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Preprints and submitted papers

  1. Farshid Rahmani* et al., Substantially larger and more amplified U.S. groundwater recharge projected by multi-objective big-data-trained models 
  2. Behroozi, Abdolmedhi*, Neural Operator #2
  3. Kraabel, Nicholas*, Jiangtao Liu*, Foundation model #2 
  4. Aboelyazeed, Doaa*, Optimization
  5. Ren et al., DMFS: Differentiable Modeling for Frozen Soil Thermodynamic Characteristics 
  6. Song, Yalan*, Kamlesh Sawadekar*, Jonathan M Frame, Ming Pan, Martyn Clark, Wouter J M Knoben, Andrew W Wood, Trupesh Patel, and Chaopeng Shen. Physics-informed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events
  7. Rahmani J., et al., Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in Prairie Pothole Region
  8. Jahangir et al., A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction  
  9. Li, Peijun*, Farshid Rahmani*, Jiangtao Liu*, Kathryn Lawson* and Chaopeng Shen. Structural bias should be addressed before effective parameter learning - Insights from SMAP soil moisture simulations using differentiable process-based models
  10. Liu, Jiangtao*, Chaopeng Shen, et al. Generative Spatiotemporal Earth Foundation Model
  11. Shen, Chaopeng. Genes of AI liberate our inquiry into the global water cycle
  12. Cui, et al., Towards Scientifically Consistent Landslide Susceptibility Mapping via Multi-Objective Physics-Informed Machine Learning
  13. Hui Tian, Allison Lassiter, and Chaopeng Shen, Predicting salinization of groundwater along the U.S. Atlantic and Gulf coasts with machine learning
  14. Chang, Shuyu, Doaa Aboelyazeed, et al., IWAND-Nitrogen: A Community Dataset for Large-Scale River Nitrogen Modeling in the United States
  15. Yalan Song*, Shen, Chaopeng, Martyn Clark, Jiangtao Liu*, James Halgren, and Kathryn Lawson*. Prominent impacts of hydrologic scaling laws on climate risks
  16. Abbas, Ather, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng*, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck. Comprehensive global assessment of 23 gridded precipitation datasets across 16,295 catchments using hydrological modeling
  17. Nagendra, Savinay, Kashif Rashid, Chaopeng Shen, and Daniel Kifer. SAMIC: Segment anything with in-context spatial prompt engineering
  18. Tang et al., Global calibration and locally-informed regionalization of hydrological model parameters using AI-based large-sample emulators 
  19. Maharjan, et al. Comparison of deep learning (DL) models to simulate discharge in ungauged glacierized high mountain regions of the world

Journal/Peer-Reviewed Conference Publications

(* indicates advisee authors; ** indicates extensively-advised collaborating authors or summer institute students; underline indicates corresponding author, CHC: Clarivate Web of Science Highly Cited, 0.1% of the papers)

  1. Ji, Haoyu*, Yalan Song*, Tadd Bindas*, Chaopeng Shen, Yuan Yang, Ming Pan, Jiangtao Liu*, Farshid Rahmani*, Ather Abbas, Hylke Beck, Kathryn Lawson* and Yoshihide Wada. (2025). Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning,  Nature Communications, doi: 10.1038/s41467-025-64367-1, Global simulation dataset. preprint
  2. Liu, Jiangtao*, Chaopeng Shen, Te Pei, Daniel Kifer, and Kathryn Lawson*  (2025) The value of terrain pattern, high-resolution data and ensemble modeling for landslide susceptibility prediction Journal of Geophysical Research - Machine Learning & Computation, doi: 10.1029/2024JH000460   preprint
  3. Liu, Jiangtao*, Chaopeng Shen, Fearghal O'Donncha, Yalan Song, Wei Zhi, Hylke E. Beck, Tadd Bindas, Nicholas Kraabel*, and Kathryn Lawson* (2025, Accepted) From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction , Hydrology and Earth System Sciences, HESS Highlight Paper.
