Quantifying soil redistribution rates is crucial for addressing environmental challenges related to land degradation and sustainable resource management. To complement conventional methodologies, alternative techniques were developed to enhance soil erosion model calibration and testing. Fallout radionuclides (FRNs), particularly ¹³⁷Cs, have been widely recognized as reliable tracers for monitoring soil movement and assessing erosion and deposition rates. Existing conversion models using FRNs to estimate soil redistribution rates often struggle to adapt to diverse soil conditions or to incorporate physical processes. RadEro is a mass balance model implemented in R, based on the compartmental, vertically-resolved, physically-based mass balance framework developed by Soto and Navas (2004, 2008). The model calculates simulated inventories by considering ¹³⁷Cs mass specific activity in the fine fraction density, effective volume (Veff), annual ¹³⁷Cs fallout, radioactive decay, and dominant vertical diffusion processes, including the effects of tillage. This enables RadEro to estimate rates while integrating the effects of soil stoniness in both ploughed and unploughed soils with either sectioned or bulk profiles. Redistribution rates are estimated by assuming that reference sites represent the natural profile distribution and decay of ¹³⁷Cs inventory for the study area. Additionally, deposition is assumed to originate from a nearby site with similar ¹³⁷Cs activity to that of the measured soil point. An accurate user-defined configuration of the model is essential for estimating reliable results. The variables in the model optimization process define the limits and resolution of the simulation sampling domain. By specifying the ranges for the diffusion coefficient (𝑘) and redistribution rates (𝑒), the model iterates to align with the measured ¹³⁷Cs inventory of a stable reference profile. The optimal 𝑘 value is then applied to estimate the corresponding soil redistribution rate (𝑚) in eroded and depositional profiles. In cases of extreme erosion or deposition, simulations with wider initial ranges for 𝑒 may be necessary to capture the full spectrum of possibilities. Our contribution highlights the need to understand the limitations of input data and model results. RadEro allows users to adjust parameters to fit their needs but relies on expert knowledge to select appropriate reference values and sampling points. By carefully evaluating input data, RadEro delivers reliable results and highlights the importance of addressing uncertainties in radionuclide distribution for accurate conclusions.

RadEro: ¹³⁷Cs Conversion Model https://github.com/eead-csic-eesa/RadEro

CRAN: Package RadEro https://cran.r-project.org/web/packages/RadEro/index.html

How to cite: Catalá, A., Latorre, B., Gaspar, L., and Navas, A.: RadEro model: A Physically-Based Model for Quantifying Soil Redistribution Rates Using 137Cs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20128, https://doi.org/10.5194/egusphere-egu25-20128, 2025.

Ephemeral gully erosion is responsible for a great part of the erosion that occurs in agricultural areas. Like any complex system, the formation of an ephemeral gully has an inherent complexity that has led many researchers to study in isolation, through experimentation and modeling, the processes involved in this phenomenon. However, by definition, in a complex system there are many interactions between processes that cannot be ignored. Therefore, it is also necessary to carry out studies that consider all the processes simultaneously. We believe that, to date, there is a lack of studies of this type on the formation of an ephemeral gully that would allow us to advance in the characterization and understanding, to test the hypotheses put forward about it, formulating other alternatives if necessary, and to evaluate the existing models, such as QAnnAGNPS. In order to fill this gap, an experiment has been initiated in November 2023 in which, first of all, an agricultural plot has been selected in an area of highly erodible silty loam soils located in Pitillas (Navarra). The plot has been tilled with conventional tillage to replicate the initial conditions of an average agricultural plot, which has been kept free of vegetation by using herbicide, and in which a rain gauge has been installed as well as moisture probes at different depths. After each precipitation event, drone flights have been carried out to obtain digital elevation models (DEM) with a resolution of less than one centimeter and orthomosaics. The DEMs and orthomosaics generated in each flight make it possible to locate the origin of the gullies formed and to determine their dimensions and their temporal evolution, in this case until November 2024, when the plot was tilled again to restart the observations. Our observations confirm the enormous complexity of the erosive phenomena, highlighting the formation and evolution of hundreds of headcuts of very different typology and size, many of them linked to ephemeral gullies. The first gullies appeared four months after tilling, after a rainfall event of 17.7 mm and high soil moisture conditions. Prior to this rainfall event, 55.4 mm accumulated in different storms, but with an intensity that did not cause the formation of gullies. Subsequent events lengthened and widened the first gullies and created new ones, resulting in a dense drainage network and very high soil losses. Our first results suggest the complexity of the phenomenon, with the formation and migration of headcuts playing a major role, confirming the suitability of the modeling of the phenomenon centered on these headcuts. The proposed experimentation represents a great opportunity to advance in the understanding of the formation and evolution of ephemeral gullies in real agricultural conditions, considering all the typical variables that affect this phenomenon.

