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.
Posters on site: Wed, 30 Apr, 14:00–15:45 | Hall X4
Sharing success stories in mitigating soil erosion is a common and effective strategy for promoting sustainable land management and fostering community engagement. These stories showcase interventions and bridge the gap between research and practice by illustrating real-world applications and inspiring broader adoption. However, while successes are celebrated and well-documented, failures often remain marginalized or hidden, despite their potential to drive critical reflection, innovation, and progress. This paper argues that embracing transparency about failures in addressing soil erosion is essential for advancing sustainable land management. Failures provide invaluable insights into the complexities and contextual challenges of soil degradation, highlighting the iterative nature of learning and the need for epistemological humility. By analysing failures alongside successes, we can refine strategies, avoid repeated mistakes, and strengthen efforts to mitigate soil erosion. The paper is structured in two parts. The first section explores key factors critical to understanding failures in soil erosion management, including (i) defining appropriate thresholds, (ii) undervaluing local populations and indigenous knowledge, (iii) neglecting scale, and (iv) overlooking processes. These discussions aim to unpack the nuanced challenges of failure, paving the way for more informed and adaptive approaches. The second section explore how failures are addressed from a systematic review of the scientific literature. Ultimately, this work underscores the importance of integrating lessons from both successes and failures to amplify the impact of investments in sustainable land management. By fostering a culture of transparency, we can build a more resilient and effective framework for addressing soil erosion in diverse contexts.
Keywords: Land degradation, Mitigation, Stakeholders, Soil Conservation
How to cite: Frankl, A.: Failing to mitigate soil erosion: a review, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5616, https://doi.org/10.5194/egusphere-egu25-5616, 2025.
Alpine regions are highly susceptible to rainfall-induced soil erosivity and to its changes in a warming climate. Thanks to their high spatio-temporal resolution and to their ability to explicitly resolve convection, recently developed convection-permitting climate models (CPMs) outperform regional models in capturing intense sub-daily rainfall. Thus, these models offer a high potential for the simulation of rainfall erosivity and its projection in a warming climate.
This study evaluates the ability of a multi-model CPM ensemble to provide reliable rainfall data for assessing rainfall erosivity. This represents a fundamental step for then analyzing the near- and far- future projected changes in rainfall erosivity under climate change scenarios. The study is carried out based on data from an transect in the Italian eastern Alps which offers an ideal case study given its rainfall variability and complex topography. Data from 174 rain gauges are used to evaluate the CPM ensemble in simulating rainfall erosivity.
Preliminary results show that the CPM multi-model ensemble is able to reproduce well the mean annual rainfall erosivity in the study region. However, the CPM simulations over predict the number of erosive events by 21% on average. Also, the quality of the CPM-based simulations is shown to be strongly impacted by terrain elevation, with simulations in low land areas being characterized by an underestimation of number of erosive events and average rainfall erosivity. The bias reverses to overestimation of both number of erosive events and average erosivity with increasing the terrain elevation. Topography also strongly influences the spread of the erosivity simulations within the model ensemble.
These findings underscore the need for bias adjustments, considering topographic influences, for the investigation of projected changes in rainfall erosivity patterns.
How to cite: Ahmed, M., Dallan, E., Vohnicky, P., and Borga, M.: Assessment of rainfall erosivity from a convection-permitting model ensemble in an alpine transect, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10462, https://doi.org/10.5194/egusphere-egu25-10462, 2025.
Human activities, including deforestation, agriculture, mining, and dam construction, significantly influence erosion and river sediment flux. However, few data exist that constrain how river sediment flux have changed compared to natural conditions. Here we compare natural erosion estimates from millennial time-scale cosmogenic nuclide measurements with sediment yields from sediment gauging and river bedload modelling to study the magnitude and driving factors of anthropogenic erosion change in the Northern Andes of Colombia.
We calculated suspended sediment yields for 139 small to medium sized rivers (10-10’000 km²) in the Northern Andes by fitting rating curves to sediment concentration and discharge measurements. Additionally, we use an empirically calibrated model to account for bedload sediment flux in these mountainous catchments and calculate the total sediment flux for time periods of 1980 to 2000 and 2000 to 2022. We convert our sediment flux to erosion rates under anthropogenic conditions and compare them to millennial time-scale natural erosion rates estimated from cosmogenic nuclide data.
Our findings reveal that river sediment flux was, on average, 78% higher than natural conditions from 1980 to 2000, and increased to 111% above baseline between 2000 and 2022, primarily due to increases in the Central Cordillera. Factors such as agriculture, rainfall erosivity, mining, and deforestation are correlated with increased erosion and sediment flux. Interestingly, the variance in sediment yield also increases with the percentage of agricultural land and rainfall erosivity. On average current river sediment yields match RUSLE soil erosion estimates, suggesting high sediment connectivity and negligible storage of eroded soils in the mountainous catchments. Our data document a doubling of sediment flux in the Northern Andes due to the joint effects of agriculture, mining, and deforestation, however, the erosional response to land use change varies with environmental conditions such as rainfall erosivity.
How to cite: Ott, R., Perez-Consuegra, N., and Restrepo Lopez, J. C.: High-Resolution Mapping of Anthropogenic Impacts on Sediment Flux in the Northern Andes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8430, https://doi.org/10.5194/egusphere-egu25-8430, 2025.
