This repository provides weekly US Drought Monitor (USDM) data aggregated to the US Census 2020 county boundaries. This dataset facilitates county-level analysis of drought conditions, supporting research, policy-making, and climate resilience planning.
📂 View the US Drought Monitor Census 2020 county archive listing here.
Federal law and FSA guidance describes a process by which county-level eligibility is determined by the intersection of county boundaries, the United States Drought Monitor weekly drought assessment, and the normal grazing period for each type of grazing land in each county.
📈 About the US Drought Monitor (USDM)
The US Drought Monitor is a weekly map-based product that synthesizes multiple drought indicators into a single national assessment. It is produced by:
- National Drought Mitigation Center (NDMC)
- US Department of Agriculture (USDA)
- National Oceanic and Atmospheric Administration (NOAA)
Each weekly map represents a combination of data analysis and expert interpretation.
The USDM weekly maps depicting drought conditions are categorized into six levels:
- None: Normal or wet conditions
- D0: Abnormally Dry
- D1: Moderate Drought
- D2: Severe Drought
- D3: Extreme Drought
- D4: Exceptional Drought
While USDM drought class boundaries are developed without regard to political boundaries, it is often aggregated by political boundaries to assist in decision-making and for regulatory purposes. This repository focuses on aggregating these data to the county level, enabling more localized analysis and decision-making.
Note: This archive is maintained by the Montana Climate Office, but all analytical authorship of the USDM drought maps belongs to the named USDM authors.
🗂 Directory Structure
usdm-counties-census-2020.R: R script that processes and aggregates weekly USDM shapefiles to county boundaries.data/: Directory containing processed county-level USDM data.README.Rmd: This README file, providing an overview and usage instructions.
Data Sources
- USDM Polygons: Weekly
.parquetfiles from sustainable-fsa/usdm - Census 2020 County Boundaries: Downloaded from the U.S. Census TIGER/Line archive for 2020.
USDM Polygons are reprojected to the same coordinate reference
system as the Census 2020 County Boundaries, geometrically
validated, and saved in .parquet format.
Processing Pipeline
The analysis pipeline is fully contained in
usdm-counties-census-2020.R and
proceeds as follows:
- Install and load dependencies:
- Uses
pak::pak()to ensure fresh, source-built installs of critical geospatial packages.
-
Download Census 2020 county boundary data. -Re-calculate county areas and save as
data/census-2020-counties.parquet -
Download and intersect:
- For each weekly USDM
.parquetfile: - Read county and drought geometries
- Perform spatial intersection
- Calculate the percent of each county area affected by each drought class
- Tabular output is saved to
data/usdm/USDM_{YYYY-MM-DD}.parquet
- Output Structure: Each output file is a non-spatial
.parquetfile with the following fields:
STATEFP,State,COUNTYFP,County,CountyLSADusdm_date: The date of the USDM mapusdm_class: One ofNone,D0,D1,D2,D3,D4usdm_percent: Proportion of the county in this drought class (as a decimal between 0 and 1)
🛠️ Dependencies
Key R packages used:
sfterraarrowtidyversecurl
The script installs all required packages using the
pak package.
📍 Quick Start: Visualize a Weekly County USDM Map in R
This snippet shows how to load a weekly GeoParquet file from the archive
and create a simple drought classification map using sf and ggplot2.
# Load required libraries
library(arrow)
library(sf)
library(ggplot2) # For plotting
library(tigris) # For state boundaries
library(rmapshaper) # For innerlines function
## Get latest USDM data
latest <-
jsonlite::fromJSON(
"manifest.json"
)$path |>
stringr::str_subset("parquet") |>
max()
# e.g., [1] "data/usdm/USDM_2025-05-27.parquet"
date <-
latest |>
stringr::str_extract("\\d{4}-\\d{2}-\\d{2}") |>
lubridate::as_date()
# Get the highest (worst) drought class in each county
usdm <-
latest |>
arrow::read_parquet() |>
dplyr::group_by(STATEFP, COUNTYFP) |>
dplyr::filter(usdm_class == max(usdm_class))
## Load the fsa-lfp-counties parquet file
counties <-
# You can read straight from online
# sf::read_sf("https://sustainable-fsa.github.io/usdm-counties-census-2020/data/census-2020-counties.parquet") |>
# sf::read_sf("data/census-2020-counties.parquet") |>
tigris::counties(cb = TRUE,
year = 2020,
resolution = "5m") |>
dplyr::filter(!(STATEFP %in% c("60", "66", "69", "78"))) |>
# transform to WGS 84
sf::st_transform("EPSG:4326") |>
sf::st_cast("POLYGON", warn = FALSE, do_split = TRUE) |>
tigris::shift_geometry() |>
dplyr::group_by(STATEFP, COUNTYFP) |>
dplyr::summarise(.groups = "drop") |>
sf::st_cast("MULTIPOLYGON")
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usdm_counties <-
usdm |>
dplyr::left_join(counties) |>
sf::st_as_sf()
# Plot the map
ggplot(counties) +
geom_sf(data = sf::st_union(counties),
fill = "grey80",
color = NA) +
geom_sf(data = usdm_counties,
aes(fill = usdm_class),
color = NA) +
geom_sf(data = rmapshaper::ms_innerlines(counties),
fill = NA,
color = "white",
linewidth = 0.1) +
geom_sf(data = counties |>
dplyr::group_by(STATEFP) |>
dplyr::summarise() |>
rmapshaper::ms_innerlines(),
fill = NA,
color = "white",
linewidth = 0.2) +
scale_fill_manual(
values = c("grey80",
"#ffff00",
"#fcd37f",
"#ffaa00",
"#e60000",
"#730000"),
drop = FALSE,
name = "Drought\nClass") +
labs(title = "US Drought Monitor",
subtitle = format(date, " %B %d, %Y")) +
theme_void()
📝 Citation & Attribution
Citation format (suggested):
US Drought Monitor authors and R. Kyle Bocinsky YYYY. US Drought Monitor Weekly Maps Aggregated to Census 2020 County Boundaries. Data processed, curated, and archived by R. Kyle Bocinsky, Montana Climate Office. Accessed via GitHub archive, YYYY-MM-DD. https://sustainable-fsa.github.io/usdm-counties-fsa-lfp/
Acknowledgments:
- Map content by USDM authors.
- Data processing, curation, and archival structure by R. Kyle Bocinsky, Montana Climate Office, University of Montana.
📄 License
- Raw USDM data (NDMC): Public Domain (17 USC § 105)
- Processed data & scripts: © R. Kyle Bocinsky, released under CC0 and MIT License as applicable
⚠️ Disclaimer
This dataset is archived for research and educational use only. The National Drought Mitigation Center hosts the US Drought Monitor. Please visit https://droughtmonitor.unl.edu.
👏 Acknowledgment
This project is part of:
Enhancing Sustainable Disaster Relief in FSA
Programs
Supported by USDA OCE/OEEP and USDA Climate Hubs
Prepared by the Montana Climate Office
📬 Contact
R. Kyle Bocinsky
Director of Climate Extension
Montana Climate Office
📧 kyle.bocinsky@umontana.edu
🌐 https://climate.umt.edu