This session will foster a discussion on the way out of the reliability crisis by rethinking current challenges in erosion modelling and proposing alternatives to push the science forward. As such, we welcome a wide range of contributions, from critical perspectives to applied research. Specifically, we encourage contributions dealing with:
(i) new approaches to modelling soil erosion
(ii) novel approaches to collecting soil-erosion data
(iii) the use of novel field and/or remote sensing techniques to improve model parameterisation and evaluation
(iv) new or improved methods for model calibration and model testing – particularly approaches that increase model falsifiability and/or that report case studies of model invalidation (if you have “bad” results, we want to hear about it!)
(v) uncertainty quantification and sensitivity analysis
(vi) the use of erosion models to develop and test hypotheses about soil systems
(vii) translating (uncertain) modelled erosion rates into risk assessments for policymakers
In addition, we expressly encourage contributions that provide critical yet constructive perspectives on soil-erosion modelling and that will enrich the session discussion. We also welcome interdisciplinary contributions bridging the gap between mathematical modelling, sociology and philosophy of science, and policy making.
Soil loss prediction models are designed to aid making decisions on land management. To do this, they predict erosion for existing and proposed land management options that are tenable on a given soil on a given topography in a given climate. The Universal Soil Loss Equation (USLE) and subsequent revisions of it provide the most widely used model for this purpose in the world. While the climate input is reportedly based on the product of storm kinetic energy (E) and the maximum 30-minute rainfall intensity (I30), the model ignores the fact that storm kinetic energy per unit of rain varies with the synoptic conditions that dominate the production of rainfall at a particular location. It also ignores the fact that runoff per unit area can vary with slope length and gradient. However, these failures exist for pragmatic reasons. The mathematical structure of the USLE models is based on predicting the long-term soil loss from the unit plot so that any method that enables that to be achieved can be used to predict erosion for situations that do not conform to the unit plot using appropriate USLE equations for factors such as slope length and gradient. WEPP has for a long time been touted as a replacement for USLE models in the USA but does not predict event soil loss on bare fallow areas better than the EI30 index. Also, parameterization of WEPP focussed on areas where crops are grown on ridges rather than planar slopes. Consequently, WEPP is not an appropriate replacement for USLE models in making land management decisions. Other models like APEX break mathematical rules.:
How to cite: Kinnell, P.: All soil loss prediction models are wrong, some more than others, some are useful, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3845, https://doi.org/10.5194/egusphere-egu25-3845, 2025.
Soil erosion models are complex and inherently uncertain. This limits their perceived trustworthiness and hinder their use in practical decision making, in particular by non-technical users. Paradoxically, understanding, reducing (when possible) and explicitly incorporating this uncertainty is crucial for building trust and improving decision-making. For this purpose, we are developing iMPACT-erosion, an open-source soil erosion modelling toolbox based on Jupyter Notebooks. Integrating interactive elements and visualization, iMPACT-erosion fosters a more fluent user-model conversation, targeting students, professors, researchers, and decision-makers. The toolbox comprises three components: iMPACT-start (basic concepts and initial steps), iMPACT-test (model evaluation), and iMPACT-explore (scenario assessment). This work focuses on iMPACT-test and iMPACT-explore, emphasizing the combination of Monte Carlo simulations and interactivity. This approach addresses not only uncertainty quantification and attribution but also tests model behaviour plausibility, identifies controlling factors and conditions leading to unsustainable soil loss rates, and reduces uncertainty by optimizing model evaluation against field measurements. This toolbox empowers users to gain a comprehensive understanding of model behaviour, assess model suitability for specific applications, and ultimately make more informed and robust decisions regarding soil conservation and land management.
How to cite: Peñuela, A.: Interactive uncertainty and sensitivity analysis: building trust on soil erosion models and making better informed decisions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3581, https://doi.org/10.5194/egusphere-egu25-3581, 2025.
Heavy precipitation and resulting erosion of arable land present ongoing challenges in disaster prevention and agricultural management. This interdisciplinary topic is gaining significant attention due to increasing frequency of extreme events linked to climate change (IPCC, 2021; Robinson et al., 2021). According to Parkin et al. (2008), the largest amount of erosion results from extreme individual events.
State-of-the-art methods for simulating heavy precipitation events involve two-dimensional, hydrodynamic-numerical models (2D models) (LUBW, 2016). Traditional erosion simulations have relied on simplified hydraulic calculations, but in this study, the hydraulics, and forces acting on the soil were precisely calculated using a 2D model as the erosion simulation critically depends on the quality of this hydraulics (Morgan et al., 1998).
