A Case Study of Geometric Modelling via 3-D Point Interpolation for the Bathymetry of the Rabasa Lakes (Alicante, Spain)

The interpolation of points by means of Information Technology programs appears as a technical tool of some relevancy in the hydrogeology in general and in the study of the humid zones in particular. Our approach has been the determination of the 3-D geometry of the humid zones of major depth of the Rabasa Lakes. To estimate the topography of the lake bed, we proceed to acquire information in the field by means of sonar and GPS equipment. A total of 335 points were measured both on the perimeter and in the lake bed. In a second stage, this information was used in a kriging program to obtain the bathymetry of the wetland. This methodology is demonstrated as one of the most reliable and cost-efficient for the 3-D analysis of this type of water masses. The bathymetric study of the zone allows us to characterize the mid- and long-term hydrological evolution of the lakes by means of depth-area-volume curves.

Comparative assessment of global irradiation from a satellite estimate model (CM SAF) and on-ground measurements (SIAR): a Spanish case study

An analysis and comparison of daily and yearly solar irradiation from the satellite CM SAF database and a set of 301 stations from the Spanish SIAR network is performed using data of 2010 and 2011. This analysis is completed with the comparison of the estimations of effective irradiation incident on three different tilted planes (fixed, two axis tracking, north-south hori- zontal axis) using irradiation from these two data sources. Finally, a new map of yearly values of irradiation both on the horizontal plane and on inclined planes is produced mixing both sources with geostatistical techniques (kriging with external drift, KED) The Mean Absolute Difference (MAD) between CM SAF and SIAR is approximately 4% for the irradiation on the horizontal plane and is comprised between 5% and 6% for the irradiation incident on the inclined planes. The MAD between KED and SIAR, and KED and CM SAF is approximately 3% for the irradiation on the horizontal plane and is comprised between 3% and 4% for the irradiation incident on the inclined planes. The methods have been implemented using free software, available as supplementary ma- terial, and the data sources are freely available without restrictions.

Comparative assessment of global irradiation from a satellite estimate model (CM SAF) and on-ground measurements (SIAR): a Spanish case study

An analysis and comparison of daily and yearly solar irradiation from the satellite CM SAF database and a set of 301 stations from the Spanish SIAR network is performed using data of 2010 and 2011. This analysis is completed with the comparison of the estimations of effective irradiation incident on three different tilted planes (fixed, two axis tracking, north-south hori- zontal axis) using irradiation from these two data sources. Finally, a new map of yearly values of irradiation both on the horizontal plane and on inclined planes is produced mixing both sources with geostatistical techniques (kriging with external drift, KED) The Mean Absolute Difference (MAD) between CM SAF and SIAR is approximately 4% for the irradiation on the horizontal plane and is comprised between 5% and 6% for the irradiation incident on the inclined planes. The MAD between KED and SIAR, and KED and CM SAF is approximately 3% for the irradiation on the horizontal plane and is comprised between 3% and 4% for the irradiation incident on the inclined planes. The methods have been implemented using free software, available as supplementary ma- terial, and the data sources are freely available without restrictions.

Downscaling of global solar irradiation in R

A methodology for downscaling solar irradiation from satellite-derived databases is described using R software. Different packages such as raster, parallel, solaR, gstat, sp and rasterVis are considered in this study for improving solar resource estimation in areas with complex topography, in which downscaling is a very useful tool for reducing inherent deviations in satellite-derived irradiation databases, which lack of high global spatial resolution. A topographical analysis of horizon blocking and sky-view is developed with a digital elevation model to determine what fraction of hourly solar irradiation reaches the Earth's surface. Eventually, kriging with external drift is applied for a better estimation of solar irradiation throughout the region analyzed. This methodology has been implemented as an example within the region of La Rioja in northern Spain, and the mean absolute error found is a striking 25.5% lower than with the original database.

