Land cover dynamics and climate change implications on water resources in South Pacific Costa Rica

Water is fundamental to human life and the availability of freshwater is often a constraint on human welfare and economic development. Consequently, the potential effects of global changes on hydrology and water resources are considered among the most severe and vital ones. Water scarcity is one of the main problems in the rural communities of Central America, as a result of an important degradation of catchment areas and the over-exploitation of aquifers. The present Thesis is focused on two critical aspects of global changes over water resources: (1) the potential effects of climate change on water quantity and (2) the impacts of land cover and land use changes on the hydrological processes and water cycle. Costa Rica is among the few developing countries that have recently achieved a land use transition with a net increase in forest cover. Osa Region in South Pacific Costa Rica is an appealing study site to assess water supply management plans and to measure the effects of deforestation, forest transitions and climate change projections reported in the region. Rural Community Water Supply systems (ASADAS) in Osa are dealing with an increasing demand of freshwater due to the growing population and the change in the way of life in the rural livelihoods. Land cover mosaics which have resulted from the above mentioned processes are characterized by the abandonment of marginal farmland with the spread over these former grasslands of high return crops and the expansion of secondary forests due to reforestation initiatives. These land use changes have a significant impact on runoff generation in priority water-supply catchments in the humid tropics, as evidenced by the analysis of the Tinoco Experimental Catchment in the Southern Pacific area of Costa Rica. The monitoring system assesses the effects of the different land uses on the runoff responses and on the general water cycle of the basin. Runoff responses at plot scale are analyzed for secondary forests, oil palm plantations, forest plantations and grasslands. The Oil palm plantation plot presented the highest runoff coefficient (mean RC=32.6%), twice that measured under grasslands (mean RC=15.3%) and 20-fold greater than in secondary forest (mean RC=1.7%). A Thornthwaite-type water balance is proposed to assess the impact of land cover and climate change scenarios over water availability for rural communities in Osa Region. Climate change projections were obtained by the downscaling of BCM2, CNCM3 and ECHAM5 models. Precipitation and temperature were averaged and conveyed by the A1B, A2 and B1 IPCC climate scenario for 2030, 2060 and 2080. Precipitation simulations exhibit a positive increase during the dry season for the three scenarios and a decrease during the rainy season, with the highest magnitude (up to 25%) by the end of the 21st century under scenario B1. Monthly mean temperature simulations increase for the three scenarios throughout the year with a maximum increase during the dry season of 5% under A1B and A2 scenarios and 4% under B1 scenario. The Thornthwaite-type Water Balance model indicates important decreases of water surplus for the three climate scenarios during the rainy season, with a maximum decrease on May, which under A1B scenario drop up to 20%, under A2 up to 40% and under B1 scenario drop up to almost 60%. Land cover scenarios were created taking into account current land cover dynamics of the region. Land cover scenario 1 projects a deforestation situation, with forests decreasing up to 15% due to urbanization of the upper catchment areas; land cover scenario 2 projects a forest recovery situation where forested areas increase due to grassland abandonment on areas with more than 30% of slope. Deforestation scenario projects an annual water surplus decrease of 15% while the reforestation scenario projects a water surplus increase of almost 25%. This water balance analysis indicates that climate scenarios are equal contributors as land cover scenarios to future water resource estimations.

Is the concept of a green economy a useful way of framing Policy Discussions and policymaking to promote sustainable development?

In the Viewpoints section, academics, practitioners and experts share their perspectives on policy questions relevant to sustainable development. In this issue, experts address the question: “Is the concept of a green economy a useful way of framing policy discussions and policymaking to promote sustainable development?

Diagnosing Causes of Water Scarcity in Complex Water Resources Systems and Identifying Risk Management Actions

From the water management perspective, water scarcity is an unacceptable risk of facing water shortages to serve water demands in the near future. Water scarcity may be temporary and related to drought conditions or other accidental situation, or may be permanent and due to deeper causes such as excessive demand growth, lack of infrastructure for water storage or transport, or constraints in water management. Diagnosing the causes of water scarcity in complex water resources systems is a precondition to adopt effective drought risk management actions. In this paper we present four indices which have been developed to evaluate water scarcity. We propose a methodology for interpretation of index values that can lead to conclusions about the reliability and vulnerability of systems to water scarcity, as well as to diagnose their possible causes and to propose solutions. The described methodology was applied to the Ebro river basin, identifying existing and expected problems and possible solutions. System diagnostics, based exclusively on the analysis of index values, were compared with the known reality as perceived by system managers, validating the conclusions in all cases

