Datasets:

skyan1002
/

flash-flood-benchmark-data

This dataset is designed to be driven by autonomous agents and scripts. Everything you need is a single flat index (manifest.jsonl) plus precomputed download URLs. No SDK, no auth, no HTML scraping.

One-paragraph model

Three levels. L1 = 42,466 reported NOAA/NCEI flash-flood episodes (1996–2025). L2 = 5,424 recent CONUS episodes (2021–2025) with observation availability annotated. L3 = 806 episode–gauge–watershed evaluation testbeds (the benchmark unit). A conservative, human-reviewed primary benchmark of 21 testbeds (14 episodes, 21 gauges) is the default set for model intercomparison. Funnel: 806 → 175 strict → 115 deduplicated → 54 NCEI-confirmed → 31 (return period ≥ 2 yr) → 21 primary. See the companion paper (TORRENT, ESSD).

The one file you need

BASE=https://huggingface.co/datasets/skyan1002/flash-flood-benchmark-data/resolve/main
curl -sL "$BASE/manifest.jsonl" -o manifest.jsonl

Each line is one JSON object. record_type=="record" rows are catalog entries; record_type=="artifact" rows are downloadable files with a resolve_url.

Agent-facing filter keys (L3 records)

key	meaning
`primary_benchmark`	true → in the 21-testbed conservative default set
`is_strict_flash_flood`	true → passed the strict flash-flood screen (175)
`dedup_selected` (`selected`)	true → deduplicated representative (115)
`ncei_confirmed`	true → in-basin NCEI report point (54)
`rp_ge_2yr`	true → LP3 return period ≥ 2 yr (31)
`nldi_recovered`	true → watershed restored by the hardened NLDI retrieval
`regulation_excluded` / `postfire_excluded`	curated out of the primary set (kept in L3)
`needs_curation_review`	true → auto-appended by the annual pipeline, awaiting human review
`water_balance_flag`	non-empty → RC > 1, use caution for volume metrics
`benchmark_tier`	`A` (in-basin NCEI point) or `B` (evidence label, not an exclusion)
`lp3_return_period_yr`, `peak_value`, `drainage_area_km2`, `unit_peak_q_cms_km2`	numeric
`primary_state_abbrev` / `all_states_abbrev`, `year`, `month`, `begin_date`	space/time

Canonical agent recipes

# The default benchmark set (21 testbeds) as clean JSON
jq -c 'select(.level=="L3" and .primary_benchmark==true)
  | {testbed_id, station_name, lp3_return_period_yr, drainage_area_km2}' manifest.jsonl

# Larger sample for robustness testing: deduplicated tier (115)
jq -c 'select(.level=="L3" and .selected==true) | {testbed_id, benchmark_tier}' manifest.jsonl

# Everything the full L3 pool offers in one state + season
jq -c 'select(.level=="L3" and .primary_state_abbrev=="TX"
  and .begin_date>="2025-07-01" and .begin_date<="2025-07-31")
  | {testbed_id, is_strict_flash_flood, primary_benchmark}' manifest.jsonl

# Download all files for one testbed (resolve URLs precomputed; -L follows the redirect)
jq -r 'select(.testbed_id=="FF_2025_07_TX_ep002__08165500" and .record_type=="artifact").resolve_url' \
  manifest.jsonl | xargs -n1 curl -L -O

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_benchmark\"=true" --data-urlencode length=100

Tool schema

Machine-readable tool/function definitions for LLM agents are in agent/tools.json (JSON Schema per tool: query_testbeds, get_testbed, list_primary_benchmark, expand_l3). They map onto the recipes above and the CLIs in tools/.

Extending the benchmark (the annual pipeline)

pipeline/expand_l3.py discovers newly qualifying testbeds from a given NCEI year and appends them to catalog/raw_csv/l3_testbeds.csv as light rows (needs_curation_review=true, no forcing). pipeline/rebuild_catalog.py regenerates the Parquet + manifest. pipeline/.github/workflows/annual_l3_update.yml runs both once a year and pushes the refreshed catalog. Forcing for a new light testbed is fetched on demand with tools/download_forcing.py.

Hard rules

Always curl -L (LFS/CDN 302 redirect; without -L you get a pointer stub).
Never awk -F,/cut -d, the raw CSVs — fields contain commas. Use jq on manifest.jsonl, the Parquet configs, or the /filter API.
primary_benchmark is the paper's default set; needs_curation_review rows are machine-appended candidates that have not yet had the human curation review.