Land degradation (LD), which affects soil, vegetation, and water resources, poses serious threats to agricultural productivity, biodiversity, and ecosystem functions. Here, we present a new approach to assess LD at the farm level, specifically within Mediterranean olive orchards, leveraging the modified Land Multidegradation Index (LMI) (Prăvălie et al., 2024) which builds on the framework established by the European Union Soil Observatory (EUSO) (Panagos et al. 2024). The approach identifies and quantifies multiple LD pathways, including soil erosion, salinity, compaction, organic matter depletion, and pollution. Field data from 53 olive orchard sites across Greece, Italy, Portugal, Spain and Marocco, as part of the Horizon Europe project SOIL O-LIVE, informed the methodological development. Indicators of LD were integrated into a scoring system, capturing the extent and interplay of LD processes. Preliminary findings are presented alongside a Shiny App web viewer developed by the Environmental Modeling and Global Change Lab (BorrelliLAB) of Roma Tre University to investigate spatial patterns of LD and land management across the Mediterranean Europe.
Acknowledgement: P.B, K.K, F.M. were funded by the European Union Horizon Europe Project Soil O-LIVE (Grant No. 101091255). P.S. was funded by the European Union Horizon Europe Project AI4SoilHealth (Grant No. 101086179).
References
Panagos, P., Borrelli, P., Jones, A., & Robinson, D. A. (2024). A 1 billion euro mission: A Soil Deal for Europe. European Journal of Soil Science, 75(1), e13466.
Prăvălie, R., Borrelli, P., Panagos, P., Ballabio, C., Lugato, E., Chappell, A., ... & Birsan, M. V. (2024). A unifying modelling of multiple land degradation pathways in Europe. Nature Communications, 15(1), 3862.
How to cite: Borrelli, P., Alewell, C., Kaffas, K., Matthews, F., Panagos, P., and Saggau, P.: Investigating spatial patterns of land degradation and land management in olive orchards across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11266, https://doi.org/10.5194/egusphere-egu25-11266, 2025.
The study presented in this communication evaluates the evolution of gully erosion over seven decades in the western Campiña of the Guadalquivir River Basin in southern Spain. Gully erosion, a major driver of soil degradation, seems to be intensified in this region in last years. The research utilizes photointerpretation techniques to track the development of gullies from 1956 to 2022, correlating this with rainfall data, land use changes, and the hydrological responses of watersheds. Nine gully systems were selected, and their areas were digitized from historical orthophotos, with volumetric erosion estimates made for select periods using available digital elevation models (DEMs).
The results show that gully development accelerated in response to a combination of factors, particularly the shift from herbaceous crops to olive groves. As olive cultivation expanded and ground cover was reduced, soil became more susceptible to erosion. This was especially evident after high-intensity rainfall events, such as those between 2009 and 2011, which caused significant gully growth. At the end of the study period, approximately 6% of the first-order catchments analyzed were occupied by gullies.
The average erosion rates recorded in this study (47 tons per hectare per year) were consistent with those reported in other Mediterranean regions, and showed a pronounced upward trend, with peak erosion rates reaching 282 tons per hectare per year during the 2008-2010 period. This increase is attributed to more frequent extreme rainfall events, as well as changes in land use. Furthermore, a comparison of gully morphology with global data suggests that the gullies in this region are shallower for a given width, possibly due to the low stability of the soils to lateral collapse.
A key finding is the role of human interventions in gully systems, such as partial filling, contour reshaping, and land leveling, which frequently modified gully erosion rates. These activities result in the underestimation of the volume eroded by up to 28%, with some particular cases showing deviations as high as 393%. This highlights the need for adjusting the temporal scale of monitoring gully erosion to capture relevant interventions, or alternatively inform gully erosion rates with detailed information on the land management between study periods.
The study concludes by recommending that the design of hydraulic structures for gully control be reevaluated in light of the increasing intensity and frequency of rainfall events with high return periods. Additionally, sustainable land use practices should be implemented from the outset to mitigate gully formation, for example, at the time of establishing a new crop or when making a change in land use. Finally, an empirical expression between contributing area and peak flow for different return periods is stablished to facilitate the implementation of control measures in a region.
Acknowledgements:
This work has been possible thanks to the contribution of the Postdoctoral fellowship (POSTDOC_21_00342) from the Andalusian Plan for Research, Development and Innovation (PAIDI 2020) and the project TUdi (GA 101000224) from European Union's Horizon 2020 research program.
How to cite: Hayas, A. and Gómez, J. A.: Analyzing gully evolution in a rolling landscape over seven decades in SW Europe. Lessons for the future, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-436, https://doi.org/10.5194/egusphere-egu25-436, 2025.
