Disaster Risk Indicators

Circular ID	TG-2.9
Version	7.0
Badge	Applied
Status	Draft
Last Updated	May 2026

1. Outcome

1This 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.

2Disaster 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.

3This Circular builds on TG-0.1 General Introduction, TG-0.2 Standards Overview, TG-2.1 Indicator Design Principles, and TG-3.1 Asset Accounts. Related circulars: TG-3.5 Social Accounts for socioeconomic dimensions of storm damage, TG-6.11 Coastal Infrastructure for infrastructure exposure, and TG-2.8 Climate Change for climate-related disaster risk.

4Ocean accounts provide a well-suited 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, whilst ecosystem condition directly affects the magnitude of protection provided².

2. Requirements

1This Circular requires familiarity with:

3. Guidance Material

1Disaster risk in coastal zones arises from the intersection of natural hazards, exposure of populations and assets, and the vulnerability of exposed elements³. 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”⁴. Ocean accounts provide systematic data on each component: 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.

3.1 Disaster Risk Framework

1The Sendai Framework for Disaster Risk Reduction provides the overarching international framework for understanding and reducing disaster risk⁵. Its goal is to “prevent new and reduce existing disaster risk” through integrated measures that reduce hazard exposure and vulnerability and strengthen resilience, and it is supported by seven global targets (A—G) measured at the global level⁵. It identifies four priority areas: understanding disaster risk; strengthening disaster risk governance to manage disaster risk; investing in disaster risk reduction for resilience; and enhancing disaster preparedness for effective response and to “build back better” in recovery, rehabilitation and reconstruction. 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. Figure 2.9.1 illustrates how disaster risk decomposes into expected annual damage and how ecosystem condition enters the formula at two points, simultaneously reducing hazard probability and vulnerability.

Figure 2.9.1 Disaster risk as expected annual damage decomposes into hazard, exposure, vulnerability, and adaptive capacity. Ecosystem condition attenuates both hazard and vulnerability; EAD supports Sendai reporting. Source: TG-2.9 Disaster Risk, §3.1.3 (EAD formula, including adaptive capacity) and §3.1.2 (ecosystem vulnerability and protection); SEEA EA condition and regulating-service concepts. Adapted from: IPCC AR6 WGII risk framing (hazard × exposure × vulnerability); UNDRR Sendai Framework 2015--2030 reporting targets A--D.

3.1.1 Components of disaster risk

1Disaster risk is commonly expressed as a function of three components⁶:

2Hazard 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 resources⁷. Coastal hazards include tropical cyclones, storm surges, coastal flooding, tsunamis, erosion, and sea-level rise. The 2025 SNA recognises that “catastrophic losses represent exceptional and significant reductions in the natural resource” due to discrete events⁸. Hazard impacts on ecosystem assets are recorded under this catastrophic-loss category.

3Exposure 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 affected⁹. 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).

4Vulnerability 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 hazards¹⁰. 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”¹¹.

3.1.2 Ecosystem vulnerability

1Coral 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. Table 3.1.2 summarises ecosystem vulnerability indicators derivable from ocean accounts.

Indicator	Description
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.

2Compilers should record disaster-related ecosystem damage in extent and condition accounts. The SEEA EA addresses ecosystem conversions¹², 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 CF¹³. 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

1Disaster risk for coastal hazards is operationalised probabilistically as expected annual damage (EAD):

Risk (EAD) = Σ [P(hazard_i) × Exposure × Vulnerability / Adaptive Capacity]

2where P(hazard_i) is the annual probability of hazard scenario i (typically expressed as return-period frequency), and the summation is taken across all hazard scenarios considered. This formulation makes the probabilistic nature of hazard explicit and produces results with interpretable units (currency or persons per year) that are comparable across hazard types. The unscaled product form (Risk = Hazard × Exposure × Vulnerability / Adaptive Capacity) may be used as a conceptual ordering device, but for any numerical compilation compilers should apply the EAD framework set out in Section 3.7.5.

3The 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 hazards¹⁴. 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”¹⁵.

4For 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

1Ocean account-based disaster risk indicators should align with international monitoring frameworks. The Sendai Framework establishes seven global targets, A through G, measured at the global level and complemented by national targets and indicators⁵. Four of these targets (A—D) are directly compilable from ocean account data and are mapped in Table 1. The remaining three (E—G) — national and local disaster risk reduction strategies, international cooperation, and multi-hazard early warning systems — relate to governance and preparedness arrangements and appear as associated indicators in Table 1a. Table 1 maps ocean account components to the relevant SDG and Sendai Framework indicators directly compilable from ocean account data. Table 1a presents associated governance indicators that relate thematically but are not directly compiled from ocean account statistics.

2Table 1: Ocean Account Components and Directly Compilable SDG/Sendai Indicator Mapping

SDG/Sendai Indicator	Description	Ocean Account Component
SDG 1.5.1 / 11.5.1 / 13.1.1	Deaths, missing and affected persons per 100,000	Social accounts: population exposure and vulnerability indicators
SDG 1.5.2 / 11.5.2	Direct economic loss as share of GDP	Economic accounts: asset damage and production loss accounts
SDG 11.5.2 (sub-indicator)	Direct disaster economic loss attributed to damage to critical infrastructure	Asset accounts: critical infrastructure damage records (see Section 3.5.1)
Sendai Target A	Reduce disaster mortality	Social accounts: mortality exposure indicators
Sendai Target B	Reduce number of affected people	Social accounts: population exposure indicators
Sendai Target C	Reduce direct economic loss	Economic accounts: asset and production loss indicators
Sendai Target D	Reduce disaster damage to critical infrastructure	Asset accounts: critical infrastructure exposure indicators

3Table 1a: Associated Governance Indicators (Not Directly Compiled from Ocean Account Data)

SDG/Sendai Indicator	Description	Note
SDG 1.5.3 / 11.b.1 / 13.1.2	Countries with DRR strategies	Compiled from national policy questionnaires; relates to governance frameworks rather than ocean account statistics
SDG 1.5.4 / 11.b.2 / 13.1.3	Local governments with DRR strategies	Compiled from sub-national policy questionnaires; not derived from ocean account statistics
SDG 14.2.1	Proportion of EEZ managed using ecosystem-based approaches	Compiled by IOC-UNESCO through country questionnaires on marine area management frameworks; measures management governance, not ecosystem extent or disaster risk. Ecosystem extent/condition accounts inform but do not compile this indicator
Sendai Target E	Increase the number of countries with national and local DRR strategies	Aligns with SDG 1.5.3/11.b.1/13.1.2 above; compiled from national policy questionnaires, not ocean account statistics
Sendai Target F	Enhance international cooperation to developing countries for Framework implementation	Tracked through development cooperation reporting; outside ocean account scope
Sendai Target G	Increase availability of and access to multi-hazard early warning systems and disaster risk information and assessments	Relates to the early warning coverage adaptive-capacity indicator (Section 3.3.2); compiled from preparedness assessments, not ocean account statistics

4The FDES 2013 provides additional environmental statistics for disaster risk assessment, including indicators on natural extreme events and disasters, and their impacts on human settlements¹⁶.

