Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Projected changes in climate and hydrological regimes of the Western Siberian lowlands

  • 207 Accesses

  • 1 Citations

Abstract

In this study, we analyse possible future climatic changes in three catchments, namely, Pyshma, Vagai and Loktinka located in the Western Siberian lowland region, and the resulting impact on hydrological regimes. It involved downscaling the GCM outputs based on the established statistical relationship between large-scale atmospheric variables and station data and simulating the effects of climate change on hydrological regimes via hydrological modelling. This was done for RCP 2.6, 4.5 and 8.5 based on second-generation Canadian Earth System Model used in the IPCC fifth assessment report. This paper provides the first climate change projections on a local scale in these catchments. The statistical downscaling showed that there will be an increase in both maximum and minimum temperature at all stations under all scenarios. The mean annual daily precipitation increased in Loktinka and Pyshma basins under all scenarios, but there was no clear trend in Vagai basin. The possible increase in annual precipitation is mostly due to the projected increase in autumn and winter precipitation. Annual streamflow tends to increase in all catchments under all scenarios.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Aizen EM, Aizen VB, Melack JM, Nakamura T, Ohta T (2001) Precipitation and atmospheric circulation patterns at mid-latitudes of Asia. Int J Climatol 21(5):535–556. https://doi.org/10.1002/joc.626

  2. Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19(3):563–572. https://doi.org/10.1002/hyp.5611

  3. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part i: model development. J Am Water Resour Assoc 34(1):73–89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x

  4. Arora VK, Boer GJ (2014) Terrestrial ecosystems response to future changes in climate and atmospheric CO2 concentration. Biogeosciences 11(15):4157–4171

  5. Bhuvandas N, Timbadiya PV, Patel PL, Porey PD (2014) Review of downscaling methods in climate change and their role in hydrological studies. Int J Environ Ecol Geol Mar Eng 8(10):648–653

  6. Bulygina ON, Groisman PY, Razuvaev VN, Korshunova NN (2011) Changes in snow cover characteristics over Northern Eurasia since 1966. Environ Res Lett 6(4):45204. https://doi.org/10.1088/1748-9326/6/4/045204

  7. Chen H, Xu C-Y, Guo S (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol 434–435:36–45. https://doi.org/10.1016/j.jhydrol.2012.02.040

  8. Dai A (2011) Drought under global warming: a review. Wiley Interdiscip Rev Clim Change 2(1):45–65. https://doi.org/10.1002/wcc.81

  9. Dai A (2012) Increasing drought under global warming in observations and models. Nature Clim Change 3(1):52–58. https://doi.org/10.1038/nclimate1633

  10. Dai A, Qian T, Trenberth KE, Milliman JD (2009) Changes in continental freshwater discharge from 1948 to 2004. J Clim 22(10):2773–2792. https://doi.org/10.1175/2008JCLI2592.1

  11. Degefie DT, Fleischer E, Klemm O, Soromotin AV, Soromotina OV, Tolstikov AV, Abramov NV (2014) Climate extremes in South Western Siberia: past and future. Stoch Env Res Risk Assess 28(8):2161–2173. https://doi.org/10.1007/s00477-014-0872-9

  12. Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol 307(1–4):145–163. https://doi.org/10.1016/j.jhydrol.2004.10.012

  13. Dibike YB, Gachon P, St-Hilaire A, Ouarda TBMJ, Nguyen VTV (2008) Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada. Theoret Appl Climatol 91(1–4):149–170. https://doi.org/10.1007/s00704-007-0299-z

  14. Fallot J-M, Barry RG, Hoogstrate D (1997) Variations of mean cold season temperature, precipitation and snow depths during the last 100 years in the former Soviet Union (FSU). Hydrol Sci J 42(3):301–327. https://doi.org/10.1080/02626669709492031

  15. Feng S, Hu Q, Huang W, Ho CH, Li R, Tang Z (2014) Projected climate regime shift under future global warming from multi-model, multi-scenario CMIP5 simulations. Global Planet Change 112:41–52. https://doi.org/10.1016/j.gloplacha.2013.11.002

  16. Fiseha BM, Melesse AM, Romano E, Volpi E, Fiori A (2012) Statistical downscaling of precipitation and temperature for the upper tiber basin in central Italy. Int J Water Sci 1(3):1–14. https://doi.org/10.5772/52890

