| 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 | 1074 | 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 | 821 | 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 | 465 | 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 | 415 | 2008 |
| 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 | 300 | 2019 |
| 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 | 285 | 2011 |
| 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 | 282 | 2011 |
| 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 | 269 | 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 | 263 | 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 | 253 | 2012 |
| Hydrologic remote sensing and land surface data assimilation H Moradkhani Sensors 8 (5), 2986-3004, 2008 | 225 | 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 | 202 | 2012 |
| Drought analysis under climate change using copula S Madadgar, H Moradkhani Journal of hydrologic engineering 18 (7), 746-759, 2013 | 186 | 2013 |
| A Bayesian framework for probabilistic seasonal drought forecasting S Madadgar, H Moradkhani Journal of Hydrometeorology 14 (6), 1685-1705, 2013 | 178 | 2013 |
| Radiance data assimilation for operational snow and streamflow forecasting C Dechant, H Moradkhani Advances in Water Resources 34 (3), 351-364, 2011 | 164 | 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 | 162 | 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 | 162 | 2012 |
| Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging S Madadgar, H Moradkhani Water Resources Research 50 (12), 9586-9603, 2014 | 156 | 2014 |
| Snow water equivalent prediction using Bayesian data assimilation methods M Leisenring, H Moradkhani Stochastic Environmental Research and Risk Assessment 25, 253-270, 2011 | 149 | 2011 |
| Spatio-temporal drought forecasting within Bayesian networks S Madadgar, H Moradkhani Journal of Hydrology 512, 134-146, 2014 | 145 | 2014 |
