Climatic Change

, Volume 141, Issue 3, pp 435–449 | Cite as

Propagation of forcing and model uncertainties on to hydrological drought characteristics in a multi-model century-long experiment in large river basins

  • L. Samaniego
  • R. Kumar
  • L. Breuer
  • A. Chamorro
  • M. Flörke
  • I. G. Pechlivanidis
  • D. Schäfer
  • H. Shah
  • T. Vetter
  • M. Wortmann
  • X. Zeng


Recent climate change impact studies studies have presented conflicting results regarding the largest source of uncertainty in essential hydrological variables, especially streamflow and derived characteristics that describe the evolution of drought events. Part of the problem arises from the lack of a consistent framework to address compatible initial conditions for the impact models and a set of standardized historical and future forcings. The ISI-MIP2 project provides a good opportunity to advance our understanding of the propagation of forcing and model uncertainties on to century-long time series of drought characteristics using an ensemble of hydrological model (HM) projections across a broad range of climate scenarios and regions. To achieve this goal, we used six regional preconditioned hydrological models set up in seven large river basins: Upper-Amazon, Blue-Nile, Ganges, Upper-Niger, Upper-Mississippi, Rhine, and Upper-Yellow. These models were forced with bias-corrected outputs from five CMIP5 general circulation models (GCMs) under two extreme representative concentration pathway scenarios (i.e., RCP2.6 and RCP8.5) for the period 1971-2099. The simulated streamflow was transformed into a monthly runoff index (RI) to analyze the attributions of the GCM and HM uncertainties on to drought magnitudes and durations over time. The results indicated that GCM uncertainty mostly dominated over HM uncertainty for the projections of runoff drought characteristics, irrespective of the selected RCP and region. In general, the overall uncertainty increased with time. The uncertainty in the drought characteristics increased as the radiative forcing of the RCP increased, but the propagation of the GCM uncertainty on to a drought characteristic depended largely upon the hydro-climatic regime. Although our study emphasizes the need for multi-model ensembles for the assessment of future drought projections, the agreement between the GCM forcings was still too weak to draw conclusive recommendations.


Hydrological Model Hydrological Drought Uncertainty Contribution Drought Characteristic Rhine 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.



This study was carried out within Helmholtz-Association climate initiative REKLIM ( This study was conducted under the Inter-Sectoral Impact Model Inter-comparison Project. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling for the CMIP5 simulations.

Supplementary material

10584_2016_1778_MOESM1_ESM.pdf (879 kb)
(PDF 879 KB)


