Abstract
Conducting an in-depth quantification of warming conditions in a given region is crucially conducive to devising more informed, credible, and effective climate actions. The traditional approach commonly is to apply a single monotonic trend test for specified beginning and ending times within a predetermined period, which is sensitive to the analyzed period. Thus, the present study aimed to apply multiple non-parametric statistical trend tests to the observed daily mean, maximum, and minimum temperature records at 12 sites located proportionally to the whole extent of the Central Highlands, Vietnam. This approach was implemented by performing Sen’s slope estimator and block bootstrapping Mann–Kendall tests repeatedly with various beginning and ending years for all possible periods of at least 10 years in length during 1980–2019. This study also delved into non-monotonic trend components in temperature means and extremes by employing an innovative trend analysis (ITA) methodology. The outcomes indicated significant warming trends in the annual mean, maximum, and minimum temperatures, with the estimated trend slopes varying mainly from 0.30–0.43, 0.09–0.25, and 0.41–0.52 °C/decade, respectively. Most extreme temperature indices (i.e., Max Tmin, Min Tmin, warm spell duration indicator, warm days/nights) were characterized mainly by positive trends. The results also pointed out higher warming levels in the annual mean and minimum temperatures than the annual maximum one, and likewise, most extreme temperature indices deriving from daily minimum temperature exhibited faster rates than those from maximum one. These findings highlight the superiority of applying the multiple trend tests and ITA method to clarify temporal trend patterns.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and material
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Alexander L, Zhang X, Peterson T, Caesar J, Gleason B, Klein Tank A, Haylock M, Collins D, Trewin B, Rahimzadeh F (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res. https://doi.org/10.1029/2005JD006290
Almazroui M, Şen Z (2020) Trend analyses methodologies in hydro-meteorological records. Earth Syst Environ 4:713–738. https://doi.org/10.1007/s41748-020-00190-6
Arnell NW, Lowe JA, Challinor A, Osborn T (2019) Global and regional impacts of climate change at different levels of global temperature increase. Clim Change 155:377–391. https://doi.org/10.1007/s10584-019-02464-z
Caesar J, Alexander LV, Trewin B, Tse-ring K, Sorany L, Vuniyayawa V, Keosavang N, Shimana A, Htay MM, Karmacharya J, Jayasinghearachchi DA, Sakkamart J, Soares E, Hung LT, Thuong LT, Hue CT, Dung NTT, Hung PV, Cuong HD, Cuong NM, Sirabaha S (2011) Changes in temperature and precipitation extremes over the Indo-Pacific region from 1971 to 2005. Int J Climatol 31:791–801. https://doi.org/10.1002/joc.2118
David Bronaugh for the Pacific Climate Impacts Consortium (2020) Climdex.pcic: PCIC implementation of climdex routines. R package version 11–11. https://CRAN.R-project.org/package=climdex.pcic. Accessed 19 Oct 2021
Dong Z, Wang L, Sun Y, Hu T, Limsakul A, Singhruck P, Pimonsree S (2021) Heatwaves in southeast Asia and their changes in a warmer world. Earth’s Future. https://doi.org/10.1029/2021EF001992
Dunn RJH, Alexander LV, Donat MG, Zhang X, Bador M, Herold N, Lippmann T, Allan R, Aguilar E, Barry AA, Brunet M, Caesar J, Chagnaud G, Cheng V, Cinco T, Durre I, de Guzman R, Htay TM, Wan Ibadullah WM, Bin Ibrahim MKI, Khoshkam M, Kruger A, Kubota H, Leng TW, Lim G, Li-Sha L, Marengo J, Mbatha S, McGree S, Menne M, de los Milagros Skansi M, Ngwenya S, Nkrumah F, Oonariya C, Pabon-Caicedo JD, Panthou G, Pham C, Rahimzadeh F, Ramos A, Salgado E, Salinger J, Sané Y, Sopaheluwakan A, Srivastava A, Sun Y, Timbal B, Trachow N, Trewin B, van der Schrier G, Vazquez-Aguirre J, Vasquez R, Villarroel C, Vincent L, Vischel T, Vose R, Bin Hj Yussof MNA (2020) Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3. J Geophys Res: Atmos 125:e2019JD032263. https://doi.org/10.1029/2019JD032263
Ebi KL, Hasegawa T, Hayes K, Monaghan A, Paz S, Berry P (2018) Health risks of warming of 1.5 °C, 2 °C, and higher, above pre-industrial temperatures. Environ Res Lett 13:0630. https://doi.org/10.1088/1748-9326/aac4bd
Eckstein D, Künzel V, Schäfer L (2021) Global climate risk index 2021, who suffers most from extreme weather events? Weather-related loss events in 2019 and 2000–2019. Germanwatch e.V., Germany. www.germanwatch.org/en/cri. Accessed 19 Oct 2021.
