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OGC Engineering Report

OGC Disaster Pilot: User Readiness Guide
OGC Engineering Report

Published

Document number:21-075r2
Document type:OGC Engineering Report
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Document stage:Published
Document language:English

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Use of this document is subject to the license agreement at https://www.ogc.org/license



I.  Contributors

All questions regarding this document should be directed to the editors or the contributors:

NameOrganizationRole
Andrew LavenderPixalytics LtdEditor
Sam LavenderPixalytics LtdEditor
Ryan AholaNatural Resources CanadaContributor
Stefano BagliGECOsistemaContributor
Omar BarrileroEuropean Union Satellite CentreContributor
Dave BorgesNASAContributor
Harrison ChoUSGS-GeoPathwaysContributor
Paul ChurchyardHSR.healthContributor
Antonio CorreasSkymanticsContributor
Katharina Demmich52 NorthContributor
Patrick DionEcereContributor
Rich FrazierUSGS-GeoPathwaysContributor
Theo GoetemannBasil LabsContributor
Ajay K GuptaHSR.healthContributor
Omar HeribaUSGS-GeoPathwaysContributor
Dean HintzSafe SoftwareContributor
Josh HusseyCompusult LimitedContributor
Jérôme Jacovella-St-LouisEcereContributor
Amy JeuGeospatial Information Systems and Mapping OrganizationContributor
Dave JonesStormCenter CommunicationsContributor
Brennan JordanUSGS-GeoPathwaysContributor
Travis KehlerDualityContributor
Albert KettnerRSS-HydroContributor
Alan LeidnerGeospatial Information Systems and Mapping OrganizationContributor
Adrian LunaEuropean Union Satellite CentreContributor
Jason MacDonaldCompusult LimitedContributor
Niall McCarthyCrust TechContributor
Vaishnavi RaghavajosyulaUSGS-GeoPathwaysContributor
Carl ReedCarl Reed and AssociatesContributor
Sara SadriUN UniversityContributor
Johannes Schnell52 NorthContributor
Guy SchumannRSS-HydroContributor
Sumit SenIIT BombayContributor
Sunil ShahDualityContributor
Harsha SomayaUSGS-GeoPathwaysContributor
John Christian SwansonUSGS-GeoPathwaysContributor
Ian TobiaUSGS-GeoPathwaysContributor
Jiin WenburnsGeospatial Information Systems and Mapping OrganizationContributor
Colin WithersCompusult LimitedContributor
Peng YueWuhan UniversityContributor

II.  Abstract

Disasters are geographic events and therefore, geospatial information, tools and applications have the potential to support the management of, and response to, disaster scenarios.

However, the use of geospatial data varies significantly across disaster and emergency communities. This can often make it difficult to share information between different organizations, and sometimes even within the same organization, involved in disaster response. This could mean that not everyone involved will have the same situational awareness information.

There are many reasons for why geospatial information is fully used and exploited, included a lack of awareness of what geospatial options are available, lack of geospatial technology and skills, lack of funding, etc. The Disaster Pilot User Guide aims to address some of these issues by providing a non-technical showcase of the workflows and tools developed by the Pilot participants demonstrating what opportunities there are for disaster and emergency management communities to use geospatial solutions in practice.

For over 20 years, the Open Geospatial Consortium (OGC) has been working on the challenges of information sharing for emergency and disaster planning, management, and response. In Disaster Pilot 23 (DP23) the aims were to:

As part of DP23, a trilogy of Guides were developed to improve knowledge and understanding of how geospatial data and tools and could support disaster and emergency communities. Alongside the User Guide is a Provider Guide giving all the detail technical details behind the work, and a companion Operational Capacity Guide describing the steps needed to develop geospatial readiness.

The User Guide contains a summary of the work undertaken in DP23, and Disaster Pilot 21 (DP21), where participants have worked on disaster scenarios relating to:

Case Studies have focused on the hazards of drought in Manitoba, Canada; wildland fires in western United States; flooding in the Red River basin, Canada; landslides and flooding in Peru; and Pandemic response in Louisiana, United States. The participants have developed various data flows, alongside tools to support the collection, discovery, or visualization of data to support disaster management and response.

Annex A describes the tools and applications developed within the Pilots alongside the benefits these can offers. The Guide finishes with details of future possibilities, and where the Disaster Pilot initiative could focus next. Annexes B to E give descriptions of the data flows developed, including the aspects of disaster management or response the data flow relates to; together with the benefits it offers and the type of decisions it can support.

This document is for first responders, emergency managers, decision-makers, and anyone interested in encouraging disaster and emergency communities to realize the value of geospatial data to save lives and limit damage.

1.  Introduction

Although there are varying definitions as to what constitutes a disaster event, the general consensus is that the number of these events are increasing. In September 2021, the World Health Organization (WHO) released ‘The Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019)’ calculated using data from the Centre for Research on the Epidemiology of Disasters (CRED). The report summarizes that over the last 50 years, 50% of all recorded disasters, 45% of related deaths and 74% of related economic losses were due to weather, climate and water-related events, translating to 2.06 million deaths, and US$ 3.6 trillion in economic losses. WHO also noted that the number of weather, climate, and water extreme events are increasing and will become more frequent and severe in many parts of the world as a result of climate change.

CRED defines a disaster as ‘a situation or event that overwhelms local capacity, necessitating a request at national or international level for external assistance; an unforeseen event that causes great damage, destruction and human suffering.’ In 2022, there were 387 events recorded by CRED, slightly above the average for the 2002-2021 period. These events resulted in 30 704 deaths, impacting the lives of 185 million people, and creating economic losses of at least US$223.8 billion. The most common disaster in 2022 was flooding with 176 events, 5% higher than the twenty-year average, followed by storms, earthquakes, droughts, landslides and wildfires. The geographical spread shows that 53% of disaster events occurred in Europe due to the heatwave last year, with 24% in Asia, 16% in Africa, and 5% in the Americas. Despite the lower geographical spread, the biggest economic losses from disasters were both in America with Hurricane Ian and drought losses accounting for US$100 billion and US$22 billion, respectively.

