| Breaking down the computational barriers to real‐time urban flood forecasting VY Ivanov, D Xu, MC Dwelle, K Sargsyan, DB Wright, N Katopodes, J Kim, ... Geophysical Research Letters 48 (20), e2021GL093585, 2021 | 48 | 2021 |
| Streamflow, stomata, and soil pits: Sources of inference for complex models with fast, robust uncertainty quantification MC Dwelle, J Kim, K Sargsyan, VY Ivanov Advances in Water Resources 125, 13-31, 2019 | 27 | 2019 |
| A novel modeling framework for computationally efficient and accurate real‐time ensemble flood forecasting with uncertainty quantification VN Tran, MC Dwelle, K Sargsyan, VY Ivanov, J Kim Water Resources Research 56 (3), e2019WR025727, 2020 | 25 | 2020 |
| On the non-uniqueness of the hydro-geomorphic responses in a zero-order catchment with respect to soil moisture J Kim, MC Dwelle, SK Kampf, S Fatichi, VY Ivanov Advances in Water Resources 92, 73-89, 2016 | 24 | 2016 |
| Academic engagement in public and political discourse: Proceedings of the Michigan meeting AJ Hoffman, K Ashworth, C Dwelle, P Goldberg, A Henderson, L Merlin, ... Ross School of Business Paper, 2015 | 18 | 2015 |
| Addressing variability in hydrologic systems using efficient uncertainty quantification M Dwelle | 1 | 2018 |
| Zooming in on hydrologic dynamics through data, probabilistic learning, and high-fidelity modeling VY Ivanov, D Xu, MC Dwelle, K Sargsyan, D Wright, J Kim Frontiers in Hydrology 2022, 424-027, 2022 | | 2022 |
| Reduction of problem dimensionality by merging hydrologic models with a probabilistic learning methodology VY Ivanov, D Xu, K Sargsyan, MC Dwelle, D Wright, J Kim, W Huang AGU Fall Meeting Abstracts 2020, H134-0001, 2020 | | 2020 |
| Pre-training of urban flood simulation for real-time flood forecasting within uncertainty quantification framework D Xu, MC Dwelle, D Wright, J Kim, K Sargsyan, VY Ivanov AGU Fall Meeting Abstracts 2019, H12B-08, 2019 | | 2019 |
| Stochastic simulation and inference of complex ecohydrologic processes with uncertainty quantification in an Amazonian catchment VY Ivanov, MC Dwelle, K Sargsyan, J Kim AGU Fall Meeting Abstracts 2019, H13Q-1993, 2019 | | 2019 |
| Uncertainty quantification of urban flooding simulation by using a reduced order modeling framework D Xu, VY Ivanov, MC Dwelle, DS McKague, K Sargsyan AGU Fall Meeting Abstracts 2018, H41L-2253, 2018 | | 2018 |
| Stochastic simulation of ecohydrological interactions between vegetation and groundwater MC Dwelle, VY Ivanov, K Sargsyan AGU Fall Meeting Abstracts 2017, H21J-1612, 2017 | | 2017 |
| Impact of extreme events on watershed dynamics J Kim, M Dwelle, A Warnock, V Ivanov, N Katopodes | | 2017 |
| Flood Dynamics Using High-Resolution Data and Probabilistic Assessment of Uncertainty MC Dwelle, J Kim, K Sargsyan, VY Ivanov AGU Fall Meeting Abstracts 2016, H34E-04, 2016 | | 2016 |
| Modeling Urban Flood Dynamics Using High-Resolution Topography and Bathymetry MC Dwelle, J Kim, VY Ivanov AGU Fall Meeting Abstracts 2015, H51E-1418, 2015 | | 2015 |
| Combining precipitation data from observed and numerical models to forecast precipitation characteristics in sparsely-gauged watersheds: an application to the Amazon River basin. MC Dwelle, VY Ivanov, V Berrocal AGU Fall Meeting Abstracts 2014, H23M-1058, 2014 | | 2014 |
| Renaissance Scientists: Collaboration across disciplines to meet the world's water-related challenges. MC Dwelle AGU Fall Meeting Abstracts 2014, ED21F-21, 2014 | | 2014 |
Khachik SargsyanSandia National LaboratoriesVerified email at sandia.gov