  4. Aboelyazeed, Doaa*, Chonggang Xu, Lianhong Gu, Xiangzhong Luo, Jiangtao Liu*, and Chaopeng Shen (2025)  Inferring plant acclimation and improving model generalizability with differentiable physics-informed machine learning of photosynthesis. Journal of Geophysical Research - Biogeosciences, doi: 10.1029/2024JG008552  preprint
  5. Behroozi, Abdolmedhi*, Chaopeng Shen, and Daniel Kifer (2025) Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations, International Conference of Learning Representations (ICLR) (a top-3-ranking general AI conference, main session)
  6. Song, Yalan*, Tadd Bindas*, Chaopeng Shen, Haoyu Ji*, Wouter J. M. Knoben, Leo Lonzarich*, Martyn P. Clark, Jiangtao Liu*, Katie van Werkhoven, Sam Lemont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani*, Cyril Thébault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Sawadekar*, and Kathryn Lawson* (2025) Water Resources Research, High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning, doi: 10.1029/2024WR038928.   National simulation dataset. preprint 
  7. Peijun Li*, Yalan Song*, Ming Pan, Kathryn Lawson, and Chaopeng Shen (2025, Accepted) Ensembling Differentiable Process-based and Data-driven Models with Diverse Meteorological Forcing Datasets to Advance Streamflow Simulation, Hydrology and Earth System Sciences
  8. Jamaat, Amirmoez*, Yalan Song*, Farshid Rahmani*, Jiangtao Liu*, Kathryn Lawson* and Chaopeng Shen (2025). Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models,   Journal of Hydrology, doi: 10.1016/j.jhydrol.2025.134137 preprint
  9. Sawadekar, Kamlesh*, Yalan Song*, Ming Pan, Hylke Beck, Rachel McCrary, Paul Ullrich, Kathryn Lawson*, and Chaopeng Shen(2025) Hydrology-informed interpretable precipitation data fusion with a differentiable hydrologic model, Journal of Hydrology, doi: 10.1016/j.jhydrol.2025.133320
  10. Yang, Yuan, Dapeng Feng, Hylke E. Beck, Weiming Hu, Agniv Sengupta, Luca Delle Monache, Robert Hartman, Peirong Lin, Chaopeng Shen, and Ming Pan (2025) Global daily discharge estimation based on grid-scale long short-term memory (LSTM) model and river routing, Water Resources Research , doi: 10.1029/2024WR039764   
  11. Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph (2025, Accepted)  Improving Streamflow Simulation through Machine Learning-Powered Data Integration and Its Implications for Forecasting in the Western U.S. Hydrology and Earth System Sciences
  12. Robert Reinecke, Lina Stein, Sebastian Gnann, Jafet C.M. Andersson, Berit Arheimer, Marc Bierkens, Sara Bonetti, Andreas Güntner, Stefan Kollet, Sulagna Mishra, Nils Moosdorf, Sara Nazari, Yadu Pokhrel, Christel Prudhomme, Jacob Schewe, Chaopeng Shen, and Thorsten Wagener (2025)  Uncertainties are a guiding light for global water model advancement , WIREs Water, doi: 10.1002/wat2.70025
  13. He, Mingxia, Jie Niu, William J. Riley, Chaopeng Shen, Yi Zheng, John M. Melack, Dongwei Gui, Han Qiu, Mengyu Xie, Liwei Sun, Dongdong Liu, Yong Fu, Qixin Wu, Shaoqi Zhou, Pan Wu, and Bill X. Hu  (2025)  Deep Learning and Remote-Sensed Observations Reveal Global Underestimation of River Obstructions, Water Resources Research, doi: 10.1029/2024WR039692
  14. Luyao Yang, Jianduo Li, Yongjiu Dai, Xingjie Lu, Chaopeng Shen, Ping Zhao, Guo Zhang and Yanwu Zhang (2025) Calibration of the high-resolution Common Land Model in simulating the soil moisture over the northeastern China using an adaptive parameter learning method , Journal of Geophysical Research - Atmosphere. doi: 10.1029/2024JD043230
  15. Khoshkalam, Yegane**, Alain N. Rousseau, Farshid Rahmani*, Chaopeng Shen, and Kian Abbasnezhadi. (2025). Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?. Journal of Hydrology. doi: 10.1016/j.jhydrol.2024.132540
  16. Yanjun Yang, Bo Ta, Alex C. Ruane, Chaopeng Shen, David S. Matteson, Rémi Cousin, and Wei Ren  (2025) Widespread advances in corn and soybean phenology in response to future climate change across the United State, Journal of Geophysical Research - Biogeosciences. doi: 10.1029/2024JG008266