Assessing erosion risk is essential for implementing effective soil and water conservation (SWC) measures, a task especially challenging in data-scarce, semi-arid regions of India. This study addresses these complexities by employing a comprehensive prioritization approach to enhance erosion management efficiency in the Devgadh Baria Watershed (DBW) in Gujarat, India. The primary goal is to systematically prioritize sub-watersheds (SWs) using geomorphometric and land use/land cover (LULC) analyses, followed by recommendations for targeted SWC interventions in high-priority areas. Through remote sensing (RS) and geographical information system (GIS) techniques, the study delineates SWs and evaluates their vulnerability based on seven key morphometric parameters and LULC classifications, including agricultural land, forest, wasteland, and built-up areas. By integrating these parameters, the analysis yields compound values for each of the 30 SWs, resulting in a refined prioritization ranking. Notably, SW26, initially ranked as very high priority due to steep slopes and low drainage density, shifted to medium priority in the combined analysis, highlighting effective agricultural practices that reduce erosion. Meanwhile, SW7 maintained its very high priority ranking across analyses, reflecting persistent erosion risk from extensive built-up areas and limited forest cover. SW30 moved from high to medium priority, influenced by balanced agricultural activities and gentler slopes, while SWs 6 and 24 dropped from very high to medium priority. SW22 remained a high priority, benefiting from moderate forest cover and soil types that mitigate erosion. This research emphasizes the scientific value of integrating morphometric and LULC analyses for accurate SW prioritization. The combined approach enhances erosion risk assessment, facilitating targeted SWC strategies vital for watershed management in semi-arid regions. These findings offer actionable insights that support global sustainability goals, contributing to improved soil conservation and water resource management.
How to cite: Koradia, A., Patel, J. N., Yadav, B., and Rana, P.: Can Remote Sensing-Based Geomorphometric Analysis Combined with LULC Provide Greater Insights for Prioritizing Soil and Water Conservation Measures in Data-Scarce Semi-Arid Regions?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-81, https://doi.org/10.5194/egusphere-egu25-81, 2025.
One of the major environmental problems of our time is soil erosion, which is a natural process reducing the fertility of soils and leading to land degradation. Soil erosion mapping helps to develop different control methods and is essential for sustainable agricultural practices. Among the modern remote sensing technologies, UAV laser scanning (ULS) is gaining importance as an accurate and efficient survey solution and can be applied in vegetated areas. It generates point clouds (PC) mapped in 3D coordinates during the survey, allowing the creation of detailed digital terrain models (DTM) which allow identification and mapping of soil runoff traces, rills and gullies.
In our research, we were conducting a monitoring study on a 10 ha vineyard in the Balaton Highlands, where three different mulching and row-cropping techniques (disking, natural cover and sown grass) were applied between the rows of vines, so that we could also investigate their impact on erosion.
The area was surveyed five times between October 2023 and December 2024 using UAV L1 LiDAR on board a DJI M350 drone to collect 3D PCs. A temporary GNSS base station was used in the northern part of the vineyard, placed in the same location for each survey. It acted as a local base station, providing RTK correction for the position calculation of the M350 drone and L1 sensor with centimetre-level accuracy. The raw PCs were later pre-processed using the R package lidR to filter for ground points using the Progressive Morphological Filter with window sizes of 1 and 3 m and distance thresholds of 0.1 and 0.3 m. The PCs representing the ground were compared for differences using the M3C2 module. The plugin allows users to select core points, adjust normal and projection scales, and calculate distances using precision maps. For the latter parameter, a constant value of 5cm was set for all PCs, representing the average measurement error of L1 LiDAR for 3D coordinates.
The results showed that rill erosion is present in all areas due to the direction of the rows planted parallel to the general slope of the vineyard, but is most dominant in areas cultivated by disking without cover crops. Surprisingly, the most severe rill erosion (~10 cm) was found along a small ditch created in a row using grass as a natural cover after the winter and spring period in 2024. We also detected a newly formed, early-stage gully in the south-western corner of the bare soil section, which channels the moving soil and sediment into a depot in this corner. Analysis of the robustness of the calculated distances showed that even low vegetation, such as grass, had a negative effect on the LiDAR measurements and PC quality, which (together with the average 3D coordinate accuracy of 5 cm) rendered the distances insignificant.
Despite its current drawbacks, the method is useful for detecting changes and formation of erosion features, and will later be tested against empirical soil erosion models to investigate similar patterns of erosion and also volumetric changes.
How to cite: Takáts, T., Zsigmond, T., Horel, Á., and Mészáros, J.: Application of 3D point clouds derived from repeated UAV LiDAR surveys for erosion mapping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15207, https://doi.org/10.5194/egusphere-egu25-15207, 2025.
