Disaster Risk Indicators

1. Outcome

This Circular provides guidance on compiling disaster risk indicators from ocean accounts, enabling countries to assess exposure, vulnerability, and adaptive capacity of coastal populations to marine and coastal hazards. Readers will understand how to integrate ecosystem accounting data with socioeconomic information to quantify hazard exposure, measure the protective services of coastal ecosystems, assess vulnerability of ocean-dependent communities, and track economic losses and recovery from coastal disasters. The guidance supports national implementation of the Sendai Framework for Disaster Risk Reduction 2015--2030 and monitoring of SDG targets 1.5 (resilience to climate-related extreme events), 11.5 (reducing disaster impacts), and 13.1 (strengthening resilience to climate-related hazards)^[1].

Disaster risk indicators derived from ocean accounts address four priority decision use cases for governments managing coastal zones. First, coastal disaster risk assessment quantifies population exposure, economic assets at risk, and ecosystem-based protection capacity, providing the evidence base for land-use planning, infrastructure investment, and emergency preparedness. Second, insurance pricing and risk transfer requires quantitative exposure and vulnerability data to enable actuarial assessment of disaster risk for properties, infrastructure, and livelihoods in coastal zones, supporting development of disaster insurance markets and parametric insurance products. Third, Sendai Framework reporting requires systematic compilation of indicator 1.5.1/11.5.1 (deaths, missing persons, and affected persons per 100,000 population) and indicator 1.5.2/11.5.2 (direct economic loss as share of GDP), both derived from asset accounts and disaster damage assessments. Fourth, coastal protection ecosystem service valuation measures the avoided damage attributable to mangroves, coral reefs, and coastal wetlands, providing economic justification for ecosystem conservation and restoration investments as nature-based disaster risk reduction measures.

This Circular builds on the conceptual framework established in TG-0.1 General Introduction, the statistical standards described in TG-0.2 Standards Overview, the indicator design principles set out in TG-2.1 Indicator Design Principles, and the asset accounting methodology from TG-3.1 Asset Accounts. The disaster risk indicators compiled using this guidance support the policy application guidance in TG-1.6 Risk Assessment. Cross-references to related circulars include TG-3.5 Ecosystem Condition for storm damage to ecosystem assets, TG-6.11 Coastal Infrastructure for exposure of built infrastructure, and TG-2.8 Climate Change for climate-related disaster risk including sea level rise and changing storm patterns.

Ocean accounts provide a unique foundation for disaster risk assessment by linking ecosystem extent and condition data with economic and social accounts. Coastal ecosystems such as coral reefs, mangroves, and seagrass meadows provide regulating services that reduce hazard exposure, while the condition of these ecosystems directly affects the magnitude of protection provided^[2]. The TNFD framework recognizes that "the prosperity and resilience of our societies and economies depend on the health and resilience of nature and its biodiversity"^[3], highlighting the direct connection between ecosystem condition and disaster risk.

2. Requirements

This Circular requires familiarity with:

• TG-0.1 General Introduction to Ocean Accounts -- for the conceptual framework connecting ecosystems to economic and social outcomes
• TG-0.2 Standards Overview -- for the international statistical standards underpinning ocean accounting, including SNA 2025, SEEA CF, SEEA EA, and their treatment of environmental-economic interactions
• TG-2.1 Indicator Design Principles -- for the general methodology for constructing indicators from ocean account data, including guidance on indicator selection, normalization, and composite index construction
• TG-3.1 Asset Accounts -- for methodology on compiling ecosystem extent and condition accounts that underpin hazard exposure assessment and ecosystem-based protection measurement, particularly the treatment of catastrophic losses to environmental assets

3. Guidance Material

Disaster risk in coastal zones arises from the intersection of natural hazards, exposure of populations and assets, and the vulnerability of exposed elements^[4]. The IPCC defines disaster risk as "the likelihood over a specified time period of severe alterations in the normal functioning of a community or a society due to hazardous physical events interacting with vulnerable social conditions"^[5]. Ocean accounts provide systematic data on each component of this risk equation: ecosystem accounts document hazard-modifying features of the coastal environment; economic accounts record exposed assets and economic activities; and social accounts capture vulnerability characteristics of coastal populations.

This section examines the disaster risk framework and its application to ocean accounting (Section 3.1), methods for compiling hazard exposure indicators (Section 3.2), vulnerability indicators for coastal communities (Section 3.3), ecosystem-based protection indicators (Section 3.4), and economic loss and recovery indicators (Section 3.5). Section 3.6 presents the compilation procedure with detailed steps from data collection through indicator reporting, while Section 3.7 provides a worked example demonstrating the full procedure using synthetic data for a coastal storm damage assessment.

3.1 Disaster Risk Framework

The Sendai Framework for Disaster Risk Reduction provides the overarching international framework for understanding and reducing disaster risk^[6]. It identifies four priority areas: understanding disaster risk; strengthening disaster risk governance; investing in disaster risk reduction for resilience; and enhancing disaster preparedness for effective response. Ocean accounts contribute to all four priorities by providing systematic, spatially explicit data on hazard exposure, ecosystem-based protection, vulnerable populations, and economic consequences of disasters.

