EDIM ADAPT and CCI-HYDR Workshop Liege, 10. January 2008 THW MEDIS Overview and Interim Results Improved methods for the estimation and mapping of flood risks Annegret Thieken, Heidi Kreibich, Bruno Merz, Klaus Piroth
EDIM Dr. Klaus Piroth Civil Engineer University Karlsruhe PhD in Hydraulic Engineering (Prof. Plate) THW Since 1995 with ARCADIS.DE Head of segment: Water Management
MEDIS: Methods for the Evaluation of Direct and Indirect flood losses R. Schwarze U. Kunert A. Thieken, I. Seifert, F. Elmer, H. Kreibich, B. Merz J. Schwarz H. Maiwald A. Gerstberger B. Kuhlmann B. Weinmann K. Piroth V. Ackermann LTV U. Müller (S. Kobsch) M. Müller Umweltamt J. Seifert ADAPT and CCI-HYDR Workshop Liege, 10. January 2008
Quantitative Risk Analysis Hazard Vulnerability Risk exceedence probability Three important questions: What can go wrong? How likely is it that it will happen? If it does happen, what are the consequences? Sources: Merz & Thieken (2004) ÖWAW 56 (3-4): 27-34; Kaplan & Garrick (1981) Risk Analysis 1(1).
Improved methods for the estimation and mapping of flood risks Model development for different sectors: Example New Model for the estimation of losses in private households - Model validation for different flood situations - Knowledge transfer by case studies and guidelines
Flood losses and influence factors
Flood risk estimation: Input data Inundation scenarios and probabilities Land use and assets (relative) damage model Example: damage to residential buildings Damage estimates / Risk curve
Flood damage data in Germany: HOWAS (1978-1994) HOWAS: Data base of the Bavarian Agency of Water Resources 1735 damage records of private houses non-parametric regression (Epanechnikovkernel, bandwidth = 0.6 m) Source: Merz et al. (2004) - NHESS, 4(1): 153-163.
Survey of flood affected private households (August 2002) 1697 computer-aided telephone interviews in April and May 2003 180 questions: Damage to buildings/contents Flood characteristics Building characteristics Flood warning Private precaution Flood experience 30 minutes (average) Source: Müller & Thieken (2005) - Versicherungswirtschaft 60(2): 145-148.
Influence of water level: median and interquartile range (IQR) Source: Thieken et al. (2005) WRR 41(12): W12430.
Flood damage augmentation by contamination 75%-Quantile Mean Median (50%-Quantile) 25%-Quantile Source: Thieken et al. (2005) WRR 41: W12430.
Flood loss reduction by precautionary measures Examples: Loss ratio of a building [%] Building use adapted Interior decoration adapted unadapted unadapted Electricity supply in upper floors in basement Source: Kreibich, Thieken et al. (2005) NHESS 5: 117-126.
Flood losses and influence factors
Flood Loss Estimation MOdel FLEMO Loss ratio of a building [%] 35 30 25 20 15 10 5 0 one-family house (semi-)detached multifamily house high building quality poor/average building quality Source: Büchele et al. (2006) NHESS 6: 485-503. 2. Modellstufe: Zu-/Abschläge < 21 cm 21-60 cm 61-100 cm 101-150 cm > 150 cm Water level (above ground surface) Contamination FLEMO+ Private precaution none good very good none 0.92 0.64 0.41 moderate 1.20 0.86 0.71 severe 1.58 --- ---
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
Databases on assets EDIM Reconstruction costs of residential buildings, reference year 2000 Municipal per-capita value Capital stock per municipality 60 branches, 3 company sizes, 2 types Source: Kleist et al. (2006) NHESS
Scaling FLEMO from micro to 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 Source: Thieken et al. submitted to J. Hydrol.
Meso-scale stage-damage-functions Source: Thieken et al. submitted to J. Hydrol.
Model validation on the basis of actual repair costs: August 2002 flood in Saxony (13 municipalities) Estimated Building Loss [Mill. Euro] 90 80 FLEMO+ 1:1 MURL (2000) 70 ICPR (2001) 60 50 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 Source: Thieken et al. submitted to J. Hydrol. Reported Building Repair Costs at SAB [Mill. Euro]
Model application in large regions Example: Inundation at the River Rhine Scenario: Extreme flood event from the ICPR-Rhine atlas Damage to residential buildings per municipality
Work in progress Further model evaluations with a focus on transferability (further case studies are welcome!) Development of software tools for (rapid) damage estimation Adaptation of FLEMO to flood problems due to rising groundwater Filter criteria for the influence of flow velocity (yes no) Improving the loss estimation model for damage of companies Development of web-services for the assessment of a flood situation with respect to the danger to people and infrastructure Development of standardised methods for loss data collection and setup of the flood loss data base HOWAS 21
Summary and conclusions Many empirical data about flood losses have been gathered after the severe flood in August 2002 in the Elbe and the Danube catchment. The new flood loss estimation model FLEMO+ not only considers water level and building use/type, but also building quality, contamination and precaution. A transparent scaling procedure for applications on the meso-scale was developed. Model validation reveals that FLEMO+ outperforms stage-damagefunctions and therefore improves results of risk analyses. Future challenges include the inclusion of further damage types, the adaptation of the model to other flood types (coastal floods, flash floods) and the transfer to other countries.