  1. Aich V, et al (2014) Comparing impacts of climate change on streamflow in four large african river basins. Hydrol Earth Syst Sci 18(4):1305–1321CrossRefGoogle Scholar
  2. Andreadis K, et al (2005) Twentieth-century drought in the conterminous United States. J Hydrometeorol 6(6):985–1001CrossRefGoogle Scholar
  3. Arnell NW (2011) Uncertainty in the relationship between climate forcing and hydrological response in UK catchments. Hydrol Earth Syst Sci 15(3):897–912CrossRefGoogle Scholar
  4. Beven K, Smith PJ, Wood A (2011) On the colour and spin of epistemic error (and what we might do about it). Hydrol Earth Syst Sci 15(10):3123–3133CrossRefGoogle Scholar
  5. Blöschl G, Montanari A (2010) Climate change impactsthrowing the dice? Hydrol Process 24:374–381Google Scholar
  6. Bosshard T, et al (2013) Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour Res 49(3):1523–1536CrossRefGoogle Scholar
  7. Briffa KR, Van Der Schrier G, Jones PD (2009) Wet and dry summers in Europe since 1750: evidence of increasing drought. Int J Climatol 29(13):1894–1905CrossRefGoogle Scholar
  8. Buizza R (2002) Chaos and weather prediction. Meteorological training course lecture series, ECMWFGoogle Scholar
  9. Cretat J, Pohl B (2012) How physical parameterizations can modulate internal variability in a regional climate model. J Atmosp Sci 69(2):714–724CrossRefGoogle Scholar
  10. Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Change 3(1):52–58CrossRefGoogle Scholar
  11. Davison AC, Hinkley DV (1997) Bootstrap methods and their applications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  12. Donnelly C, Andersson JCM, Arheimer B (2015) Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe. Hydrol Sci J 61(2):255–273CrossRefGoogle Scholar
  13. Ehret U, et al (2012) HESS opinions ”should we apply bias correction to global and regional climate model data?” Hydrol Earth Syst Sci 16:3391–3404CrossRefGoogle Scholar
  14. Giuntoli I, et al (2015) Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models. Earth Syst Dyn 6(1):267–285CrossRefGoogle Scholar
  15. Gudmundsson L, et al (2012) Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe. J Hydrometeorol 13(2):604–620CrossRefGoogle Scholar
  16. Haddeland I, et al (2011) Multimodel estimate of the global terrestrial water balance: setup and first results. J Hydrometeorol 12(5):869–884CrossRefGoogle Scholar
  17. Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19(21):5686–5699CrossRefGoogle Scholar
  18. Hempel S, et al (2013) A trend-preserving bias correction - the ISI-MIP approach. Earth Syst Dyn 4(2):219–236CrossRefGoogle Scholar
  19. Krysanova V, Hattermann F (2016) Overview of applied models and summary of results on intercomparison of climate impact assessment. Clim Change x(x):x. in rev. Special IssueGoogle Scholar
  20. Kumar R, Samaniego L, Attinger S (2013) Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resour Res 49(1):360–379CrossRefGoogle Scholar
  21. Kumar R, et al (2016) Multiscale evaluation of the standardized precipitation index as a groundwater drought indicator. Hydrol Earth Syst Sci 20(3):1117–1131CrossRefGoogle Scholar
  22. Livneh B, Kumar R, Samaniego L (2015) Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin. Hydrol Process 29(21):4638–4655CrossRefGoogle Scholar
  23. Mishra V, et al (2016) Multimodel assessment of sensitivity and uncertainty of water availability under climate change. Clim Change, 2016. In revGoogle Scholar
  24. Mueller B, Zhang X (2015) Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data. Clim Change 134(1–2):255–267Google Scholar
  25. Pappenberger F, Beven K (2006) Ignorance is bliss: or seven reasons not to use uncertainty analysis. Water Resour Res 42(5):n/a–n/aCrossRefGoogle Scholar
  26. Pechlivanidis IG, et al (2016) Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions. Clim ChangeGoogle Scholar
  27. Prudhomme C, et al (2014) Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment. Proc Nat Acad Sci 111(9):3262–3267CrossRefGoogle Scholar
  28. Samaniego L, Bárdossy A (2006) Simulation of the impacts of land use/cover and climatic changes on the runoff characteristics at the mesoscale. Ecol Model 196(1–2):45–61CrossRefGoogle Scholar
  29. Samaniego L, Kumar R, Attinger S (2010) Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour Res 46(5)Google Scholar
  30. Samaniego L, Kumar R, Zink M (2013) Implications of parameter uncertainty on soil moisture drought analysis in germany. J Hydrometeorol 14(1):47–68CrossRefGoogle Scholar
  31. Schewe J, et al (2014) Multimodel assessment of water scarcity under climate change. Proc Nat Acad Sci 111(9):3245–3250CrossRefGoogle Scholar
  32. Seneviratne SI, et al (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field CB, et al. (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge, pp 109–230Google Scholar
  33. Sheffield J, Wood EF (2008) Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim Dyn 31(1):79–105CrossRefGoogle Scholar
  34. Sheffield J, Wood EF, Roderick ML (2013) Little change in global drought over the past 60 years. Nature 491(7424):435–438CrossRefGoogle Scholar
  35. Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought. Geophys Res Lett 35(2)Google Scholar
  36. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Amer Meteorol Soc 93(4):485–498CrossRefGoogle Scholar
  37. Teng J, et al (2012) Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol 13 (1):122–139CrossRefGoogle Scholar
  38. Trenberth KE, et al (2014) Global warming and changes in drought. Nat Clim Change 4(1):17–22CrossRefGoogle Scholar
  39. 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(1):17–43CrossRefGoogle Scholar
  40. Vidal J, et al (2010) Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite. Hydrol Earth Syst Sci 14(3):459–478CrossRefGoogle Scholar
  41. Wilks DS (2011) Statistical methods in the atmospheric sciences, 3rd edn. Academic Press, AmsterdamGoogle Scholar
  42. Wang A, et al (2009) Multimodel ensemble reconstruction of drought over the continental United States. J Clim 22(10):2694–2712CrossRefGoogle Scholar
  43. Weedon G P, 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(5):823–848CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • L. Samaniego
    • 1
  • R. Kumar
    • 1
  • L. Breuer
    • 2
  • A. Chamorro
    • 2
  • M. Flörke
    • 3
  • I. G. Pechlivanidis
    • 4
  • D. Schäfer
    • 1
  • H. Shah
    • 5
  • T. Vetter
    • 6
  • M. Wortmann
    • 6
  • X. Zeng
    • 7
  1. 1.UFZ-Helmholtz Centre for Environmental ResearchLeipzigGermany
  2. 2.Justus Liebig University GießenGießenGermany
  3. 3.Universität KasselKasselGermany
  4. 4.Swedish Meteorological and Hydrological InstituteNorrköpingSweden
  5. 5.Indian Institute of Technology GandhinagarAhmedabadIndia
  6. 6.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  7. 7.Huazhong University of Science & TechnologyWuhanChina

Personalised recommendations