3.1.1 Components of disaster risk

Disaster risk is commonly expressed as a function of three components^[7]:

Hazard refers to the potential occurrence of a natural or human-induced physical event that may cause loss of life, injury, or other health impacts, as well as damage to property, infrastructure, livelihoods, service provision, and environmental resources^[8]. Coastal hazards include tropical cyclones, storm surges, coastal flooding, tsunamis, erosion, and sea-level rise. The 2025 SNA recognizes that "catastrophic losses represent exceptional and significant reductions in the natural resource" due to discrete events^[9], highlighting the accounting treatment of hazard impacts.

Exposure refers to the presence of people, livelihoods, species, ecosystems, environmental functions, services, resources, infrastructure, or economic, social, or cultural assets in places and settings that could be adversely affected^[10]. For ocean accounting, exposure is measured through spatial analysis linking hazard zones with data from asset accounts (physical infrastructure), economic accounts (industry output and employment), and social accounts (population characteristics).

Vulnerability refers to the conditions determined by physical, social, economic, and environmental factors that increase the susceptibility of an individual, a community, assets, or systems to the impacts of hazards^[11]. The SDG Framework includes indicator 1.5.1 measuring the "Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population"^[12], emphasizing the human dimensions of vulnerability.

3.1.2 Ecosystem vulnerability

Ecosystems themselves are vulnerable to hazards, and damage to ecosystems cascades to loss of protective services for coastal communities. Coral reefs suffer structural damage and bleaching from cyclone-driven wave energy and temperature anomalies. Mangroves experience windthrow and root damage during high-intensity storms. Seagrass meadows are uprooted by wave scour and buried by storm-driven sediment transport. Ecosystem vulnerability indicators derived from ocean accounts include:

• Ecosystem exposure -- extent (hectares) of each ecosystem type located within identified hazard zones, drawn from ecosystem extent accounts (see TG-3.1 Asset Accounts, Section 3.5.1)
• Condition sensitivity -- rate of condition decline following hazard events, measured through pre- and post-event condition assessments
• Recovery potential -- estimated time for ecosystem condition to return to reference levels, informed by historical recovery data and ecological literature
• Cascading risk -- the reduction in coastal protection services attributable to ecosystem damage, linking ecosystem vulnerability to human vulnerability

Compilers should record disaster-related ecosystem damage in extent and condition accounts. The SEEA EA addresses ecosystem conversions^[13], and catastrophic losses to ecosystem assets should be recorded as other changes in the volume of assets, consistent with the treatment of catastrophic losses to natural resources in the SEEA CF^[14]. Where monetary ecosystem asset accounts exist, the associated monetary losses should also be recorded. The accounting procedure for recording catastrophic losses is detailed in Section 3.6.3.

3.1.3 Risk equation for ocean accounts

Integrating these components, disaster risk can be expressed as:

Risk = Hazard x Exposure x Vulnerability / Adaptive Capacity

Where adaptive capacity represents "the ability of systems, institutions, humans and other organisms to adjust to potential damage, to take advantage of opportunities, or to respond to consequences"^[15]. For coastal communities, adaptive capacity includes both engineered protection (sea walls, drainage systems) and ecosystem-based protection (healthy coral reefs, intact mangroves).

The SEEA Ecosystem Accounting framework provides the foundation for measuring ecosystem-based adaptive capacity. Ecosystem assets in good condition provide regulating services that reduce effective exposure to hazards^[16]. Conversely, degraded ecosystems may provide diminished protection, increasing the vulnerability of coastal populations. The 2025 SNA notes that sustainability assessment requires consideration of "capacity, resilience and risk" which are related concepts all requiring "consideration of the future and the projection of potential changes to the stocks of capital"^[17].

For detailed guidance on ecosystem condition assessment that affects protective capacity, see TG-3.1 Asset Accounts, Section 3.5.2 on ecosystem condition accounts. The condition variables identified there--including structural state, compositional state, and functional state--directly influence the magnitude of coastal protection services provided.

3.1.4 Alignment with SDG and Sendai indicators

Ocean account-based disaster risk indicators should align with international monitoring frameworks. Table 1 maps ocean account components to the relevant SDG and Sendai Framework indicators, showing how compiled disaster risk indicators support national and international reporting obligations.

Table 1: Ocean Account Components and SDG/Sendai Indicator Mapping

The FDES 2013 provides additional environmental statistics for disaster risk assessment, including indicators on natural extreme events and disasters, and their impacts on human settlements^[18].

3.2 Hazard Exposure Indicators

Hazard exposure indicators quantify the people, assets, and economic activities located in areas subject to coastal hazards. Ocean accounts provide the foundation for systematic exposure assessment through integration of spatial data on hazard zones with economic and social accounts.

3.2.1 Spatial delineation of hazard zones

Exposure assessment requires spatial delineation of hazard zones for different hazard types and return periods. Key hazard zones for coastal areas include^[19]:

Coastal flood zones -- areas subject to inundation from storm surge, high tides, and sea-level rise. Flood zones are typically classified by return period (e.g., 1-in-100 year flood zone) and projected future conditions under climate change scenarios.

