Consolidated S2ID hydro-meteorological disaster records for Brazil (2013–2025) and replication package

This dataset provides a consolidated and validated extraction of Brazil’s official disaster records, together with the complete replication package for the analyses reported in the accompanying article.

Source and coverage. The primary source is the managerial report (“Relatório Gerencial — Danos Informados”) of the Integrated Disaster Information System (S2ID), maintained by Brazil’s National Secretariat for Protection and Civil Defence (Sedec). Yearly exports covering 2013–2025 were retrieved in July 2026 with all disaster types, all federative units and all processing statuses. Each record corresponds to one municipality affected by one event and contains a protocol identifier, a COBRADE hazard code, declared human and material damages, and the processing status of the federal recognition request (registered, recognised, or not recognised).

Processing. Occurrence dates were extracted from the protocol identifier, because the report’s date column records entry into the system rather than the event itself; the two diverged in year for 1,450 records. After de-duplication by protocol, the consolidated base contains 77,398 unique municipal records, of which 23,152 fall within a combined hydro-meteorological scope defined by COBRADE codes: floods, flash floods and inundations (group 1.2), rainfall-induced mass movements (subgroup 1.1.3), and storms (subgroup 1.3.2, plus cyclones 1.3.1.1).

Validation. Record counts were cross-checked against two independently produced sources: the S2ID official historical series of federal recognitions for 2013–2016 (1,832 versus 1,800 hydro-meteorological recognitions; ratio 1.02) and the federal gazette monitoring database maintained by Política Por Inteiro for 2019–2021 (1,801 versus 1,719; ratio 1.05).

Contents.

•             supplementary_analysis_s2id_v3.py — single replication script (Python 3.12): parsing, scope definition, state-level episode construction, descriptive regime classification, intensive- and extensive-margin tests, municipal Kaplan–Meier estimation, recognition models with state-clustered inference, GEE, bootstrap and leave-one-state-out checks, and the full robustness programme.

•             s2id_master_2013_2025.csv &md