When severe weather strikes an island chain, the operational problem is not only the intensity of the rain. It is the distance between agencies, the uneven availability of local data, and the difficulty of building one reliable picture from many changing hazards. During a series of storms that affected Hawaii in March 2026, flooding, landslides, dam-failure risk, and a weather-radar outage created exactly that kind of coordination challenge.
A case study published by the Pacific Disaster Center describes how the Hawaii Emergency Management Agency and partner organisations used the DisasterAWARE ecosystem to bring hazard information, field reports, and impact analysis into a shared operational picture. The account is valuable because it is not a technology demonstration detached from operations. It shows how a platform was used while agencies were managing a compound event across several islands.
The field problem: many islands, multiple hazards, one response
The storms affected a dispersed geography in which conditions could vary sharply from one island to another. Flooding in one location could coincide with landslide risk somewhere else, while a threatened dam added the possibility of rapid downstream impacts. Emergency managers needed to understand not just where rain was falling, but which populations, roads, facilities, and infrastructure were exposed.
The Pacific Disaster Center reports that the event created a demand for real-time, authoritative hazard information and a unified operational picture. It also describes a weather-radar outage affecting the Molokai radar that normally served a wider area including Oahu, Maui, Lanai, and surrounding islands. The outage did not eliminate the need for situational awareness; it increased the value of combining the remaining observations and analytical products in a common environment. [1]
This distinction matters. A common operational picture is not simply a map displayed on a large screen. It is a process for deciding which information is authoritative, how it is updated, who can contribute, and how uncertainty is communicated to decision makers. Without those rules, a digital platform can reproduce the same fragmentation found in paper reports and disconnected spreadsheets.
DisasterAWARE as a shared operational picture
According to the case study, consolidated information was provided to decision makers through the Pacific Disaster Center’s DisasterAWARE ecosystem. The platform gave agencies access to a shared view of hazards and impacts while allowing situation reports from different jurisdictions to be viewed in the same environment. [1]
Pacific Disaster Center staff supported multiple operational locations, including the U.S. Army Pacific, the Theater Joint Forces Land Component Command, the Hawaii Emergency Management Agency, and the Maui Emergency Management Agency. The important point is not merely that analysts were present. Their role connected technical analysis with the decisions being made by agencies responsible for public safety and resource allocation.
As conditions evolved, new and updated data layers were integrated. The case study identifies updated radar information, flood-zone layers, and dam-flood-inundation data. This kind of update cycle is central to disaster operations: a layer that was useful before landfall may become inadequate after a road is closed, a river changes course, or a new observation alters the expected impact area.
The value of the platform therefore came from the combination of software, data curation, analytical support, and institutional participation. A map without current data would not have been enough. Current data without a shared interface would have remained difficult to compare. A shared interface without analysts and decision makers would have risked becoming another passive repository.
Turning hazard information into decisions
The case study describes daily analytical products that included population and infrastructure exposure estimates for flood zones, dam-break analysis for potential downstream impacts, landslide-probability maps, and community-level assessments of 100-year flood exposure. These products helped translate technical hazard information into questions that emergency managers could act on: Which communities may be affected? Which critical facilities are exposed? Where could access be lost? Which areas should receive additional attention?
This translation step is often overlooked in discussions of disaster technology. Emergency managers rarely need a model output for its own sake. They need to decide whether to issue warnings, move personnel, inspect a structure, close a route, pre-position supplies, or request assistance from another jurisdiction. A useful analytical product must therefore connect hazard estimates to people, infrastructure, and response options.
| Operational question | Data and analysis contribution | Decision-support value |
|---|---|---|
| Where could flooding affect people? | Population exposure estimates within flood zones | Helps prioritise warnings, welfare checks, and evacuation support |
| Which infrastructure may be exposed? | Infrastructure layers combined with hazard footprints | Supports route planning and protection of critical facilities |
| What could happen if a dam fails? | Inundation and downstream impact analysis | Helps emergency managers understand potential cascading effects |
| Where may landslides create access problems? | Landslide-probability mapping with exposure estimates | Supports route monitoring and resource prioritisation |
| How can agencies share updates? | Common situation-report environment | Reduces conflicting versions of field information |
The value of these outputs depends on the assumptions behind them. Exposure is not the same as damage. A structure inside a modelled flood zone may remain unharmed, while a structure outside the zone may be affected by drainage failure or debris. Analytical products should therefore be treated as decision support and combined with field verification.
Compensating for a data gap
The radar outage described in the case study is a useful reminder that resilience is partly about coping with missing information. Emergency systems are often designed on the assumption that sensors, communications links, and data feeds will be available when needed. In practice, equipment fails, power is interrupted, networks become congested, and weather conditions degrade observations.
During the Hawaii event, the shared operational environment helped agencies integrate situation reports and other observations while a key radar resource was unavailable. That did not replace the radar. It helped reduce the coordination cost created by its absence. This is a more realistic understanding of resilience: a system does not need to prevent every failure if it can help organisations continue making decisions when a failure occurs.
The approach also illustrates why data provenance matters. When several agencies contribute updates, users need to know when a report was created, which area it covers, whether it has been verified, and whether it supersedes an earlier report. A platform can make information easier to share, but the response organisation still needs procedures for validation, versioning, and correction.
What the field deployment teaches
The Hawaii case offers several lessons for agencies considering a common operational picture. First, the platform must be connected to a real operating structure. PDC analysts worked across operational locations rather than treating the software as a self-service product that required no human support.
Second, the system must support multiple hazard types without forcing users to maintain separate tools for flooding, landslides, dam risk, infrastructure exposure, and situation reports. Compound events do not arrive in neat categories, and the response picture can deteriorate when each hazard is managed in a separate information silo.
Third, shared access is as important as technical sophistication. The case study describes agencies across the islands being able to contribute and view situation reports. That creates a common reference point, but it also requires agreement about who can publish information, how updates are labelled, and how conflicting observations are resolved.
Fourth, analytical products must be translated into operational language. Population and infrastructure exposure estimates are more useful when they are linked to response questions, time windows, and clear limitations. Decision makers should be able to see not only what the model indicates, but also when it was last updated and what assumptions shape the result.
Finally, the system should be tested during degraded conditions. A platform that works during normal connectivity may behave differently when data feeds are delayed, a radar is offline, or multiple agencies submit updates simultaneously. The Hawaii experience shows the importance of designing for the messy conditions that define real disaster response.
Conclusion
The Hawaii flooding case demonstrates how a common operational picture can help agencies coordinate across a dispersed geography during a compound event. DisasterAWARE supported the integration of hazard layers, situation reports, exposure analysis, and field-based decision support while agencies faced flooding, landslide risk, dam concerns, and a radar outage.
The lesson is not that one platform solves disaster coordination. The lesson is that technology becomes useful when it is embedded in a workflow that combines authoritative data, analytical expertise, local observations, and clear decision responsibility. In the field, resilience came from connecting those elements quickly enough for agencies to act on a shared understanding of a changing situation.
