Abstract
This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the normalized root mean square error (NRMSE), Cramer-von mises statistic (Sn), and Nash Sutcliffe (NS) evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe and longer droughts will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, the joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it will decrease for extreme droughts in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.Data Availability
Available on request.
Code Availability
The software used in this research will be available (by the corresponding author), upon reasonable request.
Notes
Inference Function for Margins.
References
Afshar MH, Şorman AÜ, Tosunoğlu F, Bulut B, Yilmaz MT, Danandeh Mehr A (2020) Climate change impact assessment on mild and extreme drought events using copulas over Ankara, Turkey. Theoret Appl Climatol 141:1045–1055. https://doi.org/10.1007/s00704-020-03257-6
AghaKouchak A (2014) A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought. Hydrol Earth Syst Sci 18(7):2485–2492. https://doi.org/10.5194/hess-18-2485-2014
AghaKouchak A, Farahmand A, Melton FS, Teixeira J, Anderson MC, Wardlow BD, Hain CR (2015) Remote sensing of drought: Progress, challenges and opportunities. Rev Geophys 53(2):452–480. https://doi.org/10.1002/2014RG000456
Ahmad MI, Sinclair CD, Werritty A (1988) Log-logistic flood frequency analysis. J Hydrol 98(3–4):205–224. https://doi.org/10.1016/0022-1694(88)90015-7
Amin MT, Mahmoud SH, Alazba AA (2016) Observations, projections and impacts of climate change on water resources in Arabian Peninsula: current and future scenarios. Environmental EArth Sciences 75(10):864. https://doi.org/10.1007/s12665-016-5684-4
Barker LJ, Hannaford J, Chiverton A, Svensson C (2016) From meteorological to hydrological drought using standardised indicators. Hydrol Earth Syst Sci 20(6):2483–2505. https://doi.org/10.5194/hess-20-2483-2016
Bazrafshan O, Zamani H, Shekari M (2020) A copula-based index for drought analysis in arid and semi-arid regions of Iran. Nat Resour Model 33(1):e12237. https://doi.org/10.1111/nrm.12237
Beersma JJ, Buishand TA (2004) Joint probability of precipitation and discharge deficits in the Netherlands. Water Resources Research 40(12). https://doi.org/10.1029/2004WR003265
Cammalleri C, Micale F, Vogt J (2016) A novel soil moisture-based drought severity index (DSI) combining water deficit magnitude and frequency. Hydrol Process 30(2):289–301. https://doi.org/10.1002/hyp.10578
Carrão H, Russo S, Sepulcre-Canto G, Barbosa P (2016) An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data. Int J Appl Earth Obs Geoinf 48:74–84. https://doi.org/10.1016/j.jag.2015.06.011
Danandeh Mehr A, Kahya E (2017) Climate change impacts on catchment-scale extreme rainfall variability: case study of Rize Province. Turkey Journal of Hydrologic Engineering 22(3):05016037. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001477
Das J, Jha S, Goyal MK (2020) Non-stationary and copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India. J Hydrol 580:124356. https://doi.org/10.1016/j.jhydrol.2019.124356
Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175. https://doi.org/10.1016/j.atmosres.2016.10.004
Deo RC, Şahin M (2015) Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos Res 161:65–81. https://doi.org/10.1016/j.atmosres.2015.03.018
Dorigo WA, Gruber A, De Jeu RAM, Wagner W, Stacke T, Loew A, Kidd R (2015) Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens Environ 162:380–395. https://doi.org/10.1016/j.rse.2014.07.023
Dubrovsky M, Svoboda MD, Trnka M, Hayes MJ, Wilhite DA, Zalud Z, Hlavinka P (2008) Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia. Theoret Appl Climatol 96:155–171. https://doi.org/10.1007/s00704-008-0020-x
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) empirical design and organization. Geoscientific Model Development 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Fahimirad Z, Shahkarami N (2021) The Impact of Climate Change on Hydro-Meteorological Droughts Using Copula Functions. Water Resour Manage 35(12):3969–3993. https://doi.org/10.1007/s11269-021-02918-z
FAO (2017) Drought characteristics and management in Central Asia and Turkey. Rome.