In contrast to well-known stream transport capacity approaches (e.g. Meyer-Peter & Müller), the Govers (1990) approach is effective for surface runoff and is particularly suitable for simulating soil erosion on arable land (Wang et al., 2019). Therefore, this approach was selected and integrated into the existing sediment transport module of the 2D HydroAS model.
To calibrate and validate the model, natural erosion events caused by heavy precipitation were recorded using an unmanned aerial vehicle (UAV) and analysed. Erosion areas were chosen and simulated using the combined model. The simulation results show both sheet and rill erosion. To assess the simulation results, the spatial distribution of the rill erosion and the erosion quantity were determined and compared with the natural events. Erosion on arable land can be simulated both spatially and quantitatively by coupling the Govers approach with the 2D HydroAS model. However, erosion quantities are highly dependent on the rill size and model resolution, representing minimum erosion.
Assessing sedimentation amount and its spatial distribution is also crucial for evaluating erosion risks due to heavy precipitation. The transfer of sediments from erosion areas to downstream ecosystems or settlements can negatively impact farmers, residents, and ecosystems. Sediment flow analysis is currently being conducted.
How to cite: Hinsberger, R. and Yörük, A.: Numerical Simulation of Sheet and Rill Erosion using 2D Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5175, https://doi.org/10.5194/egusphere-egu25-5175, 2025.
Traditional soil erosion models often oversimplify the mechanics of detachment by assuming vertical raindrop impacts (KEy = KEr), neglecting the critical role of shear forces (KEx) in wind-driven rain (WDR). This study investigates soil detachment rates under both wind-free rain (WFR) and WDR using four soils of differing textures: Efb (high clay and organic matter), Lda (low clay and sandy), Abd (intermediate clay and sand), and Nukerke (high clay, low organic matter, and coarse particles). Experiments conducted in a wind tunnel with a rainfall simulator evaluated the effects of vertical (KEy) and horizontal (KEx) kinetic energy fluxes on detachment rates (Du) across rainfall incidence angles (α = 0o, 53o, 68o, 73o).
The results reveal that soil detachment rates peak at α = 53o, where compressive and shear forces are balanced, enhancing detachment efficiency. Beyond this angle, as shear forces dominate and compressive forces diminish, Du declines significantly, particularly in wet soils. Soil texture and moisture content further modulate these effects, with sandy soils (e.g., Lda) being more sensitive to shear forces and cohesive soils (e.g., Efb) exhibiting higher resistance across conditions. These findings underscore the importance of integrating the dynamic partitioning of kinetic energy, soil-specific properties, and rainfall inclination into predictive erosion models to capture the complex interplay of forces driving soil detachment.
Keywords: Soil erosion modeling, Wind-driven rain (WDR), Rainfall incidence angle, Soil detachment rates
How to cite: Kaplan, S., Erpul, G., Gabriels, D., and Cornelis, W.: The Dynamic Interplay of Compressive and Shear Forces in Soil Detachment Under Wind-Driven Rain: Insights from Texturally Diverse Soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8097, https://doi.org/10.5194/egusphere-egu25-8097, 2025.
Floods are among the most common natural disasters worldwide. Caused by heavy rainfall events, they are responsible for significant damage to populations and infrastructure. Climate change is likely to increase the frequency and intensity of these extreme precipitation events. Furthermore, floods are often associated with significant sediment transport, which tends to increase damage to infrastructure and poses a threat to agriculture and river ecosystems. As a result, numerous studies have been conducted to estimate sediment production and transport, primarily using empirical models and, more recently, physically based models. However, whether empirical or physically based, it is generally difficult to estimate the parameter values of these models. Very few sediment transport measurements are available, and these generally consist of turbidity and/or suspended sediment concentration measurements at a single point, which do not provide information on the spatial variability of the processes at work.
The main objective of this study is to investigate the possible correlations between the characteristics of a catchment, such as its topography, soil texture, morphology or land-use, and its hydro-sedimentary response during flash flood. Calibration of hydro-sedimentary models could also benefit from knowledge of these correlations.
This work is based on open access databases containing discharge and suspended sediment concentration time series collected for more than 100 European and American catchments, together with complementary data on catchment characteristics. These databases are used to calculate catchment-scale indicators such as average slope, connectivity, percentage of sand or clay, precipitation intensity, etc. In addition, we characterized the hydro-sedimentary response using several signatures extracted from the time series of discharge and suspended sediment concentration, such as peak discharges, volume of sediment transported per year and per event, etc. Then, we used Spearman-rank correlations to measure the strength of the links between catchment characteristics and its hydro-sedimentary signatures. We calculated these correlations at different temporal and spatial resolutions to investigate whether the strength of these correlations is scale-dependent.