  17. Knoben, Wouter J. M., Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song*, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark. (2025) Hydrology and Earth System Sciences Technical note: How many models do we need to simulate hydrologic processes across large geographical domain, doi: 10.5194/hess-2024-279
  18. Zhigang Ou, Congyi Nai, Baoxiang Pan, Ming Pan, Chaopeng Shen, Peishi Jiang, Xingcai Liu, Qiuhong Tang, Wenqing Li, Yi Zheng (2025) Probabilistic Diffusion Models Advance Extreme Flood Forecasting, Geophysical Research Letters, doi: 10.1029/2025GL115705
  19. Duc Hai Nguyen, Amin Elshorbagy, Muhammad Naveed Khaliq, Chaopeng Shen, Mohammad Khaled Akhtar, Mohamed Ismaiel Ahmed, Fisaha Unduche, Saman Razavi, Philippe Lamontagne (2025) Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications, Results in Engineering, doi: 10.1016/j.rineng.2025.106345 
  20. Zhi, Wei, Hubert Baniecki, Jiangtao Liu*, Elizabeth Boyer, Chaopeng Shen, Gary Shenk, Xiaofeng Liu, and Li Li. (2024). Increasing phosphorus loss despite widespread concentration decline in US rivers, Proceedings of the National Academy of Sciences (PNAS). doi: 10.1073/pnas.2402028121
  21. Chang, Shuyu**, Zahra Ghahremani**, Laura Manuel**, Mohammad Erfani**, Chaopeng Shen, Sagy Cohen, Kimberly Van Meter, Jennifer L Pierce, Ehab A Meselhe, and Erfan Goharian. (2024). The geometry of flow: Advancing predictions of river geometry with multi-model machine learning, Water Resources Research.doi: 10.1029/2023WR036733
  22. Feng, Dapeng*, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu*, Ming Pan, Kathryn Lawson*, and Chaopeng Shen. (2024). Deep dive into global hydrologic simulations: Harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL),  Geoscientific Model Development. doi: 10.5194/gmd-17-7181-2024
  23. Song, Yalan*, Piyaphat Chaemchuen*, Farshid Rahmani*, Wei Zhi, Li Li, Xiaofeng Liu, Elizabeth Boyer, Tadd Bindas*, Kathryn Lawson*, and Chaopeng Shen. (2024). Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations, Journal of Hydrology. doi: 10.1016/j.jhydrol.2024.131573 
  24. Al Mehedi, Md Abdullah**, Shah Saki**, Krutikkumar Patel**, Chaopeng Shen, Sagy Cohen, Virginia Smith, Adnan Rajib, Emmanouil Anagnostou, Tadd Bindas*, and Kathryn Lawson*. (2024). Spatiotemporal variability of channel roughness and its substantial impacts on flood modeling errors, Earths' Future. doi: 10.1029/2023EF004257
  25. Song, Yalan*, Wouter Knoben, Martyn P. Clark, Dapeng Feng*, Kathryn Lawson, and Chaopeng Shen. (2024). When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling, Hydrology and Earth System Sciences. doi: hess-28-3051-2024
  26. Liu, Jiangtao*, Yuchen Bian, and Chaopeng Shen. (2024). Probing the limit of hydrologic predictability with the Transformer network, Journal of Hydrology. doi: 10.1016/j.jhydrol.2024.131389
  27. Reichert, Peter, Kai Ma**, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng*, and Chaopeng Shen. (2024). Metamorphic testing of machine learning and conceptual hydrologic models, Hydrology and Earth System Sciences. doi: 10.5194/hess-28-2505-2024
  28. Bindas, Tadd*, Wen-Ping Tsai*, Jiangtao Liu*, Farshid Rahmani*, Dapeng Feng*, Yuchen Bian, and Chaopeng Shen. (2024). Improving river routing using a differentiable Muskingum-Cunge model and physics-informed machine learning, Water Resources Research. doi: 10.1029/2023WR035337 Clarivate Highly Cited. (Data Release Link)
  29. Saha, Gourab, Cibin Raj, Jonathan Duncan, and Chaopeng Shen. (2024). Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins, Journal of Environmental Management. doi:10.1016/j.jenvman.2024.120721
  30. Liu, Xiaofeng, Yalan Song, and Chaopeng Shen. (2024). Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers, Water Resources Research. doi: 10.1029/2023WR035890
  31. Lin, Yongen, Dagang Wang, Jinxin Zhu, Wei Sun, Chaopeng Shen, and Wei Shangguan. (2024). Development of objective function-based ensemble model for streamflow forecasts, Journal of Hydrology. doi: 10.1016/j.jhydrol.2024.130861
  32. Ma, Kai, Chaopeng Shen, Ziyue Xu, and Daming He (2024). Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins, Journal of Geographical Sciences. doi: 10.1007/s11442-024-2235-x  
