Journal of Geophysical Research: Earth Surface最新文献

Greenland and Antarctica's peripheral glaciers are an important but often overlooked element in the global sea-level rise budget. Here, we use satellite laser altimetry from ICESat and ICESat-2 to assess the mass loss from Greenland's and Antarctica's peripheral glaciers for three periods: February 2003 to October 2009, October 2009 to April 2018, and October 2018 to April 2023. Over these periods, Greenland's peripheral glacier mass loss has increased from 27.3 ± 7.9 Gt yr⁻¹ during 2003–2009, to 35.8 ± 5.3 Gt yr⁻¹ during 2018–2023. The ice loss from Antarctica's peripheral glaciers underwent a more complex change during this time, with a mass loss −4.2 ± 1.3 Gt yr⁻¹ during 2003–2009, sharply rising to −16.0 ± 5.9 Gt yr⁻¹ during 2009–2018, and subsequently declining to −9.0 ± 0.7 Gt yr⁻¹ during 2018–2023. This temporal pattern of mass loss is observed across all Antarctic regions. Notably, the Antarctic Peninsula experienced a mass loss of 2.6 ± 3.1 Gt yr⁻¹ during 2003–2009 followed by gains of 2.7 ± 3.8 Gt yr⁻¹ and 11.9 ± 1.7 Gt yr⁻¹ during 2009–2018 and 2018–2023, respectively. This shift toward mass gain during 2018–2023 can be attributed to exceptional levels of precipitation during the winters of 2019 and 2020. We conclude that increased snowfall played a crucial role in mitigating glacier mass loss during this later period. Overall, our findings show accelerating mass loss of Greenland and Antarctica's peripheral glaciers with complex variability, both spatially and temporally, with certain regions experiencing mass gains through increased snowfall.

This study develops a novel general framework to project the permafrost fate with rigorous uncertainty quantification to assess dominant sources. Borehole temperature records from three sites in the Russian western Arctic are used to constrain the uncertainty of a high-fidelity freeze-thaw model. Projections from 9 Global Climate Models (GCM) are stochastically downscaled to generate future trajectories of surface ground heat flux. Under the two emission scenarios SSP2-4.5 and SSP5-8.5, the projected average thawing depths by 2100 vary from 0.4 to 14.4 m or 2.1 to 17.7 m, and the increase in the top 10 m average temperature from 2015 to 2100 is 1.2–2.7°C or 1.9–3.0°C. The results show that the freeze-thaw model uncertainty can sometimes dominate over that of GCM outputs, calling for site-specific information to improve model accuracy. The framework is applicable for understanding permafrost degradation and related uncertainties at larger scales.

Geophysical flows, governed by particle composition and channel slope, exhibit distinct kinematic properties and seismic responses under different flow regimes. This study examines the impact of particle composition on granular flow dynamics and seismic signal generation under various flow regimes using flume experiments under dam break conditions. By varying particle composition and flume inclination angles, we investigate the kinematic properties, seismic responses, and the relationship between flow regimes and seismic signal characteristics. The results reveal that particle composition significantly affects flow dynamics, with peak velocity exhibiting a non-monotonic dependence on particle size, and an optimal proportion of large particles maximizing mobility. Seismic signals, including peak amplitude and power spectral density, increase with larger particle sizes and steeper inclination angles, indicating a strong coupling between flow dynamics and seismic responses. A two-segment positive correlation between seismic signals and collisional stress highlights the role of flow regimes, with particle-ground impacts during intense collisional interactions dominating seismic signal generation, we then introduce a dimensionless amplitude parameter and establish a unified correlation with the Savage number across flow regimes. This study advances the understanding of granular flow dynamics and seismic signatures, providing a framework for interpreting seismic data in debris flow monitoring and hazard assessment. Future work to explore the interplay of frictional and collisional mechanisms to refine models of granular flow behavior and physical interpretation of seismic data is warranted.

