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The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

The dataset CO2_flux_measurements_Acoculco contains data on CO2 fluxes, coordinates (UTM), air temperature, atmospheric pressure measured in selected sites belonging to the Acoculco Geothermal Field: in particular, the areas named Lagunilla, Alcaparrosa, Los Azufres and also the area between them were investigated. CO2 flux measurements were performed using the accumulation chamber method. The dataset Field_meas_Acoculco_waters reports the ID, coordinates (UTM), Altitude (m.a.s.l.), temperature, flow rate, pH, Electrical Conductivity and Dissolved Oxygen for water samples collected in the central sector of the Acoculco geothermal field, but also in other sectors located inside and outside the Acoculco caldera. Total depth is also included for samples collected from water wells. The dataset Chemical_isotopic_data_Acoculco_waters reports major and minor chemical components and stable isotopic composition for hydrogen and oxygen determined in collected water samples in Acoculco geothermal field. Calculated partial pressures (in bars and log10-value) and CO2 concentrations of dissolved CO2 were also included. The dataset Chemical_isotopic_data_Acoculco_gas reports chemical and isotopic data for collected samples from Los Azufres and Alcaparrosa natural gas manifestations.

This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

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.

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

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.

Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier. Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050).