Evaluation of Spain's Water-Energy Nexus

This paper explores the water-energy nexus of Spain and offers calculations for both the energy used in the water sector and the water required to run the energy sector. The article takes a prospective approach, offering evaluations of policy objectives for biofuels and expected renewable energy sources. Approximately 5.8% of total electricity demand in Spain is due to the water sector. Irrigated agriculture is one of the Spanish water sectors that show the largest growth in energy requirements. Searches for more efficient modes of farm water use, urban waste water treatment, and the use of desalinated water must henceforth include the energy component. Furthermore, biofuel production, to the levels targeted for 2020, would have an unbearable impact on the already stressed water resources in Spain. However, growing usage of renewable energy sources is not threatened by water scarcity, but legislative measures in water allocation and water markets will be required to meet the requirements of using these sources. Some of these measures, which are pushed by regional governments, are discussed in concluding sections.

Irrigated agriculture: Water resources management for a sustainable environment

In the last decade, research on irrigation has mainly been aimed at reducing crop water consumption. In arid and semi-arid environments, in relation to the limited water resources, the use of low quality water in agriculture has also been investigated in order to detect their effects on soil physical properties and on crop production. More recently, even the reduction of energy consumption in agriculture, as well as the effects of external factors, climate change and agricultural policies, have been major research interests. All these objectives have been considered in the papers included in this special issue. However, in the last years, approaches aimed at reducing crop water requirements have significantly changed. Remote sensing with satellites or unmanned vehicles, and vegetation spectral measurements, among others, represent in fact the newest frontier of existing technologies. Knowledge of soil hydraulic properties, often forgotten because of the difficulty of their estimation, can also be considered as a new way to reduce water consumption.