2023 also saw significant natural disaster events, including storms in the United States, New Zealand & Mozambique; earthquakes in Turkey, Syria & Afghanistan; flooding in Australia and Asia; heat waves in Asia and Europe; and the year saw significant wildfire events across America, Canada and Europe.

1.1.  Using Geospatial Information For Disaster Planning & Response

Disasters are geographic events that occur in a specific location and impact the people, economy, and society in that, and the surrounding areas — often tens, or even hundreds, of miles away. For this reason, geospatial information has been shown to be effective supporting both the understanding of, and response to, disaster scenarios. The WHO report above also notes that the death toll from the weather, climate and water extremes have fallen significantly over the last 50 years due to the introduction of early warning systems including the use of geospatial information.

Geospatial tools and applications have the potential to save lives and limit damage, and the world is becoming better at using these resources. Unfortunately, the ability to share, use, and reuse geospatial information and applications across, and between, organizations within disaster and emergency communities, both governmental and non-governmental, requires the right partnerships, policies, standards, architecture and technologies to be in place before the disaster strikes.

For over 20 years the Open Geospatial Consortium (OGC) has worked on the challenges of information sharing for emergency and disaster planning, management, and response. The Disaster Pilot activities are part of the OGC Collaborative Solutions and Innovation Program (COSI) and aims to address the gaps, and provide support and guidance on how disasters and emergency communities can enhance their sharing and use of geospatial information and applications.

Disaster Pilot 23 (DP23) is the latest in a series of initiatives, focusing on:

  • Developing flexible, scalable, timely and resilient information workflows to support critical disaster management decisions, enabling stakeholder collaboration, and

  • Providing applications and visualization tools to promote the wider understanding of how geospatial data can support emergency and disaster communities.

This User Guide aims to provide disaster and emergency management decision makers and first responders with a non-technical showcase of the possibilities of the workflows and tools developed by DP23, and previous Pilot, participants with the hope that some of these may be integrated into operational centers and working practices.

Geospatial information offers huge potential resources to enable disaster and emergency communities to enhance their planning, prediction and response to disaster events. It is hoped that the work of the Pilot can contribute towards this, helping save more lives and reducing the impact of disasters on communities.

2.  Terms, definitions and abbreviated terms

This document uses the terms defined in OGC Policy Directive 49, which is based on the ISO/IEC Directives, Part 2, Rules for the structure and drafting of International Standards. In particular, the word “shall” (not “must”) is the verb form used to indicate a requirement to be strictly followed to conform to this document and OGC documents do not use the equivalent phrases in the ISO/IEC Directives, Part 2.

This document also uses terms defined in the OGC Standard for Modular specifications (OGC 08-131r3), also known as the ‘ModSpec’. The definitions of terms such as standard, specification, requirement, and conformance test are provided in the ModSpec.

For the purposes of this document, the following additional terms and definitions apply.

2.1.  Terms and definitions

2.1.1. ARD; Analysis Ready Data and datasets

raw data that have had some initial processing, created in a format that can be immediately integrated with other information and used within a Geographic Information System (GIS)

2.1.2. DRI; Decision Ready Information and indicators

ARDs that have undergone further processing to create information and knowledge in a format that provides specific support for actions and decisions that have to be made about the disaster

2.1.3. Indicator

realistic and measurable criteria

2.2.  Abbreviated terms

ACD

Amplitude Change Detection

AI

Artificial Intelligence

AMSR-E

Advanced Microwave Scanning Radiometer for EOS

API

Application Programming Interface

AR

Augmented Reality

ARD

Analysis Ready Data

ASMR-2

Advanced Microwave Scanning Radiometer 2

CAMS

Copernicus Atmosphere Monitoring Service

CDI

Combined Drought Indicator

CNES

French Space Agency

CONIDA

National Commission for Aerospace Research and Development’s, Peru

COSI

OGC Collaborative Solutions & Innovation Program

CRED

Centre for Research on the Epidemiology of Disasters

CSA

Canadian Space Agency

DARSIM

Disaster Augmented Reality Simulation Table

DEM

Digital Elevation Model

DP21

Disaster Pilot 21

DP23

Disaster Pilot 23

DRI

Decision Ready Indicator

DT

Digital Twin

EGS

Natural Resources Canada’s Emergency Geomatics Service

ENSO

El Niño/Southern Oscillation

EO

Earth Observation

EODMS

Natural Resources Canada’s Earth Observation Data Management Service

ESA

European Space Agency

ESIP

Earth Science Information Partners

FAIR

Findability, Accessibility, Interoperability, and Reuse of digital asset

FME

Feature Manipulation Engine (Safe Software)

GDO

Copernicus Global Drought Observatory

GEPS

Global Ensemble Predication Service

GIS

Geographic Information System

GISMO

New York City Geospatial Information System & Mapping Organization

ICU

Intensive Care Unit

IR

InfraRed

JAXA

Japan Aerospace Exploration Agency

JSON

JavaScript Object Notation

LSTM

Long Short-Term Memory

ML

Machine Learning

MRLC

Multi-Resolution Land Characteristics

MSC

Meteorological Service of Canada

MTC

Multi-Temporal and Coherence

NASA

National Aeronautics & Space Administration

NDVI

Normalized Difference Vegetation Index

NLCD

US National Land Cover Database

NOAA

US National Oceanic and Atmospheric Administration

NRCan

Natural Resources Canada’s

OGC

Open Geospatial Consortium

OSM

OpenStreetMap

PHC

Public Health Center

PPE

Personal Protective equipment

SAR

Synthetic Aperture Radar

SatCen

European Union Satellite Centre

SDG

Sustainable Development Goal

SEDAC

NASA’s Socioeconomic Data Applications Center

SPDI

Standardized Palmer Drought Index

SPEI

Standardized Precipitation Evapotranspiration index

SPoG

Single Pane of Glass

SST

Sea Surface Temperature

USGS

US Geological Survey

VIIRS

Visible Infrared Imaging Radiometer Suite

VR

Virtual Reality

WHO

World Health Organisation

WMS

Web Mapping Service

WUI

Wildland-Urban Interface

XR

Extended Reality

3.  User Guide

The User Guide is one of a trilogy of Guides being developed through Disaster Pilot 2023, alongside the Provider Guide and the Operational Capacity Guide. These three guides are complementary documents, with clear links between them to direct readers to further information, as shown in Figure 1

Fig-guides-relationship

Figure 1 — Three Guides.

