Document Type : Research Article
Authors
1 M.Sc. Graduated, Water Engineering Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
2 Associate Professor Water Engineering Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
3 Assistant Professor Water Engineering Department, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
Introduction: Drought is an extreme climate effect and a creeping phenomenon which directly affects the human life. A drought analysis usually requires characterizing drought severity, duration and frequency (SDF). These characteristic variables are commonly not independent, so this phenomenon is a complex natural disaster and climate change makes it likely to become more frequent and immense in many areas across the world. Therefore, in drought analysis, it is needed to investigate its multivariate nature and spatial variability clearly. Copula, as a model of multivariate distribution, has been used widely in hydrological studies. As the standardized precipitation index (SPI) is more accessible than other indices, it is the most commonly used indicators for analyzing the SDF of meteorological drought. Here, the study has two major focuses: 1) Fitting drought characteristics from SPI to appropriate copulas, then using fitted copulas to estimate conditional drought severity distribution and joint return periods for both historical and future time periods in Fars province. 2) Inquiring the effects of climate change on the frequency and severity of meteorological drought.
Materials and Methods: Among the weather stations of Fars province, six synoptic stations were selected, which had longer historical data than others. The data used included 24-hour precipitation during 15 (2004-2018) to 33 (1986-2018) years. Three steps were carried out. Stage one: downscaling of outputs of the large scaling (CanESM2) based on two intermediate (RCP4.5) and pessimistic (RCP8.5) scenarios using SDSM, ver. 4.2.9 during the period of 2020 to 2050. Stage two: calculation of SPIand drought characteristics in the base and future periods (2050-2020). Stage three: extracting SDF curves for the base and future periods under RCP4.5 and RCP8.5 scenarios using copula. The SPIwas used to extract the drought duration and drought severity in the Fars province using GCM models under two selected scenarios (RCP4.5 and RCP8.5) from the IPCC Fifth Assessment Report (AR5) scenarios. The gamble copula function was used to construct the joint distribution function for evaluating the drought return periods in the study area. Because short-term drought prediction is more practical than long-term prediction, we used the 1-month SPI for the copulas-based analysis. Drought severity and duration were calculated based on computed SPIvalues by using the past available data. Drought duration is defined as successive months with SPIvalue less than -1 and drought severity as the accumulative SPIvalue during the period with successive SPIvalue less than -1. The normal and log-normal functions were selected as the candidate distribution function for drought duration and drought severity.
Results and Discussion: The results showed that the frequency of drought occurrence in the Fars province will increase during the period of 2020-2050 under the both two scenarios. In the RCP8.5 scenario, the duration of the drought will also increase. The increase and decrease of monthly rainfall in RCP 4.5 and RCP 8.5 were 2.8 and 6.5%, respectively.The duration of the drought were obtained to be 5.25, 5.5 and 6 days at Shiraz station, with a 2 and 5 years return period, in the baseline and future periods under RCP4.5 and RCP8.5 scenarios, respectively. These values were estimated to be 4, 3.5 and 5 days at Bavanat Station.It is expected that the precipitation will decrease at Shiraz station under the two scenarios.Similarly, this amount is expected to increase and decrease at Bavanet station in the RCP4.5 and RCP8.5 scenarios, respectively.
Conclusion: Changing droughts based on climate change is important in many aspects. In this study, the performance of two-variable statistical distribution of severity and duration of drought was investigated based on the copula function. The comparison of the drought period calculated using the SPIshowed that due to the climate change, the frequency of drought periods is expected to increase in the base and future periods. The results showed that the value of the precipitation changes in the RCP8.5 scenario is higher than the RCP4.5 scenario. Generally, the performance criteria showed that the SDSM had a good performance for the past and the future periods in Fars province for precipitation data. It is expected that with consideration of the amendments in the sixth report of the IPCC, more precision can be obtained in precipitation modeling. Therefore, reviewing the output of the SDF curves with the availability of the results of this report is suggested.
Keywords
-
1- Abbasian M.S., and Moosavi S. 2013. Joint analysis of peak discharge and runoff volume using Copula Functions. 7th National Congress on Civil Engineering Sistan and Baluchestan University, 8p. (In Persian)
2- Arora V.K., Scinocca J.F., Boer G.J., Christian J.R., Denman K.L., Flato G.M., Kharin V.V., Lee W.G., and Merryfield W.J. 2011. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophysical Research Letters 38(5): 1-6.
