CyberSEES: Type 1: Cyber-Enabled Ensemble Data Assimilation for Drought Monitoring, Forecasting and Recovery

University Of Alabama Tuscaloosa, Tuscaloosa AL

Investigators

Abstract

There is growing concern with evidence that droughts have been intensified due to ongoing land development driven by population growth and other factors. This has correspondingly aggravated water scarcity, which threatens the long-term sustainability of water resources. Since the US is one of the largest in terms of water footprint, the country is very vulnerable to moderate and severe drought. To mitigate the drought vulnerability, an effective drought monitoring and forecasting system with assessment of drought recovery time is critical for decision makers. This project will develop a cyber-enabled ensemble data assimilation and terrestrial modeling system to characterize the land surface condition for not only assessing the agricultural drought but also providing the initial condition for probabilistic drought forecasting. These estimates will provide the basis for drought recovery estimation. The project will serve as a prototype to build capacity for large-scale drought studies and applications, and will directly enhance the ability of state water managers to take appropriate and timely measures during periods of water scarcity as a result of drought. The study relies on a variety of massive earth and environmental observational data in connection with advanced dynamical and Bayesian modeling, to account for uncertainties and provide reliable drought estimation with the goal to further freshwater resource sustainability. Ensemble modeling and probabilistic estimation to quantify the uncertainties in Earth system science by means of data assimilation has been a salient bottleneck in operationalization. Due to the multi-scale nature of hydrologic processes and under-determinedness of most hydrologic systems as well as the presence of epistemic and random uncertainties, dynamic physical-based and stochastic modeling to probabilistically characterize drought onset and predict it at seasonal scale requires proper parameterization of the system. This research proposes a modern ensemble data assimilation system operating in real time to characterize land surface conditions for monitoring drought, develops a computational framework for effective data assimilation, and implements an approach that targets computational scalability, power, and reliability in the computational framework.