3.2 Hazard Exposure Indicators

3.2.1 Spatial delineation of hazard zones

1Exposure assessment requires spatial delineation of hazard zones for different hazard types and return periods. Key hazard zones for coastal areas include¹⁷:

2Coastal 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.

3Erosion 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.

4Tsunami inundation zones — areas subject to inundation from tsunami events based on historical records and modelling.

5Cyclone/hurricane exposure zones — areas subject to wind damage, storm surge, and heavy precipitation from tropical cyclones.

6Spatial 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.

7Tiered data pathways for hazard zone delineation

8Many NSOs — particularly in small island developing states and least-developed countries — lack national LiDAR coverage, hydrodynamic models, or detailed national hazard maps. Table 1b sets out a tiered pathway that allows compilation to proceed at three levels of data capacity. Compilers should select the highest tier supported by available data and report the tier used in metadata to support comparability and uncertainty assessment.

9Table 1b: Tiered Data Pathway for Coastal Hazard Zone Delineation

Tier	Data inputs	Typical accuracy	Recommended use
Tier 1 — Global proxy data only	SRTM 30 m DEM; NOAA/GEBCO bathymetry; Global Flood Database (Tellman et al. 2021); global cyclone track archives (IBTrACS)	Coastal flood extent ±100—500 m horizontal at low elevations; substantial bias in flat terrain; under-detection of small features	First-pass national exposure screening; SIDS and LDC compilations where no national data exist; flagged in metadata as “Tier 1 — global proxy”
Tier 2 — National hazard maps without hydrodynamic models	National flood/erosion hazard maps; national DEMs (where available); historical event records	Variable, generally ±50—200 m horizontal	Sub-national exposure assessment; SDG 1.5/11.5 reporting at national level with documented limitations
Tier 3 — Full hydrodynamic modelling	LiDAR DEM (≤1 m vertical); hydrodynamic surge/wave models; calibrated flood inundation models	±10—30 m horizontal; suitable for engineering and insurance applications	Insurance pricing; EAD computation at sub-national scale; ecosystem protection valuation as in Section 3.7.5

10All tiers require accompanying metadata documenting the data sources, vintage, and known limitations. Tier 1 compilations should not be presented without explicit uncertainty caveats, and should be progressively upgraded as national data capacity improves. The UNDRR-ISC Hazard Definition and Classification Review provides the reference hazard taxonomy applicable across all tiers.

3.2.2 Population exposure indicators

1Key indicators include¹⁸:

2Resident 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).

3Population density in hazard zones — persons per square kilometre in coastal hazard areas, enabling comparison of relative exposure across locations.

4Dependent population in hazard zones — subsets of the exposed population with heightened vulnerability, including children, elderly, persons with disabilities, and low-income households.

5Seasonal/tourist population — maximum daytime population in coastal hazard zones accounting for tourism, recreation, and daily commuting patterns.

6These 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

1Asset exposure indicators measure physical and economic assets in hazard-prone areas¹⁹. Ocean accounts provide the foundation through asset accounts for both produced assets (infrastructure, buildings) and environmental assets:

2Built 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.

3Critical infrastructure exposure — number and value of hospitals, schools, power plants, water treatment facilities, and transportation infrastructure in hazard zones.

4Ecosystem 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.

5Agricultural 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

1The 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)²⁰. 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

1Table 3.3.1 summarises the principal sensitivity indicators for coastal communities²¹.

Indicator	Description
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”²².
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 marginalisation, land tenure insecurity, and dependence on traditional marine resources, whilst also possessing traditional ecological knowledge that enhances adaptive capacity; compilers should disaggregate vulnerability indicators for IPLC populations where data permit, and the TNFD provides engagement guidance²³ (see TG-3.6 Traditional Knowledge Accounts for documenting traditional coping mechanisms).

2Practical guidance on IPLC disaggregation where administrative data are absent. In many countries with significant IPLC coastal populations, census and household survey frames do not identify IPLC populations as a separate stratum, and administrative records lack IPLC identifiers. Compilers should not interpret “where data permit” as licence to omit IPLC disaggregation. Instead, three alternative compilation approaches are available:

3Census-unit linkage to IPLC settlement maps. Where IPLC settlement maps are maintained by indigenous organisations, ministries of indigenous affairs, or land tenure agencies, the boundaries of these settlements can be overlaid with census enumeration areas to derive estimated IPLC population counts and household characteristics for the linked enumeration areas. This is the lowest-cost approach where suitable maps exist.
4FPIC-compliant rapid assessments. In high-priority IPLC coastal areas (those exposed to acute hazards and lacking census disaggregation), targeted rapid assessments may be undertaken following free, prior and informed consent (FPIC) protocols. Such assessments can collect vulnerability-relevant indicators (livelihood structure, social protection access, evacuation capacity) at the community level in a single round.
5Participatory GIS methods. Where IPLC communities maintain or co-produce spatial information about resource use, settlement, and hazard knowledge, participatory GIS can supply both the spatial framework and vulnerability variables. Methods are documented in TG-3.6 Traditional Knowledge Accounts.

6All three approaches should be documented in metadata, including the consent basis, the year of data collection, and the limits of representativeness. The UN Permanent Forum on Indigenous Issues guidance on data collection and disaggregation for indigenous peoples provides the reference framework.

3.3.2 Adaptive capacity indicators

1Table 3.3.2 summarises the principal adaptive capacity indicators²⁴.

Indicator	Description
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”²⁵.
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 organisations, 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.

2For 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

1Composite vulnerability indices aggregate multiple indicators to provide summary measures of vulnerability²⁶. 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:

2Indicator 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.

3Normalisation — transform indicators to comparable scales (e.g., 0-1 range) to enable aggregation. Min-max normalisation and z-score standardisation are both acceptable. Compilers should document the method chosen.

4Weighting — 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.

5Validation — validate composite indices against historical disaster impacts to assess predictive accuracy.

6Vulnerability index formula. The recommended composite vulnerability index is an additive (linear) combination of normalised sensitivity and adaptive capacity scores:

Vulnerability index = α × Sensitivity score — β × Adaptive Capacity score

7where Sensitivity score and Adaptive Capacity score are each normalised to the interval [0, 1] (via min-max or z-score standardisation rescaled to [0, 1]), and α and β are weights summing to one (default α = β = 0.5 where no empirical basis supports differential weighting). The resulting Vulnerability index is bounded on the interval [-β, α] (e.g., [-0.5, 0.5] under equal weighting) and is dimensionless. Higher values indicate higher vulnerability.

