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Forecast Data Sources

This document provides detailed information about the environmental data sources integrated into the Rahat Anticipatory Action platform, including their APIs, data processing, and usage patterns.

Overview​

The platform integrates multiple environmental data sources to provide comprehensive climate monitoring and forecasting capabilities. These sources enable automated trigger activation based on real-time environmental conditions.

1. Department of Hydrology and Meteorology (DHM)​

Purpose​

DHM provides real-time hydrological data for river monitoring and flood prediction in Nepal. This service is critical for early warning systems in flood-prone regions.

API Integration​

Base Configuration​

// Configuration in settings
DATASOURCE: {
  DHM: {
    location: 'Babai at Chepang', // or 'Karnali at Chisapani'
    url: 'https://bipadportal.gov.np/api/v1'
  }
}

Data Synchronization​

  • Frequency: Every 5 minutes
  • Method: Automated cron job (@Cron('*/5 * * * *'))
  • Process: Fetches latest water level data and stores in local database

API Endpoints Used​

River Station Data​
// GET /api/v1/water-levels
// Returns: Array of water level readings with timestamps
{
  "data": {
    "results": [
      {
        "station": "Babai at Chepang",
        "waterLevel": 2.45,
        "timestamp": "2024-01-15T10:30:00Z",
        "location": "coordinates",
        "status": "active"
      }
    ]
  }
}
Station Information​
// GET /api/v1/stations
// Returns: List of available monitoring stations
{
  "stations": [
    {
      "id": "BABAI_001",
      "name": "Babai at Chepang",
      "location": "coordinates",
      "river": "Babai",
      "status": "active"
    }
  ]
}

Data Processing​

Water Level Monitoring​

// Trigger criteria check
async criteriaCheck(payload: AddTriggerStatement) {
  const recentData = await this.prisma.sourcesData.findFirst({
    where: {
      location: payload.location,
      source: 'DHM',
    },
    orderBy: { createdAt: 'desc' }
  });

  const currentLevel = recentData.data.waterLevel;
  const threshold = payload.triggerStatement?.waterLevel;
  
  return this.compareWaterLevels(currentLevel, threshold);
}

Threshold Comparison Logic​

compareWaterLevels(currentLevel: number, threshold: number): boolean {
  // Trigger when water level exceeds threshold
  return currentLevel >= threshold;
}

Use Cases​

1. Flood Early Warning​

  • Trigger Condition: Water level > 3.5 meters
  • Response: Immediate beneficiary notification
  • Lead Time: 2-6 hours depending on river flow

2. River Monitoring​

  • Continuous Monitoring: 24/7 water level tracking
  • Data Storage: Historical data for trend analysis
  • Alert System: Automated notifications for threshold breaches

Data Storage Schema​

-- sources_data table
CREATE TABLE sources_data (
  id SERIAL PRIMARY KEY,
  location VARCHAR(255),
  source VARCHAR(50), -- 'DHM'
  data JSONB, -- Contains water level, timestamp, station info
  created_at TIMESTAMP DEFAULT NOW()
);

2. Global Flood Awareness System (GLOFAS)​

Purpose​

GLOFAS provides global flood forecasting data with probability assessments and lead time analysis. This service enables advanced flood prediction with longer lead times than local monitoring.

API Integration​

Base Configuration​

// Configuration in settings
DATASOURCE: {
  GLOFAS: {
    location: 'Babai at Chepang',
    url: "https://ows.globalfloods.eu/glofas-ows/ows.py",
    bbox: "9066450.71499904,3117815.425733483,9405627.288509797,3456991.999244238",
    i: "89", // coordinate for station
    j: "409"
  }
}

Data Synchronization​

  • Frequency: Every hour
  • Method: Automated cron job (@Cron('0 * * * *'))
  • Process: Fetches forecast data and stores with date tracking

Integration with Trigger System​

Multi-Source Trigger Logic​

// Combined trigger assessment
async assessTriggerConditions(location: string, triggerConfig: TriggerConfig) {
  const dhmData = await this.getDHMData(location);
  const glofasData = await this.getGLOFASData(location);
  
  // DHM immediate trigger
  const dhmTriggered = this.checkDHMThreshold(dhmData, triggerConfig.dhmThreshold);
  
  // GLOFAS probability trigger
  const glofasTriggered = this.checkGLOFASProbability(glofasData, triggerConfig.probabilityThreshold);
  
  return {
    immediateTrigger: dhmTriggered,
    forecastTrigger: glofasTriggered,
    combinedRisk: this.calculateCombinedRisk(dhmData, glofasData)
  };
}

Trigger Priority System​

  1. Immediate Triggers: DHM water level thresholds
  2. Forecast Triggers: GLOFAS probability assessments
  3. Combined Triggers: Both sources indicating high risk

Data Quality and Reliability​

DHM Data Quality​

  • Accuracy: ±0.1 meter water level precision
  • Availability: 99.5% uptime
  • Latency: 5-minute data refresh
  • Coverage: 15 major river stations in Nepal

GLOFAS Data Quality​

  • Accuracy: 85-90% forecast accuracy
  • Availability: 99.9% uptime
  • Latency: 1-hour forecast updates
  • Coverage: Global coverage with regional focus

Monitoring and Alerting​

Data Source Health Monitoring​

  • Uptime Monitoring: Track API availability
  • Data Quality Checks: Validate received data format
  • Latency Monitoring: Track response times
  • Error Rate Tracking: Monitor failed requests

Alert Thresholds​

  • DHM: Alert if no data for > 15 minutes
  • GLOFAS: Alert if no data for > 2 hours
  • Data Quality: Alert if data format is invalid
  • Trigger Failures: Alert if trigger processing fails

Future Enhancements​

Planned Integrations​

  1. Satellite Data: Integration with satellite-based flood monitoring
  2. Machine Learning: Enhanced prediction models
  3. Additional Sources: Integration with regional meteorological services
  4. Real-time Processing: Stream processing for immediate analysis

Scalability Considerations​

  • Data Volume: Handle increasing data from multiple sources
  • Processing Speed: Optimize for real-time trigger assessment
  • Storage: Efficient storage of historical data
  • Reliability: Redundant data sources for critical regions