Articles | Volume 22, issue 11
Special issue:
Opinion article
01 Nov 2018
Opinion article |  | 01 Nov 2018

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Chaopeng Shen, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, and Wen-Ping Tsai

Related authors

Hybrid forecasting: blending climate predictions with AI models
Louise J. 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
Hydrol. Earth Syst. Sci., 27, 1865–1889,,, 2023
Short summary
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567,,, 2023
Short summary
A differentiable ecosystem modeling framework for large-scale inverse problems: demonstration with photosynthesis simulations
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Alex W. Jones, Chris Rackauckas, Kathryn E. Lawson, and Chaopeng Shen
Biogeosciences Discuss.,,, 2022
Revised manuscript accepted for BG
Short summary
The suitability of differentiable, learnable hydrologic models for ungauged regions and climate change impact assessment
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss.,,, 2022
Revised manuscript accepted for HESS
Short summary
Characterizing coarse-resolution watershed soil moisture heterogeneity using fine-scale simulations
W. J. Riley and C. Shen
Hydrol. Earth Syst. Sci., 18, 2463–2483,,, 2014

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups
Yuchan Chen, Xiuzhi Chen, Meimei Xue, Chuanxun Yang, Wei Zheng, Jun Cao, Wenting Yan, and Wenping Yuan
Hydrol. Earth Syst. Sci., 27, 1929–1943,,, 2023
Short summary
Reconstructing five decades of sediment export from two glacierized high-alpine catchments in Tyrol, Austria, using nonparametric regression
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, Christoph Mayer, and Axel Bronstert
Hydrol. Earth Syst. Sci., 27, 1841–1863,,, 2023
Short summary
Water and energy budgets over hydrological basins on short and long timescales
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744,,, 2023
Short summary
Hydrological response to climate change and human activities in the Three-River Source Region
Ting Su, Chiyuan Miao, Qingyun Duan, Jiaojiao Gou, Xiaoying Guo, and Xi Zhao
Hydrol. Earth Syst. Sci., 27, 1477–1492,,, 2023
Short summary
Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction
Andreas Hartmann, Jean-Lionel Payeur-Poirier, and Luisa Hopp
Hydrol. Earth Syst. Sci., 27, 1325–1341,,, 2023
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Cited articles

Abramowitz, G., Gupta, H., Pitman, A., Wang, Y., Leuning, R., Cleugh, H., Hsu, K., Abramowitz, G., Gupta, H., Pitman, A., Wang, Y., Leuning, R., Cleugh, H., and Hsu, K.: Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation, J. Hydrometeorol., 7, 160–177,, 2006. 
Abramowitz, G., Pitman, A., Gupta, H., Kowalczyk, E., Wang, Y., Abramowitz, G., Pitman, A., Gupta, H., Kowalczyk, E., and Wang, Y.: Systematic Bias in Land Surface Models, J. Hydrometeorol., 8, 989–1001,, 2007. 
Ajami, H., Khan, U., Tuteja, N. K., and Sharma, A.: Development of a computationally efficient semi-distributed hydrologic modeling application for soil moisture, lateral flow and runoff simulation, Environ. Model. Softw., 85, 319–331,, 2016. 
Albert, A., Strano, E., Kaur, J., and Gonzalez, M.: Modeling urbanization patterns with generative adversarial networks, arXiv:1801.02710, available at:, last access: 24 March 2018. 
Allamano, P., Croci, A., and Laio, F.: Toward the camera rain gauge, Water Resour. Res., 51, 1744–1757,, 2015. 
Short summary
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
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