Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
https://doi.org/10.5194/hess-22-5639-2018

Special issue: HESS Opinions 2018

Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
https://doi.org/10.5194/hess-22-5639-2018
Opinion article
01 Nov 2018
Opinion article | 01 Nov 2018

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

Chaopeng Shen et al.

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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, https://doi.org/10.1175/JHM479.1, 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, https://doi.org/10.1175/JHM628.1, 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, https://doi.org/10.1016/J.ENVSOFT.2016.09.002, 2016. 
Albert, A., Strano, E., Kaur, J., and Gonzalez, M.: Modeling urbanization patterns with generative adversarial networks, arXiv:1801.02710, available at: http://arxiv.org/abs/1801.02710, last access: 24 March 2018. 
Allamano, P., Croci, A., and Laio, F.: Toward the camera rain gauge, Water Resour. Res., 51, 1744–1757, https://doi.org/10.1002/2014WR016298, 2015. 
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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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