Abstract
We explore future changes of temperature, precipitation, and drought characteristics in the Indochina Region (ICR) based on the optimal ensemble subset of global climate models (GCMs) of the Couple Model Intercomparison Project Phase 5 (CMIP5). The optimal ensemble subset is selected from 34 GCMs using an ensemble selection method by focusing on precipitation over ICR. Bias correction procedures for the optimal ensemble subset are examined for drought analysis in ICR. Based on the bias-corrected optimal ensemble subset, mean temperature in ICR is projected to increase around 1.1 °C (0.99 °C) in near future (2011–2040), 2.5 °C (1.8 °C) in mid future (2041–2070), and 4.3 °C (2.2 °C) in far future (2071–2100) time frames under representative concentration pathway 8.5 (RCP8.5) (RCP4.5) scenario. Mean precipitation decreases in the dry season and increases in the wet season. The 3-month Standardized Precipitation Evapotranspiration Index (SPEI-3) projects larger changes of drought characteristics than those of the 3-month Standardized Precipitation Index (SPI-3), especially quite large increases of drought duration, severity, and peak. Based on SPEI-3, the potential increase of severe drought hazard is expected in ICR in the far future period under both scenarios. The most drought-prone areas are detected over Thailand and Cambodia in which the drought characteristics are projected to expand to cover most parts of ICR in the mid and far future. The potentially dry condition over ICR is clearly depicted based on SPEI-3 with more reliable estimation after selecting the optimal ensemble subset and bias correction procedure.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
27 November 2020
The original article has been corrected. Figure 3 in the original article has been replaced by this figure.
References
AMS (1997) Policy statement. Bull Am Meteorol Soc 78(5):847–852. https://doi.org/10.1175/1520-0477-78.5.847
Beguería S, Vicente-Serrano SM, Reig F, Latorre B (2013) 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
Burke EJ, Brown SJ (2008) Evaluating uncertainties in the projection of future drought. J Hydrometeorol 9(2):292–299. https://doi.org/10.1175/2007JHM929.1
Chhin R, Yoden S (2018) Ranking CMIP5 GCMs for model ensemble selection on regional scale: case study of the Indochina Region. J Geophys Res Atmos 123(17):8949–8974. https://doi.org/10.1029/2017JD028026
Dai A (2011) Drought under global warming: a review. Wiley Interdiscip Rev Clim Chang 2(1):45–65. https://doi.org/10.1002/wcc.81
Feng S, Trnka M, Hayes M, Zhang Y (2016) Why do different drought indices show distinct future drought risk outcomes in the U.S. Great Plains? J Clim 30(1):265–278. https://doi.org/10.1175/JCLI-D-15-0590.1
Furrer R, Sain SR, Nychka D, Meehl GA (2007) Multivariate Bayesian analysis of atmosphere-ocean general circulation models. Environ Ecol Stat 14(3):249–266. https://doi.org/10.1007/s10651-007-0018-z
Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble averaging (REA) method. J Clim 15(10):1141–1158. https://doi.org/10.1175/1520-0442(2002)015h1141:COAURAi2.0.CO;2
Guo H, Bao A, Liu T, Ndayisaba F, He D, Kurban A, De Maeyer P (2017) Meteorological drought analysis in the Lower Mekong Basin using satellite-based long-term CHIRPS product. Sustainability 9(6):1–21. https://doi.org/10.3390/su9060901
Haerter JO, Hagemann S, Moseley C, Piani C (2011) Climate model bias correction and the role of timescales. Hydrol Earth Syst Sci 15(3):1065–1079. https://doi.org/10.5194/hess-15-1065-2011
Hao Z, Yuan X, Xia Y, Hao F, Singh VP (2017) An overview of drought monitoring and prediction systems at regional and global scales. Bull Am Meteorol Soc 98(9):1879–1896. https://doi.org/10.1175/BAMS-D-15-00149.1
Hargreaves JC (2010) Skill and uncertainty in climate models. Wiley Interdiscip Rev Clim Chang 1(4):556–564. https://doi.org/10.1002/wcc.58
Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37(1):407–418. https://doi.org/10.1007/s00382-010-0810-6
Heim RR (2002) A review of twentieth-century drought indices used in the United States. Bull Am Meteorol Soc 83(8):1149–1166. https://doi.org/10.1175/1520-0477-83.8.1149
Herger N, Abramowitz G, Knutti R, Angélil O, Lehmann K, Sanderson BM (2018) Selecting a climate model subset to optimise key ensemble properties. Earth Syst Dyn 9(1):135–151. https://doi.org/10.5194/esd-9-135-2018
ICEM (2013) USAID Mekong ARCC climate change impact and adaptation study for the Lower Mekong Basin. Tech. rep., International Centre for Environmental Management, Bangkok
IPCC (2013) Climate change 2013: the physical science basic. Contribution of working group I to the fifth Assessment Report of the Intergovernmental Panel on Climate Change. Tech. rep., IPCC, Cambridge and New York
Jeong DI, Sushama L, Naveed Khaliq M (2014) The role of temperature in drought projections over North America. Clim Chang 127(2):289–303. https://doi.org/10.1007/s10584-014-1248-3
