Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real-time iterative water temperature forecasting system (FLARE – Forecasting Lake And Reservoir Ecosystems). FLARE is composed of: water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble-based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475-day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean squared error (RMSE) of daily forecasted water temperatures was 1.13 C at the reservoir’s near-surface (1.0 m) for 7-day ahead forecasts and 1.62C for 16-day ahead forecasts. The RMSE of forecasted water temperatures at the near-sediments (8.0 m) was 0.87C for 7-day forecasts and 1.20C for 16-day forecasts. FLARE successfully predicted the onset of fall turnover 4-14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near-sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open-source system for lake and reservoir water quality forecasting to improve real-time management.Key PointsWe created a real-time iterative lake water temperature forecasting system that uses sensors, data assimilation, and hydrodynamic modelingOur water quality forecasting system quantifies uncertainty in each daily forecast and is open-source16-day future forecasted temperatures were within 1.4°C of observations over 16 months in a reservoir case study

Evaluating the performance of a simple phenomenological model for online forecasting of ammonium concentrations at WWTP inlets

A simple model for online forecasting of ammonium (NH4+) concentrations in sewer systems is proposed. The forecast model utilizes a simple representation of daily NH4+ profiles and the dilution approach combined with information from online NH4+ and flow sensors. The method utilizes an ensemble approach based on past observations to create model prediction bounds. The forecast model was tested against observations collected at the inlet of two wastewater treatment plants (WWTPs) over an 11-month period. NH4+ data were collected with ion-selective sensors. The model performance evaluation focused on applications in relation to online control strategies. The results of the monitoring campaigns highlighted a high variability in daily NH4+ profiles, stressing the importance of an uncertainty-based modelling approach. The maintenance of the NH4+ sensors resulted in important variations of the sensor signal, affecting the evaluation of the model structure and its performance. The forecast model succeeded in providing outputs that potentially can be used for integrated control of wastewater systems. This study provides insights on full scale application of online water quality forecasting models in sewer systems. It also highlights several research gaps which – if further investigated – can lead to better forecasts and more effective real-time operations of sewer and WWTP systems.