Comprehensive CRVA & Multi-Hazard Risk Assessment

Developed by: Z M Sajjadul Islam

A Comprehensive Climate Risk and Vulnerability Assessment (CRVA) moves beyond static hazard maps to establish a dynamic, multi-layered spatial engine. It is the analytical foundation that allows local governments to shift from reactive disaster response to risk-informed, anticipatory planning.

At the highest level of technical implementation, a CRVA operationalizes the standard risk equation () by translating socioeconomic field data and meteorological models into interoperable GIS layers.

1. Hazard Characterization (The Physical Layer)

The first step requires modeling the intensity, frequency, and spatial extent of both sudden-onset (cyclones, flash floods) and slow-onset (salinity intrusion, drought) hazards.

• Probabilistic Modeling: Rather than relying solely on historical damage, probabilistic models simulate thousands of potential hazard scenarios to calculate varying return periods (e.g., 1-in-50 year vs. 1-in-100 year flood events).
• High-Resolution Spatial Inputs: Macro-level Earth Observation (EO) data provides the baseline, but localized mapping requires high-resolution photogrammetry. UAVs (drones) are deployed to generate precise Digital Elevation Models (DEMs) and orthomosaics, capturing micro-topography critical for accurate flood inundation or storm surge modeling.
• Climate Change Scenarios: Hazard layers must be adjusted dynamically using localized downscaled climate models (such as CMIP6 data) to project future hazard behavior under different emission scenarios (SSP pathways).

2. Exposure & Asset Mapping (The Inventory Layer)

Risk only materializes when people, infrastructure, or ecosystems are located within the hazard footprint. Exposure mapping digitizes the built and natural environment.

• Critical Infrastructure Base Maps: Utilizing GIS to map roads, embankments, hospitals, schools, and energy grids.
• Nature-Based Solutions (NbS) Inventory: Ecosystems are mapped as protective assets. Mangrove belts, wetland retention basins, and stabilized slopes are digitized as active infrastructure layers to assess their capacity to buffer hazard impacts.
• Household-Level Resilience Mapping: Capturing localized, climate-adaptive assets. For example, mapping nodes of decentralized food and energy security, such as households equipped with biogas digesters, solar PV, or integrated farm management (IFM) systems.

3. Vulnerability Indexing (The Socio-Economic Layer)

Vulnerability determines the propensity of exposed assets and populations to suffer adverse effects. This is where primary field data intersects with spatial analytics.

• Composite Indexing: Creating a weighted index that measures sensitivity (e.g., age demographics, housing materials, reliance on rain-fed agriculture) and adaptive capacity (e.g., access to early warning systems, alternative livelihoods, social safety nets).
• CAPI & Kobo-Toolbox Integration: To generate this index, enumerators deploy digital field surveys. Because platforms like Kobo-Toolbox capture precise geolocations alongside socioeconomic survey data, the resulting dataset can be directly exported as GeoJSON files. This allows demographic vulnerabilities to be spatially rendered and overlaid onto physical hazard maps.

a) Multi-Hazard Economic Risk Quantification

a) Multi-Hazard Economic Risk Quantification: A Transboundary Framework

i) Introduction and Paradigm Shift

In the context of a rapidly shifting climate regime, traditional single-hazard risk assessments are dangerously obsolete. The current era of climate catastrophes is characterized by compounding (e.g., simultaneous extreme heat and drought) and cascading (e.g., flooding triggering landslides, which destroy critical downstream infrastructure) hazard events.

To achieve the “whole-of-earth-as-a-whole” resilience paradigm, economic risk quantification must transcend political boundaries. When upstream nations exert unilateral control over shared landscape resources such as surface water bodies, they inherently export economic risk to downstream nations. Quantifying this multi-hazard economic risk is essential for holding actors accountable and for formulating transparent, transboundary Disaster Risk Reduction (DRR) policies.

ii) Core Components of Economic Risk

Multi-hazard economic quantification must capture two distinct categories of loss, modeled across spatial and temporal dimensions:\

Direct Losses (Stock Losses)

These are the immediate physical damages resulting directly from the hazard forces.

• Infrastructure & Assets: Destruction of structural resources, housing, and industrial facilities.
• Natural Capital: Loss of biodiversity, degradation of shared ecosystems, and destruction of arable land.
• Valuation Methodology: Replacement cost methods, damage/fragility curves tailored to multiple sequential hazards (e.g., assessing the vulnerability of a building to a flood after it has been weakened by an earthquake or prolonged drought).

1. Direct Losses (Stock Losses) in Disaster Economics

In disaster risk management and economic assessment, direct losses, frequently referred to as stock losses, represent the immediate, physical damage to or destruction of capital assets caused by a hazard event.

Because these assets represent accumulated capital “stock” at a specific point in time, measuring direct losses focuses on the total value of what was physically destroyed or structurally compromised at the moment the disaster occurred.

Core Characteristics of Stock Losses

• Immediate and Physical: These losses occur during or immediately after the hazard event (e.g., a cyclone destroying a coastal embankment, flooding ruining agricultural inventory, or an earthquake collapsing a structure).
• Asset Categorization: They encompass physical infrastructure (roads, bridges, power grids like solar PV systems), residential and commercial buildings, machinery, and existing inventories (crops already harvested and stored, livestock).
• Measurement Thresholds: In post-disaster needs assessments (PDNAs), stock losses are typically quantified using either the replacement cost (the cost to rebuild or replace the asset to its pre-disaster state) or the depreciated value (the actual economic value of the asset right before destruction).

Stock Losses vs. Flow Losses

To fully map economic risk, stock losses are always contrasted with flow losses (indirect losses).

While a stock loss is the destruction of the asset itself, a flow loss is the subsequent disruption to the flow of goods, services, and revenues over time. For example, if a biogas digester is destroyed by a flood, the physical cost of the digester is the direct stock loss. The subsequent loss of energy production and the increased household expenditure on alternative fuels over the next six months represent the indirect flow loss.

Stock Losses vs. Flow Losses

To fully map economic risk, stock losses are always contrasted with flow losses (indirect losses).

While a stock loss is the destruction of the asset itself, a flow loss is the subsequent disruption to the flow of goods, services, and revenues over time. For example, if a biogas digester is destroyed by a flood, the physical cost of the digester is the direct stock loss. The subsequent loss of energy production and the increased household expenditure on alternative fuels over the next six months represent the indirect flow loss.

b) Integration with Risk Financing and Metrics

Understanding and accurately modeling direct stock losses is the foundational step for structuring disaster risk financing frameworks and calculating key economic risk metrics

Sendai Framework Alignment

Direct stock loss is the primary driver for reporting on the Sendai Framework, particularly Indicator C-1 (Direct economic loss attributed to disasters in relation to global GDP). Accurately quantifying these physical damages is essential for national reporting and assessing the macroeconomic impact of hazards.

Probable Maximum Loss (PML) and Annual Average Loss (AAL)

 Catastrophe risk models rely on historical and simulated direct stock damage to build loss exceedance curves. The vulnerability module of a risk model translates hazard intensity (e.g., flood depth, wind speed) into a mean damage ratio for a given asset stock.

This directly feeds the calculation of the Annual Average Loss (AAL), which represents the expected long-term average of direct stock losses per year, mathematically expressed as the integral of the loss exceedance probability curve:

Where  is the direct stock loss at a given exceedance probability . Similarly, the Probable Maximum Loss (PML) provides the maximum expected direct stock loss for specific return periods (e.g., 1-in-100 years), which is critical for structuring sovereign risk transfer mechanisms or parametric insurance.

Field Assessment and Validation

Gathering accurate data on direct stock losses requires establishing precise pre-disaster baselines. This is increasingly managed by overlapping high-resolution geospatial data (GIS mapping, UAV/drone imagery) with rapid post-event field assessments. Digitized data collection tools using structured logic-such as Kobo-Toolbox deployed via Computer-Assisted Personal Interviewing (CAPI)allow for the rapid aggregation and geolocating of household and structural damage data, translating raw physical damage into exportable economic loss metrics.

