Open source software for visualization and quality control of continuous hydrologic and water quality sensor data. (August 2015)

• Record Type: Journal Article
• Authors: Horsburgh, Jeffery S.; Reeder, Stephanie L.; Jones, Amber Spackman; Meline, Jacob
• Is Part Of: Environmental modelling & software. Volume 70(2015:Aug.)
• Page Start: 32
• Page End: 44
• Publication Date: 2015-08
• Subjects: Data management -- Open source software -- Sensor data -- Python -- Hydrologic observations -- Quality control; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.04.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: It is common for in situ hydrologic and water quality data to be collected at high frequencies and for extended durations. These data streams, which may also be collected across many monitoring sites require infrastructure for data storage and management. The Observations Data Model (ODM), which is part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), was developed as a standard data model in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, an open source software application that allows users to query and export, visualize, and perform quality control post processing on time series of environmental observations data stored in an ODM database using automated Python scripting that records the corrections and adjustments made to data series in the quality control process and ensures data editing steps are traceable and reproducible. Highlights: We developed a workflow for scripting of time series data quality control. ODM Tools automatically scripts manual quality control data edits in Python. ODM Tools preserves provenance of quality control edits. ODM Tools is open source and cross platform compatible. ODM Tools demonstrates options for desktop application development and deployment using Python.