1. HYDROLOGICAL BALANCE OF THE DANUBE RIVER BASIN: AN INTERCOMPARISON STUDY Lucarini, V. (1,2), Kriegerova, I. (2), Danihlik, R. (2), Speranza, A. (1,2) 1. 1. Department of Mathematics and Computer Science, University of Camerino, Camerino, Italy 2. 2. CINFAI Unit of Camerino, Camerino, Italy Contact: valerio.lucarini@unicam.itvalerio.lucarini@unicam.it http://www.hydrocare-cadses.net Ljubljana, Slovenia, EMS-ECAC 2006

2. 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: 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.

3. 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 km 2 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) Mass conservation: Courtesy of CIA

4. Data Gridding Thiessen Polygon

5. Mean vs. Variability ERA-40 NCEP High Med Low

6. Correlation with Driving GCM Precip Evap MODELSDr.dataDr.data CLM_GKSS_germany0,910,66 HIRHAM_METNO_nor way 0,900,53 CHRM_ETH_swiss0,870,70 PROMES_UCM_spain0,87-0,16 RACMO_KNMI_netherl and 0,930,71 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_italy0,840,66P-EMODELSDr.dataObs.data CLM_GKSS_germany0.920.02 HIRHAM_METNO_nor way 0.920.05 CHRM_ETH_swiss0.890.08 PROMES_UCM_spain0.900.06 RACMO_KNMI_netherl and 0.930.11 REMO_germany0.860.15 SMHI_25_sweden0.85-0.08 SMHI_50_sweden0.890.08 DMI_12_denmark0.86-0.01 DMI_25_denmark0.85-0.01 DMI_50_denmark0.780.14 ICTP_italy0.850.19

7. P-E vs Runoff

8. Evaporation vs. Precipitation

9. Precip-Evap Feedback Precip vs Evapor 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

10. Seasonal cycle Precipitation – equivalent runoff (m 3 s -1 ) Max Min 100%

11. Seasonal cycle Evaporation – equivalent negative runoff (m 3 s -1 ) Negative balance Anticipated Delayed 100%

12. Seasonal cycle Runoff (m 3 s -1 ) Amplitude Phase Cycles of RCMS are too large and anticipated!! KNMI model

13. Danube and Mediterranean Sea  On the average, the Mediterranean Sea is a basin of “concentration” for oceanic water: the overall freshwater balance is negative → formation of saline water running into the Atlantic through the Gibraltar strait;  Excess of evaporation with respect to precipitation → the Mediterranean Sea is a source of water vapor, thus fuelling precipitations also elsewhere;  Excluding the Mediterranean coastal areas, the largest impact in terms of precipitation of the Mediterranean water vapor is in the regions downwind of the Sea, thus including especially the Balkans, central Europe, to Ukraine → Danube, Dniester depend heavily on precipitated water of Mediterranean origin, and about the same applies for Elbe, Oder, and Vistula;  Relevance of the Alps and of the Mediterranean waters in modifying and enhancing the storms of Atlantic origin for extreme precipitative events in Europe See, e.g. the "Genoa cyclone", a low-pressure system traveling from the Atlantic southeast to the Mediterranean and then turning northeast (enriched with water evaporating from the warm sea surface) across the Alps toward Central Europe See, e.g. the "Genoa cyclone", a low-pressure system traveling from the Atlantic southeast to the Mediterranean and then turning northeast (enriched with water evaporating from the warm sea surface) across the Alps toward Central Europe

14. 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