The details of the three guides are:

3.1.  User Guide

  • Audience: First responders, emergency managers, decision-makers, and associated people interested in using the geospatial data in their work, or encouraging culture change to realize the potential value of having available geospatial data to support disaster events.

  • Purpose: Aims to provide a non-technical showcase of the workflows and tools demonstrating what is possible and what opportunities there are for disaster and emergency management communities to use these solutions to support & enhance disaster planning, management, and response.

3.2.  Operational Capacity Guide

  • Audience: Disaster and emergency management administrators, operational managers, policy makers, emergency management program funding functions, together with emergency management teams and information technology support functions.

  • Purpose: Stand-alone document, providing an outline of the strategic actions required for any disaster or emergency management team who wish to establish, enhance or improve their geospatial readiness, delivering a robust and effective geospatial function to respond to disaster events.

3.3.  Provider Guide

  • Audience: Existing, and potential, data and technical application providers, data collectors, processors, publishers, emergency management information technology support functions, together with other supporting stakeholders.

  • Purpose: Describes the detailed technical requirements, data structures, and operational standards providers need to implement to integrate the data flows or tools developed in DP23.

The guides work together with each individual guide focusing on the key information for their audience and providing signposting to further details should it be required. This gives an overall structure for the guides, as shown in Figure 2

Fig-detail-guide-relationship

Figure 2 — Detailed Guide relationship.

4.  How to use this Guide?

This guide is to showcase the current possibilities offered by using geospatial data more within disaster and emergency communities, by demonstrating the tools and data workflows developed within the Disaster Pilot 2023 (DP23) that could help them better plan, manage, and respond to disasters.

The Guide includes details of each of the tools and data workflows, together with links to persistent demonstrators showing them operating. Using this Guide and the demonstrators, together with the more technical focused Provider Guide, should offer any community the information they need to operationalize any of the tools and workflows.

4.1.  Prerequisites for Using the Tools and Datasets

To use any of these tools or datasets will require a level of geospatial skills and infrastructure. Within each tool or dataset there will be a description of any specific infrastructure required to operationalize it.

In addition, the Operational Capacity Guide sets out a series of strategic actions that can help disaster and emergency communities to establish, develop, or enhance their geospatial skills and infrastructure. It includes strategic actions for:

  • Geospatial Skills

  • Technical Infrastructure

  • Geospatial Data

  • Standards

  • Operational Governance

  • Testing

4.2.  Tools to Support Use of Geospatial Data

Within Annex A there is a series of potential tools that can help disaster and emergency communities to gather, find and visualize data for any type of disaster. For each tool there is:

  • Description of the tool and what it can offer.

  • Details of the benefits the tool offers, how it can support decision making, together with the job roles who would use this tool.

  • Details of how to find the online demonstrations for the tool, and any collaborations undertaken as part of DP23.

4.3.  Data Workflows to Support Disaster Management & Response

A series of data workflows were developed by DP23 participants covering:

  • Droughts in Annex B

  • Wildfires in Annex C

  • Flooding, including landslide and pandemic impacts, in [Flooding]

  • Integration of Health & Earth Observation Data for Pandemic Response in Annex D

These data workflows will produce either Analysis Ready Datasets (ARD) or a Decision Ready Indicator (DRI), which are described in more detail below.

For each of the data workflows within the Annexes, there are details of:

  • Description of the risk or issue the data workflow aims to support, together with details of the outputs it produces.

  • Details of the benefits the workflow offers, the types of decisions it can support, and the job roles that would use the output.

  • Details of how to find the online demonstrations the tool, and any collaborations undertaken as part of DP23.

4.3.1.  Data Set Types

The data workflows take raw data — which could be any form of geospatial data such as geographic data, satellite or airborne data; fixed gauges or instrument; demographic and social data; health data, field observations; or citizen science data, and then undertake some processing to create one of two types of datasets either an ARD or a DRI as shown in Figure 3.

Pilot-Data-Model

Figure 3 — A simplified data model from the OGC Disaster Pilot

  • Analysis Ready Datasets (ARD) — This is raw data to which initial processing was undertaken to create a dataset in a format that can be immediately integrated with other information and used within a Geographic Information System (GIS). This data can be either visualized or further analyzed, interrogated, and/or combined with local knowledge, to create information upon which decisions can be made by Disaster Response Planners and Managers.

    It is most likely to be used by Data Analysts, but it could also be used by Disaster Response Planners and Managers.

  • Decision Ready Indicators (DRI) — These are ARDs that have undergone further processing to create information and knowledge in a format that provides specific support for actions and decisions that must be made about the disaster.

    This information will be useful for Disaster Response Planners and Managers, Field Responders and the Affected Public, and can be used without any specialist knowledge, skills or software.

A simplified version of the data model can be seen in Figure 3 above, with the more detailed data model available within the Provider Guide.

4.3.2.  Job Roles Who Will Use the Tools & Data Workflows

DP23 identified four job roles that are considered the most likely to use the geospatial tools and data workflows developed, although it is acknowledged that there could be more potential users. The key job roles are considered to be:

  1. Data Analysts working for the responding organizations providing insights and information for the disaster planners or field responders.

