Global streamflow modelling using process-informed machine learning

Global streamflow modelling using process-informed machine learning

We present a novel hybrid framework that incorporates information from the process-based global hydrological model PCR-GLOBWB, to reduce prediction errors in streamflow simulations. In addition to catchment attributes and meteorological data, our methodology employs simulated streamflow and state va...

Full description

Bibliographic Details
Main Authors: Michele Magni, Edwin H. Sutanudjaja, Youchen Shen, Derek Karssenberg
Format: Article
Language:English
Published: IWA Publishing 2023-09-01
Series:Journal of Hydroinformatics
Subjects:
global hydrology
hybrid streamflow modelling
machine learning
post-processing
random forests
Online Access:http://jhydro.iwaponline.com/content/25/5/1648

Internet

http://jhydro.iwaponline.com/content/25/5/1648

Similar Items