Erosion zones -- areas subject to shoreline retreat due to wave action, sediment transport, and sea-level rise. Erosion hazard mapping identifies areas where infrastructure and assets may be lost over specified time horizons.

Tsunami inundation zones -- areas subject to inundation from tsunami events based on historical records and modelling.

Cyclone/hurricane exposure zones -- areas subject to wind damage, storm surge, and heavy precipitation from tropical cyclones.

Spatial data sources for hazard zone delineation include digital elevation models derived from satellite altimetry and LiDAR, satellite imagery for flood extent mapping, and ocean observation networks for sea-level and wave climate data. For guidance on spatial data sources and methods for delineating hazard zones, see TG-4.1 Remote Sensing Data and TG-4.4 Geospatial Integration.

3.2.2 Population exposure indicators

Population exposure indicators measure the number of people residing in or using hazard-prone areas^[20]. Key indicators include:

Resident population in coastal flood zones -- number of persons residing in areas subject to coastal flooding, classified by flood return period (e.g., 1-in-10 year, 1-in-100 year).

Population density in hazard zones -- persons per square kilometre in coastal hazard areas, enabling comparison of relative exposure across locations.

Dependent population in hazard zones -- subsets of the exposed population with heightened vulnerability, including children, elderly, persons with disabilities, and low-income households.

Seasonal/tourist population -- maximum daytime population in coastal hazard zones accounting for tourism, recreation, and daily commuting patterns.

These indicators should be compiled using census data, population registers, and spatial analysis linking population distribution with hazard zone mapping. The disaggregations recommended in TG-3.5 Social Accounts (by age, sex, income) should be applied to exposure indicators where data permit.

3.2.3 Asset exposure indicators

Asset exposure indicators measure the physical and economic assets located in hazard-prone areas^[21]. Ocean accounts provide the foundation through asset accounts for both produced assets (infrastructure, buildings) and environmental assets (ecosystem assets):

Built infrastructure in hazard zones -- value of residential, commercial, industrial, and public infrastructure located in coastal hazard areas. This draws on asset account data from TG-3.1 Asset Accounts combined with spatial hazard mapping.

Critical infrastructure exposure -- number and value of hospitals, schools, power plants, water treatment facilities, and transportation infrastructure in hazard zones.

Ecosystem asset exposure -- extent (hectares) of ecosystem types located in areas subject to coastal hazards, including coral reefs, mangroves, seagrass meadows, and coastal wetlands. Ecosystem assets themselves may be damaged by disasters (e.g., coral damage from cyclones), representing both an immediate loss and a reduction in protective capacity.

Agricultural and aquaculture asset exposure -- value of productive assets in coastal hazard zones including aquaculture facilities, salt farms, and coastal agricultural land. For aquaculture-specific guidance, see TG-3.9 Aquaculture Accounts.

3.3 Vulnerability Indicators

Vulnerability indicators measure the characteristics of exposed populations and systems that affect their susceptibility to harm and capacity to cope with and recover from disasters^[22]. The IPCC distinguishes between sensitivity (the degree to which a system is affected by hazards) and adaptive capacity (the ability to adjust, take advantage of opportunities, or cope with consequences)^[23].

The vulnerability framework presented here builds on the wellbeing and equity indicators described in TG-3.5 Social Accounts, applying them specifically to disaster risk contexts.

3.3.1 Sensitivity indicators for coastal communities

Sensitivity indicators measure characteristics that increase the propensity of exposed elements to suffer harm^[24]:

Poverty rates in coastal areas -- proportion of households below the poverty line in coastal zones, as low-income households have fewer resources to prepare for, withstand, and recover from disasters. SDG target 1.5 explicitly calls for building "the resilience of the poor and those in vulnerable situations"^[25].

Informal housing -- proportion of coastal population residing in informal settlements or housing not built to building codes, which are more susceptible to damage from coastal hazards.

Livelihood dependence on coastal resources -- proportion of household income derived from fishing, aquaculture, tourism, and other ocean-dependent activities that may be disrupted by disasters. This indicator draws on data from TG-3.5 Social Accounts, Section 3.1.2 on ocean sector employment.

Nutritional dependence on seafood -- proportion of protein consumption derived from marine sources, indicating food security vulnerability when fisheries are disrupted.

Age structure -- proportion of population under 5 years and over 65 years, as these age groups face elevated risks from disasters.

Health status -- prevalence of chronic illness and disability in coastal populations, affecting capacity to evacuate and recover.

Indigenous Peoples and Local Communities -- IPLC populations may face distinct vulnerability patterns arising from historical marginalization, land tenure insecurity, and dependence on traditional marine resources, while also possessing traditional ecological knowledge that enhances adaptive capacity. Compilers should disaggregate vulnerability indicators for IPLC populations where data permit. The TNFD provides guidance on engagement with Indigenous Peoples and Local Communities in nature-related risk assessment^[26]. For guidance on documenting traditional coping mechanisms and knowledge systems relevant to disaster resilience, see TG-3.6 Traditional Knowledge Accounts.

3.3.2 Adaptive capacity indicators

Adaptive capacity indicators measure resources and capabilities that enable communities to cope with and recover from disasters^[27]:

Social protection coverage -- proportion of coastal population covered by social protection systems including unemployment insurance, disaster relief, and social assistance. SDG indicator 1.3.1 measures "Proportion of population covered by social protection floors/systems"^[28].

