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942 results for “scenarios”
CAIRT/IASI-NG/CAIRT+IASI-NG FL2S results of Case Study Scenario 4 for limb-nadir application
<p>Results of the fast level-2 simulator (FL2S) for Case Study Scenarios 4 (only SO2 files) for limb-nadir application. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak') and additional application of noise ('_aknoi') for CAIRT alone, IASI-NG alone, and the combined product CAIRT+IASI-NG.</p>
CAIRT FL2S Results of Case Study Scenario 6 (CSS6) for Auroral NO
<p>Corrected version. Results of the fast level-2 simulator (FL2S) of CAIRT developed within the Earth Explorer 11 Phase 0 Science and Requirements Consolidation Study (SciReC) – CAIRT. The files contain altitude-time cross-sections of atmospheric parameters along simulated CAIRT-orbits. The variable extensions denote the original field ('_ori'), the application of the averaging kernel ('_ak'), additional application of noise ('_aknoi'), application of systematic uncertainties ('_sys'), and application of all effects ('_aknoisys'). Further information is available from the authors. </p>
Pyrolysis process in SCENARIOS
<p>A very brief explanation of the concept of vermichar for decontaminating PFAS-contaminated soils.</p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</p>
Freshwater thickness, pycnocline depth, and depth averaged velocities from 17 model scenarios in Admiralty Bay, Antarctica.
<p>The dataset contains model results from the Admiralty Bay hydrodynamic model calculated using the Delft3D Flow. It contains freshwater thickness (FWT), pycnocline depth, and depth-averaged velocities mean values from period 1.1.2022-28.01.2022, from 17 model scenarios.</p><p>Scenarios details:</p><p>1–14 scenarios with increasing glacial influx (m^3/s per ~1 km of ice/water boundary) spread homogenously across glacial fronts with 0 m/s initial velocity: 1 - 0; 2 - 0.15 3 - 0.3; 4 - 0.6; 5 - 0.9; 6 -1.7; 7 - 3.0; 8 - 4.5; 9 - 6.0 (also described as test run H0); 10 - 8.0; 11 -11.0; 12 - 14.0; 13 - 28.0 ; 14 - 60.0;</p><p>15 - H2 test run with glacial water discharged from all glaciers, homogenously through the entirety of glacial front, with an initial velocity of 2 m/s</p><p>16 - S0 test run with glacial water discharged from all glaciers subglacially, with zero initial velocity</p><p>17 - S2 test run with glacial water discharged from all glaciers subglacially, with an initial velocity of 2 m/s</p>
Supplementary Material for "Aiding the Design of Critical Software Systems by Iterative Exploration of Distinct Requirement Violation Scenarios"
<p>This dataset provides artifacts about an industrial case study of a Steer-by-Wire system. It collects models of the system modeled in the open-source Gamma Statechart Composition Framework. You can find more information about the framework here: <a href="https://github.com/ftsrg/gamma">https://github.com/ftsrg/gamma</a>.</p>
Projected trophic changes in species-carrying capacities under climate change scenarios
<p>Climate controls the amount of energy available for plants, which in turn determines the quantity of resources available for animals. It follows that when climate changes, so should trophic communities. Using a novel modeling approach, we investigate how bird and mammal trophic communities might disassemble and reassemble under 21<sup>st</sup> century climate changes. We show that trophic structures are expected to undergo profound changes globally, chiefly in the tropics and across high latitudes in the northern hemisphere. This trophic reorganization of communities is characterized by shifts in species richness within trophic guilds. While some guilds might face population collapses, others are projected to find new opportunities to maintain stable populations in previously inhospitable areas. The proposed models offer a tool for projecting and understanding the trophic ramifications of climate change, highlighting their potential in guiding future research and conservation efforts.</p>
LuccME/INLAND land-use scenarios for Brazil 2050
