The present study aims to compare SARIMA and Holt–Winters model forecasts of mean monthly flow at the V Aniversario basin, western Cuba. Model selection and model assessment are carried out with a rolling cross-validation scheme using mean monthly flow observations from the period 1971–1990. Model performance is analyzed in one- and two-year forecast lead times, and comparisons are made based on mean squared error, root mean squared error, mean absolute error and the Nash–Sutcliffe efficiency; all these statistics are computed from observed and simulated time series at the outlet of the basin. The major findings show that Holt–Winters models had better performance in reproducing the mean series seasonality when the training observations were insufficient, while for longer training subsets, both models were equally competitive in forecasting one year ahead. SARIMA models were found to be more reliable for longer lead-time forecasts, and their limitations after being trained on short observation periods are due to overfitting problems. Article Highlights: Comparison based on rolling cross-validation revealed the models forecasts sensibility to available observations amount. HW and SARIMA models perform better when limited observations or long-view forecasting, respectively, otherwise they do similar. HW models were superior modeling less variable monthly flows while SARIMA models better forecast the highly variable periods.

Accelerated water erosion is a major soil degradation process that affects soil and water quality. In Cuba, specifically, more than 40% of agricultural lands are affected by severe erosion problems. Estimating accurate erodibility values is a crucial step for the calibration and proper application of erosion models. Several equations have been developed to estimate erodibility from soil properties; however, these are often soil- or site-specific, limiting their application. This study aims to (1) identify soil properties governing the erodibility of tropical soils from western Cuba, (2) find suitable regression models to estimate erodibility from these properties, and (3) test widely applied erodibility equations. To achieve these goals, rainfall simulation experiments were conducted on runoff plots, and erosion-related physical, chemical, and mechanical soil properties were determined for 19 different soils. The main results indicated that good correlations between erodibility and certain soil properties were achieved after clustering soils based on their cation exchange capacity (CEC) values and clay content. Soils characterized by more than 30% of clay and 40 cmol+ kg⁻¹ of CEC were excluded from the main analysis. Generally, clay content controls the erodibility of these tropical soils, exhibiting an inverse relationship. However, in the excluded soils, the clay fraction showed a positive relationship with erodibility. Soil water retention at the lowest matric potentials demonstrated the strongest correlation with soil erodibility, as this variable encompasses compound information related to clay, mineralogy, and organic matter. A new regression model to estimate erodibility based solely on the volumetric water content at 1500 kPa is presented. The optimal fitted logarithmic model accounts for 64% of the predictand variability in the studied soils. When testing known erodibility models, the nomograph was found to best mimic the erodibility trend of these soils, although it exhibited marked uncertainty and underestimation biases.