  2. Disaster Response Planners or Managers who lead the disaster readiness and response activities for the responding organizations.

  3. Field Responders who are on the ground responding to the disaster and reporting to the responding organizations.

  4. Affected public and communities who want direction and guidance on what they should do.

Each of these user groups will require different types of data or information, at different levels and presented in different ways.

5.  Case Study Areas and Hazards

In developing their tools and workflows, the Disaster Pilot participants used several case study areas and hazards as the basis for their demonstrators. In Disaster Pilot 23 (DP23), it was Manitoba in Canada and the south western United States, while Disaster Pilot 21 (DP21) focused on the Red River Basin in Canada and the United States, the Rimac and Puira Rivers in Peru, and Louisiana in the United States.

Details of the case study areas and the hazards are:

5.1.  Manitoba: Drought Hazards

Manitoba in Canada was focused on in DP23 regarding hazards associated with drought. Specifically, the area covered is the provincial boundary of Manitoba, covering the area of latitude from 49 to 52 degrees North.

Canada’s Changing Climate Report projected a substantial increase in the number of hot days for the region, highlighting the potential increased risk of droughts that will affect many aspects of Manitoba’s landscape. Droughts do not only affect agriculture; water-sensitive areas, such as power generation, fisheries, forestry, drinking water supplies, wildfires, manufacturing, recreation, wildlife, and aquatic ecosystems can be severely affected due to recurring droughts (Manitoba Green Plan, 2020).

For example, in Manitoba, from 1988 to 1990 and 2002 to 2003, drought in the Churchill/Nelson River Basin reduced agricultural production to 60% below average, caused an $80M Canadian dollars loss in reduced hydropower exports, a massive loss of wetland habitat, and an increased incidence of disease (Manitoba Green Plan, 2020). A 2012 drought caused wildfires to break out near the communities of Badger and Vita, leading to the declaration of local states of emergency. Manitoba’s recent extreme drought of 2021-22 reduced hydropower exports by $400M Canadian dollars (Manitoba Hydro, 2021) and decreased crop yield by 37%, equivalent to an estimated $100M Canadian dollars in revenue, and caused the loss of an estimated 270 jobs.

5.2.  South Western United States: Wildfire Hazards

There are three south western states selected as the case study areas for wildland fires:

  • Utah

    • Fish Lake with a 50-mile radius.

      Fish Lake is a high alpine lake in south-central Utah, which lies within the Fishlake National Forest. The lake is five miles long and one mile wide, and the surrounding forest covers 1.5 million acres. The forest is home to Pando, a huge cluster of 40,000 aspen trees covering over 100 acres, that all share the same root system. Fishlake National Forest was the site of Utah’s largest wildfire of 2023, which began with a lightning strike and burnt almost 8,000 acres during August.

    • Brian Head with a 25-mile radius.

      Brian Head is a small town in Iron County, Utah, with a population of around 100 people. It sits at 3,000 meters above sea level and is the highest town in Utah. Given its altitude, it has an alpine climate with cold winters and annual snowfall of around 9 meters, while the summer provides frequent thunder storms from the monsoons. It sits within the Dixie National Forest, which covers almost 2 million acres and the vegetation ranges from desert-type plants at lower altitudes, through to pine and juniper, and finally aspen and coniferous trees at the higher altitudes.

    • Parley’s Summit with a 25-mile radius.

      Parley’s Summit is a mountain pass at the top of Parley’s Canyon, to the east of Salt Lake City in Utah. The summit has an elevation of 2,170 meters and is the highest point of the I-80 highway in the state. In 2021, a wildfire burned almost 550 acres in the Canyon and led to thousands of evacuations.

  • Arizona

    • Tucson with a 25-mile radius.

      Tucson is the second largest city in Arizona and has a population of over a half a million people. It is situated between Saguaro National Park to the east and west, and the Coronado National Forest to north and south; it is surrounded by five minor mountain ranges. It has a hot desert climate, with the summer average daily high temperatures between 98 and 102 °F. The wildland fire risk in Tucson, on the most dangerous fire weather days, is very high and expected to increase with climate change.

    • Sedona with a 50-mile radius.

      Sedona is a small city in the north of Arizona, within the Verde Valley region. It sits within the boundaries of the Coconino National Forest and borders four wilderness areas and two state parks. It is surrounded by 1.8 million acres of mostly coniferous woodland and has a semi-arid climate with mild winters and hot summers where the average temperatures approach 100 °F during July. Increasing temperatures coupled with low levels of precipitation mean the forest is ideal fuel for any wildfires. Early in 2023 saw lightning cause the Miller Fire, which covered around 30 acres before being bought under control.

  • California

    • South Lake Tahoe with a 25-mile radius.

      South Lake Tahoe is the most populated city in El Dorado County, California, and is based within the Lake Tahoe basin. The city itself covers a total area of 43 square kilometres and lies along the southern edge of Lake Tahoe in the Sierra Nevada mountains surrounded by forest wilderness areas. It has a climate featuring chilly winters, and summers with warm to hot days and cool nights with very low humidity. It can have temperatures reaching up to 90 °F within July and August. The June 2007 Angora Wildfire was the worst forest fire in Lake Tahoe history, which burned more than 3,000 acres destroying more than 250 homes and a large area of forest. Since then, Tahoe Fire and Fuels Team have treated tens of thousands of acres of forest around Lake Tahoe and are using forest management to reduce the threat of catastrophic wildfires. In 2021, the Caldor Fire went around the populated areas due to the treated forest and firefighting effort.

5.3.  Rimac and Puira Rivers: Landslide & Flooding hazards

Peru’s Piura region in the north and the Rimac river basin near Lima are both impacted by the difficult to predict El Niño related flooding. The El Niño/Southern Oscillation (ENSO) is a naturally occurring phenomenon in the tropical Pacific coupled ocean-atmosphere system that alternates between warm and cold phases called El Nino and La Nina, respectively.

