River Management Research Articles

Regional efficiency analysis of fresh food cold chain logistics in China based on three-stage DEA model

PurposeAssessing the efficiency of fresh food cold chain logistics as accurately as possible is essential for industry development planning. This study was designed to analyze the efficiency of fresh food cold chain logistics in China.Design/methodology/approachA three-stage data envelopment analysis (DEA) model was used to analyze the efficiency of fresh food cold chain logistics in 30 provinces of China from 2013 to 2019. The stochastic frontier analysis (SFA) model in the second stage was used to eliminate the influence of external environmental factors and random disturbances on efficiency analysis results.Findings(1) The overall actual efficiency of fresh food cold chain logistics in China is unsatisfactory, with an average technical efficiency of 0.382 over the 7-year period. (2) The national average technical efficiency and average scale efficiency were overestimated by 29.9% and 40.0%, respectively, compared with the actual values. (3) The efficiency of fresh food cold chain logistics does not align with the level of regional economic development. (4) Distinct regional variations exist in the efficiency of fresh food cold chain logistics in China, with higher efficiencies observed in Northwest China and the Central Yangtze River regions, and the lowest efficiencies in the northeast regions.Originality/valueThis study applies a three-stage DEA model to assess the development and regional differences of fresh food cold chain logistics in China, enriching the application of models and empirical analysis in this field. By analyzing the situation in China, it provides ideas and references for other developing countries to develop cold chain logistics.

Assessing Land Use and Cover Changes arising from the 2022 water crisis in Southeast China: A comparative analysis of Remote Sensing Imagery classifications and Machine Learning algorithms

Abstract. The water crisis in the southeast region of China in 2022, caused by one of the worst heatwaves on record, was characterized by severe shortages of water resources, leading to challenges for local communities, agriculture, and industry. To analyze changes in land use and land cover (LULC) in the Jialing River region, Chongqing, China, we compared Remote Sensing (RS) imagery classifications before and after the intense heat waves of 2022. We evaluated the performance of two machine learning algorithms, KDTree KNN and Random Forest (RF), in LULC classifications. The classifications were carried out based on the RS images from the OLI/Landsat 8 system, NDWI index, and SRTM data. The model performances were similar, the classification accuracy showed that the RF algorithm was superior to KDTree KNN. The RF LULC classification and area calculation corroborate with the visual analysis, reaffirming the superiority of RF, which shows a decrease in water surface area, unlike DKTree KNN.

Evolution mechanism of physical morphology of oxbow lake in the middle Yangtze River

Oxbow lakes, formed through river meandering cutoffs, are recognized as key components of river ecosystems, characterized by both fluvial and lacustrine features. The focus of previous research has been placed primarily on post-disconnection changes, leaving gaps in the understanding of their long-term morphological evolution. This gap is addressed through a comparative analysis of gate-controlled and naturally connected oxbow lakes over a 30-year period. In this study, Tian’ezhou Lake (gate-controlled) and Shangchewan Lake (naturally connected), located in the middle Yangtze River, are investigated. Key morphological parameters such as curvature, shoreline development index, and shrinkage rate were measured using Google Earth Engine (GEE). The influence of hydrological and meteorological factors on lake morphology was analyzed through Redundancy Analysis (RDA). Significant morphological changes were observed from 1990 to 2000. During this period, Tian’ezhou’s curvature was found to increase by 0.003/year, while Shangchewan’s curvature decreased by 0.011/year. After the construction of the Three Gorges Dam (TGD), a reduction in the rate of morphological evolution was detected, particularly between 2000 and 2010, when Tian’ezhou’s curvature decreased by 0.071/year and Shangchewan’s increased by 0.04/year. Post-2010, no significant changes in planar morphology were recorded in either lake. It was revealed by RDA that annual precipitation from 1990 to 1999 played a dominant role in influencing the morphological evolution of both lakes. From 2000 to 2020, water level fluctuations and hydrological connectivity emerged as the primary drivers. Gate control at Tian’ezhou Lake mitigated extreme water level fluctuations, contributing to a more stable environment and a slower rate of morphological evolution compared to Shangchewan. The findings underscore the importance of balanced water management strategies, integrating both natural and engineered hydrological interventions. Practical guidance is provided for ecological restoration and river management, with an emphasis on maintaining stable habitats and ensuring ecosystem resilience in response to hydrological changes.

