A DATA PLANNING FRAMEWORK FOR DISASTER RESPONSE

KEN KEISER MANIL MASKEY*UNIVERSITY OF ALABAMA IN HUNTSVILLE

ESIP Summer Meeting Session on: Data System Architecture in Support of Disaster Response and Awareness

Preparedness vs. Reaction• “...the aftermath of a major disaster is no time to be

exchanging business cards.”• Planning and preparedness can greatly improve the quality

and latency of responses to events.•  Good planning leads to organized and effective

emergency response.  • Emergency preparedness means taking action to be ready

for emergencies before they happen. • The objective of emergency preparedness is to simplify

decision-making during emergencies.Emergency Preparedness and Response, Some Issues and Challenges  Associated with Major  Emergency Incidents,  Statement of William O. Jenkins, Jr., Director Homeland Security and Justice Issues, United States Government Accountability Office Report GAO-06-467T, 2006.

Goals of the NASA Applied Science Feasibility Project• Use Event-Driven Data Delivery (ED3) to prepare for data

needs prior to disaster (and other) events.• Demonstrate the feasibility of an ED3 framework to

support improved data preparedness for Decision Support Systems, applications, and users.

• Provide reusable framework components that can support different events, disciplines, and data and processing needs.

Acknowledgements: This research is supported by the National Aeronautic and Space Administration grant NNX12AP73G. The project team includes PI Sara Graves and Co-Is Udaysankar Nair and Ken Keiser, all at the University of Alabama in Huntsville. Frank Lindsay is the NASA Applied Science program manager for this project.

Data Preparedness Plans & Services

Service Layer

Plan Database

Events Data Workflows

Decision Systems and Users

Preparedness Plan: • For this event type• Meeting this criteria, • Do this processing

Decision Support Systems & Users

Decision Systems & Users

Custom Applications

Existing decision support systems

Service Layer

Plans Preparedness Plan: • For this event type• Meeting this criteria, • Do this processing

Any authorized system may generate and submit plans

Events Trigger Plans

Service Layer

Plans

Event Generators

Common Alerting Protocol (CAP)

Event Listener

Trigger MatchingPreparedness Plans

Event Generators

• If the plan is for this type of event, and

• if specifies this criteria,

• then execute plan

Workflow Manager

Workflow Manager

Process Plan Workflows

Service Layer

Plans

Workflow Managers

Data Access

SensorTasking

Product Generation

Process and Package

Request Open Jobs of Supported Processing

Receive Jobs To Be Processed

Data Repositories

ISERV

Workflow manager components can be specialized for different types of data and processing. Multiple workflow managers can service a single plan.

Virtual Products

Pre-negotiate access and agreements

Near –Future?

Workflow Managers

Workflow Managers

System/User Notifications

Service Layer

Plans

Event Listener Workflow

Managers

Event Detection

Workflow Processing

Plan Generation

Notifications by email, call-back functions, and others as necessary.

ED3 Use Case for NGCHC

Matching Plan is selected by the

proper Workflow Manager for

execution

Notification of plan execution and

results are sent to NGCHC viz/situational

awareness tool

Requested data is retrieved and packaged

storm notification issued and picked

up by Event Listener

Storm advisories determine potential for storm in area of

interest

DSS Creates Preparedness Plan based on occurrence

of tropical storm events

Event / Prediction

Data sources:ArchivesRegional ObservationsModelersAnalysts

Data Types:Model OutputsCSVKMLW*S

Working with RENCI for ADCIRC outputs

Aggregation of Models, Observations, and Analysis for the events with subscribed data

Example Flood Use Case

Matching Plan is selected by the

proper Workflow Manager for

execution

Notification of plan execution and

results are sent to DSS

Requested data is retrieved and

higher resolution modeling run

initiated

Flood Potential notification issued and picked up by

Event Listener

Regional flood model determines

potential for flood in area of interest

DSS Creates Preparedness Plan based on occurrence

of Flood Potential

Topography

Rainfall

Soil Moisture

Model

Event / Prediction

Data Inputs

Alabama Past and Potential Disaster Threats and Example Data Needs

• Tornado - Aerial photos as well as Landsat, SPOT, and other International Charter satellite data (April 2011 massive example)

