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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #348762

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States

item Anderson, Martha

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2018
Publication Date: 10/19/2018
Citation: Otkin, J., Zhong, Y., Lorenz, D., Anderson, M.C., Hain, C. 2018. Exploring seasonal and regional relationships between the Evaporative Stress Index and surface weather and soil moisture anomalies across the United States. Journal of Hydrometeorology. 22:5373-5386.

Interpretive Summary: Routine drought indices that provide timely information about agricultural drought conditions and potential impacts on yield will benefit monitoring programs in the United States and globally. This paper investigates factors that are important in driving moisture-related signals the Evaporative Stress Index (ESI), a remote sensing drought indictor that characterizes anomalies in evapotranspiration (ET), or crop water use. Anomalously low ET rates (negative ESI) for a given point in the growing season indicate poor crop health resulting from soil moisture deficits or other stressors, and being able to pinpoint these regions early in the season facilitates timely mitigation response – especially in cases of rapid-onset, or “flash”, droughts. This paper uses correlation analyses to determine land surface and atmospheric properties that are most related to ESI variability, to better understand ESI information content and drought response behavior. The analysis indicates that ET anomalies from remote sensing are most strongly correlated with soil moisture and vapor pressure deficit, which are both strong controls on land-surface evaporative flux. Lower but still significant correlations were obtained with precipitation and air temperature. This is consistent with prior findings that temperature and rainfall departures are less strongly related to the occurrence of flash droughts, whereas high atmospheric vapor pressure deficits can be very effective at rapidly depleting root zone soil moisture through high transpiration and soil evaporation rates.

Technical Abstract: This study used correlation analyses to explore relationships between the satellite-derived Evaporative Stress Index (ESI) – which depicts standardized anomalies in an actual to reference evapotranspiration fraction – and various land and atmospheric variables that impact evapotranspiration. Correlations between the ESI and forcing variable anomalies calculated over sub-seasonal time scales were computed at weekly and monthly intervals during the growing season. Overall, the results revealed that the ESI is most strongly correlated to anomalies in soil moisture and 2-m dew point depression. Correlations between the ESI and precipitation were also large across most of the United States; however, they were typically smaller than those associated with soil moisture and vapor pressure deficit. In contrast, correlations were much weaker for air temperature, wind speed, and radiation across most of the U.S., with the exception of the south-central U.S. where correlations were large for all variables at some point during the growing season. Together, these results indicate that changes in soil moisture and near-surface atmospheric vapor pressure deficit are better predictors of the ESI than precipitation and air temperature anomalies are by themselves. Large regional and seasonal dependencies were also observed for each forcing variable. Each of the regional and seasonal correlation patterns were similar for ESI anomalies computed over 2-, 4-, and 8-wk time periods; however, the maximum correlations increased as the ESI anomalies were computed over longer time periods and also shifted toward longer averaging periods for the forcing variables.