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121 results for “scenario modeling”
Figure 2 in Environmental niche modelling of the Chinese pond mussel invasion in Europe under climate change scenarios
Figure 2. Response curves of the environmental variables selected for prediction of S. woodiana distribution under the recent climate scenario. Each curve (green line) shows how the logistic prediction changes as each environmental variable is varied. The orange dashed line crosses the maximum value of the variable.
Future forest flux (e.g., GPP, NPP, NEP) simulated by the optimized InTEC model under four SSP-RCP scenarios
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Projected shifts in deadwood bryophytes in Sweden, data used for species distribution modelling and for climate and forest scenario analysis
<p>Climate change and habitat loss are main threats to forest biodiversity. We fitted ensembles of single species distribution models for 23 deadwood-living bryophyte species in Sweden, based on species records from the Swedish Lifewatch website. This data set comprises the species and environmental data used for species distribution modelling, and coefficients of the fitted single species distribution models (GLM, Poisson point-process, MaxEnt).</p> <p>Based on the fitted species distribution models, we conducted simulations of future species distributions given realistic climate and forest projection scenarios at the national scale of Sweden. The data used for the scenario analysis are stored here.</p>
Projected shifts in deadwood bryophytes in Sweden, data used for species distribution modelling and for climate and forest scenario analysis
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LBA-ECO LC-14 Modeled Deforestation Scenarios, Amazon Basin: 2002-2050
This data set provides the results of the two modeled scenarios for future patterns of deforestation across the Amazon Basin from 2002 to 2050. This larger defined Amazon Basin (PanAmazon area) includes the Amazon River watershed, the Legal Amazon in Brazil, and the Guiana region. The model SimAmazonia was used to simulate monthly deforestation in the Amazon Basin from 2002 to 2050 for two scenarios: (1) a "Business-as-Usual" scenario, which considered the deforestation trends across the basin and projected the rates by using historical images and their variations from 1997 to 2002 and then added to that the effect of paving a set of major roads, and (2) a "Governance" scenario, that also considered the current deforestation trends, but assumed a 50% limit imposed for deforested land within each basin's subregion, and that existing and proposed Protected Areas (PAs), play a decisive role in limiting deforestation as well (Soares et al., 2006).The provided data products include one GeoTiff (*.tif) for each year (2002 to 2050) for both model scenarios for a total of 98 files. The files have been compressed in two *.zip files, one for each model scenario. There is also one comma-delimited file that contains the model input data derived from satellite deforestation maps.
VEMAP 1: U.S. Climate Change Scenarios Based on Models with Increased CO2
The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetation type distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. Climate scenarios from eight climate change experiments are included in the data set. Seven of these experiments are from atmospheric general circulation model (GCM) 1xCO2 and 2xCO2 equilibrium runs. These GCMs were implemented with a simple "mixed-layer" ocean representation that includes ocean heat storage and vertical exchange of heat and moisture with the atmosphere, but omits or specifies (rather than calculates) horizontal ocean heat transport. The eighth scenario is from a limited-area nested regional climate model (RegCM) experiment for the U.S. which was supported by the Model Evaluation Consortium for Climate Assessment (MECCA). The CCC and GFDL R30 runs are among the high resolution GCM experiments reported in IPCC (1990). Changes in monthly mean temperature and relative humidity were represented as differences (2xCO2 climate value - 1xCO2 climate value) and those for monthly precipitation, solar radiation, vapor pressure, and horizontal wind speed as change ratios (2xCO2 climate value/1xCO2 climate value). GCM grid point change values were derived from archives at the National Center for Atmospheric Research (NCAR; Jenne 1992) and spatially interpolated to the 0.5 degree VEMAP grid. Wind speed changes are for the lowest model level. For GISS runs, we calculated winds from vector components and then determined the change ratio. Values from the 60-km RegCM grid