Search Results (152)

Earthen archaeological sites are particularly vulnerable to rain and winds, whose effects may compromise their integrity. The Huaca Chotuna (HC; 8th–16th Century AD) is an adobe platform in Peru’s semi-arid Lambayeque region, and it is in an area with exposure to rain and winds associated with the El Niño Southern Oscillation (ENSO) events. Here we present the results from an orthophotogrammetric and morphometric study aimed at quantifying the effects of erosion and deposition at the HC. The novelty of our approach consists of merging topographic, hydrological, and wind parameters to recognize the sector of the HC with exposure to potentially damaging natural climatic phenomena. We identify zones affected by erosion and deposition processes. Results of a diffusion model aimed to estimate the HC sectors where these processes will act in the next century are also presented. Gully erosion from rainfall indicates a vertical erosion rate of approximately 0.2 m/century, demonstrating the low preservation potential of the HC. Rainwater also deteriorates adobe bricks and triggers water/mud flows. Conversely, sediment-laden winds contribute to the partial burial of the HC. The findings highlight significant hazards to the HC’s structural integrity, including gravity instability. The interdisciplinary methodology we adopt offers a key framework for assessing and protecting other earthen sites globally against the escalating impacts of climate change. Full article

Gully development is a significant geomorphological and environmental process that affects land degradation worldwide, with ephemeral gullies (EGs) and permanent gullies (PGs) being the two most common types. These two gully types are often spatially connected, and with such EG-PG connectivity can accelerate erosion. However, systematic research on this phenomenon remains limited, particularly at the regional scale. This study focuses on the spatial connectivity between EGs and PGs in the Songnen black soil region of northeast China. An unequal probability stratified sampling was used to establish 977 small watershed units, and a database of gullies and their connectivity was constructed based on sub-meter imagery. Among them, 55 representative units were randomly selected within geomorphic zones for field surveys and UAV validation to ensure data accuracy. Spatial patterns of gully connectivity were analyzed, and dominant controlling factors were identified using the Geodetector, which quantifies spatial stratified heterogeneity and evaluates the explanatory power of potential driving factors. The results are as follows: (1) Gully connectivity varies significantly across the region, with hotspot areas where more than 50% of permanent gullies are connected to ephemeral gullies, and cold spot clusters elsewhere. (2) Permanent gullies connected to ephemeral gullies differ significantly from unconnected ones in both length and width, with the former exhibiting a more elongated morphology. (3) Slope length and mean annual precipitation are the primary drivers of gully connectivity, both showing significant positive effects. Moreover, the interaction between mean annual precipitation and slope length shows the strongest explanatory power, indicating that precipitation, in combination with topographic features, plays a dominant role in shaping gully connectivity. By examining the spatial patterns of gully connectivity, this study contributes to a more refined understanding of gully morphological evolution and offers empirical insights for enhancing gully erosion models and optimizing regional soil and water conservation strategies. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

The slope lands of the Loess Plateau represent a critical region impacted by soil erosion, which directly contributes to the globally recognized high sediment concentration in the Yellow River. However, the extent to which sloped farmland contributes to soil loss remains scientifically contentious. In this study, farmland with an initial slope gradient of 20° was selected for the experiment, and three decades of field monitoring data (1990s–2020s) and the Universal Soil Loss Equation (USLE) model were used for comparative calculation. The data indicated that the model-predicted soil loss rate in sloped farmland from the 1990s to the 2020s was calculated to be 62.48 t·ha⁻¹·yr⁻¹. Field-measured values averaged 45.67 t·ha⁻¹·yr⁻¹, whereas the current value is approximately 15.00 t·ha⁻¹·yr⁻¹. Anthropogenic disturbances, including tillage, manual weeding, and ovine grazing, mean that the topsoil of slope farmland has undergone cumulative displacement of 450~870 cm in 30 years, which is resulting in progressive slope gradient reduction from 20° to 5°. The soil erosion rates exhibited exponential decay characteristics, and finally gradually reached the level of flat farmland. When using the USLE model, the evolving slope gradient must be incorporated, rather than the slope angle extracted by DEM. Therefore, the key finding of this study is that the primary sources of soil loss in the Loess Plateau are non-agricultural slopes and gullies. Conversely, soil erosion on slope farmlands does not constitute a critical problem requiring urgent intervention. This finding should attract the attention of the local agricultural sector. Full article

