The Evaporative Demand Drought Index (EDDI) uses atmospheric evaporative demand (E0) anomalies across a timescale of interest relative to its climatology to indicate the spatial extent and severity of drought. The E0 is calculated using the Penman-Monteith FAO56 reference evapotranspiration formulation (0.5-m tall reference crop), driven by data on temperature, humidity, wind speed, and incoming solar radiation, with these data extracted from the operational North American Land Data Assimilation System (NLDAS-2) dataset. For a particular time-window, EDDI is estimated by standardizing the E0 anomalies relative to the same accumulation time-window in the whole period of record (1979-present), using a rank-based non-parametric method described in Hobbins et al. (2016; see the "Resources" tab). EDDI data are available at a ~12-km resolution (0.125° lat and long) across CONUS since January 1, 1980, and are updated daily.
On the EDDI Category Maps, colors indicate the frequency at which the observed E0 anomaly has occurred in the climatology, with warm colors indicating conditions that are drier than normal and cool colors indicating wetter-than-normal conditions. As an example, the ED4 category indicates that the current E0 anomaly has only been observed less than 2% of the time in the past 38 years (1979-2016), which represents the most severe drought conditions; the EW4 category means indicates that the anomaly has been exceeded 98% of the time, which represents the wettest conditions. For plotting purposes, EDDI values are binned into different percentile categories analogous to the U.S. Drought Monitor plots—however, in case of EDDI plots, both drought and anomalously wet categories are shown.
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.
Related Web Pages
- 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 (i) NOAA’s Research Transition Acceleration Program (RTAP) for the project titled “Operationalizing an Evaporative Demand Drought Index (EDDI) service for drought monitoring and early warning;” (ii) NOAA’s Sectoral Applications Research Program (SARP): Coping with Drought in Support of the National Integrated Drought Information System (NIDIS) program for the project titled “Developing a wildfire component for the NIDIS California Drought Early Warning System;” and (iii) DOI's North Central Climate Science Center for the project titled "Evaporation, Drought, and the Water Cycle across Timescales.”