Phase 1 - Data Format Standardization

The Foundation of Interoperability

Phase 1 of the WIA Drought Monitoring Standard establishes the foundational layer upon which all other phases build: standardized data formats. Without agreement on how drought information should be structured, transmitted, and stored, true interoperability remains impossible. This chapter provides detailed specifications for each drought index and monitoring parameter defined in the WIA standard.

The design philosophy for WIA data formats emphasizes:

• Human readability: JSON-based formats that developers can understand without specialized tools
• Machine processability: Strict schemas enabling automated validation and processing
• Extensibility: Ability to add new fields without breaking existing implementations
• Self-documentation: Data structures include metadata explaining their contents
• Minimal size: Efficient transmission while maintaining clarity

Core Data Schema Structure

All WIA drought data formats share a common base structure that provides essential metadata and ensures consistency across different drought indices.

Universal Header Fields

Every WIA-compliant drought data object must include:

Field	Type	Required	Description
wia_version	string	Yes	WIA standard version (e.g., "1.0")
data_type	string	Yes	Type of drought data (pdsi, spi, soil_moisture, etc.)
timestamp	ISO 8601	Yes	Date and time of observation (UTC)
location	object	Yes	Geographic location information
metadata	object	Yes	Data provenance and quality information

{
  "wia_version": "1.0",
  "data_type": "pdsi",
  "timestamp": "2025-12-26T00:00:00Z",
  "location": {
    "type": "Point",
    "coordinates": [-74.0060, 40.7128],
    "properties": {
      "region": "Northeast US",
      "elevation_m": 10,
      "land_use": "agricultural"
    }
  },
  "metadata": {
    "source_organization": "NOAA Regional Climate Center",
    "processing_date": "2025-12-26T12:00:00Z",
    "quality_flag": "good",
    "confidence_score": 0.92,
    "validation_method": "ground_station_comparison"
  }
}

Palmer Drought Severity Index (PDSI) Schema

The Palmer Drought Severity Index, developed by Wayne Palmer in 1965, remains one of the most widely used drought indices. The WIA standard provides a comprehensive schema capturing all aspects of PDSI calculation and interpretation.

PDSI Data Structure

{
  "wia_version": "1.0",
  "data_type": "pdsi",
  "timestamp": "2025-12-26T00:00:00Z",
  "location": {
    "type": "Point",
    "coordinates": [-74.0060, 40.7128],
    "properties": {
      "region": "Northeast US",
      "climate_division": "NJ-02",
      "elevation_m": 10
    }
  },
  "pdsi": {
    "value": -2.5,
    "classification": "moderate_drought",
    "classification_label": "Moderate Drought",
    "range": [-10.0, 10.0],
    "percentile": 15.3,
    "zscore": -1.2
  },
  "parameters": {
    "precipitation_mm": 45.2,
    "temperature_c": 15.3,
    "potential_evapotranspiration_mm": 114.0,
    "soil_moisture_mm": 87.5,
    "available_water_capacity_mm": 150.0,
    "runoff_mm": 12.3
  },
  "water_balance": {
    "deficit_mm": 68.8,
    "surplus_mm": 0.0,
    "recharge_mm": 5.2,
    "loss_mm": 62.5
  },
  "historical_comparison": {
    "30year_mean": 0.15,
    "30year_stddev": 2.1,
    "same_month_mean": -0.3,
    "rank_percentile": 18
  },
  "metadata": {
    "source_organization": "NOAA NCEI",
    "calculation_method": "Palmer 1965",
    "processing_date": "2025-12-26T12:00:00Z",
    "quality_flag": "good",
    "confidence_score": 0.87,
    "data_completeness": 0.98
  }
}

PDSI Classification Thresholds

PDSI Range	Classification Code	Label	Description
≥ 4.0	extremely_wet	Extremely Wet	Exceptionally moist conditions
3.0 to 3.99	very_wet	Very Wet	Significantly above normal moisture
2.0 to 2.99	moderately_wet	Moderately Wet	Above normal moisture
1.0 to 1.99	slightly_wet	Slightly Wet	Mildly above normal moisture
-0.99 to 0.99	near_normal	Near Normal	Normal moisture conditions
-1.99 to -1.0	mild_drought	Mild Drought	Incipient drought conditions
-2.99 to -2.0	moderate_drought	Moderate Drought	Established drought conditions
-3.99 to -3.0	severe_drought	Severe Drought	Serious agricultural impacts
≤ -4.0	extreme_drought	Extreme Drought	Exceptional drought conditions

Standardized Precipitation Index (SPI) Schema

The SPI quantifies precipitation deficits over multiple time scales, making it particularly useful for detecting different drought types. The WIA schema supports all standard SPI time scales.

