Comparison of different approaches for flood damage and risk assessment

Transcription

1 Perception of uncertainty in water management by stakeholders and researchers Prague, May 2007 NeWater Project Comparison of different approaches for flood damage and risk assessment Annegret H. Thieken 1, Heiko Apel 1, Heidi Kreibich 1, Guiseppe Aronica 2 GeoForschungsZentrum Potsdam, Germany

2 Risk definition Source: Merz & Thieken (2004) ÖWAW.

3 Types of flood damage damage direct indirect tangible intangible tangible intangible property damage fatalities, evacuees disruption of trade and traffic psychological damage, migration damage to residential property modified after Smith & Ward (1998): Floods.

4 Flood loss estimation: Input data Inundation scenarios Land use and assets (exposed values) Relative loss model (susceptibilty) Example: Loss ratio at residential buildings Loss ratio of building L [-] L = 0.02 h L = (2 h² + 2h)/100 L = (27 SQRT(h))/ Water level h [m above ground surface] Loss estimates [Euro]

5 Scales in flood loss estimation Meso-scale estimation / data is based on land use units (e.g. CORINE land cover), provides no damage estimate per object, only per region. Micro-scale estimation / data is based on single objects/buildings, demands very detailed input data

6 The model matrix complexity hazard linear interpolation (A) vulnerability 1D/2Dhydraulics (B) 2Dhydraulics (C) complexity simple damage functions (I) meso-scale damage models (II) micro-scale damage models (III)

7 Test area: Eilenburg (Saxony, Germany) serious damage during flood of August 2002 inundation depths at about 400 buildings (Bauhaus-University Weimar) mapped inundation extent (LfUG) Repair costs (Saxonian Reconstruction Bank) model validation

8 Model description hazard model A and B A: Linear interpolation Linear interpolation of absolute water levels between gauging stations; derivation of inundation maps by cut and fill procedure of planar water surface with DEM Required input data: DEM (25m resolution) B: 1D/2D-hydraulics (LISFLOOD-FP; Bates & de Roo, 2000) 1D-hydraulics in channel, simple geometry, kinematic; 2D- hydraulics in the floodplain, raster based, diffusion wave Required input data: DEM, channel information (cross sections)

9 Model description hazard model C C: 2D- hydraulics (Aronica et al., 1998) 2D parabolic model (St.- Venant without convective acceleration) for channel and floodplain, finite elements Required input data: DEM, channel information (detailed), building inventory

10 Model description vulnerability I: Stage- damage curves Functional relation of inundation depth to loss ratio Loss ratio of buildings d [-] MURL (2000): d = 0.02 h ICPR (2001): d = (2 h² + 2h)/100 Hydrotec (2001, 2002): d = (27 SQRT(h))/ Inudation depth h [m above ground surface]

11 Flood Loss Estimation MOdel FLEMO Loss ratio of a building [%] one-family house (semi-)detached multifamily house Source: Büchele, Kreibich et al. (2006) NHESS 6: high building quality poor/average building quality 2. Modellstufe: < 21 cm cm cm cm > 150 cm Water level (above ground surface) Private precaution Zu-/Abschläge none good very good Contamination Data basis: Interviews of 1697 households after the flood in August 2002 FLEMO+ none moderate severe

12 Vulnerability model II: FLEMO+ on the meso-scale Calculation of a mean loss model per zip code or municipality Consideration of a typical composition of building types and building quality per zip code based on census data (INFAS Geodaten, 2001) Typical composition of building types Average building quality Mean composition of residential buildings Dominated by multifamily houses Mixed: high share of multifamily homes Mixed: high share of detached houses Mixed: high share of one-family homes Dominated by one-family homes Source: Thieken et al. submitted to J. Hydrol.

