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We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.

This study aims to determine the geographic distribution of types of beaches according to their different degrees of exposure to natural and anthropogenic forcing, an approach that is important for the implementation of appropriate coastal management strategies. To do this, the morphodynamic behaviour of five Mediterranean beaches of Tetouan (northwestern Morocco) was monitored in the course of a two-year project. The results show that seasonal beach changes are not very significant; the most important variations were recorded after storms. The type and mobility of beaches are a function of their curvature and distance from headlands, exposure to waves, grain size and sediment supply. Morphological changes are greater and faster in reflective beaches located north of Cabo Negro promontory; moreover, these beaches have a greater tendency to erosion. Erosion vulnerability hazards have also increased in the northern part of the study area due to coastal constructions. The timescale of the study and the datasets obtained are, however, too short to identify the imprint of chronic erosion. Continuous dune destruction under development pressures and sand-supply deficit from the nearby wadis will, however, definitely be detrimental, especially at the more exposed beaches, leading to the destruction of the very resource that underpins tourism development. Mitigation of coastal erosion requires a nexus between science and management but also the rapid implementation of measures aimed at restoring the coastal sediment budget and that take into account the morphodynamics of the beach-dune units.

This paper explores to what extent moving towards the 30% GHG emission reductions by 2020 with respect to 1990 in the EU can be considered a transformative target. To do so, we first define the concept of transformative targets from a complex systems perspective and show a novel approach and original results using an extended application of the GEM-E3 model. Traditional macroeconomic models cannot easily handle key synergetic system effects derived from green growth and sustainability policies, and thus require additional features. We analyse the role of semi-endogenous growth driven by learning-by-doing and low-carbon investment expectations following a long-term transformative trajectory.

Abstract. The increased resolution of numerical weather prediction models allows nowadays addressing more realistically urban meteorology and air pollution processes. This has triggered new interest in modelling and describing experimentally the specific features and processes of urban areas. Recent developments and results performed within the EU-funded project FUMAPEX on integrated systems for forecasting urban meteorology and air pollution are reported here. Sensitivity studies with respect to optimum resolution, parametrisation of urban roughness and surface exchange fluxes and the role of urban soil layers are carried out with advanced meso- or sub-meso meteorological models. They show that sensible improvements can be achieved by higher model resolution that is accompanied with better description of urban surface features. Recommendations, especially with respect to advanced urban air quality forecasting and information systems, are given together with an assessment of the needed further research and data.

The realm of big data has brought new venues for knowledge acquisition, but also major challenges including data interoperability and effective management. The great volume of miscellaneous data renders the generation of new knowledge a complex data analysis process. Presently, big data technologies provide multiple solutions and tools towards the semantic analysis of heterogeneous data, including their accessibility and reusability. However, in addition to learning from data, we are faced with the issue of data storage and management in a cost-effective and reliable manner. This is the core topic of this paper. A data lake, inspired by the natural lake, is a centralized data repository that stores all kinds of data in any format and structure. This allows any type of data to be ingested into the data lake without any restriction or normalization. This could lead to a critical problem known as data swamp, which can contain invalid or incoherent data that adds no values for further knowledge acquisition. To deal with the potential avalanche of data, some legislation is required to turn such heterogeneous datasets into manageable data. In this article, we address this problem and propose some solutions concerning innovative methods, derived from a multidisciplinary science perspective to manage data lake. The proposed methods imitate the supply chain management and natural lake principles with an emphasis on the importance of the data life cycle, to implement responsible data governance for the data lake.