Journal of Geophysical Research: Earth Surface最新文献

This work introduces a physically based modeling framework to capture the spatio-temporal dynamics of dune vegetation under stochastic environmental disturbances. The model evaluates vegetation cover in response to random wind speed and runup within a cross-shore dimensionless framework. The wind speed is modeled as a compound Poisson process with Gamma-distributed properties, facilitating the computation of up-crossing times for various thresholds. The dune topography is represented by a swash zone with a Gaussian shape and a monotonic landward increase, parameterized by slope, wavelength, and height. Key disturbance conditions affecting vegetation, that is, runup-induced flooding in the swash zone and wind-induced scour on the backshore and crest, are addressed through threshold-based analysis. The model uses a state-dependent dichotomic process for vegetation dynamics, where growth and decay are influenced by external forcing and vegetation state. Analytical solutions of the master equation for the vegetation distributions reveal the impact of stochastic factors on vegetation growth and stability. Sensitivity analysis identifies dune steepness, forcing magnitude and variability, and relative roughness as critical parameters. These factors significantly affect vegetation distribution, with increased steepness leading to higher vegetation density at the backshore and reduced density at the shorefront. Validation is carried out against satellite imagery and high-resolution real elevation data from the U.S. coastline and confirms the robustness and accuracy of the proposed approach. The results enhance understanding of dune vegetation dynamics and offer a framework for coastal restoration strategies.

Barrier island resilience to climate impacts depends on sediment redistribution between the subaqueous shoreface and subaerial barrier during sea-level rise and storms. However, autogenic interactions between the upper and lower shoreface and their influence on the subaerial barrier are poorly characterized. Here, we explore the influences of various shoreface components on barrier morphology using a model of barrier and shoreface evolution under sea-level rise, the Articulated Barrier Shoreface (ABSF) Model. This reduced-complexity model divides the shoreface into upper and lower shoreface panels that respond independently to sea-level rise and deviations from the equilibrium slope. We couple the ABSF with the Lorenzo-Trueba & Ashton, 2014, https://doi.org/10.1002/2013jf002941 model (LTA), a barrier island evolution model driven by overwash and sea-level rise. Through this coupled framework, we examine the influences of upper and lower shoreface slopes, their respective depths, and sensitivity to wave climate on long-term barrier evolution. Results show that the relative depths of the upper and lower shoreface toes influence barrier response to rising seas, alongside overwash flux and closure depth. Notably, the lower shoreface response to sea-level change lags that of the upper shoreface over decades, diminishing the resilience of the barrier over centennial timescales by slowing the overall barrier response. In fact, the ABSF model predicts barriers will drown faster and more than predicted with a linear shoreface. Results highlight the shoreface as an important sediment reservoir for barrier islands and that differences in upper and lower shoreface responses can reduce barrier resilience to sea-level rise due to limited lower shoreface sediment accessibility.

Debris flows are frequent natural hazards whose destructiveness is controlled by the dynamics of their flow fronts, surges behind the front and large boulders. Understanding the mechanisms underlying the spatiotemporal variations in flow depth and velocity is limited by a lack of catchment-scale measurements. In this study, we present and analyze flow-depth and velocity measurements from a new monitoring setup which consists of high-frequency 3D LiDAR scanners installed at three different locations along the active debris-flow fan of the Illgraben. For the event analyzed herein, we observe that (a) the LiDAR-based velocities are in excellent agreement with measurements from a Pulse-Doppler (PD) radar and with manually tracked feature velocities; (b) the flow front decelerates as it travels along the fan and a watery pre-surge develops, likely due to a combination of segregation and vertical shear, which transport woody debris and small boulders to the front, as well as a horizontal velocity profile, required for transportation of large boulders through a mechanism we term “centerline advection”; (c) roll waves begin to develop on the lower part of the fan by coalescence of free surface instabilities and they exceed the front velocity by up to 2$��\times �$ to 3$��\times �$; (d) surges later in the event do not show an accumulation of boulders at the crest, but can accelerate individual boulders to velocities up to 2$��\times �$ the front. This study demonstrates that radar and LiDAR-based measurements improve our understanding of debris-flow processes, including front propagation and development of waves.

