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GRADisaster Risk FinanceIn development
STEM foundation

STEM foundation of Disaster Risk Finance

The science, technology, engineering and mathematics Disaster Risk Finance rests on. Each method has a canonical page in the STEM Commons, hosted by GCRI, and a page here on how it is applied in this field.

Open Actuarial mathematics for disaster risk finance Back to Disaster Risk Finance
Publisher
The Global Risks Alliance
Field
Disaster and crisis, Sovereign and public finance, Development finance, Insurance and catastrophe
Canvas · methods feeding this platformDesign, not a record
STEM foundation of Disaster Risk Finance: parametric trigger design and paymentA peril and an index are chosen, the trigger and payment structure designed and tested for basis risk, priced and contracted; a calculation agent monitors the index, calculates the payment and funds flow to pre-agreed delivery. Design of the process, not a record of a run.

Fig.The methods this platform draws on and what each one feeds. Design, not a record.

01In brief

The short answer.

The Disaster Risk Finance STEM foundation is the set of canonical methods its findings depend on, from actuarial mathematics to forecasting and time-series analysis. Each method has a canonical STEM Commons page and a page here on its use in the disaster and crisis field.

02Geography

Hazards now in Pacific Island states.

For STEM foundation of disaster risk finance, in GRA's spatial-finance reading, the map opens on population density, night lights and flood water with built-up and settlement extent from NASA GIBS, framed on Pacific Island states; 3 further NASA layers wait in the list.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Hazards now, for this page

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.

For STEM foundation of disaster risk finance, in GRA's spatial-finance reading, the map opens on population density, night lights and flood water with built-up and settlement extent from NASA GIBS, framed on Pacific Island states; 3 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · GDACS · 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 H3 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 H3 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 H3 cells where shown (modified). 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 H3 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 H3 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 H3 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 H3 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 H3 cells where shown (modified). 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 H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 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.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live: NASA EONET Events are indicative, not official warnings.
  • Live: GDACS (EC JRC / UN OCHA) Events are indicative, not official 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.
  • Optional layers (22 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, last day: 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
  • Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026

03Methods

The methods behind the field.

  1. Actuarial mathematicsSizing retention and transfer layers · Pricing parametric cover · Contingency fund sizingSteward · Actuarial and Quantitative RiskApplied here
    Canonical · STEM Commons
  2. Economics and development economicsCost of ex post versus ex ante finance · Household welfare effects · Fiscal impact of disastersSteward · Economic DevelopmentApplied here
    Canonical · STEM Commons
  3. Decision analysis under uncertaintyChoosing an instrument mix · Trigger threshold choice · Release rules for contingency fundsSteward · Systemic Risk ManagementApplied here
    Canonical · STEM Commons
  4. Forecasting and time-series analysisForecast verification for triggers · Seasonal outlooks for drought financeSteward · Climate and HazardsApplied here
    Canonical · STEM Commons
04Methods

The STEM methods Disaster Risk Finance applies, and why.

Each Disaster Risk Finance method is described once in the STEM Commons at GCRI and applied here to fiscal disaster risk and its financing layers.

05Why it matters

Grounding makes claims checkable.

When a finding names the methods it depends on, a reader can see its assumptions and limits, and an expert in that method can challenge it.

06Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
STEM CommonsThe shared reference of science, technology, engineering and mathematics methods for the Nexus network, hosted by GCRI, with one canonical page per method and a named steward platform.
Steward platformThe platform that maintains the canonical description of a method in the STEM Commons and reviews how other platforms apply it.
Actuarial mathematicsActuarial mathematics applies probability, statistics and financial theory to measure and price contingent risks, particularly in insurance and pensions.
Economics and development economicsEconomics and development economics study how societies produce, distribute and consume resources and how living standards change.
07Sources

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.

08Pages

Pages in STEM foundation of Disaster Risk Finance.

Method

Actuarial mathematics for disaster risk finance

Method

Economics and development economics for disaster risk finance

Method

Decision analysis under uncertainty for disaster risk finance

Method

Forecasting and time-series analysis for disaster risk finance

09Questions

Questions and answers.

What is a steward platform?
The platform that maintains the canonical description of a method and reviews how it is applied across the Consortium.
Related

Around Disaster Risk Finance.

Disaster Risk Finance

Bring your expertise to this field.

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