In the alpine region, climate warming has led to the retreat of glaciers, snow cover, and permafrost. This has intensified water cycling, soil erosion, and increased the occurrence of natural disasters in the alpine region. This study investigated the Lhasa River Basin in the southern Tibetan Plateau, serving as a representative case study of a typical alpine basin, with a specific focus on gully erosion. Based on field investigations and interpretation using high-resolution satellite remote sensing images, the Random Forest (RF) algorithm was applied to evaluate gully erosion susceptibility on watershed level. The Shapley Additive Interpretation method was then used to interpret the RF model and gain deeper insights into the influencing variables of gully erosion. The results showed that the RF model achieved an area under the receiver operating characteristic (AUC) accuracy of 0.99 and 0.98 for the training and testing datasets, respectively, indicating an outstanding performance of the model. The resulting susceptibility map based on the RF model shows that areas with moderate and higher levels of gully erosion susceptibility are covering 50 % of the basin. The model interpretation results indicated that elevation, slope, permafrost, rainstorm, silt loam topsoil, human activity, stream power, and vegetation were the explaining variables with the highest importance for gully erosion occurrence. Different variables are characterized by specific thresholds promoting gully erosion such as: i) elevations higher than 4950 m, ii) slopes steeper than 13.5°, iii) extreme rainstorms longer than 11 days per year, iv) silt loam topsoil, v) presence of permafrost, vi) stream power index higher than 1.2, and vii) normalized difference vegetation index (NDVI) lower than 0.25. Our findings provide the scientific basis to improve soil erosion control in such highly vulnerable alpine area.

Gully erosion is a serious global environmental problem associated with land degradation and ecosystem security. Examining the influencing factors of gullies and determining susceptibility hold significance in environmental sustainability. The study evaluates the spatial distribution, influencing factors, and susceptibility of gullies in the Sunshui River Basin in Sichuan Province, Southwest China. The frequency ratio method supported by satellite images and the gully inventory dataset (1614 gully head points) with different influencing factors were applied to assess the distribution and susceptibility of gullies. Additionally, gully head points were grouped into a training set (70%, 1130 points) and a test set (30%, 484 points). Spatial distribution results indicated that most gullies are located in the middle and upper part of the basin, characterized by moderate elevation (2100-3300 m), steep slopes (11.63-27.34°), abandoned farmland, and Cambisols soil, and fewer gullies are located in lower part characterized by lower elevation, gentle slopes, and low vegetation coverage. Land use and land cover influence on susceptibility is significantly greater than other factors with a prediction rate of 33.9, especially farmland abandonment, while the occurrence of gullies is also more often on southwest-orientated slopes. Gully susceptibility highlighted that the study area affected by the very low, low, moderate, high, and very high susceptibilities to these gullies covered an area of about 16%, 23%, 32%, 26%, and 3% of the total basin respectively, which indicates 61% of the study area is susceptible to gully erosion. Moderate to high susceptibility is situated in the upper and middle part, consistent with the spatial distribution of gullies in the basin, and very high susceptibility (3%) is distributed in both the lower and upper parts of the basin. These results have important implications for soil loss control, land planning, and integrated watershed management in the mountainous areas of Southwest China.

Gully is a prominent indicator of land degradation in agroecosystems, functioning as a crucial pathway connecting upslopes to downstream channels. However, little is known about how gully regulates runoff, sediment, and nutrient loss processes in the catchment during snowmelt. In this study, we monitored these processes in situ at both the gully head (the upslope accumulated catchment of the gully head, CGH) and outlet of two representative and typical gully-dominated catchments (F1 and F2) during snowmelt in Mollisols region of Northeast China. Our results showed that runoff discharge of CGH and outlet exhibited a multi-peak trend during snowmelt, driven by the transition from snow melting to soil thawing. This transition resulted in distinct runoff patterns in both CGH and outlet, with significant differences in their response to air temperature. The total runoff yield of CGH accounted for 57.8 % in F1 and 40.6 % in F2 of the total runoff yield of the outlet. Notably, the peak sediment concentration displayed a marked lag compared to the peak runoff discharge, primarily dominated by the increased sensitivity of gully erosion after the thawing of gully slopes. Gully erosion was the main source of sediment yield in the catchment, contributing 98.2 % in F1 and 96.6 % in F2. Furthermore, nutrient concentrations exhibited a decreasing trend during snowmelt. The comparison of high nutrient concentrations in CGH and relatively low nutrient concentrations in outlet highlighted the gully\'s role in intercepting and diluting runoff nutrients. Hysteresis analysis confirmed the differential contribution of CGH and gully to nutrient sources. CGH accounting for 50.9 % and 93.3 % of runoff TN and runoff TP loss, while contributing only 8.3 % and 5.8 % to sediment TN and sediment TP loss, respectively. These findings offer valuable insights for effective erosion control and nonpoint source pollution management in gully-dominated agroecosystems during snowmelt.

