INTRODUCTION

Sediment dynamics play an important role in forming the bottom geomorphology and affecting the sediment distribution in coastal waters. More and more coastal engineering projects like offshore construction or seabed pipeline routing request to estimate the potential of erosion on the seafloor bottom surface, to investigate the occurrence of special bottom morphology, e. g. tidal sand ridge distribution and development along Chinese coasts (Liu et al., 1998; Swift, 1975; Wang et al., 2012; Yang, 1989). Considering the variety of bottom sediment types and specific sediment distribution patterns, a methodology of assessing and quantifying the status of bottom dynamics is required, studying the natural dynamics of surface sediment and its interaction with human activities.

Threshold value is a widely used tool for description of bottom erosion status. Belderson (1986) found that mean spring near-surface peak tidal currents exceeding 0.5 m/s are responsible for the formation of sand waves in the North Sea, providing evidence that the magnitude of tidal currents and the occurrence of sand waves are related. To overcome the difficulty in getting reliable values of critical shear stress/velocity for the initial sediment motion and suspension, Grass (1970) demonstrated the need to use statistical tools to improve the evaluation of sediment-motion processes. Lopez and Garcia (2001) later developed this idea into a risk-based probabilistic approach; they used basic probability tools in characterization of the turbulent processes involved in the mechanics of sediment transport. This method was first applied to analyze the surface erosion risk of Mecklenburg Bight in the Baltic Sea (Bohling, 2005) where sediment characteristics and critical shear stress velocities were used to calculate the risk of sediment erosion.

In this research, we use the concept “Erosion Risk” instead of kind of threshold initial value of sediment motion to assess the bottom environment in the Bohai Sea, North Yellow Sea (BSNYS; Figure 1). BSNYS is a semi-closed shallow shelf sea, affected by the dynamic processes of tides, waves and regional currents. It receives large amounts of sediment from many rivers in China, for instance the Yellow River, and rivers from Korea (Park and Lee, 1994). The sea bottom is mostly flat and water depth less than 100 meters. The area is largely dominated by mixed semi-diurnal tidal regime, with tidal range exceeding 4 m in many coastal regions. Seasonal current patterns develop in the Yellow Sea affected by monsoonal winds. In Summer, the primary circulation in this region is characterized by NYSCW (Northern Yellow Sea Cold Water), which is anticlockwise induced by the sea-surface heating and vertical mixing of the tidal currents and bottom topography, in winter by the northernward YSWC (Yellow Sea Warm Current) and CC (Coastal Current) (Lu et al., 2011; Shi et al., 2012). Sediment at the seafloor and the hydrodynamic modeling data were gathered in a Geological Information System (GIS). Bottom erosion status indexed by erosion risk is calculated based on these data. Four sites were selected to assess the temporal and spatial change of erosion risk in the study domain, the relationship between the erosion risk distribution and the occurrence of tidal sand ridge is discussed in the final section.

Figure 1.

The study area with tidal sand ridges with schematic pattern of the regional circulation in BSNYS in summer, NYSCW (Northern Yellow Sea Cold Water), and in winter, YSWC (Yellow Sea Warm Current). CC: Coastal Current (circulation pattern adopted from Shi et al., 2012, sand ridge distribution from Wang et al., 2012)

DATA SET

Sediment data

Surface sediments characteristics used in this study were mainly adopted from a sediment distribution map published by Li et al. (2005) and other sources (Park and Lee, 1994; Shi et al., 2012; Zhu and Chang, 2000). 18 sediment types (Figure 2) using Shepard (1954) classification method were digitized into the ARCGIS database. Following the concept of “regionalized classification” (Harff and Davis, 1990), these sediments types were categorized into 5 subclasses (see Table 1, Figure 2) with reference to Bobertz and Harff (2004), Bohling (2005), Rijn (2007). Silt is primarily distributed in the coastal area of Bohai Sea with more than 80% silt content by weight, badly sorted, and with mean size of 5.5 ϕ(Shi et al., 2012). Clayey silt is one of the most widely distributed sediments inside the Bohai Sea and center of north Yellow Sea, with 60% of silt, about 2% by sand and 38% of clay, the mean grain size is 7.5 ϕ(Shi et al., 2012; Zhu and Chang, 2000). These 2 types of sediments belong to cohesive sediment, where the threshold value of critical shear stress is hard to be observed. Here, the value of critical shear stress is adopted from Rijn (2007). He showed that for sediment bed with size in the range of 8–62 μm corresponding to an approximately constant shear stress value of 0.1 N/m², will result in a critical depth-average current velocity of about 0.32 m/s. Critical shears stress of 0.2 N/m² will result in a critical depth-averaged current velocity of about 0.42 m/s with D50 < 62 μm. To simplify the classification process, 2 types of cohesive sediment were distinguished based on the literature research (Table 1). Coarse-fine silt represent the cohesive-weakly cohesive sediment which most distribute along the coastal waters of Bohai Sea (Figure 3), while clayey silt and very fine silt represent the very cohesive type which covered most area of the Bohai Sea and west part of the Yellow Sea.