Preliminary results show a strong control of hydraulic connectivity and precipitation intensity on eroded volumes. Relationships already reported in the literature are also observed here, such as those between the Lloyd index and fine particle content in soils. Contrasting relationships are also found depending on the size of the catchments or their topography. This multi-scale approach could provide a more detailed understanding of sediment mobilisation and deposition mechanisms, and consequently suggest relevant indicators to characterise the sensitivity and origin of soil loss.
Further research includes using random forests to rank the catchment characteristics based on their importance in controlling the hydro-sedimentary signatures.
How to cite: Aurélien, S., Hélène, R., Ludovic, C., and Mohamed, S.: What are the main regional and temporal controls of the hydro-sedimentary response of European and US catchments during floods ?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10075, https://doi.org/10.5194/egusphere-egu25-10075, 2025.
Rainfall-induced soil erosion is recognized as a significant threat to both human and ecosystem health, leading to habitat degradation, food insecurity, disruptions to socio-economic activities, and damage to infrastructure. Addressing and mitigating soil erosion has become a priority in global and national strategies, therefore requiring multitemporal large-scale assessments to understand how precipitation patterns, soil properties, and surface conditions interact and contribute to erosion in specific areas and over time. In these assessments, the land cover and management, and natural vegetation dynamics components play a critical role in reducing soil susceptibility to erosion. These components are represented by a specific parameter (C-factor) in the Universal Soil Loss Equation (USLE) and its updated versions. While numerous methods exist for determining the C-factor (e.g., in situ survey, remote sensing observation, data-driven models), these approaches are diverse and often have limitations, from neglecting phenological sub-annual dynamics to long timeliness, low update frequency, and coarse spatial resolution, among others. As a result, making their selection for specific purposes is challenging. In this talk, we will present a comprehensive review of existing methodologies to assess C-factor by examining their development, strengths, and drawbacks. We will provide practical examples from selected case studies across Europe to show methods’ applicability, allow cross-comparison, and guide their choice. Finally, we will explore emerging methodologies leveraging Earth Observation and Artificial Intelligence and the advances in neural networks trained on ESA Sentinel-2 data within the framework of two European-funded projects (i.e., SDGs-EYES and EO4EU).
How to cite: Latella, M., Cerveira, G., D'Anca, A., Santini, M., and Balzarolo, M.: Land cover and management factor in soil erosion assessments: where do we stand and where are we going?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15855, https://doi.org/10.5194/egusphere-egu25-15855, 2025.
Land degradation (LD) threatens soil health and ecosystems globally, which is why the European Commission formulated the objective of transitioning towards healthy soils by reducing LD by 2030. Water erosion, one of the most severe forms of LD, is influenced by management direction. This is because it affects the roughness created by tillage, seeding ridges, and tramlines, which, in turn, can either exacerbate or mitigate erosion. Therefore, management direction is an important variable in the RUSLE-framework (Revised Universal Soil Loss Equation) used within European Union Soil Observatory (EUSO) to assess and monitor soil health (Panagos et al. 2024). However, spatial data on management direction, especially at policy-relevant scales, is scarce and often not adequately integrated into model-based assessments.
This study develops a straightforward and fast approach to derive management direction and assesses its implications for soil erosion risk at the national level of Germany. Using field geometries from Tetteh et al. (2024) for 2021, we assumed that management direction follows the longest field side of the “minimum rotated rectangle” around each field. The derived management direction was validated using 1155 mapped arable fields randomly selected across Germany. In combination with a 10x10 m DEM, we then derived the contouring index (CI, 0-100%) as a measure to represent the relationship between management direction and the contour for each cell. The CI was then used to calculate the support management practice factor (P) based on the RUSLE to estimate the implications for soil erosion risk.
Results showed that the management direction was correctly modelled in 79% of observations assuming a 10° tolerance. The CI averaged at 37±26%, indicating high variability both between and within fields. Management direction of 2021 was found to reduce soil erosion risk by 7.6%. The currently low CI for Germany shows high potential for further reducing soil erosion risk by implementing contour parallel management in cropland and enhance model predictions by providing P-factor maps with high spatial resolution. Despite these promising results, challenges persist in accurately predicting management direction in fields with highly irregular shapes or unique environmental conditions. Our approach provides a feasible method to predict and monitor management direction with sufficient accuracy for large scales. The approach can be incorporated into other models requiring management direction of cropland (e.g. tillage erosion) and applied for other data sources (e.g. LPIS geometries). This can improve modelling and monitoring LD on field level for large scales, supporting landowners and policy makers towards improving soil health.
