The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/12717392

Albrecht Weerts; Sun, Y. (Yiqun) (2021): Data underlying the research of: Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/12717392.v2

Dataset

x choose version: version 1 - 2020-02-27

• Natural Hazards
• Other Environmental Sciences

Conceptual Hydrological model, Ensemble Kalman Filter (EnKF), Lowland Catchments, State Estimation, The Wageningen Lowland Runoff Simulator (WALRUS), Unscented Kalman Filter (UKF)

view on openstreetmap

hourly timestep

CC BY-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Albrecht Weerts , Y. (Yiqun) Sun

research dataset underlying peer reviewed manuscript containing hydrological streamflow forecasts (using perfect forcing) covering a period of 10 years to determine the benefits of streamflow assimilation using the WALRUS hydrological model for a Dutch lowland area (Regge catchment)

• 2020-02-27 first online

• 2021-05-12 published, posted

4TU.Centre for Research Data

media types: application/x-netcdf, application/x-tar, application/zip, text/plain

Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter

Wageningen University & Research
Hohai University, China