Datasets:

skyan1002
/

flash-flood-benchmark-data

license: cc-by-4.0
pretty_name: CONUS Flash-Flood Benchmark (L1-L3)
tags:
  - hydrology
  - flash-flood
  - benchmark
  - usgs
  - noaa
  - mrms
  - streamflow
size_categories:
  - 10K<n<100K
configs:
  - config_name: l1_episodes
    data_files:
      - split: train
        path: catalog/l1_episodes.parquet
  - config_name: l1_locations
    data_files:
      - split: train
        path: catalog/l1_locations.parquet
  - config_name: l2_episodes
    data_files:
      - split: train
        path: catalog/l2_episodes.parquet
  - config_name: l3_testbeds
    data_files:
      - split: train
        path: catalog/l3_testbeds.parquet
  - config_name: l3_hwms
    data_files:
      - split: train
        path: catalog/l3_hwms.parquet
  - config_name: manifest
    data_files:
      - split: train
        path: manifest.parquet

CONUS Flash-Flood Benchmark (L1-L3) — agent-friendly, CLI-queryable

Query by level (L1/L2/L3) + US state + time range and download from a bare terminal. Three levels: L1 national NCEI flash-flood episode catalog (1996-2025, 42,466), L2 observation-availability catalog (2021-2025, 5,424), L3 evaluation-ready gauge testbeds (688; 152 strict flash-floods; 47 NCEI-confirmed; 26 with return period >= 2 yr).

TL;DR for agents — manifest = discover, jq = filter, resolve_url = download

BASE=https://huggingface.co/datasets/skyan1002/flash-flood-benchmark-data/resolve/main
curl -sL "$BASE/manifest.jsonl" -o manifest.jsonl   # one comma-safe index of every record + file

Every line is one JSON object: record_type (record|artifact), level, ff_episode_id, gage_id, year, month, begin_date (YYYY-MM-DD), primary_state_abbrev, all_states_abbrev, plus L3 fields (benchmark_tier, flash_flood_class, is_strict_flash_flood, selected, lp3_return_period_yr, ...) and, for artifacts, artifact_kind, resolve_url, bytes, sha256.

Quickstart recipes

# 1) Screen: L3 strict flash floods in TX, July 2025
jq -c 'select(.record_type=="record" and .level=="L3" and .primary_state_abbrev=="TX"
  and .is_strict_flash_flood==true and .begin_date>="2025-07-01" and .begin_date<="2025-07-31")
  | {testbed_id, lp3_return_period_yr, peak_value}' manifest.jsonl

# 2) Download every file for one testbed (resolve URLs precomputed; -L follows the LFS/CDN redirect)
jq -r 'select(.testbed_id=="FF_2025_07_TX_ep002__08167000" and .record_type=="artifact").resolve_url' \
  manifest.jsonl | xargs -n1 curl -L -O

# 3) Just the hydrograph CSVs for the curated set (selected & RP>=2), budget by bytes first
jq -r 'select(.selected==true and .lp3_return_period_yr>=2 and .artifact_kind=="streamflow")
  | "\(.bytes)\t\(.resolve_url)"' manifest.jsonl

# 4) L1 episodes that touched TX in 2015 (any-touch via all_states_abbrev)
jq -c 'select(.level=="L1" and (.all_states_abbrev|test("(^|\\|)TX(\\||$)")) and .year==2015)
  | {ff_episode_id, primary_state_abbrev, begin_date}' manifest.jsonl

# 5) One-shot curated bundle (26 selected & RP>=2, ~0.2 GB)
curl -L "$BASE/bundles/testbeds_selected26.zip" -o selected26.zip

# 6) A single light file (no zip): one watershed polygon
curl -L "$BASE/testbeds/FF_2025_07_TX_ep002/08167000/watershed.geojson" -o ws.geojson

Optional: server-side filter (no manifest download)

curl -sG https://datasets-server.huggingface.co/filter \
  --data-urlencode dataset=skyan1002/flash-flood-benchmark-data \
  --data-urlencode config=l3_testbeds --data-urlencode split=train \
  --data-urlencode "where=\"primary_state_abbrev\"='TX' AND year=2025 AND is_strict_flash_flood=true" \
  --data-urlencode length=100

Rules: double-quote column names, single-quote strings, URL-encode, length<=100 (page with offset), re-index may lag minutes after an update.

Contracts (read before scripting)

State: filter on primary_state_abbrev (2-letter). For multi-state episodes use all_states_abbrev (pipe-joined, e.g. NJ|NY|PA).
Time: begin_date/end_date are YYYY-MM-DD (lexical compare == date range); year/month are robust integer handles across levels.
Download: always curl -L (LFS objects 302-redirect; without -L you get a tiny pointer). Use -C - to resume; check the bytes column before multi-GB pulls.
Never awk -F, / cut -d, the raw CSVs in catalog/raw_csv/ — fields contain commas. Use jq on manifest.jsonl, the Parquet configs, the /filter API, or a real CSV parser.
zarr gridded forcing ships inside packages/<id>.zip; unzip then open with zarr.open_group (NOT xr.open_zarr). The light mrms_2min_precip_basin_mean.csv is the curl-friendly alternative.

Helper CLI (optional)

ffbench (POSIX sh; needs curl, uses jq if present) wraps the same URLs:

curl -sL "$BASE/ffbench" -o ffbench && chmod +x ffbench
./ffbench query  --level l3 --state TX --start 2025-07-01 --end 2025-07-31 --strict
./ffbench get FF_2025_07_TX_ep002__08167000 --out ./tx_testbed
./ffbench bundle selected26

Layout

catalog/*.parquet (+ catalog/raw_csv/), manifest.{jsonl,csv,parquet}, manifest_schema.json, testbeds/<ep>/<gage>/ (loose light files), packages/<ep>__<gage>.zip (full per-testbed), bundles/ (selected26, strict152). Figures/interactive map: companion Space skyan1002/flash-flood-benchmark and dataset skyan1002/flash-flood-benchmark-figures.

License & citation

CC-BY-4.0. Derived from public USGS (NWIS, STN, NLDI, 3DEP) and NOAA (NCEI Storm Events, NSSL MRMS/FLASH-CREST) data. See CITATION.cff.