Algal blooms pose serious threats to water environments and the balance of aquatic ecosystems. Moreover, environmental factors may be the primary driver of bloom outbreaks. Studying the characteristics and driving factors of the evolution of cyanobacterial blooms can provide a scientific basis for the restoration of lake water environments and aquatic ecosystems. Based on the Landsat extended time series and practical ecological investigation, this study determined the bloom area and dominant species of cyanobacterial blooms in Hulun Lake, and analyzed their evolution characteristics and primary influencing factors. The results show that the area of bloom outbreak tended to decrease from 2018 to 2020, with the peak area remaining below 150 km²; in contrast, the bloom area showed an increasing trend in 2021–2022, with a maximum of 1970.55 km² in June 2022. Pearson correlation analysis of bloom area and driving factors showed that the wind speed was the main influencing factor of bloom outbreaks. In 2022, there were five dominant species of cyanobacteria in summer and three in autumn. Redundancy analysis of the dominant species of cyanobacteria and water quality indicators showed that COD, DO, TP, WT, pH, and TN were the main influencing factors of the dominant species of cyanobacteria in summer, and COD, DO, TP, and WT were the main influencing factors in autumn. In general, cyanobacteria in Hulun Lake are sensitive to changes in wind speed and nutrient concentration. An important approach towards effectively reducing the intensity of cyanobacterial blooms in Hulun Lake and coping with the ecological risks associated with the changing environment would be to reduce external loads and regulate the ecosystem structure of the lake. In the future, while studying the law of cyanobacterial bloom outbreaks, attention should be paid to the impact of the cyanobacterial physiological status on bloom outbreaks.

Climate change and eutrophication have significant impacts on aquatic ecosystems, with phytoplankton playing a critical role as primary producers. This study investigates the relationships between phytoplankton communities and environmental and meteorological factors in Wuliangsuhai Lake, a representative ecosystem in an arid and cold region of China. Using data from 25 sampling sites (April–September 2023), a generalized additive model (GAM) was employed to analyze water quality and laboratory measurements. The results showed a bimodal distribution of phytoplankton density, with peaks in July (5.33 × 10⁶ cells/L) and August (14.90 × 10⁶ cells/L). Green algae dominated in spring, while cyanobacteria became dominant in summer. GAM analysis revealed that temperature (TEMP) was the primary driver, explaining 20.7% of the deviance. When TEMP was examined together with other factors, the explanatory ability of the model was significantly enhanced, and finally, the model explained 57.10% of the deviance. GAM analysis also revealed that different algae species responded differently to environmental factors, with the cyanophyta-dominant species Pseudoalgae being more sensitive to TEMP and pH, whereas the chlorophyta-dominant species Chlorella was more affected by wind speed and salinity. Cyanobacteria dominance, exacerbated by warming, increases the risks of harmful algae blooms and ecosystem instability, highlighting the need for mitigation strategies in vulnerable freshwater ecosystems.

This study uses the Kherlen River as a case study to investigate the relationship between soil nutrients in riparian zones and water quality in inlet sections of lakes. Field sampling and experimental analyses were conducted during the high-water period (July) of 2023. An investigation was conducted on both the water quality of the river segments entering the lake and the soil nutrients. Methods such as the comprehensive water quality index (WQI), spatial heterogeneity analysis, and gray relational analysis were employed to assess water quality, soil nutrient characteristics, and their interrelationships, respectively. The results indicated that during the high-water period, the average concentrations of the permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), and dichromate oxidizability (COD_Cr) in the Kherlen River exceeded the Class V surface water quality standard thresholds. The overall WQI of the Kherlen River was 22.54, reflecting generally poor water quality, with a Global Moran’s I of 0.21, indicating a spatially clustered distribution. In the watershed, the Global Moran’s I values for pH values, TOC, TN, and TP at soil depths of 0–10 cm and 10–20 cm were 0.52, 0.90, 0.86, and 0.94 and 0.51, 0.57, 0.77, and 0.78, respectively. A significant positive correlation was found among soil nutrients, exhibiting a strong spatial aggregation characteristic, with nutrient concentrations decreasing with increasing soil depth. Moreover, the WQI of the Kherlen River demonstrated a significant correlation (R² > 0.6) with soil nutrient indicators, underscoring the substantial impact of riparian soil nutrients on river water quality. Based on these findings, targeted water management and ecological restoration measures are proposed to improve the water quality of the Kherlen River and Hulun Lake, providing new insights and scientific evidence for the restoration and sustainable development of lake ecosystems.