Search Results (246)

The rapid mobilization of sediment stored behind dams, in amounts that are large relative to mean annual sediment loads, can jumpstart river restoration but can also adversely impact habitat, infrastructure, land, and water use upstream of, within, and downstream of the former impoundment. A wide range of geomorphic and engineering assessment tools were applied to help manage sediment-related risks associated with the removal of two dams from the Elwha River in Washington State and the release of roughly 21 million m³ of sediment. Each of these tools had its strengths and weaknesses, which are explored here. The processes of sediment erosion, transport and deposition were complex. No one model was able to fully simulate all these with the accuracy necessary for predicting the magnitude and timing of coarse and fine sediment release from the reservoir. Collectively, however, the model outputs provided enough information to guide the adaptive sediment management process during dam removal. When the complexity of the morphodynamic responses to dam removal and the associated risks exceeded the capacity of any one tool to adequately assess, synoptic forecasting proved useful. The lessons learned on the Elwha have provided insights into how to use a variety of modeling techniques to address sediment management issues as dam removal scale, complexity and risk increase. Full article

This study introduces the Badland Dissection Index (BDI), a new morphometric parameter that quantifies the internal dissection and drainage maturity of badland basins. The index was applied to 87 calanchi basins developed on marine clays in the Ionian sector of Basilicata (southern Italy). BDI values range from 0.13 to 0.62, with approximately 65% of the basins exhibiting values lower than 0.30, indicating mature geomorphic stages dominated by organized fluvial incision. Pearson correlation analysis shows that BDI is strongly correlated with compactness and shape indices (r = −0.71 with circularity ratio, r = 0.74 with Gravelius compactness index, GCI), and moderately with relief (r = 0.46 with Melton ratio), highlighting the primary control exerted by basin geometry on badland dissection. A principal component analysis shows that compactness-related variables and BDI dominate the first component, which explains 38.6% of the variance, while hydrological indices define an independent second component; together the first two components account for 57.4% of total variance. A multiple regression model confirms GCI as the dominant predictor of BDI (R² = 0.58), with relief variables playing a secondary role. Owing to its simplicity, limited data requirements and clear geomorphic meaning, BDI provides a robust and scalable tool for comparing badland morphodynamics across semiarid settings and for monitoring landscape evolution where only medium-resolution topographic data are available. Full article

Background/Objectives: Glioblastoma (GBM) exhibits heterogeneous, nonlinear invasion patterns that challenge conventional modeling and radiomic prediction. Most deep learning approaches describe the morphology but rarely capture the dynamical stability of tumor evolution. We propose an AI framework that approximates a latent attractor landscape of GBM morphodynamics—stable basins in a continuous manifold that are consistent with reproducible morphologic regimes. Methods: Multimodal MRI scans from BraTS 2020 (n = 494) were standardized and embedded with a 3D autoencoder to obtain 128-D latent representations. Unsupervised clustering identified latent basins (“attractors”). A neural ordinary differential equation (neural-ODE) approximated latent dynamics. All dynamics were inferred from cross-sectional population variability rather than longitudinal follow-up, serving as a proof-of-concept approximation of morphologic continuity. Voxel-level perturbation quantified local morphodynamic sensitivity, and proof-of-concept control was explored by adding small inputs to the neural-ODE using both a deterministic controller and a reinforcement learning agent based on soft actor–critic (SAC). Survival analyses (Kaplan–Meier, log-rank, ridge-regularized Cox) assessed associations with outcomes. Results: The learned latent manifold was smooth and clinically organized. Three dominant attractor basins were identified with significant survival stratification (χ² = 31.8, p = 1.3 × 10⁻⁷) in the static model. Dynamic attractor basins derived from neural-ODE endpoints showed modest and non-significant survival differences, confirming that these dynamic labels primarily encode the morphodynamic structure rather than fixed prognostic strata. Dynamic basins inferred from neural-ODE flows were not independently prognostic, indicating that the inferred morphodynamic field captures geometric organization rather than additional clinical risk information. The latent stability index showed a weak but borderline significant negative association with survival (ρ = −0.13 [−0.26, −0.01]; p = 0.0499). In multivariable Cox models, age remained the dominant covariate (HR = 1.30 [1.16–1.45]; p = 5 × 10⁻⁶), with overall C-indices of 0.61–0.64. Voxel-level sensitivity maps highlighted enhancing rims and peri-necrotic interfaces as influential regions. In simulation, deterministic control redirected trajectories toward lower-risk basins (≈57% success; ≈96% terminal distance reduction), while a soft actor–critic (SAC) agent produced smoother trajectories and modest additional reductions in terminal distance, albeit without matching the deterministic controller’s success rate. The learned attractor classes were internally consistent and clinically distinct. Conclusions: Learning a latent attractor landscape links generative AI, dynamical systems theory, and clinical outcomes in GBM. Although limited by the cross-sectional nature of BraTS and modest prognostic gains beyond age, these results provide a mechanistic, controllable framework for tumor morphology in which inferred dynamic attractor-like flows describe latent organization rather than a clinically predictive temporal model, motivating prospective radiogenomic validation and adaptive therapy studies. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

