Journal of Geophysical Research: Earth Surface最新文献

The morphology of a coral reef terrace (CRT) is a key parameter in the interpretation and quantification of past sea-level changes, but it is directly influenced by local morphodynamic and hydrodynamic conditions. Spatial differences in terrace morphology may therefore result in over- or underestimation of paleorelative sea levels and their associated uncertainties. To investigate this, we integrate high-precision field surveys from the island of Aruba (Leeward Antilles, Caribbean Sea) with a stratigraphic forward model (DionisosFlow®) to quantify the intra-island variability of the Quaternary coral reef sequence. We establish that a possible slight North-South tectonic tilt of the island may drive differences in the elevation of CRTs and the number of emerged fossil coral reefs imprinted on the coastal landscape. However, terrace geometry is primarily defined by the basement slope and wave exposure. All together, our results show that even small-scale environmental and hydrodynamic variability can introduce meter-scale errors in sea-level reconstructions derived from CRTs.

Weathering is a fundamental driver of landslide evolution over geological timescales. Despite its ubiquity and importance, quantifying how weathering drives the progressive destabilization of rock slopes remains challenging. In this work, we develop a unified computational framework based on the particle finite element method to investigate the evolution of weathering-induced landslides, from long-term weathering to short-term slope failure and runout dynamics. The framework integrates key processes, including weathering front propagation, time-dependent strength degradation, rupture surface development, and post-failure runout dynamics. Through numerical simulation experiments, we elucidate how interactions among weathering characteristics (type, intensity, and rate law), bedrock strength, fracture distribution, and slope geometry govern the failure modes and kinematics of weathering-induced landslides. Simulations show that matrix-dominated weathering leads to shallow translational failures, whereas fracture-dominated weathering produces deep-seated rotational and compound landslides. Pre-existing fractures and slope morphology also strongly influence the movement of destabilized landmasses, affecting the failure pattern (e.g., kinematic mode and rupture surface geometry) and post-failure behavior (e.g., runout velocity). We further demonstrate that the failure time and volume of weathered slopes are governed by the competition between gravitational driving forces and cohesive resisting forces during progressive destabilization. These findings provide new insights into the fundamental mechanisms that drive the emergence of diverse failure patterns of weathering-induced landslides with important implications for landslide hazard assessment.

Long-term hillslope evolution involves complex continuous (i.e., soil creep) and discontinuous (i.e., landslide) processes. The nonlinearity introduced using discontinuous processes poses significant challenges to the numerical stability of long-term hillslope numerical simulations. Geomorphologists have proposed numerous sediment transport laws related to local slopes, each tailored to specific geomorphic processes. However, many complex hillslope sediment transport models, such as the depth–slope product model and the nonlinear depth- and slope-dependent model, lack available implicit solution schemes. We address this issue by proposing an implicit numerical scheme named “Modular Implicit Method.” Taking the nonlinear slope-dependent transport law as an example, our method splits the sediment transport law into gradient and coefficient functions and then directly computes the coefficient functions using a lagged strategy. Numerical experiments demonstrate that our method performs comparably to existing implicit methods in solving nonlinear transport models. Long-term evolution simulations on realistic hillslopes show that our method enables flexible switching and co-existence of multiple transport laws, demonstrating strong extensibility. Furthermore, the framework seamlessly couples with the stream-power incision model, exhibiting a more general compatibility.

Noise from calving icebergs, cracking ice, and melting ice dominates the underwater soundscape of glacierized fjords creating one of the loudest recorded ambient ocean environments. While progress has been made toward identifying and describing individual sound sources—including the automatic detection of calving and quantification of ice-mass loss—the relative contributions of multiple, simultaneous processes, and how these contributions evolve over time, remain underexplored, limiting robust interpretation of ice-ocean interactions. Here, we show that unsupervised machine learning separates a series of recordings captured over 8 months into five dominant sound profiles related to glacier activity. We deployed an array of hydrophones approximately 400 m from the terminus of Xeitl Sít’ (LeConte Glacier) in Southeast Alaska and recorded sound regularly between October 2016 and May 2017. Using the k-means clustering algorithm, we cluster spectral shapes of 10,440 background acoustic spectra, defined as the 25th-percentile spectral level of each recording. We identify five distinct acoustic clusters and relate their temporal occurrence to environmental time series including ice movement, meteorology, and oceanographic data. We further link spectral shapes to known glacier sources such as calving and ice melt. Our analysis reveals that these clusters correspond more closely with glacier and ice-mélange activity than with other environmental variables, confirming the dominance of glacier behavior on fjord soundscapes. This research demonstrates the effectiveness of clustering passive acoustic data and provides a framework for analyzing large, complex acoustic data sets of undersampled environments—such as glacierized fjords—to guide interpretation and track changes in dominant environmental processes.

