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Climatic Change

, Volume 141, Issue 3, pp 363–379 | Cite as

Intercomparison of climate change impacts in 12 large river basins: overview of methods and summary of results

  • Valentina Krysanova
  • Fred F. Hattermann
Article

Abstract

This paper introduces the Special Issue “Hydrological Model Intercomparison for Climate Impact Assessment”. We describe the river basins used as case studies, the input data, the hydrological models, and the climate scenarios applied in the multi-model framework. We also summarize the main results of the papers contained in this Special Issue.

Keywords

River Basin Climate Change Impact Hydrological Model Representative Concentration Pathway Arctic Basin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We are grateful to ISI-MIP for the general support of our regional-scale water sector team and for delivering climate scenarios, and to GRDC for providing discharge data. Many thanks to Shaochun Huang, David Stevens and Hannah Haacke for their help in the preparation of Figs. 2 and 3 and the analysis of climate scenarios.

Supplementary material

10584_2017_1919_MOESM1_ESM.docx (488 kb)
ESM 1 (DOCX 487 kb)
10584_2017_1919_MOESM2_ESM.docx (18 kb)
ESM 2 (DOCX 18 kb)

References

  1. Arheimer B et al (2012) Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea basin—influence of input-data quality and scale. Hydrol Res 43(4):315–329CrossRefGoogle Scholar
  2. Arnold JG et al (1993) A comprehensive surface-groundwater flow model. J Hydrol 142:47–69CrossRefGoogle Scholar
  3. Arnold JG et al (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc 34:73–89CrossRefGoogle Scholar
  4. Bergström S, Forsman A (1973) Development of a conceptual deterministic rainfall-runoff model. Nord Hydrol 4:147–170Google Scholar
  5. Boyle DP (2001) Multicriteria calibration of hydrological models. PhD Dissertation. Dep Hydrol Water Resour Univ Ariz, TucsonGoogle Scholar
  6. Christensen NS, Lettenmaier DP (2007) A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin. Hydrol Earth Syst Sci 11:1417–1434CrossRefGoogle Scholar
  7. Donnelly C et al (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
  8. Eisner S et al. (2016) An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins. Clim Change. doi: 10.1007/s10584-016-1844-5
  9. Flörke M et al (2013) Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: a global simulation study. Glob Environ Chang 23(1):144–156CrossRefGoogle Scholar
  10. Gassman PW et al (2014) Applications of the SWAT model special section: overview and insights. J Environ Qual 1–8. Doi: 10.2134/jeq2013.11.0466
  11. Gelfan A et al (2015) Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability. Hydrol Earth Syst Sci 19:2737–2754CrossRefGoogle Scholar
  12. Gelfan A et al (2016) Climate change impact on the water regime of two great arctic rivers: modeling and uncertainty issues. Clim Change. doi: 10.1007/s10584-016-1710-5 Google Scholar
  13. Gosling S et al (2016) A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C. Clim Change. doi: 10.1007/s10584-016-1773-3 Google Scholar
  14. Haddeland I et al (2014) Global water resources affected by human interventions and climate change. Proc Natl Acad Sci USA 111(9):3251–3256CrossRefGoogle Scholar
  15. Hamlet AF, Lettenmaier DP (1999) Effects of climate change on hydrology and water resources in the Columbia River Basin. J Am Water Resour Assoc 35:1597–1623CrossRefGoogle Scholar
  16. Hattermann F. et al. (2016) Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins. Clim Change. doi: 10.1007/s10584-016-1829-4
  17. Herman JD et al (2013) Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior. Water Resour Res 49:1400–1414CrossRefGoogle Scholar
  18. Huang S et al (2016) Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide. Clim Change. doi: 10.1007/s10584-016-1841-8 Google Scholar
  19. Krysanova V, White M (2015) Advances in water resources assessment with SWAT—an overview. Hydrol Sci J 60(5):771–783Google Scholar
  20. Krysanova V et al (1989) Simulation modelling of the coastal waters pollution from agricultural watershed. Ecol Model 49:7–29CrossRefGoogle Scholar
  21. Krysanova V et al (1998) Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds. Ecol Model 106:261–289CrossRefGoogle Scholar
  22. Krysanova V et al (1999) Modelling river discharge for large drainage basins: from lumped to distributed approach. Hydrol Sci J 44:313–331CrossRefGoogle Scholar
  23. Krysanova V et al (2015) Modelling climate and land use change impacts with SWIM: lessons learnt from multiple applications. Hydrol Sci J 60(4):606–635CrossRefGoogle Scholar
  24. Kumar R et al (2013a) Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resour Res 49(1):360–379CrossRefGoogle Scholar