In disaster risk financing and catastrophe modeling, Annual Average Loss (AAL) and Probable Maximum Loss (PML) are the two foundational metrics used to quantify economic risk. They translate physical hazard data into actionable financial intelligence, enabling governments and organizations to move from reactive disaster response to proactive risk management.

Both metrics are derived from a single mathematical model: the Exceedance Probability (EP) Curve.

Annual Average Loss (AAL): The Long-Term Baseline

The AAL represents the expected long-term average loss per year from a specific hazard (or multiple hazards) over a highly extended timeframe.

• The Concept: If you were to simulate thousands of years of disasters-ranging from frequent, minor floods to rare, catastrophic cyclones-and average out the financial damage per year, that number is the AAL.
• Financial Equivalent: In insurance terms, AAL is the “pure premium.” It is the exact amount of money a government or entity would need to set aside every single year to completely cover all future disaster losses over time.
• Strategic Application: AAL is the standard metric for cost-benefit analyses of resilience investments. For example, if investing in a nature-based solution or upgrading a coastal embankment costs a specific amount, that investment is economically justified if the annualized cost of the infrastructure is less than the resulting reduction in the AAL. Tracking AAL reduction is also a robust method for measuring progress toward Sendai Framework Target C.

Probable Maximum Loss (PML): The Extreme Tail Risk

While AAL looks at the average, the PML looks at the extremes. PML is the estimated maximum loss that is expected to occur for a given, low-probability “return period.”

• The Concept: PML is always tied to an Annual Exceedance Probability (AEP) or a Return Period (RP). For instance, a “1-in-100-year PML” represents the level of financial loss that has a 1% chance of being met or exceeded in any given year.
• Financial Equivalent: PML dictates capital reserve requirements. It answers the question: What is the worst-case financial shock we must be prepared to absorb without facing economic collapse?
• Strategic Application: PML is the primary metric used to structure sovereign risk transfer mechanisms. It determines the “attachment points” (when a policy starts paying out) and “exhaustion points” (the maximum payout limit) for parametric insurance policies and catastrophe bonds.

The Mathematical Relationship: The EP Curve

The Exceedance Probability (EP) Curve plots the probability of a loss occurring (X-axis) against the financial magnitude of that loss (Y-axis).

• PML represents discrete, individual points along this curve (e.g., the loss value at the  probability mark on the X-axis).
• AAL represents the total, cumulative area under the entire EP curve.

Mathematically, AAL is expressed as the integral of the loss exceedance probability:

Where  is the loss amount at a given exceedance probability .

By shifting the EP curve inward-whether through structural mitigation (like resilient housing) or through Early Warning for Early Action (EWEA) protocols that allow for the relocation of movable assets before impact-both the AAL and the PML are mathematically reduced.

c) Indirect Losses (Flow Losses & Cascading Effects)

This is where the political border-resource control paradigm shows its most devastating effects. Indirect losses are the cascading socioeconomic impacts that ripple outward from the epicenter.

• Business Interruption: Supply chain bottlenecks and halted industrial production.
• Livelihood & Job Losses: The World Bank estimates millions of full-time equivalent job losses annually due to fast-onset shocks. In agricultural basins dependent on shared transboundary water, unilateral withholding of water creates severe indirect economic stagnation downstream.
• Welfare & Human Capital: Increased poverty rates, long-term health impacts, and the economic drain of climate-induced migration.

Advanced Methodologies for Multi-Hazard Modeling

To move beyond tangled bureaucratic inefficiencies, modern risk governance must rely on objective, data-driven quantification methodologies:

Hypergraph and Dynamic Interaction Modeling

Standard GIS overlays fail to capture how one hazard triggers another. Advanced quantification utilizes Hypergraph Theory and Dynamic Modeling to simulate multi-hazard interactions. In this framework, both the hazards (e.g., heavy rainfall, upstream dam release) and the elements at risk (e.g., downstream agriculture, local markets) are modeled as interconnected nodes, allowing policymakers to visualize the cascading economic domino effect before it happens.

Macroeconomic Impact Models

To quantify the “flow losses” across a de-bordered landscape, economists utilize two primary frameworks:

• Input-Output (I-O) Models: Excellent for mapping short-term supply chain disruptions. If a flood destroys a critical bridge in a downstream nation, an I-O model quantifies the exact economic loss to the surrounding regions that relied on that trade route.
• Computable General Equilibrium (CGE) Models: These models are vital for understanding long-term, transboundary economic shifts. They simulate how a multi-hazard shock alters prices, labor markets, and global trade dynamics, forcing a holistic view of the disaster’s true cost.

Open-Source and AI-Driven Risk Engines

Tools like CLIMADA, Risk-Scape, and newly developed AI predictive models allow for the ingestion of vast geospatial datasets. By automating vulnerability assessments, these tools bypass corrupt bureaucratic bottlenecks, offering transparent, verifiable, and highly actionable economic risk metrics directly to last-mile communities.

d) Addressing the Bottlenecks: Governance and Accountability

The mathematical modeling of risk is only as effective as the governance system executing it. The quantification of multi-hazard risks exposes the true cost of unilateral resource control:

• Valuing the “Ex-Ante” Co-Benefits: Traditional bureaucracies often view DRR as a sunk cost. Modern economic quantification proves that investing in shared, transboundary resilience (green infrastructure, joint early warning systems) yields high economic returns even if the disaster never occurs, by boosting market confidence and regional stability.
• Holding Actors Accountable: By precisely quantifying the downstream economic destruction caused by human-induced hazards (e.g., engineered water scarcity), the international community can establish financial accountability mechanisms. The data removes the shield of “natural disaster” from what are often politically engineered catastrophes.

Multi-Hazard Economic Risk Quantification is not merely an academic exercise; it is the financial architecture required for a de-bordered, equitable world. By rigorously calculating the true, cascading costs of climate and human-induced hazards, we transition from reactive, localized aid to proactive, whole-of-society resilience.

e) Adaptation Financing and Integration with Local Government Planning

 To secure adaptation financing and integrate with local government planning, the CRVA must translate spatial risk into concrete financial metrics.

Conducting a Cost-Benefit Analysis (CBA) for climate adaptation requires flipping the traditional view of financial returns. Instead of measuring new revenue generated, the “benefit” in adaptation economics is calculated entirely as the avoided loss.

In the context of designing Multi-Hazard Early Warning Systems (MHEWS) or implementing nature-based solutions such as climate-adaptive household models integrated with solar PV and biogas, the baseline metric is the Annual Average Loss (AAL). When an adaptation intervention is introduced, it mathematically reduces the physical vulnerability or exposure of the asset stock, creating a new, lower “Adapted AAL.” The difference between the Baseline AAL and the Adapted AAL represents the annual financial benefit.

e) Core Metrics for Adaptation Financing

For international agencies and local governments structurally aligning with UNDRR frameworks or applying for climate financing, this avoided loss is compared against the capital expenditure (CapEx) and operational expenditure (OpEx) required to build and maintain the adaptation over its lifespan. Three primary metrics are derived:

1. Net Present Value (NPV)

NPV calculates the total value of all future avoided losses minus all costs, discounted to today’s value to account for inflation and the time value of money. A positive NPV indicates a financially viable adaptation. The formula is:

• : Avoided loss (benefit) in year
• : Maintenance cost in year
• : Discount rate
• : Upfront capital cost
• : Project lifespan

2. Benefit-Cost Ratio (BCR)

The BCR is the total discounted benefits divided by the total discounted costs. A BCR greater than 1.0 means the investment saves more money than it costs. For example, Early Warning for Early Action (EWEA) protocols typically demonstrate exceptionally high BCRs because the capital costs (communication networks, digital data collection via CAPI) are relatively low compared to the massive stock and flow losses they avert.

