The invasion and attachment of the golden mussel (Limnoperna fortunei) in hydropower engineering structures pose a significant threat to the safe and efficient operation of power stations. Biofouling caused by this species leads to obstruction of trash racks, blockage in cooling water pipes, corrosion of metal structures, and risks of unplanned shutdowns. However, existing studies primarily focus on biofouling in single engineering components, lacking a systemic understanding of the attachment characteristics and diffusion laws across the entire hydraulic system of large hydropower stations. Furthermore, the regulatory mechanisms of engineering scheduling, such as unit start-stop frequency and flow velocity thresholds, on biofouling remain unclear. Taking a large hydropower station in the Jinsha River Basin as the study object, this paper aims to systematically investigate the spatiotemporal distribution, growth rhythm, and reproductive patterns of Limnoperna fortunei in the complex hydraulic environment. Moreover, it seeks to quantitatively reveal the synergistic regulation mechanisms of hydrodynamic conditions, environmental factors, and engineering operations, thereby providing a scientific basis for constructing a comprehensive prevention and control strategy.

A systematic field investigation and sampling campaign were conducted during the maintenance periods of the study area in February and April 2024. Three key engineering structures prone to biofouling were selected for the study: trash racks, quick gate slots, and main transformer cooling water pipes. Belt transect sampling with 15 cm×15 cm quadrats was employed. For the trash racks, vertical sampling covered the intermittent submersion, stable submersion, and deep-water zones. For the quick gate slots, samples were taken from the bottom sedimentation zone and sidewalls at different elevations. For the cooling water pipes, sampling was performed in enclosed pipelines. The FiSAT Ⅱ software suite was employed for population parameter analysis. Specifically, the Bhattacharya method was used to separate cohorts from length-frequency data to identify reproductive groups. The annual reproductive pattern was inferred based on growth rates and validated by monitoring synchronous planktonic larvae from 2024 to 2025. The von Bertalanffy growth function parameters (asymptotic length L_∞ and growth coefficient K) were estimated using the ELEFAN I module to assess growth potential. In addition, the correlation between biological data and environmental/operational variables, including water level fluctuations, flow velocity, water temperature, and the start-stop records of generating units (specifically comparing Units 6 and 7), was analyzed.

The results revealed considerable spatial heterogeneity and gradient diffusion characteristics of Limnoperna fortunei attachment. 1) In terms of spatial distribution, a clear "source-sink" pattern was observed. The attachment density follows the order: cooling water pipes> trash racks> quick gate slots. In the trash racks, the density showed a vertical single-peak distribution, with the peak located 5-20 m below the minimum operating water level, shaped by the tradeoff between desiccation stress in the upper fluctuation zone and high flow shear in the deep zone. In the quick gate slots, the bottom density was markedly higher than that of the sidewalls due to gravity settlement during shutdowns. Notably, the attachment density in the gate slot of Unit 7 (66 start-stop/year) was markedly lower than that of Unit 6 (21 start-stop/year), indicating that frequent hydraulic disturbance inhibits colonization. An extreme disparity was found in the cooling water pipes; the left and right bank pipes had a density of 31 400 and 0 ind./m², respectively, attributable to a flow velocity threshold effect. The right bank velocity (0.23-0.28 m/s) was below the critical threshold (~0.3 m/s) required for maintaining sufficient food flux, whereas the left bank velocity (0.45-0.58 m/s) was optimal. 2) The population exhibited a "parent-offspring-grandspring" structure in terms of growth and reproduction, confirming a reproductive pattern of three generations per year. Growth showed strong seasonality, accelerating during the high-temperature season (June-October) and stagnating in the low-temperature season (February-April). Spatially, the growth potential (represented by L_∞ and growth performance index ϕ') decreased along the flow path from the trash racks to the cooling water pipes. Despite their high density, the number of individuals in the cooling water pipes was considerably less, attributable to metabolic inhibition due to the consistently lower water temperature (15.5-16.0 ℃) in the deep-water intake.

