Climatic Change

, Volume 141, Issue 3, pp 499–515 | Cite as

Climate change impact on the water regime of two great Arctic rivers: modeling and uncertainty issues

  • Alexander Gelfan
  • David Gustafsson
  • Yury Motovilov
  • Berit Arheimer
  • Andrey Kalugin
  • Inna Krylenko
  • Alexander Lavrenov
Article

Abstract

The ECOlogical Model for Applied Geophysics (ECOMAG) and the HYdrological Predictions for the Environment (HYPE) process-based hydrological models were set up to assess possible impacts of climate change on the hydrological regime of two pan-Arctic great drainage basins of the Lena and the Mackenzie Rivers. We firstly assessed the reliability of the hydrological models to reproduce the historical streamflow series and analyzed the hydrological projections driven by the climate change scenarios. The impacts were assessed for three 30-year periods (early- (2006–2035), mid- (2036–2065), and end-century (2070–2099)) using an ensemble of five global climate models (GCMs) and four Representative Concentration Pathway (RCP) scenarios. Results show, particularly, that the basins react with a multi-year delay to changes in RCP2.6, so-called “mitigation” scenario, and consequently to the potential mitigation measures. Then, we assessed the hydrological projections’ variability, which is caused by the GCM’s and RCP’s uncertainties, and found that the variability rises with the time horizon of the projection, and generally, the projection variability is larger for the Mackenzie than for the Lena. We finally compared the mean annual runoff anomalies projected under the GCM-based data for the twenty-first century with the corresponding anomalies projected under a modified observed climatology using the delta-change method in the Lena basin. We found that the compared projections are closely correlated for the early-century period. Thus, for the Lena basin, the modified observed climatology can be used as driving force for hydrological model-based projections and considered as an alternative to the GCM-based scenarios.

Supplementary material

10584_2016_1710_MOESM1_ESM.doc (679 kb)
ESM 1(DOC 679 kb)

References

  1. Arheimer B, Dahné J, Donnelly C (2012) Climate change impact on riverine nutrient load and land-based remedial measures of the Baltic Sea Action Plan. Ambio 41:600–612CrossRefGoogle Scholar
  2. Aziz OIA, Burn DH (2006) Trends and variability in the hydrological regime of the Mackenzie River basin. J Hydrol 319:282–294CrossRefGoogle Scholar
  3. Bartholomé E, Belward A (2005) GLC2000: a new approach to global land cover mapping from Earth observation data. Int J Remote Sens 26(9):1959–1977CrossRefGoogle Scholar
  4. Berezovskaya S, Yang D, Hinzman L (2005) Long-term annual water balance analysis of the Lena River. Glob Planet Chang 48(1–3):84–95CrossRefGoogle Scholar
  5. Chiew FHS et al (2009) Estimating climate change impact on runoff across southeast Australia: method, results, and implications of the modelling method. Water Resour Res 45(W10414):2009. doi:10.1029/2008WR007338 Google Scholar
  6. Donnelly C, Andersson JCM, Arheimer B (2015) Using flow signatures and catchment similarities to evaluate a multi-basin model (E-HYPE) across Europe. Hydrol Sci J. doi:10.1080/02626667.2015.1027710 Google Scholar
  7. Ehret U et al (2014) Advancing catchment hydrology to deal with predictions under change. Hydrol Earth Syst Sci 18:649–671. doi:10.5194/hess-18-649-2014 CrossRefGoogle Scholar
  8. Fischer G et al. (2008) Global agro-ecological zones assessment for agriculture (GAEZ 2008) IIASA, Laxenburg, Austria and FAO, Rome, ItalyGoogle Scholar
  9. Flato G et al. (2013) Evaluation of climate models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker TF et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  10. Gelfan A et al (2015a) Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability. Hydrol Earth Syst Sci 19:2737–2754. doi:10.5194/hess-19-2737-2015 CrossRefGoogle Scholar
  11. Gelfan A et al (2015b) Testing the robustness of the physically-based ECOMAG model with respect to changing conditions. Hydrol Sci J 60:1266–1285. doi:10.1080/02626667.2014.935780 CrossRefGoogle Scholar
  12. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095. doi:10.1175/2009BAMS2607.1 CrossRefGoogle Scholar
  13. Huang S et al. (2016) Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide. Climatic Change, this issueGoogle Scholar
  14. Knutti R (2010) The end of model democracy? Clim Chang 102:395–404. doi:10.1007/s10584-010-9800-2 CrossRefGoogle Scholar
  15. Kundzewicz ZW, Stakhiv EZ (2010) Are climate models “ready for prime time” in water resources management applications, or is more research needed? Hydrol Sci J 55(7):1085–1089CrossRefGoogle Scholar
  16. Lindström G et al (2010) Development and test of the ARCTIC-HYPE (Hydrological Predictions for the Environment) model—a water quality model for different spatial scales. Hydrol Res 41(3–4):295–319CrossRefGoogle Scholar
  17. Mokhov II, Semenov VA, Khon VC (2003) Estimates of possible regional hydrologic regime changes in the 21st century based on global climate models. Izv Atmos Oceanic Phys 39(2):130Google Scholar
  18. Motovilov Y et al (1999) Validation of a distributed hydrological model against spatial observation. Agric For Meteorol 98–99:257–277CrossRefGoogle Scholar
  19. Nijssen B et al (2001) Hydrologic sensitivity of global rivers to climate change. Clim Chang 50:143–175CrossRefGoogle Scholar
  20. Nohara D et al. (2006) Impact of climate change on river discharge projected by multimodel ensemble. J Hydrometeor 7:1076–1089. doi:http://dx.doi.org/10.1175/JHM531.1
  21. Pechlivanidis IG, Arheimer B (2015) Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case. Hydrol Earth Syst Sci 19:4559–4579. doi:10.5194/hess-19-4559-2015 CrossRefGoogle Scholar
  22. Pechlivanidis IG et al (2011) Catchment scale hydrological modeling: a review of model types, calibration approaches and the uncertainty analysis methods in the context of recent developments in technology and applications. Glob NEST J 13(3):193–214Google Scholar
  23. Peel MC, Blöschl G (2011) Hydrological modelling in a changing world. Prog Phys Geogr 35:249–261. doi:10.1177/0309133311402550 CrossRefGoogle Scholar
  24. Seiller G, Anctil F (2014) Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures. Hydrol Earth Syst Sci 18:2033–2047. doi:10.5194/hess-18-2033-2014 CrossRefGoogle Scholar
  25. Shiklomanov AI et al (2006) Cold region river discharge uncertainty estimates from large Russian rivers. J Hydrol 326:231–256CrossRefGoogle Scholar
  26. Teutschbein C, Wetterhall F, Seibert J (2011) Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale. Clim Dynam 37:2087–2105. doi:10.1007/s00382-010-0979-8 CrossRefGoogle Scholar
  27. van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. doi:10.1007/s10584-011-0148-z CrossRefGoogle Scholar
  28. Woo MK et al. (2008) The Mackenzie GEWEX Study: a contribution to cold region atmospheric and hydrologic sciences. In: Woo MK (Ed.), Cold Region Atmospheric and Hydrologic Studies, the Mackenzie GEWEX Experience, Atmospheric Dynamics 1:1–22.Google Scholar
  29. Xu C, Widen E, Hallding S (2005) Modelling hydrological consequences of climate change—progress and challenges. Adv Atmos Sci 22(6):789–797CrossRefGoogle Scholar
  30. Yang D et al (2002) Siberian Lena River hydrologic regime and recent change. J Geophys Res 107(D23):4694. doi:10.1029/2002JD002542 CrossRefGoogle Scholar
  31. Yang D, Shi X, Marsh P (2015) Variability and extreme of Mackenzie River daily discharge during 1973–2011. Quat Int 380–381:159–168CrossRefGoogle Scholar
  32. Ye B, Yang D, Kane DL (2003) Changes in Lena River streamflow hydrology: human impacts vs. natural variations. Water Resour Res 39(7):1200. doi:10.1029/2003WR0011991 CrossRefGoogle Scholar
  33. Yip QKY et al (2012) Climate impacts on hydrological variables in the Mackenzie River basin. Can Water Resour J 37(3):209CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Alexander Gelfan
    • 1
    • 2
  • David Gustafsson
    • 3
  • Yury Motovilov
    • 1
    • 2
  • Berit Arheimer
    • 3
  • Andrey Kalugin
    • 1
    • 2
  • Inna Krylenko
    • 1
    • 4
  • Alexander Lavrenov
    • 1
  1. 1.Water Problems Institute of RASMoscowRussia
  2. 2.P.P. Shirshov Institute of Oceanology of RASMoscowRussia
  3. 3.Swedish Meteorological and Hydrological InstituteNorrköpingSweden
  4. 4.Lomonosov Moscow State University, Faculty of GeographyMoscowRussia

Personalised recommendations