References:
Panagos, P., Borrelli, P., Jones, A., & Robinson, D. A. (2024). A 1-billion-euro mission: A Soil Deal for Europe. European Journal of Soil Science, 75(1), e13466.
Tetteh, G. O., Schwieder, M., Blickensdörfer, L., Gocht, A., & Erasmi, S. (2024). Agricultural land use (vector): National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021).https://doi.org/10.5281/zenodo.10619783
Acknowledgement:
P.S., F.M., K.K. and P.B, were funded by the European Union Horizon Europe Project AI4SoilHealth (Grant No. 101086179).
How to cite: Saggau, P., Augustin, K., Steinhoff-Knopp, B., Tetteh, G., Matthews, F., Kaffas, K., Erasmi, S., Borrelli, P., and Kuhwald, M.: Finding the right direction: An approach to determine cropland management direction and its implications for soil erosion risk assessments on large spatial scales. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10363, https://doi.org/10.5194/egusphere-egu25-10363, 2025.
The environment of the Czech Republic has long been significantly affected by anthropogenic influences that negatively impact the condition of landscape ecosystems. One such influence is accelerated soil erosion and excessive sediment loading of watercourses, primarily from agricultural landscapes. This process reduces soil fertility, degrades water quality, and increases the transfer of contaminants into aquatic systems.
This study analyzes sediment transport in the Elbe basin using monitoring data from the Labe (Elbe) and Vltava (Moldau) river basin authorities. The investigated area covers approximately 49,000 km² and includes 600 watercourse sampling profiles with monthly suspended solids measurements. The focus is on comparing measured and modeled sediment transport. WaTEM/SEDEM (based on USLE/RUSLE methods and sediment transport capacity assessment) was chosen as the modeling tool. The main objectives were to (i) assess long-term and episodic sediment loading, (ii) identify factors affecting the agreement between measured and modeled values, and (iii) evaluate the potential for model calibration and validation.
Calculations indicate that 1.5 million tons of erosive sediment enter the streams of the study area annually. Of this amount, 59% is captured in reservoirs, corresponding to 627,000 tons deposited each year. The analysis showed a strong correlation (R² = 0.94) between modeled and measured data for the entire dataset. However, after excluding 21 high-transport profiles (above 20,000 t/year), the coefficient of determination dropped to 0.50, revealing that outliers significantly affect the model’s match. This study provides a detailed comparison of modeled and measured sediment, including the influence of catchment characteristics and major rainfall episodes on model suitability.
The paper further discusses the limitations of the data sources used for both calibration and validation, with a strong emphasis on constraints arising from user-level mistakes—both minor and major—in data preparation and model computation. It is crucial for all users (and scientists) to acknowledge these limitations, address them openly, and strive to reduce errors in future modeling, validation, and publications.
Research has been supported by project TUDI (European Union's Horizon 2020 research and innovation program under grant agreement No. 101000224), QL24020309 (The Ministry of Agriculture of the Czech Republic) and TAČR SS03010332 (Technology Agency of the Czech Republic).
How to cite: Bauer, M., Dostál, T., Krása, J., Jáchymová, B., Devátý, J., and Mazancová, M.: Models or users - who is guilty? A large-scale case study of sediment transport modelling - validation datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21288, https://doi.org/10.5194/egusphere-egu25-21288, 2025.
Water-induced soil erosion is a critical threat to the sustainability of agriculture worldwide, as it destroys nutrient-rich topsoil and causes significant economic costs. Conventional soil erosion monitoring methods, such as the Universal Soil Loss Equation (USLE) and its revised version (RUSLE), often face challenges with data collection for calibration or validation. Although machine learning offers promising alternatives, they usually require large data sets, which can limit their practicality. Recent advances in deep learning have introduced several innovative techniques such as transfer learning to reduce the need for extensive data. This study presents Erosion-SAM, a novel framework that fine-tunes the Segment Anything Model (SAM) to automatically identify erosion and deposition features using high-resolution remote sensing imagery. RADOLAN radar rainfall data with a spatial resolution of 1 km was used to identify erosive events and determine erosion-prone agricultural fields including grassland, vegetated cropland, and bare cropland, in southeastern Bavaria, Germany. High-resolution orthophotos (0.2 m) were taken for fields with erosive events indicating significant erosion potential. These orthophotos were then manually segmented by experts to delineate precise erosion and deposition features and subsequently used as input data for fine-tuning SAM. Three pre-processing strategies were evaluated during the fine-tuning process: resizing, cropping, and prompt-based resizing. The prompt-based resizing method performed best, especially in grassland, with an IoU of 0.75, a Dice coefficient of 0.86, a precision of 0.82 and a recall of 0.90. While the baseline SAM performed better than the cropping method in bare cropland, it overestimated erosion and deposition, which increased the recall values. The fine-tuned methods agreed well with the actual soil erosion severity ratios, with the prompt-based resizing method achieving an R2 of 0.93, demonstrating superior predictive performance. Erosion-SAM showcases the potential to revolutionize soil erosion monitoring by automatically detecting erosion and deposition features across different land covers with high accuracy. Moreover, it generates high-quality, consistent data sets as valuable input for machine learning-based erosion modeling for different land covers. Its scalability and high spatial and temporal resolution also make it invaluable for large-scale erosion monitoring and risk assessment, including applications in the insurance and reinsurance industry.
