Was wissen wir über die Entwicklung der hydrologischen Extreme in Deutschland?

Transcription

1 Was wissen wir über die Entwicklung der hydrologischen Extreme in Deutschland? Axel Bronstert 1,2, Shaochun Huang 2, Gerd Bürger 1 1 Universität Potsdam, Institut für Erd- und Umweltwissenschaften, Lehrstuhl für Hydrologie und Klimatologie 2 PIK - Regional impacts and strategies Research Domain II - Climate Impacts & Vulnerabilities KLIFF-Tagung: Vom globalen Klimawandel zu regionalen Anpassungsstrategien Universität Göttingen, 2. und 3. September 2013

2 Contents Introduction: Development of River Floods Methods Study area and data preparation Results Calibration and validation Impacts on floods Outlook: Flash Flood conditions Conclusion Page 2

3 Historical trends in precipitation [mm] Trend in precipitation: Less precipitation in east Germany 9/17/2013 Average annual precipitation Trend in annual precipitation Data source: Wodinski, Gerstengarbe and Werner, PIK Potsdam Page 3

4 Historical trends in temperature [ C] [ C] Data source: Wodinski, Gerstengarbe and Werner, PIK Potsdam 9/17/2013 Average annual temperature Trend in annual temperature Trends in temperature Warmer in the whole Germany Page 4

5 Historical trends in flood condition Following an expert survey*, two of the main water-related problems in Germany are: More frequent and more intensive floods under climate change Uncertainty of the climate projections and hence of the impacts on floods * Data: GLOWA-Elbe and NeWater Trends in annual maximum daily discharge ( ) Aims of this study: Project the future flood condition in whole Germany using different available RCMs Investigate the uncertainty of the projections 9/17/2013 Source: Petrow and Merz, 2009 Page 5

6 Methods Page 6

7 46 0'0"N 48 0'0"N Catchment 50 0'0"N Regional Global Model system Climate change ECHAM5 Global climate Regional climate model Wettreg, CCLM, REMO 8 0'0"E 10 0'0"E 12 0'0"E 14 0'0"E SWIM Daily discharge 10 0'0"E 12 0'0"E Graph made by Shaochun, Huang, PIK Projected flood condition Uncertainty from the climate scenarios Page 7

8 Regional Climate models (RCMs) RCMs Model type Simulation period GCM based Emission scenario Realization per scenario Spatial resolution CCLM Dynamic ECHAM5 A1B, B REMO Dynamic ECHAM5 A1B, A2, B Wettreg Statisticalempirical ECHAM5 A1B, A2, B1 20 CCLM REMO Wettreg 1965 stations in Germany Page 8

9 Soil profile Bodenprofil The Model SWIM (Soil and Water Integrated Model) Klima: Strahlung, Temperatur & Niederschlag Hydrological cycle A B C Upper ground water SWIM was developed in PIK, OF-Abfluss Surface Potsdam based on Percolation Perkolation SWAT-93 and Lower MATSALU for ground Interflow GW-Neubildung recharge water climate and land use change impact studies (Krysanova et al., 1998) Basisabfluss GW flow A Climate: Global radiation, temperature, precipitation Pflanzenaufnahme Wetlands Vegetation/ Crop growth LAI Roots Land use pattern Bio mass Wasserkreislauf Flaches Grundwasser Tiefes Grundwasser B C Nitrogen cycle Landbedeckung Pflanzenwachsum LAI Biomasse Wurzeln 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 Landnutzung P res Stickstoffkreilauf N-NO 3 N o-ac N o-st N res Phosphorkreislauf P lab P m-ac P m-st P org P res Page 9

10 GEV (Generalized Extreme Value Distribution) distribution For the maxima (or minima) over certain blocks of time (e.g. Annual maximum discharge), the GEV distribution can be used (Coles, 2001): 1 G( z) With shape parameter ξ, location parameter μ and scale parameter σ (>0) Three extreme value distributions are combined ξ=0: Gumbel distribution (light tail) ξ<0: Weibull (upper limit) ξ>0: Fréchet (heavy tail) exp{ [1 ( z )] }