  33. Song, Yalan*, Wen-Ping Tsai*, Jonah Gluck**, Alan Rhoades, Colin Zarzycki, Rachel McCrary, Kathryn Lawson*, and Chaopeng Shen. (2024). LSTM-based data integration to improve snow water equivalent prediction and diagnose error sources. Journal of Hydrometeorology. doi:10.1175/JHM-D-22-0220.1
  34. Shen, Chaopeng, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng*, Praveen Kumar, Doaa Aboelyazeed*, Farshid Rahmani*, Yalan Song*, Hylke E. Beck, Tadd Bindas*, Dipankar Dwivedi, Kuai Fang*, Marvin Höge, Chris Rackauckas, Binayak Mohanty, Tirthankar Roy, Chonggang Xu, and Kathryn Lawson*. (2023). Differentiable modelling to unify machine learning and physical models for geosciences. Nature Reviews Earth and Environment. doi: 10.1038/s43017-023-00450-9. Open-Access Online PDF Clarivate Highly Cited. Clarivate Hot Paper
  35. Zhi, Wei, Wenyu Ouyang, Chaopeng Shen, and Li Li. (2023). Temperature as the predominant driver of dissolved oxygen in US rivers. Nature Water. doi: 10.1038/s44221-023-00038-z
  36. Rahmani, Farshid*, Alison Appling, Kathryn Lawson*, and Chaopeng Shen. (2023). Identifying structural priors in a hybrid differentiable model for stream water temperature modeling, Water Resources Research. doi: 10.1029/2023WR034420. (Data Release Link)
  37. Aboelyazeed, Doaa*, Chonggang Xu, Forrest M. Hoffman, Alex W. Jones, Chris Rackauckas, and Chaopeng Shen. (2023). A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations, Biogeosciences. doi: 10.5194/bg-20-2671-2023
  38. Feng, Dapeng*, Hylke Beck, Kathryn Lawson*, and Chaopeng Shen. (2023). The suitability of differentiable, learnable hydrologic models, Hydrology and Earth System Sciences. doi: 10.5194/hess-27-2357-2023 Clarivate Highly Cited.
  39. Mangukiya, Nikunj K., Ashutosh Sharma, and Chaopeng Shen. (2023). How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? Hydrological Processes. doi: 10.1002/hyp.14936
  40. Pei, Te**, Tong Qiu, and Chaopeng Shen (2023). Applying knowledge-guided machine learning to slope stability prediction,  Journal of Geotechnical and Geoenvironmental Engineering. doi: 10.1061/JGGEFK.GTENG-11053
  41. Yao, Yingying, Yufeng Zhao, Xin Li, Dapeng Feng*, Chaopeng Shen, Chuankun Liu, Xingxing Kuang, and Chunmiao Zheng. (2023). Can transfer learning improve hydrological predictions in the Alpine Regions? Journal of Hydrology. doi: 10.1016/j.jhydrol.2023.130038 
  42. Song, Yalan, Xiaofeng Liu, and Chaopeng Shen. (2023). A surrogate model for shallow water equations solvers with deep learning, Journal of Hydraulic Engineering. doi: 10.1061/JHEND8.HYENG-13190  
  43. Slater, Louise, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa. (2023). Hybrid forecasting: Using statistics and machine learning to integrate predictions from dynamical models, Hydrology and Earth System Sciences. doi: 10.5194/hess-27-1865-2023 Clarivate Highly Cited.
  44. Khoshkalam, Yegane**, Alain N. Rousseau, Farshid Rahmani*, Chaopeng Shen, and Kian Abbasnezhadi. (2023). Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long short-term memory networks with data integration, Journal of Hydrology. doi: 10.1016/j.jhydrol.2023.129682
  45. Palese, Michael, Te Pei**, Tong Qiu, Allan M. Zarembski, Chaopeng Shen, and Joseph Palese. (2023). Hazard assessment framework for statistical analysis of cut slopes using track inspection videos and geospatial information, Georisk. doi: 10.1080/17499518.2023.2222369
  46. Saha, Gourab, Farshid Rahmani*, Chaopeng Shen, Li Li, and Cibin Raj. (2023). A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds, Science of the Total Environment. doi:10.1016/j.scitotenv.2023.162930
  47. Liu, Jiangtao*, David Hughes, Farshid Rahmani*, Kathryn Lawson*, and Chaopeng Shen. (2023). Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) for current and emerging threats to crops, Geoscientific Model Development. doi: 10.5194/gmd-16-1553-2023, global soil moisture dataset.
  48. Feng, Dapeng*, Jiangtao Liu*, Kathryn Lawson*, and Chaopeng Shen. (2022). Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy, Water Resources Research. doi:10.1029/2022WR032404 Clarivate Highly Cited.
