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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

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

Usage statistics

Categories

• Natural Hazards
• Other Environmental Sciences

Keywords

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

Geolocation

view on openstreetmap

Time coverage

hourly timestep

Licence

CC BY-SA 4.0

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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)

History

• 2020-02-27 first online

• 2021-05-12 published, posted

Publisher

4TU.Centre for Research Data

Format

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

Associated peer-reviewed publication

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

Organizations

Wageningen University & Research
Hohai University, China