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Short- and Long-Term Drought Indicator Blends

Associated Agencies

University of California, Merced and the Desert Research Institute

These experimental drought blends integrate several key drought monitoring products and indices into a single short-term or long-term product, based on the methodology developed at the NOAA Climate Prediction Center (CPC). These blends are created using the Climate Engine tool, and apply the CPC weighting ratios to the high-resolution gridMET gridded dataset. The data is updated daily, with a delay of 2 to 3 days to allow for data collection and quality control.

The short-term blend combines PDSI, Z-Index, 1-month SPI, and 3-month SPI to estimate the overall short-term drought. 

The long-term blend combines PDSI, Z-Index, and 6-month, 1-year, 2-year, and 5-year SPI to estimate the overall long-term drought. 


Climate Engine: Access the short- and long-term drought indicator blends within Climate Engine.  

How To

To access the short-term and long-term drought blends in Climate Engine:

  • In the "Type" dropdown, select "Climate and Hydrology"
  • In the "Dataset" dropdown, select "gridMET Drought"
  • In the "Variable" dropdown, select either "Short-Term Drought Blend" or "Long-Term Drought Blend"
  • Select the end date for the time period you'd like to view
  • Click "Get Map Layer"


A blend of different drought indices for short and long term time scales can be useful to understand short and long term drought for a region. The experimental short- and long-term objective blends produced by the CPC are an example of blends being produced as a weighting of percentiles for different drought metrics from past data where the weights are based on expert judgment from drought experts. The blends produced by Climate Engine are instead produced as a weighting of standardized indices for the same drought metrics. The metrics that go into the experimental CPC blend are: 

  • Palmer-Z Index (Z)
  • Palmer Drought Severity Index (PDSI)
  • Standardized Precipitation Index (SPI): 30-day, 90-day, 180-day, 1-year, 2-year, and 5-year
  • Palmer Hydrological Drought Index (PDHI)
  • Soil Moisture from NOAH (SM-NOAH)

The Climate Engine blends use a weighting of the standardized indices coming from drought indices calculated from the gridMET data product (also in Climate Engine and based on 1981-2016). The weightings are based on the experimental CPC blends with some differences:

  • The weightings for SM-NOAH (soil moisture from NOAH) and PHDI (Palmer Hydrological Drought Index) are added in with the weights for PDSI. Only PDSI is used in the blend construction to represent soil moisture. 
  • In the construction, the Palmer drought indices (i.e., Z and PDSI) will be divided by 2 to put them on roughly the same scale as the standardized indices
  • The colors and bins used to visualize the drought blends will be the U.S. Drought monitor colors with non-linear standardized index bins

The precise details of the construction of the blends is: 

Short-term Blend= 0.2 *(PDSI/2) + 0.2 * SPI30d + 0.25 * SPI90d + 0.35 * (Z/2) 

  • PDSI = Palmer Drought Severity Index
  • Z = Palmer's Z-Index
  • SPI30d = 30-day Standardized Precipitation Index (SPI)
  • SPI90d = 90-day Standardized Precipitation Index (SPI)

Long-term Blend= 0.35 *(PDSI/2) + 0.15 * SPI180d + 0.2 * SPI1y + 0.2 *SPI2y + 0.1 * SPI5y 

  • PDSI = Palmer Drought Severity Index
  • SPI180d = 180-day Standardized Precipitation Index (SPI)
  • SPI1y = 1-year Standardized Precipitation Index (SPI)
  • SPI2y = 2-year Standardized Precipitation Index (SPI)
  • SPI5y = 5-year Standardized Precipitation Index (SPI)

Related Products and Research

The blends produced by the Climate Engine are one of several efforts in the drought science community to combine multiple indices into an objective blended dataset, and showcase a unique methodology in utilizing the cloud to efficiently and flexibly generate blended products. 

The NOAA Climate Prediction Center has produced their experimental blends product based on CPC Climate Division data for over 10 years. 

The National Drought Mitigation Center is developing a new generation of objective blends using Principle Component Analysis to determine the weighting factors. 

NIDIS is also funding research by NASA to determine the weighting factors through the use of machine learning analysis trained on the U.S. Drought Monitor as an automated method to reproduce the weights typically given by the U.S. Drought Monitor authors through their convergence of evidence approach.

These approaches are not meant to replace the U.S. Drought Monitor, but rather to develop enhanced information for the authors to use in their weekly analysis, as well as developing a framework to explore other types of blends optimized for various sectors and types of drought.