As an important component of reservoir ecosystems, phytoplankton is often used as an indicator to assess the health of water ecosystems such as lakes and reservoirs. The exploration the phytoplankton index of biotic integrity (P-IBI) has been proposed to assess the ecological health of the large drinking-water reservoirs. This study investigated phytoplankton communities and environmental variables at 19 sampling sites in the Danjiangkou Reservoir from October 2019 to July 2021. Results showed that 170 species of phytoplankton from 9 phyla were detected in Danjiangkou Reservoir, and the total density varied from 0.61 × 10⁵ to 36.64 × 10⁵ cells/L, with the mean value of 8.83 × 10⁵ cells/L. The P-IBI values were higher in winter and lower in spring in terms of time, and the spatial trend of P-IBI values from high to low was outlet of the reservoir > entrance of Dan Reservoir > entrance of Han Reservoir > Han Reservoir > Dan Reservoir. Linear regression analysis showed that the evaluation results of P-IBI and the comprehensive trophic level index (TLI) evaluation were generally consistent. Redundancy analysis (RDA) showed significant correlations between P-IBI and candidate indicators and major environmental factors with significant differences between seasons. The P-IBI is an effective tool to evaluate the ecological health of large drinking-water reservoirs and could provide some scientific reference for the ecological health assessment of large drinking-water reservoirs.

This study aimed to systematically evaluate the effects of different nitrogen application rates and irrigation practices on water-saving and yield enhancement in winter wheat production in the North China Plain (NCP) using a meta-analysis. By quantifying the impacts on crop yield, nitrogen use efficiency (NUE), and water use efficiency (WUE), the research provides a scientific basis for optimizing management practices in winter wheat production in this region. A comprehensive literature search was conducted across multiple databases, resulting in the inclusion of 94 eligible studies from 2018 to 2023. A random-effects model was employed to calculate the combined effect sizes, followed by subgroup and sensitivity analyses to further investigate the influence of nitrogen application rates, irrigation methods, and study regions on winter wheat production efficiency. The findings reveal that increasing nitrogen application rates and adopting deficit irrigation practices significantly improved winter wheat yield (combined effect size: 4.53 t·ha⁻¹), NUE (43.29%), and WUE (0.013 t·ha⁻¹·mm⁻¹). The subgroup analysis further elucidated the critical roles of nitrogen application ratios, irrigation methods, and study regions in determining winter wheat production efficiency, while the sensitivity analysis confirmed the robustness of these findings, as the pooled effect sizes decreased by merely 0.69% and increased by 0.61% after excluding small-sample or highly biased studies, respectively. The above meta-analysis did not incorporate long-term field trials; hence, two-year field experiments with designed irrigation and organic–inorganic fertilizer treatments were conducted, which provided further validation for the meta-analysis. Under short-term conditions (excluding CO₂ effects), we observed that chemical fertilizer exhibited a measurable inhibitory effect on crop water uptake and optimal water–fertilizer management was achieved with a 7:3 inorganic–organic fertilizer ratio combined with 450 m³·ha⁻¹ irrigation. This study demonstrates the effectiveness of optimizing nitrogen fertilization and irrigation management in enhancing winter wheat yield and resource utilization efficiency. The findings offer actionable insights for sustainable agricultural practices in the NCP and similar regions, contributing to improved crop productivity and resource conservation.