Rainfall erosivity is a critical parameter for soil degradation assessment. It is especially important in areas prone to soil erosion due to frequent or intense rainfall events. Rainfall erosivity is typically quantified through the EI30 index, which combines the rainfall's kinetic energy (E) with its maximum 30-minute intensity (I30). Rainfall data with high temporal resolution (e.g., 5-minute) are often limited in availability, particularly across large spatial scales. Gridded rainfall products usually provide half-hourly or hourly data and are frequently used despite the potential biases they introduce into erosivity calculations. In Austria, the introduction of INCA (Integrated Nowcasting through Comprehensive Analysis) has provided an opportunity to enhance rainfall erosivity studies. INCA offers rainfall data at a 15-minute temporal and 1-kilometer spatial resolution. In our study, INCA’s accuracy and limitations in capturing erosive rainfall events, especially extreme events, are carefully evaluated against high-resolution in-situ rainfall station data. Understanding the degree of underestimation or overestimation in EI30 calculations is crucial for applying INCA data in erosion simulations and according to soil conservation strategies. This study focuses on the Main Agricultural Production Zones (MAPZ) in Austria. The study evaluates the performance of INCA in calculating erosivity compared to a dense network of 5-minute resolution rainfall stations. It also investigates the occurrence probability of extreme erosive events using a probabilistic approach, providing insights into long-term erosion risks and regional differences in erosivity patterns. Eventually, the study examines the impact of temporal resolution on erosivity estimates, assessing biases introduced by coarser resolutions (15- and 30-minute) for both mean and extreme rainfall events. Results indicate that INCA overestimates 8.1% of the total event number but underestimates 3.1% of the total rainfall erosivity, with the largest under- and overestimation in east of Austria. On the other hand, the mean annual maximum EI30 was underestimated by 13.6%, and the south showed the most considerable underestimation. It was found that INCA can detect highly erosive events occurring in the in-situ datasets. The underestimation of EI30 sources from the temporally smoothened peak I30 rather than the E of the rainfall events. Long-term extreme EI30 were analyzed using the Generalized Extreme Value (GEV) distribution, revealing that EI30 values increase with longer return periods (e.g., 50 years) and that the southern region exhibits the largest EI30 values, indicating a greater risk of extreme erosive events. On the other hand, INCA may emphasize more recent, potentially intense rainfall trends, leading to larger return levels. The impact assessment by coarser temporal resolutions on EI30 confirms that the underestimation substantially increases with lower temporal resolution, primarily due to I30 rather than E. Eventually, a 15-minute temporal resolution dataset may lead to acceptable underestimations across our investigated MAPZ; underestimations ranged from 2.8–8.5% in event numbers and 1.0–10.0% in total rainfall erosivity at 15-minute resolution and from 10.2–33.7% and 5.2–26.6%, respectively, at a 30-minute resolution. The results of this study highlight the potential value of INCA data as a practical source for rainfall erosivity assessments, particularly in regions with limited high-resolution rainfall measurements.
How to cite: Vasquez, C., Klik, A., Stumpp, C., Strauss, P., Laaha, G., Pistotnik, G., Yin, S., Dostal, T., and Strohmeier, S.: Ability of Austria’s high spatiotemporal resolution of INCA for rainfall erosivity assessment in the main agricultural production zones, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7246, https://doi.org/10.5194/egusphere-egu25-7246, 2025.
Most of the mountainous regions in subtropical monsoon-dominated climatic zone experiences considerable soil erosion and related land degradation due to natural factors. Soil erosion by water is frequently regarded as the most severe form of land degradation, with substantial environmental and economic consequences, which is exacerbated by human-caused activities and has an impact on agricultural production, water resource sustainability, and ecological conservation.Mahur watershed, located in Dima Hasao District of Assam in the eastern Himalaya region, is highly vulnerable to erosionand associated geomorphic hazards due to high rainfall, young and highly erodible rock formations, fragmented reshaping geomorphology, and high soil erodibility impacting both land stability and local communities specially during monsoon season. Despite this, anthropogenic activities such as growing unplanned development, silt extraction from rivers, and shifting cultivation techniques have increased the geo-environmental sensitivity to erosion hazards in Mahur Watershed. By using Remote Sensing and GIS tools, this paper tries to understand the soil erosion characteristics, identify high-risk areas and evaluate geomorphic hazards in Mahur watershed of Dima Hasao district of Assam.
Keywords: Geospatial analysis, Soil erosion, Geomorphic Hazards Mahur watershed, North East India.
How to cite: Haflongbar, D. and Singh Rawat, M.: Geospatial Analysis of Soil Erosion and Associated Geomorphic Hazards in Mahur Watershed, Dima Hasao District of Assam, North-East India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-606, https://doi.org/10.5194/egusphere-egu25-606, 2025.
China’s croplands are facing serious threats of soil erosion, calling for long-term and spatially-explicit assessment to ensure food security and sustainable agricultural development. However, the complexities of topographical, meteorological, and land cover and management factors, which are key determinants of soil erosion, pose big challenges for estimating cropland-related soil erosion in China over an extended time span, especially across diverse agricultural regions and different crop types.
To address this issue, this study combines the Revised Universal Soil Loss Equation model with high-resolution remote sensing datasets to investigate the temporal-spatial evolution of crop-specific soil erosion in China from 1980 to 2018 at a 30 m resolution. When calculating the RUSLE factors, in addition to using well-established methods that have been validated in national and global studies to calculate the crop-specific land cover and management factors for croplands, efforts were made to localize the rainfall erosivity factor and the support practice factor. The rainfall erosivity factor was calculated using a rainfall erosivity model derived from daily rainfall data observed at China’s meteorological stations, coupled with a more accessible global daily rainfall raster dataset. Furthermore, the support practice factor for croplands was categorized based on slope, which could reflect the relationship between cropland topographical characteristics and the benefits of soil and water conservation.
The results show that about 60% of China’s croplands experienced slight erosion over the past four decades. Regions with strong and severe erosion intensity are predominantly located in the southern provinces. However, due to the agricultural policy implementations, agricultural shifts, and variations in crop planting patterns, it was revealed that soil erosion intensity in most regions has shown a downward trend. Specifically, in terms of agricultural zoning, the cropland soil erosion rate in the Sichuan Basin has decreased sharply. Moreover, different crop types exhibited differentiated spatial patterns in the cropland-related soil erosion rates. Overall, grains exhibit the highest erosion intensity, while fiber crops have the lowest.
In summary, this study constructed a high-resolution, long-term dataset of cropland soil erosion in China and analyzed its temporal-spatial dynamics and influencing factors. The outcomes can help facilitate a more comprehensive understanding of soil erosion mitigation and provide a solid foundation for sustainable agricultural production.