Research on the influence of Spillover effect on dual-channel supply chain under the background of live-streaming marketing

In the context of live streaming marketing, it is crucial for members of the supply chain to allocate resources not only to enhance product quality through quality improvement efforts but also towards marketing initiatives aimed at boosting sales. This paper focuses on a dual-channel supply chain comprising a brand vendor and a streamer and investigates the spillover effect of the live streaming channel on traditional channels and product goodwill. By analyzing the Stackelberg game and differential game, this study compares the optimal equilibrium strategy and equilibrium profit under two decentralized models: the brand vendor-dominated decentralized model and the streamer-dominated decentralized model, as well as the centralized decision-making model. Additionally, this paper presents the optimal strategy and profit function of the product goodwill trajectory for the brand vendor, streamer, and the entire supply chain, considering different spillover effects using numerical simulation. The analysis reveals that the level of quality improvement consistently increases over time, while the variation in product goodwill is more diverse. The optimal pricing of products is influenced by the market share of the channel with goods under the decision of the two decentralized models. Risk-averse brands tend to collaborate with waist and tail streamers to gain control over bargaining power, while risk-seeking brands often choose top streamers to distribute their goods when the market share of goods channels is significant. The overall profitability of the supply chain is influenced by consumers’ preferences for quality and marketing.

Stability Assessment of Weirs Reinforced with Sheet Piles

Extreme weather events induced by climate change are increasing across the world. In particular, floods resulting from unpredictable heavy rains cause significant damage to property and human life. In Korea, river and underwater structure management is performed continuously in accordance with strict regulations. However, to prepare for unprecedented downpours, the structures of river reservoirs must be reinforced and their stability must be evaluated. This study focuses on the stability evaluation of piping, which represents the main cause of weir collapse. To this end, sheet piles are applied to weirs to evaluate the effects of changes in their length and location on their seepage discharge, seepage velocity, and maximum hydraulic gradient. Additionally, piping stability evaluation of weirs is conducted using sheet piles by evaluating the critical velocity. The results derived in this study are expected to be highly beneficial for reinforcing weirs currently in operation as well as for the design, construction, and operation of future underwater structures.

A Multi-Scale Feature Fusion Deep Learning Network for the Extraction of Cropland Based on Landsat Data

In the face of global population growth and climate change, the protection and rational utilization of cropland are crucial for food security and ecological balance. However, the complex topography and unique ecological environment of the Qinghai-Tibet Plateau results in a lack of high-precision cropland monitoring data. Therefore, this paper constructs a high-quality cropland dataset for the YarlungZangbo-Lhasa-Nyangqv River region of the Qinghai-Tibet Plateau and proposes an MSC-ResUNet model for cropland extraction based on Landsat data. The dataset is annotated at the pixel level, comprising 61 Landsat 8 images in 2023. The MSC-ResUNet model innovatively combines multiscale features through residual connections and multiscale skip connections, effectively capturing features ranging from low-level spatial details to high-level semantic information and further enhances performance by incorporating depthwise separable convolutions as part of the feature fusion process. Experimental results indicate that MSC-ResUNet achieves superior accuracy compared to other models, with F1 scores of 0.826 and 0.856, and MCC values of 0.816 and 0.847, in regional robustness and temporal transferability tests, respectively. Performance analysis across different months and band combinations demonstrates that the model maintains high recognition accuracy during both growing and non-growing seasons, despite the study area’s complex landforms and diverse crops.

