Earth Surface Processes and Landforms最新文献

Slope reliability analysis is a critical aspect of geotechnical engineering, particularly under conditions of rainfall infiltration, where the spatial variability of soil parameters can significantly affect the reliability of slopes. Traditional methods like Monte Carlo simulation are often computationally intensive, severely challenging the design of cutting slopes considering the spatial variability of multiple soil parameters. To address this challenge, this study proposes a convolutional neural network (CNN)-based surrogate model to efficiently assess the reliability of unsaturated soil slopes. The CNN model is trained to establish an implicit relationship between the random field inputs of soil parameters and the corresponding slope stability outcomes, enabling rapid calculation of the probability of failure (P_f) under varying conditions. The results indicate that as rainfall intensity increases, the P_f rises. For the same slope cutting distance, a greater slope cutting angle leads to a higher P_f. Similarly, for the same slope cutting angle, increasing the slope cutting distance results in a higher P_f; and the impact of slope cutting distance on slope reliability is more significant than that of slope cutting angle. Additionally, for various rainfall conditions and slope cutting scenarios, the CNN-based surrogate model is integrated into the full probability reliability design method, and a design response surface is used to establish the relationship between design variables and reliability responses. It is found that the proposed approach can efficiently evaluate the reliability of all design schemes. A strategy for determining the optimal slope cutting scheme is finally provided as practical guidance to meet the target reliability.

The interactions of supercritical flows with sand or gravel beds in river channels or tidal inlets lead to the formation of antidunes. These bedforms are generally identified as nearly periodic sedimentary patterns of symmetrical shape that are in phase with the surface waves in the flow and have important effects on flow resistance and bedload transport. In addition, they play a fundamental role on morphodynamical processes in estuarine systems, on the scour around hydraulic infrastructure, and their bed signature can help to interpret paleofloods from sedimentary records. Despite the importance and ubiquity of antidunes in environmental flows, very few numerical simulations have captured their dynamics. In this work, we develop a model that couples the shallow-water and Exner equations in two-dimensions (2D) and demonstrate that a higher-level theory can reproduce the experimental antidune results of Pascal et al. (2021), independent of interactions at the particle scale. The flows are characterised by Froude numbers between 1.31 and 1.45, sediment diameters of $��{�d�}_{�50�}�=�2.9�$ mm and with 3° mean bed slopes. Using this information, we aim to identify the minimum requirements for a numerical model to capture in detail the migration of these bedforms. We use spectral analysis and compute statistics of bed elevation to determine the relevant temporal and spatial scales associated to the antidune propagation. The results of the model yield new insights on the mechanisms of bedform migration, providing tools to improve their description and assess the morphodynamic feedbacks.

Surficial geologic maps contribute to decisions regarding natural hazard mitigation, land-use planning and infrastructure development. However, geologic maps may not adequately convey the uncertainty inherent in the information shown. In this study, we use machine learning and lidar elevation data to produce surficial geologic maps for parts of two quadrangles in Kentucky. We measured the performance of eight supervised machine learning methods by comparing the overall accuracy and F1 scores for each geologic unit. Surficial geologic units include residuum, colluvium, alluvial and lacustrine terraces, high-level alluvial deposits and modern alluvium. The importance of 41 moving-window geomorphic variables, including slope, roughness, residual topography, curvature, topographic wetness index, vertical distance to channel network and topographic flatness, was reduced to 12 variables by ranking the importance of each variable. The gradient-boosted trees model produced the classifier with the greatest overall accuracy, producing maps with overall accuracies of 87.4% to 90.7% in areas of simple geology and 80.7% to 81.6% in areas with more complex geology. The model produced high F1 scores of up to 96.2% for colluvium but was not as good at distinguishing between units found in the same geomorphic position, such as high-level alluvium and residuum, both of which are found on ridgelines. Probability values for each geologic unit at each cell are conveyed using gradations of colour and eliminate the need for drawn boundaries between units. Machine learning may be used to create accurate surficial geologic maps in areas of simple geology; in more complex areas, highlight that additional information obtained in the field is necessary to distinguish between units.

