Climatic Change

, Volume 125, Issue 2, pp 221–235 | Cite as

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

  • Jean-François ExbrayatEmail author
  • Wouter Buytaert
  • Edison Timbe
  • David Windhorst
  • Lutz Breuer


Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data.

Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.


Emission Scenario Hydrological Model Climate Projection Statistical Downscaling Total Runoff 
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.



We thank the Deutsche Forschungsgemeinschaft (DFG) for funding the Research Unit FOR816 and subprojects B3.2, BR2238/4-1 and D4, BR2238/4-2. The first author was also funded by the Australian Research Council (ARC) through grants DP110102618 and CE110001028.

Supplementary material

10584_2014_1160_MOESM1_ESM.pdf (373 kb)
ESM 1 (PDF 373 kb)


  1. Abramowitz G, Gupta H (2008) Toward a model space and model independence metric. Geophys Res Lett 35:L05705. doi:  10.1029/2007GL032834
  2. Andersson L, Samuelsson P, Kjellström E (2011) Assessment of climate change impact on water resources in the Pungwe river basin. Tellus A. doi:  10.3402/tellusa.v63i1.15772
  3. Andres N, Vegas Galdos F, Lavado Casimiro WS, Zappa M (2013) Water resources and climate change impact modelling on a daily time scale in the Peruvian Andes. Hydrol Sci J online version. doi: 10.1080/02626667.2013.862336 Google Scholar
  4. Arnell NW (2004) Climate change and global water resources: SRES emissions and socio-economic scenarios. Glob Environ Chang 14:31–52. doi: 10.1016/j.gloenvcha.2003.10.006 CrossRefGoogle Scholar
  5. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model Development1. J Am Water Resour Assoc 34:73–89. doi: 10.1111/j.1752-1688.1998.tb05961.x CrossRefGoogle Scholar
  6. Bastola S, Murphy C, Sweeney J (2011) The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Adv Water Resour 34:562–576. doi: 10.1016/j.advwatres.2011.01.008 CrossRefGoogle Scholar
  7. Bastola S, Murphy C, Sweeney J (2013) The sensitivity of fluvial flood risk in Irish catchments to the range of IPCC AR4 climate change scenarios. Sci Total Env 409:5403–5415. doi: 10.1016/j.scitotenv.2011.08.042 CrossRefGoogle Scholar
  8. Bennett KE, Werner AT, Schnorbus M (2012) Uncertainties in Hydrologic and Climate Change Impact Analyses in Headwater Basins of British Columbia. J Clim 25:5711–5730. doi:  10.1175/JCLI-d-11-00417.1
  9. Beven K (2006) A manifesto for the equifinality thesis. J Hydrol 320:18–36. doi: 10.1016/j.jhydrol.2005.07.007 CrossRefGoogle Scholar
  10. Blöschl G, Montanari A (2010) Climate change impacts—throwing the dice? Hydrol Process 24:374–381. doi: 10.1002/hyp.7574 Google Scholar
  11. Bosilovich MG, Schubert SD, Walker GK (2005) Global changes of the water cycle intensity. J Clim 18:1591–1608. doi: 10.1175/JCLI3357.1 CrossRefGoogle Scholar
  12. Bosshard T, Carambia M, Goergen K, Kotlarski S, Krahe P, Zappa M, Schär C (2013) Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour Res 49:1523–1536. doi: 10.1029/2011WR011533 CrossRefGoogle Scholar
  13. Boulanger J-P, Martinez F, Segura EC (2006) Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America. Clim Dynam 27:233–259. doi: 10.1007/s00382-006-0134-8 CrossRefGoogle Scholar
  14. Boulanger J-P, Martinez F, Segura EC (2007) Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America. Clim Dynam 28:255–271. doi: 10.1007/s00382-006-0182-0 CrossRefGoogle Scholar
  15. Brath A, Montanari A, Toth E (2004) Analysis of the effects of different scenarios of historical data availability on the calibration of a spatially-distributed hydrological model. J Hydrol 291(3–4):232–253. doi: 10.1016/j.jhydrol.2003.12.044 CrossRefGoogle Scholar