Ge F, Peng T, Fraedrich K, Sielmann F, Zhu X, Zhi X, Liu X, Tang W, Zhao P (2019) Assessment of trends and variability in surface air temperature on multiple high-resolution datasets over the Indochina Peninsula. Theor Appl Climatol 135:1609–1627. https://doi.org/10.1007/s00704-018-2457-x
Hamed KH, Rao AR (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204:182–196. https://doi.org/10.1016/S0022-1694(97)00125-X
Ho TMH, Phan VT, Le NQ, Nguyen QT (2011) Extreme climatic events over Vietnam from observational data and RegCM3 projections. Clim Res 49:87–100. https://doi.org/10.3354/cr01021
Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5:299–314. https://doi.org/10.1080/10618600.1996.10474713
IPCC (2021) Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.
Khaliq MN, Ouarda TBMJ, Gachon P, Sushama L, St-Hilaire A (2009) Identification of hydrological trends in the presence of serial and cross correlations: a review of selected methods and their application to annual flow regimes of Canadian rivers. J Hydrol 368:117–130. https://doi.org/10.1016/j.jhydrol.2009.01.035
Kien ND, Ancev T, Randall A (2019) Evidence of climatic change in Vietnam: some implications for agricultural production. J Environ Manage 231:524–545. https://doi.org/10.1016/j.jenvman.2018.10.011
Kulkarni A, von Storch H (1995) Monte Carlo experiments on the effect of serial correlation on the Mann-Kendall test of trend. Meteorol Z 4:82–85. https://doi.org/10.1127/metz/4/1992/82
Kundzewicz ZW, Robson AJ (2000) Detecting trend and other changes in hydrological data. World climate programme—water, world climate programme data and monitoring, WCDMP-45, WMO/TD no. 1013, World Meteorological Organization, Geneva, Switzerland.
Le M-H, Kim H, Moon H, Zhang R, Lakshmi V, Nguyen L-B (2020) Assessment of drought conditions over Vietnam using standardized precipitation evapotranspiration index, MERRA-2 re-analysis, and dynamic land cover. J Hydrol 32:100767. https://doi.org/10.1016/j.ejrh.2020.100767
Maleski JJ, Martinez CJ (2017) Historical trends in precipitation, temperature and drought in the Alabama–Coosa–Tallapoosa and Apalachicola–Chattahoochee–Flint river basins. Int J Climatol 37:583–595. https://doi.org/10.1002/joc.4723
McCabe GJ, Wolock DM (2002) A step increase in streamflow in the conterminous United States. Geophys Res Lett. https://doi.org/10.1029/2002GL015999
Mohorji AM, Şen Z, Almazroui M (2017) Trend analyses revision and global monthly temperature innovative multi-duration analysis. Earth Syst Environ 1:9. https://doi.org/10.1007/s41748-017-0014-x
Ngo-Duc T (2014) Climate change in the coastal regions of Vietnam. In: Thao ND, Takagi H, Esteban M (eds) Coastal disasters and climate change in Vietnam. Elsevier, Oxford, pp 175–198. https://doi.org/10.1016/B978-0-12-800007-6.00008-3
Ngo-Duc T, Kieu C, Thatcher M, Nguyen-Le D, Phan-Van T (2014) Climate projections for Vietnam based on regional climate models. Clim Res 60:199–213. https://doi.org/10.3354/cr01234
Ngo-Thanh H, Ngo-Duc T, Nguyen-Hong H, Baker P, Phan-Van T (2018) A distinction between summer rainy season and summer monsoon season over the Central Highlands of Vietnam. Theor Appl Climatol 132:1237–1246. https://doi.org/10.1007/s00704-017-2178-6
Nguyen DQ, Renwick J, McGregor J (2014) Variations of surface temperature and rainfall in Vietnam from 1971 to 2010. Int J Climatol 34:249–264. https://doi.org/10.1002/joc.3684
Obregón GO, Marengo JA, Nobre CA (2014) Rainfall and climate variability: long-term trends in the metropolitan area of São Paulo in the 20th century. Clim Res 61:93–107. https://doi.org/10.3354/cr01241
Önöz B, Bayazit M (2012) Block bootstrap for Mann-Kendall trend test of serially dependent data. Hydrol Processes 26:3552–3560. https://doi.org/10.1002/hyp.8438
Öztopal A, Şen Z (2017) Innovative trend methodology applications to precipitation records in Turkey. Water Resour Manage 31:727–737. https://doi.org/10.1007/s11269-016-1343-5
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 4:439–473. https://doi.org/10.5194/hess-11-1633-2007
