Earth Systems and Environment

, Volume 3, Issue 3, pp 381–398 | Cite as

Downscaling and Projection of Spatiotemporal Changes in Temperature of Bangladesh

  • Mahiuddin Alamgir
  • Kamal Ahmed
  • Rajab Homsi
  • Ashraf DewanEmail author
  • Jiao-Jun Wang
  • Shamsuddin Shahid
Original Article


The objectives of this study were to: (1) evaluate possible deviations in annual and seasonal maximum (Tmx) and minimum (Tmn) temperatures, and, (2) determine the spatial pattern of these temperature changes. The study used statistical downscaling of the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) under four representative concentration pathway (RCP) scenarios. Perfect prognosis statistical downscaling models, based on support vector machine (SVM), were developed for this purpose. Biases in the GCM simulations were corrected using quantile mapping (QM) and the data were then used to determine future temperature scenarios at different locations within Bangladesh. For most of the GCMs, the mean bias was close to zero and the Nash–Sutcliffe efficiency was above 0.58 for the downscaled temperature. Non-parametric hypothesis tests showed equality in median, distribution and variance values of the observed and downscaled temperature for all GCMs. Temperature projections from the models revealed an increase in Tmx by 1.3–2.3 °C, 1.3–2.9 °C, 1.5–3.1 °C, and 2.2–4.3 °C, and Tmn by 1.8–3.0 °C, 2.1–4.2 °C, 2.4–4.5 °C, and 3.2–5.1 °C under the four RCPs during the 2070–2099 period when compared with the 1971–2000 period. The greatest increase in Tmx and Tmn was found in the more northern regions and the lowest increase was found in the southeast coastal region. Tmn tended to increase in winter, while Tmx increased predominantly during summer. Uncertainty in the temperature projections was found to be greater during the latter part of the century. The rapid rise in temperature predicted for the northern part of Bangladesh (which is historically prone to temperature extremes) may cause an increase in the frequency of temperature-related extremes in this region.


Statistical downscaling Temperature projection Global circulation model Radiative pathway scenarios Support vector machine 


Compliance with Ethical Standards

Conflict of interest

Authors declare no conflict of interest.


