Data underlying the research of: Improving forecast skill of lowland hydrological models using ensemble Kalman filter and unscented Kalman filter

DOI:10.4121/uuid:dfe80f20-2031-4d0c-a7f5-82a840248c20

Datacite citation style:

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

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Dataset

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

version 1 - 2020-02-27

Usage statistics

2521

views

citations

687

downloads

Keywords

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

Geolocation

Regge catchment

lat (N): 52.194

lon (E): 6.538

view on openstreetmap

Time coverage

hourly timestep

Licence

CC BY-SA 4.0

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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)

History

2020-02-27 first online, published, posted

Publisher

4TU.Centre for Research Data

Format

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