EDDI is a drought indicator that uses atmospheric evaporative demand (E0) anomalies across a time window of interest relative to its climatology to indicate the spatial extent and severity of drought.
The EDDI forecasts come from the CFS-gridMET dataset, which is generated daily from 4 CFS ensemble forecasts initialized at 6-hour intervals from 3 consecutive days to create a 48-member ensemble of forecasts. These raw CFS forecasts are downscaled utilizing bias-correction techniques with the gridMET climate dataset (4-km gridded resolution) as training data. Reference evapotranspiration is created using the Penman Monteith FAO56 formulation with the CFS-gridMET variables for min/max temperature, specific humidity, downward solar radiation and wind speed.
Reference evapotranspiration over the time period 1979-2018 using non-parametric probability-based methods where plotting positions are transformed to indices assuming an inverse-normal distribution.
EDDI is estimated by standardizing the reference evapotranspiration anomalies relative to the years 1979-2018, using a non-parametric method (see Hobbins et al., 2016).
The EDDI forecasts for each of the time periods are created by standardizing reference evapotranspiration totals for the next 1-7 days (Week 1), 8-14 days (Week 2), 15-21 days (Week 3), 22-28 days (Week 4), 1-14 days (Week 1-2), 1-21 days (Week 1-3) and 1-28 days (Week 1-4). The ET totals over a week are used to create a 1-week EDDI, while the ET totals over 2,3 or 4 weeks are used to create a 2-week, 3-week or 4-week EDDI.
EDDI has the potential to offer early warning of agricultural drought, hydrologic drought, and fire-weather risk by providing real-time information on the emergence or persistence of anomalous evaporative demand in a region. A particular strength of EDDI is in capturing the precursor signals of water stress at weekly to monthly timescales, which makes EDDI a strong tool for drought preparedness at those timescales.
For more information regarding these data, please contact John Abatzoglou (email@example.com).
- Abatzoglou, J. T. (2013): Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131. doi: 10.1002/joc.3413
- Suranjana Saha, and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015.1057. doi: 10.1175/2010BAMS3001.1
- Michael Hobbins, Andrew Wood, Daniel McEvoy, Justin Huntington, Charles Morton, James Verdin, Martha Anderson, and Christopher Hain (June 2016): The Evaporative Demand Drought index: Part I – Linking Drought Evolution to Variations in Evaporative Demand. J. Hydrometeor., 17(6),1745-1761. doi:10.1175/JHM-D-15-0121.1
- Daniel J. McEvoy, Justin L. Huntington, Michael T. Hobbins, Andrew Wood, Charles Morton, James Verdin, Martha Anderson, and Christopher Hain (June 2016) The Evaporative Demand Drought index: Part II – CONUS-wide Assessment Against Common Drought Indicators. J. Hydrometeor., 17(6), 1763-1779. doi:10.1175/JHM-D-15-0121.1
This work is supported in part by grants from
- NOAA’s Sectoral Applications Research Program (SARP): Coping with Drought in Support of the National Integrated Drought Information System (NIDIS) program for the project, “Developing a wildfire component for the NIDIS California Drought Early Warning System"
- NOAA through the RISA program support to Climate Impacts Research Consortium (CIRC)
- NIDIS “Expansion of DEWS Activities Through the Development of New Technologies to Improve Processing Speeds, Access, and Visualization of Drought Metrics, Impacts, and Forecasts”