13 Example: Eilenburg FLEMO mean composition of residential buildings: Cluster 2 31 % one-family homes 25 % (semi-)detached houses 44 % multifamily houses mean building quality: slightly below average mean damage ratios DR mean in Eilenburg: DR mean = 0.31 * DR EFH * DR RDH * DR MFH with: DR EFH : damage ratio for one-family homes; average building quality DR RDH : damage ratio for semi-detached houses; average building quality DR MFH : damage ratio for multifamily houses; average building quality FLEMO+ No precaution, severe contamination: 1.58

14 Vulnerability model I and II for Eilenburg 0.6 MURL ICPR Loss ratio of a building [-] Hydrotec FLEMO+ FLEMO Water level [m above ground surface] Additionally required data: Land cover data and associated building assets

15 Disaggregation of assets: Example of Dresden Asset Database of Kleist et al. (2006), NHESS EDIM

16 Vulnerability model III: FLEMO+ on the micro-scale #1 mean loss function and mean property value per affected building #2 building type specific loss functions and asset values 0.4 Loss ratio of a building [-] FLEMO+ Eilenburg One-Fam-Homes (adapted) (semi-)detached (adapted) Multi-Fam-Homes (adapted) Water level [m above ground surface] Required input data: detailed building inventory (ALK) incl. type, use, quality and assets

17 Results: Inundation simulation interpolation Source: Apel et al.(2007) IAHR 2D 1D/2D

18 Results: Inundation simulation Model performance model performance bias [m] measured inundation depths mean absolute difference [m] root mean square error [m] flood extent flood area index [%] interpolation D/2D D Source: Apel et al.(2007) IAHR

19 Inundation simulation Model performance (Bias) 8 Interpolation h simulated [m] D/2D-model h simulated [m] D-model h simulated [m] h surveyed [m] Source: Apel et al.(2007) IAHR

20 Loss estimation Official repair costs: Mio. Euro (SAB, 2005) damage inundation models models ICPR MURL HYDRO -TEC FLEMO meso FLEMO+ meso FLEMO+ micro #1 FLEMO+ micro #2 Interpolation (-48%) (-85%) (37%) (-30%) (10%) (-1%) (41%) 1D/2D (-55%) 9.78 (-87%) (23%) (-37%) (0%) (-12%) (23%) 2D (-78%) 6.04 (-92%) (-10%) (-55%) (-28%) (-40%) (-13%) Source: Apel et al.(2007) IAHR

21 Assessment of loss estimates by resampling (bootstrap) Input data Repair costs of 765 buildings in Eilenburg (SAB, 2005) Sum of repair costs: Million Euro Variation of total repair costs by resampling Statistics of the sum of samples Mean Million Euro 2.5-percentile Million Euro 97.5-percentile Million Euro

22 Evaluation of loss estimates Estimates that are within the 95% range of resampled sum: damage inundation models models ICPR MURL HYDRO -TEC FLEMO meso FLEMO+ meso FLEMO+ micro #1 FLEMO+ micro #2 Interpolation (-48%) (-85%) (37%) (-30%) (10%) (-1%) (41%) 1D/2D (-55%) 9.78 (-87%) (23%) (-37%) (0%) (-12%) (23%) 2D (-78%) 6.04 (-92%) (-10%) (-55%) (-28%) (-40%) (-13%)

23 Relevance: Comparison of Average Annual Losses (AAL) T = 10 T = 10a T = 50 T = 50a T = 100 T = 100a Input: Flood scenarios HQ10 HQ1000 LISFLOOD-FP (1D/2D) Various loss models T = 500 T = 500a Estimates of AAL: Min. (MURL): 0.3 Million Euro Max. (Hydrotec): 3.0 Million Euro FLEMO+ (meso): 2.3 Million Euro ICPR: 1.0 Million Euro

24 Conclusions Differences between inundation models are comparatively small (in this case). Variability of results by loss models is considerably larger than by inundation models. The GFZ model FLEMO+ which includes additional factors (contamination, preparedness) improves loss estimation remarkably. Inundation simulation by linear interpolation works well in this case, but fails in lowland areas as well as in mountainous terrain. 1D/2D simulations are the best compromise between data and simulation effort and required accuracy. Hazard and vulnerability (loss) models need to be calibrated and validated independently.

