Search Results (22)

Biological invasion is a major driver of biodiversity loss and ecosystem disruption, with non-native aquatic species threatening ecological integrity and economic stability. The Qinling Mountains, located in central China, serve as a crucial barrier between temperate and subtropical climate zones, and separate the Yellow and Yangtze River basins. This study investigates the role of these geographical barriers in regulating the distribution and invasion risk of non-native aquatic species. We identified 27 non-native species in Shaanxi Province based on occurrence records compiled from field survey conducted between 2012 and 2024 (and from 2019 to 2024 in the Yellow River mainstream of the Shanxi–Shaanxi Gorge), including 13 high-risk species, such as Trachemys scripta elegans, Procambarus clarkii, Sander lucioperca, and Hypomesus olidus. Using the Aquatic Species Invasiveness Screening Kit and species distribution models, we identified the Hanjiang River in the Yangtze basin and Weihe River estuary in the Yellow River basin as high-risk areas for these species. Mean annual temperature was the primary environmental factor influencing species distribution, with species adapted to cooler conditions predominantly found north of the Qinling Mountains, while those preferring warmer climates are more common in the south. Our findings highlight the Qinling Mountains as both a physical and climatic barrier, limiting cross-basin dispersal and creating distinct invasion patterns. However, human activities such as inter-basin water-transfer projects, damming, and aquaculture practices have gradually weakened the barrier’s effectiveness, facilitating the spread of invasive species. We recommend prioritizing monitoring efforts in cross-basin water-transfer regions, focusing on high-risk species adapted to both cooler and warmer climates, and incorporating environmental DNA (eDNA)-based monitoring in recipient areas of inter-basin water-transfer projects for early detection and control to minimize ecosystem damage. Full article

As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R², RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications. Full article

Developing efficient water-saving irrigation technologies that utilize high sand-laden water is an important approach to alleviating agricultural water scarcity in the Yellow River Basin. This study aims to investigate sedimentation patterns and fluid movement characteristics in drip irrigation emitters under such challenging water conditions. The dynamic changes in Dra and Cu were determined through short-period intermittent clogging tests to evaluate the anti-clogging performance of four different emitter types. The distribution and particle size composition of the deposited sediments inside the emitters were analyzed using a high-resolution electron microscope and a laser particle size analyzer. Additionally, the RNG k-ε turbulence model was used to simulate the fluid movement inside the emitters. The results showed that the B drip irrigation belt had better sediment tolerance and operational stability. The anti-clogging capacity of drip irrigation can be improved by optimizing the combination of emitter channel structure and sediment content. The fluid in the channel was divided into mainstream zone and vortex zone. Sediment particles increased in the backing-water zone and vortex center, where particles of 0.05–0.1 mm were more prone to settling due to reduced transport capacity. Energy dissipation primarily took place at the curvature of the emitter channel, and within each channel unit, gradually decreasing along the vortex flow direction, with the lowest dissipation aligning with sediment deposition zones. These findings provide a theoretical basis for mitigating clogging in high sand-laden water drip irrigation systems, offering valuable insights for improving the effective utilization of water resources in the Yellow River Basin. Full article

Soil salinization is a serious land degradation phenomenon, posing a severe threat to regional agricultural resource utilization and sustainable development. It has been a mainstream trend to use machine-learning methods to achieve monitoring of large-scale salinized soil quickly. However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. To compare the performance of different machine-learning models, this study conducted a soil sampling experiment on saline soils along the south bank of the Yellow River in Dalate Banner. The experiment lasted two years (2022 and 2023) during the spring bare soil period, collecting 304 soil samples. The soil salinity was estimated with the multi-source remote sensing satellite data by combining the extreme gradient boosting model (XGBoost), Optuna hyper-parameter optimization, and Shapley addition (SHAP) interpretable model. Correlation analysis and continuous variable projection were employed to identify key inversion factors. The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. The optimal model was selected to estimate soil salinity in the study area from 2019 to 2023. The results showed that the XGBoost model fitted optimally, the test set had high R² (0.76) and the ratio of performance to deviation (2.05), and the estimation results were consistent with the measured salinity values. SHAP analysis revealed that the salinity index and topographic factors were the primary inversion factors. Notably, the same inversion factor influenced varying soil salinity estimates at different locations. The saline soils of the study area in 2019 and 2023 were 65% and 44%, respectively, and the overall trend of soil salinization decreased. From the viewpoint of spatial distribution, the degree of soil salinization showed a gradually increasing trend from south to north, and it was most serious on the side near the Yellow River. This study is of great significance for the quantitative estimation of salinized soil in the irrigated area on the south bank of the Yellow River, the prevention and control of soil salinization, and the sustainable development of agriculture. Full article

