A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling

Water Level Fluctuation under the Impact of Lake Regulation and Ecological Implication in Huayang Lakes, China

Water level fluctuation (WLF) in shallow lakes in the middle and lower reaches of the Yangtze River has been a concern of many researchers. This work aims to investigate the effects of climate change and regulation of floodgates and the Three Gorges Dam (TGD) on WLF and lake volume in Huayang Lakes during the past 52 years. The results revealed that precipitation is the dominant factor that leads to seasonal variation of lake levels, whereas regulation of floodgates and TGD are the key drivers of hydrology regime change in the past 20 years. Natural lake regime has higher water level when there is more precipitation and less lake volume. Floodgates and TGD regulations have changed this pattern since 2003, causing less difference in water level in spite of more precipitation and lake recession. Under the combined impacts of floodgates and TGD regulations, Huayang Lakes have experienced a prolonged outflow time since 2003 and the contribution rate caused by the floodgates and TGD regulations has increased by 19.90%. Additionally, the water level of Huayang Lakes decreased by approximately 0.3~0.5 m from September to November, but it showed no alteration from January to March in the past two decades. This indicated that floodgate regulations used for agricultural irrigation and fishery culture dominate the hydrology regime in winter and early spring. This study is beneficial for aquatic ecosystem protection in floodgate-controlled lakes under the circumstance of climate change and vigorous anthropology activities

Impacts of Tide Gate Modulation on Ammonia Transport in a Semi-closed Estuary during the Dry Season—A Case Study at the Lianjiang River in South China

Recovery of tide-receiving is considered to improve the water quality in the Lianjiang River, a severely polluted and tide-influenced river connected to the South China Sea. A tide-receiving scenario, i.e., keeping the tide gate open, is compared with the other scenario representing the non-tide-receiving condition, i.e., blocking the tide flow during the flood phase, by numerical simulations based on the EFDC (Environmental Fluid Dynamics Code) model. The impacts of tide receiving were evaluated by the variation in the concentration of ammonia and its exporting fluxes, mainly in the downstream part of the river. With more water mass coming into the river, in the tide-receiving scenario, the averaged concentration of ammonia reduced by 20–40%, with the most significant decrease of 0.64 g m−3. However, the exporting flux of ammonia has decreased in the tide-receiving scenario, as the consequence of the back–forth oscillation of tidal current. In the tide-receiving scenario, the time series of ammonia concentration approximately followed the tidal oscillation, with increased concentration during the ebb tide and reduction in the flood tide. In the non-tide-receiving scenario, the ammonia concentration decreases when the tide gate is open which results in further intrusion of seawater. This was followed by an increase in ammonia concentration again after the currents shift seaward and water mass with higher concentration from the upstream part is transported downstream. Given the identical ammonia input and river runoff, the ammonia concentration stays lower in the tide-receiving scenario, except for short periods after the tide gate opening and neap tides in the downstream part which lasts for around half a day. This study highlights the importance of hydrodynamic condition, specifically tidal oscillation, in the semi-diurnal and fortnight cycles, for the transportation of waterborne materials. Furthermore, the operation of the tide gate was additionally discussed based on potential varied practical conditions and evaluation criteria

Monte Carlo Optimization for Sliding Window Size in Dixon Quality Control of Environmental Monitoring Time Series Data

Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation