Environmental Earth Sciences

, Volume 72, Issue 11, pp 4357–4369 | Cite as

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

  • Kazi RahmanEmail author
  • Christophe Etienne
  • Ana Gago-Silva
  • Chetan Maringanti
  • Martin Beniston
  • Anthony Lehmann
Original Article


Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km2. The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds.


RCM SWAT Grid size Runoff Hydrological model 



Most of the work was done during the PhD program fellowship at University of Geneva with funding from EU FP 7 project ACQWA [Assessing climate change impact on water quality and quantity] under Grant Nr. 212250. We wish to thank the Federal Office of Meteorology and Climatology MeteoSwiss for providing the daily precipitation, maximum and minimum temperatures, and wind speed required for building the hydrological model. The geographic data was obtained from Federal Office for the Environment (FOEN). We also acknowledge equally the ALPIQ and KW-MATTMARK hydropower companies for providing discharge and lake level data. The coordinates of the intake points were collected from the hydropower consulting engineers E-dric (

Supplementary material

12665_2014_3336_MOESM1_ESM.pdf (13 kb)
Supplementary document: [1] Meteorological variable conversion using MATLAB: NetCDF_SWAT_PRUDENCE


  1. Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007) Modelling hydrology and water quality in the pre-ailpine/alpine Thur watershed using SWAT. J Hydrol 333(2–4):413–430CrossRefGoogle Scholar
  2. Ahl RS, Woods SW, Zuuring HR (2008) Hydrologic calibration and validation of SWAT in a snow-dominated rocky mountain watershed, Montana, USA. J Am Water Resour Assoc 44(6):1411–1430. doi: 10.1111/j.1752-1688.2008.00233.x CrossRefGoogle Scholar
  3. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment, part 1: model development. JAWRA 34(1):73–89. doi: 10.1111/j.1752-1688.1998.tb05961.x Google Scholar
  4. Beniston M (2010) Impacts of climatic change on water and associated economic activities in the Swiss Alps. J Hydrol. doi: 10.1016/j.jhydrol.2010.06.046 Google Scholar
  5. Beniston M, Goyette S (2007) Changes in variability and persistence of climate in Switzerland: exploring 20th century observations and 21st century simulations. Glob Planet Change 57(1–2):1–15. doi: 10.1016/j.gloplacha.2006.11.004 CrossRefGoogle Scholar
  6. Beniston M, Uhlmann B, Goyettea S, Lopez-Morenob JI (2011) Will snow-abundant winters still exist in the Swiss Alps in an enhanced greenhouse climate? Int J Climatol 31(9):1257–1263. doi: 10.1002/joc.2151 CrossRefGoogle Scholar
  7. Bordoy R, Burlando P (2012) Bias correction of regional climate model simulations in a region of complex orography. J Appl Meteorol Climatol 52(1):82–101. doi: 10.1175/jamc-d-11-0149.1 CrossRefGoogle Scholar
  8. Bosshard T, Kotlarski S, Ewen T, Schaer C (2011) Spectral representation of the annual cycle in the climate change signal. Hydrol Earth Syst Sci 15(9):2777–2788. doi: 10.5194/hess-15-2777-2011 CrossRefGoogle Scholar
  9. 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. doi: 10.1016/j.jhydrol.2012.02.040 CrossRefGoogle Scholar
  10. Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Change 81:7–30CrossRefGoogle Scholar
  11. Christensen JH, Christensen OB, Lopez P, van Meijgaard E, Botzet M (1996) The HIRHAM4 regional atmospheric climate model. DMI Technical Report 96-4. DMI, Copenhagen ØGoogle Scholar
  12. Christensen JH, Carter TR, Giorgi F (2002) PRUDENCE employs new methods to assess European climate change. EOS (American Geophysical Union Newsletter) 83:13CrossRefGoogle Scholar
  13. Debele B, Srinivasan R, Gosain AK (2010) Comparison of process-based and temperature-index snowmelt modeling in SWAT. Water Resour Manag 24(6):1065–1088CrossRefGoogle Scholar
  14. Doscher R, Willén U, Jones C, Rutgersson A, Meier HM, Hansson U, Graham LP (2002) The development of the regional coupled ocean-atmosphere model RCAO. Boreal Environ Res 7(3):183–192Google Scholar
