Does the Danube exist?Does the Danube exist?Versions of reality given byVersions of reality given byvarious climate models and various climate models and

climatological datasets climatological datasets

Valerio LucariniValerio LucariniUniversity of Camerino & CINFAIUniversity of Camerino & CINFAI

soonsoonUniversity of BolognaUniversity of Bologna

IntroIntro 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: 2006-2007,

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: http://www.hydrocare-cadses.net

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:Two words on the project:

Hydrological cycle of the Hydrological cycle of the CADSES regionsCADSES regions

HYDROCAREHYDROCAREProject part-financed by the EUProject part-financed by the EU

Basic InformationBasic InformationProgramme:Programme:INTERREG IIIB – CADSES – 3rd callINTERREG IIIB – CADSES – 3rd call

Priority/MeasurePriority/Measure4 - 4 - Environment protection, resource management Environment protection, resource management and risk preventionand risk prevention4.3 - 4.3 - Promoting integrated water management and prevention Promoting integrated water management and prevention of floodsof floods

Start/End date:Start/End date:01/01/2006-31/12/200701/01/2006-31/12/2007

Total Budget:Total Budget:2.466.200,00 € (ERDF Cofinancing: 1.441.625,00 €)2.466.200,00 € (ERDF Cofinancing: 1.441.625,00 €)

Lead PartnerLead PartnerNational Consortium of Universities for the National Consortium of Universities for the Physics of Atmospheres and Hydrospheres – CINFAI (IT)Physics of Atmospheres and Hydrospheres – CINFAI (IT)

PartnershipPartnership11 Project Partners from 6 countries (IT, GR, DE, SK, PL, RO)11 Project Partners from 6 countries (IT, GR, DE, SK, PL, RO)

Project PartnersProject Partners1.1. National Consortium of Universities for the National Consortium of Universities for the

Physics of the Atmospheres and of the Physics of the Atmospheres and of the Hydrospheres (Hydrospheres (ITIT))

2.2. National Agency for the Environmental National Agency for the Environmental Protection and Technical Services - Protection and Technical Services - Department of Internal and Marine Waters Department of Internal and Marine Waters Protection (Protection (ITIT))

3.3. Marche Region - Public Works Design Service Marche Region - Public Works Design Service ((ITIT))

4.4. University of Camerino - Department of Earth University of Camerino - Department of Earth Sciences (Sciences (ITIT))

5.5. Autonomous Province of Trento - Service for Autonomous Province of Trento - Service for Hydraulic Works (Hydraulic Works (ITIT))

6.6. National Technical University of Athens - National Technical University of Athens - Department of Water Resources, School of Department of Water Resources, School of Civil Engineering (Civil Engineering (GRGR))

7.7. Municipality of Kefalonia (Municipality of Kefalonia (GRGR))

8.8. Potsdam Institute for Climate Impact Research Potsdam Institute for Climate Impact Research ((DEDE))

9.9. Slovak Hydrometeorological Institute (Slovak Hydrometeorological Institute (SKSK))

10.10. Institute of Meteorology and Water Institute of Meteorology and Water Management - Branch of Wroclaw (Management - Branch of Wroclaw (PLPL))

11.11. National Institute of Hydrology and Water National Institute of Hydrology and Water Management (Management (RORO))

11 PPs, 6 countries•Local Authorities•Technical Services•Scientific Institutions

ObjectivesObjectives Development of an integrated view of the water resource Development of an integrated view of the water resource

management, bridging the evaluation of the water resources management, bridging the evaluation of the water resources of the CADSES area with the study of the large and basin-of the CADSES area with the study of the large and basin-scale hydrological cycle. scale hydrological cycle.

Development of effective, internationally shared tools for Development of effective, internationally shared tools for

public and private institutions for the correct management of public and private institutions for the correct management of the water resources as well as for planning future the water resources as well as for planning future development of the CADSES area.development of the CADSES area.

Development of set of standards at European level for the Development of set of standards at European level for the collection, evaluation, storage and interpretation of the hydro-collection, evaluation, storage and interpretation of the hydro-meteorological data, with particular regard to extreme events meteorological data, with particular regard to extreme events of great potential impact on the welfare of the population and of great potential impact on the welfare of the population and on the state of the environment.on the state of the environment.

