This session will discuss the most recent scientific developments in soil erosion sciences and closely associated land degradation processes in agriculture, forest and rangelands. Spanning across multiple disciplines, the session will naturally integrate all driving forces of erosion (hydrological, aeolian, mechanical) focussing on water, wind, tillage and harvest (SLCH) erosion as well as the numerous anthropogenic factors which interact with these processes.
The following topics will form the areas of presentation and discussion:
• Measurements - by means of field studies or laboratory experiments developing process understanding (e.g. from interrill to gully erosion).
• Monitoring - short to long-term assessments tracking changes through time, by means of local assessments or remote sensing techniques.
• Modelling approaches – innovative simulation techniques from empirical, to process based, to data-driven, and from plot to global scale, addressing current and future land condition and climate change drivers.
• Mitigation and restoration – to address on-site and off-site impacts on soils and water.
Our objective is to discuss soil erosion processes and their impacts, while exploring strategies which support stakeholders (farmers, land managers or policy makers) and ongoing initiatives such as the Soil Monitoring Law in the European Union, the target of land degradation neutrality by 2030, and the UN Decade on Ecosystem Restoration (2021-2030).
Orals: Wed, 6 May, 10:45–12:30 | Room 0.16
Soil erosion presents a critical environmental challenge, particularly in regions exposed to climatic variability and anthropogenic pressures. In Morocco, where complex physiographic and climatic conditions prevail, the assessment of soil erosion and sediment transport is hindered by persistent data scarcity, limiting model accuracy and the effectiveness of watershed management. This thesis focuses on the development and application of robust soil erosion and sediment modeling approaches, combining both statistical and process-based methods.
The study begins with a national-scale review of soil erosion assessments in Morocco. Results indicate that research is predominantly concentrated in the Rif and Atlas Mountains, with models based on the Revised Universal Soil Loss Equation (RUSLE) or its original version, the Universal Soil Loss Equation (USLE), representing over 51% of applications. However, many studies omit key parameters such as the support practice factor and have limited spatial coverage. Erosion rates were found to be strongly influenced by geomorphological, climatic, and land use factors, although methodological inconsistencies and data limitations contribute to significant variability.
To address these challenges, an advanced modeling framework was applied in the Bouregreg watershed, a semi-arid basin in northwestern Morocco, with a focus on modeling suspended sediment concentration (SSC) and sediment yield (SY) over the period 09/01/2016–08/31/2021.
The initial approach employed four machine learning (ML) algorithms, Extra Trees, Random Forest, CatBoost, and XGBoost, were integrated with Genetic Programming to enhance predictive accuracy and robustness. Model evaluation using Root Mean Square Error (RMSE), correlation coefficient (r), and Nash–Sutcliffe Efficiency (NSE) demonstrated strong performance (NSE: 0.53–0.86; RMSE: 1.20–2.55 g/L; r: 0.83–0.91) in predicting SSC. To improve interpretability, SHapley Additive exPlanations (SHAP) analysis was employed, revealing streamflow and seasonality as the most influential predictors.
Subsequently, the RUSLE and the Modified Universal Soil Loss Equation (MUSLE) models were used to estimate soil loss and sediment yield using high-resolution soil data from INRA (1:50,000) and global FAO data (1:1,000,000). RUSLEINRA yielded more accurate results (15.56 t/ha/yr) than RUSLEFAO (10.24 t/ha/yr), while MUSLEINRA estimated 11.40 t/ha/yr. Sediment yield was validated using observed data from the Sidi Mohamed Ben Abdellah (SMBA) Dam. Projections under the SSP126 and SSP585 climate scenarios for the period 09/01/2021–12/31/2040 predict increased soil erosion (31.54–37.04 t/ha/yr), highlighting the urgent need for proactive soil conservation strategies.
This thesis contributes a scalable and interpretable modeling framework that integrates machine learning and geospatial data for improved erosion prediction and watershed management under current and future climate conditions.
How to cite: Lamane, H., Mouhir, L., Zouahri, A., and Moussadek, R.: Modeling soil water erosion and sediment transport in Bouregreg watershed (Morocco) using machine learning and climate projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-662, https://doi.org/10.5194/egusphere-egu26-662, 2026.
Marginal lands are areas with low or declining agricultural potential due to persistent soil degradation, vegetation stress, or long-term disturbances in land use. Mapping such lands is especially important in the Northeastern Region (NER) of India, where steep slopes, intense monsoonal rainfall, and widespread shifting cultivation create highly dynamic and fragile landscapes. Recent nationwide assessments have also highlighted that many districts in this region exhibit very high susceptibility to soil erosion, making the identification of marginal lands essential for restoration planning and sustainable land-use management. In this study, we propose a machine-learning framework to classify marginal lands by jointly analyzing erosion severity classes, vegetation dynamics, and bare-soil exposure. Multi-year Sentinel-2 data are used to compute pixel-wise Poor Vegetation Frequency from NDVI (Normalized Difference Vegetation Index) and Bare Soil Frequency from BSI (Bare Soil Index), providing robust indicators of vegetation stress and soil exposure. These variables are combined with potential soil loss estimates, topographic attributes, and land-use information to form a comprehensive feature set. The model is trained and evaluated using observed degradation patterns from the Desertification and Land Degradation Atlas of India, enabling an independent assessment of classification performance. The resulting marginal land maps show strong spatial agreement with known erosion-prone and degraded zones across the selected study region, with bias mass balance values generally ranging between 0.6 and 0.8. This study demonstrates the value of integrating erosion severity and vegetation dynamics within a machine-learning environment, offering a scalable approach for exploring and mapping marginal lands in complex and data-constrained regions.
How to cite: Raj, R. and Biswal, B.: Integrating Erosion Severity and Vegetation Stress Indicators for Marginal Land Mapping in Northeastern India Using a Machine Learning Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1146, https://doi.org/10.5194/egusphere-egu26-1146, 2026.
Ephemeral-gully erosion is among the most dynamic yet least monitored drivers of land degradation in agricultural landscapes. Despite its global prevalence, its multidimensional impacts across contrasting climates and management systems remain poorly quantified. Here we present a cross-regional assessment of how ephemeral-gully processes reorganize soil physical, chemical, biological, and nutrient functioning across four agricultural systems spanning semi-arid Kansas (no-till and conventional tillage) and humid-subtropical Mississippi (amended no-till). High-resolution lateral (gully axis to shoulders) and longitudinal (outlet to headcut) sampling in the 0–10 cm layer, supported by depth-resolved observations to 35 cm, revealed robust and spatially coherent degradation patterns. Across all sites, erosional shoulders and mid-gully convergence zones consistently emerged as hotspots of structural breakdown, acidification, nutrient depletion, and biological suppression. In the semi-arid no-till system, degradation was primarily surface-confined and driven by selective removal of fine particles, aggregate destabilization, and desalinization. Conventional tillage amplified these contrasts, producing pronounced carbon and nutrient redistribution and intensified biological stress. In humid amended systems, surface amendments yielded only localized improvements; upslope acidification, organic-carbon loss, and base-cation leaching persisted, with the most humid site exhibiting degradation extending well into the subsoil. Depositional hollows showed partial resilience—higher carbon availability, moisture retention, and microbial activity—but these benefits were spatially limited and insufficient to offset broader hillslope decline. Overall, our results demonstrate that ephemeral gullies function as system-level engines of land degradation rather than transient geomorphic features. By integrating multi-indicator soil responses across climates, managements, and depths, this study provides process-based insights essential for soil monitoring, erosion modelling, and targeted conservation planning, directly informing land-degradation neutrality goals, the UN Decade on Ecosystem Restoration, and the EU Soil Monitoring Law.
