Abstract
In this study, statistical downscaling models were used to project possible future patterns of precipitation and temperature in the Jhelum River basin shared by Pakistan and India. In-situ meteorological data were used to downscale precipitation and temperature using different General Circulation Models (i.e., CanESM2, BCC-CSM1–1, and MICROC5) relative to baseline (1961–1990) under the Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. The downscaling models used were the Statistical Downscaling Model (SDSM), which uses multiple linear regression and weather generator methods, and the Long Ashton Research Station Weather Generator (LARS-WG), which uses weather generators. The results showed that the SDSM performance was slightly better than that of LARS-WG during validation and that the representation of the simulated mean monthly precipitation was more correct than that of monthly precipitation. The results also revealed that BCC-CSM1–1 performed better than CanESM2 and MICROC5 in the study region. The future annual mean temperature and precipitation are expected to rise under both RCP scenarios. The changes in the annual mean temperature and precipitation with LARS-WG were relatively higher than those with SDSM. Out of four seasons, winter and autumn are expected to be more diverse with regard to precipitation changes. However, although both models yielded non-identical results, it is certain that the basin will face a hotter climate in the future.
Similar content being viewed by others
References
Ahmad, I., Ambreen, R., Sun, Z., Deng, W.: Winter-spring precipitation variability in Pakistan. Earth Environ. Sci. 4(1), 115–139 (2015). https://doi.org/10.4236/ajcc.2015.41010
Allen, M., Stott, P., Mitchell, J., Schnur, R., Delworth, T.: Quantifying the uncertainty in forecasts of anthropogenic climate change. Nature. 407(6804), 617–620 (2000). https://doi.org/10.1038/35036559
Al-Safi, H.I.J., Sarukkalige, P.R.: The application of conceptual modelling to assess the impacts of future climate change on the hydrological response of the Harvey River catchment. J. Hydro Environ. Res. (2018). https://doi.org/10.1016/j.jher.2018.01.006
Anderson, T.R., Hawkins, E., Jones, P.D.: CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today’s earth system models. Endeavour. 40(3), 178–187 (2016). https://doi.org/10.1016/j.endeavour.2016.07.002
Archer, D.R., Fowler, H.J.: Using meteorological data to forecast seasonal runoff on the river Jhelum, Pakistan. J. Hydrol. 361(1–2), 10–23 (2008). https://doi.org/10.1016/j.jhydrol.2008.07.017
Babar, Z.A., Zhi, X.F., Fei, G.: Precipitation assessment of Indian summer monsoon based on CMIP5 climate simulations. Arab. J. Geosci. 8(7), 4379–4392 (2015). https://doi.org/10.1007/s12517-014-1518-4
Babur, M., Babel, S., Shrestha, S., Kawasaki, A., Tripathi, N.K.: Assessment of climate Change impact on reservoir inflows using multi climate-models under RCPs—the case of Mangla dam in Pakistan. Water. 8(9), 389 (2016). https://doi.org/10.3390/w8090389
Charles, B.C., Elijah, P., Vernon, R.N.C.: Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: a review. Sci. Res. Essays. 12(18), 167–187 (2017). https://doi.org/10.5897/SRE2017.6521
Chen, H., Guo, J., Zhang, Z., Xu, C.Y.: Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future. Theor. Appl. Climatol. 113(3–4), 363–375 (2013). https://doi.org/10.1007/s00704-012-0793-9
Chu, J.T., Xia, J., Xu, C.Y., Singh, V.P.: Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theor. Appl. Climatol. 99(1–2), 149–161 (2010). https://doi.org/10.1007/s00704-009-0129-6
Conway, D., Wilby, R.L., Jones, P.D.: Precipitation and air flow indices over the British Isles. Clim. Res. 7, 169–183 (1996). https://doi.org/10.3354/cr007169
Dibike, Y.B., Coulibaly, P.: Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J. Hydrol. 307, 145–163 (2005). https://doi.org/10.1016/j.jhydrol.2004.10.012
Dubrovský, M., Buchtele, J., Žalud, Z.: High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Clim. Chang. 63(1–2), 145–179 (2004). https://doi.org/10.1023/B:CLIM.0000018504.99914.60