A macrotidal estuary with mountain-stream inputs (MEMSs) is characterized by strong hydrodynamic forcing, high turbidity, and complex channel morphology. This study combines field measurements (2005–2020) with a 2D hydrodynamic–sediment model to examine estuarine turbidity maximum (ETM) dynamics, erosion–deposition patterns, and the effects of engineering interventions in the Jiaojiang Estuary (JJE). Results show that the coupled influence of upstream floods and downstream macrotides produces highly seasonal and spatially variable water–sediment processes: mountain-stream floods exhibit sharp hydrodynamic fluctuations, and the estuary displays pronounced tidal-wave deformation. Over the 15-year observation period, the riverbed experienced alternating erosion (up to −3.5 m) and deposition (up to +4.2 m), with net erosion of 0.5–1.2 m occurring in most Ling River sections during high-discharge years. The ETM migrated about 30 km during spring tides, with near-bed suspended sediment concentrations reaching 50–60 kg/m³. Human activities—particularly historical sand mining—modified channel geometry and sediment composition, intensifying the exchange between bed material and suspended sediment and facilitating the formation and migration of the ETM. Extreme events further enhanced geomorphic adjustment: the post-Lekima (2019) flood produced maximum scour of −5.8 m in the upper Ling River and deposition of +3.2 m in the Jiaojiang main channel within weeks. Channel curvature and junction morphology strongly controlled flood-level distribution. Model experiments indicate that lowering shoal elevations and widening the cross-section at key constrictions can effectively reduce flood levels. Collectively, these findings clarify the morphodynamic evolution mechanisms of a MEMS system and provide quantitative guidance for flood-mitigation and estuarine-management strategies. Full article

Beach nourishment is a widely used strategy to mitigate coastal erosion, yet its long-term geological impacts remain poorly understood. This study provides a multi-decadal synthesis of shoreline change and sedimentological evolution on the nourished beaches of Cape May and Wildwood, New Jersey, USA. Using shoreline positions from 1991 to 2024, we identify contrasting trajectories: Wildwood exhibits ‘persistent transition’ with severe northern erosion (EPR: −10.0 m/yr) feeding southwards accretion, while Cape May demonstrates a ‘managed equilibrium’ with widespread accretion (mean EPR: +1.15 m/yr). Wave energy correlations account for less than 15% of shoreline variability, indicating natural drivers have been superseded by human sediment inputs. Direct sediment comparison shows substantial textural transformation, with median grain sizes increasing from 153 to 435 μm to 467–982 μm and sorting degrading from very well to moderately well sorted, reflecting sustained disequilibrium. These findings are synthesized into a conceptual model where nourishment initiates feedback cycles that create human-dependent morphodynamic trajectories. This study concludes that the long-term resilience of developed coasts will depend on a strategic evolution from managing ‘sand as volume’ toward stewarding ‘sediment as a system,’ where textural compatibility is a primary determinant of success. Full article

This study investigates the morphodynamic evolution of an embayed cobble beach located on a mesotidal cliff coast in northern Spain. La Maruca/Pinquel beach was selected for its distinctive geomorphological setting, perched on a well-sorted cobble substrate and bordered by a slightly elevated (less than 1 m) wave-cut platform. Firstly, the availability of orthophotos and the achievement of field surveys enabled a detailed topographic mapping of morphological features. Sedimentological analyses based on grain size and clast shape revealed characteristics indicative of prolonged low-energy wave conditions. A permanent sharply crested ridge and ephemeral staggered tidal berms define the morphology of the beach. Additional depositional features such as washovers, tabular structures, and lobes are also well developed. Sediment accumulation is most pronounced in the western sector, where overwash lobes migrate landward. A W-to-E gradient in cobble size and the presence of boulders in the lower foreshore can be observed. Secondly, a morphosedimentary model was developed based on the obtained data to interpret the beach’s dynamic behavior under current and projected coastal forcing. Finally, by analyzing orthophotographs spanning a 40-year period (1984–2024), the long-term geomorphological evolution of the beach was documented. The results reveal significant morphological transformations, notably a shoreline retreat of approximately 12 m and a reduction in the cobble-covered surface area, among other findings. Future analyses of sediment transport processes and lithological responses to erosion will be able to offer a deeper understanding of the complex behavior and resilience of pebble beach systems in response to changing environmental conditions. Full article

