try this Reading Guides for MHPI papers  

Preprints and submitted papers

  1. Al Mehedi  et al., New Perspective on Channel Roughness Parameterization
  2. Lin et al., Ensemble learning plays an effective role in multi-objective optimization for streamflow prediction  
  3. Chaemchuen, P.*, YL Song*, et al, Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations
  4. Zhi, W. et al., Phosphorous  
  5. Rahman, Farshid*, et al., Identifying structural priors in a hybrid differentiable model for stream water temperature modeling  
  6. Savinay Nagendra**, Daniel Kifer, Chaopeng Shen. ThreshNet: Segmentation Refinement Inspired by Region-Specific Thresholding,
  7. Liu et al., Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers 
  8. Song et al., Surrogate Model for Shallow Water Equations Solvers with Deep Learning
  9. Pei, T. **, et al., Applying Physics-Guided Machine Learning to Slope Stability Prediction
  10. Yang et al., Phenology
  11. Mangukiya, N., A. Sharma and Shen CP.
  12. Song, YL.*, et al., LSTM-based data integration to dramatically improve snow water equivalent forecast and diagnose error sources
  13. Bindas, Tadd*, Wen-Ping Tsai*, Jiangtao Liu*, Farshid Rahmani*, Dapeng Feng*, Yuchen Bian and Chaopeng Shen, Improving large-basin streamflow simulation using a modular, differentiable, learnable graph model for routing

Journal Publications

(* indicates supervisee authors. ** indicates extensively-advised collaborating authors. Underline indicates corresponding author)

