An agile and parsimonious approach to data management in groundwater science using open-source resources

Abstract

National governments and international organizations such as the European Commission, are promoting the increased use of information and communication technologies to assist scientists in the “mining” of knowledge. A typical workflow for a hydrogeologist consists of investigating and reporting hydrogeological processes in a study area, from data collection to model-based analysis. While hydrogeologists may feel insufficiently skilled to undertake the self-automatizing and digitizing process, the digitalization of the aforementioned workflow can be easily obtained by means of agile and parsimonious methodologies based on free and open-source software, and by using existing standards. This route is demonstrated for the digitalization of a vadose-zone monitoring system, where a large number of raw data related to water infiltration through the vadose zone are collected. The main aspects of the proposed methodology are a structured database (DB) where field data are stored, and a Python script to manage and process the available data. The structured DB was designed to store data recorded by field sensors and to generate inputs to run a transfer-function-based model to simulate percolation to the water table. Field data and model outputs were also exploited to automatically generate summary reports, like plots and table statistics. The proposed methodology can be generalized to other hydrogeological processes and case studies, as it is based on commonly available standards, basic knowledge of data-storage and data-management, and elementary programming skills to connect the different components of its suite.