<p>Land use and land cover change models and scenarios are essential to understand the interconnections between global and regional factors influencing land use and demand changes, especially if we consider population growth and food demand projections in 2050.</p> <p>Understanding the future of changes in land use and land cover in Brazil is fundamental for the future of global climate and biodiversity, given the richness of its five biomes. Thus, the new spatially explicit regional scenarios were developed for Brazil by 2050. Those scenarios are aligned with the Shared Socio-Economic Pathways (SSPs) and Representative Concentration Pathway (RCPs). Aim to detail global models regionally and can be used both regionally to support decision-making and enrich the overall analysis.</p> <p>For the development of these new scenarios, the LuccME spatially explicit land change allocation modeling framework and the INLAND surface model were combined to incorporate climatic variables in water deficit and biophysical, socioeconomic, and institutional factors for Brazil. The scenarios were developed for land use and land cover classes: forest vegetation, grassland vegetation, planted pasture, agriculture, mosaic of occupations, and forestry.</p> <p>The dataset comes in NetCDF format and includes the following products:</p> <p> </p> <p><strong>LUCCMEBR_land_cover_type_100km2_2000.nc</strong>: Percentage of land use and land cover for the year 2000 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2010.nc</strong>: Percentage of land use and land cover for the year 2010 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2012.nc</strong>: Percentage of land use and land cover for the year 2012 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2014.nc</strong>: Percentage of land use and land cover for the year 2014 (Observed data).</p> <p><strong>LUCCMEBR_SSP1_RCP19_land_cover_type_100km2_2015_2050.nc</strong>: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP1 and RCP1.9.</p> <p><strong>LUCCMEBR_SSP2_RCP45_land_cover_type_100km2_2015_2050.nc:</strong> Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP2 and RCP4.5.</p> <p><strong>LUCCMEBR_SSP3_RCP70_land_cover_type_100km2_2015_2050.nc</strong>: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP3 and RCP7.0.</p> <p> </p> <p><strong>Data</strong></p> <p>Percentage of land use and land cover classes: Forest vegetation (veg), Grassland vegetation (gveg), Planted pasture (pastp), Agriculture (agric), Mosaic of occupation (mosc), Forestry (fores) and Others (others).</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The scenarios are available in a spatial resolution of 0.083º x 0.083º (~100 km²) and cover the entire Brazilian territory.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2000, 2010, 2012 e 2014</p> <p>Scenario Period: 2015 – 2050 (each five-year)</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum WGS84 (EPSG4326)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as NetCDF.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors thank the project “MSA / BNDES (Environmental Monitoring by Satellite in the Amazon biome)” for financing the development of LuccMEBR.</p>
Scout Benchmark Scenarios for U.S. Building Energy and CO2 Emissions to 2050
<p><strong>Overview and Intended Use Cases</strong></p> <p>These scenarios establish a range of futures for U.S. buildings sector energy use and CO<sub>2</sub> emissions to 2050 using <a href="https://scout-bto.readthedocs.io/en/latest/">Scout</a>, a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).</p> <p>Scout benchmark scenario data are suitable for the following example use cases:</p> <ul> <li>Setting high-level policy goals for U.S. buildings sector energy use, electricity demand, and CO<sub>2</sub> emissions over both the near- and long-term (e.g., X% building CO<sub>2</sub> emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);</li> <li>Exploring the effects of key deployment dynamics driving U.S. buildings sector energy and CO<sub>2</sub> emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; improving market penetration of commercially available