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121 results for “Scenario Modelling”
Figure 1 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 1. Map of records of S. woodiana in Europe obtained from GBIF database and published sources (Vikhrev et al., 2024).
Dataset for "Narrow range of early habitable Venus scenarios permitted by modelling of oxygen loss and radiogenic argon degassing"
<p>Code and datasets required to reproduce figures in main text of Warren & Kite 2023 "Narrow range of early habitable Venus scenarios permitted by modelling of oxygen loss and radiogenic argon degassing." https://www.pnas.org/doi/full/10.1073/pnas.2209751120</p> <p>Code and usage instructions are also available at: github.com/aowarren/Venus_O2</p> <p>After downloading, code will need to be modified to find files in chosen directories for running the full model (also requires installation of VolcGases from github.com/Nicholaswogan/VolcGases) and files for re-creating plots. All .zip files contain data used to create figures in paper. Instructions to reproduce figures below:</p> <p> </p> <p><strong>1. To reproduce full dataset</strong>, download all files excluding .zip files and install VolcGases. Modify "modular_functions_clean_redox.py" to match VolcGases installation.</p> <p><em>For runaway greenhouse runs:</em> Ensure "modular_functions_clean_redox.py" line 512 is commented out. Run "adding_dissolution_clean.py" followed by "ext1line.py" to pre-run runaway greenhouse model and save output to read into full model (this speeds up running the entire suite of "melting" models, but is not strictly necessary). Next, use "gridsearch_all_redox.csv" to set model parameters, then run "input_clean_redox.py" to initiate model. </p> <p><em>For runs without runaway greenhouse melting:</em> uncomment "modular_functions_clean_redox.py" line 512. Use "gridsearch_all_redox.csv" to set model parameters, then run "input_clean_redox.py" to initiate model. </p> <p><strong>2. To reproduce Figures 2, 3, S1, and S2</strong>, either download all zip files beginning with "Fig2_" and "Fig3_", extract all files to single location, or save all new model output into a single directory and use "plot_together.py" to generate figures (instructions contained within script). Use same script to reproduce Figures S9 and S10 with data in "t_atm_sensitivity.zip" and "bH_sensitivity.zip". </p> <p><strong>3. To reproduce Figures 4 and 5</strong>, If using new model runs to generate plots, first run "gen_data_forplots.py". Alternatively, download:</p> <ul> <li>e_statistics_nomelt_CO_FMQ0.npz </li> <li>e_wd_statistics_melt_CO_FMQ0.npz</li> <li>40_Ar_hab_dlith_KU_mix_52522.csv</li> <li>40_Ar_hab_dlith_KU_nomix_52522.csv</li> </ul> <p>Then, run maintext_plotting_new.py.</p> <p><strong>4. To reproduce Figures S5 to S8</strong>, run "Ar_plots.py". Requires: "serpent_dehyd2.csv" and "eclogite_transition.csv".</p>
Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios
<p><strong>Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios</strong></p> <p>This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files. </p> <p>Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances.</p> <p>Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.</p> <p> </p> <p> </p>
cb-oura-1.0 : Generic climate scenarios from bias-adjusted CMIP5 global models
<p><strong>Context </strong></p> <p>The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, <a href="https://www.ouranos.ca/">Ouranos</a> is working to create a set of operational multipurpose climate scenarios. The initial version of “Scénarios Génériques” (generic scenarios, acronym cb-oura-1.0) is used mainly in Ouranos’ work to provide a consistent image of the changing climate over the North East of North America, principally the province of Québec. Cb-oura-1.0 was produced in 2016 by downscaling and bias-adjusting a selection of global climate model simulations available through the CMIP5 program. </p> <p><strong>Climate simulations </strong></p> <table> <caption>Climate simulations in the ensemble</caption> <thead> <tr> <th scope="col">Modeling center</th> <th scope="col">Acronym</th> <th scope="col">Model</th> <th scope="col">RCP</th> <th scope="col">Status*</th> </tr> </thead> <tbody> <tr> <td><strong>College of Global Change and Earth System Science, Beijing Normal University</strong></td> <td>GCESS</td> <td>BNU-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Canadian Centre for Climate Modelling and Analysis</strong></td> <td>CCCMA</td> <td>CanESM2</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Centro Euro-Mediterraneo per I Cambiamenti Climatici</strong></td> <td>CMCC</td> <td>CMCC-CMS</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia</strong></td> <td>CSIRO-BOM</td> <td>ACCESS1.3</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institute