Monthly drought index for the conterminous United States: 6-month and 36-month Standardized Precipitation Evapotranspiration Index (SPEI) for observed climate data, 1950-2018

Drought is an important stressor affecting forests and rangelands. The Standardized Precipitation Evapotranspiration Index (SPEI) is a meteorological drought index that can be used to investigate trends in drought over time. The SPEI allows for comparisons among locations for historical as well as future conditions, and can be computed over multiple time scales, making it useful for monitoring drought in different ecological contexts. We calculated the 6-month and 36-month SPEI, which assigns values for a given month by comparing the cumulative climatic water balance (precipitation minus potential evapotranspiration, or PET) for the previous 6- or 36-month period to the same cumulative 6- or 36-month water balance for all months in a reference period (defined here as 1950 to 2005). Because of the 6- and 36-month lags in the calculation, the first month in these data sets with data are July 1950 (6-month SPEI) and January 1953 (36-month SPEI). This data publication consists of 2 sets of geoTIFF (TIF) raster files with monthly values for the SPEI for the period 1950-2018. We calculated the SPEI using monthly gridded precipitation, minimum temperature and maximum temperature data from PRISM (Parameter-elevation Regressions on Independent Slopes Model). The spatial extent and resolution of these data match those of the PRISM data set, covering the conterminous United States at a grid cell size of approximately 4 kilometers (1/24 degree) on a side.
These data were developed to assess the recent exposure of U.S. forests and rangelands to meteorological drought in the 2020 Resources Planning Act (RPA) Assessment (https://www.fs.usda.gov/research/inventory/rpaa), chapter on Disturbances to Forests and Rangelands.
These data were published on 03/06/2023. Metadata updated on 10/17/2023 to include reference to published RPA Assessment. For more information about these data, see Costanza et al. (2023, https://doi.org/10.1002/ecs2.4525).