GRA · Disaster Risk Finance
Spatial financeWhere capital meets hazard, exposure and transition
Financial discipline overview
Disaster risk finance sits where public finance, insurance, development finance and disaster management meet. This page describes the financial discipline the platform studies: the instruments in use, the actors involved and the analytical questions they raise. It approaches financial risk and sector operations as research, not execution. It is published by the Global Risks Alliance (GRA), which does not advise on or arrange any transaction.
- STEM methods
- 4
- Canonical methods this field applies
- Topics
- 8
- Subject pages in the field
- Counterparts
- 6
- Platforms in shared fields across the network
- Report series
- 2
- Reports series it contributes to
The toolkit, in brief.
Funds and budgets
Reserve funds, budget contingencies and reallocation, used for frequent, smaller events.
Pre-arranged credit
Credit lines agreed in advance that become available when a disaster occurs or is declared.
Insurance and markets
Indemnity and parametric insurance, regional pools, reinsurance and catastrophe bonds for severe events.
Activity and exposure in Pacific Island states.
Population density, night lights and flood water over Pacific Island states, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Activity and exposure, for this page
Keyboard, with the map focused
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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.
Activity and exposure, framed on Pacific Island states.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 · CIESIN/Columbia, NASA SEDAC HBASESources 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 Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. 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
- 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
- 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.
- Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
- 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).
- Optional layers (5 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- 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
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
Who typically pays which layer.
| Layer | Typical source | Speed |
|---|---|---|
| Frequent, low-severity | Budget contingencies and reserve funds | Fast if rules allow |
| Moderate | Contingent credit such as Cat DDOs | Weeks |
| Severe, rare | Parametric insurance, pools, catastrophe bonds | Days to weeks for parametric covers |
| Residual | Post-disaster borrowing, reallocation, donor support | Months |
Shocks travel between these parts, and Disaster Risk Finance studies the links: Who pays, how fast, at each layer? The evidence base is loss history, modelled fiscal loss, instruments.
Field overview.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Who is involved.
Ministries of finance carry the fiscal exposure and usually lead disaster risk finance strategies, working with disaster management agencies that plan the response. Development institutions provide contingent credit and technical support. Insurers, reinsurers and capital markets take on transferred risk. Regional risk pools allow countries to share risk and buy cover together. Humanitarian organisations increasingly use pre-arranged finance for early action. Households and firms carry much of the loss directly, through savings, credit and, where available, insurance.
Each actor faces different questions: how much risk can the budget absorb, what does protection cost relative to its value, how quickly can funds reach those who need them, and how are decisions governed. The platform studies these questions across the system rather than for any single participant.
Protection gaps are the discipline's central measure: the difference between economic losses and the share covered by insurance or pre-arranged finance. Gaps tend to be widest in lower-income countries and for households and small firms, which is where the human cost of delay is greatest.
How the platform works.
Disaster Risk Finance studies arrangements at aggregate level and publishes research. It does not assess individual transactions, recommend instruments or providers, or constitute financial advice. GRA is a nonprofit business league for the financial services industry. It does not lend, underwrite, rate, invest, advise on or arrange transactions, and readiness work is not approval.
- How disaster shocks travel through public finance and the financial system: financial-system transmission.
- Topics in depth: risk financing layers and sovereign contingent liabilities.
- The science of disaster loss at The Global Centre for Risk and Innovation (GCRI): disaster loss accounting.
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.
Pages in Financial discipline overview.
Questions and answers.
Does GRA advise governments on disaster finance?
Is parametric insurance in scope?
Does the platform cover household and business insurance?
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