Time-varying characteristics of saturated hydraulic conductivity in grassed swales based on the ensemble Kalman filter algorithm

A case study of two long-running swales in Netherlands

Journal Article (2023)
Author(s)

Feikai Yang (IHE Delft Institute for Water Education, TU Delft - Hydraulic Structures and Flood Risk, Southeast University, Monash University)

Dafang Fu (Monash University, Southeast University)

Chris Zevenbergen (TU Delft - Urban Design, IHE Delft Institute for Water Education)

Floris C. Boogaard (Hanze Hogeschool Groningen, Deltares)

Rajendra Prasad Singh (Southeast University, Monash University)

DOI related publication
https://doi.org/10.1016/j.jenvman.2023.119760 Final published version
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Publication Year
2023
Language
English
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Journal title
Journal of Environmental Management
Volume number
351
Article number
119760
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Abstract

Saturated hydraulic conductivity (Ks) of the filler layer in grassed swales are varying in the changing environment. In most of the hydrological models, Ks is assumed as constant or decrease with a clogging factor. However, the Ks measured on site cannot be the input of the hydrological model directly. Therefore, in this study, an Ensemble Kalman Filter (EnKF) based approach was carried out to estimate the Ks of the whole systems in two monitored grassed swales at Enschede and Utrecht, the Netherlands. The relationship between Ks and possible influencing factors (antecedent dry period, temperature, rainfall, rainfall duration, total rainfall and seasonal factors) were studied and a Multivariate nonlinear function was established to optimize the hydrological model. The results revealed that the EnKF method was satisfying in the Ks estimation, which showed a notable decrease after long-term operation, but revealed a recovery in summer and winter. After the addition of Multivariate nonlinear function of the Ks into hydrological model, 63.8% of the predicted results were optimized among the validation events, and compared with constant Ks. A sensitivity analysis revealed that the effect of each influencing factors on the Ks varies depending on the type of grassed swale. However, these findings require further investigation and data support.

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