The Evaporative Demand Drought Index (EDDI): A Brief Overview and Discussion

The Evaporative Demand Drought Index (EDDI): A Brief Overview and Discussion

Predicting the onset, severity, and duration of drought remains a challenge due to the number of interconnected variables that contribute to drought evolution and recovery (e.g., soil moisture, soil type, vegetation, land-use, topography, antecedent vegetative state/stress levels, and atmospheric circulation).  The interactions among these variables span spatial and temporal scales, increasing the complexity of drought prediction.

In recent decades, more robust products to monitor and forecast drought evolution, severity, and spatial extent have been developed through applied hydroclimate research, satellite data, in-situ data, and GIS.  Examples of such products include the US Drought Monitor, drought severity indices such as the Palmer Drought Severity Index, the Palmer Hydrologic Drought Index, and MODIS-derived indices developed from MODIS Aqua and Terra platforms (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), Normalized Difference Drought Index (NDDI)). However, a newly developed and experimentally implemented index, the EDDI, is now operational.  The information/insight it provides to drought prediction and monitoring compared to other publicly available drought tools with be the focus of the remaining discussion.

The EDDI calculates the atmosphere’s influence on the rate of land surface (land surface is inclusive of soil, vegetation, and land use) evaporation and evapotranspiration across varying spatial and temporal scales. More simply stated, it quantifies the drying power (evaporative demand) that the overlying atmosphere has on the land surface. This differs from other products in that those products quantify the current dryness of the land surface to provide a starting point for determining how future atmospheric conditions may exacerbate or alleviate drought conditions. Because the EDDI determines evaporative demand, it highlights spatial areas in which hydroclimates and vegetation are likely to experience water stress, one precursor to flash droughts, and a characteristic of on-going drought conditions.

Developed using the North American Land Data Assimilation System (NLDAS), the EDDI is available from 1980 and can be computed across time scales of 1 week to 12 months.  NLDAS assimilates satellite and surface observations into a quality controlled, reanalysis land-surface model (LSM) dataset that captures soil moisture storage and energy fluxes not captured by other drought indices.  NLDAS runs operationally in near real time (~4-day lag) on a 1/8^th degree grid (approx. 10km resolution) and is available at an hourly time step. The EDDI does not include observed precipitation to quantify the index value (many other operational indices do). Because it is developed from NLDAS, the EDDI can be broken down into the primary driving soil moisture and energy flux variables that are driving evaporative demand across a given area for the specified time.

References:

• https://ldas.gsfc.nasa.gov/nldas
• https://journals.ametsoc.org/view/journals/hydr/17/6/jhm-d-15-0122_1.xml
• https://journals.ametsoc.org/view/journals/hydr/17/6/jhm-d-15-0121_1.xml
• https://psl.noaa.gov/eddi/