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

Professional Roles

Dr. Moradkhani leads a research group working on hydrologic science, water resources system analysis, and computational modeling.

Dr. Hamid Moradkhani is the Alton N. Scott Professor of Civil and Environmental Engineering at The University of Alabama, where he also serves as Director of the Center for Complex Hydrosystems Research. His work centers on hydrologic science and water resources, with an emphasis on modeling, data assimilation, and the computational treatment of uncertainty. The Center for Complex Hydrosystems Research brings together researchers and students who study how complex hydrologic systems behave, particularly under changing climatic and land surface conditions.

The research program associated with this position emphasizes computational hydrology as a means of understanding interactions among climate, land surface processes, and water availability. A recurring theme is the use of machine learning and data analytics alongside physically based models, allowing observations from remote sensing, monitoring networks, and meteorological and hydrological forecasts to be incorporated into probabilistic predictions. This combination of modeling and data assimilation supports better characterization of drought, flood, and other extreme events.

Professional roles listed for Dr. Moradkhani include an academic appointment as Alton N. Scott Professor in the Department of Civil and Environmental Engineering, and a directorship of the Center for Complex Hydrosystems Research. The center functions as a hub for faculty, staff, and student involvement, hosting research activities, courses, and collaborative projects. Teaching and mentoring are treated as core components of the role, with coursework in hydrology, modeling, and computational methods supporting graduate training in the field.

The research group contributes methods for model downscaling, Bayesian inference, and ensemble and multimodeling approaches, along with post-processing techniques that translate raw model output into usable estimates of hydrologic variability. Emphasis is placed on advancing predictive skill and quantifying uncertainty in a way that is meaningful for decision makers. Additional interests include change detection, recovery analysis, efficiency of water use, and the assessment of risk and vulnerability in water systems under nonstationary climate conditions.

Publications, course materials, and interactive maps and analysis tools are organized as resources on the site, together with information on group members, research aims, and news updates. The broader goal articulated for this work is to harness computational hydrology and machine learning to improve forecasting of drought and flood events, and to contribute to sustainable water management. The site's navigation reflects this structure, with separate sections for publications, people, courses, featured resources, and research summaries.

Research Focus

The group emphasizes the data revolution, predictive science, uncertainty analysis, machine learning, data analytics, and high-performance computing as tools for understanding water systems.

• Data Assimilation and Remote Sensing
• Drought Modeling
• Climate Variability and Change
• Hydrologic Modeling
• Extreme Events and Flood Modeling

Contact

For questions about research, collaboration, or the group's work, use the details below.