Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5493–5513, 2022
https://doi.org/10.5194/hess-26-5493-2022
Hydrol. Earth Syst. Sci., 26, 5493–5513, 2022
https://doi.org/10.5194/hess-26-5493-2022
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.

Data sets

Catchment Attributes for Large-Sample Studies (CAMELS) N. Addor, A. Newman, M. Mizukami, and M. P. Clark https://doi.org/10.5065/D6G73C3Q

CAMELS Extended Maurer Forcing Data F. Kratzert https://doi.org/10.4211/hs.17c896843cf940339c3c3496d0c1c077

CAMELS Extended NLDAS Forcing Data F. Kratzert https://doi.org/10.4211/hs.0a68bfd7ddf642a8be9041d60f40868c

A large-sample watershed-scale hydrometeorological dataset for the contiguous USA A. Newman, K. Sampson, M. P. Clark, A. Bock, R. J. Viger, and D. Blodgett https://doi.org/10.5065/D6MW2F4D

Model code and software

NeuralHydrology (v1.3.0) Data Assimilation Code G. Nearing https://doi.org/10.5281/zenodo.7063259

Executable research compendia (ERC)

Code for "Technical Note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks" Grey Nearing https://doi.org/10.5281/zenodo.7063252

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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.