Integrating multi-scale data and numerical modeling for understanding and protecting water under climate disturbances

Lijing Wang
Building climate resilience for water resources is crucial for a sustainable future. Many communities face long-term droughts or extreme storms. Protecting our water quantity and quality at scale, however, remains a major challenge under a rapidly changing climate and the inherent uncertainties within hydrologic systems. Smart decision-making requires scientists to effectively convey accurate predictions of water systems to water resource managers. My research focuses on combining multi-scale data, including geophysical survey, remote sensing data, and in-situ temperature and hydrologic measurements, and numerical modeling to provide informed hydrologic predictions with uncertainty quantification. Specifically, I develop stochastic Bayesian geomodeling and machine learning-based model calibration methods that are customized for the real-world water system at hand, and also broadly scaled up to water systems with spatiotemporally varying and uncertain geology, geochemistry, hydrology, and climate conditions.

Learn more about model-data integration work in Lijing’s group at University of Connecticut (UConn).