• Andréasson, J., , Bergstroem S. , , Carlsson B. , , Graham L. P. , , and Lindstroem G. , 2004: Hydrological change-climate change impact simulations for Sweden. Ambio,33, 228–234.

  • Berg, P., , Feldmann H. , , and Panitz H.-J. , 2012: Bias correction of high resolution regional climate model data. J. Hydrol., 448–449, 8092.

    • Search Google Scholar
    • Export Citation
  • Berg, P., , Wagner S. , , Kunstmann H. , , and Schädler G. , 2013: High resolution regional climate model simulations for Germany: Part I—Validation. Climate Dyn.,40, 401–414, doi:10.1007/s00382-012-1508-8.

  • Beurton, S., , and Thieken A. H. , 2009: Seasonality of floods in Germany. Hydrol. Sci. J., 54, 6276.

  • Booij, M. J., 2005: Impact of climate change on river flooding assessed with different spatial model resolutions. J. Hydrol., 303, 176198.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., and Coauthors, 2007: Regional climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 847–940.

  • Dankers, R., , and Feyen L. , 2008: Climate change impact on flood hazard in Europe: An assessment based on high-resolution climate simulations. J. Geophys. Res.,113, D19105, doi:10.1029/2007JD009719.

  • Deser, C., , Phillips A. , , Bourdette V. , , and Teng H. , 2012: Uncertainty in climate change projections: The role of the internal variability. Climate Dyn., 38, 527546.

    • Search Google Scholar
    • Export Citation
  • Doms, G., , and Schättler U. , 2002: A description of the nonhydrostatic regional model LM. Part I: Dynamics and numerics. COSMO Rep., Deutscher Wetterdienst, Offenbach, Germany, 140 pp. [Available online at http://www.dwd.de/bvbw/generator/DWDWWW/Content/Forschung/FE1/Veroeffentlichungen/Download/LMdocu__I__dynamics__0211,templateId=raw,property=publicationFile.pdf/LMdocu_I_dynamics_0211.pdf.]

  • Duan, Q. Y., , Sorooshian S. , , and Gupta V. K. , 1992: Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res., 28, 10151031.

    • Search Google Scholar
    • Export Citation
  • Feldmann, H., , Schädler G. , , Panitz H.-J. , , and Kottmeier C. , 2013: Near future changes of extreme precipitation over complex terrain in Central Europe derived from high resolution RCM ensemble simulations. Int. J. Climatol., doi:10.1002/joc.3564, in press.

    • Search Google Scholar
    • Export Citation
  • Graham, L. P., , Andréasson J. , , and 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. Climatic Change, 81, 293307.

    • Search Google Scholar
    • Export Citation
  • Haude, W., 1952: Zur Möglichkeit nachträglicher Bestimmung der Wasserbeanspruchung durch die Luft und ihrer Nachprüfung an Hand von Tropfversuchen und Abflussmessungen. Ber. Dtsch. Wetterdienstes, 32, 2734.

    • Search Google Scholar
    • Export Citation
  • Haylock, M., , Hofstra N. , , Tank A. M. G. K. , , Klok E. J. , , Jones P. D. , , and New M. , 2008: A European daily highresolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res.,113, D20119, doi:10.1029/2008JD010201.

  • Hewitt, C. D., , and Griggs D. J. , 2004: Ensembles-based predictions of climate changes and their impacts (ENSEMBLES). ENSEMBLES Tech. Rep. 1, Met Office, United Kingdom, 5 pp. [Available online at http://ensembles-eu.metoffice.com/tech_reports/ETR_1_vn2.pdf.]

  • Huang, S., , Hattermann F. F. , , Krysanova V. , , and Bronstert A. , 2013: Projections of climate change impacts on river flood conditions in Germany by combining three different RCMs with a regional eco-hydrological model. Climatic Change,116, 631–663, doi:10.1007/s10584-012-0586-2.

