1. 15 december 2009 Usefulness of GCM data for predicting global hydrological changes Frederiek Sperna Weiland Rens van Beek Jaap Kwadijk Marc Bierkens

2. 15 december 2009 Overview Validating GCM produced climate datasets on their usability for hydrological studies Modelling hydrological effects of climate change and distinguishing signal from noise Validating bias-corrected GCM datasets on their usability for hydrological studies

3. 15 december 2009 Hydrological impact studies GCM data hydrological model modelled discharges statistical / dynamical downscaling statistical / dynamical downscaling hydrological model hydrological model Bias- correction

4. 15 december 2009 Background-GCM General Circulation Model (GCM) Global Climate Model: Energy balance Resolution: 1.875 – 3.75 9 - 26 layers Forcings: - Greenhouse gas - Aerosols No predictions on day to day base Wikipedia, 2009

5. 15 december 2009 What has been said about GCMs…. GCM data can show large deviations from reality, especially for precipitation (Covey, 2003) Differences between GCM results are large and can be larger than differences between emission scenarios (Arnell, 2003) The model mean might show the best results (Murphy, 2004; Covey, 2003)

6. 15 december 2009 Datasets-Climate model data Intergovernmental Panel for Climate Change (IPCC): http://www.ipcc-data.org/ Provides data on a monthly timestep PCMDI data portal: Program for Climate Model Diagnosis and Intercomparison https://esg.llnl.gov:8443/index.jsp Provides data on a daily timestep

7. 15 december 2009 Datasets-Multiple AOGCM’s ModelInstituteCountryAcronym BCM2.0Bjerknes Centre for Climate ResearchNorwayBCCR CGCM3.1Canadian Centre for Climate modelling and Analysis CanadaCCCMA CGCM2.3.2Meteorological Research InstituteJapanCGCM CSIRO-Mk3.0Commonwealth Scientific and Industrial Research Organisation AustraliaCSIRO ECHAM5Max Planck InstituteGermanyECHAM ECHO-GFreie Universität BerlinBerlinECHO GFDLCM 2.0Geophysical Fluid Dynamics CentreUSAGFDL GISS ERGoddard institute for Space StudiesUSAGISS IPSL CM4Institute Pierre Simon LaplaceFranceIPSL MIROC3.2Center of Climate System ResearchJapanMIROC NCAR PCMINational Center for Atmospheric ResearchUSANCAR HADGEM1Met Office’s Hadley Centre for Climate Prediction UKHADGEM

8. 15 december 2009 Parameters - Precipitation - Temperature Calculation of potential reference evapotranspiration Penman-Monteith: - Incominging and outgoing shortwave radiation - Incoming and outgoing longwave radiation - Airpressure - Windspeed - Temperature and minimum temperature Calculation of potential reference evapotranspiration Blaney-Criddle: - Temperature

9. 15 december 2009 Reference dataset -CRU / ERA40 CRU: Climate Reasearch Unit, University of East-Anglia Timeseries with monthly values 1901-1995 ERA40: ECMWF Daily values 1957 – 2002 Validation period: 1961 - 1990 - Downscaling CRU data to daily values based on ERA40 - Projection on 0.5 degrees model grid

10. 15 december 2009 Discharge data GRDC - Global Runoff Data Centre: - Monthly discharges for 19 large rivers

11. 15 december 2009 PCR-GLOBWB (Beek, 2007) Global distributed hydrological model Daily time-step 0.5 degrees resolution (360*720) Sub-grid cell parameterisation Contains three soil layers, lakes, rivers, snow, vegetation Solves water balance per cell Direction of surface runoff calculated with drainage direction map River discharge calculated with routing scheme based on kinematic wave Natural water availability – little antropoghenic influences included

12. 15 december 2009 FEWS 12x GCM input CRU/ERA FEWS-World: Spatial/temporal interpolation Unit conversion Calculation of evaporation PCRGLOB-WB model run 13 x calculated: - Channel flow - Soil moisture - Snow cover - Actual evaporation

13. 15 december 2009 FEWS-World system

14. 15 december 2009 FEWS-World system

15. 15 december 2009 First step: Validate models PCR-GLOBWB is run for period 1961-1990 with: - data from all individual GCMs - reference meteo dataset (CRU/ERA-40) 30-year average statistics are derived for the GCM runs and reference run and observations (GRDC) GCM statistics are compared with CRU/ERA-40 and observations

16. 15 december 2009 Hydrological regime - Brahmaputra

17. 15 december 2009 Hydrological regime - Brahmaputra

18. 15 december 2009 Hydrological regime - MacKenzie

19. 15 december 2009 Hydrological regime - Rhine

20. 15 december 2009 GCM discharge compared with CRU Relative 30 year mean discharge = (Q GCM – Q CRU ) / Q CRU

21. 15 december 2009 Top 5 per catchment - mean discharge

22. 15 december 2009 Modelling hydrological effects of climate change and distinguishing signal from noise

23. 15 december 2009 selected IPCC scenarios 20CM3: Control experiment A1B: Rapid economic growth with a peak in global population in mid 21st century followed by a population decline Fast introduction of efficient technologies Decrease of social and regional differences A2: Heterogeneous world with fragmented technological developments and large regional differences Continuous increase of CO2 emission Relative negative scenarios 2000-2006: observed emissions larger than estimated (Global Carbon Project, 2008) (IPCC, 2007)

24. 15 december 2009 Modeling change Relative change for ensemble of 12 GCMs: Mean discharge control experiment, period 1971-1990 Mean discharges future experiments A1B and A2, period 2081-2100

25. 15 december 2009 Global changes and model consistency Nr. of models significant and consistent change A1B A2

26. 15 december 2009 Changes in river regimes

27. 15 december 2009 Continental change Freshwater discharge increases for all continents Freshwater inflow to oceans only decreases for Mediteranean see Large uncertainty amongst models

28. 15 december 2009 Conclusions GCM derived discharges show large deviations from observations and each other Multi-model ensembles provide a ‘relative good mean’ and give uncertainty information By quantifying significance and consistency of change, regions and catchments with high potential of hydrological change can be detected