How to cite: Barberena, I., Campo, M. Á., van Wiltenburg, K., and Casalí, J.: Field experimentation for a better understanding of the occurrence and evolution of ephemeral gullies in field conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18485, https://doi.org/10.5194/egusphere-egu25-18485, 2025.

Land degradation in African tropical mountain environments, driven by water erosion and unsustainable land use, is further exacerbated by climate change. This leads to reduced crop productivity, and significant delivery of sediment to downstream rivers and lakes. A thorough understanding of the erosion processes and its drivers, including its spatial and temporal patterns, as well as the quantification of erosion rates is key to mitigate soil erosion, reduce sediment delivery, and ensuring agricultural and environmental sustainability. Such quantitative temporal and spatial data are required to run spatially distributed erosion models that are capable of simulating the impact of various management scenarios. However, these data are often missing for tropical mountain environments such as the southern Ethiopian highlands. Indeed, typical soil erosion models such as the RUSLE or WaTEM/SEDEM require an assessment of the cover management factor, and most applications of these model use standard tabulated values that are not region-specific and are thus not representative for the spatio-temporal vegetation developments. New developments using remote sensing, however, provide an opportunity to better parameterize the crop management factor.

Here we present spatio-temporal data on vegetation and crop types using optical remote sensing for two small catchments (Charcharo, 145ha and Zaga, 87ha) in the Gamo highlands of southern Ethiopia, with the aim to better assess the changing erosion risk and to quantify local cover management factors. We have digitalized all the crop parcels in these catchments and obtained 1538 field observations of vegetation cover for different fields and crop types throughout the year. In addition, we obtained ground-based NDVI values for 264 field parcel-crop combinations at different time intervals using a crop sensor. These field-based observations were compared with the satellite-based time series per crop to produce a better assessment of temporal changes in crop cover management. We also digitized all the soil conservation measures and started monitoring stream flow and sediment sampling using a high-resolution sampler for validating erosion model predictions.

The Charcharo catchment situated at an elevation of 2890 - 3039 meters is dominated by grazing land, with barley and potato as primary crops. Vegetation cover varies between 0% to 70-85% over the course of the growing season. Terraces are the main soil conservation method used in the area with a density of 100 m/ha. Zaga catchment is situated at an elevation of 1898 to 2166 meters and is primarily agricultural land with maize, sorghum and teff as dominant crops. Vegetation cover varies between 0% and 60-80% over a four to six month growing season. Here, stone bunds are the predominant soil conservation practice with a density of 34 m/ha. Upon completion, the study will provide quantitative information on soil erosion and the crop cover management factor in particular. This information will be used to run erosion models and to simulate the impact of climate and land use change. This research will also highlight the benefits of different conservation measures, aiding the local community and governmental stakeholders.

How to cite: Demiss, F. S., Tilahun, A., Minda, T., and Verstraeten, G.: A combined field and remote sensing approach to assess spatio-temporal changes in vegetation cover for erosion modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16808, https://doi.org/10.5194/egusphere-egu25-16808, 2025.

Rain splash, the initial stage of soil erosion by water, is influenced by both rainfall erosivity and soil surface erodibility. Vegetation alters rainfall erosivity—quantified as kinetic energy—by serving either as a protective, dispersive layer or as an amplifying, drip-aggregating layer. Previous research on vegetation's impact on throughfall kinetic energy (TKE) has primarily focused on point-based vegetation data and localized erosivity measurements. However, there is a growing need for spatially continuous, area-wide predictions of vegetation's effect on rainfall erosivity to enhance erosion modeling and conservation efforts. Recent studies emphasize the role of fine-scale tree structures in creating erosivity hotspots, known as drip points.

To address this gap, we employed lidar point clouds across multiple scales to investigate the relationship between 3D vegetation structure and TKE. UAV lidar data were used to derive vegetation cover and gap fractions within a voxel framework, identifying canopy layers that contribute leaf drips reaching the ground without re-interception. Furthermore, we linked field observations of active drip points to tree skeletons extracted from TLS point clouds to establish rules governing drip formation.

Our findings reveal that temperate forest vegetation's impact on erosivity surpasses values reported in prior studies focused on plantations. We observed a strong alignment between predicted and measured vegetation effects on TKE. We could demonstrate the potential of remote sensing for comprehensive, wall-to-wall predictions of vegetation's influence on rainfall erosivity. On the tree scale, lidar can improve our understanding of stemflow and re-interception dynamics on vegetation surfaces. These detailed findings can be scaled up to enhance landscape-level erosion predictions. Overall, lidar technology offers a promising solution to bridging data gaps in conventional erosion research.