3. Break-Even Point

This represents the specific year in which the cumulative avoided losses finally surpass the upfront and ongoing maintenance costs.

 4. The Core Spatial Engine: Comprehensive CRVA & Multi-Hazard Risk Assessment

To design a system that captures both sudden-onset hazards and slow-onset climate changes, the GIS framework must blend exposure, hazard, and vulnerability layers.

• Hazard Modelling & Exposure Mapping: Integrating macro-level Earth Observation data (e.g., historical rainfall trends, sea-surface temperature anomalies, NDVI) with micro-level UAV (drone) photogrammetry to map high-risk zones, such as floodplains, landslide-prone slopes, or coastal erosion lines.
• Vulnerability Indexing: Developing a dynamic, composite vulnerability index that overlays environmental risk layers with socioeconomic data. This includes mapping household-level adaptive capacitiessuch as reliance on climate-sensitive livelihoods versus access to diversified assets.
• Economic Risk Metrics: Designing algorithms within the GIS engine to calculate Annual Average Loss (AAL) and Probable Maximum Loss (PML), giving local governments the concrete financial risk metrics required for risk-informed decision-making.

f) Primary Data Collection & PDNA Architecture

Post-Disaster Damage, Loss, and Needs Assessments (PDNA) require speed, accuracy, and standardization to unlock recovery financing (such as Sendai Framework Indicator C-1 reporting).

1. Mobile Data Collection (MDC): Standardizing field assessment forms via Computer-Assisted Personal Interviewing (CAPI) frameworks on platforms like KoboToolbox or ODK. These forms capture:

• • Structural and agricultural damage categorization.
  • Geolocation points and boundary polygons.
  • Embedded, timestamped photographic evidence.

2. Interoperable Data Pipelines: Configuring the system to automatically pipe field data out as structured CSV and GeoJSON files. This allows the incoming damage reports to instantly populate central GIS dashboards, mapping the spatial distribution of loss in real time.

3. Early Warning for Early Action (EWEA) & Preparedness Planning

A true Multi-Hazard Early Warning System (MHEWS) must bridge the gap between technical monitoring and localized response.

• Telemetry & IoT Integration: Designing API gateways that ingest continuous, real-time data streams from Automated Weather Stations (AWS) and hydrological sensors.
• Threshold Trigger Logic: Building automated alert logic based on predefined physical thresholds (e.g., water level exceeding a critical mark, or cumulative rainfall over 48 hours). When triggered, the system auto-generates localized early action protocols.
• The “Last Mile” Communication: Developing ICT tools that translate technical warnings into actionable, community-level alerts. This includes targeted SMS, automated voice messaging, and digital radio triggers tailored to local languages and contexts.

4. Nature-Based Solutions (NbS) & Locally Led Adaptation (LLA) Mapping

To scale nature-based and locally led initiatives, the ICT toolkit must treat community assets as quantifiable adaptation metrics.

• Green Infrastructure Inventories: Utilizing remote sensing and participatory GIS mapping to catalog existing ecosystemssuch as mangrove buffers, wetland retention basins, or community forestsevaluating their protective capacity against specific hazard return periods.
• Household-Level Resilience Modeling: Tracking the adoption and efficiency of localized, integrated climate adaptive models (e.g., household biogas digesters, solar PV installations, rainwater harvesting networks). The GIS system maps these as decentralized nodes of climate resilience, evaluating how localized food and energy security impacts broader community vulnerability.

5. Mainstreaming into Local Government Planning and Budgeting

The technical tools are only as effective as their integration into local institutional frameworks.

• Risk-Informed Budgeting Tools: Creating simplified, dashboard-driven decision-support software for local government officials. These tools cross-reference spatial CRVA maps with local development proposals, flagging whether a planned infrastructure project sits in a high-risk zone or violates climate-adaptive zoning.
• Participatory Planning Integration: Bridging qualitative, community-led data (from Focus Group Discussions, Transect Walks, and Participatory Risk Mapping) with quantitative spatial layers. This ensures that localized climate action plans are grounded in verified local needs while remaining technically sound for national or international financing approval.

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Climate Risk & Vulnerability Assessment……

Climate Risk & Vulnerability Assessment

MHEWC provides technical support at the country level to design, and develop the complete set of GIS and ICT tools based on comprehensive climate risk and vulnerability assessment( CRVA), Multi-hazard risk assessment, Post-disaster damage, loss and needs assessment (PDNA), multi-hazard preparedness, response and recovery planning, sector level and integrated climate change adaptation (CCA) local government planning and budgeting system, CCA, Disaster Risk Reduction (DRR), Nature-based Solution(NbS), Locally led adaptation (LLA) etc.

Undertake cross-border analysis of the gendered impacts of climate-induced hazards in Malawi, Mozambique, Malawi

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Questionnaires on Preparedness Phase ( Mozambique, Malawi, Zimbabwe )

Questionnaire for the Preparedness Phase of Cyclone, Floods and Drought

1) Type of Methodology, tools and guidelines does the duty bearer utilize  in developing preparedness plans for cyclone, flood and drought  ? 

1. Refined technical background text (you can copy–paste)

Multi-hazard and climate risk and vulnerability assessment (CRVA) and the development of risk-informed tools at national and sub-national levels form the foundation for precision-level multi-hazard early warning, impact-based forecasting, weather warning, alerting and outlook services. These products depend on a robust geospatial CRVA repository built from GIS maps that capture:

• Landscape and topographical patterns
• Environmental settings and ecological elements
• Vegetation and land use/land cover
• Soil composition
• Geological and geomorphological characteristics
• Hydrometeorological and weather patterns
• Existing physical installations and infrastructure
• Climatic and multi-hazard exposure, and
• The risk and vulnerability of exposed elements on the ground.

By overlaying weather and climate forecasts, warning outlooks, and hazard scenarios (e.g. cyclone, heavy rainfall, drought) on this geospatial CRVA repository—linked to precisely geolocated elements—it becomes possible to:

• Estimate the intensity and spatial extent of hazards
• Assess the impact level on exposed elements
• Calculate anticipatory loss and damage (L&D) to people, assets, livelihoods and critical services before an event occurs.

Within this framework, the WRD gender impact assessment seeks to review the most important CRVA reports, methodologies, processes and operational tools used at national, provincial, district, sub-district and village levels. This includes:

• GIS and remote sensing maps
• GPS-based applications for capturing coordinates of elements,
• Photographic documentation of elements and their highest recorded flood levels, and
• GIS base maps depicting local geographic features and infrastructure.

Of particular importance is the geospatial identification of women’s exposure and vulnerability, including:

• Coordinates and locations of women-headed households (based on sex, age and disability disaggregated data – SADDD)
• Women-headed farmlands and smallholder plots
• Fisheries farms/ponds
• Women-led business points and value-chain service trigger points
• Flood-proof and climate-resilient sectoral lifeline services such as WASH facilities, health centers, water supply systems, food security assets, agency-managed disaster shelters, core family shelters, public buildings, schools, boreholes and water points.

Accurate mapping of these elements over district- and village-level GIS maps enables precision emergency preparedness planning, including the pre-positioning of humanitarian assistance and services. This is critical for saving lives in situations where physical communication channels are disrupted, landscapes become isolated, and communities in hard-to-reach areas face heightened risk.