  16. Breuer L, Huisman JA, Willems P et al (2009) Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: Model intercomparison with current land use. Adv Water Resour 32:129–146. doi: 10.1016/j.advwatres.2008.10.003 CrossRefGoogle Scholar
  17. Buytaert W, Bièvre BD (2012) Water for cities: The impact of climate change and demographic growth in the tropical Andes. Water Resour Res 48, W08503. doi: 10.1029/2011WR011755 CrossRefGoogle Scholar
  18. Buytaert W, Célleri R, Timbe L (2009) Predicting climate change impacts on water resources in the tropical Andes: Effects of GCM uncertainty. Geophys Res Lett 36, L07406. doi: 10.1029/2008GL037048 CrossRefGoogle Scholar
  19. Buytaert W, Vuille M, Dewulf A, Urrutia R, Karmalkar A, Célleri R (2010) Uncertainties in climate change projections and regional downscaling in the tropical Andes: implications for water resources management. Hydrol Earth Syst Sci 14:1247–1258. doi: 10.5194/hess-14-1247-2010 CrossRefGoogle Scholar
  20. Chen J, Brissette FP, Poulin A, Leconte R (2011) Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour Res 47, W12509. doi: 10.1029/2011WR010602 CrossRefGoogle Scholar
  21. Chiew FHS, Stewardson MJ, McMahon TA (1993) Comparison of six rainfall-runoff modelling approaches. J Hydrol 147:1–36. doi: 10.1016/0022-1694(93)90073-I
  22. 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 11:1417–1434. doi: 10.5194/hess-11-1417-2007 CrossRefGoogle Scholar
  23. Crespo P, Bücker A, Feyen J, Vaché KB, Frede H-G, Breuer L (2012) Preliminary evaluation of the runoff processes in a remote montane cloud forest basin using Mixing Model Analysis and Mean Transit Time. Hydrol Process 26:3896–3910. doi: 10.1002/hyp.8382 CrossRefGoogle Scholar
  24. Dobler C, Hagemann S, Wilby RL, Stötter J (2012) Quantifying different sources of uncertainty in hydrological projections in an Alpine watershed. Hydrol Earth Syst Sci 16(11):4343–4360. doi: 10.5194/hess-16-4343-2012 CrossRefGoogle Scholar
  25. Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J (2012) HESS Opinions “Should we apply bias correction to global and regional climate model data?”. Hydrol Earth Syst Sci 16:3391–3404. doi: 10.5194/hess-16-3391-2012 CrossRefGoogle Scholar
  26. Exbrayat J-F, Viney NR, Frede H-G, Breuer L (2013) Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions. Geosci Model Develop 6:117–125. doi: 10.5194/gmd-6-117-2013 CrossRefGoogle Scholar
  27. Exbrayat J-F, Viney NR, Seibert J, Wrede S, Frede H-G, Breuer L (2010) Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment. Hydrol Earth Syst Sci 14:2383–2397. doi: 10.5194/hess-14-2383-2010 CrossRefGoogle Scholar
  28. Gleick PH (1986) Methods for evaluating the regional hydrologic impacts of global climatic changes. J Hydrol 88:97–116CrossRefGoogle Scholar
  29. Harding BL, Wood AW, Prairie JR (2012) The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin. Hydrol Earth Syst Sci 16:3989–4007. doi: 10.5194/hess-16-3989-2012 CrossRefGoogle Scholar
  30. Huisman JA, Breuer L, Bormann H, Bronstert A, Croke BFW, Frede H-G, Graff T, Hubrechts L, Jakeman AJ, Kite G, Lanini J, Leavesley G, Lettenmaier DP, Lindström G, Seobert J, Sivapalan M, Viney NR, Willems P (2009) Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III: Scenario analysis. Adv Water Resour 32:159–170. doi: 10.1016/j.advwatres.2008.06.009 CrossRefGoogle Scholar
  31. Huntington TG (2006) Evidence for intensification of the global water cycle: Review and synthesis. J Hydrol 319:83–95. doi: 10.1016/j.jhydrol.2005.07.003 CrossRefGoogle Scholar
  32. Hrachowitz M, Savenije HHG, Blöschl G, McDonnell JJ, Sivapalan M, Pomeroy JW, Arheimer B, Blume T, Clark MP, Ehret U, Fenicia F, Freer JE, Gelfan A, Gupta HV, Hughes DA, Hut RW, Montanari A, Pande S, Tetzlaff D, Troch PA, Uhlenbrook S, Wagener T, Winsemius HC, Woods RA, Zehe E, Cudennec C (2013) A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrol Sci J 58:1198–1255. doi: 10.1080/02626667.2013.803183 CrossRefGoogle Scholar
