Filters: Tags: climatologyMeteorologyAtmosphere (X)
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These files include historical downscaled estimates of decadal average monthly snow-day fraction ("fs", units = percent probability from 1 – 100) for each month of the decades from 1900-1909 to 2000-2009 at 771 x 771 m spatial resolution. Each file represents a decadal average monthly mean. Version 1.0 was completed in 2015 Version 2.0 was completed in 2018 These snow-day fraction estimates were produced by applying equations relating decadal average monthly temperature to snow-day fraction to downscaled decadal average monthly temperature. Separate equations were used to model the relationship between decadal monthly average temperature and the fraction of wet days with snow for seven geographic regions in the...
Five principal components are used to represent the climate variation in an original set of 12 composite climate variables reflecting complex precipitation and temperature gradients. The dataset provides coverage for future climate (defined as the 2040-2070 normal period) under the RCP4.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Colorado Plateau,
Great Basin,
PRISM climate data for Wyoming. Data can be accessed through the Geospatial Data Gateway http://datagateway.nrcs.usda.gov/.
Categories: Data;
Types: Downloadable;
Tags: PRISM,
Precipitation,
Wyoming,
climate,
climatologyMeteorologyAtmosphere,
These data were compiled for a manuscript in which 1) we develop a water temperature model for the major river segments and tributaries of the Colorado River basin, including the Colorado, Green, Yampa, White, and San Juan rivers; 2) we link modeled water temperature to fish population data to predict the probability native and nonnative species will be common in the future in a warming climate; and 3) assess the degree to which dams create thermal discontinuity in summer in river segments across the western US. Per goal #1, we developed a water temperature model using data spanning 1985-2015 that predicts water temperature every 1 mile (1.6-km) in rivers both now and in the future due to the potential influence...
Climate Distance Mapper is an interactive web mapping application designed to facilitate informed seed sourcing decisions and to aid in directing regional seed collections. Implemented as a shiny web application (Chang et al. 2017), Climate Distance Mapper is hosted on the web at: https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/. The application is designed to guide restoration seed sourcing in the desert southwest by allowing users to interactively match seed sources with restoration sites climatic differences – in the form of multivariate climate distance values – between restoration sites and the surrounding landscape. Climatic distances are based on a combination of variables likely to influence...
These files include climatological summaries of downscaled historical and projected decadal average monthly snowfall equivalent ("SWE", in millimeters), the ratio of snowfall equivalent to precipitation, and future change in snowfall for October-March at 771-meter spatial resolution across the state of Alaska. **Derived snow variables and summaries. Data are for summary October to March Alaska climatologies for:** 1) historical and future snowfall equivalent ("SWE"), produced by multiplying snow-day fraction by decadal average monthly precipitation and summing over 6 months from October to March to estimate the total SWE on April 1. 2) historical and future ratio of SWE to precipitation ("SFEtoP"), SFEtoP is the...
Five principal components are used to represent the climate variation in an original set of 12 composite climate variables reflecting complex precipitation and temperature gradients. The dataset provides coverage for future climate (defined as the 2040-2070 normal period) under the RCP8.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Colorado Plateau,
Great Basin,
A sensitivity analysis of groundwater-recharge estimates from a water-budget model was completed for the islands of Oahu and Maui, Hawaii (Johnson and others, 2023). Results of the sensitivity analysis were used to quantify the relative importance of selected model parameters to recharge estimates for three moisture zones (dry, mesic, and wet) on Oahu and Maui. This shapefile contains the boundaries of the moisture zones and boundaries of the model subareas that were used in the model simulations for Oahu. The shapefile attribute information includes the names of the land-cover types assigned to model subareas and the mean annual recharge values determined for the model subareas for the baseline scenario of the...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service,
Shapefile;
Tags: Hawaii,
Oahu,
Pacific Islands,
biota,
boundaries,
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster of current predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average). 0=Absence; 1=Presence*see Maxent output pdf for details on model parameters.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and baseline climate conditions, and then projecting these correlations into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-5 show...
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
This dataset is a raster summarizing the change in suitable bioclimate by looking at the difference between current and A2 2050s. Value coding:-3 = Lost bioclimate; 0 = absence (current and future); 1= maintained bioclimate; 4 = gained bioclimate
Some of the NOS rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors' discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal Mean Annual Ground Temperature at 1 Meter Depth (°C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 2km spatial resolution. It represents the A2 emissions scenario and the spatial extent is the NOS REA study area.
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