2. What the WRD gender impact assessment will review (bullet outline) ?

You can use this as a checklist or sub-headings:

1. CRVA documentation
   • National and sub-national CRVA reports (cyclone, flood, drought)
   • Methodological guidelines and technical notes
   • Risk classification and hotspot maps
2. CRVA methodologies and processes
   • Hazard, exposure, vulnerability and risk analysis methods
   • Integration of climate change projections
   • Use of impact-based forecasting and early warning
   • Inclusion of gender, disability and social vulnerability dimensions
3. CRVA tools and datasets
   • GIS base maps (topography, land cover, infrastructure, administrative units)
   • Remote sensing products (flood extent, drought indices, vegetation)
   • GPS/mobile data collection tools for elements at risk (households, farms, services)
   • Photo documentation and records of highest flood levels
   • Spatial databases / CRVA repositories at national and sub-national levels
4. Gender, SADDD and women’s livelihoods
   • SADDD databases and linkage with CRVA layers
   • Geolocated data on:
     • Women-headed households
     • Women-headed farmlands and fisheries ponds
     • Smallholder farmlands
     • Women-led businesses and value chain nodes
   • Evidence on differentiated impacts and capacities of women, men, youth, and persons with disabilities
5. Lifeline and critical basic services
   • Mapping of flood-/hazard-proof:
     • WASH facilities
     • Health centers and clinics
     • Schools and education facilities
     • Disaster shelters and core family shelters
     • Boreholes, water points, and food security infrastructure
   • Their accessibility to women and vulnerable groups during emergencies
6. Use of CRVA in preparedness and anticipatory action
   • How CRVA and impact-based forecasts inform:
     • Emergency preparedness plans
     • Pre-positioning of humanitarian assistance
     • Anticipatory actions (e.g., early cash, livelihood protection, relocation of assets)
   • Specific measures targeting women-headed households and hard-to-reach areas.

1. Availability of CRVA and PDNA reports by administrative level

1.1 CRVA coverage

• Are CRVA reports available for the following administrative levels?
  • ☐ National
  • ☐ Province / Region
  • ☐ District
  • ☐ Municipality / City Corporation
  • ☐ District town / Municipal town
  • ☐ Sub-district / Upazila / Ward (adapt to context)
  • ☐ Village / Community level

For each level, please specify:

• Year(s) of CRVA:
• Main hazards covered (cyclone, flood, drought, etc.):
• Lead institution and partners:
• Whether gender/SADDD is integrated: ☐ Yes ☐ Partially ☐ No

1.2 PDNA coverage (Post-Disaster Needs Assessment)

• Have PDNA or similar post-disaster assessment reports been conducted for major events (cyclone, flood, drought) in this area?
  • ☐ Yes ☐ No

If yes, please specify:

• Year and type of disaster:
• Administrative levels covered (province, district, municipality, village, etc.):
• Whether PDNA findings feed into emergency preparedness planning: ☐ Yes ☐ Partially ☐ No
• Whether PDNA includes gender-disaggregated and women’s livelihood analysis: ☐ Yes ☐ Partially ☐ No

2. Stakeholders involved in CRVA / PDNA and risk assessment

2.1 Types of institutions and stakeholders engaged

• Which stakeholders are engaged in CRVA / PDNA / risk assessment processes?
  Tick all that apply and add names:
• ☐ National disaster management authority / ministry:
• ☐ Hydrometeorological / meteorological and hydrology departments:
• ☐ Line ministries (agriculture, fisheries, livestock, WASH, health, education, etc.):
• ☐ Provincial / regional government departments:
• ☐ District administration and technical departments:
• ☐ Municipal / town authorities and technical units:
• ☐ Local government bodies (union councils, village councils, ward committees):
• ☐ Women’s affairs / gender ministry or department:
• ☐ Social protection and statistics/census departments:
• ☐ NGOs / CSOs (including women’s rights organizations):
• ☐ Community-based organizations (CBOs):
• ☐ Private sector (e.g., telecom, agri-business, financial services):
• ☐ Academic and research institutions / universities:
• ☐ Representatives of vulnerable groups (women-headed households, persons with disabilities, elderly, youth, minorities, etc.):

3. ICT tools and data sources for CRVA and risk mapping

3.1 Geospatial and remote sensing tools

Which ICT tools and spatial datasets are used in CRVA and risk mapping?

• ☐ GIS desktop software (e.g. QGIS, ArcGIS):
• ☐ Government-produced GIS base maps (administrative boundaries, roads, rivers, land use):
• ☐ Local government GIS maps:
• ☐ Remote sensing / satellite images (e.g. Sentinel, Landsat, commercial imagery):
• ☐ Google Earth / Google satellite imagery:
• ☐ Drone imagery for detailed local mapping:
• ☐ Online geospatial platforms / webGIS for elements at risk (please specify):
• ☐ Government statistics and census datasets for population and socio-economic data:

For each major tool/source, describe:

• Purpose (hazard mapping, exposure mapping, damage assessment, etc.)
• Scale (national, province, district, village)
• How often it is updated.

3.2 GPS / mobile tools for field data collection

• Are GPS-enabled tools or mobile applications used to capture coordinates of elements at risk?
  • ☐ Yes ☐ No

If yes, what tools/apps? (e.g. ODK/Kobo, ArcGIS Field Maps, QField, other):

• What types of elements are captured?
  • ☐ Households (including women-headed households)
  • ☐ Farmlands / smallholder plots
  • ☐ Fisheries ponds/farms
  • ☐ Business points / markets / value chain nodes
  • ☐ WASH facilities, health centers, schools
  • ☐ Disaster shelters, core family shelters, key buildings
  • ☐ Water boreholes and water points
  • ☐ Highest historical flood levels at specific locations
  • ☐ Other (specify):

4. Household-level SADDD and gender data

4.1 Data sources and methods

• Are Sex, Age and Disability Disaggregated Data (SADDD) available at household level for the area?
  • ☐ Yes ☐ Partially ☐ No

If yes:

• Main data sources:
  • ☐ National census
  • ☐ Household surveys (e.g. LSMS, DHS, MICS, etc.)
  • ☐ Social protection / beneficiary registries
  • ☐ CRVA-specific surveys
  • ☐ NGO/UN programme surveys
  • ☐ Other (specify):

4.2 Data collection tools

• What tools are used to collect household-level SADDD?
  • ☐ Paper-based questionnaires
  • ☐ Mobile data collection (Kobo, ODK, SurveyCTO, CommCare, etc.)
  • ☐ Tablets / smartphones with GPS
  • ☐ Online data entry platforms
• Are GPS coordinates of households (including women-headed households) captured?
  • ☐ Yes, systematically
  • ☐ Yes, partially
  • ☐ No
• Are SADDD and GPS data integrated into GIS / CRVA maps?
  • ☐ Yes ☐ Partially ☐ No

5. Multi-hazard Risk Atlas and update process

5.1 Existence and scope of risk atlas

• Is there a Multi-Hazard Risk Atlas (printed or digital) for:
  • ☐ National level
  • ☐ Province / region
  • ☐ District
  • ☐ Municipality / town
  • ☐ Sub-district / union / ward
  • ☐ Village / community
• Hazards included:
  • ☐ Cyclone / windstorm
  • ☐ Riverine flood
  • ☐ Flash flood
  • ☐ Storm surge
  • ☐ Drought
  • ☐ Landslide / erosion
  • ☐ Other (specify)

5.2 Consultation process for developing the atlas

• Was the atlas developed with multi-stakeholder consultation?
  • ☐ Yes ☐ Partially ☐ No

If yes, which processes were used (tick all):

• ☐ Community PRA (Participatory Rural Appraisal) exercises
• ☐ Focus Group Discussions (FGDs) with community groups (including women, youth, persons with disabilities)
• ☐ Key Informant Interviews (KIIs) with local leaders and sector experts
• ☐ Workshops with local government, sector agencies and CSOs
• ☐ Validation sessions using GIS maps with communities
• Are risks and local knowledge captured directly on GIS maps during PRA/FGD/KII (e.g. sketch maps digitized into GIS; field marking of hotspots)?
  • ☐ Yes ☐ Partially ☐ No

