S222 Monitoring Drought with Vegetation and Soil Moisture Data

Sunday, 22 January 2017
4E (Washington State Convention Center )
Theodore M.I. Hartman, Iowa State University, Ames, IA; and J. L. Matthews and J. E. Bell

Monitoring Drought with Vegetation and Soil Moisture Data

Theodore M.I. Hartman, Jessica L. Matthews and Jesse E. Bell


Iowa State University, Hollings Scholar Student

National Center for Environmental Information (NCEI)

Drought is a devastating natural phenomenon that is challenging to forecast and detect. Through soil moisture deficiency, drought has a deleterious effect on vegetation, making vegetation health indicator variables a good metric to show the evolution and extent of drought. Because of this, understanding and making use of the near-real time vegetation and soil moisture data is important for the detection of drought. The relationship between these parameters in drought conditions provides important information for the detection and mitigation process of future droughts. Using the 2012 Midwest US drought case, the responses of two soil variables, moisture and temperature, at five soil depths measured at USCRN stations, and normalized difference vegetation index, leaf area index, and fraction of absorbed photosynthetically active radiation (NDVI, LAI, and FAPAR) vegetation data are analyzed to diagnose if these data records are correlated to drought conditions. Drought determinations were made with comparisons to a thirty-year (1981-2010) climatology of vegetation climate data records. Data from 2011-2013 was used to assess pre-, during, and post-drought conditions. Linear regressions between soil and vegetation observation ratios at each USCRN station were performed to compute correlations. Comparisons of vegetation-based drought indicators were made to the United States Drought Monitor. Results show vegetation and both soil variables are significantly correlated. The strongest correlation found is with LAI and the five centimeter soil moisture observations. This study demonstrates the usefulness of vegetation and soil moisture and temperature climate record data for the detection of drought conditions.

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