This study confirms that the distribution and growth of Limnoperna fortunei in large hydropower stations are co-regulated by hydrodynamic conditions, physiological metabolism, and engineering scheduling. The trash rack area serves as the core colonization "source, " continuously supplying larvae to downstream "sinks, " such as quick gate slots and cooling water pipes. Two key physical regulatory mechanisms were identified: the "start-stop frequency effect, " in which frequent hydraulic disturbance and scouring effectively limit colonization, and the "flow velocity threshold effect, " where velocities below ~0.3 m/s inhibit survival due to insufficient trophic flux. Furthermore, low water temperature in deep intakes restricts somatic growth through metabolic suppression. On the basis of these findings, a comprehensive control strategy of "engineering protection as the main measure and operational optimization as auxiliary" is proposed. Recommendations include applying long-acting antifouling coatings in core colonization zones (trash racks), implementing UV/heat treatment and mechanical cleaning for closed systems (cooling water pipes), and attempting intermittent increases in unit start-stop frequency during noncritical periods to use hydraulic disturbance to suppress biofouling.

Environmental DNA (eDNA) technology is an emerging tool for the biological monitoring of aquatic ecosystems. It enables an efficient molecular analysis of the structural characteristics of aquatic communities by capturing DNA fragments freely present in water. This study focuses on the following objectives: (1) analyzing the spatial distribution patterns of winter zooplankton diversity and community composition in the Xiangjiaba section of the lower Jinsha River; (2) exploring the assembly processes and driving factors of winter zooplankton communities; (3) assessing the potential impacts of hydropower development on zooplankton diversity in this section during winter; and (4) identifying key environmental factors affecting zooplankton community structure and elucidating the regulatory mechanisms involved.

This study applied eDNA metabarcoding to investigate winter zooplankton communities in the Xiangjiaba section of the lower Jinsha River. Species, phylogenetic, and functional diversity indices were calculated to evaluate the α-diversity. β-diversity was partitioned into species turnover and nestedness components. Community composition variations were assessed by applying Bray-Curtis distance, principal coordinates analysis (PCoA), and permutational multivariate analysis of variance (PerMANOVA). Key taxa were identified using linear discriminant analysis effect size (LEfSe). Co-occurrence networks were constructed utilizing sparse correlations for compositional data (SparCC) to evaluate community structure and stability. Random forest models were employed to identify crucial environmental drivers shaping zooplankton diversity patterns.

The Xiangjiaba Hydropower Station significantly impacted the α-diversity of winter zooplankton in the lower Jinsha River. Specifically, the downstream area of the mainstream river exhibited markedly higher Chao1 richness (Chao1) and phylogenetic diversity than the upstream regions. Additionally, both indices gradually declined toward the dam, suggesting a "homogenization effect" caused by reservoir regulation. Regarding functional traits, functional richness (FRic) and functional divergence (FDiv) were markedly greater in the tributaries than in the mainstream, reflecting a greater ecological niche differentiation in less-regulated habitats. The composition of dominant zooplankton groups varied across different water body types. Protozoans dominated the downstream region of the mainstream, whereas copepods were predominant in the upstream tributaries. The upstream region of the mainstream exhibited moderate protozoan abundance levels but lacked a single dominant group. Co-occurrence network analysis revealed that the downstream area of the mainstream had a more complex and robust network structure, with higher connectivity and lower vulnerability, indicating enhanced community stability. At the river section scale, the β-diversity of the winter zooplankton was primarily driven by species turnover, with species replacement being the primary community assembly process. Tributaries exhibited significantly enhanced β-diversity compared to the mainstream, largely due to spatial isolation and heterogeneous environmental conditions. The upstream area of the mainstream, functioning as a transition zone between the tributaries and downstream, demonstrated greater environmental homogenization and reduced community dissimilarity.

