Publications

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

Journal Articles

  1. Ji, H., Song, Y., Bindas, T., Shen, C., Yang, Y., Pan, M., Liu, J., Rahmani, F., Abbas, A., Beck, H., Lawson, K., & Wada, Y. (2025). Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications, 16, 9169. https://doi.org/10.1038/s41467-025-64367-1
  2. Liu, J., Shen, C., Pei, T., Kifer, D., & Lawson, K. (2025). The value of terrain pattern, high-resolution data and ensemble modeling for landslide susceptibility prediction. Journal of Geophysical Research: Machine Learning and Computation, 2(3), e2024JH000460. https://doi.org/10.1029/2024JH000460
  3. Liu, L., Zhao, X., Zhou, L., Liu, J., Li, W., Zhang, B., Ling, J., & Wu, F. (2025). Comparative analysis of machine-learning-based dissolved oxygen predictions in the Yellow River Basin: The role of diverse environmental predictors. Journal of Environmental Management, 393, 127138. https://doi.org/10.1016/j.jenvman.2025.127138
  4. Jamaat, A., Song, Y., Rahmani, F., Liu, J., Lawson, K., & Shen, C. (2025). Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models. Journal of Hydrology, 663, 134137. https://doi.org/10.1016/j.jhydrol.2025.134137
  5. Aboelyazeed, D., Xu, C., Gu, L., Luo, X., Liu, J., Lawson, K., & Shen, C. (2025). Inferring plant acclimation and improving model generalizability with differentiable physics-informed machine learning of photosynthesis. Journal of Geophysical Research: Biogeosciences, 130(7), e2024JG008552. https://doi.org/10.1029/2024JG008552
  6. Song, Y., Bindas, T., Shen, C., Ji, H., Knoben, W. J., Lonzarich, L., Clark, M. P., Liu, J., van Werkhoven, K., Lamont, S., Denno, M., Pan, M., Yang, Y., Rapp, J., Kumar, M., Rahmani, F., Thébault, C., Adkins, R., Halgren, J., Patel, T., Patel, A., Sawadekar, K., & Lawson, K. (2025). High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning. Water Resources Research, 61(4), e2024WR038928. https://doi.org/10.1029/2024WR038928
  7. Zhi, W., Baniecki, H., Liu, J., Boyer, E., Shen, C., Shenk, G., Liu, X., & Li, L. (2024). Increasing phosphorus loss despite widespread concentration decline in US rivers. Proceedings of the National Academy of Sciences, 121(48), e2402028121. https://doi.org/10.1073/pnas.2402028121
  8. Liu, J., Bian, Y., Lawson, K., & Shen, C. (2024). Probing the limit of hydrologic predictability with the Transformer network. Journal of Hydrology, 637, 131389. https://doi.org/10.1016/j.jhydrol.2024.131389
  9. Bindas, T., Tsai, W. P., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson, K., & Shen, C. (2024). Improving river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning. Water Resources Research, 60(1), e2023WR035337. https://doi.org/10.1029/2023WR035337
  10. Feng, D., Beck, H., De Bruijn, J., Sahu, R. K., Satoh, Y., Wada, Y., Liu, J., Pan, M., Lawson, K., & Shen, C. (2024). Deep dive into hydrologic simulations at global scale: Harnessing the power of deep learning and physics-informed differentiable models (δ HBV-globe1.0-hydroDL). Geoscientific Model Development, 17(18), 7181–7198. https://doi.org/10.5194/gmd-17-7181-2024
  11. Zhi, W., Klingler, C., Liu, J., & Li, L. (2023). Widespread deoxygenation in warming rivers. Nature Climate Change, 13(10), 1105–1113. https://doi.org/10.1038/s41558-023-01793-3
  12. Aboelyazeed, D., Xu, C., Hoffman, F. M., Liu, J., Jones, A. W., Rackauckas, C., Lawson, K., & Shen, C. (2023). A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations. Biogeosciences, 20(13), 2671–2692. https://doi.org/10.5194/bg-20-2671-2023
  13. Liu, J., Hughes, D., Rahmani, F., Lawson, K., & Shen, C. (2023). Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats. Geoscientific Model Development, 16(5), 1553–1567. https://doi.org/10.5194/gmd-16-1553-2023
  14. Feng, D., Liu, J., Lawson, K., & Shen, C. (2022). Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy. Water Resources Research, 58(10), e2022WR032404. https://doi.org/10.1029/2022WR032404
  15. Liu, J., Rahmani, F., Lawson, K., & Shen, C. (2022). A multiscale deep learning model for soil moisture integrating satellite and in situ data. Geophysical Research Letters, 49(7), e2021GL096847. https://doi.org/10.1029/2021GL096847
  16. Tsai, W. P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J., & Shen, C. (2021). From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling. Nature Communications, 12(1), 5988. https://doi.org/10.1038/s41467-021-26107-z
  17. Ren, M., Xu, Z., Pang, B., Liu, J., & Du, L. (2020). Spatiotemporal variability of precipitation in Beijing, China during the wet seasons. Water, 12(3), 716. https://doi.org/10.3390/w12030716
  18. Liu, J., Xu, Z., Bai, J., Peng, D., & Ren, M. (2018). Assessment and correction of the PERSIANN-CDR product in the Yarlung Zangbo River Basin, China. Remote Sensing, 10(12), 2031. https://doi.org/10.3390/rs10122031
  19. Ren, M., Xu, Z., Pang, B., Liu, W., Liu, J., Du, L., & Wang, R. (2018). Assessment of satellite-derived precipitation products for the Beijing region. Remote Sensing, 10(12), 1914. https://doi.org/10.3390/rs10121914
  20. Liu, J., Xu, Z., Zhao, H., & He, J. (2019). Accuracy assessment for two satellite precipitation products: Case studies in the Yarlung Zangbo River Basin. Plateau Meteorology, 38, 386–396. (in Chinese)
  21. Liu, J., Xu, Z., Zhao, H., & Peng, D. (2018). Simulation of snowmelt runoff processes based on enhanced precipitation input module: Case studies in the Lhasa River Basin. Journal of Hydraulic Engineering, 49(11), 1396–1408. (in Chinese)
  22. Liu, J., Xu, Z., Zhao, H., Peng, D., & Zhang, R. (2018). Spatiotemporal variation of extreme precipitation events in the Yarlung Zangbo River Basin from 1973 to 2016, China. Mountain Research, 36, 750–764. (in Chinese)