How to cite: Senn, J. A. and Seitz, S.: 3d vegetation as a predictor of erosivity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19993, https://doi.org/10.5194/egusphere-egu25-19993, 2025.

Potato is the main cash crop in Atlantic Canda. Potato cropping is associated with intensive tillage and high levels of fertilizer and pest inputs. Frequent soil disturbance, together with the rolling landscape and loamy soils, has resulted in high levels of water erosion in this region. In order to reduce the water erosion risks, efforts have been made to adopt Beneficial Management Practices (BMPs) such as reduced tillage and alternative crop rotations with agro-ecological considerations. However, there is no field study to quantify the effectiveness of these BMPs. To fill this knowledge gap, rainfall simulation experiments were conducted to measure runoff and sediment generations in three crop rotations under two types of tillage. The three crop rotations examined were: the conventional Potato (PO)-Barley (BL)-Potato (POBLPO, used as the control), Corn (CO)-Spring wheat mixed with Triple mix (WT)-Potato (COWTPO) and Corn mixed with Ryegrass (CR)- Wheat mixed with Alfalfa and timothy (WA)-Potato (CRWAPO). The two types of tillage are conventional tillage (CT, used as the control) and reduced tillage (RT). A mini rainfall simulator was used to simulate 20 minutes rainfall. Runoff samples were collected every minute from which the runoff flow rate, sediment export rate and sediment concentration at every minute were determined. Cumulative measures such as runoff discharge, sediment yield and Flow-Weighted Mean Sediment Concentration (FWMSC) were calculated for 5, 10, 15 and 20 minutes durations of simulated rainfall.

The results overall demonstrate the strong effects of tillage and crop rotation on runoff and sediment generations although there were some exceptions. In the cash crop year, compared to CT, RT increased runoff discharge for short durations of rainfall but reduced it slightly for long durations of rainfall. For PO, RT only reduced sediment yield and FWMSC slightly but for CO and CR, sediment yield and FWMSC reductions by RT were more than 100 times. Compared to PO, CO and CR significantly reduced runoff discharge,  sediment yield and FWMSC. In particular, the reductions of sediment yield and FWMSC from PO to CO and CR under RT were more than 100 times. In the rotational crop year, compared to CT, RT reduced runoff discharge mostly by more than one third and reduced sediment yield and FWMSC by more than ten times. Compared to BL, WT and WA mostly reduced runoff discharge and sediment yield but the reductions were small. Based on these results, it was estimated that by switching from the conventional crop rotation (POBLPO) with conventional tillage to the alternative crop rotations (COWTPO and CRWAPO) with reduced tillage, runoff discharge, sediment yield and FWMSC on average will be reduced by 13 %, 59 % and 69 %, respectively.

How to cite: Li, S., Kupriyanovich, Y., Zheng, F., and Agomoh, I.: How much will reduced tillage and alternative crop rotations reduce runoff and sediment generations in a potato production system in Atlantic Canada?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2967, https://doi.org/10.5194/egusphere-egu25-2967, 2025.

Gully erosion is the rapid incision of soils by concentrated overland and/or subsurface runoff. Badlands, which are barren landscapes sculpted through prolonged and intense gully erosion, occur extensively across Central and Western India. These vast and immensely degraded landscapes have had several adverse effects on the regional environment and society. Consequently, they have received considerable research attention since India’s independence, while little information exists about the characteristics of gully erosion elsewhere in India. Therefore, through a detailed pan-Indian mapping of gully erosion landforms, possibly the most extensive fieldwork ever undertaken in the domain of gully erosion research and multitemporal remote sensing, this work highlights the spatial distribution and areal extents of gully erosion, the spatial variability in gully morphological attributes and the dynamics of gully erosion and reclamation in India. Overall, the findings indicate that India not only has some of the largest gullies worldwide (widths up to 412 m and depths up to 78 m) but select locales of the country also experience some of the worst long-term rates of gully erosion (up to 800 t ha^-1 yr^-1) on our planet. Although the badlands account for a large 70% of the total gullying-affected land area in India, we have found that gully erosion in Eastern India is currently a particular cause of concern not only due to the widespread occurrence, but also because of high activity rates that seldom remain within the local permissible soil loss rates. On the contrary, the badlands are stabilised, and the gullies therein exhibit limited activity, if at all, which has prompted large-scale land reclamation activities in these regions. Gully morphological attributes such as top-to-bottom width ratio, width-depth ratio and cross-sectional area differ considerably across India, with statistically significant differences observed across climes, geomorphological settings, soil types and land cover/use classes. We have also observed that stabilised gullies are considerably (by ca. 3 times on average) larger than currently active systems. Similarly, gullies in the badlands are disproportionately larger than that of gully systems elsewhere, with bank gullies characterised by the largest dimensions among the latter. By providing critical insights into the scale, nature, and severity of gully erosion in India, this project not only addresses the glaring lack of knowledge on this subject and advances scientific understanding, but the findings also support practical strategies for sustainable land management by aiding in the identification of particularly erosion-prone regions where management efforts should be prioritised, which has relevance for the ongoing national land degradation neutrality drive.

