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223 results for “future changes”
Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona
<p>This dataset contains the simulation results of the combined effects of future urban growth and climate change on irrigation water use in the Phoenix Metropolitan Area, central Arizona. The simulation is conducted with the Variable Infiltration Capacity (VIC) model at 1-km, hourly resolution from 1981-2100 and aggregated to 30-yr average in this dataset. </p> <p>The 30-yr average results are compressed and organized into three files: <strong>Baseline</strong>, <strong>ICLUS2050</strong>, and <strong>ICLUS2100</strong>. The Baseline file contains results using the historical land cover map (year 2010). The <strong>ICLUS2050</strong> and <strong>ICLUS2100</strong> contain results using future land cover maps. The filename of modeling results contains the associated land cover and climate change scenario as follows: "fluxes.irri.ICLUS_<em>$YEAR</em>_<em>$LCSCE</em>.<em>$CLSCE.$GCM</em>.nc", where <em>$YEAR</em> is the year of land cover change projection (2050 or 2100), <em>$LCSCE</em> is the land cover change scenario (SSP2 or SSP5), <em>$CLSCE</em> is the climate change scenario (RCP45 or RCP85), and <em>$GCM</em> is the GCM used (eight in total) </p> <p>More details can be found on the associated paper (this record will be updated when the paper is published):</p> <p>Wang, Z., and Vivoni, E.R. 2021. Combined Effects of Future Urban Growth and Climate Change on Irrigation Water Demand in Central Arizona. <em>Journal of the American Water Resources Association (in revision)</em>.</p>
Maps of relevant snow and frost parameters for the analysis of future vegetation changes in Swiss forests
<p>The latest climate scenarios for Switzerland (CH2018) predict significant changes in temperature and precipitation in the future. This dataset contains maps snow and frost parameters calculated from climate variables that might have significant influence on future forest development. The following factors were calculated for the reference period 1981 - 2010 and the future period 2070 - 2099: Mean first and mean last frost day of the year, date of freezing and thawing of the ground, start and end of constant snow layer, risk of drought caused by frozen ground, wet snow intensity, probability of tree damages because of wet snow. Three different results were calculated for the period 2070 - 2099, based on three different representative concentration pathways (RCP2.6, RCP4.5, RCP8.5, CH2018). Explanations concerning model structure, usability and uncertainties in the data sets can be found in the project report.</p>
Tropical and Subtropical Pacific Sources of the Asymmetric El Niño La Niña Decay and their Future Changes
<p>The model data output supporting the figures shown in the manuscript.</p> <p>For further details, please see</p> <p>Jiepeng Chen, Jin-Yi Yu, Sheng Chen, Xin Wang, Ziniu Xiao, Shih-Wei Fang (2022). Tropical and Subtropical Pacific Sources of the Asymmetric El Niño La Niña Decay and their Future Changes. Accepted at Geophysical Research Letters.</p>
Data from: Changes in waterfowl migration phenologies in central North America: implications for future waterfowl conservation
<p>Globally, migration phenologies of numerous avian species have shifted over the past half-century. Despite North American waterfowl being well researched, published data on shifts in waterfowl migration phenologies remain scarce. Understanding shifts in waterfowl migration phenologies along with potential drivers is critical for guiding future conservation efforts. Therefore, we utilized historical (1955–2008) nonbreeding waterfowl survey data collected at 21 National Wildlife Refuges in the mid- to lower portion of the Central Flyway to summarize changes in spring and autumn migration phenology. We examined changes in the timing of peak abundance from survey data at monthly intervals for each refuge and species (or species group; n = 22) by year and site-specific temperature for spring (Jan–Mar) and autumn (Oct–Dec) migration periods. For spring (n = 187) and autumn (n = 194) data sets, 13% and 9% exhibited statistically significant changes in the timing of peak migration across years, respectively, while the corresponding numbers for increasing temperatures were 4% and 9%. During spring migration, ≥80% of significant changes in the timing of spring peak