• Journal of Korean Society of Coastal and Ocean Engineers
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• v.22 no.6
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• pp.353-360
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• 2010

The Kyunggi Bay in mid-west of Korea is a relatively large estuarine system that connects the Han River system with Yellow Sea. Due to macro-tidal range of more than 8 m, the urban estuary shows deep tidal channels and wide tidal flats. Since last 30 years, the coastal development is undergoing, yielding noticeable change in environment. Particularly the tidal flat dynamics are generally accepted as being related with tidal residual flows in this area (Kim et al., 2009). We have estimated the annual variation and vertical structure of residual currents with one-year long observed flows in two major tidal channels of Kyunggi Bay. The moving average method and tidal current harmonic analysis yield nearly the same results on residual flow. The residual flow in Jangbong channel ranges from 20 cm/s in summer to 30 cm/s in winter. It is noticeable that the residual flow in Jangbong channel is flood dominant throughout the year, while the flow in Seokmo channel is ebb-dominant residual flow with current speed range of 20-40 cm/s. Due to the baroclinic response of relatively shallow estuary, significant reduction of energy in bottom layers have been observed, indicating the importance of residual circulation to the tidal flat behavior.

• Ocean and Polar Research
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• v.43 no.4
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• pp.229-243
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• 2021

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.