Data Management Project for Collaborative Groundwater Research

This project developed a comprehensive data management system designed to support collaborative groundwater research across institutions by establishing a centralized, structured database for hydrologic time series data. Built on the Observations Data Model (ODM), the system stores time series data and metadata in a relational SQLite database. Key project components included database construction, automation of data formatting and importation, development of analytical and visualization tools, and integration with ArcGIS for geospatial representation. The data import workflow standardizes and validates diverse .csv datasets by aligning them with ODM formatting. A Python-based module was created to facilitate data retrieval, analysis, visualization, and export, while an interactive map feature enables users to explore site-specific data availability. Additionally, a custom ArcGIS script was implemented to generate maps that incorporate stream networks, site locations, and watershed boundaries using DEMs from USGS sources. The system was tested using real-world datasets from groundwater wells and surface water gages across Utah, demonstrating its flexibility in handling diverse formats and parameters. The relational structure enabled efficient querying and visualization, and the developed tools promoted accessibility and alignment with FAIR principles.

本项目开发了一套综合数据管理系统，旨在通过构建水文时间序列数据的中心化结构化数据库，支持跨机构协同开展地下水研究。该系统基于观测数据模型（Observations Data Model, ODM）构建，将时间序列数据与元数据存储于关系型SQLite数据库中。项目核心组件涵盖数据库搭建、数据格式自动化处理与导入、分析与可视化工具开发，以及与ArcGIS的集成以实现地理空间可视化展示。数据导入工作流可对多样化的.csv格式数据集进行标准化处理与有效性验证，使其匹配ODM格式规范。开发了基于Python的功能模块，可便捷实现数据检索、分析、可视化与导出；同时配备交互式地图功能，支持用户查询特定监测站点的数据可用性。此外，项目还实现了自定义ArcGIS脚本，可利用美国地质调查局（United States Geological Survey, USGS）提供的数字高程模型（Digital Elevation Model, DEM）生成包含河网、监测站点位置与流域边界的专题地图。本系统使用犹他州境内地下水井与地表水测站的真实数据集进行测试，结果证明其可灵活适配多样化的数据格式与参数类型。该系统的关系型结构支持高效的数据查询与可视化操作，所开发的工具集提升了数据可及性，且符合FAIR原则（可发现、可访问、可互操作、可重用）。