Yuexia Zhou

Floods reorganize gravel‐bed rivers through coupled hydrodynamics, sediment transport and vegetation mortality. However, event‐scale morphodynamic predictions remain limited when vegetation loss is represented by a single, simplified mechanism. Here, we address how explicitly representing multiple, concurrent flood‐induced vegetation disturbance processes and their feedbacks can improve the physical realism of vegetation–hydro‐morphodynamic interactions during floods. We propose an integrated framework for modelling flood‐induced vegetation disturbance processes, including mechanical failure through overturning or breakage, followed by downstream washout, burial beneath deposited sediment or persistence in a failed but nonremoved state. To evaluate the model, we compared four configurations: Case 1 (proposed framework), Cases 2 and 3 (with different postfailure mechanisms than Case 1) and Case 4 (a static vegetation case in which flood‐induced disturbance was not considered). These four configurations were incorporated into a two‐dimensional hydro‐morphodynamic model. The framework was applied to the 2019 flood of the Chikuma River, Japan and evaluated against preflood and postflood topography and vegetation distribution data. The proposed framework successfully captured the spatial heterogeneity of vegetation removal by explicitly simulating the co‐evolution of riverbed change and vegetation disturbance processes. Furthermore, the spatial distribution of washout‐dominated vegetation removal and burial‐dominated disturbance were closely associated with bar morphology. These event‐scale feedbacks, whereby vegetation disturbance directly alters flow resistance and morphodynamic evolution, are critical for reliably predicting embankment erosion and associated infrastructure risk. Future work should extend this framework to multi‐event sequences and diverse river planform types to further validate its generalizability.

The Normalized Difference Vegetation Index (NDVI) can be effectively used for monitoring the spatial and temporal dynamics of riparian vegetation. However, quantitative and efficient evaluations of the links between NDVI and bio-hydromorphological processes remain limited, particularly in river management contexts where dense in-channel vegetation can obstruct flow and reduce conveyance capacity. Using 200 cloud-free Sentinel-2 images (2015–2024) covering a 20-km reach of the Chikuma River (Japan), we evaluated the utility of high temporal resolution NDVI and greenness index (defined as NDVI > 0.2) as quantitative indicators of bio-hydromorphological dynamics and its implications for riverine management. The analysis focused on the relationships between NDVI dynamics, flood magnitude, relative elevation along lateral channel morphology, and seasonal vegetation variability within a frequently disturbed channel. The results show that NDVI fluctuations strongly correspond to flood disturbances at lower relative elevations, whereas vegetation at higher elevations remains relatively stable. The annual maximum greenness ratio was well described by a logistic model along the cross-sectional transects. Annual greenness ratio exhibited clear seasonal patterns, showing a late-summer (August–September) greenness peak. These spatiotemporal and seasonal NDVI characteristics demonstrate the potential of Sentinel-2 imagery to operationalize both the “when” (timing) of vegetation management and the “where” (priority zones defined by relative elevation), providing a transferable, remotely sensed basis for flood-risk mitigation in frequently disturbed riverine environments.

The Normalized Difference Vegetation Index (NDVI) can be effectively used for monitoring the spatial and temporal dynamics of riparian vegetation. However, quantitative and efficient evaluations of the relationship between NDVI and bio-hydromorphological processes remain limited, particularly in the context of riverine floodplain management, where dense in-channel vegetation can obstruct flow and reduce conveyance capacity. Using 200 cloud-free Sentinel-2 images (2015–2024) for a 20-km reach of the Chikuma River (Japan), we evaluated the utility of NDVI (extracted from Sentinel-2 images) and the greenness index (defined as NDVI > 0.2) as quantitative indicators of bio-hydromorphological interactions, focusing on: (1) the relationship between NDVI dynamics, flood magnitude, and lateral channel morphology of relative elevation, and (2) the seasonal dynamics of riparian vegetation within frequently disturbed channels. Results indicated that NDVI fluctuations strongly corresponded to flood disturbances at lower elevations, while vegetation at higher elevations remained relatively stable. Along cross-channel transects, the maximum greenness ratio was well represented by a logistic model, with parameters varying according to flood magnitude from the previous year. Annual vegetation greenness additionally exhibited clear seasonal cycles, showing a late-summer greenness peak (August–September). The spatial and seasonal characteristics of NDVI displayed its potential as an indicator for operationalizing the “where” (priority bands by relative elevation) and “when” (phenological window) of vegetation control, and offered a transferable, remotely sensed basis for flood-risk mitigation and ecohydraulic planning.