were reprojected to the 0.5 degree grid. Vapor pressure (and relative humidity) were not available for the CCC run; relative humidity changes were not determined for the RegCM experiment. A key issue in the generation of altered climates based on climate model output is the strong possibility of physical inconsistencies in the new climates. Change ratios from the NCAR archive have an imposed upper limit of 5.0, providing some constraint on these changes. An exception is that the GISS wind speed change ratios do not have this limit imposed (most GISS wind speed change ratios were less than 5). For a discussion of the utility and limitations of using climate model experiment outputs for exploring ecological sensitivity to climate change, see Sulzman et al. (1995). The 8 climate model experiments are: CCC - Canadian Climate Centre (Boer, McFarlane, and Lazare 1992) GISS - Goddard Institute for Space Studies (Hansen et al. 1984) GFDL - Geophysical Fluid Dynamics Laboratory. Three experiments: (1) GFDL R15: R15 (4.5 degree by 7.5 degree grid) runs without Q- flux corrections (Manabe and Wetherald, 1987). (2) GFDL R15 Q-flux: R15 resolution (4.5 degree by 7.5 degree grid) runs with Q-flux corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990). (3) GFDL R30: R30 (2.22 degree by 3.75 degree grid) run with Q-flux corrections (Manabe and Wetherald 1990, Wetherald and Manabe 1990). OSU - Oregon State University (Schlesinger and Zhao 1989) UKMO - United Kingdom Meteorological Office (Wilson and Mitchell 1987) RegCM (MM4) - National Center for Atmospheric Research (NCAR) nested regional climate model (climate version of the Pennsylvania State University/NCAR mesoscale model MM4; Giorgi, Brodeur and Bates 1994). Conterminous U.S. simulations were on a 60-km interval grid and were driven by 1x and 2xCO2 equilibrium GCM runs (Thompson and Pollard 1995a, 1995b). 1x and 2xCO2 RegCM runs were each 3 years in length. Climate changes were based on averages for these runs. A complete users guide to the VEMAP Phase I database which includes more information about this data set can be found at ftp://daac.ornl.gov/data/vemap-1/comp/Phase_1_User_Guide.pdf. ORNL DAAC maintains additional information associated with the VEMAP Project. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 2002. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.
Pearl River Delta FVCOM model sea level rise scenarios
<p>Model data presented in: De Dominicis, M., Wolf, J., Jevrejeva, S., Zheng, P., Hu. Z. "Future interactions between sea level rise, tides and storm surges in the world's largest urban area", Geophysical Research Letters (2020).</p> <p>An FVCOM (Finite Volume Community Ocean Model) implementation for the South China Sea and the Pearl River Delta is used to understand how future sea level rise, tides and typhoon storm surges can interact and affect coastal inundation. Four future sea level rise scenarios have been analyzed: 0.3 m, 0.5 m, 0.9 m and 2.1 m. Two typhoons that impacted the PRD have been modelled: Hato (2017) and Mangkhut (2018).</p> <p>The dataset consists of water elevation data for 40 model experiments:</p> <ul> <li>EXP01-10 allow to study tide and sea level rise interactions. The Pearl River Delta model has been run for 1 month forced only by tides and sea level rise at the model boundary.</li> <li>EXP11-15 (for Hato) and EXP26-30 (for Mangkhut) allow the study of tide, surge and sea level rise interactions; in these experiments the model has been fully forced by atmospheric forcing, tides and sea level rise.</li> <li>EXP16-20 (for Hato) and EXP31-35 (for Mangkhut) allow the study of tide and sea level rise interactions and are needed for the calculation of the surge, the model has been forced by tides and sea level rise only at the model boundary. The surge can be calculated as the difference between the fully forced run (EXP11-15 and EXP26-30) and the corresponding tide-only forced run (EXP16-20 and EXP31-35) for a given sea level rise.</li> <li>EXP21-25 (for Hato) and EXP35-40 (for Mangkhut) allow the study of the surge and sea level rise interactions only (no tides); for these experiments the model has been forced by atmospheric forcing and sea level rise at the model boundary.</li> </ul>
Modelled spatiotemporally explicit fish densities at different fisheries management scenarios
