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AbstractThe potential impacts of future climate scenarios on water balance and flow regime are presented and discussed for a temporary river system in southern Italy. Different climate projections for the future (2030–2059) and the recent conditions (1980–2009) were investigated. A hydrological model (Soil and Water Assessment Tool) was used to simulate water balance at the basin scale and streamflow in a number of river sections under various climate change scenarios, based on different combinations of global and regional models (global circulation models and regional climate models). The impact on water balance components was quantified at the basin and subbasin levels as deviation from the baseline (1980–2009), and the flow regime alteration under changing climate was estimated using a number of hydrological indicators. An increase in mean temperature for all months between 0.5–2.4 °C and a reduction in precipitation (by 4–7%) was predicted for the future. As a consequence, a decline of blue water (7–18%) and total water yield (11–28%) was estimated. Although the river type classification remains unvaried, the flow regime distinctly moves towards drier conditions and the divergence from the current status increases in future scenarios, especially for those reaches classified as I‐D (ie, intermittent‐dry) and E (ephemeral). Hydrological indicators showed a decrease in both high flow and low flow magnitudes for various time durations, an extension of the dry season and an exacerbation of extreme low flow conditions. A reduction of snowfall in the mountainous part of the basin and an increase in potential evapotranspiration was also estimated (4–4.4%). Finally, the paper analyses the implications of the climate change for river ecosystems and for River Basin Management Planning. The defined quantitative estimates of water balance alteration could support the identification of priorities that should be addressed in upcoming years to set water‐saving actions.

Antarctic biota evolved under the influence of a suite of geological and climatic factors, including geographic isolation of the landmass and continental shelves, extremely low temperatures and seasonality. Current warming trends in the continent and surrounding oceans may trigger substantial shifts in community composition and biodiversity, impacting the dominance of cold-adapted over more generalist species. Until recently, the diversity of microorganisms in cold environments was investigated only in terms of distribution, with little attention to their functional roles in important environmental processes. The 'omic' methodologies now offer effective tools to investigate the relationships between biodiversity and ecosystem functioning and to understand the evolutionary principles of adaptation and tolerance/resistance to extreme conditions. In this review we summarise how cold temperatures affect the physiology of microorganisms and focus on the molecular mechanisms of cold adaptation revealed by recent biochemical and genetic studies.

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

The implementation of resource management strategies aimed at reducing the impacts of the anthropogenic activities system requires a comprehensive approach to evaluate on the whole the environmental burdens of productive processes and to identify the best recovery strategies from both an environmental and an economic point of view. In this framework, an analytical methodology based on the integration of Life Cycle Assessment (LCA), ExternE and Comprehensive Analysis was developed to perform an in-depth investigation of energy systems. The LCA methodology, largely utilised by the international scientific community for the assessment of the environmental performances of technologies, combined with Comprehensive Analysis allows modelling the overall system of anthropogenic activities, as well as sub-systems, the economic consequences of the whole set of environmental damages. Moreover, internalising external costs into partial equilibrium models, as those utilised by Comprehensive Analysis, can be useful to identify the best paths for implementing technology innovation and strategies aimed to a more sustainable energy supply and use. This paper presents an integrated application of these three methodologies to a local scale case study (the Val D'Agri area in Basilicata, Southern Italy), aimed to better characterise the environmental impacts of the energy system, with particular reference to extraction activities. The innovative methodological approach utilised takes advantage from the strength points of each methodology with an added value coming from their integration as emphasised by the main results obtained by the scenario analysis.

Hydrogen added to natural gas improves the process of combustion with the possibility to develop engines with higher performance and lower environmental impact. In this paper experimental and numerical analyses on a multi cylinder stoichiometric heavy duty engine, fuelled with natural gasehydrogen blends, are reported. Some constrains on hydrogen content and maximum load achievable have limited the scope of investigation. A specific modelling of the reference engine was developed to extend the study at full load condition and at higher hydrogen content. The results showed a higher combustion speed when hydrogen content in the fuel is increased. However, the positive effect of shorter combustion duration on thermal efficiency is mitigated by higher wall heat loss, due to higher combustion temperatures. Therefore lower CO2 emissions are due only to the substitution of natural gas with hydrogen, making crucial the way of hydrogen producing to have a benefit on well-to-wheel CO2 emissions.