Gulacha, M.M., Mulungu, D.M.M.: Generation of climate change scenarios for precipitation and temperature at local scales using SDSM in Wami-Ruvu River basin Tanzania. Phys. Chem. Earth. 100, 62–72 (2017). https://doi.org/10.1016/j.pce.2016.10.003
Hashmi, M.Z., Shamseldin, A.Y., Melville, B.W.: Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch. Env. Res. Risk A. 25(4), 475–484 (2011). https://doi.org/10.1007/s00477-010-0416-x
Hassan, Z., Shamsudin, S., Harun, S.: Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theor. Appl. Climatol. 116(1–2), 243–257 (2014). https://doi.org/10.1007/s00704-013-0951-8
Huang, J., Zhang, J., Zhang, Z., Xu, C., Wang, B., Yao, J.: Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch. Env. Res. Risk A. 25(6), 781–792 (2011). https://doi.org/10.1007/s00477-010-0441-9
Huth, R.: Statistical downscaling of daily temperature in central Europe. J. Clim. 15(13), 1731–1742 (2002). https://doi.org/10.1175/1520-0442(2002)015<1731:SDODTI>2.0.CO;2
Immerzeel, W.W., Van Beek, L.P.H., Bierkens, M.F.P.: Climate change will affect the Asian water towers. Science. 328, 1382–1385 (2010)
Ipcc. Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate Change. Intergovernmental panel on climate Change, working group I contribution to the IPCC fifth assessment report (AR5) (Cambridge Univ press, New York), 1535 (2013). https://doi.org/10.1029/2000JD000115
Khan, M.S., Coulibaly, P., Dibike, Y.: Uncertainty analysis of statistical downscaling methods. J. Hydrol. 319(1–4), 357–382 (2006). https://doi.org/10.1016/j.jhydrol.2005.06.035
Li, C., von Storch, J.S., Marotzke, J.: Deep-ocean heat uptake and equilibrium climate response. Clim. Dyn. 40(5–6), 1071–1086 (2013). https://doi.org/10.1007/s00382-012-1350-z
Mahmood, R., Babel, M.S.: Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor. Appl. Climatol. 113(1–2), 27–44 (2013). https://doi.org/10.1007/s00704-012-0765-0
Mahmood, R., Jia, S.: An extended linear scaling method for downscaling temperature and its implication in the Jhelum River basin, Pakistan, and India, using CMIP5 GCMs. Theor. Appl. Climatol. 130(3–4), 725–734 (2017). https://doi.org/10.1007/s00704-016-1918-3
Mahmood, R., Jia, S., Tripathi, N.K., Shrestha, S.: Precipitation extended linear scaling method for correcting GCM precipitation and its evaluation and implication in the transboundary Jhelum River basin. Atmosphere. 9(5), (2018). https://doi.org/10.3390/atmos9050160
Maraun, D., Wetterhall, F., Chandler, R.E., Kendon, E.J., Widmann, M., Brienen, S., … Thiele-Eich, I.: Precipitation downscaling under climate change: recent developements to bridge the gap between dynamical models and the end user. Rev. Geophys., 48(2009RG000314), 1–38 (2010). https://doi.org/10.1029/2009RG000314.1.INTRODUCTION
Mason, S.J.: Simulating climate over western North America using stochastic weather generators. Clim. Chang. 62(1–3), 155–187 (2004). https://doi.org/10.1023/B:CLIM.0000013700.12591.ca
Meaurio, M., Zabaleta, A., Boithias, L., Epelde, A.M., Sauvage, S., Sánchez-Pérez, J.M., … Antiguedad, I.: Assessing the hydrological response from an ensemble of CMIP5 climate projections in the transition zone of the Atlantic region (Bay of Biscay). J. Hydrol., 548, 46–62 (2017). https://doi.org/10.1016/j.jhydrol.2017.02.029
Meehl, G.A., Stocker, T.F., Collins, W.D., Friedlingstein, P., Gaye, A.T., Gregory, J.M., … Zhao, Z.-C.: Global Climate Projections. Climate Change 2007: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 747–846 (2007). https://doi.org/10.1080/07341510601092191
Mehan, S., Guo, T., Gitau, M., Flanagan, D.C.: Comparative study of different stochastic weather generators for long-term climate data simulation. Climate. 5(2), 26 (2017). https://doi.org/10.3390/cli5020026
Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., Van Vuuren, D.P., … Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747–756 (2010). https://doi.org/10.1038/nature08823
Pervez, M.S., Henebry, G.M.: Projections of the Ganges-Brahmaputra precipitation-downscaled from GCM predictors. J. Hydrol. 517, 120–134 (2014). https://doi.org/10.1016/j.jhydrol.2014.05.016
Peterson, T.C.: Climate Change indices. World Meteorol. Organ. Bull. 54, 83–86 (2005)
