NWS Flood Thresholds Guide
Overview
The National Weather Service (NWS) maintains flood stage thresholds for thousands of stream gages across the United States. These thresholds define when water levels become hazardous.
Data Sources
Option 1: Bulk CSV Download (Recommended for Multiple Stations)
https://water.noaa.gov/resources/downloads/reports/nwps_all_gauges_report.csv
Option 2: Individual Station Pages
https://water.noaa.gov/gauges/<station_id>
Example: https://water.noaa.gov/gauges/04118105
Flood Stage Categories
| Category | CSV Column | Description |
|---|
| Action Stage | action stage | Water level requiring monitoring, preparation may be needed |
| Flood Stage (Minor) | flood stage | Minimal property damage, some public threat. Use this to determine if flooding occurred. |
| Moderate Flood Stage | moderate flood stage | Structure inundation, evacuations may be needed |
| Major Flood Stage | major flood stage | Extensive damage, significant evacuations required |
For general flood detection, use the flood stage column as the threshold.
Downloading Bulk CSV
import pandas as pd
import csv
import urllib.request
import io
nws_url = "https://water.noaa.gov/resources/downloads/reports/nwps_all_gauges_report.csv"
response = urllib.request.urlopen(nws_url)
content = response.read().decode('utf-8')
reader = csv.reader(io.StringIO(content))
headers = next(reader)
data = [row[:43] for row in reader] # Truncate to 43 columns
nws_df = pd.DataFrame(data, columns=headers)
Important: CSV Column Mismatch
The NWS CSV has a known issue: header row has 43 columns but data rows have 44 columns. Always truncate data rows to match header count:
data = [row[:43] for row in reader]
Key Columns
| Column Name | Description |
|---|
usgs id | USGS station ID (8-digit string) |
location name | Station name/location |
state | Two-letter state code |
action stage | Action threshold (feet) |
flood stage | Minor flood threshold (feet) |
moderate flood stage | Moderate flood threshold (feet) |
major flood stage | Major flood threshold (feet) |
Converting to Numeric
Threshold columns need conversion from strings:
nws_df['flood stage'] = pd.to_numeric(nws_df['flood stage'], errors='coerce')
Filtering by State
# Get stations for a specific state
state_stations = nws_df[
(nws_df['state'] == '<STATE_CODE>') &
(nws_df['usgs id'].notna()) &
(nws_df['usgs id'] != '') &
(nws_df['flood stage'].notna()) &
(nws_df['flood stage'] != -9999)
]
Matching Thresholds to Station IDs
# Build a dictionary of station thresholds
station_ids = ['<id_1>', '<id_2>', '<id_3>']
thresholds = {}
for _, row in nws_df.iterrows():
usgs_id = str(row['usgs id']).strip()
if usgs_id in station_ids:
thresholds[usgs_id] = {
'name': row['location name'],
'flood': row['flood stage']
}
Common Issues
| Issue | Cause | Solution |
|---|
| Column mismatch error | CSV has 44 data columns but 43 headers | Truncate rows to 43 columns |
| Missing thresholds | Station not in NWS database | Skip station or use alternative source |
| Value is -9999 | No threshold defined | Filter out these values |
| Empty usgs id | NWS-only station | Filter by usgs id != '' |
Best Practices
- Always truncate CSV rows to match header count
- Convert threshold columns to numeric before comparison
- Filter out -9999 values (indicates no threshold defined)
- Match stations by USGS ID (8-digit string with leading zeros)
- Some stations may have flood stage but not action/moderate/major
