Gully erosion on agricultural land severely damages land resources and affects agricultural production. Topographic features, tillage methods, and roads are major elements constituting the farmland landscape, but the effect of their distribution in the farmland on the gully erosion is still unclear. This study examined the long-term impacts of changes in the farmland environment and climate change on gully erosion over a long temporal scale of nearly 60 years, the results showed that farmland reclamation over the past 60 years had led to a 2324.2 % increase in gully length density and a 3563.3 % increase in gully area density. The increase in annual rainfall amount and the frequency of extreme rainstorms had led to a rapid increase of gully erosion intensity in the last decade, with an average development rate in length density and area density of 61.5 m km- 2 and 778.7 m2 km- 2, respectively. Farmlands with slope aspects between 135 and 270 degrees were more prone to gully erosion, which was related to the redistribution of snow on hillslopes caused by prevailing wind directions. Tillage methods and roads simultaneously affect gully erosion, with newly formed gullies located in farmlands and roadsides accounting for 63.0 % and 29.8 %. Gullies in regions where the angle between furrows and unpaved roads exceeded 70 degrees accounted for 61.1 % of the total roadside gullies. Over the last decade, the annual average increase of gully length and area was 9.8 m yr-1 and 246.1 m2 yr-1. The development rate of gully area was significantly correlated with the drainage area.

In the black soil region of Northeast China, the issue of gully erosion persists as a significant threat, resulting in extensive damage to farmland, severe degradation of the black soil, and decreased productivity. It is therefore of utmost importance to accurately identify areas that are susceptible to gully erosion to effectively prevent and control its negative impact. This study tried to utilize geographical detectors (geodetectors) as a means to identify the factors that contribute to the distribution of gullies and assess the risk of gully erosion (GER) in five catchments within the region, with areas ranging from approximately 80 km(2)-- km(2) . By employing the geodetectors method, fourteen geo-environmental factors were analyzed, including topographic attributes (such as aspect, catchment area, convergence index, elevation, plan curvature, profile curvature, slope length, slope, stream power index, and topographic wetness index), channel network distance, vegetation index (NDVI and EVI), as well as land use/ land cover (LULC). The modeling of GER was conducted using the random forest algorithm (RFA). Out of the fourteen examined geo-environmental factors, only a subset, comprising less than or equal to 50%, demonstrated a significant (p < 0.05) influence on the spatial distribution of gullies. These selected factors were sufficient in assessing GER, with LULC (mean q-value 1 / 4 0.270) and elevation (mean qvalue 1 / 4 0.113) identified as the two most important factors. Furthermore, the RFA exhibited satisfactory performance across all catchments, achieving AUC values ranging from 0.712 to 0.933 (mean 1 / 4 0.863) in predicting GER. Overall, the catchment areas were classified into high, moderate, low, and very low-risk levels, representing 9.67%-15.95%, 19.28%-26.08%, 24.59%-30.55%, and 30.54%-39.08% of the total area, respectively. Importantly, a significant positive linear relationship (r(2) = 0.722, p < 0.05) was observed between the proportion of cropland area and the occurrence of high-level GER. Although the primary risk levels were categorized as low and very low, the proportion of high-risk levels exceeded the existing gully coverage (0.34%-3.69%). These findings highlight the substantial potential for gully erosion and underscore the necessity for intensified efforts in the prevention and control of gully erosion within the black soil region of Northeast China. (c) 2024 International Research and Training Center on Erosion and Sedimentation, China Water and Power Press, and China Institute of Water Resources and Hydropower Research. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY- NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