  15. Fette M, Weber C, Peter A, Wehrli B (2007) Hydropower production and river rehabilitation: a case study on an alpine river. Environ Model Assess 12(4):257–267CrossRefGoogle Scholar
  16. Fontaine TA, Cruickshank TS, Arnold JG, Hotchkiss RH (2002) Development of a snowfall-snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT). J Hydrol 262(1–4):209–223CrossRefGoogle Scholar
  17. Giorgi F, Marinucci MR, Bates GT (1993) Development of a second-generation regional climate model (RegCM2). Part I: Boundary-layer and radiative transfer processes. Mon Weather Rev 121(10):2794–2813Google Scholar
  18. Graham LP, Andreasson J, Carlsson B (2007) Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods: a case study on the Lule River basin. Clim Change 81:293–307. doi: 10.1007/s10584-006-9215-2 CrossRefGoogle Scholar
  19. Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143. doi: 10.1061/(asce)1084-0699(1999)4:2(135) CrossRefGoogle Scholar
  20. Hock R (2003) Temperature index melt modelling in mountain areas. J Hydrol 282(1–4):104–115. doi: 10.1016/s0022-1694(03)00257-9 CrossRefGoogle Scholar
  21. Hwang S, Graham W, Adams A, Geurink J (2013) Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model. Reg Environ Change 1–12. doi: 10.1007/s10113-013-0406-x
  22. Jiang P, Gautam MR, Zhu J, Yu Z (2013) How well do the GCMs/RCMs capture the multi-scale temporal variability of precipitation in the Southwestern United States? J Hydrol 479:75–85. doi: 10.1016/j.jhydrol.2012.11.041 CrossRefGoogle Scholar
  23. Klok EJ, Jasper K, Roelofsma KP, Gurtz J, Badoux A (2001) Distributed hydrological modelling of a heavily glaciated Alpine river basin. Hydrol Sci J 46(4):553–570CrossRefGoogle Scholar
  24. Leander R, Buishand TA (2007) Resampling of regional climate model output for the simulation of extreme river flows. J Hydrol 332(3–4):487–496. doi: 10.1016/j.jhydrol.2006.08.006 CrossRefGoogle Scholar
  25. Lenderink G, Buishand A, van Deursen W (2007) Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrol Earth Syst Sci 11(3):1143–1159CrossRefGoogle Scholar
  26. Lettenmaier DP, Wood AW, Palmer RN, Wood EF, Stakhiv EZ (1999) Water resources implications of global warming: a US regional perspective. Clim Change 43(3):537–579. doi: 10.1023/a:1005448007910 CrossRefGoogle Scholar
  27. Liechti TC, Matos JP, Boillat J-L, Schleiss AJ (2012) Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin. Hydrol Earth Syst Sci. doi: 10.5194/hess-16-489-2012 Google Scholar
  28. Liu W, Cai T, Fu G, Zhang A, Liu C, Yu H (2013) The streamflow trend in Tangwang River basin in northeast China and its difference response to climate and land use change in sub-basins. Environ Earth Sci 69(1):51–62CrossRefGoogle Scholar
  29. Masih I, Maskey S, Uhlenbrook S, Smakhtin V (2011) Assessing the impact of areal precipitation input on streamflow simulations using the SWAT model1. J Am Water Resour Assoc 47(1):179–195. doi: 10.1111/j.1752-1688.2010.00502.x CrossRefGoogle Scholar
  30. Maurer EP, Hidalgo HG (2008) Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrol Earth Syst Sci 12(2):551–563CrossRefGoogle Scholar
  31. Maurer E, Wood A, Adam J, Lettenmaier D, Nijssen B (2002) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J Clim 15:3237–3251Google Scholar
  32. Meile T, Boillat JL, Schleiss A (2010) Hydropeaking indicators for characterization of the Upper-Rhone River in Switzerland. Aquat Sci 73(1):171–182Google Scholar
  33. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans Asabe 50(3):885–900CrossRefGoogle Scholar
  34. Morid SG, Gosain AK, Keshari AK (2004) Response of different snowmelt algorithms to synthesized climatic data for runoff simulation. J Earth Space Phys 30(1):1–4Google Scholar
  35. Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12(8):2256–2284. doi: 10.1175/1520-0442(1999)012<2256:aeosad>;2 CrossRefGoogle Scholar
  36. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I: a discussion of principles. J Hydrol 10(3):282–290. doi: 10.1016/0022-1694(70)90255-6 CrossRefGoogle Scholar
