Flood mapping from optical satellite imagery is often hindered by persistent cloud cover, while historical-water-occurrence-based gap-filling may fail when flood extent exceeds previous records. This study develops a Sentinel-1/Sentinel-2 framework for flood mapping in China, Spain, and Somalia. Sentinel-1-derived water maps provide cloud-independent dynamic references. A Markov chain first fills cloud-covered pixels whose adjacent satellite observations show consistent water or non-water states, and a spatiotemporal Markov random field then reconstructs the remaining uncertain pixels using spatial neighborhood coherence and temporally weighted observations. The gap-filled Sentinel-2 maps are further combined with Sentinel-1 maps to generate denser flood sequences. Across the three cases, the framework achieved 10 m resolution, mIoU values of 0.82–0.99, and effective mapping intervals of 1.6–3.6 days. Independent PlanetScope validation, component analysis, confidence intervals, and significance tests supported its reliability. The framework can provide timely flood-extent information for disaster response and flood-risk management.

Submerged air-jet scouring is promising for underwater excavation, yet the influence of bed slope remains poorly understood. This study experimentally investigates air-jet scouring on inclined sand beds with varying jet angles (θ), nozzle heights (H), and slope directions (uphill/downhill). Using four dimensionless parameters and K-means clustering, 18 tests are classified into three distinct scour patterns. Pattern I appears as a shallow smooth crater with negligible post-cessation backfilling regardless of slope. Pattern II is a moderate-depth V-shaped crater, with uphill conditions amplifying back-filling by more than five times. Pattern III is a deep platform crater where dense granular flow reshapes the scour profile; uphill configurations significantly lower the threshold for this regime, with back-filling reaching up to 63% of the dynamic scour depth. A dimensionless gravity–momentum ratio G is proposed to quantify slope–jet coupling, which correlates well with relative backfilling intensity Δ*. Results show that downhill conditions favor predictable scour geometry, while uphill slopes pose considerable risks of intense morphological reconstruction.

High-intensity earthquakes trigger secondary geological disasters. However, a systematic understanding of their effects on basin-scale sediment yield and sediment transport during flood events remains limited. Analysis of the Minjiang, Tuojiang, and Fujiang rivers in the Longmenshan region, based on long-term (1957-2023) flow and sediment data, reveals four key insights: First, flood events contribute 40%-95% of the annual sediment load. The sediment load during flood events exhibits various phases, characterized by the convergence of sediment transport modulus curves as runoff erosion power increases. Second, an abrupt shift occurred in 2013, marking a significant increase in flood-event sediment load. This post-seismic evolution is attributed to the combined effects of high-intensity precipitation, the Wenchuan earthquake (WCE), and anthropogenic activities. Third, since 2013, these tributaries have contributed approximately 47% of the sediment inflow to the Three Gorges Reservoir (TGR), with this contribution rising to 75-81% during wet years. Last, given the long-lasting impact of the WCE, continuous monitoring of the runoff and sediment inflow to the TGR is crucial for its operational management. These findings offer critical insights for assessing sediment transport risks during floods and optimizing regional reservoir operation strategies.

Selecting an efficient operational mode for air-jet seabed scouring requires understanding how jet expansion state governs both mechanism and performance. This study experimentally compares under-expanded and fully expanded air jets impinging on a non-cohesive sand bed in quiescent water. High-speed imaging reveals two distinct mechanisms: the expanded jet drives continuous viscous shear erosion (VSE), reaching dynamic equilibrium rapidly (0.48-3.9 s) via stable wall-bounded shear flow, whereas the under-expanded jet triggers violent bearing-capacity failure (BCF), requiring significantly longer times (5.9-7.0 s) due to intermittent explosive ejections. Under identical flow input, the expanded jet demonstrated unequivocally superior performance, achieving an 11-24% increase in maximum particle entrainment height, an expansion of cumulative entrainment area by up to 1.9 times, and a remarkable enhancement of the horizontal diffusion rate by a factor of 3.9 to 7.9. Morphologically, its scour profile closely matches the classical shear-driven model (R2 = 0.98), while the underexpanded jet yields a concave, non-classical profile (R2 = 0.63) with limited downstream transport. The jet expansion state, controlled by standoff distance relative to the Mach disk, thus governs the transition between efficient shear dominated and inefficient explosion dominated regimes, providing a quantitative basis for selecting the fully expanded mode in seabed trenching and similar marine engineering applications.