Figure 2.

The digitized sediment distribution map based on Li et al., 2005

Table 1.

The corresponding properties of 5 sediment classes used in bottom sediment erosion risk model

Figure 3.

The re-classified sediment distribution map of Bohai Sea, north Yellow Sea, 5 sediment types' characteristic see Table 1

The sandy sediments are subdivided into 3 classes although previous studies put them all in one class (Shi et al., 2012; Zhu and Chang, 2000). Sand dominantly occurs in the northeast Yellow Sea, along the Laotieshan channel in the Bohai Strait. It is believed that these sandy sediments are formed after the post-glacial transgression maximum by strong tidal currents and subsequent shelf erosion (Dong et al., 1989; Li et al., 2001). Since lack of laboratory experimental observation data for the critical shear stress/velocity in this region, the critical erosion stress for noncohesive sediment is based on the Sediment-Transport Applets from USGS ( http://woodshole.er.usgs.gov/staffpages/csherwood/sedx_equations/RunSedCalcs.html). The mean velocity calculated from critical shear stress is referred to the formula derived by Prof. Ponce ( http://ponce.sdsu.edu/critical_shear_stress_versus_critical_velocity.html).

Hydrodynamic data

The hydrodynamic data are model output from Regional Ocean Modeling System (ROMS), which is a free-surface, terrain-following, primitive equations model using the hydrostatic and Boussinesq assumptions. The model domain covers the Bohai Sea, North Yellow Sea with a 5 km resolution in latitude and longitude, and with 20 layers in the vertical resolution. The time-step used for the three-dimensional baroclinic model is 300 s. The bathymetry of the model is interpolated from digitized nautical charts and ETOPO1 data set. 6 hourly forcing from NCEP reanalysis data of heat flux, wind stress are used for the model bulk driving force. The four principal and most common used tidal harmonic constituent M2, S2, K1 and O1 are applied as tidal forcing in the model, and the tidal data are derived from the suite of global and regional tide model TPXO8 dataset. For the lateral open boundaries, horizontal sea water velocity and sea surface elevation are specified from climatologically potential temperature and salinity assembled from the World Ocean Atlas 2005 (WOA05) data set. The model is initialized on 1 December, 2011 from a state of rest with the temperature and salinity specified from the WOA05, and set to run one year (2012) in this study.

The wave forcing data are derived by running the Simulating WAves Nearshore (SWAN) model offline. The SWAN model uses 6 hourly wind derived data from the NCEP reanalysis data to calculate the wind-induced wave height, wave direction, and bottom wave period in the modeling domain. The SSW-BBL bottom boundary layer module is used in the numerical computation. These wave data are used to calculate the near-bed velocity and the wave-current shear stresses which have influence on sediment suspension/deposition.

The modeled sea surface elevation results are used to calculate the harmonic constants of the tidal constituents in order to validate the model. The co-tidal and co-amplitude lines of the M2 tide, which are the dominant diurnal constituents in the Bohai Sea, North Yellow Sea, generated by the model in the BSNYS are shown in Figure 4. This structure agrees with the previous works (Bian et al., 2013; Fang et al., 2004; Lu et al., 2011; Xia et al., 2006). Sea surface elevation time-series from the model output compared with the tidal gauge measurement in May in Qinghuangdao station is shown in Figure 5, and the average correlation between modeled and observed tides is 0.9. The results show that the simulated tide agrees well with the observations.

Figure 4.

The co-tidal chart of M2 constituent from the numerical model. Solid line: phase-lag (in deg.), dashed line: amplitude (in m)

Figure 5.

Time series comparing measured tides (red line) and model output (blue line) at the tidal gauge station Qinghuangdao (black dot mar ked in Figure 4), the correlation index is 0.9

METHODS

The dynamics of the bottom boundary layer (BBL) which provide the information of hydrodynamic process by near bottom flows and waves near the seabed in a short vertical distance are well studied in coastal waters (Grant and Madsen, 1979; Jonsson and Carlsen, 1976; Madsen, 1994; Smith, 1977; Styles and Glenn, 2000). The bottom shear stress generated in the BBL plays the main role in sediment transport (Fredsoe and Deigaard, 1992; Kuhrts et al., 2004; Soulsby, 1997). If the bottom shear stress exceeds the critical values for bed load transport or resuspension, the sediment is mobilized from the seabed. On the contrary, when the shear stress falls below a critical value, the suspended sediment will settle.

