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flood-detection

Detect flood events by comparing water levels to thresholds. Use when determining if flooding occurred, counting flood days, aggregating instantaneous data to daily values, or classifying flood severity.

benchflow-ai☆ 283 Repository stars174 Repository forks

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

View source ↗Guides

Skill instructions

Flood Detection Guide

Overview

Flood detection involves comparing observed water levels against established flood stage thresholds. This guide covers how to process water level data and identify flood events.

Flood Stage Definition

According to the National Weather Service, flood stage is the water level at which overflow of the natural banks begins to cause damage. A flood event occurs when:

water_level >= flood_stage_threshold

Aggregating Instantaneous Data to Daily

USGS instantaneous data is recorded at ~15-minute intervals. For flood detection, aggregate to daily maximum:

# df is DataFrame from nwis.get_iv() with datetime index
# gage_col is the column name containing water levels

daily_max = df[gage_col].resample('D').max()
Why Daily Maximum?
Aggregation Use Case
max() Flood detection - captures peak water level
mean() Long-term trends - may miss short flood peaks
min() Low flow analysis

Detecting Flood Days

Compare daily maximum water level against flood threshold:

flood_threshold = <threshold_from_nws>  # feet

# Count days with flooding
flood_days = (daily_max >= flood_threshold).sum()

# Get specific dates with flooding
flood_dates = daily_max[daily_max >= flood_threshold].index.tolist()

Processing Multiple Stations

flood_results = []

for site_id, site_data in all_data.items():
    daily_max = site_data['water_levels'].resample('D').max()
    threshold = thresholds[site_id]['flood']

    days_above = int((daily_max >= threshold).sum())

    if days_above > 0:
        flood_results.append({
            'station_id': site_id,
            'flood_days': days_above
        })

# Sort by flood days descending
flood_results.sort(key=lambda x: x['flood_days'], reverse=True)

Flood Severity Classification

If multiple threshold levels are available:

def classify_flood(water_level, thresholds):
    if water_level >= thresholds['major']:
        return 'major'
    elif water_level >= thresholds['moderate']:
        return 'moderate'
    elif water_level >= thresholds['flood']:
        return 'minor'
    elif water_level >= thresholds['action']:
        return 'action'
    else:
        return 'normal'

Output Format Examples

Simple CSV Output
import csv

with open('flood_results.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['station_id', 'flood_days'])
    for result in flood_results:
        writer.writerow([result['station_id'], result['flood_days']])
JSON Output
import json

output = {
    'flood_events': flood_results,
    'total_stations_with_flooding': len(flood_results)
}

with open('flood_report.json', 'w') as f:
    json.dump(output, f, indent=2)

Common Issues

Issue Cause Solution
No floods detected Threshold too high or dry period Verify threshold values
All days show flooding Threshold too low or data error Check threshold units (feet vs meters)
NaN in daily_max Missing data for entire day Check data availability

Best Practices

  • Use daily maximum for flood detection to capture peaks
  • Ensure water level and threshold use same units (typically feet)
  • Only report stations with at least 1 flood day
  • Sort results by flood severity or duration for prioritization

Examples

Read the usage examples in the source repository.

View source ↗

Source history

Indexed on
2026-01-20
Index updated
2026-02-02
Branch
main

Index and sync dates describe this directory. View the source repository for code history.

GitHub ↗

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