Spur dikes are elongated structures extending from banks into rivers that mitigate erosion by forcing the flow away from the bank. The research on grouped spur dikes is insufficient in comparison with those on isolated spur dikes. Most of the studies focus on the maximum scour depth, omitting the bed morphological changes induced by local scour processes. Moreover, as yet, there is no established procedure for predicting the scour depth around spur dikes. This study aims to provide insights into the temporal and spatial morphological patterns around a single spur dike and spur dikes in cascade (three, five, and seven, consecutively). Experiments (of up to 318 h) were performed on a rectangular straight channel with dimensions of 20 m (length) × 1.0 m (width) × 1.0 m (depth). Nearly uniform sand with median grain size of 1.7 mm and sediment gradation of 1.5 was used for the mobile bed. The spacing between the elements for the spur dikes in cascade was 3b, where b is the spur dike width. All runs were conducted under a clear-water regime and steady flow conditions. Some limitations of the formulas for the equilibrium scour depth at the first spur dike reported in the literature were emphasised, with underestimations up to 160% and overestimations up to 200% at the earlier scour stages. The temporal evolution of the scour depth at the first spur dike was satisfactorily predicted with a correlation coefficient of 0.91. The scour processes at the other spur dikes were delayed and started at a dimensionless time greater than approximately 10³. However, the scour rates increased to a high degree, with the scour depths tending to match those at the first spur dike.

Daily streamflow prediction is essential for effective water resources management, particularly in small, rapidly responding Mediterranean catchments characterised by pronounced seasonal variability and flashy hydrological regimes. This study evaluates two ensemble tree-based machine learning models; LightGBM and Random Forest, for daily discharge prediction in the Fiumarella di Corleto basin (33 km²), Southern Italy, using 21 years of daily hydrometeorological observations (2002–2022). Predictor variables included lagged discharge, three-day moving averages, multi-day cumulative precipitation, and harmonic seasonal indicators derived from the day of year. Hyperparameters were optimised using an expanding walk-forward cross-validation approach to preserve the temporal structure of the time series. Model performance was primarily assessed using the Aras diagram, which quantifies total error and its decomposition into bias, variability, and correlation errors across both training and independent test periods; additional evaluation metrics included the Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), and Root Mean Squared Error (RMSE). LightGBM substantially outperformed Random Forest, achieving total errors of 5% in both periods according to the Aras diagram, compared to 21% for Random Forest. This superior performance was consistent with higher KGE (0.971 vs. 0.795), NSE (0.970 vs. 0.870), and lower RMSE (0.125 vs. 0.259 mm day⁻¹), with near-zero systematic bias for both models (PBIAS < 1%). SHAP analysis computed on the full test set identified the three-day moving average of antecedent discharge (Q_ma3) as the dominant predictor, followed by precipitation and lagged discharge variables, with cross-model agreement in feature rankings confirming the robustness of these findings. These results demonstrate that LightGBM, combined with rigorous temporal validation, Aras-based performance evaluation, and SHAP interpretability, provides a reliable and physically consistent framework for daily streamflow prediction in small Mediterranean basins.

Satellite data provide valuable information on the variations in the surface area of inland waters. These observations are crucial for monitoring remote or poorly instrumented regions and offer complementary insights to in situ measurements. To further assess this capability, we analyzed the temporal variations of Serra del Corvo Lake – located at the border between the Basilicata and Puglia regions in southern Italy – using cloud-free Sentinel-2 imagery acquired from 2015 to 2025. The satellite-derived surface water extent time series were compared with daily in situ measurements of water volume and level, achieving very high correlation (R > 0.98). This strong correlation supported the development of two predictive models, based on both linear and exponential regression method, which demonstrated satisfactory accuracy (R² consistently above 0.8 in both calibration and validation phases). These results highlight the high potential of satellitebased approaches to provide almost independent and reliable information on inland water dynamics.