Moradkhani's research team aims to make contributions to hydrologic science/water resources system analysis and computational modeling with emphasis on harnessing data revolution, predictive science, uncertainty analysis, machine learning, data analytics and high-performance computing. Our research has focused on advancing our understanding of hydrologic science through modeling climate-water-human interactions as a complex system to result in sustainable management. Our group contributes to the interactions between climate, hydrology and water resources using the data sets and methods including climate model downscaling, remote sensing, state-of-the-art data assimilation, Bayesian inference, distributed hydrologic modeling, ensemble inference, post-processing, and multi-modeling. We are interested in characterizing, quantifying, reducing and communicating uncertainties and risks in all layers of simulation and forecasting while providing reliable hydroclimate extremes analyses under nonstationarity across spatial and temporal scales to allow understanding the impact of climate variability and change on water resources and environment. These include developing a drought early warning system for monitoring and predictions and flood forecasting and inundation likelihood mapping.