5.3 Use of technology in risk mapping

• Are online GIS/GPS apps, drone imagery and satellite images used to support:
  • Local risk mapping and exposure analysis: ☐ Yes ☐ No
  • Validation of community-identified hotspots: ☐ Yes ☐ No
  • Damage and Loss assessments after events: ☐ Yes ☐ No

5.4 Regular updates

• Are multi-hazard risk, exposure and vulnerability layers regularly updated?
  • ☐ Yes, with a defined schedule (e.g. annually, after major events)
  • ☐ Yes, but ad hoc
  • ☐ No / rarely
• Are sectoral and livelihood elements (agriculture, fisheries, WASH, health, education, markets, etc.) updated in the risk atlas?
  • ☐ Yes ☐ Partially ☐ No
• Is there an impact repository (database of historical impacts, Loss & Damage) linked to the atlas?
  • ☐ Yes ☐ No

6. Women’s livelihoods and gender in CRVA and risk atlas

6.1 Mapping women’s livelihood elements

• Are women’s livelihood elements clearly plotted on maps?
  For example:
  • ☐ Women-headed farmlands and smallholder plots
  • ☐ Women-managed fisheries ponds/farms
  • ☐ Women-led business points / shops / market stalls
  • ☐ Women’s value-chain service points (input supply, processing, transport, retail, etc.)
• Is the risk and vulnerability of these women’s livelihood elements described and analyzed in CRVA reports or atlases?
  • ☐ Yes, explicitly
  • ☐ Yes, partially
  • ☐ No

6.2 Women Livelihood Risk Atlas

• Has a Women’s Livelihood Risk Atlas or similar product been developed?
  • ☐ Yes (national)
  • ☐ Yes (sub-national)
  • ☐ In progress
  • ☐ No

If yes, briefly describe:

• Scope (hazards, sectors, geographic coverage)
• How it is used for emergency preparedness and anticipatory action.

6.3 Participatory consultation with vulnerable groups

• Were vulnerable groups actively involved in risk assessment and validation?
  • ☐ Women-headed households
  • ☐ Pregnant and lactating women
  • ☐ Persons with disabilities
  • ☐ Elderly persons
  • ☐ Landless or extremely poor households
  • ☐ Ethnic/linguistic minorities
  • ☐ Youth and adolescent girls
• What methods were used?
  • ☐ Community PRA
  • ☐ FGDs (targeted women-only groups, disability groups, etc.)
  • ☐ KIIs with representatives of vulnerable groups
  • ☐ Household interviews / surveys
  • ☐ Participatory mapping using GIS or printed maps
• Were social, economic, financial and other vulnerabilities (e.g. access to credit, land tenure, social norms, GBV risk) documented?
  • ☐ Yes, systematically
  • ☐ Yes, partially
  • ☐ No

7. Use of community-based PRA, FGD, KII in risk mapping

7.1 Integration with GIS

• Are the outcomes of Community PRA, FGDs and KIIs:
  • ☐ Translated into GIS layers (e.g. digitized community risk maps)
  • ☐ Used only in narrative form, without mapping
  • ☐ Not systematically used
• Are community-identified risks and hotspots visually validated with GIS or printed maps during feedback sessions?
  • ☐ Yes ☐ Partially ☐ No

Availability of Geospatial platform based multi-hazard risk atlas  and CRVA administrative areas (Province, District, Municipality, District Town, Municipal town, Village) for facilitating the emergency preparedness planning 

1. Availability of geospatial platform and dataset accessible to local stakeholders for developing local level Preparedness  plans
2. Logn rage weather forecast /outlook being utilized for informing the early preparedness planning and sectoral  alerting  ?
3. Engaged institution linked  with Regional Early warning system in preparedness planning

• How stakeholder develop humanitarian program cyclone ? Consideration of the following factors  for preparation of preparedness plan
• CRVA informed tools based prepositioning emergency  humanitarian  assistance at local level
• Gendered classified People in Need (PIN)
• Contingencies being prepared with CRVA Classified gender needs (Sex, age, and disability disaggregated)  datasets, location,  , risk driver factor consideration.
• Informed tools driven  contingency plans minimizes  the planning gaps, resource gaps and effectively utilized as guided tools for emergency evacuation, emergency  mobilization and saving life and livelihood assets.

• What type of gap identified on gender sensitive preparedness and contingency planning ?
• Availability of geospatial platform and dataset accessible to local stakeholders for developing local level Preparedness  plans
• Logn rage weather forecast /outlook being utilized for informing the early preparedness planning and sectoral  alerting  ?
• Engaged institution linked  with Regional Early warning system in preparedness planning

• Roadmap of preparing early warning : 

1.  Review Steps , procedures  of preparing early warning system ( Cyclone)

• Review Steps, procedures  of preparing Flood early warning system :

What are the level of drought early warning system  complying the Sendai Framework Approach on Warly warning for All ?:

• Mechanism of developing Impact  based Early warning, tracking the disaster path( hotspot) based on the landscape fragility , topography, soil condition, environmental settings ( susceptibility  of landslide, mudslide, rockfall, debris fall, collapsing built-in physical infrastructure ) , epicenter, severe, very-high, high, medium impact areas ?
• Availability of national, province, district level drought early warning system  and linkage with global and regional drought early warning system?
• Having access to  IDEA FAO tracking the SMEs for preparing outlook of food security?
• Any online value chain  information  management system for supporting food security?

Developing Early Action Protocol (EAP) by IFRC, National Redcrooss, other stakeholder :

1. What is the mechanism of developing Impact based weather forecast (IFB) and District wise Anticipatory Early Action Protocol (EAP) to be developed ( with 5W) structures ?
2. What are the Forecast based EAP and Forecast based Financing Mechanism (FBF) and humanitarian resources allocations ( UN HCT-CERF/DERF-Track funds, UN-Cluster HPC- intervention , UH-Agency pool-fund , Government Contingency Funds, Local Government Contingency Funds, I-NGOs CSO, CBO, Charities, and other non-stakeholders funds ) on the basis of SADDD( so that no highly vulnerable women, adolescents, children and other vulnerable are equally being prioritized)
3. District wise Warly warning early action plan ( based on early development EAP) – 5W( who will do ,what, when , where and how)  Humanitarian contingencies ( must address comply SADDD)
4. based on the localized impact category, risk and vulnerabilities, what are the District wise Anticipatory Early Action Protocol (EAP) to be developed ( with 5W) structures ?
5. What are the Forecast based EAP and Forecast based Financing Mechanism and humanitarian resources allocations ( UN HCT-CERF/DERF-Track funds, UN-Cluster HPC- intervention , UH-Agency pool-fund , Government Contingency Funds, Local Government Contingency Funds, I-NGOs CSO, CBO, Charities, and other non-stakeholders funds ) on the basis of SADDD( so that no highly vulnerable women, adolescents, children and other vulnerable are equally being prioritized)?

8)  District wise Warly warning early action plan ( based on early development EAP) – 5W( who will do ,what, when , where and how)  Humanitarian contingencies (using the  sex, age, disability disaggregated data -SADD) ?

A. Questionnaire: Early Warning, Alerting & Observation Systems

You can number this as a new section in your tool (e.g. Section 8: Early Warning & Observation Systems).