  • Hurkmans, R., , Terink W. , , Uijlenhoet R. , , Torfs P. , , Jacob D. , , and Troch P. , 2010: Changes in streamflow dynamics in the Rhine basin under three high-resolution regional climate scenarios. J. Climate, 23, 679699.

    • Search Google Scholar
    • Export Citation
  • Kay, A. L., , Davies H. N. , , Bell V. A. , , and Jones R. G. , 2008: Comparison of uncertainty sources for climate change impacts: flood frequency in England. Climatic Change, 92, 4163.

    • Search Google Scholar
    • Export Citation
  • Kleinn, J., , Frei C. , , Gurtz J. , , Lüthi D. , , Vidale P. L. , , and Schär C. , 2005: Hydrologic simulations in the Rhine basin driven by a regional climate model. J. Geophys. Res.,110, D04102, doi:10.1029/2004JD005143.

  • Kruskal, W. H., , and Wallis W. A. , 1952: Use of ranks in one-criterion variance analysis. J. Amer. Stat. Assoc., 47, 583621.

  • Krysanova, V., , Muller-Wohlfeil D. , , and Becker A. , 1998: Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds. Ecol. Modell., 106, 261289.

    • Search Google Scholar
    • Export Citation
  • Kunstmann, H., , Schneider K. , , Forkel R. , , and Knoche R. , 2004: Impact analysis of climate change for an Alpine catchment using high resolution dynamic downscaling of ECHAM4 time slices. Hydrol. Earth Syst. Sci., 8, 10311045.

    • Search Google Scholar
    • Export Citation
  • Kunstmann, H., , Krause J. , , and Mayr S. , 2005: Inverse distributed hydrological modelling of Alpine catchments. Hydrol. Earth Syst. Sci., 2, 25812623.

    • Search Google Scholar
    • Export Citation
  • Leavesley, G. H., , Lichty R. W. , , Troutman B. M. , , and Saindon L. G. , 1983: Precipitation-runoff modeling system: User's manual. USGS Water-Resources Investigations Rep. 83-4238, 206 pp. [Available online at http://pubs.usgs.gov/wri/1983/4238/report.pdf.]

  • Lorenz, C., , and Kunstmann H. , 2012: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J. Hydrometeor., 13, 13971420.

    • Search Google Scholar
    • Export Citation
  • Marx, A., , Kunstmann H. , , Bárdossy A. , , and Seltmann J. , 2006: Radar rainfall estimates in an alpine environment using inverse hydrological modelling. Adv. Geosci., 9, 2529.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , Boer G. J. , , Covey C. , , Latif M. , , and Stouffer R. J. , 2000: The Coupled Model Intercomparison Project (CMIP). Bull. Amer. Meteor. Soc., 81, 313318.

    • Search Google Scholar
    • Export Citation
  • Menzel, L., , and Burger G. , 2002: Climate change scenarios and runoff response in the Mulde catchment (Southern Elbe, Germany). J. Hydrol., 267, 5364.

    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1975: Vegetation and the Atmosphere: Principles. Academic Press, 278 pp.

  • Morgenschweis, G., , zur Strassen G. , , Patzke S. , , and Schwanenberg D. , 2007: Estimation of the Impact of Possible Climate Change on the Management of the Reservoirs in the Ruhr Catchment Basin. Annual Rep. Ruhrwassermenge, Ruhrverband, Essen, Germany, 32–50. [Available online at http://www.talsperrenleitzentrale-ruhr.de/daten/internet/veroeffentlichungen/climate_change.pdf.]

  • Morrison, D., 1967: Multivariate Statistical Methods. McGraw-Hill, 338 pp.

  • Nash, J., , and Sutcliffe J. , 1970: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol., 10, 282290.

    • Search Google Scholar
    • Export Citation
  • Österle, H., , Gerstengarbe F. W. , , and Werner P. C. , 2006: Ein neuer meteorologischer Datensatz für Deutschland, 1951–2003. Tech. Rep., Potsdam Institut für Klimafolgenforschung, Potsdam, Germany, 3 pp.

