Abstract
Assessments of future droughts are essential tools due to the potential for serious damage to the environment, economy, and society, particularly under climate change. This study proposes a framework for assessing drought characteristics at different scales, periods, and emission scenarios modeled by phase 6 of the Coupled Model Intercomparison Project. The four drought characteristics were determined by applying the Run theory to a standardized precipitation index time series, and the severe drought areas were detected by the Jenks Natural Breaks and Kriging methods. The study produced four main findings. (1) A stochastic weather generator, AWE-GEN, captures the variability of precipitation with inter- and intra-annual stochastic properties, and presents naturally occurring variability as an ensemble. (2) According to the ensemble average of drought characteristics, future droughts are projected to become less frequent with similar durations and intensity due to future rise in precipitation. However, the ensemble (stochastic or natural) and spatial variabilities are expected to increase, making drought management difficult (e.g., future decrease of 18% in \({DE}_{max}\) for END585). (3) Different temporal scales can affect the detection and characterization of drought events. Smaller temporal scales identify mild drought events of short duration and low severity, while larger scales merge and extend drought events, resulting in more prolonged and severe droughts. (4) Severe drought areas can expand compared with a control period for drought duration and severity, but may decrease for drought interval and frequency especially for the END period (e.g., 24% and 17% increase for \({DD}_{max}\) and \({\left|DS\right|}_{max}\), and 85% and 78% decrease for \({DI}_{mean}\) and \({DE}_{max}\) for SPI3 and END585). The framework proposed in this study is expected to provide important information for the building of strategies required to adapt to and mitigate the potential impacts of drought in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alderlieste MAA, Van Lanen HAJ, Wanders N (2014) Future low flows and hydrological drought: how certain are these for Europe? 7th World FRIEND-water conference on hydrology in a changing world: environmental and human dimensions. IAHS Publication, Montpellier, France, pp 60–65
Alston M, Kent J (2004) Social impacts of drought. Citeseer
Awchi TA, Kalyana MM (2017) Meteorological drought analysis in northern Iraq using SPI and GIS. Sustain Water Resour Manag 3(4):451–463. https://doi.org/10.1007/s40899-017-0111-x
Beguería S, Vicente-Serrano SM, Reig F, Latorre B (2014) Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34(10):3001–3023. https://doi.org/10.1002/joc.3887
Bhattacharjee S, Ghosh SK, Chen J (2019) Semantic Kriging for Spatio-temporal Prediction. Springer
Charles TdS et al (2022) Estimating average annual rainfall by ordinary kriging and TRMM precipitation products in midwestern Brazil. J S Am Earth Sci 118:103937. https://doi.org/10.1016/j.jsames.2022.103937
Chen J, Arsenault R, Brissette FP, Côté P, Su T (2019) Coupling annual, monthly and daily weather generators to simulate multisite and multivariate climate variables with low-frequency variability for hydrological modelling. Clim Dyn 53(7):3841–3860. https://doi.org/10.1007/s00382-019-04750-z
Chen J, Brissette FP (2014) Stochastic generation of daily precipitation amounts: review and evaluation of different models. Clim Res 59(3):189-U45. https://doi.org/10.3354/cr01214
Chen J, Brissette FP, Leconte R (2012) WeaGETS—a Matlab-based daily scale weather generator for generating precipitation and temperature. Procedia Environ Sci 13:2222–2235. https://doi.org/10.1016/j.proenv.2012.01.211
Chen S, Shin JY, Kim T-W (2017) Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection. Stoch Environ Res Risk Assess 31(5):1061–1076. https://doi.org/10.1007/s00477-016-1279-6
Cowpertwait PSP (1998) A Poisson-cluster model of rainfall: some high-order moments and extreme values. Proc R Soc Lond Ser A Math Phys Eng Sci 454(1971):885–898. https://doi.org/10.1098/rspa.1998.0191
De la Barreda B, Metcalfe SE, Boyd DS (2020) Precipitation regionalization, anomalies and drought occurrence in the Yucatan Peninsula. Mex Int J Climatol 40(10):4541–4555. https://doi.org/10.1002/joc.6474
Doi M, Kim J (2020) Projections on climate internal variability and climatological mean at fine scales over South Korea. Stoch Environ Res Risk Assess 34(7):1037–1058. https://doi.org/10.1007/s00477-020-01807-y
Doi M, Kim J (2021) Addressing climate internal variability on future intensity-duration-frequency curves at fine scales across South Korea. Water 13:2828. https://doi.org/10.3390/w13202828