  49. Sadayappan, Kayalvizhi, Devon Kerins, Chaopeng Shen, and Li Li. (2022). Riverine nitrate concentrations predominantly driven by human, climate, and soil property in the Contiguous United States. Water Research. doi: 10.1016/j.watres.2022.119295
  50. Chen, Xi, Sheng Wang, Hongkai Gao, Jiaxu Huang, Chaopeng Shen, Qingli Li, Honggang Qi, Laiwen Zheng, and Min Liu. (2022). Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation, Journal of Hydrology. doi: 10.1016/j.jhydrol.2022.128562
  51. Owolabi, Olukunle O.**, Kathryn Lawson*, Sanhita Sengupta, Yingsi Huang, Lan Wang, Chaopeng Shen, Mila Getmansky Sherman, and Deborah A. Sunter. (2022). A robust statistical analysis of the role of hydropower on the system electricity price and price volatility, Environmental Research Communications. doi: 10.1088/2515-7620/ac7b74
  52. Nagendra, Savinay**, Daniel Kifer, Benjamin Mirus, Te Pei**, Kathryn Lawson*, Srikanth Banagere Manjunatha, Weixin Li*, Hien Nguyen, Tong Qiu, Sarah Tran, and Chaopeng Shen. (2022). Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi: 10.1109/JSTARS.2022.3177025
  53. Fang, Kuai*, Daniel Kifer, Dapeng Feng*, Kathryn Lawson*, and Chaopeng Shen. (2022). The data synergy effects of time-series deep learning models in hydrology, Water Resources Research. doi: 10.1029/2021WR029583
  54. Liu, Jiangtao*, Farshid Rahmani*, Kathryn Lawson*, and Chaopeng Shen. (2022). A multiscale deep learning model for soil moisture integrating satellite and in-situ data, Geophysical Research Letters. doi: 10.1029/2021GL096847 
  55. Liu, Binxiao, Qiuhong Tang *, Gang Zhao, Liang Gao, Chaopeng Shen, and Baoxiang Pan. (2022). Physics-guided long short-term memory network for streamflow and flood simulations in the Lancang-Mekong River Basin, Water. doi: 10.3390/w14091429
  56. Tsai, Wen-Ping*, Dapeng Feng*, Ming Pan, Hylke Beck, Yuan Yang, Kathryn Lawson*, Jiangtao Liu*, and Chaopeng Shen. (2021). From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling. Nature Communications,. doi: 10.1038/s41467-021-26107-z. Clarivate Highly Cited.
  57. Rahmani, Farshid*, Chaopeng Shen, Samantha Oliver, Kathryn Lawson*, and Alison Appling. (2021). Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins, Hydrological Processes. doi: 10.1002/hyp.14400.
  58. Xie, Kang, Pan Liu, Jianyun Zhang, Dongyang Han, Guoqing Wang, and Chaopeng Shen. (2021). Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology. doi: 10.1016/j.jhydrol.2021.127043
  59. Feng, Dapeng*, Kathryn Lawson*, and Chaopeng Shen. (2021). Mitigating prediction error of deep learning streamflow models in large data-sparse regions with ensemble modeling and soft data, Geophysical Research Letters. doi: 10.1029/2021GL092999
  60. Zhu, Fuxin**, Xin Li, Jun Qin, Kun Yang, Lan Cuo, Wenjun Tang, and Chaopeng Shen. (2021). Integration of multi-source data to estimate downward longwave radiation based on deep neural networks, IEEE Transactions on Geoscience and Remote Sensing. doi: 10.1109/TGRS.2021.3094321
  61. Che-Castaldo, Judy P., Rémi Cousin, Stefani Daryanto, Grace Deng, Mei-Ling E. Feng, Rajesh K. Gupta, Dezhi Hong, Ryan M. McGranaghan, Olukunle O. Owolabi, Tianyi Qu, Wei Ren, Toryn L. J. Schafer, Ashutosh Sharma*, Chaopeng Shen, Mila Getmansky Sherman, Deborah A. Sunter, Lan Wang, and David S. Matteson. (2021). Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects, Environment Systems and Decisions. doi: 10.1007/s10669-021-09822-2
  62. Ouyang, Wenyu**, Kathryn Lawson*, Dapeng Feng*, Lei Ye, Chi Zhang, and Chaopeng Shen. (2021). Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy, Journal of Hydrology. doi: 10.1016/j.jhydrol.2021.126455
  63. Shen, Chaopeng, Xingyuan Chen, and Eric Laloy. (2021). Editorial: Broadening the use of machine learning in hydrology, Frontiers in Water -Water and Hydrocomplexity. doi: 10.3389/frwa.2021.681023
  64. Ma, Kai**, Dapeng Feng*, Kathryn Lawson*, Wen-Ping Tsai*, Chuan Liang, Xiaorong Huang, Ashutosh Sharma*, and Chaopeng Shen. (2021). Transferring hydrologic data across continents -- leveraging data-rich regions to improve hydrologic prediction in data-sparse regions, Water Resources Research. doi: 10.1029/2020WR028600 WRR Editor's Choice Award 2021.