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), permanganate index (COD_Mn), and 5-day biochemical oxygen demand (BOD₅)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH₃-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment. Full article

Frequent soil erosion disasters in the middle reaches of the Yellow River (MRYR) have a profound effect on the sediment load of the river. This paper addresses the intertwined effects of human activities and climate change on river runoff and sediment load. Therefore, runoff and sediment loads from hydrological stations along the main and tributary rivers within the MRYR were used. The Mann–Kendall (M–K) trend test and the double mass curve analysis, among other analytical tools, were used to examine the erosion patterns of these rivers from 1950 to 2022, as well as the main factors driving these changes. The results showed that the runoff depth of the Yan River tended to decrease, and there was a significant decrease in the mainstream and nine other tributaries, with a significant decrease in the sediment transport modulus for both the mainstream and tributaries. In the main river, human activities contributed between 69.99% and 94.69% to the runoff and between 88.52% and 98.49% to the sediment load, while in the tributaries, the contribution of human activities was greater. The annual runoff and annual sediment load in the MRYR showed a decreasing trend, with a discernible impact of human activities. The results of this research are of great significance for erosion control and the restoration of the ecological balance in the Yellow River Basin. Full article

The Yellow River basin, an area of extreme water scarcity, has faced significant challenges in water quality management due to rapid economic and social development since the 1980s. This study analyzes the water quality evolution over nearly 40 years, focusing on primary pollutants like chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), and permanganate index (COD_Mn). In the 1990s, sections of the river were severely polluted, with some areas failing to meet the lowest national standards. In 2000, 32% of the river water was classified as inferior Class V. However, enhanced water resource management and stricter pollutant regulations introduced after 2000 have significantly improved water quality. By 2010, water quality reached its nadir, with 16% of water classified as inferior Class V and 25% as Class IV–V. By 2020, water quality showed marked improvement, with a significant reduction in segments classified as inferior Class V and Class IV–V. Recent years have seen water quality stabilize, with COD meeting Class I standards and NH₃-N and COD_Mn meeting Class II standards based on national criteria. The study also highlights discrepancies in water quality between the mainstream and tributaries of the Yellow River. While the mainstream generally maintains good water quality, many tributaries remain severely polluted. In 2022, 85% of the water in tributaries was classified as Class I to III, 12.3% as Class IV to V, and only 2.7% as Class V. However, all water in the mainstream reached Class I–III, with 86% achieving Class II and 14% achieving Class I. A detailed analysis of the Huayuankou section over the past three decades shows a general decline in pollution indicators. Seasonal water quality fluctuations, correlated with flow rates and temperatures, were observed, often exhibiting normal distribution patterns. These findings underscore the effectiveness of sustained pollution control and the need for continuous, adaptive management strategies to improve and maintain water quality in the Yellow River basin. Full article

Ecotourism is vital for coordinating regional ecological protection with socio-economic development. The Gansu section of the Yellow River Basin is a typical ecologically fragile area in China, and it holds a distinctive position in ecological protection and high-quality development. This study explores spatial differentiation in ecotourist perceptions and their distinct effects on ecotourist satisfaction, revisitation, and recommendation. It uses four cities (Gannan, Linxia, Lanzhou, and Baiyin) in the Gansu section of the Yellow River (mainstream) as examples, employing a questionnaire survey to collect ecotourists’ perception data and applying a random forest model and one-way ANOVA for analysis. It was found that: (1) rich ecotourism potential exists in the Gansu section of the Yellow River Basin as an ecologically fragile area; (2) there is spatial differentiation in ecotourist perceptions, and among the four regions, Baiyin stands out for its nature and atmosphere perception, and Lanzhou excels in accessibility and service perception; (3) spatial disparities exist in the influencing factors of ecotourist satisfaction, revisitation, and recommendation. Ecotourists in districts with unique natural resources, such as Gannan and Baiyin, prioritize nature perception, whereas districts with abundant natural resources and an established foundation for ecotourism development, such as Linxia and Lanzhou, emphasize service and atmosphere perception. This study constructs a new research framework to explore spatial variations in ecotourists’ perceptions, assisting ecotourism destinations to meet the needs of ecotourists from the supply side, and presents distinctive strategies and recommendations for the development of ecotourism in similar ecologically fragile areas. Full article

