GCRI · Science and methods
Risk science
Risk science asks how hazard, exposure and vulnerability combine into harm, how harm travels through networks of dependence, what causes what, and how certain we can be. The Global Centre for Risk and Innovation (GCRI) builds its evidence on these foundations, so that a decision rests on what is known and on how well it is known, not on numbers that only look precise. Part of science and methods.
01Contents
Risk science in seven parts.
In this dossier7 parts
- 01Building blocksRisk is more than the hazard
- 02Quantifying riskThe quantities risk analysts compute, with their units and meaning
- 03Decision methodsWhen probabilities are not available, decisions are tested for robustness
- 04GCRI’s partMethods for the field, published so others can test them
- 05Beyond single risksNetworks, causality and uncertainty
- 06Kinds of uncertaintyNot all uncertainty is the same
- 07RelatedFrom foundations to application
02Building blocks
Risk is more than the hazard.
The same storm can cause minor disruption in one place and a disaster in another. The difference lies in the other building blocks.
| Concept | Question it answers | Example |
|---|---|---|
| Hazard | What could happen, how often and how severely? | A river flood of a given depth |
| Exposure | Who and what is in harm’s way? | Homes, clinics and substations on the floodplain |
| Vulnerability | How badly would they be affected? | Building type, health status, income, backup power |
| Capacity | How well can they cope and recover? | Warnings, insurance, social networks, repair crews |
03Geography
Hazards and exposure worldwide.
Flood water, population density and active fires across the world, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Hazards and exposure, for this page
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
- 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 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 (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat 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
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 and exposure, framed on the world.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
- 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
- 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 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 (11 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
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 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
04Quantifying risk
The quantities risk analysts compute, with their units and meaning.
| Quantity | Definition | Unit |
|---|---|---|
| Exceedance probability | The probability that a loss or intensity is exceeded in a year | Probability per year; its inverse is the return period in years |
| Average annual loss | The loss expected per year averaged over all simulated years | Currency of a stated year, per year |
| Probable maximum loss at a return period | The loss with a stated annual exceedance probability | Currency, with the return period named |
| Damage ratio | Repair cost divided by replacement value, read from a vulnerability curve | Dimensionless, between zero and one |
| Disability-adjusted life years | Years of healthy life lost to disease or injury, the WHO and Global Burden of Disease measure | Years |
| Value at risk and expected shortfall | A loss quantile, and the mean loss beyond it, over a horizon | Currency, with confidence level and horizon |
Risk vocabulary follows ISO Guide 73 and the Society for Risk Analysis Glossary of 2018, which define risk through consequences and their uncertainty, not through probability alone. Aven’s review of 2016 explains why that matters: a probability is a judgement conditioned on knowledge, and the strength of that knowledge belongs in the result.
05Decision methods
When probabilities are not available, decisions are tested for robustness.
| Method | What it does | Limit |
|---|---|---|
| Expected value and cost-benefit analysis | Ranks options by probability-weighted outcomes | Needs credible probabilities and a discount rate; hides tails |
| Robust decision making and adaptation pathways | Finds options that perform acceptably across many futures, and signposts for switching | Requires agreement on what is acceptable; computationally heavy |
| Info-gap and stress testing | Asks how wrong the assumptions can be before a plan fails | Says nothing about which futures are more likely |
| Structured expert elicitation | Turns expert knowledge into calibrated ranges where data are absent | Sensitive to panel choice and framing; labelled as expert judgement |
Marchau and colleagues’ open-access volume of 2019 sets out these methods in practice. They suit long-lived infrastructure and adaptation choices, where the future cannot be given reliable probabilities.
06GCRI’s part
Methods for the field, published so others can test them.
GCRI develops and documents risk methods and is designed to help national teams apply them to their own systems; the method pages sit in the STEM Commons. Methods papers are designed for the Methods and Technical series, and every model used would carry a model card under NS-12 of the proposed Nexus Standards. Researchers can contribute worked examples, validation data and critiques of a method’s assumptions.
07Beyond single risks
Networks, causality and uncertainty.
Networks
Dependence carries harm
Failures spread through supply, service and financial links, studied with network science and contagion.
Causality
What causes what
Separating cause from correlation with causal inference, and saying when we cannot.
Uncertainty
How sure we are
Quantified with probability and statistics and reported with every result.
08Kinds of uncertainty
Not all uncertainty is the same.
- 01
Natural variability
Randomness in the system itself, which more data cannot remove.
- 02
Knowledge gaps
Limits in data and understanding, which better research can reduce.
- 03
Deep uncertainty
Situations where even the probabilities are unknown, explored with scenarios rather than single estimates.
09Related
From foundations to application.
These foundations are applied across systemic and cascading risks and the global challenges. Modelling practice is set out under models and scenarios. The Global Risks Alliance (GRA) applies quantitative risk methods to finance in its models and professional methods.
10Sources
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.
- ISO Guide 73:2009 Risk management, vocabulary (ISO)
- ISO 31000:2018 Risk management, guidelines (ISO)
- Society for Risk Analysis Glossary (SRA, 2018)
- Aven, Risk assessment and risk management: review of recent advances on their foundation, European Journal of Operational Research 253 (2016)
- Mastrandrea and others, Guidance note for lead authors of the IPCC Fifth Assessment Report on consistent treatment of uncertainties (IPCC, 2010)
- Marchau, Walker, Bloemen and Popper (eds), Decision Making under Deep Uncertainty: from theory to practice (open access, 2019)
- NS-12, Software, model and AI/ML artefacts, a proposed standard
11Questions
Questions and answers.
Why does GCRI separate hazard from risk?
Because reducing exposure and vulnerability is often more achievable than changing the hazard. Treating them separately shows where action can make the most difference.
Is a risk score a fact?
No. A risk score is a modelled or estimated summary with assumptions. GCRI labels it as such and shows the inputs and uncertainty behind it.
How does GCRI deal with risks that have never happened?
With scenarios, expert judgement labelled as such and stress tests. Absence from the historical record is not evidence that a risk is negligible.
Can risk science tell us what to do?
It can show options and trade-offs. Choices involve values and mandates, and they remain with competent authorities and the people affected.
Where can I learn the methods?
The STEM Commons has one page per method, and academy and learning describes supervised learning routes for experts, students and volunteers.
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