Understanding the seasonal lake dynamics is critical for water resource management and climate adaptation, yet intra-annual variability of Tibetan Plateau (TP) lakes remains poorly characterized. Here we present a monthly lake-surface area dataset covering 23,623 lakes (2000-2021), and propose a two-tiered classification framework identifying six distinct seasonal patterns. It reveals that semi-annual-cycle lakes predominate in endorheic regions while annual-cycle lakes concentrate in exorheic basins. Annual-cycle lakes are governed by single dominant factors and exhibit remarkable stability. In contrast, semi-annual-cycle lakes reflect coupled spring snowmelt and late-summer precipitation dynamics, showing high vulnerability to transitions. The Spring Peak (SP) pattern, whose shifts are attributable to intensified glacial melt and permafrost thaw, serves as a sensitive indicator of environmental changes. Seasonal complexity scales non-linearly with lake size. The post-2015 lake expansion coincided with rapid intensification of seasonal amplitude, indicating a fundamental hydrological transition that could threaten pastoral systems and water security across vulnerable endorheic regions.

Accurately measuring flow fields across diverse scenarios is essential for understanding physical phenomena. Particle Image Velocimetry (PIV) allows non-contact surface flow acquisition, but obtaining reliable prototype scale data, including in complex waterways and marine settings, remains a major challenge. This study proposes GMFlow-PIV, a novel framework that adapts the global matching optical flow model (GMFlow) to PIV applications through the design of physically-informed divergence and vorticity loss functions. Training and testing on synthetic datasets, GMFlow-PIV outperforms the original model across all test cases and reduces the average endpoint error (AEE) by 28.7% and 38.2% compared to UnLiteFlowNet-PIV and RAFT256-PIV in surface-quasi-geostrophic (SQG) flows, respectively. Parametric analysis demonstrates enhanced performance in scenarios with ultra-low/high particle diameters (<1 pixel or >5 pixels), sparse seeding densities (<0.03 particles per pixel), and large displacements up to 18 pixels. When validated on experimental PIV datasets and field flows, GMFlow-PIV exhibits improved generalization capabilities. It reduces inference time by 86.5%, 69.6% and 84.1% compared to PIV, RAFT256-PIV, and RAFT32-PIV, respectively, while maintaining competitive accuracy. By balancing high computational efficiency with robust performance, GMFlow-PIV shows promising potential for application in complex real-world scenarios.

The Tibetan Plateau (TP) is highly sensitive to climate change, yet existing Landsat-based surface water products suffer from pervasive data gaps caused by clouds, terrain shadows, and seasonal snow/ice, hindering fine-scale intra-annual hydrological analysis. Here we present a spatially complete, monthly 30-m resolution surface water dataset for the entire TP spanning 2000–2021 (TP MWH), constructed using a stepwise gap-filling (SGF) framework. The SGF method synergistically integrates Joint Research Centre (JRC)'s Yearly Water Classification History, Monthly Water Recurrence, and spatiotemporal neighborhood consistency, forming a four-step hierarchical judgment system that preserves the high precision of the original JRC classification while compensating for its inherent seasonal water omission bias. Validated against 12,141 stratified random samples covering 12 months, 12 hydrological basins, and 8 typical plateau surface features, the TP MWH dataset achieves an overall accuracy of 98.1%, a recall of 97.8%, a precision of 98.4%, an F1-score of 0.981, a Kappa coefficient of 0.961, and maintains stable high performance (>97.4% accuracy, >97.0% recall, >96.5% precision, >0.946 Kappa) throughout the year, outperforming mainstream JRC and GLAD products especially in winter freezing periods. Cross-comparison with the global monthly lake dataset confirms consistency in both interannual trends (Spearman r = 0.998) and intra-annual seasonal variability (Spearman r = 0.949). Leveraging this gap-free dataset, we reveal a three-stage interannual expansion of surface water and intensified intra-annual fluctuations after 2016, indicating growing hydrological instability of the TP. The 12 sub-basins exhibit four distinct heterogeneous evolution regimes, reflecting differentiated responses to monsoon, westerly circulation, and cryospheric meltwater changes. This dataset provides a reliable baseline for seasonal hydrological research and water resource management on the TP, and is publicly available at https://doi.org/10.5281/zenodo.13910635.