Kessler WS, McPhaden MJ (1995) The 1991–1993 El Niño in the central Pacific. Deep Sea Res Part II Top Stud Oceanogr 42(2):295–333. https://doi.org/10.1016/0967-0645(95)00041-N
Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Japan Ser II 93(1):5–48. https://doi.org/10.2151/jmsj.2015-001
L’Heureux ML, Takahashi K, Watkins AB, Barnston AG, Becker EJ, Di Liberto TE, Gamble F, Gottschalck J, Halpert MS, Huang B, Mosquera-Vásquez K, Wittenberg AT (2016) Observing and predicting the 2015/16 El Niño. Bull Am Meteorol Soc 98(7):1363–1382. https://doi.org/10.1175/BAMS-D-16-0009.1
Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res Atmos 115:D10101. https://doi.org/10.1029/2009JD012882
Li X, Zhou W, Chen YD (2015) Assessment of regional drought trend and risk over China: a drought climate division perspective. J Clim 28(18):7025–7037. https://doi.org/10.1175/JCLID-14-00403.1
Lu J, Carbone GJ, Grego JM (2019) Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models. Sci Rep 9(1):4922. https://doi.org/10.1038/s41598-019-41196-z
Maraun D, Shepherd TG, Widmann M, Zappa G, Walton D, Gutiérrez JM, Hagemann S, Richter I, PMM S, Hall A, Mearns LO (2017) Towards process-informed bias correction of climate change simulations. Nat Clim Chang 7:764–773. https://doi.org/10.1038/NCLIMATE3418
Mckee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. AMS 8th Conf Appl Clim (January):179|-184, DOI citeulike-articleid:10490403, URL http://ccc.atmos.colostate.edu/relationshipofdroughtfrequency.pdf
Miao C, Su L, Sun Q, Duan Q (2016) A nonstationary bias-correction technique to remove bias in GCM simulations. J Geophys Res Atmos 121(10):5718–5735. https://doi.org/10.1002/2015JD024159
Orlowsky B, Seneviratne SI (2013) Elusive drought: uncertainty in observed trends and short and long-term CMIP5 projections. Hydrol Earth Syst Sci 17(5):1765–1781. https://doi.org/10.5194/hess-17-1765-2013
Palmer WC (1965) Meteorological droughts. NOAA, Washington, D. C, https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf
Sanderson BM, Knutti R, Caldwell P (2015) A representative democracy to reduce interdependency in a multimodel ensemble. J Clim 28(13):5171–5194. https://doi.org/10.1175/JCLI-D-14-00362.1
Spinoni J, Naumann G, Carrao H, Barbosa P, Vogt J (2013) World drought frequency, duration, and severity for 1951–2010. Int J Climatol 34(8):2792–2804. https://doi.org/10.1002/joc.3875
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A Math Phys Eng Sci 365(1857):2053–2075
Thilakarathne M, Sridhar V (2017) Characterization of future drought conditions in the Lower Mekong River Basin. Weather Clim Extrem 17:47–58. https://doi.org/10.1016/j.wace.2017.07.004
Thirumalai K, DiNezio PN, Okumura Y, Deser C (2017) Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming. Nat Commun 8:15531
Thrasher B, Maurer EP, McKellar C, Duffy PB (2012) Technical note: bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16(9):3309–3314. https://doi.org/10.5194/hess-16-3309-2012
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7):1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Vicente-Serrano SM, Beguería S, Lorenzo-Lacruz J, Camarero JJ, López-Moreno JI, AzorinMolina C, Revuelto J, Morán-Tejeda E, Sanchez-Lorenzo A (2012) Performance of drought indices for ecological, agricultural, and hydrological applications. Earth Interact 16(10):1–27. https://doi.org/10.1175/2012EI000434.1
Vu MT, Raghavan VS, Liong SY (2015) Ensemble climate projection for hydro-meteorological drought over a river basin in Central Highland, Vietnam. KSCE J Civ Eng 19(2):427–433. https://doi.org/10.1007/s12205-015-0506-x
WMO (2012) Standardized precipitation index user guide. 1090, World Meteorological Organization, Geneva
Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) Aphrodite constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 93(9):1401–1415. https://doi.org/10.1175/BAMS-D-00122.1
Yevjevich V (1967) An objective approach to definitions and investigations of continental hydrologic droughts. Colorado State University, [Hydrology and Water Resources Program], Fort Collins, Colo., URL https://mountainscholar.org/bitstream/handle/10217/61303/HydrologyPapers{_}n23.pdf?sequence=1{&}isAllowed=y
Funding
This work was supported by JSPS KAKENHI Grant Numbers JP24224011 and JP17H01159. It was also supported by the JSPS Core-to-Core program, B. Asia-Africa Science Platforms for FY 2015–2017, and JSPS and DG-RSTHE Joint Research Program for FY 2018–2020. This work is done during the first author’s PhD program funded by ASEAN University Network/Southeast Asia Engineering Education Development Network (AUN/SEED-Net) with main support from Japan International Cooperation Agency (JICA).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(PDF 22648 kb)
Rights and permissions
About this article
Cite this article
Chhin, R., Oeurng, C. & Yoden, S. Drought projection in the Indochina Region based on the optimal ensemble subset of CMIP5 models. Climatic Change 162, 687–705 (2020). https://doi.org/10.1007/s10584-020-02850-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-020-02850-y