GCRI · Disaster Resilience and Recovery
Early warning readiness
An early warning system works only when the whole chain holds: risk knowledge, detection and forecasting, dissemination and communication, and preparedness to respond. Readiness is the last link, and it is where many systems break. This page sets out how warnings lead to action or fail to, how readiness is measured, and what evidence serves the authorities and communities that must act on warnings.
Early warning readiness on the record.
Early warning readiness: how the risk develops.
Warnings, communication and last-mile issues. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
A warning is a chain
Monitoring, forecasting, warning issue, transmission, understanding and action are separate steps run by different actors. A delay or gap at any step removes the value of the others.
Messages must be understood
Warnings in technical language, a single language or inaccessible formats fail to reach many people. Clear messages say what will happen, where, when and what to do.
Action needs means
People told to evacuate need transport, a safe destination, information about their homes and confidence that the warning is real. Without these, warnings are heard and ignored.
Trust is earned between events
False alarms, mixed messages from several sources and past experience shape whether people act. Drills and community involvement build the trust that makes action likely.
Hazards now in the Philippines.
Flood water, population density, active fires and true-colour imagery over the Philippines, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Hazards now, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Hazards now, framed on the Philippines.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (20 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
Warning readiness is a chain that crosses domains.
- WaterCouplingFlood warnings need river forecasts and local response plans.What to watchLead time against action time
- HealthCouplingHeat warnings work only if care homes and clinics act.What to watchActions taken by health services
- FoodCouplingDrought warnings need livelihood responses to prevent food crises.What to watchEarly action before harvest failure
Readiness evaluations are designed for the Evidence Brief series. The Global Status of Multi-Hazard Early Warning Systems 2024 tracks Sendai target G; warnings remain the mandate of authorised agencies.
- Warning chain evaluations.
- Community accounts of warnings received.
Anticipatory action console.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Anticipatory action console
Early warning readiness · Province C · Monsoon season
Protocol
- Readinessforecast lead 10 d
- Activationlead 3 d
- Early actions72 h window
- Responseif event
- Learningreview
River forecast at trigger gauge
Pre-agreed actions
| Cash to at-risk households | 72 h | 52 | Yes |
| Livestock evacuation | 48 h | 13 | Partial |
| Shelter kits | 72 h | 26 | Yes |
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Countries with multi-hazard early warning systems | status as reported | annual | Sendai Framework Monitor target G | Published statistics |
| Population covered by warnings | percent of population | annual | Sendai Framework Monitor target G | Published statistics |
| Local governments with evacuation plans | percent of local governments | annual | Sendai Framework Monitor target G | Published statistics |
| Warning received before impact | percent of surveyed households | per event survey | Post-event surveys | REPORTED |
| People who took protective action | percent of surveyed households | per event survey | Post-event surveys | REPORTED |
| Time from warning to dissemination | minutes | per event | Warning logs | OBSERVED |
Hazard to loss: the exceedance curve.
How often a given loss is exceeded, from a Gumbel hazard, a lognormal fragility and a protection standard; the area under the curve is the average annual loss. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Philippines; the model itself is not tied to a place.
Controls
- Typical annual hazard intensity (location)
- 1.00 intensity index
- Spread of extremes (scale)
- 0.80 intensity index
- Intensity at which half the value is lost
- 4.00 intensity index
- Dispersion of vulnerability
- 0.55
- Share of value in the hazard footprint
- 40%
- Protection standard (return period)
- 50 years
Readouts
- Average annual loss
- 0.51% of value
- Loss at 1 in 100 years
- 24.5% of value
- Loss at 1 in 250 years
- 28.4% of value
- Intensity at 1 in 100 years
- 4.68 intensity index
Numbers, methods and sources
| Return period (years) | Intensity (intensity index) | Loss (% of value) |
|---|---|---|
| 1 | 0.09 | 0.00 |
| 2 | 1.27 | 0.00 |
| 4 | 1.91 | 0.00 |
| 7 | 2.47 | 0.00 |
| 13 | 3.00 | 0.00 |
| 24 | 3.51 | 0.00 |
| 44 | 4.02 | 0.00 |
| 82 | 4.53 | 23.55 |
| 154 | 5.03 | 26.44 |
| 287 | 5.53 | 28.87 |
| 536 | 6.03 | 30.88 |
| 1,000 | 6.53 | 32.53 |
Methods
- Gumbel extreme-value hazard Gumbel (1958), Statistics of Extremes, Columbia University Press
- Lognormal fragility (vulnerability) FEMA (2003), HAZUS-MH Technical Manual
- Exceedance-probability curve and average annual loss Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
- Levee effect Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Rivers Natural Earth
- Urban extents Natural Earth
- Cities Natural Earth
- Earthquakes M4.5+, past week (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Built-up and settlement extent, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Disaster Resilience and Recovery in Foundry
- Tested at Disaster Resilience and Recovery in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Resilience and Recovery in Reports
- Taught in Faculty of Hazards, Disasters and Resilience in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Sendai Framework Monitor, target G | National reporting on early warning coverage, plans and risk information | Public, UN terms of use |
| Early Warnings for All initiative reports and country assessments | Progress on the four pillars of early warning | Public, terms per publishing agency |
| WMO Register of Alerting Authorities | Alerting authorities and their alert feeds | Public, WMO terms |
| IFRC and national society post-event evaluations | How warnings were received and acted on | Public, terms per publisher |
| Global status reports on multi-hazard early warning systems | Joint UNDRR and WMO assessment of early warning coverage | Public, UN terms of use |
Model early warning readiness as a system. Run the cascade.
