Physically Based Evaporative Demand as a Drought Metric: Historical Analysis and Seasonal Prediction
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Lack of sufficient early warning from drought monitoring and prediction tools that rely heavily on precipitation and soil moisture has prompted the need for development of new drought metrics that can account for evaporative dynamics and interactions between the land surface-atmosphere interface. Previous studies have shown that using anomalies in actual evapotranspiration (ET) estimated from satellite imagery can provide some drought early warning during the growing season, but there are a number of limitations to satellite monitoring that encourage more research on easily accessible and real-time (or forecasted) evaporative demand (E0) drought tools. The focus of this study is the development of a novel drought metric that relies only physically based E0 driven by temperature, wind speed, solar radiation, and humidity, which can all be obtained from gridded weather data and dynamical forecast model output. An evaluation of several gridded data products was first carried out using the Nevada Climate-ecohydrological Assessment Network (NevCAN) in a remote part of the Great Basin to investigate biases and deficiencies that are inherent to regions with sparse observations. It was determined that the University of Idaho Gridded Meteorological Data (METDATA) was most suitable to drive a new drought index, the Evaporative Demand Drought Index (EDDI). EDDI was computed over CONUS for the period of 1979-2013 and compared against other commonly used drought indices. During rapid onset drought, or flash drought (i.e., 2011-2012 in the Midwest) EDDI was found to lead other indicators by as much as 1-3 months. Given that E0 contains no precipitation input, the potential exists to improve seasonal drought predictions, which currently suffer from a lack of skillful precipitation forecasts. Skill of seasonal E0 anomaly forecasts were assessed over CONUS using the Climate Forecast Version 2 (CFSv2) hindcasts for 1982-2009 and METDATA was used as baseline observations. E0 forecast skill during drought events was consistently greater than precipitation, with much improved skill over parts of the central and northeast U.S. during the growing season. Results from this study suggest that continued efforts should be put towards incorporating physically based E0 in operational drought monitoring and prediction frameworks.