The strategic reduction and remediation of degraded land is a global environmental priority. This is a particular priority in the Great Barrier Reef catchment area, Australia, where gully erosion a significant contributor to land degradation and water quality deterioration. Urgent action through the prioritisation and remediation of gully erosion sites is imperative to safeguard this UNESCO World Heritage site. In this study, we analyze a comprehensive dataset of 22,311 mapped gullies within a 3480 km2 portion of the lower Burdekin Basin, northeast Australia. Utilizing high-resolution lidar datasets, two independent methods - Minimum Contemporary Estimate (MCE) and Lifetime Average Estimate (LAE) - were developed to derive relative erosion rates. These methods, employing different data processing approaches and addressing different timeframes across the gully lifetime, yield erosion rates varying by up to several orders of magnitude. Despite some expected divergence, both methods exhibit strong, positive correlations with each other and additional validation data. There is a 43% agreement between the methods for the highest yielding 2% of gullies, although 80.5% of high-yielding gullies identified by either method are located within a 1 km proximity of each other. Importantly, distributions from both methods independently reveal that ∼80% of total volume of gully erosion in the study area is produced from only 20% of all gullies. Moreover, the top 2% of gullies generate 30% of the sediment loss and the majority of gullies do not significantly contribute to the overall catchment sediment yield. These results underscore the opportunity to achieve significant environmental outcomes through targeted gully management by prioritising a small cohort of high yielding gullies. Further insights and implications for management frameworks are discussed in the context of the characteristics of this cohort. Overall, this research provides a basis for informed decision-making in addressing gully erosion and advancing environmental conservation efforts.

The fragile Loess Plateau of China suffers substantial gully erosion. It is imperative to elucidate gully erosion patterns for implementing effective erosion control strategies. However, high spatiotemporal resolution quantification of gully dynamics remains limited across the Loess Plateau landscape. We utilized the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) technique to investigate the phenomenon of gully erosion and deposition on the Dongzhiyuan tableland, which sits within the vast expanse of the Loess Plateau in China, over the period spanning 2020-2022. The tableland edges subsided while gully bottoms uplifted due to sedimentation. Low elevations underwent active deformation. Slope, aspect, and curvature modulated uplift and subsidence patterns by affecting runoff and sediment transport. Gentle downstream slopes displayed enhanced sedimentation. Southern gullies showed pronounced uplift compared to northern gullies. Heavy rainfall triggered extensive erosion followed by rapid uplift, reflecting an adaptive oscillation between erosion and deposition. Basin hydrology correlated with spatial patterns of deformation. Vegetation cover above 60 % of the maximum substantially increased InSAR error. Our study reveals intricate spatiotemporal behaviors of erosion and deposition in loess gullies using time-series InSAR. The findings provide new insights into gully geomorphology and evolution, and our study quantifies gully erosion and deposition patterns at high spatiotemporal resolution, enabling identification of the most vulnerable areas and prioritization of conservation efforts.

Gully erosion leads to the formation of deep and wide channels that increase the risk of soil loss, flooding, and water pollution. In addition, this process reduces the productivity and viability of agricultural land and natural ecosystems. Preventing gully erosion is critical for maintaining ecological balance and preserving natural resources in certain areas. This paper presents a methodology integrating remote sensing and nuclear techniques to study gully erosion. The morphometric characterization of gullies using 360-degree camera photogrammetry was introduced as a new method in erosion research. This approach aims to investigate the suitability of unmanned aerial vehicle and terrestrial photogrammetry for modeling gullies, to study the variability of erosion processes in gullies at a small scale, and to compare the differences in erosion intensity between nearby gullies. The study\'s objectives include identifying the effective and economical method for gullies monitoring and providing a starting point for controlling and safeguarding gullies. Mainly erosion process was detected in the studied gullies, while deposition was identified at only 2 out of 39 sampling locations. The results showed an average soil redistribution rate of 16.2 t ha-1 yr-1 and coefficients of variation of 32%, 59%, and 91% for three investigated gullies. It was determined that aerial photogrammetry methods were not practical under the conditions prevailing in the study area. Highly detailed 3D models of the gullies were created using 360-degree photogrammetry. It was confirmed that the micro-relief obtained by photogrammetric modeling is an essential contribution to erosion research. The 360-degree camera photogrammetry serves as a reliable tool for analyzing the morphology of gullies and, in perspective, tracking changes in gully systems over time or monitoring the effectiveness of the applied protection measures.