The Piura climate is arid but can experience very heavy rainfall associated with the high nearby Sea Surface Temperature (SST) during El Niño phases. When heavy rain occurs it can cause severe floods, which in turn can cause mudslides called huaycos. Figure 4 shows an index that indicates the El Niño phases in red and La Nina phases in blue.

Plot of the MEI

Figure 4 — ENSO index with red indicating El Niño periods, Multivariate ENSO Index Version 2 courtesy of NOAA, USA

As an example of the relationship between ENSO and flooding, El Niño brought rains caused severe flooding in 1982-1983 and again in 1998 but then, for several years, droughts and extreme heat were the main worries for these communities. Then the flooding returned again in 2002-2003 and 2017-2019. In 2017, ten times the usual amount of rain fell on Peru’s coast, swelling rivers which caused widespread flooding, and triggering huge landslides that tore through communities.

5.4.  Red River Basin: Flooding Hazards

One of the most common types of flooding is river flooding, where the river (or rivers) overflow due to high rainfall or rapid melt upstream that causes the river to expand beyond its banks. The Red River flows north from Northeast South Dakota and West Central Minnesota into Manitoba Canada, and eventually out into Hudson Bay. The relatively flat slope of the Red River valley means that the river flow is slow, allowing water runoff from the land to backfill into tributaries, particularly when the downstream river channel remains frozen. In addition, localized ice jams may impede the water flow, resulting in higher river levels.

Therefore, conditions that determine the magnitude of a spring flood include (Anatomy of a Red River Spring Flood):

  1. The freeze/melt cycle

  2. Early spring rains increase melting of the snowpack or late spring snow storms adding to the existing snow pack

  3. The actual snow pack depth and water equivalency

  4. Frost depth

  5. Ground soil moisture content

  6. River ice conditions

A typical spring thaw occurs from the middle of March across southern portions of the basin, and mid or late April across the north.

An unusually wet fall and winter, combined with spring melting, drove the water levels up in April 2020, as displayed in Figure 5, showing the April 2020/2021 water level comparison for the City of Winnipeg’s main gauge (James Avenue).

Red River April Comparison Plot

Figure 5 — Red River water level April 2020/2021 comparison, Winnipeg river levels

Winnipeg has a 48 km floodway (long excavated channel) to reduce flooding within the city, but it can only be opened when there are no ice jams. The floodway successfully protected Winnipeg from flooding during the high-water levels of 2020, and it’s estimated it resulted in around 930 million m3 of water being diverted around the City of Winnipeg.

Unfortunately, ice jam events impacting the lower reaches of the Red River, between Winnipeg and Lake Winnipeg, increased in both severity and frequency over the last century, a trend that is expected to continue and worsen in the future.

5.5.  Integration of Health and Earth Observation (EO) Data and Services for Pandemic Response in Louisiana in the United States.

The State of Louisiana is located on the coast of the Gulf of Mexico, between Texas and Mississippi. It covers a geographical area of just over 43,000 square miles divided into 64 individual parishes, and was estimated to be home to over 4,600,000 people in 2019. Almost 16% of the population are over the age of 65, and just over 23% of the population are under the age of 18. Like the rest of the planet, Louisiana suffered with COVID-19. By the middle of October 2021, over 750,000 cases of COVID-19 had been confirmed in the State, with over 14,000 deaths reported to date.

The climate within Louisiana is considered to be subtropical, and the physical geography of the area includes the Mississippi floodplain; coastal marshes; Red River Valley; terraces; and hills. It is prone to flooding and hurricanes with its largest city, New Orleans, lying five feet below sea levels protected by natural levees. During the pandemic, the area was hit by its second-most damaging and intense Hurricane, Ida, the most damaging being 2005’s Hurricane Katrina that flooded 80% of New Orleans.

6.  Future Disaster Pilot Possibilities

Disaster Pilot initiatives strive to improve the understanding, accessibility, and demonstration of what’s possible with geospatial data for disaster and emergency communities. However, it is acknowledged that there is still a significant way to travel to ensure communities utilize and benefit from geospatial data solutions.

Within this Pilot, the following areas were identified as ways to develop and enhance future Pilots to improve the take-up and use of geospatial data.

6.1.  User Centric Approach

Pilots need to ensure they focus on addressing the geospatial needs of the disaster and emergency community, particularly for the first responder and emergency manager stakeholders. It would be helpful to give these communities a greater role before a Pilot is established to help define the themes and deliverables to ensure that intended work directly address their needs.

This was identified during talks with first responders, who identified geospatial data flows they would benefit from, and it led to Disaster Pilot 23 (DP23) realizing that these requirements weren’t being directly addressed by participants. A similar conclusion was reached in the lessons learnt section of the associated Climate Resilience Initiative.

While Disaster Pilots often focus on the cutting edge of using geospatial data, it should be remembered that many disaster and emergency communities are a long way from that edge. To make a difference to disaster planning and response, the focus must include developments to address the community’s current wants and needs. However, this should be done alongside continuing to look to the future and pushing the boundaries on the use of geospatial data.

6.1.1.  Recommendations

  • Increase the involvement of first responder and emergency manager communities before the start of OGC Pilots to help define themes and deliverables, to ensure they meet their wants and needs.

  • Ensure Pilots retain work to push the boundaries of the use geospatial data in the areas the users indicate they require help and support.

6.2.  Artificial Intelligence & Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly developing technologies and could offer considerable benefits for the disaster & emergency community. While future Disaster Pilots need to deliver quick wins focused on first responder and emergency manager’s needs, as described above, it is still important that innovations and new technology is also part of the work.

AI & ML are changing facets of work across the board, and it is inconceivable that they will not cause an impact for disaster planning and response. For example: historically, Californian firefighters relied on a network of mountain top cameras and spotters to detect wildfires. During 2023, they trained an AI system to do the monitoring. In early results, the AI delivered a faster indication of a fire around 40% of the time improving response times, and identified fires where no 911 calls were ever made. Currently, the programme still requires people to make sure the AI is detecting smoke and not something else. This work began in June 2023, and is expected to be rolled out across all 21 command centers later this year.

This example demonstrates that it is vital for Disaster Pilot initiatives to keep up with this advancing trend, and work should cover areas such as the development of AI/ML solutions using various data from the diverse data sets available, and providing an assessment of the accuracy of different data sets to determine which ones are most useful and beneficial.

6.2.1.  Recommendation

  • Include AI/ML activities as part of future Disaster Pilots, work could include solution development, assessments and comparison of solutions, etc.

6.3.  Operationalizing Geospatial Data

The use of geospatial data to support decision making is not common place within all disaster and emergencies communities, and in some cases there might be relatively minimal use of geospatial data, tools and applications.

Convincing first responders, emergency managers, and disaster and emergency decision, to implement geospatial solutions, rather than their current tried and tested processes is not going to be easy. To do this there needs to a focus on developing geospatial capacity and quick wins using the benefits of geospatial data. Although, cutting edge geospatial applications, tools and data flows are exciting, the goal of Disaster Pilot initiatives must be the real-life operationalization of the data flows and tools developed, to provide benefit to the disaster and emergency communities.

Another opportunity to demonstrate the benefits of geospatial data would be greater use of real-time sensor data within future Pilots. Producing such indicators would provide a real time view of the current disaster situation, hazards, or risks, and would offer an alternative way of operationalizing the work. This would sit alongside the existing practices used by the first responding community and may highlight the advantages and benefits of geospatial data and information.

Similarly, the Operational Capacity Guide, which received positive feedback from Manitoba Emergency Community who keen to use it to develop and enhance the geospatial readiness of their communities. Further work on specific elements — such as developing example Geospatial Operating Procedures templates, or providing a Geospatial Disaster Guide for Emergency Managers — could also provide a useful and positive step in furthering the use of geospatial data.

6.3.1.  Recommendations

  • Working with first responder and emergency managers stakeholder community to identify quick wins for geospatial data and ensure these form part of the deliverables.

  • Encourage implementation of real time data, sensor based indicators, and/or dashboards in future pilots.

  • Continue working on specific elements of the Operational Capacity Guide to provide further support to help disaster and emergency communities to improve their geospatial readiness.

6.4.  Disaster and Climate Resilience

The frequency and severity of disasters are increasing as climate change causes weather and climate variations to become more extreme. Going forward disaster and emergency managers are going to need greater information about what the future scenarios could look like to better understand both possible threats and mitigation options will support improved resilience. Using more climate data to help provide forecasts will be valuable. This data needs to be made more accessible and available, and by getting this information to those directly responsible for managing and mitigating natural hazards should help them better mitigate the issue and find practical solutions at the local level.

Future Disaster Pilots will benefit from have more direct integration between indicators and climate variables and services, and the intention of bringing the Disaster & Climate Pilots will support this.

6.4.1.  Recommendations

  • Ensure climate services are part of future Pilot initiatives, in an accessible format, so that indicators can, where relevant, incorporate a future view of possible scenarios in addition to their representation of current and past impacts.

6.5.  Standing on the Shoulders of OGC Work

OGC initiatives develop benefits for the geospatial community, and the Disaster Pilot work is no different. Each Disaster Pilot, individually, produces excellent work, and it is demonstrated at the end of each Pilot. However, there is more than could be done by providing a more permanent demonstration of the OGC disaster planning and response ecosystem, providing disaster and emergency communities with a plethora of Analysis Ready Data and datasets (ARD)/Decision Ready Information and indicators (DRI) data flows, applications and tools to support their activities that would have a level of authority, and comply with data sharing and interoperability principles.

This Pilot aimed to deliver persistent demonstrators from each of the participants, as described in the Annexes, which was a positive step. This can go further, with a series of catalogs/registries listing developed data flows; supported by relevant documentation; the data flows themselves should be available and maintained for the long term; and applications and tools should be available (licence and fees dependent). This is not freezing the development of data flows and tools once they are in the ecosystem; if they can be improved within a future Pilot, then they should be. This is about providing a single place for geospatial knowledge and resources to support disaster planning and response, which is available to be used whenever a community is in a position to move forward with their use of geospatial data. This may not be today or even tomorrow, but hopefully it will happen one day. However, it is certain that if the data flows and tools are not easily accessible, they will never be used.

6.5.1.  Recommendation

  • Establish a long term persistent OGC disaster ecosystem where current, past, and future geospatial data flows and tools to support disaster planning and response can be made easily available and accessible to communities and organizations who might want to operationalize them.


Annex A
(normative)
Tools Developed

There are many types of emergency disaster events, and the tools described in this Annex can provide support to a range of events. The tools cover:

It should be noted that data generated by a disaster event itself from sensors, observations, etc., exceeds the data generated through normal day-to-day business by a degree of magnitude. Sorting through this massive quantity of data, a significant proportion of which will be from new, or less familiar sources, means that data catalogs and registry services are critical for helping to find and access the right data at the right time.

The tools developed by the Disaster Pilot 23 (DP23) participants are:

The detailed technical information about each of these tools can be seen below:

A.1.  Data/Workflow Service Registry and Discovery Tools

A.1.1.  Registry & Catalog Functions Developed by Compusult

A.1.1.1.  Introduction

Compusult enhanced the catalog of its Web Enterprise Suite to offer a registry and catalog function to enable users to discover and have access directed to datasets that the emergency and disaster communities might find helpful.

A.1.2.  Description

Compusult created two services, allowing searching and discovery of Compusult’s CSW Catalog and File MetaManager for users.

Compusult also created two services to allow users to publish their own entries into these record services.