Whereas the ability to acquire petrochronological data from detrital minerals has exploded, development of tools to analyze and interpret the multivariate data sets has not kept pace. Herein, we present a case study, which applies the recently developed non-negative Tucker-1 decomposition (NNT1) method to a multivariate detrital zircon data set from till samples collected above the Cu-bearing Guichon Creek Batholith in southern British Columbia, Canada. Zircon composition variables that we consider include age, Ce anomaly, Ce_N/Nd_N, Dy_N/Yb_N, ΔFMQ, Eu anomaly, ΣHREE/ΣMREE, Hf, Th/U, Ti temperature, and Yb_N/Gd_N. The NNT1 approach successfully deconvolves the multivariate data set into two endmembers, which are consistent with derivation either from non-oxidized and relatively anhydrous (i.e., low Cu-ore potential, Source 1) or oxidized and hydrous (i.e., potential Cu-ore bodies, Source 2) igneous rocks. Furthermore, we attribute each of the zircon grains to either the Source 1 or 2 endmember based on maximization of the likelihood that their measured multivariate geochemistry was drawn from one or the other of the learned multivariate endmembers. Finally, we demonstrate that the proportions of the Source 2 endmember decrease with increasing distance from the ore bodies, as expected due to down-ice or off-axis zircon mixing and dilution. We conclude that the NNT1 approach provides insight into geologically meaningful sediment transport processes and multivariate sediment sources even when those sources are unknown. It thus provides a basis for future petrochronological interpretations with applied and pure geoscience applications.

Variations in sediment mobility control channel morphology, its response to disturbance, and outcomes of river restoration. Previous work shows erosion tends to dominate where channels steepen and deposition where slopes moderate; over time, this should act as negative feedback that diminishes longitudinal variations in channel slope and sediment flux. We tested the hypothesis that spatial variations in sediment mobility across reach morphologies are offset by adjustments in transport efficiency using active and passive bedload tracers deployed across a stream with alternating plane-beds, pools, and step-pools. We find that critical Shields numbers, which quantify entrainment thresholds, are larger and exhibit larger seasonal increases in morphologies with greater slopes. Virtual velocities also vary with reach morphology; tracers move more slowly in step-pools than in plane beds and pools. However, if the mobile bedload layer depth scales with characteristic grain size, then bedload fluxes across reach types are approximately continuous as the thicker mobile layer on steeper reaches compensates for their slower virtual velocities. We also document lasting downstream effects of two small dams removed during the study period: lower entrainment thresholds and higher virtual velocities below former dams, consistent with elevated sediment flux from ongoing evacuation of reservoir deposits. These findings highlight a process-based mechanism—adjustment of bed grain size and transport efficiency where slope is constrained by grade controls—that can maintain sediment flux continuity across diverse reach morphologies and modulate channel response to disturbances.

Daily thermal cycles represent a persistent and uninterrupted mechanical weathering process that facilitates erosion, particularly in loess regions characterized by large diurnal temperature variations and fragile surface soils. Developing effective erosion control strategies is especially critical for vast loess territories, where population and economic activities are normally densely concentrated. A fundamental step toward this goal is to advance the understanding of the thermal behavior of loess. This study conducted laboratory simulations of ambient temperature fluctuations and measured internal temperatures, surface displacements, and surface hardness of large intact loess blocks. Numerical and theoretical analyses were employed to reproduce the observed thermal responses and validate the measurement accuracy. An indoor rainfall simulator was used to investigate the influence of thermal cycles on the erodibility of intact loess. Results indicate that soil temperature and deformation are closely coupled with fluctuating ambient temperatures. This coupling exhibits a delayed response with increasing lag at greater depths due to thermal inertia. The anisotropic microstructure of loess—featuring pronounced vertical patterning—leads to direction-dependent temperature and deformation responses. Furthermore, loess follows thermo-elastoplastic behavior—irreversible thermoplastic deformation occurs in addition to reversible thermoelastic deformation—resulting in the formation of a loose and erodible surface layer. This was corroborated by an approximately 11% reduction in Shore hardness. These findings demonstrate that the thermomechanical response of loess at the material scale acts as a significant accelerator of erosion and landscape evolution in loess terrains.

