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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Negri, Valentina; Vázquez, Daniel; Sales-Pardo, Marta; Guimerà, Roger; +1 Authors

    Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Piper, Adam T.; Manes, Costantino; Siniscalchi, Fabio; Marion, Andrea; +2 Authors

    Anthropogenic structures (e.g. weirs and dams) fragment river networks and restrict the movement of migratory fish. Poor understanding of behavioural response to hydrodynamic cues at structures currently limits the development of effective barrier mitigation measures. This study aimed to assess the effect of flow constriction and associated flow patterns on eel behaviour during downstream migration. In a field experiment, we tracked the movements of 40 tagged adult European eels (Anguilla anguilla) through the forebay of a redundant hydropower intake under two manipulated hydrodynamic treatments. Interrogation of fish trajectories in relation to measured and modelled water velocities provided new insights into behaviour, fundamental for developing passage technologies for this endangered species. Eels rarely followed direct routes through the site. Initially, fish aligned with streamlines near the channel banks and approached the intake semi-passively. A switch to more energetically costly avoidance behaviours occurred on encountering constricted flow, prior to physical contact with structures. Under high water velocity gradients, fish then tended to escape rapidly back upstream, whereas exploratory ‘search’ behaviour was common when acceleration was low. This study highlights the importance of hydrodynamics in informing eel behaviour. This offers potential to develop behavioural guidance, improve fish passage solutions and enhance traditional physical screening. Fish_detections_UL_CHFish positions derived from acoustic telemetry contained within excel file with 5 columns. 'Record' denotes tag detection numbered consecutively in sequence; 'tag_number' denotes the fish identification number; ‘PosX’ denotes fish x coordinate in UTM; ‘PosY’ denotes fish y coordinate in UTM, ‘Treatment’ denotes experimental treatment

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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
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    EASY
    Dataset . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2015
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2015
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
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      EASY
      Dataset . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2015
      License: CC 0
      Data sources: Datacite
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    Authors: Thiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 Authors

    This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rodero, María Del Rosario; Lebrero, Raquel; Serrano, Esteban; Lara, Enrique; +3 Authors

    Excel document that contains the data of the article: ‘Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor’. This dataset shows the values obtained during the experimental period and it complements the corresponding article.

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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lovato, Tomas; Peano, Daniele;

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CMCC-CM2-SR5 climate model, released in 2016, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: Balaguer-Benlliure, Victor; Roger, Moya; Johana, Gaitán-Alvarez;

    This data contents information about parental wood and charcoal characteristics of 16 tropical species growing in fast-growth condictions. The data details tha following characterist of parental wood: moisture content (PMC) and wood density (PWD). On the others hand the charcoal characteristics are: Density (CD), moisture content (CMC) and compression strength of charcoal, gross calorific value (GCV), ash and volatile matter and fixed carbon, Carbon (C), nitrogen (N), hydrogen (H), and oxygen (O) contents, C/N ratio, O/Cmol ratio and H/Cmol ratio. Besides it is presented FTIR spectra and the ignition temperature (Ti), the burnout temperature (Tf), the characteristic combustion index (S), the ignition index (Di), the time corresponding to the maximum combustion rate (tp), the ignition time (tig), and the average rate of combustion.

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    Authors: Ramirez F; Rodriguez C; Seoane J; Figuerola J; +1 Authors

    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

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    Digital.CSIC
    Dataset . 2017
    License: CC BY
    Data sources: Datacite
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    BioStudies
    Dataset . 2018
    Data sources: BioStudies
    Digital.CSIC
    Dataset . 2017 . Peer-reviewed
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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      BioStudies
      Dataset . 2018
      Data sources: BioStudies
      Digital.CSIC
      Dataset . 2017 . Peer-reviewed
      Data sources: Digital.CSIC
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dono, Gabriele; Cortignani, Raffaele; Doro, Luca; Ledda, Luigi; +10 Authors

    In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.

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    https://dx.doi.org/10.22004/ag...
    Other literature type . 2011
    Data sources: Datacite
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      https://dx.doi.org/10.22004/ag...
      Other literature type . 2011
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Input files for the ForClim model (version 4.0.1) used in the associated paper. They can be used to to reproduce results of the simulation study. The ForClim model, including the source code, executable and documentation, is freely available under an Open Access license from the website of the original developers at https://ites-fe.ethz.ch/openaccess/. The original climatic dataset used to generate the ForClim input climate files at each site in South Tyrol is freely available at https://doi.pangaea.de/10.1594/PANGAEA.924502 while the CHELSA climate data for future scenarios are available at https://www.chelsa-climate.org. If interested in using this dataset for a research study or a project, please contact Marco Mina ----------------------------------------------------------------------- Hillebrand L, Marzini S, Crespi A, Hiltner U & Mina M (2023) Contrasting impacts of climate change on protection forests of the Italian Alps. Frontiers in Forests and Global Change, 6, 2023 https://doi.org/10.3389/ffgc.2023.1240235 ABSTRACT. Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Negri, Valentina; Vázquez, Daniel; Sales-Pardo, Marta; Guimerà, Roger; +1 Authors

    Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information.