Work PackagesWork PackagesN. NAME RESPONSIBLE PP1 Project set-up and management PP1 - CINFAI (IT)2 Reconstruction of the Hydrometeorological cycle PP8 - PIK (DE)3 Hydrological analysis and design PP2 – APAT (IT)4 Water resources management PP6 – NTUA (GR)5 Dissemination and training PP1 – CINFAI (IT)

3 kinds of activities3 kinds of activities In-depth activities In-depth activities In-extension activitiesIn-extension activities Outreach activitiesOutreach activities

Reconstruction of the Reconstruction of the Hydrological cycleHydrological cycle

NCEP and ERA 40 NCEP and ERA 40 reanalysesreanalyses

IPCC 4AR global climate IPCC 4AR global climate models simulationsmodels simulations

Regional Climate Models Regional Climate Models simulations (e.g. PIK)simulations (e.g. PIK)

Observations (local and Observations (local and remote)remote)

Mostly PP1, PP2, PP5, Mostly PP1, PP2, PP5, PP6, PP8 will be involvedPP6, PP8 will be involved

Hydrological data on basins and sub-basinsHydrological data on basins and sub-basins

BratislavaBratislavadistrictdistrict(2053 km(2053 km22))

Danubian sub-basin in Slovakia (PP9)Danubian sub-basin in Slovakia (PP9)

Main river basins in Poland (PP10)Main river basins in Poland (PP10)

Watersheds in Kefalonia (PP6,7)Watersheds in Kefalonia (PP6,7)

Seawater-freshwater interaction Seawater-freshwater interaction near estuaries (PP3)near estuaries (PP3)

Erosion and Erosion and Badlands (PP4)Badlands (PP4)

Back to the Danube. BasicsBack 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 807 000 km2 – Period of 30 years: 01.01.1961 – 31.12.1990– Calculation of integral values (over the area, using GIS tools) of: P, E, R,

Precipitation – Evaporation (hydrological balance), (P - E)

Tsea

TATATA

DRdBdEPd Mass conservation:

Courtesy of CIA

Regional CM (PRUDENCE 5 FP)Regional CM (PRUDENCE 5 FP)Code Model Driving data Institute Country Data lat x lon VL

CLM GKSS

CLM HadAM3H A2

GKSS Research Centre Geesthacht Germany Daily 0.50° x 0.50° 20

HIRHAM METNO

HIRHAM HadAM3H A2

Norwegian Meteorological Institute Norway Daily 0.46° x 0.46° 19

CHRM ETH

CHRM HadAM3H A2

ETH - Swiss Federal Institute of Technology

Switzerland Daily 0.50° x 0.50° 20

PROMES UCM

PROMES HadAM3H A2

UCM - Universidad Complutense de Madrid

Spain Daily 0.50° x 0.50° 26

RACMO KNMI

RACMO HadAM3H A2

KNMI - The Royal Netherlands Meteorological Institute, University of

Reading

Netherlands,UK

Daily 0.44° x 0.44° 31

REMO REMO HadAM3H A2

MPI - Max-Planck-Institute for Meteorology

Germany Daily 0.50° x 0.50° 19

SHMI25 RCAO – high resolution

HadAM3H A2

SMHI – Swedish Meteorological and Hydrological Institute

Sweden Daily 0.22° x 0.22° 59

SHMI50 RCAO HadAM3H A2

SMHI – Swedish Meteorological and Hydrological Institute

Sweden Daily 0.44° x 0.44° 24

DMI12 HIRHAM – extra high res.

HadAM3H A2

DMI - Danish Meteorological Institute Denmark Monthly 0.15° x 0.15° 19

DMI25 HIRHAM – high resolution

HadAM3H A2

DMI - Danish Meteorological Institute Denmark Daily 0.22° x 0.22° 19

DMI50 HIRHAM HadAM3H A2

DMI - Danish Meteorological Institute Denmark Daily 0.44° x 0.44° 19

ICTP ICTP –RegCM

HadAM3H A2

ICTP The Abdus Salam Intl. Centre for Theoretical Physics

Italy Daily 0.44° x 0.44° 23

Other data (Verification)Other data (Verification)

Code Dataset Institute Country Available data

lat x lon Levels

ERA40 ERA-40, T159 resolution – Reamalyses

ECMWF –European Center for Medium-Range Weather Forecast

UK 4XDaily 2.5° x 2.5° 60

NCEP-NCAR

NCEP-NCAR - Reanalyses

National Center for Environmental Prediction – National Center for Atmospheric Research

USA 4XDaily 1.905° x 1.875°

28

HadAM3 HadAM3H model– A2 scenario (forced by observed SST and sea ice)

Hadley Centre for Climate Change - Met Office UK Daily 1.25ºx1.875º 19

Obs. Disc.

