Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events

Temporal-Spatial Fluctuations of a Phytoplankton Community and Their Association with Environmental Variables Based on Classification and Regression Tree in a Shallow Temperate Mountain River

The effects of environmental factors on phytoplankton are not simply positive or negative but complex and dependent on the combination of their concentrations in a fluctuating environment. Traditional statistical methods may miss some of the complex interactions between the environment and phytoplankton. In this study, the temporal-spatial fluctuations of phytoplankton diversity and abundance were investigated in a shallow temperate mountain river. The machine learning method classification and regression tree (CART) was used to explore the effects of environmental variables on the phytoplankton community. The results showed that both phytoplankton species diversity and abundance varied fiercely due to environmental fluctuation. Microcystis aeruginosa, Amphiprora sp., Anabaena oscillarioides, and Gymnodinium sp. were the dominant species. The CART analysis indicated that dissolved oxygen, oxidation-reduction potential, total nitrogen (TN), total phosphorus (TP), and water temperature (WT) explained 36.00%, 13.81%, 11.35%, 9.96%, and 8.80%, respectively, of phytoplankton diversity variance. Phytoplankton abundance was mainly affected by TN, WT, and TP, with variance explanations of 39.40%, 15.70%, and 14.09%, respectively. Most environmental factors had a complex influence on phytoplankton diversity and abundance: their effects were positive under some conditions but negative under other combinations. The results and methodology in this study are important in quantitatively understanding and exploring aquatic ecosystems.

Enhancing environmental data imputation: A physically-constrained machine learning framework

In water resources management, new computational capabilities have made it possible to develop integrated models to jointly analyze climatic conditions and water quantity/quality of the entire watershed system. Although the value of this integrated approach has been demonstrated so far, the limited availability of field data may hinder its applicability by causing high uncertainty in the model response. In this context, before collecting additional data, it is recommended first to recognize what improvement in model performance would occur if all available records could be well exploited. This work proposes a novel machine learning framework with physical constraints capable of successfully imputing a high percentage of missing data belonging to several environmental domains (meteorology, water quantity, water quality), yielding satisfactory results. In particular, the minimum NSE computed for meteorologic variables is 0.72. For hydrometric variables, NSE is always >0.97. More than 78 % of the physical-water-quality variables is characterized by NSE > 0.45, and >66 % of the chemical-water quality variables reaches NSE > 0.35. This work's results demonstrate the proposed framework's effectiveness as a data augmentation tool to improve the performance of integrated environmental modeling.

Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

Spatiotemporal dynamics of succession and growth limitation of phytoplankton for nutrients and light in a large shallow lake

Understanding the limiting factors of phytoplankton growth and competition is crucial for the restoration of aquatic ecosystems. However, the role and synergistic effect of co-varying environmental conditions, such as nutrients and light on the succession of phytoplankton community remains unclear. In this study, a hydrodynamic-ecological modeling approach was developed to explore phytoplankton growth and succession under co-varying environmental conditions (nutrients, total suspended solids (TSS) and variable N:P ratios) in a large shallow lake called Lake Chagan, in Northeast China. A phytoplankton bloom model was nested in the ecological modeling approach. In contrast to the traditonal ecological modeling, competition between phytoplankton species in our study was modeled at both the species/functional group and phenotype levels. Six phytoplankton functional groups, namely diatoms, green algae, Anabaena, Microcystis, Aphanizomenon and Oscillatoria and each of them with three limitation types (i.e., light-limitation, nitrogen-limitation and phosphorus-limitation) were included in the bloom model. Our results demonstrated that the average biomass proportion of the three limitation types (light-limitation, nitrogen-limitation and phosphorus-limitation) in the six phytoplankton function groups accounted for approximately 50%, 37% and 23% of the total phytoplankton biomass, respectively. TSS suppressed the growth of diatoms and green algae, but favored the dominance of cyanobacteria in Lake Chagan, especially in the turbid water phase (TSS ≥ 60 mg/L). In addition, it was reported that the potential of either N-fixing or non-N-fixing cyanobacterial blooming along the gradients of N:P ratios could exist under the influence of the co-environmental factors in the lake. The proportion of non-N-fixing cyanobacteria (i.e., Microcystis and Oscillatoria) exceeded the proportion of N-fixing cyanobacteria (i.e., Anabaena and Aphanizomenon) when the N:P ratios exceeded 20. Non-N-fixing cyanobacteria would become dominant at higher TSS concentrations and lower light intensities in the turbid water. N-fixing cyanobacteria favored lower N:P ratios and higher light intensities in the clearwater phase (where TSS ≤ 60 mg/L). To sustain a good ecological status in the lake, our results suggest that nutrient and TSS levels in the lake should be maintained at or below the thresholds (TN ≤ 1.5 mg/L; TP ≤ 0.1 mg/L; N:P ratios between 15 and 20; and TSS ≤ 60 mg/L). These findings can help improve water quality management practices to restore aquatic ecosystems.

