Dr. Jiangtao Liu CV

Jiangtao Liu, Ph.D.
 

Assistant Professor

Texas Tech University (TTU)

Email: Jiangtao.Liu@ttu.edu

Website: https://jayhydro.github.io 

LinkedIn: https://www.linkedin.com/in/jiangtaoliud

 

Education

Ph.D., Civil Engineering, The Pennsylvania State University, 2025

M.Eng., Hydrology and Water Resources, Beijing Normal University, 2019

B.Eng., Irrigation and Drainage Engineering, China Agricultural University, 2016

Publications (first-author papers listed first; full list on Google Scholar)                                       

1.       Liu, J., Shen, C., Yang, Y., Ji, H., Lawson, K. and Kraabel, N., 2026. Geospatial foundation embeddings as transferable catchment descriptors. Geophysical Research Letters, 53(16), p.e2026GL122814. https://doi.org/10.1029/2026GL122814 

2.       Liu, J., Shen, C., O'Donncha, F., Song, Y., Zhi, W., Beck, H., Bindas, T., Kraabel, N. and Lawson, K., 2025. From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction. Hydrology and Earth System Sciences, 29, 6811–6832. https://hess.copernicus.org/articles/29/6811/2025/ (selected as an Editor’s Highlight article)

3.       Liu, J., Shen, C., Pei, T., Kifer, D. and 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), p.e2024JH000460. https://doi.org/10.1029/2024JH000460 

4.       Liu, J., Bian, Y., Lawson, K. and Shen, C., 2024. Probing the limit of hydrologic predictability with the Transformer network. Journal of Hydrology, 637, p.131389. https://doi.org/10.1016/j.jhydrol.2024.131389

5.       Liu, J., Hughes, D., Rahmani, F., Lawson, K. and 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), pp.1553-1567. https://doi.org/10.5194/gmd-16-1553-2023

6.       Liu, J., Rahmani, F., Lawson, K. and Shen, C., 2022. A multiscale deep learning model for soil moisture integrating satellite and in situ data. Geophysical Research Letters, 49(7), p.e2021GL096847.

https://doi.org/10.1029/2021GL096847

7.       Liu, J., Xu, Z., Bai, J., Peng, D. and Ren, M., 2018. Assessment and correction of the PERSIANN-CDR product in the Yarlung Zangbo River Basin, China. Remote Sensing, 10(12), p.2031. https://doi.org/10.3390/rs10122031 

8.       Liu, J., Xu, Z., Zhao, H. and He, J., 2019. Accuracy assessment for two satellite precipitation products: Case studies in the Yarlung Zangbo River Basin. Plateau Meteorol, 38, pp.386-396. (in Chinese)

9.       Liu, J., Xu, Z., Zhao, H. and 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)

10.   Liu, J., Xu, Z., Zhao, H., Peng, D. and Zhang, R., 2018. Spatiotemporal variation of extreme precipitation events in the Yarlung Zangbo River basin from 1973 to 2016, China. Mountain Res, 36, pp.750-764. (in Chinese)

11.   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 

12.   Liu, L., Zhao, X., Zhou, L., Liu, J., Li, W., Zhang, B., Ling, J. and 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, p.127138. https://doi.org/10.1016/j.jenvman.2025.127138 

13.   Jamaat, A., Song, Y., Rahmani, F., Liu, J., Lawson, K. and Shen, C., 2025. Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models. Journal of Hydrology, 663, p.134137. https://doi.org/10.1016/j.jhydrol.2025.134137 

14.   Aboelyazeed, D., Xu, C., Gu, L., Luo, X., Liu, J., Lawson, K. and 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), p.e2024JG008552. 

https://doi.org/10.1029/2024JG008552 

15.   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. and Lawson, K. 2025. High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning. Water Resources Research, 61(4), p.e2024WR038928. https://doi.org/10.1029/2024WR038928 

