The use of the Soil Conservation Service-curve number (SCS-CN) model for runoff predictions after rainstorms in fire-affected forests in the Mediterranean climate is quite scarce and limited to the watershed scale. To validate the applicability of this model in this environment, this study has evaluated the runoff prediction capacity of the SCS-CN model after storms at the plot scale in two pine forests of Central-Eastern Spain, affected by wildfire (with or without straw mulching) or prescribed fire and in unburned soils. The model performance has been compared to the predictions of linear regression equations between rainfall depth and runoff volume. The runoff volume was simulated with reliability by the linear regression only for the unburned soil (coefficient of Nash and Sutcliffe E = 0.73–0.89). Conversely, the SCS-CN model was more accurate for burned soils (E = 0.81–0.97), also when mulching was applied (E = 0.96). The performance of this model was very satisfactory in predicting the maximum runoff. Very low values of CNs and initial abstraction were required to predict the particular hydrology of the experimental areas. Moreover, the post-fire hydrological “window-of-disturbance” could be reproduced only by increasing the CN for the storms immediately after the wildfire. This study indicates that, in Mediterranean forests subject to the fire risk, the simple linear equations are feasible to predict runoff after low-intensity storms, while the SCS-CN model is advisable when runoff predictions are needed to control the flooding risk.

Benchmarking techniques are useful and simple tools to analyze the performance of the collective irrigation in the Water User Associations (WUAs) towards an increase in service sustainability. Several benchmarking techniques have been proposed to process and predict performance indicators. Instead, some meaningful statistical techniques based on the distance of data samples, which overcome the limitations of the traditional benchmarking techniques, have never been applied to the collective irrigation sector. This study applies Permutational Multivariate Analysis of Variance (PERMANOVA), Multidimensional Scale Models (MDS), and Distance-Based Linear Models (DISTLM) as benchmarking techniques to evaluate the technical and financial performances of 10 WUAs in Calabria (Southern Italy). These benchmarking techniques revealed that the significant differences in the irrigated areas and financial self-sufficiency of the WUAs, shown by PERMANOVA, depend on the large variability of the remaining performance indicators. Both the MDS and DISTLM demonstrated that a higher number of associated users and larger irrigation service coverage allows an increase in the irrigated areas; this enlargement is facilitated if the water price and the size of the personnel staff decrease. The WUAs’ self-sufficiency is mainly influenced by the number of workers and the maintenance, organization, and management costs, while the impacts of the due service fees and water price are more limited; it is also convenient to increase the number of the associated farmers since this increases the economy of scale and the gross revenues of the irrigation service. Overall, from the analysis carried out for the regional case study, these benchmarking techniques seem to be powerful and easy tools to identify the problems of the irrigation service and help in planning the most suitable policies to improve the sustainability of the collective irrigation at the regional scale.