How to cite: Ganjalikhan Hakemi, F., Bridges, W., and Darnault, C.: Ephemeral Gullies as Engines of Land Degradation: A Cross-Climate, Multi-Indicator Breakdown of Soil Quality Decline, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2414, https://doi.org/10.5194/egusphere-egu26-2414, 2026.
Soil internal forces, including electrostatic, hydration, and Van der Waals forces, are underlying mechanisms responsible for aggregate breakdown and subsequent soil erosion. However, whether these forces consistently initiate raindrop-induced erosion remains unclear. This study aims to elucidate the influence of soil internal forces on raindrop-induced erosion by conducting aggregate stability and simulated rainfall experiments using two soils (Heilu soil and Aeolian sandy soil) from the Loess Plateau. Electrolyte solutions with different concentrations (from 10–5 to 1 mol L–1) were employed as soaking solutions and raindrop materials to quantitatively regulate soil internal forces. Our results indicate that an electrolyte concentration of 10–2 mol L–1 was the critical point for net pressure (NP), which varied greatly when the electrolyte concentration reduced from 1 to 10–2 mol L–1. In this case, the aggregate stability, splash erosion mass (SEM), and cumulative loss mass (CLM) of Heilu soil increased rapidly with decreasing electrolyte concentrations. A significant correlation was also observed between NP and mean weight diameter (MWD) (r = –0.909), SEM (r = 0.821), and CLM (r = 0.806), respectively (p<0.01). However, the MWD, SEM and CLM of Aeolian sandy soil remained unchanged with varying electrolyte concentrations, as it lacks an effective aggregate structure and rarely underwent the “breakdown” process during wetting. Therefore, except structureless soils, raindrop-induced erosion typically is initiated by soil internal forces and then driven by raindrop impact and overland flow. This study enhances our understanding of the driving mechanism of rainfall-induced erosion, providing a theoretical foundation for developing targeted soil erosion prevention strategies.
How to cite: li, J., hu, F., and xu, C.: Is raindrop-induced erosion controlled by the combined effects of soil internal and external forces?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4399, https://doi.org/10.5194/egusphere-egu26-4399, 2026.
[Objective] This study investigates the spatiotemporal variations of soil water erosion in the eastern region of the agro-pastoral ecotone in northern China, and firstly quantifies the interactive effects of multiple factors on water erosion dynamics, providing decision-making bases and methodological references for soil erosion control and ecological environment restoration in this region. [Methods] Based on the RUSLE model, an attribution analysis was conducted on the spatiotemporal differentiation characteristics of soil water erosion in the eastern region of the agro-pastoral ecotone in northern China from 2000 to 2023, and a quantitative analysis was performed on the main contributing factors to dynamic changes in soil water erosion. [Results] (1) The multi-year average soil water erosion modulus in the eastern region of the agro-pastoral ecotone is 777.94 t·km⁻²·a⁻¹, with most areas experiencing slight and mild erosion, accounting for 69.3% and 22.9% of the total eroded area, respectively. (2) From 2000 to 2023, the soil water erosion intensity in the study area showed a slight upward trend, with an increase rate of 0.90 t·km⁻²·a⁻¹. Soil erosion alleviated in the southeastern and northwestern regions, while intensified in the western region. (3) Areas prone to soil water erosion in the eastern region of the agro-pastoral ecotone are mainly distributed in Chifeng City in the central part, and Zhangjiakou City and Xilin Hot in the southwestern part of the study area. (4) The main contributing factor to dynamic changes in soil water erosion in the eastern region of the agro-pastoral ecotone from 2000 to 2023 is the vegetation cover management factor, with an average contribution rate of 64.18%. The combined influence of three factors(vegetation cover management factor, soil and water conservation practice factor, and rainfall erosivity factor) accounts for 23.12%, the soil and water conservation practice factor contributes 10.57% on average, and the rainfall erosivity factor contributes 2.13% on average. There are strong interactions between parameters of the RUSLE model, and the combined influence of multiple factors has a significant contribution to the dynamic changes of water erosion in the study area. [Conclusion] From 2000 to 2023, the soil water erosion situation in the eastern region of the agro-pastoral ecotone in northern China was generally stable. During the study period, with the improvement of vegetation coverage and the effective adoption of soil and water conservation measures, the aggravating effect of increased rainfall erosivity on water erosion intensity was alleviated to a certain extent. However, the soil water erosion situation in this region remains severe, and areas with low vegetation coverage and without soil and water conservation measures should be prioritized in soil erosion control efforts.
How to cite: Wu, J. and Shi, P.: Quantitative analysis of soil erosion spatiotemporal variation and contributing factors in the eastern region of the Agro pastoral Transition Zone in Northern China based on RUSLE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5378, https://doi.org/10.5194/egusphere-egu26-5378, 2026.
Underground soil loss (USL), the net removal of eroded sediment from the land surface to underground space, is a difficult-to-measure and puzzling phenomenon that typically occurs in karst areas. Compared to surface soil loss (SSL), USL has been largely ignored in soil loss assessments utilizing Universal Soil Loss Equation (USLE)-type models due to the absence of credible prediction methods. Here, we propose a framework to estimate USL locally, regionally, and globally as a first step toward developing a subsurface USLE, namely, the Underground Soil Loss Equation, as a counterpart to the surface USLE. We find that USL is a relatively minor but indispensable component of soil loss in karst areas, and its contribution increases with spatial scale and large-scale pathways. Globally, USL accounts for 5% of total soil loss and 8% of erosion-induced carbon fluxes, corresponding to an annual financial loss of $21 billion. In karst areas, the annual average soil loss and its resulting carbon fluxes would be underestimated by approximately 25% when the USL is excluded. Using a USL-included framework, we identify soil loss threats among countries with karst landscapes. Low-income countries suffer high SSL rates and should be wary of aggravated USL with insufficient conservation measures. High-income countries tend to have higher USL rates and are more likely to underestimate soil loss if USL is ignored. Middle-income countries, which account for over 3/5 of karst area and 3/4 of USL amount, should be cautious of higher SSL and USL rates as well as significant risks of underestimating soil loss. Our findings emphasize the importance of incorporating USL into soil loss-relevant modeling and assessments to more accurately identify pertinent threats and offer more strategic approaches in monitoring and conservation.
How to cite: Ke, Q., Zhang, K., Yu, B., and Zhong, D.: Macroscopic and multi-scale estimation of karst underground soil loss: toward a subsurface USLE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6491, https://doi.org/10.5194/egusphere-egu26-6491, 2026.
