The construction of bench terraces has become a common practice in north-central Portugal for establishing eucalyptus plantations on steep hillslopes. Terracing is commonly considered as an effective soil conservation technique for steep terrain, with a long tradition in agricultural practices. This method typically involves extensive redistribution of topsoil, potentially causing substantial changes in the soil's physical, chemical, and biological properties, as well as its associated functions. Additionally, modern terrace construction using bulldozers can exacerbate soil instability, leading to soil mobilization through the collapse of risers. Modern forest terracing transforms hillslopes into flat terrain, facilitating the use of machinery for planting, fertilization, vegetation control, logging, and log extraction. However, terracing removes all surface vegetation, leaving the soil exposed to raindrop impact. Moreover, the mechanical forces exerted by bulldozers break, mix, and loosen the soil, detaching particles and compromising soil structure and stability. Historically, terraces were separated by stone walls that provided structural stability and erosion control. Today, however, they are often divided by steep, unsupported sections known as risers. Despite their widespread use in forestry, the impacts of modern terracing on water and soil conservation remain poorly studied. This study aimed to (1) quantify the collapse of modern forest terrace risers during the first year after construction and (2) evaluate the effectiveness of mitigation measures such as anionic polyacrylamide (PAM) and hydromulch. In a eucalyptus plantation in north-central Portugal, nine sediment fences were installed at the base of three different risers (three pairs per riser). One riser was left as a control, while the others were randomly treated with PAM or hydromulch. Preliminary results revealed substantial soil mobilization from the risers, with a median sediment deposition of 258 Mg ha⁻¹ during the first post-terracing year. Landslides affecting risers from top to base were frequently observed, further demonstrating their instability. Both hydromulch and PAM treatments significantly reduced cumulative annual sediment deposition to averages of 113 and 105 Mg ha⁻¹, respectively. However, neither measure completely prevented the collapse of certain riser sections. These findings highlight the urgent need for multidisciplinary approaches to assess and mitigate the adverse effects of modern terracing in forest plantations.
How to cite: Martins, M., Simões, L., Basso, M., Gholamahmadi, B., González-Pelayo, O., Ben-Hur, M., and Keizer, J.: The erodibility of terrace risers in eucalyptus plantations in North-Central Portugal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14029, https://doi.org/10.5194/egusphere-egu25-14029, 2025.
Wildfires affect land surface and post-fire geomorphological activity worldwide, increasing surface runoff and soil erosion. Here, we present a global assessment of post-fire soil erosion, considering cumulative wildfire driven geomorphological changes over the last two decades. Stemmed from the largest database on wildfires occurrence and fire severity in the globe, this study estimates global trends of post fire soil erosion together with the recovery of those burned landscapes.
Our results show that when considering multiple wildfire events, global post-fire soil erosion accounts for 8.1 ± 0.72 Pg annually, representing 19% of the global soil erosion budget, and additional 5.1 ± 0.56 Pg soil erosion annually in comparison to pre-fire conditions. Moreover, soil erosion attributed to the first post-fire year represents 31% of the total soil erosion, whereas the remaining share can be attributed to previous wildfires occurrences. In what concerns the spatial distribution, Africa is the continent that is impacted the most in terms of post-fire soil erosion, given its significantly larger burned area.
The results of this study can illustrate the magnitude of post-fire soil erosion globally, and therefore support post-fire management actions towards the mitigation and restoration of affected areas, and policies towards Land Degradation Neutrality.
How to cite: Vieira, D., Borrelli, P., Scarpa, S., Liakos, L., Ballabio, C., and Panagos, P.: Post-fire soil erosion. How much land are we degrading globally?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10986, https://doi.org/10.5194/egusphere-egu25-10986, 2025.
