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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.

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安装与使用

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技能说明

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

使用示例

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来源历史

收录时间
2026-01-20
索引更新时间
2026-02-02
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

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