DyLEM-1D: a 1D physical and biochemical model for planktonic succession, nutrients and dissolved oxygen cycling

Unravelling nutrient fate and CO2 concentrations in the reservoirs of the Seine Basin using a modelling approach

Reservoirs are active reactors for the biogeochemical cycling of carbon (C) and nutrients (nitrogen: N, phosphorus: P, and silica: Si), however, our in-depth understanding of C and nutrient cycling in reservoirs is still limited by the fact that it involves a variety of closely linked and coupled biogeochemical and hydrological processes. In this study, the updated process-based Barman model was applied to three reservoirs of the Seine Basin during 2019-2020, considering the variations of carbon dioxide (CO₂) concentrations and key water quality variables. The model simulations captured well the observed seasonal variations of water quality variables, although discrepancies remained for some variables. According to the model, we found that: (1) the three reservoirs are autotrophic ecosystems and showed high removal efficiency of dissolved inorganic carbon and nutrients during 2019-2020; (2) phytoplankton assimilation, benthic denitrification, precipitation and dissolution of calcium carbonate, and gas exchange at the water-air interface are the dominant processes for water quality variations in reservoirs; (3) based on scenarios results, trophic state and mean water depth of reservoir would impact the biogeochemical processes and the retention efficiency of nitrate and dissolved silicate. Finally, we expect that the successful application of Barman model in the reservoirs of the Seine Basin could provide a useful tool for simulating reservoir water quality changes and thus evaluating the impacts of reservoirs on downstream water quality.

Spatiotemporal Variability in Phytoplankton Bloom Phenology in Eastern Canadian Lakes Related to Physiographic, Morphologic, and Climatic Drivers

Phytoplankton bloom monitoring in freshwaters is a challenging task, particularly when biomass is dominated by buoyant cyanobacterial communities that present complex spatiotemporal patterns. Increases in bloom frequency or intensity and their earlier onset in spring were shown to be linked to multiple anthropogenic disturbances, including climate change. The aim of the present study was to describe the phenology of phytoplankton blooms and its potential link with morphological, physiographic, anthropogenic, and climatic characteristics of the lakes and their watershed. The spatiotemporal dynamics of near-surface blooms were studied on 580 lakes in southern Quebec (Eastern Canada) over a 17-year period by analyzing chlorophyll-a concentrations gathered from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images. Results show a significant increase by 23% in bloom frequency across all studied lakes between 2000 and 2016. The first blooms of the year appeared increasingly early over this period but only by 3 days (median date changing from 6 June to 3 June). Results also indicate that high biomass values are often reached, but the problem is seldom extended to the entire lake surface. The canonical correlation analysis between phenological variables and environmental variables shows that higher frequency and intensity of phytoplankton blooms and earlier onset date occurred for smaller watersheds and higher degree-days, lake surface area, and proportion of urban zones. This study provides a regional picture of lake trophic state over a wide variety of lacustrine environments in Quebec, a detailed phenology allowing to go beyond local biomass assessments, and the first steps on the development of an approach exploiting regional trends for local pattern assessments.

Modelling eutrophication in lake ecosystems: A review

Eutrophication is one of the main causes of the degradation of lake ecosystems. Its intensification during the last decades has led the stakeholders to seek for water management and restoration solutions, including those based on modelling approaches. This paper presents a review of lake eutrophication modelling, on the basis of a scientific appraisal performed by researchers for the French ministries of Environment and Agriculture. After a brief introduction presenting the scientific context, a bibliography analysis is presented. Then the main results obtained with process-based models are summarized. A synthesis of the scientist recommendations in order to improve the lake eutrophication modelling is finally given before the conclusion.

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.