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Sustainable inland waterways should meet the needs of navigation without compromising the health of riverine ecosystems. Here we propose a hierarchical model to describe sustainable development of the Golden Inland Waterways (GIWs) which are characterized by great bearing capacity and transport need. Based on datasets from 66 large rivers (basin area > 100,000 km 2 ) worldwide, we identify 34 GIWs, mostly distributed in Asia, Europe, North America, and South America, typically following a three-stage development path from the initial, through to the developing and on to the developed stage. For most GIWs, the exploitation ratio, defined as the ratio of actual to idealized bearing capacity, should be less than 80% due to ecological considerations. Combined with the indices of regional development, GIWs exploitation, and riverine ecosystem, we reveal the global diversity and evolution of GIWs’ sustainability from 2015 to 2050, which highlights the importance of river-specific strategies for waterway exploitation worldwide. The exploitation of rivers has been at the detriment of river ecosystems. Here the authors propose a concept of Golden Inland Waterways (GIWs) to represent large waterways and find that the exploitation ratio threshold around the turning point for most GIWs appear to be less than 80%, subject to ecological constraints.

The intensification of land use and climate change threatens watershed sustainability. These external disturbances drive complex interactions among components within watershed socio-ecological systems (SESs). Understanding how SESs respond to these changes is crucial for developing effective integrated watershed management strategies. Nevertheless, the nonlinear responses of these systems to such changes remain poorly understood. To fill this gap, this study proposes a network analysis method focusing on the Yangtze River Basin to construct an SES network comprising six dimensions, revealing the response of coupled relationships among network elements to climate and land-use change. The results showed that changes in land-use dynamics (LUD) and the standardized precipitation evapotranspiration index (SPEI) altered the link count and importance of network nodes, with notable shifts in vegetation and landscape nodes. Importantly, a strong nonlinear response of the LUD and SPEI to the coupled relationship between SES elements was observed, and critical thresholds were identified for all network attributes. Furthermore, compared to the SPEI threshold, the LUD threshold was stable at 0.24, demonstrating stronger robustness. This study provides a new perspective for understanding watershed SESs, and has important implications for sustainable ecosystem management and planning.

The waterway freight volume through the Three Gorges ship lock (TGL) has increased significantly since it started operation in 2003 and exceeded the designed capacity of 100 million tons in 2011, 19 years earlier than expected. This made the TGL a bottleneck for the Yangtze River waterway transport development. Based on the historical freight volumes and types through the TGL, a combination of qualitative and quantitative analyses are conducted here to identify the key factors affecting freight demand. A genetic algorithm and backpropagation (GA–BP) forecasting model (optimized backpropagation neural network model using a genetic algorithm) was developed to estimate freight demand at the TGL over the period 2020–2040. We predict that freight demand will continue to increase, reaching 260.2 million tons in the basic scenario by 2040 (or 224.7 million tons in the conservative scenario and 276.7 million tons in the optimistic scenario). However, the growth rate will gradually decline. The freight composition will tend to be more stable and homogeneous, with over 64% of the freight related to investment and construction. From the perspective of freight volume evolution, the necessity of the Three Gorges New Locks Project is justified and necessary.

Rough-bed open-channel flows (OCFs) are ubiquitous in rivers and canals. However, the scaling and energy contents of very-large-scale motions (VLSMs) in such flows remain unclear. In this study, the above characteristics of VLSMs are experimentally investigated with the measurement of particle imaging velocimetry (PIV). VLSM wavelengths obtained via premultiplied spectra analysis were consistent with previously reported values. Comparisons with these studies ruled out the role of relative submergence, and suggested that the channel aspect ratio is key to controlling the VLSM wavelengths in OCFs. VLSMs carry approximately 60% of the turbulence kinetic energy (TKE) and 38–50% of the Reynolds stress in rough-bed OCFs. The VLSM-related TKE fraction in the 0.1–0.5H range increased with increasing friction Reynolds number, while variation in the Reynolds shear stress did not exhibit any explicit trend.

The adaptability of cultured fish to complex flow conditions is crucial for their survival after being released into the wild. Running water in natural environments poses significant challenges for the proliferation and release of cultured fish. This study aimed to investigate the effects of flow stimulation on the adjustment capacity of cultured fish to cope with running water. The target fish were cultured grass carp. An annular flume was used to conduct tests on training and control groups. The results demonstrated an enhancement in the adjustment capacity of cultured fish following appropriate flow stimulation training. (1) The trained fish exhibited a heightened preference for low-velocity areas. (2) The trained fish displayed the ability to select a route characterized by low energy consumption, predominantly following the periphery of the low-velocity area. This suggested that an appropriate flow velocity could improve the sensitivity of training fish to water flow information, and their adjustment capacity to cope with running water improved to a certain extent. A higher adjustment capacity allowed them to process flow rate information rapidly and identify a migration strategy with lower energy consumption. This study provides a useful reference for enhancing the survival rate of grass carp through stock enhancement initiatives and contributes to the sustainability of freshwater ecosystems.