To reduce the potential threat of soil loss due to ephemeral gullies, it is crucial to adopt Best Management Practices (BMPs) that prevent damage to landscapes by reducing sediments load. The current research evaluated the impact of five BMPs, including cover crops, grassed waterways, no-till, conservation tillage, and riparian buffer strips for reduction of sediment load from sheet/rill, and ephemeral gully erosion in an agricultural watershed in Southern Ontario, Canada. The study aimed to automatically calibrate AnnAGNPS using genetic algorithm and the most sensitive parameters of the model identified using a combination of Latin Hypercube Sampling (LHS) and One-At-a-Time (OAT) approach. It also utilized the calibrated model to simulate the effectiveness of BMPs in reducing the average seasonal and annual sediment loads from both sources of erosion (sheet/rill, and ephemeral gully) to determine the most effective practices. Riparian buffer strips were consistently successful in decreasing average seasonal sediment load of sheet/rill erosion, with an average reduction efficiency of 72 % in Spring, 64 % in Summer, 65 % in Fall, and 76 % in Winter. In terms of reducing average seasonal sediment load from ephemeral gully erosion, grassed waterways proved to be the most effective BMPs. They showed efficiency of 90 % in Spring; 83 % in Summer; 79 % in Fall; and 75 % in Winter. Considering the average annual sediment load, riparian buffer strips were consistently successful in decreasing average annual sediment load of sheet/rill erosion, with 69% reduction efficiency. Similarly, grassed waterways were the most effective BMPs for reducing average annual sediment load of ephemeral gully erosion, with an efficiency of 81 %. Additionally, grassed waterways were found to be the most efficient BMPs for reducing average annual total sediment load with reduction efficiency of 71 %. These results demonstrate the importance of implementing effective BMPs to address ephemeral gully erosion in watersheds where ephemeral gullies are the main source of erosion.

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India's Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin's GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices.

Gully erosion damages land resources and endangers human productivity and life, making it a key issue in global research on soil erosion nowadays. Gully headcut retreat (GHR) is the main form of gully erosion. Tiny concave features can be found in many retreating gully heads worldwide, and they are referred to as niche terrain in this study. To investigate the association between niche terrain and GHR, relevant research was reviewed on niches and stability analysis of gully heads with niches was modelled and analysed. Studies have shown that not all niches worldwide are identical due to regional differences in internal material-external environmental conditions. Special soil properties, joints, and cracks are the internal material conditions that lead to the formation of niche. External conditions include climate conditions, vegetation conditions, and topography. Water is the driving force for the formation of niche, while vegetation and topography are key factors. Niches can be regarded as the initial stage of GHR in areas where gully erosion is intense. In general, GHR is a composite cyclical process dominated by hydraulic erosion in the early stage and gravitational erosion in the late stage, including niche formation, inward concave formation, free face formation, overhanging soil collapse, and niche reformation. In this study, a model of gully head stability is applied, and it is found that the stability-based factor of safety decreases exponentially with increasing niche height and crack depth, increases exponentially with increasing niche angle, and decreases quadratically with increasing catchment slope. Summarizing the common characteristics of niche terrains worldwide can facilitate the study of the evolution of gully erosion globally. Niches can be regarded as the initial stage of gully head retreat. The mechanism of niches varies with regional internal material-external environmental conditions. Gully head retreat is a composite cycle process dominated by early hydraulic erosion and later gravity erosion. image

Erosion is an ongoing environmental problem that leads to soil loss and damages ecosystems downstream of agriculture. Increasingly frequent heavy precipitation causes single erosion events with potentially high erosion rates owing to gully erosion. In this study, analyses of croplands affected by heavy precipitation and linear erosion indicate that erosion occurs only on sparsely vegetated fields with land cover <= 25% and that slope gradient and length are significant factors for the occurrence of linear erosion tracks. Existing erosion models are not calibrated to the conditions of heavy precipitation and linear erosion, namely high precipitation intensities and long and steep croplands. In this study, natural linear erosion was analyzed using an unmanned aerial vehicle and erosion volumes were determined for 32 rills and gullies of different sizes. Comparisons with the RUSLE2 and EROSION-3D model values showed an underestimation of linear erosion in both models. Therefore, calibration data for erosion models used for heavy precipitation conditions must be adapted. The data obtained in this study meet the required criteria.