How to cite: Shokati, H., Engelhardt, A., Seufferheld, K., Taghizadeh-Mehrjardi, R., Fiener, P., and Scholten, T.: Automated high-resolution detection of soil erosion and deposition features in agricultural fields using the fine-tuned Segment Anything Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-827, https://doi.org/10.5194/egusphere-egu25-827, 2025.
Testing the performance of soil erosion models against observational data is a critical step in any model application. This is particularly important when models aid land-management decisions, e.g. planning and implementing soil conservation practices in agricultural landscapes. However, observational erosion data are uncertain and typically restricted to measurements of sediment fluxes at the outlet of a system, e.g. plot or watershed. This has limited utility for testing a model’s representation of landscape sediment connectivity processes, which is crucial for planning soil conservation and off-site pollution control measures. Here, the performance of a Python-implemented version of the spatially distributed soil erosion and sediment yield WaTEM/SEDEM model was evaluated for simulating sediment yields under soil conservation conditions across contrasting watersheds at an experimental farm in Southern Germany. To do so, we used an eight-year monitoring dataset (1994-2001) that includes high-resolution measurements of soil properties, plant traits, and land management operations, as well as event-based sediment yield measurements for (I) four small-scale watersheds (0.8 to 4.2 ha) primarily representing in-field erosion processes (mostly supply-limited) and (II) two cascading watersheds (5.7 to 7.8 ha) dominated by sedimentation processes along a grassed waterway (mostly transport-limited). Further, we employed a Generalised Likelihood Uncertainty Estimation (GLUE) rejectionist framework utilising Monte Carlo simulations with 25,000 iterations to condition model parameters. The model performance was evaluated across two spatial scales - from individual watersheds to aggregated supply-limited and transport-limited watershed groups - and temporal scales ranging from single-year to eight-year averages. Model iterations were considered as behavioural when their simulated sediment yields fell within an estimated error range derived from the monitoring dataset. The model demonstrated capability in simulating low sediment yields when aggregated spatially and temporally. However, the annual-scale model applications were rejected due to insufficient representation of temporal dynamics. The results indicated a systematic overestimation of sediment yields across most watersheds, with a notable exception in one transport-limited catchment where underestimation occurred. The influence of retention features within watersheds was reflected by the behavioural parameter selection: in cases of sediment yield overestimation, parameters enhancing deposition produced superior results, while in watersheds with underestimated sediment yields, parameters reducing deposition improved model performance. These observations underscore the model's capability to represent low sediment yields in agricultural landscapes under soil conservation while highlighting temporal resolution limitations and the importance of comprehensive uncertainty analysis in measured and simulated data.
How to cite: Seufferheld, K. D., Batista, P. V. G., Shokati, H., Scholten, T., and Fiener, P.: WaTEM/SEDEM's capability in simulating watershed-scale soil conservation: Using the GLUE approach to analyse the representation of in-field processes and connectivity features along thalwegs , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1799, https://doi.org/10.5194/egusphere-egu25-1799, 2025.
Caribbean islands face significant soil erosion and landslide challenges driven by extreme events such as tropical storms and hurricanes. These processes have substantial impacts on agriculture, ecosystems, infrastructure, and local communities. Despite the availability of Earth Observation (EO) and geospatial datasets, their potential for land degradation monitoring and management remains underutilized due to the lack of accessible methodologies and technical barriers to implementation.