indicated advancements, while 67% of significant changes in autumn peak timing indicated delays both across years and with increasing temperatures. Four refuges showed a consistent pattern across species of advancing spring migration peaks over time. Advancements in spring peak across years became proportionally less common among species with increasing latitude, while delays in autumn peak with increasing temperature became proportionally more common. Our study represents the first comprehensive summary of changes in spring and autumn migration phenology for Central Flyway waterfowl and demonstrates significant phenological changes during the latter part of the twentieth century.</p>
Forest land under different scenarios of future global change
<p>Forest loss is one of the most threats to biodiversity, forested lands drive a key role in the climate earth system that also affects species diversity and ecosystem services (Vale et al. 2021). Therefore, several initiatives had been driven to map land-use time series, mainly projections into the future (Chen et al. 2020 and reference therein). The availability of those products is valuable for earth science, ecology, conservation, and other research fields once land-use land-cover data are important predictors of species occurrence and biodiversity threat (Ruiz-Benito et al. 2020., Valet et al. 2021) </p> <p>A recent land-use product called <a href="https://www.nature.com/articles/s41597-020-00669-x">GCAM-Demeter</a> presents the highest global spatial resolution (0.05 º) until now (Chen et al. 2020). It provided current and future projections (2015-2100) under different scenarios of climate change (Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCP) ) and according to the most recent framework of Coupled Model Intercomparison Project phase 6 (CMIP6). The data in each year include grid-explicit fraction (in percent) of each of the 32 plant functional types (PFTs) that are widely used in current Earth system models. The complete dataset is available in five General Circulation Models (GCMs): gfdl, hadgem, ipsl, miroc, and noresm. Also includes the mean and standard deviation of those GCMs (Chen et al. 2020).</p> <p>Although the valuable contribution of the GCAM-Demeter to provide those data, it is compressed in NetCDF format, a complex file format that needs management to become usable in several analyses, especially in ecology and biodiversity analyses (Vale et al., 2021 and reference therein). Here I managed the outputs of the mean of five GCMs (harmonized projection) based on the sum analysis of plant functional types considering:</p> <p>1- Global Extent at 0.05-degree resolution </p> <p>2- Years 2020, 2030 and 2050</p> <p>3- SSPs and RCP as follow: SSP1_RCP2, SSP2_RCP45, SSP4_RCP6, SSP5_RCP85</p> <p>The goals are to assess quantitatively the forest lands under different scenarios of global change and make these data available in the Tag Image File Format (TIFF) which is a more friendly and useable format to incorporate in several spatial analyses, mainly in ecology and biodiversity studies for conservation purposes.</p> <p><strong>Methods</strong></p> <p>I downloaded the GCAM-Demeter NetCDF files (the outputs of the mean of five GCMs and the first version, i.e the harmonized projection) freely available at <a href="https://release.datahub.pnnl.gov/released_data/1190">DataHub</a> (Chean et al. 2020). I selected, extracted, and performed the sum analysis of plant functional types (codes PTF1 to PTF11- described in README attached) considering:</p> <p>1- Global Extent at 0.05-degree resolution </p> <p>2- Years 2020, 2030 and 2050</p> <p>3- SSPs and RCP as follow: SSP1_RCP2, SSP2_RCP45, SSP4_RCP6, SSP5_RCP85</p> <p>The data manipulation and analysis were done using ncdf4 and raster packages in the R environment (R Core Team 2020, Pierce 2019; Hijmans et al. 2020). The outputs range from 0 to 100 and can be identified by their file name, for example: 2020_SSP5_RCP85_Forest_GCAM-Demeter_GCMsMean_Harmonized.tif . Also, outputs are provided in the Tag Image File Format (TIFF) which is a more friendly and useable format (Vale et al. 2021 and references therein). The methods and the quantitative results for forested areas are detailed better in the <a href="https://github.com/Tai-Rocha/Forest_Scenarios.github.io">GitHub repository</a> .</p> <p><strong>Acknowledgments</strong>.