The characterization of dissolved organic matter (DOM) is important for better understanding of the migration and transformation mechanisms of DOM in water bodies and its interaction with other contaminants. In this work, fluorescence characteristics and molecular compositions of the DOM samples collected from the mainstream, tributary, and sewage outfall of the Inner Mongolia section of the Yellow River (IMYR) were determined by using fluorescence spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). In addition, concentrations of potentially toxic elements (PTEs) in the relevant surface water and their potential relationships with DOM were investigated. The results showed that the abundance of tyrosine-like components increased significantly in downstream waters impacted by outfall effluents and was negatively correlated with the humification index (HIX). Compared to the mainstream, outfall and tributaries have a high number of molecular formulas and a higher proportion of CHOS molecular formulas. In particular, the O₅S class has a relative intensity of 41.6% and the O_5-7S class has more than 70%. Thirty-eight PTEs were measured in the surface water samples, and 12 found above their detective levels at all sampling sites. Protein-like components are positively correlated with Cu, which is likely indicating the source of Cu in the aquatic environment of the IMYR. Our results demonstrated that urban wastewater discharges significantly alter characteristics and compositions of DOM in the mainstream of IMYR with strongly anthropogenic features. These results and conclusions are important for understanding the role and sources of DOM in the Yellow River aquatic environment. Full article

In a transboundary river basin, downstream states frequently express concerns regarding the potential utilization of water resources by upstream states as a tool for exerting coercion. This fact contributes to instilling doubt in the applicability of negotiations, even in transboundary basins that possess strong international agreements. In an effort to address the issue, this paper introduces the painted water concept. It divides upstream states’ available water into three triage color volumes before reaching downstream states in ascending order of negotiability: green, yellow, and red. Additionally, downstream states must consider the dynamics of transitions of painted water classes over time when developing their negotiation strategies and water policies. In order to assess the concept’s contribution in practice, we analyze trilateral riparian negotiations along the Blue Nile River basin, based on a “what-if” analysis approach under four global future scenarios. These results could shed light on part of the complexity of the Blue Nile negotiation and mainstream the water policies and perspectives of riparian states. Here, this paper shows that the painted water concept can provide multidisciplinary insights into proactive water negotiations. The inclusion of such a concept can help to deepen theories, approaches principals, and any disciplines pertinent to transboundary water negotiations. Full article

The Xiaolangdi Reservoir is the second largest water conservancy project in China and the last comprehensive water conservancy hub on the mainstream of the Yellow River, playing a vital role in the middle and lower reaches of the Yellow River. To study the effects of the construction of the Xiaolangdi Reservoir (1997–2001) on the runoff and sediment transport in the middle and lower reaches of the Yellow River, runoff and sediment transport data from 1963 to 2021 were based on the hydrological stations of Huayuankou, Gaocun, and Lijin. The unevenness coefficient, cumulative distance level method, Mann-Kendall test method, and wavelet transform method were used to analyze the runoff and sediment transport in the middle and lower reaches of the Yellow River at different time scales. The results of the study reveal that the completion of the Xiaolangdi Reservoir in the interannual range has little impact on the runoff in the middle and lower reaches of the Yellow River and a significant impact on sediment transport. The interannual runoff volumes of Huayuankou station, Gaocun station, and Lijin station were reduced by 20.1%, 20.39%, and 32.87%, respectively. In addition, the sediment transport volumes decreased by 90.03%, 85.34%, and 83.88%, respectively. It has a great influence on the monthly distribution of annual runoff. The annual runoff distribution is more uniform, increasing the runoff in the dry season, reducing the runoff in the wet season, and bringing forward the peak flow. The runoff and Sediment transport have obvious periodicity. After the operation of the Xiaolangdi Reservoir, the main cycle of runoff increases and the second main cycle disappears. The main cycle of Sediment transport did not change obviously, but the closer it was to the estuary, the less obvious the cycle was. The research results can provide a reference for ecological protection and high-quality development in the middle and lower reaches of the Yellow River. Full article

