Skill: wu-uk/flood-risk-analysis-flood-detection (version 0.1.0)

--- name: flood-detection description: 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. license: MIT --- # 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: ```python # 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: ```python 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 ```python 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: ```python 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 ```python 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 ```python 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


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