The objective of the Spanish government-funded project GESMO (Gestión integral del acuífero 08.29 Mancha Oriental), is to develop new tools for the evaluation and monitoring of water policies. These tools have to be capable of matching resource exploitation with reserve sustainability, applied to aquifer 08.29 in the Eastern Mancha, Spain. A decision support system (DSS), was developed as part of the GESMO project, that integrates two different systems within one computer application. One, an hydrogeological model, simulates the River Júcar basin and its associated aquifer. The other, an econometric system, is capable of predicting the evolution of regional crop maps, crop yields and crop prices, thus allowing the determination of the regional gross product of crops. This paper describes mainly the economic system of the DSS, a set of econometric models. Those used for crop allocations are the most important for the DSS. The approach followed for the specification of the DSS is proposed as a provisional method based on information from the pre-quota period to estimate likely responses of farmers in a post-quota period. A brief description of the overall structure of the DSS and an example of one of its possible applications are also included in the paper.Additional key words: decision support system, econometric models, simulation, water management. ResumenUn sistema econométrico para medir el impacto económico de políticas de restricción de agua en EspañaEl objetivo del proyecto GESMO (Gestión integral del acuífero 08.29 Mancha Oriental) financiado por el gobierno español, consiste en desarrollar nuevas herramientas para la evaluación y el seguimiento de las políticas del agua. Dichas herramientas deben ser capaces de adecuar la explotación de recursos a la reserva sostenible, aplicándolo al acuífero 08.29 de la Mancha Oriental, España. Como parte del proyecto GESMO se ha desarrollado un sistema de soporte a la decisión (DSS), que integra dos sistemas distintos en una aplicación informática. Uno de ellos consiste en un modelo hidrogeológico que simula la cuenca del río Júcar y su acuífero asociado. El otro es un sistema economé-trico capaz de predecir la evolución del mapa regional de cultivos, el rendimiento de las cosechas y sus precios, todo ello encaminado al cálculo del producto regional bruto de los cultivos. Este artículo describe principalmente el sistema económico del DSS, formado por un conjunto de modelos econométricos. Los más importantes para el DSS son los que se utilizan para la asignación de cultivos. La aproximación seguida para su especificación se propone como una solución provisional al problema de la estimación de respuestas probables de los agricultores en un período postcuota, basándose en la información proporcionada por el periodo pre-cuota. Se incluyen también en este artículo una breve descripción de la estructura completa del DSS y un ejemplo de una de sus posibles aplicaciones.Palabras clave adicionales: gestión del agua, modelos econométricos, simulación, sistema de soporte...
The interactions among abiotic, biotic, and anthropic factors and their influence at different scales create a complex dynamic in landscape evolution. Scaling and multifractal analysis have the potential to characterise landscapes in terms of the statistical signature of the selected measure, in this case, altitude. This work evaluates the multifractality of altitude data points along transects that are obtained in several directions using Detrended Fluctuation Analysis (DFA) in a protected area adjacent to Madrid. The study data set consist of a matrix 2048 x 2048 pixels obtained at a 5 m resolution and extracted from a digital terrain model (DTM) using a Geographic Information System (GIS). We found that the distribution of altitude fluctuations at small scales revealed a non-Gaussian character in the statistical moments, indicating that Fractional Brownian modelling is not appropriate. Generalised Hurst dimensions (H(q)) were calculated on several transects crossing the area under study, all of which exhibited multifractality within a certain scale range. The results show a persistent behaviour in all directions because all of the H(q) values exceeded 0.5 and because there were differences in the intensities of the multifractality. The analysis of the directionality by means of a generalised Hurst rose plot showed differences in the scaling characteristics both along and across rivers and reservoirs. This indicates a clear anisotropy that is mainly due to the directions of the two river basins located in the area and the basement movement as a consequence of gradual tectonic displacement, which must be considered in two-dimensional DFAs.
Although frost can cause considerable crop damage, and practices have been developed to mitigate forecasted frost, frost forecasting technologies have not changed for years. This paper reports on a new method based on successive application of two models to forecast the number of monthly frost days for several Community of Madrid (Spain) meteorological stations. The first is an autoregressive integrated moving average (ARIMA) stochastic model that forecasts minimum monthly absolute temperature (t min) and average monthly minimum temperature (micro t) following Box and Jenkins methodology. The second model relates monthly temperatures (t min, micro t) to the minimum daily temperature distribution during one month. Three ARIMA models were identified. They present the same seasonal behaviour (integrated moving average model) and different non-seasonal part: autoregressive model (Model 1), integrated moving average model (Model 2) and autoregressive and moving average model (Model 3). The results indicate that minimum daily temperature (t dmin) for the meteorological stations studied followed a normal distribution each month with a very similar standard deviation through out the years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures produced the best frost days forecast. This procedure provides a tool for crop managers and crop insurance companies to assess the risk of frost frequency and intensity, so that they can take steps to mitigate frost damage and estimate the damage that frost would cause.
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