Predicting Lake Eutrophication Responses to Multiple Scenarios of Lake Restoration: A Three-Dimensional Modeling Approach

Monitoring, simulation and early warning of cyanobacterial harmful algal blooms: An upgraded framework for eutrophic lakes

Cyanobacterial Harmful Algal Bloom (CyanoHAB) is a global aquatic environmental issue, posing considerable eco-environmental challenges in freshwater lakes. Comprehensive monitoring and accurate prediction of CyanoHABs are essential for their scientific management. Nevertheless, traditional satellite-based monitoring and process-oriented prediction methods of CyanoHABs failed to satisfy this demand due to the limited spatiotemporal resolutions of both monitoring data and prediction results. To address this issue, this paper proposes an upgraded framework for comprehensive monitoring and accurate prediction of CyanoHABs. A collaborative CyanoHAB monitoring network was firstly constructed by integrating space, aerial, and ground-based monitoring means. As a result, CyanoHAB conditions were assessed frequently covering the entire lake, its key areas, and core positions. Furthermore, by overcoming technical limitations associated with high-precision simulation of the growth-drift-accumulation process of CyanoHABs, such as the unclear drifting process of CyanoHABs and the mechanism of its coastal accumulation, the multi-scale CyanoHAB prediction was realized interconnecting the entire lake and its nearshore areas. The implemented framework has been applied in Lake Chaohu for over three years. It provided high-frequency and high-spatial-resolution CyanoHAB monitoring, as well as its multi-scale and accurate simulation. The application of this framework in Lake Chaohu had significantly improved the accuracies of CyanoHAB monitoring, simulation, and early warning. This advancement holds significant scientific value and offers potential for CyanoHAB prevention and control in eutrophic lakes.

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

The development of anthropic activities during the 20th century increased the nutrient fluxes in freshwater ecosystems, leading to the eutrophication phenomenon that most often promotes harmful algal blooms (HABs). Recent years have witnessed the regular and massive development of some filamentous algae or cyanobacteria in Lake Geneva. Consequently, important blooms could result in detrimental impacts on economic issues and human health. In this study, we tried to lay the foundation of an HAB forecast model to help scientists and local stakeholders with the present and future management of this peri-alpine lake. Our forecast strategy was based on pairing two machine learning models with a long-term database built over the past 34 years. We created HAB groups via a K-means model. Then, we introduced different lag times in the input of a random forest (RF) model, using a sliding window. Finally, we used a high-frequency dataset to compare the natural mechanisms with numerical interaction using individual conditional expectation plots. We demonstrate that some HAB events can be forecasted over a year scale. The information contained in the concentration data of the cyanobacteria was synthesized in the form of four intensity groups that directly depend on the P. rubescens concentration. The categorical transformation of these data allowed us to obtain a forecast with correlation coefficients that stayed above a threshold of 0.5 until one year for the counting cells and two years for the biovolume data. Moreover, we found that the RF model predicted the best P. rubescens abundance for water temperatures around 14°C. This result is consistent with the biological processes of the toxic cyanobacterium. In this study, we found that the coupling between K-means and RF models could help in forecasting the development of the bloom-forming P. rubescens in Lake Geneva. This methodology could create a numerical decision support tool, which should be a significant advantage for lake managers.

Calibration and quantifying uncertainty of daily water quality forecasts for large lakes with a Bayesian joint probability modelling approach

Correcting the systematic bias and quantifying uncertainty associated with the operational water quality forecasts are imperative works for risk-based environmental decision making. This work proposes a post-processing method for addressing both bias correction and total uncertainty quantification for daily forecasts of water quality parameters derived from dynamical lake models. The post-processing is implemented based on a Bayesian Joint Probability (BJP) modeling approach. The BJP model uses a log-sinh transformation to normalize the raw forecasts and corresponding observations, and uses a bivariate Gaussian distribution to characterize the dependence relationship. The posterior distribution of the transformation parameters is inferenced through Metropolis Monte Carlo Markov chain sampling; it generates unbiased probabilistic forecasts that account for uncertainties from all sources. The BJP is used to post-processing raw daily forecasts of dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations of Lake Chaohu, the fifth largest lake in China with lead times from 0 to 5 days. Results suggest that an average 93.1% forecast bias has been removed by BJP. The root mean square error in probability skill scores range from 5.8% for NH to 68.2% for TP, and the non-parametric bootstrapping test suggests that 67.7% forecasts are significantly improved averaged across all sampling sites, water quality parameters and lead times. The probabilities of the calibrated forecasts are reasonably consistent with the observed relative frequencies, and have appropriate spread and thus correctly quantify forecast uncertainty. The BJP post-processing method used in this study can be a useful operational tool that help to better realize the potential of water quality forecasts derived from dynamical models.