Soil and water conservation is a pressing global challenge, exacerbated by land use changes, cover transitions, and extreme rainfall events. Rainfall plays a pivotal role in soil erosion due to its ability to detach and transport soil particles. Understanding its spatial and temporal variability is critical for devising effective conservation strategies. Rainfall erosivity studies typically focus on the RUSLE R-factor to quantify monthly and annual trends. However, these broader timescales may overlook event-based dynamics captured by metrics like EI30, which considers storm kinetic energy and maximum 30-minute rainfall intensity. Such event-based analyses are especially important in regions with irregular rainfall patterns or increasing extreme weather events. Developing countries lack spatial representativeness of sub-hourly monitoring stations, demanding modelling strategies to address these data limitations. Relying only on the traditional R-factor may underestimate the soil loss quantification in regions experiencing intense land use transition (e.g., deforestation and urban expansion) and significant changes in rainfall patterns due to climate change. Such underestimations can have severe consequences in the management of river and reservoir silting, loss of agricultural land, and increased landslide risks. In this context, technological solutions based on Artificial Intelligence (AI) and satellite data present a promising alternative for addressing these challenges in countries like Brazil. In this study, we proposed a general framework to estimate the EsRE in a spatiotemporal fashion by leveraging the strengths of AI and the temporal and spatial availability of satellite-derived rainfall data, such as CHIRPS (available since 1981). The traditional and well-stablished event-based seasonal model is used as baseline model. As case study, both the AI-based model and the seasonal model used daily rainfall and the fortnight of the event as inputs to estimate the EsRE (retrieved from monitoring stations available since 2015 in the metropolitan region of Belo Horizonte, Brazil). The predictive performance of the models was evaluated using R², Nash-Sutcliffe Efficiency (NSE), and Pbias. Additionally, a bootstrap approach was employed to assess the uncertainty of the models. To evaluate the local applicability of the proposed model, we analyzed the impacts of the improved EsRE on the erosion process in the peri-urban Ibirité watershed, which faces intensified erosion and reservoir siltation. The AI-based model outperformed the traditional model, achieving R², NSE, and Pbias of 0.73, 0.73, and 2.4%, respectively, compared to R², NSE, and Pbias of 0.62, 0.62, and 0.13% for the traditional model. The observed uncertainty on EsRE simulation was expected due to the parsimonious model and problem complexity. Nevertheless, the AI-based model was able to track the overall spatiotemporal pattern, shedding light on the potential of this approach in modelling EsRE in poorly monitored regions. When replacing rainfall observations with CHIRPS-retrieved rainfall, uncertainty of EsRE increased, which was improved after bias correction. The bias correction addressed challenges related to intense rainfall events, such as convective storms, which are often underrepresented in satellite-derived data. The AI-based model improved the spatiotemporal analysis in the Ibirité watershed. Future studies should test other AI-based structures, input variables, and calibration strategies to decrease uncertainties on EsRE simulations.
How to cite: Rodrigues, A., Brentan, B., Bezerra, R., and Eleutério, J.: Spatiotemporal assessment of event-scale rainfall erosivity (EsRE): a modelling approach based on artificial intelligence and satellite information, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4767, https://doi.org/10.5194/egusphere-egu25-4767, 2025.
Soil erosion research is essential for sustainable development due to its significant impact on soil health through different erosive processes. One of these processes is subsurface erosion by soil piping, which is often overlooked in research. Recently, some progress has been made in detecting surface piping features using UAVs, while the identification of underground pipes remains challenging. Therefore, this study focuses on the innovative geophysical approach in studying subsurface soil erosion. The main aim is to assess the effectiveness of electrical resistivity tomography (ERT) in detecting soil pipes. Field experiments were conducted in the Bieszczady Mountains (Carpathians, SE Poland), alongside theoretical modelling using Resistivity 2D software. The findings were compared with existing research and validated through trenching. We evaluated different measurement settings, including array configurations (Wenner – W, Wenner-Schlumberger – WS and dipole-dipole – DD), electrode spacing, and measurement directions along the pipe system, to assess their effect on detecting pipes regarding their size, shape, and depth. We performed six ERT profiles in the field and we modelled the electrical response of a theoretical void at various subsurface positions, assuming the root-mean-squared error (RMS) of 0% and 5%. The results revealed that higher resistivity anomalies correspond to pipes, with the DD configuration showing lower resistivity (105 Ωm) compared to the W and WS configurations (268–427 Ωm). A comparison with other studies suggests that there is no universal threshold for confirming the presence of soil pipe; rather, a clear electrical contrast with the surrounding area is crucial. Our findings suggest that while all tested configurations effectively detect pipes, the choice of configuration impacts image quality. We recommend using the WS configuration for detecting both vertical and horizontal features. The number of anomalies influences the RMS and should be critically evaluated during surveys. These findings can help researchers and practitioners in designing more effective ERT studies in different environments to detect subsurface soil pipes.
The study is supported by the National Science Centre, Poland within the first author’s project SONATINA 1 (UMO-2017/24/C/ST10/00114).
How to cite: Bernatek-Jakiel, A. and Kondracka, M.: Assessing electrical resistivity tomography (ERT) to detect soil pipes: theoretical modelling and field experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-571, https://doi.org/10.5194/egusphere-egu25-571, 2025.
