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(2) Where the paper and the authors' results disagree, the implementation follows the results and the authors' scripts: TOPSIS uses vector normalisation (scripts) rather than the sum of Eq. 4; sensitivity weights are children 0.3 and elderly singles 0.7 (Eq. 2 and the results), while the text of Sect. 3.1.1 gives 0.7 and 0.3; the entropy factor is applied in all three TOPSIS steps. (3) Hazard rings exclude every building footprint, not only the building's own, as the authors' stored intermediate fractions require; the paper mentions this only for semi-detached houses. (4) Buffers are drawn in the coordinate system of the authors' buildings (Web Mercator), as in their data; fractions are ratios, so area units cancel. (5) The number of iterations of the class rule (three) is inferred from the paper's four non-zero risk classes. (6) For comparison only, the FAIR2Adapt urban_pfr toolbox (an existing open implementation) is also run as is; I co-wrote parts of it. It is not used to compute the reproduced results. https://w3id.org/sciencelive/np/RAwD-DCUir9JDcqk8lQkRnT91aRGfRZ_p2N7j_uQ9mBw0/vogelbacher2026-pfr-independent-reproduction https://w3id.org/sciencelive/o/terms/hasDiscipline http://www.wikidata.org/entity/Q42250 https://w3id.org/sciencelive/np/RAwD-DCUir9JDcqk8lQkRnT91aRGfRZ_p2N7j_uQ9mBw0/vogelbacher2026-pfr-independent-reproduction https://w3id.org/sciencelive/o/terms/hasMethodologyDescription An independent implementation in Python (geopandas, shapely, scipy), written from the paper's Sect. 3 and the authors' exported scripts on Zenodo (CC BY 4.0), without ArcGIS and without other existing implementations. Inputs are the authors' layers from their ArcGIS layer package. Social vulnerability: TOPSIS for sensitivity (children, elderly singles), coping capacity (welfare recipients, school leavers without diploma) and their combination, each multiplied by the Shannon-entropy index, then the flood-sensitivity transformation (SV + mean/4)^2. Exposure: residents per building and per ground floor as given in the authors' input layer. Hazard to well-being: for each depth 30-100 cm, the flooded fraction of a 2 m ring around each building, passed through a log-normal CDF (sigma 0.25, x4) and summed. Hazard to mobility: the maximum of the flooded fraction of a 5 m ring and of the street area within the 0-15 m and 15-30 m rings at 30 cm, through the same CDF. Risk: social vulnerability x exposure x hazard (Eqs. 14-15). Risk classes: the authors' iterated-mean rule. Every index is compared building by building with the authors' ArcGIS output layer, and the classes with the classes that rule gives on the authors' values. The run is a Snakemake pipeline from download to figures. https://w3id.org/sciencelive/np/RAwD-DCUir9JDcqk8lQkRnT91aRGfRZ_p2N7j_uQ9mBw0/vogelbacher2026-pfr-independent-reproduction https://w3id.org/sciencelive/o/terms/hasScopeDescription Whether the building-level pluvial flood risk of the paper's framework, and the pattern that risk is highest for buildings close to flooded areas and streets where exposure and hazard coincide, is reproduced from the authors' published inputs. In scope: the authors' worked example as deposited on Zenodo (version 2, the version the paper cites): synthetic building-level data for a Hamburg city quarter (37 buildings), the 100-year design rainfall scenario, flood depths 30-100 cm; every intermediate index (sensitivity, coping capacity, social vulnerability, exposure, both hazard indices), both risk indices and the five risk classes shown in the paper's maps. 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