Access to financial services -- proportion of coastal households with access to savings accounts, insurance, and credit, enabling self-financing of recovery.

Disaster insurance coverage -- proportion of coastal properties and livelihoods covered by flood, cyclone, or fisheries insurance.

Educational attainment -- average years of education in coastal populations, associated with greater awareness and ability to access information and resources.

Social capital -- strength of community organizations, cooperatives, and social networks that support collective action in disasters.

Early warning coverage -- proportion of coastal population with access to effective early warning systems for coastal hazards.

Evacuation capacity -- availability of evacuation routes and shelters relative to exposed population, and historical evacuation rates.

For governance arrangements affecting adaptive capacity, see TG-3.7 Governance Accounts, which documents institutional frameworks for disaster risk management.

3.3.3 Composite vulnerability indices

Composite vulnerability indices aggregate multiple indicators to provide summary measures of vulnerability^[29]. While construction of composite indices requires methodological choices about indicator selection, normalization, and weighting, they can provide useful summary measures for policy communication. The general principles for composite indicator construction set out in TG-2.1 Indicator Design Principles apply directly to disaster risk composite indices. Key considerations include:

Indicator selection -- include indicators across sensitivity, adaptive capacity, and exposure dimensions, ensuring coverage of economic, social, and physical vulnerability. Compilers should select indicators for which consistent, regularly updated data are available across all geographic units being compared.

Normalization -- transform indicators to comparable scales (e.g., 0-1 range) to enable aggregation. Min-max normalization and z-score standardization are both acceptable; compilers should document the method chosen.

Weighting -- apply weights reflecting the relative importance of different vulnerability dimensions, with transparent documentation of weighting choices. Equal weighting is the default approach where no strong empirical or theoretical basis exists for differential weighting. Where expert elicitation or statistical methods (such as principal components analysis) are used to derive weights, the methodology should be documented.

Validation -- validate composite indices against historical disaster impacts to assess predictive accuracy.

Countries should adapt composite index methodologies to reflect local data availability and policy priorities while documenting deviations from the general framework to support cross-country comparability.

3.4 Ecosystem-Based Protection Indicators

Ecosystem-based protection indicators measure the contribution of coastal and marine ecosystems to reducing hazard exposure and disaster risk. The SEEA EA recognizes coastal protection as a regulating service: "ecosystem contributions that regulate or maintain environmental conditions that benefit people"^[30].

This section connects to the broader treatment of ecosystem services in TG-3.2 Flows from Environment to Economy, with specific focus on coastal protection services. It also relates to the ecosystem goods and services indicators described in TG-2.4 Ecosystem Goods and Services.

3.4.1 Coastal protection services from ecosystem accounts

Coastal ecosystems provide protection through multiple mechanisms^[31]:

Wave attenuation -- coral reefs, seagrass beds, and mangroves reduce wave energy before it reaches the shoreline. Research indicates that coral reefs reduce wave energy by an average of 97%, with reef crests alone responsible for 86% of this reduction^[32].

Storm surge reduction -- mangroves and coastal wetlands attenuate storm surge through friction as water flows through vegetation. Studies estimate that mangroves reduce storm surge by approximately 0.5 metres per kilometre of mangrove width^[33].

Shoreline stabilization -- seagrass meadows and mangrove roots stabilize sediments, reducing erosion rates. Root systems also trap sediments, enabling shorelines to accrete and keep pace with sea-level rise.

Flood water storage -- coastal wetlands store floodwaters, reducing peak flood levels and extending the time over which flooding occurs.

The SEEA EA states that "ecosystem services encompass services that are both predominantly biotic (e.g. air filtration services provided by forests) and predominantly abiotic (e.g. coastal protection services provided by sand dunes)"^[34], indicating that both living and physical features of coastal ecosystems contribute to protection.

For ecosystem-specific guidance on coral reef protection services, see TG-6.1 Coral Reef Accounts. For mangrove protection services, see TG-6.2 Mangrove and Wetland Accounts. For seagrass protection services, see TG-6.3 Seagrass Accounts.

3.4.2 Ecosystem-based adaptation service matrix

Table 2 summarizes the principal ecosystem types that provide coastal protection, the hazards they mitigate, the physical mechanisms through which protection occurs, and the recommended quantification methods. This matrix supports compilers in identifying which ecosystem condition variables to prioritize when assessing protective capacity.

Table 2: Ecosystem-Based Adaptation Service Matrix

The condition variables from ecosystem condition accounts (see TG-3.1 Asset Accounts, Section 3.5.2) that affect protective capacity vary by ecosystem type. Table 3 links key condition variables to their effect on protection mechanisms, supporting the compilation of protection service flows from condition account data.

Table 3: Ecosystem Condition Variables Affecting Protective Capacity

3.4.3 Ecosystem protection indicators

Key ecosystem-based protection indicators derived from ocean accounts include^[35]:

Ecosystem extent in protective positions -- hectares of coral reefs, mangroves, seagrass beds, and coastal wetlands located seaward of coastal populations and infrastructure, quantifying the "natural infrastructure" available for protection.

