Inland River Research Articles

An agent-based socio-hydrological modeling to identify the feedbacks between agricultural irrigation and ecological water conveyance tradeoffs in Hotan River basin

Study regionHotan River basin in Northwest China Study focusThe conflict between agricultural irrigation and ecological water conveyance is particularly critical in arid inland river basins. Water resource systems involve multiple stakeholders and sectors, complicating the identification of dynamic feedback between agriculture and ecology. This study presents an agent-based socio-hydrological modeling (ABSHM) framework to address these interactions. New hydrological insights for the regionFeedback is quantified using dynamic state variable of oasis decision-making sensitivity in the Hotan River basin. When this sensitivity ranges from 0.03 to 0.06, water managers prioritize agricultural irrigation, increasing its usage from 8 to 47 million m3 at the expense of ecological water. Conversely, when sensitivity is from 0.015 to 0.03, ecological health requires increasing ecological water use from 27 to 66 million m³ by reducing irrigation. The ABSHM framework effectively captures these dynamic feedback processes, supporting basin water resources management and decision-making. The uniqueness of the ABSHM framework lies in its ability to capture the impact of individual decision-making behaviors on the overall water resource system. This offers new perspectives and approaches for addressing current and future water resource management challenges.

Study on the Spatial–Temporal Variation of Groundwater Depth and Its Impact on Vegetation Coverage in Ejina Oasis

Ejina, a representative inland river basin situated in the arid region of northwest China, exhibits a delicate ecological environment and its vegetation coverage is intrinsically linked to regional ecological security. Based on MOD13Q1-NDVI data from 2018 to 2023 and groundwater depth monitoring data during the same period, this study analyzed the spatial–temporal variation characteristics of vegetation coverage and its relationship with groundwater depth in Ejina. It is found that the vegetation coverage in Ejina is generally low and mainly distributed along the riverbanks in the form of strips. During the study period, the overall trend of vegetation coverage showed a fluctuating pattern of first increasing and then decreasing, revealing the fragility of the regional ecology. The groundwater depth shows the characteristic of being higher in the east river than the west, and the trend of groundwater depth along the river flow is first increasing and then decreasing. The spatial groundwater depth indicates that the east river is higher than that of the west river, and the groundwater depth along the river flow first increases and then decreases. In terms of inter-annual changes, the groundwater depth experiences a process of first decreasing and then stabilizing. Further analysis indicates that vegetation growth and coverage in Ejina are significantly affected by water conditions, and areas with high Normalized Difference Vegetation Index (NDVI) values are mainly distributed along the riverbanks. In addition, there is a certain degree of correlation between groundwater depth and NDVI. When the depth of groundwater is too deep or too shallow, the positive correlation between NDVI and groundwater depth increases slightly and the negative correlation decreases slightly. The findings of this study are of great significance for understanding and predicting the response of vegetation coverage to groundwater changes in arid areas, and provide a scientific basis for water resources management and ecological protection in Ejina.

An Evaluation Study on the Spatial and Temporal Evolution of Water Ecological Security in the Hotan River Basin

With the intensification of global climate change, inland river basins in arid desert regions are facing serious challenges in water supply and ecological and environmental protection. A water ecological security assessment is important as a key management tool in the context of inland river basins situated in arid desert areas. This study evaluated the water ecological security of the Hotan River Basin based on a combination of the Ecology–Produce–Life Space perspective and the Drive–Pressure–Status–Influence–Respond (DPSIR) model. The entropy value method and the composite index method were employed for this purpose, and a regression analysis was used to establish a prediction model to forecast the future water ecological security status. The results show that from 2013 to 2020, the water ecological security status of the Hotan River Basin exhibited a fluctuating upward trend, shifting from an average to a good status. The pressure layer has the greatest impact on water ecological security, while the ecological space within the Ecology–Produce–Life Space is increasing in the overall share year by year. In the future, the water ecological safety condition of the Hotan River Basin is expected to improve and remain in good condition. Taking the Hotan River Basin as an example, the results of this study, combining the Ecology–Produce–Life Space perspectives and the DPSIR model, provide in-depth theoretical and practical value for the evaluation and prediction of water ecological security in inland river basins in arid desert areas, provide a scientific basis and feasible suggestions for relevant decision-making, and emphasize the importance of ecological spatial protection and restoration for the sustainable development of human society and ecosystems.