To address this gap, we developed a multi-scale geospatial framework integrating open-access satellite data and national LiDAR datasets for monitoring surface processes and soil erosion in Saint Lucia and Dominica. The methodology aims to build capacity among local officers, equipping them to identify erosion hotspots and implement mitigation measures effectively, with potential for replication in similar contexts. At the island scale, global Digital Elevation Models (DEMs) were employed to compute hydrological and geomorphological indicators, delineating areas at risk of erosion and sediment transport. At the local scale, high-resolution LiDAR data facilitated detailed analyses, including: (1) identifying erosion-prone areas and sediment transport pathways; (2) assessing road infrastructure to detect drainage inefficiencies and processes contributing to slope destabilization; (3) analyzing runoff dynamics from upslope regions to coastal zones.
Our framework does not aim to improve model accuracy for purely research purposes. Instead, it adopts a simplified, yet science-based modeling approach tailored to the unique challenges of small-island settings. By focusing on actionable insights, it provides local experts with practical, science-based strategies to address land degradation issues and contributes to building resilience within local communities.
How to cite: Straffelini, E., Pijl, A., and Tarolli, P.: Embracing imperfection: how an unvalidated remote-sensing based land degradation workflow helped Caribbean experts in disaster management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8372, https://doi.org/10.5194/egusphere-egu25-8372, 2025.
Mankind has been actively reshaping and altering the soil system since the dawn of agriculture in the Neolithic. As our impact on the soil system has grown throughout the millennia, so has the need to understand its complex dynamics. This has led to the creation of mechanistic soil-landscape evolution models to simulate the long-term development of soils and landscapes. These models discretize the soil column in layers, which represent how the soil profile is handled in the model, rather than real geopedological horizons. As a result, the layer thickness directly impacts the resolution of the geomorphic and soil forming processes being modeled. It is still unclear how different layering options impact the outcome of the simulation and therefore which discretization would be preferable when developing or choosing a soil-landscape evolution model.
This work aims to bridge this gap by investigating how different soil layering options impact simulations of soil thickness, organic matter content, soil texture and computational efficiency.
We tested the impact of a) the number of layers and b) the thickness of these layers. The former was investigated by comparing simulations carried out with 2, 8 and 16 layers, while the latter was explored by simulating layers of uniform thickness (UT) and layers which increase in thickness with depth (IT), which provides a higher resolution closer to the surface. To ensure a general yet realistic setting, the model was applied to a silty soil profile from Canneto Pavese, in the Oltrepò Pavese region (Italy).
For this work, the mechanistic soil-landscape evolution model LORICA was chosen due to its ability to simulate both geomorphic and soil forming processes in the entire soil profile and for its ability to simulate soil layers with varying and dynamic thickness. We simulated the processes of bedrock weathering, physical and chemical weathering and carbon cycle dynamics and observed their impacts on soil properties 10, 100, 1000 and 10000 years of calculation.
We found that a greater vertical interaction between layers resulted in differences in the outputs, which occur both when a higher number of layers is adopted, and in the IT mode compared to the UT mode.
This work provides insight into the impact of layering options in soil-landscape evolution models, so that other researchers will be able to select the most apt and efficient set up for their simulations depending on their specific circumstances and needs. In the future, the action of water erosion will additionally be assessed on a landscape scale to consider spatial variations in soil development as well.
How to cite: Giarola, A., van der Meij, M., Meisina, C., Bordoni, M., and Temme, A.: The impact of soil layering in a mechanistic soil-landscape evolution model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11244, https://doi.org/10.5194/egusphere-egu25-11244, 2025.
Soil erosion, hydrogeologic risk, and land degradation are serious issues that receive due attention in policy and scientific research. As soil scientists with years of experience in both research and (agricultural) projects, we observed erosion issues in both simulations and field measurements - yet at the same time we observed another interesting trend. The researcher sometimes seems more concerned with long-term soil loss processes than the person owning and working that very soil: the farmer.
This leads to the interesting question: is erosion an actual concern for the average farmer?
In our most recent scientific work within the https://phito.eu/ project, we conducted an elaborate questionnaire among 650+ smallholder farmers throughout Europe. Two questions in particular returned surprising insights in this farmer perspective on erosion:
- To the question what kind of map data would be useful for their farming, soil erosion was ranked as the least useful information out of 11 variables. In fact, more than ⅓ of farmers rate soil erosion risk maps as not useful at all. This was notably low compared to water- and meteorology-related variables (e.g. temperature and precipitation maps were desired by >88% of farmers).
- To another question about the importance of climate-induced risks, land degradation was ranked as the lowest concern out of 8 risks, again in contrast to concerns about water- and meteorology-related events (heatwaves and drought). Tellingly, even ecological degradation was ranked higher (biodiversity being a concern among > ½ of farmers) than land degradation (soil erosion, landslides and hydrological risk not being a concern for ~ ½ of farmers).