Integración de la calidad del aire en la planificación física

En esta tesis se presenta una nueva aproximación para la realización de mapas de calidad del aire, con objeto de que esta variable del medio físico pueda ser tenida en cuenta en los procesos de planificación física o territorial. La calidad del aire no se considera normalmente en estos procesos debido a su composición y a la complejidad de su comportamiento, así como a la dificultad de contar con información fiable y contrastada. Además, la variabilidad espacial y temporal de las medidas de calidad del aire hace que sea difícil su consideración territorial y exige la georeferenciación de la información. Ello implica la predicción de medidas para lugares del territorio donde no existen datos. Esta tesis desarrolla un modelo geoestadístico para la predicción de valores de calidad del aire en un territorio. El modelo propuesto se basa en la interpolación de las medidas de concentración de contaminantes registradas en las estaciones de monitorización, mediante kriging ordinario, previa homogeneización de estos datos para eliminar su carácter local. Con el proceso de eliminación del carácter local, desaparecen las tendencias de las series muestrales de datos debidas a las variaciones temporales y espaciales de la calidad del aire. La transformación de los valores de calidad del aire en cantidades independientes del lugar de muestreo, se realiza a través de parámetros de uso del suelo y de otras variables características de la escala local. Como resultado, se obtienen unos datos de entrada espacialmente homogéneos, que es un requisito fundamental para la utilización de cualquier algoritmo de interpolación, en concreto, del kriging ordinario. Después de la interpolación, se aplica una retransformación de los datos para devolver el carácter local al mapa final. Para el desarrollo del modelo, se ha elegido como área de estudio la Comunidad de Madrid, por la disponibilidad de datos reales. Estos datos, valores de calidad del aire y variables territoriales, se utilizan en dos momentos. Un momento inicial, donde se optimiza la selección de los parámetros más adecuados para la eliminación del carácter local de las medidas y se desarrolla cada una de las etapas del modelo. Y un segundo momento, en el que se aplica en su totalidad el modelo desarrollado y se contrasta su eficacia predictiva. El modelo se aplica para la estimación de los valores medios y máximos de NO2 del territorio de estudio. Con la implementación del modelo propuesto se acomete la territorialización de los datos de calidad del aire con la reducción de tres factores clave para su efectiva integración en la planificación territorial o en el proceso de toma de decisiones asociado: incertidumbre, tiempo empleado para generar la predicción y recursos (datos y costes) asociados. El modelo permite obtener una predicción de valores del contaminante objeto de análisis en unas horas, frente a los periodos de modelización o análisis requeridos por otras metodologías. Los recursos necesarios son mínimos, únicamente contar con los datos de las estaciones de monitorización del territorio que, normalmente, están disponibles en las páginas web viii institucionales de los organismos gestores de las redes de medida de la calidad del aire. Por lo que respecta a las incertidumbres de la predicción, puede decirse que los resultados del modelo propuesto en esta tesis son estadísticamente muy correctos y que los errores medios son, en general, similares o menores que los encontrados con la aplicación de las metodologías existentes. ABSTRACT This thesis presents a new approach for mapping air quality, so that this variable of physical environment can be taken into account in physical or territorial planning. Ambient air quality is not normally considered in territorial planning mainly due to the complexity of its composition and behavior and the difficulty of counting with reliable and contrasted information. In addition, the wide spatial and temporal variability of the measurements of air quality makes his territorial consideration difficult and requires georeferenced information. This involves predicting measurements in the places of the territory where there are no data. This thesis develops a geostatistical model for predicting air quality values in a territory. The proposed model is based on the interpolation of measurements of pollutants from the monitoring stations, using ordinary kriging, after a detrending or removal of the local character of sampling values process. With the detrending process, the local character of the time series of sampling data, due to temporal and spatial variations of air quality, is removed. The transformation of the air quality values into site-independent quantities is performed using land use parameters and other characteristic parameters of local scale. This detrending of the monitoring data process results in a spatial homogeneous input set which is a prerequisite for a correct use of any interpolation algorithm, particularly, ordinary kriging. After the interpolation step, a retrending or retransformation is applied in order to incorporate the local character in the final map at places where no monitoring data is available. For the development of this model, the Community of Madrid is chosen as study area, because of the availability of actual data. These data, air quality values and local parameters, are used in two moments. A starting point, to optimize the selection of the most suitable indicators for the detrending process and to develop each one of the model stages. And a second moment, to fully implement the developed model and to evaluate its predictive power. The model is applied to estimate the average and maximum values of NO2 in the study territory. With the implementation of the proposed model, the territorialization of air quality data is undertaken with the reduction in three key factors for the effective integration of this parameter in territorial planning or in the associated decision making process: uncertainty, time taken to generate the prediction and associated resources (data and costs). This model allows the prediction of pollutant values in hours, compared to the implementation time periods required for other modeling or analysis methodologies. The required resources are also minimal, only having data from monitoring stations in the territory, that are normally available on institutional websites of the authorities responsible for air quality networks control and management. With regard to the prediction uncertainties, it can be concluded that the results of the proposed model are statistically very accurate and the mean errors are generally similar to or lower than those found with the application of existing methodologies.

