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942 results for “Scenarios”
Modelling pan-Arctic peatland carbon dynamics under alternative warming scenarios
<p>The purpose of this study is to simulate peatland carbon dynamics in the future climate conditions for four major future warming scenarios. The study examines whether less pronounced warming could further enhance the peatland carbon sink capacity and buffer the effects of climate change. It will also determine which trajectory peatland carbon balance will follow, what the main drivers are and which one will dominate in the future.</p> <p>In this study, LPJGUESS Peatland has been employed across the pan-Arctic and we carried out four sets of simulations. The data files contain the information about carbon accumulation, NEE, NPP and ice fraction.</p>
Data from 666 earthquake/tsunami scenario simulations targeting Nankai subduction
<p><strong>Summary: </strong></p> <ul> <li>Data from 666 earthquake/tsunami scenario simulations targeting Nankai subduction</li> <li>Tsunami simulation solver: TUNAMI-N2</li> <li>Fault rupture model: Okada model(Okada, 1985)</li> <li>Each scenario data consists of 71 ASCII files containing the simulated wave sequences at the synthetic gauges </li> <li>Each single scenario is tagged as "Nankai-XYZE", where XYZW would be the number from <strong>0003</strong> to <strong>1470</strong>.</li> <li>Some of the synthetic gauges are located based on the real ocean gauge points (e.g., DONET2, NOPHAS) nearby Shikoku region, Japan.</li> </ul> <p> </p> <p><strong>Details of each file:</strong></p> <ul> <li><strong>wave_sequenses.tar.gz</strong>: A series of wave sequences (4-hour wave history-data, recorded every 5 seconds) at 71 synthetic gauges are provided in ascii type format. <br> Note that <strong>5.8GB additional storage would be required</strong> to fully unzip this archive file with the following command. <pre><code>tar xzvf wave_sequences.tar.gz</code></pre> <p>You can get all scenario data as follows:</p> <pre><code>wave_sequences ├ Nankai-0003 ├ Nankai-0004 ├… ├ Nankai-1467 └ Nankai-1470 </code></pre> <p>Each directory contains the 71 files, head with 'pntX_Y.asc',</p> <pre><code>Nankai-0003 ├ pnt1_01.asc ├ pnt1_02.asc ├... ├ pnt5_46.asc └ pnt5_47.asc</code></pre> <p>which contains the wave sequence data. The ASCII file, the time (minute) elapsed from the fault rupture is aligned in the left column, and the wave displacements \eta (meter) from the original surface location are in the right column.</p> </li> </ul> <ul> <li> <p><strong>quake_params.csv</strong>: The parameter used to generate 666 earthquake scenarios caused by the rupture of rectangular fault, by means of Okada model. (Okada 1985)</p> </li> </ul> <ul> <li><strong>synthetic_gauges.csv</strong>: The locations of 71 synthetic gauges. </li> </ul>
Stiffness of randomly sampled stainless steel frames under gravity and gravity plus wind load scenarios
<p>Data was generated using the general purpose finite element software ABAQUS and performing advanced nonlinear analyses. The database is comprised of vertical and lateral system stiffness values corresponding to different random samples of six different nominal stainless steel frames under gravity and gravity plus wind load combinations. The values of the random variable assignments are given for each case. </p> <p>The full details of the finite element model can be found in: Arrayago, I.; Rasmussen, K.J.R. Reliability of stainless steel frames designed using the Direct Design Method in serviceability limit states. Journal of Constructional Steel Research 196, 107425, 2022. DOI: https://doi.org/10.1016/j.jcsr.2022.107425</p> <p>The data included in the dataset corresponds to the vertical & lateral stiffness of each frame under different load conditions.</p> <p>Although the data has been generated using the finite element software ABAQUS, no special software is required to read or interpret the data.</p>
Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030
<p>Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).</p>
Marine plastics alter the organic matter composition of the air-sea boundary layer, with influences on CO2 exchange: a large-scale analysis method to explore future ocean scenarios
