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

• Main Authors: Georgy Ayzel, Liubov Kurochkina, Dmitriy Abramov, Sergei Zhuravlev
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Hydrology
• Subjects: runoff; modeling; reanalysis; dataset; neural networks; LSTM
• Online Access: https://www.mdpi.com/2306-5338/8/1/6

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<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mo>°</mo></msup></semantics></math></inline-formula> 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.