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Global warming and direct anthropogenic impacts, such as water extraction, are largely affecting water budgets in Mediterranean wetlands, thereby increasing wetland salinities and isolation, and decreasing water depths and hydroperiods (duration of the inundation period). These wetland features are key elements structuring waterbird communities. However, the ultimate and net consequences of these dynamic conditions on waterbird assemblages are largely unknown. We combined a regular sampling on waterbird presence through the 2008 annual cycle with in-situ data on these relevant environmental predictors of waterbird distribution to model habitat selection for 69 individual species in a typical Mediterranean wetland network in south-western Spain. Species association with environmental features were subsequently used to predict changes in habitat suitability for each species under three climate change scenarios (encompassing changes in environment that ranged from 10% to 50% change as predicted by climatic models). Waterbirds distributed themselves unevenly throughout environmental gradients and water salinity was the most important gradient structuring the distribution of the community. Environmental suitability for the guilds of diving birds and vegetation gleaners will be reduced according to future climate scenarios, while most small wading birds will benefit from changing conditions. Resident species and those that breed in this wetland network will be also more impacted than those using this area for wintering or stopover. We provide here a tool that can be used in a horizon-scanning framework to identify emerging issues on waterbird conservation and to anticipate suitable management actions : Datasets as supporting information to article “How will climate change affect endangered Mediterranean waterbirds?” to be published in PLOS ONE. Address questions to Francisco Ramírez: ramirez@ub.edu

Format: raw binary. The raw binary archive is composed of 576 zipped files, corresponding to the SPEI index at time scales between 1 and 48 months for the whole World and divided by decades (except the last file, containing only data for the period 2001-2006). Each zipped file contains three files, one with the data itselt (.img), and two headers (.doc and .hdr). The information contained in the header files is equivalent, and allows direct access to the data using some widely used commercial programs. Naming convention: spei[tempscale]_[decade].zip, where [tempscale] is a number between 1 and 48 indicating the temporal scale of the index (months), and [decade] indicates the years of data contained in the file. Example: spei12_1910-1919.zip. All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. Use of the newest version is recommended. Older versions are still available to allow replicability. The dataset is freely available on the web repository of the Spanish National Research Council (CSIC) in three different formats (NetCDF, binary raster, and plain text).

[ES] La base de datos consta de dos archivos en formato .txt con las mallas de anomalías de precipitación (Standardized Precipitation Index) calculadas a 1 y 12 meses sobre el dominio peninsular español, cubriendo el periodo 12/2015_12/2020. Estas han sido calculadas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). Además, se incluye un análisis descriptivo de los 40 episodios de sequía identificados según los criterios de intensidad de la sequía (SPI12 =20 % de la superficie de la malla). Para cada episodio se incluyen las series temporales del SPI01 y SPI12 promedio de toda la malla (expresadas en anomalías); el área de la malla en condiciones de sequía (SPI12 =< -0.84) (expresada en tanto por uno); los mapas integrales del episodio atendiendo a su duración (expresada en número de meses) e intensidad (promedio de las celdas en condiciones de sequía); y los mapas que representan la propagación espacial del episodio. Este registro se corresponde con la versión 1.0.0 del conjunto de datos. La base de datos se distribuye bajo una licencia abierta (Open Data Commons Attribution, ODC-By). [EN] The database consists of two files in .txt format with the precipitation anomaly grids (Standardized Precipitation Index) calculated at 1 and 12 months over the Spanish peninsular domain, covering the period 2015/12_2020/12. These have been calculated from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). In addition, a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =20 % of the grid area) is included. For each episode we include the time series of the SPI01 and SPI12 average of the whole grid (expressed in anomalies); the area of the grid under drought conditions (SPI12 =< -0.84) (expressed in percent per one); the integral maps of the episode according to its duration (expressed in number of months) and intensity (average of the cells under drought conditions); and the maps representing the spatial propagation of the episode. This record corresponds to version 1.0.0 of the dataset. The database is distributed under an open license (Open Data Commons Attribution, ODC-By). [ES] Malla SPI01: texto plano. 5219 filas -descontando el encabezado- y 1261 columnas -descontando las coordenadas X e Y-. Malla SPI12: texto plano. 5219 filas -descontando el encabezado- y 1250 columnas -descontando las coordenadas X e Y-. Archivos descriptivos de los episodios: mapas integrales de duración e intensidad, promedios de SPI’1 y SPI12 y mapas de la propagación espacial. [EN] SPI01 grid: plain text. 5219 rows (excluding the header) and 1261 columns (excluding the X and Y coordinates). SPI12 grid: plain text. 5219 rows (excluding the header) and 1250 columns (excluding the X and Y coordinates). Episode descriptive files: duration and intensity integral maps, SPI01 and SPI12 averages, and spatial propagation maps. Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science. Open Data Commons Attribution (ODC-By 1.0). Peer reviewed

From the perspective of the recent orientations of virtual geographies, the idea of smart and novelty villages in the context of renovated material rural worlds is conceptualized. The sum of new virtual and new materiality produces virtual and novelty spaces and places, which acquire a precise territorial dimension in the rural policy and politics of smart villages. Smart villages can not only be framed in global smart contexts, but they can also play a fundamental role in de-global territorial horizons as an instrument of resistance to global processes of rural restructuring. The smart political idea or orientation takes shape in each rural community with a different expression in the form of new local materials. The concept of quality virtuality is developed theoretically along three axes: the encounter between smart, novelty and new materials; the smart in the equitable rural community; and the right to disconnection in remote rural areas.

