CAS-Climate: CAREER: Analytical methods for understanding and predicting agricultural flash droughts in a changing climate

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Flash drought is an extreme hydro-climate event characterized by sudden onset, rapid intensification, and devastating impact on communities. It rapidly depletes soil moisture, posing significant water and heat stresses for plant growth and agricultural productions over non-irrigated lands. Flash drought is challenging to predict because of its fast onset and development, and complex land-ocean-atmosphere factors that contribute to or affect their formation. This project of interlinked activities study the causes and predictability of flash droughts, improve their forecasts and projections, assess their impact on climate change, and promote education and outreach. The proposed research and educational programs will benefit society, especially agricultural communities vulnerable to droughts, and pave the way towards better-informed drought management, climate adaptation and improved resilience.The research will disentangle underlying drivers of agricultural flash droughts using machine learning-based causal inference analysis, develop and evaluate agricultural flash drought forecasts at the sub-seasonal timescale using deep learning approaches, and assess changes in agricultural flash drought under contemporary and future climate, based on coupled general circulation models large ensembles. These research objectives will be integrated with an education plan focusing on developing and implementing innovative lessons on drought through the 4-H (“head, heart, hands, and health”) program, conducting workshops on sub-seasonal forecasts and decision making with agricultural stakeholders through the climate learning network, mentoring undergraduate and graduate students, and involving graduate students in educational activities besides research. Deliverables from this project will contribute to an improved understanding and predictions of droughts at the regional scale and will provide a framework for analyses of a broader class of extreme climate events, which will be broadly applicable at different locations around the world. This award is co-funded by the Hydrologic Sciences and Climate and Large-Scale Dynamics programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.