<ol> <li>Conflicts of interest between resource extraction and conservation are widespread, and negotiating such conflicts or trade-offs is a key issue for ecosystem managers. One such trade-off is resource competition between fisheries and marine top predators. Managing this trade-off has so far been difficult due to a lack of knowledge regarding the amount and distribution of prey required by top predators.</li> <li>Here, we develop a framework that can be used to address this gap: a bio-energetic model linking top predator breeding biology and foraging ecology with forage fish ecology and fisheries management.</li> <li>We apply the framework to a Baltic Sea colony of common guillemots <i>Uria aalge</i> and razorbills <i>Alca torda</i>, two seabird species sensitive to local prey depletion, and show that densities of forage fish (sprat <i>Sprattus sprattus</i> and herring <i>Clupea harengus</i>) corresponding to the current fisheries management target B<sub>MSY</sub> are sufficient for successful breeding. A previously proposed fisheries management target for conserving seabirds, 1/3 of historical maximum prey biomass (B<sub>1/3</sub>), was also sufficient.</li> <li>However, the results highlight the importance of maintaining sufficient prey densities in the vicinity of the colony, suggesting that fine-scale spatial fisheries management is necessary to maintain high seabird breeding success.</li> <li>Despite foraging on the same prey, razorbills could breed successfully at lower prey densities than guillemots but needed higher densities for self-maintenance, emphasizing the importance of considering species-specific traits when determining sustainable forage fish densities for top predators.</li> <li> <i>Synthesis and application.</i> Our bio-energetic modelling framework provides spatially explicit top predator conservation targets that can be readily integrated with current fisheries management. The framework can be combined with existing management approaches such as Dynamic Ocean Management, Marine Spatial Planning and Management Strategy Evaluation to inform ecosystem-based management of marine resources.</li> </ol>
Implementation of Inverse Model Policy based on KineNN in Physical Robot for Pick and Place Scenario
<p>Inverse Model Policy based on KineNN was trained in a simulation. After that, the policy is evaluated with the physical robot. The policy is used to drive the robot to specific target such as: Pick Position, Safe Pick Position, Place Position, Safe Place Position. </p>
Climate-economy model scenario runs and sensitivity analysis using DICE-2016
<p>This paper assesses the prospects for climate stabilization from both positive and normative economic perspectives, and with an eye to the conditions necessary for collective action across the three domains: domestic, international, and intergenerational. While it is well-established that international freeriding and transaction costs pose major impediments to successful environmental agreements, this analysis identifies the intergenerational domain as the source of intractability due to long delays between enduring mitigation costs and enjoying their eventual climate benefits. This lag causes the net benefits for median-aged voters' to be negative over their expected remaining lifespans. Drawing on estimates from several Integrated Assessment Models of the benefits and costs of climate stabilization actions, the analysis concludes that a program of domestic and international climate actions will be hopelessly stymied by the failure of the actions to pass individual and collective rationality tests. However, the dire implications of this conclusion leaves the door open to the possibility that some change in circumstances could undercut this conclusion. The assignment of rights, in particular, has that potential. Indeed, these circumstances echo the canonical insights from Ron Coase's observation in The Problem of Social Cost (1960) that the arrangement of rights can have large effects on welfare when transaction costs for an externality are high. Current climate rights amount to a de facto open access right to pollute the atmosphere. Were a right to a stable climate for both for current and future generations recognized, added weight or leverage would apply in support of climate stabilization policies and international agreements. These legal changes could represent a counterweight to offset the inadequacy of support from the current self-interested generation. Indeed, some recent climate litigation argues that many nations' constitutions already encompass an affirmative right to a stable climate, a proposition that could represent an inimitable means to break the climate impasse.</p>
The Effect of Benner's Jigsaw Teaching Technique Based on the Novice to Expert Model and Scenario-Based Peripheral Intravenous Catheterization Instruction on Skill Acquisition of Nursing Students: An
ClinicalTrials.gov study NCT07298681. IPD Sharing: NO. Countries: 1. Publications: 0.