How to cite: Majhi, A., Mallick, K., Barman, D., Harris, A., Evans, M., Shuttleworth, E., and Bhattacharjee, P.: Gully erosion in India: Land degradation, geomorphology and dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-563, https://doi.org/10.5194/egusphere-egu25-563, 2025.

This research investigates the impact of climate change on streamflow and sediment yield within the Carapelle basin, located in the Apulia Region of Italy, a Mediterranean environment. The study utilized three climate model projections, which were adjusted for bias to enhance accuracy. Statistical evaluations demonstrated an improved agreement between observed data and the corrected projections. The Soil and Water Assessment Tool (SWAT) was employed to model hydrological processes and sediment transport, with calibration and validation conducted using data from 2004 to 2011. The model exhibited satisfactory performance in simulating both streamflow and sediment load. Future projections for 2030-2050 indicate a potential temperature rise of up to 1.3°C and a reduction in average annual rainfall by as much as 38% relative to the baseline. These changes are expected to result in decreased water yield and sediment load. Among the climate models, the CMCC projection suggested the most significant decline in mean annual flow (67%), followed by reductions of 35% and 7% predicted by the MPI and EC-EARTH models, respectively. Sediment load reductions were estimated at 52.8% for CMCC, 41.7% for MPI, and 18.1% for EC-EARTH. Spatial analysis indicated that soil erosion remains a critical issue under future climate scenarios, particularly in areas with steep slopes and wheat cultivation, where sediment yield exceeds 10 t ha⁻¹. These findings underscore the necessity for proactive water resource management to address the anticipated decrease in water availability and highlight the importance of adopting sustainable agricultural practices to mitigate soil erosion.

How to cite: Abdelwahab, O. M. M., Ricci, G. F., Gentile, F., and De Girolamo, A. M.: Assessing the Effects of Climate Change on streamflow and soil erosion in a Mediterranean Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11105, https://doi.org/10.5194/egusphere-egu25-11105, 2025.

Recent studies show that machine learning methods (ML) have a high potential to model various soil erosion processes. The main focus of this research is on qualitative modelling (risk classes, binary occurrence) on smaller scales e.g., probability maps for gully erosion. In this study, we used four machine learning methods to reproduce spatial explicit information on soil loss rates in a 5 m resolution obtained in the Lower Saxonian (Germany) soil erosion monitoring program in the years 2000 - 2020. The monitoring data includes information on soil loss by water erosion (sheet and rill erosion) on 465 ha of cropland in northern Germany derived by continuous erosion feature mapping after erosive rainfall events (see Steinhoff-Knopp & Burkhard (2018) for methods and results). We applied the ML methods Random Forest (RF), a Single-Layer Neural Network (SLNN), a Deep Neural Network with multiple hidden layers (DNN) and a Convolutional Neural Network (CNN) to reproduce the mapped soil loss rates. Prediction variables included are up to 19 soil, land use, rainfall and DEM-derived topographic parameters. All ML methods were able to reproduce the soil erosion patterns. The comparison between the different models shows that the CNN model outperforms all other tested models in nearly all metrics. Its RMSE of 1.05 and MAE of 0.41 are significantly lower than those of the RF (RMSE: 1.31, MAE: 0.58) and SLNN (RMSE: 1.48, MAE: 0.63). Only the DNN performs similarly, with a slightly higher RMSE of 1.1 and MAE of 0.58. However, the classification performance of the RF, DNN, and CNN models is comparable, with F1 scores ranging from 0.68 to 0.70 and AUC values between 0.92 and 0.94. Additionally, the permutation importance was calculated to assess the influence of the predictor variables. In all four models, the variable with the highest importance is the DEM. Its importance ranges from 15% to 18.3%, depending on the model. All models also strongly rely on USLE C and R factors. Our findings emphasize the high potential of ML-driven erosion predictions and will be rolled out to predict soil erosion rates on cropland in northern Germany.

Steinhoff-Knopp, B. and Burkhard, B.: Soil erosion by water in Northern Germany: long-term monitoring results from Lower Saxony, CATENA, 165, 299–309, https://doi.org/10.1016/j.catena.2018.02.017, 2018.