P-wave velocity profiles from seismic refraction reveal deep critical zone (CZ) architecture along profiles hundreds of meters long. However, extrapolating local velocity measurements to infer CZ architecture at regional scales (1–20 km²) remains challenging. Here, we present a strategy that transforms seismic observations from individual profiles into maps of CZ architecture spanning tens of square kilometers. Data from 15 seismic refraction profiles (approximately 6.6 km total length) collected in weathered crystalline rocks of the South Carolina Piedmont, USA, revealed approximately 400,000 m² of deep CZ architecture. Using casing depths from four boreholes, we show that the boundary dividing saprolite and fractured rock corresponds to a velocity of 1,870 m/s. Using velocity measurements from an outcrop within the survey area, we identify the bedrock velocity as 4,550 m/s. These velocities define a three-layer CZ structure comprising soil and saprolite, fractured bedrock, and unweathered bedrock. We developed an empirical relationship between CZ structure and minimum and maximum principal curvatures, enabling prediction of CZ architecture over approximately 17 km². The correlation between seismically inferred CZ structure and principal curvatures at our study site suggests that curvature metrics can be used to predict CZ structure at larger scales in crystalline terrains under subtropical climates. However, the empirical relationship struggled to predict CZ structure where landscape curvatures were near zero, suggesting that other variables likely contribute to local heterogeneity. Given that curvature is an important variable for erosion and groundwater flow, our results suggest it could be a promising metric for predicting CZ structure.

Seagrass meadows fulfill many essential ecological functions, of which an important one is to stabilize sediment. Therefore, they are perceived as a nature-based addition or alternative to conventional rigid coastal protection. The impact of seagrass meadows depends on their morphology, such as canopy height, shoot density, and spatial extent. However, deciduous, intertidal seagrass species are often simplified in modeling studies by adopting their annual mean height and density. This can lead to an erroneous estimate of their impact on hydro-morphodynamics and misconceptions about their contribution to coastal protection. Here, we assess the importance of seasonal changes in seagrass properties for morphological development, using a tidal basin in the Wadden Sea as an example. We applied numerical modeling to simulate the annual growth cycle of seagrass meadows and their interaction with hydro-morphodynamics. Based on validated seasonal changes in seagrass properties from field surveys and comparisons between scenarios of seagrass growth, our results show that adopting static seagrass parameters in modeling can lead to over- or underestimation of morphological changes induced by seagrass meadows. In some cases, it may even predict results contrary to simulations that consider seasonal changes in seagrass properties, particularly regarding the net sediment volume change in the intertidal zone. This highlights the essential necessity of considering the natural growth and decline cycles of seagrass meadows when assessing their role in coastal protection, especially in temperate zones where seasonal changes in seagrass properties are distinct.

Understanding earthquake-induced secondary hazards is critical for effective disaster mitigation and risk reduction. However, while most research focuses on hazards triggered by major earthquakes, moderate-magnitude events can also cause severe disasters in specific geo-environmental configurations. The 2023 Ms 6.2 (Mw 6.0) Jishishan earthquake, occurring in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau in China, serves as an ideal case to investigate this “moderate quake but numerous hazards” phenomenon. To systematically characterize its impacts, we generated a comprehensive inventory of 16,544 co-seismic landslides through visual interpretation utilizing high-resolution spaceborne and airborne remote sensing images, and illuminated the driving factors of landslide evolution by machine learning methods. Random Forest and Light Gradient Boosting Machine models, which achieve an area under the receiver operating characteristic curve (ROC AUC) >0.96, are effective in landslide susceptibility assessment. Our results demonstrate that the occurrence of co-seismic landslides is primarily controlled by large peak ground acceleration and the distribution of sedimentary lithology (loess). The proxy for ground disturbance derived from interferometric synthetic aperture radar (InSAR) data succeeds in highlighting the spatial pattern of earthquake damage. Furthermore, multi-source remote sensing analysis of an enormous lateral spread disaster triggered by this earthquake revealed that flood irrigation over sandy soils, indicated by Sentinel-2 soil moisture indices, is assumed to induce a liquefied process under ground shaking, subsequently leading to lateral spread. Our findings advance the understanding of landslide occurrence and multi-hazard interaction in moderate seismic events within vulnerable landscapes.

The Pearl River, originating from the southeastern Tibetan Plateau and transporting substantial sediments into the northern South China Sea, records the coupled influence of tectonics, climate, and surface processes in East Asia. However, the timing and mechanisms of its reorganization remain debated. Here we use the landscape evolution model Badlands to explore hypotheses for the Pearl River's drainage history since the Cenozoic, incorporating reconstructions of dynamic topography, lithospheric deformation, and surface processes. Our simulations suggest that the early Cenozoic landscape of South China was westward-tilted with a paleo-coastline ∼300 km south of its present-day location, where the western tributaries and the paleo-Pearl River evolved independently. Intensified precipitation associated with the East Asian monsoon may have promoted headward erosion and drainage integration, establishing a Pearl River–scale network at the end of the Late Oligocene. Models with overestimated precipitation produce premature sediment flux peaks, whereas stepwise precipitation increases reproduce the timing of river integration inferred from detrital zircon provenance shifts. Dynamic topography and tectonic forcing shaped the broader landscape but played a secondary role compared to precipitation in controlling drainage development. Although Badlands simplifies fluvial and hillslope processes, the experiments provide testable hypotheses for the climate–tectonics–surface-process coupling that drives the evolution of large monsoon-influenced river systems.