SPI Data Structure

{
  "wia_version": "1.0",
  "data_type": "spi",
  "timestamp": "2025-12-26T00:00:00Z",
  "location": {
    "type": "Point",
    "coordinates": [-119.4179, 36.7783],
    "properties": {
      "region": "California Central Valley",
      "station_id": "USC00043500"
    }
  },
  "spi": {
    "value": -1.8,
    "time_scale_months": 12,
    "classification": "severely_dry",
    "classification_label": "Severely Dry",
    "probability": 0.036,
    "percentile": 3.6
  },
  "precipitation": {
    "period_total_mm": 285.4,
    "historical_mean_mm": 450.2,
    "historical_stddev_mm": 91.5,
    "percent_of_normal": 63.4,
    "deficit_mm": 164.8
  },
  "distribution": {
    "fitted_distribution": "gamma",
    "alpha_parameter": 2.45,
    "beta_parameter": 183.7,
    "goodness_of_fit": 0.94
  },
  "multi_scale": {
    "spi_1_month": -0.9,
    "spi_3_month": -1.4,
    "spi_6_month": -1.6,
    "spi_12_month": -1.8,
    "spi_24_month": -1.2
  },
  "metadata": {
    "source_organization": "California DWR",
    "calculation_method": "McKee et al. 1993 (gamma fit)",
    "reference_period": "1991-2020",
    "processing_date": "2025-12-26T06:00:00Z",
    "quality_flag": "good",
    "data_completeness": 1.0
  }
}

SPI Classification System

SPI Range	Classification Code	Category	Probability (%)
≥ 2.0	extremely_wet	Extremely Wet	2.3
1.5 to 1.99	very_wet	Very Wet	4.4
1.0 to 1.49	moderately_wet	Moderately Wet	9.2
-0.99 to 0.99	near_normal	Near Normal	68.2
-1.49 to -1.0	moderately_dry	Moderately Dry	9.2
-1.99 to -1.5	severely_dry	Severely Dry	4.4
≤ -2.0	extremely_dry	Extremely Dry	2.3

Soil Moisture Data Schema

Soil moisture is a critical indicator of agricultural drought. The WIA schema supports multi-depth measurements from both in-situ sensors and satellite retrievals.

Soil Moisture Structure

{
  "wia_version": "1.0",
  "data_type": "soil_moisture",
  "timestamp": "2025-12-26T14:30:00Z",
  "location": {
    "type": "Point",
    "coordinates": [-96.7970, 32.7767],
    "properties": {
      "region": "Texas",
      "field_id": "TX-FARM-001-FIELD-03",
      "soil_type": "clay_loam"
    }
  },
  "soil_moisture": {
    "measurement_type": "volumetric",
    "unit": "percent",
    "layers": [
      {
        "depth_range_cm": [0, 10],
        "depth_center_cm": 5,
        "moisture_percent": 12.5,
        "saturation_percent": 45.0,
        "field_capacity_percent": 35.0,
        "wilting_point_percent": 15.0,
        "available_water_percent": 20.0,
        "stress_level": "severe"
      },
      {
        "depth_range_cm": [10, 30],
        "depth_center_cm": 20,
        "moisture_percent": 18.3,
        "saturation_percent": 45.0,
        "field_capacity_percent": 35.0,
        "wilting_point_percent": 15.0,
        "available_water_percent": 20.0,
        "stress_level": "severe"
      },
      {
        "depth_range_cm": [30, 60],
        "depth_center_cm": 45,
        "moisture_percent": 22.1,
        "saturation_percent": 45.0,
        "field_capacity_percent": 35.0,
        "wilting_point_percent": 15.0,
        "available_water_percent": 20.0,
        "stress_level": "high"
      },
      {
        "depth_range_cm": [60, 100],
        "depth_center_cm": 80,
        "moisture_percent": 25.8,
        "saturation_percent": 45.0,
        "field_capacity_percent": 35.0,
        "wilting_point_percent": 15.0,
        "available_water_percent": 20.0,
        "stress_level": "moderate"
      }
    ]
  },
  "measurement_info": {
    "method": "TDR",
    "sensor_model": "Campbell CS616",
    "calibration_date": "2025-01-15",
    "accuracy_percent": 2.0
  },
  "historical_comparison": {
    "30day_mean_percent": 28.5,
    "same_date_mean_percent": 31.2,
    "percentile": 12.5,
    "anomaly_percent": -12.9
  },
  "metadata": {
    "source_organization": "Texas A&M AgriLife",
    "processing_date": "2025-12-26T15:00:00Z",
    "quality_flag": "good",
    "confidence_score": 0.95
  }
}

Soil Moisture Stress Levels

The stress level is determined from the available water fraction, i.e. (current moisture − wilting point) ÷ (field capacity − wilting point). In the example above, the 0-10 cm layer (12.5%) is below the wilting point (15%) and the 10-30 cm layer (18.3%) is at about 17% of available water, so both are "severe"; the 30-60 cm layer (22.1%) is at about 36%, which is "high"; and the 60-100 cm layer (25.8%) is at about 54%, which is "moderate".

