Christine Lew will be moderating a session at an upcoming annual meeting (date TBD) of the California Water and Environmental Modeling Forum (CWEMF) entitled “Tools for Groundwater and Surface Water Analysis, Modeling, and Management of Data”. She will be presenting “A Groundwater Data Management System for SGMA Applications”.
Development and maintenance of a data management system for storing and reporting of information is a requirement in the SGMA regulations for Groundwater Sustainability Plans. Choosing and implementing the right system is important in meeting SGMA requirements in an efficient and timely manner and key aspects of a suitable system include the ability to share data among stakeholders, perform trend evaluation, and assist with annual report creation. EnDAR (Environmental Database for Acquisition and Reporting) is a groundwater data management system developed by Tetra Tech for data collection, storage, analysis, and reporting. The system features a sample planner, mobile field data collection app, a website for data uploads and data access, data processors for import of data from data loggers and geotechnical software, a SQL server database, a notification system, and reporting tools, all with secured data access. The system incorporates a number of features to improve management of data and has a flexible design allowing for easy customization. For data visualization and reporting, the system includes a data dashboard containing interactive maps, charts, and tables that can be shared among stakeholders. With a few clicks of the mouse, the user can drill down to investigate specific data, evaluate trends in the data, and customize what data are displayed for exporting to include in an annual report or presentation. This presentation will provide an overview of the system, factors considered during development, and discussion of its broad applicability to various aspects of groundwater monitoring, including meeting SGMA requirements.
Katherine Heidel made a presentation at the GRA Conference on Groundwater Monitoring: Measurements, Management, and Applications (March 3-4. 2020) on “A Groundwater Data Management System for Acquisition, Storage, and Reporting”.
EnDAR (Environmental Database for Acquisition and Reporting) is a groundwater data management system developed by Tetra Tech for data collection, storage, analysis, and reporting. The system features a sample planner, mobile field data collection app, a website for data uploads and data access, data processors for import of data from data loggers and geotechnical software, a SQL server database, a notification system, and reporting tools, all with secured data access. EnDAR was developed by a team of scientists and engineers with a strong understanding of monitoring programs and associated data management needs. The system incorporates a number of features to streamline and improve management of data at a facility or site. Techniques such as auto-fill are used to avoid data entry errors and improve data quality. Immediate, interactive feedback on data uploads reduces time spent finding and fixing errors. Tracking of who uploaded data and when and maintaining a history of all changes allows for complete traceability of the data. Another key feature of the system is its flexible design; addition of new tables or fields to the database are automatically consumed by the system software so no code changes are required. This allows the system to be easily customized to suit the needs of a particular groundwater monitoring program. For data visualization and reporting, the system includes a data dashboard containing interactive maps, charts, and tables that can be shared among stakeholders. With a few clicks of the mouse, the user can drill down to investigate specific data, evaluate trends in the data, and customize what data are displayed for exporting to include in an annual report or presentation. Customized versions of EnDAR are used for data management at several large electric utility companies and at coordinated integrated monitoring programs in Southern California. This presentation will provide an overview of the system and discuss its broad applicability to various aspects of groundwater monitoring, including meeting SGMA requirements.
Christine Lew presented at the 19th Conference on Artificial Intelligence for Environmental Science as part of the American Meteorological Society Annual Conference held in January 2020. Her presentation was entitled, “A hybrid empirical-Bayesian artificial neural network model of salinity in the San Francisco Bay-Delta estuary”.
With the growing maturity of artificial neural network (ANN) applications in the environmental literature, it has become clear that the “black-box” model relationship between inputs and outputs embodied in ANNs may not adequately represent the physical system being modeled. Thus, a trained and validated ANN model may fit the aggregate response to multiple inputs well, even though the sensitivity to a specific input is not physically meaningful, or in some cases, not physically plausible. The condition of representing inputs and outputs in a manner that is physically plausible, given an a priori understanding of a system, is termed “structural” validity, and is needed for developing robust environmental models. This paper reports the refinement of a published empirical model of salinity in the San Francisco Bay-Delta estuary by integration with a Bayesian ANN model and incorporation of additional inputs. Performance goals established for the resulting hybrid model are based on the quality of fit to observed data (replicative and predictive validation) as well as sensitivity when compared with a priori knowledge of system behavior (structural validation). ANN model parameters were constrained to provide plausible sensitivity to coastal water level, a key input introduced in the hybrid formulation. In addition to representing observed data better than the underlying empirical model while meeting structural validation goals, the hybrid model allows for characterization of prediction uncertainty. This work demonstrates a real-world application of a general approach--integration of a preexisting model with a Bayesian ANN constrained by knowledge of system behavior--that has broad application for environmental modeling. Christine presented this work on behalf of her colleagues John Rath, Paul Hutton, Limin Chen and Sujoy Roy. The work is published in: Rath, J.S., P.H. Hutton, L. Chen, and S.B. Roy. A hybrid empirical-Bayesian artificial neural network model of salinity in the San Francisco Bay-Delta estuary. Environmental Modeling & Software 93 (2017) 193-208.
