Earth Surface Processes and Landforms最新文献

An attempt is made in this study to advance the understanding of the sand movement on Mars by studying the bedform migration at Gale, Jezero and Pasteur craters. The study on the grain size distribution at Gale Crater using Curiosity rover (MAHLI and APXS) observations reveals that the grains with smaller diameters (~50–150 μ) are more prone to migration and vice-versa, which gives an idea of the necessary requirements that initiate bedform migration. The chemical analysis of the surface materials at the Gale crater revealed elevated concentrations of P₂O₅, SO₃, Cl and Zn in soil compared to sand and active transportation processes for sand but not soil. The comprehensive chemical makeup of the Martian soil (inactive bedforms) and sand (active bedforms) is characterized by its basaltic nature, with enriched volatile elements such as sulphur, chlorine and zinc, and the presence of minerals like plagioclase, pyroxene and olivine due to the cohesive nature of inactive bedforms. Physical weathering and wind flow velocity play a pivotal role in the formation of different sedimentary bodies, impacting grain size distribution and mineralogy. The effect of dust-lifting on surface features is studied by analysing Perseverance-MEDA observations at the Jezero crater to understand the short-term changes in the bedform. These events are found to involve the redistribution of only a small amount of materials and, thereby, changing surface features on Mars over a short period. To detect the bedform migration in the Pasteur crater, several HiRISE images acquired over different time intervals were used. The changes in the ripple crest (~0.29–1.18 m/Earth year) and dune slip face suggest new grain flow events. In the Pasteur crater, extensive changes in sand deposits near the dunes signify a widespread bedform migration. The stronger north-westerly and north-easterly winds dominate these changes. Thus, the bedform migration in the three tropical craters exhibits significant variability driven by localized aeolian processes. This variability is crucial for understanding Mars' geological history, current surface dynamics and eventually, helps in planning future missions.

Saltating particle tracking (SPT) is an essential visualized channel to understand the dynamics of aeolian saltation at sand particle size scale, while the published SPTs could have low recall or accuracy rate and misestimate further saltation intensity. Hence, a Kalman filter-Hungarian algorithm with a postprocessor (KF-H-k) was proposed, where the Kalman filter was employed for predicting particle motion, and the Hungarian algorithm for optimizing global assignment, as well as the postprocessor with k-means cluster for correcting the erroneous recovered tracks by Kalman filter-Hungarian algorithm. The new SPT was validated in a digital high-speed video with various particle concentrations from a wind tunnel experiment. It demonstrated that compared with the previous SPTs, KF-H-k kept the highest and most stable accuracy (85% ~ 93%), the best spatial resolution, the moderate recall rate (50% ~ 70%) and time cost. The present work offers a new hybrid scheme for tracking sand particles accurately but alsodatasets for automatically identifying saltating tracks via machine learning models, very critical for insight into new hypothesis on sand ripple formation.

Earthworks such as earthen berms have been constructed across the western US since the late 1800s to mitigate erosion in landscapes where water is both the dominant driver of erosion and the limiting resource for biota. Berms alter hydrologic, geomorphic and ecologic processes by intercepting runoff and altering patterns of water availability in the landscape. Understanding site-specific changes in process dynamics requires accurate mapping of berm locations and knowledge of their condition. This paper presents an automated, object-based framework for identifying earthen berms from 1 m LiDAR-derived digital elevation models in the western US rangelands. Geomorphon, a computer vision tool, was used to classify landforms and identify berm-like landforms, including summits and ridges. Ten geomorphic and geometric attributes associated with each potential berm object were used to develop a machine-learning model for distinguishing berms from natural summits and ridges. The model was trained and applied to independent test sites to identify and map berms. The mapped berms were compared with manually identified reference berms for accuracy assessment. The identification results achieved 79% to 87% recall, 82% to 92% precision and 81% to 89% F-measure. We also explored the influence of training sample selection on model performance and conducted an analysis of attribute relative importance. The automated framework has the potential to be scaled up to larger areas in semi-arid environments.