technologies; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);</li> <li>Determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO<sub>2</sub> emissions reductions and/or changes in total consumption by fuel type to 2050 under a given set of assumptions;</li> <li>Identifying the energy and CO<sub>2</sub> impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio; and/or</li> <li>Exploring the total cost of deploying different portfolios of building energy efficiency and end-use electrification measures, as well as the total consumer energy cost savings potential of those portfolios. </li> </ul> <p><strong>Scenario Summary</strong></p> <p>A total of 5 scenarios explore total building energy use, CO<sub>2</sub> emissions, and technology and energy costs from 2024–2050 under varying levels of demand-side deployment of building efficiency and electrification measures and parallel decarbonization of buildings’ electricity supply. Narrative descriptions of these scenarios are as follows:</p> <ul> <li><strong>Stated Policies: </strong>Existing policies and regulations (mainly IRA for buildings) lead to modestly accelerated deployment of HPs/HPWHs but not other efficiency measures in the buildings sector. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> <li><strong>Mid: </strong>Policy makers rely mostly on market-based instruments to moderately increase deployment of efficient technology and fuel switching to heat pumps. The power sector decarbonizes consistent with a “Mid-case with 95% Decarbonization by 2050 (without tax credit phaseout)” scenario.</li> <li><strong>High: </strong>Policy makers use both regulations and market-based instruments to dramatically accelerate deployment of high efficiency technologies and fuel switching to heat pumps, though building technologies with breakthrough increases in performance at low cost do not materialize on the market. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Breakthrough: </strong>Research and innovation breakthroughs lead to market availability of cost-effective, high-performance building technologies by 2030; these, coupled with accelerated deployment of high efficiency technologies and fuel switching to heat pumps, lead to aggressive buildings sector transformation. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Inefficient Electrification Sensitivity: </strong>Policy makers use regulations and market-based instruments to encourage fuel switching but do not include provisions that require switching to efficient heat pumps, resulting in a substantial amount of switching to inefficient electric resistance heating and water heating technologies. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> </ul> <p>The key input dimensions that are varied to produce the above range of scenarios are as follows:</p> <ul> <li><u>Market-available technology performance range:</u> the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;</li> <li><u>Load electrification rate and efficiency:</u> the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment; </li> <li><u>Early retrofits:</u> the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and</li> <li><u>Power grid decarbonization:</u> the annual average CO<sub>2</sub> emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2024–2050), resolved by grid region. </li> </ul> <p>Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Execution” XLSX.</p> <p>Results data are reported as an annual time series (2024–2050) at both a national and regional (<a href="https://www.eia.gov/outlooks/aeo/pdf/nerc_map.pdf">EMM grid region</a>) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.</p> <p><strong>What's New in This Version</strong></p> <p><strong><em>Note: v6.1 updates the file ./Results/Results_Summary.xlsx to reflect the latest scenario runs. Please disregard the outdated version of this file that was posted in v6.