1. Reconstructing daily streamflow and floods from large-scale atmospheric variables with feed-forward and recurrent neural networks in high latitude climates.
- Author
-
Hagen, Jenny Sjåstad, Hasibi, Ramin, Leblois, Etienne, Lawrence, Deborah, and Sorteberg, Asgeir
- Subjects
- *
RECURRENT neural networks , *CLIMATE change models , *STREAMFLOW , *FLOODS , *HYDROLOGIC models , *REGRESSION trees , *DOWNSCALING (Climatology) - Abstract
With climate change, decreases in winter snow storage and increases in precipitation duration and intensities will alter the occurrence of floods in high-latitude countries. The state-of-the-art hydrological climate-impact model chain consists of one or more global climate models, downscaling and bias-correction techniques, and one or more hydrological models. Machine learning offers a complementary approach to hydrological climate-impact modelling by facilitating direct downscaling from large-scale atmospheric variables to streamflow. This paper presents the development of multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks benchmarked against regression tree models for reconstruction of daily streamflow and floods from atmospheric reanalysis data with comparable resolution to global climate model outputs. Catchment-specific, physically-based input features representing the dominant flood drivers were identified for 27 catchments in Norway. Overall, the LSTM obtained the highest accuracy. This article provides a springboard for future research on hydrological climate-impact modelling with neural networks in high-latitude countries. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF