| 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 | 335 | 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 | 283 | 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 | 233 | 2018 |
| 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 | 167 | 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 | 130 | 2018 |
| 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 | 125 | 2020 |
| The data synergy effects of time‐series deep learning models in hydrology K Fang, D Kifer, K Lawson, D Feng, C Shen Water Resources Research 58 (4), e2021WR029583, 2022 | 91 | 2022 |
| Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions K Fang, D Kifer, K Lawson, C Shen Water Resources Research 56 (12), e2020WR028095, 2020 | 86 | 2020 |
| Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US K Fang, C Shen Water Resources Research 53 (9), 8064-8083, 2017 | 44 | 2017 |
| The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics C Shen, WJ Riley, KR Smithgall, JM Melack, K Fang Water Resources Research 52 (2), 880-902, 2016 | 38 | 2016 |
| Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model C Shen, J Niu, K Fang Environmental Modelling & Software 59, 146-161, 2014 | 35 | 2014 |
| Improving Budyko curve‐based estimates of long‐term water partitioning using hydrologic signatures from GRACE K Fang, C Shen, JB Fisher, J Niu Water Resources Research 52 (7), 5537-5554, 2016 | 34 | 2016 |
| Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions K Fang, C Shen, D Kifer arXiv preprint arXiv:1906.04595, 2019 | 13 | 2019 |
| Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin K Fang, X Ji, C Shen, N Ludwig, P Godfrey, T Mahjabin, C Doughty Advances in Water Resources 130, 12-28, 2019 | 12 | 2019 |
| HESS Opinions: Deep learning as a promising avenue toward knowledge discovery in water sciences C Shen, E Laloy, A Albert, FJ Chang, A Elshorbagy, S Ganguly, K Hsu, ... Hydrology and Earth System Sciences Discussions 2018, 1-21, 2018 | 10 | 2018 |
| Revealing causal controls of storage-streamflow relationships with a data-centric bayesian framework combining machine learning and process-based modeling WP Tsai, K Fang, X Ji, K Lawson, C Shen Frontiers in Water 2, 583000, 2020 | 8 | 2020 |
| Modeling continental US stream water quality using long-short term memory and weighted regressions on time, discharge, and season K Fang, J Caers, K Maher Frontiers in Water 6, 1456647, 2024 | | 2024 |
| Electrodialysis and Nitrate Reduction: An Electrochemical Reactive Separations Platform for Distributed Ammonia Recovery from Wastewaters J Guo, M Liu, CM Laguna, S Blair, B Clark, D Miller, Z Perzan, C Wong, ... 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Modeling continental US stream water quality using Long-Short Term Memory (LSTM) and Weighted Regressions on Time, Discharge, and Season (WRTDS) K Fang, K Maher AGU Fall Meeting Abstracts 2022, H13D-05, 2022 | | 2022 |
| WaterNet: a process-based and explainable deep learning framework modeling stream discharge K Fang, Z Perzan, K Maher AGU Fall Meeting Abstracts 2022, H45L-1540, 2022 | | 2022 |