Danube discharge at Ceatal Izmail station

Global Runoff Data Center Germany Monthly

Est. Obser.

Danube basin runoff reconstructed as in Hagemann et al. (2004)

Global Runoff Data Center Germany Monthly

Data GriddingData GriddingVoronoi Polygon

Statistics of the Yearly time seriesStatistics of the Yearly time series

Balance (Precipitation – Evaporation)Balance (Precipitation – Evaporation) PrecipitationPrecipitation EvaporationEvaporation RunoffRunoff

Mean vs. VariabilityMean vs. Variability

ERA-40

NCEP

High

Med

Low

136600 smDMAXT

μ

σ

P vs. EP vs. E

μ(E)

μ(P)

136600 smDMAXT

ERA-40

NCEP

Med

Low

High

Correlation with Driving AGCM (1)Correlation with Driving AGCM (1)Precip EvapPrecip Evap

MODELSMODELS C(P,P)C(P,P) C(E,E)C(E,E)CLM_GKSS_germanyCLM_GKSS_germany 0,910,91 0,660,66

HIRHAM_METNO_norwayHIRHAM_METNO_norway 0,900,90 0,530,53CHRM_ETH_swissCHRM_ETH_swiss 0,870,87 0,700,70PROMES_UCM_spainPROMES_UCM_spain 0,870,87 -0,16-0,16RACMO_KNMI_netherlandRACMO_KNMI_netherland 0,930,93 0,710,71REMO_germanyREMO_germany 0,880,88 0,720,72SMHI_25_swedenSMHI_25_sweden 0,840,84 0,750,75SMHI_50_swedenSMHI_50_sweden 0,890,89 0,790,79DMI_12_denmarkDMI_12_denmark 0,850,85 0,730,73DMI_25_denmarkDMI_25_denmark 0,870,87 0,760,76DMI_50_denmarkDMI_50_denmark 0,800,80 0,750,75ICTP_italyICTP_italy 0,840,84 0,660,66

P-E FeedbackP-E FeedbackP vs EP vs E   

DRIVING DATADRIVING DATA 0,900,90CLM_GKSS_germanyCLM_GKSS_germany 0,810,81HIRHAM_METNO_norwayHIRHAM_METNO_norway 0,580,58CHRM_ETH_swissCHRM_ETH_swiss 0,840,84PROMES_UCM_spainPROMES_UCM_spain 0,040,04RACMO_KNMI_netherlandRACMO_KNMI_netherland 0,690,69REMO_germanyREMO_germany 0,820,82SMHI_25_swedenSMHI_25_sweden 0,870,87SMHI_50_swedenSMHI_50_sweden 0,890,89DMI_12_denmarkDMI_12_denmark 0,910,91DMI_25_denmarkDMI_25_denmark 0,900,90DMI_50_denmarkDMI_50_denmark 0,920,92ICTP_italyICTP_italy 0,800,80NCEP/NCARNCEP/NCAR 0.720.72ERA40ERA40 -0.41-0.41

Correlation with Driving AGCM (2)Correlation with Driving AGCM (2)P-E=BP-E=B

MODELSMODELS C(B,B)C(B,B)CLM_GKSS_germanyCLM_GKSS_germany 0.920.92

HIRHAM_METNO_norwayHIRHAM_METNO_norway 0.920.92CHRM_ETH_swissCHRM_ETH_swiss 0.890.89PROMES_UCM_spainPROMES_UCM_spain 0.900.90RACMO_KNMI_netherlandRACMO_KNMI_netherland 0.930.93REMO_germanyREMO_germany 0.860.86SMHI_25_swedenSMHI_25_sweden 0.850.85SMHI_50_swedenSMHI_50_sweden 0.890.89DMI_12_denmarkDMI_12_denmark 0.860.86DMI_25_denmarkDMI_25_denmark 0.850.85DMI_50_denmarkDMI_50_denmark 0.780.78ICTP_italyICTP_italy 0.850.85

Runoff vs BalanceRunoff vs Balance

ERA-40

NCEP

High

Med

Low

μ(R)

μ(B) Not good!

Seasonal CycleSeasonal Cycle

Balance (Precipitation – Evaporation)Balance (Precipitation – Evaporation) PrecipitationPrecipitation EvaporationEvaporation RunoffRunoff

PRECIPITATION

Max

Max

Min Min

100%

EVAPORATION

Max

Min

100%

Min

Negative balance

BALANCEMin

100%

Max

Negative balance

RUNOFFMinMax

Amplitude

Phase

Geographical limits to water transport?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 ..Sligthly tragically ..

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