Does the Danube exist? Versions of reality given by various climate models and climatological datasets Valerio Lucarini University of Camerino & CINFAI soon University of Bologna

Intro   Territorial planning and management require the development of deep knowledge concerning some key hydro meteorological and hydrological processes: – –Water is central to human and environmental welfare; – –About 70% of all natural disasters in the world are caused by hydro- meteorological events   HYDROCARE (INTERREG IIIB – CADSES): Time: , Budget: 2.5 M€; Partnership 11 institutions from 6 countries (Italy, Germany, Greece, Poland, Romania, and Slovakia). Lead Partner, CINFAI, Italy – –Mission: Analysis of the hydrological cycle of the CADSES area by adopting an integrated and multidisciplinary approach. – –Web-site:   The assessment of the reliability of the current RCMs for the climatology of the water balance (mean value & variability), of the basin of the Danube river is crucial, because of its relevance at social, economical and environmental level. This the reason for its centrality in the project HYDROCARE.

Two words on the project: Hydrological cycle of the CADSES regions HYDROCARE Project part-financed by the EU

Basic Information Programme: INTERREG IIIB – CADSES – 3rd call Priority/Measure 4 - Environment protection, resource management and risk prevention Promoting integrated water management and prevention of floods Start/End date: 01/01/ /12/2007 Total Budget: ,00 € (ERDF Cofinancing: ,00 €) Lead Partner National Consortium of Universities for the Physics of Atmospheres and Hydrospheres – CINFAI (IT) Partnership 11 Project Partners from 6 countries (IT, GR, DE, SK, PL, RO)

Project Partners 1.National Consortium of Universities for the Physics of the Atmospheres and of the Hydrospheres (IT) 2.National Agency for the Environmental Protection and Technical Services - Department of Internal and Marine Waters Protection (IT) 3.Marche Region - Public Works Design Service (IT) 4.University of Camerino - Department of Earth Sciences (IT) 5.Autonomous Province of Trento - Service for Hydraulic Works (IT) 6.National Technical University of Athens - Department of Water Resources, School of Civil Engineering (GR) 7.Municipality of Kefalonia (GR) 8.Potsdam Institute for Climate Impact Research (DE) 9.Slovak Hydrometeorological Institute (SK) 10.Institute of Meteorology and Water Management - Branch of Wroclaw (PL) 11.National Institute of Hydrology and Water Management (RO) 11 PPs, 6 countries Local Authorities Technical Services Scientific Institutions

Objectives  Development of an integrated view of the water resource management, bridging the evaluation of the water resources of the CADSES area with the study of the large and basin- scale hydrological cycle.  Development of effective, internationally shared tools for public and private institutions for the correct management of the water resources as well as for planning future development of the CADSES area.  Development of set of standards at European level for the collection, evaluation, storage and interpretation of the hydro- meteorological data, with particular regard to extreme events of great potential impact on the welfare of the population and on the state of the environment.

Work Packages N.NAME RESPONSIBLE PP 1Project set-up and management PP1 - CINFAI (IT) 2Reconstruction of the Hydrometeorological cycle PP8 - PIK (DE) 3Hydrological analysis and design PP2 – APAT (IT) 4Water resources management PP6 – NTUA (GR) 5Dissemination and training PP1 – CINFAI (IT) 3 kinds of activities  In-depth activities  In-extension activities  Outreach activities

Reconstruction of the Hydrological cycle  NCEP and ERA 40 reanalyses  IPCC 4AR global climate models simulations  Regional Climate Models simulations (e.g. PIK)  Observations (local and remote)  Mostly PP1, PP2, PP5, PP6, PP8 will be involved

Hydrological data on basins and sub-basins Bratislava district (2053 km 2 ) Danubian sub-basin in Slovakia (PP9) Main river basins in Poland (PP10) Watersheds in Kefalonia (PP6,7)

 Seawater-freshwater interaction near estuaries (PP3)  Erosion and Badlands (PP4)

Back to the Danube. Basics   Data sources: – –ERA-40 reanalysis data – –NCEP/NCAR reanalysis data – –Regional Climate Models Control data – Prudence project – –Global Runoff Data Center – GRDC – –Met Office, Hadley Center, UK (driving data)   Daily values of: – –Precipitation (P) – –Evaporation (E) – –Runoff (R) – –Observed discharge data (GRDC)   Area of interest: – –Danube: length river 2850 Km, Area basin km 2 – –Period of 30 years: – – –Calculation of integral values (over the area, using GIS tools) of: P, E, R, Precipitation – Evaporation (hydrological balance), (P - E) Mass conservation: Courtesy of CIA

Regional CM (PRUDENCE 5 FP) CodeModelDriving dataInstituteCountryDatalat x lonVL CLM GKSS CLMHadAM3H A2 GKSS Research Centre GeesthachtGermanyDaily0.50° x 0.50°20 HIRHAM METNO HIRHAMHadAM3H A2 Norwegian Meteorological InstituteNorwayDaily0.46° x 0.46°19 CHRM ETH CHRMHadAM3H A2 ETH - Swiss Federal Institute of Technology SwitzerlandDaily0.50° x 0.50°20 PROMES UCM PROMESHadAM3H A2 UCM - Universidad Complutense de Madrid SpainDaily0.50° x 0.50°26 RACMO KNMI RACMOHadAM3H A2 KNMI - The Royal Netherlands Meteorological Institute, University of Reading Netherlands, UK Daily0.44° x 0.44°31 REMO HadAM3H A2 MPI - Max-Planck-Institute for Meteorology GermanyDaily0.50° x 0.50°19 SHMI25RCAO – high resolution HadAM3H A2 SMHI – Swedish Meteorological and Hydrological Institute SwedenDaily0.22° x 0.22°59 SHMI50RCAOHadAM3H A2 SMHI – Swedish Meteorological and Hydrological Institute SwedenDaily0.44° x 0.44°24 DMI12HIRHAM – extra high res. HadAM3H A2 DMI - Danish Meteorological InstituteDenmarkMonthly0.15° x 0.15°19 DMI25HIRHAM – high resolution HadAM3H A2 DMI - Danish Meteorological InstituteDenmarkDaily0.22° x 0.22°19 DMI50HIRHAMHadAM3H A2 DMI - Danish Meteorological InstituteDenmarkDaily0.44° x 0.44°19 ICTPICTP – RegCM HadAM3H A2 ICTP The Abdus Salam Intl. Centre for Theoretical Physics ItalyDaily0.44° x 0.44°23

