Critical feedbacks between fluid motion, sediment dynamics, and particle interactions—such as size sorting—drive surface process variability, with implications ranging from hydraulic engineering and hazard risk management to predicting landscape and ecosystem responses.
A) Particle-Scale Interactions and Transport Mechanics:
-Entrainment mechanics in both fluvial and aeolian flows
-Turbulent energy and momentum transfer to particles
-Statistical approaches to upscaling stochastic sediment movement
-Dynamics of granular flow in dry and submerged scenarios
-Effects of grain morphology on sediment and granular transport
-Interactions among mixed-size sediment grains and segregation processes
-Discrete element modeling and upscaling into continuum frameworks
B) Reach-Scale Fluvial and Geomorphic Dynamics:
-Relationships among flow hydraulics, sediment transport, bedform development, and stratigraphy
-Equation development and solution for multiphase flows in rivers and air
-Shallow-water hydro-sediment-morphodynamic modelling
-Characterizing complex, unsteady flows including flash floods and granular mass movements
-Extreme event impacts: flood waves, debris flows, landslides
C) Engineering Applications and Earthcasting hazards:
-Dam failure processes (natural and engineered) and cascading hazards
-Coastal sediment transport (long-shore, cross-shore) and shoreline evolution
-Reservoir management and sediment process interactions
-Hydraulic structure design (e.g., fish passes, spillways) with consideration of sediment impacts
-Maintenance and management of waterways: dredging, regulation in large river systems
-Calibration and validation methodologies for Earth's surface hazards forecasts.
Orals: Mon, 4 May, 08:30–12:30 | Room -2.93
Dense sedimentary flows underpin a wide range of geomorphic processes, from bedload transport in rivers to debris-laden shallow flows, yet their rheological description across regimes remains incomplete. In particular, the transition from viscous-dominated to inertia-dominated behavior in dense suspensions poses a central challenge for constitutive modeling of subaqueous sediment transport. Here, we present a unified numerical investigation of the viscous–inertial transition in sheared sedimentary flows using particle-resolved Direct Numerical Simulations (pr-DNS), spanning idealized rheometric configurations and flow-driven sediment beds.
We employ both pressure-imposed and volume-imposed rheological frameworks to systematically probe the role of fluid viscosity, shear rate, granular pressure, particle friction, confinement, and boundary roughness. Across configurations, we characterize rheology in terms of the macroscopic friction and solid volume fraction expressed as functions of combined viscous and inertial control parameters. Our results confirm that the transition can be described by an additive scaling of visco-inertial stresses but reveal that different rheological quantities respond differently to inertia.
In pressure-imposed simulations of dense frictional suspensions, we find that the viscous–inertial transition occurs at Stokes numbers ranging from 5 to around 8, consistent with recent experiments. Notably, shear stress exhibits a more gradual transition than particle pressure, indicating a decoupling of stress components. Microstructural analysis shows that this behavior arises from the combined action of lubrication and tangential contact forces, as particles progressively shift from rolling to sliding contacts. This shift is governed not only by the Stokes number, but also by proximity to jamming and inter-particle friction.
Complementary volume-imposed simulations between rough confining walls demonstrate that boundary conditions strongly influence the measured rheology through particle layering and inter-layer mixing. Wall roughness and cell height modulate stress levels and effective friction, including weakening of the macroscopic friction during the transition, while preserving a consistent viscous–inertial scaling across cases. Despite a reduction in contact number, increased force magnitudes on remaining contacts drive the inertial regime.
Finally, simulations of pressure-driven shallow flows over sediment beds show that the transition occurs at Stokes numbers comparable to those of our numerical and experimental results of pressure-imposed rheometry, with distinct scaling coefficients for volume fraction and macroscopic friction. Together, these results highlight the complex, multi-scale nature of sediment rheology and underscore the need for refined constitutive laws that explicitly account for microstructure, confinement, and stress anisotropy in geomorphic sediment transport.
How to cite: Vowinckel, B., Khodabakhshi, A., Konidena, S., and Tapia, F.: Rheology of sedimentary flows across the viscous–inertial transition, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12701, https://doi.org/10.5194/egusphere-egu26-12701, 2026.
The study-to-study variability of bedload flux measurements in turbulent sediment transport borders an order of magnitude, even for idealized laboratory conditions. This uncertainty stems from physically poorly supported, empirical methods to account for channel geometry effects in the determination of the transport-driving bed shear stress and from study-to-study grain shape variations. Here, we derive a universal procedure of bed shear stress determination. It consists of a physically-based definition of the bed surface and a channel sidewall correction that does largely not rely on empirical elements, except for well-established scaling coefficients associated with Kolmogorov's theory of turbulence. Application of this procedure to bedload transport of spherical grains---to rule out grain shape effects---collapses data from existing laboratory measurements and grain-resolved CFD-DEM simulations for various channel geometries onto a single curve. By contrast, classical sidewall corrections, such as the Einstein-Johnson method, as well as an alternative bed surface definition, are unable to universally capture these data, especially those from shallow or very narrow channel flows. The sidewall correction method is also independently supported by data from systematic experiments of open-channel flows over fixed rough beds with various width-to-depth ratios.
How to cite: Pähtz, T., Chen, Y., Yu, H., Wei, M., and Duran, O.: Bed shear stress in bedload transport: new methods of sidewall correction and bed surface determination, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16411, https://doi.org/10.5194/egusphere-egu26-16411, 2026.
The prediction of bedload sediment transport remains challenging due to the multi-scale interactions that link grain-scale dynamics, bed morphology, and turbulence. Grain-scale processes are influenced by particle shape and contribute to the spread in existing bedload models. Experimental access to these processes is limited, making numerical simulations a valuable complementary tool. Most numerical studies to date represent sediment grains as spheres to reduce computational cost or intentionally exclude shape effects. More recent work has demonstrated the importance of shape using ellipsoidal approximations which capture the overall grain form but loose finer surface irregularities. Only a few simulations have employed more realistic clumped-sphere grain approximations and have shown that grain shape introduces significant uncertainty in entrainment and transport predictions.
This contribution advances the quantification of grain-shape effects by using realistic representations of grain geometry obtained from measurements in the literature. It presents direct numerical simulations of turbulent bedload transport with low particle loading in a highly mobile regime using fully resolved, realistic sand grains. Three simulations with monodisperse but polymorph particles are considered, such that only grain shape is varied. One configuration represents smooth, well-rounded sand grains, the second consists of more angular and irregular grains. A third simulation with uniform spheres serves as a reference. The realistic grain samples are generated statistically following an established methodology that yields two distinct sand populations. The grains in these populations are characterized using sphericity and roundness and are classified using the Zingg diagram. Although the Zingg-class of all grains is spheroid, the grain populations can be subdivided based on the distributions of the other two shape descriptors, with sphericity capturing larger-scale morphology and roundness reflecting smaller-scale surface irregularities.
Across the three simulations, the Shields parameter increases with increasing grain irregularity. Furthermore, the particle ensembles in all simulations show oscillatory dynamics during statistically steady bedload transport, attributed to the recurring formation of particle clusters, with the characteristic period increasing with grain irregularity. Shape-conditioned statistics obtained through double averaging show that in the case of the angular grain population more rounded grains accumulate near the channel bottom, while more angular grains are transported to higher elevations. This sorting is not observed for the well-rounded grain population. Additionally, for both grain populations, the rotational energy of the grains increases with irregularity, although rotation remains overall weak compared to translational motion in the present highly mobile regime.
How to cite: Rebel, R. and Fröhlich, J.: Resolved DNS of bedload transport with realistic grain morphology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13379, https://doi.org/10.5194/egusphere-egu26-13379, 2026.
