| 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 | 187 | 2021 |
| 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 | 133 | 2022 |
| A multiscale deep learning model for soil moisture integrating satellite and in situ data J Liu, F Rahmani, K Lawson, C Shen Geophysical Research Letters 49 (7), e2021GL096847, 2022 | 50 | 2022 |
| Widespread deoxygenation in warming rivers W Zhi, C Klingler, J Liu, L Li Nature Climate Change 13 (10), 1105-1113, 2023 | 48 | 2023 |
| Assessment of satellite-derived precipitation products for the Beijing region M Ren, Z Xu, B Pang, W Liu, J Liu, L Du, R Wang Remote Sensing 10 (12), 1914, 2018 | 28 | 2018 |
| Improving river routing using a differentiable Muskingum‐Cunge model and physics‐informed machine learning T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Water Resources Research 60 (1), e2023WR035337, 2024 | 24 | 2024 |
| A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations D Aboelyazeed, C Xu, FM Hoffman, J Liu, AW Jones, C Rackauckas, ... Biogeosciences 20 (13), 2671-2692, 2023 | 22 | 2023 |
| Assessment and correction of the PERSIANN-CDR product in the Yarlung Zangbo River Basin, China J Liu, Z Xu, J Bai, D Peng, M Ren Remote Sensing 10 (12), 2031, 2018 | 20 | 2018 |
| Probing the limit of hydrologic predictability with the Transformer network J Liu, Y Bian, K Lawson, C Shen Journal of Hydrology 637, 131389, 2024 | 15 | 2024 |
| Spatiotemporal variability of precipitation in Beijing, China during the wet seasons M Ren, Z Xu, B Pang, J Liu, L Du Water 12 (3), 716, 2020 | 12 | 2020 |
| Accuracy assessment for two satellite precipitation products: Case studies in the Yarlung Zangbo River Basin J Liu, Z Xu, H Zhao, J He Plateau Meteorol 38, 386-396, 2019 | 10 | 2019 |
| Evaluating a global soil moisture dataset from a multitask model (GSM3 v1. 0) with potential applications for crop threats J Liu, D Hughes, F Rahmani, K Lawson, C Shen Geoscientific Model Development 16 (5), 1553-1567, 2023 | 8 | 2023 |
| From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data WP Tsai, M Pan, K Lawson, J Liu, D Feng, C Shen arXiv preprint arXiv:2007.15751 430, 2020 | 8 | 2020 |
| Deep dive into global hydrologic simulations: Harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1. 0-hydroDL) D Feng, H Beck, J de Bruijn, RK Sahu, Y Satoh, Y Wada, J Liu, M Pan, ... Geoscientific Model Development Discussions 2023, 1-23, 2023 | 6 | 2023 |
| Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Authorea Preprints, 2022 | 6 | 2022 |
| A new rainfall-induced deep learning strategy for landslide susceptibility prediction J Liu, C Shen, T Pei, K Lawson, D Kifer, S Nagendra, ... AGU Fall Meeting Abstracts 2021, NH35E-0504, 2021 | 6 | 2021 |
| Improving large-basin river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Authorea Preprints, 2023 | 4 | 2023 |
| Differentiable, learnable, regionalized process-based models with physical 505 outputs can approach state-of-the-art hydrologic prediction accuracy D Feng, J Liu, K Lawson, C Shen | 2 | 2022 |
| Harnessing the power of deep learning and physics-informed differentiable models for accurate global hydrologic modeling D Feng, C Shen, H Beck, J De Bruijn, R Sahu, Y Satoh, Y Wada, J Liu, ... AGU fall meeting abstracts 2023, H33B-01, 2023 | 1 | 2023 |
| Impact of cross-validation strategies on machine learning models for landslide susceptibility mapping: a comparative study T Pei, J Liu, C Shen, D Kifer AGU Fall Meeting Abstracts 2023 (717), NH13D-0717, 2023 | 1 | 2023 |