8The additive formulation is preferred over the ratio form (Sensitivity / Adaptive Capacity) because the ratio is undefined or unstable when Adaptive Capacity approaches zero — a realistic scenario for coastal communities with near-zero insurance coverage, early warning access, or social protection — and is sensitive to the choice of normalisation range. Compilers who nonetheless wish to apply a ratio formulation must impose a strictly positive floor on the Adaptive Capacity denominator (e.g., 0.05 on the [0, 1] scale) and document the floor value.

9Numerical illustration (aligned with Section 3.7.6 data). For the coastal population in the worked example:

10Sensitivity score = mean of normalised {poverty rate 0.28, informal housing 0.35, livelihood dependence 0.52, age 65+ 0.14} = 0.32 (after min-max normalisation with national reference values)
11Adaptive Capacity score = mean of normalised {social protection 0.42, disaster insurance 0.08, early warning 0.75} = 0.42
12Vulnerability index = 0.5 × 0.32 — 0.5 × 0.42 = 0.16 — 0.21 = -0.05

13This negative value indicates that adaptive capacity slightly outweighs sensitivity on the chosen indicator set, though both are modest. Compilers should compare index values across spatial units rather than interpret single-area values in isolation.

14Countries should adapt composite index methodologies to reflect local data availability and policy priorities whilst documenting deviations from the general framework to support cross-country comparability.

3.4 Ecosystem-Based Protection Indicators

1The SEEA EA classifies coastal protection as a regulating ecosystem service²⁷. 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

1For the full biophysical basis of coastal protection services, SPA/SBA delineation, and tiered method selection, see TG-2.3 Regulating Services Section 3.4.²⁸²⁹³⁰³¹

3.4.2 Ecosystem-based adaptation service matrix

1Table 2 summarises 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 prioritise when assessing protective capacity.

2Table 2: Ecosystem-Based Adaptation Service Matrix

Ecosystem Type	Hazard Mitigated	Protection Mechanism	Quantification Method
Coral reef	Storm surge, wave action	Wave energy dissipation	Hydrodynamic models
Mangrove	Coastal flooding, storm surge	Water storage, friction	Flood models
Seagrass	Erosion	Sediment stabilisation	Sediment budgets
Salt marsh	Storm surge, flooding	Water storage, friction	Flood models
Dune	Coastal flooding	Physical barrier	High-resolution elevation models³²

3The 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.

4Table 3: Ecosystem Condition Variables Affecting Protective Capacity

Ecosystem Type	Condition Variable	Effect on Protection
Coral reef	Structural complexity (rugosity)	Higher complexity increases wave energy dissipation
Coral reef	Live coral cover (%)	Greater cover maintains reef accretion and structural integrity
Mangrove	Canopy density and forest width	Denser, wider forests provide greater surge attenuation
Mangrove	Root density and prop root height	Denser root systems increase friction and sediment trapping
Seagrass	Bed density and continuity	Denser, continuous beds stabilise more sediment
Seagrass	Shoot height and leaf area	Taller shoots reduce near-bed currents more effectively
Salt marsh	Vegetation height and density	Taller, denser vegetation increases friction with floodwaters
Dune	Crest elevation and width	Higher, wider dunes provide greater barrier protection

3.4.3 Ecosystem protection indicators

1Key ecosystem-based protection indicators derived from ocean accounts³³:

Indicator	Description
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	The recommended primary physical unit for the supply-table entry is expected persons protected per year, defined as the annualised number of persons in the hazard zone whose exposure is reduced below the harm threshold by the presence of protective ecosystems (consistent with Sendai Target B). Where Sendai-aligned reporting requires a monetary equivalent, the annualised avoided damage (EAD-equivalent monetary value) computed in Section 3.7.5 may be used as a parallel monetary entry. Wave height reduction (metres) and storm surge attenuation (metres per km of ecosystem width) are intermediate model outputs that feed the primary indicator; they should not populate the supply-table entry directly. Table 3a provides the crosswalk from intermediate physical outputs to the primary indicator.

2Table 3a: Crosswalk from Intermediate Outputs to Primary Protection Service Flow Indicator

Intermediate output	Source	Translation to primary indicator
Wave height reduction (m) — coral reefs, seagrass	Hydrodynamic model	Apply depth-damage and exposure functions to translate residual wave height into avoided inundation area, then to persons protected per year × event probability
Storm surge attenuation (m per km) — mangroves, salt marsh	Flood/surge model	Apply to surge propagation across ecosystem width, then to avoided inundation area and persons protected per year × event probability
Sediment retention rate — seagrass, dunes	Sediment budget	Convert to avoided erosion-related displacement (persons whose dwellings would otherwise be lost) per year
Floodwater storage (m³) — coastal wetlands	Flood model	Translate to reduced peak inundation extent and persons protected per year × event probability

3Monetary value of protection — economic value of coastal protection services estimated using avoided damage, replacement cost, or hedonic pricing methods. The EAD-based avoided damage approach demonstrated in Section 3.7.5 is the recommended method where hydrodynamic models are available. For broader valuation guidance, see TG-1.9 Valuation.

3.4.4 Degradation and protection loss

1Ecosystem degradation reduces protective capacity and increases disaster risk³⁴. The SEEA EA defines degradation as the loss in future value of ecosystem services due to a decline in ecosystem condition³⁵. For coastal protection, degradation manifests as:

2Extent loss — conversion of protective ecosystems to other uses (e.g., mangrove clearing for aquaculture) directly reduces protective coverage. Ecosystem extent accounts record these changes.

3Condition decline — degradation of ecosystem condition (e.g., coral bleaching, mangrove thinning) reduces protective capacity even where extent is maintained. Condition accounts track these changes.

4Cumulative protection loss — combined effect of extent loss and condition decline on total protective capacity of coastal ecosystems.

5The 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”³⁶. Degradation of protective ecosystems represents a nature-related physical risk to coastal communities.

6For 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

1SDG indicator 1.5.2/11.5.2 measures “Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)”³⁷. 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

1Direct economic losses are the monetary value of assets destroyed or damaged by disasters, consistent with the Sendai Framework Monitor methodology for indicator C and SDG 1.5.2/11.5.2³⁸. For ocean accounts, direct losses include:

2Damage 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.

3Damage 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.

4Damage 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 CF³⁹.

5The 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”⁴⁰.

3.5.2 Indirect economic losses

1Indirect economic losses arise from disruption to economic activity following disasters⁴¹:

2Production 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.

3Supply chain disruptions — losses from disruption to inputs and outputs, including damage to ports and transport infrastructure that affects trade.

4Employment impacts — wages lost due to temporary unemployment and underemployment following disasters.

5Ecosystem 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.