  17. Frey KE, Smith LC (2003) Recent temperature and precipitation increases in West Siberia and their association with the Arctic Oscillation. Polar Res 22(2):287–300. https://doi.org/10.1111/j.1751-8369.2003.tb00113.x

  18. Gagnon S, Singh B, Rousselle J, Roy L (2005) An application of the statistical downscaling model (SDSM) to simulate climatic data for streamflow modelling in Québec. Can Water Resour J 30(4):297–314. https://doi.org/10.4296/cwrj3004297

  19. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250. https://doi.org/10.13031/2013.23637

  20. Groisman PY, Blyakharchuk TA, Chernokulsky AV, Arzhanov MM, Marchesini LB, Bogdanova EG, Vygodskaya NN (2013) Climate changes in Siberia. In: Groisman PY, Gutman G (eds) Regional environmental changes in siberia and their global consequences (pp 57–109). Springer Environmental Science and Engineering, Berlin. https://doi.org/10.1007/978-94-007-4569-8_3

  21. Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377(1–2):80–91

  22. Guse B, Kail J, Radinger J, Schröder M, Kiesel J, Hering D, Wolter C, Fohrer N (2015) Eco-hydrologic model cascades: Simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates. Sci Total Environ 533:542–556

  23. Hassan Z, Shamsudin S, Harun S (2014) Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoret Appl Climatol 116(1–2):243–257. https://doi.org/10.1007/s00704-013-0951-8

  24. Hessami M, Gachon P, Ouarda TBMJ, St-Hilaire A (2008) Automated regression-based statistical downscaling tool. Environ Model Softw 23(6):813–834. https://doi.org/10.1016/j.envsoft.2007.10.004

  25. Htut AY (2014) Forecasting climate change scenarios in the Bago River Basin, Myanmar. J Earth Sci Clim Change 5:9. https://doi.org/10.4172/2157-7617.1000228

  26. Hu Y, Maskey S, Uhlenbrook S (2013) Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods. Theoret Appl Climatol 112(3–4):447–460. https://doi.org/10.1007/s00704-012-0745-4

  27. IPCC (2013) Summary for policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate Change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  28. Jarvis A, Reuter HI, Nelson A, Guevara E (2008) Hole-Filled Seamless SRTM Data V4. International Centre for Tropical Agriculture (CIAT) [online] http://srtm.csi.cgiar.org/. Accessed May 2013

  29. Kabanov MV, Lykosov VN (2006) Monitoring and modeling of climatic changes in Siberia. Atmos Ocean Opt 19(9):675–685

  30. Kalnay E, Kanamisu M, Kistler R, Collins W (1996) The NCEP/NCAR 40 year reanalysis project. BAMS 77:437–472

  31. Khon VC, Mokhov II (2012) The hydrological regime of large river basins in Northern Eurasia in the XX–XXI centuries. Water Resour 39(1):1–10. https://doi.org/10.1134/S0097807812010058

  32. Kiesel J, Fohrer N, Schmalz B, White MJ (2010) Incorporating landscape depressions and tile drainages of a northern German lowland catchment into a semi-distributed model. Hydrol Process 24(11):14721486. https://doi.org/10.1002/hyp.7607

  33. Kiesel J, Pfannerstill M, Schmalz B, Khoroshavin V, Sheldukov A, Veshkurseva T, Fohrer N (2018) Modelling of hydrological processes in snowmelt-governed permafrost-free catchments of the Western Siberian Lowlands. Int J Hydrol Sci Technol 8(3):289–316. https://doi.org/10.1504/IJHST.2018.10007182

  34. Kiesel J, Gericke A, Rathjens H, Wetzig A, Kakouei K, Jähnig SC, Fohrer N (2019) Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions. Ecol Eng 127:404–416

  35. Liu L, Liu Z, Ren X, Fischer T, Xu Y (2011) Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model. Quatern Int 244(2):211–220. https://doi.org/10.1016/j.quaint.2010.12.001

  36. Liu Z, Mehran A, Phillips TJ, Aghakouchak A, Res C, Liu Z, Aghakouchak A (2014) Seasonal and regional biases in CMIP5 precipitation simulations. Clim Res 60(1):35–50. https://doi.org/10.3354/cr01221