The Normalized Difference Vegetation Index (NDVI) can be effectively used for monitoring the spatial and temporal dynamics of riparian vegetation. However, quantitative and efficient evaluations of the relationship between NDVI and bio-hydromorphological processes remain limited, particularly in the context of riverine floodplain management, where dense in-channel vegetation can obstruct flow and reduce conveyance capacity. Using 200 cloud-free Sentinel-2 images (2015–2024) for a 20-km reach of the Chikuma River (Japan), we evaluated the utility of NDVI (extracted from Sentinel-2 images) and the greenness index (defined as NDVI > 0.2) as quantitative indicators of bio-hydromorphological interactions, focusing on: (1) the relationship between NDVI dynamics, flood magnitude, and lateral channel morphology of relative elevation, and (2) the seasonal dynamics of riparian vegetation within frequently disturbed channels. Results indicated that NDVI fluctuations strongly corresponded to flood disturbances at lower elevations, while vegetation at higher elevations remained relatively stable. Along cross-channel transects, the maximum greenness ratio was well represented by a logistic model, with parameters varying according to flood magnitude from the previous year. Annual vegetation greenness additionally exhibited clear seasonal cycles, showing a late-summer greenness peak (August–September). The spatial and seasonal characteristics of NDVI displayed its potential as an indicator for operationalizing the “where” (priority bands by relative elevation) and “when” (phenological window) of vegetation control, and offered a transferable, remotely sensed basis for flood-risk mitigation and ecohydraulic planning.

Vegetation colonizing fluvial sandbars provides many noteworthy functions in river and floodplain systems, but it also influences hydrodynamic processes, mainly during flooding events. Numerical modelling is generally used to evaluate the impact of floods, but its reliability is very much connected with the accuracy of the bed and bank roughness, which is eventually altered by the presence of vegetation and its height. However, for the sake of simplicity, most models tend to ignore how the sandbar roughness varies over space and time, as a function of the local vegetation dynamics (spatial distribution and height). To determine the long-term dynamic vegetation condition using remote sensing multispectral indexes, this study leverages a deep-learning method to establish a relationship between vegetation height (h), a critical parameter for vegetation roughness estimation, and vegetation indexes (VIs) collected by an uncrewed aerial vehicle (UAV). A field campaign was performed in October 2024 covering the Baishazhou sandbar, located along a straight section of the Wuhan reach of the Changjiang River Basin, China. The results show that the R² and RMSE between the real and predicted vegetation height by the trained Fully Connected Neural Network (FCNN) are 0.85, 1.10 m, and the relative error reaches a maximum of 17.2%, meaning that the trained FCNN model performs rather well. Despite being tested on a single case study, the workflow presented here demonstrates the opportunity to use UAVs for depicting vegetation characteristics such as height over large areas, eventually using them to inform numerical models that consider sandbar roughness.

The Normalized Difference Vegetation Index (NDVI) can be effectively used for monitoring the spatial and temporal dynamics of riparian vegetation. However, quantitative and efficient evaluations of the relationship between NDVI and bio-hydromorphological processes remain limited, particularly in the context of riverine floodplain management, where dense in-channel vegetation can obstruct flow and reduce conveyance capacity. Using 200 cloud-free Sentinel-2 images (2015–2024) for a 20-km reach of the Chikuma River (Japan), we evaluated the utility of NDVI (extracted from Sentinel-2 images) and the greenness index (defined as NDVI > 0.2) as quantitative indicators of bio-hydromorphological interactions, focusing on: (1) the relationship between NDVI dynamics, flood magnitude, and lateral channel morphology of relative elevation, and (2) the seasonal dynamics of riparian vegetation within frequently disturbed channels. Results indicated that NDVI fluctuations strongly corresponded to flood disturbances at lower elevations, while vegetation at higher elevations remained relatively stable. Along cross-channel transects, the maximum greenness ratio was well represented by a logistic model, with parameters varying according to flood magnitude from the previous year. Annual vegetation greenness additionally exhibited clear seasonal cycles, showing a late-summer greenness peak (August–September). The spatial and seasonal characteristics of NDVI displayed its potential as an indicator for operationalizing the “where” (priority bands by relative elevation) and “when” (phenological window) of vegetation control, and offered a transferable, remotely sensed basis for flood-risk mitigation and ecohydraulic planning.

The destruction of trees in the river (riparian trees) has been mainly analyzed with consideration of flood loading recently, and the decreasing of the critical resistive moment used to evaluate the destruction condition was not analyzed in those studies though the actual resistive moment may decrease with substrate scouring by the heavy flood. In the context of climate change, which increases the likelihood of simultaneous floods and storms, the mechanism of riparian tree destruction under both flood and wind load and the change of critical resistive moment with riverbed erosion should be investigated. We established a mechanical model and analyzed the criteria for the destruction of riparian trees under flood and wind loads. One simple model was also proposed with considerations of root-soil plate radius and erosion depth of river bed to analyze the decreasing of the critical resistive moment. The taller and smaller trees are more vulnerable to be destructed than medium trees, with the consideration of both flood and storm. The critical resistive moment decreased as compared to the previous study, and the usefulness of the established model was verified to some extent.