Stress Level	Moisture Range	Agricultural Impact	Management Action
None	More than 80% of available water (> 80%)	Optimal growing conditions	Normal operations
Low	60-80% of available water	Minimal crop stress	Monitor closely
Moderate	40-60% of available water	Beginning crop stress	Prepare irrigation
High	20-40% of available water	Significant crop stress	Irrigate immediately
Severe	Less than 20% of available water (< 20%)	Crop damage occurring	Emergency irrigation

NDVI Vegetation Index Schema

The Normalized Difference Vegetation Index from satellite imagery provides early drought detection through vegetation health monitoring.

NDVI Data Structure

{
  "wia_version": "1.0",
  "data_type": "ndvi",
  "timestamp": "2025-12-20T10:30:00Z",
  "location": {
    "type": "Polygon",
    "coordinates": [
      [
        [-96.8050, 32.7850],
        [-96.7890, 32.7850],
        [-96.7890, 32.7680],
        [-96.8050, 32.7680],
        [-96.8050, 32.7850]
      ]
    ],
    "properties": {
      "region": "North Texas",
      "area_hectares": 64.5,
      "crop_type": "winter_wheat"
    }
  },
  "ndvi": {
    "value": 0.45,
    "range": [-1.0, 1.0],
    "interpretation": "moderate_vegetation",
    "anomaly": -0.15,
    "percentile": 22.5,
    "zscore": -1.3
  },
  "reflectance": {
    "red_band": 0.093,
    "nir_band": 0.245,
    "calculation": "(NIR - Red) / (NIR + Red)"
  },
  "satellite_info": {
    "satellite": "MODIS Terra",
    "sensor": "MOD13Q1",
    "resolution_m": 250,
    "cloud_cover_percent": 5.2,
    "quality_assessment": "good",
    "acquisition_date": "2025-12-20",
    "processing_level": "L3"
  },
  "historical_comparison": {
    "same_period_mean": 0.60,
    "same_period_stddev": 0.12,
    "10year_max": 0.78,
    "10year_min": 0.32,
    "current_rank_percentile": 18
  },
  "drought_indicators": {
    "vegetation_stress": "moderate",
    "drought_likelihood": "probable",
    "trend_14day": "declining",
    "rate_of_change": -0.03
  },
  "metadata": {
    "source_organization": "NASA EOSDIS",
    "processing_date": "2025-12-21T03:15:00Z",
    "quality_flag": "good",
    "atmospheric_correction": "6S algorithm"
  }
}

NDVI Interpretation Guidelines

NDVI Range	Classification	Vegetation Health	Drought Indication
0.8 - 1.0	Dense Vegetation	Excellent	No drought stress
0.6 - 0.8	Moderate to Dense	Good to Very Good	Minimal stress
0.4 - 0.6	Moderate Vegetation	Fair to Good	Possible drought stress
0.2 - 0.4	Sparse Vegetation	Poor	Probable drought stress
0.0 - 0.2	Very Sparse	Very Poor	Severe drought stress
< 0.0	No Vegetation/Water/Snow	N/A	N/A

Evapotranspiration Data Schema

Evapotranspiration (ET) represents water loss from soil and plant surfaces, a critical component of drought assessment.

{
  "wia_version": "1.0",
  "data_type": "evapotranspiration",
  "timestamp": "2025-07-15T00:00:00Z",
  "location": {
    "type": "Point",
    "coordinates": [-120.6625, 35.3050],
    "properties": {
      "region": "California Coast",
      "station_id": "CIMIS-043"
    }
  },
  "evapotranspiration": {
    "et0_mm_day": 4.2,
    "etc_mm_day": 3.8,
    "kc_coefficient": 0.90,
    "crop_type": "almond_orchard",
    "growth_stage": "mid_season"
  },
  "weather_inputs": {
    "temperature_max_c": 24.5,
    "temperature_min_c": 12.3,
    "solar_radiation_mj_m2": 22.4,
    "wind_speed_2m_ms": 2.1,
    "relative_humidity_percent": 55.0,
    "precipitation_mm": 0.0
  },
  "calculation_method": {
    "algorithm": "FAO-56 Penman-Monteith",
    "reference": "FAO Irrigation and Drainage Paper No. 56 (Allen et al., 1998)",
    "quality_assessment": "high"
  },
  "water_balance": {
    "daily_deficit_mm": 3.8,
    "7day_deficit_mm": 26.6,
    "30day_deficit_mm": 95.4,
    "cumulative_season_mm": 785.2
  },
  "metadata": {
    "source_organization": "California CIMIS",
    "processing_date": "2025-07-15T06:00:00Z",
    "quality_flag": "excellent",
    "confidence_score": 0.96
  }
}

Data Validation and Quality Control

All WIA-compliant data must undergo validation to ensure accuracy and reliability. The standard defines required quality control procedures and metadata.

Required Quality Metadata Fields

Field	Type	Purpose	Valid Values
quality_flag	string	Overall data quality assessment	excellent, good, fair, poor, invalid
confidence_score	number	Numerical confidence (0-1)	0.0 to 1.0
validation_method	string	How data was validated	ground_truth, cross_validation, model_based
data_completeness	number	Percentage of expected data available	0.0 to 1.0
uncertainty_estimate	number	Estimated measurement uncertainty	Varies by parameter