Granular sediments in rivers, coasts, and pipelines often undergo particle size segregation due to the action of the carrier fluid and particle-particle interactions. This process can significantly affect geomorphology and the associated geohazards, but our mechanistic understanding of granular segregation in fluid-driven bedload transport remains elusive. In this study, a particle-scale numerical simulation based on the coupled computational fluid dynamics–discrete element method (CFD-DEM) is conducted to investigate the segregation of a bidisperse bed sheared by high-viscosity fluids. The evolution of segregation under varying shear intensities, characterized by the Shields number, is systematically analyzed in laminar flow. The results show that: (1) under various Shields numbers, the granular bed can be divided into an upper bedload layer (fluid-like, fast-moving) and a lower creep layer (solid-like, slowly moving), with the bedload layer thickening and the creep layer thinning linearly as the shear intensity increases; (2) particle segregation evolves exponentially over time, and at the same duration, the final degree of segregation for the entire bed increases linearly with the Shields number; (3) the segregation timescale shows a non-monotonic dependence on the Shields number, governed by the competing effects of increasing segregation velocity and active layer thickness as the Shields number is increased; and (4) the segregation timescale follows a power-law relationship with the shear rate in laminar flow, showing similarities to dry granular flow behavior. Future work will focus on developing a predictive model that captures the evolution of coarse and fine particle concentration profiles, thereby enhancing our modeling capabilities of granular segregation and its feedback effects on the mobility of sediment transport.
How to cite: Li, X. and Jing, L.: Size Segregation of Bidisperse Granular Beds in Laminar Shear Flow: A CFD-DEM Investigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8573, https://doi.org/10.5194/egusphere-egu26-8573, 2026.
Wave-induced seabed liquefaction is a common factor leading to submarine instability, primarily occurring in silty seabeds. Under wave action, the pore water pressure within the seabed continuously increases. When the pore water pressure approaches or exceeds the total stress of the soil, the effective stress of the soil tends toward zero, resulting in liquefaction. Currently, most models of seabed dynamic response are based on macroscopic constitutive equations derived from Biot’s consolidation theory, making it difficult to accurately reveal the mesoscale mechanisms of seabed behavior under wave loading. This study employs a coupled numerical approach integrating the Lattice Boltzmann Method (LBM), the Immersed Boundary Method (IBM), and the Discrete Element Method (DEM) to systematically investigate the dynamic response and liquefaction process of a seabed under wave action. In this model, DEM is used to describe the motion and interactions of seabed sediment particles, LBM is applied to simulate fluid flow behavior, and IBM handles the coupling effects between particles and the fluid. Additionally, to improve computational efficiency, local grid refinement is applied near the seabed region, enhancing overall calculation performance. Using this coupled model, the periodic variations of wave-induced pore water pressure and effective stress in the seabed are studied, and the simulation results are validated against experimental data. The results show good agreement between simulations and experiments, accurately reflecting the dynamic response characteristics of the seabed under wave action. The model not only reveals the interaction mechanisms between soil particles and pore fluid from a microscopic perspective but can also be further extended to study the coupled effects of liquefaction and scour on near-bed sediment transport, offering significant theoretical insights and practical engineering value.
How to cite: Zhang, J., Zhang, Q., Li, Z., and Liu, G.: A Fully-Resolved Simulation Study of Seabed Liquefaction and Dynamic Response Using a Coupled LBM-IBM-DEM Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2481, https://doi.org/10.5194/egusphere-egu26-2481, 2026.
Bedload transport is ubiquitous in natural environments and encompasses flow regimes from saltation to sheet-flow, characterized by distinct fluid–particle interaction mechanisms. Accurately capturing these processes requires a numerical approach that can capture both turbulence and fluid-particle interactions, for which challenges exist due to the constraints of grid resolution on the coupling accuracy. In this study, we propose a semi-resolved LES–DEM framework to overcome such limitations in the conventional CFD-DEM paradigm. A feedback-controlled body-force term is also proposed to maintain a prescribed discharge under periodic boundary conditions in turbulent open-channel flow simulations. Three benchmark cases are conducted to assess the accuracy and robustness of the proposed framework, including clear-water turbulent channel flow as well as bedload transport in both saltation and sheet-flow regimes. The present method is demonstrated to effectively overcome the conventional grid-size limitation and thus allows the fluid field to be resolved on sufficiently fine grids while preserving accurate fluid-particle coupling. We further investigate the micromechanical processes underlying the transition from the saltation to sheet-flow regimes and quantify the thickness of the transport layers as functions of the Shields number. Overall, this framework provides a unified and reliable numerical tool for simulating sediment transport across a broad range of flow regimes, offering a solid basis for micromechanical analysis and the development of continuum models.
How to cite: Liu, Y., Jing, L., Wu, Z., and Fu, X.: Semi-resolved LES–DEM simulations of turbulent bedload transport from saltation to sheet flow regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5469, https://doi.org/10.5194/egusphere-egu26-5469, 2026.
There are no theoretical formulas that can accurately predict the sand transport rate (Qm) over the Gobi surface. We report herein high-frequency field observations of wind-blown sand processes over the Gobi surface under extremely high winds in eastern Xinjiang, China. The results reveal that the power-law exponent of the scaling relationship between Qₘ and friction wind velocity (uτ) in the extremely high winds with high gravel coverage Gobi area can reach 15.51, significantly exceeding that on sandy surfaces. Meanwhile, there is a favorable power-law between Qm and the fluctuation intensity of the vertical wind velocity (Iw), whereas the correlation between Qₘ and the streamwise fluctuation intensity (Iu) is weak. Therefore, Iw has a significant application in constructing the prediction model for Qₘ over such Gobi surfaces. This study provides a new insight into the quantitative analysis of the aeolian transport over the windy Gobi areas.
How to cite: Wang, T.: Sand Transport Rate and Turbulent Fluctuation in AeolianTransportation Over the Gobi Surface Under ExtremelyHigh Winds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2690, https://doi.org/10.5194/egusphere-egu26-2690, 2026.
Braided rivers are dynamic systems that support diverse habitats associated with their shifting mosaic of anabranches, backwaters, bars and islands. These units are characterized by chaotic, but not random, distributions of substrate, elevation and hydraulics at multiple scales. Modelling sediment supply or flow-driven changes in riverbed composition is notoriously difficult given the inherent dynamism and multiple scales defining large braided rivers.
In this research, we present a new data-rich modelling framework which combines census-scale [1 m] substrate classification with detailed 2D hydraulic models. The resulting transport capacity estimates can, for a static bed, be quickly applied to any transient flow scenario while retaining spatial detail. We then use the information gathered during the 2D modelling to parameterise a 1.5-dimensional transport solution in the time-evolving CASCADE sediment routing framework.
The 2D model uses a substrate map of a 56-km reach of the Rangitata [Rakitata] River, Aotearoa New Zealand, derived by machine learning based on high-fidelity helicopter lidar and orthophotography. A library of 2D steady-state hydraulic models is then run over the substrate map to predict the spatial and temporal capacity for sediment transport.
The adapted 1.5D-CASCADE model captures a defining feature of braided rivers, width variability, with a reach-specific hypsometric solver, tested against the 2D results, that predicts flow and sediment transport. The half-dimension is width, discretised against height using the 2D predictions of inundation and active area. The 1.5D model can then evolve bed composition both laterally across the braidplain and longitudinally down the river, within hydraulic geometry set by the template survey, including storage and remobilisation in side channels and floodplains.