Ghabaei Sough M, Mosaedi A (2013) Wheat drought monitoring by using generalized Scalogram model at Mashhad and Shiraz synoptic stations. Iranian J Irrig Drain 7(1):23–35
Ghabaei Sough M, Zare Abyaneh H, Mosaedi A (2018) Assessing a multivariate approach based on Scalogram analysis for agricultural drought monitoring. Water Resour Manage 32(10):3423–3440. https://doi.org/10.1007/s11269-018-1999-0
Hameed M, Ahmadalipour A, Moradkhani H (2018) Apprehensive drought characteristics over Iraq: results of a multidecadal spatiotemporal assessment. Geosciences 8(2):58. https://doi.org/10.3390/geosciences8020058
Hameed M, Ahmadalipour A, Moradkhani H (2020) Drought and food security in the middle east: An analytical framework. Agric for Meteorol 281:107816. https://doi.org/10.1016/j.agrformet.2019.107816
Hang Q, Xiao M, Singh VP (2015) Uncertainty evaluation of copula analysis of hydrological droughts in theEast River basin, China. Global Planet Change 129:1–9. https://doi.org/10.1016/j.gloplacha.2015.03.001
Hao Z, AghaKouchak A (2013a) A multivariate multi-index drought modeling framework. Journal of Hydrometeorol 15, 89–101
Hao Z, AghaKouchak A (2013b) Multivariate standardized drought index: a parametric multi-index model Adv Water Resour 57:12-18
Hao Z, AghaKouchak A (2014) A nonparametric multivariate multi-index drought monitoring framework. J Hydrometeorol 15(1):89–101. https://doi.org/10.1175/JHM-D-12-0160.1
Hao Z, AghaKouchak A, Nakhjiri N, Farahmand A (2014) Global integrated drought monitoring and prediction system. Scientific Data 1(1):1–10. https://doi.org/10.1038/sdata.2014.1
Huang S, Huang Q, Leng G, Liu S (2016) A nonparametric multivariate standardized drought index for characterizing socioeconomic drought: A case study in the Heihe River Basin. J Hydrol 542:875–883. https://doi.org/10.1016/j.jhydrol.2016.09.059
IPCC-TGCIA (2007) General guidelines on the use of scenario data for climateimate impact and adaptation assessment. Alfsen K, Barrow E, Bass B, Dai X, Desanker P, Gaffin SR, Giorgi F, Hulme M, Lal M, Mata LJ, Mearns LO, Mitchell JFB, Morita T, Moss R, Murdiyarso D, Pabon-Caicedo JD, Palutikof J, Parry ML, Rosenzweig C, Seguin B, Scholes RJ, Whetton PH, Task Group on Data and Scenario Support for Impact and Climate Assessment
Joe H (1997) Multivariate models and multivariate dependence concepts. CRC Press
Kendall DR, Dracup JA (1992) On the generation of drought events using an alternating renewal–reward model. Stoch Hydrol Hydraul 6(1):55–68. https://doi.org/10.1007/BF01581675
Keyantash JA, Dracup JA (2004) An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resources Research 40(9). https://doi.org/10.1029/2003WR002610
Khajeh S, Paimozd S, Moghaddasi M (2017) Assessing the impact of climate changes on hydrological drought based on reservoir performance indices (case study: ZayandehRud River basin, Iran). Water Resour Manage 31(9):2595–2610. https://doi.org/10.1007/s11269-017-1642-5
Li Q, Li P, Li H, Yu M (2015) Drought assessment using a multivariate drought index in the Luanhe River basin of Northern China. Stoch Env Res Risk Assess 29(6):1509–1520. https://doi.org/10.1007/s00477-014-0982-4
Liu YY, Dorigo WA, Parinussa RM, De Jeu RAM, Wagner W, McCabe MF, Van Dijk AIJM (2012) Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens Environ 123(280–297):14. https://doi.org/10.1016/j.rse.2012.03.014
Liu YY, Parinussa RM, Dorigo WA, De Jeu RAM, Wagner W, Van Dijk AIJM, Evans JP (2011) Developing an improved soil moisture dataset by blending passive andactive microwave satellite-based retrievals. Hydrol Earth Syst Sci 15(2):425–436. https://doi.org/10.5194/hess-15-425-2011
Ma MW, Ren LL, Song SB, Song JL, Jiang SH (2013) Goodness-of-fit tests for multi-dimensional copulas: expanding application to historical drought data. Water Sci Engineering 6:18–30. https://doi.org/10.3882/j.issn.1674-2370.2013.01.002
Mathier L, Perreault L, Bobe B, Ashkar F (1992) The use of geometric and gamma-related distributions for frequency analysis of water deficit. Stoch Hydrol Hydraul 6(4):239–254. https://doi.org/10.1007/BF01581619
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 17(22):179–183
McKee TB, Doesken NJ, Kleist J (1995) Drought monitoring with multiple time scales. In: 482 9th Conference on Applied Climatology, Am Meteor Soc 233–236