</p> <p>This initiative was possible due to the high-quality data maintained and made publicly available by GCAM-Demeter authors. Also, the study was developed within the scope of the Earth System Modeling Program funded by CAPES (Coordination for the Improvement of Higher Education Personnel - Grant No. 88887.373031/2019-00) </p> <p> </p> <p><strong>References</strong></p> <p>Chen, M., Vernon, C. R., Graham, N. T., Hejazi, M., Huang, M., Cheng, Y., & Calvin, K. (2020). Global land use for 2015–2100 at 0.05 resolution under diverse socioeconomic and climate scenarios. <em>Scientific Data</em>, <em>7</em>(1), 1-11. </p> <p>Hijmans, R. J. (2020). raster: Geographic Data Analysis and Modeling (R package version 3.3-13)[Computer software]. <em>Retrieved form https://CRAN. R-project. org/package= raster</em>.</p> <p>Pierce, D. (2019). ncdf4: Interface to Unidata netCDF (Version 4 or earlier) Format Data Files. R package version 1.16.</p> <p>Ruiz-Benito P, Vacchiano G, Lines ER, Reyer CP, Ratcliffe S, Morin X, Hartig F, Mäkelä A, Yousefpour R, Chaves JE, Palacios-Orueta A. Available and missing data to model impact of climate change on European forests. Ecological Modelling. 2020 Jan 15;416:108870.</p> <p>Team, R. C. (2020). R: A language and environment for statistical computing.</p> <p>Vale, M. M., Lima-Ribeiro, M. S., & Rocha, T. C. (2021). GLOBAL LAND-SE AND LAND-COVER DATA: HISTORICAL, CURRENT AND FUTURE SCENARIOS. Biodiversity Informatics, 16, 2021, pp. 28-38.</p> <p> </p> <p> </p>
Future changes in mangrove coastal water properties
<p>This dataset contains data on future changes in sea-surface water properties along the global distribution of mangrove forests. This includes the coordinates of mangrove occurence and associated information on present (2000-2014) and future (2090-2100) sea-surface temperature (SST), sea-surface salinity (SSS), and sea-surface density (SSD, derived from SST and SSS, using the UNESCO EOS-80 equation of state polynomial for seawater - 'sw_dens.m' in MATLAB). SST and SSS fields from which the data were extracted, are from the Bio-ORACLE database. Additionally, for each of the mangrove occurence points (longitude, latitude), information is provided about the main mangrove biogeographical region and associated province as provided in the Marine Ecoregions of the World dataset (Spalding et al., 2007, <em>BioScience</em>), alowing for a spatial analysis of the data. More information and details about the data are provided in the README sheet of the Excel data file.</p>
Orbitrap analysed non-volatile compound data from blue swimmer crab (Portunus armatus) flesh for manuscript: "Climate-driven changes to taste and aroma determining metabolites in an economically valuable portunid (Portunus armatus) have implications for future harvesting"
<p>Accurate mass measurements of non-volatile metabolites conducted on a Q-Exactive Orbitrap LC-MS (Thermo Scientific, Scoresby, VIC, Australia) equipped with a heated electrospray ionization (H-ESI) source. Source conditions were as follows: spray voltage (positive ion 3.9 kV), sheath gas 60 (arbitrary units), auxiliary gas 10 (arbitrary units) and sweep gas 1 (arbitrary units), capillary temperature of 350 °C and auxiliary gas heating temperature of 400 °C.</p>
Data from: Predicting range shifts of Davidia involucrata Ball. under future climate change
<p>Understanding and predicting how species will respond to climate change is crucial for biodiversity conservation. Here, we assessed future climate change impacts on the distribution of a rare and endangered plant species, Davidia involucrate in China, using the most recent global circulation models developed in the sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC6). We assessed the potential range shifts in this species by using an ensemble of species distribution models (SDMs). The ensemble SDMs exhibited high predictive ability and suggested that the temperature annual range, annual mean temperature, and precipitation of the driest month are the most influential predictors in shaping distribution patterns of this species. The projections of the ensemble SDMs also suggested that D. involucrate is very vulnerable to future climate change, with at least one-third of its suitable range expected to be lost in all future climate change scenarios and will shift to the northward of high-latitude regions. Similarly, at least one-fifthof the overlap area of the current nature reserve networks and projected suitable habitat is also expected to be lost. These findings suggest that it is of great importance to ensure that adaptive conservation management strategies are in place to mitigate the impacts of climate change on D. involucrate.</p>