Ecosystem condition indicators -- condition variables from ecosystem condition accounts (see TG-3.1 Asset Accounts, Section 3.5.2) that affect protective capacity, including coral reef structural complexity, mangrove canopy density and forest width, seagrass bed density and continuity, and wetland vegetation height and density.

Protected coastline proportion -- proportion of coastline length fronted by protective ecosystems at or above reference condition thresholds.

Protection service flow -- estimated physical flow of coastal protection services, measured in hectares of land protected or metres of wave height reduction attributed to ecosystems.

Monetary value of protection -- economic value of coastal protection services estimated using avoided damage, replacement cost, or hedonic pricing methods. For valuation guidance, see TG-1.9 Valuation.

3.4.4 Degradation and protection loss

Ecosystem degradation reduces protective capacity and increases disaster risk^[36]. The SEEA EA defines degradation as "the decline in condition multiplied by the associated loss of future ecosystem service flows"^[37]. For coastal protection, degradation manifests as:

Extent loss -- conversion of protective ecosystems to other uses (e.g., mangrove clearing for aquaculture) directly reduces protective coverage. Ecosystem extent accounts record these changes.

Condition decline -- degradation of ecosystem condition (e.g., coral bleaching, mangrove thinning) reduces protective capacity even where extent is maintained. Condition accounts track these changes.

Cumulative protection loss -- combined effect of extent loss and condition decline on total protective capacity of coastal ecosystems.

The TNFD framework identifies physical risks from nature loss including "acute physical risks stemming from specific, short-term changes in nature that are, for example, event-driven, such as...exposure to natural hazards such as flooding, storms, wildfires, droughts, pollution events and disease outbreaks"^[38]. Degradation of protective ecosystems represents a nature-related physical risk to coastal communities.

For guidance on accounting for ecosystem degradation in monetary terms, see TG-3.1 Asset Accounts, Section 3.5.3 on monetary ecosystem asset accounts.

3.5 Economic Loss and Recovery Indicators

Economic loss and recovery indicators measure the monetary impacts of coastal disasters and the pace of economic recovery. SDG indicator 1.5.2/11.5.2 measures "Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)"^[39].

This section integrates with the economic activity measures in TG-3.3 Economic Activity and the asset accounts in TG-3.1 Asset Accounts.

3.5.1 Direct economic losses

Direct economic losses are the monetary value of assets destroyed or damaged by disasters^[40]. For ocean accounts, direct losses include:

Damage to produced assets -- destruction or damage to buildings, infrastructure, machinery, and inventories. Measured as the value of assets at pre-disaster prices that would be required to restore assets to their pre-disaster condition.

Damage to natural assets -- destruction or damage to fish stocks, aquaculture assets, and ecosystem assets. Fish kills from storm-related water quality impacts, damage to aquaculture cages from storms, and physical damage to coral reefs from cyclones represent direct losses to natural assets.

Damage to ecosystem assets -- physical damage to ecosystem extent and condition from disasters. Physical damage (hectares of coral reef damaged, mangroves destroyed) should be recorded in extent accounts as other changes in the volume of assets. Where monetary ecosystem asset accounts exist, the monetary loss should be recorded as an other volume change in the asset account, consistent with the treatment of catastrophic losses in the SEEA CF^[41].

The SEEA CF notes that "catastrophic losses are recorded only for natural resources since, by definition, losses of cultivated biological resources are recorded as outputs and losses of products are recorded as changes in inventories"^[42].

3.5.2 Indirect economic losses

Indirect economic losses arise from disruption to economic activity following disasters^[43]:

Production losses -- value of output lost due to disruption of productive activities. For ocean industries, this includes lost fishing days, suspended aquaculture production, and reduced tourism activity.

Supply chain disruptions -- losses from disruption to inputs and outputs, including damage to ports and transport infrastructure that affects trade.

Employment impacts -- wages lost due to temporary unemployment and underemployment following disasters.

Ecosystem service flow disruption -- reduction in ecosystem service flows following disaster damage to ecosystems. Damaged coral reefs provide reduced fish habitat, coastal protection, and tourism services until recovery occurs.

3.5.3 Recovery indicators

Recovery indicators track the restoration of economic activity, assets, and ecosystem services following disasters^[44]:

Asset reconstruction rate -- proportion of damaged built assets restored to pre-disaster condition over time.

Economic activity recovery -- restoration of output, employment, and trade to pre-disaster levels in disaster-affected areas and sectors.

Ecosystem recovery rate -- restoration of ecosystem extent and condition following disaster damage. Coral reefs may require decades to recover from severe cyclone or bleaching damage, while mangroves can recover more quickly if propagule sources remain. Recovery rates inform projections of when protective services will be restored.

Livelihood recovery -- restoration of household incomes and food security in disaster-affected communities.

Build back better indicators -- extent to which reconstruction improves on pre-disaster conditions through improved building standards, relocated assets, and enhanced ecosystem protection.

3.6 Compilation Procedure

This section presents a step-by-step procedure for compiling disaster risk indicators from ocean accounts, from initial data collection through accounting entry and policy reporting.

