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.

Nitrogen is one of the most important elements involved in ecosystem biogeochemical cycling. However, little is known about the characteristics of nitrogen cycling during the ice-covered period in seasonally frozen lakes. In this study, shotgun metagenomic sequencing of subglacial water and sediment from Lake Ulansuhai was performed to identify and compare nitrogen metabolism pathways and microbes involved in these pathways. In total, ammonia assimilation was the most prominent nitrogen transformation pathway, and Bacteria and Proteobacteria (at the domain and phylum levels, respectively) were the most abundant portion of microorganisms involved in nitrogen metabolism. Gene sequences devoted to nitrogen fixation, nitrification, denitrification, dissimilatory nitrate reduction to ammonium, and ammonia assimilation were significantly higher in sediment than in surface and subsurface water. In addition, 15 biomarkers of nitrogen-converting microorganisms, such as Ciliophora and Synergistetes, showed significant variation between sampling levels. The findings of the present study improve our understanding of the nitrogen cycle in seasonally frozen lakes.

It is imperative to elucidate the process of evaporation in lakes, particularly those that are freshwater and are situated in middle and high latitudes. Based on one-year evaporation and high-frequency meteorological–water quality data of Lake Wuliangsuhai, this study analyzed the applicability and driving mechanism of the evaporation model. These dynamics are elucidated by the vorticity covariance method combined with the multivariate constrained evaporation Modelling method. The findings of this study revealed that (1) Lake evaporation (ET) is affected by multiple meteorological–water quality constraints, and the water quality indicators significantly related to ET are also affected by lake stratification. The coupled meteorological–water quality evaporation model can explain 93% of the evaporation change, which is 20% higher than the traditional meteorological Modelling evaporation model. (2) The nighttime ET is mainly affected by the thermal inertia lag, and the nighttime ET loss in Lake Wuliangsuhai accounts for 37.34% of the total evaporation, which cannot be ignored. (3) The actual water surface evaporation of the lake is much smaller than that measured by the pan conversion method and the regional empirical C formula method. The cumulative evaporation of Lake Wuliangsuhai from the non-freezing period to the early glacial period converted from meteorological station data is 1333.5 mm. The total evaporation in the non-freezing period is 2.77~3.68 × 10⁸ m^3, calculated by the lake area of 325 km², while the evaporation calculated by the eddy station is 1.91 × 10⁸ m³. In addition, the ET value measured by the cumulative C formula method was 424.2% higher than that of the model method and exceeded the storage capacity. Low-frequency and limited environmental index observations may lead to an overestimation of the real lake evaporation. Therefore, in situ, high-frequency meteorological–water quality monitoring and the eddy method deserve more consideration in future research on lake evaporation.