Assembled from the Nexus Standards area profile Early warning and anticipatory action: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Ice-jam flood
- Tropical cyclone
- Heatwave
- HIV epidemic
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Extreme fire weather (set off downstream)
- Wildfire (set off downstream)
- Heat-driven grid stress (set off downstream)
- Agricultural drought (set off downstream)
- Flash drought (set off downstream)
- Snow drought (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-C7Early warning, triggers and anticipatory actionNRS-S9System profile: NXS-AAP (Anticipatory action and triggers)NRS-H117Hazard family: Social cohesion and atrocity risk (NHS-1 soc.cohesion)NRS-H83Hazard family: Health system capacity failure (NHS-1 health.system)NRS-H98Hazard family: Agentic AI acting at scale (NHS-1 tech.ai-agentic)NRS-H22Hazard family: Solar-terrestrial storms (NHS-1 space.solar-storm)
Observation
- NWS API active alerts (CAP 1.2 as GeoJSON) (listed for reference)
- GloFAS global flood forecasts and reanalysis (listed for reference)
- ECMWF open data (IFS and AIFS real-time forecasts) (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- Number of countries that have multi-hazard early warning systems
- Number of countries that have multi-hazard monitoring and forecasting systems
- Number of people per 100,000 covered by early warning information through local governments or national dissemination mechanisms
- Number of countries with accessible, understandable, usable and relevant disaster risk information and assessment
Example geography
- Southeast Asia: 51 hexagonal cells
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface air temperature, daytime (AIRS), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
Who decides, and what the evidence serves.
Disaster Resilience and Recovery prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| National disaster management authorities | Which links in the warning chain need strengthening first | Warning chain reviews; readiness assessments |
| Local governments and civil protection | How evacuation, shelters and transport are organised for warned communities | Evacuation plan coverage; post-event surveys |
| National meteorological and hydrological services | How warning content and format should change | Survey evidence on understanding and action |
Outputs this topic produces
- Warning chain reviews from detection to household action
- Readiness assessments against the Early Warnings for All pillars
- Post-event survey designs and analyses
- Message design guidance for clear, accessible warnings
How early warning readiness is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for early warning readiness could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
What experts ask.
Warning researchers ask what happens between a forecast and a protective action.
- Who receives warnings, through which channels, and who is missed?
- Do people understand the warning, believe it applies to them and know what to do?
- How do false alarms and missed events affect trust and future response?
- How are warnings coordinated across agencies, hazards and borders?
- What prevents people from acting, even when they understand the risk?
The chain from forecast to action.
Warning is a chain, and it breaks at its weakest link. Forecasts may be skilful but issued in technical terms; messages may be clear but sent through channels that some groups do not use; people may receive and understand them but lack transport, a safe place to go or the means to protect livelihoods. Social research shows that people typically seek confirmation from others before acting, and that trust in the source, past experience and the specificity of the message all shape response.
The last mile is where warnings most often fail: remote communities, people with disabilities, migrants who do not speak the language of the warning, and those without phones or power. Impact-based warnings, which describe what a hazard will do rather than only its physical parameters, and messages that state specific actions, generally improve response.
How the evidence is built, and where it stops.
- 01
Forecast verification
Measuring forecast skill with forecasting and time-series analysis. Skill on hazard does not measure warning effectiveness.
- 02
Warning response research
Surveys and interviews on how people received and acted on warnings, with social and behavioural science. Recall after events is imperfect.
- 03
Coverage analysis
Mapping channel reach against population with geospatial information systems. Reach is not the same as reception or understanding.
- 04
Post-event review
Tracing the warning chain in a real event. Reviews are often internal and unpublished.
How it is studied, and what the methods cannot say.
- 01Forecasting and time-series analysisTrigger verification · Flood forecast lead time · Seasonal outlooks for drought preparednessApplied here
- 02Social and behavioural scienceWarning response surveys · Participatory risk assessment · Social vulnerability analysisApplied here
- 03Geospatial information systemsExposure mapping · Rapid damage assessment · Common operational dataApplied here
- 04Decision analysis under uncertaintyCost-benefit of risk reduction · Acting on forecasts · Recovery prioritisationApplied here
Where this page's references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What are the four pillars of early warning?
Why do people ignore warnings?
What is a people-centred early warning system?
Does the platform issue warnings?
Why do accurate warnings sometimes fail?
Do false alarms reduce trust?
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