Earth Surface Processes and Landforms最新文献

The variations in soil erosion significantly impact regional ecological security. Under rapid urbanisation, extensive ecological restoration and climate change, soil erosion development in the red soil region of southern China is ambiguous. Therefore, this study investigated the current (1980s–2020) and future (2050) erosion characteristics in a typical soil erosion control catchment (Changting section catchment) in this region by using the Cellular Automata Markov model and CMIP6 data to predict future scenarios and the Revised Universal Soil Loss Equation to estimate soil erosion. The results showed significant changes in the vegetation coverage of major land uses from 1980s to 2020, which was mainly caused by continuous soil and water conservation (SWC). The land use subtypes that were obtained by reclassifying land use based on the threshold of vegetation cover on soil erosion control, reflect a continuous transformation from those with poor SWC effectiveness to those with great SWC effectiveness. Therefore, the estimated soil erosion intensity continued to decrease from 1980s to 2020, and the contribution of land use/land cover (LULC) impacts ranged from 74%–195%. However, predictions of land use subtypes indicated that LULC may be stable after 2020; thus, soil erosion changed little when the climate was almost unchanged in 2050. Under climate change scenarios, soil erosion may increase by 111%–121%, and the contribution of precipitation impacts was 63%–66%. The major driving factor of soil erosion changes may shift from LULC to precipitation after 2020. Therefore, in the future, the potential for reducing soil erosion by vegetation restoration may be limited, and more engineering measures should be applied to address the erosion risk caused by climate changes. This study provides prospects for land use/land cover and soil erosion in the red soil region of southern China.

Splays—fan-shaped depositional landforms produced by overbank deposition by unconfined flows—can damage structures, degrade arable land and incur substantial mitigation costs. Splay-related hazards along many rivers are likely to worsen with the increasing magnitude and frequency of major floods. The highly incomplete understanding of splays on braided streams is a conspicuous knowledge gap in a changing world with more frequent and intense floods. The largest recorded flood on the braided, sand-dominated lower Platte River (eastern Nebraska, USA) in March 2019 resulted from the rapid melting of a deep, moist snowpack during an extreme rain-on-snow, bomb-cyclone event. This flood produced 32 large (as much as 234 ha) splays that buried structures and cropland under sand. A total of 1,438 ha of row crop was buried, equating to 1.2 million dollars in lost revenue. These splays diverged from the channel by 14° to 104° along a 122 km reach. The topography of preexisting abandoned channels strongly controlled the shape and orientation of most splays, although forested areas tended to trap or divert sediment. The flood eroded 2.2 to 202 m² m⁻¹ of the streambank at 11 of the splays. The five largest splays (>100 ha) deposited as much as 2.4 m of sand. Ground-penetrating radar profiles of the largest splay indicate that it consisted almost entirely of overbank deposits exhibiting simple downstream accretion that buried the pre-flood soil under ≤ 1 m or less of sand. Locally, however, this soil was eroded during the flood. Climate models predict increasing winter precipitation in the Platte River basin; therefore, the frequency of major floods should increase, making splays recurrent hazards. Our geomorphic assessment of the splays on the lower Platte River illustrates the need for future hazard and mitigation planning.

The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national-scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland-specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non-wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Clustering and machine learning-based predictions are increasingly used for environmental data analysis and management. In fluvial geomorphology, examples include predicting channel types throughout a river network and segmenting river networks into a series of channel types, or groups of channel forms. However, when relevant information is unevenly distributed throughout a river network, the discrepancy between data-rich and data-poor locations creates an information gap. Combining clustering and predictions addresses this information gap, but challenges and limitations remain poorly documented. This is especially true when considering that predictions are often achieved with two approaches that are meaningfully different in terms of information processing: decision trees (e.g., RF: random forest) and deep learning (e.g., DNNs: deep neural networks). This presents challenges for downstream management decisions and when comparing clusters and predictions within or across study areas. To address this, we investigate the performance of RF and DNN with respect to the information gap between clustering data and prediction data. We use nine regional examples of clustering and predicting river channel types, stemming from a single clustering methodology applied in California, USA. Our results show that prediction performance decreases when the information gap between field-measured data and geospatial predictors increases. Furthermore, RF outperforms DNN, and their difference in performance decreases when the information gap between field-measured and geospatial data decreases. This suggests that mismatched scales between field-derived channel types and geospatial predictors hinder sequential information processing in DNN. Finally, our results highlight a sampling trade-off between uniformly capturing geomorphic variability and ensuring robust generalisation.

