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The difference in Spring temperature (F) between the reference time period of 1980-2014 and the current time period 2015-2019. Winter months include March, April, and May. Data used are sourced from DAYMET, Daily Surface Weather and Climatological Summaries, Oak Ridge National Laboratory. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

The U.S. Climate Reference Network (USCRN) was designed to monitor the climate of the United States using research quality instrumentation located within representative pristine environments. The USCRN hourly standardized soil moisture anomalies and percentiles were evaluated during changes in U.S. Drought Monitor (USDM) status, with a focus on onset, worsening, and improving drought conditions. The purpose of this study was to explore timescales (i.e., 1 to 6 weeks) and soil moisture at individual (i.e., 5, 10, 20, 50, and 100 cm) and aggregated layer (i.e., TOP and COLUMN) depths to determine their alignment with evolving drought conditions. Standardized measures of soil moisture were found to be useful in drought monitoring applications during these critical stages of drought formation and recovery. Indices based on weekly average standardized soil moisture and counts of hours below the 30th percentiles and above the 70th percentile were selected for use as drought indices, and they are provided daily for each layer in this dataset. File names are structured at CRNDI0101-STATIONNAME.csv. DI stands for Drought Indices. The first two digits of the trailing integer indicate major version and the second two digits minor version of the product.

This data release includes data-processing scripts, data products, and associated metadata for a remote-sensing based approach to characterize vegetation sensitivity to droughts from 2000 through 2016 in the U.S. states of Washington, Oregon, and Idaho. Drought sensitivity analysis was conducted in minimally-disturbed (‘intact’) forest and shrub-steppe ecosystems, defined as 1-km pixels (i.e., grid cells) that had not experienced major recent insect mortality or fire. Drought conditions were assessed using the multi-scalar standardized precipitation evapotranspiration index (SPEI), for which positive values indicate wetter that average conditions and negative values indicate drier than average conditions for a given site (Vicente-Serrano and others, 2010). A multi-scalar drought sensitivity index (S’) was developed for two drought intensity levels (L): moderate drought (-1.5 < SPEI ≤ -1) and severe drought (SPEI ≤ -1.5). Vegetation response to droughts was quantified using remotely sensed Enhanced Vegetation Index (EVI) from the Moderate-resolution Imaging Spectroradiometer (MODIS) for summer months (June, July, and August) from 2000 through 2016. EVI is a vegetation index calculated from the blue, red, and near-infrared spectral bands representing atmospherically corrected surface reflectance and has advantages over other similar indices in its abilities to represent areas of dense vegetation (Huete and others, 2002). For each pixel, S’ represents the percent decrease in EVI under drought conditions relative to baseline (non-drought, non-pluvial) conditions. Relationships between S’ and a variety of landscape characteristics representing climatic water balance, topography, soil characteristics, and shallow groundwater availability were examined using Boosted Regression Tree (BRT) modeling, a machine-learning algorithm. For detailed descriptions of data-release components, including analysis methods and modeling, please consult the appropriate metadata documents that accompany the processing scripts and data products.

This data release includes data-processing scripts, data products, and associated metadata for a remote-sensing based approach to characterize vegetation sensitivity to droughts from 2000 through 2016 in the U.S. states of Washington, Oregon, and Idaho. Drought sensitivity analysis was conducted in minimally-disturbed (‘intact’) forest and shrub-steppe ecosystems, defined as 1-km pixels (i.e., grid cells) that had not experienced major recent insect mortality or fire. Drought conditions were assessed using the multi-scalar standardized precipitation evapotranspiration index (SPEI), for which positive values indicate wetter that average conditions and negative values indicate drier than average conditions for a given site (Vicente-Serrano and others, 2010). A multi-scalar drought sensitivity index (S’) was developed for two drought intensity levels (L): moderate drought (-1.5 < SPEI ≤ -1) and severe drought (SPEI ≤ -1.5). Vegetation response to droughts was quantified using remotely sensed Enhanced Vegetation Index (EVI) from the Moderate-resolution Imaging Spectroradiometer (MODIS) for summer months (June, July, and August) from 2000 through 2016. EVI is a vegetation index calculated from the blue, red, and near-infrared spectral bands representing atmospherically corrected surface reflectance and has advantages over other similar indices in its abilities to represent areas of dense vegetation (Huete and others, 2002). For each pixel, S’ represents the percent decrease in EVI under drought conditions relative to baseline (non-drought, non-pluvial) conditions. Relationships between S’ and a variety of landscape characteristics representing climatic water balance, topography, soil characteristics, and shallow groundwater availability were examined using Boosted Regression Tree (BRT) modeling, a machine-learning algorithm. For detailed descriptions of data-release components, including analysis methods and modeling, please consult the appropriate metadata documents that accompany the processing scripts and data products.

