You’re building weather-aware logistics and need a reliable way to detect drought-driven disruptions at specific ports without wiring up multiple hazard feeds. By the end of this post, you’ll know how to query the FreightPulse Drought Disruptions lookup, work within guest access limits (no key required), parse the response envelope safely, and design for production with lagged congestion metrics and natural-hazard-only scope.
What the Drought Disruptions lookup covers (weather-only)
The Drought Disruptions lookup focuses strictly on natural hazard signals that intersect with logistics, with drought as the filter. Data sources are natural hazards only (GDACS/USGS/NOAA). Labor actions, canal scheduling, policy changes, or other non-weather causes are intentionally out of scope.
If you also correlate port congestion, note that FreightPulse uses IMF PortWatch for congestion, lagged roughly 6–7 days. It is a 7-day average benchmarked against a 90-day baseline, not a live AIS wait-time feed. Treat it as a contextual background metric rather than a real-time queue.
Access model and authentication
You can start without an API key: guest access allows up to 100 requests per day per IP. That’s enough for prototyping and light automation. For sustained workloads or multiple ports, register for a key to avoid guest caps.
- Header-based auth: X-API-Key: YOUR_API_KEY
- Guest without key: 100 requests/day per IP
- Response envelope: {"success": true, "data": {...}} (see the official sample below)
Keep the envelope contract in mind when you parse results. Always check success before reading data, and prefer timestamp fields when present to drive caching and freshness logic.
Make your first request for weather-driven drought disruptions
The Drought Disruptions lookup is queried by port code and type=drought. Here is the official curl you can copy and run. If you have a key, include it in the X-API-Key header as shown; if not, omit the header and you will be served under guest limits.
curl -H "X-API-Key: YOUR_API_KEY" "https://freightpulsehq.com/api/v1/disruptions?port=USLAX&type=drought"Key notes before you wire this into a job:
- Parameters: port=USLAX (example), type=drought (weather category).
- Rate limits: guest 100/day per IP (no key). Register for higher or predictable throughput.
- Hazard scope: natural hazards only (GDACS/USGS/NOAA). Strikes or canal ops are not included.
Official sample response format (envelope and meta)
FreightPulse responses follow a stable envelope. The following is the official sample you should use to implement and test your parsing logic. Copy it exactly; do not substitute fields or values.
{
"success": true,
"data": {
"timestamp": "2026-10-25T00:00:21+00:00",
"source": "EIA (U.S. Energy Information Administration)",
"currency": "USD",
"unit": "gallon",
"data": {
"diesel": {
"national_average": 6.276,
"change_week": 0.097,
"change_percent": 1.57,
"updated_at": "2026-10-19",
"regions": {
"east_coast": 5.984,
"midwest": 6.352,
"gulf_coast": 5.903,
"rocky_mountain": 6.422,
"west_coast": 7.394,
"california": 8.242
}
},
"gasoline": {
"national_average": 4.284,
"change_week": -0.047,
"change_percent": -1.09,
"updated_at": "2026-10-19",
"grades": {
"regular": 4.284,
"midgrade": 4.915,
"premium": 5.314
}
},
"bunker_fuel": {
"available": false,
"note": "No known free source (Rotterdam/Singapore/Houston) — requires a paid provider (e.g. Ship & Bunker, Argus)."
}
},
"historical": {
"diesel_30d_avg": 6.259,
"diesel_90d_avg": 5.88,
"diesel_yoy_change": 73.4,
"gasoline_30d_avg": 4.359,
"gasoline_90d_avg": 4.213,
"gasoline_yoy_change": 41.9
}
},
"meta": {
"request_id": "a21d7505-6ff3-4230-b93e-c9dd1d17e0a3"
}
}How to use this structure in your client:
- success: boolean flag to gate your parsing; stop early if false.
- data: the payload node you’ll consume. Rely on data.timestamp (ISO 8601, UTC offset) when present to manage freshness and caching TTLs.
- meta.request_id: useful for idempotency keys on retries and for support tickets or logging correlation.
