Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method

Suspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of model...

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Main Authors: Wenrui Chen, Daosheng Wang, Xiujuan Liu, Jun Cheng, Jicai Zhang
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/1/148
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author Wenrui Chen
Daosheng Wang
Xiujuan Liu
Jun Cheng
Jicai Zhang
author_facet Wenrui Chen
Daosheng Wang
Xiujuan Liu
Jun Cheng
Jicai Zhang
author_sort Wenrui Chen
collection DOAJ
description Suspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of modeling the suspended sediment concentrations (SSCs) with newly developed techniques. Based on a three-dimensional suspended cohesive sediment transport model, the transport of suspended sediment and SSCs are simulated by assimilating SSCs retrieved from the Geostationary Ocean Color Imager (GOCI) with the adjoint data assimilation in the Hangzhou Bay, a typical strong tidal estuary along the coast of the East China Sea. To improve the effect of the data assimilation, the penalty function method, in which the reasonable constraints of the estimated model parameters are added to the cost function as penalty terms, will be introduced for the first time into the adjoint data assimilation in the SSCs modeling. In twin experiments, the prescribed spatially varying settling velocity is estimated by assimilating the synthetic SSC observations, and the results show that the penalty function method can further improve the effect of data assimilation and parameter estimation, regardless of synthetic SSC observations being contaminated by random artificial errors. In practical experiments, the spatially varying settling velocity is firstly estimated by assimilating the actual GOCI-retrieved SSCs. The results demonstrate that the simulated results can be improved by the adjoint data assimilation, and the penalty function method can additionally reduce the mean absolute error (MAE) between the independent check observations and the corresponding simulated SSCs from 1.44 × 10<sup>−1</sup> kg/m<sup>3</sup> to 1.30 × 10<sup>−1</sup> kg/m<sup>3</sup>. To pursue greater simulation accuracy, the spatially varying settling velocity, resuspension rate, critical shear stress and initial conditions are simultaneously estimated by assimilating the actual GOCI-retrieved SSCs to simulate the SSCs in the Hangzhou Bay. When the adjoint data assimilation and the penalty function method are simultaneously used, the MAE between the independent check observations and the corresponding simulated SSCs is just 9.90 × 10<sup>−2</sup> kg/m<sup>3</sup>, which is substantially less than that when only the settling velocity is estimated. The MAE is also considerably less than that when the four model parameters are estimated to be without using the penalty function method. This study indicates that the adjoint data assimilation can effectively improve the SSC simulation accuracy, and the penalty function method can limit the variation range of the estimated model parameters to further improve the effect of data assimilation and parameter estimation.
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spelling doaj.art-4a908c75d0b949659dc000c081c8bc2b2023-12-03T15:02:29ZengMDPI AGRemote Sensing2072-42922022-12-0115114810.3390/rs15010148Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function MethodWenrui Chen0Daosheng Wang1Xiujuan Liu2Jun Cheng3Jicai Zhang4Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, ChinaHubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, ChinaHubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, ChinaDepartment of Environmental and Sustainability Sciences, Kean University, Union, NJ 07083, USAState Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, ChinaSuspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of modeling the suspended sediment concentrations (SSCs) with newly developed techniques. Based on a three-dimensional suspended cohesive sediment transport model, the transport of suspended sediment and SSCs are simulated by assimilating SSCs retrieved from the Geostationary Ocean Color Imager (GOCI) with the adjoint data assimilation in the Hangzhou Bay, a typical strong tidal estuary along the coast of the East China Sea. To improve the effect of the data assimilation, the penalty function method, in which the reasonable constraints of the estimated model parameters are added to the cost function as penalty terms, will be introduced for the first time into the adjoint data assimilation in the SSCs modeling. In twin experiments, the prescribed spatially varying settling velocity is estimated by assimilating the synthetic SSC observations, and the results show that the penalty function method can further improve the effect of data assimilation and parameter estimation, regardless of synthetic SSC observations being contaminated by random artificial errors. In practical experiments, the spatially varying settling velocity is firstly estimated by assimilating the actual GOCI-retrieved SSCs. The results demonstrate that the simulated results can be improved by the adjoint data assimilation, and the penalty function method can additionally reduce the mean absolute error (MAE) between the independent check observations and the corresponding simulated SSCs from 1.44 × 10<sup>−1</sup> kg/m<sup>3</sup> to 1.30 × 10<sup>−1</sup> kg/m<sup>3</sup>. To pursue greater simulation accuracy, the spatially varying settling velocity, resuspension rate, critical shear stress and initial conditions are simultaneously estimated by assimilating the actual GOCI-retrieved SSCs to simulate the SSCs in the Hangzhou Bay. When the adjoint data assimilation and the penalty function method are simultaneously used, the MAE between the independent check observations and the corresponding simulated SSCs is just 9.90 × 10<sup>−2</sup> kg/m<sup>3</sup>, which is substantially less than that when only the settling velocity is estimated. The MAE is also considerably less than that when the four model parameters are estimated to be without using the penalty function method. This study indicates that the adjoint data assimilation can effectively improve the SSC simulation accuracy, and the penalty function method can limit the variation range of the estimated model parameters to further improve the effect of data assimilation and parameter estimation.https://www.mdpi.com/2072-4292/15/1/148sediment transport modeladjoint data assimilationpenalty functionGOCI
spellingShingle Wenrui Chen
Daosheng Wang
Xiujuan Liu
Jun Cheng
Jicai Zhang
Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
Remote Sensing
sediment transport model
adjoint data assimilation
penalty function
GOCI
title Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
title_full Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
title_fullStr Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
title_full_unstemmed Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
title_short Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
title_sort improve the accuracy in numerical modeling of suspended sediment concentrations in the hangzhou bay by assimilating remote sensing data utilizing combined techniques of adjoint data assimilation and the penalty function method
topic sediment transport model
adjoint data assimilation
penalty function
GOCI
url https://www.mdpi.com/2072-4292/15/1/148
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AT xiujuanliu improvetheaccuracyinnumericalmodelingofsuspendedsedimentconcentrationsinthehangzhoubaybyassimilatingremotesensingdatautilizingcombinedtechniquesofadjointdataassimilationandthepenaltyfunctionmethod
AT juncheng improvetheaccuracyinnumericalmodelingofsuspendedsedimentconcentrationsinthehangzhoubaybyassimilatingremotesensingdatautilizingcombinedtechniquesofadjointdataassimilationandthepenaltyfunctionmethod
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