How to cite: Steinhoff-Knopp, B., Barthel, N., Ott, S., and Burkhard, B.: Using Machine Learning Methods to Predict Water Erosion Patterns in Northern Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9574, https://doi.org/10.5194/egusphere-egu25-9574, 2025.

Gully erosion has long been recognized as an important driver of soil loss and land degradation. However, modeling this process—particularly at larger spatial scales—remains challenging. While recent advancements have significantly improved our ability to simulate the spatial patterns of gully occurrence, understanding their activity and the factors controlling their erosion rates remains a much greater challenge. Similarly, the broader impacts of gullies across diverse environments are still poorly understood and under-researched.

This talk highlights recent progress in modeling gully erosion from regional to global scales. We demonstrate that gully occurrence and activity are governed by distinct yet complementary sets of factors. For instance, land cover plays a dominant role in determining gully occurrence patterns across Africa, whereas activity rates are more strongly influenced by recent land use changes and rainfall intensities. These findings suggest that projected changes in climate and land use could result in far greater increases in gully erosion than predicted by models focusing solely on gully occurrence.

Drawing on case studies from the Global South, we further explore some of the severe and often unconsidered impacts of gully erosion. Key examples include significant crop yield losses due to altered cropping practices and the emergence of highly destructive gullies in urban environments. Notably, many of these impacts are concentrated in regions that are already highly vulnerable to environmental change.

Taken together, these insights suggest that the challenges posed by gully erosion in our changing world may be far greater than previously anticipated. Nonetheless, our findings also highlight that effective land management practices can mitigate or even prevent many of these issues.

How to cite: Vanmaercke, M., Chen, Y., De Geeter, S., Mekonnen, Y. Y., Dujardin, E., Ilombe Mawe, G., Lutete Landu, E., and Poesen, J.: Gully erosion might become a larger problem than hitherto anticipated: insights from fieldwork and recent modelling advancements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9577, https://doi.org/10.5194/egusphere-egu25-9577, 2025.

•  Gully erosion represents one of the most severe forms of land degradation. In regional management decisions, gully density serves as a crucial metric. As an essential measure under China’s national strategy for protecting black soil, gully erosion control projects rely on accurate simulation of gully density at the regional scale to enable more efficient and precise management. Taking the northeast China’s Songnen typical black soil region as the study area, this research employed a stratified unequal probability systematic sampling method to select 977 small watershed sample units. Using sub-meter resolution Google Earth imagery, gully density on farmland was visually interpreted. To ensure the interpretation’s accuracy, 55 typical small watersheds were randomly selected as validation units for field investigations. On this basis, the RF algorithm was used with 13 selected factors to predict farmland gully length density. The results showed the following: 1) The Random Forest model demonstrated high accuracy and applicability, with an NSE exceeding 0.5. Residuals primarily centered around -0.1 km/km². 2) Slope was identified as the key influencing factor for farmland gully density, followed by multi-year average May precipitation, slope length, and multi-year average rainstorm volume. Threshold analysis revealed a significant increase in gully density when slope exceeded 1.21° and slope length surpassed 74.15 m, but a weakening effect was observed when the slope and slope length reached certain thresholds. 3) The prediction results indicated higher gully densities in low mountains and hilly areas. Regions with a density range of 0–0.05 km/km², followed by 0.05–0.25 km/km². As density increased, the proportion of area gradually declined, with areas >1 km/km² accounting for no more than 15% of the total. High-density regions were concentrated in low mountains and hilly areas, with average gully densities of 1.33 km/km² and 1.80 km/km², respectively, whereas low-density regions were concentrated in plains, with densities close to 0 km/km². This study provides theoretical and technical support for regional gully management decisions in the black soil region of Northeast China, contributing to the protection of black soil resources.

• Keywords: Permanent gully; Songnen typical black soil region; Random Forest; Regional scale; Gully length density

How to cite: Liu, H., Wang, C., Chen, Y., Ma, L., Zhang, C., Long, Y., and Yang, Q.: Random forest-based prediction of gully density on farmland in the Songnen typical black soil region of the Northeast, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9463, https://doi.org/10.5194/egusphere-egu25-9463, 2025.