In this study, we present a synthetic model summarising the main sedimentary and morphologic factors that drive spatial–temporal variations in bank stability through an exemplar macrotidal estuary. In contrast to previous studies that tend to only consider localised variations in the stability of small-scale banks, here the focus is on understanding the bank stability patterns at the scale of the whole Severn Estuary (UK). The results show that during falling tides, the bank sediments persist in a near-saturated state giving elevated bank pore pressures that coincide in time with declines in the hydrostatic confining pressure, leading to destabilisation of the bank. In contrast, bank stabilisation predominantly occurs during rising tides when the hydrostatic confining pressure is able to dominate over the destabilisation processes. Cohesive macrotidal estuaries similar to the Severn Estuary, tend to present a generalised decrease in the instability moving from the outer estuary where the tidal oscillations are more significant, to the inner part of the system where such oscillations are reduced and coupled with less high banks.

Controlled experiments on natural forms involve a traditional view of explanation that prioritizes control, manipulation and replication over more nuanced understanding of the phenomena under investigation. This is an issue if the phenomenon, like tafoni, is historically contingent. In particular, the entanglement of the environment is explicitly severed in controlled experiments. The failure to adequately reproduce the ordered forms associated with tafoni in laboratory experiments suggests that simulation and field case studies could provide a more informative route to understanding tafoni. This will, however, require a more systematic recognition of how such research enables researchers to understand the initial conditions of weathering and modes of development to tafoni observed.

Pio XI Glacier (49°S) is the largest and one of the very few advancing glaciers in Patagonia. Satellite data indicate that the main glacier terminus transitioned from calving to land-based around 2010, effectively resulting in the formation of an ice-contact delta at the head of Eyre Fjord. Here, we investigate how this ice-contact delta formation affected sediment transport processes in Eyre Fjord. Sediment cores and seismic profiles collected along the fjord provide evidence for a relatively abrupt increase in the number and magnitude of turbidity currents, coeval with the formation of the ice-contact delta. This observation is supported by modern summer hydrographic observations and bathymetric data. We posit that the ice-contact delta formation resulted in sediment input being concentrated at specific subaerial locations across the fjord head, which favoured the development of plume-triggered turbidity currents. This suggests that a sudden increase in turbidite thickness in fjord sediment records could represent ice-contact delta development at fjord heads.

The complex superimposition of different kinematics and nested sectors within landslide systems amplifies the challenge of interpreting their heterogeneous displacement pattern and targeting effective mitigation solutions. As an example of such peculiar spatio-temporal behaviour, the DeBeque Canyon Landslide (Colorado, USA) is emblematic of the application of interferometric post-processing analysis for a detailed, remotely-based investigation. We employed a multi-geometry Persistent Scatterers (PS) InSAR dataset to provide continuous information on the spatio-temporal scale and achieve a solid representation of the segmented kinematics and timings. Using an updated geomorphological map of the landslide system, we performed a two-dimensional decomposition of the Persistent Scatterers (PS) dataset to determine the displacement orientation and inclination for each internal sector of the landslide system. We then conducted statistical analyses on the displacement vector characteristics and time series data. These analyses enabled us to spatially characterize the segmented activity patterns of the landslide system and identify abrupt changes in trends associated with preparatory and triggering factors. A clear differentiation of the rotational or translational kinematics within the landslide system was accomplished solely using surface displacement measures. Moreover, the application of a Bayesian model on the bi-dimensional vector time series leads to the identification of significant differences in the deformational behaviour of each sector with respect to precipitation and temperature factors. Our approach represents a replicable method for local-scale characterization and monitoring of landslides exhibiting complex spatio-temporal displacement patterns and providing an effective, low-cost solution for transportation agencies from a risk-reduction perspective.

Amy E. East, Andrew W. Stevens, Andrew C. Ritchie, Patrick L. Barnard, Pamela Campbell-Swarzenski, Brian D. Collins and Christopher H. Conaway. Earth Surface Processes and Landforms, 43 (12), 2562–2577. https://doi.org/10.1002/esp.4415

In the referenced article, the authors would like to correct text in the first paragraph on page 2571, Figure 9 and its caption. The changes reflect errors made in the processing of the rainfall intensity-duration data used to compare storms to published debris flow triggering thresholds. The correctly processed data does not change the interpretations made in the paper. The revision to the data processing does correctly indicate that the investigated storms did not exceed the rainfall intensity – duration threshold of Cannon (1988) but did significantly exceed the debris flow triggering threshold of Wieczorek (1987).

