Pedro Velloso Gomes Batista

Soil erosion “risk” maps are almost exclusively produced by estimating long-term average soil loss rates with USLE-based models, after which at-risk areas are ranked according to poorly justified thresholds. This approach fails to engage with well-established concepts from more mature fields in risk science.Soil erosion is a natural process and only becomes a hazard once vulnerable systems are exposed to individual or successive erosion events. Hence, meaningful erosion risk assessments require (i) a clear definition of the exposed elements considered to be at risk (e.g. soil itself, farmers, downstream infrastructure and water bodies); (ii) their biophysical and/or socio-economic vulnerability to erosion; (iii) the consequences that erosion may generate (e.g. crop yield losses, increased vulnerability to droughts and floods, off-site pollution); and (iv) the time frame over which these consequences occur. Crucially, meaningful erosion risk assessments must describe erosion in probabilistic terms rather than as deterministic averages.In this contribution, we outline several steps towards more progressive erosion risk assessments. First, we demonstrate how the probability of occurrence of severe water-erosion events on individual arable fields can be estimated using a simple machine-learning approach and dynamic input factors. This enables the identification of short-term risks of crop or off-site damage and can lead to the development of early warning systems. Second, to address medium- to long-term risks associated with erosion-induced soil change, we propose a dynamic, process-oriented multi-model framework that represents how erosion alters soils and their functioning over time. This framework accommodates different erosion processes and their interactions within a multi-hazard perspective. Our contribution will be presented in an interactive poster format, inviting the soil erosion research community to co-develop a more useful and consequence-oriented erosion risk assessment framework.

Soil erosion models are essential tools for soil conservation planning. Although these models are generally well-tested against plot and field data for in-field soil management, challenges arise when scaling up to the landscape level, where sediment trapping along landscape features becomes increasingly critical. At this scale, a separate analysis of model performance in representing erosion, sediment transport, and deposition processes is both challenging and often lacking. In this study, we assessed the capacity of the spatially distributed erosion and sediment transport model WaTEM/SEDEM to simulate sediment yields in six micro-scale watersheds ranging from 0.8 to 7.8 ha, monitored over eight years from 1994 to 2001. The watersheds were comprised of two groups: four field-dominated watersheds characterised by arable land with minimal landscape structures, and two structure-dominated watersheds featuring a combination of arable land and linear landscape structures (mainly grassed waterways along thalwegs) that minimise sediment connectivity. This setup enabled a separate analysis of model performance for both watershed groups. A Generalised Likelihood Uncertainty Estimation (GLUE) framework was employed to account for measurement and model uncertainties across multiple spatiotemporal scales. Our results show that while WaTEM/SEDEM generally captured the magnitude of the very low measured sediment yields in the monitored watersheds, the model did not meet our pre-defined limits of acceptability when operating on annual timesteps. However, the WaTEM/SEDEM's performance improved substantially when model realisations were aggregated across the eight-year monitoring period and over the two watershed groups, with mean absolute errors of 0.11 t ha⁻¹ yr⁻¹ for field-dominated and 0.18 t ha⁻¹ yr⁻¹ for structure-dominated watersheds. Our findings demonstrate that the model can represent the influence of soil conservation measures on reducing soil erosion and sediment delivery but performs better for long-term conservation planning at larger scales than for precise annual predictions in individual micro-scale watersheds with specific conservation practices.

Soil erosion models are essential tools for soil conservation planning. Although these models are generally well-tested against plot and field data for in-field soil management, challenges arise when scaling up to the landscape level, where sediment trapping along landscape features becomes increasingly critical. At this scale, a separate analysis of model performance in representing erosion, sediment transport, and deposition processes is both challenging and often lacking. In this study, we assessed the capacity of the spatially distributed erosion and sediment transport model WaTEM/SEDEM to simulate sediment yields in six micro-scale watersheds ranging from 0.8 to 7.8 ha, monitored over eight years from 1994 to 2001. The watersheds were comprised of two groups: four field-dominated watersheds characterised by arable land with minimal landscape structures, and two structure-dominated watersheds featuring a combination of arable land and linear landscape structures (mainly grassed waterways along thalwegs) that minimise sediment connectivity. This setup enabled a separate analysis of model performance for both watershed groups. A Generalised Likelihood Uncertainty Estimation (GLUE) framework was employed to account for measurement and model uncertainties across multiple spatiotemporal scales. Our results show that while WaTEM/SEDEM generally captured the magnitude of the very low measured sediment yields in the monitored watersheds, the model did not meet our pre-defined limits of acceptability when operating on annual timesteps. However, the WaTEM/SEDEM's performance improved substantially when model realisations were aggregated across the eight-year monitoring period and over the two watershed groups, with mean absolute errors of 0.11 t ha⁻¹ yr⁻¹ for field-dominated and 0.18 t ha⁻¹ yr⁻¹ for structure-dominated watersheds. Our findings demonstrate that the model can represent the influence of soil conservation measures on reducing soil erosion and sediment delivery but performs better for long-term conservation planning at larger scales than for precise annual predictions in individual micro-scale watersheds with specific conservation practices.

