Berg, P.

Duethmann, D.

Ihringer, J.

Kunstmann, H.

Liebert, J.

Merz, B.

Ott, I.

Schaedler, G.

Wagner, S.

Werhahn, J.

English

KITopen

KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz AssociationCEDIM – Center for Disaster Management and Risk Reduction Technology  High resolution regional climate modeling for flood hazard impact study in GermanyS. Wagner1; P. Berg2; D. Duethmann4; J. Ihringer3; H. Kunstmann1; J. Liebert3; B. Merz4; I. Ott1; G. Schaedler2; J. Werhahn1(1) IMK-IFU, KIT, Garmisch-Partenkirchen, Germany (2) IMK-TRO, KIT Karlsruhe, Germany (3) IWG, KIT, Karlsruhe, Germany. (4) GFZ, Potsdam, Germany 2010 AGU Fall Meeting, Poster ID# A13D-0245Contact: Sven Wagner IMK-IFU, KIT Kreuzeckbahnstr. 1982467 Garmisch-PartenkirchenGermanyEmail: sven.wagner@kit.edu• An increased variability in precipitation and temperature for the warming future climate is expected.• The development of adaptation strategies very often requires impact studies on a regional scale, e.g.for flood hazard studies in small and medium sized river catchments.• Therefore, ensembles of coupled climate-runoff simulations are performed for the assessment ofchanges in flood hazard for small and medium sized river catchments in Germany. This project isfunded by CEDIM (Center for Disaster Management and Risk Reduction Technology).• Our ensemble includes 2 GCMs (ECHAM5, CCCma3) and for one GCM (ECHAM5) three realizationswith different initial conditions, 2 RMCs (CCLM, WRF) with a final spatial resolution of 7km and 1hour output timestep, and at least two hydrological models for each catchment (see Figure1). The regional scale of the analysis allows for addressing the following questions:- How will the flood characteristics for small and medium catchments alter ina changing climate within the next decades?- What uncertainties are to be expected?INTRODUCTIONControl (1971-2000) + Scenario A1B (2021-2050)GCM2GCM1 (ECHAM5) GCM2 ( CCma3)Realization 1--3CCLM (IMK-TRO) WRF (IMK-IFU)PRMS (IWG) WASIM (IMK-IFU) SWIM (GFZ)ERA40CCLM and WRFBias CorrectionFigure 1: Schematic over the ensemble simulations strategy. The additional step of bias correction is included on the right side of the diagramPART1: Regional Climate Modeling    SETUP Double nesting strategy of both RCMs to downscale GCM results:- Domain1:  ~ 50km covering Europe - Domain2:  7km covering Germany and surroundings- 41 vertical levelsRESULTS VALIDATION CLIMATE CHANGE SIGNAL (1971-2000) (2021-2050 versus 1971-2000)Figure 3left: Annual mean bias for the CCLM, WRFand REMO control simulations (E520C) forrealizations R1-3, plus an CCCma3 drivensimulation with CCLM. Spatial averages areshown for Germany (“G”), Ammer (“A”), Mulde(“M”), and Ruhr (“R”). Temperatures arecompared to the E-OBS data set [Haylock etal., 2008], and precipitation to the REGNIEdata set (DWD)Validation of RCM results:• CCLM model is colder thanobservations by about 0.5 to 2 K• WRF and REMO have a warm bias ofaround 0.5 K• Precipitation is overestimated by allmodels (partially also due to GCM input)• Wet bias is larger in winter for CCLMand WRF (not shown)Figure 2: RCM Domain 2 (7km) and river catchments under study: Ammer (south), Mulde (east) and Ruhr (west)Temperature Figure 5 left: Spatial distribution of relative annualmean precipitation bias for the CCLM, REMOand WRF control simulations (E520C) forrealization 1 (top line) and CCCma3, CCLMR2, REMO R2, and CCLM R3 (bottom line)Figure 4 left: Spatial distribution of annual meantemperature bias for the CCLM, REMO andWRF control simulations (E520C) forrealization 1 (top line) and CCCma3, CCLMR2, REMO R2, and CCLM R3 (bottom line)Figure 6: Normalizedprecipitation intensitydistributions [mm] ofCCLM and WRF E520Cand E5A1B1 forrealization 1 (left) andCCLM E520C andE5A1B1 realization 1-3(right)Bias correction of RCM results:•Biases in the RCMs needs to becorrected before coupled to thehydrological modeling• Bias correction method: quantilemapping (not shown)• Method implementation and applicationin this project see Berg et al., 2010Climate Change Signals:• Temperature increase between 0.8 and1.3 K• Change of annual precipitation sumbetween -2% and 9%; varying spatialdistributions for different RCMs andrealizations• Intensity range of realizations 1 to 3 iswider for control simulations compared tothe scenario ones• Increase of high precipitation intensitiesfor WRF and CCLM realization 2 and 3.References:Berg, P., D. Düthmann, H. Feldmann, J. Liebert, andS. Wagner (2010), Assessing uncertainties inobservations and RCM bias correction, submitted toInternational Journal of Climatology.Haylock, M., N. Hofstra, A.M.G. Klein Tank, E.J. Klok,P.D. Jones, and M. New (2008), A European dailyhigh-resolution gridded dataset of surfacetemperature, precipitation and sea-level pressure, J.Geophys. Res., 113,D20119,doi:10.1029/2008JD010201.right: Climate change signal of the RCM simulations described aboveright: Climate change signal of the RCM simulations described aboveright: Climate change signal of the RCM simulations described abovePrecipitationTemperature PrecipitationKIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz AssociationCEDIM – Center for Disaster Management and Risk Reduction Technology  CONCLUSIONS Regional climate modeling analysis has shown that  the selection of the GCM, the realization of the GCM, and the selection of the RCM significantly impacts the simulation result. All ensemble simulations estimate a temperature increase of around 1°C and an increase in annual precipitation (except the CCCma3) for the scenario period 2021-2050 for Germany.  