Spatial distribution of the soil carbon pool in a Holm oak dehesa in Spain

Aims Dehesas are agroforestry systems characterized by scattered trees among pastures, crops and/or fallows. A study at a Spanish dehesa has been carried out to estimate the spatial distribution of the soil organic carbon stock and to assess the influence of the tree cover. Methods The soil organic carbon stock was estimated from the five uppermost cm of themineral soil with high spatial resolution at two plots with different grazing intensities. The Universal Kriging technique was used to assess the spatial distribution of the soil organic carbon stocks, using tree coverage within a buffering area as an auxiliary variable. Results A significant positive correlation between tree presence and soil organic carbon stocks up to distances of around 8 m from the trees was found. The tree crown cover within a buffer up to a distance similar to the crown radius around the point absorbed 30 % of the variance in the model for both grazing intensities, but residual variance showed stronger spatial autocorrelation under regular grazing conditions. Conclusions Tree cover increases soil organic carbon stocks, and can be satisfactorily estimated by means of crown parameters. However, other factors are involved in the spatial pattern of the soil organic carbon distribution. Livestock plays an interactive role together with tree presence in soil organic carbon distribution.

Influencia de la maquinaria agrícola sobre la variabilidad espacial de la compactación del suelo. Aplicación de la metodología geoestadística-fractal

El cultivo de la caña de azúcar es uno de los más importantes en muchos países del mundo. Los suelos dedicados a este cultivo son usualmente compactados por el tránsito de la maquinaria en el proceso de cosecha. El uso combinado de la geoestadística con el análisis fractal ha demostrado ser útil para el estudio de los mismos. El objetivo del trabajo fue determinar los cambios espaciales de la resistencia a la penetración del suelo debido a la influencia del tránsito de la maquinaria en el proceso de cosecha de la caña de azúcar en un Vertisol, aplicando la metodología geoestadística-fractal. La investigación se llevó a cabo en el período de cosecha 2008-2009. Se evaluó la resistencia a la penetración en dos momentos, antes y después de la cosecha. El muestreo se realizó sistemáticamente en cuadrícula y en transecto, seleccionando 144 y 100 observaciones antes y después de la cosecha, respectivamente, y 221 para el transecto en diagonal. También se determinó el contenido de humedad del suelo por el método gravimétrico, para lo que se tomaron 288 muestras aleatorias en todo el campo. Los resultados demuestran que los valores de resistencia a la penetración (RP) presentaron una distribución normal a partir de los 5 cm de profundidad, el tránsito de la maquinaria agrícola para la cosecha de la caña de azúcar provocó concentración de la variabilidad espacial a escalas inferiores a la del muestreo (el efecto pepita aumentó), un aumento del rango de correlación espacial y una redistribución de las zonas de compactación (las variaciones de los mapas de Krigeaje). También indujo anti-persistencia y anisotropía en algunas direcciones horizontales. Se observó un comportamiento irregular de (RP) verticalmente en el transecto, donde no solamente influyó la maquinaria, sino que también otros factores influyeron como: la hilera, borde de la hilera y grietas. ABSTRACT The cultivation of the cane of sugar is one of the most important in many countries of the world. The soils dedicated to this cultivation are usually compacted by the traffic of the machinery in the harvest process. The combined use of the geostatistics with the fractal analysis has demonstrated to be useful for the study of the same ones. The objective of the work was to determine the space changes from the resistance to the penetration of the floor due to the influence of the traffic of the machinery in the harvest process of harvest of the cane of sugar in a Vertisol applying the geostatistic-fractal methodology. The investigation was carried out in the period of harvest 2008-2009. The resistance to the penetration at two moments was evaluated, before and after the harvest. The sampling was realized systematically in grid and transect, selecting 144 and 100 observations before and after the harvest, respectively, and 221 for transect in diagonal. Also the soil moisture content of the ground by the gravimetric method was determined, so 288 random samples in the entire field were taken. The results shown that resistance to penetration values presented a normal distribution deeper than 5 cm before and after harvest. The transit of the agricultural machinery for sugar cane harvest concentrated the space variability at lower distances than the sampling one, reflected an increase in the nugget effect. At the same time, an increase space correlation rank and a redistribution of compaction areas were observed studying the variations in kriging maps. Another effect of the agricultural machinery transit was to induce antipersistence and anisotropy in some horizontal directions. However, in vertical direction of the longest transect an irregular behaviour was induced not only by the machinery as by another factors such as soil cracks, crop rows and allocation.

Spatial Estimation of Sub-Hour Global Horizontal Irradiance Based on Official Observations and Remote Sensors.

This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of +-2 standard deviations).

Diffuse soil CO2 flux to assess the reliability of CO2 storage the reliability of CO2 storage in the Mazarrón-Gañuelas Tertiary Basin (Spain)

In the framework of a global investigation of the Spanish natural analogues of CO2 storage and leakage, four selected sites from the Mazarrón?Gañuelas Tertiary Basin (Murcia, Spain) were studied for computing the diffuse soil CO2 flux, by using the accumulation chamber method. The Basin is characterized by the presence of a deep, saline, thermal (?47 ?C) CO2-rich aquifer intersected by two deep geothermal exploration wells named ?El Saladillo? (535 m) and ?El Reventón? (710 m). The CO2 flux data were processed by means of a graphical?statistical method, kriging estimation and sequential Gaussian simulation algorithms. The results have allowed concluding that the Tertiary marly cap-rock of this CO2-rich aquifer acts as a very effective sealing, preventing any CO2 leak from this natural CO2 storage site, being therefore an excellent scenario to guarantee, by analogy, the safety of a CO2 storage.

