| 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 | 1146 | 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 | 889 | 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 | 520 | 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 …, 2009 | 463 | 2009 |
| 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 | 386 | 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 | 313 | 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 | 297 | 2006 |
| 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 | 285 | 2011 |
| 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 | 276 | 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 | 275 | 2012 |
| Hydrologic remote sensing and land surface data assimilation H Moradkhani Sensors 8 (5), 2986-3004, 2008 | 257 | 2008 |
| 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 | 227 | 2019 |
| Examining the effectiveness and robustness of data assimilation methods for calibration and quantification of uncertainty in hydrologic forecasting C DeChant, H Moradkhani Water Resources Research 48 (4), W04518, 2012 | 224* | 2012 |
| Drought analysis under climate change using copula S Madadgar, H Moradkhani Journal of hydrologic engineering 18 (7), 746-759, 2013 | 206 | 2013 |
| A Bayesian framework for probabilistic seasonal drought forecasting S Madadgar, H Moradkhani Journal of Hydrometeorology 14 (6), 1685-1705, 2013 | 188 | 2013 |
| MXenes: The two-dimensional influencers MD Firouzjaei, M Karimiziarani, H Moradkhani, M Elliott, B Anasori Materials Today Advances 13, 100202, 2022 | 184 | 2022 |
| Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index L Xu, P Abbaszadeh, H Moradkhani, N Chen, X Zhang Remote Sensing of Environment 250, 112028, 2020 | 184 | 2020 |
| DeepYield: A combined convolutional neural network with long short-term memory for crop yield forecasting K Gavahi, P Abbaszadeh, H Moradkhani Expert Systems with Applications 184, 115511, 2021 | 180 | 2021 |
| 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 | 180 | 2012 |
| In-situ and triple-collocation based evaluations of eight global root zone soil moisture products L Xu, N Chen, X Zhang, H Moradkhani, C Zhang, C Hu Remote Sensing of Environment 254, 2021 | 179 | 2021 |