The validation of the RCM results  has shown the need for bias correction using the quantile mapping method before coupling to the hydrological model. The flood impact study performed so far indicates that the selection of the RCM, the selection of the GCM and its realization impacts the hydrological modeling results significantly. Furthermore, the selection of the hydrological model causes different  modeling results.  Climate impact analysis considers so far only simulation results of two hydrological models for one catchment. It is expected that the ensemble simulations in particular for the other two catchments support a more detailed assessment of climate change signals in context of the large uncertainties involved within the modeling chain.  PART2: Flood hazard impact study    High resolution regional climate modeling for flood hazard impact study in GermanyS. Wagner1; P. Berg2; D. Duethmann4; J. Ihringer3; H. Kunstmann1; J. Liebert3; B. Merz4; I. Ott1; G. Schaedler2; J. Werhahn1(1) IMK-IFU, KIT, Garmisch-Partenkirchen, Germany (2) IMK-TRO, KIT Karlsruhe, Germany (3) IWG, KIT, Karlsruhe, Germany. (4) GFZ, Potsdam, Germany 2010 AGU Fall Meeting, Poster ID# A13D-0245INTRODUCTIONControl (1971-2000) + Scenario A1B (2021-2050)GCM2GCM1 (ECHAM5) GCM2 ( CCma3)Realization 1--3CCLM (IMK-TRO) WRF (IMK-IFU)PRMS (IWG) WASIM (IMK-IFU) SWIM (GFZ)ERA40CCLM and WRFBias CorrectionFigure 1: Schematic over the ensemble simulations strategy. The additional step of bias correction is included on the right side of the diagram.SETUP - Hydrological simulations are performed for three representative small to medium sized catchments in   Germany (Ammer, Mulde, Ruhr) with catchment areas between 700 and 6000 km2 applying at least  two different hydrological models per catchment- All model setups are calibrated and validated using meteorological observations before applying RCM results (not shown)RESULTS mean monthly discharge [m³/s] maximum monthly discharge [m³/s]  (30 year average) (30 year average)Figure 10left: Mean monthly discharge at Bad Duebenof the WaSiM and SWIM simulations with thebias corrected 7km ECHAM5 realization R1,R2, and R3 using the RCM CCLM for thescenario ( A1B1, 2021-2050) periodDiscussion:• The selection of the RCM significantlyimpacts the hydrological modeling result forboth mean and maximum discharge inspite of bias correction• Both RCMs estimate higher mean andmaximum discharge in winter and spring. Insummer the signal is different• The selection of the hydrological modelalso impacts the hydrological modelingresult significantly• Seasonal cycle of mean and maximumdischarge of both hydrological models iscomparable, but there is an offset if usingWaSiM• The offset decreases partially for thescenario period• The impact of the selection of realizationR1,R2, or R3 of the GCM is minor for themean values compared to theaforementioned differences. But for thescenario period the differences increase• For the maximum discharge values theimpact is in the same order of magnitudecompared to the selection of the RCM andthe hydrological modelFigure 7: Digital elevation model of theMulde catchment and location of the gaugeof the gauge Bad Dueben• An increased variability in precipitation and temperature for the warming future climate is expected.• The development of adaptation strategies very often requires impact studies on a regional scale, e.g.for flood hazard studies in small and medium sized river catchments.• Therefore, ensembles of coupled climate-runoff simulations are performed for the assessment ofchanges in flood hazard for small and medium sized river catchments in Germany. This project isfunded by CEDIM (Center for Disaster Management and Risk Reduction Technology).• Our ensemble includes 2 GCMs (ECHAM5, CCCma3) and for one GCM (ECHAM5) three realizationswith different initial conditions, 2 RMCs (CCLM, WRF) with a final spatial resolution of 7km and 1hour output timestep, and at least two hydrological models for each catchment (see Figure1). The regional scale of the analysis allows for addressing the following questions:- How will the flood characteristics for small and medium catchments alter ina changing climate within the next decades?- What uncertainties are to be expected?right: Maximum monthly discharge of  the simulation results described aboveright: Maximum monthly discharge of  the simulation results described aboveright: Maximum monthly discharge of  the simulation results described aboveFigure 8left: Mean monthly discharge at Bad Duebenof the WaSiM simulations with the biascorrected 7km ECHAM5 realization R1 datausing the RCMs CCLM and WRF for thecontrol (CTR, 1971-2000) and scenario(A1B1, 2021-2050) periodFigure 9:left: Mean monthly discharge at BadDueben of the WaSiM and SWIMsimulations with the bias corrected 7kmECHAM5 realization R1, R2, and R3 usingthe RCM CCLM for the control (CTR, 1971-2000) periodCTR (1971-2000), A1B1(2021-2050)CTR (1971-2000)A1B1(2021-2050)CTR (1971-2000), A1B1(2021-2050)CTR (1971-2000)A1B1(2021-2050)Contact: Sven Wagner IMK-IFU, KIT Kreuzeckbahnstr. 1982467 Garmisch-PartenkirchenGermanyEmail: sven.wagner@kit.edu

High resolution regional climate modeling for flood hazard impact study in Germany

https://publikationen.bibliothek.kit.edu/220088106/3816149

High resolution regional climate modeling for flood hazard impact study in Germany

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