  • Pakosch, S., 2011: Development of a fuzzy rule based expert system for flood forecasts within the meso-scale upper main basin. Ph.D. thesis, University of the Federal Armed Forces, 204 pp.

  • Piani, C., , and Haerter J. O. , 2012: Two dimensional bias correction of temperature and precipitation copulas in climate models. Geophys. Res. Lett.,39, L20401, doi:10.1029/2012GL053839.

  • Priestley, C. H. B., , and Taylor R. J. , 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 8192.

    • Search Google Scholar
    • Export Citation
  • Reifen, C., , and Toumi R. , 2009: Climate projections: Past performance no guarantee of future skill? Geophys. Res. Lett.,36, L13704, doi:10.1029/2009GL038082.

  • Roeckner, E., and Coauthors, 2003: The atmospheric general circulation model ECHAM5: Part 1: Model description. Rep. 349, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 127 pp. [Available online at http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf.]

  • Schulla, J., , and Jasper K. , 2007: Model Description WaSiM-ETH. Tech. Rep., 181 pp. [Available online at http://wasim.ch/downloads/doku/wasim/wasim_2007_en.pdf.]

  • Scinocca, J. F., , McFarlane N. A. , , Lazare M. , , Li J. , , and Plummer D. , 2008: The CCCma third generation AGCM and its extension into the middle atmosphere. Atmos. Chem. Phys. Discuss., 8, 78837930.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Smiatek, G., , Kunstmann H. , , Knoche R. , , and Marx A. , 2009: Precipitation and temperature statistics in high-resolution regional climate models: Evaluation for the European Alps. J. Geophys. Res.,114, D19107, doi:10.1029/2008JD011353.

  • Wagner, S., , Berg P. , , Schädler G. , , and Kunstmann H. , 2013: High resolution regional climate model simulations for Germany: Part II—Projected climate changes. Climate Dyn.,40, 415–427, doi:10.1007/s00382-012-1510-1.

  • Wilby, R. L., , and 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.

  • Wilby, R. L., , Hay L. E. , , Gutowski W. J. , , Arritt R. W. , , Takle E. S. , , Pan Z. , , Leavesley G. H. , , and Clark M. P. , 2000: Hydrological responses to dynamically and statistically downscaled climate model output. Geophys. Res. Lett., 27, 11991202.

    • Search Google Scholar
    • Export Citation
  • Xu, Z., , and Yang Z. L. , 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. J. Climate, 25, 62716286.

    • Search Google Scholar
    • Export Citation
  • Yang, D., , Ishida S. , , Goodison B. E. , , and Gunther T. , 1999: Bias correction of daily precipitation measurements for Greenland. J. Geophys. Res., 104 (D6), 61716181.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 147 147 34
Full Text Views 1 1 0
PDF Downloads 1 1 0

High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

View More View Less
  • 1 * Atmospheric Environmental Research, Institute for Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
  • | 2 Section 5.4–Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
  • | 3 Hydrology, Institute for Water Resources and River Basin Management (IWG), Karlsruhe Institute of Technology, Karlsruhe, Germany
  • | 4 Rossby Centre, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
  • | 5 ** Troposphere Research, Institute for Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
  • | 6 Atmospheric Environmental Research, Institute for Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, and Department of Geography, University of Augsburg, Augsburg, Germany
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

Current affiliation: Department of Geography, University of Augsburg, Augsburg, Germany.

Corresponding author address: Irena Ott, University of Augsburg, Department of Geography, Universitaetsstrasse 10, 86135 Augsburg, Germany. E-mail: irena.ott@geo.uni-augsburg.de

Abstract

The impact of climate change on three small- to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021–50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM–realization–RCM–HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

Current affiliation: Department of Geography, University of Augsburg, Augsburg, Germany.

Corresponding author address: Irena Ott, University of Augsburg, Department of Geography, Universitaetsstrasse 10, 86135 Augsburg, Germany. E-mail: irena.ott@geo.uni-augsburg.de
Save