The Big Picture on Flood Modeling Webinar
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In this webinar, RMS top global flood experts take a close look at flood peril best practices—from technologies to underwriting guidelines—and answer your most pressing questions and concerns to help you understand, manage and grow your flood business.
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The Big Picture on Flood Modeling Webinar
- 1. 1Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved. THE BIG PICTURE ON FLOOD MODELING Questions and Answers June 25, 2015 1Copyright © 2015 Risk Management Solutions, Inc. All Rights Reserved.
- 2. 2Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE?
- 3. 3Copyright © 2015 Risk Management Solutions, Inc. MEET THE EXPERTS Laurent Marescot Europe Model Product Strategy Ludovico Nicotina Europe Flood Model Development Navin Peiris Global Vulnerability Model Development Rajkiran Vojjala Climate Vulnerability Model Development Falk Niehorster Global Model Product Strategy Maurizio Savina Europe Flood Model Product Management
- 4. 4Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHAT TYPES OF FLOOD CAN BE MODELED?
- 5. 5Copyright © 2015 Risk Management Solutions, Inc. Inland flood • Precipitation based - all sources • Hydrological modeling • On- and off-floodplain Storm surge • Model surge through entire storm life cycle • Physical numerical approach Tsunami • Subduction-zone induced events • Entire tsunami life cycle modeled • Near- and far-field
- 6. 6Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHY IS IT NECESSARY TO SIMULATE PRECIPITATION?
- 7. 7Copyright © 2015 Risk Management Solutions, Inc. Model based on longer and higher- quality data (rainfall vs. discharge) Continuous simulation captures soil wetness conditions prior to flood events Capture correlations in space and time ADVANTAGES OF MODELING PRECIPITATION
- 8. 8Copyright © 2015 Risk Management Solutions, Inc. RAIN, FLOW AND LOSS CORRELATION RAIN FLOW LOSS
- 9. 9Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHAT DATA IS IMPORTANT?
- 10. 10Copyright © 2015 Risk Management Solutions, Inc. 5m DTM, LIDAR TOPOGRAPHIC DATA Non-filtered airborne-derived DTM example Ghent, Belgium 50m DTM, Ground-truthed High Resolution ≠ Higher Accuracy High-resolution is sometimes misleading Resampling coarse data to higher resolution is easy, but does not improve accuracy
- 11. 11Copyright © 2015 Risk Management Solutions, Inc.Copyright © 2015 Risk Management Solutions, Inc.. RIVER GAUGE DATA • Daily data is vitally important – peaks • Broad spatial coverage over as long a period as possible • Used for calibration of models
- 12. 12Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHAT ABOUT FLOOD DEFENSES?
- 13. 13Copyright © 2015 Risk Management Solutions, Inc. Collect Data Represent Potential Failure Estimate Design Standard (SoP)
- 14. 14Copyright © 2015 Risk Management Solutions, Inc. • Coverage is more varied • Requires different approach • Location specific defense definition is important COASTAL DEFENSES
- 15. 15Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? HOW IS DAMAGE ESTIMATED?
- 16. 16Copyright © 2015 Risk Management Solutions, Inc. MODES OF FLOOD DAMAGE STRUCTURAL SUPERFICIAL / INTERNAL
- 17. 17Copyright © 2015 Risk Management Solutions, Inc. LIKELIHOOD OF FLOODING AT SUBJECT AT RISK
- 18. 18Copyright © 2015 Risk Management Solutions, Inc.Copyright © 2015 Risk Management Solutions, Inc.. FLOOD DAMAGE – ANALYTICAL MODELING • Engineering-based approach to estimate flood damage • Granular risk differentiation by location-specific attributes • Enhanced treatment of damage and elevation uncertainty Source: RMS
- 19. 19Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK?
- 20. 20Copyright © 2015 Risk Management Solutions, Inc. VERTICAL CHARACTERISTICS TO CONSIDER Building Elevation Exposure Distribution Elevation Variability h Ground-floor Elevation
- 21. 21Copyright © 2015 Risk Management Solutions, Inc. Unfinished IMPORTANCE OF DEFINING BASEMENTS: LESSONS FROM SANDY Basement Flooding and Resultant BI in Manhattan Source: RMS Field Recon Basement Damage Varies by Occupancy - Logic Tree Approach Finished Basement Value Restaurant Special Storage Parking MEP
- 22. 22Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHAT DOES HD MEAN FOR FLOOD MODELING?
- 23. 23Copyright © 2015 Risk Management Solutions, Inc. ADVANTAGES OF HIGH-DEFINITION MODELS Improved Correlation of Secondary Uncertainty Clustering Hours Clause Nth-event Covers Consistent Marginal Impact Multi- parameter Damage Function Simple Loss Analytics Cross-peril Correlation Ground-up Simulation Temporal Simulation
- 24. 24Copyright © 2015 Risk Management Solutions, Inc. • High resolution peril requires high resolution data • What to do when detailed data is not available? • Sophisticated disaggregation techniques offer one solution AGGREGATE DATA
- 25. 25Copyright © 2015 Risk Management Solutions, Inc. Flood Event 1 Flood Event 3Flood Event 2 TEMPORAL SIMULATION: SOLVING FOR HOURS CLAUSE RiskLink Models: Flood events hard-wired, one month long, and based on the IED High-Definition Models: Simulation contains physical master events; actual loss events dynamically defined by applying the hours-clause to portfolio and maximizing cedant losses Jan Feb Mar Apr Loss Event 1 Loss Event 3 Loss Event 2
- 26. 26Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE?
- 27. 27Copyright © 2015 Risk Management Solutions, Inc. Current RMS Flood Products
- 28. 28Copyright © 2015 Risk Management Solutions, Inc. Current RMS Flood Products Phase I HD Europe Flood Model and Maps Taiwan Flood Maps
- 29. 29Copyright © 2015 Risk Management Solutions, Inc. Current RMS Flood Products Phase I Phase II U.S. Flood Maps Thailand Flood Maps HD Japan Typhoon Model HD New Zealand Earthquake and Tsunami Model
- 30. 30Copyright © 2015 Risk Management Solutions, Inc. Current RMS Flood Products Phase I Phase II Phase III HD U.S. Inland Flood Model Indonesia Flood Maps HD TWTY and SKTY HD Japan Earthquake and Tsunami Model HD North America Earthquake and Tsunami Model
- 31. 31Copyright © 2015 Risk Management Solutions, Inc. WHAT TYPES OF FLOOD CAN BE MODELED? HOW IS DAMAGE ESTIMATED? WHY PRECIPITATION? WHAT EXPOSURE CHARACTERISTICS IMPACT FLOOD RISK? WHAT DATA IS IMPORTANT? WHAT DOES HD MEAN FOR FLOOD MODELING? WHAT ABOUT FLOOD DEFENSES? WHAT IS YOUR FLOOD PRODUCTS ROADMAP AND COVERAGE? ADDITIONAL QUESTIONS?
- 32. 32Copyright © 2015 Risk Management Solutions, Inc. “Prediction is very difficult, especially if it's about the future..” - Niels Bohr, Physicist
- 33. ©2015 Risk Management Solutions, Inc. RMS and the RMS logo are registered trademarks of Risk Management Solutions, Inc. All other trademarks are property of their respective owners.