Soil erosion by water remains a major environmental threat in Europe, with rainfall erosivity (R-factor) acting as a key driver of its magnitude and spatiotemporal variability, and with indications that erosion pressures may intensify under future climatic conditions (Panagos et al., 2021). At the European scale, event-based rainfall erosivity has been robustly characterized through the REDES database, resulting in a mean multi-annual R-factor map (Panagos et al., 2015) and monthly climatological R-factor maps derived from REDES and WorldClim data (Ballabio et al., 2017), together providing a consistent spatial framework for erosion assessments. Building on these established frameworks, we extend these products by reconstructing temporally resolved annual and monthly rainfall erosivity for Europe from 1950 to present, combining the spatial patterns of REDES with the temporal variability captured by ERA5-Land hourly precipitation.
ERA5-Land hourly precipitation is used to identify erosive rainfall events at each grid cell using standard criteria (Renard et al., 1997). Rainfall erosivity (EI₆₀) is calculated for each erosive event, and monthly erosivity is obtained by aggregating event contributions and applying month-specific conversion factors to express erosivity on a 30-min basis (Panagos et al., 2016), ensuring consistency with established formulas and enabling comparable monthly and seasonal analyses across heterogeneous recording intervals. Annual and monthly reconstruction is performed by scaling the REDES multi-annual and monthly climatological erosivity maps using ERA5-derived annual and monthly anomalies, therefore preserving the spatial patterns of REDES while transferring ERA5-captured interannual and intra-annual variability. The workflow produces gridded annual and monthly ERA5 erosivity and reconstructed erosivity maps accompanied by statistics and extreme-event diagnostics.
Results indicate that most years and months exhibit scaling ratios close to unity across large parts of Europe, while a limited fraction of pixels shows substantial anomalies and are subjected to targeted hotspot analysis. By extending rainfall erosivity reconstruction beyond the reference period to the present and transforming established multi-annual and monthly climatological R-factor products into fully time-resolved annual and monthly maps, we enable a consistent assessment of recent variability and extremes fully aligned with European erosivity mapping, providing a robust basis for erosion modeling, risk assessment, and climate-impact studies.
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:
Ballabio, C., Borrelli, P., Spinoni, J., Meusburger, K., Michaelides, S., Beguería, S., ... & Panagos, P. (2017). Mapping monthly rainfall erosivity in Europe. Science of the Total Environment, 579, 1298-1315.
Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., ... & Alewell, C. (2015). Rainfall erosivity in Europe. Science of the Total Environment, 511, 801-814.
Panagos, P., Borrelli, P., Spinoni, J., Ballabio, C., Meusburger, K., Beguería, S., ... & Alewell, C. (2016). Monthly rainfall erosivity: conversion factors for different time resolutions and regional assessments. Water, 8(4), 119.
Renard, K.G., et al., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE) (Agricultural Handbook 703). US Department of Agriculture, Washington, DC, p. 404.
How to cite: Kaffas, K., Matthews, F., Saggau, P., Panagos, P., and Borrelli, P.: Annual and Monthly Reconstruction of Historical R-Factor from REDES and ERA5-Land, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13715, https://doi.org/10.5194/egusphere-egu26-13715, 2026.
The cover-management factor for non-agricultural land (Cnon-crop) is a critical parameter in the USLE-based soil erosion models, and its estimation accuracy directly affects the reliability of regional and global soil erosion simulations. This study developed a dynamic global estimation framework for Cnon-crop by integrating a pixel-based spectral mixture model (NDVI-SWIR32) and rainfall erosivity weighting within the Google Earth Engine platform. Using MOD43A4 and MOD09GA data, we retrieved global fractional cover of photosynthesizing vegetation (fPV), non-photosynthesizing vegetation (fNPV), and bare soil (fBS) at 500 m resolution from 2000 to 2024. The fNPV during the non-growing season in deciduous forests was explicitly incorporated as a proxy for annual understory coverage to better represent its role in erosion mitigation. Combined with ERA5 rainfall data and land use information, soil loss ratios across 24 half-month periods were weighted by rainfall erosivity to generate a continuous global Cnon-crop product. The results show that the mean Cnon-crop values for forests, shrublands, and grasslands were 0.017, 0.014, and 0.029, respectively, which align closely with values reported in the literature (0.020, 0.018, and 0.032), with absolute errors ranging between 0.003 and 0.004. This confirms the stability and applicability of the proposed method across different ecological zones. Approximately 82.8% of the global land area exhibited a Cnon-crop value below 0.2, primarily distributed in temperate and subtropical regions. High Cnon-crop values (>0.5) were concentrated in arid and semi-arid regions such as North Africa and Central Asia, indicating weaker vegetation protection and higher ecosystem vulnerability. The explicit inclusion of understory vegetation significantly improved the parameterization of soil erosion factors in forest ecosystems and effectively reduced the systematic bias associated with relying solely on canopy cover.
How to cite: Shi, H., Liu, B., Li, R., Yang, Q., Zhang, X., and Zhang, H.: Global Estimation of the Soil Erosion Cover-Management Factor for Non-Agricultural Land (Cnon-crop) Using Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15627, https://doi.org/10.5194/egusphere-egu26-15627, 2026.
Mitigating soil erosion and muddy floods with vegetative barriers in Belgium's open agricultural landscapes
Glenn Desplentere, Amaury Frankl
Climate and Earth Lab, Department of Geography, Ghent University, Gent, Belgium
Vegetative barriers are widely implemented nature-based solutions in open agricultural landscapes to mitigate the off-site impacts of soil erosion and reduce the occurrence of muddy floods. Although their effectiveness and functioning are well documented in laboratory settings, relatively few studies have evaluated their performance under field conditions. Using hilly Flanders (Belgium) as a case study, this research assessed (i) the quality of field implementation, (ii) the effectiveness in reducing peak runoff, and (iii) potential negative geomorphological feedbacks, across 244 small agricultural catchments treated with vegetative barriers. The barriers consisted of woodchips, coconut-fibre bales, willow brushwood, and straw bales. To assess whether field-installed barriers function as intended, 40 randomly selected barriers were inspected in situ. Barrier capacity to attenuate peak runoff was evaluated using a semi-quantitative scoring grid, based on criteria including structural continuity, evidence of bypass flow, and the presence of soil piping. Peak runoff was simulated for all barriers under the assumption of fully functional performance. Long-term runoff time series were generated from historical rainfall records, and peak flows were derived using peak-over-threshold (POT) frequency analysis. Simulated peak inflows were then compared with barrier outflow capacity to assess effectiveness. Results indicate a generally poor implementation quality: only a limited fraction of barriers were fully functional, while more than half were classified as dysfunctional (i.e., unable to mitigate peak flow). Woodchip barriers performed best overall, whereas straw-bale barriers exhibited the lowest reliability. Modelled peak runoff suggests that approximately half of all barriers are unable to buffer peak flows during high-intensity rainfall events, whereas well-maintained barriers show a clear capacity to attenuate peak discharges. In addition, negative geomorphological feedbacks were observed: ephemeral gullying occurred on nearly half of cropland areas downstream of barriers. While nature-based solutions are increasingly promoted to mitigate off-site erosion impacts and reduce muddy-flood risk, these findings highlight substantial management challenges and potential unintended geomorphological effects. Field-based evidence such as presented here is crucial, as modelling approaches may otherwise overestimate barrier effectiveness. Muddy floods can be viewed as a symptom of structurally and hydrologically degraded soils; vegetative barriers can contribute to mitigation, but only when carefully implemented and maintained, and when guided by a robust geomorphological understanding of the treated catchments.
