Discretized U.S. drought data to support statistical modeling

Drought is a costly and disruptive natural disaster, with widespread
implications for agriculture, wildfire, and urban planning.  We
present a novel data set on US drought built to enable computationally
efficient spatio-temporal statistical and probabilistic models of drought.
We converted drought data obtained from the widely-used US Drought Monitor
(USDM) from continuous shape files to a 0.5-degree regular lattice. These
data cover the Continental US from 2000 to mid-2022. Known environmental
drivers of drought include those obtained from the North American Land
Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow
data, and National Oceanic and Atmospheric Administration (NOAA)
teleconnections data. The USGS streamflow data is itself a new gridded
data product, aggregating point-referenced stream discharges from across
the US to a common lattice using watersheds to combine nearby stream data.
The resulting data set permits statistical and probabilistic modeling of
drought with explicit spatial and/or temporal dependence.  Such
models could be used to forecast short-range and even season-to-season
future droughts with uncertainty, extending the reach and value of the
current US Drought Outlook produced by the National Weather Service
Climate Prediction Center.