Accurate estimation of discharge through submerged spillways is critical for reservoir operation and structural maintenance. Traditional methods using empirical formulas or intrusive measurements fail to characterize high-speed pressurized flow hydrodynamics. We introduce a novel Eulerian monocular photogrammetry (EMP) method that non-intrusively quantifies discharge by integrating pixel-level jet surface geometry into a theoretical velocimetry framework. This framework further combines image-derived streamlines and cross-sectional flow analysis to establish a robust relationship between jet images and flow discharge. The methodology was validated through controlled laboratory experiments and a field application at Xiluodu dam. In laboratory tests, EMP-derived discharge estimates showed excellent agreement with direct measurements and demonstrated robustness against variations in camera positioning. Field application matched theoretical/numerical results with minor underestimation, consistent with similar projects. Bridging visual observation and quantitative analysis, EMP enhances spillway discharge estimation reliability and efficiency for modern hydraulic engineering.

MobileViT v2, a Transformer-CNN hybrid deep learning model, was restructured and successfully applied to predict urban pluvial flood maximum water depth for the first time. The hybrid model leverages the advantages of both Transformers and CNNs to enhance global and local modeling capabilities, thereby achieving high prediction accuracy even on unseen terrains. Furthermore, we proposed a data augmentation approach to satisfy the substantial data requirements of the Transformer blocks and mitigate overfitting caused by limited training data. Moreover, we utilized an attribution method to analyze the effectiveness and physical plausibility of both the hybrid model and the data augmentation, thereby enhancing the interpretability of the proposed framework. Results indicate that the hybrid model with data augmentation demonstrated a significant improvement in performance compared to the standard ViT and a modest enhancement compared to representative CNN baselines. These findings demonstrate the hybrid model’s superior generalization capability and greater potential for further improvements. Additionally, the results validate data augmentation as an easy-to-implement and widely applicable approach for enhancing prediction accuracy with limited data in flood prediction tasks. Finally, this study presents a promising method for enhancing prediction accuracy in flood prediction tasks and represents the successful application of hybrid models to this task across diverse unseen spatial terrain patches.

In this study, a novel spatiotemporal hydrodynamic prediction task framework, named single frame prediction, was developed. The framework could generate results based on boundary conditions and a single flood map from the last time step, relying on hydrodynamic principles rather than historical trends, and doesn't require the assistance of traditional hydrodynamic models. Moreover, a post-processing method based on physical laws was developed to refine the outputs of deep learning models at each time step, aiming to reduce accumulated errors in long-term predictions. The performance of a widely used convolutional neural network-based model, U-Net, was evaluated to assess the feasibility of single frame prediction and the impact of the proposed post-processing method. The experiments showed that single frame prediction could produce accurate flood maps, demonstrating the feasibility of the novel framework. Furthermore, the results indicated that the physics-based post-processing method could mitigate errors at each step, thereby enhancing prediction accuracy across entire flood event, showing strong effectiveness and applicability in flood prediction. Additionally, an ablation experiment was conducted to assess the effectiveness of each step in the method. The single frame prediction provided a more comprehensive and interpretable depiction of flood prediction processes with essential hydrodynamic variables, including water depth and unit discharge on all grid cells. The post-processing method significantly reduced the accumulated error in the later stages of single frame prediction to an acceptable range with an average root-mean-square error of 0.041 m for water depth and 0.003 m2/s for unit discharge, suggesting a new technique for long-term flood predictions.

