Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-5639-2018
Special issue:
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, 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

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Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
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Improving Streamflow Simulation through Machine Learning-Powered Data Integration and Its Implications for Forecasting in the Western U.S.
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From RNNs to Transformers: benchmarking deep learning architectures for hydrologic prediction
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A novel hybrid fine-tuning method for supercharging deep learning model development for hydrological prediction
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Ensembling Differentiable Process-based and Data-driven Models with Diverse Meteorological Forcing Datasets to Advance Streamflow Simulation
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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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