Publications

Google Scholar
All papers are available upon request
*Indicates co-first authors

Journal Articles

  1. Song, Y.; Bindas, T.; Shen, C.; Ji, H.; Knoben, W. J. M.; Lonzarich, L.; Clark, M. P.; Liu, J.; van Werkhoven, K.; Lemont, S.; Denno, M.; Pan, M.; Yang, Y.; Rapp, J.; Kumar, M.; Rahmani, F.; Thébault, C.; Sawadekar, K.; Lawson, K. High‑Resolution National‑Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‑Informed Machine Learning. Water Resour. Res. 2025, 61(4), e2024WR038928.
  2. Zhi, W.; Baniecki, H.; Liu, J.; Boyer, E.; Shen, C.; Shenk, G.; Liu, X.; Li, L. Increasing Phosphorus Loss Despite Widespread Concentration Decline in US Rivers. Proc. Natl. Acad. Sci. 2024, 121(48), e2402028121. https://doi.org/10.1073/pnas.2402028121
  3. Liu, J.; Bian, Y.; Lawson, K.; Shen, C. Probing the Limit of Hydrologic Predictability with the Transformer Network. J. Hydrol. 2024, 637, 131389. https://doi.org/10.1016/j.jhydrol.2024.131389
  4. Bindas, T.; Tsai, W.-P.; Liu, J.; Rahmani, F.; Feng, D.; Bian, Y.; Lawson, K.; Shen, C. Improving River Routing Using a Differentiable Muskingum-Cunge Model and Physics-Informed Machine Learning. Water Resour. Res. 2024, 60(1), e2023WR035337. https://doi.org/10.1029/2023WR035337
  5. Feng, D.; Beck, H.; de Bruijn, J.; Sahu, R. K.; Satoh, Y.; Wada, Y.; Liu, J.; Pan, M.; Lawson, K.; Shen, C. Deep Dive into Hydrologic Simulations at Global Scale: Harnessing the Power of Deep Learning and Physics-Informed Differentiable Models (δHBV-Globe1.0-hydroDL). Geosci. Model Dev. 2024, 17(18), 7181–7198. https://doi.org/10.5194/gmd-17-7181-2024
  6. Zhi, W.; Klingler, C.; Liu, J.; Li, L. Widespread Deoxygenation in Warming Rivers. Nat. Clim. Change 2023, 1–9. https://doi.org/10.1038/s41558-023-01793-3
  7. Aboelyazeed, D.; Xu, C.; Hoffman, F. M.; Liu, J.; Jones, A. W.; Rackauckas, C.; Lawson, K.; Shen, C. A Differentiable, Physics-Informed Ecosystem Modeling and Learning Framework for Large-Scale Inverse Problems: Demonstration with Photosynthesis Simulations. Biogeosciences 2023, 20(13), 2671–2692. https://doi.org/10.5194/bg-20-2671-2023
  8. Liu, J.; Hughes, D.; Rahmani, F.; Lawson, K.; Shen, C. Evaluating a Global Soil Moisture Dataset from a Multitask Model (GSM3 v1.0) with Potential Applications for Crop Threats. Geosci. Model Dev. 2023, 16(5), 1553–1567. https://doi.org/10.5194/gmd-16-1553-2023
  9. Feng, D.; Liu, J.; Lawson, K.; Shen, C. Differentiable, Learnable, Regionalized Process-Based Models with Multiphysical Outputs Can Approach State-of-the-Art Hydrologic Prediction Accuracy. Water Resour. Res. 2022, 58(10), e2022WR032404. https://doi.org/10.1029/2022WR032404
  10. Liu, J.; Rahmani, F.; Lawson, K.; Shen, C. A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and in Situ Data. Geophys. Res. Lett. 2022, 49(7), e2021GL096847. https://doi.org/10.1029/2021GL096847
  11. Tsai, W.-P.; Feng, D.; Pan, M.; Beck, H.; Lawson, K.; Yang, Y.; Liu, J.; Shen, C. From Calibration to Parameter Learning: Harnessing the Scaling Effects of Big Data in Geoscientific Modeling. Nat. Commun. 2021, 12(1), 5988. https://doi.org/10.1038/s41467-021-26107-z
  12. Ren, M.; Xu, Z.; Pang, B.; Liu, J.; Du, L. Spatiotemporal Variability of Precipitation in Beijing, China during the Wet Seasons. Water 2020, 12(3), 716
  13. Liu, J.; Xu, Z.; Bai, J.; Peng, D.; Ren, M. Assessment and Correction of the PERSIANN-CDR Product in the Yarlung Zangbo River Basin, China. Remote Sens. 2018, 10(12), 2031
  14. Ren, M.; Xu, Z.; Pang, B.; Liu, W.; Liu, J.; Du, L.; Wang, R. Assessment of Satellite-Derived Precipitation Products for the Beijing Region. Remote Sens. 2018, 10(12), 1914
  15. Liu, J.; Xu, Z.; Zhao, H.; He, J. Accuracy assessment for two satellite precipitation products: Case studies in the Yarlung Zangbo River Basin. Plateau Meteorol. 2019, 38, 386–396 (in Chinese)
  16. Liu, J.; Xu, Z.; et al. Simulation of snowmelt runoff processes based on enhanced precipitation input module: Case studies in the Lhasa River basin. J. Hydraul. Eng. 2018, 49(11), 1396–1408 (in Chinese)
  17. Liu, J.; Xu, Z.; Zhao, H.; Peng, D.; Zhang, R. Spatiotemporal variation of extreme precipitation events in the Yarlung Zangbo River Basin from 1973 to 2016, China. Mountain Res. 2018, 36, 750–764 (in Chinese)