<p>Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications in the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used six large scale mesocosms to simulate future ocean scenarios of high plastic concentration. Each mesocosm was filled with 3 m3 of seawater from the oligotrophic Sea of Crete, in the Eastern Mediterranean Sea. A known amount of standard polystyrene microbeads of 30 μm diameter was added to three replicate mesocosms, while maintaining the remaining three as plastic-free controls. Over the course of a 12-day experiment, we explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of carbohydrate-like and proteinaceous marine gel compounds in the sea-surface microlayer. Importantly, this resulted in a 3 % reduction in the concentration of dissolved CO2 in the underlying water. This reduction was associated to both direct and indirect impacts of microplastic pollution on the uptake of CO2 within the marine carbon cycle, by modifying the biogenic composition of the sea's boundary layer with the atmosphere.</p>
Scenarios simulations of Bergen (TURBAN - D06)
<h3>Basic information</h3> <p>This dataset contains simulation results for the so-called Danmarksplass domain, an area in the city of Bergen, Norway. Danmarksplass is the major trafic conjuction point in Bergen. Danmarksplass is located in a densily built-up and populated urban district subjected to many environmental challenges among them air pollution by NOx and aerosols (particulate matter PM2.5 and PM10) are considered as significant health threats. Detailed studies of the Danmarksplass meteorological conditions, air quality, and structure of the air polution could be found in Wolf et al. (2014a,b; 2017; 2020; 2021). </p> <p>This dataset contains two collections of new simulations of air quality at Danmarksplass. The simulations were performed with the PALM modeling system v23.04 with additional modules developed in the TURBAN project (Radovic et al., 2024; Resler et al. to be submitted). This two collections of the PALM runs spans two air pollution episodes :</p> <ul> <li>Summer episode (2019-07-20 to 2019-07-27) of the record breaking summer heat wave in Bergen (on July 26, 2019; <a title="33.4 C - the new heat record in Bergen" href="https://www.bt.no/nyheter/lokalt/i/QoE3rA/naa-er-334-den-nye-varmerekorden-i-bergen" target="_blank" rel="noopener">https://www.bt.no/nyheter/lokalt/i/QoE3rA/naa-er-334-den-nye-varmerekorden-i-bergen</a>) when considerable problems (haze, smell) from the aerosol air pollution (PM2.5 and PM10) were noted (Esau et al., 2022). The main sources of pollution are ships in the port of Bergen and road traffic.</li> <li>Winter episode (2021-02-04 to 2021-02-12) of the prolonged cold wave in Bergen (<a title="Historically long cold weather" href="https://www.bt.no/nyheter/lokalt/i/lEOVm9/kulde-med-historisk-sus-i-bergen" target="_blank" rel="noopener">https://www.bt.no/nyheter/lokalt/i/lEOVm9/kulde-med-historisk-sus-i-bergen</a>) when considerable problems (haze, smell) from the aerosol air pollution (PM2.5 and PM10) were noted (Esau et al., 2022). the main source of pollution is household wood combustion for heating.</li> </ul> <p>Thus, the dataset presents multiple daily (24 h) runs driven by the results of model downscaling of ERA5 reanalysis with WRF model (produced by K. Eben and M. Bures). The aerosol emission sources are described in Wolf et al. (2020; 2021). The runs where combined into a single dataset in post-processing. </p> <p>The observational data for these two episodes collected in the TURBAN project are avaialble in Esau et al. (2023).</p> <p>For more detailed description of the experiments see the TURBAN project website at <a title="TURBAN" href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>.</p> <h3>General organisation</h3> <p>The dataset organized as follows. There are four folders named as "<em><strong>scenario_Bergen_{episode}_PALM_set0_{domain}_domain</strong></em>" where</p> <ul> <li><em><strong>{episode}</strong></em> is either the selected summer scenario <em><strong>{episode} = summer_2019-07</strong></em> or the winter scenario <em><strong>{episode} = winter_2021-02</strong></em></li> <li><em><strong>{domain}</strong></em> is either the larger coarse resolution domain smaller fine resolution domain <em><strong>{domain} = child</strong></em> </li> </ul> <p>Each scenario folder contains daily dynamic, static, chemistry drivers and the PALM configuration files in subfolders <em><strong>INPUT</strong></em> in folders designated by the day, e.g., <em><strong>dpc_set0_D0_D1_20210215/INPUT</strong></em>, to rerun all simulations if necessery. For convinience, the common static driver (<em><strong>set0_2021-02_static.nc</strong></em>) and the combined model output averaged over 3 h intervals (<em><strong>combined_set0_2021-02_av_xy.nc</strong></em>) are provided.