Transformative Household-level flood and hurricane risk ...devika/evac/Rice-OEM... ·...

transcript

Transformative Household-level flood and hurricane risk assessment tools

for the City of Houston

Leonardo Dueñas-Osorio (CEVE)

Robert Stein (POLI)

Devika Subramanian (COMP)

Candase Arnold, Brian Bue, Rachel CarlsonWilliam McGuinness, Alexander Tribble, Jeffrey Weed

Office of Emergency ManagementHouston, Texas

January 22, 2008

Presentation Outline

I. Research Motivation

II. Risks from wind and flood hazards

III. Structural risk estimation

IV. Risk computation and visualization

V. Perceived risk characterization

VI. Summary and future work

I. Research motivation

Collective Dynamics

• Hurricane Rita and Houston’s Infrastructure Usage

Evacuation Zoning

• Current disaster management strategies

ZIP code level evacuation policy

- Requires large scale evacuation

- Relies on intercity sheltering

- Aggregates data at the ZIP code level

Structural Safety Tagging

• Transformative disaster management strategies

- Avoids unnecessary evacuation

- Encourages intra-city sheltering

- Offers flexibility of data aggregation

- Reveals geospatial variability of risk

Household-level risk assessment strategy

SAFESAFESAFE

SAFESAFEUNSAFEUNSAFE

UNSAFE

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Agent-Based Modeling

• Multi-disciplinary Approach

Estimation of floodand wind risk at

individualhouseholds

Behavioralcharacterization

of households

Agent-basedmodeling of

evacuation andsheltering

Data-Rich Environment

Demographic characterization

Post-disaster utility availability

Best intra-city sheltering and escape routes

Evacuation policies

What is the actual risk?

What is the perceived risk?

What are the resulting collective dynamics?

II. Risks from wind and flood hazards

Geocoding

Visualization

Database of residences

Computational Risk Assessment

Risk Estimation Methodology

Data Source 1

• Harris County Appraisal District (HCAD) data

– Aimed towards keeping detailed Harris County property tax records

– Exterior wall type, property dimensions, assessed structure quality (A-F), foundation type, year built

– Contains approx. 1 million residential properties

Data Source 2

• Hurricane Rita survey data (Stein, 2005)

– Performed shortly after the events of Rita (09/05)

– Input from 648 homeowners

– 216/407 total in Harris County

– 175/216 located in HCAD db (thus far)

– Wind and flood risks manually assessed by civil engineers in our group (214/216)

Geocoding

• Geocoding: street address → (latitude, longitude), elevation

• Addresses are automatically coded via Google maps web interface

• Performance: ~ 10k addresses / hour

Geocoding is essential for assessing geospatial proximity – relevant for individual household flood and wind risk assessment

Automated Risk Assessment

• Problem with source data 1: far too many properties in HCAD to characterize manually

• Approach: computationally assess risks by analyzing residence characteristics in HCAD data for given hazard scenarios. Use geospatial proximity and similarity of structural properties to scale up household level evaluation sample size.

III. Structural risk estimation

HAZUS-MH

• HAZards United States – Multi-Hazards

• GIS-Based Program freely distributed by FEMA

• Developed for institutions

• Evaluates hurricanes, flooding, and earthquakes

Leveraging the HAZUS Framework

• HAZUS

– Evaluates vulnerability at the census-tract level

– Informs about evacuation for entire areas, regardless of building vulnerability

– Contains specific structural performance data that is useful when detailed inventory is available

– Available only to GIS users

– Complex data outputs

• Rice/OEM

– Evaluates vulnerability at the building level

– Allows for household-level evacuation decisions

– Uses public HCAD inventory that is useful when structural behavior data for individuals households is available

– Available to anyone with Internet access / mobile phone

– Easy to use output scale

Hazard Scenarios for Pilot Test

• Choose scenario for:– Wind (“Rita-like”

hurricane)– Flooding (100-yr flow

+ surge)

• Determine wind risk and flood risk for area households

• Once one scenario is run, ANY scenario can be run

III. a. Wind scenario

HAZUS Fragility Curves

• A fragility curve provides the probability of specific damage states as a function of hazard intensity.