  65. Zhi, Wei, Dapeng Feng*, Wen-Ping Tsai*, Gary Sterle, Adrian Harpold, Chaopeng Shen, and Li Li. (2021). From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? Environmental Science & Technology. doi: 10.1021/acs.est.0c06783 Clarivate Highly Cited.
  66. Rahmani, Farshid*, Kathryn Lawson*, Wenyu Ouyang**, Alison Appling, Samantha Oliver, and Chaopeng Shen. (2021). Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data, Environmental Research Letters. doi: 10.1088/1748-9326/abd501
  67. Fang, Kuai*, Daniel Kifer, Kathryn Lawson*, and Chaopeng Shen. (2020). Evaluating the potential and challenges of an uncertainty quantification method for long short-term memory models for soil moisture predictions, Water Resources Research. doi: 10.1029/2020WR028095
  68. Tsai, Wen-Ping*, Kuai Fang*, Xinye Ji*, Kathryn Lawson*, and Chaopeng Shen. (2020). Revealing causal controls of storage-streamflow relationships with a data-centric Bayesian framework combining machine learning and process-based modeling. Frontiers in Water-Water and Hydrocomplexity. doi:10.3389/frwa.2020.583000
  69. Feng, Dapeng*, Kuai Fang*, and Chaopeng Shen. (2020). Enhancing streamflow forecast and extracting insights using continental-scale long-short term memory networks with data integration, Water Resources Research. doi: 10.1029/2019WR026793 Clarivate Highly Cited.
  70. Fang, Kuai*, and Chaopeng Shen. (2020). Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel, Journal of Hydrometeorology, JHM-D-19-0169.1. doi:10.1175/JHM-D-19-0169.1
  71. Fang, K*, XY. Ji*, CP. Shen, N. Ludwig, P. Godfrey, T. Mahjabin, and C. Doughty, Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin, Advances in Water Resources 130, 12-28 (2019), doi: 10.1016/j.advwatres.2019.05.008
  72. Ji, XY*, L. Lesack, JM. Melack, S. Wang*, WJ. Riley, and CP. Shen. (2019). Seasonal and inter-annual patterns and controls of hydrological fluxes in an Amazon floodplain lake with a surface-subsurface processes model, Water Resources Research, 55(4), 3056-3075. doi: 10.1029/2018WR023897
  73. Sun, N*, K. Fang*, and CP. Shen. (2019). Toward a priori evaluation of relative worth of head and conductivity data as functions of data densities in inverse groundwater modeling, Water, 11(6). doi: 10.3390/w11061202
  74. Fan, YR., et al. (2019). Hillslope hydrology in global change research and earth system modeling, Water Resources Research, 55(2), 1737-1772. doi:10.1029/2018WR023903
  75. Shen, CP., Laloy, E., Albert, A., Chang, F.-J., Elshorbagy, Bales, J., A., Ganguly, S., Hsu, K.-L., Kifer, D., Fang, Z., Fang, K.*, Li, D., Li, X., and Tsai, W.-P.* (2018). HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community, Hydrol. Earth Syst. Sci., 22, 5639–5656. doi: 10.5194/hess-22-5639-2018
  76. Fang, K*, M. Pan, and CP. Shen. (2018). The value of SMAP for long-term soil moisture estimation with the help of deep learning, Transactions on Geoscience and Remote Sensing, 57(4), 2221-2233. doi: 10.1109/TGRS.2018.2872131 
  77. Shen, CP.. (2018). A trans-disciplinary review of deep learning research and its relevance for water resources scientists, Water Resources Research. 54(11), 8558-8593. doi: 10.1029/2018WR022643 Clarivate Highly Cited.