The models are tested and applied to simulate the effects of flow regulation on bed composition in the Rangitata River. The model’s longitudinal consistency was only possible when using the spatial substrate data, and predictions are corroborated by lidar change detection. Results demonstrate that subtle changes in flow regime can alter where sediment is stored across the braidplain, with sedimentation impacts focused on side channels. Transfer of the model to other rivers indicates that width-varying solvers produce more stable sediment routing predictions than any single width, while remaining computationally efficient.
How to cite: Rogers, J. and Brasington, J.: Adapting CASCADE for braided rivers: A 1.5D sediment transport approach with variable substrate and width, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15789, https://doi.org/10.5194/egusphere-egu26-15789, 2026.
Landslide Dam-Break Outburst Floods (LDBOF) are devastating natural hazards that drastically reshape downstream river morphologies. However, their inaccessibility, high risk of equipment loss, and sparse field data collection severely hinder hazard understanding and timely warning capabilities. The 2000 Yigong LDBOF event in China is one of the most significant modern recorded cases, yet it suffers from limited observational data. To address this gap, we integrated multi-source data—including open-source elevation datasets, literature-derived records, satellite-based flood inundation extents, and direct field observations—to develop a comprehensive input dataset for hydro-morphodynamic modeling of the event. Model validation against field observations and comparable studies confirmed the reasonableness of simulated lake emptying, dam breaching, flood inundation, bank erosion, and channel infilling processes. Our results reveal key morphodynamic characteristics of the Yigong LDBOF: dam material transport was dominated by translational motion during the flood rising stage and dispersive transport during the falling stage. The outburst flood peak discharge reached ~60 times that of typical meteorological floods, significantly amplifying the effects of river width on dam material transport. We further proposed a sediment transport equation that incorporates the regulatory effect of large boulders. Post-event channel recovery simulations, validated with remote sensing data, indicated minimal planform changes, with bed incision driven by headward erosion as the dominant morphological adjustment. Large boulders acted as a stabilizing factor, limiting upstream erosion and forming sediment supply-limited reaches. This study provides a robust multi-source data integration and modeling framework for LDBOF events with sparse observations, offers new insights into cross-scale hydro-morphodynamic processes of extreme floods, and the proposed sediment transport equation improves the accuracy of simulating boulder-influenced sediment dynamics—supporting hazard risk assessment and downstream river management for future LDBOF events.
How to cite: Lei, Y., Hassan, M., Rosatti, G., Fraccarollo, L., Zugliani, D., Fu, X., and Shi, H.: Morphodynamic responses to the 2000 Yigong dam-break flood: Insights from back-analysis and cross-scale modelling challenges, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9737, https://doi.org/10.5194/egusphere-egu26-9737, 2026.
To effectively assess the growing hazard related to debris flows, it is crucial to simulate these natural
grain-fluid flows at a reasonable computational cost. To complement existing depth-averaged grain-fluid flow
models with an upper-fluid layer, we propose here a model with an upper-solid layer, as a first step towards the
development of unified models describing all possible configurations. This model accounts for granular mass
dilatancy and pore fluid pressure feedback and solves for solid and fluid velocity in the mixture and for the
upper-solid velocity. Simulation in uniform configurations reveals the rich behaviour of the flow and shows that
the upper-solid and upper-fluid models may predict very different behaviour. Our work highlights the need of
developing two-layer models accounting for dilatancy and unifying upper-solid and upper-fluid configurations
in the same framework.
How to cite: Mangeney, A., Bouchut, F., Fernandez-Nieto, E., and Narbona-Reina, G.: A depth-averaged grain-fluid model with dilatancy and an upper-solid layer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5017, https://doi.org/10.5194/egusphere-egu26-5017, 2026.
Field observations in the Jiangjia Ravine show that surge characteristics evolve systematically along the flow path. As the flows transition from steep upstream slopes to gentler downstream reaches, surge forms shift from high-frequency low-amplitude surges to low-frequency high-amplitude surges. To explain the spatial evolution of surges, we develop a mechanistic model governed by slope geometry and yield stress. The shear stress can fall below the yield stress at slope breaks, temporarily blocking the flow. Subsequent surges with higher shear stress exceed the yield stress and remobilize the stored material. Experimental results show that on the steep slope, the mixture generates unsteady roll waves. Once these waves reach the gentle slope, they further amplify and evolve into distinct surge fronts, confirming the proposed model. These findings establish a conceptual framework for understanding the accelerated evolution of debris-flow surges.
How to cite: Song, D. and Liu, Y.: Debris Flow Surges Amplification Controlled by Topography and Rheology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2920, https://doi.org/10.5194/egusphere-egu26-2920, 2026.
Particle-size segregation is a common phenomenon in granular materials that has attracted increasing attention in recent years. Yet segregation under recirculation remains underexplored compared to segregation in simple-sheared gravity-driven flows. In this study, we investigated the dynamics of a bi-dispersed granular mixture flowing over an inclined conveyor belt. This belt pulled particles upstream, creating a recirculating flow. We visualized the internal structure of granular flow in a vertical plane by matching the refractive indices of the fluid and particles, and then located the particles. We observed an upstream accumulation of small particles and downstream accumulation of large particles, these two regions being separated by a curved interface. We think that this separation resulted from the interplay between particle recirculation and segregation: 1) surface particles moved downstream while bottom particles moved upstream; 2) segregation led to particles separating during recirculation, with full separation achieved at the channel ends. We developed a depth-averaged advection-diffusion equation to quantify this phenomenon by treating the recirculation as convection. This study provides new insights into the coupled mechanisms of recirculation and segregation in granular materials.
How to cite: Chen, Y. and Ancey, C.: Size segregation of granular mixtures under recirculation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7103, https://doi.org/10.5194/egusphere-egu26-7103, 2026.
Vertical velocity profiles in erosive multiphase mass flows control how momentum is transferred from a moving landslide to the underlying bed and therefore govern erosion, entrainment, and mass enhancement. Although erosion is known to increase landslide mobility, the particle-scale mechanisms by which internal shear drives sediment mobilisation remain poorly constrained. In particular, the vertical distribution of velocity in erosive granular flows is largely unknown, despite providing the critical link between flow dynamics and bed response. Field measurements document a wide range of velocity profile shapes but lack the spatial resolution required to quantify shear close to the bed. By contrast, previous laboratory studies either failed to resolve internal kinematics under erosive conditions or relied on artificial, rounded particles that suppress the frictional interactions characteristic of natural sediments. Consequently, differences between landslide velocity and the velocity of the eroded bed, as well as the vertical shear rates underpinning erosion-entrainment-mobility formulations, remain largely unconstrained by empirical data.
Here we present a new experimental dataset that directly addresses this gap. We conducted controlled laboratory experiments on landslide-like granular flows moving over an erodible bed composed of naturally crushed sand-gravel mixtures. A measurement approach based on Particle Image Velocimetry combines lateral imaging with plan-view observations, allowing continuous vertical velocity profiles to be reconstructed across the full flow depth during active erosion and entrainment. The experiments include dry granular flows and flows with varying water content, two representative grain-size classes, and systematic comparisons between erosive runs and reference cases over a rigid bed.
The results show that both the inertia of the erodible material and the time-dependent erosion rate fundamentally alter the vertical velocity profile. The velocity of the moving landslide and that of the erodible bed can now be clearly distinguished, enabling direct calculation of entrainment velocity and erosion drift. Shear mainly occurs near the bed-flow interface, evolving dynamically as material is entrained and creating velocity gradients that cannot be captured by depth-averaged approximations. These measurements provide the first quantitative characterisation of vertical shear under fully erosive conditions using realistic sediment properties. By resolving particle-scale velocity gradients, this study establishes the experimental basis required to calibrate and verify erosion-mobility models that explicitly depend on shear-rate-controlled entrainment, thereby advancing the predictive modelling of erosive landslides.