An ever more environmentally conscious society demands the use of green, sustainable and high-efficiency renewable energy resources. However, large-scale energy storage remains a challenge for a deep penetration of power produced from renewables into the grid. The Calcium-Looping (CaL) process, based on the reversible carbonation/calcination of CaO, is a promising technology for thermochemical energy storage (TCES) in Concentrated Solar Power (CSP) plants. Natural limestone to be used as CaO precursor is cheap, non-toxic and abundant. Nevertheless, recent works have shown that carbonation of CaO derived limestone at optimum conditions for TCES is limited by pore-plugging, which leads to severe deactivation for large enough particles to be employed in practice. In our work, we have synthesized inexpensive CaO/SiO composites by means of a biotemplate method using rice husk as support. The morphological and compositional features of the biomorphic materials synthesized help improve the CaO multicycle activity under optimum CSP storage conditions and for particles sufficiently large to be managed in practical processes. Peer Reviewed

Coastal areas are more densely populated than inland areas and present faster rates of population increase and urbanization. This trend is expected to continue in the coming decades, and thus, the demand of natural resources in coastal areas, such as water and energy resources, increasing the pressure and impact on the environment, superposed to the effects of climate change. Currently, in Europe, the demand for heating in buildings and businesses outnumbers the demand for cooling. However, the latter is gradually catching up due to rising demand for air cooling or refrigeration for industry such as food, technological and medical supplies. The energy required to cool buildings in Europe is expected to increase by more than 70% by 2030, while energy used to heat buildings may decrease by 30% (UE, 2018). Low Temperature Geothermal Energy (LTGE) is most likely the green energy production method for heating and cooling with the highest potential to provide affordable and clean energy and meet the CO2-emissions reduction goals of the Green Deal. Despite advances on LTGE technologies, the efficiency of these systems remains inherently sensitive to changes in hydrodynamics and the media (e.g., changes in the groundwater thermal regime). Groundwater, on the other hand, is the world's largest freshwater resource, and it is especially important in coastal areas because interactions between aquifer systems and sea water may lead to salinization and resource loss. Because geothermal systems and coastal aquifers interact directly, specially at groundwater discharge areas, it is clear that a better understanding of the potential interactions of geothermal systems with current and prospective coastal aquifer processes is essential for their design and foreseeing potential environmental effects. To address these issues, we model variable-density groundwater coupled with heat transport to simulate the long-term evolution of groundwater salinity and aquifer thermal energy discharge. We find that the heating/cooling-induced water density variations affect the seawater intrusion. Understanding the behavior of the groundwater system is required to ensure sustainable water, energy, and coastal ecosystem management.

Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean values as surrogates of the instantaneous ones in several studies that aimed to assess the impact of global warming on fire. In this paper we test the sensitivity of FWI values to both instantaneous and daily mean values, analyzing their effect on mean seasonal fire danger (seasonal severity rating, SSR) and extreme fire danger conditions (90th percentile, FWI90, and FWI>30, FOT30), with a special focus on its influence in climate change impact studies. To this aim, we analyzed reanalysis and regional climate model (RCM) simulations, and compared the resulting instantaneous and daily mean versions both in the present climate and in a future scenario. In particular, we were interested in determining the effect of these datasets on the projected changes obtained for the mean and extreme seasonal fire danger conditions in future climate scenarios, as given by a RCM. Overall, our results warn against the use of daily mean data for the computation of present and future fire danger conditions. Daily mean data lead to systematic negative biases of fire danger calculations. Although the mean seasonal fire danger indices might be corrected to compensate for this bias, fire danger extremes (FWI90 and specially FOT30) cannot be reliably transformed to accommodate the spatial pattern and magnitude of their respective instantaneous versions, leading to inconsistent results when projected into the future. As a result, we advocate caution when using daily mean data and strongly recommend the application of the standard definition for its calculation as closely as possible. Threshold-dependent indices derived from FWI are not reliably represented by the daily mean version and thus can neither be applied for the estimation of future fire danger season length and severity, nor for the estimation of future extreme events. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project). J.F. acknowledges nancial support from the Spanish R&D&I programme through grant CGL2010-22158-C02 (CORWES project). The ESCENA project (200800050084265) of the Spanish \Strategic action on energy and climate change" provided the WRF RCM simulation used in this study. We acknowledge three anonymous referees for their useful comments that helped to improve the original manuscript.

Abstract Two different strains of microalgae, one raphidophyte and one dinoflagellate, were tested under different abiotic conditions with the goal of enhancing lipid production. Whereas aeration was crucial for biomass production, nitrogen deficiency and temperature were found to be the main abiotic parameters inducing the high-level cellular accumulation of neutral lipids. Net neutral lipid production and especially triacylglycerol (TAG) per cell were higher in microalgae (&gt;200% in Alexandrium minutum, and 30% in Heterosigma akashiwo) under treatment conditions (25°C; 330 μM NaNO3) than under control conditions (20°C; 880 μM NaNO3). For both algal species, oil production (free fatty acids plus TAG fraction) was also higher under treatment conditions (57 mg L−1 in A. minutum and 323 mg L−1 in H. akashiwo). Despite the increased production and accumulation of lipids in microalgae, the different conditions did not significantly change the fatty acids profiles of the species analyzed. These profiles consisted of saturated fatty acids (SAFA) and polyunsaturated fatty acids (PUFA) in significant proportions. However, during the stationary phase, the concentrations per cell of some PUFAs, especially arachidonic acid (C20:4n6), were higher in treated than in control algae. These results suggest that the adjustment of abiotic parameters is a suitable and one of the cheapest alternatives to obtain sufficient quantities of microalgal biomass, with high oil content and minimal changes in the fatty acid profile of the strains under consideration.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.