  78. Wang, S-Y., R. Gilles, O-Y. Chung, and CP. Shen. (2018). Cross-basin decadal climate regime connecting the Colorado River and the Great Salt Lake, Journal of Hydrometeorology. doi: 10.1175/JHM-D-17-0081.1
  79. Ji, XY.*, and Shen, CP.. (2018). The introspective may achieve more: enhancing existing Geoscientific models with native-language emulated structural reflection, Computers and Geosciences. doi: 10.1016/j.cageo.2017.09.014
  80. Fang, K.*, CP. Shen, D. Kifer, and X. Yang. (2017). Prolongation of SMAP to spatio-temporally seamless coverage of Continental US using a deep learning neural network, Geophysical Research Letters. doi: 10.1002/2017GL075619 Clarivate Highly Cited. First LSTM paper in Hydrology
  81. Fang, K*, and CP. Shen. (2017). Full-flow-regime storage-streamflow correlation patterns provide insights into hydrologic functioning over the continental US, Water Resources Research. doi: 10.1002/2016WR020283
  82. Niu, Jie, CP. Shen, J. Chambers, JM. Melack, and WJ. Riley. (2017). Interannual Variation in Hydrologic Budgets in an Amazonian Watershed with a Coupled Subsurface - Land Surface Process Model. Journal of Hydrometeorology. doi: 10.1175/JHM-D-17-0108.1
  83. Fang, K.*, CP. Shen, JB. Fisher, and J. Niu. (2016). Improving Budyko curve-based estimates of long-term water partitioning using hydrologic signatures from GRACE, Water Resources Research. doi: 10.1002/2016WR018748
  84. Liu, X., Y. Chen, and CP. Shen. (2016). Coupled Two-dimensional Surface Flow and Three-dimensional Sub-surface Flow Modeling for the Drainage of Permeable Road Pavement, Journal of Hydrologic Engineering. doi: 10.1061/(ASCE)HE.1943-5584.0001462
  85. Shen, CP., SL. Wang*, and X. Liu. (2016). Geomorphological significance of at-many-stations hydraulic geometry, Geophysical Research Letters, Vol. 43, No. 8. doi: 10.1002/2016GL068364
  86. Fatichi, S., ER. Vivoni, FL. Ogden, VY. Ivanov, B. Mirus, D. Gochis, CW. Downer, M. Camporese, JH. Davison, B. Ebel, N. Jones, J. Kim, G. Mascaro, R. Niswonger, P. Restrepo, R. Rigon, CP. Shen, M. Sulis, and D. Tarboton. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology, Journal of Hydrology. doi:10.1016/j.jhydrol.2016.03.026 Clarivate Highly Cited.
  87. Shen, CP., WJ. Riley, KR. Smithgall*, JM. Melack, and K. Fang*. (2016). The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics, Water Resources Research. doi: 10.1002/2015WR018086
  88. Pau, GHS, CP. Shen, WJ. Riley, and Y. Liu. (2016). Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models, Water Resources Research. doi: 10.1002/2015WR017782
  89. Ji, XY*, CP. Shen, and WJ. Riley. (2015). Temporal evolution of soil moisture statistical fractal and controls by soil texture and regional groundwater flow. Advances in Water Resources, 86, 155-169.doi:10.1016/j.advwatres.2015.09.027
  90. Clark, M. P., Y. Fan, D. M. Lawrence, J. C. Adam, D. Bolster, D. J. Gochis, R. P. Hooper, M. Kumar, L. R. Leung, D. S. Mackay, R. M. Maxwell, CP. Shen, S. C. Swenson, and X. Zeng. (2015). Improving the representation of hydrologic processes in Earth System Models, Water Resources Research. doi: 10.1002/2015WR017096 Clarivate Highly Cited.
  91. Niu, J., CP. Shen, S-G Li, and M.S. Phanikumar. (2014). Quantifying Storage Changes in Regional Great Lakes Watersheds Using a Coupled Subsurface - Land Surface Process Model and GRACE, MODIS Products, Water Resources Research. doi: 10.1002/2014WR015589
  92. Riley, WJ., and CP. Shen. (2014). Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations, Hydrol. Earth Syst. Sci., 18(7), 2463-2483. doi:10.5194/hess-18-2463-2014
  93. Trebotich, D., M.F. Adams, S. Molins, C.I. Steefel, and CP. Shen. (2014). High Resolution Simulation of Pore Scale Reactive Transport Processes Associated with Carbon Sequestration, Computing in Science and Engineering, Nov/Dec Issue. doi: 10.1109/MCSE.2014.77
  94. Molins, S., D. Trebotich, J.B. Ajo-Franklin, T.J. Ligocki, CP. Shen, and C.I. Steefel, Pore-scale controls on Calcite dissolution rates from flow-through laboratory and numerical experiments, Environmental Science & Technology (2014), doi:10.1021/es5013438