Phuong DND, Cuong DK, Dam DT, Loi NK (2019) Long–term spatio–temporal warming tendency in the Vietnamese Mekong delta based on observed and high–resolution gridded datasets. Eur J Clim Ch 1:01–16. https://doi.org/10.3415/2019-EJCC-0101-01-16/euraass
Phuong DND, Hai LM, Dung HM, Loi NK (2022) Temporal trend possibilities of annual rainfall and standardized precipitation index in the Central Highlands. Vietnam Earth Syst Environ 6:69–85. https://doi.org/10.1007/s41748-021-00211-y
R Core Team (2021) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/. Accessed 19 Oct 2021
Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17:1042–1046. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556
Şen Z (2014) Trend identification simulation and application. J Hydrol Eng 19:635–642. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000811
Şen Z (2017) Innovative trend significance test and applications. Theor Appl Climatol 127:939–947. https://doi.org/10.1007/s00704-015-1681-x
Seo L, Kim T-W, Kwon H-H (2012) Investigation of trend variations in annual maximum rainfalls in South Korea. KSCE J Civ Eng 16:215–221. https://doi.org/10.1007/s12205-012-0004-3
Sonali P, Nagesh Kumar D (2013) Review of trend detection methods and their application to detect temperature changes in India. J Hydrol 476:212–227. https://doi.org/10.1016/j.jhydrol.2012.10.034
Venkata Rao G, Venkata Reddy K, Srinivasan R, Sridhar V, Umamahesh NV, Pratap D (2020) Spatio-temporal analysis of rainfall extremes in the flood-prone Nagavali and Vamsadhara Basins in eastern India. Weather Clim Extremes 29:100265. https://doi.org/10.1016/j.wace.2020.100265
Vogel E, Donat MG, Alexander LV, Meinshausen M, Ray DK, Karoly D, Meinshausen N, Frieler K (2019) The effects of climate extremes on global agricultural yields. Environ Res Lett 14:054010. https://doi.org/10.1088/1748-9326/ab154b
World Meteorological Organization (2009) Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation (Albert M.G. Klein Tank, Francis W. Zwiers and Xuebin Zhang). (WMO-TD No. 1500). Geneva.
World Meteorological Organization (2017) WMO Guidelines on the Calculation of Climate Normals (WMO-No. 1203). Geneva.
Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manage 18:201–218. https://doi.org/10.1023/B:WARM.0000043140.61082.60
Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Processes 16:1807–1829. https://doi.org/10.1002/hyp.1095
Zhang Z, Dehoff AD, Pody RD (2010a) New approach to identify trend pattern of streamflows. J Hydrol Eng 15:244–248. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000179
Zhang Z, Dehoff AD, Pody RD, Balay JW (2010b) Detection of streamflow change in the Susquehanna River Basin. Water Resour Manage 24:1947–1964. https://doi.org/10.1007/s11269-009-9532-0
Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wires Clim Change 2:851–870. https://doi.org/10.1002/wcc.147
Zhang Z, Pody RD, Dehoff AD, Balay JW (2012) Analysis of Streamflow Trend in the Susquehanna River Basin, USA. In: Machiwal D, Jha MK (ed) Hydrologic Time Series Analysis: Theory and Practice. Springer Netherlands, Dordrecht, pp 181–200.
Zhu S, Ge F, Fan Y, Zhang L, Sielmann F, Fraedrich K, Zhi X (2020) Conspicuous temperature extremes over Southeast Asia: seasonal variations under 1.5 °C and 2 °C global warming. Clim Change 160:343–360. https://doi.org/10.1007/s10584-019-02640-1
Acknowledgements
The authors thank the editor and reviewers for their constructive comments as well as supportive suggestions.
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Contributions
Conceptualization: DNDP, TTN, NKL; methodology: DNDP, NTH, LHT; Formal analysis and investigation: DNDP, LHT, PTH; Writing—original draft preparation: DNDP, NTH, LHT; Writing—review and editing: DNDP, PTH, TTN; Resources: TTN, NKL; supervision: NKL.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Responsible Editor: Clemens Simmer.
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
Phuong, D.N.D., Huyen, N.T., Tu, L.H. et al. Identifying temporal trend patterns of temperature means and extremes over the Central Highlands, Vietnam. Meteorol Atmos Phys 134, 47 (2022). https://doi.org/10.1007/s00703-022-00886-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00703-022-00886-6