Agroforestry is considered a sustainable form of land management that optimizes the use of natural resources (nutrients, radiation, water). Agroforestry is defined as the deliberate integration of woody vegetation with agricultural activities in the lower story. It provides a higher biomass production per unit of land, while providing more ecosystem services than woody-less agricultural lands, such as the reduction of soil erosion and nitrogen leaching, and increase carbon sequestration and landscape biodiversity. The objective of this paper is to evaluate the past and current European Union Common Agricultural policies aiming at promoting the afforestation or reforestation of lands, as the introduction of trees can be seen as a first step to carry out agroforestry practices in former agricultural or forest lands. Agroforestry was a traditional land use system in Europe before modern times. However, before the sixties land intensification and consolidation destroyed millions of trees all over Europe. On the contrary, some good examples of agroforestry promotion are found in Eastern European countries in order to reduce the effect of extreme events such as winds, flooding at the beginning and mid of the last century. In Western European countries, the introduction of trees in the land has been promoted by agroforestry, afforestation and reforestation at the end of the last century. Afforestation of agricultural lands have been the most successful CAP measure (over 1 million hectares) while agroforestry measures were not extensively adopted which may be explained by the funds associated to afforestation measure which compensated the losses of income 15 or 20 years in afforested lands. Agroforestry was poorly adopted in the CAP 2007-2013, having a better success in the CAP 2014-2020 due to the recognition of woody vegetation and the compensation of 5 years given for maintenance once agroforestry is established. However, policy rules ensuring Pillar I payment when agroforestry measure is adopted such as a management plans ensuring that maximum tree density (100 trees per hectare) is not reached, should be pursued.

The Global Geothermal Energy Database of the International Geothermal Association (IGA) is an internet-based platform providing access to the world geothermal production data. This platform is unique for the geothermal sector, and provides an excellent tool for showing geothermal use in the world and promoting the development of geothermal energy. The platform is also an important example of organization and access to widely distributed data, since it allows analyzing, synthetizing and quickly interpreting stored data. The global and country-specific information regard both electricity generation and direct use applications. The platform, which has been built using an open-source Business Intelligence application and can be accessed through the IGA website, allows to access, navigate and organize information in various ways. Data lists related to geothermal fields and plants, direct uses, geothermal turbine manufactures and geothermal companies can be accessed, aggregated and filtered, producing reports, charts and maps. Aggregation and filtering options synthetize and organize data for direct use, power plants and installed capacity by region, category and operative status. The mapping tool, providing geothermal fields location, power plants and direct uses geographical references, allows also map browsing, and to zoom, to measure distance, to pan and to query for further information. The chart analysis produces pie charts and bar diagrams of data, dynamically sorted and aggregated.

A reliable energy supply is fundamental to ensure energy security and support the mitigation of climate change by promoting the use of renewable sources and reducing carbon emissions. Energy system analysis provides a sound methodology to assess energy needs, allowing to investigate the energy system behavior and to individuate the optimal energy-technology configurations for the achievement of strategic energy and environmental policy targets. In this framework, the estimation of future trends of exogenous variables such as energy demand has a fundamental importance to obtain reliable and effective solutions, contributing remarkably to the accuracy of models' input data. This study illustrates an application of regression analysis to predict energy demand trends in end use sectors. The proposed procedure is applied to characterize statistically the relationships between population and gross domestic product (independent variables) and energy demands of Residential, Transport and Commercial in order to determine the energy demand trends over a long-term horizon. The effectiveness of linear and nonlinear regression models for energy demand forecasting has been validated by classical statistical tests. Energy demand projections have been tested as input data of the bottom-up TIMES model in two applications (the TIMES-Basilicata and TIMES-Italy models) confirming the validity of the forecasting approach. (C) 2020 Elsevier Ltd. All rights reserved.

AbstractAs a consequence of land‐use change and the burning of fossil fuels, atmospheric concentrations of CO2 are increasing and altering the dynamics of the carbon cycle in forest ecosystems. In a number of studies using single tree species, fine root biomass has been shown to be strongly increased by elevated CO2. However, natural forests are often intimate mixtures of a number of co‐occurring species. To investigate the interaction between tree mixture and elevated CO2, Alnus glutinosa, Betula pendula and Fagus sylvatica were planted in areas of single species and a three species polyculture in a free‐air CO2 enrichment study (BangorFACE). The trees were exposed to ambient or elevated CO2 (580 μmol mol−1) for 4 years. Fine and coarse root biomass, together with fine root turnover and fine root morphological characteristics were measured. Fine root biomass and morphology responded differentially to the elevated CO2 at different soil depths in the three species when grown in monocultures. In polyculture, a greater response to elevated CO2 was observed in coarse roots to a depth of 20 cm, and fine root area index to a depth of 30 cm. Total fine root biomass was positively affected by elevated CO2 at the end of the experiment, but not by species diversity. Our data suggest that existing biogeochemical cycling models parameterized with data from species grown in monoculture may be underestimating the belowground response to global change.