The Yellow River is a valuable resource in the Ningxia Hui Autonomous Region and plays a vital role in local human activities and biodiversity. Bacteria are a crucial component of river ecosystems, but the driving factors and assembly mechanisms of bacterial community structure in this region remain unclear. Herein, we documented the bacterial community composition, determinants, co-occurrence pattern, and assembly mechanism for surface water and sediment. In comparison to sediment, the bacterioplankton community showed significant seasonal variation, as well as less diversity and abundance. The network topology parameters indicated that the sediment bacterial network was more stable than water, but the bacterioplankton network had higher connectivity. In this lotic ecosystem, COD_Mn, Chl a, and pH affected the structure of the bacterioplankton community, while TP was the primary factor influencing the structure of the sediment bacterial community. The combined results of the neutral community model and the phylogenetic null model indicate that Bacterial communities in both habitats were mainly affected by stochastic processes, with ecological processes dominated by ecological drift for bacterioplankton and dispersal limitation for sediment bacteria. These results provide essential insights into future research on microbial ecology, environmental monitoring, and classified management in the Ningxia section of the Yellow River.

To understand the characteristics of the water’s environmental carrying capacity in the Aiyi River in Yinchuan, Ninxia, China, a BP artificial neural network analysis based on water environmental factors and aquatic biodiversity metrics were conducted. Twelve indicators were used to construct an evaluation index system to examine the water environment carrying capacity of the river, namely ratio of water supply and water requirements; ratios of water environmental capacity to pollutant emissions of COD_Mn, BOD₅, TN, NH_3-N, and TP; self-purification rate; biodiversity indices of phytoplankton, zooplankton zoobenthos, and aquatic macrophytes; and a comprehensive nutrition state index. A model of water environmental carrying capacity was constructed according to the BP artificial neural network. The model, which quantitatively analyzed the water environmental carrying capacity of the Aiyi River, found it to be in the basic carrying state.

Rainwater is the main water source in arid and semiarid areas of the Loess Plateau, where rainfall is generally insufficient, ineffective and underutilized during the growing season. Thus, improving rainwater utilization efficiency is essential for sustainable agricultural development. A new system composed of rainwater harvesting, an infiltrator bucket with multiple holes and mulching (RHM), was designed to maintain soil moisture at a proper level in rain-fed orchards in arid and semiarid areas of the Loess Region of China. However, there is a lack of clarity on the effectiveness of RHM. In this study, changes in the soil environment and the growth and physiology of apricot trees were monitored via two treatments: (1) Rain-harvesting irrigation system (RHM) treatment and (2) traditional orchard treatment (CK) as a baseline. The results showed that (1) RHM could effectively improve soil water storage at depths of 0–45 cm and at a horizontal distance of 40 cm from the trunk. For the 1.4 mm light rain event, the soil water content increased by 6.3–12%, and for the two moderate rains, the soil water content increased by 12–25%. The change in the soil relative water content predicted by the LSTM model is consistent with the overall trend of the measured value and gradually decreases, and the prediction accuracy is high, with an error of 0.65. (2) The average soil temperatures at 5 cm, 20 cm and 40 cm under RHM were 17.0% (2.4 °C), 13.6% (1.9 °C) and 7.5% (1 °C) greater than those under CK, respectively. (3) Compared with the control treatment, RHM improved the growth and WUEL of apricot trees. The results highlighted the efficiency of the RHM system in enhancing the soil environment and regulating the growth and physiology of apricot trees, which has greater popularization value in arid and semiarid areas.

The time scale of rainfall data limits the accuracy and application scope of hydrologic models, especially when low-accuracy observed rainfall data are used in physically based distributed hydrologic models. In this study, an optimized rainfall method based on maximum rainfall intensity and self-similarity was established to provide different rainfall data for the physically based distributed hydrological model. The results showed the following: (1) the increase of time scale resulted in decreased rainfall intensity and an evenly distributed rainfall pattern; (2) the established disaggregation method for rainfall well described the uneven distribution in time; (3) the influence of time scale could be divided into 1-20, 20-120, and 120-360 min; (4) the conversion method between rainfall intensity and saturated hydraulic conductivity was effective at ensuring the physical meaning of the parameters on a time scale of 20-90 min. Furthermore, results showed that the reasonable time scale of application of the CASC2D model is less than 120 min. For longer time scales, the model was able to simulate peak discharge but unable to describe the flood process.

The Taiyangshan Wetland, a valuable wetland resource in the arid zone of central Ningxia, is critical for flood storage and drought resistance, climate regulation, and biodiversity protection. Nevertheless, the community structure and diversity of bacterioplankton in the Taiyangshan Wetland remains unclear. High-throughput sequencing was used to analyze the differences in bacterioplankton structure and major determinants in the Taiyangshan Wetland from April to October 2020. The composition and diversity of the bacterioplankton community varied significantly in different sampling periods but showed negligible differences across lake regions. Meanwhile, the relative abundances of bacterioplankton Bacteroidetes, Actinobacteria, Firmicutes, Chloroflexi, Tenericutes, Epsilonbacteraeota, and Patescibacteria were significantly different in different sampling periods, while the relative abundances of Cyanobacteria in different lake regions were quite different. Network analysis revealed that the topological attributes of co-occurrence pattern networks of bacterioplankton were high, and bacterioplankton community compositions were complicated in the month of July. A mantel test revealed that the bacterioplankton community in the entire wetland was affected by water temperature, electrical conductivity, dissolved oxygen, salinity, total nitrogen, ammonia nitrogen, chemical oxygen demand, fluoride, and sulfate. The bacterioplankton community structure was affected by ten environmental parameters (e.g., water temperature, dissolved oxygen, salinity, and permanganate index) in April, while the bacterioplankton community was only related to 1~2 environmental parameters in July and October. The bacterioplankton community structure in Lake Region IV was related to seven environmental parameters, including dissolved oxygen, pH, total nitrogen, and chemical oxygen demand, whereas the bacterioplankton community structures in the other three lake regions were related to two environmental parameters. This study facilitates the understanding of the bacterioplankton community in wetlands in arid areas and provides references to the evaluation of aquatic ecological management of the Taiyangshan Wetland.