Development of water quality forecasting system with ensemble stream prediction method in the Geum River Basin, Korea

Data from Automatic water quality monitors and gauging stations on the Bedford Ouse are telemetered to the Cambridge office of the Creat Ouse River Division of the Anglian Water Authority. A mini-computer has been installed to log data from the out-stations, convert data to meaningful units, prepare data summaries, check for alarm conditions and forecast flow and quality up to 80 hours ahead at key locations along the river system. The system provides information on flow and quality conditions in real time and has been used to forecast the movement of pollutants along the river system. A generalised suite of computer programs have been developed for data management and forecasting and a micro-processor controlled water quality monitor is currently under development.

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

10.1101/2020.01.22.915538 ◽

2020 ◽

Cited By ~ 1

Author(s):

R. Quinn Thomas ◽

Renato J. Figueiredo ◽

Vahid Daneshmand ◽

Bethany J. Bookout ◽

Laura K. Puckett ◽

...

Keyword(s):

Water Quality ◽

Data Assimilation ◽

Water Temperature ◽

Open Source ◽

Real Time ◽

Thermal Conditions ◽

Temperature Forecasting ◽

Forecasting System ◽

Water Quality Forecasting ◽

Daily Forecast

AbstractFreshwater 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

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A Stochastic Water Quality Forecasting System for the Yiluo River

Journal of Environmental Informatics ◽

10.3808/jei.200300010 ◽

2003 ◽

Vol 1 (2) ◽

pp. 18-32 ◽

Cited By ~ 11

Author(s):

H.C. Guo

Keyword(s):

Water Quality ◽

Forecasting System ◽

Water Quality Forecasting

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

Water ◽

10.3390/w13192661 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2661

Author(s):

Nigel W. T. Quinn ◽

Michael K. Tansey ◽

James Lu

Keyword(s):

Water Quality ◽

River Basin ◽

Forecasting Model ◽

Assimilative Capacity ◽

Web Based ◽

Salt Load ◽

San Joaquin River ◽

Beneficial Uses ◽

Water Quality Forecasting ◽

Salinity Management

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.

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