1D-2D hydrodynamic and sediment transport modelling using MIKE models

A comprehensive modeling framework utilizing hydrodynamic and sediment transport models (MIKE Hydro River, MIKE 21 FM, MIKE 21C) was applied to the 33.57 km stretch of the Banshadhara River from Banshadhara bridge, Gunupur to Kashinagar town. Daily discharge, water levels, and sediment data from 2013 to 2022 of Gunupur and Kashinagar gauge stations was used as model input, and calibration–validation of models. Additionally, satellite remote sensing data used for determined Manning’s roughness (n) values as well compared the model outputs. Calibration procedures ensured high accuracy, with MIKE Hydro River achieving 98.3% agreement between observed and simulated water levels. 1D sediment transport model showing significant variability in total bed load values ranging from 0.3 to 3.83 m^3/s. The Wilcock & Crowe gravel bed load formula accurately predicted bed load transport within the expected magnitude, albeit tending to overestimate transport, indicative of supply-limited conditions. 2D MIKE 21 FM simulations highlighted flow dynamics, with rapid flood arrival at Kashinagar and varying current speeds along the river, especially at bends. MIKE 21C, focused on the central portion, indicated erosion processes at river bends with high discharge. Sediment transport model accuracy was approximately 86.7%, capturing general trends despite occasional discrepancies. Over a 10-year simulation, erosion trends and potential river course shifting were observed, particularly near bends. The analysis provides valuable insights for river management, flood risk mitigation, water resource management, infrastructure planning, and environmental preservation in the Banshadhara River basin and similar environments.

Research on the Impact of Social Media Platform Brand Promotion: Take TikTok Platform Online Selling as an Example

Nowadays, it has been discovered that more and more online selling platforms are developing. More and more people tend to do shopping online, including Taobao, Tmall, and Pinduoduo. However, live-streaming e-commerce is also stepping forward at a rapid speed. More and more online shopping platforms are appearing. This article will mainly focus on the impact of the TikTok platform’s brand promotion. The research methods used in the research are the literature review method and the case analysis method. By digging deep into a specific example of a certain selling product on the Tiktok platform’s live streaming channel, the article jumps to the conclusion that the Tiktok platform utilizes consumers’ desire for convenience and inner mind to simplify the consumption process to attract the consumers and promote their brand quality. After completing the research, it has been discovered that most social media platforms utilize consumers’ inner activity to attract users to increase their brand influence.

Regional Factors Affecting Smallmouth Bass and Largemouth Bass Recruitment in Midwestern USA Reservoirs

ABSTRACTRegional factors correlated with recruitment of black bass (largemouth bass; Micropterus nigricans and smallmouth bass; Micropterus dolomieu), two important fishes, are rarely studied, despite the importance of recruitment variation in influencing fish populations. We sought to identify factors that drove variation in age‐0 and age‐1 black bass abundance. Age‐0 or age‐1 black bass catch per unit effort (CPUE) in reservoirs in Kansas and Illinois, USA, was positively correlated with local short‐term (i.e., mean April precipitation) environmental variables. In contrast, age‐0 or age‐1 black bass CPUE was generally negatively correlated with long‐term environmental variables and river regulation metrics (i.e., reservoir elevation) in the Mississippi and Missouri river reservoirs. Our findings highlight that consideration of spatiotemporal scale is important when managing black bass populations.

Experimental Study of Bedform Development Characteristics and Their Effects on Sediment Transport

Bedforms are complex and varied features on riverbeds created by the interaction between flow and sediment particles. Bedform evolution is crucial for understanding sediment transport, bed resistance, and the ecological environment of rivers. In this study, a series of movable-bed dune experiments were conducted utilizing advanced 3D topographic laser scanning technology to precisely measure the 2D and 3D dune bedforms under various conditions. This enabled the detailed analysis of the bedform parameters, including the dune length and height, the crest height, and the trough depth. Furthermore, this study examined the relationship between bed resistance and dune morphology and investigated how the intensity of bedload transport affects bed resistance. The findings indicate that bedload mass transport on the riverbed significantly contributes to increased bed resistance. Given the limitations of the traditional bedload transport rate formulas for dune bedforms, this research introduces a new formula derived from the experimental data. The accuracy of this formula was confirmed using the nonlinear least squares method. Our study enhances the accuracy of sediment transport predictions and provides scientific support for river management and engineering practices.

The LTAR Croplands Common Experiment at the Gulf Atlantic Coastal Plain.

