OCCRI Researchers

Title: Associate Professor
Name: Hamid Moradkhani
Institution: Portland State University
Department: Civil and Environmental Engineering
Education:



Research Theme: surface water hydrologic/hydraulic modeling, integrated water resources management, uncertainty assessment in estimating hydrologic states; model calibration and data assimilation in distributed hydrologic systems; application of artificial intelligence and evolutionary methods in water resources system analysis; and impact assessment of climate change and variability on water resources systems.

Research Fields:

Water Policy and Planning

Professional Activities:

Associate Editor, AGU Water Resources Research

Associate Editor, ASCE Journal of Hydrologic Engineering

Editorial Advisory Board, International Journal of Science and Technology

NOAA-MAPP Drought Task Force

Chair of “Risk and Uncertainty in Water Resources Systems” Technical Committee at EWRI

Selected Publications:

Jaeger, W.K., A.J. Plantinga, H. Chang, G. Grant, D. Hulse, J. McDonnell, H. Moradkhani, A.T. Morzillo, P. Mote, A. Nolin, M. Santelmann, J. Wu (accepted), Toward a formal definition of water scarcity in natural-human systems, Water Resources Research.

Samadi S.Z., C.A. Wilson, H. Moradkhani, S. Gummeneni, (2013), Uncertainty Analysis of Statistical Downscaling Models Using Hadley Centre Coupled Model Journal of Theoretical and Applied Climatology, 1–18, doi:10.1007/s00704-013-0844-x.

Fattahi, M.H., N. Talebbeydokhti, H. Moradkhani, Nikooee (in Press), Revealing the chaotic nature of river flow, Journal of Science and Technology.

Chang, H. I. Jung, A. Strecker, D. Wise, M. Lafranz, V. Shandas, H. Moradkhani, A. Yeakly, Y. Pan, R. Bean, G. Johnson, M. Psaris (2013), Water Supply, Demand, and Quality Indicators for Assessing the Spatial Distribution of Water Resource Vulnerability in the Columbia River Basin, USA Atmosphere and Ocean, 1–18, http://dx.doi.org/10.1080/07055900.2013.777896.

Moradkhani H., C.M. DeChant and S. Sorooshian (2012), Evolution of Ensemble Data Assimilation for Uncertainty Quantification using the Particle Filter-Markov Chain Monte Carlo Method, Water Resources Research, VOL. 48, W12520, 14 PP., doi:10.1029/2012WR012144.