Grass-planting measure is a crucial vegetation approach to mitigate understory soil erosion and improve ecological environment in the red soil region of southern China. This study aimed to quantify the effects of grass (Paspalum wettsteinii Hackel.)-planting measures on runoff and sediment reduction on slopes of Masson pine plantations under rainstorm conditions. We conducted a rainfall simulation experiment at a rainfall intensity of 2.0 mm/min, comparing single strip (MT1, strip spacing: 145 cm), double strips (MT2, strip spacing: 70 cm), and triple strips (MT3, strip spacing: 45 cm) grass-planting measures on slope surface runoff generation and soil erosion processes of the young Masson pine (MT0, no grass strip) plantation, and the bare slope (CK) was selected as the control. Results revealed that grass-planting measures significantly decreased slope erosion parameters compared to CK and MT0. As the average grass coverage increased (MT1 from 10% to 25%, MT2 from 7.5% to 22.5%, MT3 from 7.3% to 25%), the slope surface erosion parameters under the grass-planting measures decreased, resulting in significantly improved runoff and sediment reduction benefits. The runoff reduction effect could reach 32%, while the sediment reduction effect could reach 88%. Moreover, MT3 demonstrated superior performance over MT2 and MT1, with minimal runoff and sediment reduction effects observed for the MT0. Overall, this study suggests that grass-planting measures, coupled with the increasing of grass coverage rates, significantly improve runoff and sediment reduction benefits on slopes in regions experiencing heavy rainfall. Among the tested configurations, MT3 emerged as most effective measure for controlling understory soil erosion in Masson pine plantations, especially when its average grass coverage rate reached 25%. These findings provide valuable insights for selecting appropriate grass-planting strategies, as well as for understanding the underlying mechanisms of how these measures mitigate soil erosion. This scientific reference will aid in the design and implementation of soil and water conservation measures in the region.

Accurate cirque classification is essential for understanding their formation and palaeoclimatic implications. Longitudinal-profile-based cirque classification offers advantages over expert classification and parameter-based methods. This classification fits exponential or power functions to cirque profiles, employing linear classifiers based on the exponential coefficient (c-value) and cirque height, or a threshold approach based on the power coefficient (b-value) to classify cirques and non-cirques. However, previous studies were limited to small sample sets. Our study extends this methodology to more extensive datasets on the southeastern Tibetan Plateau, evaluating its effectiveness across two larger sample sets. Both c-value and b-value based methods are tested with two classifiers: the original classifier from previous studies and the parameter-refitted classifier trained by datasets of this study. The results show that the c-value-based method effectively classifies typical cirques and non-cirques, with notable enhancements in performance based on the refitted classifier compared to the original one. The b-value-based method with the refitted classifier performs well in typical cirque identification but is less effective for non-cirques compared to the original classifier. For all-type cirques and non-cirques, both methods demonstrated improved performance in non-cirque classification although there was a slight trade-off of cirque classification. Additionally, c-value based non-linear classifiers and b-value optimal threshold for classifying cirque and non-cirque have been developed, and their improved performance in this classification is discussed. Overall, the longitudinal-profile-based classification is more effective for typical cirques and non-cirques, with potentials for further improvement by considering additional spatial structure information of cirques.

Geomorphological, geochronological and paleoenvironmental data were integrated to examine how Holocene environmental changes have influenced geomorphic responses, particularly recurrent landslides and colluvial deposition on the steep slopes of the Rio de Janeiro mountains. This study focuses on a pilot hillslope (32°) with a concave-up topographic hollow and associated shallow translational landslides, enhancing our understanding of how extreme climatic events shaped landscape evolution in the region prior to significant human impact. Recently, the hillslope was reactivated by a shallow landslide triggered by the extreme rainfall event of January 2011, which resulted in over 3600 landslides in the region. Subsequent deep gullying exposed a deposit (>3 m) containing buried, organic-rich colluvial layers. Comprehensive analysis was conducted on sedimentological, geochronological (AMS ¹⁴C), palynological, taphonomic and carbon isotope (δ¹³C) data from this deposit. The geochronological results indicate that the hillslope's evolution involves recurrent shallow translational landslides throughout the Holocene, with organic colluvial layers dating from 10 148 cal years BP at the base to 663 cal years BP at the top. Charcoal fragments of various sizes suggest frequent paleofires during the Holocene. Palynological and taphonomic analyses reveal post-fire herbaceous-shrubby vegetation and ferns (Asteraceae, Poaceae, Alchornea, Baccharis, Celtis and Polypodium), with predominant pollen grains and spores showing mechanical damage indicative of high transport energy and physical stress conditions. The rarefied post-fire vegetation and pioneer forest genera identified in the deposit are associated with a hydrological and mechanical dynamics that likely created less stable conditions on the slope system, promoting the occurrence of shallow landslides throughout the Holocene.

