Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks

Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) netwo...

Full description

Bibliographic Details
Published in:Hydrology
Main Authors: Georgy Ayzel, Liubov Kurochkina, Dmitriy Abramov, Sergei Zhuravlev
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
runoff
modeling
reanalysis
dataset
neural networks
LSTM
Science
Q
Northwest Russia
Online Access:https://doi.org/10.3390/hydrology8010006
https://doaj.org/article/890ee245490446de9e19ed12ad312326
Description
Summary:Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5 <semantics> ° </semantics> spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia.

Similar Items