</p> <p>The simulation results for each scenario contain:</p> <ul> <li>The combined PALM runs output average over 3 hours and presented on certain model levels (the files "<strong>*_av_xy*.nc</strong>"). The files are in the NetCDF4 format.</li> <li>The maps in PNG format visualizing the most relevant results for stakeholds (files in folder <em><strong>NMAP</strong></em>)</li> </ul> <p>In addition, we included the template of a Python script that is used to read the data and create Nmaps.</p> <h3>Modelled variables</h3> <p>Each subfolder includes 4 subfolders with variables. Variable <em>kc_PM10</em> is the concentration of PM2.5 at the 3rd model level, <em>theta_2m</em> is the potential temperature at 2m above ground, <em>tsurf</em> is the surface temperature and <em>wspeed_10m</em> is the wind speed at 10m above ground.</p> <h3>File nomenclature</h3> <p>Each file (PNG) has the same nomenclature. An example (<em><strong>set0_kc_PM10_2021-02-04T0300.png</strong></em>) could be parsed as: domain name (set0), variable name (kc_PM10), date and time of the output (2021-02-04T0300) and averaged period (from (03:00 - 3h) to 03:00). So, the result is a map with 3 hourly averaged PM2.5 concentrations for 4 February 2021 between 00:00 and 03:00 UTC.</p> <h3>Important note</h3> <p>During the processing phase a few potentially important problems were identified and need to be analysed in detail. One of them are extremely overestimated concentrations due to stable conditions from boundary condition inputs. In certain situations it can happen that the best regional meteorological model can provide inappropriate input conditions for some episode. This needs to be checked in detail before any following interpretation.</p> <h3>References</h3> <p>Esau, I.: <strong>TURBAN – Observational datasets for studies of urban air quality hazard scenarios in Bergen, Norway</strong>, DataverseNO, V1, <a href="https://doi.org/10.18710/QHUAZ2">https://doi.org/10.18710/QHUAZ2</a>, 2023.</p> <p>Radović, J., Belda, M., Resler, J., Eben, K., Bureš, M., Geletič, J., Krč, P., Řezníček, H., Fuka, V., 2024. Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM. Geosci. Model Dev. 17, 2901–2927. https://doi.org/10.5194/gmd-17-2901-2024</p> <p>Wolf, T., Esau, I., Reuder, J., 2014a. Analysis of the vertical temperature structure in the Bergen valley, Norway, and its connection to pollution episodes. J. Geophys. Res. 119. https://doi.org/10.1002/2014JD022085</p> <p>Wolf, T., Esau, I., 2014b. A proxy for air quality hazards under present and future climate conditions in Bergen, Norway. Urban Clim. 10, 801–814. https://doi.org/10.1016/j.uclim.2014.10.006</p> <p>Wolf-Grosse, T., Esau, I., Reuder, J., 2017. The large-scale circulation during air quality hazards in Bergen, Norway. Tellus A Dyn. Meteorol. Oceanogr. 69, 1406265. https://doi.org/10.1080/16000870.2017.1406265</p> <p>Wolf, T., Pettersson, L.H., Esau, I., 2020. A very high-resolution assessment and modelling of urban air quality. Atmos. Chem. Phys. 20, 625–647. https://doi.org/10.5194/acp-20-625-2020</p> <p>Wolf, T., Pettersson, L.H., Esau, I., 2021. Dispersion of particulate matter (PM2.5) from wood combustion for residential heating: optimization of mitigation actions based on large-eddy simulations. Atmos. Chem. Phys. 21, 12463–12477. https://doi.org/10.5194/acp-21-12463-2021</p> <h3>Acknowledgements</h3> <p>The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Norwegian SIGMA2 facilities. The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.</p>
Dataset SUC1/S1-S4: Cyberattack scenarios on DER energy management and control