How to cite: Baselt, I., Krautblatter, M., Pudasaini, S., and Wetterauer, K.: Vertical Velocity Profiles in Erosive Landslides, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16480, https://doi.org/10.5194/egusphere-egu26-16480, 2026.
The Var River in southeastern France represents an example of a Mediterranean fluvial system profoundly modified by human activity. Over the past eight decades, engineering works, gravel mining, and urbanization have progressively confined and simplified the channel, while the river remains a vital water resource for the city of Nice and its surroundings.
We present a multi-temporal and multi-scale analysis of the lower Var River’s morphological evolution between 1940 and 2025. Historical aerial and satellite images (n > 50) were analyzed to quantify changes in braided index, channel confinement ratio, slope–width relationships, and channel morphology classes. A high-resolution UAV survey conducted in 2025 covered 20 km of the lower valley, producing detailed orthomosaics and digital elevation models from over 80,000 images. Additional sedimentological analyses combining terrestrial photogrammetry and laboratory measurements thoroughfully characterized grain size and lithology of fluvial landforms.
Results reveal a complex spatial pattern in channel form: the lower Var alternates between multi-thread and single-thread morphologies along its 20 km course, with transitions occurring over distances of less than one km. These abrupt shifts are linked to local confinement, engineered structures (among which weirs which underwent recent lowering), and bedload disconnection. Overall, the river has undergone strong simplification and narrowing, with active-channel reductions exceeding 60% in channelized reaches. The present morphology reflects a hybrid, fluvial state shaped by human regulation and contrasted hydrology. These findings provide new insights into the geomorphic resilience of Mediterranean rivers and inform sediment management and corridor planning for the Nice Metropolitan region, since it present the first high-resolution analysis of this fluvial corridor.
How to cite: Granados-Bolaños, S., Picourlat, F., Ouassanouan, Y., Billaud, F., Chapuis, M., and Abily, M.: Fluvial geomorphology and historical evolution of the Var River, France: a case study of a highly anthropic Mediterranean braided channel, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7135, https://doi.org/10.5194/egusphere-egu26-7135, 2026.
Fluvial dynamics hinge on sediment erosion, transport, and deposition. These are the forcing factors responsible for changing channel morphology and landform evolution. Our study analyses these processes at the confluence of the Damodar River and the Barakar River in eastern India. It is a transitional zone between Archaean-Proterozoic crystalline rocks, lower Gondwana formations, and Quaternary alluvium. The landscape remains in a constant state of change, shaped by the annual pulse of flood and profoundly altered by two hundred years of anthropogenic activity.
An integrated framework evaluates boundary conditions, morphologic responses, fluvial drivers, and terrace archives. Despite comparable flow velocities in active channels, rivers transport distinct grain sizes and lithologies. The right-bank remnants of the Damodar River evidence past high-energy regimes and are absent on the left bank. Terrace sequences are unpaired, sedimentologic units are unmatched, and bank structures preserve ancient high-velocity signatures.
How to cite: Kundu, S., Sinha, S., and Haque, S. M.: Geomorphic and sedimentary records for deciphering the landform evolution at the Damodar and Barakar River confluence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16521, https://doi.org/10.5194/egusphere-egu26-16521, 2026.
Coupled interactions between climate, vegetation, and geomorphic processes control sediment export from rapidly eroding badlands; however, their relative roles under future climate scenarios remain poorly constrained. We present a coupled landscape evolution model (LEM) and a dynamic vegetation model, CLIMBAD, applied to the Laval catchment (Draix-Bléone CZO, SE France) in a badland setting, to quantify how fluvial and hillslope erosion, together with frost weathering and vegetation dynamics, drive historical and projected sediment fluxes. The LEM is forced by temperatures (acting on frost weathering) and precipitation events, including depth, duration, and peak intensity. The dynamic vegetation model is calibrated to 1982-2021 vegetation maps and driven by topographic and climatic variables. Future climate (2022-2099) is generated using a stochastic weather generator calibrated on observations from historical data and future projections obtained from a regional climate model.
Model evaluation for 1985-2021 shows that coupling dynamic vegetation to the LEM improves agreement with observed annual sediment fluxes at the catchment outlet (i.e., R2 increased from ~0.60 to ~0.66), demonstrating the importance of vegetation-erosion feedbacks. To isolate climatic controls, we ran four scenarios for future climate: (i) changing temperature (T) and precipitation (P), (ii) constant T with changing P, (iii) changing T with constant P, and (iv) constant T and P. These results help disentangle the relative contribution of a change in the precipitation regime and a change in temperature on sediment fluxes in a coupled system, with direct implications for sediment hazard assessment in climate‑sensitive badland landscapes.
How to cite: Sharma, H., Le Bouteiller, C., and Boulangeat, I.: Coupling Landscape Evolution and Dynamic Vegetation Models to Simulate Sediment Fluxes under Historical and Future Climate in the Laval Catchment (Draix-Bleone CZO, SE France), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19059, https://doi.org/10.5194/egusphere-egu26-19059, 2026.
Extreme hydro-meteorological events are among the primary drivers of hydrologic and geomorphic hazards, posing an increasing threat to societies worldwide. The combined effects of climate change, and increased exposure and vulnerability in hazard-prone areas have led to a continuous rise in disaster risk.
This contribution addresses some key challenges in forecasting and managing hydro-meteorological processes across two main interrelated contexts—data-rich or data-scarce regions—which, despite their known differences, share common issues of scale, complexity, and uncertainty in hazard–society interactions.
In both environments, local-scale factors such as small-scale processes, and human disturbances interact with regional climate variability and large-scale atmospheric drivers to shape evolving hydro-geomorphic processes. At the same time, decisions happen at national, basin, or urban scales, often creating cross-scale mismatches between where hydro-meteorological processes materialize and where decisions are taken.
The presentation discusses how Earthcasting-oriented approaches, such as the integration of remote sensing, reanalysis products, crowd-sourced information, and qualitative socio-economic data, can partially address these gaps. While these data sources introduce new uncertainties, they also provide opportunities to improve awareness and support process-based forecasts and decision-making in regions where conventional data are unavailable, or they might not be enough.
Building on recent advances in technology but also risk science, this talk advocates for integrated assessment frameworks that explicitly account for cross-scale interactions, feedbacks, and data limitations, also highlighting implications for communication strategies. Ultimately, advancing such integrated approaches is essential for translating scientific knowledge into an added social value of the predictability of Earth surface processes.
How to cite: Sofia, G.: From Climate Data to Decisions in the Age of Extremes: Challenges and Opportunities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3013, https://doi.org/10.5194/egusphere-egu26-3013, 2026.
Rainfall erosivity is a major driver of soil erosion and is highly sensitive to short-duration precipitation extremes, which are expected to intensify under climate change. Great advancement on climate data have been seen in the last decade, and convection-permitting climate models (CPMs) offer new opportunities to simulate rainfall characteristics relevant to erosivity. However, their performance in complex terrain remains insufficiently quantified.
We evaluate rainfall erosivity (RUSLE R-factor) simulated by a nine-member CPM ensemble from the CORDEX Flagship Pilot Study on Convective Phenomena over Europe, focusing on the Great Alpine Region. CPM estimates are compared with long-term, high-resolution rain-gauge observations from ~500 stations spanning a wide elevation range. We quantify and apply a temporal adjustment to reconcile hourly model output with 10-minute observations, then model performance vs observations is assessed for key erosivity-related variables, including rainfall intensity, event depth, frequency of erosive events, and mean annual erosivity. The CPM ensemble reproduces the spatial variability of rainfall erosivity with good skill and overall low bias, but exhibits clear elevation-dependent biases. Erosivity is underestimated at low elevations and increasingly overestimated at higher elevations, reflecting biases in rainfall intensity and/or event frequency. While low-elevation biases are largely consistent with sampling variability, high-elevation biases are predominantly systematic.
