Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

Quantifying the contribution of external loadings and internal hydrodynamic processes to the water quality of Lake Okeechobee

The water quality of a waterbody is determined by internal hydrodynamic processes as well as external loadings. Understanding the interaction between the external loading and internal process of a waterbody is essential for efficient water management and water quality improvement. Studies and efforts have focused on water and nutrient loading from drainage watersheds, but the contribution of the waterbody's internal process to water quality is often ignored and not well documented. This study investigated how the water quality of Lake Okeechobee is controlled by external and internal factors using statistical and numerical modeling approaches. Water quantity and quality observed at the outlets of the Lake Okeechobee drainage basins and 19 monitoring sites located within the lake were statistically analyzed using multilinear regression. A three-dimensional numerical model, namely Environmental Fluid Dynamics Code (EFDC), was calibrated to the observations to mathematically represent the lake's internal hydrodynamic process. The multilinear regression found that the water quality was the most sensitive to air temperature, the total phosphorus (TP) concentration of inflow entering the lake from the Kissimmee River basins, and the amount of outflow discharged from the lake among external factors. However, the regression models and their explanatory power were substantially varied by the monitoring stations. The model parameter sensitivity analysis of the calibrated EFDC model showed that model parameters related to the lake's internal algal processes including algal growth, predation, and basal metabolism rates had greater impacts on algal biomass than other model parameters controlling nutrient-related processes such as nutrient half-saturation and hydrolysis rates. The EFDC input data sensitivity analysis found that wind (speed) is the major driving force for the internal hydrodynamic processes; its impact on algal biomass was greater than those of the external loadings. In addition, the algal biomass was found to have an inverse relationship with wind-induced horizontal currents. The results demonstrate the dynamic contribution of the internal and external drivers to the water quality of Lake Okeechobee, suggesting the need to consider both internal hydrodynamic and external loading processes for efficient water quality improvement of the lake.

Too ill to cure? - An uncertainty-based probabilistic model assessment on one of China's most eutrophic lakes

Eutrophication is a global challenge, which is exemplified by the tremendous efforts but little results in restoring the sixth largest and also one of the most eutrophic freshwater lakes in China, Lake Dianchi. Considering large parametric uncertainties in water quality modeling, the traditionally used deterministic water quality model is expanded to a probabilistic model to explore the Lake Dianchi's potential responses to different levels of pollutant load reductions. The results show that, given the long pollution history and severe pollution state in Lake Dianchi, a minimum pollution load reduction by half (base year 2003) is required to maintain the water quality state as it is now in 40 years. At least a 60% nutrient load reduction is required to generate any likelihood of water quality improvement, however, the system stabilizes quickly after about 10 years, which may explain why tremendous investments have generated little results. 80% of nutrient load reduction for 40 years has 95% probability of meeting the TN target but only a below 50% (45%) probability in meeting the TP target, and even less to meet water quality target for Chla. The feasibility of ever reaching the Chinese drinking water standards for total phosphorous and total nitrogen is questionable.

Development and application of a new sensitivity analysis model for the remote sensing retrieval of heavy metals in water

This work proposes a new sensitivity analysis model, referred to as the D-δ(ε) model, for the remote sensing retrieval of heavy metals in bodies of water. By defining the reflectance ratio function (δ(ε)), we deduce the mathematical relationships between the heavy metal concentration sequences (D_i) that can be effectively used for remote sensing retrievals and the radiometric resolution (ε) of the remote sensing instrument. Then, as a function of wavelength, we obtain the curve of the lower limit of the heavy metal concentrations in water that can be retrieved by remote sensing. To demonstrate the advantages of this model, we take two compounds, copper sulphate (CuSO₄) and cadmium sulphide (CdS), as examples to discuss the remote sensing sensitivity of different wavelengths when retrievals are performed using the Chinese HJ-1A's hyperspectral imager (HSI). The results showed that the lowest detectable concentration of CuSO₄ in the wavelength range of 460.04-496 nm (corresponding to bands 1-17 of the HSI image) can be below 0.15 mg/L, while the concentration of CdS can be lower than 0.001 mg/L in the separate ranges of 460.04-493.59 nm (bands 1-16) and 526.885-594.79 nm (bands 29-51). This model clearly demonstrates the mathematical relationship obeyed by "D-ε". Additionally, this model can not only calculate the retrieval concentration sequences at any observation wavelength but also intuitively provide the curve of the lower concentration limit for heavy metal retrievals. This work provides a theoretical basis for the selection of the most sensitive bands for remote sensing retrieval using hyperspectral images in the future.

Exploring the type and strength of nonlinearity in water quality responses to nutrient loading reduction in shallow eutrophic water bodies: Insights from a large number of numerical simulations

Reducing the load of nutrients is essential to improve water quality while water quality may not respond to the load reduction in a linear way. Despite nonlinear water quality responses being widely mentioned by studies, there is a lack of comprehensive assessment on the extent and type of nonlinear responses considering the seasonal changes. This study aimed to measure the strength of nonlinearity of theoretically possible water quality responses and explore their potential types in shallow eutrophic water bodies. Hereto, we generated 14,710 numerical water body cases that describe the water quality processes using the Environmental Fluid Dynamics Code (EFDC) and applied eight load reduction scenarios on each water body case. Inflows are simplified from Lake Dianchi. The climate conditions consider three cases: Lake Dianchi, Wissahickon Creek, and Famosa Slough. We then developed a nonlinearity strength indicator to quantify the strength and frequency of nonlinear water quality responses. Based on the quantification of nonlinearity, we clustered all the samples of water quality responses using K-Means, an unsupervised Machine Learning algorithm, to find the potential types of nonlinear water quality responses for TN (total nitrogen), TP (total phosphorus), and Chla (chlorophyll a). Results show linear or near-linear response types account for 90%, 69%, and 20% of TN, TP, and Chla samples respectively. TP and Chla could perform more types of nonlinearity. Representative nonlinear water quality responses include disproportional improvement, peak change (disappear, move forwards or afterward), and seasonal deterioration of TN after load reduction. This study would contribute to the current understanding of nonlinear water quality responses to load reduction and provide a basis to study under which conditions the nonlinear responses may emerge.