Agricultural-driven land-use changes have extensively reshaped landscapes, leading to increased soil erosion and water demand. Achieving long-term agricultural sustainability requires a balanced interaction among water resources, land cover, and climate. However, how this interconnected system will respond to climate change remains uncertain. This study aims to assess (i) the current impacts of land cover changes on water balance variables and soil erosion and (ii) the projected impacts of future climate conditions. Field observations were conducted on 100 m² experimental plots in Brazil, maintained over the past decade. We evaluated the long-term trade-offs between common agricultural land covers—sugarcane, pasture, and soybean—and their effects on runoff and soil loss rates. Results were compared to those for native forest (wooded Cerrado) and bare soil. A significant difference between agricultural land and native forest were found. For instance, areas converted to pasture experienced nearly 20 times higher runoff, while sugarcane cultivation resulted in soil loss rates five times greater than native forest. To analyze future impacts, we applied the Universal Soil Loss Equation (USLE) and Hydrus model, integrating them with CMIP6 climate projections under SSP2-4.5 and SSP5-8.5 scenarios for the intermediate (2040–2070) and distant future (2071–2100). The results indicated that climate change will variably affect water flux components in a hierarchical sequence: soil-water storage, bottom flux, infiltration, surface flux, evaporation, and root uptake. For example, we estimated an increase of 23% in root water uptake and reduction of 8% in soil-water storage in sugarcane. This pattern was consistent across all types of land cover, differing primarily in magnitude. Regarding soil erosion, our projections indicated increases of 4.9% under SSP2-4.5 and 7.6% under SSP5-8.5 scenarios for all land covers. The observed soil loss rates highlight the critical need for sustainable land management to mitigate soil degradation. Notably, ongoing land cover changes pose a greater risk to water fluxes than projected climate changes. However, shifts in rainfall patterns due to climate change are likely to increase rainfall erosivity, amplifying soil erosion potential. Consequently, land conversion presents substantial risks to soil stability at both local and continental scales. These findings underscore the urgency of adopting targeted soil and water conservation strategies in the Cerrado biome. By mitigating soil erosion and promoting sustainable land-use practices, these strategies can help balance agricultural productivity with ecological preservation under current and future climate scenarios.
How to cite: Schwamback, D., R. Amorim Brandão, A., Zhang, L., Berndtsson, R., Wendland, E., and Persson, M.: What is more impactful on water fluxes and soil erosion: changes in land cover or climate variability? , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12570, https://doi.org/10.5194/egusphere-egu25-12570, 2025.
Land cover in areas undergoing long-term shifting cultivation typically represents a mosaic of agricultural fields, fallows in different stages of regrowth, remnant forest, and degraded grasslands. The Ankeniheny Zahamena corridor in eastern Madagascar represents a case in point. Previous research revealed major differences in hydrological response between forests, fallows or reforested sites, and degraded grasslands. We used these field data in the physically-based RoGeR_Dyn model[1] to examine the effects of topography and land cover on hydrological processes at the meso-catchment scale (58 km²). Using historic land-cover maps, predicted deforestation rates, and a theoretical reforestation rate, we created eight land-cover scenario’s with forest fractions ranging from 25 to 97% and combined these with five rainfall scenario’s (range: 1050 – 2100 mm/year).
The simulations showed that total evapotranspiration increased with rainfall and was ~20% less for the most degraded land-cover scenario than for a land cover dominated by (non-degraded) forest in all rainfall scenario’s. Topsoil infiltration capacities were reduced following soil degradation but still exceeded maximum hourly rainfall intensities. The lower vegetation water use in the degraded scenario produced wetter soil conditions, leading in turn to moderate increases in saturation-excess overland flow as well as deep percolation (baseflows). Deep percolation increased >2.4 times when annual rainfall was increased two times (regardless of land cover). The total stormflow in the dry scenario was more than three times larger for the most degraded land cover than under full forest (48 mm vs. 14 mm) and increased by an order of magnitude when rainfall was doubled (359 mm vs. 220 mm, respectively). Restoring forest on degraded soils is thus seen to change runoff processes, mostly by reducing overland flow and stormflow, and to a lesser extent by decreasing deep percolation due to increased evapotranspiration.
Above all, the simulations show that a hydrological model driven by measurements at the point- or plot-scale and very limited calibration, can provide useful information on how climate, land cover and soil degradation together affect the occurrence of high and low flows.
[1] Schwemmle, R., Leistert, H., Steinbrich, A., and Weiler, M.: RoGeR v3.0.5 – a process-based hydrological toolbox model in Python, Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, 2024.
How to cite: Zwartendijk, B., van Meerveld, I., Bruijnzeel, S., Batenburg, S., Ghimire, C., Leistert, H., Weiler, M., and Teuling, R.: Combining field data and a spatially distributed model to understand the effects of land cover, soil degradation, and climate variability on the hydrological response of a meso-scale catchment in Eastern Madagascar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1687, https://doi.org/10.5194/egusphere-egu25-1687, 2025.