  33. Immerzeel WW, Beek LPH, Konz M et al (2012) Hydrological response to climate change in a glacierized catchment in the Himalayas. Clim Chang 110:721–736. doi: 10.1007/s10584-011-0143-4 CrossRefGoogle Scholar
  34. IPCC (2007) Climate change 2007: the physical science basis, contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New-YorkGoogle Scholar
  35. IPCC (2013) Climate change 2013: the physical science basis, contribution of working group I to the fourth assessment report of the Intergovernmental panel on climate change. Cambridge University Press, Cambridge and New-YorkGoogle Scholar
  36. Jung I-W, Chang H (2011) Assessment of future runoff trends under multiple climate change scenarios in the Willamette River Basin, Oregon, USA. Hydrol Process 25:258–277. doi: 10.1002/hyp.7842 CrossRefGoogle Scholar
  37. Klemeš V (1986) Operational testing of hydrological simulation models. Hydrol Sci J 31:13–24. doi : 10.1080/02626668609491024
  38. Lavado Casimiro WS, Labat D, Guyot JL, Ardoin-Bardin S (2011) Assessment of climate change impacts on the hydrology of the Peruvian Amazon-Andes basin. Hydrol Process 25:3721–3734. doi: 10.100Google Scholar
  39. Leung LR, Mearns LO, Giorgi F, Wilby RL (2003) Regional climate research. Bull Am Meteorol Soc 84:89–95. doi: 10.1175/BAMS-84-1-89 CrossRefGoogle Scholar
  40. Lindgren GA, Wrede S, Seibert J, Wallin M (2007) Nitrogen source apportionment modeling and the effect of land-use class related runoff contributions. Nord Hydrol 38:317–331. doi: 10.2166/nh.2007.015 CrossRefGoogle Scholar
  41. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003. doi: 201010.1029/2009RG000314Google Scholar
  42. Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25:2814–2826. doi: 10.1002/hyp.8043 CrossRefGoogle Scholar
  43. Nielsen SA, Hansen E (1973) Numerical simulation of the rainfall runoff process on a daily basis. Nord Hydrol 4:171–190Google Scholar
  44. Perrin C, Oudin L, Andréassian V, Rojas-Serna C, Michel C, Mathevet T (2007) Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models. Hydrol Sci J 52(1):131–151. doi: 10.1623/hysj.52.1.131 CrossRefGoogle Scholar
  45. Plesca I, Timbe E, Exbrayat J-F, Windhorst D, Kraft P, Crespo P, Vaché KB, Frede H-G, Breuer L (2012) Model intercomparison to explore catchment functioning: results from a remote montane tropical rainforest. Ecol Model 239:3–13. doi: 10.1016/j.ecolmodel.2011.05.005 CrossRefGoogle Scholar
  46. Rollenbeck R, Bendix J (2011) Rainfall distribution in the Andes of southern Ecuador derived from blending weather radar data and meteorological field observations. Atmos Res 99:277–289. doi: 10.1016/j.atmosres.2010.10.018 CrossRefGoogle Scholar
  47. Seibert J (1997) Estimation of parameter uncertainty in the HBV model. Nord Hydrol 28:247–262Google Scholar
  48. Seibert J (1999) Regionalisation of parameters for a conceptual rainfall-runoff model. Agric For Meteorol 98–99:279–293. doi: 10.1016/S0168-1923 (99) 00105-7 CrossRefGoogle Scholar
  49. Seibert J, Beven KJ (2009) Gauging the ungauged basin: how many discharge measurements are needed? Hydrol Earth Syst Sci 13:883–892. doi: 10.5194/hess-13-883-2009 CrossRefGoogle Scholar
  50. Seiller G, Anctil F, Perrin C (2012) Multimodel evaluation of twenty lumped hydrological models under constrasted climate conditions. Hydrol Earth Syst Sci 16:1171–1189. doi: 10.5194/hess-16-1171-2012 CrossRefGoogle Scholar
  51. Sivapalan M, Ruprecht JK, Viney NR (1996) Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 1. Small catchment water balance model. Hydrol Process 10:393–411. doi:  10.1002/(SICI) 1099-1085 (199603) 10:3<393:: AID-HYP307>3.0.CO;2-#
