Methodology
Expected damage follows Eq. 3 of Assab (2025), Journal of Climate Finance 11:100066: probability-weighted damage across return periods, depth-damage functions from Huizinga et al. (2017). The financial translation applies the paper's regression on 951 project-finance loans matched to Moody's data: +99 bps probability of default per one standard deviation of expected damage; enforced flood protection standards −4%. Full papers: 10.1016/j.jclimf.2025.100066 · 10.3390/jrfm17040133.
Data provenance & licences
- WRI Aqueduct Floods v2 hazard maps — CC BY 4.0
- WRI Global Power Plant Database v1.3 — CC BY 4.0
- OurAirports open data — public domain
- NGA World Port Index (Pub 150) — public domain
- JRC global depth-damage functions (Huizinga et al. 2017) — published research
Architecture & auditability
The platform is a static dataset plus client-side computation: no black box between data and screen. Every number displayed can be traced to a row of the published results database and recomputed from the cited equations. Hazard data refreshes are versioned; analyses record the data version they were computed on.
Known limitations (stated, not hidden)
- Hazard is undefended: local flood defences are not netted out of depths.
- Default-probability baselines are region × sector panel averages, not loan-specific assessments.
- Assets are represented as single points at ~1 km resolution.
- FloodGate is a screening tool — not an engineering survey, credit rating, or investment advice.
Security & privacy
The free product stores registration locally and requires acceptance of the usage policy. Premium portfolio data is private to the customer's profile by default and never enters the public database without explicit consent.
Questions
Written answers within two business days: a.assab@sms.ed.ac.uk.