Prasanna, V.: Regional climate change scenarios over South Asia in the CMIP5 coupled climate model simulations. Meteorog. Atmos. Phys. 127, 561–578 (2015)
Sachindra, D.A., Perera, B.J.C.: Statistical downscaling of general circulation model outputs to precipitation accounting for non-stationarities in predictor-predictand relationships. PLoS One. 11(12), 1–21 (2016). https://doi.org/10.1371/journal.pone.0168701
Semenov, M.A., & Barrow, E.M.: A Stochastic Weather Generator for Use in Climate Impact Studies. User Manual, Hertfordshire, UK, (August), 0–27 (2002)
Semenov, M.A., Stratonovitch, P.: Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim. Res. 41(1), 1–14 (2010). https://doi.org/10.3354/cr00836
Semenov, M.A., Brooks, R.J., Barrow, E.M., Richardson, C.W.: Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim. Res. 10(2), 95–107 (1998). https://doi.org/10.3354/cr010095
Sharma, A., Sharma, D., Panda, S.K., Dubey, S.K., Pradhan, R.K.: Investigation of temperature and its indices under climate change scenarios over different regions of Rajasthan state in India. Glob. Planet. Chang. 161(December 2017), 82–96 (2018). https://doi.org/10.1016/j.gloplacha.2017.12.008
Taylor, K.E.: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106(D7), 7183–7192 (2001). https://doi.org/10.1029/2000JD900719
Taylor, K.E., Stouffer, R.J., Meehl, G.A.: An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93(4), 485–498 (2012). https://doi.org/10.1175/BAMS-D-11-00094.1
Turco, M., Quintana-Seguí, P., Llasat, M.C., Herrera, S., Gutiérrez, J.M.: Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain. J. Geophys. Res. Atmos. 116(18), 1–14 (2011). https://doi.org/10.1029/2011JD016166
Wigley, T.M.L., Jones, P.D., Briffa, K.R., Smith, G.: Obtaining subgrid scale information from coarse-resolution general circulation model output. J. Geophys. Res. 95, 1943–1953 (1990)
Wilby, R., & Dawson, C.: Using SDSM version 3.1- A decision support tool for the assessment of regional climate change impacts, 17, 147–159 (2004)
Wilby, R.L., Dawson, C.W., Barrow, E.: SDSM: a decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw. 17(2), 145–157 (2002)
Wilby, R.L., Whitehead, P.G., Wade, A.J., Butterfield, D., Davis, R.J., Watts, G.: Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: river Kennet, UK. J. Hydrol. 330(1–2), 204–220 (2006). https://doi.org/10.1016/j.jhydrol.2006.04.033
Wilks, D.S.: Multisite downscaling of daily precipitation with a stochastic weather generator. Clim. Res. 11(2), 125–136 (1999). https://doi.org/10.3354/cr011125
Wilks, D.S., Wilby, R.L.: The weather generation game: a review of stochastic weather models. Prog. Phys. Geogr. 23(3), 329–357 (1999). https://doi.org/10.1191/030913399666525256
Zhang, Y., You, Q., Chen, C., Ge, J.: Impacts of climate change on streamflows under RCP scenarios: a case study in Xin River basin, China. Atmos. Res. 178–179, 521–534 (2016). https://doi.org/10.1016/j.atmosres.2016.04.018
Zhou, T., Yu, R.: Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J. Clim. 19(22), 5843–5858 (2006). https://doi.org/10.1175/JCLI3952.1
Acknowledgements
We are thankful to the Institute of Hydrology and Meteorology, Chair of Meteorology, Technical University Dresden for giving the opportunity to perform this research. Mr. Naeem Saddique also acknowledge the Higher Education Commission of Pakistan (HEC) Pakistan and German Academic Exchange Service (DAAD) Germany for providing him financial support for his PhD studies. The financial support for this project was extremely useful for the completion of this research endeavor and is greatly appreciated. The authors wish to extend a special thanks to the organizations WAPDA, PMD and IMD for providing access to meteorological data utilized in this research.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: Soon-Il An.
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
Saddique, N., Bernhofer, C., Kronenberg, R. et al. Downscaling of CMIP5 Models Output by Using Statistical Models in a Data Scarce Mountain Environment (Mangla Dam Watershed), Northern Pakistan. Asia-Pacific J Atmos Sci 55, 719–735 (2019). https://doi.org/10.1007/s13143-019-00111-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13143-019-00111-2