Hamid Moradkhani
Hamid Moradkhani
Professor of Civil Engineering, University of Alabama
Verified email at - Homepage
Cited by
Cited by
Dual state–parameter estimation of hydrological models using ensemble Kalman filter
H Moradkhani, S Sorooshian, HV Gupta, PR Houser
Advances in water resources 28 (2), 135-147, 2005
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter
H Moradkhani, KL Hsu, H Gupta, S Sorooshian
Water resources research 41 (5), 2005
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities
Y Liu, AH Weerts, M Clark, HJ Hendricks Franssen, S Kumar, ...
Hydrology and earth system sciences 16 (10), 3863-3887, 2012
General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis
H Moradkhani, S Sorooshian
Hydrological modelling and the water cycle: Coupling the atmospheric and …, 2008
Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter
C Montzka, H Moradkhani, L Weihermüller, HJH Franssen, M Canty, ...
Journal of hydrology 399 (3-4), 410-421, 2011
Assessing the uncertainties of hydrologic model selection in climate change impact studies
MR Najafi, H Moradkhani, IW Jung
Hydrological processes 25 (18), 2814-2826, 2011
Future drought risk in Africa: Integrating vulnerability, climate change, and population growth
A Ahmadalipour, H Moradkhani, A Castelletti, N Magliocca
Science of the Total Environment 662, 672-686, 2019
Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response
Y Hong, K Hsu, H Moradkhani, S Sorooshian
Water resources research 42 (8), 2006
Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
H Moradkhani, K Hsu, HV Gupta, S Sorooshian
Journal of Hydrology 295 (1-4), 246-262, 2004
Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method
H Moradkhani, CM DeChant, S Sorooshian
Water Resources Research 48 (12), 2012
Hydrologic remote sensing and land surface data assimilation
H Moradkhani
Sensors 8 (5), 2986-3004, 2008
Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting
CM DeChant, H Moradkhani
Water Resources Research 48 (4), 2012
Drought analysis under climate change using copula
S Madadgar, H Moradkhani
Journal of hydrologic engineering 18 (7), 746-759, 2013
A Bayesian framework for probabilistic seasonal drought forecasting
S Madadgar, H Moradkhani
Journal of Hydrometeorology 14 (6), 1685-1705, 2013
Radiance data assimilation for operational snow and streamflow forecasting
C Dechant, H Moradkhani
Advances in Water Resources 34 (3), 351-364, 2011
Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method
P Abbaszadeh, H Moradkhani, X Zhan
Water Resources Research 55 (1), 324-344, 2019
Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation
MA Parrish, H Moradkhani, CM DeChant
Water Resources Research 48 (3), 2012
Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging
S Madadgar, H Moradkhani
Water Resources Research 50 (12), 9586-9603, 2014
Snow water equivalent prediction using Bayesian data assimilation methods
M Leisenring, H Moradkhani
Stochastic Environmental Research and Risk Assessment 25, 253-270, 2011
Spatio-temporal drought forecasting within Bayesian networks
S Madadgar, H Moradkhani
Journal of Hydrology 512, 134-146, 2014
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