The first paragraph should read:

We can, however, assess whether precipitation conditions during the 2016–2017 wet season were sufficient to cause substantial landsliding based on proxies developed for previous large storms in this region (the San Francisco Bay area; Wieczorek, 1987; Cannon, 1988). Hourly precipitation data from NOAA COOP station USC00040673 reveals that multiple storms between December 2016 and February 2017 had rain intensity and duration well-above minimum threshold conditions for debris flow triggering (Wieczorek, 1987) and reached ~60% of the threshold (Cannon, 1988) that delineates previous record-setting regional landslide activity (Figure 9). Before these storms, antecedent rainfall of 254 mm (10 in.) had been reached by mid-November, a baseline value generally considered necessary to achieve subsurface hydrologic conditions suitable to generate landslides in this region (Cannon, 1988). Two long-duration, early January storms (2 January and 7 January), with maximum rainfall intensities of the season, coincided with the increases in SSC and sand content in the San Lorenzo River (Figure 5), and the steep increase in sand proportion during the 19 January flow peak (Figure 5) coincided with the most intense rainfall event of the season (Figure 9).

We apologize for this error.

Physical wear during bedload transport influences downstream changes in the size distributions and shapes of riverbed sediment, which in turn affect myriad fluvial processes ranging from incision into bedrock to provision of aquatic habitat. Here we use laboratory tumbling experiments to address several remaining knowledge gaps, including the roles of lithologic susceptibility to fragmentation, particle size distributions and particle angularity in controlling wear rates and the size distribution of wear products. To focus on the dynamics of particle wear in headwater channels, we used initially angular sediment and ran the experiments until 10% of the initial bedload particle mass had been lost to fine-grained wear products. We measured individual particle mass and diameter by hand and used photo analysis to quantify particle shape and angularity. We find strikingly different wear patterns between limestone and welded tuff. Although wear rates in the tuff were an order of magnitude slower than the limestone, tuff wear was primarily by fragmentation while limestone wear was dominantly by attrition. Fragmentation of the tuff resulted in a widening of the bedload size distribution, a lack of consistent particle rounding and a fine-wear product dominated by sand. In contrast, limestone particles rounded substantially and produced silt-sized wear products. Differences in wear product size and susceptibility to fragmentation may reflect contrasts in the size distribution of mineral crystals in each lithology. Wear rates in both rock types declined substantially with increasing cumulative mass loss, due to rounding in the limestone and reduced fragmentation in the tuff. These results suggest that applications of the conventional exponential model to predict mass loss with travel distance need to account for lithologic influence on susceptibility to fragmentation and the influence of rounding on particle wear rates.

Lakes on the Qinghai–Tibet Plateau (QTP) are important indicators of climate change. The water level and area of Zonag Lake have increased sharply since 2000, and the west lake bottom area was exposed after an outburst of flood in September 2011. The drained basin was exposed to the cold environment, causing rapid permafrost forming. In this paper, time-series synthetic aperture radar interferometry (InSAR) was utilized to monitor the surface deformation of the drained basin using Sentinel-1A images within a 4-year period (2017–2020). This research focused on characterizing the spatial and temporal dynamics of ground deformation and exploring changes in the permafrost base around the drained basin based on the deformation information. The experimental results revealed spatially variable ground deformation, with long-term deformation rates ranging from −78.3 mm/year to 72.8 mm/year and seasonal amplitudes of 0–14 mm. Subsidence was prominent in the lakeshore, the thermokarst region and the slope drainage area, indicative of the thawing of ice-rich permafrost. Conversely, uplift was concentrated in the exposed unconsolidated sediment region, with a maximum displacement of 110 mm over the nearly 4-year observation period, suggesting ground ice aggradation. The uplift information was used to estimate the change in the permafrost base in the drained basin, yielding a maximum value of 4 m. The estimated permafrost base change closely aligned with simulated results, with an error of 0.28 m. This study demonstrates the capability of InSAR in monitoring permafrost stability and improving the understanding of the dynamic permafrost formation process.