Classifies flood severity and aggregates water level data.

Install

mkdir -p .claude/skills/flood-detection && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2941" && unzip -o skill.zip -d .claude/skills/flood-detection && rm skill.zip

Installs to .claude/skills/flood-detection

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

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.
203 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Aggregate instantaneous water level data
  • Detect flood events based on thresholds
  • Classify flood severity levels
  • Generate flood reports in CSV or JSON

How it works

It resamples instantaneous sensor data to daily maximums and compares these values against defined flood stage thresholds.

Inputs & outputs

You give it
Instantaneous water level data and thresholds
You get back
Flood event report or daily statistics

When to use flood-detection

  • Detecting flood event timing
  • Aggregating sensor data into daily reports
  • Classifying flood severity levels

About this skill

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?

AggregationUse 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

IssueCauseSolution
No floods detectedThreshold too high or dry periodVerify threshold values
All days show floodingThreshold too low or data errorCheck threshold units (feet vs meters)
NaN in daily_maxMissing data for entire dayCheck 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

When not to use it

  • Low flow analysis
  • Long-term trend analysis without peak detection

Limitations

  • Requires accurate threshold values
  • Units must be consistent between data and thresholds

How it compares

It provides a structured approach to peak-based flood detection rather than simple mean-based aggregation.

Compared to similar skills

flood-detection side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
flood-detection (this skill)16moNo flagsIntermediate
quant-analyst1032moNo flagsAdvanced
umap-learn62moReviewIntermediate
embedding-strategies82moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

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