The Global Disaster-Related Statistics Framework: Why Statisticians and Disaster Managers Must Finally Speak the Same Language

During a consultancy with a UN agency, I needed to integrate disaster impact data from the national disaster management commission with population statistics from the national statistical agency for a climate-informed cash targeting model. The two agencies' offices were close by. Their data might as well have been on different planets.

The disaster commission used sub-national geographic codes based on their own internal taxonomy. The statistical agency used the official p-code system aligned with OCHA's Common Operational Datasets. The disaster commission counted "affected households." The statistical agency counted "individuals" using census definitions. The disaster commission classified events by operational response type. The statistical agency needed events classified by internationally comparable hazard categories.

Neither dataset was wrong. They were produced by different institutional cultures for different purposes using different standards — and they could not be combined without weeks of manual harmonisation. I'm a disaster risk and humanitarian data systems architect who has spent a decade working at this exact fault line, and the experience has convinced me that the single most important development in disaster data governance this decade is not a new platform or a new indicator. It is the Global Disaster-Related Statistics Framework (G-DRSF) — endorsed by the UN Statistical Commission on 9 March 2026 — which for the first time gives disaster managers and statisticians a shared vocabulary, shared standards, and a shared reason to work together.

The G-DRSF is the first internationally harmonised framework for producing disaster-related statistics. Developed through comprehensive global consultation in 2025, it provides the statistical standards, definitions, and methodologies that bridge two institutional worlds: the National Disaster Management Agencies (NDMAs) who collect operational disaster data, and the National Statistical Offices (NSOs) who produce the official statistics that governments and international bodies rely on for policy and finance decisions.

Before the G-DRSF, these two worlds operated in parallel. NDMAs collected data for operational purposes — which villages were flooded, how many houses were damaged, how many people needed emergency assistance. NSOs produced statistics for policy purposes — poverty rates, GDP impacts, population demographics. The data rarely met. When it did, the reconciliation was manual, ad-hoc, and unreproducible.

The G-DRSF changes this by establishing:

Shared definitions for what constitutes a "disaster," a "hazardous event," a "loss," and a "damage" — aligned with the Sendai Framework's terminology and the WMO-CHE hazard classification system.

Shared geographic standards using p-codes and official administrative boundary systems, ensuring that disaster data can be linked to census data, health data, education data, and economic data without geographic reconciliation.

Shared quality assurance protocols that specify what completeness, accuracy, timeliness, and consistency mean for disaster data — giving NSOs a framework for certifying NDMA data as official statistics.

Shared disaggregation requirements mandating that disaster impact data be broken down by geography, sector, sex, age, and disability — aligning with both the Sendai Framework's Leave No One Behind commitment and the SDG disaggregation standards.

The most consequential design feature of the G-DRSF is what UNDRR calls the "one-report-two-purposes" principle. Data entered once to meet the 38 Sendai Framework indicators — covering mortality (Target A), affected people (Target B), economic losses (Target C), infrastructure damage (Target D), DRR strategies (Target E), international cooperation (Target F), and early warning systems (Target G) — automatically feeds 12 SDG indicators across targets 1.5, 11.5, 11.b, and 13.1.

This is not a minor efficiency gain. For developing countries with limited statistical capacity, the elimination of double-reporting is transformative. Many national statistics offices have between 2-5 staff dedicated to disaster-related statistics. Asking them to separately compile Sendai reports and SDG reports — using different methodologies, different formats, and different timelines — was a capacity burden that many countries simply could not meet.

The reporting cycle that the G-DRSF standardises follows global milestones in April and October, allowing countries to synchronise their disaster data production with both the Sendai Framework Monitor reporting windows and the SDG Voluntary National Review calendar. This synchronisation means that the same dataset, produced once, is valid for multiple international accountability mechanisms.

The G-DRSF provides the framework. Making it work requires solving the hardest problem in disaster data governance: the institutional relationship between the NDMA and the NSO.