Collisional and frictional shape evolution of coastal and fluvial pebbles has long been at the focus of geomorphological research. Interestingly, the well-known rounded pebble shapes show remarkable similarities all around the world. The almost universal axis ratios observed in naturally occurring pebbles suggest the existence of a stable shape toward which pebbles converge during abrasion. However, no widely accepted and robust explanation for this phenomenon exists to date. The aim of the present work is to provide a novel perspective on the shape evolution of rounded pebbles. The investigation focuses on dominant motions that depend on the shape and the abrasion processes that are expected to be induced by these motions. Motivated by the big picture of shape-dependent motions of pebbles and the corresponding predicted abrasion, a highly intuitive heuristic model is constructed, in which a motion-dependent, selective curvature-driven abrasion reveals a self-exciting process that may occur during the long-term motion. Unlike previous models, the introduced approach suggests an unstable ellipsoidal shape near the axis ratios characterizing natural pebbles. In this state, changes in axis ratios are slower because of the statistical variety of expected motions, whereas for shapes that differ significantly, the self-exciting effect accelerates shape change as a dominant mode of a motion emerges. Experiments were also conducted to validate the most critical predicted behavior of the model.

Changes in precipitation rate (a climate proxy) affect erosion rates in mountain belts, driving river profile adjustment and leading to fluctuations in sediment flux and downstream sediment thickness. Here, we use a numerical model to investigate the response of the mountain-basin system to cyclic climate variations. Model results show that rivers exhibit an erosion saturation effect when the precipitation forcing period $��P�$ exceeds the system response time $��\tau �$, corresponding to low-frequency climate variations. In this state, maximum erosion occurs before the precipitation peak, and the response amplitude is significantly lower than for . Consequently, there exists a specific $��P�/�\tau �$ range that maximizes the amplitude of the sediment flux response before the system reaches erosion saturation. Our modeling also shows that sedimentation amplifies the sediment flux response and accelerates erosion saturation, while parameters such as uplift rate and bedrock erodibility seem to have no influence. Compared to the sediment flux response in the mountain belt, variations in basin sediment thickness consistently lag behind precipitation changes, with a smaller response amplitude when . As the forcing period $��P�$ increases, this lag gradually decreases and eventually approaches zero, while the response amplitude progressively increases. Our modeling suggests that the mountain belt and basin should be analyzed as a coupled system, rather than as independent source and sink regions, because they reach their maximum responses at different forcing periods.

Loess tablelands, plateau-like landforms built of wind-blown dust, store large volumes of sediment on its path from bedrock sources to sedimentary basins. Long-term storage of organic carbon also occurs in buried soils beneath loess tableland summits. The evolution of loess tablelands can be conceptualized as a process of relief generation through loess accumulation, raising tableland summits above local base level, followed by stream dissection driven by the increased local relief. We investigated how these landforms of highly erodible material have persisted over 10,000–100,000 years timescales in the central Great Plains, USA, using stratigraphic data, field measurements of soil strength, geospatial analysis, and numerical modeling. Where subsurface data are adequate, it appears that loess tablelands developed on pre-existing bedrock tablelands. The presence of many closed depressions on tableland summits is the key factor limiting the rate of tableland dissection, rather than an erosion-resistant cap as in bedrock tablelands. These depressions capture most runoff on tableland summits, limiting the drainage area of channels eroding the steep tableland margins. Numerical modeling of loess tableland evolution with the Landlab toolkit produces tablelands similar to those of our study area; tableland summits with many closed depressions persisted substantially longer than those without depressions. Modeling also indicates that depression-breaching by gullies is an important process in tableland dissection, and there is clear evidence of this process in tablelands of our study area.

Badlands are sensitive components of the Earth's surface where weathering, erosion and transport processes can be observed on human timescales. Within the Draix-Bléone Critical Zone Observatory (CZO) in SE France, both water and sediment fluxes and their climatic drivers have been recorded for >35 yrs, making it an ideal natural laboratory to develop a landscape-evolution model (LEM) for badland evolution calibrated with field data. The aim of this study is to develop an LEM that reproduces the intra-annual sediment-flux variability observed in the Draix-Bléone CZO, in particular the transition from transport-limited to supply-limited conditions that occurs during summer, and the associated hysteresis between rainfall and sediment fluxes. The model predicts soil thickness and sediment export at monthly timescales, providing a potential link between “classical” LEMs that pertain to long (≫1 yr) timescales and event-scale models. Sediment supply is limited in the model by winter sediment production induced by frost-cracking. We include the impact of rainfall intensity, identified as the main trigger of sediment mobility both on hillslopes and in streams, to reproduce the dynamics of the transport-limited regime. Parameter calibration is performed using average annual sediment-export and seasonal soil-depth data in specific compartments of the catchment. The progressive increase in model complexity leads to the identification of a minimum number of process laws needed to reproduce the observed badland dynamics. Our model constitutes an important first step toward modeling observed annual morphologic changes in badlands and more accurately predicting badland evolution in the context of anthropogenic climate change.