Mesbahzadeh T, Mirakbari M, Mohseni Saravi M, Soleimani Sardoo F, Miglietta MM (2020) Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP). Meteorol Appl 27(1):e1856. https://doi.org/10.1002/met.1856
Mirabbasi R, Fakheri-Fard A, Dinpashoh Y (2012) Bivariate drought frequency analysis using the copula method. Theoret Appl Climatol 108(1):191–206. https://doi.org/10.1007/s00704-011-0524-7
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology: A Journal of the Royal Meteorological Society 25(6):693–712. https://doi.org/10.1002/joc.1181
Moghaddasi M, Anvari S, Akhondi N (2022) A trade-off analysis of adaptive and non-adaptive future optimized rule curves based on simulation algorithm and hedging rules. Theoretical and Applied Climatology, 148, 65–78. https://doi.org/10.1007/s00704-022-03930-y
Mohammed R, Scholz M (2017) The reconnaissance drought index: a method for detecting regional arid climatic variability and potential drought risk. J Arid Environ 144:181–191. https://doi.org/10.1016/j.jaridenv.2017.03.014
Mortuza MR, Moges E, Demissie Y, Li HY (2019) Historical and future drought in Bangladesh using copula-based bivariate regional frequency analysis. Theoret Appl Climatol 135(3):855–871. https://doi.org/10.1007/s00704-018-2407-7
Motevali Bashi Naeini E, Akhoond-Ali AM, Radmanesh F, Koupai JA, Soltaninia S (2021) Comparison of the Calculated Drought Return Periods Using Tri-variate and Bivariate Copula Functions under Climate Change Condition. Water Resour Manage 35(14):4855–4875. https://doi.org/10.1007/s11269-021-02965-6
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 Resour Manage 34(13):4217–4231. https://doi.org/10.1007/s11269-020-02666-6
Nelsen RB (2006) An Introduction to Copulas. Springer, New York. MR2197664
New M, Hulme M, Jones PD (1999) Representing twentieth-centuryspace-time climate variability.Part I: development of a 1961–90 mean monthly terrestrial climatology. J Clim 12:829–856. https://doi.org/10.1175/1520-0442(1999)012%3c0829:RTCSTC%3e2.0.CO;2
Rahmani Kam A (2015) Soil moisture routing using remote sensing products, Mater’s thesis, University of Shahrood, Iran
Rajsekhar D, Singh VP, Mishra AK (2015) Multivariate drought index: An information theory based approach for integrated drought assessment. J Hydrol 526:164–182. https://doi.org/10.1016/j.jhydrol.2014.11.031
Rezaeianzadeh M, Stein A, Cox JP (2016) Drought forecasting using Markov chain model and artificial neural networks. Water Resour Manage 30(7):2245–2259. https://doi.org/10.1007/s11269-016-1283-0
Sajeev A, Deb Barma S, Mahesha A, Shiau JT (2021) Bivariate drought characterization of two contrasting climatic regions in India using copula. J Irrig Drain Eng 147(3):05020005. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001536
Salvadori G, Michele CD, Kottegoda NT, Rosso R (2007) Extremes in nature: an approach using copulas. Water Science and Technology Library Series. Springer, Dordrecht
Scanlon BR, Ruddell BL, Reed PM, Hook RI, Zheng C, Tidwell VC, Siebert S (2017) The food-energy-water nexus: Transforming science for society. Water Resour Res 53(5):3550–3556. https://doi.org/10.1002/2017WR020889
Sheffield J, Wood EF (2008) Projected changes in drought occurrence under future global warming from multi-model, multi-scenario. IPCC AR4 simulations. Clim Dyn 31:79–105. https://doi.org/10.1007/s00382-007-0340-z
Shiau JT (2006) Fitting drought duration and severity with two-dimensional copulas. Water Resour Manage 20(5):795–815. https://doi.org/10.1007/s11269-005-9008-9
Sklar M (1959) Fonctions de repartition an dimensions etleurs marges. Publ Inst Statist Univ Paris 8:229–231
Solomon S, Manning M, Marquis M, Qin D (2007) Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge University Press
Svoboda M, Fuchs B (2016) Handbook of Drought Indicators and Indices
Thilakarathne M, Sridhar V (2017) Characterization of future drought conditions in the Lower Mekong River Basin. Weather and Climate Extremes 17:47–58. https://doi.org/10.1016/j.wace.2017.07.004
Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38(1):55–94. https://doi.org/10.2307/210739
Tirado MC, Clarke R, Jaykus LA, McQuatters-Gollop A, Frank JM (2010) Climate change and food safety: A review. Food Res Int 43(7):1745–1765. https://doi.org/10.1016/j.foodres.2010.07.003