The Spatiotemporal Characteristics of Water Quality and Main Controlling Factors of Algal Blooms in Tai Lake, China

Taking Tai Lake in China as the research area, a 3D water environment mathematical model was built. Combined with the LHS and Morris uncertainty and sensitivity analysis methods, the uncertainty and sensitivity analysis of total phosphorus (TP), total nitrogen (TN), dissolved oxygen (DO), and chlorophyll a (Chl-a) were carried out. The main conclusions are: (1) The performance assessment of the 3D water environment mathematical model is good (R2 and NSE > 0.8) and is suitable for water quality research in large shallow lakes. (2) The time uncertainty study proves that the variation range of Chl-a is much larger than that of the other three water quality parameters and is more severe in summer and autumn. (3) The spatial uncertainty study proves that Chl-a is mainly present in the northwest lake area (heavily polluted area) and the other three water quality indicators are mainly present in the center. (4) The sensitivity results show that the main controlling factors of DO are ters (0.15) and kmsc (0.12); those of TN and TP are tetn (0.58) and tetp (0.24); and those of Chl-a are its own growth rate (0.14), optimal growth temperature (0.12), death rate (0.12), optimal growth light (0.11), and TP uptake rate (0.11). Thus, TP control is still the key treatment method for algal blooms that can be implemented by the Chinese government.

Seasonal Dynamics of Algal Net Primary Production in Response to Phosphorus Input in a Mesotrophic Subtropical Plateau Lake, Southwestern China

A comprehensive 3-dimensional hydrodynamic and eutrophication model, the environmental fluid dynamics code model (EFDC) with three functional phytoplankton groups, was applied to simulate the algal dynamics in a mesotrophic P-limited subtropical plateau lake, Lake Erhai, Southwestern China. Field investigations revealed the seasonal patterns in external total phosphorus (TP) input and TP concentration, as well as the composition of the phytoplankton community. The model was calibrated to reproduce qualitative features and the succession of phytoplankton communities, and the net primary production was calculated. The modeled daily net primary production (NPP) ranged between −16.89 and 15.12 mg C/m2/d and exhibited significant seasonal variation. The competition for phosphorus and temperature was identified as the primary governing factor of NPP by analyzing the parameter sensitivity and limitation factors of the lake. The simulation of four nutrient loading reduction scenarios suggested high phytoplankton biomass and NPP sensitivity to the external TP reduction. A significant positive correlation was found among NPP, total phytoplankton biomass and TP concentration. Overall, this work offers an alternative approach to estimating lake NPP, which has the potential to improve sustainable lake management.

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage

Massive cyanobacterial blooms in river water causes adverse impacts on aquatic ecosystems and water quality. Complex and diverse data sources are available to investigate the cyanobacteria phenomena, including in situ data, synthetic measurements, and remote sensing images. Deep learning attention models can process these intricate sources to forecast cyanobacteria by identifying important variables in the data sources. However, deep learning attention models for predicting cyanobacteria have rarely been studied using an assemblage of various datasets. Thus, in this study, a convolutional neural network (CNN) model with a convolutional block attention module (CNN_an) was developed to predict cyanobacterial cell concentrations by using the observed cell data from field monitoring, chlorophyll-a distribution map from hyperspectral image sensing, and simulated water quality outputs from a hydrodynamic model. Then, the prediction performance of the CNN_an model was compared to an environmental fluid dynamics code (EFDC) simulation and a CNN model without an attention network. The seasonal variations of the predicted cyanobacteria that was obtained from CNN_an showed the best agreement with the observed variations with Nash-Sutcliffe efficiency values higher than 0.76 when compared to the EFDC and CNN predictions. The daily hydrodynamic outputs allowed the prediction of cyanobacteria cells, while the rich information of the chlorophyll-a map contributed to the improvement of the prediction performance at certain periods. Moreover, the attention network visualized the importance of the additional chlorophyll-a map and improved the CNN_an model prediction performance by refining the input features. Therefore, this study demonstrated that a deep learning model with data assemblage is practically feasible for predicting the presence of harmful algae in inland water.

Internal positive feedback promotes water quality improvement for a recovering hyper-eutrophic lake: A three-dimensional nutrient flux tracking model

Nutrient dynamics in lakes are determined by the combined effects of external and internal nutrient fluxes. However, the feedback loop of nutrient fluxes and water quality changes is still an open question. An integrated three-dimensional flux tracking approach based on the Environmental Fluid Dynamics Code model was established to quantify the long-term dynamic changes in external and internal processes in Lake Dianchi (one of the three most hyper-eutrophic lakes in China), and to explore the potential causes of water quality improvement during 2012-2018. The long-term trends and relative influences of nutrient fluxes on water quality were identified. The results showed that the inflow flux was the largest input source and declined by 50% between 2003 and 2012, which was followed by a stable trend from 2012 to 2018. The second largest input source was benthic release, which exhibited a significant interannual reduction. Algae sedimentation was the largest removal process, and declined by 45% between 2012 and 2018. An integrated analysis demonstrated that, following an external loading reduction prior to 2012, the positive feedback of internal fluxes promoted water quality improvement during 2012-2018. Considering the long timescale of the nutrient-flux feedback mechanism, reducing external loading is still the top priority for a long-term virtuous cycle of water quality in the process of eutrophic lake restoration.

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.