Data for global agricultural water scarcity assessment incorporating blue and green water availability under future climate change
<p>This dataset is for the publication Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change by Liu et al., 2022 (Earth's Future, doi: <a href="http://doi.org/10.1029/2021EF002567">10.1029/2021EF002567</a>).</p> <p>Three observation-based global meteorological datasets, namely PGMFD v.2, GSWP3, and WFDEI, were used to calculate ETc over the baseline period. The bias-corrected climate projections of four GCMs (namely GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) provided by the ISIMIP phase 2b (ISIMIP2b) were used to calculate the ETc over the future period.</p> <p> </p> <p>Liu, X., Liu, W., Tang, Q., Liu, B., Wada, Y., & Yang, H. (2022). Global agricultural water scarcity assessment incorporating blue and green water availability under future climate change. Earth's Future, 10, e2021EF002567. <a href="https://doi.org/10.1029/2021EF002567">https://doi.org/10.1029/2021EF002567</a></p>
Climate change and energy policy control the future of Myanmar's rivers
<p>This data repository holds input and output data for the paper Jin XY., Chowdhury, A.K., Dang, T.D., Deshmukh, R. and Galelli, S. “Climate change and energy policy control the future of Myanmar's rivers”. See Readme for more details. </p>
Supplementary material 2 from: Dickey JWE, Cuthbert RN, Rea M, Laverty C, Crane K, South J, Briski E, Chang X, Coughlan NE, MacIsaac HJ, Ricciardi A, Riddell GE, Xu M, Dick JTA (2018) Assessing the relative potential ecological impacts and invasion risks of emerging and future invasive alien species. NeoBiota 40: 1-24. https://doi.org/10.3897/neobiota.40.28519
Table S2. Summary of the GB online survey outlining which of the four species of turtle was being sold :
Figure 2 in Niche evolution and diversification in Middle Eastern stream salamanders (Paradactylodon): vulnerability to future climate change
Figure 2. Recent (A: 1970-2000) and future (2081-2100) habitat suitability of Paradactylodon species based on the consensus model under optimistic (B: ssp126) and pessimistic (C: ssp585) scenarios.
Figure 1 in Niche evolution and diversification in Middle Eastern stream salamanders (Paradactylodon): vulnerability to future climate change
Figure 1. Study area. The occurrence records of Paradactylodon species with different colors are shown on the map.
Figure 3 in Niche evolution and diversification in Middle Eastern stream salamanders (Paradactylodon): vulnerability to future climate change
Figure 3. Panels (A-D) illustrate the niche overlap values between two species distribution ranges (see table 3), along the
Data from: Future suitability of habitat in a migratory ungulate under climate change
With climate change, the effect of global warming on snow cover is expected to cause range expansion and enhance habitat suitability for species at their northern distribution limits. However, how this depend on landscape topography and sex in size-dimorphic species remains uncertain, and is further complicated for migratory animals following climate-driven seasonal resource fluctuations across vast landscapes. Using 11 years of data from a partially migratory ungulate at their northern distribution ranges, the red deer (Cervus elaphus), we predicted sex-specific summer and winter habitat suitability in diverse landscapes under medium and severe global warming. We found large increases in future winter habitat suitability, resulting in expansion of winter ranges as currently unsuitable habitat became suitable. Even moderate warming decreased snow cover substantially, with no suitability difference between warming scenarios. Winter ranges will hence not expand linearly with warming, even for species at their northern distribution limits. Although less pronounced than in winter, summer ranges also expanded and more so under severe warming. Summer habitat suitability was positively correlated with landscape topography and ranges expanded more for females than males. Our study highlights the complexity of predicting future habitat suitability for conservation and management of size-dimorphic, migratory species under global warming.