The Loess Plateau is threatened by severe gully erosion and tableland retreat, primarily driven by uncontrolled surface runoff. Numerical simulations of Gully Consolidation and Highland Protection (GCHP) demonstrate that individual measures such as check dams, terraces, and gully head backfilling can reduce sediment by 31–35% in the short term, but their effectiveness declines after approximately 10 years. This study classifies GCHP models into four types, progressively integrating drainage, filling, slope protection, and ecological measures. Simulation results confirm that the most comprehensive model—coupling all four types—offers the highest and most sustainable effectiveness in both erosion control and ecological restoration. To address long-term challenges, the study proposes a Sustainable Natural GCHP Management Method, combining cascade interception, guided drainage, and ecological retention, thereby enhancing project resilience and supporting China’s Yellow River Basin ecological protection strategy. Full article

Engineered loess-filled gullies, which are widely distributed across China’s Loess Plateau, face significant stability challenges under extreme rainfall conditions. To elucidate the regulatory mechanisms of antecedent rainfall on the erosion and failure processes of such gullies, this study conducted large-scale flume experiments to reveal their phased erosion mechanisms and hydromechanical responses under different antecedent rainfall durations (10, 20, and 30 min). The results indicate that the erosion process features three prominent phases: initial splash erosion, structural reorganization during the intermission period, and runoff-induced gully erosion. Our critical advancement is the identification of antecedent rainfall duration as the primary “pre-regulation” factor: short-duration (10–20 min) rainfall predominantly induces surface crack networks during the intermission, whereas long-duration (30 min) rainfall directly triggers substantial holistic collapse. These differentiated structural weakening pathways are governed by the duration of antecedent rainfall and fundamentally control the initiation thresholds, progression rates, and channel morphology of subsequent runoff erosion. The long-duration group demonstrated accelerated erosion rates and greater erosion amounts. Concurrent monitoring demonstrated that transient pulse-like increases in pore-water pressure were strongly coupled with localized instability and gully wall failures, verifying the hydromechanical coupling mechanism during the failure process. These results quantitatively demonstrate the critical modulatory role of antecedent rainfall duration in determining erosion patterns in engineered disturbed loess, transcending the prior understanding that emphasized only the contributions of rainfall intensity or runoff. They offer a direct mechanistic basis for explaining the spatiotemporal heterogeneity of erosion and failure observed in field investigations of the engineered fills. The results directly contribute to risk assessments for land reclamation projects on the Loess Plateau, underscoring the importance of incorporating antecedent rainfall history into stability analyses and drainage designs. This study provides essential scientific evidence for advancing the precision of disaster prediction models and enhancing the efficacy of mitigation strategies. Full article

The Jinghe River flows through the gully area of the Loess Plateau, where soil erosion is relatively severe. With the intensification of human activities, quantitatively evaluating the impact of land use/cover change (LUCC) on runoff is of paramount importance. This study is based on the Soil and Water Assessment Tool (SWAT) and Patch-generating Land Use Simulation (PLUS) models, and quantitatively analyzes the effect of LUCC on runoff in the Jinghe River Basin (JRB) through land use data from 2000 to 2020 and predicted scenarios for 2030 that encourage development, farmland protection, and ecological protection. The results show that reductions in farmland, grassland, and forest areas promote runoff, while increases in construction land similarly contribute to greater runoff. In all 2030 scenarios, the JRB is dominated by farmland and grassland. The mean annual runoff of LUCC under the three simulated prediction scenarios shows an increasing trend compared to LUCC in 2020, and the distribution of mean annual runoff depth is roughly the same. In addition, there is a strong interconnection between land use types and runoff in their dynamic relationship. Within the LUCC scenario, the decrease in farmland and forest land, along with the growth of construction land area promote runoff, while grassland plays a suppressive role in runoff. The results can offer a scientific foundation for improving soil erosion as well as optimizing land use patterns in the JRB. Full article

The advent of geomatic techniques and novel sensors has opened the road to new approaches in mapping, including morphological ones. The evolution of a land portion and its graphical representation constitutes a fundamental aspect for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, which are useful for modernizing traditional, geomorphological mapping, become necessary for the creation of scalable digital representation of processes and landforms. A fully remote mapping approach, based on multi-source and multi-sensor applications, was implemented for the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of ‘calanchi’ (i.e., badlands). Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e., Geomorphons, was adopted to map landforms at the slope scale. Simultaneously, by collecting and digitizing a time-series of historical orthoimages, a multi-temporal analysis was performed. Finally, surveying the area with an unmanned aerial vehicle, exploiting the high-resolution digital terrain model and orthoimage, a local-scale geomorphological map was produced. The proposed approach has proven to be well capable of identifying the variety of processes acting on the pilot area, identifying various genetic types of geomorphic processes with a nested hierarchy, where runoff-associated landforms coexist with gravitational ones. Large ancient mass movement characterizes the upper part of the basin, forming deep-seated gravity deformation, highly remodeled by a set of widespread runoff features forming rills, gullies, and secondary shallow landslides. The extended badlands areas imposed on Plio-Pleistocene clays are typically affected by sheet wash and rill and gully erosion causing high potential of sediment loss and the occurrence of earth- and mudflows, often interfering and affecting agricultural areas and anthropic elements. This approach guarantees a multi-scale and multi-temporal cartographic model for a full-coverage representation of landforms, representing a useful tool for land planning purposes. Full article