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; +13 Authors

    Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Piper, Adam T.; Manes, Costantino; Siniscalchi, Fabio; Marion, Andrea; +2 Authors

    Anthropogenic structures (e.g. weirs and dams) fragment river networks and restrict the movement of migratory fish. Poor understanding of behavioural response to hydrodynamic cues at structures currently limits the development of effective barrier mitigation measures. This study aimed to assess the effect of flow constriction and associated flow patterns on eel behaviour during downstream migration. In a field experiment, we tracked the movements of 40 tagged adult European eels (Anguilla anguilla) through the forebay of a redundant hydropower intake under two manipulated hydrodynamic treatments. Interrogation of fish trajectories in relation to measured and modelled water velocities provided new insights into behaviour, fundamental for developing passage technologies for this endangered species. Eels rarely followed direct routes through the site. Initially, fish aligned with streamlines near the channel banks and approached the intake semi-passively. A switch to more energetically costly avoidance behaviours occurred on encountering constricted flow, prior to physical contact with structures. Under high water velocity gradients, fish then tended to escape rapidly back upstream, whereas exploratory ‘search’ behaviour was common when acceleration was low. This study highlights the importance of hydrodynamics in informing eel behaviour. This offers potential to develop behavioural guidance, improve fish passage solutions and enhance traditional physical screening. Fish_detections_UL_CHFish positions derived from acoustic telemetry contained within excel file with 5 columns. 'Record' denotes tag detection numbered consecutively in sequence; 'tag_number' denotes the fish identification number; ‘PosX’ denotes fish x coordinate in UTM; ‘PosY’ denotes fish y coordinate in UTM, ‘Treatment’ denotes experimental treatment

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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
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    B2FIND
    Dataset . 2015
    Data sources: B2FIND
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    EASY
    Dataset . 2015
    Data sources: EASY
    DRYAD
    Dataset . 2015
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2015
      License: CC 0
      Data sources: ZENODO
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      B2FIND
      Dataset . 2015
      Data sources: B2FIND
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      EASY
      Dataset . 2015
      Data sources: EASY
      DRYAD
      Dataset . 2015
      License: CC 0
      Data sources: Datacite
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    Authors: Thiery, Wim; Lange, Stefan; Rogelj, Joeri; Schleussner, Carl-Friedrich; +33 Authors

    This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
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    Authors: Rodero, María Del Rosario; Lebrero, Raquel; Serrano, Esteban; Lara, Enrique; +3 Authors

    Excel document that contains the data of the article: ‘Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor’. This dataset shows the values obtained during the experimental period and it complements the corresponding article.

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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC BY NC ND
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC BY NC ND
      Data sources: Datacite
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    Authors: Lovato, Tomas; Peano, Daniele;

    Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CMCC-CM2-SR5 climate model, released in 2016, includes the following components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0. The model was run by the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce 73100, Italy (CMCC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

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    World Data Center for Climate
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      World Data Center for Climate
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Authors: Balaguer-Benlliure, Victor; Roger, Moya; Johana, Gaitán-Alvarez;

    This data contents information about parental wood and charcoal characteristics of 16 tropical species growing in fast-growth condictions. The data details tha following characterist of parental wood: moisture content (PMC) and wood density (PWD). On the others hand the charcoal characteristics are: Density (CD), moisture content (CMC) and compression strength of charcoal, gross calorific value (GCV), ash and volatile matter and fixed carbon, Carbon (C), nitrogen (N), hydrogen (H), and oxygen (O) contents, C/N ratio, O/Cmol ratio and H/Cmol ratio. Besides it is presented FTIR spectra and the ignition temperature (Ti), the burnout temperature (Tf), the characteristic combustion index (S), the ignition index (Di), the time corresponding to the maximum combustion rate (tp), the ignition time (tig), and the average rate of combustion.

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    Authors: Ramirez F; Rodriguez C; Seoane J; Figuerola J; +1 Authors

    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

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    Digital.CSIC
    Dataset . 2017
    License: CC BY
    Data sources: Datacite
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    BioStudies
    Dataset . 2018
    Data sources: BioStudies
    Digital.CSIC
    Dataset . 2017 . Peer-reviewed
    Data sources: Digital.CSIC
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      Digital.CSIC
      Dataset . 2017
      License: CC BY
      Data sources: Datacite
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      BioStudies
      Dataset . 2018
      Data sources: BioStudies
      Digital.CSIC
      Dataset . 2017 . Peer-reviewed
      Data sources: Digital.CSIC
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    Authors: Dono, Gabriele; Cortignani, Raffaele; Doro, Luca; Ledda, Luigi; +10 Authors

    In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.

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    https://dx.doi.org/10.22004/ag...
    Other literature type . 2011
    Data sources: Datacite
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      https://dx.doi.org/10.22004/ag...
      Other literature type . 2011
      Data sources: Datacite
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