TH scores update: TSUNAMI

[matamadio]

Hazard Processing Method

• Data Source: 3 return periods (RP100, RP500, RP2500) - inundation depth rasters
• Unit: m (meters)
• Default RP-Specific Thresholds:
  • RP100: 2.0 m
  • RP500: 1.0 m
  • RP2500: 0.5 m
• Default Area Threshold: 0%

Workflow: Different from Earthquake/Cyclone - uses MAJORITY values

1. Extract all pixel values for each RP raster within each admin unit
2. For each RP, calculate:
   • MAJORITY inundation depth = most common positive value (ignoring zero/nodata)
   • Area percentage = % of pixels above RP-specific threshold > using 0% due to sparse data format
3. Check if unit has ANY inundation data (majority > 0 in any RP)
4. Check if majority value meets RP-specific threshold AND area threshold % is met for each RP
5. Score = count of RPs meeting both thresholds (0-3)

Scoring Logic (different from earthquake/cyclone):

• No inundation data in any RP → Score -1 (not affected)
• Has inundation but 0 RPs meet thresholds → Score 0 (below threshold)
• 1 RP meets thresholds → Score 1
• 2 RPs meet thresholds → Score 2
• 3 RPs meet thresholds → Score 3

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Comparison

New score distribution: ADM2

New score distribution: URBAN

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Original (2019) vs. 2025 Update

Data Overview

• Original Dataset: 38,229 administrative regions (ADM2 level)
• 2025 Dataset: 39,370 administrative regions (ADM2 level)
• High-Quality Matches: 33,207 regions (90%+ matching confidence)
• Unmatched Original: 5,022 regions
• Unmatched 2025: 6,268 regions

Technical Notes

• Hazard ranks: -1 = N/A, 0 = Very Low, 1 = Low, 2 = Medium, 3 = High
• Original data used text codes (VLO, LOW, MED, HIG), converted to numeric for easier comparison (delta)
• Matching threshold: 90% similarity required for inclusion in analysis
• Medium-quality matches (70-90%) available in separate sheet for manual review

Matching Method

Fuzzy string matching using hierarchical location names (Country | Admin1 | Admin2).
Only high-quality matches (≥90% similarity) were used for hazard comparison.

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• Regions Compared: 4,729
• Agreement: 96.0% (4,540 unchanged) ✓ VERY HIGH AGREEMENT
• Net Trend: Slight downgrades (169 downgrades vs 20 upgrades)
• Average Change: -0.81 ranks
• Most Stable: Minimal changes overall

Comment

The slight changes only reflect the boundaries update and the minor differences in approach compared to original.

[matamadio]

The existing tsunami layer is a point-on-coast dataset which only reports on potential wave height. It comes in 3 scenarios.

There is a new prototype layer I've been building for BRI: it combines identification of tsunami-prone coastal lenghts from CDRI/GIRI, buffering of 10 km as max flooding extent, and coastal DTM altitude values to set a threshold to max water depth.

The DTM information allows to correctly identify exposed coastal plains. The drawback is that it is deterministic layer, not probabilistic; yet we could still build 3 different scenarios setting different DTM depths thresholds.