11 Discharge (m 3 /s) GEV (Generalized Extreme Value Distribution) distribution fit for the annual maxima Estimation of 30-year flood level Estimation of relative changes in 50-year flood level Annual maxima return level plot Simulated with CCLM reference Simulated with CCLM scenario 95% confidence level Q1 Q0 Relative change in % Q1 Q0 *100 Q Return period (Year) Page 11

12 Study area and data preparation Page 12

13 Study areas Intschede Versen Frankfurt Rockenau Calbe- Grizehne Hofkirchen 5 main catchments in Germany Ems Weser Elbe Upper Danube Rhine basin 12 gauges used for calibration and 15 gauges for validation Calibration period: Validation period: Control period Scenario periods Page 13

14 Digital elevation map Digital elevation map (DEM) Source: Shuttle radar topography mission data (SRTM 90 m) DEM Germany meter less than ,000 1,000-1,500 more than 1,500 Resolution used: 250 m Page 14

15 Soil map German soil map 1:1,000,000 (BÜK 1000) Source: Federal Institute for Geosciences and Natural Resources Czech soil map Sourec: Koskova et al., 2007 European soil map Source: European Communities - DG Joint Research Centre Germany Soil of the coastal area and bog soils Soils in broad river valleys Soils in undulating lowlands and hilly area Soils in Loess areas Mountains and hill soils Alpine soils Anthrosols and water Soils in other countries Page 15

16 Landuse map Corine land cover (2000) Source: European Environment Agency Germany Water Settlement, industry, road Cropland Grassland Forest mixed Forest evergreen Forest deciduous Wetland Bare soil Page 16

17 46 0'0"N 48 0'0"N 50 0'0"N Subbasin map 8 0'0"E 10 0'0"E 12 0'0"E 14 0'0"E 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: 206 Weser: 536 Elbe: 2268 Donau: 796 Rhein: '0"E 12 0'0"E Graph made by Shaochun, Huang, PIK Page 17

18 Climate input Climate data needed in SWIM: (Minimum T, Mean T, Maximum T, Precipitation, Radiation, Humidity) Source: PIK, DWD, ENSEMBLES project Germany! climate and precipitation stations Climate data from models Daily high-resoluation gridded climate data set for Europe Page 18

19 Results Calibration and validation Impacts on floods Page 19

20 Q (m 3 /s) Q (m 3 /s) Calibration results two examples (a) Q observed Q simulated Intschede (Weser) Calibration period Efficiency: 0.90 Deviation: 1% (b) Hofkirchen (Danube) Calibration period 3000 Efficiency: Deviation: 0% Page 20

21 Discharge (m 3 /s) Discharge (m 3 /s) Validation results example at gauge Versen 1000 Generalized Extreme Value (GEV) plots for the annual maxima of daily discharge observed and simulated during control period (a) Versen (Ems) 1000 (a) Versen (Ems) Return period (year) Return period (year) Observed Simulated with observed climate Simulated with observed climate Simulated with REMO Simulated with CCLM, realization 1 Simulated with CCLM, realization 2 Medium fitting to simulation with Wettreg Including 20 fittings to simulation with Wettreg Including 10 fittings to simulation with Wettreg Page 21

22 Simulated discharge Simulated discharge Simulated discharge Simulated discharge Validation results 10-year and 50-year flood level estimated for the control period and compared with observed discharges 10-year flood 50-year flood Driven by observed climate data Driven by REMO control run Floods are well simulated by SWIM using observed climate data Observed discharge Driven by CCLM control runs Observed discharge Driven by Wettreg control runs Simulated flood driven by different models: Wettreg > REMO > CCLM 10-year flood 50-year flood Observed discharge Observed discharge Page 22

23 Results Calibration and validation Impacts on floods Page 23

24 30-year flood level (m 3 /s) 30-year flood level (m 3 /s) Results flood generation over time 30-year flood level (m 3 /s) 30-year flood level (m 3 /s) Versen B1 Versen, B2 from REMO Versen A1B Versen, A1B from REMO Year Year 30-year flood level estimated 95% confidence level 30-year flood level for period % confidence level for period Versen (Ems) A1B realization 2 Versen, A1B from CCLM realization Versen A1B Versen, A1B from Wettreg Year 800 REMO and CCLM: dynamic flood generation Wettreg: flood level smoothly changed Year Page year flood level estimated

