DOI: 10.5061/dryad.g1jwstqw7
title: Discretized U.S. drought data to support statistical modeling

description: 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. 
description: # 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,
 aggregating point-referenced stream discharges from across the US to the
 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. ## Description
 of the data and file structure The dataset is saved as a CSV file. Each
 row in the spreadsheet corresponds to a grid cell for a particular week.
 The columns correspond to the variables which are described below. Index
 variables: * 'time' is YYYYMMDD * 'grid' is an
 arbitrary naming convention based on lat/lon. Letters index lat, and
 numbers index lon. See RasterizeUSDMForDryad.R for details. *
 'lon' is the longitude of the centroid for the grid cell *
 'lat' is the latitude for the centroid for the grid cell USDM
 variable: * 'drought' is a factor with levels 0, D0, D1, D2, D3,
 D4 and refer to the value of the USDM at the particular time and at the
 grid centroid. NLDAS-2 processed variables: * APCP: weekly total
 precipitation [kg/m2], originally from Forcing File A * EVP:  weekly
 average evapotranspiration [kg/m2], originally from NOAH Land Surface
 Model * LAI: weekly average leaf area index [-], originally from NOAH Land
 Surface Model * PEVAP: weekly average potential evaporation [kg/m2],
 originally from Forcing File A * PEVPR: weekly average potential latent
 heat flux [W/m2], originally from NOAH Land Surface Model * SOILM: weekly
 average soil moisture content [kg/m2], originally from NOAH Land Surface
 Model * SNOD: weekly average snow depth [m], originally from NOAH Land
 Surface Model * SNOM: weekly average snow melt [kg/m2], originally from
 NOAH Land Surface Model * SNOWC: weekly average snow cover fraction [-],
 orginally from NOAH Land Surface Model * SSRUN: weekly average surface
 runoff [kg/m2], originally from NOAH Land Surface Model * TSOIL: weekly
 average soil temperature [K], originally from NOAH Land Surface Model *
 WEASD: weekly average water equivalent of accumulated snow depth [kg/m2],
 originally from NOAH Land Surface Model Streamflow variables: For each
 in-situ stream gauge, 30-year empirical distributions of average 7-day,
 14-day, and 28-day streamflows were computed, and all individual values
 are expressed as averages of percentiles of the corresponding empirical
 distribution. The three variables below show mean percentiles for teach
 time/grid over 7, 14, and 28 days. For any combination where no gauges are
 present within a grid cell, an NA appears. * 'percFlow7day' *
 'percFlow14day' * 'percFlow28day' To infill missing
 data, averages of the same data are taken over watersheds as defined by
 hydrologic unit code (HUC) of increasing sizes needed to infill data. HUC
 8 is the smallest watershed, and HUC6 and HUC4 are larger. These averages
 can be either unewighted (e.g. avg.HUC.7day), or weighted by inverse
 distance between each gauge and the grid cell centroid (e.g.
 avgDist.HUC8.7day). The following are all averages taken of data from
 different lengths (7, 14, 28), HUC (8, 6, 4) and weighting (none of
 avgDist). * 'avg.HUC8.7day' * 'avgDist.HUC8.7day' *
 'avg.HUC6.7day' * 'avgDist.HUC6.7day' *
 'avg.HUC4.7day' * 'avgDist.HUC4.7day' *
 'avg.HUC8.14day' * 'avgDist.HUC8.14day' *
 'avg.HUC6.14day' * 'avgDist.HUC6.14day' *
 'avg.HUC4.14day' * 'avgDist.HUC4.14day' *
 'avg.HUC8.28day' * 'avgDist.HUC8.28day' *
 'avg.HUC6.28day' * 'avgDist.HUC6.28day' *
 'avg.HUC4.28day' * 'avgDist.HUC4.28day'
 Teleconnections variables: * 'pna' is the weekly average of the
 Pacific/North American Pattern * 'nao' is the weekly average of
 the North Atlantic Oscillation * 'ao' is the weekly average of
 the Arctic Oscillation * 'enso' is the weekly average of El Nino
 3.4 index * 'enso14' is the 14-day average of El Nino 3.4 index
 * 'enso28' is the 28-day average of El Nino 3.4 index *
 'enso84' is the 84-day average of El Nino 3.4 index ##
 Sharing/Access information All raw data are publicly and freely available
 at the following: *
 [https://droughtmonitor.unl.edu/DmData/GISData.aspx](https://droughtmonitor.unl.edu/DmData/GISData.aspx) * [https://disc.gsfc.nasa.gov/datasets?keywords=NLDAS](https://disc.gsfc.nasa.gov/datasets?keywords=NLDAS) * [https://waterdata.usgs.gov/nwis](https://waterdata.usgs.gov/nwis) * [https://www.cpc.ncep.noaa.gov/data/indices/wksst9120.for](https://www.cpc.ncep.noaa.gov/data/indices/wksst9120.for) * [https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.pna.index.b500101.current.asci](https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.pna.index.b500101.current.asci) * [https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii](https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii) * [https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.ao.index.b500101.current.ascii](https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.ao.index.b500101.current.ascii) ## Code/Software All code files needed to process the raw data to output this data set can be found on [https://github.com/heplersa/USDMdata](https://github.com/heplersa/USDMdata)