Preprints

  1. Liu, J., Shen, C., O’Donncha, F., Song, Y., Zhi, W., Beck, H., Bindas, T., Kraabel, N., & Lawson, K. (2025). From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction. EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2025-1706
  2. Kraabel, N., Liu, J.*, Bian, Y., Kifer, D., & Shen, C. (2025). StefaLand: An efficient geoscience foundation model that improves dynamic land-surface predictions. *arXiv preprint. (* Indicates equal contribution). https://arxiv.org/html/2509.17942v1
  3. Li, P., Shen, C., Liu, J., Rahmani, F., & Lawson, K. E. (2025). Structural bias should be addressed before effective parameter learning: Insights from SMAP soil moisture simulations using differentiable process-based models. ESS Open Archive. https://doi.org/10.22541/essoar.175855507.72741303/v1
  4. Song, Y., Clark, M., Liu, J., Halgren, J., Lawson, K., & Shen, C. (2024). Prominent impacts of hydrologic scaling laws on climate risks. Research Square. https://doi.org/10.21203/rs.3.rs-4584048/v1
  5. Rahmani, F., Shen, C., Appling, A., Wada, Y., Song, Y., Ji, H., Liu, J., & Lawson, K. (2025). Substantially larger and more amplified U.S. groundwater recharge projected by multi-objective big-data-trained models. Nature Water.