These results highlight the potential of CPMs for rainfall erosivity assessment and the importance of accounting for elevation-dependent biases in mountainous regions.
How to cite: Borga, M., Mansoor, A., Dallan, E., and Francesco, M.: Evaluation of convection-permitting models for rainfall erosivity in an Alpine region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21488, https://doi.org/10.5194/egusphere-egu26-21488, 2026.
Granular media is observed in a variety of natural contexts. Whether they come in the form of landslides, debris flows, pyroclastic density currents, bed load, fault gouge, or magmatic crystals, they can fail catastrophically and jam. Here we introduce a characterisation that examines collective motion within granular systems to probe their stability as they are pushed towards the point of failure and stoppage. Using particle-resolved simulations, we show that this characterisation gives early indication of weakening prior to external measures. This characterisation is agnostic to the method of destabilisation, whether it be from increasing slope angles or fluid injection. Applying this characterisation to analogue experiments shows that it can easily demarcate between a static deposit, agitated particles, and an actively destabilizing layer, showing promise in using remote signals to probe the stability of natural systems.
How to cite: Zrelak, P., Breard, E., Makris, S., and Dufek, J.: The Edge of Stability: From Collective Vibrations to Jamming and Failure in Granular Media, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15476, https://doi.org/10.5194/egusphere-egu26-15476, 2026.
Understanding the impact of geophysical flows on the channel bed is essential for assessing erosion processes of bed material. In this study, the discrete element method (DEM) is used to simulate idealized, steady-state, fully-developed granular flows impacting the channel bed with systematically varying total particle number (1000-30000), grain size (2-16mm), and slope angle (28-34°) to investigate the probability distributions of the basal force. The probability density functions of the basal force, normalized to the mean force, were calculated and fitted with ten probability distributions. Four indices, namely R2, Residual Sum of Squares (RSS), Wasserstein distance, and information entropy, are introduced to evaluate the goodness of fit for each probability density distribution. By comparison, the broad probability density distribution of normalized basal force can be well-described by Gamma distributions (GD) with its shape and scale parameters. The shape parameter of the Gamma distribution is positively correlated with the total particle number and grain size, but negatively correlated with the slope angle. An opposite relationship is revealed in the scale parameter of the Gamma distribution. Additionally, we analyzed flow kinematics by calculating the coordination number, dimensionless velocity, shear rate, inertial number, and volume fraction, and linking these variables to the shape and scale parameters. The coordination number, shear rate, inertial number, and volume fraction serve as effective proxies for the shape and scale parameters, enabling interpretation of the statistical characteristics of monitored basal forces in geophysical mass flows.
How to cite: Tang, H., Fang, J., Cui, Y., Turowski, J., Jing, L., and Kong, Y.: Basal force distribution from steady fully-developed granular flows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12336, https://doi.org/10.5194/egusphere-egu26-12336, 2026.
Coherent, energetic airflow structures control the incipient aeolian entrainment of coarse sediment and plastic debris, but their effect is poorly captured by classical, time-averaged shear-stress thresholds. This contribution showcases the results from recently published work [1, 2] that combines a particle-scale energy framework with wind-tunnel observations to quantify how individual sweeps and related structures trigger rocking, creep and full rolling, thereby regulating geomorphic work and debris mobility at Earth’s surface.
Wind-tunnel experiments were conducted in a 30 m environmental facility over a fixed rough bed of identical 40 mm celluloid spheres, representing idealized gravel and light plastic debris under fully turbulent, near-threshold flow (U ≈ 7.5–8.2 m s⁻¹). Synchronous 1 kHz measurements of near-bed airflow (2D hot-film) and particle displacement (0.1 mm laser distance sensor) resolve intermittent rocking and episodic rolling of a single exposed particle on a regular bed, under an atmospheric boundary layer with logarithmic mean profile and near-surface turbulence intensities up to ~20%.
A micromechanical model defines “energetic airflow events” as intervals where instantaneous drag exceeds an initial resistance level and persists for a finite duration, and relates their energy content Ef ∝ ∫u³dt to the minimum mechanical work F_g z_cr required to push a particle over its micro-topographic barrier. The resulting work-based criterion C_eff∫u³dt ≥ const introduces a normalized efficiency C_eff, estimated from the ratio of drag work ∫u²vdt to event energy, which partitions motion regimes from creep through rocking to incipient rolling and near-saltation. Quadrant analysis of uw shows that >85% of both rocking and rolling events are associated with Q4 sweeps; a simple peak-force condition u²_f,p ≥ u²_cr,0 is necessary for motion but insufficient for full entrainment, whereas the energy criterion correctly classifies ≈90–95% of observed rocking vs. rolling events. These results provide a transferable, event-based description of how coherent turbulent structures drive low-mobility aeolian transport, including mechanical sieving on gravel-mantled megaripples and the mobilisation of meso- to micro-plastic debris.
References
[1] Valyrakis, M., Zhao, X., Pähtz, T., & Li, Z. (2025). The role of energetic flow structures on the aeolian transport of sediment and plastic debris. Acta Mechanica Sinica, 41(1), 324467. https://doi.org/10.1007/s10409-024-24467-x.
[2] Zhao, X. H., Valyrakis, M., Pähtz, T., & Li, Z. S. (2024). The role of coherent airflow structures on the incipient aeolian entrainment of coarse particles. Journal of Geophysical Research: Earth Surface, 129(5), e2023JF007420. https://doi.org/10.1029/2023JF007420.
How to cite: Valyrakis, M., Pähtz, T., and Zhao, X.: From sweeps to sieving: a particle scale work-based criterion for intermittent aeolian entrainment of gravel and plastics under coherent turbulent structures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11556, https://doi.org/10.5194/egusphere-egu26-11556, 2026.
Understanding sediment grain size distribution in riverbeds is fundamental to analyses of sediment transport, riverbed morphology, and ecological habitats. Recent advances in unmanned aerial vehicle (UAV)–Structure-from-Motion (SfM) photogrammetry have enabled indirect characterization of sediment grain size (D) using surface roughness (R) derived from point cloud analyses. However, the relationships between grain size and roughness, as obtained using different roughness metrics in mountain rivers, remain insufficiently investigated.
In this study, manual sediment sampling and high-resolution UAV surveys were conducted across multiple mountainous river reaches in Taiwan, characterized by coarse bed materials and wide grain size distributions. SfM-derived point clouds were used to compute three roughness metrics: roughness height (RH), standard deviation of elevations (σ), and detrended standard deviation (σd). Linear relationships were established between local grain sizes (Di, where i = 16, 25, 50, 75, and 84) and their corresponding percentile roughness values (Ri). In addition, integrated power-law relationships were developed by pooling all paired Di–Ri data across the study reaches.
The results indicate that all three roughness metrics (RH, σ, and σd) exhibit strong correlations with grain size in gravel-bed rivers when analyses are conducted within the same river reach. The linear Di–Ri relationships show moderate to strong correlations (R² = 0.57–0.95), with the D50–R50 relationship demonstrating the highest consistency across all three metrics. Similarly, the integrated power-law relationships derived from the three roughness metrics yield high correlations (R² = 0.89–0.93). However, notable differences emerge when these relationships are applied to other river reaches. The RH-based relationship maintains more consistent predictive performance, whereas relationships derived from σ and σd exhibit larger deviations. These results suggest that RH-based roughness metrics offer superior applicability for estimating sediment grain size in mountain rivers. Overall, this study provides practical insights into the selection of suitable roughness metrics for grain size estimation in coarse-grained riverbeds.