Surface Coal Mining activities have significant effects on vegetation and land use/land cover (LULC), resulting in environmental degradation and changes in ecosystem services. In this study, geospatial methods, incorporating spatial autocorrelation tools like Moran's I, were adopted to identify and assess notable spatial patterns of Normalized Difference Vegetation Index (NDVI) and LULC alterations in areas impacted by mining for the years 1997 and 2022 in the Damodar River basin, India. Spatial autocorrelation assessments were performed in ArcGIS to identify patterns of clustering and dispersion in NDVI and LULC changes to get an idea about the emerging hot spot and cold spot patterns influenced by surface coal mining in the Damodar River basin, India. The methodology involves calculating Global Moran’s I to analyze overall spatial trends and the Mann Kendall Trend test to analyse the temporal trend. Following this, Hot Spot Analysis (Getis-Ord Gi*) are employed to identify regions undergoing notable vegetation decline or shifts in land use. The spatial weights matrix, an essential element for these evaluations, is configured to reflect spatial relationships, such as contiguity or distance-based interactions.
Initial findings reveal significant clusters of NDVI decline in active mining areas, aligning with widespread deforestation and land cover transformations from natural green cover to mining infrastructure, the mining area shows an increase of 6.89 per cent of the total geographical area of the basin. Hotspot analysis indicates crucial locations that necessitate prompt environmental intervention. The whole basin exhibits a statistically significant temporal trend of high-value aggregation of NDVI. This research underscores the effectiveness of spatial autocorrelation tools in tracking and managing the ecological consequences of mining operations. The results offer valuable information for policymakers and environmental managers to focus restoration efforts and adopt sustainable land use strategies.
How to cite: Anjali, K. and Remesan, R.: Hotspots Analysis and Spatial-temporal Trends of NDVI and Land Use Land Cover Changes in Surface Coal Mining -Affected Regions Using Spatial Autocorrelation Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19697, https://doi.org/10.5194/egusphere-egu25-19697, 2025.
The world faces unprecedented land-use pressures, and one of our societal roles as geoscientists is to document, measure, and analyse landscape degradation, producing understanding that can mitigate or help prevent ongoing damage. We do this in a framework of prior studies that shape the way we understand the system and how it operates, which thereby also shapes how we construct research questions and design data collection. This is, of course, how science operates. However, sometimes we need to step back and examine the foundations of our guiding framework.
Doing this exercise for geomorphologic interpretations of landscape change in the Global South reveals a buried legacy of colonial-era assumptions and assertions about harmful impacts of indigenous/traditional land-use practices: a “narrative of blame” that targets Global South populations. Global North colonists, seeing unfamiliar countryside managed with unfamiliar techniques, wrote interpretive descriptions of what they perceived as degraded landscapes—but which were based primarily on their experiences elsewhere and/or which drew on gut feelings coming from a lack of local knowledge and inherent disdain for the native population and their methods. These ideas were published, repeated, restated and rephrased, achieving over time the status of received wisdom. They are still recycled today, as part of literature review and project justification. They provide rationalisation for assumptions that we build into project design, and they give license for interpretations on the basis that overarching controls have already been established; e.g. “It is well known that …… and therefore …. “. But tracing individual precepts back through the literature often reveals that in fact the variables in question have never been subject to rigorous testing or verifiable measurement.
Examples of the impacts of these colonial narratives on modern science are widespread, and include over-interpretation of small amounts of data (e.g. short-term and/or small-scale measurements in areas of high erosion being extrapolated to represent regional or national erosion rates) as well as conclusions being formulated without perceived need to perform measurements or comparative analysis (e.g. inferring that because deforestation elsewhere has been linked elsewhere with erosion, tree removal in a study site must also have caused rapid and intense soil loss).
This is not to say that humans do not cause erosion or landscape degradation. Damage that we do throughout the world is indisputably documented. But there is a clear imbalance in the way we measure and analyse geomorphic change, and—particularly in the Global South—there is a history of embedded assumptions, fed by strong implicit bias that indigenous and traditional land-use practices are inherently damaging. This means that many projects are (unintentionally) preconditioned to return results that will be in line with expectations set by the governing assumptions. Which of course strengthens those assumptions. To properly quantify and understand anthropogenic impacts on the landscape we must test all our embedded expectations. The colonial-era narrative of blame is pervasive and deeply entangled in our science. It is our job to learn to identify it and uproot it. And to avoid setting expectations in project design and analysis.
How to cite: Cox, R. and Rakotondrazafy, A. F. M.: Setting expectations: how colonial narratives continue to shape our analysis of geomorphology in the Global South, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7130, https://doi.org/10.5194/egusphere-egu25-7130, 2025.
