Treffer: A database application framework toward data-driven vertical connectivity analysis of rivers.
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The description of complex river environments requires interdisciplinary approaches to collect and manage manifold data types and sources. Deriving comprehensive knowledge from complex data sources is challenging and necessitates not only knowledge of environmental science but also statistics and Software engineering. This study introduces a relational database framed in an application called River Analyst for creating and managing river data with open-source standards (Python3 and Django). We conceptualize data models of river environments, which describe sediment characteristics and hydraulics related to hyporheic exchange. River Analyst enabled us to derive novel insights for restoring rivers affected by so-called riverbed clogging, notably, fine sediment infiltration in the hyporheic zone. The database analysis reveals that clogging is not a dominant control process when the fraction of fine sediment exceeds 50%–55%. In conclusion, the new Software holds promise for data-informed advancements in augmenting knowledge to restore ecologically functional hydro-environments. • A database app in Django enables scalable, centralized management of fluvial data. • Database scheme enhances data analysis through linked parametrical data. • Open-source framework can leverage data-driven decisions for river restoration. [ABSTRACT FROM AUTHOR]
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