[Objective]Traditional methods for floating and transporting immersed tunnel elements at sea often involve the use of tugboats for towing.This approach results in the vessel and the tunnel element moving independently,making it difficult to control the attitude of the immersed tube and leading to low navigation speeds.The Shenzhen-Zhongshan Bridge project in China,however,utilized an integrated vessel for the transportation and installation of immersed tubes.This specialized construction boat combines the operations of floating,positioning,immersion,and installation of tunnel elements.The integrated vessel measures 190.40 m in length,75.00 m in beam,14.70 m in depth,and 23 200 t in weight.It is equipped with two main propulsion systems,each capable of delivering 9 280 kW and eight side thrusters ranging from 2 600 to 3 000kW.The integrated vessel,connected rigidly to the immersed tube through supports and cables,demonstrated rapid floating capabilities in the Shenzhen-Zhongshan Bridge project,achieving a maximum navigation speed of 5.8 kn and covering a 47.0 km floating route in just 7-8h.While this high-speed floating navigation enhances operational efficiency,the safety of both the vessel and the transported elements during floating remains a significant concern.A notable issue observed is the synchronization of the attitude between the element and the vessel.During acceleration,a relatively significant longitudinal tilt occurs,necessitating in-depth analysis to understand the hydrodynamic mechanisms behind this trim occurrence during high-speed floating of oversized immersed tubes,as well as to assess the impact of sustained trim on the safety of floating navigation and the loss of propulsion efficiency for the vessel.[Methods]This paper presents a theoretical analysis comparing the resistance distributions of immersed tunnel elements in calm water with those under navigation at specific speeds.In situ measurements were conducted to observe attitude changes during the floating process.A numerical model describing the floating condition of a single tube element was developed using FLOW-3D software to analyze the resistance distributions and attitude changes at approximately 4.0 kn.Additionally,a comprehensive numerical model of the vessel-tube connection was established using computational fluid dynamics methods,with a scaling ratio of 1:40 for model-scale simulations.These models simulated the flow field changes around the integrated vessel and the immersed tube at navigation speeds of 4.0 and 6.0 kn.[Results]Through theoretical analysis,in situ measurements,and numerical simulations,the following conclusions were drawn:(1)The geometric shape of the immersed tube,which was a nonstreamlined rectangular box,resulted in significantly greater end face(bow face)resistance than streamlined vessels.This end face resistance was the main component of the navigation resistance for the immersed tube.(2)At certain navigation speeds,a downward flow field formed by the water at the bottom of the bow end was identified as the primary cause of the bow-down tilt of the immersed tube.This vertical flow field decreased the water pressure in the area near the bow end,leading to a significant trim phenomenon.(3)The total frictional resistance caused by the viscosity of water was found to be only approximately 1.50％ of the total resistance,making its impact almost negligible.[Conclusions]Measurements of the integrated vessel's attitude during the rapid floating of immersed tubes indicate a significant longitudinal tilt.A relationship between the trim angle and navigation speed is established through these measurements.By combining numerical and theoretical analysis methods,it is possible to analyze the state of the flow field around the immersed tube under high-speed floating conditions.The analysis suggests that the longitudinal tilt of the immersed tube is related to the flow field formed at the bow of the immersed tube,which reduces the dynamic pressure at the bottom of the bow end.This reduction in pressure generates a rotational moment in the bow tilting of the immersed tube.

A set of high-resolution, time-resolved particle image velocimetry measurements were conducted in an open channel, with closely arranged glass spheres of 6 mm used to rough the bed, at low to moderate Reynolds numbers ( Re-tau approximate to 600-2000) and intermediate to high relative submergences ( h/k(s) = 6.3-14.7, where h is the flow depth and k(s) is the equivalent roughness height). Analyses of the wall-attached motions (WAMs) in rough-wall open channel flows (OCFs) are performed using linear coherence spectra at various wall-normal positions, and results of two smooth-wall OCFs are also included for comparison ( Re-tau approximate to 500 and 900). The WAMs in rough-wall OCFs exhibit self-similar properties in the region of 0.2