Suspended sediment concentration, flux, and river discharge are essential indicators of river ecosystem health and reflect watershed-scale processes. Monitoring these variables is labor-intensive, leading to sparse and geographically biased observations and the development of models to fill in the observational gaps. These models generally use either climatological data or satellite images to estimate one of these variables. In this work, we present a novel deep learning model that can leverage multiple data sources with different temporal characteristics to produce continuous daily estimates of suspended sediment concentration (SSC), suspended sediment flux (SSF), and discharge. The model first encodes daily hydrological data from the ERA5-Land reanalysis using a Long Short-Term Memory network and water color data from Landsat satellites using a Multi-Layer Perceptron network, then merge these encoded data sources using a cross-attention decoder. We train and test the model on a large data set of in situ observations from 630 river sites over 43 years in the contiguous United States, covering a wide range of watersheds and conditions. We produce SSC, SSF, and discharge predictions with respective relative errors of 49%, 57%, and 44%, and relative bias of −2.5%, 2.6%, and 3.7%. We use our model to create a data set of continuous daily SSC, SSF, and discharge for all large rivers in the contiguous United States. This new model architecture provides a valuable tool for monitoring river systems, addressing limitations of single-source models and offering a framework applicable to other Earth systems monitoring problems where integrating diverse data streams may be useful.

The search for predictions about the natural world is as old as science itself, but it has never lost its urgency. In 2022, we launched a special collection under the umbrella of three AGU journals—the Journal of Geophysical Research: Earth Surface, Earth's Future, and Earth and Space Science—to reflect on predictions in coastal geomorphology. We invited contributions that made and explored predictions as well as papers that looked into potential limits to predictability. The collection ultimately brought together 27 papers spanning diverse coastal environments, tools, and timescales. The contributions in this collection fall broadly into four approaches: (a) those that look into predictions from physical laws and idealized models. (b) Others draw on large data sets, including satellite records and machine learning, to uncover patterns. (c) A third group combines physical laws with data, and (d) a fourth group includes studies that aim to improve prediction methods.

Understanding the flow dynamics of granular materials and their impacts on obstacles is crucial for disaster prediction and mitigation. Despite its importance, the effects of particle segregation on the flowability and impact behavior of granular flows remain insufficiently understood. This study addresses this gap by conducting numerical simulations to systematically investigate the influence of key factors—slope length, slope angle, and particle size—on segregation and granular flow behavior. The results indicate that segregation is more pronounced in the flow depth direction than in the flow direction, with the diameter ratio playing a critical role in driving segregation. Flow-barrier interactions are shown to suppress segregation in granular flows. Moreover, particle segregation significantly impacts both peak and residual impact forces, with larger diameter ratios leading to greater impact forces on barriers and generating distinct pulse-like features in the force profiles. Steeper slopes and longer flow lengths further amplify impact forces on barriers, while segregation changes the spatial distribution of these forces along the barriers. Segregation also influences the flowability of granular materials by inducing velocity differences between large and small particles, especially at the rear of the flow. The enhanced activity of large particles produces a U-shaped flow depth profile, while small particles migrate downward during segregation. This downward migration causes small particles to act like rolling elements at the base, transforming the frictional interaction in the granular flow from sliding friction to rolling friction. This transition reduces frictional resistance, thereby promoting fluidization. These findings underscore the pivotal role of segregation in granular flow dynamics, offering some insights for engineering design and for understanding natural processes such as landslide runout distances, debris flow initiation, and the formation of depositional landforms.

Ripples are the most fundamental and ubiquitous aeolian bedforms formed on sandy surfaces, but their small size and fast response times make them inherently difficult to measure. However, these attributes also make ripples excellent flow indicators, and they have been used extensively in planetary locations for this purpose. Here, we use terrestrial laser scanning to measure ripple morphometry and celerity coincidently, as well as saltation height above rippled surfaces. We find that although ripple height and wavelength respond linearly to increased shear velocity, under strong winds ripple celerity exhibits a non-linear increase. This relationship at high wind speeds is also reflected in the response of aerodynamic roughness and saltation dynamics, with a greater maximum saltation height present over ripple lee slopes. Importantly, when using ripple patterns as indicators of flow conditions, celerity or height should be used in preference to wavelength as their dynamics respond faster to changing wind speed. In planetary and stratigraphic settings where measuring celerity is not possible, wavelength should be considered as indicative of consistent wind conditions rather than the full range of sand transporting wind speeds.