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81 results for “climate change scenarios”
Data and code of Land use scenario for 'Development of common socio-economic scenarios for climate change impact assessments in Japan'
<p>Land use scenario calculation: Executable files, source code files and data files<br> This dataset contains program codes and input data used for reproducing land use scenarios explained in Chapter 5.2 in Yoshikawa et al. (submitted to GMDD).</p> <p>We found a few fatal errors in the following code.<br> These code were fixed from version 2 (http://dx.doi.org/10.5281/zenodo.7090670).<br> /Step3/a01_calc_land_use.py<br> /Step3/a01_calc_land_use_std.py<br> /Step3/a01_calc_land_use_rate.py<br> /Step3/run03.bat</p>
Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 3. Location map of the study area.
Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.
Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 1. High-resolution flow chart of the Random Forest (RF) model.
Laboratory modeling of gap-leaping and intruding western boundary currents under different climate change scenarios
<p>Western boundary currents (WBCs), such as, the Kuroshio and the Gulf Stream, are very intense currents flowing along the western boundaries of the oceans.<br>WBCs -and their respective extensions- have an important effect on climate because of their huge heat transports, the corresponding air–sea interactions and the role they play in sustaining the global conveyor belt. It is therefore very relevant to analyze WBC dynamics not only through observations and numerical modelling, but also by means of laboratory experiments; to this respect several rotating tank experiments have been performed in recent years.<br>The new laboratory experiments proposed here for the Hydralab+ 19GAPWEBS project are aimed at analyzing the interactions of a WBC with gaps located along the western coast. Examples of such processes include the Gulf Stream leaping from the Yucatan to Florida and the Kuroshio leaping, and partly penetrating, through the South and East China Seas and through the wider gap separating Taiwan to Japan. In the experiments the WBC is produced by a horizontally unsheared current flowing over a topographic beta slope; along the western lateral boundary a sequence of gaps of different widths simulate the openings present in the above mentioned locations.</p>
Code and data to reproduce the results of the paper: "Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece"
<p>Code and data to reproduce the results of Knitter et al. (2019): Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece. ERL.</p>
Simulation data of European seabass and meagre growth in Greece (C12A) under climate change scenarios
<p>The dataset contains excel files with the biological predictions for European seabass and meagre generated within ClimeFish C12A. Simulations are done for climate scenarios RCP45 and RCP85 and at three time scales denoting short (2015-2025)-, mid (2025-2035)- and long (2045-2055)- term projections. The temperature data used for the simulations are also included as well as a file containing metadata.</p>
Data from: Predicting range shifts of pikas (Mammalia, Ochotonidae) in China under scenarios incorporating land-use change, climate change, and dispersal limitations
<p><span>Two of the most important forces affecting biodiversity are land-use change (LUC) and global climate change (GCC). Previous studies have modeled their impacts on species separately and together, but few have done so for multiple species with dispersal limitations incorporated into the models.</span></p> <p><span>We integrate species distribution models plus a dispersal model to predict LUC and GCC impacts on the ranges of five species of pikas in the Qinghai-Tibet Plateau region of China. Pikas are sensitive to land-use and climate change, and have limited dispersal abilities.</span></p> <p><span>The predicted impacts of LUC and GCC on pikas vary between species as well as between LUC and GCC projections. Incorporation of dispersal limitations appreciably restricts the amount of colonized habitat. For all five species, the amount of habitat abandoned or colonized when LUC and GCC are modeled together is less than the sum of LUC and GCC modeled separately. Three of the five species experience a net increase in occupied habitat by 2080 relative to their current ranges under all modeled projections. However, relative to a "Dispersal Only" baseline scenario that assumes no environmental change but continued range expansion into suitable, unoccupied habitat, all five species suffer a net loss of occupied habitat by 2080 under some or all projections.</span></p> <p><span>Predictions of future distributions of species based solely on LUC or GCC, as well as predictions assuming additive impacts, can be misleading. Inclusion of dispersal limitations in models markedly alters predicted future distributions of species. The use of a "Dispersal Only" scenario provides a different and perhaps more accurate way to gauge net impacts to species. Future work should consider incorporating all these parameters to better predict the impacts of LUC and GCC on biodiversity.</span></p>
Overtopping events in breakwaters under climate change scenarios [Dataset]. Zenodo
<p>Reliable prediction of wave run-up/overtopping and structure damage is a key task in the design and safety assessment of coastal and harbor structures. Run-up/overtopping and damage must be below acceptable limits, both in extreme and in normal operating conditions, to guarantee the stability of the structure and the safety of people and assets on and behind the structure. The mean-sea-level rise caused by climate change and its effects on wave climate may increase the number and intensity of run-up/overtopping events and make the existing coastal/harbor structures more vulnerable to damage.</p> <p>Accurate estimates, through physical modelling, of the statistics of overtopping waves for a set of climate change conditions, are needed. The research project HYDRALAB+ (H2020-INFRAIA-2014-2015) gathers an advanced network of environmental hydraulic institutes in Europe, which provides access to a suite of environmental hydraulic facilities. They play a vital role in the development of climate change adaptation strategies, by allowing the direct testing of adaptation measures and by providing data for numerical model calibration and validation. The use of physical (scale) models allows the simulation of extreme events as they are now, and as they are projected to be under different climate change scenarios.</p> <p>The enclosed dataset refers to the experimental work developed at LNEC within HYDRALAB+ and considers 2D damage and overtopping tests for a rock armor slope, with four different approaches to represent storms. Data of free surface elevation, overtopping and damage is presented.</p>
Data from: Predicting range shifts of pikas (Mammalia, Ochotonidae) in China under scenarios incorporating land-use change, climate change, and dispersal limitations
Open the record for dataset details and reuse information.