The Vietnamese Mekong River Delta (VMD) is one of the largest and lowest elevated deltas on Earth, shaped over the past thousands of years following delta progradation and sediment deposition. The geologically young delta sediments have high porosity and compressibility, resulting in high natural sediment consolidation (also known as autocompaction). Autocompaction is a natural intrinsic process that governs the spatio-temporal morphological evolution and shallow compaction (i.e., land subsidence) in a delta. As a delta aggrades and progrades, the weight of accumulated sediments increases the effective stress experienced by underlying sediments, driving internal shallow compaction processes. Compaction of shallow sediments considerably contributes to land subsidence in the VMD, influencing the morphology and elevation of the delta plain and increasing the deltas exposure to natural hazards like flooding and relative sea-level rise. In this study, we introduce a novel methodology to quantify sediment accumulation and autocompaction while taking into account the depositional history and heterogeneous nature of subsurface sediments in deltas like the VMD. We derived the depositional history, spatial heterogeneity and palaeo-sedimentation rates by combining extensive datasets with lithological borelogs, sediment datings and geomechanical characterization of the delta's most representative lithologies. To simulate the spatio-temporal formation and evolution of the delta over the last 4000 years, we employ the NATSUB3D finite element model to simulate sediment deposition and consolidation over time using an adaptive three-dimensional mesh. The resulting 3D hydro-stratigraphical and geomechanical characterization provides unique insights on past Holocene spatio-temporal evolution of the VMD and current autocompaction dynamics. The model enables the prediction of shallow compaction rates under future sediment deposition and can facilitate process-based quantification of delta elevation evolution under natural and human-engineered sedimentation. This unlocks new opportunities to evaluate the effectiveness of nature-based solutions and sediment enhancing strategies aimed to prevent elevation loss and combat relative sea-level rise in the Mekong delta and similar lowly elevated coastal-deltaic landforms elsewhere.

Assessing tsunami risk requires knowledge of the potential inundation area, which can be inferred from the spatial distribution of tsunami deposits. However, field surveys of tsunami deposits are time-consuming and occasionally pose challenges, such as disturbance of sedimentary evidence by human and natural causes. Here, we propose a novel technique capable of mapping tsunami deposits using remote sensing, which was tested along a coastal stretch of central Chile following the tsunami of 27 February 2010. We trained a classification model using high-resolution satellite images from before (September 2004 and January 2005) and after (April 2010) the 2010 tsunami to map the sand deposit, yielding an overall accuracy of about 86%. Our satellite mapping of the deposit was validated with field observations in pits and eyewitness interviews conducted about a decade after the tsunami. The field data matched the model predictions by 88%. Likewise, our satellite mapping was also contrasted with the inundation area reported by previous post-tsunami surveys. The spatial distribution of the tsunami sand deposit inferred from our model reproduces a minimum inundation area, which was almost as extensive as the actual inundation area. Sand inundation ranged from 50 to 600 m inland, matching about 90% of water inundation. Both sand and water inundation were controlled by the land slope. Application of our technique to a satellite image from 11 years after the tsunami (May 2021) shows that the detection ability of the sand deposit was lost by about 86%, which is attributed to human intervention and masking by new soil development. Our results suggest that extensive tsunami deposits can be accurately mapped by a supervised classification model in a lesser time than that employed in field surveys.

Catchment-wide erosion and sedimentation behaviour is influenced by variety of controls. One of these controls is erodibility, which may be determined by the lithological properties (e.g. texture, porosity) or the (density of) structural discontinuities (e.g. faults, fractures). In this study, the potential role of different erodibility of lithology and faults in spatio-temporal erosion and sedimentation behaviour was evaluated using the landscape evolution model, Landscape Process Modelling at Multi-dimensions and Scales (LAPSUS). The study area, Kula Badlands (western Turkey), is known for dense badland gully networks, incised into fine-grained sediments in one of the tributaries of the Gediz River, the Geren catchment. An earlier field-based study demonstrated the fault-controlled net erosion and consequent sedimentation in these badlands. Here, we test the role of lithology and faults in landscape development with scenario-based modelling. A reconstructed PalaeoDEM, representing a 30-ka-old landscape, was used as an input. Scenario simulations were conducted with lithology- and fault-related erodibility and sedimentability factors. Simulation results demonstrate a significant difference in spatial erodibility and sedimentability and catchment erosion and sedimentation behaviour. Incorporating higher erodibility factors for fault zones caused not only a considerable amount of within-catchment erosion in fault-determined erosion zones, but also a decrease in overall catchment sediment export. In addition, high constant sedimentability lowers the sediment export considerably whilst slightly increasing total erosion rates. These outcomes indicate that fault zones with higher erodibility can increase accommodation spaces, producing temporary (re)sedimentation locations, which decrease overall sediment delivery from its catchment on the long run. The model simulations suggest that fault-related higher erodibility and sedimentability can be important factors in controlling landscape dynamics at the local and catchment scale.