To reduce the potential threat of soil loss due to ephemeral gullies, it is crucial to adopt Best Management Practices (BMPs) that prevent damage to landscapes by reducing sediments load. This study combines two approaches to evaluate and optimize BMPs for reducing sediment load from sheet/rill and ephemeral gully erosion. The research applied a novel methodology integrating a genetic algorithm with the Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) to optimize the model and also strategically select and place BMPs in Southern Ontario, Canada, to reduce sediment load cost-effectively. The study assessed five BMPs: cover crops, grassed waterways, no-till, conservation tillage, and riparian buffer strips. Considering the average annual sediment load, riparian buffer strips were consistently successful in decreasing average annual sediment load of sheet/rill erosion, with 69% reduction efficiency. Similarly, grassed waterways were the most effective BMPs for reducing average annual sediment load of ephemeral gully erosion, with an efficiency of 81%. These BMPs were integrated into a cost-optimization framework, demonstrating that strategic placement of BMPs could enhance their efficiency. The optimized placement reduced sheet/rill sediment load by 84.6%, ephemeral gully by 85.4%, and total erosion by 86.3%, achieving these results at minimal cost. The study highlights the significance of targeted BMP placement rather than uniform implementation across entire watersheds. This integrated approach is a viable solution for watersheds with limited resources, facilitating decision-makers and aiding in the adoption of BMPs that can comprehensively reduce sediment load. The developed model in the current study can be applied by decision makers in other watersheds with limited resources for implementing BMPs.
How to cite: Daggupati, P., Mohebzadeh, H., Biswas, A., Rudra, R., Devris, B., and Yang, W.: Optimizing Best Management Practices for Efficient Sediment Load Reduction in Agricultural Watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12661, https://doi.org/10.5194/egusphere-egu25-12661, 2025.
Savanna ecosystems cover about 50% of Africa, forming a spectrum from grasslands with scattered trees to bush thickets. These ecosystems are shaped by water availability, seasonal rainfall patterns, grazing by wild and domestic animals, and soil and landform features that influence nutrient and water distribution. The Greater Maasai Mara Ecosystem (Mara) in Kenya is a prime example of such an ecosystem, rich in biodiversity but increasingly degraded by invasive species expansion, climate change and agricultural activities. Degradation processes such as reduced vegetation cover due to overgrazing from livestock herds, leads to soil exposure, increased soil erosion and decreased organic matter inputs, and subsequent impacts on nutrient cycling and biotic activity.
In response to soil degradation in the Mara, afforestation using native plant communities has been proposed as a restoration strategy. Research has focused on identifying optimal tree species for soil restoration through experimental treatments. Three 50 m x 50 m treatment plots were established: Savanna Mimic, reflecting native woodland patterns; Diversity-Rich, with 30 tree species; and Themed Species Assortments, focusing on bioculturally relevant species. A fourth control plot outside the fenced area represented degraded conditions.
Soil samples collected from the plots revealed significant insights. The control plot exhibited the lowest soil organic carbon (SOC) stock, about 40% lower than the treatments. This decline in SOC was linked to overgrazing, which limits plant growth and rhizodeposition, reducing carbon inputs to the soil. The control area also had the highest Carbon Quality Index (CQI = 0.64), indicating highly resistant organic matter, and a high dsDNA:SOC ratio (4.20 mg g–1), suggesting microbial communities are under stress due to limited organic carbon.
The Diversity-Rich plot emerged as the most effective restoration strategy, promoting SOC accumulation with higher carbon quality (CQI = 0.57). This treatment avoided nitrogen (N) and phosphorus (P) limitations, with the highest ratios of microbial C:N, N:P and C:P acquiring enzymes and available P concentration (18.7 mg kg–1). Enhanced nutrient cycling in the Diversity-Rich treatment highlights the role of plant diversity in restoring degraded soils.
Overall, the study demonstrates that overgrazing significantly depletes SOC and disrupts ecosystem functions. High-diversity planting schemes offer the most promising outcomes for soil restoration, enhancing carbon storage, nutrient availability, and microbial activity, thus contributing to the resilience of savanna ecosystems. However, we are aware that further monitoring activities should be carried out to have a more reliable picture about the effect of the applied restoration on the investigated soil properties.
How to cite: Di Bonito, M., De Feudis, M., Falsone, G., Trenti, W., Guatelli, F., Vittori Antisari, L., Boumezgane, A., Fornasier, F., Gichira, A., Nabaala, H., Njenga, P. K., Warui, C. M., and Limbua, P. G.: Restoring degraded savannas: a case study of soil carbon and nutrient dynamics in Kenya's Maasai Mara., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5949, https://doi.org/10.5194/egusphere-egu25-5949, 2025.