How to cite: Xie, Y., Zhang, T., Zhang, Z., and Wu, X.: Spatial-temporal pattern of cropland soil erosion in China over the past four decades, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3955, https://doi.org/10.5194/egusphere-egu25-3955, 2025.
Furrow irrigation is one of the most common irrigation methods across the globe but also contributes to significant sediment discharge to downstream surface waters. Accurate furrow erosion prediction tools are needed but no modeling approach has been widely adopted. In this study, we compare two process-base modeling approaches for furrow irrigation erosion: 1) the transport capacity Tc concept and 2) a semi-empirical approach in which furrow erodibility exponential decreases with length. A dynamic soil erodibility modeling approach whereby soil erodibility is allowed to decrease as erosion progressed was also tested to improve erosion prediction performance. Performance assessment demonstrated the strength of the Tc model when combined with the dynamic soil erodibility approach. The study also highlighted weaknesses of the Tc model in accounting for observed deposition patterns. The proposed process-based furrow erosion functions can be directly coupled with furrow flow routing models or other hillslope erosion models.
How to cite: Nouwakpo, K., Bjorneberg, D., and King, B.: Modeling furrow irrigation-induced erosion using a process-based approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4472, https://doi.org/10.5194/egusphere-egu25-4472, 2025.
Soil degradation threatens ecosystem stability and global food security by undermining soil health and functionality. Certain soil degradation processes can be further intensified under changing climate and with land use alterations. Here we combine projections from 18 global climate models under two emission scenarios (SSP2-4.5 and SSP5-8.5) with land use fractions from the Land Use Harmonization (LUH2) dataset, to assess future soil vulnerability to degradation across Europe. Utilizing a machine learning framework, we linked the Soil Vulnerability Index (SVI), to topography, soil texture, climate, and land use factors. Our SVI projections for the near future (2031–2060) and far future (2071–2100) show that northern European countries, such as Estonia and Latvia will experience increments in SVI by up to 16% driven by climate factors. Conversely, southern countries such as Spain and Italy may experience declines in SVI, reflecting potential improvements in soil health conditions associated with land use changes. Moreover, our results show that land use changes in arid zones may lower SVI for 45% of observations under SSP2-4.5 scenario. Meanwhile, in colder regions, change in climate factors heightens SVI in 55% of observations under SSP5-8.5 scenario. Our findings highlight the need for targeted soil management strategies that address both land cover management and climate change adaptation to mitigate negative impacts on soil health under future climate scenarios.
How to cite: Afshar, M. H., Hassani, A., Borrelli, P., Panagos, P., Robinson, D. A., Or, D., and Shokri, N.: Effects of the projected changes in land use and climate on soil vulnerability in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6131, https://doi.org/10.5194/egusphere-egu25-6131, 2025.
Land degradation is a critical environmental issue that poses significant threats to ecosystem stability, especially in semi-arid regions, which are highly susceptible to erosion. Addressing this challenge necessitates innovative technologies for accurate and efficient prediction. This study leverages the Revised Universal Soil Loss Equation (RUSLE) framework, remote sensing, and soil analyses to identify factors driving soil erosion. By integrating these methodologies with machine learning, the research offers a novel approach for precise, real-time monitoring and detection of soil degradation.
The study characterises each RUSLE factor encompassing soil physicochemical properties, rainfall intensity, soil type, land cover, and topography to estimate soil loss and identify erosion-prone areas. Two complementary data sources were utilised: digital data, comprising time-series satellite imagery (LANDSAT, DEM, CHIRPS) processed through Google Earth Engine, Earth Explorer, and ArcGIS to generate RUSLE factor maps spanning 1987 to 2023; and field data, consisting of soil samples collected from various locations to validate digital results and calculate average soil loss across the study area.
Results indicate that the average annual soil loss during this period is approximately 20.65 tons/ha/year, significantly higher than findings from comparable studies. By combining field and digital datasets using the Random Forest model, a predictive map was developed to highlight erosion-prone areas, providing detailed visualisations of spatial erosion patterns across the region. The analysis further identifies the region's geographic characteristics and irregular, extreme climatic conditions as primary drivers of soil erosion. These findings underscore the critical role of advanced data integration and machine learning techniques in developing effective strategies for soil degradation management.
How to cite: Kelkouli, N., Bouacha, M. I., Tarquis Alfonso, A. M., and Maatoug, M.: Integrating RUSLE, Remote Sensing, and Machine Learning for Precise Soil Erosion Assessment in Semi-Arid Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9289, https://doi.org/10.5194/egusphere-egu25-9289, 2025.
Viticulture in North Italy is a very important sector for the local and global economy because it generates a positive impact on cultural and natural landscapes. Traditional vineyard management has been preserved over the years, creating a perfect environment for growing vines also in steep and very steep slope areas.
Climate change emphasizes intense and localized rainfall, which increases the risk of erosive processes in steep slope areas, making here the vineyard farm much delicate.
The study, carried out within the scope of the Italian research program AGRITECH (Spoke 4), develops an erosion index called Potential Erosion Index (PEI), using stations and satellite data from Google Earth Engine for Veneto and Friuli-Venezia Giulia regions. PEI estimates 5 levels of climate-topographical aggressiveness against the soil. Therefore, an application of a USLE-type tool was studied to identify areas where precise field verification is needed for protective practices, creating a map on the degree of protection requirement (Pr). Future climate models from CHELSA dataset were implemented to detect possible future trends in soil erosion and soil protection requirement.
