fema’s recent lessons: gis modeling and analytics

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FEMA’s Recent Lessons: GIS Modeling and Analytics MOTF Analytics Briefing Doug Bausch, [email protected] April 1 st , 2015

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Page 1: FEMA’s Recent Lessons: GIS Modeling and Analytics

FEMA’s Recent Lessons: GIS Modeling and

Analytics

MOTF Analytics Briefing

Doug Bausch, [email protected]

April 1st, 2015

Page 2: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected] 2

FEMA’s Modeling Task Force (MOTF)

On-call, distributed analysis • Real-time analysis to affect decisions

• Some pre-analysis

Ground-Truthing

• Field observations

• Imagery based assessments

Applications

Sandy, Katrina examples

Page 3: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

MOTF Aligns to NRCC Organization

3

Situational Awareness

Section Chief

Geospatial and Technical Group

Supervisor

GIS Unit Leader Technical Unit

Leader Modeling Task Force (MOTF)

Remote Support:

Denver, CO

MOTF

NRCC & Regions

Academia & Applied

Research

DHS S&T/National

Labs

State & Local Field

Observations Data Collection

Remote Sensing

Other Federal Agencies

(USGS, NHC, USACE)

MOTF

NRCC Situational Awareness

Rental Assistance

State/local partners

Housing Task Force

Power Task Force

FEMA Regions

Public Assistance

Page 4: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected] HAZUS - Role of

Clearinghouses

June

19th, 2007

Benefits of Depth Grids

Cost effective approach when good terrain and flood surface data are available

Data collection focus=HWM and DEM (lidar)

Can be generated from pre-event modeling and scenarios

Supports multiple phases of disaster response. Access determinations, expedited aid, etc.

Page 5: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

• “Ground-truthed” depth grid

Page 6: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Oct 26 Oct 29 Nov 1 Nov 5 Nov 7 Nov 11

Hurricane Sandy MOTF Timeline

NRCC Activation,

USGS Deployment Hurricane Sandy County

Risk Matrix

Landfall Migration to impact

analysis, Hindcast

SLOSH, portal updates

Hybrid hindcast

SLOSH, preliminary

HWM observations

(±150), expedited

rental assistance,

portal updates

Field verified inundation

(±300 observations),

medium resolution,

portal, expedited

assistance, NFIP

updates

Field verified inundation

(±1,000 observations),

high resolution, LiDAR-

based, major portal,

expedited assistance,

NFIP updates

High resolution (3 ft

LiDAR) observed

inundation NYC,

Nor’easter surge

modeling

Page 7: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Individual Assistance Damage Levels

Housing Task

Force

Building

Inspection

Response

Substantial

Damage

Estimation

Debris

Estimation

Inundation

Model to

Observations

Imagery

Assessment

IA Inspection

(FVL)

Affected

Minor

Major

Destroyed

Affected

Assessment

Source Applications

Page 9: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Extensive

metadata and

download

link: https://content.fem

adata.com/

Page 10: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Hurricane Sandy County Impact Analysis

Page 11: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Sandy, 2012: Field Observed

Flood Surface

Page 12: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected] 12

Sandy 2012: Expedited Assistance

-44K households identified as damaged and inaccessible

-Over $130M in expedited assistance based on accurate and rapid flood depth information

Page 13: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Analysis for NFIP Impacts Flood depths assigned to

200,000+ policies

Integrated into request to increase borrowing authority – $9.7 billion

Page 14: FEMA’s Recent Lessons: GIS Modeling and Analytics

[email protected]

Questions?

Lessons?

Ground-truthing—Obtaining and incorporating field based observations

Requires year round dedicated effort—pre-analysis, coordination, methodology, product development and calibration.