  37. Marvel K, Bonfils C (2013) Identifying external influences on global precipitation. Proc Natl Acad Sci USA 110(48):19301–19306. https://doi.org/10.1073/pnas.1314382110

  38. MDHS (1961–1988) Hydrological yearbooks. Main Department of the Hydrometeorological Service, Council of Ministers, Russia

  39. Mehran A, Aghakouchak A, Phillips TJ (2014) Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations. J Geophys Res Atmos 119(4):1695–1707. https://doi.org/10.1002/2013JD021152

  40. Miao C, Duan Q, Sun Q, Huang Y, Kong D, Yang T, Gong W (2014) Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia. Environ Res Lett 9(5):55007. https://doi.org/10.1088/1748-9326/9/5/055007

  41. Min S-K, Zhang X, Zwiers F (2008) Human-induced Arctic moistening. Science 320(5875):518–520. https://doi.org/10.1126/science.1153468

  42. Min S-K, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470(7334):378–381. https://doi.org/10.1038/nature09763

  43. Moriasi DN, Arnold JG, Liew MW, Van Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Am Soc Agric Biol Eng 50(3):885–900. https://doi.org/10.13031/2013.23153

  44. NOAA (2013) National Climatic Data Center, Precipitation and Temperature Data, National Oceanic and Atmospheric Administration [online] http://www.ncdc.noaa.gov/. Accessed Sept 2014

  45. Omernik JM, Bailey RG (1997) Distinguishing Between Watersheds and Ecoregions. J Am Water Resour Assoc 33(5):935–949

  46. Peterson BJ, Holmes RM, McClelland JW, Vörösmarty CJ, Lammers RB, Shiklomanov AI, Rahmstorf S (2002) Increasing river discharge to the Arctic Ocean. Science 298(5601):2171–2173. https://doi.org/10.1126/science.1077445

  47. Pfannerstill M, Guse B, Fohrer N (2014) A multi-storage groundwater concept for the SWAT model to emphasize nonlinear groundwater dynamics in lowland catchments. Hydrol Processes 28(22):5599–5612

  48. Pfannerstill M, Guse B, Reusser D, Fohrer N (2015) Process verification of a hydrological model using a temporal parameter sensitivity analysis. Hydrol Earth Syst Sci 19(10):4365–4376

  49. Radojevic M (2014) The ensemble of daily predictor variables developed from the CanESM2 CMIP5 experiments. Canadian Centre for Climate Modelling and Analysis (CCCma), Canada

  50. Rawlins M, Steele M, Holland MM, Adam JC, Cherry JE, Francis J, Zhang T (2010) Analysis of the Arctic system for freshwater cycle intensification: observations and expectations. J Clim 23(21):5715–5737. https://doi.org/10.1175/2010JCLI3421.1

  51. SASCHA (2014) Annual report: SASCHA-sustainable land management and adaptation strategies to climate change for the Western Siberian Corn belt. Muenster, Germany

  52. Serreze MC, Walsh JE, Chapin FS III, Osterkamp T, Dyurgerov M, Romanovsky V, Barry RG (2000) Observational evidence of recent change in the Northern high-latitude environment. Clim Change 46:159–207. https://doi.org/10.1023/A:1005504031923

  53. Shiklomanov AI, Lammers RB, Lettenmaier DP, Polischuk YM, Savichev OG, Smith LC, Chernokulsky AV (2013) Regional environmental changes in Siberia and their global consequences (Google eBook). In: Groisman PY, Gutman G (eds) Regional environmental changes in Siberia and their global consequences (pp 111–154). Springer Environmental Science and Engineering, Berlin. https://doi.org/10.1007/978-94-007-4569-8

  54. Shulgina TM, Genina EY, Gordov EP (2011) Dynamics of climatic characteristics influencing vegetation in Siberia. Environ Res Lett 6(4):45210. https://doi.org/10.1088/1748-9326/6/4/045210

  55. Smith LC, Pavelsky TM, MacDonald GM, Shiklomanov AI, Lammers RB (2007) Rising minimum daily flows in northern Eurasian rivers: a growing influence of groundwater in the high-latitude hydrologic cycle. J Geophys Res 112(G4):G04S47. https://doi.org/10.1029/2006JG000327

