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

Feikai Yang; Dafang Fu; Chris Zevenbergen; Floris C Boogaard; Rajendra Prasad Singh

doi:10.1016/j.jenvman.2023.119760

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

J Environ Manage. 2024 Feb:351:119760. doi: 10.1016/j.jenvman.2023.119760. Epub 2023 Dec 12.

Authors

Feikai Yang¹, Dafang Fu², Chris Zevenbergen³, Floris C Boogaard⁴, Rajendra Prasad Singh⁵

Affiliations

¹ School of Civil Engineering, Southeast University, Nanjing 210096, China; Southeast University-Monash University Joint Research Centre for Future Cities, Nanjing 210096, China; IHE-Delft Institute for Water Education, P.O. Box 3015, 2611DA Delft, the Netherlands; Department of Civil Engineering, Delft University of Technology (TU Delft), Gebouw 23, Stevinweg 1, 2628CN Delft, the Netherlands.
² School of Civil Engineering, Southeast University, Nanjing 210096, China; Southeast University-Monash University Joint Research Centre for Future Cities, Nanjing 210096, China.
³ IHE-Delft Institute for Water Education, P.O. Box 3015, 2611DA Delft, the Netherlands; Department of Civil Engineering, Delft University of Technology (TU Delft), Gebouw 23, Stevinweg 1, 2628CN Delft, the Netherlands.
⁴ Research Centre for Built Environment NoorderRuimte, Hanze University of Applied Sciences, 9747 AS Groningen, the Netherlands; Deltares, Daltonlaan 600, 3584 BK Utrecht, the Netherlands.
⁵ School of Civil Engineering, Southeast University, Nanjing 210096, China; Southeast University-Monash University Joint Research Centre for Future Cities, Nanjing 210096, China. Electronic address: rajupsc@seu.edu.cn.

PMID: 38086124
DOI: 10.1016/j.jenvman.2023.119760

Abstract

Saturated hydraulic conductivity (K_s) of the filler layer in grassed swales are varying in the changing environment. In most of the hydrological models, K_s is assumed as constant or decrease with a clogging factor. However, the K_s 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 K_s of the whole systems in two monitored grassed swales at Enschede and Utrecht, the Netherlands. The relationship between K_s 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 K_s 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 K_s into hydrological model, 63.8% of the predicted results were optimized among the validation events, and compared with constant K_s. A sensitivity analysis revealed that the effect of each influencing factors on the K_s varies depending on the type of grassed swale. However, these findings require further investigation and data support.

Keywords: Ensemble Kalman filter; Grassed swales; Multivariate nonlinear regression; Nature based solution; Saturated hydraulic conductivity.

MeSH terms

Chemical Phenomena
Hydrology
Netherlands
Poaceae*
Soil*

Substances

Soil