Deep reinforcement learning for collision avoidance of autonomous ships in inland river

Due to the characteristics of large inertia, large time delay, and nonlinearity in ship motion, the maneuverability of ships is an important issue related to the safety of ship navigation. The manipulation of ships significantly affects the safety of ship navigation, and its importance will gradually increase with the development of autonomous ships. Collision accidents are common due to the complexity of inland waterways and density of the ships. Herein, an autonomous learning framework with deep reinforcement learning (DRL) is constructed for autonomous driving tasks. The state space, action space, reward function and neural network structure of DRL are designed based on the ship’s maneuvering characteristics and control requirements. A DDPG algorithm is then used to implement the controller. Finally, some representative route segments of the inland waterway is selected for simulation research based on the virtual simulation environment. The designed DRL controller can quickly converge from the training and learning process to meet the control requirements. By comparing with the previous experimental results, the effectiveness of this algorithm is verified. Therefore, this study provides a reference for future research on the collision avoidance technology of autonomous ships in inland rivers.

Factors influencing carbon and water use efficiency in changing environments

IntroductionVegetation plays a crucial role in terrestrial ecosystems, acting as a vital link connecting the lithosphere, hydrosphere, and atmosphere in terms of energy flow and material cycling. Changes in surface vegetation significantly regulate the water cycle, energy flow within terrestrial surfaces, and global carbon balance.MethodsThis study focuses on nine major river basins in China to quantitatively investigate the impacts of climate factors, vegetation dynamics, and land use changes on carbon use efficiency (CUE) and water use efficiency (WUE).ResultsThe primary controlling factors of WUE trends are NDVI (average contribution: 33.75% ± 6.90%) and VPD (average contribution: 28.04% ± 3.98%). NDVI predominates in the Haihe, Yellow River, Yangtze River, Pearl River, and Songliao River basins, while shortwave radiation (Srad) dominates in the southeastern rivers and inland river areas, and humidity (Shum) in the southwestern river basins. For CUE trends, the main controlling factors are Srad (average contribution: 36.46% ± 3.40%) and precipitation (Pre) (average contribution: 26.72% ± 5.20%). NDVI negatively influences the Huaihe River and southeastern river basins, while Pre negatively influences the Songliao River and Yellow River basins, and Srad negatively influences the Huaihe and southwestern river basins. Pre predominates in the Huaihe, Songliao, Haihe, southwestern river basins, and inland river areas, while Srad predominates in the Pearl River, Yangtze River, and Yellow River basins.DiscussionClimate factors and vegetation dynamics have significant regional impacts on WUE and CUE across different river basins, especially the roles of NDVI and VPD on WUE, and Srad and precipitation on CUE. These differences underscore the importance of developing region-specific management strategies to optimize ecosystem services in each basin.

Response of drought to climate extremes in a semi-arid inland river basin in China

AbstractAgainst the backdrop of global warming, climate extremes and drought events have become more severe, especially in arid and semi-arid areas. This study forecasted the characteristics of climate extremes in the Xilin River Basin (a semi-arid inland river basin) of China for the period of 2021–2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, a linear regression, a wavelet analysis, and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070. The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios (SSP5-8.5>SSP2-4.5>SSP1-2.6) in the future. The SPEI trends under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were estimated as −0.003/a, −0.004/a, and −0.008/a, respectively, indicating a drier future climate. During the historical period (1970–2020), the SPEI and SDI trends were −0.003/a and −0.016/a, respectively, with significant cycles of 15 and 22 a, and abrupt changes occurring in 1995 and 1996, respectively. The next abrupt change in the SPEI was projected to occur in the 2040s. The SPEI had a significant positive correlation with both summer days (SU) and heavy precipitation days (R10mm), while the SDI was only significantly positively correlated with R10mm. Additionally, the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4–6 a, indicating a robust interdependence between the two indices. These findings have important implications for policy makers, enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.

Wireless Channel Propagation Model for Inland Waterway Bridge Scenario

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

No-Tillage Treatment with Total Green Manure Mulching Reduces Soil Respiration by Regulating Soil Water Content Affecting Heterotrophic Respiration