Tsakiris G, Pangalou D, Vangelis H (2007) Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manage 21(5):821–833. https://doi.org/10.1007/s11269-006-9105-4
Van Loon AF (2015) Hydrological drought explained. Wiley Interdiscip Rev Water 2(4):359–392. https://doi.org/10.1002/wat2.1085
Van Loon AF, Ploum SW, Parajka J, Fleig AK, Garnier E, Laaha G, Van Lanen HA (2014) Hydrological drought typology: temperature-related drought types and associated societal impacts. Hydrology & Earth System Sciences Discussions 11(9)
Vergni L, Todisco FL, Mannocchi F (2015) Analysis of agricultural drought characteristics through a two-dimensional copula. Water Resour Manage 29(8):2819–2835. https://doi.org/10.1007/s11269-015-0972-4
Vicente-Serrano SM, Beguería S, Lopez-Moreno JI (2010) A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index - SPEI. J Clim 23:1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Vicente-Serrano SM, Beguería S, Lorenzo-Lacruz J, Camarero JJ, López-Moreno JI, Azorin-Molina C, Revuelto J, Morán-Tejeda E, Sánchez-Lorenzo A (2012) Performance of drought indices for ecological, agricultural and hydrological applications. Earth Interact 16:1–27. https://doi.org/10.1175/2012EI000434.1
Wagner W, Dorigo W, de Jeu R, Fernandez D, Benveniste J, Haas E, Ertl M (2012) Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture. InXXII ISPRS Congress, Melbourne, Australia 315–321
Waseem M, Ajmal M, Kim TW (2015) Development of a new composite drought index for multivariate drought assessment. J Hydrol 527:30–37. https://doi.org/10.1016/j.jhydrol.2015.04.044
Wilhite DA, Glantz MH (1985) Understanding: the drought phenomenon: the role of definitions. Water International 10(3):111–120. https://doi.org/10.1080/02508068508686328
Wilhite DA, Sivakumar MV, Pulwarty R (2014) Managing drought risk in a changing climate: The role of national drought policy. Weather and Climate Extremes 3:4–13. https://doi.org/10.1016/j.wace.2014.01.002
Won J, Choi J, Lee O, Kim S (2020) Copula-based Joint Drought Index using SPI and EDDI and its application to climate change. Sci Total Environ 744:140701. https://doi.org/10.1016/j.scitotenv.2020.140701
Yan H, Moradkhani H, Zarekarizi M (2017) A probabilistic drought forecasting framework: A combined dynamical and statistical approach. J Hydrol 548:291–304. https://doi.org/10.1016/j.jhydrol.2017.03.004
Zhang L, Liu Y, Ren L, Jiang S, Yang X, Yuan F, Wei L (2019) Drought Monitoring and Evaluation by ESA CCI Soil Moisture Products Over the Yellow River Basin. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12(9):3376–3386. https://doi.org/10.1109/JSTARS.2019.2934732
Zhang L, Wang Y, Chen Y, Bai Y, Zhang Q (2020) Drought risk assessment in Central Asia using a probabilistic copula function approach. Water 12(2):421. https://doi.org/10.3390/w12020421
Zhu J, Zhou L, Huang S (2018) A hybrid drought index combining meteorological, hydrological, and agricultural information based on the entropy weight theory. Arab J Geosci 11(5):1–12. https://doi.org/10.1007/s12517-018-3438-1
Acknowledgements
The authors would like to express special thanks to Dr. Kaveh Mohammadpour for sharing with them his rich experience in working with gridded datasets and climatology.
Funding
The authors did not receive support from any organization for the submitted research.
Author information
Authors and Affiliations
Contributions
M.M.: Conceptualization, Methodology, Technical Investigation, Writing, Reviewing and Editing, Validation, Visualization, Supervision; A.Sh.: Software, Data Curation, Editing; K.N.: writing, original draft preparation.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable, because this article does not contain any studies with human or animal subjects.
Consent to Participate
The research data were not prepared through a questionnaire.
Consent for Publication
There is no conflict of interest regarding the publication of this article.
Conflict of Interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Naderi, K., Moghaddasi, M. & shokri, A. Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change. Water Resour Manage 36, 2865–2888 (2022). https://doi.org/10.1007/s11269-022-03186-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-022-03186-1