Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5493–5513, 2022
Hydrol. Earth Syst. Sci., 26, 5493–5513, 2022
Technical note
04 Nov 2022
Technical note | 04 Nov 2022

Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks

Grey S. Nearing et al.

Related authors

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models
Louise 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. Discuss.,,, 2022
Preprint under review for HESS
Short summary
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032,,, 2022
Short summary
Deep learning rainfall–runoff predictions of extreme events
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 26, 3377–3392,,, 2022
Short summary
Uncertainty estimation with deep learning for rainfall–runoff modeling
Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing
Hydrol. Earth Syst. Sci., 26, 1673–1693,,, 2022
Short summary
A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling
Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 25, 2685–2703,,, 2021
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
A comprehensive open-source course for teaching applied hydrological modelling in Central Asia
Beatrice Sabine Marti, Aidar Zhumabaev, and Tobias Siegfried
Hydrol. Earth Syst. Sci., 27, 319–330,,, 2023
Short summary
Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment
Aniket Gupta, Alix Reverdy, Jean-Martial Cohard, Basile Hector, Marc Descloitres, Jean-Pierre Vandervaere, Catherine Coulaud, Romain Biron, Lucie Liger, Reed Maxwell, Jean-Gabriel Valay, and Didier Voisin
Hydrol. Earth Syst. Sci., 27, 191–212,,, 2023
Short summary
Technical note: Extending the SWAT model to transport chemicals through tile and groundwater flow
Hendrik Rathjens, Jens Kiesel, Michael Winchell, Jeffrey Arnold, and Robin Sur
Hydrol. Earth Syst. Sci., 27, 159–167,,, 2023
Short summary
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
Wencong Yang, Hanbo Yang, Changming Li, Taihua Wang, Ziwei Liu, Qingfang Hu, and Dawen Yang
Hydrol. Earth Syst. Sci., 26, 6427–6441,,, 2022
Short summary
Disentangling scatter in long-term concentration–discharge relationships: the role of event types
Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
Hydrol. Earth Syst. Sci., 26, 6227–6245,,, 2022
Short summary

Cited articles

Abrahart, R. J. and See, L.: Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments, Hydrol. Proc., 14, 2157–2172, 2000. a
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313,, 2017.  a, b, c, d
Bannister, R.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, 2017. a
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks, arXiv [preprint],, 2015. a
Cameron, D., Kneale, P., and See, L.: An evaluation of a traditional and a neural net modelling approach to flood forecasting for an upland catchment, Hydrol. Proc., 16, 1033–1046,, 2002. a
Short summary
When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.