</em></strong></p> <p>This set of benchmark scenarios provides an update to <a href="../records/8087519">Version 5</a> of the Scout Benchmark Scenarios (June 2023) using the same scenario definitions but an updated set of baseline and measure input data alongside several minor methodological changes. </p> <p>The following scenario features are new in this dataset:</p> <ul> <li>Reference case data and energy use projections updated to <a href="https://www.eia.gov/outlooks/aeo/">AEO 2023</a>, including updates to energy and stock and technology cost, performance, and lifetime data; updated site-source energy conversions, CO2 emissions intensities, and energy prices; and revised peak and take period definitions that are consistent with 2023 EMM projections.</li> <li>Integration of federal and state cost incentives from AEO 2023 (see <a href="https://www.eia.gov/outlooks/aeo/IIF_IRA/pdf/IRA_IIF.pdf">AEO2023 Issues in Focus: Inflation Reduction Act Cases</a> in the AEO2023 for details); these incentives reduce the initial cost of upgrades for applicable measures.</li> <li>Revised method for allocating end use electricity baselines in AEO from census divisions to EMM regions and states by using <a href="https://www.nrel.gov/buildings/end-use-load-profiles.html">End Use Load Profiles</a> (EULP) data. EULP data now also underpin updated, EMM-resolved hourly load baseline shapes.</li> <li>Retail price projections for grid scenarios are updated to match those produced by NREL under the Department of Energy’s DECARB Initiative (these are similar to but differ in slight ways from NREL’s <a href="https://www.nrel.gov/analysis/standard-scenarios.html">Standard Scenarios</a>). Three scenarios are included: <ul> <li><em>Stated Policies</em>: includes moderate estimates for inputs such as technology costs, fuel prices, and demand growth with no nascent technologies and electric sector policies that match current federal laws and regulations (including IRA & BIL); achieves an 88% reduction in building site electricity emissions <em>intensity</em> (Mt CO2/quad site) from 2005 levels by 2050.</li> <li><em>Mid</em>: consistent with<em> Stated Policies</em> except achieves 97% reduction in building site electricity emissions intensity from 2005 levels by 2050.</li> <li><em>High:</em> includes low demand growth projections with advanced inputs for technology costs and allowance of transmission expansion between regions (without limitations based on historical build rates); federal policies are consistent with implemented laws (including IRA & BIL); building electricity is fully decarbonized after 2035.</li> <li>The previous version of the benchmark datasets used retail price data from EIA’s <a href="https://www.eia.gov/outlooks/aeo/">Annual Energy Outlook</a> scenarios.</li> </ul> </li> <li>In contrast to <a href="https://doi.org/10.5281/zenodo.8087519">Version 5</a>, measures in the “best available” measure tier are not deployed with load flexibility features. </li> </ul>
SLIIDERS: Sea Level Impacts Input Dataset by Elevation, Region, and Scenario
<p>This record includes the Sea Level Impacts Input Dataset by Elevation, Region, and Scenario (SLIIDERS) dataset. It also includes source code to generate this product as well as necessary inputs that are not available for download elsewhere. Both the dataset and the source code are consistent with version 1.2. <strong>Note</strong>: The version associated with <a href="https://gmd.copernicus.org/articles/16/4331/2023/">Depsky et al., 2023</a> is v1.1.</p> <p>The zipped SLIIDERS Zarr store can be downloaded and accessed locally or can be directly accessed via code similar to the following:</p> <pre><code>from fsspec.implementations.zip import ZipFileSystem import xarray as xr xr.open_zarr(ZipFileSystem(url_of_file_in_record}}).get_mapper())</code></pre> <p><strong>File Inventory</strong></p> <p><em>Products</em></p> <ul> <li><strong>sliiders-v1.2.zarr.zip</strong>: SLIIDERS. A global dataset containing 18 socioeconomic variables, reflecting present day socioeconomic and geophysical characteristics of 11,980 coastal regions and projecting capital stock, GDP, and population growth trajectories through 2100 for five SSPs and two economic growth models. These variables are used as inputs to the pyCIAM modeling platform detailed in Depsky et al. 2023.</li> <li><strong>sliiders-v1.2.nc</strong>: Same as the original SLIIDERS dataset, but in netcdf format.