The payload above demonstrates the envelope and timestamp patterns. Your Drought Disruptions lookup will still follow the same envelope style. Do not assume or invent additional fields for drought events beyond what is documented; consult the linked docs for field-level details.
End-to-end example in Python
The following Python sample calls the same endpoint and shows safe parsing of the envelope. It avoids guessing at inner field names by checking success, then working with the data node and any timestamp/meta fields that are present. Replace YOUR_API_KEY with your key or remove the header to use guest access.
import os
import sys
import json
import time
from urllib.parse import urlencode
import requests
API_BASE = "https://freightpulsehq.com/api/v1/disruptions"
def fetch_drought_disruptions(port_code, api_key=None, timeout=10):
params = {"port": port_code, "type": "drought"}
headers = {}
if api_key:
headers["X-API-Key"] = api_key
url = f"{API_BASE}?{urlencode(params)}"
r = requests.get(url, headers=headers, timeout=timeout)
r.raise_for_status()
payload = r.json()
# Envelope safety checks
if not isinstance(payload, dict) or "success" not in payload:
raise ValueError("Unexpected response shape (no 'success' flag).")
if not payload.get("success"):
# For production, consider logging payload.get("meta") for request_id
raise RuntimeError("API indicated failure.")
data = payload.get("data", {})
meta = payload.get("meta", {})
# Useful common fields across endpoints
timestamp = data.get("timestamp") # ISO 8601 if provided
request_id = meta.get("request_id")
# For demonstration, print what we reliably know without guessing schema
print("Request ID:", request_id or "(none)")
print("Data timestamp:", timestamp or "(none)")
print("Top-level data keys:", list(data.keys()))
# If your integration expects to cache by timestamp, you can compute TTLs here.
# Example: default 6 hours if timestamp exists, else 1 hour fallback.
default_ttl_seconds = 6 * 3600 if timestamp else 3600
return {
"url": url,
"timestamp": timestamp,
"meta_request_id": request_id,
"data_keys": list(data.keys()),
"cache_ttl_seconds": default_ttl_seconds,
"raw": payload,
}
if __name__ == "__main__":
port = os.environ.get("PORT_CODE", "USLAX")
api_key = os.environ.get("FREIGHTPULSE_API_KEY") # optional
try:
result = fetch_drought_disruptions(port, api_key)
print(json.dumps({
"url": result["url"],
"timestamp": result["timestamp"],
"meta_request_id": result["meta_request_id"],
"data_keys": result["data_keys"],
"cache_ttl_seconds": result["cache_ttl_seconds"]
}, indent=2))
except Exception as e:
print(f"Error: {e}", file=sys.stderr)
sys.exit(1)This approach ensures your client is resilient even if specific fields for drought events evolve. You always validate success, log request_id, prefer timestamps when available, and avoid making assumptions about nested structures.
Working within weather constraints and production realities
When you build weather-aware features, it’s tempting to treat any congestion metric as live. Don’t. FreightPulse congestion is derived from IMF PortWatch and is lagged roughly 6–7 days. It’s a 7-day average relative to a 90-day baseline. Treat it as a slow-moving signal that complements faster hazard alerts rather than a minute-by-minute queue.
- Units and currency: when present, unit and currency fields follow explicit strings (e.g., “USD”, “gallon” in the official sample). Avoid unit inference.
- Timestamps: ISO 8601 with timezone offset (e.g., 2026-10-25T00:00:21+00:00). Normalize to UTC in your pipeline.
- Caching: use data.timestamp to set TTLs. Without a timestamp, use conservative fallbacks and back off on repeated identical meta.request_id values.
- Retries: standard network backoff (e.g., 250ms, 1s, 2s) with a hard cap; preserve idempotency by logging meta.request_id on failures when available.
- Pagination: if your response can span multiple pages, consult the Documentation for parameters and next-page cursors. Do not guess field names.
Common patterns for drought-driven weather workflows
Here are concrete integration patterns that stay within the natural-hazards-only scope and help you ship faster:
- Pre-carriage screening: call type=drought for the next leg’s destination port before you finalize drayage pickup windows. If a drought flag exists, lengthen buffers or flip the route.
