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Water Metrics

Overview

The water metrics endpoint provides comprehensive hydrological analysis including precipitation/PET ratio, soil moisture percentile, evapotranspiration anomaly, and water stress index. These metrics are calculated using sophisticated climatological comparisons against historical data.

Parameters

A request to /water_stress/ uses standard query parameters. Parameters that are hidden from the public OpenAPI schema are intentionally omitted here.

Parameter Type Required Default Description
geometry string (WKT) Yes - Input geometry (WKT).
area int No 4 Area in hectares when geometry is a POINT.
start_date string No 2017-01-01 Start date (YYYY-MM-DD).
date string No today (UTC) End date (YYYY-MM-DD).
thumbnail bool No true Whether to return a thumbnail.
map_tile bool No true Whether to return XYZ map tile URLs.

Metrics

1. Precipitation/PET Ratio

Calculation: precipitation / potential_evapotranspiration

Data Sources:

  • Precipitation: CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) pentad precipitation
  • PET: MODIS (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration

Interpretation:

  • > 1.0: More precipitation than PET (wet conditions)
  • < 1.0: Less precipitation than PET (dry conditions)
  • = 1.0: Balanced conditions

2. Soil Moisture Percentile

Calculation: Climatological percentile based on historical SMAP data

Data Source: NASA SMAP (Soil Moisture Active Passive) Level 3 Enhanced

Methodology: 1. Historical Climatology: Uses 10 years of historical SMAP data 2. Statistical Analysis: Calculates mean and standard deviation for each pixel 3. Z-Score Calculation: (current_value - climatological_mean) / climatological_std 4. Percentile Conversion: Uses normal distribution to convert z-score to percentile 5. Range Clamping: Ensures values are between 0-100%

Interpretation:

  • 0-20%: Extremely dry conditions
  • 20-40%: Below normal moisture
  • 40-60%: Normal moisture conditions
  • 60-80%: Above normal moisture
  • 80-100%: Extremely wet conditions

Edge Case Handling:

  • Zero Standard Deviation: Uses minimum threshold (0.001) to avoid division by zero
  • Missing Data: Handles gaps in historical data gracefully

3. Evapotranspiration Anomaly

Calculation: Standardized anomaly (z-score) against climatological mean

Data Source: MODIS evapotranspiration data

Methodology: 1. Historical Climatology: Uses 10 years of historical ET data 2. Standardized Anomaly: Calculates z-score relative to climatological statistics 3. Range Clamping: Clamps values to [-3, 3] standard deviations for meaningful interpretation

Interpretation:

  • Negative values: Below normal evapotranspiration (reduced vegetation activity)
  • Positive values: Above normal evapotranspiration (increased vegetation activity)
  • Magnitude: Indicates severity of deviation from normal conditions

4. Water Stress Index

Calculation: Composite index combining all water metrics

Methodology: 1. Component Normalization: Each metric is normalized to 0-1 scale 2. Weighted Combination: Combines precipitation stress (40%), soil moisture stress (40%), and ET stress (20%) 3. Stress Interpretation: Higher values indicate greater water stress

Interpretation:

  • 0.0-0.3: Low water stress
  • 0.3-0.6: Moderate water stress
  • 0.6-1.0: High water stress

Data Sources and Accuracy

Primary Datasets

  • CHIRPS: ±5mm/day accuracy for precipitation
  • MODIS ET: ±15% accuracy for evapotranspiration
  • SMAP: ±0.04 m³/m³ accuracy for soil moisture

Use Cases

Agricultural Monitoring

  • Drought Detection: Low soil moisture percentiles indicate drought conditions
  • Irrigation Planning: ET anomalies help optimize irrigation schedules
  • Crop Yield Prediction: Water stress indicators correlate with yield potential

Climate Analysis

  • Trend Analysis: Long-term changes in water availability
  • Extreme Event Detection: Identification of unusual hydrological conditions
  • Climate Change Impact: Assessment of changing water patterns

Water Resource Management

  • Reservoir Management: ET anomalies inform water release decisions
  • Groundwater Monitoring: Soil moisture trends indicate recharge patterns
  • Flood Risk Assessment: High precipitation/PET ratios indicate flood potential

API Response

{
  "uuid": "unique-identifier",
  "precipitation_pet_ratio": 1.2,
  "soil_moisture_percentile": 65.5,
  "evapotranspiration_anomaly": -0.1,
  "water_stress_index": 0.3,
  "ingestion_date": "2024-01-15T10:30:00",
  "monitoring_start": "2023-01-01",
  "monitoring_end": "2024-01-15",
  "thumbnail_url": "https://storage.googleapis.com/...",
  "uncertainty_sd": 0.15,
  "imagery_pre": "https://earthengine.googleapis.com/...",
  "imagery_post": "https://earthengine.googleapis.com/...",
  "water_metrics_map": "https://earthengine.googleapis.com/...",
  "input_plot_area": 25.5,
  "input_plot_geom": "POLYGON(...)"
}

Future Enhancements

Planned Improvements

  1. Extended Historical Data: Integration of longer climatological periods
  2. Seasonal Adjustments: Account for seasonal variations in climatology
  3. Uncertainty Quantification: Provide confidence intervals for all metrics
  4. Machine Learning: Use ML models for improved anomaly detection
  5. Real-time Updates: Near real-time processing capabilities

Additional Metrics

  1. Groundwater Recharge: Estimate groundwater recharge rates
  2. Surface Water Availability: Assess surface water storage and flow
  3. Water Quality Indicators: Include water quality metrics
  4. Irrigation Efficiency: Calculate irrigation water use efficiency
  5. Crop Water Requirements: Estimate crop-specific water needs