3.6.1 Step 1: Data collection and spatial framework

The compilation process begins with establishing a spatial framework for the coastal zone and assembling source data from multiple data streams.

Establish spatial framework. Define the accounting area covering the coastal zone, typically extending from the inland limit of coastal influence (tidal extent, storm surge reach) to the seaward limit of territorial waters or the EEZ. Subdivide the accounting area into spatial units (administrative districts, coastal segments, ecosystem spatial units) that serve as the geographic basis for indicator compilation. Detailed guidance on geospatial frameworks is provided in TG-4.4 Geospatial Integration.

Delineate hazard zones. Using digital elevation models, hydrodynamic models, and historical hazard records, delineate spatial boundaries of:

• Coastal flood zones by return period (1-in-10 year, 1-in-50 year, 1-in-100 year)
• Tsunami inundation zones
• Erosion-prone coastal segments
• Cyclone/hurricane wind damage zones

Collect ecosystem data. From ecosystem extent and condition accounts (see TG-3.1 Asset Accounts):

• Hectares of mangroves, coral reefs, seagrass meadows, coastal wetlands by spatial unit
• Condition variables affecting protective capacity (canopy density, reef rugosity, bed continuity)
• Spatial position of ecosystems relative to populations and infrastructure

Collect population and asset data. From census, administrative records, and economic accounts:

• Population counts by spatial unit, with age and income disaggregations
• Value of residential, commercial, and critical infrastructure by spatial unit
• Employment and economic activity in coastal zones

Collect historical disaster data. From emergency management records, insurance databases, and post-disaster assessments:

• Historical disaster events by type, date, and affected area
• Damage estimates (lives lost, persons affected, economic loss)
• Recovery timelines from past events

3.6.2 Step 2: Exposure indicator compilation

With the spatial framework and source data assembled, compile exposure indicators by overlaying hazard zones with population and asset data.

Population exposure calculation. For each hazard zone and return period:

Population exposed (N persons) = Sum of population in spatial units intersecting hazard zone

Disaggregate by age group, income quintile, and IPLC status where data permit.

Asset exposure calculation. For each hazard zone:

Asset value at risk (currency units) = Sum of asset values in spatial units intersecting hazard zone

Distinguish residential, commercial, industrial, critical infrastructure, and natural assets.

Ecosystem exposure calculation. For ecosystems vulnerable to disasters:

Ecosystem extent at risk (hectares) = Sum of ecosystem area in spatial units subject to hazard

Record separately for each ecosystem type (mangroves, coral reefs, seagrass).

3.6.3 Step 3: Recording catastrophic losses in asset accounts

When disasters occur, physical and monetary losses must be recorded in asset accounts following the other changes in volume of assets (OCVA) framework from the SEEA CF and SEEA EA.

Physical asset account entry. For natural aquatic resources (fish stocks), ecosystem extent, or other environmental assets affected by disasters, record the physical loss in the asset account under "Catastrophic losses" in the reductions section. This entry applies to "exceptional and significant reductions in the natural resource" that result from discrete disaster events^[41:1].

Example for coral reef extent account:

Monetary asset account entry. Where monetary ecosystem asset accounts are compiled, the monetary value of catastrophic losses is recorded as an other volume change, consistent with SEEA CF para 5.50. The monetary loss equals the physical loss multiplied by the unit value of the ecosystem asset:

Monetary catastrophic loss (currency) = Physical loss (hectares) × Ecosystem asset value per hectare

This entry reduces the monetary value of the ecosystem asset without representing consumption of fixed capital or depletion, as it results from an unpredictable event rather than use in production.

Condition account treatment. Where disasters affect ecosystem condition without complete conversion (e.g., coral bleaching, mangrove canopy damage), record the condition decline in the condition account rather than as extent loss. The ecosystem degradation that results from condition decline is recorded in the monetary ecosystem asset account as described in TG-3.1 Asset Accounts, Section 3.5.3.

3.6.4 Step 4: Vulnerability and adaptive capacity assessment

Compile sensitivity indicators. For the exposed population identified in Step 2, calculate sensitivity indicators:

• Poverty rate (proportion of exposed population below poverty line)
• Informal housing rate
• Nutritional dependence on seafood (average protein share from marine sources)
• Livelihood dependence on ocean sectors (proportion of household income from ocean-related employment)

Data sources include household surveys, census records, and social accounts compiled as described in TG-3.5 Social Accounts.

Compile adaptive capacity indicators. For the same exposed population:

• Social protection coverage (proportion with access to disaster relief, unemployment insurance)
• Disaster insurance coverage
• Educational attainment (average years of education)
• Access to early warning systems

Construct composite vulnerability index (optional). Following the methodology in TG-2.1 Indicator Design Principles, normalize sensitivity and adaptive capacity indicators and aggregate using chosen weights:

Vulnerability index = (Exposure × Sensitivity) / Adaptive capacity

3.6.5 Step 5: Ecosystem protection service quantification

Identify protective ecosystems. For each spatial unit containing population or infrastructure in hazard zones, identify the extent and condition of protective ecosystems located seaward of the exposure.