Análisis de la distribución espacial de los parámetros acústicos en recintos

El principal objetivo de este estudio es la evaluación de la distribución espacial de los parámetros acústicos en un recinto a través de la aplicación de técnicas geoestadísticas como el método Kriging. Mediante el uso de la herramienta de análisis espacial ArcMap, perteneciente a la plataforma ArcGIS, se ha analizado el comportamiento acústico del Salón de Actos común a la Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST) y la Escuela Técnica Superior de Ingeniería de Sistemas Informáticos (ETSISI), ambas situadas en el Campus Sur de la Universidad Politécnica de Madrid. Se han realizado mediciones in-situ del recinto no ocupado utilizando la herramienta de medición DIRAC Room Acoustics y el método de la respuesta impulsiva integrada, extrayéndose los parámetros acústicos de tiempo de reverberación (RT), tiempo de reverberación inicial (EDT), fuerza sonora relativa (Grel), claridad (C80), tiempo central (Ts), definición (D50) e Índice de Transmisión Rápida de la Palabra (RASTI). Se ha analizado la adecuación de los valores observados de Grel, C80 y Ts al modelo teórico de Barron y estimado, mediante el método Kriging Ordinario, el conjunto de parámetros medidos en el recinto, obteniéndose los semivariograma y mapas de estimación correspondientes. Además, se ha evaluado la calidad de la estimación en base a un número de puntos de medición reducido. A la vista de los resultados obtenidos, en general, el método Kriging puede considerarse un buen interpolador de los parámetros acústicos en un recinto, observándose que los parámetros que evalúan relaciones energéticas, especialmente la fuerza sonora relativa (Grel) proporcionan mejores estimaciones en comparación con aquellos relacionados con la reverberación y la inteligibilidad del habla. El coeficiente de determinación (R2) constituye una medida útil para evaluar la precisión de la estimación. Además, la entropía de los datos observados puede ser un buen indicador a priori de la precisión de la estimación. Asimismo, se ha demostrado que, basándose en un reducido número de puntos de medición, es posible obtener una estimación precisa de los parámetros acústicos de fuerza sonora relativa (Grel) y tiempo central (Ts). ABSTRACT. This project aims to evaluate the feasibility of using geostatistical techniques such as Kriging on the analysis of the spatial distribution of the acoustic parameters in rooms. The acoustic behaviour of the Assembly Hall of the ETSIST and ETSISI (Universidad Politécnica de Madrid) is investigated using ArcMap, which is the main component of ArcGIS suite of geospatial processing programs. For this purpose, in-situ acoustic measurements are carried out in the unoccupied room using DIRAC Room Acoustics software. The following acoustic parameters are measured by means of the integrated impulse response method for further examination: Reverberation Time (RT), Early Decay Time (EDT), Relative Strength (Grel), Clarity (C80), Centre Time (Ts), Definition (D50) and Rapid Speech Transmission Index (RASTI). Goodness-of-fit of measured Grel, C80 and Ts values to Barron’s theory is determined and Ordinary Kriging is applied to all the measured parameters in order to calculate the semivariogram and prediction surfaces. The prediction performance is also analysed when significantly fewer receiver positions are used for the prediction. The experimental results obtained lead to conclude that Kriging can be successfully applied to room acoustics. Energy\based acoustic parameters can be estimated with higher accuracy compared to those related to reverberation and speech intelligibility. Coefficient of determination (R2) is a reliable statistic for assessing the prediction accuracy, for which measured data entropy can also be a good a priori indicator. Furthermore, based on fewer receiver positions, it is demonstrated that accurate predictions of Grel and Ts can be achieved.

Predicción espacio-temporal de la irradiancia solar global a corto plazo en España mediante geoestadística y redes neuronales artificiales