This study combined the characteristics of rock mass structure, channel characteristics of main and tributary rivers, and flow conditions in the Three Gorges Reservoir area of the Yangtze River and designed and constructed a three-dimensional granular rock shallow-water landslide surge model test system under dynamic water flow conditions. The main influencing variables, including the volume of the landslide body, the inclination of the sliding surface, the water depth of the channel, and the flow velocity, were determined. A four-factor four-level orthogonal test design and a single-factor test design were combined to determine 28 sets of test conditions. Based on existing field surveys and physical model test simulation results, the motion process of the landslide body was divided into four stages: starting, accelerating, slowly accelerating or decelerating, and rapidly decelerating. The variations in the motion and velocity of the landslide body over time were determined by analyzing the image sequence extracted using a high-speed camera. The morphological characteristics of the landslide accumulation body were analyzed, and the submerged rate of the landslide body was statistically calculated. The relationship between the maximum bottom width of the landslide underwater accumulation body and the volume of the underwater accumulation body was established.

The acoustic method, which enables continuous monitoring with great temporal resolution, is an alternative technique for detecting bedload movement. In order to record the sound signals produced by the impacts between gravel particles and detect the bedload motion, in this study, a hydrophone is placed close to the riverbed at the upper Yangtze River. Three categories of raw audio signals—moving gravel particles, ship engines, and flow turbulence—are collected and investigated. Signal preprocessing is performed using spectral subtraction to reduce the noise of the background sound, and the sound signal characteristic parameters are then calculated. In this paper, we propose a novel method for detecting and extracting bedload motion parameters, including peak frequency, pitch frequency, and energy eigenvector. When a segment of a speech signal meets the indicators for all three feature parameters simultaneously, the segment signal is classified as a bedload motion sound signal. Further work will be conducted to investigate bedload transport using the extracted audio signal.

Millions of river barriers have been constructed worldwide for flood control, hydropower generation, and agricultural irrigation. The lack of comprehensive records on river barriers’ locations and types, particularly small barriers including weirs, limits our ability to assess their societal and environmental impacts. Integrating satellite imagery with object detection algorithms holds promise for the automatic identification of river barriers on a global scale. However, achieving this objective requires high-quality image datasets for algorithm training and testing. Hence, this study presents a large-scale dataset named the River Barrier Object Detection (RBOD). It comprises 4,872 high-resolution satellite images and 11,741 meticulously annotated oriented bounding boxes (OBBs), encompassing five classes of river barriers. The effectiveness of the RBOD dataset was validated using five typical object detection algorithms, which provide performance benchmarks for future applications. To the best of our knowledge, RBOD is the first publicly available dataset for river barrier object detection, providing a valuable resource for the understanding and management of river barriers.

Bedload sediment transport is critical in the natural river environmental and ecological system. It is quite challenging to measure the bedload sediment transport rate in rivers with any degree of accuracy. In this study, the authors developed the Gravel Pressure and Voice Synchronous observation system (GPVS) to estimate the bedload sediment transport rate in rivers. The core of the GPVS includes an underwater high-fidelity audio recorder and pressure sensor. The audio recorder is intended to monitor the low bedload sediment transport rate, whereas the pressure sensor is utilized to detect relatively substantial bedload sediment transport. The GPVS is tested by running flume experiments with natural gravel to evaluate the system’s reliability and feasibility. Results reveal that the GPVS has a very high sensitivity for detecting bedload transport. When the bedload sediment transport rate is relatively low, the audio recorder can measure it quantitatively, showing that the system is not impacted by ambient noise.

In this study, 18S rRNA high-throughput sequencing was applied to investigate the eukaryotic community in a full-scale drinking water treatment plant. Eukaryotic species and microbial functions in raw water and filter biofilms were identified by metagenomic sequencing. The eukaryotic species richness and diversity presented declining trends throughout the treatment process. The lowest eukaryotic species richness was observed in disinfected water. Arthropoda, Ciliophora, Ochrophyta, and Rotifera were the dominant eukaryotic phyla and exhibited high variations in relative abundance among the different treatment units. Sedimentation significantly decreased the abundance of all eukaryotes except Arthropoda. Biological activated carbon (BAC) filtration and chlorine disinfection exerted strong effects on community composition. The eukaryotic communities in water were distinct from those in filter biofilms, as were the communities of different filter biofilms from each other. In contrast, communities were functionally similar among different filter biofilms, with the category metabolism being the dominant category represented, within which amino acid transport and metabolism (E) and energy production and conversion (C) dominated among subcategories. Seventy-one eukaryotic species pathogenic to humans were identified in raw water and filter biofilms. Quantitative PCR (qPCR) results showed that Acanthamoeba spp. and Vermamoeba vermiformis were present during some treatment processes, with concentrations of 12–1.2 × 10 5 copies/mL and 1 copy/mL, respectively. Neither of the two pathogenic amoebae was found in disinfected water. Canonical correspondence analysis (CCA) showed that pH was the most important environmental factor affecting eukaryotic community composition. Overall, the results provide insights into the eukaryotic community diversity in drinking water treatment plants and the potential eukaryotic hazards involved in drinking water production.