  1. Shen, CP. et al., Differentiable modelling to unify machine learning and physical models for geosciences. Nature Reviews Earth and Environment (2023, Accepted), preprint
  2. Aboelyazeed, Doaa*, Chonggang Xu, Forrest M. Hoffman, Alex W. Jones, Chris Rackauckas and Chaopeng Shen, A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: Demonstration with photosynthesis simulations, Biogeosciences (2023, Accepted). preprint
  3. Feng Dapeng*, Hylke Beck, Kathryn Lawson* and Chaopeng Shen, The suitability of differentiable, learnable hydrologic models, Hydrology and Earth System Sciences (2023, Accepted) HESS Discussion
  4. Louise Slater, 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, Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models, Hydrology and Earth System Sciences (2023), doi: 10.5194/hess-27-1865-2023
  5. Yegane Khoshkalam, Alain N. Rousseau, Farshid Rahmani*, Chaopeng Shen, Kian Abbasnezhadi, Applying Transfer Learning Techniques to Enhance the Accuracy of Streamflow Prediction Produced by Long Short-term Memory Networks with Data Integration, Journal of Hydrology (2023), doi: 10.1016/j.jhydrol.2023.129682
  6. Zhi, Wei, Wenyu Ouyang**, Chaopeng Shen, Li Li, Temperature as the predominant driver of dissolved oxygen in US rivers. Nature Water (2023), doi: 10.1038/s44221-023-00038-z
  7. Michael Palese, Te Pei, Tong Qiu, Allan M. Zarembski, Chaopeng Shen, and Joseph Palese, Hazard assessment framework for statistical analysis of cut slopes using track inspection videos and geospatial information, Georisk (2023, Accepted)
  8. Saha, Gourab, Farshid Rahmani*, Cibin Raj, Chaopeng Shen, and Li Li. A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds, Science of the Total Environment (2023), doi: 10.1016/j.scitotenv.2023.162930
  9. Liu, Jiangtao*, David Hughes, Farshid Rahmani*, Kathryn Lawson* and Chaopeng Shen, Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1.0) for current and emerging threats to crops, Geoscientific Model Development (2023), doi: 10.5194/gmd-16-1553-2023
  10. Feng Dapeng*, Jiangtao Liu*, Kathryn Lawson* and Chaopeng Shen, Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy, Water Resources Research (2022), doi:10.1029/2022WR032404 
  11. Kayalvizhi Sadayappan, Devon Kerins, Chaopeng Shen, Li Li, Riverine nitrate concentrations predominantly driven by human, climate, and soil property in the Contiguous United States. Water Research (2022), doi: 10.1016/j.watres.2022.119295
  12. Xi Chen, Sheng Wang, Hongkai Gao, Jiaxu Huang,  Chaopeng Shen; Qingli Li; Honggang Qi; Laiwen Zheng; Min Liu, Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation, Journal of Hydrology (2022) , doi: 10.1016/j.jhydrol.2022.128562
  13. Olukunle O. Owolabi, Kathryn Lawson*, Sanhita Sengupta, Yingsi Huang, Lan Wang, Chaopeng Shen, Mila Getmansky Sherman, Deborah A. Sunter, A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility, Environmental Research Communications (2022), doi: 10.1088/2515-7620/ac7b74,  preprint
  14. Savinay Nagendra**, Daniel Kifer, Benjamin Mirus, Te Pei**, Kathryn Lawson*, Srikanth Banagere Manjunatha, Weixin Li*, Hien Nguyen, Tong Qiu, Sarah Tran, and Chaopeng Shen, 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 (2022), doi: 10.1109/JSTARS.2022.3177025,  preprint.
  15. Kuai Fang*, Daniel Kifer, Dapeng Feng*, Kathryn Lawson* and Chaopeng ShenThe data synergy effects of time-series deep learning models in hydrology, Water Resources Research (2022), doi: 10.1029/2021WR029583,  preprint.
  16. Jiangtao Liu*, Farshid Rahmani*, Kathryn Lawson* and Chaopeng Shen, A multiscale deep learning model for soil moisture integrating satellite and in-situ data, Geophysical Research Letters (2022), doi: 10.1029/2021GL096847 
  17. Binxiao Liu, Qiuhong Tang *, Gang Zhao, Liang Gao, Chaopeng Shen, Baoxiang Pan, Physics-guided long short-term memory network for streamflow and flood simulations in the Lancang-Mekong River Basin, Water (2022), doi: 10.3390/w14091429
  18. Wen-Ping Tsai*, Dapeng Feng*, Ming Pan, Hylke Beck, Yuan Yang, Kathryn Lawson*, Jiangtao Liu*, and Chaopeng Shen. From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling. Nature Communications, (2021). doi: 10.1038/s41467-021-26107-z.
  19. Farshid Rahmani*, Chaopeng Shen, Samantha Oliver, Kathryn Lawson* and Alison Appling, Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins, Hydrological Processes (2021), doi: 10.1002/hyp.14400.
  20. Kang Xie; Pan Liu; Jianyun Zhang; Dongyang Han; Guoqing Wang; Chaopeng Shen. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology (2021),  doi: 10.1016/j.jhydrol.2021.127043
  21. Feng, DP.*, K. Lawson and CP. Shen, 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 (2021)
  22. Fuxin Zhu, Xin Li, Jun Qin, Kun Yang, Lan Cuo, Wenjun Tang, Chaopeng Shen, 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
  23. Judy P. Che-Castaldo, 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, David S. Matteson, Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects, Environment Systems and Decisions, (2021) preprint link
  24. Ouyang WY.**, K. Lawson*, DP. Feng*, L. Ye, Chi Zhang, CP. Shen, 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 (2021), preprint link
  25. Shen, CP., XY. Chen and E. Laloy, Editorial: Broadening the Use of Machine Learning in Hydrology, Frontiers in Water -Water and Hydrocomplexity, doi: 10.3389/frwa.2021.681023 (2021)
  26. Ma, K.**, DP. Feng*, K. Lawson*, W-P Tsai*, C. Liang, XR. Huang, A. Sharma*, and CP. Shen, Transferring hydrologic data across continents -- leveraging data-rich regions to improve hydrologic prediction in data-sparse regions, Water Resources Research, doi: 10.1029/2020WR028600 (2021) pre-review preprint
  27. Zhi, W., DP. Feng*, WP Tsai*, G. Sterle, A. Harpold, CP. Shen and L. Li, 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 (2021) pre-review preprint