<p><span>This sandboxing use case focuses on advanced energy management and control applications for DERs, which are essential for optimizing their operation and achieving key objectives. These objectives include tracking the awarded power generation according to energy market clearing processes, avoiding intense power imbalances caused by intermittent weather-based RES, and increasing the profitability of DER owners. By leveraging real-time control strategies, the management system can dynamically adjust flexible DER operations to improve the overall response of aggregated DERs, based on both RES and Battery Storage Systems (BSS). Since this use case requires active control of an BSS and its operation can be severely affected in case of a cyber-attack, it is crucial to examine this scenario in a controlled and non-invasive environment enabled by a sandboxing testing environment (the KIOS CoE Cyber-physical sandbox) to avoid any disturbance to the actual power infrastructure.</span></p> <p><span>This dataset collection is related to the four comprehensive cyber attack scenarios which target the Modbus TCP communication ptotocol used for transmitting m<span>easurements from smart meters of RESs and DERs to DER controller, as well as s<span>et-points sent to flexible DER inverters from DER controller, aiming to </span></span>affect the proper operation of the DER energy managemnet and control function. The four scenarios are:</span></p> <p><span><span>SUC1/S1 - MITM with FDI cyber-attack on wind farm measurements<br></span></span><span>SUC1/S2 - MITM with FDI cyber-attack on BSS measurements</span></p> <p><span>SUC1/S3 - MITM with FDI cyber-attack on BSS set-points</span></p> <p><span>SUC1/S4 - MITM with DoS cyber-attack</span></p> <p><span><span>The dataset includes electrical measurements of the active power generation of the wind farm (attacked signal and actual state) and the BSS operation, as well as the set-point generated by the DER controller. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files. The measurements were recorded with a 5-second time resolution by the DER controller. <span> </span></span></span></p>
Projected fresh water use from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016
<p>The dataset contains projections of fresh water withdrawal and consumption from the European energy sector on NUTS2 level by 2050 following EU Energy Reference Scenario 2016.</p> <p>The energy sector in this scope includes energy production (production of coal, oil and gas) and energy transformation in oil refineries and power plants (nuclear, solid fuels, oil, gas, biomass and geothermal).</p> <p>The information in provided on NUTS 2 level following the NUTS2 2013 definition.</p> <p>The dataset is explained in more detail in the report <a href="https://ec.europa.eu/jrc/en/publication/projected-fresh-water-use-european-energy-sector">Projected fresh water use from the European energy sector</a>.</p>
Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment
<p>This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.</p> <p> </p> <p> </p> <p> </p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
EJPSOIL_SERENA: Maps of Soil Organic Carbon Loss Scenarios in Elva Parish, Estonia
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. </p> <p>The study examined the effects of winter cropping systems on long-term soil fertility and their potential to mitigate SOC (Soil Organic Carbon) loss compared to bare soil during the winter months. It analyzed changes in SOC stocks (0–30 cm) at the field level in Elva Parish over the period 2020–2040, under different land-use scenarios. The modeling was based on a SOC stock map layer for Estonian mineral arable soils, developed by the Centre of Estonian Rural Research and Knowledge, which represented the baseline conditions in 2020. SOC stock projections were made using the RothC model, which simulates soil carbon turnover. </p> <p>In the first scenario (Scenario 1), the average SOC stock in Elva Parish by 2040 was estimated assuming the land would remain bare, without vegetation, during the winter months from October to April. In the second scenario (Scenario 2), the SOC stock projection accounted for the presence of winter vegetation, which means the soil is covered with vegetation year-round. The dataset includes four files: a projected SOC stock map for Elva Parish in 2040 and the stock changes from 2020–2040 under Scenario 1, along with a projected SOC stock map for 2040 and the stock changes from 2020–2040 under Scenario 2. </p>
Review of existing modelling studies focusing on specific soil-based ecosystem services (SES) and threats (ST) including climate change, management and land use change scenarios.