• Environmental - Satellite and aerial data (color and IR) (DeepwaterHorizon as an example)

• Hurricane – Aerial imagery, LANDSAT, LIDAR, MODIS for during/after event analysis

• Winter/Ice Storms - Soil-based data such as USDA/NRCS soils vector and tabular data

• Earthquake – (pre and post-event) high-res satellite imagery, aerial imagery, RADAR and LiDAR data (to generate interferograms)

• Landslide - Aerial imagery, high-res elevation data, RADAR and LIDAR, for change detection and slope analysis

• Sinkholes - LiDAR data and aerial imagery (4-band)• Flood – Aerial and LIDAR, elevation and floodplain

data• Drought – LANDSAT, aerial imagery and spectral data

for vegetation classification and analysis• Wildfire – Thermal data during fires, and visual land

data for pre/post comparisons and analysis• Tsunami - Aerial imagery, LANDSAT, LIDAR, MODIS

for analysis during and after the event• Radiological – Thermal and reflected data for both

aerial and satellite coverage

Alabama State Emergency Support  Functions and Responsible Agencies/Departments• Transportation: AL DOT and AL EMA• Communications: AL EMA• Public Works and Engineering: AL DOT and public Utilities• Firefighting: AL Forestry Commission• Emergency Management: AL EMA• Mass Care: Emergency Assistance and Housing and Human Services• Logistics Management and Resource Support: AL EMA• Public Health and Medical Services: Dept of Public Health• Search and Rescue: AL EMA• Oil and Hazardous Materials Response: ADEM• Agriculture and Natural Resources: AL Dept of Agriculture and Industries,

AL Dept of Conservation and Natural Resources• Energy: ADECA (utilities)• Public Safety and Security: AL Dept of Public Safety• Long-Term Community Recovery: Govenor’sOffice• External Affairs: AL EMA

Ongoing and Planned Use Cases• Hurricane/Coastal Impacts – UAH ITSC with Northern

Gulf Coastal Hazards Collaboratory (NG CHC) participants. • Land Slide Potential – UAH Atmospheric Science and

Geological Survey of Alabama, NH CHC• SERVIR – Integrating event notifications with SERVIR data

processing workflows to provide more rapid response to some events and potential tasking of the ISERV instrument.

• Flood Prediction – UAH Atmospheric Science, SERVIR (international), and GSA (S.E U.S. regional)

• Super Fog Transportation Conditions – UAH Atmospheric Science, ITSC and Alabama Forestry Commission

• Intelligence Community – ITSC and IC partners

Use Case: Super Fog Transportation Conditions

• Interface with previous Applied Science project that models the air quality and visibility impacts of controlled burns within the state.

• Collaboration with the Alabama Forestry Commission• Potential event generator for transportation warnings and impacts

on other smoke sensitive features.

Alabama Forestry Commission

PermitDB

University of Alabama In Huntsville

NASA Data

Atmospheric Conditions

Dispersionand AQ Models

Output

CurrentPermits

GISDb

GeoServer

Web GIS Environment

Virtual Alabama

1

2

3

4

6

7

High-Level Architecture

Current AQ conditions

AQ and VisibilityWarnings/Notifications

5

Challenges• Generically handling event notices from multiple sources

• Generalizing event criteria across event types

• Supporting and handling all data workflows

• Communicating processing notices in various forms to support variety of DSS approaches

Lessons Learned• Keep architecture loosely coupled to external components

• Identify interested stakeholders early and work closely with them to identify requirements

• Reference Implementations are extremely useful to work out integration problems

Project Participants• Project Team

• Sara Graves (PI) – UAH Information Technology and Systems Center• Udaysankar Nair (Co-I) UAH Atmospheric Science Dept.

• Current Collaborators• Global Hydrology Resource Center (GHRC) – NASA/MSFC• Northern Gulf Coastal Hazards Collaboratory (LA, MS, AL)• Geological Survey of Alabama• SERVIR/ISERV

• Reference Implementation• Http://ed3test.itsc.uah.edu/

Acknowledgements: This research is supported by the National Aeronautic and Space Administration grant NNX12AP73G. Frank Lindsay is the NASA Applied Science program manager for this project.