The Gulf Atlantic Coastal Plain (GACP) Long-Term Agroecosystem Research (LTAR) Network site is characterized by hot and humid summers with low gradient stream channels surrounded by wetland forests and croplands. Beneath its sandy soils, a confining layer stifles recharge to the deeper aquifer system, so a substantial proportion of streamflow is driven by shallow subsurface baseflow. Agricultural practices in the area consist of forage and livestock production, forestry, and rotational cropping systems dominated by cotton (Gossypium hirsutum) and peanut (Arachis hypogaea). Certain factors impose challenges to the viability of GACP agricultural systems, like changing economics and demographics, as well as pest and disease pressures. The GACP communicates with stakeholders from various backgrounds, who provide their perspectives as agricultural research service scientists execute their research plans. The GACP LTAR common experiment (CE) is carried out via plot- and field-scale studies. The plot-scale CE compares prevailing practices, determined from regional data, with an alternative treatment including winter covers, such as the biofuel feedstock carinata (Brassica carinata, A. Braun), to provide both economic and environmental benefits. The field-scale CE is observational; key variables are monitored for two farms where management practices largely emulate the prevailing treatment. Data collection efforts quantify vegetation, hydrology, soils, and climate data to produce datasets for modeling and statistical analysis. Research teams quantify relationships between land management, environment, and socioeconomic benefits. Ultimately, the GACP LTAR site works to facilitate agricultural system health and wellbeing at local, regional, and national scales by providing long-term science-based solutions.

Delineating the drought vulnerability zones in Bangladesh

The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Description of a relict aspen parkland-associated grassland in the Peace River region of British Columbia, Canada

The aspen parkland-associated grasslands of the Peace River region, British Columbia (BC), have been severely reduced in area, primarily because of agricultural and urban development. In this region, the species composition of plant communities is similar to that of prairie grasslands and is topographically influenced, occurring primarily on warm-aspect slopes along the Peace River and some of its tributaries. Historical records show that non-forested grass- and sedge-dominated plant communities occurred on flat and gently rolling terrain in a parkland ecosystem near what are now the communities of Dawson Creek and Fort St. John. The Peace grasslands are not represented in BC’s biogeoclimatic ecosystem classification, perhaps leading to their neglect in regional natural resource management and conservation planning. Here, I describe the vascular plant community of a level-terrain relict aspen parkland-associated grassland in the Peace River region. Its species composition differs from nearby warm-aspect grasslands and includes provincially listed plant species. Increased awareness of grassland communities may support conservation, ecosystem restoration, and climate change adaptation in the southern boreal region of BC.

Analyzing the distribution patterns and dynamic niche of Magnolia grandiflora L. in the United States and China in response to climate change.

Magnolia grandiflora L. (southern magnolia) is native to the southeastern coastal areas of the United States, from North Carolina to eastern Texas (USDA Cold Hardiness Zone 8). It is currently widely cultivated in Zones 5-10 in the U.S. and in southern Yangtze River regions in China. Limited studies have examined the effects of climate change and human activities on the geographical distribution and adaptability of M. grandiflora during its introduction to China. We selected 127 occurrence points in the U.S. and 87 occurrence points in China, along with 43 environmental variables, to predict suitable habitat areas for M. grandiflora using present climate data (1970-2000) and projected future climate data (2050-2070) based on a complete niche ensemble model (EM) using the Biomod2 package. We also predicted the niche change of M. grandiflora in both countries using the 'ecospat' package in R. The ensemble models demonstrated high reliability, with an AUC of 0.993 and TSS of 0.932. Solar radiation in July, human impact index, and precipitation of the wettest month were identified as the most critical variables influencing M. grandiflora distribution. The species shows a similar trend of distribution expansion under climate change scenarios in both countries, with predicted expansions towards the northwest and northeast, and contractions in southern regions. Our study emphasizes a practical framework for predicting suitable habitats and migration of Magnoliaceae species under climate change scenarios. These findings provide valuable insights. for species conservation, introduction, management strategies, and sustainable utilization of M. grandiflora.

How Does the Tibetan Plateau Land Thermal Initial Condition Influence the Subseasonal Prediction of 2020 Record‐Breaking Mei‐Yu Rainfall

AbstractAccurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.