How to cite: Desplentere, G. and Frankl, A.: Mitigating soil erosion and muddy floods with vegetative barriers in Belgium's open agricultural landscapes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17622, https://doi.org/10.5194/egusphere-egu26-17622, 2026.
The Congo Basin, home to the world’s second largest tropical rainforest and river network, plays a crucial role in the global carbon (C) cycle. However, rapid population growth and land-use changes are intensifying geomorphic and biogeochemical disturbances. Consequently, the basin is experiencing accelerated soil redistribution, whose impacts on lateral C transfer along the land–ocean aquatic continuum (LOAC) remain largely unknown. By integrating the most comprehensive observation dataset available with a state-of-the-art land surface model (ORCHIDEE-Clateral), this study quantified the magnitude and temporal evolution of lateral C fluxes in the forms of particulate organic carbon (POC), dissolved organic carbon (DOC), and carbon dioxide (CO₂) over the past five decades, and assessed the impact of these lateral C transfers on the terrestrial C budget. The calibrated ORCHIDEE-Clateral model explains 73%, 84%, 78%, and 84% of the spatial variation in observed river water discharge, sediment discharge, POC concentration, and DOC concentration in the Congo River network, respectively. It also captures well the seasonal variations in riverine water discharge, sediment discharges, water surface extent, and riverine CO₂ partial pressure. Using the calibrated model, we reconstructed the historical evolution of C fluxes and transformations along the LOAC. Since 1970, lateral C (i.e., POC, DOC, and CO2) input from land to river has increased significantly (Mann–Kendall P < 0.001), with POC, DOC, and CO2 rising by 51%, 20%, and 29%, respectively. The increase in POC is primarily driven by land-use change, followed by climate change and rising atmospheric CO₂. Of the terrestrial C entering the Congo River network, 61% of DOC and 67% of POC remain within the river–floodplain complex—approximately two and three times the proportion retained in the Amazon and European river networks, respectively. These results suggest that the majority (> 60%) of the laterally transported terrestrial C is stored or transformed inside the Congo Basin rather than being exported to the ocean or released to the atmosphere. With the projected rapid population growth and land-use expansion in the Congo Basin, lateral C fluxes along the LOAC are expected to intensify further, reinforcing the Congo Basin’s role as a major inland C buffer that reshapes the regional land–ocean C balance.
How to cite: Zhao, P., Ma, M., Carlier, N., de Groot, L., de Clippele, A., Barthel, M., Drake, T. W., Hemingway, J. D., Zhang, H., Hastie, A., Six, J., Van Oost, K., and Regnier, P. A. G.: A Half-Century Intensification of Lateral Carbon Transfer along the Congo Basin’s Land–Ocean Aquatic Continuum, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21715, https://doi.org/10.5194/egusphere-egu26-21715, 2026.
Posters on site: Wed, 6 May, 08:30–10:15 | Hall X3
How to cite: Liu, H., Shi, P., and Wang, Y.: The Impact of Open-Pit Mining on Soil Erosion in the Mining Area of Ordos Plateau in China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3033, https://doi.org/10.5194/egusphere-egu26-3033, 2026.
Water erosion is one of the main processes of soil degradation in semi-arid mountain watersheds, due to its role in accelerated sediment export, loss of soil fertility, and disruption of water resources. In the upper Tassaoute watershed, located in Morocco's High Atlas Mountains, these dynamics result in increased suspended sediment flows and the development of gullies, revealing the increased vulnerability of slopes to climatic and anthropogenic stresses.
In this context, this study proposes a comparative analysis of two complementary approaches—remote sensing and machine learning—to assess and map spatial susceptibility to water erosion. The first approach is based on the use of Sentinel-2A optical images and statistical analysis of spectral indices describing soil condition and vegetation cover. Four vegetation indices and nine soil indices are calculated and then aggregated to construct a composite explanatory variable. Regression analyses are performed between this variable and the individual indices to estimate the correlation and determination coefficients (R²), allowing their relative contribution to erosion to be assessed. Principal Component Analysis (PCA) is then applied to reduce redundancy between indices and structure the multispectral information. The first component is mainly associated with soil signatures (moisture, roughness, minerality), while the second reflects more the condition and vigor of the vegetation. On this basis, a predictive model is developed by weighting the indices according to their explanatory power and factorial contribution, leading to the development of a map classifying soils into four levels of susceptibility to degradation.
The second approach uses machine learning techniques to map susceptibility to gully erosion. An inventory of 400 occurrences, comprising 200 gullied sites and 200 non-gullied sites, was compiled based on field observations and interpretation of satellite images. These occurrences are correlated with 21 predisposing factors grouped into topographical, geological, climatic, pedological, anthropogenic, and land use variables. Five models (GLM, GBM, ANN, Random Forest, and SVM) are evaluated according to different data partitioning scenarios, with hyperparameter optimization by cross-validation. Performance is assessed using AUC-ROC and classification indicators, before producing probabilistic maps reclassified into four levels of vulnerability.
The results show that remote sensing provides a consistent and easily updatable reading of surface conditions, while machine learning significantly improves predictive capacity by integrating non-linear relationships and multiple environmental factors. Their combination provides a robust decision-making framework for targeting priority areas, guiding anti-erosion actions, and supporting land-use planning in fragile mountain environments.
Keywords: Water erosion, remote sensing, machine learning, upstream Tassaoute watershed, Morocco.
How to cite: Nait-taleb, O., El Goumi, S., Bimouhen, M., Ouchkir, I., Ismaili, M., El Kamouni, F. E., Elomari, S., Krimissa, S., Namous, M., and Elaloui, A.: Comparative analysis of remote sensing and machine learning approaches for mapping susceptibility to water erosion in the Moroccan High Atlas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4531, https://doi.org/10.5194/egusphere-egu26-4531, 2026.
The accurate characteristics of raindrops play a crucial role in various fields, including meteorology, hydrology, agriculture, horticulture, weather forecasting and atmospheric physics. This can be important, especially in the context of soil splash erosion caused by the impact of raindrops and the consequent transport of soil microorganisms, such as pathogens. At present, the most common technique for measuring precipitation drops is the use of disdrometers, while high-speed imaging technique are becoming more popular. Therefore, the aim of the study was to present the possibilities of using high-speed cameras in order to characterize large falling drops and to compare this methodology with the use of selected disdrometers.
The study was based on the formation of single water drops with specified diameters (3.2, 4.3 and 5.3 mm) and different heights of drop release (1, 3 and 5 m). The analyses included a comparison of data obtained from a single high-speed camera, a set of synchronized high-speed cameras with 3D PTV module (i.e. particle tracking velocimetry) and two types of laser disdrometers (Thies Clima LPM and Parsivel2). Based on the evaluation of the suitability of selected methods for measuring the properties of large drops, it has been shown that high-speed cameras allowed a very accurate analysis of the parameters of individual drops (i.e. size, velocity and shape descriptors) in contrast to the tested disdrometers which showed substantial variability in the results. The calculated coefficient of variation for the measured parameters was up to 5.5% for the cameras, and up to 13.6% for the disdrometers. In this context, high-speed cameras offer an alternative method of measuring processes subject to significant errors, such as those related to the irregularity and variability of the drop shape, in disdrometer-based measurements like throughfall phenomenon. They also serve as a valuable tool for validating widely used instruments.