25 Results changes in 50-year flood level REMO A1B scenario CCLM A1B scenario Realization 1 Wettreg A1B scenario Page 25

26 REMO Results changes in 50-year flood level Page 26

27 Results changes in 50-year flood level CCLM Page 27

28 Results changes in 50-year flood level Wettreg Page 28

29 Number Number Number Number Results changes in flood seasons Changes in flood seasons (example: river Saale) (a) REMO (d) Wettreg Control period A1B scenario A2 scenario B1 scenario Month Month 8 (b) CCLM realization 1 6 (c) CCLM realization Month Month number of monthly discharges over 95 percentile observed and simulated with the three RCMs under A1B, A2 and B1 scenarios in both control period and scenario period at the gauge Calbe-Grizehne (Saale, Elbe) Page 29

30 Outlook: Flash Flood Conditions Development of extreme precipitation intensities Page 30

31 a) Change in high-intensity rainfall at Essen ( ; time steps: 1min 30 min) c) b) d) Bubble plot diagrams for Essen: a) positive trend increase for storms with given duration, intensity and depth for time period 1940s 2009, (b) as a for the time period , (c) as b, but only with trend increases which are statistically significant, (d) as c, but only with trend increases which are considered erosion-relevant. Bubbles are scaled uniformly; the largest occurring bubble diameter (in plot at b) corresponds to a trend increase of 0.5 events/year, and a pixel points to a trend increase of zero; Müller & Pfister, JoH, 2011

32 Raqinfall Analysis for three Alpine Stations (A): Brixenbachtal, Längental, Ruggbachtal Temperature Precipitation sum 24h min daily temperature bins All daily data points (n= )

33 Daily P, 99.9% quantiles

34 15min precipitation, means based on wet events (Brixenbachtal, Längental, Ruggbachtal)

35 15min precipitation, 99.9% quantiles based on wet events (Brixenbachtal, Längental, Ruggbachtal)?

36 99.9 %-tile of 15min. precipitation units: mm per minute Dependence of 99% percentile of daily P on temperature (dots) for three regions in the DACH Area. Source: Bürger & Bronstert (under preparation)

37 Future P: basic idea from GCMs etc. T daily P 15min

38 Pre-Conclusion Usefulness of CC scenarios for hydrological impact assessment mean (seasonal) runoff dynamics STAR WettReg REMO GCM evapotranspiration soil moisture/ groundwater recharge snow melt flooding conditions (intermed. return intervals) flooding conditions (large return intervals) low flow conditions Evaluation ref. adequateness for different hydrological processes / conditions (0: fail; 1: hardly enough; 2: satisfactory; 3: good) Bronstert et al., JoC, 2007

39 Conclusion The floods simulated under REMO and Wettreg control runs agreed better with the observations than those simulated under CCLM runs. The changes in flood conditions vary in different gauges and time slices. There is no one gauge, for which change pattern can be agreed by the three models. The uncertainty in estimating extreme event remains very high due to different RCM structures, emission scenarios and various realizations. The dynamic climate models may have the capability of projecting the shift of flood seasons while the statistical model cannot. The analysis of precipitation intensity change and temperature increase may enable the detection and simulation of more robust signals regarding flash flood developments. Page 39

40 Publications BRONSTERT, A., KOLOKOTRONIS, V., SCHWANDT, D., STRAUB, H. (2007): Comparison and evaluation of regional climate scenarios for hydrological impact analysis: general scheme and application example. International Journal of Climatology, 27, BUERGER, G., BRONSTERT, A.: Sub-daily Clausius-Clapeyron disaggregation, Journal of Hydrometeorlogy, (under preparation) HUANG, S., KRYSANOVA, V., BRONSTERT, A., HATTERMANN, F.F (2013): Projections of climate change affected river flood conditions in Germany by combining three different RCMs with a regional eco-hydrological model. Climatic Change, 116, MUELLER, EN., PFISTER, A. (2011) Increasing occurrence of high-intensity rainstorm events relevant for the generation of soil erosion in a temperate lowland region in Central Europe, Journal of Hydrology, 411, Page 40

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