Quantify protection service flow. Using hydrodynamic models, flood models, or empirically calibrated relationships:

Wave height reduction (metres) = f(ecosystem extent, condition variables)

Storm surge attenuation (metres per km) = f(mangrove width, canopy density)

Estimate avoided damage (monetary valuation). Calculate the expected annual damage with and without ecosystem protection, using the approach demonstrated in Section 3.7.2:

Avoided damage = EAD (without ecosystems) - EAD (with ecosystems)

This provides a monetary estimate of the ecosystem protection service flow that can be recorded in ecosystem service supply tables as described in TG-3.2 Flows from Environment to Economy.

3.6.6 Step 6: Policy reporting and communication

Compile headline indicators. Summarize the analysis in policy-relevant headline indicators:

• Total population exposed to 1-in-100 year coastal flood (persons, proportion of national population)
• Total asset value at risk in coastal hazard zones (currency units, proportion of GDP)
• Expected annual damage from coastal hazards (currency units, proportion of GDP)
• Ecosystem protection value (currency units per year)
• Coastal exposure dependency ratio (proportion of population and assets in hazard zones)

Report for SDG and Sendai monitoring. Where historical disaster data are available, compile retrospective indicators:

• SDG 1.5.1/11.5.1: Deaths, missing, and affected persons per 100,000 (from disaster records)
• SDG 1.5.2/11.5.2: Direct economic loss as share of GDP (from asset damage assessments)

Document limitations and uncertainty. Transparently report:

• Hazard probability uncertainties (particularly for rare extreme events)
• Asset valuation uncertainties
• Model uncertainties in ecosystem protection quantification
• Data gaps and use of proxy indicators

3.7 Worked Example: Coastal Storm Damage Assessment

This section presents a worked example demonstrating the full compilation procedure using synthetic data for a coastal zone affected by a severe tropical storm. All figures are illustrative and designed to show the accounting mechanics; actual compilations would draw on national data following the procedure in Section 3.6.

3.7.1 Scenario description

Setting. A hypothetical 150 km stretch of tropical coastline with a coastal population of 85,000 persons (12% of the national population of 720,000). The coastal zone includes:

• 2,400 hectares of mangrove forest
• 1,800 hectares of coral reef
• 600 hectares of seagrass meadows

Disaster event. A Category 4 tropical cyclone (estimated 1-in-50 year event) made landfall, generating a 3.5 metre storm surge, sustained winds of 210 km/h, and extreme rainfall. The disaster occurred in accounting period 2025.

Data sources. Pre- and post-disaster satellite imagery for extent change detection; field surveys at 60 monitoring stations for condition assessment; household surveys of 1,200 households for socioeconomic impacts; insurance claims and government disaster relief records for economic loss; hydrodynamic models for counterfactual analysis of ecosystem protection benefits.

3.7.2 Step 1: Pre-disaster asset accounts (baseline)

Prior to the disaster, asset accounts recorded the following baseline stocks:

Ecosystem extent account (hectares)

Ecosystem condition indicators (pre-disaster)

3.7.3 Step 2: Post-disaster damage assessment

Immediate extent losses (physical)

Post-disaster satellite imagery and field surveys revealed catastrophic losses to ecosystem extent:

• Mangroves: 240 hectares destroyed (10% of stock), 180 hectares severely damaged
• Coral reefs: 180 hectares structurally damaged (10% of stock)
• Seagrass: 90 hectares buried by sediment (15% of stock)

Condition changes

For ecosystems not converted, condition degradation was recorded:

• Mangrove canopy density declined from 75% to 62% (average across remaining extent)
• Live coral cover declined from 42% to 28%
• Seagrass bed density declined from 380 to 290 shoots/m²

Human impacts

• 8 deaths directly attributed to storm surge and wind damage
• 32,000 persons displaced (38% of coastal population)
• 1,200 homes destroyed, 3,800 homes damaged

Economic losses (direct)

• Residential property damage: USD 85 million
• Commercial and industrial damage: USD 42 million
• Critical infrastructure damage (hospital, schools, roads): USD 18 million
• Aquaculture facility damage: USD 9 million
• Total direct loss: USD 154 million (2.8% of national GDP)

3.7.4 Step 3: Recording catastrophic losses in asset accounts

Physical extent account with disaster entry (hectares)

The catastrophic losses entry isolates the exceptional reduction attributable to the discrete disaster event, distinguishing it from gradual changes (conversions, degradation) that would occur in normal years.

Monetary ecosystem asset account (million USD)

Pre-disaster ecosystem asset valuations (based on NPV of ecosystem services as described in TG-1.9 Valuation):

• Mangrove: USD 180,000 per hectare
• Coral reef: USD 250,000 per hectare
• Seagrass: USD 80,000 per hectare

The catastrophic losses entry records the monetary value of the ecosystem extent destroyed by the cyclone (240 ha × USD 180,000/ha = USD 43.2 million for mangroves). This represents the lost natural capital from the disaster, distinct from annual depreciation or depletion.

Condition account treatment

The condition decline in remaining ecosystems (mangrove canopy density 75% → 62%, coral cover 42% → 28%) is recorded in the condition account and manifests in the monetary asset account as ecosystem degradation (not shown in detail here but calculated from the loss of future service flows attributable to condition decline multiplied by the appropriate discount factor).

