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

Nearing, Grey S.; Klotz, Daniel; Frame, Jonathan M.; Gauch, Martin; Gilon, Oren; Kratzert, Frederik; Sampson, Alden Keefe; Shalev, Guy; Nevo, Sella

doi:https://doi.org/10.5194/hess-26-5493-2022

Articles | Volume 26, issue 21

https://doi.org/10.5194/hess-26-5493-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-5493-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 21

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, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo

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

Interactive computing environment

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

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.