Soil erosion models are essential tools for soil conservation planning. Although these models are generally well-tested against plot and field data for in-field soil management, challenges arise when scaling up to the landscape level, where sediment trapping along landscape features becomes increasingly critical. At this scale, a separate analysis of model performance in representing erosion, sediment transport, and deposition processes is both challenging and often lacking. In this study, we assessed the capacity of the spatially distributed erosion and sediment transport model WaTEM/SEDEM to simulate sediment yields in six micro-scale watersheds ranging from 0.8 to 7.8 ha, monitored over eight years from 1994 to 2001. The watersheds were comprised of two groups: four field-dominated watersheds characterised by arable land with minimal landscape structures, and two structure-dominated watersheds featuring a combination of arable land and linear landscape structures (mainly grassed waterways along thalwegs) that minimise sediment connectivity. This setup enabled a separate analysis of model performance for both watershed groups. A Generalised Likelihood Uncertainty Estimation (GLUE) framework was employed to account for measurement and model uncertainties across multiple spatiotemporal scales. Our results show that while WaTEM/SEDEM generally captured the magnitude of the very low measured sediment yields in the monitored watersheds, the model did not meet our pre-defined limits of acceptability when operating on annual timesteps. However, the WaTEM/SEDEM's performance improved substantially when model realisations were aggregated across the eight-year monitoring period and over the two watershed groups, with mean absolute errors of 0.11 t ha⁻¹ yr⁻¹ for field-dominated and 0.18 t ha⁻¹ yr⁻¹ for structure-dominated watersheds. Our findings demonstrate that the model can represent the influence of soil conservation measures on reducing soil erosion and sediment delivery but performs better for long-term conservation planning at larger scales than for precise annual predictions in individual micro-scale watersheds with specific conservation practices.

Soil erosion models are essential tools for soil conservation planning. Although these models are generally well-tested against plot and field data for in-field soil management, challenges arise when scaling up to the landscape level, where sediment trapping along landscape features becomes increasingly critical. At this scale, a separate analysis of model performance in representing erosion, sediment transport, and deposition processes is both challenging and often lacking. In this study, we assessed the capacity of the spatially distributed erosion and sediment transport model WaTEM/SEDEM to simulate sediment yields in six micro-scale watersheds ranging from 0.8 to 7.8 ha, monitored over eight years from 1994 to 2001. The watersheds were comprised of two groups: four field-dominated watersheds characterised by arable land with minimal landscape structures, and two structure-dominated watersheds featuring a combination of arable land and linear landscape structures (mainly grassed waterways along thalwegs) that minimise sediment connectivity. This setup enabled a separate analysis of model performance for both watershed groups. A Generalised Likelihood Uncertainty Estimation (GLUE) framework was employed to account for measurement and model uncertainties across multiple spatiotemporal scales. Our results show that while WaTEM/SEDEM generally captured the magnitude of the very low measured sediment yields in the monitored watersheds, the model did not meet our pre-defined limits of acceptability when operating on annual timesteps. However, the WaTEM/SEDEM's performance improved substantially when model realisations were aggregated across the eight-year monitoring period and over the two watershed groups, with mean absolute errors of 0.11 t ha⁻¹ yr⁻¹ for field-dominated and 0.18 t ha⁻¹ yr⁻¹ for structure-dominated watersheds. Our findings demonstrate that the model can represent the influence of soil conservation measures on reducing soil erosion and sediment delivery but performs better for long-term conservation planning at larger scales than for precise annual predictions in individual micro-scale watersheds with specific conservation practices.

The DynAWI Extreme Weather Toolbox represents an innovative approach to addressing climate-related challenges in agriculture. This publicly accessible web application offers three primary functions: a historical agricultural weather indicator atlas, a dynamic configurator for calculating user-specified weather indexes, and a forecast model for predicting reduced yields or complete crop failure due to weather extremes. The web application can perform real-time analyses based on multi-dimensional spatio-temporal data.The technical implementation is based on a client-server architecture, utilizing a scalable geodata infrastructure and an array database management system rasdaman, enabling efficient processing of multidimensional geodata. The system allows real-time analysis of extreme weather events, such as droughts, heatwaves, and heavy rainfall, dating back to 1995. The toolbox aims to provide stakeholders—from farmers to policymakers—with a comprehensive platform for weather-related risk assessment and decision support in agriculture.In a live demonstration, we will showcase the platform's key features, emphasizing its interactive capabilities and extensive parameter customization options.

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.

A Lakatosian history of soil-erosion modelling as a scientific research programmeHere 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).ReferencesBatista, 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.