These are different organisations with different mandates, different cultures, and different relationships with political authority. NDMAs operate under operational urgency — data needs measured in hours and days. NSOs operate under statistical rigour — data needs measured in quarters and years. An NDMA data officer reporting "approximately 5,000 households affected" is doing good disaster management. An NSO statistician requiring sampling methodology and confidence intervals is doing good statistics. Both are right. The G-DRSF gives them a protocol for reconciling their rightness.

Data ownership and p-codes. Where disaster data has political sensitivity — which is most countries — the question of data ownership is contested. A Memorandum of Understanding (MoU) between the NDMA and NSO — signed before data collection begins — specifies data flows, validation protocols, publication authority, and dispute resolution. This governance document reflects a political agreement about how disaster data will be produced and certified.

Equally critical: the standardisation of geographic identifiers (p-codes). P-codes are the bridge between operational disaster data and statistical population data. Without valid p-codes, a flood impact cannot be linked to census figures or health facility density. With p-codes, the linkage is automatic. Ensuring consistent p-code usage is one of the highest-impact, lowest-cost interventions in disaster data quality. DELTA Resilience mandates this. Many legacy systems did not.

The G-DRSF's March 2026 endorsement positions it as the data backbone for the post-COP30 reporting landscape. The 59 Belém Adaptation Indicators adopted at COP30 require countries to monitor adaptation progress across agriculture, health, infrastructure, and livelihoods — many requiring historical disaster loss baselines.

The COP30 "State of Loss and Damage Report" will rely on data produced through national DELTA Resilience systems aligned with G-DRSF standards. Countries that have not operationalised the G-DRSF will find their loss claims unverifiable — and in a resource-scarce environment where the Loss and Damage Fund has $768 million against $580 billion in estimated need, unverifiable claims will not be funded.

This creates a direct financial incentive for G-DRSF adoption. It is no longer about good practice. It is about access to climate finance.

The G-DRSF provides the standards. DELTA Resilience provides the system that turns those standards into a working data ecosystem.

DELTA's data model is built around G-DRSF definitions. Its hazard classification uses WMO-CHE. Its disaggregation structure implements G-DRSF requirements for sex, age, disability, and geographic breakdown. Its API architecture enables automated data exchange between NDMA and NSO systems.

The Data Ecosystem Maturity Assessment (DEMA) is conducted before DELTA deployment, assessing data governance, technical infrastructure, data quality, and human capacity. DELTA begins with governance and builds technology on institutional foundations — a sequencing that distinguishes it from predecessors like DesInventar.

If you work in disaster data at any level — national, regional, or global — here are three immediate actions:

Read the G-DRSF. The e-learning course on UN SDG:Learn is free, self-paced, and takes approximately 8 hours. It covers the framework's structure, definitions, and practical application. This is now essential knowledge for anyone working in DRR data.

Map your current data against G-DRSF standards. Take your national disaster database — whatever system it uses — and check: are your hazard classifications aligned with WMO-CHE? Are your geographic identifiers using valid p-codes? Is your disaggregation capturing sex, age, and disability? Is your mortality data cross-referenced with affected population data for consistency?

Start the NDMA-NSO conversation. If your country does not have a formal data-sharing agreement between the disaster management agency and the statistical office, begin that conversation now. The G-DRSF gives you the framework. The Loss and Damage Fund gives you the incentive. But the MoU is something that must be negotiated locally, and it takes time.

The G-DRSF is not a statistics reform. It is a governance reform. It changes the institutional relationship between the organisations that collect disaster data and the organisations that certify it. It creates shared accountability for data quality. It establishes shared standards that make data interoperable across national and international systems.

And governance reforms only succeed when the people who collect the data and the people who certify the data learn to trust each other. The G-DRSF provides the framework for that trust. The rest is politics, patience, and the slow, unglamorous work of building institutional partnerships that outlast project cycles.