</li> </ul> <p><em>Inputs</em></p> <p>All provided inputs are manually created or adjusted points used to create the coastline segments of SLIIDERS:</p> <ul> <li><strong>ciam_segment_pts_manual_adds.parquet</strong>: A list of segment points manually added to those that come from the extreme sea level model CoDEC (<a href="https://doi.org/10.5281/zenodo.3660926">Muis et al. 2020</a>)</li> <li><strong>gtsm_stations_ciam_ne_coastline_snapped.parquet:</strong> Stations from CoDEC snapped to coastlines from <a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">Natural Earth</a></li> <li><strong>gtsm_stations_eur_tothin.parquet</strong>: A list of European points in CoDEC to thin. CoDEC provides ~10km resolution in Europe and ~50km elsewhere. For consistency, SLIIDERS uses ~50km spacing for its coastal segments globally.</li> </ul> <p><em>Source Code</em></p> <ul> <li><strong>sliiders-1.2.zip</strong>: The source code used to generate SLIIDERS v1.1. See READMEs within this code for more details. This is consistent with release v1.2 of the code maintained on github at <a href="https://github.com/ClimateImpactLab/SLIIDERS">https://github.com/ClimateImpactLab/SLIIDERS</a></li> </ul>
Land use and land cover scenarios for the Maurienne valley (French Alps) at 2085 horizon produced using CLUMPY model
<p>We built three contrasted future LULC scenarios from 2020 to 2085 with the CLUMPY model (Mazy and Longaretti, 2022). The CLUMPY model is an innovative model of land use and land cover change comprising a calibration-estimation module separate from a non-biased allocation module. It is calibrated by using time series of past LULC maps (Mazy and Longaretti, 2022). The model then calculates transition probabilities for each LULC class according to relevant spatial explanatory variables. Next, the model can produce maps of future LULC distributions according to information it learned during the calibration-estimation phase. This model has the benefits of being easy to use, proposing nonbiased allocation methods and producing scenarios of future LULC change either by adjusting manually the matrix of LULC transitions probabilities (used for the Conservation and Tourism scenarios) or by training the model on specific areas of the past time series (only used for the Conservation scenario).</p>
Fig. 4 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 4. Distribution of pairwise values of genetic distances amongst species with allopatric areas: 1 — for the Western Palearctic genus Sylvaemus; 2 — for the Eastern Palearctic genera Apodemus and Alsomys; 3 — for the Palearctic Muridae as a whole, including species of genera Micromys and Mus.
Fig. 3 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 3. Distribution of pairwise intraspecies genetic distances within: 1 — the Western Palearctic genus Sylvaemus; 2 — the Eastern Palearctic genera Apodemus and Alsomys; 3 — in general for the Palearctic Muridae, including Micromys and Mus.
Fig. 2 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 2. Phenogram of genetic distances (Tamura, Nei, 1993) calculated from cytb sequences amongst representatives of the genera/subgenera Alsomys, Apodemus and genera Micromys, Mus, Rattus, constructed using the UPGMA algorithm. Representatives of the Arvicolidae and Cricetidae as well as S. s. dichrurus, S. flavicollis, S. (K.) mystacinus and S. (K.) epimelas were taken as outgroups.
Fig. 5 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 5. Distribution of pairwise genetic distances amongst taxa: 1 — Western Palearctic genus Sylvaemus, 2 — Eastern Palearctic genera Apodemus, Alsomys, 3 — Western Palearctic genus Sylvaemus and contrarily Eastern Palearctic genera Apodemus, Alsomys.
Fig. 1 in Taxonomic Hierarchy And Evolutionary Scenario Of The Genus Group Apodemus S. L. (Muridae) Of The Palaearctic Based On Genetic Differentiation In The Gene
Fig. 1. Phenogram of genetic distances calculated from cytb sequences amongst representatives of the genera Sylvaemus, Rattus, constructed using the UPGMA algorithm, as mentioned above. Microtus arvalis (Arvicolidae) and Cricetus cricetus (Cricetidae) are used as outgroups.
