Drought is a natural hazard that inflicts costly damage to the environment and human communities. Although ample literature exists on the climatological aspects of drought, little is known on whether existing drought indices can predict the damages and how different human communities respond and adapt to the hazard. This project examines (1) whether existing drought indices can predict the occurrence of drought events and their actual damages; (2) how the adaptive capacity (i.e., resilience) varies across space; and (3) what public outreach and engagement effort would be most effective for mitigation of risk and impacts. The study region includes all 503 counties in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. This data set was created to examine the first objective of the project. The Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) data, available only at the climate-division level, were downscaled into county-level indices over the 1975-2010 period. The drought damage data, acquired from the Spatial Hazards Events and Losses Database for the United States (SHELDUSTM), were tabulated for the same time period. Statistical correlations were conducted between drought indices and drought damages to test whether these indices accurately represent the drought damage in the study region. This data set contains the two county-level drought indices and drought damage for the period 1975-2010, which should be useful to future related studies.

Drought is a natural hazard that inflicts costly damage to the environment and human communities. Although ample literature exists on the climatological aspects of drought, little is known on whether existing drought indices can predict the damages and how different human communities respond and adapt to the hazard. This project examines (1) whether existing drought indices can predict the occurrence of drought events and their actual damages; (2) how the adaptive capacity (i.e., resilience) varies across space; and (3) what public outreach and engagement effort would be most effective for mitigation of risk and impacts. The study region includes all 503 counties in Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. This data set was created to examine the first objective of the project. The Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) data, available only at the climate-division level, were downscaled into county-level indices over the 1975-2010 period. The drought damage data, acquired from the Spatial Hazards Events and Losses Database for the United States (SHELDUSTM), were tabulated for the same time period. Statistical correlations were conducted between drought indices and drought damages to test whether these indices accurately represent the drought damage in the study region. This data set contains the two county-level drought indices and drought damage for the period 1975-2010, which should be useful to future related studies.

This dataset adds attributes describing the self-calibrated Palmer Drought Severity Index (PDSI) during the observation year of wet/dry streamflow observations collected in the Pacific Northwest, USA. Streamflow observation locations are linked to the nearest National Hydrography Dataset high-resolution (NHD-HR) stream segment to obtain stream order and stream permanence (perennial/non-perennial) from NHD-HR. Additionally, the PDSI and precipitation percentile for 7.5 minute quadrangle map extents, within the extent of the conterminous United States (https://carto.nationalmap.gov/arcgis/rest/services/map_indices/MapServer), during the map survey year are presented. NHD perennial/non-perennial classifications derive from the topographic maps.

This dataset adds attributes describing the self-calibrated Palmer Drought Severity Index (PDSI) during the observation year of wet/dry streamflow observations collected in the Pacific Northwest, USA. Streamflow observation locations are linked to the nearest National Hydrography Dataset high-resolution (NHD-HR) stream segment to obtain stream order and stream permanence (perennial/non-perennial) from NHD-HR. Additionally, the PDSI and precipitation percentile for 7.5 minute quadrangle map extents, within the extent of the conterminous United States (https://carto.nationalmap.gov/arcgis/rest/services/map_indices/MapServer), during the map survey year are presented. NHD perennial/non-perennial classifications derive from the topographic maps.

This dataset contains values of the Palmer Hydrological Drought Index (PHDI) for annual and seasonal periods from 1985-2012. Values are derived from data based on National Climatic Data Center (NCDC) climate divisions, of which there are 344 in the U.S.

This dataset contains values of the Palmer Hydrological Drought Index (PHDI) for annual and seasonal periods from 1985-2012. Values are derived from data based on National Climatic Data Center (NCDC) climate divisions, of which there are 344 in the U.S.

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.