Delineation of areas susceptible to gully erosion with high accuracy and low cost using significant factors and statistical model is essential. In the present study, a gully susceptibility erosion map (GEM) was developed using hydro-geomorphometric parameters and geographic information system in western Iran. For this aim, a geographically weighted regression (GWR) model was applied, and its results compared to frequency ratio (FreqR) and logistic regression (LogR) models. Almost twenty effective parameters on gully erosion were detected and mapped in the ArcGIS®10.7 environment. These layers and gully inventory maps (375 gully locations) were prepared using aerial photographs, Google Earth images, and field surveys divided into 70% and 30% (263 and 112 samples) ArcGIS®10.7. The GWR, FreqR, and LogR models were developed to generate gully erosion susceptibility maps. The area under the receiver/relative operating characteristic curve (AUC-ROC) was calculated to validate the generated maps. Based on the LogR model results, soil type (SOT), rock unit (RUN), slope aspect (SLA), Altitude (ALT), annual average precipitation (AAP), morphometric position index (MPI), terrain surface convexity (TSC), and land use (LLC) factors were the most critical conditioning parameters, respectively. The AUC-ROC results show the accuracy of 84.5%, 79.1%, and 78% for GWR, LogR, and FreqR models, respectively. The results show high performance for the GWR compared to LogR and FreqR multivariate and bivariate statistic models. The application of hydro-geomorphological parameters has a significant role in the gully erosion susceptibility zonation. The suggested algorithm can be used for natural hazards and human-made disasters such a gully erosion on a regional scale.

Gully erosion causes high soil erosion rates and is an environmental concern posing major risk to the sustainability of cultivated areas of the world. Gullies modify the land, shape new landforms, and damage agricultural fields. Gully erosion mapping is essential to understand the mechanism, development, and evolution of gullies. In this work, a new modeling approach was employed for gully erosion susceptibility mapping (GESM) in the Golestan Dam basin of Iran. The measurements of 14 gully erosion (GE) factors at 1042 GE locations were compiled in a spatial database. Four training datasets comprised of 100%, 75%, 50%, and 25% of the entire database were used for modeling and validation (for each data set in the common 70:30 ratio). Four machine learning models-maximum entropy (MaxEnt), general linear model (GLM), support vector machine (SVM), and artificial neural network (ANN)- were employed to check the usefulness of the four training scenarios. The results of random forest (RF) analysis indicated that the most important GE effective factors were distance from the stream, elevation, distance from the road, and vertical distance of the channel network (VDCN). The receiver operating characteristic (ROC) was used to validate the results. Our study showed that the sample size influenced the performance of the four machine learning algorithms. However, the ANN had a lower sensitivity to the reduction of sample size. In addition, validation results revealed that ANN (AUROC = 0.85.7-0.90.4%) had the best performance based on all four sample data sets. The results of this research can be useful and valuable guidelines for choosing machine learning methods when a complete gully inventory is not available in a region.

A highly visible form of soil erosion is gully, a significant geomorphological feature, resulting from water erosion and causing land degradation and deterioration. In arid and semi-arid environment, gully erosion is conceived as an important source of sediment supply washing out the top fertile soil and exposing lower soil layers. The present study is conducted on the lateritic terrain of Rupai watershed of eastern plateau fringe of India, where water erosion is a serious concern. In order to prepare a gully erosion vulnerability mapping, the analytical hierarchy process (AHP) model coupled with geospatial technology is adopted taking into account thirteen bio-physical factors. It is revealed that around 49% area of the watershed belongs to high to very high gully erosion vulnerability zone (GEVZ) followed by moderate risk zone of 31.64%. This model is validated performing an accuracy assessment, which is calculated to be 90.91%, and the value of Kappa co-efficient is 0.86. It is imperative to estimate the average annual soil loss alongside of delineating GEVZ; thus, the revised universal soil loss equation (RUSLE) model is used with geospatial technology. It unveils that the average estimated soil loss of the watershed varies from < 15 to 431 t ha-1 y-1. Around 29% of the study area experiences high to very high (57 to > 147 t ha-1 y-1) soil erosion risk, where 68% area endures low level of soil erosion risk (< 15 t ha-1 y-1). The study of gully morphology depicts gully depth ranging from < 1 to 5 m (small to medium gully) with V and U shapes. Results obtained from this study may help in planning and management of land use and soil erosion conservation.

A gully system is an important indicator that reflects the development of regional topography and landforms, and topography is one of the most important factors affecting the development of gullies. However, at present, research on the impact of topography on the development of gully systems in the mountainous area of Ningnan dry-hot valley still needs to be strengthened. In order to study the characteristics of gullies and the influence of topography on the development of gully systems, based on both the visual interpretation of remote sensing images and field investigations, five topographic factors (elevation, slope gradient, aspect, relief, and dissection) were employed and three gully erosion indexes (gully length, density, and frequency) were calculated. The geographical information system was used in this study to carry out the spatial analysis, Ward’s hierarchical clustering and correlation analysis. Results showed that the development of gully systems is greatly affected by the degree of relief and dissection, and there is a significant positive correlation (p < 0.01; p < 0.05), while elevation, slope gradient and aspect have little influence on it. Analysis of the gully systems showed that the gully erosion is the most intense in the area with an elevation of 2800−3200 m and slope gradients ≥ 38°. Furthermore, the degree of erosion on shady slopes was greater than that on sunny slopes. These results will help us to understand the spatial distribution and formation of gully systems in mountainous areas.