| 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 | 238 | 2017 |
| 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 | 237 | 2020 |
| 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 | 213 | 2018 |
| 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 | 103 | 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 | 98 | 2020 |
| 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 | 70 | 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 | 51 | 2022 |
| 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 | 45 | 2023 |
| 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 | 39 | 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 | 37 | 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 | 34 | 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 | 32 | 2016 |
| Differentiable modeling to unify machine learning and physical models and advance Geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... arXiv preprint arXiv:2301.04027, 2023 | 11 | 2023 |
| 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 | 11 | 2019 |
| 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 | 10 | 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 | 6 | 2020 |
| On the data synergy effect of large-sample multi-physics catchment modeling with machine learning C Shen, F Rahmani, K Fang, Z Wei, WP Tsai EGU General Assembly Conference Abstracts, EGU21-16108, 2021 | | 2021 |
| Transcending the uniqueness of places with large-sample multi-physics catchment modeling based on machine learning C Shen, F Rahmani, W Zhi, K Fang, WP Tsai, L Li, K Lawson AGU Fall Meeting Abstracts 2020, H103-08, 2020 | | 2020 |
| Multisource Data Integration under a Deep Learning Framework to Improve Streamflow Forecast Ability D Feng, C Shen, K Fang 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
