Flood damage modelling

Transcription

1 Flood damage modelling Laurens Bouwer (dept. of Flood Risk Management) with Dennis Wagenaar, Andreas Burzel, Kymo Slager, Karin de Bruijn, Marcel van der Doef, and many others

2 Introduction Why model flood damages? Delft-FIAT Examples: Viet Nam hot spot identification South Florida rainfall and surge Netherlands multi-variable models New York hurricane Sandy 7 juni

3 Changing global flood risk Increasing hazard? Increasing exposure Changing vulnerability? Increasing damages Bangladesh IPCC SREX (2012) Mechler & Bouwer (2015) Climatic Change 7 juni

4 Flood risk indicators Indicators to monitor and quantify changes in flood risk Set targets for risk reduction efforts acceptable risk Monetised risk: basis for CBA 7 juni

5 Flood risk management: minimise total costs Costs (dollar) (Marginal benefits = marginal costs) Total costs Investment costs Expected damage cost Levee height (inch) Protection standard (1/year) 07-Jun-17

6 Delft-FIAT: Flood Impact Assessment Tool Model set-up Model run Python Object maps Impact functions Max damage Website (WPS) model config file Operational FEWS calculation core 7 juni 2017

7 Delft-FIAT: current implementations Country Location Type Client Remarks Netherlands Netherlands Coast/river Rijkswaterstaat (SSM 2015) Official national model Netherlands Meuse river River H2020 IMPREX Research Europe Europe River H2020 BASE Research United States Florida River NOAA / FEMA Consultancy Bangladesh Bangladesh Coast/river National government Consultancy Afghanistan Afghanistan River World Bank Consultancy United States New York Coast Deltares Research Sri Lanka Colombo River World Bank Consultancy Viet Nam Viet Nam Coast World Bank Consultancy 7 juni

8 SSM2015 (Delft-FIAT web processing service) 7 juni 2017

9 Example 1: coastal Viet Nam (World Bank) Objectives Rapid Screening: quantitative overview of risks in Northern and Central coastal regions related to coastal hazards: typhoons (wind, surge, rain) and erosion, incl. effects of climate change (2050); Socioeconomic impacts of super typhoon scenarios as prepared by MARD

10 Viet Nam: coastal screening for hot-spots Results from GTSM model

11 Coastal flooding results Hotspot ranking for coastal floods (GTSM results) (all criteria same weight) Flood damages as part of MCA people at risk 2015 (per year) - poor people at risk % affected of total province AAL on residential buildings Total commune level GDP and crops (in million dollar) score Rank Hotspots Province value score weight value score weight value score weight value score weight Bình Định % Bình Định Đà Nẵng % Đà Nẵng Hà Tĩnh % Hà Tĩnh Hải Phòng % Hải Phòng Khánh Hòa % Khánh Hòa Nam Định % Nam Định Nghệ An % Nghệ An Ninh Bình % Ninh Bình Ninh Thuận % Ninh Thuận Phú Yên % Phú Yên Quảng Bình % Quảng Bình Quảng Nam % Quảng Nam Quảng Ngãi % Quảng Ngãi Quảng Ninh % Quảng Ninh Quảng Trị % Quảng Trị Thái Bình % Thái Bình Thanh Hóa % Thanh Hóa Thừa Thiên - Huế % Thừa Thiên - Huế Max. 5, %

12 Example 2: South Florida Booster pumps Urban drainage and resilience Canal conveyance and bank improvements 7 juni

13 Input data maps Digital Elevation Model Building polygons and points HAZUS damage functions

14 Damage results: Storm October 2000 Total estimated direct damages: $ 36.2 million (2000 price level) 7 juni

15 Projected flood risk (EAD) XP-SWMM hydraulic model - +2% +6% +18% Areas adjacent to C-7 Canal ONLY Scenario Current - Scenario 1 Scenario 2 Scenario 3 Sea-level rise in ft ft ft - +31% +71% +244% 7 juni

16 Flood damage risk (EAD) 7 juni

17 Equity: Residential damages / capita 7 juni

18 Example 3: Multi-variable models Variable Source Pearson correlation on damage td Total damage Original dataset 1 sd Structure damage Original dataset 0.85 cd Content damage Original dataset 0.83 df dg Water depth relative to floor Water depth relative to DEM Original dataset 0.18 Flood simulation 0.18 bs Basement Original dataset dh Detached house Original dataset 0.08 hs Household size Original dataset 0.17 fv Flow velocity Flood simulation 0.04 fd Flood duration Flood simulation 0.05 rp Return period Flood simulation ba Building age Cadastre 0.01 la Floor area for living Cadastre 0.04 fa Footprint area building Cadastre td sd cd df hs bs dh fa ba dg la fv fd rp td sd cd df hs bs dh fa ba dg la fv fd rp 7 juni 2017

19 1: Decision tree learning 2: Bayesian Network Regression tree works better than creating a damage function based on only water depth with regression. Even better results with ensemble methods based on multiple trees made from resampled data (e.g. Random Forest) Expert network cd= content damage, ba=building age, bs=basement, td=total damage, fd=flood duration, df= water depth floor, rp= return period, hs=household size, sd= stucture damage, dg= water depth ground, fa= footprint area house. 7 juni 2017

20 Example 4: New York / Nassau County Nature based solutions (Living with the Bay) Delft 3D simulations: Height wave crest Mill River Hurricane Sandy flood damages (Delft-FIAT output) Water depth 7 juni

21 Prototype: Single-story residential buildings Inundation only Inundation AND waves Applied to: - Residential category - Recreation category

22 Area wave function applied

23 Delft-FIAT damage model results Waves included: 3,328 M$ No waves: 3,125 M$ No waves, wave function 3,197 M$ Waves included (energy restriction): 3295 M$ Benefits lower waves: 203 M$ Explained by wave function: 72 M$ Energy restriction, benefits are: 170 M$ In sum: some ~100 M$ can potentially be saved

24 Thank you for your attention! 7 juni