El enriquecimiento del conocimiento sobre la Irradiancia Solar (IS) a nivel de superficie terrestre, así como su predicción, cobran gran interés para las Energías Renovables (ER) - Energía Solar (ES)-, y para distintas aplicaciones industriales o ecológicas. En el ámbito de las ER, el uso óptimo de la ES implica contar con datos de la IS en superficie que ayuden tanto, en la selección de emplazamientos para instalaciones de ES, como en su etapa de diseño (dimensionar la producción) y, finalmente, en su explotación. En este último caso, la observación y la predicción es útil para el mercado energético, la planificación y gestión de la energía (generadoras y operadoras del sistema eléctrico), especialmente en los nuevos contextos de las redes inteligentes de transporte. A pesar de la importancia estratégica de contar con datos de la IS, especialmente los observados por sensores de IS en superficie (los que mejor captan esta variable), estos no siempre están disponibles para los lugares de interés ni con la resolución espacial y temporal deseada. Esta limitación se une a la necesidad de disponer de predicciones a corto plazo de la IS que ayuden a la planificación y gestión de la energía. Se ha indagado y caracterizado las Redes de Estaciones Meteorológicas (REM) existentes en España que publican en internet sus observaciones, focalizando en la IS. Se han identificado 24 REM (16 gubernamentales y 8 redes voluntarios) que aglutinan 3492 estaciones, convirtiéndose éstas en las fuentes de datos meteorológicos utilizados en la tesis. Se han investigado cinco técnicas de estimación espacial de la IS en intervalos de 15 minutos para el territorio peninsular (3 técnicas geoestadísticas, una determinística y el método HelioSat2 basado en imágenes satelitales) con distintas configuraciones espaciales. Cuando el área de estudio tiene una adecuada densidad de observaciones, el mejor método identificado para estimar la IS es el Kriging con Regresión usando variables auxiliares -una de ellas la IS estimada a partir de imágenes satelitales-. De este modo es posible estimar espacialmente la IS más allá de los 25 km identificados en la bibliografía. En caso contrario, se corrobora la idoneidad de utilizar estimaciones a partir de sensores remotos cuando la densidad de observaciones no es adecuada. Se ha experimentado con el modelado de Redes Neuronales Artificiales (RNA) para la predicción a corto plazo de la IS utilizando observaciones próximas (componentes espaciales) en sus entradas y, los resultados son prometedores. Así los niveles de errores disminuyen bajo las siguientes condiciones: (1) cuando el horizonte temporal de predicción es inferior o igual a 3 horas, las estaciones vecinas que se incluyen en el modelo deben encentrarse a una distancia máxima aproximada de 55 km. Esto permite concluir que las RNA son capaces de aprender cómo afectan las condiciones meteorológicas vecinas a la predicción de la IS. ABSTRACT ABSTRACT The enrichment of knowledge about the Solar Irradiance (SI) at Earth's surface and its prediction, have a high interest for Renewable Energy (RE) - Solar Energy (SE) - and for various industrial and environmental applications. In the field of the RE, the optimal use of the SE involves having SI surface to help in the selection of sites for facilities ES, in the design stage (sizing energy production), and finally on their production. In the latter case, the observation and prediction is useful for the market, planning and management of the energy (generators and electrical system operators), especially in new contexts of smart transport networks (smartgrid). Despite the strategic importance of SI data, especially those observed by sensors of SI at surface (the ones that best measure this environmental variable), these are not always available to the sights and the spatial and temporal resolution desired. This limitation is bound to the need for short-term predictions of the SI to help planning and energy management. It has been investigated and characterized existing Networks of Weather Stations (NWS) in Spain that share its observations online, focusing on SI. 24 NWS have been identified (16 government and 8 volunteer networks) that implies 3492 stations, turning it into the sources of meteorological data used in the thesis. We have investigated five technical of spatial estimation of SI in 15 minutes to the mainland (3 geostatistical techniques and HelioSat2 a deterministic method based on satellite images) with different spatial configurations. When the study area has an adequate density of observations we identified the best method to estimate the SI is the regression kriging with auxiliary variables (one of them is the SI estimated from satellite images. Thus it is possible to spatially estimate the SI beyond the 25 km identified in the literature. Otherwise, when the density of observations is inadequate the appropriateness is using the estimates values from remote sensing. It has been experimented with Artificial Neural Networks (ANN) modeling for predicting the short-term future of the SI using observations from neighbor’s weather stations (spatial components) in their inputs, and the results are promising. The error levels decrease under the following conditions: (1) when the prediction horizon is less or equal than 3 hours the best models are the ones that include data from the neighboring stations (at a maximum distance of 55 km). It is concluded that the ANN is able to learn how weather conditions affect neighboring prediction of IS at such Spatio-temporal horizons.

Towards downscaling of aerosol gridded dataset for improving solar resource assessment, an application to Spain

Solar radiation estimates with clear sky models require estimations of aerosol data. The low spatial resolution of current aerosol datasets, with their remarkable drift from measured data, poses a problem in solar resource estimation. This paper proposes a new downscaling methodology by combining support vector machines for regression (SVR) and kriging with external drift, with data from the MACC reanalysis datasets and temperature and rainfall measurements from 213 meteorological stations in continental Spain. The SVR technique was proven efficient in aerosol variable modeling. The Linke turbidity factor (TL) and the aerosol optical depth at 550 nm (AOD 550) estimated with SVR generated significantly lower errors in AERONET positions than MACC reanalysis estimates. The TL was estimated with relative mean absolute error (rMAE) of 10.2% (compared with AERONET), against the MACC rMAE of 18.5%. A similar behavior was seen with AOD 550, estimated with rMAE of 8.6% (compared with AERONET), against the MACC rMAE of 65.6%. Kriging using MACC data as an external drift was found useful in generating high resolution maps (0.05° × 0.05°) of both aerosol variables. We created high resolution maps of aerosol variables in continental Spain for the year 2008. The proposed methodology was proven to be a valuable tool to create high resolution maps of aerosol variables (TL and AOD 550). This methodology shows meaningful improvements when compared with estimated available databases and therefore, leads to more accurate solar resource estimations. This methodology could also be applied to the prediction of other atmospheric variables, whose datasets are of low resolution.

Development of Shape-Optimization Tools for the Aerodynamic Design of Turbomachinery Blades

The aim of this work is to develop an automated tool for the optimization of turbomachinery blades founded on an evolutionary strategy. This optimization scheme will serve to deal with supersonic blades cascades for application to Organic Rankine Cycle (ORC) turbines. The blade geometry is defined using parameterization techniques based on B-Splines curves, that allow to have a local control of the shape. The location in space of the control points of the B-Spline curve define the design variables of the optimization problem. In the present work, the performance of the blade shape is assessed by means of fully-turbulent flow simulations performed with a CFD package, in which a look-up table method is applied to ensure an accurate thermodynamic treatment. The solver is set along with the optimization tool to determine the optimal shape of the blade. As only blade-to-blade effects are of interest in this study, quasi-3D calculations are performed, and a single-objective evolutionary strategy is applied to the optimization. As a result, a non-intrusive tool, with no need for gradients definition, is developed. The computational cost is reduced by the use of surrogate models. A Gaussian interpolation scheme (Kriging model) is applied for the estimated n-dimensional function, and a surrogate-based local optimization strategy is proved to yield an accurate way for optimization. In particular, the present optimization scheme has been applied to the re-design of a supersonic stator cascade of an axial-flow turbine. In this design exercise very strong shock waves are generated in the rear blade suction side and shock-boundary layer interaction mechanisms occur. A significant efficiency improvement as a consequence of a more uniform flow at the blade outlet section of the stator is achieved. This is also expected to provide beneficial effects on the design of a subsequent downstream rotor. The method provides an improvement to gradient-based methods and an optimized blade geometry is easily achieved using the genetic algorithm.