Future fire-driven landscape changes along a southwestern US elevation gradient
<p>Over the 21st century, the combined effects of increased fire activity and climate changes are expected to altered forest composition and structure in many ecosystems by changing post-fire successional trajectories and recovery. The southwestern US mountains encompass varied vegetation types and species according to elevation which do not respond the same to changing climate and fire regime. Moreover, fire exclusion applied during the early 20<sup>th</sup> century has altered forest structure and fuel loads compared to their natural states (i.e. without fire suppression). Consequently, uncertainties persist about future vegetation shifts along the elevation gradient.</p> <p>In this study, we simulated future vegetation dynamics along an elevation gradient in the southwestern US comprising pinyon-juniper woodlands, ponderosa pine forests and mixed conifer forests for the period 2000-2099, to quantify the effects of future climate conditions and projected wildfires on species productivity and distribution.</p> <p>While we expected to find larger changes at low elevation due to warmer and drier conditions, the largest changes occurred at high elevation in mixed-conifer forests and were caused by wildfire. The largest increase in high-severity and large fires were recorded in this vegetation type, leading to high mortality of the dominant species, <i>Picea engelmannii</i> and <i>Abies lasiocarpa</i>, which are not adapted to fire. The loss of these two species reduced biomass productivity at high elevation. In ponderosa pine forests and pinyon-juniper woodlands, fewer vegetation changes occurred due to higher abundance of well-adapted species to fire and the lower fuel loads mitigating projected fire activity, respectively.</p> <p>Thus, future research should prioritize understanding of the processes involved in future vegetation shifts in mixed-conifer forests in order to mitigate the risk of loss of diversity specific to high-elevation forests and the decrease in biomass productivity, and thus carbon storage capacity, of these ecosystems due to wildfires.</p>
Data from: Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change
Boreal forests and arctic tundra cover 33% of global land area and store an estimated 50% of total soil carbon. Because wildfire is a key driver of terrestrial carbon cycling, increasing fire activity in these ecosystems would likely have global implications. To anticipate potential spatiotemporal variability in fire-regime shifts, we modeled the spatially explicit 30-yr probability of fire occurrence as a function of climate and landscape features (i.e. vegetation and topography) across Alaska. Boosted regression tree (BRT) models captured the spatial distribution of fire across boreal forest and tundra ecoregions (AUC from 0.63–0.78 and Pearson correlations between predicted and observed data from 0.54–0.71), highlighting summer temperature and annual moisture availability as the most influential controls of historical fire regimes. Modeled fire–climate relationships revealed distinct thresholds to fire occurrence, with a nonlinear increase in the probability of fire above an average July temperature of 13.4°C and below an annual moisture availability (i.e. P-PET) of approximately 150 mm. To anticipate potential fire-regime responses to 21st-century climate change, we informed our BRTs with Coupled Model Intercomparison Project Phase 5 climate projections under the RCP 6.0 scenario. Based on these projected climatic changes alone (i.e. not accounting for potential changes in vegetation), our results suggest an increasing probability of wildfire in Alaskan boreal forest and tundra ecosystems, but of varying magnitude across space and throughout the 21st century. Regions with historically low flammability, including tundra and the forest–tundra boundary, are particularly vulnerable to climatically induced changes in fire activity, with up to a fourfold increase in the 30-yr probability of fire occurrence by 2100. Our results underscore the climatic potential for novel fire regimes to develop in these ecosystems, relative to the past 6000–35 000 yr, and spatial variability in the vulnerability of wildfire regimes and associated ecological processes to 21st-century climate change.
Figure 4 in Will future anthropogenic climate change increase the potential distribution of the alien invasive Cuban treefrog (Anura: Hylidae)?
Figure 4. Maps of the potential distribution of O. septentrionalis as expected for 2020, 2050 and 2080 assuming A2a and B2a conditions. Maps show mean values of Maxent values derived from models projected onto CCCMA, CISRO and HADCM3 scenarios.
Figure 3 in Will future anthropogenic climate change increase the potential distribution of the alien invasive Cuban treefrog (Anura: Hylidae)?
Figure 3. Comparison between the known distribution of O. septentrionalis (A) (source: Johnson (2007)) and model prediction in Florida (B). Spread history of O. septentrionalis is indicated.
Figure 2 in Will future anthropogenic climate change increase the potential distribution of the alien invasive Cuban treefrog (Anura: Hylidae)?
Figure 2. Potential distribution of O. septentrionalis under current climate conditions within the Caribbean. Higher Maxent values suggest higher climatic suitability. Native records are indicated as points and invasive records as triangles.
ScienceDex guides
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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.