This data release includes data-processing scripts, data products, and associated metadata for a remote-sensing based approach to characterize vegetation sensitivity to droughts from 2000 through 2016 in the U.S. states of Washington, Oregon, and Idaho. Drought sensitivity analysis was conducted in minimally-disturbed (‘intact’) forest and shrub-steppe ecosystems, defined as 1-km pixels (i.e., grid cells) that had not experienced major recent insect mortality or fire. Drought conditions were assessed using the multi-scalar standardized precipitation evapotranspiration index (SPEI), for which positive values indicate wetter that average conditions and negative values indicate drier than average conditions for a given site (Vicente-Serrano and others, 2010). A multi-scalar drought sensitivity index (S’) was developed for two drought intensity levels (L): moderate drought (-1.5 < SPEI ≤ -1) and severe drought (SPEI ≤ -1.5). Vegetation response to droughts was quantified using remotely sensed Enhanced Vegetation Index (EVI) from the Moderate-resolution Imaging Spectroradiometer (MODIS) for summer months (June, July, and August) from 2000 through 2016. EVI is a vegetation index calculated from the blue, red, and near-infrared spectral bands representing atmospherically corrected surface reflectance and has advantages over other similar indices in its abilities to represent areas of dense vegetation (Huete and others, 2002). For each pixel, S’ represents the percent decrease in EVI under drought conditions relative to baseline (non-drought, non-pluvial) conditions. Relationships between S’ and a variety of landscape characteristics representing climatic water balance, topography, soil characteristics, and shallow groundwater availability were examined using Boosted Regression Tree (BRT) modeling, a machine-learning algorithm. For detailed descriptions of data-release components, including analysis methods and modeling, please consult the appropriate metadata documents that accompany the processing scripts and data products.

This data release includes data-processing scripts, data products, and associated metadata for a remote-sensing based approach to characterize vegetation sensitivity to droughts from 2000 through 2016 in the U.S. states of Washington, Oregon, and Idaho. Drought sensitivity analysis was conducted in minimally-disturbed (‘intact’) forest and shrub-steppe ecosystems, defined as 1-km pixels (i.e., grid cells) that had not experienced major recent insect mortality or fire. Drought conditions were assessed using the multi-scalar standardized precipitation evapotranspiration index (SPEI), for which positive values indicate wetter that average conditions and negative values indicate drier than average conditions for a given site (Vicente-Serrano and others, 2010). A multi-scalar drought sensitivity index (S’) was developed for two drought intensity levels (L): moderate drought (-1.5 < SPEI ≤ -1) and severe drought (SPEI ≤ -1.5). Vegetation response to droughts was quantified using remotely sensed Enhanced Vegetation Index (EVI) from the Moderate-resolution Imaging Spectroradiometer (MODIS) for summer months (June, July, and August) from 2000 through 2016. EVI is a vegetation index calculated from the blue, red, and near-infrared spectral bands representing atmospherically corrected surface reflectance and has advantages over other similar indices in its abilities to represent areas of dense vegetation (Huete and others, 2002). For each pixel, S’ represents the percent decrease in EVI under drought conditions relative to baseline (non-drought, non-pluvial) conditions. Relationships between S’ and a variety of landscape characteristics representing climatic water balance, topography, soil characteristics, and shallow groundwater availability were examined using Boosted Regression Tree (BRT) modeling, a machine-learning algorithm. For detailed descriptions of data-release components, including analysis methods and modeling, please consult the appropriate metadata documents that accompany the processing scripts and data products.

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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 climate scenarios. These data represent geographic patterns in simulated ecological drought metrics based on SOILWAT2 simulations under climate conditions representing historical (current) time period (1980-2010) and two future projected time periods (2020-2050, d40yrs) and (2070-2100, d90yrs) for two representative concentration pathways (RCP4.5, RCP8.5) as medians across simulation runs based on output from each of the available downscaled global circulation models that participated in CMIP5 (RCP4.5, 37 GCMs; RCP8.5, 35 GCMs; Maurer et al. 2007). Additional information about the setup of SOILWAT2 simulation experiments can be found in Bradford et al. 2020. These data were created in 2020 and 2021 for the area of the sagebrush region in the western North America. These data were created by a collaborative research project between the U.S. Geological Survey and Yale University. These data can be used with the high-resolution matching algorithm (Renne et al., 202X), within the scope of Bradford et al. 2020, and as defined by the study. These data may also be used to evaluate the potential impact of changing climate conditions on robust ecological drought metrics within the scope defined by the study.