LiDAR remote sensing applied to forest resources assessment

Disponer de información precisa y actualizada de inventario forestal es una pieza clave para mejorar la gestión forestal sostenible y para proponer y evaluar políticas de conservación de bosques que permitan la reducción de emisiones de carbono debidas a la deforestación y degradación forestal (REDD). En este sentido, la tecnología LiDAR ha demostrado ser una herramienta perfecta para caracterizar y estimar de forma continua y en áreas extensas la estructura del bosque y las principales variables de inventario forestal. Variables como la biomasa, el número de pies, el volumen de madera, la altura dominante, el diámetro o la altura media son estimadas con una calidad comparable a los inventarios tradicionales de campo. La presente tesis se centra en analizar la aplicación de los denominados métodos de masa de inventario forestal con datos LIDAR bajo diferentes condiciones y características de masa forestal (bosque templados puros y mixtos) y utilizando diferentes bases de datos LiDAR (información proveniente de vuelo nacionales e información capturada de forma específica). Como consecuencia de lo anterior, se profundiza en la generación de inventarios forestales continuos con LiDAR en grandes áreas. Los métodos de masa se basan en la búsqueda de relaciones estadísticas entre variables predictoras derivadas de la nube de puntos LiDAR y las variables de inventario forestal medidas en campo con el objeto de generar una cartografía continua de inventario forestal. El rápido desarrollo de esta tecnología en los últimos años ha llevado a muchos países a implantar programas nacionales de captura de información LiDAR aerotransportada. Estos vuelos nacionales no están pensados ni diseñados para fines forestales por lo que es necesaria la evaluación de la validez de esta información LiDAR para la descripción de la estructura del bosque y la medición de variables forestales. Esta información podría suponer una drástica reducción de costes en la generación de información continua de alta resolución de inventario forestal. En el capítulo 2 se evalúa la estimación de variables forestales a partir de la información LiDAR capturada en el marco del Plan Nacional de Ortofotografía Aérea (PNOA-LiDAR) en España. Para ello se compara un vuelo específico diseñado para inventario forestal con la información de la misma zona capturada dentro del PNOA-LiDAR. El caso de estudio muestra cómo el ángulo de escaneo, la pendiente y orientación del terreno afectan de forma estadísticamente significativa, aunque con pequeñas diferencias, a la estimación de biomasa y variables de estructura forestal derivadas del LiDAR. La cobertura de copas resultó más afectada por estos factores que los percentiles de alturas. Considerando toda la zona de estudio, la estimación de la biomasa con ambas bases de datos no presentó diferencias estadísticamente significativas. Las simulaciones realizadas muestran que las diferencias medias en la estimación de biomasa entre un vuelo específico y el vuelo nacional podrán superar el 4% en áreas abruptas, con ángulos de escaneo altos y cuando la pendiente de la ladera no esté orientada hacia la línea de escaneo. En el capítulo 3 se desarrolla un estudio en masas mixtas y puras de pino silvestre y haya, con un enfoque multi-fuente empleando toda la información disponible (vuelos LiDAR nacionales de baja densidad de puntos, imágenes satelitales Landsat y parcelas permanentes del inventario forestal nacional español). Se concluye que este enfoque multi-fuente es adecuado para realizar inventarios forestales continuos de alta resolución en grandes superficies. Los errores obtenidos en la fase de ajuste y de validación de los modelos de área basimétrica y volumen son similares a los registrados por otros autores (usando un vuelo específico y parcelas de campo específicas). Se observan errores mayores en la variable número de pies que los encontrados en la literatura, que pueden ser explicados por la influencia de la metodología de parcelas de radio variable en esta variable. En los capítulos 4 y 5 se evalúan los métodos de masa para estimar biomasa y densidad de carbono en bosques tropicales. Para ello se trabaja con datos del Parque Nacional Volcán Poás (Costa Rica) en dos situaciones diferentes: i) se dispone de una cobertura completa LiDAR del área de estudio (capitulo 4) y ii) la cobertura LiDAR completa no es técnica o económicamente posible y se combina una cobertura incompleta de LiDAR con imágenes Landsat e información auxiliar para la estimación de biomasa y carbono (capitulo 5). En el capítulo 4 se valida un modelo LiDAR general de estimación de biomasa aérea en bosques tropicales y se compara con los resultados obtenidos con un modelo ajustado de forma específica para el área de estudio. Ambos modelos están basados en la variable altura media de copas (TCH por sus siglas en inglés) derivada del modelo digital LiDAR de altura de la vegetación. Los resultados en el área de estudio muestran que el modelo general es una alternativa fiable al ajuste de modelos específicos y que la biomasa aérea puede ser estimada en una nueva zona midiendo en campo únicamente la variable área basimétrica (BA). Para mejorar la aplicación de esta metodología es necesario definir en futuros trabajos procedimientos adecuados de medición de la variable área basimétrica en campo (localización, tamaño y forma de las parcelas de campo). La relación entre la altura media de copas del LiDAR y el área basimétrica (Coeficiente de Stock) obtenida en el área de estudio varía localmente. Por tanto es necesario contar con más información de campo para caracterizar la variabilidad del Coeficiente de Stock entre zonas de vida y si estrategias como la estratificación pueden reducir los errores en la estimación de biomasa y carbono en bosques tropicales. En el capítulo 5 se concluye que la combinación de una muestra sistemática de información LiDAR con una cobertura completa de imagen satelital de moderada resolución (e información auxiliar) es una alternativa efectiva para la realización de inventarios continuos en bosques tropicales. Esta metodología permite estimar altura de la vegetación, biomasa y carbono en grandes zonas donde la captura de una cobertura completa de LiDAR y la realización de un gran volumen de trabajo de campo es económica o/y técnicamente inviable. Las alternativas examinadas para la predicción de biomasa a partir de imágenes Landsat muestran una ligera disminución del coeficiente de determinación y un pequeño aumento del RMSE cuando la cobertura de LiDAR es reducida de forma considerable. Los resultados indican que la altura de la vegetación, la biomasa y la densidad de carbono pueden ser estimadas en bosques tropicales de forma adecuada usando coberturas de LIDAR bajas (entre el 5% y el 20% del área de estudio). ABSTRACT The availability of accurate and updated forest data is essential for improving sustainable forest management, promoting forest conservation policies and reducing carbon emissions from deforestation and forest degradation (REDD). In this sense, LiDAR technology proves to be a clear-cut tool for characterizing forest structure in large areas and assessing main forest-stand variables. Forest variables such as biomass, stem volume, basal area, mean diameter, mean height, dominant height, and stem number can be thus predicted with better or comparable quality than with costly traditional field inventories. In this thesis, it is analysed the potential of LiDAR technology for the estimation of plot-level forest variables under a range of conditions (conifer & broadleaf temperate forests and tropical forests) and different LiDAR capture characteristics (nationwide LiDAR information vs. specific forest LiDAR data). This study evaluates the application of LiDAR-based plot-level methods in large areas. These methods are based on statistical relationships between predictor variables (derived from airborne data) and field-measured variables to generate wall to wall forest inventories. The fast development of this technology in recent years has led to an increasing availability of national LiDAR datasets, usually developed for multiple purposes throughout an expanding number of countries and regions. The evaluation of the validity of nationwide LiDAR databases (not designed specifically for forest purposes) is needed and presents a great opportunity for substantially reducing the costs of forest inventories. In chapter 2, the suitability of Spanish nationwide LiDAR flight (PNOA) to estimate forest variables is analyzed and compared to a specifically forest designed LiDAR flight. This study case shows that scan angle, terrain slope and aspect significantly affect the assessment of most of the LiDAR-derived forest variables and biomass estimation. Especially, the estimation of canopy cover is more affected than height percentiles. Considering the entire study area, biomass estimations from both databases do not show significant differences. Simulations show that differences in biomass could be larger (more than 4%) only in particular situations, such as steep areas when the slopes are non-oriented towards the scan lines and the scan angles are larger than 15º. In chapter 3, a multi-source approach is developed, integrating available databases such as nationwide LiDAR flights, Landsat imagery and permanent field plots from SNFI, with good resultos in the generation of wall to wall forest inventories. Volume and basal area errors are similar to those obtained by other authors (using specific LiDAR flights and field plots) for the same species. Errors in the estimation of stem number are larger than literature values as a consequence of the great influence that variable-radius plots, as used in SNFI, have on this variable. In chapters 4 and 5 wall to wall plot-level methodologies to estimate aboveground biomass and carbon density in tropical forest are evaluated. The study area is located in the Poas Volcano National Park (Costa Rica) and two different situations are analyzed: i) available complete LiDAR coverage (chapter 4) and ii) a complete LiDAR coverage is not available and wall to wall estimation is carried out combining LiDAR, Landsat and ancillary data (chapter 5). In chapter 4, a general aboveground biomass plot-level LiDAR model for tropical forest (Asner & Mascaro, 2014) is validated and a specific model for the study area is fitted. Both LiDAR plot-level models are based on the top-of-canopy height (TCH) variable that is derived from the LiDAR digital canopy model. Results show that the pantropical plot-level LiDAR methodology is a reliable alternative to the development of specific models for tropical forests and thus, aboveground biomass in a new study area could be estimated by only measuring basal area (BA). Applying this methodology, the definition of precise BA field measurement procedures (e.g. location, size and shape of the field plots) is decisive to achieve reliable results in future studies. The relation between BA and TCH (Stocking Coefficient) obtained in our study area in Costa Rica varied locally. Therefore, more field work is needed for assessing Stocking Coefficient variations between different life zones and the influence of the stratification of the study areas in tropical forests on the reduction of uncertainty. In chapter 5, the combination of systematic LiDAR information sampling and full coverage Landsat imagery (and ancillary data) prove to be an effective alternative for forest inventories in tropical areas. This methodology allows estimating wall to wall vegetation height, biomass and carbon density in large areas where full LiDAR coverage and traditional field work are technically and/or economically unfeasible. Carbon density prediction using Landsat imaginery shows a slight decrease in the determination coefficient and an increase in RMSE when harshly decreasing LiDAR coverage area. Results indicate that feasible estimates of vegetation height, biomass and carbon density can be accomplished using low LiDAR coverage areas (between 5% and 20% of the total area) in tropical locations.