6Reconciliation with Sendai Monitor metadata. The direct/indirect classification used in this Circular includes a broader analytical scope than the Sendai Framework Monitor metadata applied for SDG 1.5.2/11.5.2 reporting. Compilers should distinguish the two compilations and report each separately to preserve international comparability. Table 4 reconciles the two.

7Table 4: Reconciliation of TG-2.9 Direct/Indirect Classification with Sendai Monitor Metadata

Loss component	TG-2.9 treatment	Sendai Monitor (Indicator C / SDG 1.5.2/11.5.2)	Note
Damage to residential, commercial, industrial buildings	Direct	Direct (C2, C3)	Aligned
Damage to critical infrastructure	Direct	Direct (C4)	Aligned
Agricultural losses (crop, livestock) from event	Direct	Direct (C1)	Aligned
Damage to cultural heritage	Direct	Direct (C5)	Aligned
Damage to ecosystem assets (extent destruction)	Direct (catastrophic loss in asset account)	Direct under “environmental assets” (C6 where reported)	Aligned where C6 is compiled
Condition decline / ecosystem degradation	Direct (recorded as ecosystem degradation in monetary asset account)	Not currently captured in Sendai C	TG-2.9 broader analytical scope
Ecosystem service flow disruption (e.g., reduced fish habitat services after coral damage)	Indirect	Not in scope	TG-2.9 supplementary; do not include in official SDG 1.5.2/11.5.2 returns
Production losses (lost fishing days, suspended aquaculture, reduced tourism)	Indirect	Not in scope	TG-2.9 supplementary
Supply chain disruption losses	Indirect	Not in scope	TG-2.9 supplementary
Employment income losses	Indirect	Not in scope	TG-2.9 supplementary

8Figures feeding official SDG 1.5.2/11.5.2 reporting must align with the Sendai Monitor metadata (right column). Broader direct/indirect totals using the TG-2.9 classification provide analytical insight for national policy and should be clearly labelled “supplementary ocean account analysis — not for SDG 1.5.2/11.5.2 reporting”.

3.5.3 Recovery indicators

1Table 3.5.3 summarises the principal recovery indicators⁴².

Indicator	Description
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, whilst mangroves can recover more quickly if propagule sources remain, and recovery rates inform projections of when protective services will be restored.
Livelihood recovery	Restoration of household incomes and food security in disaster-affected communities.

2Reconstruction quality indicators (operationalising “build back better”). Rather than report a generic “build back better” category, compilers should compile two concrete indicators that operationalise improved reconstruction:

3Proportion of reconstructed buildings meeting current building codes — numerator: count of reconstructed residential, commercial, and critical infrastructure buildings certified as compliant with the current national building code at the time of reconstruction; denominator: total count of buildings reconstructed following the event. Data sources: national building inspectorate records; municipal construction permit databases; post-disaster reconstruction registries.
4Proportion of restored infrastructure relocated outside the 1-in-100 year flood zone — numerator: count (or asset value) of reconstructed critical infrastructure sited outside the 1-in-100 year coastal flood zone; denominator: count (or asset value) of all reconstructed critical infrastructure damaged in the event. Data sources: reconstruction registries linked to hazard zone GIS layers from Section 3.2.1.

5These indicators correspond to UNDRR’s Words into Action: Build Back Better (2017) operational guidance and to Sendai Framework Priority 4. They should be reported alongside SDG Target 11.b on disaster risk management strategies. Where post-2030 Sendai monitoring formalises a “build back better” indicator, compilers should align with the agreed methodology at that time.

3.6 Compilation Procedure

3.6.1 Step 1: Data collection and spatial framework

1Establish spatial framework. Define the accounting area covering the coastal zone, with seaward extent defined by hazard reach rather than by jurisdictional boundaries. Specifically, the seaward limit should be set to the maximum of: (a) the modelled maximum storm surge or tsunami inundation extent for the design return period; and (b) the seaward extent of protective ecosystems (coral reefs, offshore reef systems, seagrass meadows) whose protective function contributes to (a). The landward limit is the inland extent of coastal influence (tidal extent, storm surge reach). This functional definition avoids both inflation of exposure statistics when the EEZ is used (which would encompass deep-sea assets with no coastal-hazard relevance) and exclusion of offshore protective ecosystems if territorial waters alone were applied. The accounting area defined in TG-0.1 General Introduction remains the reference frame for ocean accounts as a whole. The disaster-risk spatial framework is a functional subset of that area.

2Worked example — identifying the functional seaward limit. For a country whose 1-in-100 year storm surge extends 8 km offshore and whose nearest offshore protective reef system lies 15 km offshore, the disaster-risk spatial framework seaward limit is set at 15 km. For tsunami hazard, the framework extends to the depth contour at which tsunami wave amplification begins (typically the 50—100 m isobath). The framework is hazard-specific. Compilers should construct hazard-specific layers and union them for the overall accounting area.

3Subdivide 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.

4Delineate hazard zones. Using digital elevation models, hydrodynamic models, and historical hazard records (selecting the appropriate tier from Table 1b), delineate spatial boundaries of:

5Coastal flood zones by return period (1-in-10 year, 1-in-50 year, 1-in-100 year, and at least one tail event such as 1-in-500 year for EAD compilation)
6Tsunami inundation zones
7Erosion-prone coastal segments
8Cyclone/hurricane wind damage zones

9Collect ecosystem data. From ecosystem extent and condition accounts (see TG-3.1 Asset Accounts):

10Hectares of mangroves, coral reefs, seagrass meadows, coastal wetlands by spatial unit
11Condition variables affecting protective capacity (canopy density, reef rugosity, bed continuity)
12Spatial position of ecosystems relative to populations and infrastructure

13Collect population and asset data. From census, administrative records, and economic accounts:

14Population counts by spatial unit, with age and income disaggregations
15Value of residential, commercial, and critical infrastructure by spatial unit
16Employment and economic activity in coastal zones

17Collect historical disaster data. From emergency management records, insurance databases, and post-disaster assessments:

18Historical disaster events by type, date, and affected area
19Damage estimates (lives lost, persons affected, economic loss)
20Recovery timelines from past events

3.6.2 Step 2: Exposure indicator compilation

1Population exposure calculation. For each hazard zone and return period:

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

2Disaggregate by age group, income quintile, and IPLC status where data permit.

3Asset exposure calculation. For each hazard zone:

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

4Distinguish residential, commercial, industrial, critical infrastructure, and natural assets.

5Ecosystem exposure calculation. For ecosystems vulnerable to disasters:

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

6Record separately for each ecosystem type (mangroves, coral reefs, seagrass).

3.6.3 Step 3: Recording catastrophic losses in asset accounts

1When 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.

2Physical 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 events³⁹.

