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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...
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,
The South Florida Water Management District (SFWMD) and the U.S. Geological Survey (USGS) have evaluated projections of future droughts for south Florida based on climate model output from the Multivariate Adaptive Constructed Analogs (MACA) downscaled climate dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The MACA dataset includes both Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5). A Portable Document Format (PDF) file is provided which presents boxplots of future overall drought-event characteristics based on 6-mo. and 12-mo. averaged balance anomaly timeseries derived from climate models downscaled by the MACA method assuming the Kruijt stomatal resistance curve...
The South Florida Water Management District (SFWMD) and the U.S. Geological Survey (USGS) have evaluated projections of future droughts for south Florida based on climate model output from the Multivariate Adaptive Constructed Analogs (MACA) downscaled climate dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The MACA dataset includes both Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5). A Microsoft Excel workbook is provided which tabulates mean future (2056-95) anomalies derived from climate models downscaled by the MACA method assuming historical-standard stomatal resistance for four regions: (1) the entire South Florida Water Management District (SFWMD), (2) the Lower...
Categories: Data;
Tags: Florida,
Florida,
South Florida Water Management District,
Southern Florida,
climate,
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in the year 2040) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates projected future...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in the year 2040) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in the year 2040) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in the year 2040) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in the year 2040) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in the year 2040) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change...
We apply a monthly water-balance model (MWBM) to simulate components of the water balance for the period 1950-2099 under RCP4.5 and RCP8.5 for the Contiguous United States. We use the statistically downscaled MACAv2-METDATA temperature and precipitation data from 20 General Circulation Models (GCMs) from the Climate Model Intercomparison Program Phase 5 (CMIP5) as input to the water balance model. This dataset supports the USGS National Climate Change Viewer. The statistically downscaled dataset is: MACAv2-METDATA: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by METDATA, Abatzoglou, 2013) Users interested in the downscaled temperature and precipitation files are referred to...
The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates...
Categories: Data;
Tags: Florida,
Florida,
South Florida Water Management District,
Southern Florida,
climatologyMeteorologyAtmosphere,
The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided which tabulates quantiles of change factors derived from various...
Categories: Data;
Tags: Florida,
Florida,
South Florida Water Management District,
Southern Florida,
climatologyMeteorologyAtmosphere,
With increasing concerns about the impact of warming temperatures on water resources, more attention is being paid the relationship between runoff and precipitation, or runoff efficiency. Temperature is a key influence on Colorado River runoff efficiency, and warming temperatures are projected to reduce runoff efficiency. Here, we investigate the nature of runoff efficiency in the upper Colorado River (UCRB) basin over the past 400 years, with a specific focus on major droughts and pluvials, and to contextualize the instrumental period. We first verify the feasibility of reconstructing runoff efficiency from tree-ring data. The reconstruction is then used to evaluate variability in runoff efficiency over periods...
Categories: Data;
Tags: Climatology,
Climatology,
Colorado River,
Hydrology,
Lees Ferry (historical),
These data were compiled using a new multivariate matching algorithm that transfers simulated soil moisture conditions (Bradford et al. 2020) from an original 10-km resolution to a 30-arcsec spatial resolution. Also, these data are a supplement to a previously published journal article (Bradford et al., 2020) and USGS data release (Bradford and Schlaepfer, 2020). The objectives of our study were to (1) characterize geographic patterns in ecological drought under historical climate, (2) quantify the direction and magnitude of projected responses in ecological drought under climate change, (3) identify areas and drought metrics with projected changes that are robust across climate models for a representative set of...
These data were compiled to allow further understanding of how aboveground net primary production of different plant functional types in ecosystems along an elevation gradient in the southwestern U.S. respond to extreme changes in warm-season precipitation (drought and water addition) associated with the North American Monsoon. The objectives of the study were to 1) determine how primary production responds to warm-season precipitation extremes over time; 2) compare production sensitivities to warm-season precipitation (slopes of production – precipitation relationships) across an elevation gradient; 3) evaluate whether the sensitivity of production differed under extreme dry and wet years compared to ambient precipitation....
These BioLake raster data provide global estimates (~10.0 x 12.4 km resolution) of twelve bioclimatic variables based on estimated lake temperature. Eleven of these twelve variables (BioLake01 - BioLake11) are estimated for each of three lake strata: lake mix (surface) layer, lake bottom, and total lake water column. These eleven variables correspond to CHELSA (Climatologies at high resolution for the earth's land surface areas) bioclimatic variables BIO1 - BIO11, except that these BioLake variables are based on lake water temperature and CHELSA BIO1 - BIO11 variables are based on air temperature. CHELSA BIO is also calculated a finer spatial resolution (~1 x 1 km). The twelfth variable (BioLake20; months with non-zero...
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