Integrated assessment of the water environment has become widespread in many rivers, lakes, and reservoirs; however, aquatic organisms in freshwater are often overlooked in this process. Zooplankton, as primary consumers, are sensitive and responsive to changes in the water environment. Water and zooplankton samples were collected on-site at Shanxi Reservoir quarterly to determine 12 water environmental indicators and to quantify the abundance of zooplankton of Cladocera, Copepoda and Rotifera by using the ZooScan zooplankton image-scanning analysis system, combined with OLYMPUS BX51 using machine learning recognition classification. The aim was to explore the relationship between water environmental factors and zooplankton through their spatial and temporal heterogeneity. Through principal component analysis, redundancy analysis and cluster analysis, variations in the factors driving zooplankton population growth in different seasons could be identified. At the same time, different taxa of zooplankton can form clusters with related water environmental factors during the abundant water period in summer and the dry water period in winter. Based on long-term monitoring, zooplankton can be used as a comprehensive indicator for water environment and water ecological health evaluation, as well as providing scientific support for regional water resources deployment and management.

In plant ecology and environmental remediation, the characterization of riparian plant communities and the influence of environmental factors have been widely discussed. However, the delineation of plant communities from different spatial perspectives is often overlooked, especially in hillstreams. In this study, the Lingshan River is taken as the research area, which is a quintessential hillstream and is characterized by a plethora of hydraulic structures lining its course by 20 weirs. We aim to investigate the multidimensional spatial distribution of riparian plants and their main environmental factors through plant field surveys combined with cluster analysis and redundancy analysis (RDA). The main findings are as follows: (1) In this study, a total of 104 herbaceous species were investigated, distributed among 12 families, in which Poaceae (16.67%) and Compositae (9.65%) showed significant dominance. (2) Plant community delineation was based on the complete linkage clustering. Five plant communities were classified along the longitudinal scale of the river, four plant communities were in the near-water zones, and three plant communities were in the far-water zones. (3) Riparian plant diversity and community distribution in longitudinal and lateral dimensions exhibits significant differentiation. Longitudinally, there was a significant decrease in plant diversity from upstream to downstream. Laterally, the plant biomass in the near-water zone was higher than in the far-water zone, while diversity demonstrated a reverse trend in the midstream. (4) The main environmental factors influencing plant distribution varied for different spatial dimensions. Longitudinally, the physical factor of soil is domination, particularly soil texture, which exhibits the strongest correlation with plant communities. Laterally, the chemical factor of soil is domination, such as soil organic matter and soil nitrate nitrogen content. This study enhances our understanding of the riparian area’s ecology, and provides a scientific basis for plant cover restoration and ecological environment protection, and their management.

As a result of global climate change and human production activities, algal blooms are occurring in aquatic environments. The problem of eutrophication in water bodies is becoming increasingly severe, affecting the safety of drinking water sources. In this study, an algal bloom risk index model combining the Improved Fuzzy Analytic Hierarchy Process (IFAHP), Entropy Weight Method (EWM), and Game Theory (GT) was proposed for the Shanxi Reservoir based on the TOPSIS method. After the seasonal and spatial variability in algal bloom risk from 2022 to 2023 was analyzed, an adaptive simplification of the algal bloom risk index calculation was proposed to optimize the model. To enhance its practical applicability, this study proposed an adaptive simplification of the algal bloom risk index calculation based on an improved TOPSIS approach. The error indexes R² for the four seasons and the annual analysis were 0.9884, 0.9968, 0.9906, 0.9946, and 0.9972, respectively. Additionally, the RMSE, MAE, and MRE values were all below 0.035, indicating the method’s high accuracy. Using the adaptively simplified risk index, a risk grading and a spatial delineation of risk areas in Shanxi Reservoir were conducted. A comparison with traditional risk classification methods showed that the error in the risk levels did not exceed one grade, demonstrating the effectiveness of the proposed calculation model and risk grading approach. This study provides valuable guidance for the prevention and control of algal blooms in reservoir-type drinking water sources, contributing to the protection of drinking water sources and public health.