How to cite: Lai, T. Y., Jan, C. D., Lai, K. C., and Hsu, Y. C.: An Evaluation of Riverbed Roughness Metrics Derived from UAV–SfM Point Clouds and Their Relationships with Grain Size Distribution in Mountain Rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4395, https://doi.org/10.5194/egusphere-egu26-4395, 2026.
The incipient motion of coarse particles critically governs bed stability, sediment transport dynamics, and geomorphic evolution in turbulent flows, with profound implications for riverbed destabilization, flood risk, and the integrity of hydraulic infrastructure. Despite extensive research, the ways under which microtopographic pocket arrangements—clusters or depressions formed by particle packing— modulate entrainment thresholds remains relatively underexplored.
This presentation aims to outline the effects of varied pocket configurations on the critical hydraulic conditions required for particle entrainment under turbulent flow fields. Utilizing instrumented particles equipped with inertial measurement units (IMUs) [1, 2] to record high-fidelity particle accelerations and angular velocities, we probe both particle kinematics and dynamics, at the onset of motion. Novel flow-particle interaction metrics, derived from these measurements, reveal the underlying physical mechanisms—such as torque imbalances and lift generation—that drive or resist entrainment.
We hypothesize that subtle differences in pocket geometry and orientation can substantially elevate or lower the entrainment threshold, necessitating distinct flow field characteristics (e.g., shear stress and turbulence intensity) for motion initiation [3, 4]. Preliminary results from controlled flume experiments demonstrate threshold shifts across configurations, underscoring the sensitivity of bed stability to local topography.
These insights aim to highlight the transformative potential of IMU-based instrumentation for real-time risk assessment of riverbed and bank destabilization in natural streams, as well as scour development in engineered channels, for sustainable river management and infrastructure resilience.
References
1. Al-Obaidi K, Xu Y, Valyrakis M. The design and calibration of instrumented particles for assessing water infrastructure hazards. J Sens Actuator Netw. 2020;9(3):36. doi:10.3390/jsan9030036.
2. Al-Obaidi K, Valyrakis M. A sensory instrumented particle for environmental monitoring applications: development and calibration. IEEE Sens J. 2021;21(8):10153-10166. doi:10.1109/JSEN.2021.3053080.
3. Al-Obaidi K, Valyrakis M. Linking the explicit probability of entrainment of instrumented particles to flow hydrodynamics. Earth Surf Process Landf. 2021;46(12):2448-2465. doi:10.1002/esp.5178.
4. Al-Obaidi K, Valyrakis M. Coherent flow structures linked to the impulse criterion for incipient motion of coarse sediment. Appl Sci (Basel). 2023;13(19):10656. doi:10.3390/app131910656.
How to cite: Papadaki, A. and Valyrakis, M.: Influence of Pocket Geometry on the Incipient Entrainment of Coarse Particles in Turbulent Flows , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11632, https://doi.org/10.5194/egusphere-egu26-11632, 2026.
In foreland basins adjacent to collisional mountain belts, rivers exhibit an abrupt gravel-sand transition (GST) at ~10-40 km downstream of mountain fronts, where surface median grain size reduces from ~10 mm to ~1 mm. This is the only abrupt downstream reduction in grain size in fluvial systems. Existing theories attribute GST formation to size-selective transport of bimodal sediment, rapid deposition of sand from the washload, and gravel exhaustion. These mechanisms predict that GST location should respond systematically to changes in hydraulic conditions (channel gradient, flow strength), sediment supply (gravel flux), and accommodation space (subsidence rate). However, observations from the Himalayan foreland basin reveal significant along-strike variability in GST locations despite similar gravel lithology, comparable subsidence rates, and uniform climatic forcing. This unexplained spatial variability indicates that additional controls on GST formation remain poorly understood.
Here, we hypothesize that particle shape—an intrinsic sediment property traditionally considered secondary to grain size—exerts first-order control on GST location through its influence on gravel mobility. To test this hypothesis, we developed a force-balance framework accounting for drag, lift, and rotational forces to model gravel transport as a function of particle shape. Experiments with varying bed matrix characteristics demonstrate that gravel mobility is strongly modulated by shape variations under identical hydraulic conditions. Field measurements of particle shape distributions from Himalayan foreland rivers reveal that GST locations coincide spatially with downstream increases in the proportion of low-mobility shapes (equant and platy forms). Progressive accumulation of these less mobile shapes reduces the bulk mobility of the gravel bedload, causing the gravel front to stall.
Our results demonstrate that particle shape exerts first-order control on GST formation and location, operating independently of climate and tectonic forcing. This intrinsic control has likely influenced sediment routing in both ancient and modern foreland basins worldwide. The findings suggest that GST positions in stratigraphic records reflect the evolution of particle shape rather than solely changes in external forcing. Understanding this shape-controlled mechanism is essential for interpreting sedimentary archives, predicting downstream sediment delivery, and refining landscape evolution models in mountain-foreland systems.
How to cite: Panda, S. K., Kundu, S., Mandal, S. K., and Scherler, D.: Shape Matters: How particle morphology affects the location ofthe Gravel-Sand Transition., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16255, https://doi.org/10.5194/egusphere-egu26-16255, 2026.
A numerical model is developed for the transport of mixed natural sediment and plastic particles, accounting for multiple size classes and material densities. The conceptual model drawn from a classico Hirano layered description. It includes a transport layer, an active layer and a substratum. In the transport layer mass conservation consists on fraction-wise Exner equations, including pickup and deposition rates, convective and diffusive transport in the transport layer, and local accumulation for each size and density class. Convective fluxes are the product of particle activity for each size and density classes (the conservative variables) and particle bulk velocity. The latter computed using a modified Luque and van Beek formulation, adapted to account for different sizes and particle densities. Thresholds for incipient motion are taken as calibration coefficients. A flux limiter is implemented to avoid over-saturation of the transport layer and to ensure positivity of particle activity. The pickup function is derived from probabilistic descriptions of sediment entrainment (taking into account density) and deposition rates are functions of actual particle activity and sedimentation velocity. The dynamics of the active layer is determined by empirical availability functions by size. The volume of the active layer is kept constant and scaling with the initial d90 of the mixture. Instantaneous mixing is assumed. As a consequence, during deposition the composition of the active is transferred to the substratum. During erosion, the composition of the substratum is incorporated in the active layer.
The model is calibrated with laboratory experiments conducted under steady and overfeeding flow conditions. Two flumes were employed. A 5.2 m long, 25 cm wide, and 35 cm deep flume was used to conduct flat-bed experiments in two scenarios: (i) homogeneous bed composed of plastic granules, and (ii) gravel or sand bed mixed with plastic granules, which were manually seeded at clastic bed surface for different covering percentage. A 12 m long, 40 cm wide channel was used to conduct gravel-sand sediment sorting experiments, leading to surface coarsening, and overfeeding experiments. Calibration consisted in finding best fits to threshold values of grain velocities, ensuring the observed equal mobility characteristics of poorly sediments with the same density. After calibration, the simulations reproduce the observed grave-sand sheet propagation in the overfeeding experiments. Simulations of the tests of different density classes indicate that coarse, low-density particles exhibit higher mobility than equally sized quartz particles, while the mobility of fine, low-density particles is comparable to that of natural sand of similar size. The model provides a consistent and conservative framework for representing the coupled transport and sorting of sediment–plastic mixtures in open-channel flows.
