Search for: All records

We monitored water quality in Beaverdam Reservoir (Vinton, Virginia, USA, 37.31288, -79.8159) with high-frequency (10-minute and 15-minute) sensors in 2016-2022. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of 2 data sets: BVR_sensor_string_2016_2020.csv and BVR_platform_data_2020_2022.csv. BVR_sensor_string_2016_2022.csv consists of a temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by a dissolved oxygen logger at 5 m or 10 m depending on the time of year. A sonde measuring temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was deployed at ~1.5 m depth. This initial data set spans 2016 to 2020, with no additional data collection beyond the last observation. The second data set is BVR_platform_data_2020_2022.csv, with data collection still ongoing. This data set contains 1) a temperature string with 13 temperature sensors ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; 2) two oxygen sensors, one in the middle of the string and one sensor above the sediments; and 3) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 data set is also included in this 2020-2022 data set, deployed at 1.5 m below the surface. The temperature string with the thermistors, dissolved oxygen sensor, and pressure sensor are permanently fixed to the platform and water level changes around them. In the methods we describe how to add a depth measurement to each observation. 

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 temperature 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 square 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 1.62°C for 16‐day ahead forecasts. The RMSE of forecasted water temperatures at the near‐sediments (8.0 m) was 0.87°C for 7‐day forecasts and 1.20°C 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.