16.   Zhi, W., Baniecki, H., Liu, J., Boyer, E., Shen, C., Shenk, G., Liu, X. and Li, L., 2024. Increasing phosphorus loss despite widespread concentration decline in US rivers. Proceedings of the National Academy of Sciences, 121(48), p.e2402028121. https://doi.org/10.1073/pnas.2402028121 

17.   Bindas, T., Tsai, W.P., Liu, J., Rahmani, F., Feng, D., Bian, Y., Lawson, K. and Shen, C., 2024. Improving river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning. Water Resources Research, 60(1), p.e2023WR035337. https://doi.org/10.1029/2023WR035337

18.   Feng, D., Beck, H., De Bruijn, J., Sahu, R.K., Satoh, Y., Wada, Y., Liu, J., Pan, M., Lawson, K. and 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), pp.7181-7198. https://doi.org/10.5194/gmd-17-7181-2024

19.   Zhi, W., Klingler, C., Liu, J. and Li, L., 2023. Widespread deoxygenation in warming rivers. Nature Climate Change, 13(10), pp.1105-1113. https://doi.org/10.1038/s41558-023-01793-3

20.   Aboelyazeed, D., Xu, C., Hoffman, F.M., Liu, J., Jones, A.W., Rackauckas, C., Lawson, K. and Shen, C., 2023. A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations. Biogeosciences, 20(13), pp.2671-2692. https://doi.org/10.5194/bg-20-2671-2023

21.   Feng, D., Liu, J., Lawson, K. and 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), p.e2022WR032404. https://doi.org/10.1029/2022WR032404

22.   Tsai, W.P., Feng, D., Pan, M., Beck, H., Lawson, K., Yang, Y., Liu, J. and Shen, C., 2021. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling. Nature communications, 12(1), p.5988. https://doi.org/10.1038/s41467-021-26107-z

23.   Ren, M., Xu, Z., Pang, B., Liu, J. and Du, L., 2020. Spatiotemporal variability of precipitation in Beijing, China during the wet seasons. Water, 12(3), p.716. https://doi.org/10.3390/w12030716 

24.   Ren, M., Xu, Z., Pang, B., Liu, W., Liu, J., Du, L. and Wang, R., 2018. Assessment of satellite-derived precipitation products for the Beijing region. Remote Sensing, 10(12), p.1914. https://doi.org/10.3390/rs10121914 

Preprints and Manuscripts Under Review                                                                               

1.       Liu, J., Shen, C., Xu, C., Bian, Y. and Lawson, K., 2026. Strong Landscape Interconnections Can Help Predict Natural Forest Changes. Earth’s Future (under review)

2.       Kraabel, N., Liu, J. (co-first author), Bian, Y., Kifer, D. and 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. and 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. and 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. One Earth.

Manuscripts in Preparation                                                                               

1.       Liu, J., Shen, C., Kraabel, N., Lawson, K. and Aboelyazeed, D., 2026. StefaLand: A Global Annual Land-Surface Environmental Embedding Dataset for 1980–2024. Manuscript in preparation.

2.       Liu, J., Pan, M., Yang, Y. and Shen, C., 2026. When River Topology Matters for Streamflow Prediction. Manuscript in preparation.

3.      Liu, J., Shen, C., Xu, C., McDill, M. and Lawson, K., 2026. Data-Anchored Projections Show Large Stress on Global Tropical and Mountain Forests. Manuscript in preparation.

