Trigger verification
Historical forecasts are compared with observed events to estimate how often a proposed trigger would have hit, missed or falsely activated.
GCRI · Disaster Resilience and Recovery
Forecasting for early action
Forecasting supplies the lead time that early warning and anticipatory action depend on. In this field the emphasis is on how forecasts become triggers and warnings, and on verifying them against what happened so that action thresholds rest on demonstrated skill.
01Contents
Forecasting for early action in five parts.
02Application
What forecasting and time-series analysis does here.
Forecasting and time-series analysis predict future values of variables from past observations and explanatory information. They support early warning and planning across hazards, markets and health. In disaster and crisis and climate and hazards, forecasts trigger early action and anticipatory finance before shocks materialise. Probabilistic forecasts allow decisions to account for uncertainty across connected systems. The canonical description is on the STEM Commons.
Flood forecast lead time
The lead time at which flood forecasts reach usable skill is estimated for each river reach, setting how early action can begin.
Seasonal outlooks for drought preparedness
Seasonal rainfall outlooks are evaluated for the regions and seasons where they show skill, and used only there to trigger preparedness.
03Geography
Hazards now in the Philippines.
Flood water, rain, population density 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
- 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
- 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
- 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
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
04Data
What the method consumes.
Datasets and records used by forecasting and time-series analysis on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| GloFAS forecasts and reforecasts | Flood trigger design | FORECAST |
| Seasonal forecast archives | Drought trigger design | FORECAST |
| Observed event records | Verification | OBSERVED |
| Impact databases | Linking hazard to impact | REPORTED |
05Monitor
Forecast verification.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Forecast verification
Disaster resilience · Forecasting for early action · Run R-6827
Skill above referenceGCRI · Disaster Resilience and Recovery
Forecast with range
Method card
- Method
- Forecasting and time-series analysis
- Reference
- climatology
- Lead times
- 1 to 6 steps
- Label
- MODELLED
Verification by lead time
held-out period
| T+2 | 0.11 | 0.14 | 0.31 | Skilful |
| T+4 | 0.25 | 0.23 | 0.10 | Skilful |
| T+6 | 0.21 | 0.14 | 0.21 | Skilful |
| T+8 | 0.38 | 0.22 | 0.26 | Marginal |
06Validation
How results are checked.
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
| Check | How it is done |
|---|---|
| Hindcast verification | Triggers are tested on past forecasts before use |
| Contingency table scores | Hit rate, false alarm ratio and related scores are reported |
| Impact link check | Thresholds are checked against recorded impacts, not hazard values alone |
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
07Twin and cascade simulator
Model forecasting for early action 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
08Connections
Topics that rely on this method.
09Methods and evidence
How forecasting for early action 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 forecasting for early action 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
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
10Stewardship
Who maintains the method.
Climate and Hazards stewards forecasting and time-series analysis for the Consortium and reviews how it is applied across platforms.
11Process
How the process runs.
Process explorer · Process
Forecasting for early action: Flood forecasting and warning chain
9 steps·3 stages·1 gate
Process
Forecasting for early action: Flood forecasting and warning chain
The WMO flood forecasting manual and Early Warnings for All describe the chain from forecast to action, across EW4All's four pillars.
01 Forecast
- 01Rainfall forecastSourceNumerical weather prediction and nowcasts supply rainfall input. Ensembles can apply to weather prediction and hydrological models.
- 02Hydrological modelModel · MunicipalityA hydrological model turns rainfall into runoff and discharge. The manual covers model choice, parameters and calibration.
- 03Hydraulic modelModelA hydraulic model routes the discharge to water levels along the river and floodplain. The place is set by the page.
- 04Inundation mapsDatasetInundation forecasting combines a level-and-flow model with a digital flood plain surface, the manual's most sophisticated public service.
02 Warning
- 05ThresholdsGateForecasts are compared with thresholds agreed in advance, as in a threshold-based alert service. Impact-based guidance was not read.
- 06Warning decisionDecision · MunicipalityThe agency responsible for forecasts and warnings decides on the warning. It should understand the roles of the other agencies concerned.
- 07Warning disseminationStepWarnings are communicated through the channels people use. This is EW4All pillar 3, warning dissemination and communication.
03 Response
- 08Early actionStepPeople and institutions act on the warning. This is EW4All pillar 4, preparedness and response capabilities.
- 09Forecast verificationStepForecasts are verified using hits, misses and false alarms. The manual lists probability of detection, false alarm rate and threat score.
Neutral outline of a published external standard or method, from its issuing body. Not a record of operation. Nexus does not perform, certify or approve any step shown. A gate or decision belongs to the party the source names.
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of forecasting and time-series analysis in this field?
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
How are results checked?
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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