Acknowledgement: this work was financed from the National Science Centre, Poland project no. 2022/45/B/NZ9/00605 and the CTU in Prague project no. SGS23/155/OHK1/3T/11.
Reference: Beczek M., Neumann M., Mazur R., Zumr D., Dostal T., Bieganowski A.: Challenges in measuring the size and velocity of large raindrops: a comparison of selected methods Journal of Hydrology 662, Part B, 133932, 2025
How to cite: Beczek, M., Mazur, R., Neumann, M., Zumr, D., Dostal, T., and Bieganowski, A.: Possibilities of using a high-speed camera to characterize falling drops, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4861, https://doi.org/10.5194/egusphere-egu26-4861, 2026.
Rainfall erosivity is a key driver of the soil erosion process, and it varies greatly across Europe due to differences in climate regimes, precipitation patterns, and storm intensities. The rainfall erosivity (R-factor) database in Europe needs to be updated. The rainfall erosivity database is required to develop robust input data for soil erosion models. This contribution presents an updated Rainfall Erosivity Database at European Scale (REDES 2.0), which is based on high-frequency, sub-hourly and hourly data from 9,138 stations across Europe. All the high-frequency data were converted to a 30-minute time step using data aggregation or disaggregation using a multiplicative cascade model. The 30-minute time step was then used to calculate R-factor following the Revised Universal Soil Loss Equation (RUSLE) methodology. Over 2.4 million erosive events were consequently identified, primarily covering the period from 2010 to 2025, with an average station data length of 15 years and approximately 265 erosive events per station. Spatial patterns of the annual R-factor show pronounced differences across Europe, with the highest values concentrated in the Mediterranean and Alpine regions. In contrast, northern, eastern and central Europe exhibit comparatively lower values. These spatial patterns and the country-averaged R-factor values resemble those of the previous REDES 1.0 database somewhat, though there are some differences in countries such as Greece and Italy. This can be attributed to the increased spatial density of stations in REDES 2.0 and to some extreme storms in recent years. REDES 1.0 and 2.0 are overlapping at 1062 locations around Europe and average annual R-factor values for these stations are 792 MJ*mm*ha-1*h-1*year-1 and 861 MJ*mm*ha-1*h-1*year-1 for REDES 1.0 and 2.0, respectively. The corresponding standard error of the annual R-factor in REDES 2.0 using all available data averaged at around 18%. Using only data from 2010 onwards did not significantly alter the annual R-factor or the corresponding standard error compared to using all available data in the calculation of the annual R-factor. The relationship between the annual R-factor and station characteristics, such as location and elevation, was generally weak. This indicates the complex drivers of rainfall erosivity in Europe. REDES 2.0, an updated version of the original database (REDES 1.0), provides essential input for soil erosion modelling, land management planning and climate adaptation strategies. It can also be used to develop methods for dynamic estimation of continental rainfall erosivity.
Acknowledgment: The N. Bezak contribution was supported by the Slovenian Research and Innovation Agency (ARIS) through grant P2-0180.
How to cite: Bezak, N., Borrelli, P., Cesarini, L., Müller-Thomy, H., Philipp Saggau, F. M., Liakos, L., Brettin, J., Galdies, C., Petan, S., Valiukas, D., Giannaklis, C.-P., Lagouvardos, K., Gomes, G., Salamon, P., Begueria, S., Lakatos, M., Perčec Tadić, M., Chervenkov, H., Michaelides, S., and Panagos, P.: Rainfall erosivity in Europe: an update of the REDES database, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4979, https://doi.org/10.5194/egusphere-egu26-4979, 2026.
The Revised Universal Soil Loss Equation, Version 2 (RUSLE2), is the primary water erosion prediction tool used by the USDA Natural Resources Conservation Service (NRCS) for land management planning across the United States. Despite its widespread adoption, RUSLE2’s reliance on a personal computer-based model limits its capacity for large-scale, dynamic applications. This research addresses these constraints by developing a novel cloud-based platform to host and enhance RUSLE2, enabling server-based computation, geospatial data integration, and scalable modeling capabilities. Built on Amazon Web Services (AWS), the platform integrates web-based user interfaces, spatial databases, and geoprocessing tools to streamline soil erosion modeling. It incorporates historical data on soil properties, weather patterns, and land use practices to support precise assessments of rill and interrill erosion. A redesigned database architecture ensures computational efficiency, data security, and collaborative development. Scientific advancements in RUSLE2 include quantifying the effects of precipitation variability and land use on the spatiotemporal dynamics of key soil properties. Leveraging advanced field and laboratory methods, remote sensing, and machine learning, the platform improves the measurement and mapping of soil erodibility and soil loss across diverse U.S. agricultural landscapes. These enhancements enable more accurate forecasts of erosion risk under evolving environmental scenarios and support flexible land management strategies. This transformative, cloud-based platform delivers innovative tools to guide land use practices and improve long-term agricultural productivity. By integrating cutting-edge technologies and data-driven modeling, this work addresses longstanding challenges in erosion science and enhances regional and national resilience in soil resource management.
How to cite: Darnault, C., Ghorbani, M., Genc Kildirgici, G., Ghimire, B., Sisk, C., Cunningham, K., Calhoun, J., Momm, H., Yoder, D., Vieira, D., Bingner, R., Locke, M., Wells, R., and Ferruzzi, G.: Advancing Revised Universal Soil Loss Equation, Version 2 (RUSLE2) Development: Integrating Cutting-Edge Science and Cloud-Based Innovations for Transformative Soil Erosion Modeling and Land Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6166, https://doi.org/10.5194/egusphere-egu26-6166, 2026.
Soil loss is a major environmental concern because it can compromise the provision of ecosystem services on both local and global scales. Therefore, developing effective mitigation measures and soil protection strategies is essential and should be grounded in a solid understanding of the main factors and processes that trigger and predispose erosion. Water related soil erosion is increasing in temperate as well as tropical and subtropical areas, especially in hilly and mountainous environments where cultivation and land-use changes reduce vegetation cover. Regions in southern Italy are characterized by a morphological configuration that increases the vulnerability to water erosion, making them relevant case studies representative of the Mediterranean area. In this context, accurately estimating the R-factor, which quantifies the potential of precipitation to cause soil erosion, is essential for assessing erosion risk and supporting effective soil conservation planning.
This study aims to update estimates of the R-factor across three regions of southern Italy (i.e., Campania, Sardinia, and Sicily) and to analyze its spatio-temporal evolution over the past 72 years. The first part of the study uses high resolution (10-minute) database of recent rainfall observations (2002-2023) to derive point-scale reference values of the R-factor at 169 stations in Campania, 134 in Sardinia and 92 in Sicily. These benchmark values, calculated using the RUSLE-2 method for rainfall kinetic energy combined with an innovative approach for identifying erosive events, were then used to locally calibrate and validate a selection of simplified empirical models at the regional level. Such simplified models can estimate the R-factor based on coarser resolution (i.e. daily) rainfall data, enabling broader regional applicability while maintaining accuracy.