  28. Rahmani, F.*, K. Lawson*, WY. Ouyang**, A. Appling, S. Oliver and CP. Shen, 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 (2021)
  29. Fang, K.*, D. Kifer, K. Lawson* and CP. Shen, 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 (2020) preprint link
  30. Tsai, W-P.*, K. Fang*, X. Ji*, K. Lawson*, CP. Shen, 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 (2020)
  31. Feng, DP*, K. Fang* and CP. Shen, Enhancing streamflow forecast and extracting insights using continental-scale long-short term memory networks with data integration, Water Resources Research, doi: 10.1029/2019WR026793 (2020) preprint
  32. Fang, K.* and CP. Shen, 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 (2020)
  33. 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 (2019), 130, 12-28, doi: 10.1016/j.advwatres.2019.05.008
  34. Ji, XY*, L. Lesack, JM. Melack, S. Wang*, WJ. Riley, CP. Shen, 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 (2019)
  35. Sun, N*, K. Fang*, CP. Shen, Toward a Priori Evaluation of Relative Worth of Head and Conductivity Data as Functions of Data Densities in Inverse Groundwater Modeling, Water (2019), 11(6), doi: 10.3390/w11061202
  36. Fan, YR., et al., Hillslope Hydrology in Global Change Research and Earth System Modeling, Water Resources Research, 55(2), 1737-1772, doi:10.1029/2018WR023903 (2019)
  37. 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.*, 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, (2018)
  38. Fang, K*, M. Pan, and CP. Shen, 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  (2018)
  39. Shen, CP., 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 (2018)
  40. Shen, CP., Deep learning: A next-generation big-data approach for hydrology, Eos, 99, (2018)
  41. Wang, S-Y., R. Gilles, O-Y. Chung, CP. Shen, 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 (2018)
  42. Ji, XY.* and Shen, CP., 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 (2018)
  43. Fang, K.*, CP. Shen, D. Kifer and X. Yang, Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network, Geophysical Research Letters, doi: 10.1002/2017GL075619, preprint accessible at: arXiv:1707.06611 (2017)
  44. Fang, K* and CP. Shen, Full-flow-regime storage-streamflow correlation patterns provide insights into hydrologic functioning over the continental US, Water Resources Research, doi: 10.1002/2016WR020283 (2017)
  45. Niu, Jie, CP. Shen, J. Chambers, JM. Melack and WJ. Riley, 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 (2017)
  46. Fang, K.*, CP. Shen, JB. Fisher and J. Niu. Improving Budyko curve-based estimates of long-term water partitioning using hydrologic signatures from GRACE, Water Resources Research, doi: 10.1002/2016WR018748 (2016)
  47. Liu, X., Y. Chen, CP. Shen, 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 (2016)
  48. Shen, CP., SL. Wang* and X. Liu, Geomorphological significance of at-many-stations hydraulic geometry, Geophysical Research Letters, Vol. 43, No. 8, doi: 10.1002/2016GL068364 (2016)
  49. 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, 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 (2016)
  50. Shen, CP., WJ. Riley, KR. Smithgall*, JM. Melack and K. Fang*, The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics, Water Resources Research, doi: 10.1002/2015WR018086 (2016)
  51. Pau, GHS, CP. Shen, WJ. Riley and Y. Liu, Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models, Water Resources Research, doi10.1002/2015WR017782 (2016)
  52. Ji, XY*, CP. Shen and WJ. Riley, 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 (2015)
  53. 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, Improving the representation of hydrologic processes in Earth System Models, Water Resources Research, doi:10.1002/2015WR017096 (2015)
  54. Niu, J., CP. Shen, S-G Li and M.S. Phanikumar, 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 (2014)
  55. Riley, WJ. and CP. Shen: 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 (2014).
  56. Trebotich, D., M.F. Adams, S. Molins, C.I. Steefel and CP. Shen, High Resolution Simulation of Pore Scale Reactive Transport Processes Associated with Carbon Sequestration, Computing in Science and Engineering, Nov/Dec Issue, (2014), doi: 10.1109/MCSE.2014.77
  57. 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, doi:10.1021/es5013438 (2014)
  58. Shen, CP., J. Niu and K. Fang*, 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 (2014)
  59. 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, 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 (2014)
  60. Shen, CP., J. Niu and M.S. Phanikumar, 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 (2013)
  61. Molins S., D. Trebotich, C.I. Steefel and CP. Shen, 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 (2012)
  62. Shen, CP., J.-M. Qiu and A. Christlieb, 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/ (2011)
  63. Shen, CP. and M.S. Phanikumar, 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 (2010)
  64. Shen, CP., J. Niu, E.J. Andersen and M.S. Phanikumar, 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 (2010)
  65. Shen, CP. and M.S. Phanikumar, 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 (2009)
  66. Shen, CP., M.S. Phanikumar, T.T. Fong, I. Aslam, S.L. Molloy and J.B. Rose, 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 (2008)
  67. Phanikumar,M.S., I. Aslam, CP. Shen, D.T. Long and T.C. Voice, 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 (2007)


Counting chickens

  1. Sharma, monthly (under prep)
  2. Pei Te, et al., Interactive system



  1. Water Resources Chapter, in Shortle J. et al., Pennsylvania Climate Change Impact Assessment Update (2015)
  2. Shen, CP. and K. Lawson, Chapter 19. 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