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Preprints and submitted papers

1. Li, Jiangtao*, Chaopeng Shen, et al. Generative Spatiotemporal Earth Foundation Model
2. Shen, Chaopeng. Genes of AI liberate our inquiry into the global water cycle
3. Aboelyazeed, Doaa*, Chonggang Xu, Lianhong Gu, Xiangzhong Luo, Jiangtao Liu*, and Chaopeng Shen. Inferring plant acclimation and improving model generalizability with differentiable physics-informed machine learning of photosynthesis
4. Liu, Jiangtao*, Chaopeng Shen, Te Pei, Daniel Kifer, and Kathryn Lawson*. The value of terrain pattern, high-resolution data and ensemble modeling for landslide susceptibility prediction
5. 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, Kamlesh Sawadekar*, and Kathryn Lawson*. High-resolution national-scale water modeling is enhanced by multiscale differentiable physics-informed machine learning
6. Song, Yalan*, Kamlesh Sawadekar*, Jonathan M Frame, Ming Pan, Martyn Clark, Wouter J M Knoben, Andrew W Wood, Trupesh Patel, and Chaopeng Shen. Improving physics-informed, differentiable hydrologic models for capturing unseen extreme events
7. Shen, Chaopeng, Yalan Song*, Martyn Clark, Jiangtao Liu*, James Halgren, and Kathryn Lawson*. Prominent impacts of hydrologic scaling laws on climate risks
8. 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
9. Nagendra, Savinay, Kashif Rashid, Chaopeng Shen, and Daniel Kifer. SAMIC: Segment anything with in-context spatial prompt engineering
10. 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. Technical note: How many models do we need to simulate hydrologic processes across large geographical domains
11. Sawadekar, Kamlesh*, Yalan Song*, Ming Pan, Hylke Beck, Rachel McCrary, Paul Ullrich, Kathryn Lawson*, and Chaopeng Shen. Hydrology-informed interpretable precipitation data fusion with a differentiable hydrologic model
12. Yang, Yuan, Dapeng Feng*, Hylke E. Beck, Weiming Hu, Agniv Sengupta, Luca Delle Monache, Robert Hartman, Peirong Lin, Chaopeng Shen, and Ming Pan. Global daily discharge estimation based on grid-scale long short-term memory (LSTM) model and river routing.
13. Reinecke, et al. Uncertainties are a guiding light for global water model advancement
14. He, et al. Global Underestimation of River Obstructions
15. Maharjan, et al. Comparison of deep learning (DL) models to simulate discharge in ungauged glacierized high mountain regions of the world
16. Yang, et al. Phenology

Journal/Peer-Reviewed Conference Publications

(* indicates advisee authors; ** indicates extensively-advised collaborating authors or summer institute students; underline indicates corresponding author)

1. Behroozi, Abdolmedhi*, Chaopeng Shen, and Daniel Kifer. (2025, Accepted). Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations, International Conference of Learning Representations (ICLR) (a top-2-ranking general AI conference, main session)
2. 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
3. Khoshkalam, Yegane**, Alain N. Rousseau, Farshid Rahmani*, Chaopeng Shen, and Kian Abbasnezhadi. (2024). 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
4. 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
5. 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
6. 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 
7. 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
8. 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
9. 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
10. 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
11. 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 (Data Release Link)
12. 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
13. 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
14. 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
15. 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  
16. 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
17. 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 
18. 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
19. 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)
20. 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
21. 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
22. 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
23. 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
24. 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 
25. 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  
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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 
38. 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
39. 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.
40. 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.
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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 
60. 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
61. Shen, CP.. (2018). Deep learning: A next-generation big-data approach for hydrology, Eos (Editor's vox), 99. https://doi.org/10.1029/2018EO095649
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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. 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
2. 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
3. Nagendra, Savinay**, Daniel Kifer, and Chaopeng Shen. (2022). ThreshNet: Segmentation Refinement Inspired by Region-Specific Thresholding, https://arxiv.org/abs/2211.06560 
4. 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). 
5. Shen, Chaopeng. (2015). Water Resources Chapter, in Shortle J. et al., Pennsylvania Climate Change Impact Assessment Update.