for Numerical Mathematics</strong></td> <td>INM</td> <td>INM-CM4</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institut Pierre-Simon Laplace</strong></td> <td>IPSL</td> <td>IPSL-CM5A-LR</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td>IPSL-CM5B-LR</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Met Office Hadley Centre</strong></td> <td>MOHC</td> <td>HadGem2</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)</strong></td> <td>MPI-M</td> <td>MPI-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Norwegian Climate Centre</strong></td> <td>NCC</td> <td>NorESM</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>NOAA Geophysical Fluid Dynamics Laboratory</strong></td> <td>NOAA-GFDL</td> <td>GFDL-ESM2M</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> </tbody> </table> <p>From the complete ensemble of RCP 4.5 and 8.5 driven CMIP5 climate simulations, a selection of 22 simulations (11 per RCP) was made using a clustering ensemble reduction methodology (Casajus et al. 2016). This objective selection method identifies a reduced number of simulations that best represent the overall ensemble. Input criteria for the reduction were the monthly changes between the present (1981-2010) and two future horizons (2041-2070 and 2071-2100), at 15 regions distributed across Canada, for three variables (mean daily maximum temperature, mean daily minimum temperature and total precipitation). An initial selection of 16 simulations shows a distribution projected changes for the 12 (months) x 2 (horizons) x 15 (regions) x 3 (variables) indices that is not statistically different from the complete ensemble. A small number of simulations were subsequently added to have a complete set with both RCPs represented equally (11 members for each emission scenario). </p> <p><strong>Reference dataset </strong></p> <p>The bias-adjustment reference (or target) is a gridded observation dataset produced by Natural Resources Canada (McKenney et al., 2011; et Hutchinson et al. ,2009). It uses the ANUSPLIN interpolation method over station observations to derive daily grids of minimum and maximum temperature, as well as total precipitation for the Canadian landmass. The grid has a resolution of 10 km x 10 km and cover the time period from 1950 to 2013. </p> <p>As this dataset is not available over the United States, it was merged with another observation interpolation dataset produced by Livneh et al. (2015) in order to enable the production of bias-corrected climate scenarios covering a portion of the northern United States. </p> <p><strong>Coverage </strong></p> <p>The final version of this dataset covers a region covering the Atlantic provinces, Québec, Ontario, Manitoba and Saskatchewan and part of the northern United States: From 120°W to 54°W and from 40°N to 62°N. </p> <p>It contains the daily minimum temperature, daily maximum temperature and daily precipitation flux, covering the period 1950 to 2100. </p> <p><strong>Bias-adjustment </strong></p> <p>The global simulations where downscaled to the reference grid using bilinear interpolation and then bias-adjusted with a 1-D quantile mapping method, as described by Gennaretti et al. (2015). A moving window of 31 days was used to adjust each day of the year, using 50 quantiles to define the statistical distributions to match. The long-term linear trends of the temperature variables were preserved explicitly. </p> <p><strong>Climate indicators </strong></p> <p>This dataset is used to in the first versions (up to 1.3) of Ouranos’ <a href="https://www.ouranos.ca/en/climate-portraits">Climate Portraits </a>website. A selection of 26 seasonal and annual climate indicators were computed from the daily scenarios, using the xclim software package (Logan et al. 2022). The "virtual indicator module" used for the computation is made available here in the "indicators.yml" file.</p> <p>On the Climate Portraits website, the information is presented from three aspects: spatial, temporal and summary. This repository stores the reduced ensemble data as shown on the website. Filenames are constructed as "{aspect}_{indicator}_{season}.nc".</p> <ul> <li> <p>Maps (files "spatial_*") : Climate indicators for each bias-adjusted climate simulation and for a given RCP emission scenario are averaged over 30-year horizons. Ensemble percentiles are computed in order to summarize climate model uncertainty. In particular the 10, 25, 50, 75 and 90th percentiles over the 11 members are calculated for each RCP. </p> </li> <li> <p>Timeseries (files "temporal_*") : Climate indicators for each bias-adjusted simulation are averaged spatially over each region for every time step (annual or seasonal). The ensemble statistics are computed by first pooling all regional average values for the 11 members using within a centred 30-year window and then calculating percentile values (same as above) on the pooled data. </p> </li> </ul> <ul> <li> <p>Summary (files "summary_*") : The indicators are averaged over each region and then over 30-year horizons. The ensemble statistics (same as above) are then computed. </p> </li> </ul> <p>In versions 2.x of the app, this data will be presented as "CMIP5".