| A transdisciplinary review of deep learning research and its relevance for water resources scientists C Shen Water Resources Research 54 (11), 8558-8593, 2018 | 845 | 2018 |
| An overview of current applications, challenges, and future trends in distributed process-based models in hydrology S Fatichi, ER Vivoni, FL Ogden, VY Ivanov, B Mirus, D Gochis, ... Journal of Hydrology 537, 45-60, 2016 | 559 | 2016 |
| Improving the representation of hydrologic processes in Earth System Models MP Clark, Y Fan, DM Lawrence, JC Adam, D Bolster, DJ Gochis, ... Water Resources Research 51 (8), 5929-5956, 2015 | 487 | 2015 |
| Hillslope hydrology in global change research and earth system modeling Y Fan, M Clark, DM Lawrence, S Swenson, LE Band, SL Brantley, ... Water Resources Research 55 (2), 1737-1772, 2019 | 388 | 2019 |
| An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation S Molins, D Trebotich, CI Steefel, C Shen Water Resources Research 48 (3), 2012 | 351 | 2012 |
| Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks RM Maxwell, M Putti, S Meyerhoff, JO Delfs, IM Ferguson, V Ivanov, J Kim, ... Water resources research 50 (2), 1531-1549, 2014 | 310 | 2014 |
| Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales D Feng, K Fang, C Shen Water Resources Research 56 (9), e2019WR026793, 2020 | 307 | 2020 |
| Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network K Fang, C Shen, D Kifer, X Yang Geophysical Research Letters 44 (21), 11,030-11,039, 2017 | 273 | 2017 |
| HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community C Shen, E Laloy, A Elshorbagy, A Albert, J Bales, FJ Chang, S Ganguly, ... Hydrology and Earth System Sciences 22 (11), 5639-5656, 2018 | 235 | 2018 |
| A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling C Shen, MS Phanikumar Advances in Water Resources 33 (12), 1524-1541, 2010 | 224 | 2010 |
| From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? W Zhi, D Feng, WP Tsai, G Sterle, A Harpold, C Shen, L Li Environmental Science & Technology 55 (4), 2357-2368, 2021 | 212 | 2021 |
| Pore-scale controls on calcite dissolution rates from flow-through laboratory and numerical experiments S Molins, D Trebotich, L Yang, JB Ajo-Franklin, TJ Ligocki, C Shen, ... Environmental science & technology 48 (13), 7453-7460, 2014 | 193 | 2014 |
| From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling WP Tsai, D Feng, M Pan, H Beck, K Lawson, Y Yang, J Liu, C Shen Nature communications 12 (1), 5988, 2021 | 172 | 2021 |
| Differentiable modelling to unify machine learning and physical models for geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... Nature Reviews Earth & Environment 4 (8), 552-567, 2023 | 126 | 2023 |
| The value of SMAP for long-term soil moisture estimation with the help of deep learning K Fang, M Pan, C Shen IEEE Transactions on Geoscience and Remote Sensing 57 (4), 2221-2233, 2018 | 124 | 2018 |
| Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface‐land surface processes model C Shen, J Niu, MS Phanikumar Water Resources Research 49 (5), 2552-2572, 2013 | 124 | 2013 |
| Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel K Fang, C Shen Journal of Hydrometeorology 21 (3), 399-413, 2020 | 114 | 2020 |
| Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K Xie, P Liu, J Zhang, D Han, G Wang, C Shen Journal of Hydrology 603, 127043, 2021 | 113 | 2021 |
| Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models L Slater, L Arnal, MA Boucher, AYY Chang, S Moulds, C Murphy, ... Hydrology and Earth System Sciences Discussions 2022, 1-35, 2022 | 109* | 2022 |
| Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy D Feng, J Liu, K Lawson, C Shen Water Resources Research 58 (10), e2022WR032404, 2022 | 106 | 2022 |
Mantha S. PhanikumarProfessor, Michigan State UniversityVerifisert e-postadresse på egr.msu.edu