The results indicate that, under the SSP585 scenario, the PEI values give rise to a potential worsening of soil erosion in the northern part of the region and to an improvement in the southern part during the period 2071-2100. The present condition of Pr shows areas that have a Pr > 50% and 75% affecting 30% and 13% of the total area, respectively. Part of these areas are also localized in vineyard farms. Future projections indicate also an increase of the Pr in the 2041-2070 period, less visible in the 2071-2100 period.
The findings of this study highlight the importance of an upgradeable investigation on the future trends of soil erosions in steep agricultural land. In fact, their quantification can be supportive, particularly in a variable climatic scenario, for drafting management guidelines and planning policies in cooperation with stakeholders.
How to cite: Baldan, V., Tarolli, P., and D'Agostino, V.: Assessing soil erosion risk and soil protection requirement in North Italy’s vineyards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10727, https://doi.org/10.5194/egusphere-egu25-10727, 2025.
Overland flow processes possess key importance for flood generation, erosion and sediment redistribution. Hortonian overland flow occurs often during flash flood events in sub-humid and semi-arid regions in response to high intensity convective rainstorms. The recent increase of these events aggravated by climate change, as well as impervious surface expansion and associated increase in runoff generation, highlights the need for a deeper understanding of the mass, momentum and energy balances of overland flow to predict, prevent and mitigate the response processes such as flooding and erosion.
As erosion is driven by the physical work flowing water performs on the land surface, this study investigates Hortonian overland flow formation from an energetic perspective. Key emphasis is on the partitioning of overland flow and its kinetic energy between sheet and rill domains. Rill formation requires an accumulation of overland flow to provide the necessary shear stress, while rills speed up overland flow velocities due to an enlarged hydraulic radius, which in return increases the shear stress. Due to this positive feedback, we hypothesize that hillslope scale rill networks evolve towards a steady-state configuration, which will result in equal partitioning of the kinetic energy flux into sheet and rill flow. The latter would imply that erosion is neither detachment nor transport limited.
We specifically revisited the hillslope-scale rainfall-runoff experiments of Gerlinger in the Weiherbach catchment located in the Kraichgau region, analyzing in a first step the interplay of surface runoff volume, flow velocity and erosion processes. Secondly, we calibrated the physically based numerical model CATFLOW-SED to reproduce overland flow hydrographs and the observed splitting into sheet and rill flow components using the “open book” approach. In a third step, we determined the spatial distribution of potential and kinetic energy within both domains and their relation to flow accumulation in the rills and surface roughness.
Our findings revealed that overland flow formation correlates positively with antecedent soil water content and negatively with surface roughness. A greater surface roughness promotes increased flow accumulation into the rill domain leading to a reduced particle detachment compared to the sheet domain. The simulation results indicate further that the partitioning of overland flow into both domains was generally well matched.
The analysis of the steady-state spatial energy patterns revealed, furthermore, a local maximum in total potential energy, separating the upslope laminar flow regime from downslope turbulent flow regime, where rills emerge. Moreover, higher roughness values corresponded to a stronger flow accumulation into the more energy efficient rill domain. While sheet domain accounted for the greater portion of potential energy along the hillslope, experiments associated with higher flow accumulation coefficients showed near-equal to equal kinetic energy for both domains at the foot of the hillslope.
To conclude, our study highlights the critical role of soil physical properties and flow characteristics on overland flow formation and erosion processes. The results indicate that emergence of rills suggest a steady-state energy-efficient configuration that balances erosion and transport dynamics.
How to cite: Yahşi, D. and Zehe, E.: Modelling overland flow and its partitioning into sheet and rill domains: An energetic perspective on the dynamics of Hortonian overland flow and erosion processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12710, https://doi.org/10.5194/egusphere-egu25-12710, 2025.
Soil erosion is a critical environmental issue in Mediterranean regions, caused by climate change and unsustainable farming practices. The combination of intense rainfall, extended drought periods, and conventional farming techniques significantly degrades topsoil, creating risks for soil fertility and crop productivity. This study aims to assess soil erosion risks in hilly olive orchards using cutting-edge technologies -drone-based digital elevation models (DEMs) and surface runoff monitoring- to quantify soil loss with high precision.
In the first phase of our research, we focused on direct measurements of soil loss collected from an experimental setup. An experimental area allowed for the collection of sediment samples after rainfall events, which were analyzed to estimate soil loss. Simultaneously, we employed an advanced drone equipped with a LiDAR camera (DJI MATRICE 350 RTK) to create high-resolution DEMs before and after rainfall events. The Difference of DEMs (DoD) methodology was used to calculate soil displacement, with millimeter-level accuracy. These values were then validated using direct soil loss measurements from the experimental runoff collection system.
The second phase of our study explored the use of a more affordable, commercial drone (DJI Phantom 4) for soil erosion analysis. This drone, equipped with an RGB camera, offers a cost-effective alternative but requires orchards with low density vegetation. A newly planted 0.2-hectare olive orchard with low tree density provided an ideal test site. Preliminary findings indicate that tractor operations such as tillage and plowing in combination with intense rainfall events caused the displacement of approximately 80 m³ of soil. As we continue to monitor the site, we are awaiting the end of the rainy season in late spring to use the drone again and estimate additional erosion caused by rainfall events.