In the second part of the study, the calibrated models were forced with daily rainfall data arising from a spatially distributed database (spatial resolution: 10 km x 10 km) covering the three regions over the period 1951-2022. This allowed the generation of an updated R-factor map for the regions and enabled an unprecedented analysis of trends over the 72-year time series. Specifically, trend detection was performed using the non-parametric Modified Mann-Kendall test at a significance level of α = 0.05, while trend magnitude was estimated using Sen’s slope estimator. The R-factor was computed over moving time windows of 16 years with a 4-year lag, also considering a multi-model ensemble mean approach. Additionally, Moran’s autocorrelation indices were used to investigate the spatial distribution of trends.
The results of the study revealed an overall positive trend in the R-Factor across the study area, indicating an increase in erosive potential of rainfall over the past years in all examined regions, with a more pronounced intensification in inland areas. The observed trends in southern Italy, consistent with broader patterns observed across the Mediterranean region, highlight the need for proactive soil conservation measures aimed at planning resilient and sustainable management strategies for regional agroecosystems.
How to cite: Ippolito, M., Nasta, P., Langousis, A., Romano, N., Deidda, R., and Pumo, D.: Rainfall erosivity in semi-arid Mediterranean areas: a multi-decadal spatio-temporal analysis of the R-factor (1951–2022), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10598, https://doi.org/10.5194/egusphere-egu26-10598, 2026.
Water erosion poses a significant threat to agricultural systems, causing soil degradation, loss of fertile topsoil, and adverse impacts on water quality. Climate change is expected to exacerbate these effects, as increasing rainfall intensities enhance water-induced soil erosion. Potential soil erosion by water is commonly assessed using the Universal Soil Loss Equation (USLE) and its revised version (RUSLE), which combine information on soil properties, topography, land cover, conservation practices, and rainfall erosivity, expressed by the R-factor. While the R-factor is typically derived from high-temporal-resolution precipitation measurements and extrapolated using regression-based approaches, its projection into future climates remains challenging. Uncertainties arise primarily from the strong sensitivity of rainfall erosivity to short-duration precipitation extremes, which are poorly represented in conventional climate projections. As a result, existing methods either apply present-day relationships to future conditions or rely on convection-resolving simulations that are limited in temporal coverage and resolution, as well as ensemble size, hindering a robust assessment of future erosion risk and associated uncertainties.
In this study, we determine the R-factor on a monthly basis for Austria at a spatial resolution of 1 km × 1 km for a reference period (1995–2015), the mid-century period (2036–2065, RCP8.5), the far future (2071–2100, RCP4.5 and RCP8.5), and under a global warming level of 3 °C. We first develop multiple linear regression models using rainfall data of 184 stations in Austria. For future periods, the regression models were modified applying Clausius-Clapeyron scaling and Austrian climate projection data (ÖKS15). This framework allows us to spatially extrapolate rainfall erosivity across Austria, while retaining high temporal resolution and explicitly accounting for model and scenario uncertainty.
Our results indicate that rainfall erosivity is highest in August and increases most strongly in alpine regions, where the R-factor already has the highest present-day values. The largest increases, but also the greatest model uncertainties, are associated with the RCP8.5 scenario in the far future. In this scenario, the median increase in the R-factor in August is at least 72% - in some regions, even over 100%. Overall, all climate scenarios consistently project an increase in rainfall erosivity in the future. Incorporating the projected R-factors into the RUSLE model enables the estimation of future water-induced soil erosion and supports more robust risk assessment and adaptation planning.
How to cite: Wöckinger, S., Wittholm, J., Johannsen, L., Schmaltz, E., and Haslinger, K.: Multi-model high-resolution projections of rainfall erosivity in Austria , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12122, https://doi.org/10.5194/egusphere-egu26-12122, 2026.
Iceland has a long history of land degradation lasting over a millennium. Prior to human settlement, heath plant communities were widespread, and mountain birch covered around 40% of the country. Today, birch cover about 1% after episodes of degradation and extensive erosion. It is furthermore estimated that 40% of the land cover is still in degraded or eroded state. The drivers behind this shift are both anthropogenic and environmental with considerable interactions. One critical factor is the vulnerability of Andisols, the dominating soil order, due to their lack of particle cohesion and low bulk density. However, Andisols can contain considerable amounts of soil organic carbon and soil water, both contributing to potentially high fertility. Degraded lands, where the Andisols have been lost, do therefore have a high potential for carbon sequestration through revegetation. The project presented here focuses on monitoring soil and vegetation changes in reclamation areas dating back to 1990. The sampling unit is a 10 x 10 m plot located on a random 1x1 km² national grid. Five subplots (50 x 50 cm) are used to collect composite samples and estimate vegetation cover using a modified Braun-Blanquet scale. Soil samples are divided into 0-5, 5-10, 10-20, and 20-30 cm depth intervals. These plots have now been sampled twice within 10 years. C and N content was analyzed in all samples. Preliminary results indicate a general increase in vegetation cover and carbon stocks over the 10-year sampling period but trends do differ depending on approaches and location. This variability highlights the importance of finding the most suitable reclamation approach depending on local environmental and geographical conditions. Further statistical analysis will focus on identifying the key factors affecting changes in carbon stocks and vegetation covers, and how we can use the different reclamation approaches to effectively revegetate the degraded land.
How to cite: Benediktsdottir, E. B., Sanchez, S., Fridriksdottir, H. V., Gudmundsdottir, S. B., and Thorsson, J.: Revegetation in Icelandic eroded areas: monitoring over a 10-year period, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12577, https://doi.org/10.5194/egusphere-egu26-12577, 2026.
Soil erosion by water and wind is a major land degradation process with wide-ranging impacts on food production, ecosystem functioning, and socioeconomic systems (Shokri et al., 2025). Intensified precipitation extremes and increasing aridity could influence the relative dominance of water- and wind-driven erosion in complex and spatially heterogeneous ways. However, a systematic understanding of where and how these two erosion mechanisms will respond to future climate variability at the global scale remains largely lacking. Current global assessments predominantly rely on empirical models. However, these approaches are highly parameterized, require extensive calibration, and are often difficult to apply consistently under changing climate conditions. Here, we aim to present a new framework to quantify potential soil erosion risk, in terms of the probability of erosion occurrence, rather than absolute soil loss. The study will use km-scale global simulations from ICON (~10 km) (Hohenegger et al., 2023), together with satellite and global soil datasets, to assess potential water and wind erosion risk under present and future climate conditions. The resulting high-resolution maps provide insight into present-day erosion hot spots, projected changes in erosion likelihood under different scenarios, and contrasting responses of water- and wind-driven erosion systems to variations in precipitation regimes, wind, and land surface conditions. This establishes an integrated and climate-informed basis (Shokri et al., 2023) for identifying priority regions for soil conservation and land management.