The Mediterranean coastlines are densely populated zones which host key socio-economic and commercial activities. For this reason, coastal areas are vulnerable sites in case of natural disasters as tsunamis that can strike coasts causing widespread damage to the population and facilities. For these reasons, several studies were performed over the last decade to study the impact of tsunami waves on the coasts. This research assessed the inundation risk due to a tsunami wave which can hit the southeastern coast of Lampedusa Island. The coastal low-lying geomorphological setting of the southeastern part of the island led to significant socio-economic growth, but Lampedusa falls within the Mediterranean Sea, a high-tsunamigenic area, therefore, the need to investigate tsunami propagation and coastal flooding of this sensitive site emerged. For this scope, a calculation chain model was implemented incorporating three steps: the DELFT-3D software for earthquake effects modelling, MIKE 21 Flow Model FM for nearshore propagation and HEC-RAS for onshore tsunami inundation modelling. The simulations illustrate the impact of three tsunami scenarios with different magnitudes (M_w 8.5, 7.5, 6.5) generated by hypothetical earthquakes in the Hellenic Arc. In the M_w 8.5 magnitude scenario, significant flooding occurs in the harbour region, with maximum water depths reaching approximately 3.5 m. The maximum water velocity in this scenario reaches about 15 m/s in the eastern portion, adjacent to cliffs impacted by the tsunami wave. In contrast, the M_w 7.5 magnitude scenario demonstrates reduced flooded areas, with the cliffs containing the waves and preventing further flooding. Water depths and velocities in the M_w 7.5 scenario remain minimal. Changes in both propagation and flooding are not significant between scenarios M_w 7.5 and M_w 6.5. This methodology can be employed for more accurate tsunami wave simulations not only in the Mediterranean region but also in various case studies.

Sediment transported from debris-flow initiation zones is typically stored in a topographic feature called a debris-flow fan, the formation process of which governs secondary sediment transport further downstream. Although sediment transport from debris-flow fans can impact sediment regimes and change landforms, the determinants of progressive fan formation are not well constrained. To identify such determinants, this study monitored debris flows and performed topographic surveys of debris-flow fans in the Ichino-sawa torrent (Japan) during 2016–2017. In this period, eight debris flows occurred, two of which eroded the existing fan and formed a new channel with a short recurrence interval (~40 days). Consequently, these two cases induced substantial sediment transport further downstream from the fan. The examined rainfall indices did not provide a threshold for diagnosing the occurrence of such sediment transport. Debris flows with a large flow depth and a long duration led to changes in the runout direction and subsequently formed new channels. Before these processes, the existing channel was backfilled and plugged by previous debris flows, forming a steep fan surface around the fan apex. The results suggest that increasing the magnitude and the duration of debris flows potentially triggers sediment transport from fans coupled with channel plugging. The annual sediment transport from the fan exceeded almost all sediment yields of the world rivers and was found comparable with that linked with volcanic eruptions and their aftermath. Thus, the fan-formation process can induce substantial sediment transport, independent of volcanic perturbations and extreme climatic events, and is dependent on the sediment supply from repeated occurrence of debris flows in the initiation zones.

Geomorphological classification is affected by classification principles, indicators, methods, and data resolution, which can lead to uncertainty in the results. Such uncertainty directly affects the quality and subsequent applications of geomorphological classification. To quantify and control the uncertainty, it is important to select an appropriate and effective method for evaluating the uncertainty of geomorphological classification. This study evaluated the uncertainty of geomorphological classification of Shaanxi Province at the ground-feature class and image scales, which derived from rough set theory: rough entropy, approximate classification quality, and approximate classification accuracy. The three indicators helped effectively assess the uncertainty of geomorphological classification at multi-scale and measured the degree to which different factors affected the uncertainty of geomorphological classification. The relative impacts of three factors on the uncertainty of classification decreased in the order of classification methods, data resolution, and classification indicators. This finding is helpful to objectively evaluate and control the uncertainty generated in the process and results of geomorphological classification, and can provide targeted reference and guidance for future geomorphological classification work, which is more conducive to decision-making and application. At the same time, this study is also a beneficial supplement to the geomorphological research based on digital terrain analysis.