8.1 Geospatial server–based alerting and dashboards

1. Geospatial server and web-based platforms

• Is there a geospatial server / web-GIS platform used for real-time or near–real-time early warning and alert visualization?
  • ☐ Yes ☐ Partially ☐ No
• If yes, please specify:
  • Hosting institution (NMHS, DRM authority, university, etc.):
  • Main functions:
    • ☐ Display of hazard forecasts (cyclone, flood, drought, etc.)
    • ☐ Live or near–real-time monitoring (water levels, rainfall, AWS)
    • ☐ Display of exposure layers (settlements, infrastructure, women’s livelihoods, etc.)
    • ☐ Hotspot / incident maps (flooded villages, blocked roads, damaged infrastructure)
    • ☐ Other (specify):

8.2 Common Alerting Protocol (CAP) and standardized warnings

2. Common Alerting Protocol (CAP)

• Does the country / agency use Common Alerting Protocol (CAP) for public warnings?
  • ☐ Yes, institutionalized nationally
  • ☐ Yes, pilot or partial
  • ☐ No
• If yes:
  • Which hazards are disseminated via CAP?
    • ☐ Cyclone / storm
    • ☐ Flood
    • ☐ Heavy rainfall / severe weather
    • ☐ Heatwave / cold wave
    • ☐ Drought-related advisories
    • ☐ Other (specify):
  • Are CAP messages integrated with:
    • ☐ Mobile networks (SMS/Cell broadcast)
    • ☐ Web / app notifications
    • ☐ TV & radio
    • ☐ Siren systems
    • ☐ Social media

8.3 Real-time & multi-hazard alerting systems

3. Real-time / near–real-time weather alerting system

• Is there a real-time weather alerting system operated by the Met / NMHS?
  • ☐ Yes ☐ Partially ☐ No
• Hazard coverage:
  • ☐ Cyclone & associated storm surge
  • ☐ Heavy rainfall / severe local storms
  • ☐ Riverine / flash floods (linked to hydro data)
  • ☐ Drought / seasonal outlooks
  • ☐ Strong winds, lightning, landslides, etc.

4. Multi-hazard alerting

• Is there a multi-hazard alerting system (single platform/channel that covers multiple hazards)?
  • ☐ Yes, fully multi-hazard
  • ☐ Yes, but hazard-specific components
  • ☐ No
• Does it:
  • ☐ Prioritize high-risk hotspots using CRVA layers
  • ☐ Allow targeted alerts (e.g. specific districts, villages, livelihood groups)
  • ☐ Include clear action-oriented messages (what to do, by whom, where, when)

8.4 Hotspot tracking and incident alerting

5. Hotspot monitoring and incident tracking

• Is there a system to track hotspots and incidents in real time (e.g. flooded villages, damaged infrastructure, blocked roads)?
  • ☐ Yes (digital platform / app)
  • ☐ Yes (manual / spreadsheet-based)
  • ☐ No
• Functions (tick all that apply):
  • ☐ Field teams report incidents via mobile/app
  • ☐ Incidents displayed as points/polygons on GIS maps
  • ☐ Integrated with EOC / command center dashboards
  • ☐ Linked to resource deployment / logistics decision making
  • ☐ Used to update CRVA and risk atlas after events

8.5 Incident Command System (ICS) and military involvement

6. Incident Command System (ICS)

• Is an Incident Command System (ICS) or similar structure used for disaster response and early action?
  • ☐ Yes, formally adopted
  • ☐ Yes, partially / informally
  • ☐ No
• Main lead institution:
  • ☐ Disaster management authority
  • ☐ Military / armed forces
  • ☐ Civil defense / fire service
  • ☐ Police / internal security
  • ☐ Other (specify):
• Does ICS:
  • ☐ Receive and act on real-time multi-hazard alerts
  • ☐ Coordinate with NMHS and hydrological agencies
  • ☐ Coordinate with local government and communities
  • ☐ Integrate gender and protection focal points (e.g. WRD, social services)

8.6 Hybrid observation network (automated + community-based)

7. Telemetric community-based flood early warning mechanisms

• Are there community-based flood early warning mechanisms using telemetric or semi-automatic gauges?
  • ☐ Yes, in multiple locations
  • ☐ Yes, in a few pilots
  • ☐ No
• Features:
  • ☐ River/stream gauges monitored by community volunteers
  • ☐ Telemetric transmission (SMS, GSM, radio) to central systems
  • ☐ Local warning dissemination (flags, sirens, mosque/temple/church announcements, megaphones, etc.)
  • ☐ Linkage with formal national flood forecasting system

8. NMHS telemetric hydrological observation system

• Does the national hydrological or water resources authority operate a telemetric hydrological network (river level, discharge, reservoir levels)?
  • ☐ Yes ☐ Partially ☐ No
• If yes:
  • Number of key telemetric stations (approx.):
  • Are data:
    • ☐ Real-time or near–real-time
    • ☐ Integrated into flood forecasting models
    • ☐ Shared with DRM and ICS for alerting & response
    • ☐ Visualized on geospatial dashboards

9. Meteorological observation network

• Automatic Weather Stations (AWS) / telemetric stations
  • Are AWS deployed and operational?
    • ☐ Yes, dense network
    • ☐ Yes, limited / partial
    • ☐ No
  • Are AWS data:
    • ☐ Transmitted telemetrically in near–real-time
    • ☐ Used in forecasting and impact-based alerts
    • ☐ Integrated into GIS/CRVA maps
• Human weather observers
  • Are there manual / human observation stations (synoptic, agro-met, rainfall observers)?
    • ☐ Yes ☐ Partially ☐ No
  • Are these observations:
    • ☐ Digitized / transmitted regularly
    • ☐ Used to validate and calibrate AWS and models
    • ☐ Feeding into early warning messages

10. Integration: hybrid observation and warning chain

☐ Yes ☐ Partially ☐ No

Are all warning and alerting systems (CAP, multi-hazard alerting, hotspot incident tracking, ICS) fed by a hybrid observation network combining:

☐ Telemetric community-based EWS

☐ Telemetric hydrological network

☐ AWS and telemetric meteorological stations

☐ Human observers and community reports

Is there redundancy so that if one system fails (e.g. telecommunication outage), others (community observers, radio, local sirens) can still issue warnings?

10) Development of Point based weather, multi-hazard observation, weather outlook, weather warning, disaster alert, hotspot of the disaster incidence capture, current event situation reporting

The assessment will examine the development and use of point-based weather and multi-hazard services that provide localized forecasts, outlooks, warnings, disaster alerts, and real-time situation reports for geolocated high-value elements. These elements include settlement clusters (remote villages, small townships, municipalities and urban centers), marketplaces, industrial and mining areas, critical government and utility infrastructure, water control and hydrological structures, standing crop lands, value-chain structures, sector-specific service delivery facilities, and other critical assets.

The review will focus on the availability of a geospatial database of high-value elements, the extent to which point-based observation and forecast products are generated and updated, and how disaster incidence hotspots and current event situations are captured and mapped. It will also assess whether these point-based products are systematically used to inform humanitarian decision-making, including prioritization of locations for lifesaving assistance, pre-positioning of relief, anticipatory action, and targeted support to vulnerable settlements and livelihood systems.

10. Point-based weather & multi-hazard services for high-value elements

10.1 Geolocated “high-value elements” database

1. Existence of point-based geospatial database

• Is there a point-based geospatial database (with coordinates) of high-value elements at risk?
  • ☐ Yes, nationally
  • ☐ Yes, in selected provinces/districts
  • ☐ Pilot/partial
  • ☐ No

2. Types of high-value elements mapped (tick all that apply)

• Settlements & population clusters
  • ☐ Clustered rural houses / hamlets
  • ☐ Remote / hard-to-reach villages
  • ☐ Small townships and growth centers
  • ☐ Municipal / urban centers
• Economic & livelihood elements
  • ☐ Markets and trading centers
  • ☐ Industrial zones / factories / warehouses
  • ☐ Mining areas / extraction sites
  • ☐ Standing crop lands (by crop type)
  • ☐ Livestock production areas
  • ☐ Fisheries ponds / aquaculture farms
  • ☐ Value-chain structures (collection centers, storage, processing, transport hubs, retail outlets)
• Critical government & utility infrastructure
  • ☐ Government administrative offices
  • ☐ Health facilities (clinics, hospitals)
  • ☐ Education facilities (schools, colleges)
  • ☐ WASH facilities (water treatment plants, pumping stations, reservoirs, main pipelines)
  • ☐ Power infrastructure (power plants, substations, main transmission lines)
  • ☐ Telecommunication hubs / data centers
• Hydrological & water control infrastructure
  • ☐ Dams, barrages, regulators
  • ☐ Embankments / levees / dykes
  • ☐ Flood gates, sluices, regulators
  • ☐ Irrigation canals, intakes, major drains
  • ☐ Water structures (check dams, retention ponds, tanks, etc.)
• Other critical structures
  • ☐ Disaster shelters and safe buildings
  • ☐ Key logistics hubs (ports, major bridges, bus/rail terminals)
  • ☐ Strategic storage for food/relief items
  • ☐ Other (specify):

