Storm Risk Heatmap Calculator

Turn hazard data into storm risk map. Adjust weights, spot hotspots, and log mitigations quickly. Export results to support audits, disclosures, and faster decisions.

Calculator Inputs

Tune hazard weights, vulnerability factors, and preparedness to produce an ESG-ready heatmap signal.
Used in exports and reporting.
Helps interpret proximity and surge sensitivity.
Use to align with ESG materiality narratives.
Scenario label for comparison.
Longer windows smooth short-term variability.
Use observed or modeled averages.
Max sustained or peak gust proxy.
Represents extreme precipitation day.
Set 0 for non-coastal sensitivity.
Closer distances increase hazard via proximity.
Lower elevations increase flood and surge sensitivity.
5 is best drainage; 1 is poor.
Asset condition, building code, and exposure.
Higher values reduce residual risk.
Operational importance and financial materiality.
Used as limited risk-transfer reduction.
Capture mitigations for audits and disclosures.

Hazard Weights (must be meaningful)

Weights are auto-normalized. Use them to reflect local hazard relevance.

Composite Weights

Balances physical vulnerability, operational importance, and readiness.
Clear Result
After submission, results appear above this form for quick review.

Formula Used

This calculator produces an overall risk score from a hazard score and a composite factor. All hazard components are normalized to a 0–100 scale, then combined using your weights.

1) Hazard normalization
FrequencyN = scale(storm_freq, 0..12)
WindN = scale(max_wind, 0..300)
RainN = scale(rain_mm, 0..400)
SurgeN = scale(surge_m, 0..8)
scale(x, a..b) = clamp((x−a)/(b−a),0..1) × 100
2) Hazard modifiers
ProximityBonus = (1 − distance_km/200) × 0.20
ElevationBonus = if elev≤50 then (1−elev/50)×0.25 else −min((elev−50)/500,1)×0.05
DrainageBonus = (1 − (drainage−1)/4) × 0.20
Wind and surge use proximity; surge also uses elevation. Rain uses drainage and low elevation.
3) Hazard score (weighted)
Hazard = Σ(AdjComponent × NormWeight)
NormWeight = Weight / Σ(Weights)
Weights are auto-normalized so totals do not need to equal 100.
4) Composite factor and overall risk
CompositeIndex = Vuln×wV + Crit×wC + (100−Prep)×wP
Multiplier = (0.60 + 0.80×CompositeIndex/100) × (1−PrepReduction) × (1−InsReduction)
OverallRisk = clamp(Hazard × Multiplier, 0..100)
Preparedness reduces up to 30%; insurance reduces up to 20%.

How to Use This Calculator

  1. Enter hazard inputs using observed or modeled values.
  2. Set proximity, elevation, and drainage for flood sensitivity.
  3. Rate vulnerability, preparedness, and criticality from 1 to 5.
  4. Adjust weights to match your ESG materiality assumptions.
  5. Submit to generate a heatmap cell and a 0–100 score.
  6. Export CSV for portfolio rollups, PDF for audit evidence.

Example Data Table

Use these rows to test the calculator and compare risk bands.
SiteRegionFreqWindRainSurgeDistanceElevationVulnPrepCrit
Coastal Hub ACoastal4.21802103.185435
River Depot BRiverine2.41201600.62522344
Inland Plant CInland1.295900160140243

Physical hazard inputs and normalization

The calculator converts four storm drivers into comparable 0–100 signals: frequency (0–12 events/year), wind (0–300 km/h), rainfall (0–400 mm/day), and surge (0–8 m). Values beyond these bounds are clamped, which prevents outliers from distorting portfolio comparisons and keeps the score interpretable for board reporting. Increasing frequency from 3 to 6 events/year raises the signal proportionally.

Location modifiers that reflect flood dynamics

Proximity and elevation adjust hazard intensity where water and surge matter most. A site at 0 km distance can add up to 20% to wind and surge components, while a low-elevation facility (≤50 m) can add up to 25% surge sensitivity. Drainage quality (1–5) can add up to 20% to the rainfall component, reflecting surface runoff limitations. Modifiers are applied before weighting, so they shape the heatmap placement.

Weighting for scenario and materiality alignment

Hazard weights are normalized automatically, so they do not need to total 100. This supports scenario design: coastal sites may emphasize surge, while inland sites may emphasize rainfall. Composite weights balance vulnerability, criticality, and preparedness; the preparedness term is inverted (100−preparedness) so stronger readiness lowers the composite index. Teams can keep weights fixed and rerun seasons for trend lines.

Residual risk controls and governance signals

Preparedness reduces the multiplier by up to 30% and insurance reduces it by up to 20%, acting as a controlled proxy for mitigation and risk transfer. These caps keep the model conservative: even well-insured assets remain exposed when hazard is extreme. The output includes subscores for hazard, composite index, and multiplier to support audit trails and internal controls. Use the notes field to document mitigation timing and verification evidence.

Heatmap interpretation for ESG disclosures

The heatmap cell is derived from likelihood (1–5) based on the hazard score and impact (1–5) based on the composite index. The 5×5 matrix provides a consistent language for prioritization: low and moderate cells support monitoring plans, while high to severe cells justify CAPEX, continuity testing, and disclosure-ready narratives across sites and seasons. Pair the risk band with the 0–100 score to communicate thresholds clearly. Exported tables help justify prioritization to auditors and stakeholders in reviews.

FAQs

1) What does the 0–100 risk score represent?
It is a normalized indicator of storm risk intensity after weighting, modifiers, and residual controls. Use it for comparisons across sites, seasons, and portfolios rather than as a forecast of exact loss.

2) How should I choose hazard weights?
Start with equal weights, then shift emphasis toward the dominant local driver. Coastal assets often raise surge weight; inland assets often raise rainfall. Keep weights consistent for quarterly portfolio comparisons.

3) Why do proximity and elevation change the score?
They act as physical amplifiers for surge and flooding sensitivity. Shorter distance increases exposure, and lower elevation increases inundation potential. This keeps the heatmap responsive to location-specific flood dynamics.

4) Does insurance remove the risk?
No. Insurance is modeled as limited risk transfer, capped at a 20% reduction to keep results conservative. High hazard can still produce high scores even with strong coverage.

5) How is the heatmap cell selected?
Likelihood is derived from the hazard score and impact from the composite index. Both are mapped into 1–5 buckets, then combined in a 5×5 grid to produce the displayed heatmap level.

6) Can I use exports for ESG reporting?
Yes. The CSV supports portfolio rollups, and the PDF captures a timestamped view for assurance. Add mitigation notes and keep weight settings stable for traceable, disclosure-ready evidence.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.