Soil erosion by water is a critical factor contributing to eutrophication in water bodies, acting as a significant nitrogen and phosphorus input from land. While many models predict soil erosion and sediment transport into lakes and rivers, and the connection between soil erosion triggering eutrophication is considered textbook knowledge, there is, indeed, limited scientific data-based evidence of a direct link between eutrophication and soil erosion. We assessed the impact of soil erosion on eutrophication, considering other covariates such as slope, elevation, phosphorus, nitrogen, and water temperature, by analysing buffer zones of varying sizes around lakes in six different regions of Europe:  Austria (82 lakes), France (313), Germany (294), Hungary (79), Poland (478), and the UK (320). We utilized multispectral Sentinel-2 satellite remote sensing data at 20m spatial resolution for 2021 and 2022 to estimate the Floating Algae Index (FAI) of lakes. Bloom occurrence (BO) – the frequency of detected algal blooms – and maximum bloom extent (MBE) – the total area affected by blooms during the study period were correlated with the aforementioned covariates within contributing terrestrial zones of 100m, 200m, 500m, and 1km using machine learning algorithms. Initial results indicate that soil erosion itself is a the most important driver of eutrophication for many of the selected European regions, with water temperature and elevation also playing important roles. Moreover, the significance of soil erosion varies depending on contributing terrestrial zone across different regions. This study underscores the utility of remote sensing in assessing the impact of soil erosion on eutrophication providing a data based scientific link between the two processes.

How to cite: Gupta, S., Scheper, S., Borrelli, P., Panagos, P., and Alewell, C.: Exploring the influence of soil erosion on lake eutrophication through remote sensing across Europe , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1329, https://doi.org/10.5194/egusphere-egu25-1329, 2025.

Soil erosion poses a significant global challenge, with an estimated 25–40 billion tons of soil lost annually, threatening food security, water quality, and ecological balance. In Europe alone, soil losses exceed 970 million tons annually, emphasizing the urgent need for precise modeling to assess and mitigate erosion risks. The Revised Universal Soil Loss Equation (RUSLE) serves as a widely used empirical model for quantifying soil erosion risk, incorporating key parameters such as rainfall erosivity (R), soil erodibility (K), cover and management (P and C), and the Length and Steepness factor (LS). The LS factor, which accounts for slope gradient and length effects on runoff velocity and soil detachment, is critical but often constrained by static Digital Elevation Models (DEMs) that lack the temporal and spatial resolution to capture dynamic topographical changes.  This study introduces the integration of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), a cutting-edge remote sensing technique, with high-resolution Sentinel-1 SAR data and DEMs to enhance LS factor predictions within the RUSLE framework. PSInSAR enables millimetre-scale monitoring of terrain displacement over time, identifying subtle ground deformations that influence soil stability. Applying this approach to two watersheds in Israel—Yarkon-Ayalon and HaBsor—the study refines slope length and steepness estimates with sub-centimetre vertical accuracy, addressing the limitations of conventional DEM-based methods. Time-series displacement analyses derived from Sentinel-1 SAR data (2017–2023) reveal slope deformations, with subsidence rates reaching -14.49 mm/year and uplift rates up to 4.60 mm/year. Stable areas exhibited negligible displacement trends, validating the precision of the method. These displacement trends, supported by statistically significant p-values (< 0.01), highlight the spatial variability of erosion potential and topographical changes. The enhanced LS factor significantly improves soil erosion risk assessments under diverse climatic and land-use conditions. By integrating PSInSAR with the RUSLE model, this study advances soil erosion research and supports sustainable land management and policy development in erosion-prone areas. The findings provide actionable insights for reducing erosion risks and promoting soil sustainability on a broad scale.

Keywords: Sentinel-1, Synthetic Aperture Radar, Soil erosion modelling, Interferometric SAR, SNAP, Persistant Scatterer Interferometry, Digital Elevation Model.

How to cite: Paul, A. and Paz-Kagan, T.: Unveiling Erosion Dynamics: Integrating PSInSAR into Length and Steepness Factor in RUSLE Model., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2510, https://doi.org/10.5194/egusphere-egu25-2510, 2025.

Wars are usually associated with degradation of natural resources. With the objective of quantifying the effect of the two years’ (2020-2022) Tigray war on degradation of natural resources that have been under restoration, 10 ex-battlefield (ex-BF) and 12 non-battlefield (NBF) sites were selected. The ex-BF sites were further divided into exclosures (n = 5) and farmlands (n = 5), while NBFs were divided into exclosures (n = 6) and farmlands (n = 6). Detail field observations were carried out through transect walks and farmers were interviewed (n=500). To measure the impacts of the war on land degradation, field measurements were conducted on bunds (n = 324), check dams (n = 87), war fortifications (n = 102), tree plots (n = 143), footpaths (n = 17), waterways (n = 44), and gullies (n = 85). These were verified using high resolution Google Earth Imageries as well as  Normalized Difference Vegetation Index (NDVI) data calculated from Sentinel 2 satellite images that were acquired before and after the war and through interviewing farmers (n=500). The findings reveal that the mean proportion of war-induced damaged bunds was 0.246 ± 0.185. A significant difference in bund destruction was observed between BFs and NBFs (p < 0.0001). Combatants used stones from bunds to construct war trenches up to 650 m long. In addition, 52% of the farmers perceived that the war disrupted exclosure management. Plot level analysis also shows that mean proportion of destroyed trees was 0.31 ± 0.15, with greater tree loss in BFs (46% ± 13%) compared to NBFs (19% ± 12%) (p < 0.0001). Besides, 44% of the check dams were damaged across the sites, with 78.3% of check dams in BFs classified as being in poor condition compared to 31.1% in NBFs. Moreover, the average pit volume in BFs and NBFs was 0.503 ± 0.389 m³, with mean sediment displacement in BFs (0.82 ± 0.17 m³) higher than in NBFs (p<0.006). New and reactivated gullies were also found with variable volumes, ranging from 134.3 ± 92.4 m³ in NBFs to 362.7 ± 629.4 m³ in BFs. In conclusion, the war resulted in obstruction of restoration process of the natural resources that have been undergoing in the degraded region over the last three decades. Therefore, integrated post-war rehabilitation strategies are needed to mitigate the environmental problems caused by the war.

