Automated QA/QC For Data Management, Curation, And Standardization Of Hydrological, Meteorological, And Biogeochemical Datasets at the Rifle Field Site

Friday, 18 December 2015
Poster Hall (Moscone South)
Boris Faybishenko1, Roelof J Versteeg2, Charuleka Varadharajan1 and Deb Agarwal1, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)Subsurface Insights, Hanover, NH, United States
As part of the U.S. Department of Energy (DOE) “Genomes to Watershed” Science Focus Area field effort at the Rifle site in Colorado, USA, sensor-based hydrological and meteorological datasets and data from laboratory characterization of groundwater samples have been curated and archived in a database (http://ifrcrifle.org). We have developed automated quality assurance (QA) and quality control (QC) methods to detect and identify errors made while recording, manipulating, formatting, transmitting and archiving data, or due to the malfunctioning of sensors. The focus was on developing and implementing basic QA/QC for the DOE Legacy Management installed SOARS network that collects data from the water-level pressure transducers, vadose zone and groundwater thermistors, as well as the meteorological stations. We developed and implemented QA/QC procedures to identify and flag the sources of erroneous data and cleaned up the water-level time series data using outlier filtering methods. Based on the analysis of field water-level data, we provided recommendations on the reinstallation and calibration of pressure transducers installed in monitoring wells. Additionally, in support of the QC of the geochemical dataset, we developed an approach of flagging the samples based on the evaluation of ionic balance of water samples. We also advanced a visualization system to allow users to plot and download raw data and perform QA/QC of time series data masked by the quality flags.