Editor's Notes
- Most of the questions we received are about the Modeling of antecedent conditions for inland flood (EUFL, JPTY, EUFL, all our maps, etc based on continuous hydrological modleing) The need of linking hurricane and inland flood model (CNTY, JPTY, USFL) How we can capture and flash flooding (minor river component in some of current flood models as well as EUFL, JPTY, USFL, etc.) As these topics are also covered with next answers, now I give just an overview on different methodologies. Take talking points from the below material.. Inland: Precipitation based – All sources of rain Continuous hydrological modeling Flood defenses Capturing major and minor river floods Surge: (Ensure that we reference Japan TC FL and SS here as well) Pressure low Model surge through entire storm life cycle Physical numerical approach, Mike 21, DHI, world leading physical based numerical model, based on shallow water equation Hazard stored at VRG and ZIP code Bespoke storm surge defenses (is this correct?), user able to change defense height, binary (overtopping, no stochastic component). Velocity: There are two sets of vulnerability functions in the storm surge model: one representing the damage caused by low-velocity flooding and a second for modeling the additional damage caused by wave action. Flow chart: Tropical Cyclone Parameters (stochastic track set, parameters and rates) and Tidal Boundary Conditions + Bathymetry and Topography Hydrodynamic Model Surge Elevation Location Specific Factors Storm surge hazard module—generates the storm surge elevation footprint for stochastic events that produce damageable surge levels. The physical storm surge model is forced by time-stepping (5min) wind (at 3sec gust over local terrain) and pressure fields generated by the wind field module, along with tides. The footprints are computed on the storm surge variable resolution grid (VRG) and stored in the stochastic surge hazard database. Storm surge is the abnormal rise of water generated by a storm, above the predicted astronomical tide. This abnormal elevation results from a combination of two main factors: wind stress, and the inverse barometric effect. Wind stress refers to the way that high winds push water towards the shore The inverse barometric effect refers to the bulge in the ocean's surface that is caused by the low atmospheric pressure in the hurricane. The ocean surface expands by up 10 cm for every 10 hPa drop in atmospheric pressure. While this is a small height, its effect extends over a large area in a hurricane and has a cumulative impact. Riding on top of the storm surge are wind-induced waves that increase the energy of the storm surge and its height, and thus can significantly increase the damage at an affected location. The rise in sea level can be augmented by astronomical tides, particularly if the storm surge occurs at high tide. The sea level rise from storm surge occurs over a period of a few hours before and after the storm makes landfall, and is a complex combination of the storm intensity, forward speed, size (radius of maximum winds), angle of approach to the coast, central pressure, and the shape and characteristics of coastline, including ocean bathymetry. Stochastic Storm Track Generation: The storm surge model uses the same stochastic track set that is used for the wind peril model (the development of which is described in the Stochastic Event Module section of the North Atlantic Hurricane Model Methodology), to evaluate the combined or separate impact of wind and surge. Individual events in the stochastic event set only include storm surge footprints if the simulated water elevation is greater than the minimum value needed to cause flooding and damage. The full stochastic event set for the version 15.0 North Atlantic Hurricane Models represents 100,000 years of possible hurricanes that may impact the study area. This set was “boiled down” by removing storms with similar characteristics and adjusting the contribution of the remaining storms to the final hazard statistics. The storm surge model uses individual stochastic hurricane tracks from this reduced event set to create two-dimensional time-varying wind and pressure fields along the entire lifecycle of each hurricane. The Storm Surge Hazard Module section of this document describes how these wind and pressure fields then force the various storm surge model domains in order to determine storm surge hazard values. Tsunami: Subduction-zone induced events Entire tsunami life cycle modeled Near- and far-field Flow chart: Identify Source Subduction Zones Source Characterization Event Generation Ocean Wave Propagation Coastal Inundation First ever global tsunami catalogue The scenarios are generated from earthquakes on subduction zones around the world, with magnitudes ranging between M8.9 – 9.6 (tohoku earthquake in 2011 was 9.0) These footprints take into consideration near-field (local) and far-field (basin-wide) impacts and as a result, many of the scenarios are global in extent. Variable resolution, from 10m (Japan) to 30m (US), 25m (NZ) and 90m all other countries. Identify Sources Eleven historical tsunami events (of local or global significance) were considering in developing this model Two recent earthquakes exceeded the maximum magnitude expected for their respective subduction zones: the 2004 Indian Ocean event on the Sumatra-Andaman subduction zone and the 2011 Tohoku event on the Japan Trench. To account for these types of unanticipated tail events, the seismic hazard community has started to consider the possibility of great earthquakes on various subduction zones. RMS modeled M9 events on various subduction zones around the world where such great earthquakes have not necessarily occurred in the historical past. Source Characterization The tsunami source event characterization model represents an earthquake as a rupture that slips during an earthquake, releasing seismic energy. The model defines the rupture characteristics based on the magnitude of the event and an assumed slip distribution pattern. Event Generation RMS models the seafloor deformation based on the event rupture model. The bathymetry and topography are adjusted to account for any uplifting or subsidence that would occur as a result of seafloor deformation. RMS generates initial wave conditions in the near-field from the seafloor deformation. Ocean Wave Propagation After the initial seafloor deformation and subsequent water displacement, RMS models the ocean wave propagation. RMS developed a numerical solver, implemented on Graphic Processing Units (GPUs), which uses a finite volume approach to approximate 2D shallow water wave equations over both the ocean and complex topography. In the near-field, inundation is sensitive to initial seafloor deformation, while in the far- field, inundation is more sensitive to magnitude and location of rupture Coastal Inundation As the wave enters shallow water and approaches the coast, RMS models the movement of water along the wet/dry interface using the RMS GPU-based solver, considering variable land friction.
