Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index

Abstract

Reliable indicators of rapid drought onset can help to improve the effectiveness of drought early warning systems. In this study, the evaporative stress index (ESI), which uses remotely sensed thermal infrared imagery to estimate evapotranspiration (ET), is compared to drought classifications in the U.S. Drought Monitor (USDM) and standard precipitation-based drought indicators for several cases of rapid drought development that have occurred across the United States in recent years. Analysis of meteorological time series from the North American Regional Reanalysis indicates that these events are typically characterized by warm air temperature and low cloud cover anomalies, often with high winds and dewpoint depressions that serve to hasten evaporative depletion of soil moisture reserves. Standardized change anomalies depicting the rate at which various multiweek ESI composites changed over different time intervals are computed to more easily identify areas experiencing rapid changes in ET. Overall, the results demonstrate that ESI change anomalies can provide early warning of incipient drought impacts on agricultural systems, as indicated in crop condition reports collected by the National Agricultural Statistics Service. In each case examined, large negative change anomalies indicative of rapidly drying conditions were either coincident with the introduction of drought in the USDM or lead the USDM drought depiction by several weeks, depending on which ESI composite and time-differencing interval was used. Incorporation of the ESI as a data layer used in the construction of the USDM may improve timely depictions of moisture conditions and vegetation stress associated with flash drought events.

Monitoring the Impacts of Severe Drought on Southern California Chaparral Species using Hyperspectral and Thermal Infrared Imagery

“…These complementary capabilities give hyperspectral data strong potential for monitoring drought impacts on vegetation [25,26], but a paucity of hyperspectral time series datasets has prevented the development of drought monitoring applications. Thermal infrared time series data also have considerable potential for monitoring drought due to drought impacts on evapotranspiration and canopy temperature [27][28][29][30]. NASA has proposed the Hyperspectral Infrared Imager (HyspIRI) satellite mission, which would combine a hyperspectral imaging spectrometer covering the visible, near infrared, and shortwave infrared spectral regions (VSWIR) with a multispectral thermal infrared (TIR) sensor [31].…”

A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations

“…However, since satellite-derived soil moisture is a relatively new product most operational remote sensing-based drought indicators rely on rainfall [39], vegetation [40], a combination of rainfall and temperature [32,33,41], rainfall and evapotranspiration [34] or land surface temperature and a surface energy balance model [42,43]. Neglecting the soil moisture component in drought monitoring is also critical due to erroneous satellite-derived rainfall observations [44,45].…”

Forecasting and Monitoring Agricultural Drought in the Philippines

“…In the U.S., the Department of Agriculture (USDA) currently employs an agricultural drought index called Evaporative Stress Index (ESI), which can detect rapid onset of drought events or flash droughts (Otkin et al, 2013;Otkin et al, 2014). ESI denotes standardized anomalies in the ratio of actual evapotranspiration to potential evapotranspiration, indicating areas with anomalously high or low rates of water use across the U.S.…”

“…To measure the likelihood of drought, the revised ESI (RESI) was used, which assessed the presence and severity of drought events. ESI developed to detect flash droughts (Otkin et al, 2013) was modified by computing the standardization based on the entire time series instead of calculating it for each month. This will capture the month-to-month variability in the local conditions of the environment.…”