Gullying is one of the problems that cause soil degradation in semi-arid areas and should be predicted to mitigate its damaging effects. Three machine learning models have been employed in this work to map the susceptibility to gully erosion in the N'fis river basin in the Moroccan High Atlas. Utilizing high-resolution images from Google Earth alongside fieldwork data, we digitized 434 gully erosion events to construct the comprehensive inventory map. These data were divided into two groups: training (70%) and test (30%). Based on the literature research and the multicollinearity test, 11 conditioning factors were selected. The receiver operating characteristic (ROC) approach and other statistical measures were used to quantify the model's accuracy. The study findings highlight the significance of drainage density, slope, NDVI, and distance from roads as crucial factors influencing gully erosion in the study area. Among the evaluated machine learning algorithms, the random forest (RF) model exhibited the highest performance, with an area under the curve (AUC) value of 0.932. It was followed by adaptive boosting (AB) with an AUC of 0.902 and gradient-boosted decision trees (GBDT) with an AUC of 0.893. The maps produced reveal that the southern and central regions of the study area have the classes of very high and high gully erosion susceptibility. The outputs of the current study can be used by decision-makers to improve prevention planning and mitigation techniques against gully erosion damage.

Over the years, Njaba has been known to be bedeviled with worrying gully erosion challenges. This is attributed to the geologic makeup of the soil and other environmental factors. Geophysical and geotechnical characterization of the soil within Njaba and its environs has been carried out with the aim of determining the potentials in the development and enhancement of gullies within the area. 23 vertical electrical sounding were conducted using Schlumberger array configuration with maximum current electrode spacing of 350 m, while two soil samples were also collected for geotechnical studies. The first layers were used to categorize the soils into competence and corrosive soils. Qualitative interpretation reveals that the sites are characterized by six (6) resistivity type curves, namely; 6-HK, 2-AK, 3-KH, 2-QH, 4-KK, 2-AH. The results from the iso-resistivity showed that the resistivities of the layers increased progressively with depth as the maximum electrode spacing increased. The transverse resistance calculated varies from 1408950 to 30987 ohm m, and the longitudinal conductance varies from 0.03163 to 0.00099876 mho. From the competence and corrosivity rating, the soils were categorized from highly competent to moderately competent for the competence categories with varying resistivities of 2800 to 154 ohm m and for the corrosivity category, soils were categorized from essentially non-corrosive to mildly corrosive soils with varying resistance of 182.0 to 154.0 ohm m. The coefficient of anisotropy determined averages 1.25, 1.14 and 1.06 at three different sites. The results of the compaction tests indicate that the Optimum Moisture Content ranged from 11.1-11.5% is required to achieve Maximum Dry Density of the samples ranging from 0.18-2.15 mg/m3. From the study, it can be ascertained that the soil is of low compressibility. The study suggests among others that surface and subsurface flows and drainage should be controlled by directing water through concrete channels into lined artificial reservoirs or straight into lakes or river plains.

Climate change results in physical changes in permafrost soils: active layer thickness, temperature, soil hydrology and abrupt thaw features in ice-rich soils. Abrupt thaw features create new landforms such as ponds, lakes and erosion phenomena. In this chapter, current observations of physical changes in permafrost soils are discussed, including their effect on the soil carbon cycle. For the carbon cycle changes, the results of observations and experimental studies are emphasized. First, the effects of soil warming without further geomorphological change is considered. The potential effect of self-amplifying soil warming by heat production from bacterial production is discussed. Next, the changes in geomorphological processes expressed by formation of thaw ponds, lakes and erosion features are considered. These contribute to an increase of CO2 and non-CO2 greenhouse gas emissions. Hydrological changes include the effects of permafrost thaw on the water cycle via groundwater flow and directly climate-driven changes in precipitation and evapotranspiration. These result in river discharge changes with effects on floodplains, and influence transport of carbon from permafrost regions to the Arctic Ocean. Soil hydrology changes - wetting or drying - induce changes in the pattern of greenhouse gas emissions of permafrost soils.