Stochastic simulation of phytoplankton biomass using eighteen years of daily data - predictability of phytoplankton growth in a large, shallow lake

During the past decades, on-line monitoring of freshwater lakes has developed rapidly. To use high frequency time-series in lake management, novel models are needed that are simple and provide insight into the complexity of phytoplankton dynamics. Chlorophyll a (Chl), a proxy for phytoplankton biomass and environmental drivers were monitored on-line in large, shallow Lake Balaton during the vegetation periods between 2001 and 2018. Growth and non-growth (G and non-G) states of algae were deduced from daily change in Chl. Random forests (RF) were used to find stochastic response rules of phytoplankton to growth-supporting environmental habitat templates. The stochastic G/non-G state was translated into long-term daily biomass dynamics by a deterministic biomass model to assess uncertainty and to distinguish between inevitable and unpredictable blooms. A biomass peak was qualified as inevitable or unpredictable if the lower 95% confidence limit of simulations exceeded or remained at the baseline Chl level, respectively. Compared to a stochastic null model based on monthly Markovian transition probabilities, RF-based models captured wax and wane of biomass realistically. Timing of peaks could be better simulated than their magnitude, likely because habitat templates were primarily determined by light whereas peak sizes might depend on unmeasured processes, such as phosphorus availability. In general, algal growth was favored by wind-induced sediment resuspension that decreased light availability but simultaneously enhanced the P supply. Seasonal temperature and an integral of departures from the "normal" seasonal temperature over 2 to 3 generations were important drivers of phytoplankton growth, whereas short-term (diel and day to day) changes in water temperature appeared to be irrelevant. Four types of years could be distinguished during the study period with respect to algal growth conditions. The present modeling approach can reasonably be used even in highly variable aquatic environments when 3 to 4 years of daily data are available.

Rapid freshwater discharge on the coastal ocean as a mean of long distance spreading of an unprecedented toxic cyanobacteria bloom

Cyanobacterial toxic blooms are a worldwide problem. The Río de la Plata (RdlP) basin makes up about one fourth of South America areal surface, second only to the Amazonian. Intensive agro-industrial land use and the construction of dams have led to generalized eutrophication of main tributaries and increased the intensity and duration of cyanobacteria blooms. Here we analyse the evolution of an exceptional bloom at the low RdlP basin and Atlantic coast during the summer of 2019. A large array of biological, genetic, meteorological, oceanographic and satellite data is combined to discuss the driving mechanisms. The bloom covered the whole stripe of the RdlP estuary and the Uruguayan Atlantic coasts (around 500 km) for approximately 4 months. It was caused by the Microcystis aeruginosa complex (MAC), which produces hepatotoxins (microcystin). Extreme precipitation in the upstream regions of Uruguay and Negro rivers' basins caused high water flows and discharges. The evolution of meteorological and oceanographic conditions as well as the similarity of organisms' traits in the affected area suggest that the bloom originated in eutrophic reservoirs at the lower RdlP basin, Salto Grande in the Uruguay river, and Negro river reservoirs. High temperatures and weak Eastern winds prompted the rapid dispersion of the bloom over the freshwater plume along the RdlP northern and Atlantic coasts. The long-distance rapid drift allowed active MAC organisms to inoculate freshwater bodies from the Atlantic basin, impacting environments relevant for biodiversity conservation. Climate projections for the RdlP basin suggest an increase in precipitation and river water flux, which, in conjunction with agriculture intensification and dams' construction, might turn this extraordinary event into an ordinary situation.

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.