Mapping Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality

Pen aquaculture is the main form of aquaculture in some shallow lakes in eastern China. It is valuable to map the spatiotemporal changes of pen aquaculture in eutrophic lakes to assess its effect on water quality, thereby helping the relevant decision-making agencies to manage the water quality (WQ) of lakes. In this study, an automatic approach for extracting the pen aquaculture area was developed based on Landsat data. The approach integrates five algorithms, including grey transformation, discrete wavelet transform, fast Fourier transform, singular value decomposition and k-nearest neighbor classification. It was successfully applied in the automatic mapping of the pen aquaculture areas in Lake Yangcheng from 1990 to 2016. The overall accuracies were greater than 92%. The result indicted that the practice of pen aquaculture experienced five stages, with the general area increasing in the beginning and decreasing by the end of the last stage. Meanwhile, the changes of nine WQ parameters observed from 2000 to 2016, such as ammonia (NH3-N), pH, total nitrogen (TN), total phosphorus (TP), chlorophyll a, biochemical oxygen demand (BOD), chemiluminescence detection of permanganate index (CODMn), Secchi disk depth (SDD) and dissolved oxygen (DO), were analyzed in the lake sectors of Lake Yangcheng, and then their relationships were explored with the percentage of pen aquaculture area. The result suggested that the percentage of pen aquaculture area exhibits significantly positive correlations with NH3-N, TN, TP, chlorophyll a, BOD and CODMn, but significantly negative correlations with SDD and DO. The experimental results may offer an important implication for managing similar shallow lakes with pen aquaculture expansion and water pollution problems.

Analysis of the Theoretical Performance of the Wind-Driven Pulverizing Aerator in the Conditions of Góreckie Lake—Maximum Wind Speed Method

The eutrophication of surface waters is a natural process; however, anthropogenic activities significantly accelerate degradation processes. Most lakes in Poland and in the world belong to the poor and unsatisfactory water quality class. It is therefore necessary to limit negative anthropogenic impacts and introduce restoration methods, in particular those that are safe for the aquatic ecosystem. One of these is a pulverizing aeration Podsiadłowski method that uses only wind energy. The method allows for the moderate oxygenation of hypolimnion water, which maintains the oxygen conditions in the overlying water zone in the range of 0–1 mg O2·dm-1. The purpose of the work was to develop a new method of determining the efficiency of the aerator pulverization unit in the windy conditions of the lake. The method consists in determining the volumetric flow rates of water in the aerator pulverization unit, based on maximum hourly wind speeds. The pulverization efficiency in the conditions of Góreckie Lake was determined based on 6600 maximum hourly wind speeds in 2018. Based on the determined model, the theoretical performance of the machine was calculated, which in the conditions of Góreckie Lake in 2018 amounted to less than 79,000 m3 per year (nine months of the effective aerator operation).

Development and evaluation of a real-time forecasting framework for daily water quality forecasts for Lake Chaohu to Lead time of six days

The socioeconomic benefits associated with informative water quality forecasts for large lakes are becoming increasingly evident. However, it remains an enormous challenge to produce forecasts of water quality variables that are accurate enough to meet public demand. In this study, we developed and evaluated a new forecast framework for real-time forecasting of daily dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations at lead times from one to six days for Lake Chaohu, the fifth largest freshwater lake in China. The forecast framework is based on a 3-D hydrodynamic ecological model referred to as EcoLake. We used hydrological, meteorological and water quality data from multiple sources to generate initial conditions and forcing functions. Solar radiation and inflows from tributaries which are not readily available were calculated using forecasted cloud cover and rainfall. Forecast skill was evaluated based on 122 forecasts produced on different days in 2017 and for each of the 12 sampling sites. Results indicate that the skill of the forecast framework varies considerably across water quality variables, sampling sites, and lead times. Generally, the forecast framework is more skillful than the persistence forecasts, which use the most recent observations as forecasts. The TN forecasts tend to be the most skillful with a mean RMSE skill score of 28.5% averaged across the six lead times. The DO forecasts tend to have the lowest skill with an average value of 10.9%. Model sensitivity experiments further revealed that errors in the raw air temperature and wind speed forecasts have a noticeable impact on the overall skill of DO and NH forecasts. The forecast framework proposed here could be a useful operational forecasting tool to enhance the effectiveness of the drinking water supply and public health protection based on the water quality management of Lake Chaohu.