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Adaptive forest management (AFM) aims to adapt a forest to water availability by means of an artificial regulation of the forest structure and density. Areas vulnerable to water scarcity situations, such as the Mediterranean region, might require AFM to optimize the hydrological cycle under normal and future global change conditions. This study uses the process based model (PBM) BIOME-BGC to predict the effects of AFM in an unmanaged semi-arid Mediterranean Aleppo pine plantation. At the same time, it seeking to increase the spatially explicit information to initialize the model runs. To this end, the model has been slightly modified, and canopy average specific leaf area and canopy water interception coefficient have both been introduced as functions of canopy coverage, which was obtained using airborne laser scanning (LiDAR) technology. The model was then calibrated and evaluated using sap flow, soil moisture and throughfall field data obtained during one year from three forest coverages (85, 73 and 26%, respectively). Calibration and evaluation of the model show acceptable accuracy, with the Nash-Sutcliffe coefficient ranging between 0.39 and 0.76 for calibration and 0.35 and 0.75 for evaluation. The model was then applied to analyze and predict the need for forest management in a Mediterranean public forest indicated a possible optimization of the hydrological cycle to establish a new equilibrium between blue and green water. This new scenario reduced water interception and plant transpiration (green water), and increased water runoff and/or percolation (blue water). (C) 2015 Elsevier B.V. All rights reserved.