NB: the 650+ respondents are diverse Spanish, Portuguese, Italian, Romanian, Albanian, Hungarian and EU-overseas smallholder farmers, the majority of which are working in hilly environments.
These results offer the soil science community a rich basket of food for thought, straight from the farmer. In this session, alongside a unique (3D) poster illustrating the different views on erosion, we welcome an open discussion to exchange ideas about possible explanations, its implications for our discipline, and possible solutions that are supported by farmers.
How to cite: Pijl, A., Straffelini, E., Pezzutti, C., Vogel, T., and Tarolli, P.: Soil erosion: who cares? An EU farmer perspective (in 3D)., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13804, https://doi.org/10.5194/egusphere-egu25-13804, 2025.
Current process-based soil erosion models face significant challenges stemming from data availability, parameter uncertainty, and the dynamic nature of evolving environmental conditions. These limitations restrict the precision and applicability of existing models in capturing the complexities of soil erosion processes. To address some of these challenges, this study introduces an innovative approach that leverages nested, continuous, high-resolution spatio-temporal data obtained through Structure from Motion (SfM) photogrammetry. The technique enables detailed monitoring of soil surface changes caused by precipitation events across multiple spatial scales, ranging from plot-scale observations to slope-scale and micro-catchment-scale analyses.
The study's methodology incorporates an extensive and unprecedented dataset, blending time-lapse photogrammetry, comprehensive field measurements, and data collected via uncrewed aerial vehicles (UAVs) over an extensive period of 3.5 years. This robust dataset allows for a detailed monitoring of soil erosion dynamics, including flow velocity, soil consolidation and compaction process measurements. Moreover, it provides a critical foundation for the calibration and evaluation of soil erosion models, demonstrated by its application in refining the RillGROW model.
To further advance the field, the study offers an open-access dataset to the scientific community, intended for model parameterisation, calibration, and testing. Researchers are invited to build on this work by employing similar methods to collect complementary soil erosion data, thereby contributing to an expanded, high-resolution dataset. This collective effort aims to foster the development of more accurate and reliable soil erosion models, ultimately improving our ability to predict and mitigate soil degradation in diverse environmental contexts.
How to cite: Eltner, A., Epple, L., Grothum, O., and Bienert, A.: High-Resolution Insights from Photogrammetry: Tracking Soil Surface Changes Across Scales with Time-Lapse Data over 3.5 Years for Enhanced Soil Erosion Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15752, https://doi.org/10.5194/egusphere-egu25-15752, 2025.
Terrigenous inputs from hillslopes to hydrosystems can alter the quality of Fort-de-France Bay’s waters and sediments and so the balance of ecosystems (notably the degradation of coral reefs). Depending on the intensity of rainfall episodes, terrigenous inputs increase the turbidity the bay’s water and potentially result in the arrival of pollutants (including pesticides) in coastal waters leading to the ban of certain coastal economic activities. Another major impact is land degradation in upstream watersheds, leading to the impoverishment of agricultural soils. Furthermore, there has been much less research in soil erosion and sediment transfer in these tropical volcanic island settings compare to other environments.
In order to analyze the rate and the spatio-temporal dynamics of water and suspended particles fluxes, the objective of this study is to model runoff and erosion on the two main watersheds (100 and 65 km²) draining into the Fort-de-France Bay, including a variety of soil type, land use and morphology. The modelling approach was used to perform an in-depth analysis of runoff and erosion processes. Various hypotheses were tested at different scales, particularly on the genesis of runoff (hortonian or soil saturation), soil depth, soil percolation. etc. Our results suggest the importance of saturation processes in controlling flood event occurrence. Compared with more traditional soil erosion modelling studies, this study on large watersheds have also enabled us to analyze upscaling effects’ aspects.
This calibrated runoff and erosion model will be used to simulate different management or remediation plan to reduce sediment exports to the bay. Management plan will deal with agricultural practices and soft hydraulic systems such as grass strips, hedges (planted with local or endemic plant species) or fascines.
How to cite: Vandromme, R., Bizeul, R., Grangeon, T., Nachbaur, A., Evrard, O., and Cerdan, O.: Runoff and soil erosion modelling at catchment scale in a tropical volcanic Island (Martinique, Lesser Antilles), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18474, https://doi.org/10.5194/egusphere-egu25-18474, 2025.
A Lakatosian history of soil-erosion modelling as a scientific research programme
Here I employ a Lakatosian theory (Lakatos, 1978) to explain the history of soil-erosion modelling as a research programme that went through a progressive phase during the 20th century and early 2000s, with the formulation of novel models with excess empirical content over their predecessors and the prediction of new facts that were corroborated or at least falsifiable by empirical evidence. I argue that the research programme then entered a so-called degenerative phase with an increased prediction of truisms, lack of falsifiability, and theory lagging behind the empirical evidence (Parsons, 2019).