Green manure is widely applied in agricultural production due to its beneficial soil modification and fertilization effects. However, the mechanisms underlying the effects of green manure return methods on soil respiration (Rs) and its components remain unclear. This study aimed to investigate the effects of green manure return methods on Rs in maize fields by quantifying Rs levels. A field experiment was conducted from 2021 to 2023 in the inland river oasis irrigation area of Gansu, with five treatment conditions: tillage with a full quantity of green manure incorporated into the soil (TG), no tillage with a full quantity of green manure mulched on the soil surface (NTG), tillage with roots incorporated into the soil and above-ground green manure removed (T), no tillage with above-ground manure removed (NT), and conventional tillage and leisure (CT). The results showed that, compared with CT, the NTG treatment increased the maize grain yield while reducing the soil heterotrophic respiration rate (Rh) by 8.5–9.8% and Rs by 6.7–8.7%, but did not significantly affect the soil autotrophic respiration rate (Ra), and decreased the carbon emission efficiency (CEE) by 20.8–25.6%. The increase in the soil water content (SWC) significantly reduced Rh during all growth periods, which was the primary factor in the reduction of Rs. Additionally, the net ecosystem productivity carbon sequestration (NEP-C) of the farmland ecosystem was positive under this system, indicating that the soil acts as a carbon “sink”. Therefore, a no-tillage treatment with a full quantity of green manure mulched on the soil surface can be used as a reasonable green manure return method to reduce carbon emissions from farmland in arid oasis irrigation regions.

An Unmanned Traffic Command System for Controlled Waterway in Inland River: An Edge-centric IoT Approach

An Unmanned Traffic Command System for Controlled Waterway in Inland River: An Edge-centric IoT Approach

Evaluation of Water System Connectivity Based on Node Centrality in the Tarim River Basin, Xinjiang, China

Water system connectivity is an important measure to optimize the balanced spatial allocation of water resources and water security patterns. Inland river basins in arid zones are generally insufficiently connected, so the scientific evaluation of the current status of water system connectivity and the centrality of its nodes in the water system network has practical significance for the scientific construction of regional water networks. Taking the Tarim River Basin in Xinjiang, China, as an object, this study conducted a comprehensive evaluation of basin water system connectivity by constructing a water system connectivity evaluation system with a total of 12 indicators for the three aspects of pattern connectivity, structural connectivity and functional connectivity. Subsequently, the TOPSIS model, with combined weights of the analytic hierarchy process and the entropy weight method, was used to comprehensively evaluate the connectivity of the watershed’s water system. The research evaluated the node centrality of the water system network that was closely related to the basin water system connectivity by using complex network analyses. The study results indicated the following: (1) among the source streams in the Tarim River Basin, the connectivity of the Aksu Basin was the best and that of the Keriya Basin was the worst and (2) the distribution patterns of the eigenvector centrality and betweenness centrality of the basin hydrological network nodes were similar, with nodes of highest centrality concentrated in the vicinity of the mainstems and the source–mainstem intersections. This work provides a basis and reference for the construction of water system connectivity and the selection of key control sections for ecological flow in the Tarim River Basin.

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.

An adaptive meta-imitation learning-based recommendation environment simulator: A case study on ship-cargo matching

High-quality shipping is one of the effective ways for sustainable cities in inland river basins to improve transportation efficiency and reduce energy consumption. Currently, the biggest challenge faced by shipping is the high empty-ship rate, which makes it impossible to directly apply machine learning methods due to the cold-start problem. Although some researchers have tried to utilize deep reinforcement learning(DRL)-based recommendation that do not rely on manually labeled data to alleviate the cold-start problem, progress has been slow due to the lack of available training environment. Therefore, this paper introduces an adaptive meta-imitation learning-based recommendation environment simulator, termed AMIL-Simulator. Specifically, we construct a conditionally guided diffusion model to simulate shipowner behavior in a dynamically changing environment. Moreover, we propose a shipowner reward model based on adaptive meta-imitation learning, enabling the learning of shipowner rewards across multiple tasks, even when confronted with limited samples and imbalanced categories. By conducting extensive quantitative experimental evaluations and shipowner-cargo matching studies, the results demonstrate the effectiveness of AMIL-Simulator, particularly in smaller-scale and cold-start environments.

Climate warming positively affects hydrological connectivity of typical inland river in arid Central Asia

Hydrological connectivity is crucial for understanding water-ecosystem dynamics, as it serves as a key link between different landscape units. However, the variability of hydrological connectivity in Central Asia remains unexplored, which poses challenges to a comprehensive understanding of ecohydrological processes. This study investigates the spatiotemporal patterns and driving mechanisms of hydrological connectivity in the Tarim River Basin (TRB), Central Asia, from 1990 to 2020, employing a novel approach that integrates remote sensing and reanalysis data. The results indicate an increasing trend in the hydrological connectivity index (HCI), with approximately 60% of the TRB exhibiting significant increases. Climate change exerts the greatest direct (0.59) and total (0.64) effects on HCI, with potential evapotranspiration (19.2%) and temperature (12.6%) being the dominant factors. In mountainous regions, climate change (0.65) is the primary driver, while human activities have a greater impact in the plains (−0.27). These findings offer a new framework for studying ecohydrological processes in arid regions.

