Light and temperature controls of aquatic plant photosynthesis downstream of a hydropower plant and the effect of plant removal.

Many rivers worldwide are regulated, and the altered hydrology can lead to mass development of aquatic plants. Plant invasions are often seen as a nuisance for human activities leading to costly remedial actions with uncertain implications for aquatic biodiversity and ecosystem functioning. Mechanical harvesting is often used to remove aquatic plants and knowledge of plant growth rate could improve management decisions. Here, we used a simple light-temperature theoretical model to make a priori prediction of aquatic plant photosynthesis. These predictions were assessed through an open-channel diel change in O2 mass balance approach. A Michaelis-Menten type model was fitted to observed gross primary production (GPP) standardised at 10 °C using a temperature dependence from thermodynamic theory of enzyme kinetics. The model explained 87 % of the variability in GPP of a submerged aquatic plant (Juncus bulbosus L.) throughout an annual cycle in the River Otra, Norway. The annual net plant production was about 2.4 (1.0-3.8) times the standing biomass of J. bulbosus. This suggests a high continuous mass loss due to hydraulic stress and natural mechanical breakage of stems, as the biomass of J. bulbosus remained relatively constant throughout the year. J. bulbosus was predicted to be resilient to mechanical harvesting with photosynthetic capacity recovered within two years following 50-85 % plant removal. The predicted recovery was confirmed through a field experiment where 72 % of J. bulbosus biomass was mechanically removed. We emphasise the value of using a theoretical approach, like metabolic theory, over statistical models where a posteriori results are not always easy to interpret. Finally, the ability to predict ecosystem resilience of aquatic photosynthesis in response to varying management scenarios offers a valuable tool for estimating aquatic ecosystem services, such as carbon regulation. This tool can benefit the EU Biodiversity Strategy and UN Sustainable Development Goals.

Water quality assessment and catchment-scale nutrient flux modeling in the Ramganga River Basin in north India: An application of INCA model.

The present study analyzes the water quality characteristics of the Ramganga (a major tributary of the Ganga river) using long-term (1991-2009) monthly data and applies the Integrated Catchment Model of Nitrogen (INCA-N) and Phosphorus (INCA-P) to the catchment. The models were calibrated and validated using discharge (1993-2011), phosphate (1993-2010) and nitrate (2007-2010) concentrations. The model results were assessed based on Pearson's correlation, Nash-Sutcliffe and Percentage bias statistics along with a visual inspection of the outputs. The seasonal variation study shows high nutrient concentrations in the pre-monsoon season compared to the other seasons. High nutrient concentrations in the low flows period pose a serious threat to aquatic life of the river although the concentrations are lowered during high flows because of the dilution effect. The hydrological model is satisfactorily calibrated with R2 and NS values ranging between 0.6-0.8 and 0.4-0.8, respectively. INCA-N and INCA-P successfully capture the seasonal trend of nutrient concentrations with R2>0.5 and PBIAS within ±17% for the monthly averages. Although, high concentrations are detected in the low flows period, around 50% of the nutrient load is transported by the monsoonal high flows. The downstream catchments are characterized by high nutrient transport through high flows where additional nutrient supply from industries and agricultural practices also prevail. The seasonal nitrate (R2: 0.88-0.94) and phosphate (R2: 0.62-0.95) loads in the catchment are calculated using model results and ratio estimator load calculation technique. On average, around 548tonnes of phosphorus (as phosphate) and 77,051tonnes of nitrogen (as nitrate) are estimated to be exported annually from the Ramganga River to the Ganga. Overall, the model has been able to successfully reproduce the catchment dynamics in terms of seasonal variation and broad-scale spatial variability of nutrient fluxes in the Ramganga catchment.