Acknowledgement: This works was supported by the Portuguese Foundation for Science and Technology (FCT) through project Project DT4Rivers COMPETE2030‐FEDER‐00760800 and European Union through Interreg Atlantic 2021-2017 project TRAP – EAPA_0122/2024
How to cite: Ricardo, A. M., L Ferreira, R. M., Varrani, A., Guerrero, M., Rowinski, P., and Mrokowska, M.: A model for the fluvial transport of different size and density classes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22114, https://doi.org/10.5194/egusphere-egu26-22114, 2026.
The central challenge in understanding extreme hydro-geomorphologic events is the persistent lack of integrated, quantitative observations capable of developing and constraining predictive models. While flash floods and associated sediment transport represent an escalating hazard under climate change, their underlying dynamics remain poorly understood across the spatio-temporal scales required for effective risk mitigation. Existing monitoring is often fragmented, with upcoming novel approaches only partly resolving key unknowns when used in isolation. For instance, optical methods such as UAV-based photogrammetry and camera gauges provide high resolution surface process data but cannot resolve subsurface bedload dynamics, whereas environmental seismic methods capture particle-riverbed interactions and signatures of turbulence but produce indirect, composite signals that are difficult to isolate and quantify.
To bridge this gap, we envision a multi-modal approach that moves beyond those single-technique or single-sensor proxies. To reliably and robustly observe temporarily evolving interlinked key parameters, i.e., water level, flow velocity, and hydraulic geometry, major steps involve using stereo-vision for precise scaling and channel cross-section updates, alongside AI-based optical flow for complex velocity fields. By integrating low-cost, event-triggered sensors (e.g., thermal & multispectral cameras, seismometers, and LiDAR), we can automate the retrieval of discharge as well as additional parameters such as turbidity and granulometry. Using photogrammetric change detection and AI-driven image processing we can further bridge terrestrial and aerial perspectives (e.g., from UAV), moving toward a physically consistent characterization of extreme events. By integrating high-resolution 3D imaging and seismic data inversion, it becomes possible to capture water and sediment dynamics simultaneously, resulting in unique complementary information on the same event.
In this framework, laboratory experiments provide the necessary controlled conditions to infer the capabilities, caveats and calibration measures for this sensor integration. Highly resolved computational fluid dynamics multiphase flow modelling will generate synthetic reference datasets to disentangle environmental signals and sensor noise. These heterogeneous data streams are integrated via AI-based fusion and uncertainty modelling to resolve non-linear relationships governing coupled water–sediment dynamics. Ultimately, hydrological and hydraulic modelling serves as a testbed for upscaling, in which models are informed by improved process knowledge-based observation data and its uncertainty to evaluate how small-scale insights alter catchment-scale predictions.
From this framework, significant gaps emerge that define the current research frontier. A critical unresolved challenge is the systematic separation of source terms from the superimposed signals generated by the actively evolving sediment-carrying river during flood events. Furthermore, the transition from "data-rich" local observations to “data-poor” but "process-informed" regional models is still hindered by the lack of scalable frameworks that can maintain physical consistency across different scales, i.e., climatic and geomorphological regimes. Addressing these gaps requires a coordinated shift from observing isolated parameters to an integrative, physics-based monitoring loop that can provide truly scalable, model-ready information for extreme events.
How to cite: Eltner, A., Dietze, M., Kowalski, J., Aberle, J., Grundmann, J., and Vowinckel, B.: Multi-Modal Monitoring and Modelling of Extreme Hydro-Geomorphological Events: Bridging the Gap Between Local Dynamics and Catchment-Scale Predictions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12572, https://doi.org/10.5194/egusphere-egu26-12572, 2026.
Landscape evolution models typically solve three main processes: the conversion of rainfall to runoff, flow routing, erosion, and sediment transport, for a given precipitation time series, and a topographic surface. They can help to predict watershed dynamics in response to potential extreme events and anticipate potential damages. To that purpose, models must accurately represent the studied catchment and reproduce available observations, such as water discharge and sediment flux. This requires adjusting the model parameters representing the catchment characteristics, which can be challenging due to long simulation times, many uncertain characteristics and modeling errors.
This study focuses on modeling the Pommeroye catchment — a 0.54 km² elementary watershed in the Canche River basin in northern France. The objective is to identify models able to reproduce the twenty extreme events identified in the data collected during the 2016-2017 hydrological year for discharge and suspended sediment at the catchment outlet. Topography is derived from a high-resolution (1m) LiDAR-derived digital elevation model. CAESAR-Lisflood is considered for dynamic simulation. The rainfall-to-runoff is modeled with a local storage term that has an exponential recession and is controlled by the water storage depth parameter “m”. From the generated surface runoff, the model continuously computes the flux of water and sediment across cells. Flow routing is solved via a reduced solution to the shallow water equations, where the friction term is computed via the Manning-Strickler model and hence controlled by the Manning’s roughness. Sediment transport follows the Wilcock and Crowe parameterization, with multiple controlling parameters. For the Pommeroye catchment, model run times are long, e.g. up to 24 hours on 36 CPUs, limiting the number of simulations that can be performed in practice. To overcome this, we developed a workflow combining machine learning-based surrogate models with sensitivity analysis and calibration. Gaussian processes are considered to mimic CAESAR-Lisflood from a limited training set and provide fast estimations of the simulator outputs for any input parameter values within given ranges. Instead of CAESAR-Lisflood, these predictions are used for variance-based sensitivity analysis (Sobol’ indices) and optimization (Efficient Global Optimization), drastically reducing the computation times.
A first sensitivity analysis highlighted that the m parameter mainly affects water discharge. However, no single m parameter value enables the model to correctly reproduce all data: the best fit is obtained with increasing values throughout the year, starting with low values in winter. In a second study, we thus added flexibility with time-dependent monthly values for m, leading to an improved match with water discharge data. Finally, the EGO approach - with fixed monthly m values - was considered to better reproduce suspended sediment data, identifying settling velocity and Manning’s roughness as key factors.
How to cite: uchasara huarachi, M. R., gervais, V., armitage, J., franke, C., and alary, C.: Surrogate-based sensitivity analysis and calibration for the hydro-sedimentary modeling of an elementary agricultural catchment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19202, https://doi.org/10.5194/egusphere-egu26-19202, 2026.
Debris flows are a major natural hazard in mountainous regions worldwide and significantly impact the sediment budgets in alpine areas. However, the development of debris flow frequency under climate change conditions has not yet been conclusively clarified, as long-term, comprehensive event records (i.e. not biased towards large events) are scarce. As alpine debris flows are predominantly triggered by high-intensity and short-duration rainfall events, precipitation records can be useful for inferring potential triggers, particularly under transport-limited conditions. As high-resolution precipitation measurements are rarely available over long periods of time, we employed dynamical downscaling of a Regional Climate Model (RCM) based on the Advanced Weather Research and Forecasting model (WRF). This approach resulted in a high-resolution climate model dataset covering most of the Central Alps, with a spatial resolution of 2x2 km and a temporal resolution of 15 minutes. This model enabled us to analyse high-intensity, short-duration rainfall events since the end of the Little Ice Age in 1850.
We compared the RCM with a debris flow record in the Horlachtal catchment in Tyrol, Austria. By analysing remote sensing datasets such as historical and recent aerial imagery, airborne lidar data and lichenometric dates, we identified 991 individual debris flows in the area between 1947 and 2022. Combining the observation dataset with the RCM rainfall data enabled us to take an integrated approach to assessing changes in debris flow frequency in the Horlachtal and their climatic drivers since 1850. The results provide insights into possible future trends in debris flow frequency in a changing climate, showing a weak positive long-term trend for the Horlachtal. The RCM's coverage allows for similar studies in other Alpine regions, offering more detailed insights into the spatial variability of changes in debris flow activity within the Central Alps.