3- Azam M., Maeng S.J., Kim H.S., and Murtazaev A. 2018. Copula-Based Stochastic Simulation for Regional Drought Risk Assessment in South Korea. Water 10: 359-388.
4- Azizabadi M., Bakhtiari B., Qaderi K., and Rezapour M. 2016. The survey of climate change impact on drought severity-duration-frequency curves using Copulas 47(4): 743-754.
5- Castellvi F., Mormeneo I., and Perez P.J. 2004. Generation of daily amounts of precipitation from standard climatic data: a case study for Argentina. Journal of Hydrology 289: 286–302.
6- Chung C.H., and Salas J.D. 2000. Drought occurrence probabilities and risks of dependent hydrologic processes. Journal of Hydrologic Engineering ASCE 5(3): 259–268.
7- Chebana F., and Ouarda T.B.M.J. 2009. Index flood–based multivariate regional frequency analysis. Water Resources Research 45(10): 1-15.
8- Chen L., Singh V.P., Guo S., Hao Z., and Li T. 2012. Flood coincidence risk analysis using multivariate Copula functions. Journal of Hydrologic Engineering 17(6): 742-755.
9- De Michele C., and Salvadori G. 2003. A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-copulas. Journal of Geophysical Research 108(D2): 40-67.
10- De Michele C., Salvadori G., Canossi M., Petaccia A., and Rosso R. 2005. Bivariate statistical approach to check adequacy of dam spillway. Journal of Hydrologic Engineering 10(1): 50–57.
11- Edwards D.C., and McKee T.B. 1997. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report Number 97–2 Department of Atmospheric Science Colorado State University Fort Collins Colorado, 155.
12- Farrokhnia A., and Morid S. 2008. Analysis of drought severity and duration using Copula functions. 4th National Congress on Civil Engineering, 6-8 May. (In Persian with English abstract)
13- Favre A.C., El Adlouni S., Perreault L., Thie’monge N., and Bobe’e B. 2004. Multivariate hydrological frequency analysis using Copulas. Water Resources Research 40: 1–12.
14- Genest C., Favre A.C., Beliveau J., and Jacques C. 2007. Metaelliptical Copulas and their use in frequency analysis of multivariate hydrological data. Water Resources Research 43(9): 1-12.
15- Halwatura D, Lechner A.M., and Arnold S. 2015. Drought severity-duration-frequency curves: a foundation for risk assessment and planning tool for ecosystem establishment in post-mining landscapes. Hydrology and Earth System Sciences 19(2): 1069-1091.
16- Heim R.R. 2002. A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society 83:1149-1165.
17- IPCC. 2014. Climate Change 2014 Synthesis Report Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team R K Pachauri and L A Meyer (eds.)] IPCC Geneva Switzerland, 151.
18- Kao S.C., and Govindaraju R.S. 2008. Trivariate statistical analysis of extreme rainfall events via the Plackett family of Copulas. Water Resources Research 44(2): 1-11.
19- Kao S.C., Govindaraju R.S., and Niyogi D. 2009. A spatio-temporal drought analysis for the midwestern US. Proceedings of World Environmental and Water Resources Congress 2009 World Environmental and Water Resources Congress 2009 Great Rivers 342: 4654-4663.
20- Khalili A. 1997. Integrated water plan of Iran. Meteorological studies Ministry of power Iran.
21- Liu C.L., Zhang Q., Singh V.P., and Cui Y. 2011. Copula-based evaluations of drought variations in Guangdong South China. Natural Hazards 59(3): 1533-1546.
22- Ma M.W., Song S.b., Ren L.L., Jiang S.H., and Song J.L. 2012. Multivariate drought characteristics using trivariate Gaussian and Student t Copulas. Hydrological Processes 27(8): 1175-1190.
23- Mackee B., Nolan T., Dooesken J., and Kleist J. 1995. Drought monitoring with multiple timescales 9th Conference on Applied Climatology 15-20. January Boston Massachusetts:223-236 drought indices for drought assessment and monitoring in Greece 8th International Conference on Environmental Science and Technology Lemons Island, 8-10 September:484-491.