Metabolomics demonstrates divergent responses of two Eucalyptus species to water stress

Past studies of water stress in Eucalyptus spp. generally highlighted the role of fewer than five “important” metabolites, whereas recent metabolomic studies on other genera have shown tens of compounds are affected. There are currently no metabolite profiling data for responses of stress-tolerant species to water stress. We used GC–MS metabolite profiling to examine the response of leaf metabolites to a long (2 month) and severe (Ψpredawn < −2 MPa) water stress in two species of the perennial tree genus Eucalyptus (the mesic Eucalyptus pauciflora and the semi-arid Eucalyptus dumosa). Polar metabolites in leaves were analysed by GC–MS and inorganic ions by capillary electrophoresis. Pressure–volume curves and metabolite measurements showed that water stress led to more negative osmotic potential and increased total osmotically active solutes in leaves of both species. Water stress affected around 30–40% of measured metabolites in E. dumosa and 10–15% in E. pauciflora. There were many metabolites that were affected in E. dumosa but not E. pauciflora, and some that had opposite responses in the two species. For example, in E. dumosa there were increases in five acyclic sugar alcohols and four low-abundance carbohydrates that were unaffected by water stress in E. pauciflora. Re-watering increased osmotic potential and decreased total osmotically active solutes in E. pauciflora, whereas in E. dumosa re-watering led to further decreases in osmotic potential and increases in total osmotically active solutes. This experiment has added several extra dimensions to previous targeted analyses of water stress responses in Eucalyptus, and highlights that even species that are closely related (e.g. congeners) may respond differently to water stress and re-watering