</p> <p><strong>Data availability </strong></p> <p>This repository stores the climate indicator ensemble statistics as shown on the Climate Portraits website and described above. The complete daily dataset is too large for this platform.</p> <p>The complete daily dataset is available through the public THREDDS server of the PAVICS platform maintained by Ouranos. This data might be removed in the future. When this is the case, please contact us for data requests.<br> <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html</a> </p> <p>The annual and season indicators of the Climate Portraits website are available on the same server, along with a few more indicators not shown on the app. <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html</a></p> <p><em>Terms of use</em>: Use of this dataset should be acknowledged as 'Data produced and provided by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change'. Furthermore, the modeling groups from which the bias-adjusted climate scenarios were constructed must also be acknowledged, please refer to: The Coupled Model Intercomparison Project <a href="https://pcmdi.llnl.gov/mips/cmip5/citation.html.">https://pcmdi.llnl.gov/mips/cmip5/citation.html.</a></p>
Simple Biosphere model version 4.2 (SiB4) simulations for the present day atmosphere with 500 ppt OCS and the two OCS geoengineering scenarios with 4.8 ppb and 35.5 ppb OCS.
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figures 1 and 2.</p> <p>Simple Biosphere model version 4.2 (SiB4, Haynes et al., 2019; Sellers et al., 1986) was used to calculate (i) the average increase in evapotranspiration anticipated under an elevated OCS scenario for the years 2000-2021 on a 0.5 ° latitude x 0.5 ° longitude grid and (ii) OCS uptake by plants and soils, per month, at baseline (500 ppt) and elevated (4.8 and 35.5 ppb) OCS levels averaged over the years 2000-2021.</p> <p>- - - - - - - - - - -</p> <p><em>File 1: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_DeltaEvapotranspiration_GloballyGridded_SiB4.nc</em></p> <p>File Format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> latitude</p> <p> longitude</p> <p>Parameters:</p> <p> percent_diff_et: relative increase in % of evapotranspiration in a scenario where 20% of terrestrial plants exhibit a 50% increase in stomatal conductance under high OCS</p> <p>- - - - - -</p> <p><em>File 2: vonHobe_et_al_2023_CarbonylSulfideGeoengineeringScenarios_BiosphereUptake_MonthlyIntegrated_SiB4.csv</em></p> <p>File Format:</p> <p> comma delimited text file (.csv)</p> <p>Index Variable:</p> <p> time: monthly, format m/dd/yy</p> <p>Parameters:</p> <p> ocs_veg_base: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_soil_base: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 500 ppt</p> <p> ocs_veg_4.8ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_soil_4.8ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 4.8 ppb</p> <p> ocs_veg_35.5ppb: simulated monthly OCS uptake by terrestrial vegetation at an atmospheric OCS mole fraction of 35.5 ppb</p> <p> ocs_soil_35.5ppb: simulated monthly OCS uptake by soils at an atmospheric OCS mole fraction of 35.5 ppb</p> <p>- - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Haynes, K. D., Baker, I. T., Denning, A. S., Stöckli, R., Schaefer, K., Lokupitiya, E. Y., and Haynes, J. M.: Representing<br> Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple<br> Biosphere Model (SiB4), Journal of Advances in Modeling Earth Systems, 11, 4423-4439, 10.1029/2018ms001540, 2019.</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Sellers, P. J., Mintz, Y., Sud, Y. C., and Salcher, A.: A Simple Biosphere Model (SiB) for Use within General Circulation Models, Journal of the Atmospheric Sciences, 43, 505-531, 1986.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) ,</p>
Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES
<p>Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water. </p> <p> </p> <p>This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1).</p> <p> </p> <p>Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.</p> <p> </p>
NPJ Climate Action AR6 scenarios database submission histograms by model family and project