  56. Viñas M-J (2014) NASA: four decades of Arctic sea ice from space. http://www.reportingclimatescience.com/news-stories/article/nasa-four-decades-of-arctic-sea-ice-from-space.html. Accessed 8 July 2015

  57. von Storch H, Hewitson B, Mearns L (2000) Review of empirical downscaling techniques. In: Regional climate development under global warming. General Technical Report No. 4, Conference Proceedings, Torbjornrud, Norway

  58. Wilby RL, Hassan H, Hanaki K (1998) Statistical downscaling of hydrometeorological variables using general circulation model output. J Hydrol 205(1–2):1–19. https://doi.org/10.1016/S0022-1694(97)00130-3

  59. Wilby RL, Dawson CW, Barrow EM (2002) SDSM—A decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17(2):147–159. https://doi.org/10.1016/S1364-8152(01)00060-3

  60. Wilby RLL, Charles SPP, Zorita E, Timbal B, Whetton P, Mearns LOO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Prepared for consideration by the IPCC at the request of its Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA)

  61. WWF (2014) The Terrestrial Ecoregions of the World Base Global Dataset. World Wildlife Fund Conservation Science Data and Tools [online] http://www.worldwildlife.org/pages/conservation-science-data-and-tools. Accessed Sept 2014

  62. Yang D, Kane DL, Hinzman LD, Zhang X, Zhang T, Ye H (2002) Siberian Lena River hydrologic regime and recent change. J Geophys Res Atmos 107(23):1–10. https://doi.org/10.1029/2002JD002542

  63. Yang D, Ye B, Shiklomanov A (2004) Discharge characteristics and changes over the Ob river watershed in Siberia. J Hydrometeorol 5(4):595–610. https://doi.org/10.1175/1525-7541(2004)005%3C0595:DCACOT%3E2.0.CO;2

  64. Yao T, Thompson L, Yang W, Yu W, Gao Y, Guo X, Joswiak D (2012) Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Clim Change 2(9):663–667. https://doi.org/10.1038/nclimate1580

  65. Zemtsov Va, Paromov VV, Kopysov SG, Kouraev aV, Negrul SV (2014) Hydrological risks in Western Siberia under the changing climate and anthropogenic influences conditions. Int J Environ Stud 71(5):611–617. https://doi.org/10.1080/00207233.2014.942530

  66. Zhang L, Lu W, Yang Q, An Y, Li D, Gong L (2012) Hydrological impacts of climate change on streamflow of Dongliao River watershed in Jilin Province, China. Chin Geogr Sci 22(5):522–530

  67. Zuo D, Xu Z, Zhao J, Abbaspour KC, Yang H (2015) Response of runoff to climate change in the Wei River basin, China. Hydrol Sci J. https://doi.org/10.1080/02626667.2014.943668

Download references

Acknowledgements

This work was conducted as part of project SASCHA (Sustainable land management and adaptation strategies to climate change for the Western Siberian grain belt). We are grateful for funding by the German Government, Federal Ministry of Education and Research within their Sustainable Land Management funding framework (funding reference 01LL0906C). JK acknowledges funding through the “GLANCE” project (Global change effects on river ecosystems; 01LN1320A) supported by the German Federal Ministry of Education and Research (BMBF). Further thanks go to our Russian partners of Tyumen State University (TSU) and State Agrarian University of the Northern Transurals (GAUSZ) for a great and successful cooperation. We would also like to thank Dr. D.T. Degefie, Dr. Laurent Terray and Ms. Milka Radojevic for their guidance in statistical downscaling and Dr. Matthias Pfannerstill for valuable discussions and support regarding SWAT-3S.

Author information

Correspondence to Rajesh Sada.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is a part of a Topical Collection in Environmental Earth Sciences on Climate Effects on Water Resources, edited by Drs. Zongzhi Wang and Yanqing Lian.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sada, R., Schmalz, B., Kiesel, J. et al. Projected changes in climate and hydrological regimes of the Western Siberian lowlands. Environ Earth Sci 78, 56 (2019). https://doi.org/10.1007/s12665-019-8047-0

Download citation

Keywords

  • Temperature change
  • Precipitation change
  • Statistical downscaling
  • Hydrological modelling
  • Western Siberia