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.

复杂河流环境的描述需要跨学科的方法来收集和管理多种数据类型和来源。从复杂的数据源中获取全面的知识具有挑战性，不仅需要环境科学知识，还需要统计学和软件工程知识。本研究介绍了一个名为 River Analyst 的应用程序中构建的关系数据库，用于使用开源标准（Python3 和 Django）创建和管理河流数据。我们概念化了河流环境的数据模型，它描述了与潜流交换相关的沉积物特征和水力学。River Analyst 使我们能够获得新颖的见解，以恢复受所谓河床堵塞影响的河流，特别是潜流区的细沉积物渗透。数据库分析表明，当细泥沙含量超过 50%~55% 时，堵塞不再是主要控制过程。总之，新软件有望在增强知识以恢复具有生态功能的水文环境方面实现数据知情的进步。