Other data (Verification) CodeDatasetInstituteCountryAvailable data lat x lonLevels ERA40ERA-40, T159 resolution – Reamalyses ECMWF –European Center for Medium-Range Weather Forecast UK4XDaily2.5° x 2.5°60 NCEP- NCAR NCEP-NCAR - Reanalyses National Center for Environmental Prediction – National Center for Atmospheric Research USA4XDaily1.905° x 1.875° 28 HadAM3HadAM3H model– A2 scenario (forced by observed SST and sea ice) Hadley Centre for Climate Change - Met OfficeUKDaily1.25ºx1.875º19 Obs. Disc. Danube discharge at Ceatal Izmail station Global Runoff Data CenterGermanyMonthly Est. Obser. Danube basin runoff reconstructed as in Hagemann et al. (2004) Global Runoff Data CenterGermanyMonthly

Data Gridding Voronoi Polygon

Statistics of the Yearly time series  Balance (Precipitation – Evaporation)  Precipitation  Evaporation  Runoff

Mean vs. Variability ERA-40 NCEP High Med Low μ σ

P vs. E μ(E) μ(P) ERA-40 NCEP Med Low High

Correlation with Driving AGCM (1) Precip Evap MODELSC(P,P)C(E,E) CLM_GKSS_germany0,910,66 HIRHAM_METNO_norway0,900,53 CHRM_ETH_swiss0,870,70 PROMES_UCM_spain0,87-0,16 RACMO_KNMI_netherland0,930,71 REMO_germany0,880,72 SMHI_25_sweden0,840,75 SMHI_50_sweden0,890,79 DMI_12_denmark0,850,73 DMI_25_denmark0,870,76 DMI_50_denmark0,800,75 ICTP_italy0,840,66

P-E Feedback P vs E DRIVING DATA 0,90 CLM_GKSS_germany0,81 HIRHAM_METNO_norway0,58 CHRM_ETH_swiss0,84 PROMES_UCM_spain0,04 RACMO_KNMI_netherland0,69 REMO_germany0,82 SMHI_25_sweden0,87 SMHI_50_sweden0,89 DMI_12_denmark0,91 DMI_25_denmark0,90 DMI_50_denmark0,92 ICTP_italy0,80 NCEP/NCAR0.72 ERA

Correlation with Driving AGCM (2) P-E=B MODELSC(B,B) CLM_GKSS_germany0.92 HIRHAM_METNO_norway0.92 CHRM_ETH_swiss0.89 PROMES_UCM_spain0.90 RACMO_KNMI_netherland0.93 REMO_germany0.86 SMHI_25_sweden0.85 SMHI_50_sweden0.89 DMI_12_denmark0.86 DMI_25_denmark0.85 DMI_50_denmark0.78 ICTP_italy0.85

Runoff vs Balance ERA-40 NCEP High Med Low μ(R) μ(B) Not good!

Seasonal Cycle  Balance (Precipitation – Evaporation)  Precipitation  Evaporation  Runoff

PRECIPITATION Max Min 100%

EVAPORATION Max Min 100% Min Negative balance

BALANCE Min 100% Max Negative balance

RUNOFF Min Max Amplitude Phase

Geographical limits to water transport?   The Mediterranean Sea play a relevant role in the hydrology of the Danubian region both for the mean state and the extreme events.   The largest impact in terms of precipitation of the Mediterranean water vapor is in the regions downwind of the Sea, thus including Central-Eastern Europe.   The Danube depends almost entirely on precipitated water of Mediterranean origin. Similarly, a very strong Mediterranean influence exists for Elbe, Oder, and Vistula, since they or their main tributaries originate from mountains (Carpatians, Sudety, Erzebirge) which catalyze the precipitation of Mediterranean water

  Most of the major floodings occurred in central-eastern Europe are due to a typical Mediterranean meteorological pattern, the Genoa cyclone.

Conclusions   NCEP and ECMWF Reanalyses are largely inadequate for representing the hydrology of the Danube basin;   RCMs feature large discrepancies for the climatology of water balance: most underestimate the discharge of the Danube; they act as differently parameterized downscaling of the driving GCM;   Only few models (METNO, SHMI, KNMI) provide estimates which are consistent with the observed discharge values of the Danube at its Delta;   Most RCMs have a large and anticipated mean seasonal cycle (small damping); problems in representation of snow depletion: KNMI model agrees remarkably well with observed data;   The agreement between mean integrated P-E and runoff is not perfect;   The considered approach relies on the mass conservation principle at the air-land interface and bypasses the details of soil modelling and will be used for analyzing climate change scenarios.   Analysis of meteorological processes and of transport of water vapor of Mediterrabean origin is crucial – –Meteorological Hydrological Cycle, not Geographical Hydrological Cycle

Sligthly tragically..  While the RCMs actually act as strongly constrained downscaling models, at the same time, once outputs are upscaled via spatial integration procedure on a finite - not too large, not too small domain, as discussed earlier - domain, information may be, and actually in most cases is, degraded.