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 ..., , , # Discretized US 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 variables 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 spatially varying variables have been processed to represent weekly averages on the common lattice that is used for drought. The USGS streamflow data is itself a new gridded data product, aggregati...