Presentations and Talks                                                                    

1.       Transfer Learning Across Architectures, Space, and Time for Flood Forecasting. 2026 CW3E Annual Meeting(June 2026)

2.       A Pretrained Foundation AI Model Reveals Global Landscape Dynamics and Human-driven Ecosystem Deviations. AGU Fall Meeting (Dec 2025)

3.       Improving Landslide Susceptibility Prediction with Terrain Patterns, High-Resolution Data, and Ensemble Models. AGU Fall Meeting (Dec 2025, Poster)

4.       Benchmarking Deep Learning Models for Improved Streamflow Prediction. 2025 River Symposium, Bucknell University (October 2025)

5.       A Generative AI Approach to Assess Ecosystem Risks and Restoration Potential. Accelerating Computing for Emerging Sciences (ACES) Workshop (July 2025)

6.       Advancing Hydrological Predictions at National Scales with Deep Learning. Tethys Summit (July 2025)

7.       A Foundation Model for Understanding Landscape-Ecosystem Coevolution. Lawrence Berkeley National Laboratory (June 2025, Poster)

8.       From RNN to Transformer: a comprehensive evaluation of time-series deep learning models. PIERS 2025 (April 2025)

9.       Foundation Model for Understanding Landscape Hydrologic Coevolution. AGU Fall Meeting (Dec 2024)

10.   A General Framework for Hydrological Modeling: From RNN to Transformer. AGU Fall Meeting (Dec 2024)

11.   Can Attention Models Surpass LSTM in Hydrology? EGU Conference (April 2024)

12.   Exploring Transformer Architecture in Predictive Hydrologic Modeling. AGU Fall Meeting (Dec 2023)

13.   Enhancing Soil Moisture Forecasting Using the PatchTST Model for Agricultural Risk Management. IBM Research (Aug 2023)

14.   A multiscale deep learning model integrating satellite-based and in-situ data for high-resolution soil moisture predictions. EGU Conference (April 2023)

15.   Evaluating a multitask LSTM-based soil moisture model on the global scale for current and emerging threats to crops. AGU Fall Meeting (Dec 2022, Poster)

16.   The machine learning strategy for rainfall-induced landslide susceptibility prediction. Google Geo for Good Summit (Oct 2022)

17.   A global soil moisture dataset based on a multi-scale deep learning framework. HydroML Symposium (May 2022)

18.   Deep learning strategies for landslide detection. AGU Fall Meeting (Dec 2021, Poster)

19.   Automated deep-learning-based soil moisture planning and forecast system for planning against natural disasters. AGU Fall Meeting (Dec 2020)

20.   Simulation of runoff and sediment processes in a semi-arid reservoir catchment using SWAT model. The 2016 International SWAT Conference (July 2016, Poster)

Honors and Awards                                                                      

1.       Travel Award, CIROH Developers Conference, CUAHSI, 2026

2.       Stipend Award, CIROH HydroLearn Workshop & Hackathon, CIROH, 2026

3.       Vice Provost and Dean of the Graduate School Student Persistence Scholarship, Penn State, Summer 2025

4.       Travel Grant, 2025 Deep Learning for Science (DL4SCI) Summer School, Computing Sciences at Berkeley Lab (awarded through competitive selection with 35% acceptance rate; covered registration fees and accommodation for 5 days).

5.       Travel Grant, Tethys Summit 2025 

6.       Travel Grant, Texas A&M High Performance Research Computing (HPRC) for the third ACES Researcher Workshop 2025 

Journal Reviewer                                                                         

Geophysical Research Letters; Water Resources Research; Hydrology and Earth System Sciences; Journal of Hydrometeorology; Journal of Hydrology; Remote Sensing of Environment; Journal of Geophysical Research: Biogeosciences; Journal of Geophysical Research: Machine Learning and Computation; Earth and Space Science; Computational Geosciences; npj Hydrosphere; Ecological Informatics , among others.

Service to the Academic and Science Communities                                             

1.       Primary Convener, American Geophysical Union (AGU) Fall Meeting 2026. AI-Driven Hydrologic Modeling and Prediction: Methods, Applications, and Emerging Frontiers (Session ID#: 280083).

2.       Primary Convener, American Geophysical Union (AGU) Fall Meeting 2025. Frontier AI Models Transforming Water Science (Session ID#: 249965).

3.       Guest Editor, Infrastructures (MDPI) – Special Issue: Advances in Geohazards for Infrastructures: Present and Future (2025–present).