The land surface model (LSM) is extensively utilized to simulate terrestrial processes between land surface and atmosphere in the Earth system. Hydrology simulation is the key component of the model, which can directly reflect the capability of LSM. In this study, three offline LSM simulations were conducted over China using the Community Land Model version 5.0 (CLM5) driven by different meteorological forcing datasets, namely China Meteorological Forcing Dataset (CMFD), Global Soil Wetness Project Phase 3 (GSWP3), and bias-adjusted ERA5 reanalysis (WFDE5), respectively. Both gridded and in situ reference data, including evapotranspiration (ET), soil moisture (SM), and runoff, were employed to evaluate the performance levels of three CLM5-based simulations across China and its ten basins. In general, all simulations realistically replicate the magnitudes, spatial patterns, and seasonal cycles of ET over China when compared with remote-sensing-based ET observations. Among ten basins, Yellow River Basin (YRB) is the basin where simulations are the best, supported by the higher KGE value of 0.79. However, substantial biases occur in Northwest Rivers Basin (NWRB) with significant overestimation for CMFD and WFDE5 and underestimation for GSWP3. In addition, both grid-based or site-based evaluations of SM indicate that systematic wet biases exist in all three CLM5 simulations for shallower soil layer over nine basins of China. Comparatively, the performance levels in simulating SM for deeper soil layer are slightly better. Moreover, all three types of CLM5 simulate reasonable runoff spatial patterns, among which CMFD can capture more detailed information, but GSWP3 presents more comparable change trends of runoff when compared to the reference data. In summary, this study explored the capacity of CLM5 driven by different meteorological forcing data, and the assessment results may provide important insights for the future developments and applications of LSM.

Cationic polyacrylamide (CPAM) is a commonly used flocculant for water treatment. Factors that affect the flocculation effect and can be controlled manually include the type and dosage of CPAM, wastewater pH, stirring time and settling time, and their reasonable setting is critical to the flocculation effect of CPAM. In this paper, the optimal flocculation conditions of a novel CPAM were studied. First, single-factor tests were conducted to preliminarily explore the optimal range of factors that influence CPAM flocculation, and then response surface methodology (RSM) tests were performed to accurately determine the optimums of the influencing factors. The results showed that the flocculation effect was better when the intrinsic viscosity was larger or the cationic degree of CPAM was higher. The CPAM dosage, wastewater pH and stirring time significantly impacted the flocculation effect, and inflection points were observed. A model that could guide CPAM-8.14-40.2 flocculation was obtained by RSM tests. The model optimization showed that the optimal flocculation conditions of CPAM-8.14-40.2 for treating wastewater prepared with kaolin were as follows: the CPAM dosage, wastewater pH and stirring time were 5.83 mg·L⁻¹, 7.28, and 5.95 min, respectively, and the turbidity of the treated wastewater was reduced to 6.24 NTU.

In order to study the vertical distribution characteristics of phytoplankton in the Danjiangkou Reservoir, the water source of the Middle Route Project of the South-North Water Diversion, seven sampling sites were set up in the Reservoir for quarterly sampling from 2017 to 2019, and water environment surveys were conducted simultaneously. The results showed that 157 species (including varieties) were identified, belonging to 9 phyla and 88 genera. In terms of species richness, Chlorophyta had the largest number of species, accounting for 39.49% of the total species. The Bacillariophyta and Cyanobacteria accounted for 28.03% and 13.38% of the total species, respectively. From the whole Danjiangkou Reservoir, the total phytoplankton abundance varied from 0.09 × 10² to 20.01 × 10⁶ cells/L. In the vertical distribution, phytoplankton were mainly observed in the surface–thermospheric layer (I–II layer) and the bottom layer, while the Shannon–Wiener index showed a trend of gradually decreasing from the I–V layer. The Surfer model analysis showed that there was no significant stratification in the Q site’s water temperature (WT) and dissolved oxygen (DO) levels in the water diversion area during the dynamic water diversion process. A canonical correspondence analysis (CCA) showed that DO, WT, pH, electrical conductivity (Cond), chemical oxygen demand (COD_Mn), total phosphorus (TP), ammonia nitrogen (NH₄⁺-N), and total nitrogen (TN) had significant effects on the vertical distribution of phytoplankton (p < 0.05). A partial Mantel analysis showed that the vertical distribution of the phytoplankton community was related to WT, and the phytoplankton community structure at the other sites, except for Heijizui (H) and Langhekou (L), was affected by DO. This study has positive significance for exploring the vertical distribution characteristics of a phytoplankton community in a deep-water dynamic water diversion reservoir.

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development.