Treffer: GeoFabrics 1.0.0: An open-source Python package for automatic hydrological conditioning of digital elevation models for flood modelling.

Digital Elevation Models (DEMs) are essential for two-dimensional flood modelling. Remote sensing provides ever-increasing coverage and quality of elevation and other data for DEM generation, but we lack standalone tools for integrating this data. DEMs must be hydrologically conditioned if they are to produce accurate flood models. Hydrological conditioning is the process of adjusting a DEM to better represent flow-paths through the removal of spurious-obstructions. We present GeoFabrics , an open-source Python package, that provides a flexible, multistage, and automated framework for assimilating elevation, natural-feature, and infrastructure data into hydrologically conditioned DEMs. Unlike existing tools, it is fully automated requiring only a single instruction-file to produce a hydrological conditioning DEM. We demonstrate GeoFabrics' utility at two sites by showing how it can assimilate a wide-range of data into hydrologically conditioned DEMs. We then highlight the impact that different stages of the hydrological conditioning process can have on a generic flood event. [Display omitted] • GeoFabrics provides a fully automated process for producing hydrologically conditioned digital elevation models. • GeoFabrics can assimilate a wide range of elevation, infrastructure and natural feature data into digital elevation models. • GeoFabrics produced digital elevation models are self-documenting and provide layers to improve data traceability. • We demonstrate GeoFabrics capabilities and versatility across case studies of two flood prone towns. [ABSTRACT FROM AUTHOR]

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