The Evaporative Demand Drought Index: The Physical Basis.

Thursday, 18 December 2014
Mike Hobbins, National Integrated Drought Information System, Boulder, CO, United States, Daniel McEvoy, University of Nevada Reno, Reno, NV, United States, Justin Lee Huntington, Desert Research Institute Reno, Reno, NV, United States, Andrew W Wood, National Center for Atmospheric Research, Boulder, CO, United States and James P Verdin, USGS/EROS, Boulder, CO, United States
Operational drought monitoring has long suffered from poor representation of evaporative dynamics, with popular drought indices—such as the PDSI that informs much of the US Drought Monitor (USDM)—primarily relying on precipitation and temperature (T) to represent hydroclimatic anomalies. When evaporative demand (Eo) has been used, it has most often been derived from poorly performing T-based parameterizations that are then used in LSMs to derive actual evapotranspiration (ET). Our goal is to create a drought index that both improves the representation of evaporative dynamics in drought and offers a useful leading indicator of both flash and sustained droughts. This presentation will outline the physical basis for such an index: the Evaporative Demand Drought Index (EDDI).

EDDI measures the physical response of Eo to surface drying anomalies that occur due to two distinct land surface/atmosphere interactions. In sustained drought, when moisture is limited at the land surface, Eo and ET vary in a complementary relationship: as ET declines, Eo increases due to the energy balance tipping to favor sensible heating. In flash droughts (before surface moisture limitations), ET and Eo both rise in response to increases in advection, radiation, or temperature, or decreases in humidity. Thus, Eo rises in response to both drought types (in contrast to ET-based drought measures), suggesting strong potential for use as a leading indicator of both drought types.

For Eo, we use reference ET (ETo) from the ASCE Standardized Reference ET equation forced by North American Land Data Assimilation System (NLDAS) drivers (T, specific humidity, downwards SW radiation, and wind speed), run daily across CONUS from 1980 to the present. Anomalies from a 30-year climatological mean ETo are accumulated across a given time-window and double-standardized: first by standardizing the anomaly against climatologic ETo; then generating a Z–score. Positive EDDI indicates drier than normal conditions (and so, drought).

This poster will summarize these different responses and how they combine to make a useful drought indicator such as EDDI, examine the long-term performance of EDDI against the USDM in four basins from across CONUS’s hydroclimatic spectrum, and demonstrate the promise of EDDI as a leading indicator of drought.