El parámetro de capacidad de retención de agua en el suelo (CRA) es un modelo de base física ampliamente utilizado por técnicos forestales e investigadores en ecología forestal del territorio español, como uno de los factores estimadores de las disponibilidades de agua para las plantas y, por ende, de la calidad de estación. Dentro del proyecto Caracterización de suelos forestales de la provincia de Huelva se han apreciado una serie de anomalías en cuanto a los valores obtenidos para el parámetro CRA, especialmente en lo referente a la influencia de la pendiente sobre el modelo para su cálculo, así como en la determinación del agua disponible para la vegetación, en relación a la reserva total. Este trabajo plantea una sencilla modificación del modelo que permite obtener valores de capacidad de retención de agua más acordes con la calidad de la estación, en los terrenos forestales de fuerte pendiente, y también extiende el modelo al cálculo de la máxima reserva de agua disponible en el suelo. La bondad de estos parámetros se contrasta mediante el análisis de correlaciones frente a un índice de calidad de estación, con resultados satisfactorios. | In Spain, a physical model to estimate the soil water capacity, which is called the CRA parameter, was implemented by Gandullo (1985). This parameter is broadly used in forest management and forest ecology research in relationship with site index and soil quality variables. While running a soil mapping project in the southwest of Spain some problems related to the CRA model were detected, mainly related to the influence of slope on total soil water capacity, as well as the estimation of available water capacity. A simple correction of the model is proposed in this paper, as well as a complementary parameter for available water capacity. The quality of those new parameters is tested by means of correlation analysis against Site Index variable.

This study attempt to simulate the effect of forest structure on the distribution of rainfall water in the soil. Nutrient supplies to the forest soil are estimated in 5 hypothetical stages of development in the Mediterranean forest, based upon the measurements of water flows and their corresponding chemical compositions made in Poblet holm-oak forest.

An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor’s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09 %. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.