Data from: Expected spatial patterns of alien woody plants in South Africa’s protected areas under current scenario of climate change
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Impacts of anthropogenic emission change scenarios on U.S. water and carbon balances at national and state scales in a changing climate
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Interaction between climate change scenarios and biological invasion reveals complex cascading effects in freshwater ecosystems
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FIGURE 1 in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina
FIGURE 1. General Niche-Environment System Factor Analysis (GNESFA) and Factor Analysis of the Niche, Taking the Environment as the Reference (FANTER) for Pristidactylus species. Left column: grey points show the distribution of the RUs (here the pixels) on the axes found by the analysis and black points correspond to the RUs used by the species. Right column: correlations between the environmental variables and the axes. References: P. achalensis A–B; P. nigroiugulus C–D.
FIGURE 5 in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina
FIGURE 5. Area models for suitability habitat from the averaged replications output for: P. achalensis, A) Present model = 5008.55 km², B) Model for 2050 RCP 45 = 4054.00 km², C) Model for 2050 RCP 85 = 2677.83 km²; P. nigroiugulus, 2) Present model = 71957.34 km², E) Model for 2050 RCP 45 = 56162.45 km², F) Model for 2050 RCP 85 = 38501.27 km². References: Country / province names, protected areas perimeters dashed-green lines, protected areas intersected with suitable areas filled in solid green, localities in red dots, and defined accessible area (M) in the upper left box.
FIGURE 4 in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina
FIGURE 4. True skill statistic (TSS) performed on the replicates for each species. References: mod, number of model replicate; values close to 1 indicates perfect agreement, values near zero indicates a performance no better than random.
FIGURE 3. RUs histograms for P in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina
FIGURE 3. RUs histograms for P. nigroiugulus. The white columns show the distributions of available RUs, whereas grey columns show the distributions of used RUs.
FIGURE 2. RUs histograms for P in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina
FIGURE 2. RUs histograms for P. achalensis. The white columns show the distributions of available RUs, whereas grey columns show the distributions of used RUs.
A companion dataset to the paper Scenarios of future climate zone changes in Europe based on EURO-CORDEX regional model ensemble by Holtanová et al., to be submitted to Regional Environmental Change
<p>The content of the dataset is described in the metadata.txt file. </p>
Responses of Surface Evaporative Fluxes in Montane Cloud Forests to the Climate Change Scenario
<p>CL_surfobs_raw.mat and LHC_nodew_new_raw.mat are the analyzed CLM simulation output with atmospheric observations in Chi-Lan and Lien-Hua-Chih as input forcings.</p> <p>CLatm_LHC_prec*.mat are the analyzed CLM simulation output in sensitivity tests for the rainfall pattern.</p> <p>CL_*_transform_raw.mat are the analyzed CLM simulation output in CTL simulation and climate change sensitivity tests. </p> <p>Fig_*.m are matlab code files to reproduce figures in the article.</p> <p>Fig_11_ttest.m and Fig_11_LE_p_value.mat are the t-test for the decrease of latent heat flux in the diurnal cycle under climate change scenario and the resulting p-value.</p> <p>Fig_quantile.m and Fig_4_6_7_8_9_10_11_quantile.mat are to obtain first and third quantile of the changes of canopy water and surface heat fluxes in the diurnal cycle under climate change scenarios.</p>
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Allen Brain Atlas
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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.