By combining both high-resolution LiDAR-based DEM analysis and direct measurements of soil loss, this study demonstrates the potential of drone technology -both advanced and commercial- to accurately assess soil erosion. These methodologies offer valuable insights into the effects of different land management practices and can inform sustainable farming strategies in Mediterranean olive orchards facing the challenges of climate change.
How to cite: Pantazis, C., Nastos, P., Solomos, S., Fountoulakis, I., Konsolaki, A., and Zerefos, C.: High-Resolution Soil Erosion Assessment in Mediterranean Olive Orchards Using Drone-Based Digital Elevation Models and Surface Runoff Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12941, https://doi.org/10.5194/egusphere-egu25-12941, 2025.
How precipitation recharges the soil water is essential for managing water resources of semiarid rangeland ecosystems. This research assessed the soil water content (SWC) dynamics at soil depths of 0.05 m, 0.25 m, 0.5 m, and 1 m over an annual cycle under four vegetation types—shrubs, tussock grass, meadow, and dwarf heath shrub. In addition, the specific conductivity was measured at each location and used as a tracer in a simple two-component mixing model.
The time series analysis indicates that the SWC response to precipitation differs in different land covers. Shrubs exhibited higher SWC at shallow depths, while tussock grass showed lower SWC compared to other vegetation types in the upper layers. Instead, meadow and dwarf heath shrubs had a similar temporal variability and amplitude which dampened towards deeper soil layers. The temporal variability of new water varied among the different vegetation types. Shallow soil layers in the meadow had a high temporal variable but where stable in deeper layers.
These findings provide insights into the connectivity and interplay between the atmosphere, vegetation and different soil layers. These first findings offering a better understanding of ecohydrological processes at locations with different landcover and will help to improve our understanding of rainfall-runoff processes at catchment scale to develop water and landscape management strategies for these delicate semiarid ecosystems.
Acknowledgements: P. H-F thanks SIA 85220121 and FONDECYT N°1240314 for funding. While, R. M-A and S. R-S thank FONDEF ID22I10139.
How to cite: Hervé-Fernández, P., Fischer, B., Salinas, C., Muñoz-Arriagada, R., and Radic-Schilling, S.: Contribution of event water to soil water content using specific conductivity as a tracer under different land covers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14117, https://doi.org/10.5194/egusphere-egu25-14117, 2025.
The sedimentation issue in Taiwan's reservoirs has been a longstanding problem, significantly diminishing the operational lifespan of these reservoirs and impacting the water supply to the northern regions. To project future sedimentation trends at Shimen Reservoir, this study utilizes numerical simulations to estimate sediment yield, taking into account both landslides and soil erosion. Aerial photography and depth-area relationships were employed alongside a landslide risk assessment of the upstream Sqzyacing watershed to evaluate landslide-derived sediment. Furthermore, this research implemented the Universal Soil Loss Equation (USLE), integrating future rainfall projections from the Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP) and streamflow data from the past decade to calculate soil erosion rates. Sediment dynamics within the Sqzyacing watershed were modeled using the SRH-2D hydrodynamic simulation, with parameters such as flow velocity, average slope, and watershed area aiding in the sediment yield estimates from individual sub-watersheds. Accompanying these calculations, sediment yields for Shimen Reservoir under various flow conditions, including Q5, Q10, Q25, Q50, and Q100, were estimated.
How to cite: Liu, S. J. and Liao, K.-W.: Numerical Simulation of Sediment Yield and Reservoir Sedimentation Under Varied Hydrological Conditions: A Case Study of Shimen Reservoir, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14279, https://doi.org/10.5194/egusphere-egu25-14279, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 3
EGU25-2178 | ECS | Posters virtual | VPS14
Assessment of Water Erosion in the Semi-Arid Oued Beht WatershedUsing Satellite Data and Comparative Modeling ApproachesTue, 29 Apr, 14:00–15:45 (CEST) | vP3.1
Water accumulation is a critical challenge in arid and semi-arid regions, significantly degrading soil quality and threatening land sustainability. This study focuses on the Oued Beht watershed in Morocco, covering 6,200 km², representative of semi-arid geographical conditions. Using satellitebased Earth observation data, including Landsat 9 and SRTM, this research assesses water erosion by comparing two models: PAP/CAR, a qualitative approach, and RUSLE, a quantitative model.
Key datasets, such as NDVI, slope, and land use, were extracted from satellite imagery to calibrate and validate the models. For the RUSLE model, the rainfall erosivity factor (R) was estimated using two distinct methods. The first applies the formula developed by Renard and Freimund (1994), which links annual precipitation to erosivity. The second employs a modified formula by Rango and Arnoldus (1987), adapted to Moroccan conditions, using monthly and annual precipitation to estimate erosivity.
Rainfall data covering 65 years (1958–2023), obtained from 23 meteorological stations, were utilized to ensure robust and reliable analysis. By comparing the performance of these two RUSLE methods with the PAP/CAR model, this study aims to determine their respective effectiveness in
evaluating erosion risks.
The findings contribute to advancing knowledge on erosion processes, offering valuable insights for sustainable land management practices and mitigating land degradation in semi-arid environments. This research underscores the critical role of satellite data and modeling in
addressing natural hazards, aligning closely with the conference’s focus on leveraging Earth observation technologies for risk assessment and management.