A.1.2.1.  Benefits

Compusult’s catalog will allow external users to have access to register, search and discover products and services.

This activity improved access and sharing of data and services, and provided an easy-to-use graphical interface to support users to discover and use available data services to support disaster and emergency planning and response.

A.1.3.  Collaborations

Compusult’s catalog was made available to all participants to contribute and share data. Content from other participants was harvested into the catalog and an account provided to others to allow them to query for data.

A.1.4.  Climate Data Catalog, Data Service and Registry Tool Developed by Safe Software

A.1.4.1.  Introduction

Safe Software’s Climate Data Catalog, Data Service and Registry Tool component is a tool that could help users search, find, and access various climate-related datasets, with a particular focus on Analysis Ready Datasets (ARD) implemented using the FME platform. In addition, the service has the capacity to incorporate base map, Earth Observation (EO), and a wide range of other datasets.

Whatever the type of natural disaster, whether fire, flood, drought, or other hazards, increasingly the severity of natural disasters is exacerbated by the effects of climate change. The challenge of trying to manage and mitigate these changes poses difficulties for geospatial data both in terms of understanding the areas impacted and the timescale. There is a need to translate the outputs of global climate models into specific impacts at the local levels.

The FME platform is a spatial data integration platform, produced by Safe Software, and it can help explore options for bridging this gap given its ability to read datasets produced by climate models and then filter, aggregate, interpolate and transform it as needed. It is configured using no code data transformation models that can bridge the gaps between disparate systems using its support for hundreds of different spatial and nonspatial data formats and services. It can also combine climate data with higher resolution local data, and then output it to whatever format or service is most appropriate for a given application domain or user community.

DP23 had a particular emphasis on incorporating and serving climate model output ARD, which is essential to support disaster management in our changing world. It also explored approaches to support data search and cataloging, to allow users to provide data to the service in a simple way. The component also has a service to support metadata harvesting — in order to supply the information needed to enable the data to be found by other users through searches.

A.1.4.2.  Description

A.1.4.2.1.  Analysis Ready Data (ARD) Service

The chosen challenge for the ARD service component was to take climate model results and use them to feed forecast and impact models related to the hazards of interest such as drought, fire or flood. The workflow transforms raw data, or data from other sources, into a form of ARD which is more easily integrated in local GIS solutions to enable users to use, analyze, or visualize the data. The underlying goal is to feed the data value chain from raw source data — in this case climate model data cubes, through to ARD in order to feed decision and impact indicator workflows (DRI — Decision Ready Information).

The climate model source data was made available using OGC standards approved processes, which was key to making the data more widely accessible and usable by those likely to be affected by its potential impacts.

For selected climate scenarios, this solution supported the analysis of estimated drought risk impacts over time via simple queries. The primary climate scenario for this work was the drought hazard for the province of Manitoba, Canada. Safe Software provided a projected precipitation time series to fellow DP23 participant, Pixalytics, who used it as an input to their drought severity indicator. More information on this can be found in the Annex B indicator section.

The data service also allows end users to access the climate data using queries and retrieve the environmental variables and statistics for their specific geographic extent and time period of interest. The service itself supports a range of query parameters which can allow users to explore various value ranges and extremes inherent in the climate scenario projections. Multiple environmental variables such as temperature, precipitation and change in precipitation relative to historic are available on the time series points. Users can then ask questions to look for times and places of concern relative to specific natural hazards such as drought, fire, heat or flood.

As example, if the following request was made to the service: “Find all time step points over the next 40 years for southern Manitoba where projections indicate > 25% dryer and mean monthly temperature > 23C.” The user would enter the parameters using the following screen

D101_SAFE_ExampleRequest

Figure A.1 — OGC API Features response to above query: 63 temporal points with associated temperature and precipitation values, as shown in FME Data Inspector client.

This would produce the output shown below:

D101_SAFE_63temporalpoints

Figure A.2 — Response to above query: 63 temporal points with associated temperature and precipitation values, as shown in FME Data Inspector client.

This data is displayed in Safe Software’s Data Inspector client. This result shows climate model points derived the query above, these points show from August 2048 and 2058 represent the hot and dry areas and times that satisfy the query above and could constitute increased drought and fire risk. The ultimate goal is to make climate model outputs more accessible in a form and structure easy to consume by those used to working with GIS tools.

A.1.4.2.2.  Metadata Harvest & Catalog Service

The Metadata harvest workflow allows users to provide data to the service, and it reads the source data and automatically extracts the key properties and information required to create catalog of the data to enable it to be discovered by other users when searching.

This workflow is a standalone service. In the future, it is hoped that this service could be used to auto-generate a description, which could include the contents contained in the dataset. It could also auto-populate the catalogue entry, and add additional information from the dataset.

Alongside the Metadata Harvest Service, Safe Software also implemented a simply catalog service that could be used to register and make available the data making it easier for other components and users to locate and use response datasets as they become available.

The service is only a basic implementation that allows other components in DP23 to interrogate the catalog service and use the resulting metadata to assess and query other feature data services. It is a read-only catalog service that serves to publish metadata on the datasets and services Safe Software contributed to DP23.

A.1.5.  Benefits

When a disaster occurs, responders need to quickly locate, access, register and share a wide range of datasets. The Metadata Harvesting component can be used to quickly extract metadata from datasets as they come in and register them with the available catalog services. The catalog component can then be used to register and make available item metadata to make it easier for other components and users to locate and use response datasets as they become available.

One such data service for DP23 was the ARD service associated with climate model future projections. Disaster planners can use this service to query for temperature and precipitation anomalies in order to give them a better understanding of environmental extremes that might be expected in the future and test the resilience of their systems to those extremes. The hope is that by testing a range of climate scenarios, planners will be able to explore when and where hazard risks are more pronounced and take steps to mitigate those risks. One key capability is to allow users to directly interact with and interrogate climate projection data, so that they can see what type of environmental variable value ranges they might expect to see in the future, and what trends to be aware of.