How to cite: Asfaha, T. G., Nyssen, J., Annys, S., Gebregergis, H. M., Welemaram, Z. T., gebremeskel, E. N., and Frankl, A.: War-induced obstruction of natural resources restoration: quantitative evidences from the Tigray Region, Ethiopia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15722, https://doi.org/10.5194/egusphere-egu25-15722, 2025.

Soil structural degradation has been found to be widespread across different countries, including from a 2015/2016 winter survey across multiple catchments in Scotland. Winter is when soil structure can be most degraded, and this work found an extreme storm event exacerbated soil structure degradation over time. The previous research was limited to a simple Visual Evaluation of Soil Structure (VESS), so a repeat study in 2022/2023 at the same locations tested the hypothesis that soil structure degradation would recover over time, and that VESS would relate to quantitative soil physical data of penetration resistance, and bulk density, hydraulic conductivity and water retention characteristics from intact soil cores (2-7 cm depth). This research therefore aimed to explore the extent of soil structure degradation and its resilience over time, comparing visual and quantitative methods. Across 42 separate fields, three replicate samples were taken from in-field locations, three from degraded regions with visible damage to the soil surface from heavy traffic, and three from less disturbed field margins. From VESS scores, 59.5% of in-field and 78.5% of margin locations had a good soil structure, compared to 11.1% for degraded soils. This pattern continued for the quantitative core data. For bulk density, in-field soils were 8.3% denser, and degraded soils were 12.7% denser than margins, which was also reflected in porosity. Furthermore, organic carbon content was 10.6% less for in-field and 11.3% less for degraded compared with field margins. Much of the in-field soils had no degradation from VESS scores, but indicators of erosion and structural damage from quantitative soil core data were found. Land use also significantly impacted soil structure, with saturated hydraulic conductivity being highest for in-field soils, which is likely due to tillage practices. Grasslands presented the least degraded physical structure, with significantly greater porosity compared to stubble, ploughed soils and winter cereal cropland. Although we found VESS to be a valuable and rapid tool, data from quantitative measurements found more structural degradation, demonstrating that VESS scoring alone may not provide a holistic assessment of soil structural degradation. These results emphasise the need for improved land management practices in Scotland to maintain good soil structure and retain land productivity.

How to cite: Brook, J., Gaffney, P., Geris, J., Gilbert, P., Baggaley, N., Hall, R., Lilly, A., and Hallett, P.: Broadscale on-farm sampling suggests extensive soil physical degradation in Scotland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13066, https://doi.org/10.5194/egusphere-egu25-13066, 2025.

In semi-arid zones, wind erosion is both a consequence and a cause of soil degradation, as it particularly affects unprotected soils and can deplete them of nutrients and organic matter. Thus it represents a major concern for rural populations who depend on soil productivity for income and subsistence. During the last 60 years in the Sahel, an intense drought affected the region between the 1970s and 1980s while major socio-economic changes were (and still are) underway, leading to a profound alteration of agropastoral systems and a decline in soil fertility in some places. In this context, estimating aeolian sediment fluxes generated from cultivated plots and disentangling climate effects from anthropic ones would allow for a better understanding of the role of wind erosion in the perceived land degradation, as well as paving the way for trajectories simulation with future climate projections. 
In this study, we aim to understand and model how the combined effect of climatic and agropastoral changes affected wind erosion in Sahelian conditions for the 1960-2020 period, using the Senegalese groundnut basin as a case study. The Senegalese groundnut basin is the agricultural heartland of Senegal and home to different sociolinguistic groups whose approach to agriculture led to varying responses to the region’s conditions. Using a modeling approach relying on an extensive dataset, we simulated vegetation growth (STICS and STEP models) and estimated the horizontal flux of aeolian sediment (DPM model) resulting from several land uses and managements at the plot scale. We used ERA5 meteorological time series (ECMWF) combined with an extensive review of the literature on the dynamics of land use in the groundnut basin to develop several realistic trajectories for horizontal flux generation in 3 different parts of the groundnut basin (North, Center, South) over the last 60 years (1960-2020). On top of integrating climate variability, these trajectories take into account the use and management of different crops, as well as crop rotation systems, fallow periods, the use of mineral and organic fertilizers, trees and livestock. Average biomass production and yield found in the literature were used to verify the model's reliability.
We found that aeolian flux generation has increased overall since 1960, especially in the northern part of the groundnut basin. The most erosive period took place after 1980 when the Senegalese groundnut basin suffered from drought and the end of government subsidies for agriculture. 