The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m³, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups. Full article

Establishment of a watershed ecological compensation mechanism between multiple subjects is an effective means to realize the collaborative governance of water pollution and maintain the security of water ecology. This paper breaks through the conventional upstream and downstream perspectives of watershed ecological compensation design research and combines them with uncertainty factors. The watershed ecological compensation mechanism for the mainstream and branches was established based on the evolutionary game and the random process. Then, taking the midstream of the Yellow River as an example, some constraint conditions and influencing factors were explored. Results show that: (1) The branch government (i.e., the Shanxi provincial government) is the key to establishing an ecological compensation mechanism between the river mainstream and branches. (2) The proportion of pollution transferred by other branches, the initial probability and the random factors are the main factors affecting the decision-making of branch governments (Shanxi and Shaanxi provincial governments). (3) The compensation and reward of the mainstream government to the branch government and the compensation of the branch government to the mainstream government are the main factors affecting the decision-making of mainstream and branch governments (Shanxi–Henan provincial governments, Shaanxi–Henan provincial governments). The study may provide scientific guidance for the construction of a watershed ecological compensation mechanism between mainstream and multiple branches. Full article

In the northern part of the Ningxia Autonomous Region, there are rich lake resources, which are known as the “South of the Seas”. In recent years, the natural evolution of the water system and human activities have caused significant changes in the lake area. In order to fully understand the evolution of lakes in the northern Ningxia Yellow Irrigation Area, Landsat, Sentinel-2 images and ArcGIS were used to extract relevant information, and the cumulative distance level curve and Mann–Kendall trend analysis were used to analyze the trends of each driving factor in depth. The results showed that (1) the lake surface area in the northern Yellow Diversion Irrigation Area showed a significant increasing trend from 1986 to 2019. (2) The annual average temperature in the Ningxia Yellow River Irrigation Area has shown an increasing trend over the past 39 years, and no year has obvious cyclical changes, but in 1998, there was a sudden change in temperature and the temperature began to rise sharply; the annual average precipitation showed an increasing trend with a large variation, and the annual average precipitation from 1980 to 2018 showed a fluctuating increasing trend. (3) There is no significant linear pattern of runoff from upstream during 1986–2015, and it is characterized by fluctuating changes; the precipitation in the Yellow Irrigation Area is much lower than the average level in Ningxia, and it is classified as a typical arid area; the water consumption is all decreasing, but its linear trend is not significant; the most significant impact of the change in the substratum on the water surface is the construction of fields around the lake after 1990, followed by the Lake engineering treatment. (4) The water surface area of the mainstream is significantly and positively correlated with the incoming water from upstream, is significantly and negatively correlated with the area of grassland, and is significantly and positively correlated with the areas of arable land and construction land. The effect of land cover on the water surface area of the mainstream is lower than that on the water surface area other than the mainstream. Full article

Algorithm design and implementation for the detection of large herbivores from low-altitude (200 m–350 m) UAV remote sensing images faces two key problems: (1) the size of a single image from the UAV is too large, and the mainstream algorithm cannot adapt to it, and (2) the number of animals in the image is very small and densely distributed, which makes the model prone to missed detection. This paper proposes the following solutions: For the problem of animal size, we optimized the Faster-RCNN algorithm in terms of three aspects: selecting a HRNet feature extraction network that is more suitable for small target detection, using K-means clustering to obtain the anchor frame size that matches the experimental object, and using NMS to eliminate detection frames that have sizes inconsistent with the size range of the detection target after the algorithm generates the target detection frames. For image size, bisection segmentation was used when training the model, and when using the model to detect the whole image, we propose the use of a new overlapping segmentation detection method. The experimental results obtained for detecting yaks, Tibetan sheep (Tibetana folia), and the Tibetan wild ass in remote sensing images of low-altitude UAV from Maduo County, the source region of the Yellow River, show that the mean average precision (mAP) and average recall (AR) of the optimized Faster-RCNN algorithm are 97.2% and 98.2%, respectively, which are 9.5% and 12.1% higher than the values obtained by the original Faster-RCNN. In addition, the results obtained from applying the new overlap segmentation method to the whole UAV image detection process also show that the new overlap segmentation method can effectively solve the problems of the detection frames not fitting the target, missing detection, and creating false alarms due to bisection segmentation. Full article