How to cite: moutaoikil, N., Benzougagh, B., Mastere, M., Bounouira, H., El Fellah, B., Ouallali, A., and Lamrani, H.: Assessment of Water Erosion in the Semi-Arid Oued Beht WatershedUsing Satellite Data and Comparative Modeling Approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2178, https://doi.org/10.5194/egusphere-egu25-2178, 2025.
EGU25-9666 | ECS | Posters virtual | VPS14
Topographic Characteristics of River Embankment Damage and Soil and Water Conservation Benefits Under Extreme Rainfall ConditionsTue, 29 Apr, 14:00–15:45 (CEST) | vP3.2
In recent years, the frequency of extreme rainfall events has significantly increased worldwide, posing severe challenges to river embankments and other soil and water conservation measures. This study focused on the core disaster area of the "July 29, 2023 extreme rainfall" event—the Beizhi River Basin in Lincheng County, China. Using GIS technology, the study analyzed the damage patterns of embankments with different construction standards, the critical topographic conditions, and their protective benefits for land under extreme rainfall conditions. The results showed that: 1) River embankment damage was severe, with the affected areas primarily located in the middle reaches of the river. The overall damage proportion was significant, and embankments built to higher standards suffered less damage than those built to lower standards, indicating greater stability. 2) The damage characteristics of embankments were influenced by a combination of river slope and catchment area. The developed S-A topographic critical model indicated that high-standard embankments required higher critical topographic conditions to sustain damage, demonstrating their ability to maintain structural integrity under harsher conditions. 3) Embankments had significant soil and water conservation benefits. Compared to segments without embankments, areas with embankments experienced significantly less land damage. High-standard embankments exhibited greater efficiency in protecting land compared to low-standard embankments. This study could make an important contribution to the theory of river soil and water conservation under the backdrop of increasing extreme rainfall events due to climate change. It may provide valuable guidance for improving embankment design standards and optimizing soil and water conservation measures.
Keywords: Extreme rainfall; embankment damage; topographic critical conditions; soil and water conservation benefits
How to cite: Sun, Z., Wang, C., Shen, H., and Wang, Q.: Topographic Characteristics of River Embankment Damage and Soil and Water Conservation Benefits Under Extreme Rainfall Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9666, https://doi.org/10.5194/egusphere-egu25-9666, 2025.
EGU25-14166 | Posters virtual | VPS14
Nested Catchment Delineation at the European Scale: A Tool for Fine-Scale Environmental AnalysisTue, 29 Apr, 14:00–15:45 (CEST) | vP3.3
The delineation of hydrological catchments and river networks is fundamental for hydrographic and hydrological information, environmental analysis, modeling, and decision-making. However, many existing datasets are limited in their spatial resolution, which can constrain their ability to accurately represent localized processes such as floodplain dynamics and soil erosion patterns. Building on the concepts of the new vector-based global river network dataset by Lin et al. (2021), Catchment Characterisation and Modelling (CCM) by the Joint Research Centre (JRC) (Vogt et al., 2003), as well as HydroSHEDS by the World Wildlife Fund US (Lehner and Grill, 2013), we aim to introduce a finer spatial scale that captures regional nuances and enhances hydrological detail. Using high-resolution digital elevation data, this study applies a hierarchical coding system to delineate nested catchments across Europe, achieving basin sizes reduced to a fine scale. The methodology ensures the accurate representation of catchments and associated river networks, with a focus on maintaining hydrological connectivity.
This delineation approach allows for the creation of a comprehensive geospatial dataset that integrates detailed catchment and river attributes. Our work complements existing large-scale datasets, providing critical insights for regional and local hydrological and environmental applications. The product/dataset will support environmental analysis by enabling the calculation of catchment-scale statistics for a wide range of environmental, soil, and land degradation parameters, including soil properties, soil erosion and land degradation, hydrological factors, ecological indicators, land use and land cover characteristics across Europe.
By generating a high-resolution, hierarchically nested dataset, this project addresses various environmental challenges at both regional and European scales, while meeting the increasing demand for spatially detailed environmental data that covers specific regional needs. The resulting data will support applications in land management, soil conservation, and environmental policy, providing a robust framework for both scientific research and practical implementation.
Acknowledgement: K.K, F.M., P.B, were funded by the European Union Horizon Europe Project Soil O-LIVE (Grant No. 101091255). P.S. was funded by the European Union Horizon Europe Project AI4SoilHealth (Grant No. 101086179).
References:
Lehner, B., & Grill, G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15), 2171-2186.
Lin, P., Pan, M., Wood, E. F., Yamazaki, D., & Allen, G. H. (2021). A new vector-based global river network dataset accounting for variable drainage density. Scientific data, 8(1), 28.
Vogt, J., Colombo, R., Paracchini, M. L., de Jager, A., & Soille, P. (2003). CMM river and catchment database. Version, 1, 1-32.
How to cite: Kaffas, K., Matthews, F., Saggau, P., and Borrelli, P.: Nested Catchment Delineation at the European Scale: A Tool for Fine-Scale Environmental Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14166, https://doi.org/10.5194/egusphere-egu25-14166, 2025.
Additional speakers
- Mirco Barbero, European Commission, Belgium
- Bavo Peeters