Assessment of spatial-temporal dynamics of water fluxes in Germany under climate change

Transcription

1 Assessment of spatial-temporal dynamics of water fluxes in Germany under climate change Potsdam Institute for Climate Impact Research

2 Contents Introduction Method: SWIM model Data preparation Results Calibration and validation in different catchments Water components in Germany Climate scenarios Conclusion and outlook 2

3 Germany Location: central Europe Total Area:357,21 km 2 Climate: maritime and continental. Average annual temperature : +9 C prevailing winds: westerly. Precipitation: occurs in all seasons In the lowlands of northern Germany, <5-7 mm Upland areas: 7-15 mm The Alps: over 2 mm (Statistical Yearbook 28). Source: 3

4 Global change in Germany Historical trends in precipitation Average annual precipitation Trend in annual precipitation [mm] Data source: Wodinski, Acrobat-Dokument Gerstengarbe and Werner, PIK Potsdam Trend in precipitation: Less precipitation in east Germany 4

5 Global change in Germany Historical trends in temperature Average annual temperature Trend in annual temperature [ C] [ C] Trend in temperature Warmer in the whole Germany Data source: Wodinski, Gerstengarbe and Werner, PIK Potsdam Aim: to assess the dynamics of water fluxes in Germany under climate change. 5

6 Model Structure Climate change ECHAM5 Global climate Regional climate model STAT CCLM (Steppeler et al., 23) (Orlowsky et al., 28) Regional Global SWIM (Krysanova et al., 1998) 1) Water resources and extremes 2) Change in vegetation growth (crop production) 1 realisations Catchment 6

7 SWIM (Soil and Water Integrated Model) Climate: Global radiation, temperature, precipitation SWIM was developed in PIK, Potsdam based on SWAT-93 and MATSALU for climate and land use change impact studies (Krysanova et al., 1998) Hydrological cycle Soil profile A B C Upper ground water Lower ground water LAI Roots Land use pattern Vegetation/ Crop growth Bio mass Nitrogen cycle N-NO 3 N o-ac N o-st N res Phosphorus cycle P lab P m-ac P m-st & land management P org P res 7

8 Spatial disaggregation in SWIM Catchment Subbasins Hydrotopes Routing in river (water, N, P, sediments) Aggregation of lateral flows Water, N, P cycles vegetation growth 8

9 Study areas 5 main catchments in Germany Ems Weser Elbe including Czech Republic Upper Danube including the areas in Austria; Rhine basin in Germany, France and Luxemburg. Other catchments Oder Mass Coastal regions 9

10 Digital elevation map Resolution used: 25 m 1

11 Soil map 11

12 Landuse map Corine land cover (2) does not have information for Switzerland. 12

13 Subbasin map German standard subbasin map (UBA map) Source: Federal Environment Agency Czech subbasin map Source: T.G.M. Water Research Institute Other regions: generated by DEM map. Number of subbasins in Ems: 26 Weser: 536 Elbe: 2268 Donau: 796 Rhein:

14 Climate input - The observed climate data in Austria and Switzerland is now in processing. - STAR scenario is only available now for Germany!, DWD, ENSEMBLES project Daily high-resoluation gridded climate data set for Europe 14

15 Water discharge gauges Versen (Ems) Intschede (Weser) Neu- Darchau (Elbe) Calibration: Gauges: Versen, Intschede, Neu-Darchau, Hofkirchen, Frankfurt (Main). Period: Time step: daily Frankfurt A. M. (Main) Hofkirchen (Danube) Gage sources: GRDC & PIK Validation: Spatial: 24 additional gauges (most drainage areas 5 km 2 ) Ems: 3 Weser: 5 Elbe: 5 Donau: 5 Rhein: 6 Temporal: Time step: daily Evaluation of results: Nash-Sutcliffe efficiency Deviation of water discharges 15

16 Calibration and validation at the 5 main gauges Q (m3/s) Q (m3/s) Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan Intschede (Weser) Versen (Ems) 16 Q observed Q simulated Q observed Q simulated Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-9 Calibration period Efficiency:.9 Deviation: % Validation period Efficiency:.88 Deviation: -4% Calibration period Efficiency:.87 Deviation: % Validation period Efficiency:.86 Deviation: -5%