Preprints

  1. Liu, J.; Shen, C.; Xu, C.; McDill, M.; Bian, Y. Global Ecosystem Anomalies and Trajectories Captured by Foundation AI. 2025 (submitted)
  2. Liu, J.; Shen, C.; O’Donncha, F.; Song, Y.; Zhi, W.; Beck, H.; Bindas, T.; Kraabel, N.; Lawson, K. From RNN to Transformer: a Comprehensive Evaluation of Time‑Series Deep Learning Models. Hydrol. Earth Syst. Sci. 2025 (under review)
  3. Liu, J.; Shen, C.; Pei, T.; Kifer, D.; Lawson, K. The value of terrain pattern, high-resolution data and ensemble modeling for landslide susceptibility prediction. J. Geophys. Res.: Machine Learning and Computation 2024 (under review)
  4. Ji, H.; Song, Y.; Bindas, T.; Shen, C.; Yang, Y.; Pan, M.; Liu, J.; Rahmani, F.; Abbas, A.; Beck, H.; Wada, Y.; Lawson, K. Distinct Hydrologic Response Patterns and Trends Worldwide Revealed by Physics‑Embedded Learning. Nat. Commun. 2025 (under review)
  5. Jamaat, A.; Song, Y.; Rahmani, F.; Liu, J.; Lawson, K.; Shen, C. Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models. arXiv preprint arXiv:2502.16444. 2025 (under review)
  6. Liu, L.; Zhao, X.; Zhou, L.; Liu, J. Comparative Analysis of Dissolved Oxygen Predictions in the Yellow River Basin Using Different Environmental Predictors Based on Machine Learning. Available at SSRN 4861890. (under review)
  7. Aboelyazeed, D.; Xu, C.; Gu, L.; Luo, X.; Liu, J.; Shen, C. Inferring plant acclimation and improving model generalizability with differentiable physics-informed machine learning of photosynthesis. J. Geophys. Res.: Biogeosciences 2024. (under review)
  8. Song, Y.; Clark, M.; Liu, J.; Halgren, J.; Lawson, K.; Shen, C. Prominent Impacts of Hydrologic Scaling Laws on Climate Risks. Nature Water 2024 (under review)