Shifted Flood and Ecology Regimes Due To Channel Bar Greening and Increased Flow Resistance in a Large Dammed River

AbstractDamming profoundly affects downstream flow‐sediment regimes, altering channel bar dynamics and thereby affecting floods and riverine biodiversity. Here, we investigate the response of bars to upstream damming by examining patterns, mechanisms, and impacts in the Middle Yangtze River (∼1,000 km). Over a decade of post‐damming observational data reveal substantial increases in bar revegetation and Leaf Area Index. Shorter flood duration and stable bar size collectively drive bar greening. Consequently, denser vegetation has slowed flow velocity by 17% ± 2% and increased flow resistance by 21% ± 5%, offsetting the water‐level decrease from channel expansion due to scouring and even causing a slight rise in floodwater levels. Furthermore, damming has markedly altered river connectivity, thermal regimes, and solute dynamics, detrimentally affecting fish habitats and aquatic life. These findings, along with refined river stage simulation considering flow‐sediment‐vegetation interactions, facilitate sustainable reservoir operation and river management in big river systems.

Fish Distribution Pattern and Management in the Tinau River, Palpa

For study direct observation, survey, questionnaire, interviews and discussion method are used to meet the objectives. The main objectives of the study were fish distribution pattern and fish management. The field work was performed between September 2016 to june 2017 of different five stations in Tinau river. Local fishermen were also used for the collection of fishes from sampling sites. Cast net, hook, Bhureli jal and Nets (Jhal) other locally available devices were used for the collection of fish sample. Altogether 5 orders, 9 families, 14 genera and 25 species were found in the study area. Among them 19 fish species from Station I, 16 fish Species from Station II and station III, 10 fish species from Station IV and 7 species from Station V were collected with the help of local fisherman. Fish species such as Channa punctatus, Puntius conchonius and Glyptothorax pectinopterus were found to be clumped in distribution while the rest of the fishes were uniformly distributed. Cyprinids were found to be the most common and dominant among all the species. The human impacts like construction of dam, bridges, riverbed extraction, bank erosion, illegal fishing and use of poisonous chemicals effects on the fish distribution and management directly of the Tinau river. For the management practices like implementation of strict rules and regulations, made ladder for fish passing, construction of fish barrier, plantation, control water pollution and awareness programmes should be done with the help of local people and other institutions. This study helps the local people to pay more attention towards the knowledge about indigenous or locally found fishes and proper management of the Tinau river.

Groundwater productivity analysis with geology in the Geumgang River region, Korea

Groundwater productivity analysis with geology in the Geumgang River region, Korea

High resolution (1-km) surface soil moisture generation from SMAP SSM by considering its difference between freezing and thawing periods in the source region of the Yellow River

Soil moisture (SM) is a critical component of the land surface hydrological cycle, significantly impacting various sectors such as hydrology, meteorology, and agriculture. Accurate, high-resolution SM data are essential for effective flood forecasting, water resource management, and understanding the soil freeze-thaw processes in cold regions. This study aims to generate 1 km resolution liquid surface SM (SSM) data with a twice-daily update frequency by downscaling SMAP Level-4 SSM data using random forest (RF) and multiple linear regression (MLR) in the source region of the Yellow River (SRYR), by considering the differences in SM changes between freezing and thawing periods. To obtain the SSM data, 16 downscaling schemes of both RF and MLR were designed for each of the three scenarios. In each downscaling process, both land surface temperature (LST) and normalized difference vegetation index (NDVI) were utilized in MLR and RF models, alongside various combinations of additional variables such as albedo, elevation, leaf area index (LAI), soil texture. Results showed that during the freezing period, RF produced superior SSM estimates when supplemented with NDVI, LST, albedo, elevation, LAI, and soil texture. MLR was more effective during the thawing period when paired with NDVI, LST, elevation, LAI, and soil texture. During the freezing period, the downscaled SMAP SSM exhibited average R, RMSE, ubRMSE of 0.76, 0.029 m3·m-3, and 0.023 m3·m-3, respectively, when compared with in-situ measurements. During the thawing period, the average R, MAE, RMSE, and ubRMSE between the downscaled SMAP SSM and in-situ measurements were 0.52, 0.057 m3·m-3, 0.067 m3·m-3, and 0.054 m3·m-3, respectively, compared to 0.45, 0.070 m3·m-3, 0.083 m3·m-3, and 0.060 m3·m-3 for the original SMAP SSM. Thus, the research significantly enhances both the accuracy and spatial resolution of SMAP SSM estimations, underscoring its vital role in advancing hydrological studies within the SRYR.