References:
Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., et al. (2023). ICON‐Sapphire: Simulating the components of the Earth system and their interactions at kilometer and subkilometer scales. Geoscientific Model Development, 16(2), 779–811. https://doi.org/10.5194/gmd-16-779-2023
Shokri, N., Robinson, D.A., Afshar, M., Alewell, C., Aminzadeh, M., Arthur, M., Broothaerts, N., Campbell, G.A., Eklund, L., Gupta, S., Harper, R., Hassani, A., Hohenegger, C., Keller, T., Kiener, M., Lebron, I., Madani, K., Marwala, T., Matthews, F., Moldrup, P., Nemes, A., Panagos, P., Prăvălie, R., Rillig, M.C., Saggau, P., Shokri-Kuehni, S.M.S., Smith, P., Thomas, A., Wollesen de Jonge, L., Or, O. (2025). Rethinking Global Soil Degradation: Drivers, Impacts, and Solutions, Rev. Geophys. 63, e2025RG000883, https://doi.org/10.1029/2025RG000883
Shokri, N., Stevens, B., Madani, K., Grabe, J., Schlüter, M., Smirnova, I. (2023). Climate Informed Engineering: An essential pillar of Industry 4.0 transformation, ACS Eng. Au, 3, 1, 3–6, https://doi.org/10.1021/acsengineeringau.2c00037
How to cite: Deng, S., Hohenegger, C., and Shokri, N.: Global patterns and climate sensitivity of water- and wind-driven soil erosion risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14262, https://doi.org/10.5194/egusphere-egu26-14262, 2026.
Soil erosion in the Ethiopian highlands poses a persistent threat to land productivity, downstream water bodies, and long-term watershed sustainability. While both climate change and land-use transitions are recognized as major drivers of erosion, their combined and relative effects remain insufficiently quantified at watershed scale. This study investigates how projected climate change and land-use change interact to shape future soil erosion dynamics in the Gumara–Maksegnit watershed, a critical tributary of Lake Tana in the Upper Blue Nile Basin. An integrated modeling framework was developed by coupling the Revised Universal Soil Loss Equation (RUSLE) with probabilistic climate projections and data-driven land-use simulations. Future rainfall was derived from five CORDEX-Africa regional climate models, bias-corrected and combined using Bayesian Model Averaging (BMA) to reduce model uncertainty and improve representation of rainfall erosivity. Land-use and land-cover changes were mapped for 2003 and 2023 using multi-temporal Landsat imagery and Random Forest classification, and future land-use patterns (2083) were simulated using an artificial neural network–based Cellular Automata approach. Soil erosion was quantified for historical, current, and future periods under land-use change only, climate change only (RCP4.5 and RCP8.5), and combined scenarios. Results indicate a continued expansion of cultivated land at the expense of forest and grazing areas, accompanied by a progressive increase in rainfall erosivity. Mean annual soil loss increased substantially from historical to current conditions and is projected to intensify further under future scenarios. Climate change exerts a stronger marginal influence on soil erosion than land-use change alone; however, their interaction amplifies erosion non-linearly, leading to the highest erosion rates under the combined land-use change and RCP8.5 scenario. Persistent erosion hotspots are concentrated on steep northern and northeastern slopes, where high topographic control coincides with limited conservation practices. The findings emphasize the importance of integrating climate uncertainty and land-use dynamics in soil erosion assessments and highlight the need for slope-targeted, climate-adaptive soil and water conservation strategies to mitigate future land degradation in the Upper Blue Nile highlands.
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Getu Legese Abebaw is funded for his PhD study by the Scholarship for Christian Young People.
How to cite: Getu, L. A., Szegedi, S., Addis, H. K., and Túri, Z.: Climate and Land Use Controls on Future Soil Erosion in the Upper Blue Nile Highlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15013, https://doi.org/10.5194/egusphere-egu26-15013, 2026.
Understanding future changes in rainfall erosivity is crucial for soil erosion management and related hazard mitigation in regions with complex topography. Rainfall erosivity is strongly controlled by short-duration precipitation characteristics, which are more realistically represented by convection-permitting climate models (CPMs) operating at convection-resolving scales, than by coarser regional models. Despite their advantages, CPM-based projections remain challenging due to limited simulation lengths and ensemble sizes. This study assesses projected changes in rainfall erosivity by analyzing both the intensity and the frequency of individual erosive rainfall events. The focus is on the Greater Alpine Region, a climatically and topographically heterogeneous area particularly sensitive to rainfall-driven erosion processes. An ensemble of nine CPM simulations from the CORDEX-FPS initiative at 3-km resolution is analyzed for a historical period (1996–2005), and a far future (2090–2099) under the RCP8.5 scenario.
The results indicate a widespread increase in projected rainfall erosivity across the region, driven by intensification of erosive rainfall events and, in some areas, by their increased frequency. Notably, the strongest relative increases are projected for high-elevation areas (particularly in the Eastern Alps), where present-day rainfall erosivity is comparatively low, while lowland regions, characterized by higher current erosivity, exhibit more moderate future changes. This altitudinal contrast points to a partial redistribution of erosive potential. These findings highlight a growing erosion risk in alpine environments under future climate conditions.
How to cite: Ahmed, M., Dallan, E., Panagos, P., Vohnicky, P., and Borga, M.: Future Evolution of Rainfall Erosivity in the Greater Alpine Region from Convection-Permitting Climate Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18825, https://doi.org/10.5194/egusphere-egu26-18825, 2026.
In wildfire-affected landscapes, climate variability and fire burn severity jointly determine how long the soil surface remains exposed and how quickly vegetation recovers. In practice, annual basis soil loss estimates often overlook the seasonal dynamics between exposure and stabilization, which are strongly driven by rainfall and drought timing. This study examines how rainfall and drought impact vegetation recovery and the risk of soil erosion in the Upper Cache Creek watershed, following the 2018 Ranch Fire in the southwestern United States. We monitored biome-specific vegetation cover dynamics and postfire recovery using seasonal time series of the Normalized Difference Vegetation Index (NDVI) derived from Landsat-8 imagery spanning 2016 to 2024. We mapped fire burn severity using the differenced Normalized Burn Ratio (dNBR) and quantified drought stress from the Gridded Surface Meteorological (GRIDMET) dataset using the Standardized Precipitation–Evapotranspiration Index. Rainfall erosivity density was reconstructed by integrating long-term mean annual precipitation from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) with a high-temporal-resolution station-based erosivity dataset, thereby providing an updated rainfall erosivity factor for the study period. We further derived high-resolution soil erodibility and topographic parameters from the Soil Survey Geographic database and the Shuttle Radar Topography Mission digital elevation model, respectively. Geospatial data processing and model parameterization were conducted using Google Earth Engine and Quantum Geographic Information System. Results show that forest and shrubland exhibited an exponential recovery pattern in moderate-to-high-burn-severity areas, with a half-recovery period of about 2 to 3 years. By the sixth year following the fire, the two vegetation types had significantly rebounded, reaching 69% recovery in forests and 76% in shrublands compared to prefire conditions. Grasslands responded erratically, marked by rapid greening during the first postfire wet season and declines in subsequent drought years. In line with these vegetation trends, RUSLE estimates indicate that the largest erosion pulse occurred in the first postfire year, when high rainfall erosivity coincided with widespread soil exposure. As a result, mean annual soil loss rates in fire-affected areas were up to fourfold relative to the prefire values. With rainfall erosivity closer to baseline conditions in 2024, erosion remained double due to intervening drought years, which suppressed early recovery gains by up to 40%. RUSLE soil loss estimates were validated with observed sediment yield at the watershed’s outlet and showed strong agreement across most postfire years. The observed sediment yield in 2017 remained notably higher relative to the estimated value. This anomaly was likely influenced by seasonal sediment-flushing operations, given the presence of two large upstream reservoirs. The results show that the interaction between rainfall and drought events governs postfire recovery and erosion, highlighting the importance of accounting for their timing, especially in annual assessments of postfire erosion.
