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.

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.

Dramatic environmental changes have altered the connectivity pattern of aquatic ecosystems and thus fish spawning habitats. Although connectivity processes in local riverine habitats are crucial for fish reproduction, existing connectivity simulation approaches do not allow for quantitatively describing the fine-scale connectivity structure driven by hydrogeomorphic variables. Here we proposed the fine-scale connectivity theory (FSCT) that tackled the challenge of nonlinearity issue in modeling asymmetric connectivity in water environments, aiming to clarify the mechanism of fine-scale functional connectivity structure on fish natural reproduction. The FSCT was applied to the spawning ground of the Chinese sturgeon, a critically endangered migratory fish of the utmost concern in the Yangtze River, China. Results demonstrated that our method outperformed present connectivity models with an accuracy improvement of 25.1%. This study revealed a high correlation between the connectivity of spawning habitats and spawning capacity of the Chinese sturgeon, with a value of 0.947. Our findings showed a significant decline in habitat connectivity within the Chinese sturgeon spawning ground since 2003, which was associated with the shift from an aggregated to a decentralized connectivity structure. This study can facilitate theoretical and technical support for habitat restoration and conservation efforts of endangered fish populations in dammed rivers.