Generalización de variables medioambientales mediante interpolación GIS

La investigación de esta tesis se centra en el estudio de técnicas geoestadísticas y su contribución a una mayor caracterización del binomio factores climáticos-rendimiento de un cultivo agrícola. El inexorable vínculo entre la variabilidad climática y la producción agrícola cobra especial relevancia en estudios sobre el cambio climático o en la modelización de cultivos para dar respuesta a escenarios futuros de producción mundial. Es información especialmente valiosa en sistemas operacionales de monitoreo y predicción de rendimientos de cultivos Los cuales son actualmente uno de los pilares operacionales en los que se sustenta la agricultura y seguridad alimentaria mundial; ya que su objetivo final es el de proporcionar información imparcial y fiable para la regularización de mercados. Es en este contexto, donde se quiso dar un enfoque alternativo a estudios, que con distintos planteamientos, analizan la relación inter-anual clima vs producción. Así, se sustituyó la dimensión tiempo por la espacio, re-orientando el análisis estadístico de correlación interanual entre rendimiento y factores climáticos, por el estudio de la correlación inter-regional entre ambas variables. Se utilizó para ello una técnica estadística relativamente nueva y no muy aplicada en investigaciones similares, llamada regresión ponderada geográficamente (GWR, siglas en inglés de “Geographically weighted regression”). Se obtuvieron superficies continuas de las variables climáticas acumuladas en determinados periodos fenológicos, que fueron seleccionados por ser factores clave en el desarrollo vegetativo de un cultivo. Por ello, la primera parte de la tesis, consistió en un análisis exploratorio sobre comparación de Métodos de Interpolación Espacial (MIE). Partiendo de la hipótesis de que existe la variabilidad espacial de la relación entre factores climáticos y rendimiento, el objetivo principal de esta tesis, fue el de establecer en qué medida los MIE y otros métodos geoestadísticos de regresión local, pueden ayudar por un lado, a alcanzar un mayor entendimiento del binomio clima-rendimiento del trigo blando (Triticum aestivum L.) al incorporar en dicha relación el componente espacial; y por otro, a caracterizar la variación de los principales factores climáticos limitantes en el crecimiento del trigo blando, acumulados éstos en cuatro periodos fenológicos. Para lleva a cabo esto, una gran carga operacional en la investigación de la tesis consistió en homogeneizar y hacer los datos fenológicos, climáticos y estadísticas agrícolas comparables tanto a escala espacial como a escala temporal. Para España y los Bálticos se recolectaron y calcularon datos diarios de precipitación, temperatura máxima y mínima, evapotranspiración y radiación solar en las estaciones meteorológicas disponibles. Se dispuso de una serie temporal que coincidía con los mismos años recolectados en las estadísticas agrícolas, es decir, 14 años contados desde 2000 a 2013 (hasta 2011 en los Bálticos). Se superpuso la malla de información fenológica de cuadrícula 25 km con la ubicación de las estaciones meteorológicas con el fin de conocer los valores fenológicos en cada una de las estaciones disponibles. Hecho esto, para cada año de la serie temporal disponible se calcularon los valores climáticos diarios acumulados en cada uno de los cuatro periodos fenológicos seleccionados P1 (ciclo completo), P2 (emergencia-madurez), P3 (floración) y P4 (floraciónmadurez). Se calculó la superficie interpolada por el conjunto de métodos seleccionados en la comparación: técnicas deterministas convencionales, kriging ordinario y cokriging ordinario ponderado por la altitud. Seleccionados los métodos más eficaces, se calculó a nivel de provincias las variables climatológicas interpoladas. Y se realizaron las regresiones locales GWR para cuantificar, explorar y modelar las relaciones espaciales entre el rendimiento del trigo y las variables climáticas acumuladas en los cuatro periodos fenológicos. Al comparar la eficiencia de los MIE no destaca una técnica por encima del resto como la que proporcione el menor error en su predicción. Ahora bien, considerando los tres indicadores de calidad de los MIE estudiados se han identificado los métodos más efectivos. En el caso de la precipitación, es la técnica geoestadística cokriging la más idónea en la mayoría de los casos. De manera unánime, la interpolación determinista en función radial (spline regularizado) fue la técnica que mejor describía la superficie de precipitación acumulada en los cuatro periodos fenológicos. Los resultados son más heterogéneos para la evapotranspiración y radiación. Los métodos idóneos para estas se reparten entre el Inverse Distance Weighting (IDW), IDW ponderado por la altitud y el Ordinary Kriging (OK). También, se identificó que para la mayoría de los casos en que el error del Ordinary CoKriging (COK) era mayor que el del OK su eficacia es comparable a la del OK en términos de error y el requerimiento computacional de este último es mucho menor. Se pudo confirmar que existe la variabilidad espacial inter-regional entre factores climáticos y el rendimiento del trigo blando tanto en España como en los Bálticos. La herramienta estadística GWR fue capaz de reproducir esta variabilidad con un rendimiento lo suficientemente significativo como para considerarla una herramienta válida en futuros estudios. No obstante, se identificaron ciertas limitaciones en la misma respecto a la información que devuelve el programa a nivel local y que no permite desgranar todo el detalle sobre la ejecución del mismo. Los indicadores y periodos fenológicos que mejor pudieron reproducir la variabilidad espacial del rendimiento en España y Bálticos, arrojaron aún, una mayor credibilidad a los resultados obtenidos y a la eficacia del GWR, ya que estaban en línea con el conocimiento agronómico sobre el cultivo del trigo blando en sistemas agrícolas mediterráneos y norteuropeos. Así, en España, el indicador más robusto fue el balance climático hídrico Climatic Water Balance) acumulado éste, durante el periodo de crecimiento (entre la emergencia y madurez). Aunque se identificó la etapa clave de la floración como el periodo en el que las variables climáticas acumuladas proporcionaban un mayor poder explicativo del modelo GWR. Sin embargo, en los Bálticos, países donde el principal factor limitante en su agricultura es el bajo número de días de crecimiento efectivo, el indicador más efectivo fue la radiación acumulada a lo largo de todo el ciclo de crecimiento (entre la emergencia y madurez). Para el trigo en regadío no existe ninguna combinación que pueda explicar más allá del 30% de la variación del rendimiento en España. Poder demostrar que existe un comportamiento heterogéneo en la relación inter-regional entre el rendimiento y principales variables climáticas, podría contribuir a uno de los mayores desafíos a los que se enfrentan, a día de hoy, los sistemas operacionales de monitoreo y predicción de rendimientos de cultivos, y éste es el de poder reducir la escala espacial de predicción, de un nivel nacional a otro regional. ABSTRACT This thesis