Hydrological dynamics (e.g., water depth, flow velocity, and water level fluctuations) and water quality (e.g., temperature and turbidity) are the main environmental factors influencing α-diversity patterns of winter zooplankton. Variations in nutrient levels (e.g., chlorophyll a) and water quality (e.g., conductivity and water temperature) are the key drivers of β-diversity and its components, particularly species turnover. These findings suggest that developing hydropower stations and associated environmental changes notably influence zooplankton community structure and assembly processes. Tributary inflow and dam-induced habitat modifications are critical in shaping spatial biodiversity patterns in regulated river systems.

Intermittent rivers, distinguished by sporadic flow cessation, account for more than half of the global river network and are particularly prevalent in semiarid and arid zones, such as the northwest region of China. These watercourses are pivotal in hydrological modeling, sediment transport simulations, and evaluation of riverine ecosystems. Nevertheless, precise extraction of riverbed and cross-sectional data for intermittent rivers during low-flow periods poses a formidable challenge, largely due to the paucity of data and the constraints inherent in conventional measurement techniques. The current methodologies, including digital elevation models (DEM) and empirical equations, fail to deliver continuous and accurate data on river morphology. This study tackles these challenges by introducing a graphics processing unit (GPU)-accelerated random forest (GRF)-convolutional neural network (CNN) algorithm, referred to as the GRF-CNN algorithm, that harnesses multisource remote sensing information and integrates soil moisture downscaling to delineate riverbed characteristics under bankfull and dry states.

This research centers on the Wuding River Basin, a typical intermittent river system located on the Loess Plateau of China. This study develops and validates the GRF-CNN algorithm via integration and optimization of advanced techniques for river morphology extraction. Multisource remote sensing datasets, such as thermal infrared data from the Landsat-8 and ZY1-02E satellites and soil moisture data from the soil moisture active passive (SMAP) satellite and the Sentinel-1 synthetic aperture radar, are used for comprehensive surface and subsurface analyses. Building on the parallel random forest-artificial neural network (PRF-CNN) framework, the GRF-CNN algorithm incorporates CNN modules to enhance the downscaling of thermal infrared data and improve soil moisture inversion. A hybrid statistical-physical model, combining the water cloud model, Dubois model, and advanced integral equation model, along with the deep supervised neural network, is employed to increase the spatial resolution of soil moisture data to 10 m. Specific soil moisture thresholds for different river levels (4th-7th order) are established to accurately extract riverbeds and cross-sectional boundaries during dry seasons. The algorithm's performance is confirmed via hydrological station measurements and high-resolution satellite images.

The results demonstrate that the downscaled soil moisture data achieve an R² of 0.81 and a root mean square error of < 0.059 m³/m³, outperforming conventional soil moisture products. The GRF-CNN algorithm achieves 95.7% accuracy for bankfull river surfaces and 90.3% for dry season riverbeds, with a 27.3% improvement in accuracy of generalized cross-sectional bottom width extraction versus the PRF-ANN algorithm. Furthermore, GRF-CNN surpasses widely recognized machine learning models, including DiCNN-4, Bi-LSTM, and Transformer-based methods, by a margin of at least 10% in terms of Kappa coefficient, overall accuracy, classification accuracy, F₁ score, and recall, particularly in the extraction of intermittent river features.

This study underscores the potential of integrating multisource remote sensing data with sophisticated machine learning algorithms to tackle the challenges associated with the extraction of river morphology in intermittent river systems. The GRF-CNN algorithm offers a scalable and precise solution for reconstructing river boundaries and estimating cross-sectional parameters in data-scarce regions. Moreover, it illustrates the benefits of incorporating soil moisture dynamics into riverbed morphology extraction processes. We believe the results have far-reaching implications for hydrological modeling, sediment transport studies, and developing digital twin watersheds for resource management and ecological restoration in arid and semiarid areas. By addressing the gap in river morphology data extraction, this study provides substantial technical and data support for increasing the accuracy of hydrological simulations and advancing sustainable watershed management strategies.