Posters on site: Wed, 30 Apr, 16:15–18:00 | Hall X4
Larger samples of measurement data unlock far-reaching opportunities to improve our predictive capabilities and process understanding in erosion and sediment load modelling through: (i) streamlining model applications to compare performance and understand model generalizability across differing environments, (ii) improving upscaling capacity and predictive capabilities in unmonitored locations via data-oriented approaches, and (iii) developing new modelling approaches better suited to data ingestion. Despite the tangible benefits of applying soil erosion models over multiple spatial domains, indicative overviews of modelling efforts show efforts in 3 or more catchments remain considerably less abundant. This study synthesises the currently available measurement data available for in-stream monitoring of hillslope sediment fluxes to stream channels in Europe. By combining purpose-compiled community data from small to medium catchments in EUSEDcollab (a European Union Soil Observatory initiative) with other open-access water quality data (e.g. GEMSTAT) and other resources, we give an overview of the current state-of-the-field. Key findings show: (i) data is significantly less abundant from small catchment drainage areas, limiting the potential inferences on hillslope processes, (ii) catchment data on the long-term average annual sediment load (e.g. statistical aggregations) is significantly more abundant than time series data, reflecting limited open sharing of historical measurement data, (iii) sediment load measurements are decreasing in modern time periods, limiting our potential to capitalise on modern revolutions in domain-agnostic geospatial data (e.g. remote sensing data). Further community efforts to compile current and legacy data across Europe with FAIR (Findable, Accessible, Interoperable, Reusable) standards are vital for scientific advancements and data rescue, following similar data sharing efforts (e.g. CAMELS for hydrology). Extensions of catchment data with large-scale feature compilations of (time series) of hydrometeorological, soil and management attributes data may further strengthen efforts to provide ready-to-use data for models. To conclude, open data is pivotal for multi-scale, open, and collaborative research which requires ongoing collaboration between research groups, national agencies, and multi-national institutions.
How to cite: Matthews, F., Vieira, D., Panagos, P., Saggau, P., Kaffas, K., Tan, F., and Borrelli, P.: Towards large-sample data availability for applications in soil erosion and sediment transport studies in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10939, https://doi.org/10.5194/egusphere-egu25-10939, 2025.
A variety of applications are informed by hydrological and agricultural models that simulate soil erosion using point-scale precipitation inputs. This becomes a challenge in an era of climate change because most climate projections are only available over coarse grids, yet soil erosion modeling is sensitive to the spatial resolution of precipitation input. This study aims to demonstrate a recently developed global dataset of CLIGEN parameters in terms of driving soil erosion models. The study examines the sensitivity of soil erosion to spatial resolutions of precipitation inputs of CLIGEN and other popular grid datasets. Case studies of the climate drivers involved model simulations at selected international sites using the Water Erosion Prediction Project (WEPP) model and the Rangeland Hydrology and Erosion Model (RHEM). The modeling results show point-scale precipitation leads to considerably higher erosion rate than the grid-scale precipitation. Such scale dependence is expected to be more pronounced under the future climate due to the intensification of storm intensity. Overall, the research outcome can facilitate environmental assessments globally and provide insight into the expected changes in soil erosion and conservation under climate change.
How to cite: Gao, S. and Fullhart, A.: A Global CLIGEN Parameter Dataset to Enable Soil Erosion Modeling Driven by Point-Scale Precipitation Dynamics , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2619, https://doi.org/10.5194/egusphere-egu25-2619, 2025.
Identifying anthropic infrastructure and soil erosion systems is critical for analyzing the P-factor of RUSLE (Revised Universal Soil Loss Equation) formula which plays a crucial role in assessing the effectiveness of conservation practices at reducing soil erosion. SEEDs is a CMCC (Mediterranean Centre for Climate Change) project for IFOA (Training organization) focused on the production of high-resolution assessments of soil erosion risk, combining Earth Observation data and advanced analytics to support sustainable land management in Italy. The current work explores semantic segmentation of Sentinel-2 imagery, utilizing both its native 10-meter resolution and super-resolved 3-meter images, to map urban infrastructure (buildings and roads) and agricultural terraces critical for soil erosion analysis, within the context of SEEDs project.
It is focused on two distinct areas in Italy: the Lattari Mountains (Campania, southern Italy) and the Idice river basin (Emilia-Romagna, northern Italy), which was impacted by significant flood events in May 2023 and September 2024. These regions differ in landscape characteristic and erosion control measures, providing a valuable comparison to evaluate influence of these factors on erosion dynamics.
The methodology incorporates deep learning U-Net architectures, fine-tuned using Sentinel-2 multispectral data and elevation (DEM) data. Mask preparation for training and validation involves data from OpenStreetMap, Corine Land Cover and visual interpretation using QGIS software, specifically for Liguria terraces mapping, which were used as a unique training dataset for identifying terraced landscapes in the study areas, due to the scarcity of available labelled datasets.
The analysis mapped key infrastructures in the study areas using both original resolution and super-resolved imagery. In addition, preliminary results indicated differences in infrastructure before and after the flood events of 2023, suggesting potential impacts on both agricultural lands and urban areas. This approach demonstrates potential for precise urban and agricultural mapping in erosion-prone landscapes. Ongoing work focuses on refining model performance and validating results across diverse terrains and regions, ultimately enhancing soil erosion risk assessments and supporting more effective land management strategies.