The effects of environmental evolution on runoff and hydrological health variations in the upper Shule River, Northwest China

Promptly grasping the process of runoff change and the mechanism of hydrological health (H) variation in inland river basins is essential for the optimal use of water resources in the arid area of Northwest China. In this study, the upper Shule River (USR) was selected as the research area. The Budyko equation and the slope changing ratio of cumulative quantity method (SCRCQ) were used to analyze the response of environmental changes to the evolution of runoff and hydrological health during 1956–2022. The findings showed that the streamflow at various time scales in the USR showed a significantly increased trend from 1956 to 2022. The change rate of annual maximum runoff (Rmax) was 3.3 times that of annual runoff (R), the change rate of R in the flood period was 2.4 times that in the non-flood period, and the change rate in summer was 5 times that in winter. The H of the USR indicated a downtrend during 1956–2022, and the H in the Ta period was better than that in the Tb period. June and August were the main periods of runoff increase and hydrological health evolution, and precipitation (P) was the main driving factor. The response of Rmax increased to climate variability and anthropogenic stressors was 50.42 % and 49.58 %, respectively. The response degrees of H to climate variability and anthropogenic stressors were 62.89 % and 37.11 %, respectively. In general, climate variation was the major factor in the increase of R and the decrease of H in the USR. The findings are useful for getting the streamflow evolution process and hydrologic cycle mechanism and playing a supporting role in the sustainable management of water resources in the future.

Sustainable groundwater regulation in typical irrigation areas of inland river basins based on ecological indicators and the MIKE-SHE hydrological model

Study regionThe study area is located in the Nukus irrigation area on the lower reaches of the Amudarya River in Uzbekistan. Study focusThe extensive agricultural land development has introduced a substantial amount of water resources into the irrigation area, leading to a significant rise in regional groundwater levels. Quantifying regional groundwater resource issues and achieving sustainable groundwater management under conditions of high spatiotemporal heterogeneity are key scientific issues for regional ecological protection. Thus, this study used the concept of ecological water level to quantify groundwater issues, used the MIKE-SHE hydrological model to conduct scenario simulations, formulated and subsequently evaluated a groundwater regulation plan with spatiotemporal attributes. New hydrological insights for the regionRegional sustainable development groundwater level should be within the range of 1.78–2.78 m. During the irrigation and non-irrigation periods, the groundwater levels in approximately 84.3 % and 72.2 % of the study area respectively exceeded the upper and lower ecological water level limits. Under the background of gradually increasing groundwater fluctuations, it will pose a serious threat to regional ecology. Therefore, this paper established a monthly-scale groundwater zoning control plan, which can make the area of regional groundwater within the ecological water level reach 87.53 %. All research not only contributes to the protection of the local ecological environment but also provides valuable insights for water resources management in other regions.

Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field

In order to ensure the safety of navigation in a bridge area, and based on the theory of the safety potential field, a method of ship safety assessment and early warning in an inland river bridge area is proposed. Firstly, the risk elements associated with ship collisions in a bridge area are classified. Secondly, these risks are quantified using the potential energy field, the boundary potential field and the behavioural field, and then the ship state under the influence of wind flow, predicted by the Kalman filter, is quantified using the kinetic energy field. Then, the above four potential energy fields are merged to obtain a superposition field, and the magnitude of the instantaneous risk in the bridge area is obtained based on its magnitude. Finally, the change of field strength values under different moments is used for early warning. The results of the simulation of a ship passing through the piers of the Baijusi Bridge show that the model can effectively quantify the risk of a ship–bridge collision in the inland bridge area and provide real-time warning of the risk of a ship–bridge collision in the bridge area, which is of great significance for improving the safety of the inland bridge area.