  25. Kumar R et al (2013b) Towards computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme. Water Resour Res 49(9):5700–5714CrossRefGoogle Scholar
  26. Liang X et al (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99:14415–14428CrossRefGoogle Scholar
  27. Lindström G et al (2010) Development and test of the HYPE (Hydrological Predictions for the Environment) model—a water quality model for different spatial scales. Hydrol Res 41(3–4):295–319CrossRefGoogle Scholar
  28. Menzel L et al (2006) Impact of climate change on the regional hydrology—scenario-based modelling studies in the German Rhine catchment. Nat Hazards 38:45–61CrossRefGoogle Scholar
  29. Mishra V et al (2016) Multimodel assessment of sensitivity and uncertainty of evapotranspiration and a proxy for available water resources under climate change. Clim Chang (in press). This SIGoogle Scholar
  30. Moore RJ (1985) The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J 30(2):273–297CrossRefGoogle Scholar
  31. Motovilov YG (2013) ECOMAG: a distributed model of runoff formation and pollution transformation in river basins solution. IAHS Publ 361:227–234Google Scholar
  32. Motovilov YG et al (1999) Validation of a distributed hydrological model against spatial observation. Agric For Meteorol 98–99:257–277CrossRefGoogle Scholar
  33. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part I—a discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  34. Neitsch SL et al (2011) Soil & water assessment tool theoretical documentation version 2009. Texas Water Resources Institute Technical Report No. 406 Texas A&M University System College Station, TX, pp. 647Google Scholar
  35. Pechlivanidis I, Arheimer B (2015) Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case. Hydrol Earth Syst Sci Discuss 12:2885–2944CrossRefGoogle Scholar
  36. Pechlivanidis I et al (2016) Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions. Clim Change. doi: 10.1007/s10584-016-1723-0 Google Scholar
  37. Rakovec O et al (2015) Multiscale and multivariate evaluation of water fluxes and states over European river basins. J Hydrometeorol. doi: 10.1175/JHM-D-15-0054.1 Google Scholar
  38. Samaniego L et al (2010) Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour Res 46:W05523. doi: 10.1029/2008WR007327 Google Scholar
  39. Samaniego L et al (2016) Propagation of forcing and model uncertainties on to hydrological drought characteristics in a multi-model century-long experiment in large river basins. Clim Change. doi: 10.1007/s10584-016-1778-y Google Scholar
  40. Schewe J et al (2014) Multimodel assessment of water scarcity under climate change. Proc Natl Acad Sci USA 111(9):3245–3250CrossRefGoogle Scholar
  41. Schneider C et al (2013) How will climate change modify river flow regimes in Europe? HESS 17(1):325–339. doi: 10.5194/hess-17-325-2013 Google Scholar
  42. Strauch M et al (2016) Adjustment of global precipitation data for enhanced hydrologic modelling of tropical Andean watersheds. Clim Change. doi: 10.1007/s10584-016-1706-1 Google Scholar
  43. Su B et al (2016) Impacts of climate change on streamflow in the upper Yangtze River basin. Clim Change. doi: 10.1007/s10584-016-1852-5
  44. Teklesadik AD et al (2016) Intercomparison of hydrological impacts of climate change on the Upper Blue Nile basin using ensemble of hydrological models and global climate models. Clim Chang (in press). This SIGoogle Scholar
  45. Verzano K (2009) Climate change impacts on flood related hydrological processes: Further development and application of a global scale hydrological model, no. 71–2009 in Reports on Earth System Science, 71. Max Planck Institute for Meteorology, Hamburg, GermanyGoogle Scholar
  46. Vetter T et al (2015) Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents. Earth Syst Dyn 6:17–43CrossRefGoogle Scholar
  47. Vetter T et al (2016) Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Clim Change. doi: 10.1007/s10584-016-1794-y Google Scholar
  48. Vrugt JA et al (2005) Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation. Water Resour Res 41:W01017. doi: 10.1029/2004WR003059 CrossRefGoogle Scholar
  49. Wang X et al (2016) Analysis of multi-dimensional hydrological alterations under climate change for four major river basins in different climate zones. Clim Change. doi: 10.1007/s10584-016-1843-6 Google Scholar
  50. Warszawski L et al (2014) The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework. Proc Natl Acad Sci USA 111(9):3228–3232CrossRefGoogle Scholar
  51. Weedon GP et al (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J Hydrometeorol 12:823–848CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany

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