Note that the FME Data Inspector tool also supports consumption of more than 500 spatial and non spatial data formats and services including more than 30 OGC standards, other open standards such as Open Street Map, as well as vendor proprietary standards. This can help disaster analysts rapidly review any available data in a rapid response situation and quickly determine which datasets are most useful to support assessment and response efforts

A.1.5.1.  Collaborations

To support future drought risk estimates for Manitoba, Safe Software provided a projected precipitation time series to Pixalytics as an input to their drought analytics and Decision Ready indicators (DRI) component. Their component provides a more sophisticated indicator of drought probability since in addition to precipitation it also takes into account soil moisture and vegetation. The goal was to extract precipitation totals per time step from the climate variable outputs for Manitoba based on model results obtained from Environment Canada. Pixalytics then ran their drought model based on these precipitation estimates in order to assess potential future drought risk in southern Manitoba.

Further information on Safe Software’s contributions to OGC Disaster & Climate Pilots can be found at https://community.safe.com/s/article/OGC-Disaster-and-Climate-Resilience-Pilots

The persistent demonstrators for the services can be found at:

A.1.6.  Geospatial Data Registry Services Developed by USGS-GeoPathways

A.1.6.1.  Introduction

USGS-GeoPathways established a community with resources and data services to enable the public, scientists, and policymakers to boost disaster risk resilience.

A.1.6.2.  Description

The resources available are:

  • Terria Disaster Community Registry (Open Source) — This uses the web-based TerriaJS application to provide 3D visualizations of disaster-related datasets, for fire, drought, and more, with terrain elevation context.

  • ArcGIS Disaster Risk Resilience Registry (Proprietary) — This Esri ArcGIS Hubsite includes apps, data catalogs, and external resource links on disaster risk and resilience. This includes:

    • Knowledge Hub — This is an experience builder application that allows individuals to input disaster-related information about websites, terms, data, apps, and the metaverse, so the wider public can search for disaster-related information.

    • Extended Reality Immersive Spatial Market Catalog — A guide to using 220 of the world’s best virtual reality and augmented reality headsets and can be used by anyone interested in metaverse technology to determine which extended reality (XR) headset will work best for their project.

  • Voyager Search Disaster Risk Resilience Registry (Hybrid) — This web-based application by Voyager Search provides an indexed registry of disaster-related datasets. It facilitates the visualization of data across various map viewer software and enables the integration of AI/ML workflow models with indexed data. Voyager’s advanced indexing encompasses millions of datasets from commercial, organizational and government data. Voyager’s software includes a powerful search function, exposing API data without complicated API requests. Voyager doesn’t store data but reveals endpoints and uses metadata for optimal searchability and data accessibility. Voyagers efficient API connector framework processes entire government and organizational data APIs in minutes for quick data extraction, enrichment, transformation, and deliverability.

    As part of the partnership with Voyager Search, work was undertaken on indexing and testing Esri’s Living Atlas and USGS models with workflows that detect, extract, and assess analysis ready data. Workflows are critical to automating repeated tasks that would increase overall efficiency as well as reduce the risk of data errors. Moreover, models operating on indexed data generate fresh data that is automatically integrated into the registry, thereby expanding the volume of data accessible to users.

A.1.6.3.  Benefits

Users will receive updates on technological capabilities, response strategies, breaking news, and relief resources. The dashboard dynamically updates metrics based on selected queries or locations. It links each data record to a geographical location, enhancing analysis of XR hardware and allowing pilot participants to share workflow information.

A.1.6.4.  Collaborations

To develop these resources USGS-GeoPathways worked with USGS, US Forest Service, Federal Geographic Data Committee, NASA, ESRI, AmeriGEOSS, NOAA, GeoPathways Peru, Google, USDA, Microsoft, & Voyager Search.

Further information and demonstrations of this work can be found at:

A.2.  Emergency Location and Language Application Developed by GISMO-Basil Labs

A.2.1.  Introduction

The Emergency Location and Language Application (Ella) is a mobile crowdsourcing survey & reporting application that aims to give a client the opportunity to design a survey script to collect information directly from individual citizens via a smartphone. The aim is that Ella would act as a supplement to the 9-1-1 system when that system is overwhelmed and important information is being missed.

The Ella application is designed to be user friendly, and easily modifiable by non-technical personnel. Individuals who are comfortable using their smartphone for conversations, texting, and photographing should have no difficulty becoming comfortable with the application. Ella applications should be easy to use by citizens caught within a disaster area, responder teams within a disaster area, and by disaster managers at operations centers, or from vehicles. User-friendly dashboards and analytics can be designed for use both by citizens and by responders, while more technical personnel can be provided with more sophisticated outputs.

A.2.2.  Description

Ella is designed to capture information and use it for various outputs that could be quickly reviewed by the response community, and made available to responders in the field. It can also provide up-to-the-minute status reports of conditions within the disaster zone, and — unlike the way 9-1-1 is utilized — could be modified and redeployed whenever there were changes in the nature of the disaster. Operational capabilities that can be designed into Ella include:

  • Rapid survey design using application templates allowing non-programmers to rapidly modify a survey, or quickly create wholly new surveys through the use of simple pull down menus.

  • Ability to re-issue surveys as the situation on the ground changes, or when a data refresh is required.

  • Collect information and intelligence from people within a disaster zone or other area of interest.

  • Support communications between first responders in the field, disaster response managers, and people caught within a disaster zone. Ella can allow response managers to transmit guidance to all those within a disaster area, or to specialized groups, such as those evacuating using vehicles or those identified to be in immediate threat.

  • Support communications between different teams of responders dispatched to the same or adjoining areas for improved coordination.

Examples of the visualized outputs available from Ella on survey results are shown below. All responses can be visualized question by question, and are mappable either by exact location from mobile device if given, or general location via IP address.