River level predicting underpins the management of water resource projects, steers navigational activities in rivers, and protects the lives and properties of riverside communities, etc. Traditionally, hydrological-hydraulic coupled models have been at the forefront of simulating and predicting river levels, achieving notable success. Despite their utility, these models encounter limitations due to the exhaustive demand for various data types-often difficult to obtain-and the ambiguity in determining downstream boundary conditions for the hydraulic model. Responding to these limitations, this study utilizes Long Short-Term Memory (LSTM) model, a deep learning technique, to predict river levels using upstream discharges. Three approaches were used to further enhance the accuracy and reliability of our model. Firstly, we incorporated historical water level data at or downstream of the predicted station as input, secondly, we classified the datasets based on physical principles, and thirdly, we employed data augmentation techniques. These methods were evaluated within the JingjiangDongting river-lake system in China. It achieves high prediction accuracy of water level and can mitigate the impact of input inaccuracies. The incorporation of water level data as input and the Classification-Enhanced LSTM model that segregates the input data according to rising and recession trends of water level, significantly improve prediction accuracy under extreme water level conditions compared with other deep learning approaches. The proposed model uses easily accessible data to predict water levels, offering enhanced robustness and new strategies for improving prediction accuracy under extreme conditions. It is applicable for predicting water levels at any hydrological station along a river and can enhance the prediction accuracy of hydraulic models by proving a robust downstream boundary condition.

The relative magnitude of the suspended sediment concentration (SSC) to the suspended sediment transport capacity (SSTC) is an important indicator for determining riverbed evolution. In this paper, with the use of an extensive dataset, Ruijin Zhang’s formula is employed to calculate the SSTC at representative hydrological stations in the Upper Yangtze River, thereby examining the fluctuations in the ratio of the SSC to the SSTC (SSC/SSTC) across varying flow rates (Q) for the first time, yielding the following three important insights. First, the results confirm that except for some hydrological stations located within the reservoir area, the remaining hydrological stations are generally characterized by a sub-saturated state of flow with respect to sediment, as evidenced by predominantly lower SSC/SSTC values, which are typically less than 1. Second, the results emphasize the substantial influence of changes in the upstream sediment load on the SSC/SSTC values. Third, a distinctive V-shaped correlation is identified between the Q and SSC/SSTC values at each station, characterized by a clear minimum point. This minimum point defines the “critical flow rate”, which can be attributed to the morphology of river channel.

Dunes are ubiquitous riverbed forms, yet how they modify the most recently documented energy-containing turbulent structures—very-large-scale motions (VLSMs)—remains unclear. To address this issue, high fidelity experiments were conducted in open-channel flows (OCFs) over isolated dunes with contrasting morphologies. Time-resolved, long-duration, and high-resolution velocity fields with extended streamwise coverage were obtained using an in-house multi-camera particle image velocimetry system, enabling detailed quantification of dune effects over long distances with fine spatial resolutions. Consistent with canonical OCFs, VLSMs are evident in both dune cases upstream of the lee face, where dune effects are minimal, as revealed by the bimodal features of the streamwise velocity spectrum, indicating two dominant energy-containing turbulent structures: large-scale motions and VLSMs. The scales and strength of these motions are comparable to those in canonical OCFs. In contrast, over the lee face and downstream side, pronounced dune morphology–dependent effects emerge. For low lee-slope dunes, VLSMs persist throughout the flow depth, whereas for steep dunes, their spectral signature is eliminated in the near-bed region and remains only near the free surface. Furthermore, the strength of VLSMs, measured by their contributions to streamwise turbulent kinetic energy (TKE) and Reynolds shear stress (RSS), is substantially reduced: from 60% to 30% for TKE and from 50% to 15% for RSS in the near-dune region. With increasing distance downstream, dune effects weaken, and VLSM strength gradually recovers.