Glacial watersheds provide an ideal setting for studying chemical weathering, sediment transport and circulation at high altitudes. This study analyzed cryoconite, river sediments, and water samples from the Muztagh glacial watershed in the Pamir Plateau. The results showed that the cryoconite dust in the glacier originated mainly from the Asia arid regions. The (²³⁴U/²³⁸U) activity ratio of river sediments ranges from 0.998 to 0.976, in which the uranium comminution age indicates that the fine-particles age from bedrock is approximately 3.3–43.6 ka. Correlation analysis with topographic, climatic, and hydrological parameters reveals that glacial (physical) erosion is the primary factor driving the variability of sediment surface processes between the tributary and mainstream in the glacier watershed. Glacier erosion contributes a mean of 67 ± 25% of the sediment input of the glacier-fed tributaries of the Muztagh watershed, while down to the main stream in the Gaizi River, the contribution drops to 60 ± 26%. The concentrations of [U] and [Li] in river water increased along the glacier to downstream area, while U-Li isotope ratios showed high (²³⁴U/²³⁸U) and δ⁷Li values at the glacier terminus, showing a gradual decrease mode subsequently. These findings suggest that glacial action in the Muztagh Glacier region causes extensive physical comminution of mineral particles, leading to strong α recoil, in which ²³⁴U is preferentially ejected from damaged crystal lattice sites, while limited chemical weathering. In contrast, non-glacial regions experience reduced recoil effects and enhanced chemical weathering. This study provides new insights into the sediment production and transported process in glacial watersheds.

Tectonic fracturing in uplifting mountains facilitates fluid-rock interactions, causing downward propagation of chemical weathering fronts. In contrast, erosion in uplifting mountains removes fractured and chemically altered bedrock, thinning the weathering zone. The interplay of these processes sets weathering zone thickness, but despite the disproportionate influence of chemical weathering in mountains on global biogeochemical cycles, it is unclear where within the weathering zone those chemical reactions predominantly occur. Here we present geochemical data from a 300 m-deep drill core and results from reactive transport modeling to assess weathering zone characteristics in the Southern Alps/Kā Tiritiri o te Moana of New Zealand/Aotearoa. Our findings indicate that soil is thin and chemical weathering fronts are shallow, with only apatite (and likely calcite) weathering extending below the soil-bedrock boundary. Simulations indicate that soil thickness is primarily controlled by porosity-generating plagioclase weathering and that simulated soil thicknesses are consistent with local precipitation and denudation rates. However, simulations also show that if all 6 m of annual precipitation infiltrated bedrock, chemical weathering fronts would extend substantially deeper than observed. We infer that the porosity contrast between soil and rock limits bedrock fluid flow, slowing the propagation of chemical weathering. Erosion and limited fluid-mineral interaction in deep fractures result in a thin weathering zone, suggesting that silicate weathering in uplifting mountains occurs primarily within soil, rather than bedrock. Our measurements suggest that oxidative weathering of petrogenic carbon has been overestimated previously, but, consistent with prior work, surface processes in the study area result in net consumption of atmospheric CO₂.

Instream large wood (LW) plays a vital role in river morphology and ecology, but its transport can pose risks to infrastructure during floods. Monitoring LW transport during flood events remains limited due to technical and logistical limitations. This study employs drone-based video monitoring and machine learning to analyze LW dynamics during an experimental flood in the Spöl River, Swiss Alps. Using three drones covering a 200-m stretch, we created a high-resolution data set of over 560 pieces and 36,000 wood detections (including individual pieces captured in multiple frames). Convolutional neural networks (CNNs) detected and tracked LW, enabling detailed analysis of trajectories, rotation, and velocity, complemented with flow field characteristics (i.e., surface velocity) derived from Large-Scale Particle Image Velocimetry (LSPIV). Results showed that LW transport was concentrated in high-velocity flow paths and influenced by wood piece dimensions. Longer, thinner pieces moved faster, while thicker pieces faced greater resistance. Flow convergence aligned wood pieces with flow direction, reducing rotation, especially for larger pieces. Although wood piece rotation increased with flow velocity, it plateaued at the highest velocities. Large pieces, while fewer, represented 65% of the total transported volume, emphasizing their role in LW dynamics. By leveraging unmanned aerial vehicles (UAVs) and convolutional neural networks (CNNs), this study offers new insights into interactions between flow conditions, wood size, and transport behavior. Our findings contribute to the understanding of LW dynamics in flood conditions and provide valuable information that can enhance flood risk assessment, support early warning systems, and inform sustainable river management strategies.