3. Attributes recorded for each point

• ☐ Type and function of element
• ☐ Ownership / managing agency
• ☐ Population served / dependent users
• ☐ Historical flood level / hazard impact history
• ☐ Sensitivity to specific hazards (flood, cyclone, drought, landslide, etc.)
• ☐ Criticality classification (e.g. high / medium / low)
• ☐ Linkage to SADDD / gender data (e.g. service used by women-headed households)

10.2 Point-based weather, multi-hazard observation & outlook products

4. Point-based weather observation

• Are point-based weather and hydrological observations (e.g. from AWS, rain gauges, river gauges) linked directly to high-value elements?
  • ☐ Yes, systematically
  • ☐ Yes, partially
  • ☐ No

5. Point-based weather outlook and warnings

• Does the Met / NMHS or DRM authority produce point- or location-based:
  • ☐ Short-term weather forecasts (daily/3-day) for specific settlements and key structures
  • ☐ Multi-day weather outlooks for key livelihood and infrastructure points
  • ☐ Hazard-specific alerts (e.g. river level exceeding danger near a specific town, strong winds near an industrial zone)
• Are these products:
  • ☐ Generated automatically through a GIS/database system
  • ☐ Manually interpreted and issued by forecasters
  • ☐ Not yet produced

6. Multi-hazard point-based warning

• For the mapped high-value elements, are there point-based multi-hazard warnings (not just district-level)?
  • ☐ Yes, multiple hazards (flood, cyclone, heavy rainfall, etc.)
  • ☐ Yes, for some hazards only
  • ☐ No
• Are thresholds defined at element level, e.g.:
  • ☐ Water level at X gauge threatening Y settlement
  • ☐ Rainfall intensity over Z catchment threatening specific villages
  • ☐ Wind speed forecast threatening specific industrial facilities or power lines

10.3 Disaster incidence hotspots & current event situation reporting

7. Hotspot capture of disaster incidents

• Is there a system to capture disaster incidents at point level, such as:
  • Flooded villages / neighborhoods
  • Damaged or non-functional infrastructure (roads, bridges, schools, health centers, power, water points)
  • Disrupted markets, industry, mining sites
  • Losses to standing crops and key livelihoods
• Reporting mechanism:
  • ☐ Mobile or web-based incident reporting app
  • ☐ Phone/SMS-based reporting consolidated into a map
  • ☐ Paper-based forms later digitized into GIS
  • ☐ Ad hoc / not standardized

8. Current situation reporting (situation reports, sitreps)

• During an event (e.g. flood, cyclone), are current situation reports generated that:
  • ☐ Use point-based maps of high-value elements affected
  • ☐ Summarize impacts by element type (settlements, markets, infrastructure, crops, etc.)
  • ☐ Identify access constraints and isolated locations
  • ☐ Feed into national/provincial EOCs and ICS for decisions
• Frequency of updates:
  • ☐ Real-time / daily
  • ☐ Every few days
  • ☐ Only once per major event
  • ☐ Irregular

10.4 Use for humanitarian actions and decision-making

9. Use of point-based products to inform humanitarian action

• Are point-based weather, hazard outlooks, warnings and incident/hotspot maps explicitly used to:
  • ☐ Prioritize locations for evacuation and lifesaving support
  • ☐ Decide where to pre-position food, NFIs, WASH supplies, medical items
  • ☐ Trigger anticipatory actions (cash transfers, livelihood protection, movement of livestock, protection of assets)
  • ☐ Plan access routes and logistics (roads, bridges, alternative routes)
  • ☐ Target support to particularly vulnerable settlements (e.g. remote villages, informal settlements, women-headed household clusters)

10. Formal linkages and SOPs

• Are there formal SOPs or decision trees that connect:
  • Point-based warnings →
  • Hotspot/impact maps →
  • Pre-defined humanitarian actions and resource allocation?
  • ☐ Yes, clearly documented
  • ☐ Yes, but informal / partial
  • ☐ No

11. Gender and inclusion in point-based actions

• Do point-based products include or link to:
  • ☐ Data on women-headed household clusters
  • ☐ Locations with high proportion of vulnerable groups (persons with disabilities, elderly, etc.)
  • ☐ Women’s livelihood elements (markets, processing centers, farmlands, ponds)
• Are these used to prioritize assistance and protection measures for women and vulnerable groups?
  • ☐ Yes ☐ Partially ☐ No

B. What are the key gaps and how has the crises impacted on these key gender gaps

  1) Availability of gender responsive preparedness plan for cyclone ?

11. CRVA-Guided Preparedness Planning, Evacuation & Sectoral Readiness

11.1 CRVA-guided preparedness plan, maps and risk atlas

1. Existence of CRVA-guided preparedness plan

• Is there an emergency preparedness plan explicitly guided by CRVA (multi-hazard & climate risk and vulnerability assessment)?
  • ☐ Yes, nationally
  • ☐ Yes, sub-nationally (province/district)
  • ☐ Yes, at local level (municipality / sub-district / village)
  • ☐ Partially / pilot only
  • ☐ No
• Does the plan:
  • ☐ Show all key elements at risk on maps (settlements, infrastructure, livelihoods, services)
  • ☐ Use risk ranking (e.g. very high / high / medium / low) by area or element
  • ☐ Include a Risk and Vulnerability Atlas (printed or digital)
  • ☐ Show locations of vulnerable women-headed households based on SADDD
  • ☐ Integrate gender and social vulnerability narrative analysis

2. Risk and Vulnerability Atlas

• Is there a Risk and Vulnerability Atlas that:
  • ☐ Is multi-hazard (cyclone, flood, drought, others)
  • ☐ Shows exposure and vulnerability of key sectors and elements
  • ☐ Highlights clusters of vulnerable women, children, elderly, persons with disabilities
  • ☐ Is updated regularly and used in planning exercises

11.2 Evacuation planning, routes, shelters and coordination

3. Evacuation route mapping and guidance

• Do GIS and geospatial map–based guidelines exist showing:
  • ☐ Official evacuation routes from villages and settlements
  • ☐ Alternative routes for floods, road blockage, bridge damage
  • ☐ Mode of transport for evacuation (boat, truck, bus, non-motorized, etc.)
  • ☐ Key constraints (narrow roads, weak bridges, waterlogged areas, landslide-prone sections)
• Are these routes:
  • ☐ Printed and shared with communities
  • ☐ Available in digital/webGIS form
  • ☐ Displayed at community notice boards/shelters

4. Shelter mapping and classification

• Do GIS maps show all types of shelters and safe structures?
  • ☐ Core family shelters
  • ☐ Public disaster shelters / cyclone shelters
  • ☐ Multi-purpose community centers used as shelters
  • ☐ Schools and public buildings designated as temporary shelters
  • ☐ Government installations that can be used for sheltering in emergencies
• Are shelters mapped with attributes like:
  • ☐ Capacity (number of people)
  • ☐ Structural safety (cyclone/flood resistance)
  • ☐ Accessibility for women, children, elderly, persons with disabilities
  • ☐ WASH facilities, lighting, privacy arrangements