- Sailing plan adjustments: schedule a nightly job for target ports. Combine drought signals with lagged congestion to choose between feasible berthing windows. Do not treat lagged congestion as live AIS.
- Warehouse and yard staffing: surface drought disruption presence to WMS/TMS as a binary gate for inbound slots. Target minimal data exposure: success boolean, timestamp, and a small subset of event descriptors documented in the API.
- Pricing models: you can use drought presence as a risk factor but do not quote a freight-rates USD fare as today’s price. The API does not provide “today’s freight rate.”
Error handling and validation for weather-only events
Because the lookup is constrained to natural hazards (GDACS/USGS/NOAA), validate your downstream logic accordingly. For instance, if your product tracks labor strikes or canal operations, keep those systems separate and never conflate them with drought disruptions from this API.
- Input validation: restrict type to drought when using this workflow. Reject or ignore other, non-weather categories.
- Port codes: sanitize and log the requested port code (e.g., USLAX) to ensure your caching keys don’t collide.
- Idempotency and traceability: persist meta.request_id to a request log. It helps debug intermittent network errors and correlate support requests.
- Guest budgeting: monitor 100 requests/day/IP usage in CI or staging by adding a counter to your job scheduler. Register for a key before production rollout.
Testing with the official sample
Use the official sample response to assert your parser’s behavior. Even though the sample payload demonstrates fuel-specific fields, the contract you need to test is the envelope: success, data, optional timestamp inside data, and meta.request_id. Your tests should:
- Fail if success is missing or false.
- Accept ISO 8601 timestamps with timezone offsets.
- Handle extra fields in data without breaking (forward compatibility).
- Record meta.request_id for observability.
Operational tips that save time
- Backfill strategy: drought signals may not shift hourly. Cache results per port with a TTL aligned to your operational cadence (e.g., 3–6 hours), then refresh opportunistically on user demand.
- Clock drift: align job schedulers to UTC to avoid timestamp misreads around DST transitions.
- Change detection: store a hash or ETag equivalent of the normalized data node so you can short-circuit downstream pipelines when nothing changed.
- Alert fatigue: avoid noisy Slack alerts by setting a minimum re-alert interval per port unless the timestamp has advanced.
Quick checklist before you ship
- You can make a guest request to the disruptions endpoint and receive a success=true envelope.
- Your client verifies success, extracts data safely, reads data.timestamp if present, and logs meta.request_id.
- Your cron or queue respects guest 100/day/IP limits or uses a registered key.
- Your UX labels congestion as a lagged IMF PortWatch average (7-day vs 90-day baseline), not a live AIS wait time.
- You do not quote any freight-rates USD fare as today’s price.
- Your hazard logic remains weather-only: drought from natural hazard sources (GDACS/USGS/NOAA).
FAQ
Do I need an API key to start?
No. Guest access is available with 100 requests per day per IP. Omit the X-API-Key header to use guest mode. For sustained throughput, register for a key.
Is port congestion live?
No. FreightPulse port congestion uses IMF PortWatch and is lagged roughly 6–7 days. It is a 7-day average versus a 90-day baseline, not a live AIS wait-time feed.
Can I retrieve non-weather disruptions like strikes or canal scheduling?
No. The Drought Disruptions lookup is weather-only and limited to natural hazards (GDACS/USGS/NOAA). Do not use it for labor actions or canal operations.
What response fields can I safely rely on?
Always rely on the envelope: success, data, and meta (including meta.request_id). Use data.timestamp when provided to drive caching and freshness. For field-level payload details, see the Documentation.
Can I use these signals to set freight prices today?
You should not quote a freight-rates USD fare as today’s price based on this lookup. Use drought presence and lagged congestion as risk/context signals, not as live price quotes.
Ready to move beyond guest limits and productionize your weather-aware disruption checks? Create an account, get your API key, and ship your integration: Register. For field-by-field schemas and advanced topics like pagination or filters, see the Documentation.