GCRI · Society and Human Development
Humanitarian risk
Humanitarian risk is the chance that a crisis will overwhelm people’s ability to cope and require outside assistance. It combines hazards and conflict, people’s vulnerability and the capacity of institutions to respond. Assessing it helps agencies prepare and act early. This page sets out how humanitarian risk is analysed, how needs are estimated, and what evidence serves the humanitarian and development actors who use it. It names no third-party risk index.
Humanitarian risk on the record.
Humanitarian risk: how the risk develops.
Humanitarian and displacement analysis under safeguards. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Hazard, vulnerability and capacity combine
Humanitarian need arises where hazards or conflict meet vulnerable people and weak response capacity, so all three must be assessed together.
Crises compound
Conflict, economic collapse, disease and disasters often occur together, with each making the others harder to address.
Early action needs early analysis
Analysis of risk before crises peak lets agencies preposition supplies and act early, which costs less than late response.
Needs estimates shape funding
Estimates of people in need drive appeals and allocations, so their methods and assumptions matter greatly.
Hazards now in Latin America and the Caribbean.
Population density, flood water and active fires over Latin America and the Caribbean, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Hazards now, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds and overpass timing leave gaps)
- Source
- VIIRS NOAA-20 375 m (FIRMS) (NASA LANCE FIRMS)
- Provider
- NASA LANCE FIRMS
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds and overpass timing leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 500 m grid
- Uncertainty
- Satellite hot-spot detections for the day shown, including gas flares and industrial heat; not a confirmed fire, an incident report or a warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
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 Latin America and the Caribbean.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 JPL OPERA DIST-ALERTSources 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
- 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
- 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
- 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
- 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
- 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 JPL OPERA DIST-ALERT (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
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- 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
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
Humanitarian risk indices combine every domain’s hazards.
Findings are designed for the Country Systems Risk and Resilience series. Composite humanitarian risk methods are documented as methods; Nexus pages show indicators domain by domain, never a ranking.
- Humanitarian data through HDX.
- Critiques of composite indices.
Humanitarian needs overview.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
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 |
|---|---|---|---|---|
| People in need of humanitarian assistance | count | annual | Humanitarian needs overviews | ESTIMATED |
| Population in acute food insecurity | people by IPC phase | per analysis | Integrated Food Security Phase Classification | ESTIMATED |
| Humanitarian appeal funding coverage | percent funded | annual | OCHA Financial Tracking Service | REPORTED |
| Conflict events | count | weekly | Conflict event data | REPORTED |
| Anticipatory action triggers activated | count | per season | Agency records | REPORTED |
| Access constraints | count of reported incidents | monthly | Humanitarian access monitoring | REPORTED |
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 Latin America and the Caribbean; 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 Society and Human Development in Foundry
- Tested at Society and Human Development in Universe
- Codified in Governance, Security and Society in Standards
- Published in Society and Human Development in Reports
- Taught in Faculty of Health and Society 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 |
|---|---|---|
| OCHA Global Humanitarian Overview and Humanitarian Needs Overviews | People in need and appeal requirements | Public, UN terms of use |
| Integrated Food Security Phase Classification analyses | Acute food insecurity classifications | Public, IPC terms |
| ACLED conflict event data | Political violence and protest events | Free with registration, ACLED terms |
| OCHA Humanitarian Data Exchange | Shared humanitarian datasets | Open, licence per dataset |
| FEWS NET food security outlooks | Food security projections | Public domain, US government (to check current status) |
Model humanitarian risk as a system. Run the cascade.
Assembled from the Nexus Standards area profile Humanitarian and civil society: 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
Systems and couplings
- Humanitarian and civil society
- Agriculture and land use Coupling
- Health systems Coupling
- International organisations and multilaterals Coupling
- Defence and civil protection Coupling
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-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-D3Humanitarian response and resilient recoveryNRS-E1Annual programme cycle, exercises and gatesNRS-H18Hazard family: Eruption products and unrest (NHS-1 geo.eruptive)
Observation
- Humanitarian Data Exchange (listed for reference)
- Financial Tracking Service (listed for reference)
- IATI Datastore (listed for reference)
- IPC food insecurity classifications (listed for reference)
- Refugee Data Finder (listed for reference)
- INFORM Risk Index (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale
- Percentage of population exposed to or at risk from disasters protected through pre-emptive evacuation following early warning
- Number of directly affected people attributed to disasters, per 100,000 population
Example geography
- East Africa: 189 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
Who decides, and what the evidence serves.
Society and Human Development 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 |
|---|---|---|
| Humanitarian coordinators and agencies | Where to prepare and prioritise assistance | Risk analyses; needs estimates |
| Donors | How to allocate humanitarian and anticipatory funding | Funding and risk evidence |
| National disaster and social protection authorities | How to link national systems with humanitarian response | Early action evidence |
Outputs this topic produces
- Humanitarian risk analyses combining hazard, vulnerability and capacity
- Needs estimation method reviews
- Early warning and early action evidence summaries
- Evidence notes for humanitarian planning
How humanitarian risk 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.
System model
The couplings between the systems in humanitarian risk: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
The questions experts ask.
- Where are crises likely to cause the greatest humanitarian need in coming months?
- Which combinations of hazard, conflict and vulnerability drive that need?
- How reliable are early indicators, and how much lead time do they offer?
- How should uncertainty in forecasts be communicated to decision-makers?
- Which data uses create risks for affected people, and how are they prevented?
Composite risk and early warning.
Humanitarian risk models typically combine three dimensions: hazard and exposure, including conflict; vulnerability of the population; and lack of coping capacity, including weak institutions and infrastructure. Indicators are aggregated into indices that rank or classify areas. Forecasting of food insecurity, displacement or disease outbreaks uses climate forecasts, market prices, conflict data and seasonal patterns to give warning before needs peak.
Anticipatory approaches link forecasts to pre-agreed actions, aiming to act before a shock becomes a crisis. Their value depends on forecast skill, the time needed to act and the cost of acting when the event does not occur. Conflict-driven crises are harder to anticipate than seasonal hazards, and compound crises challenge models built for single shocks.
Verification closes the loop. Comparing past forecasts with what actually happened shows whether indicators and models deserve trust, which thresholds work and where biases lie. Publishing that verification, including failures, is part of responsible humanitarian analysis.
Methods and safeguards.
Composite indicators
Indices summarise risk across many indicators; weighting choices and data gaps shape results.
Predictive models
Statistical and machine-learning forecasts of need; skill varies and must be verified.
Responsible data
Aggregation, access controls and review prevent data from identifying or targeting people.
Limits: indices can mask local drivers, models trained on past crises may miss new patterns, and false alarms and missed events both carry costs. Data about locations of vulnerable groups can be dangerous in conflict settings.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceSurvey design for hard-to-reach groups · Qualitative and participatory researchApplied here
- 02Spatial statistics and geostatisticsSmall-area poverty estimation · Vulnerability and hazard overlayApplied here
- 03Causal inference and impact evaluationEvaluating cash transfers · Effects of disasters on households · Education programme evaluationApplied here
- 04EconometricsPoverty dynamics with panel data · Price shock incidence · Inequality decompositionApplied 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 is the IPC?
How is humanitarian need estimated?
Why act before a crisis peaks?
Does the platform issue humanitarian alerts?
Are risk rankings published?
How are people protected in this analysis?
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