  52. Sivapalan M, Takeuchi K, Franks SW, Gupta VK, Karambiri H, Lakshmi V, Liang X, McDonnell JJ, Mendiondo EN, O’Connell PE, OKI T, Pomeroy JW, Schertzer D, Uhlenbrook S, Zehe E (2003) IAHS Decade on predictions in Ungauged Basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrol Sci J 48:857–880. doi: 10.1623/hysj.48.6.857.51421 CrossRefGoogle Scholar
  53. Smith MB, Seo DJ, Koren VI, Reed SM, Zhang Z, Duan Q, Moreda F, Cong S (2004) The distributed model intercomparison project (DMIP): motivation and experiment design. J Hydrol 298:4–26. doi: 10.1016/j.jhydrol.2004.03.040 CrossRefGoogle Scholar
  54. Sun WC, Ishidaira H, Bastola S (2010) Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet. Hydrol Earth Syst Sci 14:2011–2022. doi: 10.5194/hess-14-2011-2010 CrossRefGoogle Scholar
  55. Teng J, Vaze J, Chiew FHS, Wang B, Perraud J-M (2012) Estimating the relative ncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol 13:122–139. doi: 10.1175/JHM-d-11-058.1 CrossRefGoogle Scholar
  56. 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
  57. Urrutia R, Vuille M (2009) Climate change projections for the tropical Andes using a regional climate model: Temperature and precipitation simulations for the end of the 21st century. J Geophys Res 114, D02108. doi: 10.1029/2008JD011021 Google Scholar
  58. USACE (2001) HEC-HMS Hydrologic Modeling System, User’s Manual for Version 2.11. Report CPD-74A, US Army Corps of Engineers, Hydrologic Engineering Center, Davis, CAGoogle Scholar
  59. Velázquez JA, Schmid J, Ricard S, Muerth MJ, Gauvin St-Denis B, Minville M, Chaumont D, Caya D, Ludwig R, Turcotte R (2013) An ensemble approach to assess hydrological models’ contribution to uncertainties in the analysis of climate change impact on water resources. Hydrol Earth Syst Sci 17:565–578. doi: 10.5194/hess-17-565-2013 CrossRefGoogle Scholar
  60. Viney NR, Bormann H, Breuer L et al (2009) Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions. Adv Water Resour 32:147–158. doi: 10.1016/j.advwatres.2008.05.006 CrossRefGoogle Scholar
  61. Wade AJ, Jackson BM, Butterfield D (2008) Over-parameterised, uncertain “mathematical marionettes” — How can we best use catchment water quality models? An example of an 80-year catchment-scale nutrient balance. Sci Total Environ 400:52–74. doi: 10.1016/j.scitotenv.2008.04.030 CrossRefGoogle Scholar
  62. Wilby RL, Dawson CW, Barrow EM (2002) sdsm — a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:145–157. doi: 10.1016/S1364-8152 (01) 00060-3 CrossRefGoogle Scholar
  63. Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the River Thames, UK. Water Resour Res 42, W02419. doi: 10.1029/2005WR004065 CrossRefGoogle Scholar
  64. Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107:4429. doi: 10.1029/2001JD000659 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Jean-François Exbrayat
    • 1
    • 2
    Email author
  • Wouter Buytaert
    • 3
  • Edison Timbe
    • 4
    • 5
  • David Windhorst
    • 4
  • Lutz Breuer
    • 4
  1. 1.School of GeoSciences and National Centre for Earth ObservationUniversity of EdinburghEdinburghUK
  2. 2.Climate Change Research Centre and ARC Centre of Excellence for Climate System ScienceUniversity of New South WalesSydneyAustralia
  3. 3.Civil and Environmental Engineering & Grantham Institute for Climate Change, Imperial College LondonLondonUK
  4. 4.Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ)Justus-Liebig-Universität GießenGießenGermany
  5. 5.Grupo de Ciencias de la Tierra y del Ambiente, DIUCUniversidad de CuencaCuencaEcuador

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