The Jing River–Dongting Lake (DTL), a critical river–lake complex system in the Middle Yangtze River, China, plays a vital role in flood regulation and ecological sustainability. Recent decades have experienced significant morphology adjustments due to upstream reservoir operations; however, the long-term high-resolution hydro-morphodynamic evolution and its impacts on river–lake interactions remain insufficiently quantified. To address this gap, a two-dimensional hydro-morphodynamic model based on HEC-RAS was employed to simulate three decades of hydro-morphology evolution under projected flow–sediment conditions. The model was validated against observed data and reproduced erosion–deposition trends consistent with previous numerical studies. The results indicate sustained channel incision in the Jing River, with a cumulative erosion volume of 462 million m³, in contrast to net deposition in the DTL area totaling 276 million m³ over three decades. A comparison of results under a sediment reduction regulation shows that the overall spatial pattern of erosion and deposition remains largely consistent, although local areas, particularly the confluence of the three major inlets feeding the lake, exhibit pronounced sensitivity to sediment variations. Furthermore, continuous mainstream incision intensifies a draining effect on the lake during dry seasons, leading to declines in both water levels and surface area in the DTL. This effect is most pronounced in the eastern lake area, with reductions being markedly greater in dry periods than in wet periods. Finally, the lake’s storage capacity progressively decreases, with an average annual loss of approximately 36.5 million m³ in the wet periods, underscoring significant impairment of its flood-regulation function. This study provides a validated modeling framework and critical insights for predicting morphological evolution and informing adaptive management in large river–lake systems. Full article

In Slovenia, flood hazard mapping follows a standardized model-based procedure, defined by the EU Floods Directive. On the other hand, erosion hazard mapping remains largely empirical due to the absence of a unified methodology. Recent advances in 2D hydraulic and morphodynamic modelling offer new opportunities to integrate sediment processes into hazard assessments. This study enhances the current empirical approach to erosion hazard mapping by applying a combined 2D hydraulic-sediment transport model using HEC-RAS software version 6.4.1 and the Selška Sora River (Slovenia) as the case study area, comprising 300 ha of floodplains. Incorporating hydraulic variables and soil characteristics, the method identifies erosion-prone zones and classifies hazard levels. For the case study area, 21.9 ha of floodplains were identified as subject to erosion, and 23.9 ha as prone to deposition. Additionally, 43.8 ha of floodplains fall within the low, 1.9 ha within the medium, and 0.1 ha within the high erosion hazard class. The presented approach not only supports the national hazard mapping framework in Slovenia but also has potential applicability within broader EU hazard assessment practices. Full article

The stage-discharge relationship in the Jingjiang Reach of the Yangtze River has undergone significant alterations due to post-Three Gorges Reservoir (TGR) operation effects, notably bed erosion and roughness variation. This study employs a calibrated 1D hydrodynamic model based on Saint-Venant equations. The model was validated with high accuracy (Nash-Sutcliffe efficiency >0.94 at key stations) using long-term hydrological data (1996–2022). Four scenarios were simulated: pre-dam conditions, post-dam topography with pre-dam roughness, pre-dam topography with increased roughness, and coupled post-dam changes. A novel scenario-based decomposition framework was developed to isolate individual and coupled factor contributions, advancing beyond traditional descriptive approaches. The results indicate that upstream water level changes are mainly controlled by riverbed erosion (e.g., at the Zhicheng Station: the topographic contribution rate exceeds 80% at a flow rate of 5000 m³/s, resulting in a water level drop of approximately 1.7 m), while downstream, an increase in roughness becomes the dominant factor (e.g., at the Jianli Station: causing a water level rise of about 1.0 m at a flow rate of 13,000 m³/s, with such changes being particularly pronounced under low-flow conditions). Spatially, topographic influence attenuates downstream, whereas roughness sensitivity amplifies in high-sinuosity reaches (bend coefficient: 3.0). Seasonally, the topographic contribution rate remains stable overall during the low-flow period, e.g., within a narrow range of 0.88–0.98 at Zhicheng Station, while roughness effects exhibit negative values in dry periods (November) due to fine sediment deposition. The coupling effect in mid-discharge ranges (15,000–20,000 m³/s) at Jianli partially offsets stage reductions. These findings not only provide critical insights for flood forecasting and navigation management in the Jingjiang Reach but also offer a transferable methodology for quantifying hydro-morphodynamic interactions in global regulated rivers, highlighting the model’s utility in predictive water resource management. Full article

Riparian and floodplain vegetation play a key role in controlling flow resistance, sediment transport, and channel morphology, shaping the dynamics of riverine ecosystems. Accurately representing these vegetation–flow–sediment interactions in numerical models is essential for predicting system responses and supporting sustainable river management. This study introduces an enhanced biomorphodynamic model in the open-source framework openTELEMAC, which combines multiple vegetation friction approaches with a method for predicting sediment transport in vegetated flows. The modular structure of the framework enables flexible configurations for different vegetation types (rigid or flexible) and flow conditions (emergent or submerged) by selecting suitable vegetation friction approaches, improving usability and extending model applicability. Model performance is evaluated using two laboratory experiments on bedload transport through emergent and submerged rigid vegetation. Simulations reproduce the measured bed and water surface profiles with high accuracy, yielding low goodness-of-fit errors (RMSE ≈ 0.5–1.8 cm, MAE ≈ 0.5–1.6 cm). The results highlight the sensitivity of predictions to vegetational input parameters such as the drag coefficient. Overall, the enhanced biomorphodynamic model advances the representation of vegetation–sediment interactions and provides an adaptable, open-source tool for eco-hydraulic and morphodynamic research. Full article

We present a coupled large-eddy simulation (LES) and bed morpho-dynamics study to investigate the influence of sediment dynamics on the performance of a utility-scale marine hydrokinetic vertical-axis turbine (VAT) parametrized by an actuator surface model. By resolving the interactions between turbine-induced flow structures and bed evolution, this study offers insights into the environmental implications of VAT deployment in riverine and marine settings. A range of tip speed ratios is examined to evaluate wake recovery, power production, and bed response. The actuator surface method (ASM) is implemented to capture the effects of rotating vertical blades on the flow, while the immersed boundary method accounts for fluid interactions with the channel walls and sediment layer. The results show that higher TSRs intensify turbulence, accelerate wake recovery over rigid beds, and enhance erosion and deposition patterns beneath and downstream of the turbine under live-bed conditions. Bed deformation under live-bed conditions induces asymmetrical wake structures through jet flows, further accelerating wake recovery and decreasing turbine performance by about 2%, compared to rigid-bed conditions. Considering the computational cost of the ASM framework, which is nearly 4% of the turbine-resolving approach, it provides an efficient yet robust tool for assessing flow–sediment–turbine interactions. Full article

Coastal zones in arid regions are particularly vulnerable to climate change because of their limited sediment supply and high sensitivity to marine and aeolian forces. This study provides probabilistic projections of coastal evolution for a 130 km segment of the Duba shoreline, Saudi Arabia, a rapidly developing region that includes the NEOM mega-project. An integrated modeling framework was developed by combining a four-decade (1985–2024) diachronic analysis of shoreline evolution from Landsat imagery with a cascade of numerical models. Specifically, climate projections from CMIP6 (under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) were dynamically downscaled using the regional climate models COSMO-CLM and RegCM, which provided boundary conditions for the SWAN hydrodynamic model to simulate the wave dynamics. The SWAN outputs were then used to force the Delft3D morphodynamic model to project future shoreline evolution. A Bayesian framework was applied to systematically quantify and integrate the uncertainties across all modeling steps, enabling robust probabilistic forecasts. Results indicate an accelerated trend of shoreline retreat, with mean Net Shoreline Movement (NSM) by 2100 ranging from −8.1 m under the low-emission SSP1-2.6 scenario to a critical −25.6 m under the high-emission SSP5-8.5 scenario, with 95% confidence intervals reaching −47.9 m. This erosion is mainly driven by a projected relative sea-level rise of up to 48.3 cm (±15.8 cm) and an increase in significant wave height of up to 40% (mean of 1.95 m). By delivering probabilistic rather than deterministic results, this study provides a solid scientific basis to guide sustainable coastal management, inform the design of risk-sensitive infrastructure, and support the development of climate-resilient adaptation strategies in one of the world’s most rapidly transforming coastal regions. Full article

Canals have played a significant role in promoting the prosperity of the shipping industry worldwide. Meanwhile, canal construction can alter the hydro-morphodynamic processes in coastal tidal channels. The Fangchenggang Canal is an extension route of the Pinglu Canal, which connects southwestern regions to the Beibu Gulf in the South China Sea by cutting across approximately 20 km of intertidal and dry land of the Qisha peninsula. A two-dimensional numerical model based on MIKE21 has been established to investigate the variations of tidal current structures and sediment transport characteristics. The maximum flow velocity within the main channel increases up to 1.18 m/s in the marine section. A bidirectional flow pattern has been observed in the land excavation segment. Numerical simulations of the sedimentation processes demonstrated potential erosion in the land excavation section due to the increased bed shear stress. The present study shares useful insights into the response mechanism of hydro-morphodynamic processes under canal construction. The quantitative simulations would support the environmental assessment and route planning of canal projects. Full article