In this study, changes in abiotic environmental factors were analyzed based on measured data from Dawangtan Reservoir in Nanning City during 2021–2022. The Carlson Integrated Trophic State Index method was applied to evaluate water quality and eutrophication in the reservoir, considering both exogenous inputs and internal water quality conditions. Additionally, by investigating water quality and biological factors (zooplankton and fish) in the reservoir, this study identified the main drivers influencing phytoplankton outbreaks in Dawangtan Reservoir through redundancy analysis (RDA) and Pearson correlation analysis. The results showed that the combined trophic state index of the reservoir varied between 29.2 and 56.5 throughout the year, with each water quality indicator performing worse during the summer months when temperatures were higher. There was a positive correlation between zooplankton biomass and phytoplankton biomass, and the increase in the proportion of phytophagous fish contributed to the reduction in phytoplankton. The redundancy analysis revealed that the distribution of phytoplankton species was significantly correlated with total phosphorus (TP), pH, dissolved oxygen (DO), and chlorophyll a (Chl-a), with different phytoplankton species responding variably to these environmental factors. Finally, a multiple stepwise regression analysis was used to construct the optimal response equation between Chl-a concentration and environmental factors. The results indicated that pH, Secchi disk (SD), DO, and biological oxygen demand (BOD) were the main factors affecting Chl-a concentration. Therefore, the outcomes of abiotic environmental factors and lake biological resources should be considered in the restoration of eutrophic lakes to achieve the desired restoration effect.

Differences in land use and landscape patterns have become crucial factors affecting regional water quality. In order to investigate the effects of different land use and landscape patterns on water quality, this study used dissolved oxygen (DO), ammonia nitrogen (NH₄⁺-N), and potassium permanganate index (COD_Mn) from 147 conventional water quality monitoring stations in the Pearl River basin of China from January to December 2021 as representative water quality parameters. The quantitative relationship between land use, landscape pattern, and water quality in the Pearl River basin was investigated using geographic information system technology (GIS) and partial least squares (PLS). The results showed that the overall water quality of the Pearl River basin was relatively positive and mainly threatened by organic pollution. The water quality of the Pearl River basin was affected by the spatial characteristics of land use and landscape pattern, showing a poorer spatial pattern on the eastern and western ends and a better one in the central part of the basin. The developed PLS regression model could better explain the quantitative relationship between water quality, land use, and landscape pattern, concluding that unused urban land has the greatest impact on water quality, with an impact coefficient of more than 0.10. The interspersion juxtaposition index (IJI) for representing landscape patterns had the greatest impact on water quality indicators, with an impact coefficient of −0.15 on DO, 0.13 on NH₄⁺-N, and 0.15 on COD_Mn, respectively. Meanwhile, land use types such as unused land and water and landscape patterns indicated by the Shannon diversity index (SHDI) and the contagion index (CONTAG) had significant effects on watershed water quality. The results of the study provide a reference value for the optimal adjustment of land use structure and water quality improvement in the basin.

Hydropower development brings benefits in terms of power generation and flood control, but it also has inevitable ecological impacts. These impacts must be considered and addressed in order to ensure sustainable development and minimize harm to the environment. This study utilized the MIKE 11 HD modeling system to construct a hydrological and hydrodynamic model of the Jianhe River basin. The model incorporates the flow demand of ecologically sensitive targets for scheduling purposes and was calibrated and validated using hydrological data from 2014 to 2022. The hydrodynamic model was then applied to analyze the evolution characteristics of the water level in the main stream of the Jianhe River, identify key areas and periods for hydropower station operation, and calculate the minimum ecological water requirement using verification and estimation methods. Based on these findings, an ecological dispatching scheme for the cascade hydropower stations in the Jianhe River basin was developed. The results demonstrate satisfactory performance of the constructed NAM model for rainfall runoff and the 1D hydrodynamic MIKE 11 HD model for the Jianhe River basin. The deterministic coefficients exceed 0.8, and the relative errors in the total water volume are below 5.5%. The critical time and space interval for hydropower station operation in the main stream of the Jianhe River is identified as December to February of the following year, with the highest risk of flow interruption occurring in January, primarily concentrated between the Duoluo II and Huahai hydropower stations. If the appropriate dispatching scheme is not implemented in the areas prone to flow interruption during critical periods, it will have a negative impact on the ecological environment. These findings provide a scientific basis and decision support for developing multi-objective ecological flow guarantee schemes for rivers.