Hamid Moradkhani Alton N. Scott Professor of Civil and Environmental Engineering · Director, Center for Complex Hydrosystems Research

For course-related inquiries, contact Dr. Hamid Moradkhani by phone at (205) 348 9125 or by email at hmoradkhani@moradkhani.net.

Teaching at the University of Alabama connects directly to Dr. Hamid Moradkhani's research in hydrologic science and water resources system analysis. Courses in the Department of Civil and Environmental Engineering draw on his group's work in hydrologic modeling, drought assessment, climate variability and change, and the analysis of extreme events such as flooding. Rather than treating coursework as separate from the laboratory, students are introduced to the computational and data analytics methods that underpin current practice in the field, including model downscaling, ensemble forecasting, and probabilistic post-processing of meteorological and hydrological output.

A recurring theme in the instruction areas is the treatment of uncertainty. Because water management decisions are made under uncertain conditions, students are exposed to frameworks for quantifying and communicating uncertainty in predictions, along with multi-model approaches for combining simulations from different sources. This emphasis mirrors the group's contribution to methods for data assimilation, Bayesian inference, and remote sensing retrievals, helping learners understand how observational information can be folded into model-based forecasts and how forecast skill can be evaluated objectively rather than assumed.

Students also encounter the practical monitoring side of hydrology, including satellite-based soil moisture products and soil moisture data assimilation, which link ground measurements with remote sensing information. Course discussions often connect drought monitoring and recovery to broader questions of vulnerability, risk, and the efficiency of water resource systems, encouraging participants to consider how probabilistic information supports sustainable management decisions in the face of climate change and natural variability.

The center's involvement in the Center for Complex Hydrosystems Research gives courses a natural bridge between theory and application, since the group's members work across the full chain from data collection to forecasting and decision support. Course projects and exercises are structured around interactive maps, visualization, and analysis of observed and simulated time series, giving students hands-on experience with the tools used in operational hydrology. The emphasis remains on understanding complex interactions within hydrologic systems and on building the computational reasoning needed to interpret them responsibly.

Overall, the teaching program reflects an academic and computational emphasis: harnessing modern data analytics, machine learning, and high-performance computing to advance predictive capability while acknowledging irreducible uncertainty. Course content is designed to prepare students for research careers in hydrology, water resources, and related environmental fields, and to build habits of rigorous, reproducible analysis. Instruction areas and related research themes are presented as a coherent set, linking coursework in the classroom with the ongoing work of the research group and its collaborators.