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.
Sujoy Roy made a presentation at the Department of Water Resources DSM2 User Group Meeting on “Evaluating Salinity Trends in the Delta Using Data from 1922-2012.”
The location of the low salinity zone in San Francisco Bay where the bottom salinity is 2 parts per thousand (ppt) (termed as X2 and reported as the distance in kilometers from Golden Gate), has been used as the basis for outflow management in the estuary. There is great interest in understanding how the low salinity zone in general, and the X2 position in particular, has changed over time under different conditions of hydrology, exports, and development. This work presents a compilation and analysis of X2 of data over a nine-decade period. Contact Sujoy Roy for additional information.
2014 Bay Delta Science Conference. Phil Bachand and colleagues moderated a session on the Agriculture and Food Research Initiative (AFRI) Rice Project in the Delta.
A regional solution is needed to mitigate or reverse the problem of island subsidence in the Delta, and to support the sustainability and water supply reliability. The primary goal of this project is to demonstrate rice-based cropping systems as an agricultural solution in the Delta. Rice is expected to result in important environmental benefits: concurrently mitigating greenhouse gas (GHG) emissions, soil loss, and subsidence while also reducing risks to California water supply and protecting water quality. Please see the project description for additional information.
Karen Summers recently presented at the “Groundwater Monitoring and Corrective Action Workshop” for the Utilities Solid Waste Activities Group (USWAG) in Washington, D.C. on July 8, 2014.
The focus of the workshop was on the proposed US EPA requirements for groundwater monitoring at surface impoundments and landfills containing coal combustion residuals. The workshop was attended by over 30 utility representatives and 20 additional participants by Webcast. In June, 2010, the US EPA proposed regulations for managing coal combustion residuals (CCR) as either hazardous waste under RCRA Subtitle C or non-hazardous waste under RCRA Subtitle D. The regulations have not been finalized, but the Subtitle D option is thought to be the more likely option. A key component of the proposed Subtitle D option is the use of groundwater monitoring to protect human health and the environment from potential impacts of releases to surface- or groundwater from CCR disposal sites. At the USWAG workshop, Karen presented a summary of the required statistical testing of the monitoring results and on statistical testing methods that are most appropriate for the anticipated monitoring results at CCR sites where the required measurements of trace-substance concentration data will exhibit a high percentage of non-detect values and varying detection limits. Contact Karen Summers for additional information.
Tetra Tech conducted simulations of potential plant, pipeline, and post-project subsurface releases
US Department of Energy approves $167 million in cost-shared funding for NRG’s W.A. Parish Plant post-combustion CO2 Capture and Sequestration Project. The project includes post-combustion capture of 90 percent of the CO2 (1.6 million tons per year) from a 250-MWe equivalent slip stream from a coal-fired unit using an advanced amine technology, compression and dehydration of the CO2 for transport in an 80-mile pipeline to an existing oil field in southeast Texas for enhanced oil recovery. Tetra Tech conducted simulations of potential plant, pipeline, and post-project subsurface releases for the health and safety section of the Environmental Impact Statement.
Ernest Moniz, newly appointed head of the US Department of Energy, is supportive of CCS and CO2 utilization
Tetra Tech conducted simulations of potential post-project, plant, and pipeline subsurface releases
Wide local support expressed for the FutureGen Alliance’s FutureGen 2.0 project in central Illinois at the public hearing held on the Draft Environmental Impact Statement on May 21, 2013. This project includes repowering an existing coal-fired plant with a 168 MWe oxy-fuel combustion unit, capture of about 90 percent of the CO2 (1.2 million tons per year), compression and dehydration of the CO2 for transport in a 30-mile pipeline, and geologic storage using horizontal wells in the Mt. Simon Formation in Illinois. Tetra Tech conducted simulations of potential plant, pipeline, and post-project subsurface releases for the health and safety section of the Environmental Impact Statement.
Tetra Tech staff presented a poster on “Use of Experiments to Improve Modeling of CO2 Pipeline Rupture Hazards” at the 12th Annual CCUS Conference.
12th Annual CCUS Conference held in Pittsburgh, PA showed that interest in CCUS by US and international government and industry partners is high, in spite of recent economic challenges. Tetra Tech staff presented a poster on “Use of Experiments to Improve Modeling of CO2 Pipeline Rupture Hazards”. The scoping study showed that few experiments have been conducted using CO2, and that most were not under supercritical conditions. None of the experiments were at a large-enough scale to represent the near-field phase changes that can occur from a pipeline rupture.
