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ABSTRACT Urban surface-flow modelling is highly sensitive to the representativeness, resolution, and conditioning of geospatial inputs, especially in heterogeneous catchments containing engineered barriers and drainage pathways. This study presents an automated, auditable workflow that transforms routinely available elevation, land-use, infrastructure, and design-rainfall datasets into Hydrological Analysis-Ready Data (H-ARD) for high-resolution urban applications. The workflow comprises five stages – Acquisition, Preparation, Enrichment, Processing, and Validation – and produces versioned outputs, audit artefacts, and machine-readable run logs. It was applied to four Norwegian 1 m LiDAR DEM tiles (Bergen, Oslo, Stavanger, and Hamar), each subdivided into urban and rural zones, to quantify how conditioning alters drainage connectivity, stream-network topology, land-use thematic detail, runoff-coefficient fields, and the representativeness of nearest-station versus gridded IDF rainfall assignments. Results show that conditioning materially changes inferred hydrological connectivity in anthropogenically modified areas, refines land-surface parameter fields, and reveals location-dependent disagreement between precipitation assignment approaches. The contribution is methodological: a reproducible workflow and a standardised set of input-impact diagnostics for evaluating conditioned versus raw geospatial products. Hydrological model outputs are not evaluated in this study; any claim of improved discharge, water level, inundation, or flood-risk performance requires separate validation against observations.
Svellingen et al. (Fri,) studied this question.