Abstract Soil erosion, particularly when intensified by heavy precipitation, is a natural process and a persistent challenge in agricultural management. To date, this problem has been addressed using existing erosion models. However, these models often rely on simplified hydraulic approaches, whereas two-dimensional (2D) hydrodynamic numerical models are state-of-the-art for overland flow simulations. In this study, the combination of a 2D model and a soil erosion approach allowed for a more precise consideration of the hydraulics. The Govers approach was used with the 2D HydroAS model and evaluated using experimental plot data and naturally occurring field-scale erosion data, sourced from the literature. Results indicate that the combined model simulated sheet erosion and produced reasonable estimates for small rills using the Govers transport capacity approach. However, larger rills require calibration of this method. Additionally, the resolution of the digital elevation model (0.25 m) used as the basis for the simulation was of great importance to avoid overestimating smaller rills. Sensitivity analysis revealed that these smaller rills were particularly influenced by the input grain diameter and surface roughness. Comparisons with other erosion models underscore that incorporating an improved hydraulic approach and adapting the topography at each simulation time step enhances estimation of the spatial distribution and quantity of erosion of the rills. Knowledge about the occurrence and quantification of rill erosion can help planners develop geoecological solutions for flooding and erosion.

Abstract This study evaluates the effectiveness of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) for monitoring vegetation health and soil moisture dynamics in rehabilitated mining quarries. The research focuses on developing an integrated framework of processing and analyzing these indices using remote sensing data and employing machine learning techniques to address challenges such as missing or defected pixels. Sentinel-2 Level-2 A imagery, known for its high spatial and temporal resolution, was employed to calculate these indices. To address potentially missing or affected pixels in such works a Self-Organizing Map (SOM) machine learning technique was utilized for data interpolation, ensuring accuracy and continuity. Cross-validation techniques were applied to validate the performance of SOM, yielding low Mean Absolute Error (MAE) values for NDVI and NDMI, indicating minimal bias in the interpolated data. Results demonstrate significant improvements in vegetation health and moisture retention in rehabilitated areas, though inconsistencies remain compared to natural sites. The combined use of NDVI and NDMI provided complementary insights, highlighting areas requiring further intervention. This methodology offers a solid framework for monitoring rehabilitation progress and guiding future restoration efforts, not only in rehabilitated mining quarries but also in other rehabilitated urban and industrial sites.

Urban aquifers are a potential alternative, with which to cover the rising demand for freshwater. However, they are commonly polluted by a wide range of anthropogenic substances, including rare earth elements (REEs). In this context, it is essential to investigate the presence and behaviour of these pollutants, in order to safely use urban groundwater. An example of these contaminants is Gadolinium, which can reach the environment after it has been used for medical purposes. Gadolinium anomalies were detected in hydraulically connected surface and subsurface water bodies in Barcelona, northeast Spain. A striking factor in this study, is that Gadolinium pollution in groundwater was less frequent and more attenuated than in surface water, suggesting that Gadolinium attenuation may be prevalent in the subsurface. Batch experiments were conducted and modelled to ascertain processes affecting Gadolinium in the sampled aquifers. Experimental results have (i) demonstrated that the concentration of dissolved Gadolinium decreased 90% in 24 days, and (ii) suggested that sorption plays a key role in Gadolinium attenuation. The modelling of the experiments provides essential parameters, with which to predict the behaviour of Gadolinium. Our results have significant implications for understanding the evolution of anthropogenic Gadolinium in aquifers in general, which is of paramount importance for determining where groundwater can be safely used after its natural or artificial infiltration.

Assessing the environmental or ecological flow (EF) of rivers constitutes a crucial endeavor in safeguarding them against increased overexploitation, climate change impacts, and the proliferation of dams along their courses. However, the frequent lack of observation data jeopardizes the accurate and reliable assessment of rivers’ minimum streamflow requirements. The research implements a methodological framework to estimate EF in partially gauged basins by forcing the Tennant, Tessman and the Global Environmental Flow Calculator EF methods with river discharges generated by the LISFLOOD large scale hydrological model over a 31-year period (1992–2022). The outputs which correspond to nine major rivers in Greece, many of which are transboundary ones, are compared to the environmental flow estimates derived from the Greek legislation model. The findings indicate that the Greek legislation produces low EFs on an intermonthly timescale, often aligning with the EFs from other methods during the dry season, suggesting the need for a revision of the current legislation. The Tennant method tends to yield relatively low EFs in the dry period, whereas the Tessman and Global Environmental Flow Calculator methods produce EF curves that closely mimic the natural streamflow curve of each river. The methodological framework presented here is adaptable to any hydrosystem, offering a valuable base for enhanced EF assessment in the future.