explores geostatistical techniques and their contribution to a better characterization of the relationship between climate factors and agricultural crop yields. The crucial link between climate variability and crop production plays a key role in climate change research as well as in crops modelling towards the future global production scenarios. This information is particularly important for monitoring and forecasting operational crop systems. These geostatistical techniques are currently one of the most fundamental operational systems on which global agriculture and food security rely on; with the final aim of providing neutral and reliable information for food market controls, thus avoiding financial speculation of nourishments of primary necessity. Within this context the present thesis aims to provide an alternative approach to the existing body of research examining the relationship between inter-annual climate and production. Therefore, the temporal dimension was replaced for the spatial dimension, re-orienting the statistical analysis of the inter-annual relationship between crops yields and climate factors to an inter-regional correlation between these two variables. Geographically weighted regression, which is a relatively new statistical technique and which has rarely been used in previous research on this topic was used in the current study. Continuous surface values of the climate accumulated variables in specific phenological periods were obtained. These specific periods were selected because they are key factors in the development of vegetative crop. Therefore, the first part of this thesis presents an exploratory analysis regarding the comparability of spatial interpolation methods (SIM) among diverse SIMs and alternative geostatistical methodologies. Given the premise that spatial variability of the relationship between climate factors and crop production exists, the primary aim of this thesis was to examine the extent to which the SIM and other geostatistical methods of local regression (which are integrated tools of the GIS software) are useful in relating crop production and climate variables. The usefulness of these methods was examined in two ways; on one hand the way this information could help to achieve higher production of the white wheat binomial (Triticum aestivum L.) by incorporating the spatial component in the examination of the above-mentioned relationship. On the other hand, the way it helps with the characterization of the key limiting climate factors of soft wheat growth which were analysed in four phenological periods. To achieve this aim, an important operational workload of this thesis consisted in the homogenization and obtention of comparable phenological and climate data, as well as agricultural statistics, which made heavy operational demands. For Spain and the Baltic countries, data on precipitation, maximum and minimum temperature, evapotranspiration and solar radiation from the available meteorological stations were gathered and calculated. A temporal serial approach was taken. These temporal series aligned with the years that agriculture statistics had previously gathered, these being 14 years from 2000 to 2013 (until 2011 for the Baltic countries). This temporal series was mapped with a phenological 25 km grid that had the location of the meteorological stations with the objective of obtaining the phenological values in each of the available stations. Following this procedure, the daily accumulated climate values for each of the four selected phenological periods were calculated; namely P1 (complete cycle), P2 (emergency-maturity), P3 (flowering) and P4 (flowering- maturity). The interpolated surface was then calculated using the set of selected methodologies for the comparison: deterministic conventional techniques, ordinary kriging and ordinary cokriging weighted by height. Once the most effective methods had been selected, the level of the interpolated climate variables was calculated. Local GWR regressions were calculated to quantify, examine and model the spatial relationships between soft wheat production and the accumulated variables in each of the four selected phenological periods. Results from the comparison among the SIMs revealed that no particular technique seems more favourable in terms of accuracy of prediction. However, when the three quality indicators of the compared SIMs are considered, some methodologies appeared to be more efficient than others. Regarding precipitation results, cokriging was the most accurate geostatistical technique for the majority of the cases. Deterministic interpolation in its radial function (controlled spline) was the most accurate technique for describing the accumulated precipitation surface in all phenological periods. However, results are more heterogeneous for the evapotranspiration and radiation methodologies. The most appropriate technique for these forecasts are the Inverse Distance Weighting (IDW), weighted IDW by height and the Ordinary Kriging (OK). Furthermore, it was found that for the majority of the cases where the Ordinary CoKriging (COK) error was larger than that of the OK, its efficacy was comparable to that of the OK in terms of error while the computational demands of the latter was much lower. The existing spatial inter-regional variability between climate factors and soft wheat production was confirmed for both Spain and the Baltic countries. The GWR statistic tool reproduced this variability with an outcome significative enough as to be considered a valid tool for future studies. Nevertheless, this tool also had some limitations with regards to the information delivered by the programme because it did not allow for a detailed break-down of its procedure. The indicators and phenological periods that best reproduced the spatial variability of yields in Spain and the Baltic countries made the results and the efficiency of the GWR statistical tool even more reliable, despite the fact that these were already aligned with the agricultural knowledge about soft wheat crop under mediterranean and northeuropean agricultural systems. Thus, for Spain, the most robust indicator was the Climatic Water Balance outcome accumulated throughout the growing period (between emergency and maturity). Although the flowering period was the phase that best explained the accumulated climate variables in the GWR model. For the Baltic countries where the main limiting agricultural factor is the number of days of effective growth, the most effective indicator was the accumulated radiation throughout the entire growing cycle (between emergency and maturity). For the irrigated soft wheat there was no combination capable of explaining above the 30% of variation of the production in Spain. The fact that the pattern of the inter-regional relationship between the crop production and key climate variables is heterogeneous within a country could contribute to one is one of the greatest challenges that the monitoring and forecasting operational systems for crop production face nowadays. The present findings suggest that the solution may lay in downscaling the spatial target scale from a national to a regional level.