USDM County Aggregations

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This repository provides weekly US Drought Monitor (USDM) data aggregated to US Census county boundaries. This dataset facilitates county-level analysis of drought conditions, supporting research, policy-making, and climate resilience planning.

📂 View the US Drought Monitor county archive listing here.

The goal of this repository is to aggregate county-level US Drought Monitor data in a consistent and reproducible way, using authoritative US Census county boundaries. Given the regulatory role played by the interaction between USDM and county boundaries (e.g., 7 CFR 1416.205, 7 CFR 759.5), it is essential that this work be authoritative, well-documented, and available in a persistent, and findable archive established by a Federal agency. This work seeks to create a framework for such an archive, with the ultimate goal of conforming with the Foundations for Evidence-Based Policymaking Act of 2018 (“Evidence Act”, Public Law 115–435), the Geospatial Data Act of 2018 (enacted as part of Public Law 115–254), and Executive Order 14303, Restoring Gold Standard Science.

📈 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.R: R script that processes and aggregates weekly USDM shapefiles to county boundaries.
data/: Directory containing processed county-level USDM data and US Census county boundary files.
README.Rmd: This README file, providing an overview and usage instructions.

Data Sources

USDM Polygons: Weekly .parquet files from sustainable-fsa/usdm
U.S. Census County Boundaries: Downloaded from the U.S. Census TIGER/Line archive for 2000–2024.
For each USDM date, the county boundary vintage used is the most recently available TIGER/Line file as of that year.
For example:
USDM maps from 2000 use 2000 vintage counties
USDM maps from 2010 use 2000 vintage counties
USDM maps from 2011 use 2010 vintage counties
USDM maps from 2014 use 2013 vintage counties
USDM maps from 2024 use 2023 counties (the latest available)

All boundaries are reprojected to EPSG:4326, geometrically validated, and saved in .parquet format.

Processing Pipeline

The analysis pipeline is fully contained in usdm-counties.R and proceeds as follows:

Install and load dependencies:

Uses pak::pak() to ensure fresh, source-built installs of critical geospatial packages.

Download county boundary shapefiles for each year (2000–2024) directly from US Census and store in data/census/raw.
Convert each shapefile to (Geo)Parquet:

Extract and clean up attributes (STATEFP, COUNTYFP, NAME, NAMELSAD)
Encode using UTF-8, fix geometries, cast to MULTIPOLYGON, and calculate area
Output is saved to data/census/parquet/{year}-counties.parquet

Match USDM dates with appropriate county vintage:

Each USDM date is paired with the most recent county boundary file available for that year
The full list of USDM dates is generated starting from 2000-01-04, up to two days before the current date

Download and intersect:

For each weekly USDM .parquet file:
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 .parquet file with the following fields:

STATEFP, State, COUNTYFP, County, CountyLSAD
usdm_class: One of None, D0, D1, D2, D3, D4
percent: Proportion of the county in this drought class (as a decimal between 0 and 1)

🛠️ Dependencies

Key R packages used:

sf
terra
arrow
tidyverse
curl

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))

counties <- 
  tigris::counties(cb = TRUE, 
                   resolution = "5m",
                   progress_bar = FALSE) |>
  dplyr::filter(
    !(STATE_NAME %in% c("Guam", 
                        "American Samoa", 
                        "United States Virgin Islands", 
                        "Commonwealth of the Northern Mariana Islands"))
  ) |>
  sf::st_cast("POLYGON", warn = FALSE, do_split = TRUE) |>
  tigris::shift_geometry()

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("#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. US Drought Monitor Weekly Maps. Data curated and archived by R. Kyle Bocinsky, Montana Climate Office. Accessed via GitHub archive, YYYY. https://sustainable-fsa.github.io/usdm/

Acknowledgments:

Map content by USDM authors.
Data curation and archival structure by R. Kyle Bocinsky, Montana Climate Office, University of Montana.

📄 License

Raw USDM data (NDMC): Public Domain (17 USC § 105)

⚠️ 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