Improved ore processing assessed by digital image análisis of the ores: a key to the sustainable development of the Iberian Pyrite Belt

Mining in the Iberian Pyrite Belt (IPB), the biggest VMS metallogenetic province known in the world to date, has to face a deep crisis in spite of the huge reserves still known after ≈5 000 years of production. This is due to several factors, as the difficult processing of complex Cu-Pb-Zn-Ag- Au ores, the exhaustion of the oxidation zone orebodies (the richest for gold, in gossan), the scarce demand for sulphuric acid in the world market, and harder environmental regulations. Of these factors, only the first and the last mentioned can be addressed by local ore geologists. A reactivation of mining can therefore only be achieved by an improved and more efficient ore processing, under the constraint of strict environmental controls. Digital image analysis of the ores, coupled to reflected light microscopy, provides a quantified and reliable mineralogical and textural characterization of the ores. The automation of the procedure for the first time furnishes the process engineers with real-time information, to improve the process and to preclude or control pollution; it can be applied to metallurgical tailings as well. This is shown by some examples of the IPB.

End-User Development Success Factors and their Application to Composite Web Development Environments

The Future Internet is expected to be composed of a mesh of interoperable Web services accessed from all over the Web. This approach has not yet caught on since global user-service interaction is still an open issue. Successful composite applications rely on heavyweight service orchestration technologies that raise the bar far above end-user skills. The weakness lies in the abstraction of the underlying service front-end architecture rather than the infrastructure technologies themselves. In our opinion, the best approach is to offer end-to-end composition from user interface to service invocation, as well as an understandable abstraction of both building blocks and a visual composition technique. In this paper we formalize our vision with regard to the next-generation front-end Web technology that will enable integrated access to services, contents and things in the Future Internet. We present a novel reference architecture designed to empower non-technical end users to create and share their own self-service composite applications. A tool implementing this architecture has been developed as part of the European FP7 FAST Project and EzWeb Project, allowing us to validate the rationale behind our approach.

Counting, scoring and classifying hunger to allocate resources targeted to solve the problem

A proper allocation of resources targeted to solve hunger is essential to optimize the efficacy of actions and maximize results. This requires an adequate measurement and formulation of the problem as, paraphrasing Einstein, the formulation of a problem is essential to reach a solution. Different measurement methods have been designed to count, score, classify and compare hunger at local level and to allow comparisons between different places. However, the alternative methods produce significantly reach different results. These discrepancies make decisions on the targeting of resource allocations difficult. To assist decision makers, a new method taking into account the dimension of hunger and the coping capacities of countries, is proposed enabling to establish both geographical and sectoral priorities for the allocation of resources.

Counting, scoring and classifying hunger to allocate resources targeted to solve the problem

A proper allocation of resources targeted to solve hunger is essential to optimize the efficacy of actions and maximize results. This requires an adequate measurement and formulation of the problem as, paraphrasing Einstein, the formulation of a problem is essential to reach a solution. Different measurement methods have been designed to count, score, classify and compare hunger at local level and to allow comparisons between different places. However, the alternative methods reach significantly different results. These discrepancies make decisions on the targeting of resource allocations difficult. To assist decision makers, a new method taking into account the dimension of hunger and the coping capacities of countries is proposed enabling to establish both geographical and sectoral priorities for the allocation of resources

Building resilience to water scarcity in Southern Spain: A case study of rice farming in Doñana protected wetlands

Agricultural water management needs to evolve in view of increased water scarcity, especially when farming and natural protected areas are closely linked. In the study site of Don?ana (southern Spain), water is shared by rice producers and a world heritage biodiversity ecosystem. Our aim is to contribute to defining adaptation strategies that may build resilience to increasing water scarcity and minimize water conflicts among agricultural and natural systems. The analytical framework links a participatory process with quantitative methods to prioritize the adaptation options. Bottom-up proposed adaptation measures are evaluated by a multi-criteria analysis (MCA) that includes both socioeconomic criteria and criteria of the ecosystem services affected by the adaptation options. Criteria weights are estimated by three different methods?analytic hierarchy process, Likert scale and equal weights?that are then compared. Finally, scores from an MCA are input into an optimization model used to determine the optimal land-use distribution in order to maximize utility and land-use diversification according to different scenarios of funds and water availability. While our results show a spectrum of perceptions of priorities among stakeholders, there is one overriding theme that is to define a way to restore part of the rice fields to natural wetlands. These results hold true under the current climate scenario and evenmore so under an increased water scarcity scenario.