Modelled spatiotemporally explicit fish densities at different fisheries management scenarios
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Climate-economy model scenario runs and sensitivity analysis using DICE-2016
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Future land use/cover change (LUCC) simulated by FLUS and SD model under four SSP-RCP scenarios
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Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)
<p>Parameters and Sets files for an updated model built for Italy's energy system model (Electricity, Heat and Hydrogen), with 6 region resolution simulation under PNIEC2019 scenario. (April 2024)</p> <p>* Data are designed as an input for Hypatia Modelling Framework</p> <p>** Model was develeped for master thesis study " Investigating the regional contributions to the Italian decarbonization: an Energy Modelling multi-regional approach." C. Lo Guidice, F. Cruz, K. Gad, E. Colombo</p>
Permafrost model for the Argentinian Andes - Results and climatic scenarios
<p><strong>Supplementary information to the following publication: </strong></p> <p><strong>Tapia Baldis C, Trombotto Liaudat D. 2020. Permafrost debris-model in Central Andes of Argentina (28°-33° S). Cuadernos de Investigación Geográfica 46, <a href="http://doi.org/10.18172/cig.3802">http://doi.org/10.18172/cig.3802</a></strong></p> <p>To predict regional-scale spatial patterns of permafrost occurrence, especially over remote environments with limited data, empiric-statistical models are widely used. This kind of approach correlates permafrost occurrence with topo-climatic factors (altitude, geographic position, slope, aspect, air temperature, ground temperature, solar radiation, etc.) easily available, in some cases. Different combinations of empiric-statistical models were tested to evaluate the permafrost spatial distribution in the study area.</p> <p>The study area (28° to 33°S and 70°30’ to 69°W) comprises the middle portion of the South American (Argentinian side) Central Andes (17°30’ to 35°S), named Dry Andes. The landscape is expressed as mountain ranges and valleys with 50% of the terrain surface above 3000 m a.s.l. The highest elevations are represented by mountain peaks such us Mercedario (6850 m a.s.l.) or La Ramada (6400 m a.s.l.). The Dry Andes could be further separated into Desert Andes (17°30’ to 31°S) and Central Andes (31° to 35°S), according to precipitation rates and landscape geomorphological characteristics. </p> <p>Models were trained in a calibration area to evaluate the correlation between geomorphological permafrost indicators (named explanatory variable) and the topoclimatic parameters (predictive variable). A logistic regression model with a logit link function was chosen as a mathematical approach.</p> <p>Data for model calibration was obtained from the Bramadero river basin, located at 31°50’ S and 70°00’ W in the Central Andes. From a geomorphological point of view, the landscape of the Dry Andes is characterized by the interdigitation of glacial, periglacial, alluvial, fluvial, and gravitational processes. The Bramadero river basin was largely glaciated during the LGM, even today it is possible to recognize erosive forms and glacial deposits all over the main valley and subordinated creeks. Even though Quaternary glacial stages modeled the landscape; periglacial features prevail today. Currently, periglacial processes are active in elevations exceeding 2700 m a.s.l. (lowest limit of seasonal freezing), however, a wide variety of periglacial deposits and permafrost indicating cryoforms occur between 3400 and >4500 m a.s.l. (permafrost periglacial belt).</p> <p>The complete geomorphological characterization of the Bramadero river basin and the geomorphometric data extracted from every kind of landform were used to set up the permafrost predictive categories. The first predictive category (presence) includes geoforms that indicate current permafrost, such as; active rock glaciers, inactive rock glaciers, protalus lobes, cryoplanation surfaces, and perennial snow patches. The second category (absence) includes geoforms without current permafrost (relict or fossil rock glaciers, bedrock outcrops, glacial abrasion surfaces, debris/mud flows, and Andean wetlands/peatlands types). It also includes geoforms where the presence of permafrost could not be certainly assessed such us: frozen and unfrozen talus slopes, glaciers and covered glaciers, moraines and morainic complexes, debris/snow avalanches, rock avalanches, and rock slides.</p> <p><strong>The following link can accede data from the calibration area: </strong></p> <p><strong>Tapia Baldis, Carla. (2018). Permafrost model for the Argentinian Andes - Calibration data set [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7229569">https://doi.org/10.5281/zenodo.7229569</a></strong></p>
Code and data from : Using a spatially explicit population model to evaluate cost-effective management scenarios for an invasive deer population
<p>This record contains the following:</p> <p>-"WoJ SEPM.Rmd": Script used to build our spatially explicit population model, run simulations for our scenarios and calculate population summary statistics</p> <p>-"WoJ cpue.Rmd": Script used to run our catch-per-unit-effort model that estimates relationship between deer density and number of deer shot per hour</p> <p>-"Cost estimates.Rmd": Script used to calculate costs for each scenario</p> <p>-"Costs functions.R": Functions that are called in the "Cost estimates" script.</p> <p> </p> <p>In addition, all datafiles required to run the scrips are included here.</p>
Energy Consumption Reduction in Historic Urban Residential Sectors: A Case Study of Kyoto City Using Bottom-Up Modeling and Future Climate Scenarios
<p>This dataset is a detailed simulation result of 21 scenarios in the paper. The results include:</p> <ol> <li>Annual daily energy consumption data divided by energy source and residential type.</li> <li>Photovoltaic power generation data, direct photovoltaic use, battery use, and photovoltaic power generation consumed by apartment houses and Kyomachiya through P2C systems. </li> <li>Annual daily net energy consumption data divided by energy source and residential type.</li> </ol>
Cycles agroecosystem model weather files under nuclear winter scenarios
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A Possibilistic Simulation Model for Multiplayer Game Scenarios Using CPN Tools
<p>Apresentação para o SBES 2020 </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.