We present an extensive electromagnetic induction imaging (EMI) survey across a 66 ha catchment to quantify spatial variations in soil hydraulic properties controlling surface-groundwater interactions. The proposed methodology involves three steps: (1) inversion of EMI data using a deep learning (DL) network that permits rapid prediction of 1D electrical conductivity (EC) depth models, (2) correlation of predicted electrical conductivity (EC) with soil textural information and (3) prediction of saturated hydraulic conductivity (K_s) from the predicted soil textural information using a recalibrated pedotransfer function (PTF) developed for the catchment. The performance of the DL inversion is evaluated through comparison with the classical deterministic inversion approach, with both methods yielding similar EC sections without significant discrepancies. The experimental petrophysical relationships between EC and soil textural information at 300 sampling locations revealed reasonably well-defined correlations with silt (R2=0.4) and clay (R2=0.33), but no correlation with sand and the predicted soil profiles capture measured trends, with minor discrepancies of 2-5%. The recalibrated PTF demonstrates effective performance (R2=0.52) in estimating K_s and was evaluated using split-sample validation. The predicted K_s maps reveal substantial variability in shallow soil hydraulic properties, likely influenced by agricultural practices, with a shift towards lower K_s values at greater depths. The proposed approach, combining DL inversion of EMI data, site-specific petrophysical relationships, and a field-scale PTF, provides a framework for predicting hydraulic properties from EMI data in catchments with heavy soils, enhancing understanding of runoff generation mechanisms and enabling improved land management strategies.

Every application of soil erosion models brings the need of proper parametrization, i.e., finding physically or conceptually plausible parameter values that allow a model to reproduce measured values. No universal approach for model parametrization, calibration and validation exists, as it depends on the model, spatial and temporal resolution and the nature of the datasets used. We explored some existing options for parametrization, calibration and validation for erosion modelling exemplary with a specific dataset and modelling approach. A modified version of the Morgan-Morgan-Finney (MMF) model was selected, representing a balanced position between physically-based and empirical modelling approaches. The resulting calculator for soil erosion (CASE) model works in a spatially distributed way on the timescale of individual rainfall events. A dataset of 142 high-intensity rainfall experiments in Central Europe (AT, HU, IT, CZ), covering various slopes, soil types and experimental designs was used for calibration and validation with a modified Monte-Carlo approach. Subsequently, model parameter values were compared to parameter values obtained by alternative methods (measurements, pedotransfer functions, literature data). The model reproduced runoff and soil loss of the dataset in the validation setting with R 2 adj of 0.89 and 0.76, respectively. Satisfactory agreement for the water phase was found, with calibrated saturated hydraulic conductivity (k sat) values falling within the interquartile range of k sat predicted with 14 different PTFs, or being within one order of magnitude. The chosen approach also well reflected specific experimental setups contained in the dataset dealing with the effects of consecutive rainfall and different soil water conditions. For the sediment phase of the tested model agreement between calibrated cohesion, literature values and field measurements were only partially in line. For future applications of similar model applications or datasets, the obtained parameter combinations as well as the explored methods for deriving them may provide guidance.