How to cite: Magazzino, S., Fazzini, N., Ricciardelli, A., Folegani, M., and Houël, M.: Semantic Segmentation of Super Resoluted Sentinel-2 Images for Urban and Agricultural Surface Mapping in Soil Erosion Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9981, https://doi.org/10.5194/egusphere-egu25-9981, 2025.
Splash erosion is one of the form of water erosion, where the falling raindrops hit the soil surface and result in detachment and ejection of splashed material, and transport thereof over different distances. This process causes the loss of soil material, the breakdown of soil aggregates or is responsible for the displacement of microorganisms, pathogens, and pollutants within the ejected particles. The ejection of a mixture of solid (soil) and liquid (water) phases is one of the aspects of the energy dissipation of the impacting drop during the splash. Therefore, the aim of the study was to present the method for the estimation of the falling drop energy that was used for the transportation of the ejected material, while considering that such material was a mixture of solid soil particles and water droplets and therefore, “two-phase”.
The use of an innovative multi-method approach allowed for the characterization of ejected particles during soil splash phenomenon affected by a single drop impact. The mass measurements provided by a splash cup and image analysis method based on high-speed cameras gave the possibility to determine average density of the splashed material, the number of ejected particles, their sizes and masses, and their ejection velocities. Consequently, it enabled the calculation of the summed kinetic energy of ejected particles expressed as a percentage of falling drop energy transferred to splashed material. Based on the obtained results, this value ranged from 1% to 14 % and was strongly dependent on soil texture as well as initial moisture content. The study should be considered, in the context of physical modelling of splash erosion, as an important step for calculation of the energy balance of a single drop impact, i.e., the energy dissipation allowing for the determination of which processes absorb the kinetic energy of the falling drop during soil splash phenomenon.
This work was partly financed from the National Science Centre, Poland; project no. 2022/45/B/NZ9/00605.
References:
Beczek M., Mazur R., Ryżak M., Sochan A., Polakowski C., Beczek T., Bieganowski A.: How much raindrop energy is used for transportation of the two-phase splashed material? GEODERMA 425, 2022
Beczek M., Ryżak M., Sochan A., Mazur R., Polakowski C., Bieganowski A.: A new approach to kinetic energy calculation of two-phase soil splashed material. GEODERMA 396, 2021
How to cite: Beczek, M., Ryżak, M., Mazur, R., Sochan, A., Polakowski, C., Beczek, T., and Bieganowski, A.: Soil splash phenomenon - how much energy of raindrop is used for transportation of the splashed material?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-345, https://doi.org/10.5194/egusphere-egu25-345, 2025.
It is crucial to monitor soil loss at the plot scale to evaluate the effectiveness of different soil management practices in mitigating erosion. The present study presents the first-year results from a monitoring experiment conducted in Umbria, Central Italy, on six plots (22-metre-long with a 16% slope) under three soil management systems, replicated twice: i) control (CTR), i.e., traditional soil management with bare soil during the autumn-winter period and soil preparation in spring for sowing the cash crop (sunflower); ii) cover crop (CC) traditional management (CCT) where a CC mixture of hairy vetch (Vicia villosa Roth.) and rye (Secale cereale L.) is sown in September-October and devitalized by plowing in early spring before the sowing of the cash crop; iii) CC mulch-based no-tillage management (CCM), where the same CC mixture is sown in September-October and devitalized by roller-crimping in early spring, after which the following cash crop is directly sown in the mulched CC biomass. Despite the implementation of the CC, no statistically significant differences were observed in soil loss or runoff among the treatments during the first year. The primary factor contributing to this outcome was the occurrence of erosive events shortly after the sowing of the CC, which resulted in the formation of rills in the plots where seedbeds had been prepared. These rills, in turn, exacerbated runoff and soil loss during successive erosive events, thereby undermining the anticipated benefits of the CC. In contrast, conventionally managed soils, which did not necessitate seedbed preparation, did not demonstrate such rill formation. While the study reaffirms the importance of vegetative cover for soil conservation, it also highlights a potential drawback: soil disturbance associated with CC establishment can occasionally offset the benefits of the CC under certain conditions. This finding underscores the need for careful consideration of soil preparation practices in erosion-prone areas.
The study was financed by European Union-Next-GenerationEU-National Recovery and Resilience Plan (NRRP)–MISSION 4 COMPONENT 2, INVESTIMENT N. 1.1, CALL PRIN 2022 D.D. 104 02-02-2022–(Soil Conservation for sustainable AgricuLture in the framework of the European green deal-SCALE) CUP N. J53D23010340006.