24- Mishra A., and Singh V.P., 2010. A review of drought concepts. Journal of Hydrology 391: 202-216.
25- Mirabbasi R., Fakheri-Fard A., and Dinpashoh Y. 2012. Bivariate Drought Frequency Analysis Using the Copula Method. Theoretical and Applied Climatology 108(1-2): 191-206.
26- Mirakbari M., Ganji A., and Fallah S.R. 2010. Regional Bivariate Frequency Analysis of Meteorological Droughts. Journal of Hydrologic Engineering 15(12): 985-1000.
27- Nelsen R.B. 2007. An introduction to Copulas (2thed.). New York Springer.
28- Palmer W.C. 1965. Meteorological drought. USWB, Res 45.
29- Pirnia A., Golshan M., Bigonah S., and Solaimani K. 2018. Investing the drought characteristics of Tamar basin (upstream of Golestan Dam) using SPI and SPEI indices under current and future climate conditions. EcoHydrologi 5(1): 215-228.
30- Reddy M.J., and Ganguli P. 2011. Application of Copulas for derivation of drought severity-duration- frequency curves. Hydrological Processes 26(11): 1672-1685.
31- Salas J.D., Fu C., Cancelliere A., Dustin D., Bode D., Pineda A., and Vincent E. 2005. Characterizing the severity and risk of drought in the Poudre River, Colorado. Journal of Water Resources Planning and Management ASCE 131(5): 383–393.
32- Serinaldi F., Bonaccorso B., Cancelliere A., and Grimaldi S. 2009. Probabilistic characterization of drought properties through Copulas. Journal of Physics and Chemistry of the Earth 34: 596–605.
33- Shiau J.T. 2006. Fitting drought duration and severity with two-dimensional Copulas. Water Resources Management 20: 795–815.
34- Shiau J.T., and Modarres R. 2009. Copula-based drought severity-duration-frequency analysis in Iran. Meteorological Applications 16(4): 481-489.
35- Sklar A. 1959. Distribution functions of n Dimensions and Margins. Publications of the Institute of Statistics of the University of Paris 8: 229-231. (In French)
36- 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 and Modelling Software 17: 145-157.
37- Wilby R.L., Dawson C.W., Murphy C., Connor P.O., and Hawkin E.h. 2014. tati tical own calling Mo l − ci ion ntric (SDSMDC) conceptual basis and applications. Climate Research 61(3): 251-268.
38- Wong G., Van Lanen H.A.J., and Torfs P.J.J.F. 2013. Probabilistic analysis of hydrological drought characteristics using meteorological drought. Hydrological Sciences Journal 58(2): 253-270.
39- Wu H., Hayes M.J., Weiss A., and Hu Q. 2001. An Evaluation of the standardized precipitation index the china-z index and the statistical z-score. International Journal of Climatology 21:745-758.
40- Yan B., Guo S., Xiao Y., and Fang B. 2007. Analysis on Drought Characteristics Based on Bivariate Joint Distribution. Arid Zone Research 24(4): 537-542. (In Chinese)
41- Yusof F., Hui-Mean F., Suhaila J., and Yusof Z. 2013. Characterization of Drought Properties with Bivariate Copula Analysis. Water Resources Management 27(12): 4183–4207.
42- Zhang L., and Singh V.P. 2007. Bivariate rainfall frequency distributions using Archimedean Copulas. Journal of Hydrology 332:93-109.
43- Zhang Q., Li J., Singh V.P., and Xu C.Y. 2012. Copula-based spatiotemporal patterns of precipitation extremes in China. International Journal of Climatology 35(5): 1140-1152.
44- Zhang Q., Xiao M., Singh V.P., and Chen X. 2013. Copula-based risk evaluation of droughts across the Pearl River basin China. Theoretical and Applied Climatology 111(1-2): 119-131.
45- Zhang J., Ding Z., and You J. 2014. The joint probability distribution of runoff and sediment and its change characteristics with multi-time scales. Journal of Hydrology and Hydromechanics 62(3): 218–225.
)
Send comment about this article