  95. Shen, CP., J. Niu, and K. Fang*. (2014). Quantifying the Effects of Data Integration Algorithms on the Outcomes of a Subsurface - Land Surface Processes Model, Environmental Modelling & Software.. doi: 10.1016/j.envsoft.2014.05.006
  96. Maxwell, RM., M. Putti, SB. Meyerhoff, J. Delfs, I. Ferguson, V. Ivanov, J. Kim, O. Kolditz, S. Kollet, M. Kumar, S. lopez, J. Niu, C. Paniconi, Y. Park, MS. Phanikumar, CP. Shen, E. Sudicky, and M. Sulis. (2014). Surface-subsurface model inter-comparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 50(2), pp. 1531-1439. doi: 10.1002/2013WR013725
  97. Shen, CP., J. Niu, and M.S. Phanikumar. (2013). Evaluating Controls on Coupled Hydrologic and Vegetation Dynamics in a Humid Continental Climate Watershed Using a Subsurface - Land Surface Processes Model, Water Resources Research, 49(5), pp. 2552 - 2572. doi: 10.1002/wrcr.20189
  98. Molins S., D. Trebotich, C.I. Steefel, and CP. Shen. (2012). An Investigation of the Effect of Pore Scale Flow on Average Geochemical Reaction Rates Using Direct Numerical Simulation, Water Resources Research, 48(3), W03527. doi:10.1029/ 2011WR011404
  99. Shen, CP., J.-M. Qiu, and A. Christlieb. (2011). Adaptive Mesh Refinement Based on High order Finite Difference WENO Scheme for Multi-scale Simulations, Journal of Computational Physics, 230(10), pp. 3780 - 3802. doi: 10.1016/j.jcp.2011.02.008
  100. Shen, CP., and M.S. Phanikumar. (2010). A Process-Based, Distributed Hydrologic Model Based on a Large-Scale Method for Surface - Subsurface Coupling, Advances in Water Resources, 33(12), pp. 1524 - 1541. doi: 10.1016/j.advwatres.2010.09.002
  101. Shen, CP., J. Niu, E.J. Andersen, and M.S. Phanikumar. (2010). Estimating Longitudinal Dispersion in Rivers Using Acoustic Doppler Current Profilers, Advances in Water Resources, 33(6), pp. 615-623. doi: 10.1016/j.advwatres.2010.02.008
  102. Shen, CP., and M.S. Phanikumar. (2009). An Efficient Space-Fractional Dispersion Approximation for Stream Solute Transport Modeling, Advances in Water Resources, 32(10), pp. 1482-1494. doi: 10.1016/j.advwatres.2009.07.01
  103. Shen, CP., M.S. Phanikumar, T.T. Fong, I. Aslam, S.L. Molloy, and J.B. Rose. (2008). Evaluating Bacteriophage P22 as a Tracer in a Complex Surface Water System: The Grand River, Michigan, Environmental Science & Technology , Vol. 42, No. 7. doi: 10.1021/es702317t
  104. Phanikumar,M.S., I. Aslam, CP. Shen, D.T. Long, and T.C. Voice. (2007). Separating Surface Storage from Hyporheic Retention in Natural Streams Using Wavelet Decomposition of Acoustic Doppler Current Profiles, Water Resources Research, Vol. 43, No. 5. doi: 10.1029/2006WR005104

Non-Journal/Non-Peer-Reviewed Publications

  1. Enrico Camporeale, Raffaele Marino, John Rundle, Doris Folini, Yangkang Chen, Donald D. Lucas, Xiaofeng Li, Thomas E. Berger, Geoffrey C. Fox, Yixin Wen, Chaopeng Shen, and Renata Wentzcovitch, The First 18 Months of JGR: MLC, (2025), Journal of Geophysical Research: Machine Learning and Computation, doi: 10.1029/2025JH000797
  2. Shen, Chaopeng and Kathryn Lawson*, Chapter 19. (2021). Applications of Deep Learning in Hydrology. Deep learning for the Earth Sciences: With Applications and R, Second Edition. Editors: Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein. doi: 10.1002/9781119646181.ch19
  3. Dwivedi, Dipankar, Xingyuan Chen, Chaopeng Shen, and Harihar Rajaram. (2023). Advancing AI and machine learning beyond predictive capabilities, Eos (Editor's Vox). doi: 10.1029/2023EO235032
  4. Nagendra, Savinay**, Daniel Kifer, and Chaopeng Shen. (2022). ThreshNet: Segmentation Refinement Inspired by Region-Specific Thresholding, https://arxiv.org/abs/2211.06560 
  5. Fang, Kuai*, Chaopeng Shen, and Daniel Kifer. (2019). Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions, International Conference on Machine Learning (ICML) 2019 Workshop (double-blind review). 
  6. Shen, CP.. (2018). Deep learning: A next-generation big-data approach for hydrology, Eos (Editor's vox), 99. https://doi.org/10.1029/2018EO095649
  7. Shen, Chaopeng. (2015). Water Resources Chapter, in Shortle J. et al., Pennsylvania Climate Change Impact Assessment Update.