<p>Based on submissions to the IPCC AR6 Scenarios Database, this datasets uses the metadata to construct histograms of the submitted scenarios by model family and project, noting the total submissions, vetted scenarios, and climate assessed scenarios. A total of 2304 scenarios were submitted to the global emissions database, of these, 618 did not passing vetting for sufficiently consistency with historical energy and emissions data, and a further 484 did not have sufficient data to perform a climate assessment, leaving a total of 1202 used in the primary assessment of scenarios. </p> <p>The database based on the scenario metadata. The ‘model family’ was determined by removing version numbers from the full model name. The ‘project family’ was obtained using the ‘Scenario family’ variable in metadata, supplemented by manually checking against cited literature. The classification of vetted scenarios was based on the variable ‘Historical vetting’ and the climate assessment on the ‘Climate Category’.</p> <p>This version is based on version 1.0 of the AR6 scenarios database.</p>
Isotope mixing scenarios for: To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection
<p><span>We selected 10 types of distinct isotope signatures that can be found in the samples of natural water. Every 3–10 types of hypothetical isotope signatures were conceptually grouped together. There would be 968 possible combinations based on combinatorics theory. However, we needed distinct mixing polygons to facilitate our determination of model capacity in dealing with uncertainties. Therefore, we </span><span>kept </span><span>only 240 such groups in </span><span>the </span><span>final</span><span> analysis</span><span>. Each group was designated with a </span><span>predefined</span><span> mixing ratio. After that, we ran all the examined models through these mixing scenarios to </span><span>obtain</span><span> the model estimation of the mixing ratios.</span></p>
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
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Isotope mixing scenarios and machine learning model in: To what extent are the source mixing models accurate: evaluation of the model accuracy and guidelines for the site-specific model selection
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Data for: Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling
<p>This dataset is associated to the article "Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling " published in Seismological Research Letters (<a href="https://pubs.geoscienceworld.org/ssa/srl/article-abstract/doi/10.1785/0220200278/592607/Historical-Earthquake-Scenarios-for-the-Middle?">link</a>).</p> <p>It includes the following:</p> <ul> <li>The annotated photographs of the EAE (Earthquake Archeological Effects) inventoried in Iznik ("EAE_xxx.pdf").</li> <li>The 3D displacement signals used as input for obelisk modeling ("Displacement_xxx").</li> <li>The output obelisk displacement curves and final block shift values relative to base ("Obelisk_block_motion.pdf").</li> </ul>
Experimental Data for Natural Disaster Mobility Model and Typhoon Haiyan Scenario
<p>The experimental data set for running the <em>Typhoon Haiyan</em> scenario with the <em>Natural Disaster Mobility Model</em> presented in the paper:</p> <p>Milan Stute, Max Maass, Tom Schons, and Matthias Hollick, “<strong>Reverse Engineering Human Mobility in Large-scale Natural Disasters</strong>,” to appear in <em>ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)</em>, November 2017, Miami Beach, USA.</p>
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Evaluating Effects of Climate Change, Restoration Scenarios, and Hatchery Effects on Chinook Salmon in the Stillaguamish River Basin with the HARP Model
<p>Model code (R) to accompany the 2023 NOAA report "<strong>Evaluating Effects of Climate Change, Restoration Scenarios, and Hatchery Effects on Chinook Salmon in the Stillaguamish River Basin with the HARP Model</strong>"</p>
tRIBS Model Scenarios: Forest Treatment Effects on Watershed Responses under Warming in the Beaver Creek (2003-2018)
