Dr. Moreno’s research advances machine learning applications in geoscience, focusing on the interconnected dynamics of water, energy, and biogeochemical cycles across diverse spatio-temporal scales. His work addresses critical challenges posed by natural variability and anthropogenic change, particularly for water security, extreme event forecasting, and societal resilience.By integrating machine learning with geoscientific analysis, Dr. Moreno investigates atmospheric science, surface water, and groundwater hydrology, drawing on related disciplines like hydrometeorology, hydroclimatology, ecohydrology, surface hydrology, and hydroinformatics. His lab leverages diverse data sources, including atmospheric, surface, and subsurface field observations, remotely sensed information from satellites, radars, and unmanned systems, and high-performance, distributed hydrological models.This multidisciplinary approach enables the development of advanced platforms for decision-making under uncertainty, merging machine learning's predictive power with comprehensive environmental datasets. By applying these tools to understand and forecast hydro-system dynamics, Dr. Moreno’s research aims to enhance sustainable water management and mitigate risks associated with extreme events, offering innovative solutions to global challenges.