In recent years, the significant change in the runoff-sediment distribution in the upper Yangtze River has led to an increased sediment contribution from the Minjiang River Basin (MRB) to the Three Gorges Reservoir. However, previous studies on sediment load changes in the MRB have focused mainly on annual-scale characteristics, thereby neglecting features driven by floods. Therefore, this study focused on examining the changes in flood-event sediment loads in the MRB based on mathematical statistics and comprehensive measurement data. The results indicated that human activities, climate change, and seismic events have caused an increasing trend in the flood-scale sediment load in the upper MRB and a decreasing trend in the lower MRB. The changes in the flood-scale sediment modulus at low runoff erosion power levels were greater than those at high runoff erosion power levels at different abrupt-change stages. During extreme flood events, the actual sediment concentration in the MRB remained below the sediment-carrying capacity. This study provides novel insights into water resource management during the flood season in the MRB and similar basins. HIGHLIGHTS Changes in the sediment load at the flood-event scale show opposite trends upstream and downstream of the Minjiang River Basin (MRB). After an abrupt change, the variation in sediment transport is smaller under high-flow conditions than under low-flow conditions. Under extreme flood conditions, the actual sediment concentration in the MRB does not reach the sediment-carrying capacity.

Exploring very-large-scale motions (VLSMs) in open channel flows (OCFs) is crucial for comprehensively understanding material transport and energy exchange. While previous studies focused on OCFs in ideal flumes with simple boundaries, this paper presents large eddy simulation (LES) results on the existence and scale characteristics of VLSMs in complex river morphologies with the Minjiang River in southwestern China as a case study. This research demonstrates that the OpenFOAM-based LES model is capable of accurately reconstructing the time-averaged flow field and providing instantaneous velocity data that capture VLSMs with sufficient resolution. Spectrum analysis of the streamwise fluctuating velocity shows that VLSMs are present in the straight segment of the river, with streamwise wavelengths approximately (16–22) times the water depth but are absent in the bend and confluence segments due to the suppression by secondary currents. This article validates the effectiveness of LES in examining the characteristics of VLSMs in natural rivers, thereby laying a good foundation for further studies on the impacts of such structures on sediment transport and pollutant dispersion.

Satellite altimetry data has become essential for studying the dynamics of water bodies, especially in regions with limited or inaccessible data. Traditional low-resolution mode (LRM) satellites' accuracy cannot be guaranteed when it comes to assessing water levels in small- (< 200 m in width) and medium-sized (200-800 m in width) rivers. Synthetic aperture radar (SAR) altimeters, exemplified by Sentinel-3 A, have shown great potential for inland water altimetry. Nevertheless, developing algorithms to retrack the raw data remains an essential requirement in this context. This is attributed to the width of small-sized rivers, which is often narrower than the along-track resolution of both LRM and SAR altimeters. In addition, new altimeters may have long revisit cycles and different spatial coverage and cannot yield historical data necessary in some situations. To address these challenges, this study proposed a conditional threshold retracker (CTR). The CTR algorithm is well-designed and facilitates accurate water level monitoring. Moreover, we proposed an enhanced footprint filter (EFF), thus significantly bolstering the number of available cycles. Our findings demonstrate that the developed method substantially enhances the temporal and spatial resolution of both LRM and SAR altimetry satellites during water level monitoring in rivers of different climate types. The width of the thirteen selected rivers is on the order of 85-630 m. The CTR significantly improved the water level monitoring accuracy by 68 %- 78 %. Furthermore, the EFF increased the number of water level cycles by approximately 49 %-68 %. These findings have practical implications for obtaining accurate water level data, estimating river discharge and improving hydraulic model calibration.

Optical satellite imaging for surface water mapping often encounters significant challenges owing to persistent spatial data gaps caused by clouds, shadows, and sensor errors. This study presents a novel Stepwise Gap-Filling (SGF) method, designed to enhance the monthly surface water mapping and monitoring. The SGF method leverages temporal similarities and spatial correlations to reconstruct gap pixels originally classified as invalid observations. We validated this approach against historical high-resolution Google Earth images from 2887 sample points in the Siling Co Basin of the Tibetan Plateau. The results demonstrated substantial improvements in mapping accuracy, achieving an overall accuracy of 98.93%, a producer’ accuracy of 98.59%, and a user’ accuracy of 99.11%, markedly reducing the uncertainties in the original dataset. Importantly, the SGF method offers detailed insights into monthly surface water dynamics, which are closely aligned with annual trends. This study highlights the effectiveness of the SGF method for filling data gaps and its potential for widespread application in the monitoring and management of global water resources.