  37. Neitsch SL, Arnold JG, Kiniry J, Williams JR (2005) Soil and water assessment tool theoretical documentation. USDA Agricultural Research Service and TexasA & MBlackland Research Center, Temple, TexasGoogle Scholar
  38. Obeysekera J (2013) Validating climate models for computing evapotranspiration in hydrologic studies: how relevant are climate model simulations over Florida? Reg Environ Change 1–10. doi: 10.1007/s10113-013-0411-0
  39. Pal JS, Small EE, Eltahir EA (2000) Simulation of regional‐scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J Geophys Res Atmos (1984–2012) 105(D24):29579–29594Google Scholar
  40. Pal JS, Giorgi F, Bi X (2004) Consistency of recent European summer precipitation trends and extremes with future regional climate projections. Geophys Res Lett 31(13)Google Scholar
  41. Pavlik D, Söhl D, Pluntke T, Mykhnovych A, Bernhofer C (2012) Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km. Environ Earth Sci 65(5):1475–1482CrossRefGoogle Scholar
  42. Pepin N, Losleben M (2002) Climate change in the Colorado Rocky Mountains: free air versus surface temperature trends. Int J Climatol 22(3):311–329. doi: 10.1002/joc.740 CrossRefGoogle Scholar
  43. Pradhanang SM, Anandhi A, Mukundan R, Zion MS, Pierson DC, Schneiderman EM, Matonse A, Frei A (2011) Application of SWAT model to assess snowpack development and streamflow in the Cannonsville watershed, New York, USA. Hydrological Processes n/a–n/a. doi: 10.1002/hyp.8171
  44. Rahman K, Maringanti C, Beniston M, Widmer F, Abbaspour K, Lehmann A (2013) Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: the upper Rhone River watershed case in Switzerland. Water Resour Manag 27(2):323–339. doi: 10.1007/s11269-012-0188-9 CrossRefGoogle Scholar
  45. Raneesh KY, Santosh GT (2011) A study on the impact of climate change on streamflow at the watershed scale in the humid tropics. Hydrol Sci J 56(6):946–965CrossRefGoogle Scholar
  46. Salzmann N, Mearns LO (2012) Assessing the performance of multiple regional climate model simulations for seasonal mountain snow in the upper Colorado River Basin. J Hydrometeorol 13(2):539–556. doi: 10.1175/2011jhm1371.1 CrossRefGoogle Scholar
  47. Schaefli B, Hingray B, Musy A (2007) Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties. Hydrol Earth Syst Sci 11(3):1191–1205CrossRefGoogle Scholar
  48. Schoetter R, Hoffmann P, Rechid D, Schluenzen KH (2012) Evaluation and bias correction of regional climate model results using model evaluation measures. J Appl Meteorol Climatol 51(9):1670–1684. doi: 10.1175/jamc-d-11-0161.1 CrossRefGoogle Scholar
  49. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmospheres 106(D7):7183–7192CrossRefGoogle Scholar
  50. van Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio A, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324(1–4):10–23. doi: 10.1016/j.jhydrol.2005.09.008 CrossRefGoogle Scholar
  51. Wang X, Melesse AM (2005) Evaluation of the swat model’s snowmelt hydrology in a northwestern Minnesota watershed. Trans Asae 48(4):1359–1376CrossRefGoogle Scholar
  52. Zhang XS, Srinivasan R, Debele B, Hao FH (2008) Runoff simulation of the headwaters of the Yellow River using the SWAT model with three snowmelt algorithms. J Am Water Resour Assoc 44(1):48–61. doi: 10.1111/j.1752-1688.2007.00137.x CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Kazi Rahman
    • 1
    • 2
    Email author
  • Christophe Etienne
    • 1
  • Ana Gago-Silva
    • 1
  • Chetan Maringanti
    • 3
  • Martin Beniston
    • 1
  • Anthony Lehmann
    • 1
    • 2
    • 4
  1. 1.Institute for Environmental SciencesUniversity of GenevaCarougeSwitzerland
  2. 2.Forel InstituteUniversity of GenevaVersoixSwitzerland
  3. 3.Risk Modeling UnitZurich Financial Services Ltd.ZurichSwitzerland
  4. 4.United Nations Environment Programme, Division of Early Warning and AssessmentGlobal Resource Information Database Geneva, International Environment HouseChâtelaineSwitzerland

Personalised recommendations