  1. Acton QA (2012) Advances in machine learning research and application, 2012th edn. ScholarlyEditions, AtlanataGoogle Scholar
  2. Ahammed S, Chung ES, Shahid S (2018) Parametric assessment of pre-monsoon agricultural water scarcity in bangladesh. Sustainability 10(3):819CrossRefGoogle Scholar
  3. Ahammmed SJ, Homsi R, Khan N, Shahid S, Shiru MS, Mohsenipour M (2019) Assessment of changing pattern of crop water stress in Bangladesh. Environment, Development and Sustainability. Springer, New York, pp 1–19Google Scholar
  4. Ahmed K, Shahid S, Harun SB, Wang XJ (2015) Multilayer perceptron neural network for downscaling rainfall in arid region: a case study of Baluchistan, Pakistan. J Earth Syst Sci 124(6):1325–1341CrossRefGoogle Scholar
  5. Ahmed K, Shahid S, Nawaz N, Khan N (2019) Modeling climate change impacts on precipitation in arid regions of Pakistan: a non-local model output statistics downscaling approach. Theor Appl Climatol 137:1347–1364CrossRefGoogle Scholar
  6. Alamgir M, Pour SH, Mohsenipour M, Hasan MM, Ismail T (2016) Predictors and their domain for statistical downscaling of climate in Bangladesh. J Teknol 78:6–12Google Scholar
  7. Alamgir M, Mohsenipour M, Homsi R, Wang XJ, Shahid S, Shiru MS, Alias NE, Yuzir A (2019) Parametric assessment of seasonal drought risk to crop production in Bangladesh. Sustainability 11(5):1442CrossRefGoogle Scholar
  8. Asefa T, Kemblowski MW, Urroz G, Mckee M, Khalil A (2004) Support vectors—based groundwater head observation networks design. Water Resour Res 40(11):W11509CrossRefGoogle Scholar
  9. Bandara JS, Cai Y (2014) The impact of climate change on food crop productivity, food prices and food security in South Asia. Econ Anal Policy 44(4):451–465. CrossRefGoogle Scholar
  10. Bartlett MS (1937) Properties of sufficiency and statistical tests. Proc R Soc Lond Ser A Math Phys Sci 160:268–282CrossRefGoogle Scholar
  11. Chowdhury AFMK, Kar KK, Shahid S, Chowdhury R, Rashid MM (2019) Evaluation of spatio-temporal rainfall variability and performance of a stochastic rainfall model in Bangladesh. Int J Climatol. CrossRefGoogle Scholar
  12. Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15:208CrossRefGoogle Scholar
  13. Easterling DR, Horton B, Jones PD, Peterson TC, Karl TR, Parker DE et al (1997) Maximum and minimum temperature trends for the globe. Science 277(5324):364–367CrossRefGoogle Scholar
  14. HadiPour S, Harun S, Arefnia A, Alamgir M (2016) Transfer function models for statistical downscaling of monthly precipitation. J Teknol 78:4–9Google Scholar
  15. IPCC (2013) Climate change 2013: the physical science basis. In: Stocker TF et al (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  16. IPCC (2014) Climate change 2014–impacts, adaptation and vulnerability: regional aspects. Cambridge University Press, CambridgeGoogle Scholar
  17. Jhajharia D, Singh VP (2011) Trends in temperature, diurnal temperature range and sunshine duration in Northeast India. Int J Climatol 31(9):1353–1367CrossRefGoogle Scholar
  18. Karl TR, Kukla G, Razuvayev VN, Changery MJ, Quayle RG, Heim RR et al (1991) Global warming: evidence for asymmetric diurnal temperature change. Geophys Res Lett 18(12):2253–2256CrossRefGoogle Scholar
  19. Khadam IM, Kaluarachchi JJ (2004) Use of soft information to describe the relative uncertainty of calibration data in hydrologic models. Water Resour Res 40(11):W11505CrossRefGoogle Scholar
  20. Khan N, Pour SH, Shahid S, Ismail TB, Ahmed K, Chung ES, Nawaz N, Wang XJ (2019) Spatial distribution of secular trends in rainfall indices of Peninsular Malaysia in the presence of long-term persistence. Meteorol Appl. CrossRefGoogle Scholar
  21. Liu SC, Fu C, Shiu CJ, Chen JP, Wu F (2009) Temperature dependence of global precipitation extremes. Geophys Res Lett 36(17):L17702CrossRefGoogle Scholar
  22. Mainuddin M, Kirby M (2009) Agricultural productivity in the lower Mekong Basin: rends and future prospects for food security. Food Secur 1:71–82Google Scholar
  23. Manabe S, Stouffer RJ, Spelman MJ, Bryan K (1991) Transient responses of a coupled ocean-atmosphere model to gradual changes of atmospheric CO2. Part I. Annual mean response. J Clim 4(8):785–818CrossRefGoogle Scholar
  24. Mishra A, Liu SC (2014) Changes in precipitation pattern and risk of drought over India in the context of global warming. J Geophys Res Atmos 119(13):7833–7841CrossRefGoogle Scholar
  25. Mohsenipour M, Shahid S, Chung ES, Wang XJ (2018) Changing pattern of droughts during cropping seasons of Bangladesh. Water Resour Manag 32(5):1555–1568CrossRefGoogle Scholar
  26. Mora C, Wei CL, Rollo A, Amaro T, Baco AR, Billett D, Gooday AJ (2013) Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century. PLoS Biol 11(10):e1001682CrossRefGoogle Scholar
  27. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual model. Part 1—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  28. Nashwan MS, Shahid S, Wang XJ (2019) Uncertainty in estimated trends using gridded rainfall data: a case study of Bangladesh. Water 11(2):349CrossRefGoogle Scholar