• HAZUS contains specific fragility curves for multiple building types.

• 80/112 applicable residential housing fragility types for Houston.

Wind fragility curve

Parametric HAZUS Fragility Curves

• HAZUS curves are prototypical of common structural types. Parameters include:– Design (stories, garage, roof shape)

– Construction (roofing nail size, roof/wall connection)

– Location (surrounding terrain)

• HCAD data contains indirect information to determine HAZUS parameters:– Year Built � Existence of roof straps

– Examining perimeter of floor v. total sq. footage �Number of Floors

– Exterior wall type � Structure type

Coupling HCAD and HAZUS

• Use HAZUS fragility curves to develop HCAD fragility curves

• Report risk assessment at the household level

• Validate and compare Rice/OEM approach with HAZUS and building inspectors

- Average nailing patterns per structural type

- Average roof configurations

- Average of houses with and without shutters

HCAD Parameters in HAZUS

HAZUS Fragility Curves

Implies highly accurate knowledge of individual structures

Creating HCAD Fragility Curves

• Group similar HAZUS types– Averaged the fragility of similar homes

– Clustered 80 curves -> 24 curves– Maintained variability with confidence bands

• Obtained log-normal probability distribution parameters– Creates smooth graph

– Searchable for ANY wind speed

– Useful to report risk estimates at the household level

An HCAD fragility curve

Wind Risk Assessment

• HCAD fragility curves determine damage likelihood for a given wind speed

• Plug scenario-determined wind and estimate vulnerability from curve

• Determine risk from a Rita-like storm to all area households

III. b. Flooding scenario

Floodplain Map Challenges

• Floodplain Maps

– Hazard based

– Flooded or not flooded

– Few rainfall scenarios

– Only one surge scenario

• Rice/OEM Output

– Risk-based

– Allow for water depth to be calculated

– ANY rainfall scenario

– ANY surge scenario

Flooding Profiles

Using TSARP Models in Corps of Engineers’ HEC-RAS – Harris Gully to create Floodplain Map

Longitudinal Centerline Profile

Stream Cross-section

Flooding Area

Overlaying LIDAR Data in ArcGIS –Harris Gully 100-yr storm

Flooding Vulnerability

Comparing Data and Creating Coordinate-Searchable Map – Harris Gully 100-yr storm with 7’ surge

Expanding scenario to area within Beltway 8

Creation of “Category 5” scenario – 500-yr storm, 25’ surge

IV. Risk computation and visualization

Output Visualization

Web Interface

• Web access:– Risk assessment

information tailored to each address

• Access via Google Earth:– All data can be

exported to a Google Earth-compatible format (for offline access)

Household-Level Flood Risk Analysis

Regional Flood Hazard Analysis

Image credit: TSARP

Perceived vs. Estimated Flood Risk

Perceived risk Estimated risk

For the Houston Beltway Area:

V. Perceived risk characterization

The Policy Problem

• Increasing numbers of persons (households) are making choices in the face of severe weather experiences that put themselves and others at risk (e.g., congestion of roadways and shelters).

Multidisciplinary Research Questions

• How are perceptions about risk from severe weather experiences related to estimated risk?

• How are perceptions about risk from severe weather experiences and estimated risk related to evacuation behavior?

• What are the most efficacious strategies for informing/persuading (as opposed to coercing) the public about the estimated risks from severe weather experiences and how best to respond?

Research Design

• Rice/Houston Chronicle Survey– Sept. 29 – Oct. 3, 2005– 650 Households from 8 county Houston area– 85% response rate– Analysis limited to Harris County residents

N=216 with some missing data.

• Residential property’s vulnerability to flooding in Harris County

Evacuation behavior Harris County

• Did you evacuate or attempt to evacuate your home to go someplace safer before Hurricane Rita hit?

• Yes: 51%• No: 49%

N = 216

Estimated vulnerability to Flooding

• Likelihood residential structure would flood in severe storm from Tropical Storm Allison Recovery Project:

– 5: houses are well within the 100-yr floodplain

– 4: houses on the fringe of the 100-yr floodplain

– 3: houses outside the 100-yr floodplain

– 2: houses are the edge of the 500-yr floodplain

– 1: houses far from any floodplains

Perceived vulnerability

• Now, aside from the risk of an initial storm surge, consider the risks of flooding from rain and rising water from hurricane Rita. In your opinion was your residence/home located in a high risk, a medium risk or a low-risk area for flooding from rain and rising water from Hurricane Rita.

High: 19%Medium: 19%Low: 60%Don’t know 2%

Evacuation behavior by location

Chi-square: 22.7Df: 1Sig.: .000

80 49 129

76.9% 45.0% 60.6%

24 60 84

23.1% 55.0% 39.4%

104 109 213

100.0% 100.0% 100.0%

% within Decisionto evacuate

Not in a mandatoryevacuation zone

In mandatoryevacuation zone

Evacuationarea

Did notevacuate

Evacuatedor attempted

Decision to evacuate

Percent by column

Perceived and Estimated Flooding Risk

Chi-square: 8.6Df: 8Sig: .3711-3 =low, 4=medium, 5=high

17 16 5 5 4 47

18.7% 22.5% 25.0% 29.4% 44.4% 22.6%

19 11 6 4 3 43

20.9% 15.5% 30.0% 23.5% 33.3% 20.7%

55 44 9 8 2 118

60.4% 62.0% 45.0% 47.1% 22.2% 56.7%

91 71 20 17 9 208

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

% within NewFlood rain vulner

Medium

Perceived riskfrom flooding

1-Low 2 3 4 5-High

Estimated flood risk

Percent by column

Misperception of Risk for Cat. 5 Event

43 20.2 20.7

47 22.1 43.3

118 55.4 100.0

208 97.7

213 100.0

Under estimated risk

Correctly estimated risk

Over estimated risk

SystemMissing

Frequency PercentCumulative

Percent

Perceived Risk and Evacuation Behavior

25 42 196 263

17.7% 29.2% 56.6% 41.7%

116 102 150 368

82.3% 70.8% 43.4% 58.3%

141 144 346 631

100.0% 100.0% 100.0% 100.0%

Did not evacuate

Evacuated or attempted

Decision toevacuate

High Medium Low

Perceived risk from Flooding

Chi square: 74.2DF: 2Sig.: .000

Percent by column

Estimated Risk and Evacuation Behavior

Chi-square: 5.5Df: 2Sig.: .06

38 41 25 104

40.0% 57.7% 53.2% 48.8%

57 30 22 109

60.0% 42.3% 46.8% 51.2%

95 71 47 213

100.0% 100.0% 100.0% 100.0%

% within New_Risk_Flood

Did not evacuate

Decision toevacuate

Low Medium High

Risk from Flooding

Percent by column

Estimation of Risk and Evacuation Behavior

Chi-square: 5.8Df: 2Sig. .05

18 18 67 103

41.9% 38.3% 56.8% 49.5%

25 29 51 105

58.1% 61.7% 43.2% 50.5%

43 47 118 208

100.0% 100.0% 100.0% 100.0%

Did not evacuate

Decision toevacuate

Underestimated risk

Correctlyestimated risk

Overestimated risk

Estimated Risk

Percent by column

VI. Summary and future work

Summary

• Citizens generally over estimate their risk leading to risk averse behavior i.e., evacuation

• Risks due to wind and flooding can be estimated dynamically at the household level

• Information about risk can be disseminated to the resident in real time

• Can behavior be modified by the dissemination of information about estimated risk?

Future Work

• Demonstrate:– Combined risk assessment for wind, flood

and surge– Adequacy of evacuation strategies– Location of shelter for optimal coverage

• Quantify human factors in evacuation planning and response

• Develop agent-based models• Explore unconventional disaster

management strategies and perform sensitivity analysis

Thank You

Questions and Discussion

Transformative Household-level flood and hurricane risk ...devika/evac/Rice-OEM... ·...

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