Insights on the impact of systematic model errors on data assimilation performance in changing catchments

Insights on the impact of systematic model errors on data assimilation performance in changing... •Systematic errors in observations or model simulations have traditionally been shown to degrade data assimilation quality.•We investigate this issue in the context of data assimilation and prediction in catchments with changing system properties (i.e. land cover conditions).•Experiments on a range of catchments show that the impacts of systematic errors due to unknown land cover changes are dependent on the inherent model prediction uncertainty that persists even in pre-change conditions.•Systematic errors introduced by unresolved dynamic system properties do not always negatively impact assimilation/forecast quality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Water Resources Elsevier

Insights on the impact of systematic model errors on data assimilation performance in changing catchments

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0309-1708
eISSN
1872-9657
D.O.I.
10.1016/j.advwatres.2017.12.006
Publisher site
See Article on Publisher Site

Abstract

•Systematic errors in observations or model simulations have traditionally been shown to degrade data assimilation quality.•We investigate this issue in the context of data assimilation and prediction in catchments with changing system properties (i.e. land cover conditions).•Experiments on a range of catchments show that the impacts of systematic errors due to unknown land cover changes are dependent on the inherent model prediction uncertainty that persists even in pre-change conditions.•Systematic errors introduced by unresolved dynamic system properties do not always negatively impact assimilation/forecast quality.

Journal

Advances in Water ResourcesElsevier

Published: Mar 1, 2018

References

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