Hydrological management strategies for the control of algal blooms in regulated lowland rivers

High‐resolution monitoring of water quality and ecosystem functioning over large spatial scales in expansive lowland river catchments is challenging. Therefore, we need modeling tools to predict these processes at locations where observations are absent. Here, we present a new approach to estimate ecosystem metabolism underpinned by a high‐resolution, process‐based model of in‐stream flows and water quality. The model overcomes the current challenges in metabolism modeling by accounting for oxygen transport under varying flows and oxygen transformations due to biogeochemical processes. We implement the model in a 62‐km‐long stretch of the River Thames, England, using observations spanning 2 yr. Model outputs suggest that the river is primarily autotrophic from mid‐spring to mid‐summer due to high biomass during low‐flow periods, and is heterotrophic during the rest of the year. Ecosystem respiration in upstream reaches is driven mainly by biochemical oxygen demand, autotrophic respiration, and nitrification processes, whereas downstream sites also show a control of benthic oxygen demand in addition to the aforementioned processes. Using empirical modeling, we analyze the sensitivity of our estimated metabolism rates to multiple environmental stressors. Results demonstrate that empirical models could be useful for rapid river health assessments, but need improvements to reproduce peak metabolism rates. The process‐based model, although more complex than existing in situ approaches to metabolism quantification, allows inference when gaps in continuous observations are present. The model offers additional benefits for predicting metabolism rates under future scenarios of environmental change incorporating multiple stressor effects.

/pdf/high-resolution-water-quality-and-ecosystem-metabolism-2ynbjrrr.pdf

High‐resolution water‐quality and ecosystem‐metabolism modeling in lowland rivers

Abstract. Large agricultural streams receive excessive inputs of nitrogen. However, quantifying the role of these streams in nitrogen processing remains
limited because continuous direct measurements of the interacting and highly time-varying nitrogen processing pathways in larger streams and rivers are very complex. Therefore, we employed a monitoring-driven modelling approach with high-frequency in situ data and the river water quality model Water Quality Analysis Simulation Program (WASP) 7.5.2 in the 27.4 km reach of the sixth-order agricultural stream called Lower Bode (central Germany) for a 5-year period (2014–2018). Paired high-frequency sensor data (15 min interval) of discharge, nitrate, dissolved oxygen, and chlorophyll a at upstream and downstream stations were used as model boundaries and for setting model constraints. The WASP model simulated 15 min intervals of discharge, nitrate, and dissolved oxygen with Nash–Sutcliffe efficiency values higher than 0.9 for calibration and validation, enabling the calculation of gross and net dissolved inorganic nitrogen uptake and pathway rates on a daily, seasonal, and multiannual scale. Results showed daily net uptake rate of dissolved inorganic nitrogen ranged from −17.4 to 553.9 mgNm-2d-1. The highest daily net uptake could reach almost 30 % of the total input loading, which occurred at extreme low flow in summer 2018. The growing season (spring and summer) accounted for 91 % of the average net annual uptake of dissolved inorganic nitrogen in the measured period. In spring, both the DIN gross and net uptake were dominated by the phytoplankton uptake pathway. In summer, benthic algae assimilation dominated the gross uptake of dissolved inorganic nitrogen. Conversely, the reach became a net source of dissolved inorganic nitrogen with negative daily net uptake values in autumn and winter, mainly because the release from benthic algae surpassed uptake processes. Over the 5 years, average gross and net uptake rates of dissolved inorganic nitrogen were 124.1 and 56.8 mgNm-2d-1, which accounted for only 2.7 % and 1.2 % of the total loadings in the Lower Bode, respectively. The 5-year average gross DIN uptake decreased from assimilation by benthic algae through assimilation by phytoplankton to denitrification. Our study highlights the value of combining river water quality modelling with high-frequency data to obtain a reliable budget of instream dissolved inorganic nitrogen processing which facilitates our ability to manage nitrogen in aquatic systems. This study provides a methodology that can be applied to any large stream to quantify nitrogen processing pathway dynamics and complete our understanding of nitrogen cycling.


/pdf/how-do-inorganic-nitrogen-processing-pathways-change-29b14q51.pdf

How do inorganic nitrogen processing pathways change quantitatively at daily, seasonal, and multiannual scales in a large agricultural stream?

Successful modelling of river dissolved oxygen dynamics requires knowledge of stream channel environments

A water quality prediction approach for the Downstream and Delta of Dongjiang River Basin under the joint effects of water intakes, pollution sources, and climate change

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°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 m3 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.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028773

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

Whole-stream metabolism characterizes energy and carbon transformations, thus providing an estimate of the food base and CO2 emission sources from streams and rivers. Metabolism models are generally implemented with a steady flow assumption that does not hold true for many systems with sub-daily flow variation, such as river sections downstream of dams. The steady flow assumption has confined metabolism estimation to a limited range of river environments, thus limiting our understanding about the influence of hydrology on biological production in rivers. Therefore, we couple a flow routing model with the two-station stream metabolism model to estimate metabolism under unsteady flow conditions in rivers. The model's applicability is further extended by including advection-dispersion processes to facilitate metabolism estimation in transient storage zones. Metabolism is estimated using two approaches: (a) an accounting approach similar to the conventional two-station method and (b) an inverse approach that estimates metabolism parameters using least squares minimization method. Both approaches are complementary since we use outputs of the accounting approach to constrain the inverse model parameters. The model application is demonstrated using a case study of an 11 km long stretch downstream of a hydropower plant in the River Otra in southern Norway. We present and test different formulations of the model to show that users can make an appropriate selection that best represents hydrology and solute transport mechanism in the river system of interest. The inclusion of unsteady flows and transient storage zones in the model unlocks new possibilities for studying metabolism controls in altered river ecosystems. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2022JG007245

Metabolism Modeling in Rivers With Unsteady Flow Conditions and Transient Storage Zones

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

Metabolism Modeling in Rivers With Unsteady Flow Conditions and Transient Storage Zones