Air Pollutant Emission Factors of Inland River Ships under Compliance

Inland river ships (IRSs) use diesel with a lower sulfur content and emit relatively low emissions, making it challenging to monitor their emissions. Sniffer monitoring equipment was installed from August 2020 to June 2022 at the Gezhou Dam of the Yangtze River and monitored emissions from 8,238 IRSs passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oil samples for direct fuel sulfur content (FSC) detection, which determined the actual FSC of the passing ships. The monitoring data from these 100 ships indicated that the relative error of the SO2 emission factors (EFs) and FSC results is significant at the 10-parts-per-million level. The monitoring data from the remaining 8,138 ships showed that the EFs of NO, NO2, PM2.5, and PM10 were 24.02 ± 16.92 g kg−1, 10.30 ± 18.08 g kg−1, 0.72 ± 0.60 g kg−1, and 0.92 ± 0.70 g kg−1, respectively. The NOx EFs of container ships are higher than those of other ship types, while the PM EFs for different ship types do not significantly differ. Based on these EFs, we calculated the average emission rates for different types of ships passing through locks, which is a real-time measurement method for estimating ship emissions. In addition, a comparison of ship EF measurements over the past 20 years revealed that EF values for SO2, NOx, and PM exhibited a downward trend, with the calculated results of the current study determined to be the lowest numerical level.

Water resource safety assessment of the Hexi inland river of China based on the PSR model

ABSTRACT The Hexi Corridor is located in northwest China, and its water resources mainly rely on meltwater from the Qilian Mountains and limited precipitation. The Hexi Corridor is an important regional population center and grain production area. However, limited rainfall restricts regional development due to inadequate water resources. Assessing water resource security in the Hexi region and providing targeted recommendations are crucial for rational planning and utilization of water resources, protecting the ecological environment, and promoting development. This study employs the entropy weight method to construct a pressure-state-response (PSR) system, assessing the water resource security of various river basins in the Hexi inland area. The evaluation results indicate that since the 21st century, the comprehensive water resource security scores of the three river basins have shown an upward trend. Specifically, the economic and social systems of the Shule River Basin and Heihe River Basin are improving, while their natural systems are declining; all three systems in the Shiyang River Basin are improving. Based on these results, targeted recommendations for different areas are proposed, providing a basis for enhancing water resource security in the region.

Inland river health assessment based on statistics and matter-element theory: a case study of the Shiyang River in China

ABSTRACT A method combining statistics and matter-element theory was proposed to evaluate the health status of the main stream of the Shiyang River in Gansu Province, China. The river health evaluation indicators and their weight values were derived, constructing an evaluation system with four criteria layers: ‘basin’, ‘water’, ‘biology’, and ‘social service function’, encompassing 11 indicators. The Shiyang River was divided into three sections: Xiying, Liangzhou, and Minqin. The fuzzy evaluation method and the matter-element model were used to assess health grades. The weights of the ‘basin’, ‘water’, ‘biology’, and ‘social service function’ criteria are 29, 15, 24, and 32%, respectively. The comprehensive evaluation results for the Xiying, Liangzhou, and Minqin sections are ‘very healthy’, ‘unhealthy’, and ‘sub-healthy’, respectively. The main factors leading to lower scores in the Liangzhou and Minqin sections are ‘ecological flow’, ‘macrobenthic integrity’, and ‘fish diversity’. This study provides a comprehensive, scientific method for assessing and maintaining the health of the Shiyang River.

Remote sensing ecological index (RSEI) affects microbial community diversity in ecosystems of different qualities

Soil microorganisms are key to ecological environment stability, but climate change and human activities exacerbate ecological environment changes. Therefore, assessment of ecological environment quality impacts on microbial diversity is needed. The Tarim River is the largest inland river in China and plays a crucial role in supporting regional biodiversity, maintaining ecological balance, and preventing desertification. In this study, we used the Remote Sensing-based Ecological Index (RSEI) to assess the ecological quality of habitats in the Tarim River Basin and explore the effects of habitat quality (extreme, semi-extreme, and general) on the structural diversity of microbial (bacterial and fungal) communities, biogeographic patterns, co-occurrence networks, and community assembly processes. Study results show that soil physicochemical characteristics varied significantly with habitat quality; highly resilient microorganisms are more abundant in habitats with low ecological quality. RSEI affects changes in microbial communities, and the positive correlation ratio of the network is inversely proportional to RSEI. The interspecific relationships of microbial communities in the Tarim River Basin are dominated by positive correlations, and community assembly is strongly influenced by stochastic processes. RSEI directly affects soil microbial diversity, with its contribution to both bacterial and fungal diversity being 0.27. Total nitrogen (TN) also directly affects microbial diversity, with effects of 0.11 on bacteria and 0.07 on fungi, respectively. This study provides scientific evidence and technical support for understanding microbial diversity in environments and for the development of regional sustainable development policies.