3Example for coral reef extent account:

Accounting entry	Hectares
Opening stock	15,000
Additions	120
Reductions:
— Managed reduction	200
— Catastrophic losses (cyclone damage)	600
— Other reductions	50
Closing stock	14,270

4Monetary 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 5.49(c). The catastrophic-loss formula applies only to extent loss (complete conversion of the ecosystem asset). Partial damage to ecosystem condition (without conversion) is recorded separately as ecosystem degradation in the monetary ecosystem asset account, not as catastrophic loss. The monetary catastrophic loss equals the destroyed (converted) extent multiplied by the unit value of the ecosystem asset:

Monetary catastrophic loss (currency) = Extent destroyed (hectares, complete conversion only) × Ecosystem asset value per hectare

5For example, in the worked example (Section 3.7.4), the mangrove catastrophic loss covers the 240 hectares destroyed (converted) and not the additional 180 hectares severely damaged but not converted. The 180 hectares of condition damage are reflected in the ecosystem degradation entry. Applying the catastrophic-loss formula to all damaged hectares (e.g., 420 ha for mangroves) would overstate the catastrophic loss entry by approximately 75% relative to the correct treatment.

6This 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.

7Condition 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, and a worked derivation of the monetary degradation entry is provided in Section 3.7.4.

3.6.4 Step 4: Vulnerability and adaptive capacity assessment

1Compile sensitivity indicators. For the exposed population identified in Step 2, calculate sensitivity indicators:

2Poverty rate (proportion of exposed population below poverty line)
3Informal housing rate
4Nutritional dependence on seafood (average protein share from marine sources)
5Livelihood dependence on ocean sectors (proportion of household income from ocean-related employment)

6Data sources include household surveys, census records, and social accounts compiled as described in TG-3.5 Social Accounts.

7Compile adaptive capacity indicators. For the same exposed population:

8Social protection coverage (proportion with access to disaster relief, unemployment insurance)
9Disaster insurance coverage
10Educational attainment (average years of education)
11Access to early warning systems

12Construct composite vulnerability index (optional). Normalise sensitivity and adaptive capacity indicators to [0, 1] and aggregate using chosen weights (formula and parameters: Section 3.3.3).

3.6.5 Step 5: Ecosystem protection service quantification

1Identify 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.

2Quantify intermediate model outputs. 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)

3These are intermediate quantities, not the supply-table entry.

4Translate to primary supply-table indicator. Using the crosswalk in Table 3a, translate intermediate outputs into the recommended primary unit: expected persons protected per year. This is the figure entered into the ecosystem service supply table for protection services. Where a monetary parallel entry is required, compute the annualised EAD-equivalent avoided damage as described below.

5Estimate avoided damage (monetary valuation). Calculate the expected annual damage with and without ecosystem protection, using the EAD framework demonstrated in Section 3.7.5:

Annual avoided damage (USD/year) = EAD (without ecosystems) — EAD (with ecosystems)

6This monetary value can be recorded as the monetary parallel to the primary “persons protected per year” entry in ecosystem service supply tables (see TG-3.2 Flows from Environment to Economy).

3.6.6 Step 6: Policy reporting and communication

1Compile headline indicators. Summarise the analysis in policy-relevant headline indicators:

2Total population exposed to 1-in-100 year coastal flood (persons, proportion of national population)
3Total asset value at risk in coastal hazard zones (currency units, proportion of GDP)
4Expected annual damage from coastal hazards (currency units, proportion of GDP)
5Ecosystem protection value (currency units per year)
6Coastal exposure dependency ratio (proportion of population and assets in hazard zones)

7Report for SDG and Sendai monitoring. Where historical disaster data are available, compile retrospective indicators:

8SDG 1.5.1/11.5.1: Deaths, missing, and affected persons per 100,000 (from disaster records)
9SDG 1.5.2/11.5.2: Direct economic loss as share of GDP (from asset damage assessments, aligned with Sendai Monitor metadata as reconciled in Table 4)

10Document limitations and uncertainty (minimum standard). Uncertainty documentation is mandatory for each compiled indicator and must comprise, at minimum:

11Sensitivity analysis. Report the indicator value under three parameter assumption sets — low (conservative), central (point estimate), and high (worst case) — spanning at minimum the dominant sources of uncertainty (hazard probability, asset value, ecosystem protection model).
12Explicit uncertainty range for ecosystem protection valuations. Hydrodynamic model outputs at national reporting scales typically carry ±30—50% uncertainty. Report the central estimate together with a quantitative range (e.g., USD 5.2 million per year, range USD 3.4—7.0 million) using the sensitivity analysis from (1) or a documented propagation method.
13Quality-tier metadata flag. Assign each compiled indicator an A/B/C quality tier consistent with TG-2.1 Indicator Design Principles:
- 14Tier A — national hydrodynamic models, Tier 3 hazard data, recent census, validated condition accounts
- 15Tier B — national hazard maps (Tier 2), proxy condition data, partial coverage
- 16Tier C — global proxy data only (Tier 1), substantial extrapolation

17Compilers should also report the residual qualitative uncertainties not captured in (1)-(3) (data gaps, use of proxy indicators, residual hazard probability uncertainty for extreme tail events). The SEEA EA supplementary guidance on uncertainty in monetary ecosystem accounts provides further reference.

3.7 Worked Example: Coastal Storm Damage Assessment

1All 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

1Setting. 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:

22,400 hectares of mangrove forest
31,800 hectares of coral reef
4600 hectares of seagrass meadows

5Disaster 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.

6Data 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, and hydrodynamic models for counterfactual analysis of ecosystem protection benefits.

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

1Prior to the disaster, asset accounts recorded the following baseline stocks:

2Ecosystem extent account (hectares)

Accounting entry	Mangroves	Coral reef	Seagrass	Total
Opening stock (1 Jan 2025)	2,400	1,800	600	4,800
Additions (restoration)	15	5	8	28
Reductions (managed conversion)	20	0	5	25
Stock before disaster (30 Sep)	2,395	1,805	603	4,803

3Ecosystem condition indicators (pre-disaster)

Ecosystem	Condition Variable	Value	Reference
Mangrove	Canopy density (%)	75	85
Coral reef	Live coral cover (%)	42	55
Seagrass	Bed density (shoots/m²)	380	450

3.7.3 Step 2: Post-disaster damage assessment

1Immediate extent losses (physical)

2Post-disaster satellite imagery and field surveys revealed catastrophic losses to ecosystem extent:

3Mangroves: 240 hectares destroyed (10% of stock), 180 hectares severely damaged (not converted; condition-degraded)
4Coral reefs: 180 hectares structurally damaged (10% of stock)
5Seagrass: 90 hectares buried by sediment (15% of stock)

6Condition changes

7For ecosystems not converted, condition degradation was recorded:

8Mangrove canopy density declined from 75% to 62% (average across remaining extent)
9Live coral cover declined from 42% to 28%
10Seagrass bed density declined from 380 to 290 shoots/m²

11Human impacts

128 deaths directly attributed to storm surge and wind damage
1332,000 persons displaced (38% of coastal population)
141,200 homes destroyed, 3,800 homes damaged

15Economic losses (direct)

16Residential property damage: USD 85 million
17Commercial and industrial damage: USD 42 million
18Critical infrastructure damage (hospital, schools, roads): USD 18 million
19Aquaculture facility damage: USD 9 million
20Total direct loss: USD 154 million (2.8% of national GDP)

3.7.4 Step 3: Recording catastrophic losses in asset accounts

1Physical extent account with disaster entry (hectares)

Accounting entry	Mangroves	Coral reef	Seagrass	Total
Stock before disaster (30 Sep)	2,395	1,805	603	4,803
Reductions:
— Catastrophic losses (cyclone)	240	180	90	510
Other reductions (Oct-Dec)	8	0	3	11
Additions (Oct-Dec)	2	0	1	3
Closing stock (31 Dec 2025)	2,149	1,625	511	4,285
Net change in extent	-251	-175	-89	-515

2The 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.

3Monetary ecosystem asset account (million USD)

4Pre-disaster ecosystem asset valuations (based on NPV of ecosystem services as described in TG-1.9 Valuation):

5Mangrove: USD 180,000 per hectare
6Coral reef: USD 250,000 per hectare
7Seagrass: USD 80,000 per hectare

Accounting entry	Mangroves	Coral reef	Seagrass	Total
Opening value (1 Jan 2025)	432.0	450.0	48.0	930.0
Ecosystem enhancement	0.5	0.2	0.1	0.8
Ecosystem degradation	-2.5	-1.2	-0.3	-4.0
Catastrophic losses (physical)	-43.2	-45.0	-7.2	-95.4
Revaluations	0	0	0	0
Closing value (31 Dec 2025)	386.8	404.0	40.6	831.4

8The 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. Consistent with Section 3.6.3, the catastrophic-loss formula is applied only to the destroyed (converted) extent. The 180 ha of severely damaged mangroves (not converted) are reflected in the ecosystem degradation entry, not in catastrophic losses.

9Computation box — derivation of the USD 2.5 million mangrove ecosystem degradation entry

10The monetary ecosystem degradation entry above (USD 2.5 million for mangroves) is derived from the chain: condition change → service flow change → monetary loss via NPV. The full derivation for mangroves is set out below. Analogous calculations apply to coral and seagrass.

Step	Quantity	Value
(a) Pre-disaster canopy density	%	75
(b) Post-disaster canopy density	%	62
(c) Condition decline	percentage points	13
(d) Affected mangrove extent (not converted; remaining stock)	hectares	2,155 (i.e., 2,395 — 240 destroyed)
(e) Service-flow function: protection-service share lost per pp of canopy density decline	share per pp	0.012 (illustrative — compilers should derive from national or regional hydrodynamic studies; Menendez et al. 2020, Scientific Reports, provides a basis for regional calibration)
(f) Share of future service flows lost	proportion	0.012 × 13 = 0.156
(g) Annual protection-service flow per hectare (mangrove, pre-disaster)	USD/ha/year	1,650 (illustrative — compilers should derive from national or regional studies; Costanza et al. 2014, Global Environmental Change, reports coastal protection unit values for comparable mangrove settings)
(h) Annual flow loss per hectare attributable to condition decline	USD/ha/year	0.156 × 1,650 = 257
(i) Total annual flow loss across affected extent	USD/year	257 × 2,155 = 554,000
(j) Time horizon	years	30
(k) Discount rate (real)	%	4
(l) NPV factor at 4% over 30 years	—	17.292
(m) Monetary degradation = (i) × (l)	USD	554,000 × 17.292 = 9.58 million (gross)
(n) Recovery adjustment (assumed partial recovery over horizon)	proportion	0.26 (avoided losses through partial natural recovery)
(o) Net monetary degradation entry = (m) × (1 — recovery) — rounding	USD million	~2.5

11Note (row (d)): The 13 pp canopy density decline in row (c) is an extent-weighted average across the 2,155 ha of remaining mangrove stock. Compilers with stratified condition data may apply differentiated degradation rates to severely damaged versus moderately affected extents rather than a single average rate.

12The discount rate (4%) and time horizon (30 years) are aligned with national NPV conventions. Compilers should document the rate and horizon used and apply consistent assumptions across ecosystem types. See TG-1.9 Valuation for further NPV guidance.

13Condition account treatment

14The 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 the ecosystem degradation entry derived above.

3.7.5 Step 4: Ecosystem protection service valuation

1Counterfactual analysis

2Hydrodynamic modelling was conducted to estimate storm surge height with and without the protective ecosystems (mangroves and coral reefs) that existed prior to the disaster.

3Scenario A (with ecosystem protection — actual):

4Mangroves attenuated storm surge by 1.2 metres (2.4 km average forest width × 0.5 m/km, consistent with Section 3.4.1)
5Coral reefs attenuated wave height by 1.2 metres (65% wave energy dissipation)
6Resulting peak surge at shoreline: 3.5 metres
7Inundation extent: 4.2 km² (420 hectares)
8Estimated damage: USD 154 million (actual)

9Scenario B (without ecosystem protection — counterfactual):

10No mangrove or reef attenuation
11Peak surge at shoreline: 5.9 metres (3.5 + 1.2 + 1.2)
12Inundation extent: 7.8 km² (780 hectares)
13Estimated damage: USD 245 million (modelled, using depth-damage functions)

14Ecosystem protection value (avoided damage):

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

15This 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.

16Annualised protection value (EAD with trapezoidal integration)

17The expected annual value of protection is estimated using expected annual damage (EAD) integrated across the damage-probability curve with the trapezoidal rule. Discrete summation of a small number of return-period scenarios systematically underestimates EAD because it assumes zero damage below the smallest scenario and constant damage between scenarios. Trapezoidal integration across all scenarios — including at least one tail event (e.g., 1-in-500 year) — captures the contribution of both frequent moderate events and rare extreme events.

Storm scenario	Return period (yr)	Annual exceedance probability p	Damage with ecosystems (USD M)	Damage without ecosystems (USD M)
Moderate surge	10	0.100	12	22
Severe surge	50	0.020	154	245
Extreme surge	100	0.010	380	620
Tail extreme	500	0.002	720	1,150

18Trapezoidal EAD computation between consecutive scenarios (i, i+1) uses:

Δ EAD = 0.5 × (p_i — p_{i+1}) × (D_i + D_{i+1})

19plus a tail contribution above the rarest scenario (here truncated at p = 0.002):

Tail EAD = p_{tail} × D_{tail}

20The tail term integrates from p = p_tail down to p = 0 under the assumption that damage remains constant at D_tail for all events rarer than the truncation scenario (i.e., it is a rectangle of width p_tail and height D_tail). This is a conservative lower-bound assumption: actual damages from rarer events would likely exceed D_tail, so the tail term understates tail risk. Compilers with data to support a higher-damage tail extrapolation (e.g., a constant exceedance ratio or Pareto tail) should apply that formulation and report the sensitivity of EAD to the tail assumption.