25 Acknowledgements Funding: German Ministry of Education and Research (BMBF) MEDIS Methods for the Evaluation of Direct and Indirect flood losses More information: Dr. Annegret Thieken Engineering Hydrology Section Telegrafenberg D Potsdam Additional funding:

26 References and further reading I APEL, H., G. ARONICA, H. KREIBICH, A.H. THIEKEN: Evaluation of Different Modelling Strategies for Flood Risk Assessment in Urban Areas. In: Proceedings of the 32nd Congress of IAHR Harmonizing the Demands of Art and Nature in Hydraulics, Venice, Italv, 1-6 July 2007 (in press) ARONICA, G., NASELLO C., TUCCIARELLI, T. (1998): 2D multilevel model for flood wave propagation in flood-affected areas. Journal of Water Resources Planning and Management, 124(4): ARONICA, G., LANZA, L. (2005), Drainage efficiency in urban environment. Hydrological Processes, 19 (5), BATES, P.D., DE ROO, A.P.J. (2000): A simple raster-based model for flood inundation simulation. Journal of Hydrology, 236(1-2): BÜCHELE, B., H. KREIBICH, A. KRON, A. THIEKEN, J. IHRINGER, P. OBERLE, B. MERZ, F. NESTMANN (2006): Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks. NHESS 6(4): , ( nhess/6/nhess pdf). HYDROTEC (2001): Hochwasser-Aktionsplan Angerbach. Teil I: Berichte und Anlagen. Studie im Auftrag des StUA Düsseldorf, Aachen. HYDROTEC (2001): Hochwasser-Aktionsplan Lippe. Teil I: Berichte und Anlagen. Studie im Auftrag des StUA Lippstadt, Aachen. ICPR (2001): Atlas on the risk of flooding and potential damage due to extreme floods of the Rhine. International Commission for the Protection of the Rhine (ICPR). KLEIST, L., A.H. THIEKEN, P. KÖHLER, M. MÜLLER, I. SEIFERT, D. BORST, U. WERNER (2006): Estimation of the regional stock of residential buildings as a basis for comparative risk assessment for Germany. NHESS 6(4): ( KREIBICH, H., A.H. THIEKEN, TH. PETROW, M. MÜLLER, B. MERZ (2005): Flood loss reduction of private households due to building retrofitting - Lessons learned from the Elbe floods in August NHESS 5:

27 References and further reading II MERZ, B., A.H. THIEKEN (2005): Separating Natural and Epistemic Uncertainty in Flood Frequency Analysis. - Journal of Hydrology 309: MERZ, B., A.H. THIEKEN (2004): Flood risk analysis: Concepts and challenges. - Österreichische Wasser- und Abfallwirtschaft 56(3-4): MURL (2000): Potentielle Hochwasserschäden am Rhein in NRW - Ministerium für Umwelt. Raumordnung und Landwirtschaft des Landes Nordrhein-Westfalen. Düsseldorf. SMITH, K., WARD, R. (1998): Floods: Physical processes and human impacts. John Wiley and Sons, Chichester. THIEKEN, A.H., M. MÜLLER, L. KLEIST, I. SEIFERT, D. BORST, U. WERNER (2006): Regionalisation of asset values for risk analyses. NHESS 6(2): , ( THIEKEN, A.H., M. MÜLLER, H. KREIBICH, B. MERZ (2005): Flood damage and influencing factors: New insights from the August 2002 flood in Germany. Water Resources Research 41(12), W THIEKEN, A.H., H. KREIBICH, T. NELTCHINOVA, B. MERZ: A new model for the estimation of flood losses on the meso-scale. Journal of Hydrology (submitted).