Haian Depression is one of the key areas for oil and gas resource replacement in Jiangsu Oilfield. Since the 13th cycle of the Five Year Plan, with the continuous improvement in the exploration level of the Taizhou Formation (K₂t), the difficulty of tapping potential has gradually increased. It is urgent to change our thinking and expand new exploration layers. From the perspective of oil and gas display frequency in different layers of the Haian Depression, except for K₂t, the oil and gas systems with the Fusan Member (E₁f₃) as the main reservoir have good oil and gas display frequency, demonstrating great exploration potential. This study of sedimentary characteristics is the basis of analyzing the sedimentary environment and lithofacies paleogeographic conditions and is of great significance for determining the distribution range of subtle oil and gas reservoirs. Based on this understanding, this study was specially established to systematically analyze the logging curves of forty-three wells in the research area, combined with core observations of eighteen coring wells and the analysis of eight seismic profiles. The results show that the low slope, warm and humid climate, sufficient provenance, and frequent lake level rise and fall cycles during the deposition period of the E₁f₃ member of the Haian Sag provide a favorable depositional background for the development of shallow water delta in the study area. There are many gullies in the research area, mainly consisting of U-shaped gullies and W-shaped gullies. Slope breaks are mainly affected by structural factors leading to fractures, and the types are mostly fault terrbreakslope breaks. In the study area, the shallow water delta deposits during the deposition period of the four key sand groups in the Fu3 Formation are dominated by the shallow water delta front and shallow water prodelta. The shallow water delta plain subfacies are not significantly developed because of erosion. The sand bodies are mainly distributed in the Sunjiawa Subdepression, and the Fuan Subdepression in the north of the depression, and the sand bodies in the plane show the filling characteristics of the strip. Based on the above research, a sedimentary model of shallow water delta during the E₁f₃ section of the Haian Depression was established, providing a geological basis for the design of exploration and development plans for hidden oil and gas reservoirs in the next step. Full article

Gully erosion is one of the significant environmental issues facing the black soil regions in Northeast China, and its formation is closely related to various environmental factors. This study employs multiple machine learning models to assess gully erosion susceptibility in this region. The primary objective is to evaluate and optimize the top-performing model under high-resolution UAV data conditions, utilize the optimized best model to identify key factors influencing the occurrence of gully erosion from 11 variables, and generate a local gully erosion susceptibility map. Using 0.2 m resolution DEM and DOM data obtained from high-resolution UAVs, 2,554,138 pixels from 64 gully and 64 non-gully plots were analyzed and compiled into the research dataset. Twelve models, including Logistic Regression, K-Nearest Neighbors, Classification and Regression Trees, Random Forest, Boosted Regression Trees, Adaptive Boosting, Extreme Gradient Boosting, an Artificial Neural Network, a Convolutional Neural Network, as well as optimized XGBOOST, a CNN with a Multi-Head Attention mechanism, and an ANN with a Multi-Head Attention Mechanism, were utilized to evaluate gully erosion susceptibility in the Dahewan area. The performance of each model was evaluated using ROC curves, and the model fitting performance and robustness were validated through Accuracy and Cohen’s Kappa statistics, as well as RMSE and MAE indicators. The optimized XGBOOST model achieved the highest performance with an AUC-ROC of 0.9909, and through SHAP analysis, we identified roughness as the most significant factor affecting local gully erosion, with a relative importance of 0.277195. Additionally, the Gully Erosion Susceptibility Map generated by the optimized XGBOOST model illustrated the distribution of local gully erosion risks. Full article

The gullies and valleys of the Loess Plateau, as key ecological zones for soil erosion control, play a critical role in the region’s sustainable development under increasing urbanization. This study employed the Soil and Water Assessment Tool (SWAT) to analyze the impacts of land use/cover changes (LUCC) on runoff at multiple spatial scales and locations within the Sanchuan River Basin (SRB) in the loess hilly and gully region. The methodology integrates SWAT modeling with LUCC scenario analysis, focusing on spatial and scale effects of land use changes on hydrological processes. The results revealed distinct spatial differences, with diminishing LUCC impacts on streamflow from the upper to lower reaches of the basin, regardless of land use type. Scale effects were also evident: grassland effectively controlled runoff within 300 m of riparian zones, while forest land was most effective beyond 750 m. A relatively insensitive range for runoff changes was observed between 300 and 750 m. These findings highlight the critical role of LUCC in influencing runoff patterns and underscore the importance of region-specific and scale-sensitive land use management strategies. This research provides valuable guidance for sustainable land planning, particularly in riparian zones, to enhance runoff control and optimize ecological benefits. Full article

