Drought Monitor

Standardised Precipitation Index (SPI)

The Standardised Precipitation Index (SPI) is a widely used index to characterise meteorological drought on a range of timescales. SPI shows the actual precipitation compared to the probability of precipitation for various time frames.

The SPI is an index based on precipitation only. It can be used on a variety of time scales, which allows it to be useful for both short-term agricultural and long-term hydrological applications.

A drought event occurs when the SPI is continuously negative and reaches an intensity of -1.0 or less. The drought event is considered to be ongoing until SPI reaches a value of 0. The event ends when the SPI becomes positive.

Each drought event, therefore, has a duration defined by its beginning and end, and an intensity for each month that the event continues. The positive sum of the SPI for all the months within a drought event can be termed the drought’s “magnitude”.

The ability of SPI to be calculated at various timescales allows for multiple applications. SPI values for 3 months or less might be useful for basic drought monitoring, values for 6 months or less for monitoring agricultural impacts and values for 12 months or longer for hydrological impacts.

Key Strengths:

  • Uses precipitation only (reduced data needs)
  • Can characterise drought at different time scales.

Key Limitations:

  • As a measure of water supply only, the SPI does not account for evapotranspiration, and this limits its ability to capture the effect of increased temperatures (associated with climate change) on moisture demand and availability.
  • Sensitive to the quantity and reliability of the data used to fit the distribution; 30-50 years recommended.
  • Does not consider the intensity of precipitation and its potential impacts on runoff, streamflow, and water availability within the system of interest.

Standardised Precipitation Evapotranspiration Index (SPEI)

The SPEI is a relatively new drought index based on the Standardised Precipitation Index (SPI), however it includes an evapotranspiration component as well as precipitation, allowing the index to account for the effect of temperature on drought development through a basic water balance calculation. It can be calculated for time steps of as little as 1 month up to 48 months or more. Monthly updates allow it to be used operationally and the longer the time series of data available, the more robust the results will be. The index can be used for determining the onset, duration and magnitude of drought conditions with respect to normal conditions in a variety of natural and managed systems such as pastures, crops, ecosystems, rivers and water resources.

Key Strengths:

  • The inclusion of temperature along with precipitation data allows the SPEI to account for the impact of temperature on a drought situation.
  • The output is applicable for all climate regimes, with the results being comparable because they are standardised. With the use of temperature data, SPEI is an ideal index when looking at the impact of climate change in model output under various future scenarios.

Key Limitations:

  • The requirement for a serially complete dataset for both temperature and precipitation may limit its use due to insufficient data being available.
  • Sensitive to the quantity and reliability of the data used to fit the distribution; 30-50 years recommended.

Self-Calibrated Palmer Drought Severity Index (sc-PDSI)

The Self-Calibrated Palmer Drought Severity Index (sc-PDSI) is a variant of the Palmer Drought Severity Index (PDSI), which is calibrated dynamically based upon the characteristics present at each location, giving it the ability to adapt to local climate.

The Palmer Drought Severity Index (PDSI), also known operationally as the Palmer Drought Index (PDI), is the longest used index for monitoring agricultural drought. It attempts to measure long-term drought based on current weather patterns plus the cumulative patterns of previous months.

The PDSI allows for a categorisation of various levels of wetness and dryness that are prominent over an area. The PDSI uses temperature and precipitation data along with information on the water-holding capacity of soils to estimate soil moisture levels. It takes into account moisture received (precipitation) as well as moisture stored in the soil, and accounts for the potential loss of moisture due to temperature influences.

Palmer values may lag emerging droughts by several months. It has been reasonably successful at quantifying long-term drought. Monthly PDSI values do not capture droughts on time scales less than about 12 months.

Key Strengths:

  • Effective in determining long-term drought, especially over low and middle latitudes.
  • The use of surface air temperature, soil data and a physical water balance model makes it quite robust for identifying drought.
  • Takes precedent (prior month) conditions into account.

Key Limitations:

  • sc-PDSI has a timescale of approximately nine months, which leads to a lag in identifying drought conditions based upon simplification of the soil moisture component within the calculations. This lag may be up to several months, which is a drawback when trying to identify a rapidly emerging drought situation.

Palmer Z-Index

The Palmer Z Index can be used to track short-term agricultural drought on a monthly scale, since it responds quickly to changes in soil moisture. The index is sometimes referred to as the ‘Moisture Anomaly Index’, and the derived values provide a comparable measure of the relative anomalies of a region for both dryness and wetness when compared to the entire record for that location. The Palmer Z Index is calculated on a monthly scale along with PDSI output as the moisture anomaly.

Key Strengths:

  • Responds to short-term conditions better than the sc-PDSI.
  • The use of surface air temperature, soil data and a physical water balance model makes it quite robust for identifying drought.
  • Takes precedent (prior month) conditions into account.

Key Limitations:

  • Greater data needs

Australian Combined Drought Indicator

The Australian Combined Drought Indicator (CDI) is based on the U.S. Drought Monitor (USDM) concept, which was developed at the National Drought Mitigation Center at the University of Nebraska-Lincoln in the late 1990s. The Australian CDI for the Northern Australia Climate Program (NACP) is a scaled down version of the U.S. Drought Monitor, using only four selected drought indicators.

The Australian CDI uses a combination of rainfall, soil moisture, evapotranspiration and Normalized Difference Vegetation Index (NDVI) from satellite to produce a drought indicator tailored for Australia.

Key Strengths:

  • The CDI is specifically calibrated for accuracy within Australia

Key Limitations:

  • The Australian CDI is a relatively new tool that is still being assessed by our Climate Mates, Extension Officers, and producers who are providing feedback on its capacity to accurately represent on-ground conditions.

Normalised Difference Vegetation Index (NDVI)

The Normalised Difference Vegetation Index (NDVI) is a measure of vegetation "greenness" as observed by satellite. The satellite data is sourced from the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites operated by the U.S.

The value of the NDVI depends upon greater density and "greenness" of the plant canopy. NDVI decreases as leaves come under water stress, become diseased, or die.

Key Strengths:

  • Since data is sourced by satellite, it offers high-quality continuous coverage of areas outside of stations.

Key Limitations:

  • The NDVI standardised anomaly analysis using a record of 17 years may be less robust than analyses using rainfall (available for 100+ years)
  • NDVI is not an absolute measure of primary production.
  • Areas of reduced coverage or reduced data quality exist, sometimes during winters due to very low sun elevations.

Total Rootzone Soil Moisture

Total Rootzone Soil Moisture is a measure of the percentage of water stored in the top one metre root-zone soil profile. The data is sourced from the Australian Landscape Water Balance model (AWRA-L v6), which simulates the available water content using air temperature, precipitation, solar radiation, and wind data and a sophisticated water balance model.

Key Limitations:

  • Simulates available water content only, and is not a measure of primary production.