- Say something about the 3 categories By modeling precip. we have a realistic view of antecedant conditions, which can dramatically change the downstream effect of a given precipitation patterns. Again, not possible if looking just at the final realization represented by observed (wrong) Q obs. (does AIR model precip too?) low availability of Q data only few cases with correct data as difficult/impossible to have correct Q observation under severe flooding conditions (rating curve based on normal flows) pluvial flooding not captured by Q obs Model flood everywhere in un/gauged basins, thus 1D + 2D + residual risk Overcome issue of data congruence across countries
- By modelling precipitation as the beginning of the modelling chain and capturing within the model the physics of the process which leads rainfall into becoming river flow, the spatial rainfall correlation naturally propagates through our flow, inundation and financial model. The space-time correlation of the modelled losses that emerges at the end of this process, is then the result of the physical interactions that transforms the input signals into the output variable and not and imposed artificial correlation that depends on the amount a
- Some talking points: Third party data TIV/popul. Based assumptions Bespoke flood defenses (inland only or also Surge (e.g., JPTY) Defenses fail stochastically (always or only inland?) Mention challenge posed by defences in Asia; need to assess defended and undefended views
- …So, why do we do this? The answer is that it provides a number of advantages over what could be achieved within the RiskLink framework, and becomes feasible with the computing power that wasn’t available when RiskLink was designed. Some of the advantages you see on this slide and there are many more actually. We can… [name a few examples]
- As an integrated part of the financial model we dynamically analyze the HC, portfolio dependent on the fly to identify the time windows of maximum losses as it should be…
- North America: USA: Storm Surge Model, Enhanced FEMA Flood Zone Data (Inland and Storm Surge), Tsunami Scenario Catalog Canada and Mexico: Tsunami Scenario Catalog Europe: UK: Storm Surge Model, Inland Flood Model, UK Flood Maps (Inland and Storm Surge), UK Flood PRDs (Inland and Storm Surge) Germany, Belgium: Inland Flood Model Greece, Italy, Portugal, Spain, Cyprus, Turkey: Tsunami Scenario Catalog Asia-Pacific: Australia, Guam, Japan: Storm Surge Model and Tsunami Scenario Catalog China, Hong Kong: Storm Surge and Precipitation-Induced Inland Flooding included in Typhoon Model, Tsunami Scenario Catalog. South Korea: Inland Flood Maps Bangladesh, Cambodia, India, Indonesia, Malaysia, Myanmar, New Zealand, Philippines, Taiwan, Thailand, Vietnam, Russia, Sri Lanka, Papua New Guinea, Pacific Islands, Indian Ocean Islands: Tsunami Scenario Catalog Caribbean: Cayman Islands: Storm Surge Model The Bahamas: Storm Surge Model, Tsunami Scenario Catalog Turks and Caicos: Storm Surge Model, Tsunami Scenario Catalog Anguilla, Antigua and Barbuda, Aruba, Bermuda, British Virgin Islands, Cuba, Curacao, Dominican Republic, Grenada, Guadeloupe, Haiti, Martinique, Montserrat, Puerto Rico, Saba, Sint. Maartin, St. Barthelemy, St. Kitts and Nevis, St. Lucia, St. Martin, St. Vincent and the Grenadines, Trinidad and Tobago, Turks and Caicos, U.S. Virgin Islands: Tsunami Scenario Catalog Central and South America: Belize, Costa Rica, Honduras, Nicaragua, Panama, Argentina, Brazil, Chile, Ecuador, Peru, Venezuela, Falkland Islands: Tsunami Scenario Catalog Middle East and Africa: Israel, Lebanon, Syria, Egypt, Morrocco, Algeria, Cape Verde, Namibia, South Africa, Madagascar: Tsunami Scenario Catalog
- Europe Flood: Inland flooding models and maps for the UK, France, Belgium, Germany, Austria, Czech Republic, Hungary, Lichtenstein, Luxembourg, Poland, Slovakia and Switzerland, capturing pluvial and fluvial flooding. Taiwan Flood Maps: Inland Flood maps covering both monsoon and typhoon driven events.