The current setting of most Tyrrhenian coastal plains in central-southern Italy is the result of the interaction between sedimentary inputs, tectonic movements, and sea level changes during the Quaternary. Based on a comprehensive review of data from the literature on the stratigraphic setting of the coastal plains of Volturno and Garigliano Rivers, and with the final output being a validated 3D geological model, this study provides new elements for improved definition of the chronological intervals of fault activity. Specifically, the ages of tectonic deformations and/or subsidence are crucial for future estimates of coastal hazards induced by both seismicity and coastal inundations. Our multidisciplinary approach includes (i) definition of the Late Quaternary sedimentary architecture by revision of a large amount of core data, (ii) acquisition of offshore seismic reflection data and their correlation with sedimentary bodies of the coastal plains, and (iii) structural analysis of the main faults. These investigations were conducted on the marine segment offshore Mount Massico and on contiguous portions of the Volturno and Garigliano alluvial–coastal plains. The acquisition of seismic and core data enabled the definition of the sedimentary architecture of the coastal sectors of the plains. The Mt. Massico ridge (northern Campania), comprising Mesozoic–Cenozoic units of the orogenic chain and morphologically separating the two plains, was the subject of mesostructural analysis of fault orientation and kinematics. The seismic lines were calibrated correctly using two close stratigraphic core logs from the Garigliano Plain. The identification of correlatable and/or coeval stratigraphic/seismic units reveals land–sea correlations. These units are clearly affected by recent faulting expressed by complex deformation patterns, such as flower structures and strike-slip faults.

Introducing large wood (LW) into streams for restoration purposes is a common practice, as it creates habitat through processes like scouring, deposition and sediment sorting. However, while monitoring often focuses on short-term (<3 years) or long-term (>10 years) changes in habitat features, there is a lack of understanding regarding annual geomorphic changes over relatively long periods. In this study, we investigated annual geomorphic adjustments (channel geometry and substrate size) over 7 years in three tributaries of the Mill Creek watershed (Oregon, USA). The 7-year period included moderate to high flows, with peak annual flow exceeding bankfull flow (Q_bf) 2–5 times and flows being above half Q_bf on average 4–20 days per year. Data included topographic surveys and surface pebble counts collected from 2014 (1 year before LW) to 2021 (6 years after LW). We quantified scour and deposition and estimated sediment grain sizes and sorting from topographic surveys and pebble counts. Our analysis revealed that stream size influenced geomorphic adjustment, with smaller streams experiencing more scouring compared with larger streams over the 6 years. LW structures promoted increased scouring at the cross-section scale, with a strong relationship found between volumetric blockage ratio and scour. In our case, the most significant scouring changes were associated with volumetric blockage ratios between 35% and 50%; further research is needed to investigate scouring for higher blockage ratios. Instream changes in scour and deposition peaked around 3–4 years after LW introductions but persisted until the end of the monitoring period. Sediment size dynamics were influenced more by time since restoration than by proximity to LW jams. While LW introductions increased sediment sorting into patches, the degree of sorting declined 5–6 years post-restoration at all sites. Our findings offer insights into the long-term persistence and magnitude of instream changes associated with LW introductions.

We conducted monthly surveys, from October 2020 to May 2021, of coastal topography in Holgate, New Jersey. Using unmanned aerial vehicle (UAV)-photogrammetry and RTK-GNSS equipment, we generated digital elevations models and cross-section profiles, capturing spatiotemporal variability in volumetric change. We measured a total loss of 27 500 ± 10 500 m³ of subaerial sediment through the study. From October 2020 to February 2021, over 59 600 ± 10 500 m³ of sediment was eroded, followed by 32 100 ± 10 500 m³ of deposition from February to May 2021. We developed a semi-empirical model correlating the measured geomorphological change to wave energy and water-level variations. The calibrated model identified storm conditions that caused erosion and that waves from the south to southeast caused more erosion than those from the east to northeast. These results emphasise that alongshore transport represents a critical component of sediment transport dynamics relevant to beach erosion. Using the calibrated model, we quantified the impact of water-level variations and wave energy on net sediment transport for a stretch of barrier coastline. Specifically, a water-level increase of 0.14 m (equivalent to a 1σ variation) generated slightly less erosion (1.18 m³/m per 48 h) than the same variance-based increase in wave energy, which generates 1.44 m³/m of erosion per 48 h. These variables strongly covary. Alongshore transport modulates the relationship, increasing erosion 0.9 m³/m per 48 h with a 1σ shift in wave energy directed alongshore. Forcing from strong storms, hindcast from 8 years of data, can produce 15–20 m³/m of erosion per 48 h. This modelling approach represents a methodology to produce estimates of potential erosion under predicted storm conditions, which is inherently valuable to coastal management and resilience planning. Our study demonstrates cost-effective data collection and robust analytical methods that can be applied globally to benefit both the understanding of coastal geomorphology and local communities through data-driven natural resource management.