Benggangs are a unique type of soil erosion commonly found in southern China, with the gully wall being the most dynamic component of the Benggang system and is crucial for assessing its overall progression. The unsaturated shear strength of soil in Benggang areas is a key factor influencing the stability of the gully wall. However, quantitative analyses of the unsaturated shear strength in the gully walls of Benggangs remain limited. In this study, the soil–water characteristic curves (SWCC) and shear strengths of undisturbed soil samples from four different soil layers in the gully wall of Benggang were measured using a pressure membrane meter and a quadruple direct shear apparatus. The results revealed that the water holding capacity of the soil decreased gradually with increasing matrix suction, and the order of the water holding capacity was the sandy soil layer > transition layer > laterite layer > clastic layer. With an increasing soil water content (SWC), the shear strength, cohesion (c), and internal friction angle (φ) of the four soil layers decreased significantly, and the φ showed a power function decreasing curve (p < 0.05), whereas c in the laterite layer and transition layer exhibited a power function decreasing curve (p < 0.01). The c of the sandy soil layer and clastic layer decreased linearly and logarithmically (p < 0.01) with increasing SWC, respectively. The unsaturated shear strength model for the four soil layers was developed based on the Vanapalli model. The root mean square error (RMSE) of the simulated and measured values was less than 29.349, while the Nash–Sutcliffe efficiency (NSE) and R² values were greater than 0.638 and 0.788, respectively. The model can be used to analyze and predict the unsaturated shear strength in different layers of Benggang gully walls, providing a theoretical foundation for studying the erosion mechanisms of Benggangs. Full article

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts. Full article

The hilly and gully region of the Loess Plateau represents one of China’s most ecologically vulnerable landscapes, characterized by severe soil erosion, intensive land use, and pronounced disturbances to the structure and functionality of ecosystem services. Taking Zichang City as a case study, this research integrates grid-scale analysis with the InVEST-PLUS model and bivariate spatial autocorrelation techniques to examine the spatiotemporal dynamics and inter-relations of four critical ecosystem services—carbon storage, water yield, biodiversity, and soil retention—under varying land use intensity scenarios from 1990 to 2035. The findings indicate that (1) between 1990 and 2020, land use intensity in Zichang City steadily declined, exhibiting a spatial distribution pattern typified by central-area clustering and gradual peripheral transitions. (2) Across three development scenarios, the spatial distribution of the four ecosystem services aligned with the patterns observed in 2020, with central areas showing pronounced fluctuations, whereas peripheral regions experienced relatively minor changes. Specifically, from 1990 to 2020, the proportion of low-carbon storage areas increased by 2.89%, and high water yield areas expanded by 9.45%, while the shares of low habitat quality and low soil retention areas decreased by 5.59% and 6.25%, respectively. (3) A significant spatial autocorrelation was observed between land use intensity and the four ecosystem services, with widespread cold and hot spots reflecting dynamic spatial clustering patterns. These results offer valuable insights for optimizing land use strategies, improving ecosystem service performance, and advancing ecological conservation and sustainable development initiatives. Full article

Water erosion is a significant global threat due to the high soil loss rate and all its consequent implications. Technologies to predict erosion are strongly related to measurements and vice versa. Measurements can simply provide empirical evidence of the erosion process and are hard to extrapolate in time and space. Measurements were used to develop some erosion models, such as the Universal Soil Loss Equation (USLE), and also for their calibration and validation. Several measurement techniques are used to collect soil erosion data at different spatial and temporal scales, but they cannot be considered fully accurate in any experimental condition. Each technique exhibits advantages and disadvantages, so extensive knowledge of their feasibility, accuracy, and limitations is required to correctly plan experiments and use the performed measurements. In this paper, recent scientific developments on the measurement of rainfall erosivity, soil loss at the plot scale, and rill and gully erosion using close-range photogrammetry are presented. Further considerations are made on the quality of soil erosion measurements and the usefulness and importance of measuring plot soil loss. Our critical analysis highlighted that the techniques reported in the literature are a solid basis, which, however, should be developed to improve their range of applicability and data quality. Full article