21EAD (with ecosystems) — trapezoidal:

220.5 × (0.100 — 0.020) × (12 + 154) = 0.5 × 0.080 × 166 = USD 6.64 M
230.5 × (0.020 — 0.010) × (154 + 380) = 0.5 × 0.010 × 534 = USD 2.67 M
240.5 × (0.010 — 0.002) × (380 + 720) = 0.5 × 0.008 × 1,100 = USD 4.40 M
25Tail (p = 0.002, D = 720): 0.002 × 720 = USD 1.44 M
26EAD (with ecosystems) ≈ USD 15.2 million per year

27EAD (without ecosystems) — trapezoidal:

280.5 × (0.100 — 0.020) × (22 + 245) = 0.5 × 0.080 × 267 = USD 10.68 M
290.5 × (0.020 — 0.010) × (245 + 620) = 0.5 × 0.010 × 865 = USD 4.33 M
300.5 × (0.010 — 0.002) × (620 + 1,150) = 0.5 × 0.008 × 1,770 = USD 7.08 M
31Tail (p = 0.002, D = 1,150): 0.002 × 1,150 = USD 2.30 M
32EAD (without ecosystems) ≈ USD 24.4 million per year

Annual ecosystem protection value = USD 24.4M — USD 15.2M ≈ USD 9.2 million per year

33The upper return-period truncation (here 1-in-500 year) materially affects the EAD estimate. In this example, truncating the exceedance curve at 1-in-100 year — dropping the 1-in-500 trapezoid (USD 4.40 M) and the tail rectangle (USD 1.44 M) while applying a replacement tail at the new truncation point (0.010 × 380 = USD 3.80 M) — gives EAD ≈ USD 13.1 million, an understatement of approximately 14% relative to the USD 15.2 million estimate. Without a replacement tail the understatement reaches approximately 39%. The magnitude of truncation bias depends heavily on the tail assumption applied. Compilers should always state the truncation point and whether a tail term is included, and conduct sensitivity analysis with at least one additional tail scenario (e.g., 1-in-1000 year) where data allow. Reference: UNDRR Global Assessment Report on Disaster Risk Reduction 2022, Chapter 3 on probabilistic risk; FEMA P-58 EAD methodology.

34This annualised avoided damage estimate (USD 9.2 million per year) represents the recurring ecosystem service flow from coastal protection and can be recorded in ecosystem service supply tables as the monetary parallel entry to the primary “persons protected per year” indicator (see TG-3.2 Flows from Environment to Economy).

35Uncertainty range. Applying the minimum uncertainty standard from Section 3.6.6, with ±35% propagated uncertainty from hydrodynamic model outputs, the protection value is reported as USD 9.2 million per year (range USD 6.0—12.4 million per year; quality tier B).

3.7.6 Step 5: Vulnerability indicators

1Sensitivity indicators (pre-disaster)

2For the 85,000 coastal population:

3Poverty rate: 28% (national average: 18%)
4Informal housing: 35%
5Livelihood dependence on ocean sectors: 52% (fishing, aquaculture, tourism)
6Nutritional dependence on seafood: 68% of protein intake
7Population aged 65+: 14%

8Adaptive capacity indicators (pre-disaster)

9Social protection coverage: 42% (national average: 58%)
10Disaster insurance coverage: 8%
11Early warning system coverage: 75%

12Composite vulnerability index. Applying the additive formulation from Section 3.3.3, the coastal population scores Vulnerability index = 0.5 × 0.32 − 0.5 × 0.42 = −0.05 (full step-by-step derivation: Section 3.3.3). The national reference index is −0.15. The coastal index (−0.05) exceeds the national reference by 0.10 units, confirming higher vulnerability driven principally by higher livelihood dependence on ocean resources (0.52 vs 0.25) and lower disaster insurance coverage (0.08 vs 0.18).

13Post-disaster outcomes validation

14The higher-than-national vulnerability indicators correlated with disproportionate disaster impacts:

15Mortality rate: 9.4 deaths per 100,000 coastal population (national rate for comparable disasters: 5.2 per 100,000)
16Displacement rate: 38% of population (national average: 22%)
17Recovery time: 24 months to restore pre-disaster livelihood levels (national average: 14 months)

18This retrospective validation supports the predictive validity of the vulnerability framework.

3.7.7 Step 6: SDG and Sendai reporting

1SDG Indicator 1.5.1 / 11.5.1 (deaths, missing, affected persons per 100,000)

SDG indicator (national population denominator): Deaths per 100,000 = (8 deaths / 720,000 population) × 100,000 = 1.1 per 100,000

SDG indicator (national population denominator): Affected persons per 100,000 = (32,000 displaced / 720,000) × 100,000 = 4,444 per 100,000

2Note on “affected persons” definition for SDG 1.5.1/11.5.1 compliance. The Sendai Framework Monitor definition of “directly affected persons” (Sendai Indicators B-1 through B-5) is broader than displaced persons alone. It includes persons injured or ill, evacuated, displaced, relocated, and those whose dwellings or livelihoods were directly affected. The figure of 32,000 used here represents the displaced subset only. For full SDG 1.5.1/11.5.1 reporting, compilers must sum all Sendai B-1 to B-5 categories. In this example, the scenario records an additional 1,200 homes destroyed and 3,800 homes damaged, which would add further affected persons under the Sendai definition (occupants of damaged dwellings plus persons with directly affected livelihoods). Compilers should report the displaced-only figure as a memorandum item and clearly label the official indicator value as the broader Sendai-defined total.

3For sub-national analysis, coastal-specific rates using the coastal population denominator provide additional insight:

Coastal rate: Deaths per 100,000 = (8 deaths / 85,000 coastal population) × 100,000 = 9.4 per 100,000

Coastal rate: Affected persons per 100,000 = (32,000 displaced / 85,000) × 100,000 = 37,647 per 100,000 (displaced subset only; see Sendai definition note above)

4SDG Indicator 1.5.2 / 11.5.2 (direct economic loss as share of GDP)

5National GDP = USD 5,500 million

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

6This figure aligns with the Sendai Monitor metadata column in Table 4 (built/critical infrastructure and aquaculture). Supplementary TG-2.9 totals including ecosystem catastrophic losses are reported separately.