How to cite: Vinci, A., Tosti, G., Parisi, A., and Vergni, L.: Impact of Soil Management Practices on Soil Loss and Runoff: First-Year Results from a Plot-Scale Experiment in Central Italy , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2123, https://doi.org/10.5194/egusphere-egu25-2123, 2025.
Hope Mwanake1, Gabriel Stecher1, Bano Mehdi-Schulz1, Karsten Schulz1, Nzula Kitaka2, Luke Olang3, Mathew Herrnegger1
1Institute of Hydrology and Water Management (HyWa), University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
2Department of Biological Sciences, Egerton University, P.O. Box 536 – 20115 Egerton-Njoro, Kenya
3Department of Biosystems and Environmental Engineering, Technical University of Kenya, Nairobi, Kenya
Abstract
The complexity of shared governance, the variety of land use practices that affect water quantity and quality, and the fluctuating socioeconomic conditions that lead to different regional priorities are some of the reasons that pose particular challenges for sustainable resource management for transboundary river basins. This study carried out in the Sio Malaba Malakisi River Basin (SMMRB), shared by Kenya and Uganda, examines how combining a scientific evaluation of local soil erosion risk together with farmer-reported views of soil erosion and field conservation measures enhances soil conservation efforts.
Through participatory surveys involving 200 farming households to gather data on their estimations of soil loss, and on their farm management practices, together with the regional application of geospatial analysis to estimate soil erosion with the Universal Soil Loss Equation (USLE), we identified significant discrepancies between farmer perceptions and estimated USLE soil erosion outcomes. While 60% of farmers reported visible soil erosion and 92% noted declining soil fertility, the spatial modeling of the USLE estimates revealed that over 76% of the region lacks effective soil and water conservation practices (SWCPs), leaving vast areas vulnerable to erosion. These findings highlight a substantial gap between farmer perceptions and modeled estimates, emphasizing the need for targeted interventions. Furthermore, transboundary water quality assessments revealed nutrient "hotspots" linked to erosion, stressing the need for joint management strategies to address shared challenges.
To address these challenges, a Best Management Practices (BMPs) scenario was developed based on the knowledge of the 200 farmers interviewed within the basin and the practices of "best-practice farmers" from the survey results. This scenario assumes that farmers are more likely to adopt measures already practiced in their vicinity, such as terracing for steep slopes or crop rotation for maize and beans, reflecting local topography and cropping systems. The BMP scenario predicts a 25% reduction in severely eroded areas, demonstrating the transformative potential of scaling up the SWCPs to reduce soil loss. These outcomes reinforce the importance of leveraging local knowledge to design regionally relevant conservation strategies.
In this study, farmers expressed a strong willingness to share their insights on soil erosion and conservation, highlighting the need for community-driven conservation efforts that integrate farmer knowledge into scientific frameworks. These efforts can lead to more effective erosion control and sustainable land management by fostering a sense of ownership and encouraging locally informed decision-making.
This research highlights the need for data-driven, context-specific conservation strategies in data-scarce regions like the SMMRB, emphasizing the importance of local data, and farmer engagement for the adoption of BMPs. The results provide a scalable model for comparable data scarce areas around the world and highlight the significance of coordinated transboundary collaborations, and inclusive capacity-building balancing between ecosystem restoration and sustainable livelihoods.
How to cite: Mwanake, H.: Integrating Farmers’ perspectives and Scientific Knowledge to Manage Transboundary River Basins Sustainably: A case study of the Sio Malaba Malakisi River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15493, https://doi.org/10.5194/egusphere-egu25-15493, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 3
EGU25-15186 | ECS | Posters virtual | VPS14
A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer OptimizationTue, 29 Apr, 14:00–15:45 (CEST) | vP3.4
Tilling, a common agricultural practice, is being done excessively on farms leading to about 2.35 billion tons of soil erosion from US croplands annually. This causes soil erosion, soil infertility, carbon release, nutrient runoff, and fertilizer over-usage. This paper evaluates whether optimizing tillage intensity, timing, and fertilizer quantity will address these problems. A convolutional neural network based machine learning model utilizes a camera-captured field image to determine existing tilling intensity on a 7-point scale. This machine learning output, along with soil sensor and external forecast data, flows into a 10-parameter algorithm that determines optimal tilling and fertilizer levels. A fully functional tractor prototype demonstrates the above. A 30-year simulation comparing conventionally-tilled and algorithm-tilled farms showed a reduction in carbon emission by 57%, fertilizer usage by 43%, and runoff by 86% demonstrating the transformative potential of this algorithm. Additionally, a stationary prototype was deployed in 155 farms across 5 countries.
How to cite: Magesh, S.: A Convolutional Neural Network Model and Algorithm Driven Prototype for Sustainable Tilling and Fertilizer Optimization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15186, https://doi.org/10.5194/egusphere-egu25-15186, 2025.
