Water Resources Research. 14 (3), 139-125. [in Persian]

Nash, JE.,and Sutcliffe, J. V., 1970. River flow forecasting through conceptual models. A discussion of principles, J Hydrol, 10:282–290.

Nelsen, R. B., 2006, An Introduction to Copulas, Springer, New York. 269 pp.

Salvadori, G., De Michele C.and Durante, F. 2011. On the return period and design in a multivariate framework . Hydrology and Earth System Sciences Discussions, 8:5523-5558. doi:10.5194/hessd-8-5523-2011.

Schwarz, G .1978. Estimating the dimension of a model. The annals of statistics 6(2):461-464.

Semonov, M. A., and Stratonovitch, P. 2010. Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Research. 41: 1-14.

Shiau JT, 2006. Fitting drought duration and severity with two-dimensional copulas. Water Resources Management 20,795–815.https://doi.org/10.1007/s11269-005-9008-9.

Shiau JT, Feng S and Nadarajah S, 2007. Assessment of hydrological droughts for the Yellow River, China, using copulas. Hydrological Processes 21(16): 2157–2163.

Sklar, A., 1959. Fonctions de répartition à n dimensions et leurs marges, Publications de l'Institut de Statistique de L'Université de Paris, 8: 229-231.

Wilby R.L., Dawson C.W.,and Barrow, E.M. 2002. SDSM- a decision support tool for the assessment of regional climate change impacts, Environmental Modeling & Software, 17: 147-159

Zhang, Y., You, Q., Chen, C.,and Ge, J., (2016), Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China, Atmospheric Research, 178, 521-534.

Zhao, P., Lü, H., Yang, H., Wang, W. and Fu, G., 2019. Impacts of climate change on hydrological droughts at basin scale: A case study of the Weihe River Basin, China. Quaternary international, 513, pp.37-46.