I revisit a scientometric analysis (Batista et al., 2019) using new data that suggests that soil-erosion modelling is becoming increasingly polarised between application- and understanding-driven research clusters, with little connection between experimental work and model applications. Moreover, the scientometric analysis demonstrates a decreasing interest in process-oriented models in favour of USLE-type approaches.
I argue that questionable modelling practices, e.g. extrapolation of empirical models outside their domain and ignoring or hiding uncertainties, have entrained the research programme and are likely to persist without targeted action (Smaldino and O’Connor, 2022). I explain this scenario as being caused by both internal, i.e. related to particular developments in the research programme, and external drivers, e.g. the current incentive structures in science (Tunç and Pritchard, 2022).
References
Batista, P. V. G., Davies, J., Silva, M. L. N. and Quinton, J. N.: On the evaluation of soil erosion models: Are we doing enough?, Earth-Science Rev., 197, 102898, doi:10.1016/j.earscirev.2019.102898, 2019.
Lakatos, I.: The methodology of scientific research programmes, Cambridge Univeristy Press., 1978.
Parsons, A. J.: How reliable are our methods for estimating soil erosion by water?, Sci. Total Environ., 676, 215–221, doi:10.1016/j.scitotenv.2019.04.307, 2019.
Smaldino, P. E. and O’Connor, C.: Interdisciplinarity can aid the spread of better methods between scientific communities, Collect. Intell., 1, 263391372211318, doi:10.1177/26339137221131816, 2022.
Tunç, D. U. and Pritchard, D.: Collective epistemic vice in science: Lessons from the credibility crisis, [Preprint], 2022.
How to cite: Batista, P.: A Lakatosian history of soil-erosion modelling as a scientific research programme, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18554, https://doi.org/10.5194/egusphere-egu25-18554, 2025.
The EU’s proposed Soil Monitoring Law recognizes soil erosion as major threat to soil health and requests Member States to monitor different soil erosion processes. Amongst the major soil erosion processes tillage erosion has received rather limited attention. Unlike water and wind erosion, whose effects are often easily visible in the landscape, the extent and severity of erosion caused directly by soil tillage only become evident after decades of tillage through spatial variations in soil properties. Tillage redistributes large amounts of soil from convexities to concavities within fields of rolling topography thus driving a spatially heterogeneous evolution of soil nutrient and carbon stocks. Currently, the few tillage erosion models available are based on a modest number of field surveys and tillage tracer experiments, and we are lacking operational tools for monitoring tillage erosion and its impact on soil health in the long-term.
To explore the potential impact of tillage erosion on the crop land in Denmark we have done a scenario analysis comparing the output from a robust soil redistribution model with remote sensing data of topsoil carbon contents. Our study aimed to (1) map tillage-induced soil redistribution across Denmark at a 10-meter resolution and (2) assess its impact on estimating topsoil organic carbon (SOC) content on arable land. Running the WaTEM model with a LiDAR-derived DEM smoothed to different degrees and assuming a typical tillage intensity, we estimated tillage-induced annual soil redistribution rates. We then used Sentinel 2A-derived bare soil composites and other co-variates together with the soil redistribution rates as predictors for mapping SOC via machine learning.
Our modelling results showed that without smoothing the 10-m resolution DEM, 23% of the arable land in Denmark had tillage-induced soil loss rates >2.5 t ha-1 a-1 while 12% exceeded 5 t ha-1 a-1. At the highly eroding sites, measured plough layer SOC contents obtained from a national survey tended to be lowest. Soil redistribution modelled with the less smoothed DEM showed stronger correlations with the bare soil composite bands in erosional zones. While the bare soil composite was the main predictor for SOC contents, tillage erosion rate was the only other important predictor at a national scale.
In lack of a mechanistic model for mapping the effect of tillage on SOC stock evolution and other soil properties on the arable land across Denmark, our scenario analysis highlights the unsustainability of current intensive tillage practices. To comply with the Soil Monitoring Law the development of operational tools for mapping actual tillage erosion and its impact on soil health must be prioritized.
How to cite: Heckrath, G., Gutierrez, S., de Carvalho Gomes, L., and Greve, M. H.: Tillage Erosion in Denmark and Its Effects on Soil Organic Carbon Distribution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20911, https://doi.org/10.5194/egusphere-egu25-20911, 2025.
