name: nws-flood-thresholds description: Download flood stage thresholds from NWS (National Weather Service). Use when determining flood levels for USGS stations, accessing action/minor/moderate/major flood stages, or matching stations to their flood thresholds. license: MIT

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

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

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

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