  29. OECD (2003) Development and climate change in Bangladesh: focus on coastal flooding and the Sundarbans. In: Agrawala S, Ota T, Ahmed AU (eds) Organization for economic co-operation and development (OECD) report COM/ENV/EPOC/DCD/DAC (2003)3/FINAL, Paris, France. Accessed 20 June 2015
  30. Panofsky HA, Brier GW (1968) Some applications of statistics to meteorology: earth and mineral sciences continuing education. College of Earth and Mineral Sciences, PhiladelphiaGoogle Scholar
  31. Pour SH, Shahid S, Harun SB, Wang XJ (2013) Genetic programming for downscaling extreme rainfall events. In: 2013 1st International conference on artificial intelligence, modelling and simulation, pp 331–334Google Scholar
  32. Pour SH, Harun S, Shahid S (2014) Genetic programming for the downscaling of extreme rainfall events on the East Coast of Peninsular Malaysia. Atmosphere 5(4):914–936CrossRefGoogle Scholar
  33. Pour SH, Shahid S, Chung ES (2016) A hybrid model for statistical downscaling of daily rainfall. Proced Eng 154:1424–1430CrossRefGoogle Scholar
  34. Pour SH, Shahid S, Chung ES, Wang XJ (2018) Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh. Atmos Res 213:149–162CrossRefGoogle Scholar
  35. Pour SH, Khairi AWA, Shahid S, Wang XJ (2019) Spatial pattern of the unidirectional trends in thermal bioclimatic indicators in Iran. Sustainability 11(8):2287CrossRefGoogle Scholar
  36. Rahman MM, Islam MN, Ahmed AU, Georgi F (2012) Rainfall and temperature scenarios for Bangladesh for the middle of 21st century using RegCM. J Earth Syst Sci 121(2):287–295. CrossRefGoogle Scholar
  37. Rashid HE (1991) Geography of Bangladesh. University Press, DhakaGoogle Scholar
  38. Roy SS, Balling RC (2005) Analysis of trends in maximum and minimum temperature, diurnal temperature range, and cloud cover over India. Geophys Res Lett 32(12):L12702CrossRefGoogle Scholar
  39. Sa’adi Z, Shahid S, Chung ES, Ismail TB (2017) Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models. Atmos Res 197:446–460CrossRefGoogle Scholar
  40. Sachindra DA, Ahmed K, Rashid MM, Shahid S, Perera BJC (2018a) Statistical downscaling of precipitation using machine learning techniques. Atmos Res 212:240–258CrossRefGoogle Scholar
  41. Sachindra DA, Ahmed K, Shahid S, Perera BJC (2018b) Cautionary note on the use of genetic programming in statistical downscaling. Int J Climatol 38(8):3449–3465CrossRefGoogle Scholar
  42. Sachindra DA, Ahmed K, Rashid MM, Sehgal V, Shahid S, Perera BJC (2019) Pros and cons of using wavelets in conjunction with genetic programming and generalised linear models in statistical downscaling of precipitation. Theor Appl Climatol. CrossRefGoogle Scholar
  43. Salem GAS, Kazama S, Shahid S, Dey NC (2018) Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region. Agric Water Manag 208:33–42CrossRefGoogle Scholar
  44. Shahid S (2010) Recent trends in the climate of Bangladesh. Clim Res 42(3):185–193CrossRefGoogle Scholar
  45. Shahid S (2011) Trends in extreme rainfall events of Bangladesh. Theor Appl Climatol 104(3–4):489–499CrossRefGoogle Scholar
  46. Shahid S (2012) Vulnerability of the power sector of Bangladesh to climate change and extreme weather events. Reg Environ Change 12(3):595–606CrossRefGoogle Scholar
  47. Shahid S, Wang XJ, Harun SB, Shamsudin SB, Ismail T, Minhans A (2016) Climate variability and changes in the major cities of Bangladesh: observations, possible impacts and adaptation. Reg Environ Change 16(2):459–471CrossRefGoogle Scholar
  48. Shahid S, Pour SH, Wang XJ, Shourav SA, Minhans A, Ismail TB (2017) Impacts and adaptation to climate change in Malaysian real estate. Int J Clim Change Strateg Manag 9(1):87–103CrossRefGoogle Scholar
  49. Shourav MSA, Mohsenipour M, Alamgir M, Pour SH, Ismail T (2016) Historical trends and future projection of climate at Dhaka City of Bangladesh. J Teknol 78(6–12):69–75Google Scholar
  50. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  51. Vapnik V, Golowich SE, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Adv Neural Inf Process Syst 281–287Google Scholar
  52. Wang X, Piao S, Ciais P, Friedlingstein P, Myneni RB, Cox P, Yang H (2014) A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506(7487):212–215CrossRefGoogle Scholar
  53. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80–83CrossRefGoogle Scholar

Copyright information

© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahiuddin Alamgir
    • 1
  • Kamal Ahmed
    • 1
    • 2
  • Rajab Homsi
    • 1
  • Ashraf Dewan
    • 3
    Email author
  • Jiao-Jun Wang
    • 4
    • 5
  • Shamsuddin Shahid
    • 1
  1. 1.School of Civil Engineering, Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  2. 2.Lasbela University of Agriculture, Water and Marine SciencesUthalPakistan
  3. 3.School of Earth and Planetary Sciences (EPS), Faculty of Science and EngineeringCurtin UniversityBentleyAustralia
  4. 4.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringNanjing Hydraulic Research InstituteNanjingChina
  5. 5.Research Center for Climate Change, Ministry of Water ResourcesNanjingChina

Personalised recommendations