<p><span>We </span><span>reviewed existing modelling studies focusing on soil ecosystem services (SES) and soil threats (ST) including climate change, land use change and management scenarios. A publication has been submitted and is currently being reviewed. The title of the manuscript is: </span><span>Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review. </span></p> <p><span>Work was split between various authors. All Co-authors were working on either one or more SES or one ST. Excel sheets were prepared by INRA and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The final list of related publications is reported here. <br></span></p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (Pal J et al., 2024 - <a href="https://doi.org/10.5281/zenodo.14035736" target="_blank" rel="noopener">10.5281/zenodo.14035736</a>). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Short description of the datase:</p> <p>This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios
<p>This repository contains the data and results from the paper <strong>Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities</strong> published in <em>Nature Medicine</em> (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>).</p> <p>It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals. </p> <p>The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>.</p> <h3>Content</h3> <p>This repository contains three zip files, each with an internal codebook:</p> <ul> <li><em>data.zip</em>: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li><em>results_csv.zip</em>: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li><em>results_parquet.zip</em>: contains the same information as the <em>results_csv.zip</em> but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> <p>It is recommended to only download <em>results_csv.zip</em> for a quick exploration of the results, or only <em>results_parquet.zip</em> when the results are to be loaded into a software for deeper analysis.</p> <p> </p>
Data for Marine Ecological Niche Models, for 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species
<p>Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.</p>
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Solutions and Genetic algorithm dataset of the Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )
<p>This dataset contains the <strong>solution </strong>of the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case (link).</p> <p>The solution are computed by a Genetic Algorithm developped by Nicolas Durand.<br> <br> Inside, one can find:</p> <p>-One archive, "GA_Scenario_Solution_Dataset.zip", containing 10 couple of files (so 20 files). Each couple of file "sol_X_1.csv" and "sols_X_1.csv" are reciprocally the solutino given by the Genetic Algorithm to scenario X, and all the candidate solution explroed by the GA while solving scenario X. This archive also contain other versions of the solutions made by the GA with other parameters.<br> <br> -One archive, "GA_Toy_Dataset.zip" , containing solution to random scenarios, used to develop the first interfaces.</p> <p>Those solutions are used to developp the heatmatrix and heatmaps of the project (link), and visualisations for the validation (link).</p>
Dataset for "Best organic farming deployment scenarios for pest control: a modeling approach" V3
<p>Organic Farming (OF) has been expanding recently in response to growing consumer demand and as a response to environmental concerns. The area under OF is expected to further increase in the future. The effect of OF expansion on pest densities in organic and conventional crops remains difficult to predict because OF expansion impacts Conservation Biological Control (CBC), which depends on the surrounding landscape context. In order to understand and forecast how pests and their biological control may vary during OF expansion, we modeled the effect of spatial changes in farming practices on population dynamics of a pest and its natural enemy. We investigated the impact on pest density and on predator to pest ratio of three contrasted scenarios aiming at 50% organic fields through the progressive conversion of conventional fields. Scenarios were 1) conversion of Isolated conventional fields first (IP), 2) conversion of conventional fields within Groups of conventional fields first (GP), and 3) Random conversion of conventional field (RD). We coupled a neutral spatially explicit landscape model to a predator-prey model to simulate pest dynamics in interaction with natural enemy predators. The three OF expansion scenarios were applied to nine landscape types differing in their proportion and fragmentation of semi-natural habitat. We further investigated if the ranking of scenarios was robust to pest control methods in OF fields and pest and predator dispersal abilities.</p> <p>We found that organic farming expansion affected more predator densities than pest densities for most landscape types. The impact of OF expansion on final pest and predator densities was also stronger in organic than conventional fields and in landscapes with large proportions of highly fragmented semi-natural habitats. Based on pest densities and the predator to pest ratio, our results suggest that a progressive organic conversion with a focus on isolated conventional fields (scenario IP) could help promote CBC. Careful landscape planning of OF expansion appeared most necessary when pest management was substantially less efficient in organic than in conventional crops, and in landscapes with low proportion of semi-natural habitats.</p> <p><strong>This dataset contains simulation outputs and the R script that was used to describe, display and analyse data. The model itself can be found at <a href="https://doi.org/10.17605/OSF.IO/Z2QCX">https://doi.org/10.17605/OSF.IO/Z2QCX</a></strong></p> <p><strong>Please note that this is the third version of this dataset, following recommendations from the PCI Ecology reviewers and editor.</strong></p>
Cyber-attack scenarios for super-heaters system
<p>Simulated cyber-attacks for super-heaters system. For detailed description refer to pdf file. Dataset is in the form of tab separated txt files.</p> <p>In case of any questions please contact michal.syfert@pw.edu.pl or anna.sztyber@pw.edu.pl.</p> <p>Please cite: Sztyber-Betley, A.; Syfert, M.; Kościelny, J.M.; Górecka, Z. Controller Cyber-Attack Detection and Isolation. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2778. https://doi.org/10.3390/s23052778</p>
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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