The environmental and economic consequences of drought are among the most serious of all natural disasters. However, not all droughts are the same. The onset and intensification of drought can occur at exceptionally rapid rates. Such events, called flash droughts can precede long term drought with both types of drought resulting in devastating impacts on agriculture, depletion of water resources, and through placing excessive moisture stress on both managed and natural ecosystems. In a warming climate, drought is expected to increase in frequency, duration, and intensity at both regional and global scales. Improving analysis and prediction of all drought types requires the combination of multiple data sources, including remote sensing data, surface observations, and even indicators of societal impact. Satellite hydrological variables and vegetation indices have contributed dramatically to understand the mechanisms of drought occurrence and development, as well as de-couple the drought signals from normal hydrological conditions and vegetation status. Remotely sensed land observations are used to force or parameterize models, and the hydrological outputs provide the foundation for existing drought indicators. However, taking drought monitoring and prediction to the next level not only requires advances in understanding drought mechanisms, but also the societal impacts and how to better manage water resources. There are still many open scientific questions related to data fusion, integration of drought indicators, emerging social media data sources and the optimal combination of these data sets for providing insights to climate, environmental and societal changes with respect to drought events. This session invites submissions that advance our understanding of the causes and characteristics of both flash drought and long term drought, through climatological analyses, case studies of recent events, impacts of land-atmosphere interactions, and numerical simulations. Application of remote sensing land observations, social media data or the fusion of the two for understanding, monitoring and predicting drought are especially encouraged.

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