Estimation of spatially varying parameters in three-dimensional cohesive sediment transport models by assimilating remote sensing data

Zhang, Jicai; Chu, Dongdong; Wang, Daosheng; Cao, Anzhou; Lv, Xianqing; Fan, Daidu

doi:10.1007/s00773-017-0477-3

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…(2:1) where x i (i=1,2,…,n) are IP, which can be selected according to the IPS (Guo et al, 2017;Pan et al, 2017;Zhang et al, 2018). x is any depth between x 1 and x n .…”

Section: Cubic Spline Fitting Methodsmentioning

confidence: 99%

Research on outlier detection in CTD conductivity data based on cubic spline fitting

et al. 2022

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Outlier detection is the key to the quality control of marine survey data. For the detection of outliers in Conductivity-Temperature-Depth (CTD) data, previous methods, such as the Wild Edit method and the Median Filter Combined with Maximum Deviation method, mostly set a threshold based on statistics. Values greater than the threshold are treated as outliers, but there is no clear specification for the selection of threshold, thus multiple attempts are required. The process is time-consuming and inefficient, and the results have high false negative and positive rates. In response to this problem, we proposed an outlier detection method in CTD conductivity data, based on a physical constraint, the continuity of seawater. The method constructs a cubic spline fitting function based on the independent points scheme and the cubic spline interpolation to fit the conductivity data. The maximum fitting residual points will be flagged as outliers. The fitting stops when the optimal number of iterations is reached, which is automatically obtained by the minimum value of the sequence of maximum fitting residuals. Verification of the accuracy and stability of the method by means of examples proves that it has a lower false negative rate (17.88%) and false positive rate (0.24%) than other methods. Indeed, rates for the Wild Edit method are 56.96% and 2.19%, while for the Median Filter Combined with Maximum Deviation method rates are 23.28% and 0.31%. The Cubic Spline Fitting method is simple to operate, the result is clear and definite, better solved the problem of conductivity outliers detection.

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“…(2:1) where x i (i=1,2,…,n) are IP, which can be selected according to the IPS (Guo et al, 2017;Pan et al, 2017;Zhang et al, 2018). x is any depth between x 1 and x n .…”

Section: Cubic Spline Fitting Methodsmentioning

confidence: 99%

Research on outlier detection in CTD conductivity data based on cubic spline fitting

et al. 2022

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“…Here, A(t) and B(t) can be solved by the IP scheme (Lu and Zhang 2006;Guo et al 2017;Pan et al 2017;Zhang et al 2018). Assume that N is the length of the entire time series and select K time points that are uniformly distributed in the time domain as IPs.…”

Section: A Pff-vpmentioning

confidence: 99%

A Methodology for Fitting the Time Series of Snow Depth on the Arctic Sea Ice

Wang

Pan

Wang

et al. 2019

Journal of Atmospheric and Oceanic Technology

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Snow depth is an important geophysical variable for investigating sea ice and climate change, which can be obtained from satellite data. However, there is a large number of missing data in satellite observations of snow depth. In this study, a methodology, the periodic functions fitting with varying parameter (PFF-VP), is presented to fit the time series of snow depth on Arctic sea ice obtained from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The time-varying parameters are obtained by the independent point (IP) scheme and cubic spline interpolation. The PPF-VP is validated by experiments in which part of the observations are artificially removed and used to compare with the fitting results. Results indicate that the PPF-VP performs better than three traditional fitting methods, with its fitting results closer to observations and with smaller errors. In the practical experiments, the optimal number of IPs can be determined by only considering the fraction of missing data, particularly the length of the longest gaps in the snow-depth time series. All the experimental results indicate that the PPF-VP is a feasible and effective method to fit the time series of snow depth and can provide continuous data of snow depth for further study.

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“…The transport of sediment in Hangzhou Bay is complex and influenced by various physical and chemical processes, such as current, mixing, settlement, and flocculation. Traditional fixed-point measurements are insufficient to finely portray the material transport in the entire bay, and satellite remote sensing means are limited to the surface layer of the water body, while the numerical model of suspended sediment can make up for the deficiencies of traditional field observation and remote sensing methods [31,32]. Incomplete factors in the numerical sediment model with a low grid resolution in Hangzhou Bay were considered in previous studies [27,28,30], and the water and sediment transport and their dynamic mechanism are still unclear.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Analysis of Water and Suspended Sediment Transport in Hangzhou Bay, China

Huang

Yuan

Zhu

2022

JMSE

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Hangzhou Bay is a large, high-turbidity shallow bay located on the southern side of the Changjiang Estuary, China. The process and dynamic mechanisms of water and sediment transport in the bay are not yet clear. An improved three-dimensional sediment numerical model that combined various dynamic factors was established to simulate and analyze these mechanisms. The residual current cannot properly represent the net water and sediment transport, and the residual unit width water flux (RUWF) and residual unit width sediment flux (RUSF) were used to explain the water and sediment transport. The results of numerical simulations indicate that in summer, the surface RUWF from the Changjiang Estuary near Nanhui Cape flows westward along the coast, in which the major part flows southward to the Zhenhai area, and the small part flows further westward along the north coast and then turns to the south coast and eastward, forming the water transport pattern of north-landward and south-seaward, which is stronger in the spring tide than in the neap tide. The bottom RUWF near Zhenhai flows northward to Nanhui Cape in the neap tide, which is larger in the neap tide than in the spring tide. In the middle and western parts of the bay, the RUWF has the same pattern as the surface water transport and is stronger in the spring tide than in the neap tide. The pattern of RUSF is roughly similar to the water flux transport. During the spring tide, the water and sediment transport fluxes near Nanhui Cape are from the Changjiang Estuary into Hangzhou Bay, but from Hangzhou Bay into the Changjiang Estuary during the neap tide. In the winter, the distributions of RUWF, RUSF, and suspended sediment concentration (SSC) are similar to those in the summer. In addition, the distance of surface water transport westward along the north coast is shorter than that in the summer, the magnitude of the bottom RUWF is smaller than that in the summer due to the weaker salinity gradient, and the bottom RUSF near Nanhui Cape is weaker than that in the summer during the neap tide. The net transect water flux (NTWF) and the net transect sediment flux (NTSF) near Nanhui Cape are from the Changjiang Estuary into Hangzhou Bay during the spring tide; during the neap tide, the NTWF is still from the Changjiang Estuary into Hangzhou Bay, but the NTSF is from Hangzhou Bay into the Changjiang Estuary because the SSC is much higher in the bottom layer than in the surface layer. The dynamic reason for the temporal and spatial variation in RUWF and RUSF is that the barotropic pressure gradient force is larger than the baroclinic pressure gradient force during the spring tide and is the opposite during the neap tide.

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Estimation of spatially varying parameters in three-dimensional cohesive sediment transport models by assimilating remote sensing data

Cited by 10 publications

References 32 publications

Research on outlier detection in CTD conductivity data based on cubic spline fitting

Research on outlier detection in CTD conductivity data based on cubic spline fitting

A Methodology for Fitting the Time Series of Snow Depth on the Arctic Sea Ice

Numerical Simulation and Analysis of Water and Suspended Sediment Transport in Hangzhou Bay, China

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