Hourly Prediction of Phytoplankton Biomass and Its Environmental Controls in Lowland Rivers

High-resolution river modeling is valuable to study diurnal scale phytoplankton dynamics and understand biomass response to short-term, rapid changes in its environmental controls. Based on theory contained in the Quality Evaluation and Simulation Tool for River-systems model, a new river model is developed to simulate hourly scale phytoplankton growth and its environmental controls, thus allowing to study diurnal changes thereof. The model is implemented along a 62 km stretch in a lowland river, River Thames (England), using high-frequency water quality measurements to simulate flow, water temperature, dissolved oxygen, nutrients, and phytoplankton concentrations for 2 years (2013-2014). The model satisfactorily simulates diurnal variability and transport of phytoplankton with Nash and Sutcliffe Efficiency (NSE) > 0.7 at all calibration sites. Even without high-frequency data inputs, the model performs satisfactorily with NSE > 0.6. The model therefore can serve as a powerful tool both for predictive purposes and for hindcasting past conditions when hourly resolution water quality monitoring is unavailable. Model sensitivity analysis shows that the model with cool water diatoms as dominant species with an optimum growth temperature of 14 degrees C performs the best for phytoplankton prediction. Phytoplankton blooms are mainly controlled by residence time, light and water temperature. Moreover, phytoplankton blooms develop within an optimum range of flow (21-63 m(3) s(-1)). Thus, lowering river residence time with short-term high flow releases could help prevent major bloom developments. The hourly model improves biomass prediction and represents a step forward in high-resolution phytoplankton modeling and consequently, bloom management in lowland river systems.

Automated summary

High-resolution river modeling has been developed to simulate hourly scale phytoplankton growth and environmental controls in the River Thames. The model successfully captures diurnal variability and transport of phytoplankton, serving as a powerful tool for predictive purposes and past conditions reconstruction. Model sensitivity analysis shows that cool water diatoms as dominant species with an optimum growth temperature of 14 degrees C perform the best for phytoplankton prediction.