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.

Total phosphorus (TP) dynamics between reservoirs and inflowing rivers critically affect eutrophication risks, but their multi-scale interactions remain insufficiently quantified. This study applied wavelet transform analysis to 8-year TP time series data from the Shanxi Reservoir and its inflowing rivers. Key findings include the following: (1) Morlet wavelet decomposition revealed dominant 8–16-month cycles for reservoir TP, contrasting with 4–8-month cycles in river TP; (2) wavelet coherence analysis identified a 90° phase lag (2–4 months delay) between reservoir and river TP at the 8–16-month scale; and (3) the time–frequency localization capability quantified rapid responses—reservoir TP reacted within 2 months to abrupt river TP increases, showing stronger intensity. Multi-resolution analysis further distinguished the driving mechanisms: interannual cycles (>12 months) governed reservoir TP variations, while seasonal cycles (<8 months) controlled river TP fluctuations. The study demonstrated wavelet analysis’ dual strengths: resolving scale-specific interactions through multi-scale decomposition and quantifying transient responses via phase coherence metrics. The 90° phase shift exposes hysteresis in TP transport, and the 2-month response threshold defines critical intervention timing. An adaptive monitoring framework is proposed as follows: ≤8-month sampling under stable conditions and 2-month intervals during TP surges, providing a time–frequency decision tool for precise reservoir water quality 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.