AWESOME CGE scenarios and modelling outputs
<p>The primary goal of this analysis was to study the impacts of climate change on food security given the potential supply of alternative water sources: desalination and reused (treated) water within the WEFE nexus in the Mediterranean Sea Basin. Accordingly, the first step of the analysis is to explicitly introduce desalination and treated water into the global CGE model and database. Using the standard GTAP model (Corong, Hertel, McDougall, Tsigas, & van der Mensbrugghe, 2017) and GTAP10A dataset (Aguiar, Chepeliev, Corong, McDougall, & & van der Mensbrugghe, 2019), the water sector was divided into three main categories using the SplitCom application (Horridge, 2008) and including (a) natural water that refers to distribution of water, (b) desalinated water that refers mostly to seawater desalination and (c) treated water that refers primarily to wastewater and brackish water treatment. The resulting modelling framework is used as the basis of the second step, that assesses the externalities associated with the different water sources incorporated in the model.</p> <p>To introduce alternative water sources in the CGE, several assumptions were made. First, desalinated water is produced from the abundant resource – seawater and therefore the scarcity value that is attributed to depletable natural resources is not applicable. Accordingly, desalinated water production can be treated as a sector disaggregated from the existing water sector in the GTAP database. For example, the economic activities of the desalination sector were reported as part of the water sector in input-output tables (United Nations, 2008).</p>
AWESOME Climate Scenarios over the Nile River Basin
<p>This record includes the climate data produced in the AWESOME project, downscaled/bias-adjusted over the Nile River Basin, considering the following temporal frames:</p> <ul> <li>control period CP: 1981-2005 (1983-2005 for temperature)</li> <li>future scenarios RCP2.6, 4.5, 8.5: 2006-2100, which rely on the <a href="https://www.ipcc.ch/assessment-report/ar5/">IPCC 5th assessment (2014)</a></li> </ul> <p>The Nile River Basin is subdivided into ten sub-basins, extracted from <a href="https://www.hydrosheds.org/page/hydrobasins">HydroBASINS (Lehner and Grill, 2013):</a></p> <ul> <li>Main Nile</li> <li>Atbara</li> <li>Blue Nile</li> <li>White Nile</li> <li>Baro-Akobo-Sobat</li> <li>Bahr El Jebel</li> <li>Bahr El Ghazal</li> <li>Lake Albert</li> <li>Victoria Nile</li> <li>Lake Victoria</li> </ul> <p>The regional data are retrieved from <a href="https://esgf-data.dkrz.de/search/cordex-dkrz/">CORDEX</a> Africa 44° (models GCM: ICHEC-EC-EARTH; RCM: SMHI-RCA4), while the observation data are taken from <a href="https://www.chc.ucsb.edu/data">CHIRPS and CHIRTS.</a></p> <p>In particular, this record includes:</p> <ul> <li>the downscaled/bias-adjusted mean daily data of mean precipitation ‘pav’ [mm/d], mean temperature ‘tas’ [°C], minimum 'Tmin' and maximum temperatures 'Tmax', saved in .txt files (day, variable) aggregated over the 10 Nile main subbasins (.zip folder)</li> <li>the downscaled/bias-adjusted gridded daily data of mean precipitation ‘pav’ [mm/d], mean temperature ‘tas’ [°C] and extreme temperatures (‘tasmax’ and ‘tasmin’ [°C]), saved in .nc files (netcdf format) at high resolution (5°) for the 10 Nile main subbasins (<a href="https://131.175.15.9/share.cgi?ssid=89f41d79528f436b993bcc904053047a">link</a> to the POLIMI-hosted server to access the data)</li> <li>Deliverable D2.2 Climate Scenarios, which reports on the climate scenarios developed for the project, including the analysis of downscaled climate change projections across the different spatial scales requested by the AWESOME modelling framework (PDF file).</li> </ul> <p> </p>
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).</strong></p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_LUC_2019.tif</strong> : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_LUC_2050.tif :</strong> land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><u>. </u>This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p> <p> </p> <p> </p>
Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis
<p><strong>Dataset Name:</strong><br><em>Literature Data, Archetype Parameter Sheets, and Schedules for the publication, named Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis</em>.</p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources, archetypal data, and schedules for Nigerian residential dwelling typologies.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> Nigeria<em>_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> Nigeria<em>_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> Nigeria<em>_Schedules.xlsx:</em> Excel sheet containing the operation schedules compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format.</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Nigerian residential buildings. The literature sources included in the Nigeria_LiteratureSources.xlsx and Nigeria_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Chibuikem Chrysogonus Nwagwu, Sahin Akin, and Edgar G. Hertwich. 2024. “Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis” https://doi.org/10.5281/zenodo.10995123</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) as well as full material and energy use result sheets can be provided on request. If you have any questions or comments about the dataset, please contact <strong>chibuikem.nwagwu@sintef.no, the corresponding author.</strong></p>
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