This is an elementary page that collects work related to our group on hydrologic deep learning.



1. AGU tutorial
2. AGU sessions


1. fork our hydroDL GitHUB
2. Jupyter note for the first hydroDL tutorial on LSTM.


  1. 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 (2021, Accepted), preprint link
  2. 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 (2021), preprint link
  3. 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)
  4. 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
  5. 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)
  6. 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 (2020, Accepted)
  7. 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 (2020), doi: 10.1029/2020WR028095 preprint:
  8. 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
  9. 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 Reserach, (2020), doi: 10.1029/2019WR026793, preprint:
  10. 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)
  11. 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)
  12. Fang, K.*, CP. Shen and D. Kifer, Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions, International Conference on Machine Learning (ICML) Workshop, Climate Change: What can AI do?Long Beach, CA, June 2019 (Spotlight talk, double-blind peer reviewed pdf, non-archival).
  13. Shen, CP.A trans-disciplinary review of deep learning research and its relevance for water resources scientistsWater Resources Research. 54(11), 8558-8593, doi: 10.1029/2018WR022643 (2018)
  14. 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)
  15. Shen, CP., Deep learning: A next-generation big-data approach for hydrology, Eos, 99, (2018)
  16. 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 NetworkGeophysical Research Letters, doi: 10.1002/2017GL075619, preprint accessible at: arXiv:1707.06611 (2017)
  17. 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)



Hydrologic Deep Learning


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, (2017), doi: 10.1002/2017GL075619, preprint accessible at: arXiv:1707.06611.

Here at Multi-scale Hydrology, Processes and Intelligence group, we study how mother nature works using state-of-the-art machine learning technique, especially times series deep learning. We first utilized time series deep learning to examine large, raw hydrologic data. Deep learning is not the end. It is a means to better advance our understanding of hydrology and a path toward stronger predictive capability.

We utilized the Long Short-Term Memory (LSTM) to reproduce soil moisture dynamics. We show it is not only viable but also more robust than simpler statistical methods. This research shows it is possible to hindcast soil moisture dynamics using deep learning, opening up a range of new possibilities for advancing science.


In all tests, LSTM shows stronger performance for the test set, when compared to simple feedforward neural network, Auto-regressive models (AR), and regularized linear regression (LR).

LSTM outperforms other methods


LSTM is also suitable for long-term hindcasting:

long-term hindcast using LSTM