Doi M, Kim J (2022) Future projections and uncertainties of CMIP6 for hydrological indicators and their discrepancies from CMIP5 over South Korea. Water 14:2926. https://doi.org/10.3390/w14182926
Edwards B, Gray M, Hunter B (2019) The social and economic impacts of drought. Aust J Soc Issues 54(1):22–31
Erdélyi D et al (2023) Predicting spatial distribution of stable isotopes in precipitation by classical geostatistical- and machine learning methods. J Hydrol 617:129129. https://doi.org/10.1016/j.jhydrol.2023.129129
Evin G, Favre AC, Hingray B (2018) Stochastic generation of multi-site daily precipitation focusing on extreme events. Hydrol Earth Syst Sci 22(1):655–672. https://doi.org/10.5194/hess-22-655-2018
Eyring V et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9(5):1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Fatichi S, Ivanov VY, Caporali E (2011) Simulation of future climate scenarios with a weather generator. Adv Water Resour 34(4):448–467. https://doi.org/10.1016/j.advwatres.2010.12.013
Fatichi S et al (2016) Uncertainty partition challenges the predictability of vital details of climate change. Earths Future 4(5):240–251. https://doi.org/10.1002/2015ef000336
Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578. https://doi.org/10.1002/joc.1556
Gidden MJ et al (2019) Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci Model Dev 12(4):1443–1475. https://doi.org/10.5194/gmd-12-1443-2019
Haile GG et al (2020) Projected impacts of climate change on drought patterns over East Africa. Earth’s Future 8(7):e2020EF001502. https://doi.org/10.1029/2020EF001502
Hao Z, AghaKouchak A (2013) Multivariate standardized drought index: a parametric multi-index model. Adv Water Resour 57:12–18. https://doi.org/10.1016/j.advwatres.2013.03.009
Hertig E, Tramblay Y (2017) Regional downscaling of Mediterranean droughts under past and future climatic conditions. Glob Planet Chang 151:36–48. https://doi.org/10.1016/j.gloplacha.2016.10.015
Heydari Alamdarloo E, Khosravi H, Nasabpour S, Gholami A (2020) Assessment of drought hazard, vulnerability and risk in Iran using GIS techniques. J Arid Land 12(6):984–1000. https://doi.org/10.1007/s40333-020-0096-4
Ivanov VY, Bras RL, Curtis DC (2007) A weather generator for hydrological, ecological, and agricultural applications. Water Resour Res 43(10)
Jehanzaib M, Yoo J, Kwon H-H, Kim T-W (2021) Reassessing the frequency and severity of meteorological drought considering non-stationarity and copula-based bivariate probability. J Hydrol 603:126948. https://doi.org/10.1016/j.jhydrol.2021.126948
Keller EJ (1992) Drought, war, and the politics of famine in Ethiopia and Eritrea. J Mod Afr Stud 30(4):609–624
Keshavarzi A, Tuffour HO, Brevik EC, Ertunç G (2021) Spatial variability of soil mineral fractions and bulk density in Northern Ireland: assessing the influence of topography using different interpolation methods and fractal analysis. CATENA 207:105646. https://doi.org/10.1016/j.catena.2021.105646
Khazaei MR, Hasirchian M, Zahabiyoun B (2021) An improved daily weather generator for the assessment of regional climate change impacts. Theor Appl Climatol 146(1–2):475–487. https://doi.org/10.1007/s00704-021-03753-3
Khazaei MR, Zahabiyoun B, Hasirchian M (2020) A new method for improving the performance of weather generators in reproducing low-frequency variability and in downscaling. Int J Climatol 40(12):5154–5169. https://doi.org/10.1002/joc.6511
Kim J, Ivanov VY (2015) A holistic, multi-scale dynamic downscaling framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics. J Hydrol 522:645–660. https://doi.org/10.1016/j.jhydrol.2015.01.025
Kim J, Ivanov VY, Fatichi S (2016a) Climate change and uncertainty assessment over a hydroclimatic transect of Michigan. Stoch Environ Res Risk Assess 30(3):923–944. https://doi.org/10.1007/s00477-015-1097-2
Kim J, Ivanov VY, Fatichi S (2016b) Environmental stochasticity controls soil erosion variability. Sci Rep. https://doi.org/10.1038/srep22065
Kim J, Ivanov VY, Fatichi S (2016c) Soil erosion assessment-Mind the gap. Geophys Res Lett 43(24):12446–12456. https://doi.org/10.1002/2016gl071480
Kim J, Tanveer ME, Bae D-H (2018) Quantifying climate internal variability using an hourly ensemble generator over South Korea. Stoch Environ Res Risk Assess 32(11):3037–3051. https://doi.org/10.1007/s00477-018-1607-0
Kimwatu DM, Mundia CN, Makokha GO (2021) Developing a new socio-economic drought index for monitoring drought proliferation: a case study of Upper Ewaso Ngiro River Basin in Kenya. Environ Monit Assess 193(4):213. https://doi.org/10.1007/s10661-021-08989-0
