Preprints

  1. Fang, K.* et al., The data synergy effects of time-series deep learning models in hydrology
  2. Nagendra et al.*, Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Learning Without Forgetting
  3. Chen et al., Comparison between a typical deep learning model and a process-based model in a glacio-hydrology simulation
  4. Liu et al.*. Multiscale deep learning models integrating multi-source data for high-resolution soil moisture predictions.

Journal Publications

  1. 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.
  2. 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.
  3. 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. Accepted), doi: 10.1016/j.jhydrol.2021.127043
  4. 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)
  5. 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
  6. 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
  7. 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
  8. 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)
  9. 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
  10. 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
  11. 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)
  12. 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
  13. 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)
  14. 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
  15. 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)
  16. 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
  17. 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)
  18. 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
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. Shen, CP., Deep learning: A next-generation big-data approach for hydrology, Eos, 99, https://doi.org/10.1029/2018EO095649 (2018)
  24. 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)
  25. 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)
  26. 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)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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)
  32. 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)
  33. 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)
  34. 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)
  35. 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)
  36. 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)
  37. 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)
  38. 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).
  39. 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
  40. 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)
  41. 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)
  42. 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)
  43. 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)
  44. 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)
  45. 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/j.jcp.2011.02.008 (2011)
  46. 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)
  47. 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)
  48. 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)
  49. 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)
  50. 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)

(* indicates supervisee authors. ** indicates visiting students under Shen's supervision. Underline indicates corresponding author)

Counting chickens

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

 

Non-journal

  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