7Sendai Target C (reduce direct economic loss)

8The baseline disaster loss ratio (2.8% of GDP) establishes the reference for tracking progress.

9Policy implications

10The worked example demonstrates several key findings:

11Ecosystem catastrophic losses of USD 95.4 million equal 62% of the direct economic loss to produced assets (USD 154 million). Total asset losses, produced and natural combined, reach USD 249.4 million when both are counted
12Ecosystem protection provided USD 91 million in avoided damage during this single event and USD 9.2 million per year on an annualised EAD basis (range USD 6.0—12.4 million per year). These figures quantify the climate adaptation value of nature-based solutions in monetary terms
13The coastal population exhibits higher vulnerability than national averages, indicating priority areas for social protection extension and risk reduction investment
14The coastal exposure dependency ratio (12% of national population in hazard-prone coastal zones) indicates moderate national-level exposure concentration

4. Acknowledgements

1This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

2Authors: [To be confirmed]

3Reviewers: [To be confirmed]

5. References

1
United Nations, Transforming our world: the 2030 Agenda for Sustainable Development, SDG Targets 1.5, 11.5, 13.1. ↩
2
SEEA EA, para 6.36 and Table 6.3. Coastal protection services are classified as regulating ecosystem services, with sand dunes cited as an example of predominantly abiotic ecosystem services providing coastal protection. ↩
3
IPCC, 2022, Annex II: Glossary, Climate Change 2022: Impacts, Adaptation and Vulnerability. ↩
4
IPCC, 2022, Annex II: Glossary, definition of “Disaster risk”. ↩
5
United Nations Office for Disaster Risk Reduction, Sendai Framework for Disaster Risk Reduction 2015-2030 (A/CONF.224/CRP.1). Expected outcome and goal, paras 16-17; the seven global targets (A-G), para 18; guiding principles, para 19; the four priorities for action, para 20. ↩ ↩² ↩³
6
IPCC, 2022, Box 1.1, Concepts for risk framing. ↩
7
IPCC, 2022, Annex II: Glossary, definition of “Hazard”. ↩
8
SEEA CF, para 5.49(c). ↩
9
IPCC, 2022, Annex II: Glossary, definition of “Exposure”. ↩
10
IPCC, 2022, Annex II: Glossary, definition of “Vulnerability”. ↩
11
United Nations, Global indicator framework for SDGs, Indicator 1.5.1. ↩
12
SEEA EA, paras 4.25-4.27 on ecosystem conversions and reclassifications. ↩
13
SEEA CF, para 5.49(c). Catastrophic losses to natural resources are recorded as other changes in the volume of assets. ↩
14
SEEA EA, para 6.36 and Table 6.3. ↩
15
2025 SNA, Chapter 35, para 35.114. ↩
16
FDES 2013, Component 4: Environmental resources and their use, and Sub-component 1.3: Natural extreme events and disasters. ↩
17
FDES 2013, Sub-component 1.3: Natural extreme events and disasters; UNDRR-ISC Hazard Definition and Classification Review (2020); Tellman et al. (2021), “Satellite imaging reveals increased proportion of population exposed to floods”, Science. ↩
18
SDG indicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population. ↩
19
SDG indicator 11.5.2: Direct economic loss in relation to global GDP, damage to critical infrastructure. ↩
20
IPCC, 2022, Annex II: Glossary, definitions of “Sensitivity” and “Adaptive capacity”. ↩
21
SF-MST, para 5.46 on impacts on wellbeing; OECD/JRC (2008), Handbook on Constructing Composite Indicators, Chapter 6 on aggregation methods; UN Permanent Forum on Indigenous Issues, Data Collection and Disaggregation for Indigenous Peoples (2004). ↩
22
United Nations, 2030 Agenda for Sustainable Development, SDG Target 1.5. ↩
23
TNFD Recommendations, Annex 4: Additional guidance for engagement with Indigenous Peoples and Local Communities. ↩
24
TNFD Recommendations, Strategy C on resilience. ↩
25
United Nations, Global indicator framework for SDGs, Indicator 1.3.1. ↩
26
IPCC, 2022, Chapter 16: Key risks across sectors and regions. ↩
27
SEEA EA, para 6.3, classification of ecosystem services. ↩
28
SEEA EA, para 6.36 and Table 6.3 on coastal protection services; see also para 13.86 on ocean ecosystem services including coastal protection and tidal surge mitigation. ↩
29
Ferrario et al. (2014), The effectiveness of coral reefs for coastal hazard risk reduction and adaptation, Nature Communications. ↩
30
McIvor et al. (2012), The response of mangrove soil surface elevation to sea level rise, Natural Coastal Protection Series. ↩
31
SEEA EA, para 6.36. ↩
32
Dune quantification requires LiDAR at 1 m horizontal resolution or better to capture dune crest elevation, which is a sub-grid feature in 30 m global DEM products (SRTM, NASADEM). Where LiDAR data are unavailable, dune protection should be excluded from the indicator compilation and flagged in metadata as “dune protection not assessed — data inadequate”. See Stockdon et al. (2006), Coastal Engineering; USACE Coastal Engineering Manual EM 1110-2-1100. ↩
33
SEEA EA, Table 6.3, regulating and maintenance services; SEEA EA paras 8.19—8.24 on ecosystem service flow measurement. ↩
34
TNFD Recommendations, Section 2.3 on nature-related risks. ↩
35
SEEA EA, para 12.30. ↩
36
TNFD Recommendations, Table 2.2, Physical risks. ↩
37
United Nations, Global indicator framework for SDGs, Indicator 1.5.2/11.5.2. ↩
38
UNDRR, Sendai Framework Monitor Technical Guidance, Indicator C methodology; SDG 1.5.2/11.5.2 metadata (UNDRR 2022); SEEA CF, para 5.49(c) on catastrophic losses. ↩
39
SEEA CF, para 5.49(c). Other changes in the volume of assets include catastrophic losses, providing the accounting mechanism for recording disaster-related ecosystem damage. See also SEEA EA paras 4.25—4.27 and 12.30. ↩ ↩²
40
SEEA CF, paras 5.49-5.52. The SEEA CF treatment distinguishes catastrophic losses to natural resources from losses of cultivated biological resources and product inventories. ↩
41
2025 SNA, Chapter 35 on environmental-economic accounting. See also SEEA CF paras 5.49-5.52 on recording catastrophic losses and other volume changes in asset accounts; UNDRR Sendai Framework Monitor Technical Guidance for direct/indirect classification used in SDG 1.5.2/11.5.2 reporting. ↩
42
SDG Target 11.b on implementing disaster risk management strategies; UNDRR, Words into Action: Build Back Better (2017); Sendai Framework Priority 4 (para 20) and guiding principle (k) (para 19(k)) on “Building Back Better” in the recovery, rehabilitation and reconstruction phase. ↩