Outputs of the InVEST erosion model for application at a national scale; Hooftman et al. 2026 PLOS One

This dataset belongs to the published paper of Hooftman et al. 2026 in PLOS one titled “Modification and validation of the InVEST erosion model for application at a national scale”. Doi to be added upon publication of the paper. In this Zenodo repository we provide the Geotiff files belonging to the presented output figure 4 of Hooftman et al. (2026), being a summation of 23 individual periodic runs of the InVEST SDR model. The Geotiffs are accompanied by a respective ArcGIS layer-file for colouring, as well as a 600dpi depiction. We strongly refer to the published paper for any details (Doi to be added upon publication of the paper). The abstract is provided below. Outputs include: The modelled realised exort of sediments that reaches the streams (Realised sediment export) The avoided erosion by the vegetation (Avoided erosion) The proportion avoided erosion of all possible export (Proportion avoided erosion) In addition we provide: The 23 periodic outputs as ZIP-files of the respective Geotiffs for reaslised sediment export, avoided erosion and proportion avoided erosion, The single per year run, also as Zip file, see the paper for details. Abstract Hooftman et al. 2026 PLOS One. Soil erosion is an important ecological impact of human activities such as agriculture, and models can aid in identifying areas most at risk. However, to aid targeting of actions, predictions must simultaneously cover large spatial extents and account for site-specific variation in retention, while achieving adequate parameterisation and evaluation of models remains demanding. To address these issues, we adapted and assessed an erosion model using the InVEST platform, which applies the Revised Universal Soil Loss Equation (RUSLE), for Great Britain (GB). We parametrised the model using GB-specific input data, for multiple crop types, and integrating new factors including sub-annual periodicity, a GB-specific erosivity layer, and Normalized Difference Vegetation Index (NDVI) derived cover management metrics. Our modelled predictions validated well against sediment concentration measurements in rivers, with a predictive accuracy of 78% on normalised data and rank correlation of 0.46 between predictions and observations. However, absolute model values were overestimated 28-times, especially at higher levels of sediment erosion. This seems related to a lack of measurements of sediment concentrations at peak flows in the validation data, combined with known RUSLE methodological issues. To allow for future model training, we call for improvements to national sediment load monitoring in rivers, by including measurements during peak flows especially. Next to inclusion of a within stream sedimentation function in the InVEST Sediment Delivery Ratio model. Our InVEST model parametrisation example provides a valuable tool for relative risk mapping, comparing regions and targeting where ecosystems are more prone to erosion. Cover management factors based on NDVI observations are a substantial methodological improvement for RUSLE modelling that can be readily reproduced in other locations. Whereas sub-annuality did not provide difference with an annual model in this all-year around rain area but serves as enhancement example for more seasonal areas.