How to cite: Rom, J., Pfeiffer, M., Marzeion, B., Heckmann, T., Haas, F., and Becht, M.: Evaluation of a downscaled Regional Climate Model for analysing the frequency of debris flows since 1850, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9692, https://doi.org/10.5194/egusphere-egu26-9692, 2026.
Authors:
Ezeaba Assumpta1, Dallan Eleonora1, Vohnicky Petr1, Borga Marco1
1 Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Type of presentation:
Poster
Title:
Interplay Between Event Frequency and Intensity in Future Rainfall Erosivity revealed by Convection-permitting climate models
Abstract:
Soil erosion represents a critical environmental and economic challenge facing agricultural landscapes, and its severity could be amplified by the rising intensity of extreme rainfall in a warming climate. Rainfall erosivity, a key driver of erosion, depends on both rainfall intensity and the frequency of erosive events, making it highly sensitive to their ongoing and future changes. High resolution convection-permitting models (CPMs) offer enhanced representation of sub-daily rainfall extremes, yet their application to soil erosion studies remains limited.
This work assesses the skill of an hourly CPM in reproducing historical rainfall erosivity in a Mediterranean Island, Sicily, and evaluates its future changes under RCP4.5 and RCP8.5 scenarios. Modelled rainfall was first bias-corrected using intensity thresholds and scaling factors derived from high temporal-resolution observations. The CPM shows underestimate in maximum rainfall intensity and erosive event frequency, and thus in mean annual erosivity, especially in lowland and coastal areas. These biases highlight challenges in simulating short-duration convective events, sea-land interactions, and mismatches between point-based and gridded datasets. Future projections show divergent outcomes: under RCP4.5 moderate frequency decrease combines with higher intensities leading to a moderate net increase in erosivity, whereas under the RCP8.5 scenario a marked (17%) reduction in event frequency dominates the signal, yielding lower future erosivity despite rainfall intensification.
The results demonstrate that bias correction procedures should consider topographic dependence and different erosivity-related variables, and that future erosivity cannot be inferred from intensity changes alone; event frequency is equally relevant. Incorporating high-resolution climatic models and explicitly accounting for frequency-intensity interactions are therefore essential for robust erosion risk assessments and climate adaptation strategies.
This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next‐GenerationEU (National Recovery and Resilience Plan—NRRP, Mission 4, Component 2, Investment 1.3—D.D. 1243 2/8/2022, PE0000005); and within the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005.
How to cite: Ezeaba, A., Dallan, E., Vohnicky, P., and Borga, M.: Interplay Between Event Frequency and Intensity in Future Rainfall Erosivity revealed by Convection-permitting climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1632, https://doi.org/10.5194/egusphere-egu26-1632, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 3
EGU26-11678 | ECS | Posters virtual | VPS26
Rare-event detection of incipient sediment motion from smart-particle time series using deep learningTue, 05 May, 14:48–14:51 (CEST) vPoster spot 3
Incipient sediment motion in turbulent flows remains difficult to characterize and predict because the underlying hydrodynamic forces are highly intermittent and events are sparse in time, even in well-controlled experiments. This study investigates whether temporal deep-learning architectures can detect the onset of particle motion directly from high-frequency velocity time series measured by an instrumented “smart sphere” [1, 2], without explicit force or torque measurements. The workflow includes detrending and cleaning of raw signals, physics-informed signal transforms (e.g. smoothed velocity, acceleration, jerk, and kinematic impulse proxies), segmentation with sliding windows, and supervised training of temporal deep-learning architectures, including recurrent, convolutional, and attention-based models, using class-imbalance mitigation such as focal loss, class weighting, and data augmentation.
Hyperparameter optimization is performed automatically with Optuna, and model performance is assessed using ROC and precision–recall curves, confusion matrices and time-resolved prediction performance. Results show that all tested architectures can learn consistent kinematic signatures preceding incipient motion from single-axis velocity time series, with models incorporating attention mechanisms achieving the highest recall on rare motion-onset events, consistent with their ability to focus on intermittent, high-magnitude kinematic bursts preceding entrainment. These findings demonstrate that deep learning applied to smart-particle sensor data can provide an efficient, non-intrusive tool for particle-scale sediment transport monitoring and real-time–capable event detection. The approach is directly relevant to the session’s focus on particle-scale transport mechanics and data-driven upscaling, and opens avenues for integrating deep-learning-based event detection into multi-scale sediment transport models in geophysical and engineered flows.
References
[1] Al-Obaidi, K., Xu, Y., & Valyrakis, M. (2020). The design and calibration of instrumented particles for assessing water infrastructure hazards. Journal of Sensor and Actuator Networks, 9(3), 36.
[2] AlObaidi, K., & Valyrakis, M. (2021). Linking the explicit probability of entrainment of instrumented particles to flow hydrodynamics. Earth Surface Processes and Landforms, 46(12), 2448-2465.
How to cite: Mavris, I. and Valyrakis, M.: Rare-event detection of incipient sediment motion from smart-particle time series using deep learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11678, https://doi.org/10.5194/egusphere-egu26-11678, 2026.
EGU26-11152 | ECS | Posters virtual | VPS26
Efficient Hydrodynamic Modeling at the Landscape Scale: Quantifying River Width and Shear Stress Variability to Decode Tectonic SignalsTue, 05 May, 14:57–15:00 (CEST) vPoster spot 3
Basal shear stresses exerted by river flow control the capacity of river to erode and transport sediment. Material properties (e.g. lithology, grain size) modulate how basal shear stress translates into morphological change. Quantifying the spatial variability of basal shear stress is therefore essential to assess fluvial erosion processes and to infer the tectonic and climatic forcings recorded in landscape morphology.
Direct and systematic measurement of the basal shear stress in rivers is not feasible at large scales, making numerical hydrodynamic modelling the primary tool for its estimation. However, applications beyond the reach scale remain computationally prohibitive due to (i) the need for high-resolution topography to resolve channels, banks, and bars, and (ii) the numerical cost of solving the Shallow Water Equations (SWEs), which require small time steps to propagate changes induced and complex solvers.
Here, we present a novel numerical framework that substantially reduces the computational cost of hydrodynamic modelling for morphometric analysis, enabling simulations over large, high-resolution DEMs and ranges of hydrological conditions. The approach reformulates the SWEs into a simplified stationary scheme, linearizing algorithmic complexity, and allowing scalable computations. In addition, we employ GPU-accelerated, graph-based flow accumulation algorithms to compute discharge efficiently. Together, these developments reduce computation time by up to three orders of magnitude compared to conventional hydraulic modelling approaches.
The method is implemented in the pyfastflow package within the TopoToolbox ecosystem. We apply it to more than 100 watersheds in the Mendocino Triple Junction (California, USA), a region characterized by strong spatial gradients in tectonic uplift. Hydrodynamics are computed for five hydrological states constrained by precipitation data, spanning low flow to flood conditions. We quantify spatial variations in river width and shear stress and show that these metrics capture complementary temporal signatures of uplift timing and magnitude. Basin-wide shear stress responds quickly to uplift onset but exhibits a significantly delayed response during relaxation, whereas channel width displays a more variable and spatially contrasted transient signal upstream of the onset.
How to cite: Gailleton, B., Steer, P., Cordonnier, G., and Clubb, F.: Efficient Hydrodynamic Modeling at the Landscape Scale: Quantifying River Width and Shear Stress Variability to Decode Tectonic Signals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11152, https://doi.org/10.5194/egusphere-egu26-11152, 2026.