<p>This dataset contains the model simulation setup and scenario results of the individual and combined effects of forest thinning and warming on the forest hydrologic response in the Beaver Creek watershed of central Arizona. The simulations were conducted with the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model at a variable resolution of about 120 m, hourly resolution from 2003-2018 and aggregated daily values in this dataset. Raw model outputs at hourly resolution are available upon request from the authors, but not included here due to their excessive size. </p> <p>The model setup files are organized into the tar gzipped file: <strong>BCmodel.tar.gz</strong>. This contains the following files: (1) a series of input files (*.in) used for the model simulations, and (2) the ancillary data sets required for model execution (terrain model, soil map, land cover map, initialization file, data descriptor tables). </p> <p>The model rainfall forcing files are organized into the tar gzipped file: <strong>BCrain.tar.gz</strong>. This contains the following directories: (1) rainfall data from the bias-corrected NEXRAD product, and (2) rainfall data from the NLDAS-2 product.</p> <p>The model meteorological forcing files are organized into the tar gzipped file: <strong>BCweather.tar.gz</strong>. This contains the following data from bias-corrected, adjusted NLDAS-2: (1) atmospheric pressure, (2) wind speed, (3) air temperature, (4) incoming solar radiation, and (5) relative humidity. </p> <p>The daily values of the model outputs are stored in the file <strong>tRIBSModelScenarios_DailyOutputs.xlsx</strong>. This includes the scenarios (BC0, BC1, BC2, BC4, BC6, PT0, PT1, PT2, PT4, PT6) and the variables streamflow (Q), total evapotranspiration (ET), snowmelt (M), ground sublimation (Subg), and canopy sublimation (Subc). All values in mm/day over the period 10/01/2002 to 09/30/2018 (16 water years). A readme file is provided. </p> <p>More details can be found in the associated paper (this record will be updated when the paper is published):</p> <p>Cederstrom, C., Vivoni, E.R., Mascaro, G., and Svoma, B. 2024. Forest Treatment Effects on Watershed Responses under Warming.<em> Water Resources Research. (in revision)</em>.</p>
Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'
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WEAP Modelling Scenarios
<p><span>The WEAP water balance model, developed by the Stockholm Institute of Environment, has been set up for the Barapullah Drain (New Delhi) and the Kanpur Metropolitan Area to examine how the demand for water and quality of water in the river and the underlying aquifers change over time in accordance with various socio-economic and environmental drivers (population growth, growth in per capita demand, changes in climatic conditions, especially rainfall conditions and changes in wastewater treatment technologies and capacities) and how the supply of water in terms of quantity and quality to meet the requirements (demand) of water changes under various technological interventions.</span></p>
SESMG model scenarios of the study "Indicators for the optimization of sustainable urban energy systems based on energy system modeling"
<p>This folder contains the model scenarios belonging to the publication "<strong>Indicators for the optimization of sustainable urban energy systems based on energy system modeling</strong>" (<a href="https://doi.org/10.1186/s13705-021-00323-3">https://doi.org/10.1186/s13705-021-00323-3</a>).</p> <p>The individual scenarios can be executed and evaluated with the <strong>Spreadsheet Energy System Model Generator (<a href="https://github.com/chrklemm/SESMG">SESMG</a>)</strong> <a href="https://doi.org/10.5281/zenodo.5412027">v0.0.4</a>, respectively <a href="https://doi.org/10.5281/zenodo.5520513">v0.2.0</a>.</p> <p>The file names are to be understood as follows:</p> <p><em>"scenario name"_"(dispatch) optimization criterion"_"scenario concretization"_"further scenario concretization"_"associated program version"</em>.xlsx.</p> <p>For example, the title name "<em>Scenario3_C_4MW_Biogas_SESMGv0.0.4.xlsx</em>" contains the following information:<br> - This file belongs to scenario 3 (see main publication for details).<br> - Dispatch optimized according to energy costs C (see main publication for details).<br> - The scenario contains 4 MW biogas CHP capacity (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.0.4.</p> <p>Another example. The title name "<em>optimization_C_80PercentDemand_70PercentEmissions_SESMGv0.1.1.xlsx</em>" contains the following information:<br> - This file belongs to the optimization scenario (see main publication for details).<br> - The primary optimization criterion is energy costs C (see main publication for details).<br> - Energy demand was capped at 80 percent and emissions at 70 percent of baseline (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.1.1.<br> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors would like to thank Prof. Dr. Peter Vennemann (Münster University of Applied Sciences) for the constructive discussion regarding this article. This research has been conducted within the R2Q project, funded by the German Federal Ministry of Education and Research (BMBF) - grant number 033W102A and the junior research group energy sufficiency funded by the German Federal Ministry of Education and Research (BMBF) as part of its Social-Ecological Research funding priority, funding number 01UU2004A. </p>
Supplementary Material on "Early timing analysis based on scenario requirements and platform models"
<p>This dataset provides supplementary material on the submission “Early timing analysis based on scenario requirements and platform models” to the SoSyM theme issue on Model-Driven Requirements Engineering. It provides software and models for illustrating the paper's example application results as well as more detailed evaluation data.</p> <p>MSD-CCSL-TimingAnalysis.zip contains our approach and encompasses the following artifacts (Java 8 and not later required; if needed modify the GemocStudio.ini and point the vm to a corresponding Java version via "-vm <PathToJava8>\jre\bin"):</p> <ul> <li>Development workspace: <ul> <li>ECL specification under /de.fraunhofer.iem.swt.msd.tam.dse/ecl/MSDLanguage.ecl</li> <li>MoCCML constraints under /de.fraunhofer.iem.swt.msd.tam.mocc/mocc/MSDLanguageComplete.moccml</li> <li>TAM profile under /de.fraunhofer.iem.swt.msd.tam.tamProfile/model/tam.profile.uml</li> </ul> </li> <li>Runtime workspace: <ul> <li>Models under "01_ExampleModels"</li> <li>Exemplary traces under "02_ExampleTraces"</li> <li>QVT-O Transformations (e.g., Preprocessing) needed when modifying the models</li> </ul> </li> </ul> <p>Papyrus-CCSLEditor-Measurement.zip contains the plugins and artifacts that we used for measuring the particular modeling operations for the evaluation of the hypothesis H2 (see further documents below). It requires Java 11; if needed modify the eclipse.ini and point the vm to a corresponding Java version via "-vm <PathToJava11>\jre\bin". Contained plugins and artifacts:</p> <ul> <li>Development workspace: <ul> <li>is.ru.cs.PapyrusActivityLogger: Our adapted version of ModRec, particularly extended by an Xtext document listener</li> <li>org.eclipse.gemoc.moccml.*: MoCCML editor prerequisites for the CCSL runtime model</li> <li>org.scenariotools.msd.profile and de.fraunhofer.iem.swt.msd.tam.tamProfile: Profiles that we partially use in the Papyrus runtime model</li> </ul> </li> <li>Runtime workspace: <ul> <li>CCSL Measuring Project: Measuring project for CCSL models</li> <li>Papyrus Measuring Project: Measuring project for Papyrus models</li> </ul> </li> </ul> <p>Further documents:</p> <ul> <li>MSD-CCSL-TimingAnalysisTutorial.pdf: Tutorial on starting the simulative timing analysis</li> <li>EvaluationData_H1_TimingEffectTestResults: Test results for the particular timing effects based on several models for hypothesis H1</li> <li>Files for hypothesis H2: <ul> <li>EvaluationData_H2.xlsx: Spreadsheet containing the particular model element amounts of MSD-spec-1--4 and CCSL-model-1--4, the measurements for the categorized atomic model operation kinds, the multiplication scheme for predicting the raw overall effort, and the measured transformation execution times</li> <li>EvaluationData_H2_MSD-specification-effort.pdf: PDF extract of the spreadsheet contents for the MSD specification effort and computation</li> <li>EvaluationData_H2_CCSL-model-effort.pdf: PDF extract of the spreadsheet contents for the CCSL model effort and computation</li> <li>EvaluationData_H2_transformationExecTimes.pdf: PDF extract of the spreadsheet contents for the transformation execution times</li> <li>EvaluationData_H2_MSD-specification_measurement-timestamps.txt: Raw timestamp logs for the conducted measurements for model operations on MSD specifications</li> <li>EvaluationData_H2_CCSL-model_measurement-timestamps.txt: Raw timestamp logs for the conducted measurements for model operations on CCSL models</li> </ul> </li> </ul>
Model results of reduced wood harvest and forest protection scenarios using MAgPIE 4.3.5
<p>The files here contain MAgPIE 4.3.5 results of reduced wood harvest and forest protection scenarios.</p> <p>MAgPIE requires <em>GAMS</em> (<a href="https://www.gams.com/">https://www.gams.com/</a>) including licenses for the solvers <em>CONOPT</em> and (optionally) <em>CPLEX</em> for its core calculations. As the model benefits significantly from recent improvements in <em>GAMS</em> and <em>CONOPT4</em> it is recommended to work with the most recent versions of both.<br> <br> The results of the model run here have been cleaned up to avoid bulky uploads. The fulldata.gdx is the technical output of the GAMS optimization and contains all quantities that were used during the optimization in unchanged form. The mif-file is a CSV file of a specific format and is synthetized from the fulldata.gdx by post-processing scripts. It can be read in any text editor or spreadsheet program and is well suited for a brief look at the results and for further analysis.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.