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Soil-based Flash Drought Identification and Prediction

Presentation Date
Wednesday, January 31, 2024 at 2:00pm - Wednesday, January 31, 2024 at 2:15pm
The Baltimore Convention Center - 318/319



Flash droughts are caused by heat, atmospheric aridity, precipitation deficit, or their combinations, and are often manifested as a rapid decrease of soil moisture. While multiple drought monitors based on various hydrometeorological variables are currently in use, soil moisture provides the most direct measurement of drought severity and impact. Another important variable reflecting drought impact is plant photosynthesis, which responds to both soil moisture stress and increased atmospheric aridity. The variation of plant photosynthesis can be captured by the spaceborne solar-induced chlorophyll fluorescence (SIF), which provides an emerging technology for drought impact monitoring. Moreover, our earlier study showed that a strong deviation of SIF trajectory from its climatological norm during the early stage of drought development, as reflected by a large negative value of the SIF Rapid Change Index (RCI), can forecast flash drought occurrence with a lead time of weeks to months for two major U.S. flash drought events. In this study, based on multiple gridded soil moisture datasets in combination with information on soil hydraulic properties, we develop an inventory of droughts and flash droughts in the U.S., and compare with the U.S. Drought Monitor (and other drought indices) to examine commonality and differences. The data is then compared with SIF data to identify hotspots where flash droughts have the most impact on terrestrial ecosystems including agriculture. Meanwhile, we make use of multiple products of SIF to derive the RCI over the US during the period. The resulting SIF RCI data are then compared with flash drought inventories to assess drought predictability and the performance of SIF RCI as a drought predictor. By analyzing drought events that can be accurately predicted by SIF RCI as well as false positives and false negatives, we identify the conditions for good performance and causes for failure, which will help advance flash drought monitoring and prediction in the U.S. at the subseasonal timescale.

Funding Program Area(s)