- HD Japan Typhoon Model: Includes storm surge and precipitation driven inland flooding. HD New Zealand Earthquake Model: Includes a probabilistic tsunami model. US Flood Maps: Cover fluvial and pluvial inland flood, plus inland flooding events driven by precipitation from hurricanes. Thailand Flood Maps: Captures both typhoon driven and monsoon driven inland flooding.
- HD U.S. Inland Flood Model: Probabilistic flood model covering fluvial and pluvial inland flood, plus inland flooding events driven by precipitation from hurricanes. HD Asia Typhoon Model: Basin-wide event set to capture loss correlation between countries. New South Korea and Taiwan typhoon models to be incorporated with the Japan typhoon 2016 update in 2017. Beyond 2017 China, Vietnam and the Philippines to be added. The Pan-Asian model will capture both storm surge and precipitation driven inland flooding. Indonesia Flood Maps: Captures both typhoon driven and monsoon driven inland flooding. HD North America Earthquake Model: Includes a probabilistic tsunami model for Canada, Mexico and the U.S. HD Japan Earthquake Model: Includes a probabilistic tsunami model.
- From RMS 2013/2014 UK WS-FL report: The recent publication from Jongman et al. (2014) gave robust evidence that flood risk in Europe is increasing. The study estimates that, mainly because of socio-economic growth and change in precipitation patterns, European flood risk could more than double by 2050. Large events are forecasted to hit across political boundaries, with particular evidence in case of unfavorable antecedent conditions as the one leading to the large Central Europe Flood in June 2013 (RMS Blog, 2013) , which produced major losses in Germany, Austria, Czech Republic and Slovakia. Climate models forecast increased episodes of flooding for the U.K. under climate change conditions (U.K. Meteorological Office, 2011). Some commentators claim that an upward trend in extreme rainfall events over the past years is already apparent in the meteorological record (Harrabin, 2013). Peer-reviewed scientific research, performed by academics in collaboration with RMS scientists, found that climate change increased the likelihood of the floods that impacted England and Wales in the year 2000 (Pall et al., 2011). With regard to the wind peril, initial climate projections from the Intergovernmental Panel on Climate Change (IPCC) indicated a potentially more active storm track for Europe and a greater penetration of storms into western Europe (e.g., Bengtsson et al., 2006). However, more recent studies show weaker signals (e.g., Zappa et al., 2013). The current view from the IPCC is that ―substantial uncertainty and thus low confidence remains in projecting changes in Northern Hemisphere storm tracks, especially for the North Atlantic Basin.‖ Although RMS continues to monitor scientific developments in this crucial area of research, RMS considers that understanding the inherent storm variability is more relevant and of greater concern for the insurance industry than climate change. The climate change signal is currently weak, and the impact may spread over several decades, thus leaving time for the industry to adapt to it. RMS discussed this issue together with external academic experts at a workshop that RMS jointly hosted with the Bermuda based Risk Prediction Initiative (RPI) in October 2013. A key conclusion from this workshop was that a sophisticated catastrophe model should give the user the possibility of exploring different views around storm variability, based on different wind period calibrations (as reported in Marescot & Mark, 2013). RMS is therefore working on solutions that will help clients further Winter 2013/2014 Storms in Europe Modeling Challenges CONFIDENTIAL 29 explore the uncertainty and variability inherent to this complex peril, by giving access to different view of risks.