17 Calibration and validation at the 5 main gauges Q (m3/s) Q (m3/s) Frankfurt A. M. (Main) Q observed Q simulated Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-9 Neu-Darchau (Elbe) 4 Q observed Q simulated Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-9 Calibration period Efficiency:.8 Deviation: 3% Validation period Efficiency:.78 Deviation : 4% Calibration period Efficiency:.86 Deviation: % Validation period Efficiency:.86 Deviation: -1% 17

18 Calibration and validation at the 5 main gauges Q (m3/s) Hofkirchen (Danube) (Donau) Q observed Q simulated Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-9 Calibration period Efficiency:.83 Deviation : % Validation period Efficiency:.82 Deviation : -3% Snow melting from Alps observed simulated For the 5 calibration and validation gauges: m 3 /s 8 Calibration period Validation period 4 Efficiency:.8.9 Deviation: % - 3% Efficiency: Deviation: -5% - 4% Average day

19 Spatial validation at different gauges River basin Gauges Nashefficiency Deviation of water balance Ems Dalum.83 3% Rheine.78 1% Greven.87 4% Weser Voltho.86 % Guntershausen (Fulda).57 5% Letzter Heller (Werra).71 4% Marklendorf (Aller).76 9% Schwarmstedt (Leine).68-3% Donau Achleiten.74 6% Pfelling.79 1% Passau Ingling (Inn).61 12% Burghausen (Salzach, Inn).56-3% Donauwoerth.75-2% Elbe Havelberg (Havel).43-31% Calbe-Grizehne (Saale).74-4% Laucha (Unstrut).6-13% Bad Dueben (Mulde).59-2% Schoena.6 3% Rees.83 6% Rhein Andernach.81 5% Rocknau (Neckar).75-8% Trier (Mosel).21 57% Maxau.84-4% Schermbeck (Lippe).78-5% Relative frequency Havel: lowland + mining activity French part Havel CZ and French part Deviation of water balance(%) Conclusion The water discharges were well simulated at most of the intermediate gauges Due to lack of climate data outside Germany and human interference of natural hydrological, the hydrographs are difficult to reproduce Relative frequency Nash-efficiency 19

20 Average annual evaportranspiration distributions ( ) Estimation in Hydrological Atlas of Germany (HAD, 2) Simulation result from SWIM

21 Average annual total runoff distributions ( ) Estimation in Hydrological Atlas of Germany (HAD, 2) Simulation result from SWIM

22 Average annual groundwater recharge distributions ( ) Estimation in Hydrological Atlas of Germany (HAD, 2) Simulation result from SWIM

23 Regional climate model STAR Difference of the annual average precipitation (a), temperature (b) medium realization (251-26) reference ( ) (a) (b) Source: Deutschlanddatensatz, EWD, PIK, 28 Differences [ C] Differences [mm] Less Precipitation in east and south Warmer in the whole Germany 23

24 Seasonal changes in river discharges in catchment Ems (gauge Versen) Versen (Ems) Discharge in m 3 /s realizations included 8 realizations included Medium realization simulated Average day m 3 /s observed simulated Discharge in m 3 /s realizations included 8 realizations included Medium realization simulated Average day Average day

25 Seasonal changes in river discharges in catchment Weser (gauge Intschede) Intschede (Weser) Discharge in m 3 /s realizations included 8 realizations included Medium realization simulated Average day m 3 /s observed simulated Discharge in m 3 /s realizations included 8 realizations included Medium realization simulated Average day Average day

26 Seasonal changes in river discharges in catchment Saale (gauge Calbe-Grizehne) Calbe- Grizehne (Saale) Discharge in m 3 /s realizations included 8 realizations included Medium realization simulated Average day m 3 /s observed simulated Discharge in m 3 /s realizations included 8 realizations included Medium realization simulated Average day Average day

27 Seasonal changes in river discharges in catchment Main (gauge Frankfurt A. M.) Frankfurt A. M. (Main) Discharge in m 3 /s realizations included 8 realizations included Medium realization Reference Average day m 3 /s observed simulated Discharge in m 3 /s realizations included 8 realizations included Medium realization Reference Average day Average day

