WUR Data Librarian; Sun, Y. (Yiqun) (2020): Data underlying the research of: Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:dfe80f20-2031-4d0c-a7f5-82a840248c20

Dataset

x choose version: version 2 - 2021-05-12 (latest)

• 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)

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hourly timestep

CC BY-SA 4.0

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

by WUR Data Librarian, 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, published, posted

4TU.Centre for Research Data

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

Wageningen University & Research;
Hohai University, China