Łabędzki L (2007) Estimation of local drought frequency in central Poland using the standardized precipitation index SPI. Irrig Drain 56(1):67–77. https://doi.org/10.1002/ird.285
Laimighofer J, Laaha G (2022) How standard are standardized drought indices? Uncertainty components for the SPI & SPEI case. J Hydrol 613:128385. https://doi.org/10.1016/j.jhydrol.2022.128385
Lee J, Kim Y, Wang D (2022) Assessing the characteristics of recent drought events in South Korea using WRF-Hydro. J Hydrol 607:127459. https://doi.org/10.1016/j.jhydrol.2022.127459
Li C et al (2019) Drought hazard assessment and possible adaptation options for typical steppe grassland in Xilingol League, Inner Mongolia. China Theor Appl Climatol 136(3):1339–1346. https://doi.org/10.1007/s00704-018-2563-9
Ma F, Luo LF, Ye AZ, Duan QY (2019) Drought characteristics and propagation in the Semiarid Heihe River Basin in Northwestern China. J Hydrometeorol 20(1):59–77. https://doi.org/10.1175/jhm-d-18-0129.1
McBride LA et al (2021) Comparison of CMIP6 historical climate simulations and future projected warming to an empirical model of global climate. Earth Syst Dyn 12(2):545–579. https://doi.org/10.5194/esd-12-545-2021
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. Proceedings of the 8th conference on applied climatology, pp 179–183
Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1–2):202–216
Mitra S, Srivastava P, Lamba J (2018) Probabilistic assessment of projected climatological drought characteristics over the Southeast USA. Clim Chang 147(3):601–615. https://doi.org/10.1007/s10584-018-2161-y
Moraga JS, Peleg N, Molnar P, Fatichi S, Burlando P (2022) Uncertainty in high-resolution hydrological projections: partitioning the influence of climate models and natural climate variability. Hydrol Process. https://doi.org/10.1002/hyp.14695
Nagarajan R (2010) Drought indices, drought assessment. Springer, Netherlands, Dordrecht, pp 160–204. https://doi.org/10.1007/978-90-481-2500-5_5
Nasrollahi M, Khosravi H, Moghaddamnia A, Malekian A, Shahid S (2018) Assessment of drought risk index using drought hazard and vulnerability indices. Arab J Geosci 11(20):606. https://doi.org/10.1007/s12517-018-3971-y
Nerini D, Besic N, Sideris I, Germann U, Foresti L (2017) A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform. Hydrol Earth Syst Sci 21(6):2777–2797
Nicks AD, Gander GA (1994) CLIGEN—a weather generator for climate inputs to water-resource and other models, Computers in agriculture 1994—proceedings of the 5th international conference, pp 903–909
Norris J, Chen G, Neelin JD (2019) Thermodynamic versus dynamic controls on extreme precipitation in a warming climate from the Community Earth System Model Large Ensemble. J Clim 32(4):1025–1045
Panofsky HA, Brier GW, Best WH (1958) Some application of statistics to meteorology
Peleg N, Fatichi S, Paschalis A, Molnar P, Burlando P (2017) An advanced stochastic weather generator for simulating 2-D high-resolution climate variables. J Adv Model Earth Syst 9(3):1595–1627. https://doi.org/10.1002/2016ms000854
Qi W et al (2021) Weakening flood, intensifying hydrological drought severity and decreasing drought probability in Northeast China. J Hydrol Reg Stud 38:100941. https://doi.org/10.1016/j.ejrh.2021.100941
Raja NB, Aydin O, Türkoğlu N, Çiçek I (2017) Space-time kriging of precipitation variability in Turkey for the period 1976–2010. Theor Appl Climatol 129(1):293–304. https://doi.org/10.1007/s00704-016-1788-8
Ramkar P, Yadav SM (2018) Spatiotemporal drought assessment of a semi-arid part of middle Tapi River Basin, India. Int J Disaster Risk Reduct 28:414–426. https://doi.org/10.1016/j.ijdrr.2018.03.025
Deo RC, M.Ş. (2015) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Elsevier.https://doi.org/10.1016/j.atmosres.2014.10.016
Rhee J, Cho J (2016) Future changes in drought characteristics: regional analysis for South Korea under CMIP5 projections. J Hydrometeorol 17(1):437–451. https://doi.org/10.1175/JHM-D-15-0027.1
Rhee J, Im J (2017) Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agric for Meteorol 237–238:105–122. https://doi.org/10.1016/j.agrformet.2017.02.011
Riahi K et al (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Chang 42:153–168
Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17(1):182–190
Rochford P (2016) SkillMetrics: A Python package for calculating the skill of model predictions against observations
Ryu J-H et al (2019) Different agricultural responses to extreme drought events in neighboring counties of South and North Korea. Remote Sens. https://doi.org/10.3390/rs11151773
Sadri S, Burn DH (2012) Nonparametric methods for drought severity estimation at ungauged sites. Water Resour Res. https://doi.org/10.1029/2011WR011323
Sattar MN, Lee J-Y, Shin J-Y, Kim T-W (2019) Probabilistic characteristics of drought propagation from meteorological to hydrological drought in South Korea. Water Resour Manag 33(7):2439–2452. https://doi.org/10.1007/s11269-019-02278-9
Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10(2):95–107. https://doi.org/10.3354/cr010095
Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett. https://doi.org/10.1029/2007GL032487
Song YH, Nashwan MS, Chung ES, Shahid S (2021) Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea. Atmos Res. https://doi.org/10.1016/j.atmosres.2020.105261
Stöckle CO, Campbell GS, Nelson R (1999) ClimGen manual. Biological Systems Engineering Department, Washington State University, Pullman, WA, 28
Tabari H, Nikbakht J, Hosseinzadeh Talaee P (2013) Hydrological drought assessment in Northwestern Iran based on streamflow drought index (SDI). Water Resour Manag 27(1):137–151. https://doi.org/10.1007/s11269-012-0173-3
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192. https://doi.org/10.1029/2000JD900719
Tebaldi C, Mearns LO, Nychka D, Smith RL (2004) Regional probabilities of precipitation change: a Bayesian analysis of multimodel simulations. Geophys Res Lett. https://doi.org/10.1029/2004GL021276
Thom HCS (1966) Some methods of climatological analysis, 81. Secretariat of the World Meteorological Organization Geneva
Tsakiris G, Vangelis H (2004) Towards a drought watch system based on spatial SPI. Water Resour Manag 18(1):1–12. https://doi.org/10.1023/B:WARM.0000015410.47014.a4
Vicente-Serrano SM, Quiring SM, Pena-Gallardo M, Yuan S, Dominguez-Castro F (2020) A review of environmental droughts: increased risk under global warming? Earth Sci Rev 201:102953
Wackernagel H (2003) Multivariate geostatistics: an introduction with applications. Springer Science & Business Media
Wang Q et al (2022) An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018. Sci Data 9(1):124. https://doi.org/10.1038/s41597-022-01201-z
Wilson EB, Hilferty MM (1931) The distribution of chi-square. Proc Natl Acad Sci 17(12):684–688
Xu DH, Ivanov VY, Kim J, Fatichi S (2019) On the use of observations in assessment of multi-model climate ensemble. Stoch Environ Res Risk Assess 33(11–12):1923–1937. https://doi.org/10.1007/s00477-018-1621-2
Xu ZG, Wu ZY, Shao QX, He H, Guo X (2023) From meteorological to agricultural drought: propagation time and probabilistic linkages. J Hydrol Reg Stud. https://doi.org/10.1016/j.ejrh.2023.101329
Yevjevich VM (1967) Objective approach to definitions and investigations of continental hydrologic droughts, An, Colorado State University. Libraries
Yin S-Q, Wang Z, Zhu Z, Zou X-K, Wang W-T (2018) Using Kriging with a heterogeneous measurement error to improve the accuracy of extreme precipitation return level estimation. J Hydrol 562:518–529. https://doi.org/10.1016/j.jhydrol.2018.04.064
Yu J, Lim J, Lee K-S (2018) Investigation of drought-vulnerable regions in North Korea using remote sensing and cloud computing climate data. Environ Monit Assess 190(3):126. https://doi.org/10.1007/s10661-018-6466-0
Zhai JQ et al (2020) Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmos Res 246:18. https://doi.org/10.1016/j.atmosres.2020.105111
Zhang S, Li J, Zhang T, Feng P, Shi H (2023) How does dewfall affect drought assessment in different climate regions in China. J Hydrol 616:128601. https://doi.org/10.1016/j.jhydrol.2022.128601
Acknowledgements
This work was supported by a 2022-MOIS63-002 of Cooperative Research Method and Safety Management Technology in National Disaster, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2008584).
Funding
This work was supported by Cooperative Research Method and Safety Management Technology in National Disaster (Grant No. 2022-MOIS63-002), and Korea government (MSIT) (Grant No. NRF- 2022R1A2C2008584).
Author information
Authors and Affiliations
Contributions
TQV: Conceptualization; Methodology; Modeling; Formal analysis; Visualization; and Main manuscript writing MVD: Conceptualization; Methodology; and Modeling JK: Conceptualization; Methodology; Main manuscript writing review and editing; Funding acquisition; Resources; Supervision All authors reviewed the manuscript
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Vo, T.Q., Van Doi, M. & Kim, J. Assessment of future changes in drought characteristics through stochastic downscaling and CMIP6 over South Korea. Stoch Environ Res Risk Assess 38, 1955–1979 (2024). https://doi.org/10.1007/s00477-024-02664-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-024-02664-9