Keywords
real-time; salinity; stakeholder; sensors; participation; decision-suppo
Start Date
6-7-2022 9:00 AM
End Date
6-7-2022 9:30 AM
Abstract
Real-time monitoring data quality assurance for participatory real-time salinity management Nigel W.T. Quinn Berkeley National Laboratory Climate and Ecosystem Sciences, Bld 64-209 Berkeley, CA 94720 Improvements in the accuracy and reliability of environmental sensors and cellular telemetry and falling costs have increased their deployment for hydrologic and water quality monitoring. These deployments support the continuous operation of water quality simulation and forecasting models that provide decision support to stakeholders engaged in a program to manage salinity in real-time a major river basin in California. Real-time quality assurance of data assimilated from monitoring stations in the river basin is key for stakeholder confidence and to ensure long-term stakeholder involvement in the program. The workflow associated with real-time salinity forecasting includes: (a) downloading continuous sensor data from dataloggers; (b) screening the downloaded data for missing data values, data spikes, sensor drift and other errors; (c) correcting erroneous time series data and archiving of the quality assured product; and (d) uploading and parsing data at the appropriate time interval for use in water quality forecasting models. Although there are several popular commercial environmental hydrologic data management systems that have been used successfully complete this data quality assurance workflow, the high cost and complexity of these commercial software products require trained, dedicated staff not available to small public and private water agencies, non-profits and private entities involved in salinity compliance monitoring. An easy-to-implement and accessible data screening and management platform using public domain software is described in this paper focused on decision support for small water districts managing irrigated agriculture and seasonally managed wetlands both of which export salt loads to the San Joaquin River. The Python-based scripts that comprise the platform are versatile and can be incorporated in a number of workflows ranging from associated short-term scientific research projects to the long-term, basin-scale, interagency real-time salinity management program.
Real-time monitoring data quality assurance for participatory realtime salinity management
Real-time monitoring data quality assurance for participatory real-time salinity management Nigel W.T. Quinn Berkeley National Laboratory Climate and Ecosystem Sciences, Bld 64-209 Berkeley, CA 94720 Improvements in the accuracy and reliability of environmental sensors and cellular telemetry and falling costs have increased their deployment for hydrologic and water quality monitoring. These deployments support the continuous operation of water quality simulation and forecasting models that provide decision support to stakeholders engaged in a program to manage salinity in real-time a major river basin in California. Real-time quality assurance of data assimilated from monitoring stations in the river basin is key for stakeholder confidence and to ensure long-term stakeholder involvement in the program. The workflow associated with real-time salinity forecasting includes: (a) downloading continuous sensor data from dataloggers; (b) screening the downloaded data for missing data values, data spikes, sensor drift and other errors; (c) correcting erroneous time series data and archiving of the quality assured product; and (d) uploading and parsing data at the appropriate time interval for use in water quality forecasting models. Although there are several popular commercial environmental hydrologic data management systems that have been used successfully complete this data quality assurance workflow, the high cost and complexity of these commercial software products require trained, dedicated staff not available to small public and private water agencies, non-profits and private entities involved in salinity compliance monitoring. An easy-to-implement and accessible data screening and management platform using public domain software is described in this paper focused on decision support for small water districts managing irrigated agriculture and seasonally managed wetlands both of which export salt loads to the San Joaquin River. The Python-based scripts that comprise the platform are versatile and can be incorporated in a number of workflows ranging from associated short-term scientific research projects to the long-term, basin-scale, interagency real-time salinity management program.
Stream and Session
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