5. Forecast-based evacuation triggers and stages

• Are there national forecast-based guidelines on:
  • ☐ When to begin evacuation preparation based on cyclone stage / warning level
  • ☐ When women, children, elderly and persons with disabilities should move to shelters
  • ☐ Who orders evacuation (which authority, at which warning level)
• Are the guidelines:
  • ☐ Clearly linked to official forecast stages (e.g., watch, warning, danger)
  • ☐ Communicated regularly to local government and communities
  • ☐ Used in simulation/drills

6. Stakeholder coordination map and guideline

• Is there a stakeholder coordination map for preparedness and evacuation that shows:
  • ☐ Lead and supporting institutions at each level (national, provincial, district, municipality, village)
  • ☐ Roles of local government, CPP/volunteers, military, NGOs/CSOs, women’s groups, etc.
  • ☐ Lines of communication and reporting (who informs whom, and how)
• Are there written guidelines/SOPs describing:
  • ☐ Roles and responsibilities in evacuation and shelter management
  • ☐ Coordination with the Emergency Operations Center (EOC) and Incident Command System (ICS)
  • ☐ Coordination with gender and protection actors

11.3 Gender-sensitive evacuation, sheltering and pre-positioning

7. Guidelines for gender-friendly evacuation and sheltering

• Are there formal guidelines that address:
  • ☐ Gender-sensitive evacuation (ensuring safe, dignified movement for women and children)
  • ☐ Special assistance for pregnant and lactating women, persons with disabilities, elderly
  • ☐ Protection from GBV and exploitation during evacuation and at shelters
• Are these guidelines:
  • ☐ Known to local authorities, CPP volunteers and security forces
  • ☐ Explained to communities
  • ☐ Practiced through drills

8. Gender sensitivity and safety in shelter guidelines

• Do disaster shelter guidelines include:
  • ☐ Separate spaces or partitions for women and families
  • ☐ Secure, gender-sensitive WASH facilities (separate toilets, bathing spaces, lighting)
  • ☐ Safe sleeping arrangements for women and children
  • ☐ Codes of conduct and complaint mechanisms for GBV and harassment
  • ☐ Inclusion of women in shelter management committees

9. Shelter status and pre-positioning information

• Is there a system (map or database) showing shelter status, including:
  • ☐ Availability and pre-positioning of relief items (NFIs, hygiene kits, dignity kits)
  • ☐ Food stocks and cooking arrangements
  • ☐ Basic medical and first aid supplies
  • ☐ Water and sanitation facilities
  • ☐ Power and lighting
• Is this information:
  • ☐ Updated before hazard season
  • ☐ Accessible to decision-makers at EOC/ICS
  • ☐ Used to identify gaps and prioritize logistics

11.4 Sectoral operational preparedness plans & forecasts

10. Agriculture sector operational preparedness

• Are there sector-specific operational preparedness plans for agriculture that include:
  • ☐ Forecast-based estimates of possible loss and damage to standing crops
  • ☐ Early action advice so women smallholder farmers can protect crops, fisheries, livestock
  • ☐ Recommended measures (e.g. early harvesting, drainage management, fodder storage, moving livestock, protecting seed and inputs)
• Are these operational forecasts and advisories:
  • ☐ Location-specific (district/village level)
  • ☐ Time-specific (linked to forecast dates and lead times)
  • ☐ Disseminated through channels accessible to women (radio, SMS, local meetings, women’s groups, extension workers, etc.)

11. Preparedness guidelines for other key sectors

For each sector below, indicate whether there are written preparedness guidelines and operational forecasts/risk outlooks informed by CRVA:

• Nutrition
  • ☐ Guidelines ☐ Operational outlooks ☐ Neither
• Transport, logistics and communication
  • ☐ Guidelines ☐ Operational outlooks ☐ Neither
• Food security & livelihoods
  • ☐ Guidelines ☐ Operational outlooks ☐ Neither
• Shelter and Camp Management
  • ☐ Guidelines ☐ Operational outlooks ☐ Neither
• Water, Sanitation and Hygiene (WASH)
  • ☐ Guidelines ☐ Operational outlooks ☐ Neither
• Health
  • ☐ Guidelines ☐ Operational outlooks ☐ Neither
• Protection (including GBV, child protection)
  • ☐ Guidelines ☐ Risk alerts/outlooks ☐ Neither
• Do these sectoral plans:
  • ☐ Explicitly reference CRVA/risk atlas
  • ☐ Include gender and age-specific responsibilities and actions
  • ☐ Include triggers for anticipatory action and early response

12. Functional map of sectoral stakeholders

• Is there a functional stakeholder map showing:
  • ☐ Lead sector ministries and agencies
  • ☐ Local government roles
  • ☐ NGOs/CSOs and UN/INGO partners
  • ☐ Private sector (transport, telecoms, logistics, agri-business)
  • ☐ Community structures, women’s groups, CBOs

11.5 Gender-responsive operational forecasts

13. Operational forecasts for women-headed smallholder farmers

• Are operational weather and agrometeorological forecasts specifically designed for smallholder farmers, including women-headed households?
  • ☐ Yes ☐ Partially ☐ No
• Content includes:
  • ☐ Short-term weather (rainfall, temperature, wind)
  • ☐ Water stress/drought indicators
  • ☐ Crop-stage specific advice (sowing, fertilizer, pest control, harvesting)
  • ☐ Advice for fisheries and livestock
• Are these forecasts:
  • ☐ Tailored to local languages and literacy levels
  • ☐ Communicated through farmer field schools, women’s groups, extension, radio/SMS, apps
  • ☐ Monitored for reach and usefulness among women-headed households

14. Geospatial tools for managing women-headed household risk

• Are operational forecasts integrated into geospatial tools targeted at women-headed households, such as:
  • ☐ Maps highlighting locations of women-headed households in high-risk zones
  • ☐ Dashboards showing forecasted hazard impacts on these locations
  • ☐ Target lists for forecast-based cash or in-kind assistance
  • ☐ Tools used by local government/NGOs to prioritize outreach and preparedness support

11.6 EOC, CPP and humanitarian operational guidelines

15. Emergency Operations Center (EOC) functional guidelines

• Does the EOC have written functional guidelines/SOPs that:
  • ☐ Integrate CRVA, risk atlas and sectoral risk information
  • ☐ Define how warnings and forecasts trigger actions
  • ☐ Define roles for each cluster/sector (health, WASH, shelter, protection, etc.)
  • ☐ Include gender and protection responsibilities (WRD, social services, GBV focal points)

16. Cyclone Preparedness Program (CPP) guidelines

• Are there operational guidelines for the Cyclone Preparedness Program (CPP) (or equivalent community-based warning and evacuation program) that cover:
  • ☐ Early warning dissemination and last-mile communication
  • ☐ Evacuation support, especially for women, children, elderly, and persons with disabilities
  • ☐ Coordination with local government, EOC, and humanitarian partners
  • ☐ Gender-sensitive volunteer conduct and protection standards

17. Humanitarian action guidelines

• Are there national or sub-national humanitarian guidelines that:
  • ☐ Define minimum standards for shelter, WASH, health, food security, protection, etc.
  • ☐ Incorporate gender, age, disability and protection considerations
  • ☐ Are explicitly linked to CRVA and forecast-based triggers (anticipatory action)
  • Operational guidelines of all stakeholders with 5W.

C. What are the key gaps and how has the crises impacted on these key gender gaps

[1] Showed in below diagram

1. Query formats for International and National Non-Government  Organizations (I-NGOs) :

1. I-NGOs support Protection in  reducing Prevention of sexual exploitation and abuse (PSEA) and Sex and Geder Based Violence (SGBV) during disaster emergency.  
2. Local level stakeholder map
3. Identification of root causes and strategy development for reduction PSEA and SGBV.

• I-NGOs support  the  strengthening  Food Security governance system:  

• I-NGOs support the WASH  services delivery : 

• I-NGOs support  the  Health Services at District and Village  : 

Questions : Prevention Phase

Questions: Recovery Planning Phase

Questions : Response Phase