Keywords: Wildfire, Soil Erosion, Vegetation Recovery, RUSLE, Rainfall Erosivity
How to cite: Damtie, M. T., De Michele, C., and Ravi, S.: The Role of Postfire Rainfall and Drought Timing in Vegetation Recovery and Soil Erosion Risk, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19844, https://doi.org/10.5194/egusphere-egu26-19844, 2026.
The vegetation of South Africa’s Northern Cape forms part of the Succulent Karoo biome, a globally significant dryland system characterized by exceptionally high succulent diversity and endemism. During drought periods and because of land-use change, reduced vegetation cover can enhance the frequency and intensity of aeolian activity by facilitating sand transport. This process reinforces wind erosion and increases the susceptibility of the remaining vegetation to mechanical disturbance.
Although succulent species are generally well adapted to arid environments, their responses to intensified aeolian impacts remain insufficiently understood. Increased abrasion, tissue damage, and partial burial caused by sandblasting may impair plant functioning and survival, thereby weaken the stabilizing role of vegetation and promoting positive feedbacks between vegetation loss and soil degradation. Despite the importance of these mechanisms for dryland degradation, the direct effects of aeolian abrasion on succulent vegetation and their contribution to land degradation dynamics are still poorly quantified.
This study investigates the effects of sandblasting on individual succulent plants using controlled wind tunnel experiments. Experimental treatments combined wind speeds ranging from 2 to 12 m s⁻¹ with systematically varied sediment loads, including differences in sand concentration and grain-size composition, to determine damage thresholds and evaluate the effects of repeated sandblasting events. The experimental framework enables a clear separation of aerodynamic and sediment-related controls, reflecting both present-day conditions and plausible future sandstorm scenarios in degraded dryland environments. Plant responses were quantified at the individual scale using high-resolution imaging to document abrasion patterns, tissue degradation, and structural alterations.
The results are expected to provide quantitative insights into the role of aeolian processes in driving vegetation degradation and reinforcing land degradation in the Northern Cape. By linking wind erosion intensity to plant-level damage, this study contributes to a mechanistic understanding of degradation pathways in arid and semi-arid rangelands.
How to cite: Fister, W., Nyffenegger, L., and Niederberger, D.: Effects of Wind-Induced Sand Abrasion on Succulent Plant Communities in the Northern Cape, South Africa., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22132, https://doi.org/10.5194/egusphere-egu26-22132, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 2
EGU26-20591 | Posters virtual | VPS17
Study on Sediment Sorting Characteristic sand Transport Mechanism of Engineering Accumulation Slope ErosionWed, 06 May, 14:09–14:12 (CEST) vPoster spot 2
In order to reveal the sorting characteristics and transport mechanism of sediment on the steep slope Of engineering accumulation driven by runoff,three simulated runoff scour experiments were designed under The conditions of 10,20,and30 L/min from above to analyze the particle distribution characteristics of erosion sediment on the steep slope(32°) of the accumulation body of the Yangling project. The results showed that the clay and fine silt in the eroded sediment (before dispersion) increased significantly compared with the original soil, which was easy to produce erosion. The influence of runoff on aggregate fragmentation of erosion sediment clay content and the influence of runoff on pellet crushing effect on clay content is negative when runoff power is less than 1.709N/(m•s), but positive when runoff power is greaterthan3.89N/(m•s). In sediment，fine and coarse silt particles are mainly transported in the form of single grain, while clay and sand particles are mostly transported in the form of aggregates. Clay particles are enriched and sand particles are depleted. The sediment particle size determines the main transport mode,<0.11mm sediment particles are mainly suspended saltation transport,>0.11mm sediment particles are mainly rolling transport,More than 80% erosion sediment particles are transported by suspended saltation, and the contribution rate of rolling transport increases first and then decreases with the increase of runoff transport capacity. The conclusion of this study will help to reveal the micro mechanism of slope water erosion process of engineering accumulation body, and provide scientific basis for improving the prediction accuracy of slope water erosion model of engineering accumulation.
How to cite: Gao, Z.: Study on Sediment Sorting Characteristic sand Transport Mechanism of Engineering Accumulation Slope Erosion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20591, https://doi.org/10.5194/egusphere-egu26-20591, 2026.
EGU26-14894 | ECS | Posters virtual | VPS17
Toposequence-driven variability on soil properties redistribution at irrigated semi-arid landscape, Northeastern AlgeriaWed, 06 May, 14:12–14:15 (CEST) vPoster spot 2
Agriculture in semi-arid Mediterranean regions contributes significantly to local food production and rural livelihood. Nevertheless, it depends strongly on irrigation to sustain crop production and soil fertility. With the terrain complexity present, irrigation can lead to downward and lateral transfer of soil particles and nutrients, thus intensifying and accelerating the complex interplay between leaching and erosion, which in turn, reduce soil productivity and create spatial fertility imbalances. This study addresses the lack of knowledge about these processes to support better soil management in Bir Bouhouch irrigated perimeter with complex terrain characteristics in northeastern Algeria , which represents a strategic agricultural area mainly producing cereals .The area has been used for intensive agriculture since the expansion of irrigation schemes in recent decades .In This study the vertical and catenary variability of physicochemical characteristics of soils were examined. Four soil profiles along a toposequence from the summit (P1), to the toeslope (P4) were described and soils samples were collected in different depth to physicochemical characterization (texture, pH and electrical conductivity in water (EC), active lime, organic matter (OM), total nitrogen and extractable phosphorus. All profiles showed alkaline pH (8.10–8.60) with low EC (0.23–0.58 dS/m) that increased progressively from the summit to the teslope as well as with depth. Surface horizons (0- 60 cm) at downslope profiles showed finer textures with transition from silty clay to loamy clay and higher OM contents (up to 17.2 g/kg) compared with the summit (9.00 g/kg), indicating possible downslope colluvial accumulation. Active lime increased but followed a bi-profile sequence along surface (from 55 to 145 g/kg for P1-P2 and from 35 to 105 g/kg for P3-P4) and with depth reflecting a possible carbonate leaching and re-precipitation under alkaline conditions, locally forming caliche horizons. Extractable P concentrations ranged from 0.05 to 0.07 mg/kg and were enriched at lower slope positions at surface horizons. Besides total N (0.8–1.5 g/kg) showed limited vertical and lateral variation. These patterns demonstrate that soil variability along the transect can be mainly controlled by the topography-driven redistribution and carbonate dynamics enhanced by irrigation.
This work was funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the projects UIDB/04129/2020 and UID/04129/2025 (LEAF) and LA/P/0092/2020 (TERRA).
How to cite: Kouider, K., Benhalima, Y., Mazouz, E. H., Santos, E., and Arán, D.: Toposequence-driven variability on soil properties redistribution at irrigated semi-arid landscape, Northeastern Algeria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14894, https://doi.org/10.5194/egusphere-egu26-14894, 2026.
