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As a prerequisite and foundation for studying the evolution mechanism of river channels, an in-depth understanding of the cross-sectional morphology adjustment is required. As a starting point, it is crucial to systematically summarize and generalize the research findings on channel morphological adjustment obtained to date, particularly in the context of the significant changes in the water and sediment conditions of large rivers that have occurred worldwide. This paper provides a comprehensive review of the research findings on the three following aspects of the Lower Yellow River: the transverse distribution of overbank flow velocity, the transverse distribution of suspended sediment concentration, and the morphological adjustment of the river cross-section. There are various equations available to predict the lateral depth–average flow velocity distribution. These equations are classified into the two following categories: empirical and theoretical formulas. Theoretical formulas are obtained through consideration of the cross-sectional morphology, accounting for inertial force terms caused by secondary flow, and momentum transfer between the main channel and its floodplain. Similarly, empirical equations and theoretical formulas for sediment concentration transverse distribution are also summarized, given the different influencing factors and assumptions. We also discuss the morphological adjustment of river cross-sections based on the analysis of measured data, mathematical model calculation, and the physical model test. In particular, we propose the idea of revealing channel cross-section morphology evolution mechanisms from the theoretical level of water and sediment movement and distribution. This review aims to enhance understanding of overbank flow, sediment transport, and channel morphology in the Lower Yellow River and may also serve to some extent as a reference for the evolution and management of channels in other rivers.

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination (R2) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.

Soil salinization is one of the main hazards affecting the sustainable development of agriculture in the Hetao Irrigation District (HID) of Inner Mongolia. To grasp the water and salt transport patterns and spatial–temporal distribution characteristics of the HID at the regional scale, the improved Soil and Water Assessment Tool with a salinity module (SWAT-Salt) model was used to establish the distributed water and salt transport model for the watershed in this study. The results demonstrated that the modified model could more accurately represent the process of water and salt changes in the HID. The coefficient of determination (R2) in the simulation of streamflow and discharge salt loading was 0.83 and 0.86, respectively, and the Nash–Sutcliffe efficiency (NSE) was 0.80 and 0.74, respectively. Based on this, different hydrological processes (surface runoff, lateral flow, groundwater, soil seepage) as well as spatial–temporal distribution characteristics of water salinity in groundwater and soil were analyzed in the HID. Differences in groundwater and soil salinity in different land uses and soil types were also compared. Of these, surface runoff and lateral flow salt discharge loading are concentrated in the southwestern portion of the basin, while groundwater salt discharge loading is concentrated in the eastern as well as southwestern portions of the basin. The salt discharge loading from groundwater accounts for about 98.7% of the total salt discharge loading from all hydrological pathways and is the major contributing part of salt discharge from the irrigation area. Soil salinity increases gradually from west to east. Groundwater salinity (2946 mg/L) and soil water electrical conductivity (0.309 dS/m) were minimized in the cropland. Meanwhile, rational allocation of irrigation water can appropriately increase the amount of salt discharge loading. In conclusion, the model could provide a reference for the investigation of soil salinization and water–salt management measures in irrigation areas. [ABSTRACT FROM AUTHOR]

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM. [ABSTRACT FROM AUTHOR]

Soil salinization is a pressing issue that needs to be addressed in current agricultural production. In this study, we utilized novel materials, unfunctionalized multi-walled carbon nanotubes (MWCNT) and functionalized multi-walled carbon nanotubes (MWCNT-OH), to explore the effects of soil carbon and nitrogen cycles in saline soil. We set up four treatments, which were exposed to two exposure doses of 1 g/kg and 1 μg/kg and two MWCNT types of functionalized MWCNT-OH and unfunctionalized MWCNT. Our results demonstrate that exposure of saline soil to 1 g/kg functionalized MWCNT-OH significantly increased the soil inorganic nitrogen (p < 0.05), while also promoting the soil microbial biomass. This exposure can also potentially enhance greenhouse gas emissions from saline soil. Moreover, exposure to MWCNTs significantly increased the proportion of Actinobacteria and Proteobacteria, two dominant phyla (p < 0.05), which in turn improved their contribution to the carbon and nitrogen cycling processes within saline soil. High exposure dose treatments (1 g/kg) significantly increased the abundance of functional genes associated with carbon metabolism, carbon fixation, methane metabolism, and nitrogen cycling processes within saline soil. In contrast, low exposure dose treatments (1 μg/kg) had no significant effect on the abundance of functional genes related to nitrogen cycling, but significantly increased the abundance of special functional genes related to carbon cycling. Redundancy analysis revealed that the microbial community composition within saline soil was significantly impacted by the soil total carbon, total nitrogen, and nitrate nitrogen content. Furthermore, it was observed that over 80% of the carbon and nitrogen cycling processes within the saline soil were contributed by the dominant phyla. In summary, our research confirms the potential applicability of MWCNTs within saline soil. Notably, exposure of saline soil to 1 g/kg functionalized MWCNT-OH exhibited the most significant promoting effect on the carbon and nitrogen cycles. [ABSTRACT FROM AUTHOR]

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