How to cite: Raynal, P.-A., Rajot, J.-L., Marticorena, B., Roehrig, R., and Pierre, C.: 60 years in the Senegalese Groundnut basin : Modeling wind erosion in a changing social and climatic context, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9434, https://doi.org/10.5194/egusphere-egu25-9434, 2025.

Abstract

Like many areas along the border of the main Ethiopian Rift, the Shafe catchment is severely affected by gully erosion. Many earlier studies hypothesized that this can lead to important crop yield losses and, by extent, other socio-economic impacts. Nonetheless, as in other regions, these effects remain poorly quantified and understood. This research aims to quantify and understand these impacts, a critical step for designing effective and targeted mitigation measures. Given the challenges of accurately quantifying these impacts, a multi-method approach was employed. Household interviews were conducted with 171 randomly selected farmers, each having at least one gully-affected plot, to gather insights on perceived impacts, the allocation of uncropped buffer zones near gullies, and crop desiccation effects. Drone mapping of 85 gully-affected plots was carried out to quantify land losses due to gullies. Daily soil moisture measurements were recorded at varying distances from the gully edge (1m, 5m, 10m, 20m, 25m, and 40m). To quantify crop yield reduction, sorghum, wheat, and haricot bean samples were collected from gully-affected plots over two different seasons, with grain and biomass yields measured. Composite soil samples were also collected and analyzed to determine whether crop yield differences may be linked to soil nutrient contents. The preliminary results indicate that farmers experience direct losses of cropland, reduced crop yields due to a combination of factors, and the need for buffers around gullies as the most significant impacts of gully erosion. Soil moisture analysis indicate significant variations across depth and distance from the gully, with relatively higher moisture levels recorded at 40 m compared to closer distances, highlighting reduced moisture availability near the gully. Such variation in soil moisture corresponds to the observed crop yield trends, which increase with distance from the gully. Among the crop samples collected, sorghum showed the highest sensitivity to desiccation (from 0.1 kg/m² at 1 m to 0.47 kg/m² at 40 m). These preliminary results underline the significant impacts that gully erosion can have but also enhance our understanding for the development of more feasible, site-specific mitigation strategies.

How to cite: Mekonnen, Y., Kervyn, M., Belayneh, L., Tsegaye, G., De Bleser, J., and Vanmaercke, M.: Quantifying the impacts of gully erosion on farmers' livelihoods in the Shafe catchment, southern main Ethiopian Rift: a multi-method approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1909, https://doi.org/10.5194/egusphere-egu25-1909, 2025.

Abandoned terraced landscapes face heightened risks of soil erosion and slope instabilities, contributing to significant sediment dynamics that threaten environmental sustainability and land productivity. This study leverages the LISEM (Limburg Soil Erosion Model) to assess sediment sources, transport pathways, and soil erosion sources within these fragile terrains. By combining very high-resolution spatial data, soil profiling, and hydrological modelling, we identified major sediment-generating zones and quantified their contributions under varying rainfall scenarios.

Our analysis highlights the critical role of land use and land cover (LULC) changes in driving erosion processes, with unmanaged vegetation and abandoned terraces emerging as key contributors to sediment mobilization and slope failures. The model outputs reveal spatial variability in sediment yield and erosion intensity, pinpointing critical hotspots requiring targeted conservation measures.

These findings emphasize the importance of nature-based solutions and sustainable land management practices to mitigate erosion and sediment transport risks. The research contributes to developing adaptive strategies for stabilizing slopes and restoring abandoned terraced landscapes, offering actionable insights for global applications in similar environments.

How to cite: Niyokwiringirwa, P., Maerker, M., Lombardo, L., and Rellini, I.: Modeling Sediment Dynamics and Identifying Key Erosion Sources in Abandoned Terraced Landscapes: Insights from Land Cover Change Analysis. The case study of Vernazza Catchment, Liguria, Italy., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20723, https://doi.org/10.5194/egusphere-egu25-20723, 2025.