The Hydrologic Engineering Center – Hydrologic Modelling System (HEC‐HMS) model has been widely applied for estimating hydrological variables at event scale. The choice of the most suitable infiltration method simplifies model's applicability under different environmental conditions. A proper case study for evaluating the runoff prediction capability of HEC‐HMS by the available infiltration methods is the semi‐arid torrents typical of Southern Italy as they are small and intermittent water courses, often subject to high‐magnitude flash floods and erosive events. HEC‐HMS performance of the ‘SCS‐CN’, ‘Green‐Ampt’ and ‘Initial and Constant’ infiltration methods in predicting runoff volume and peak flow was evaluated at the outlet of the Mésima torrent, Calabria, Southern Italy. Fourteen rainfall–runoff events were simulated by HEC‐HMS and compared with the corresponding observations by a quantitative approach. A good accuracy in predicting runoff volume was achieved using the ‘SCS‐CN’ method after calibration of the initial curve numbers. Peak flow was better estimated using the ‘Initial and Constant’ method, also thanks to calibration of the ‘constant rate’ parameter. The calibrated hydrographs were very similar to the observations for both ‘SCS‐CN’ and ‘Initial and Constant’ methods. Adopting the ‘Green‐Ampt’ equations, however, showed low reliability. The evaluation of the time to flood peak was in some cases inadequate. Copyright © 2016 John Wiley & Sons, Ltd.

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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.

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