28 Seasonal changes in river discharges in catchment Donau (gauge Hofkirchen) Discharge in m 3 /s Hofkirchen (Danube) 1 realizations included 8 realizations included Medium realization Reference Average day m 3 /s observed simulated Discharge in m 3 /s realizations included 8 realizations included Medium realization Reference Average day Average day

29 Average annual changes in water components in Germany Components in scenario (251 26) components in reference ( ) (a) actual evapotranspiration (b) runoff generation 29

30 Conclusion SWIM is a powerful tool to simulate hydrological cycles in the large basins. The climate scenarios estimated that less water in streams in South and East part of Germany, especially in summer; more water in coastal regions in winters; more actual evapotranspiration due to higher temperature. Outlook To complete the Rhein basin project and improve the results in Elbe and Danube basins with more observed climate data. Improvements for the snow melting processes in the upper Danube. (Preliminary results are presentated in the paper). Cross-comparison of different scenarios (e.g. scenarios from models STAR, CCLM, REMO) Extreme events analysis for the scenario period 3

31 Outlook Discharge in m 3 /s Example for cross-comparison: in Weser basin (gauge Intschede) Simulated by STAT 1 realizations included 8 realizations included Medium realization Reference Discharge in m 3 /s Simulated by CCLM Reference Scenario Discharge in m 3 /s Average day realizations included 8 realizations included Medium realization Reference Discharge in m 3 /s Average day Reference Scenario Average day Average day

32 Outlook Example for flood analysis: Annual maximum discharge in Weser (gauge Intschede) Weser (gauge Intschede) GEV fit Return level (m3/s) e-1 1e+ 1e+1 1e+2 1e+3 Return period 32

33 References GRDC (The Global Runoff Data Centre), 5668 Koblenz, Germany. HAD (Hydrologischer Atlas von Deatschland), 2. Edited by Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit. ISBN Kosková R., Nemecková, S., Hesse, C., 27. Using of the Soil Parametrisation Based on Soil Samples Databases in Rainfall-Runoff Modelling. In Jakubíková, A., Broza, V., Szolgay, J. (Eds.): Proceedings of the Adolf Patera Workshop "Extreme hydrological events in catchments" Bratislava, pp ISBN [in czech] Krysanova V., Müller-Wohlfeil D.I., Becker, A.,1998. Development and test of a spatially distributed hydrological / water quality model for mesoscale watersheds. Ecol. Model. 16, Orlowsky, B., Gerstengarbe, F.-W., Werner, P., 28. A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM. Theor. Appl. Climatol., 92: Statistisches Bundesamt Deutschland, 28. Statistical Yearbook 28. Available in German ( /Geographie,property=file.pdf) (asscessed: ). Steppeler, J., Doms, G., Schaettler, U., Bitzer, H.W., Gassmann, A., Damrath, U., Gregoric, G. 23. Mesogamma scale forecasts using the nonhydrostatic model LM. Meteorol. Atmos. Phys. 82:75 96 T.G.M. Water Research Institute. Hydroecological Information system: (asscessed: ) 33

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35 Climate scenarios gauge Schermbeck (Lipper) m 3 /s scenario included 5 scenario included mean scenario simulated observed simulated Average day scenario included 5 scenario included mean scenario simulated m 3 /s 8 m 3 /s Average day Average day

36 Climate scenarios gauge Rockenau (Neckar) m 3 /s scenario included 5 scenario included mean scenario simulated observed simulated Average day scenario included 5 scenario included mean scenario simulated m 3 /s 2 m 3 /s Average day Average day

37 Climate scenarios gauge Donauwörth (Donau) m 3 /s scenario included 5 scenario included mean scenario simulated observed simulated Average day scenario included 5 scenario included mean scenario simulated m 3 /s 2 m 3 /s Average day Average day

38 Climate scenarios gauge Vlotho (Weser) m 3 /s scenario included 5 scenario included mean scenario simulated m 3 /s observed simulated m 3 /s Average day scenario included 5 scenario included mean scenario simulated Average day Average day

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