3.7.5 Step 4: Ecosystem protection service valuation

Counterfactual analysis

Hydrodynamic modelling was conducted to estimate storm surge height with and without the protective ecosystems (mangroves and coral reefs) that existed prior to the disaster.

Scenario A (with ecosystem protection -- actual):

• Mangroves attenuated storm surge by 0.8 metres (2.4 km average forest width × 0.33 m/km)
• Coral reefs attenuated wave height by 1.2 metres (65% wave energy dissipation)
• Resulting peak surge at shoreline: 3.5 metres
• Inundation extent: 4.2 km² (420 hectares)
• Estimated damage: USD 154 million (actual)

Scenario B (without ecosystem protection -- counterfactual):

• No mangrove or reef attenuation
• Peak surge at shoreline: 5.5 metres (3.5 + 0.8 + 1.2)
• Inundation extent: 7.8 km² (780 hectares)
• Estimated damage: USD 245 million (modelled, using depth-damage functions)

Ecosystem protection value (avoided damage):

Avoided damage = Scenario B damage - Scenario A damage = USD 245 million - USD 154 million = USD 91 million

This is the one-time avoided damage attributable to ecosystem protection during the disaster event. It provides evidence of the protective service provided by coastal ecosystems and can inform benefit-cost analysis of ecosystem restoration investments.

Annualized protection value

To estimate the expected annual value of protection (accounting for event probability), we apply the expected annual damage (EAD) framework. For this coastal zone, multiple storm surge scenarios and their probabilities are integrated:

EAD (with ecosystems) = (0.10 × 12M) + (0.02 × 154M) + (0.01 × 380M) = 1.2M + 3.1M + 3.8M = USD 8.1 million per year

EAD (without ecosystems) = (0.10 × 22M) + (0.02 × 245M) + (0.01 × 620M) = 2.2M + 4.9M + 6.2M = USD 13.3 million per year

Annual ecosystem protection value = USD 13.3M - USD 8.1M = USD 5.2 million per year

This annualized avoided damage estimate (USD 5.2 million per year) represents the recurring ecosystem service flow from coastal protection and can be recorded in ecosystem service supply tables as described in TG-3.2 Flows from Environment to Economy. The estimate provides a minimum economic justification for ecosystem conservation: any investment in maintaining or restoring the 4,800 hectares of coastal ecosystems costing less than USD 5.2 million per year would generate positive net benefits from disaster risk reduction alone, before accounting for other ecosystem service co-benefits such as fisheries habitat, carbon sequestration, and tourism.

3.7.6 Step 5: Vulnerability indicators

Sensitivity indicators (pre-disaster)

For the 85,000 coastal population:

• Poverty rate: 28% (national average: 18%)
• Informal housing: 35%
• Livelihood dependence on ocean sectors: 52% (fishing, aquaculture, tourism)
• Nutritional dependence on seafood: 68% of protein intake
• Population aged 65+: 14%

Adaptive capacity indicators (pre-disaster)

• Social protection coverage: 42% (national average: 58%)
• Disaster insurance coverage: 8%
• Early warning system coverage: 75%

Post-disaster outcomes validation

The higher-than-national vulnerability indicators correlated with disproportionate disaster impacts:

• Mortality rate: 9.4 deaths per 100,000 coastal population (national rate for comparable disasters: 5.2 per 100,000)
• Displacement rate: 38% of population (national average: 22%)
• Recovery time: 24 months to restore pre-disaster livelihood levels (national average: 14 months)

This retrospective validation supports the predictive validity of the vulnerability framework.

3.7.7 Step 6: SDG and Sendai reporting

SDG Indicator 1.5.1 / 11.5.1 (deaths, missing, affected persons per 100,000)

Deaths per 100,000 = (8 deaths / 85,000 population) × 100,000 = 9.4 per 100,000

Affected persons per 100,000 = (32,000 displaced / 85,000) × 100,000 = 37,647 per 100,000

SDG Indicator 1.5.2 / 11.5.2 (direct economic loss as share of GDP)

National GDP = USD 5,500 million

Direct economic loss / GDP = (USD 154 million / USD 5,500 million) × 100 = 2.8% of GDP

Sendai Target C (reduce direct economic loss)

The baseline disaster loss ratio (2.8% of GDP) establishes the reference for tracking progress. The Sendai Framework calls for substantial reduction in disaster economic loss relative to GDP by 2030.

Policy implications

The worked example demonstrates several critical findings:

1. Ecosystem catastrophic losses of USD 95.4 million represent a significant share (62%) of total direct economic loss, highlighting the economic importance of natural assets
2. Ecosystem protection provided USD 91 million in avoided damage during this single event, demonstrating the climate adaptation value of nature-based solutions
3. The coastal population exhibits higher vulnerability than national averages, indicating priority areas for social protection extension and risk reduction investment
4. The coastal exposure dependency ratio (12% of national population in hazard-prone coastal zones) indicates moderate national-level exposure concentration

4. Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

Authors: Gerald Singh, Mikael JA Maes, Sarah Taylor, Crystal Bradley

Reviewers: [To be assigned]

5. References