Due to the fact that the sediment erosion takes place gradually over a wide range of shear stress velocities as the flow velocity is increasing (Grass, 1970), the idea of using stochastic method to describe the nature of the sediment erosion was developed (Lavelle and Mofjeld, 1987). Based on the assumption that the erosion risk is equal to the probability and its distribution follow Gaussian normal distribution, Lopez and Garcia (2001) developed a risk-based probabilistic approach using polynomial approximation. The erosion risk Ri can be estimated as:if

Here, a₁ = 0.196854, a₂ = 0.115194, a₃ = 0.000344, a₄ =0.019527, τ_c, τ_f represent the resistance and the loading force for the initial state of the sediment, and σ_c, σ_f is the standard deviation of the two forces mentioned above. To calculate the risk of erosion, we use an approximate methodology here, τ_c is the mean shear stress velocity derived from the critical shear stress (Table. 1) (m/s), τ_f is the mean flow-wave velocity above the seabed from the model output (m/s), σ_c is the standard deviation of critical shear stress velocity (m/s) with reference to the method of Bohling (2005), and σ_f is the standard deviation of flow-wave induced velocity (m/s). To estimate the variability of the instantaneous flow- and wave- induced shear stress velocity, we use σ_f = 0.4 τ_f, this follows the approach by Grass (1970), which was also applied by Lopez and Gacia (2001), and Bohling (2005). By using the GIS spatial analysis, all data sets including the sedimentologic and hydrodynamic data were interpolated at the same grid. The erosion risk is calculated on every grid point with different time slot data.

RESULTS

As the hydrodynamic model uses climatological reanalysis of wind data as a driving force, the low temporal resolution output cannot resolve for the calculation of strong storm events, which normally last for short time but produce significant bottom erosion risk. Here, our main concern is the bottom environment under long term forcing. A mean erosion risk distribution map is made based on the calculation of one month hour by hour model output (Figure 6). Seven erosion risk zones (A–G) can be recognized referring to the erosion risk index. The Yalujiang Estuary (A), West of Korea Bay (B), northern area of the Bohai Bay (G) and surroundings of Laotieshan Channel in the northern part of Bohai Strait (C) show significant high values, and their corresponding sediment types are mostly sand. The eastern waters of Shandong Peninsula (D) show a relative high risk value compared to that of zones A, B, C. In the north-east of Liaodong Bay, Zone E has mostly the fine sand at sea bottom, and also shows a comparative medium value. Zone F is located in the west of Liaodong Bay, and the erosion risk distribution has a similar erosion risk pattern, showing slightly higher values compared to surrounding areas. As sediment transport takes place when a certain critical shear stress is exceeded, the same pattern can be found in the bottom shear stress distribution from model output (Figure 7). Since wave induced bottom shear stress is large where water depth is less than 10 m, and almost zero where water depth is more than 20 m (Figure 8), it can be shown that the mean erosion risk is strongly related to bottom shear stress induced by the current.

Figure 6.

The average erosion risk distribution in the study area with seven risk zones (A–G), the colors in the study domain represent the erosion index from 0 to 1

Figure 7.

Bottomshear-stress distribution induced by currents and waves from model output

Figure 8.

Bottomshear-stress caused by waves from the model output

4 sites representing 4 typical sedimentation areas in the BSNYS (T1, T2, T3 and T4 in Figure 6) were selected to check the erosion risk status through one month of simulation (Figure 9), and the time serials of each pattern follows the track of tidal cycle. T1 is close to the central part of Bohai Sea, where the circulation in the Bohai Sea is relatively weak (Lu et al., 2011), and its erosion risk index in one tidal cycle generally less than 0.5, the average value is 0.1568. T2 is located in the west part of the Laotieshan water channel, where small elongated tidal sand ridges exist in the surrounding area (Wang et al., 2012). Affected by the strong current through the tidal channel, its risk index can up to 0.9 during the strong tidal force, and the mean value is 0.5019. T3 is in the east part of Shandong Peninsula, close to the central of north Yellow Sea, affected by different seasonal circulation, and the erosion risk varied during the whole tidal period with a mean value of 0.3744. T4 is located in the western part of Korea Bay, where the best development of tidal-current ridges can be found at bay-heads (Chough et al., 2002), and it is also the place where the strong tide current play a role. The erosion risk is all the way in a high value (up to 0.9) and its average value is 0.7285.

Figure 9.

The erosion risk histogram chart of 4 specific locations in the modeling domain

DISCUSSION

SUMMARY AND CONCLUSION

Estimation of erosion risk can be a helpful indicator to describe the bottom dynamic conditions of the Bohai Sea, North Yellow Sea. Seven areas of high erosion risk were recognized from average erosion risk distribution, which is based on the risk calculation from hydrodynamic model output and GIS sedimentary database. The seven zones fit well with the occurrence of tidal sand ridges from literature findings. It has been found that areas with mean erosion risk above 0.5 correspond directly to the tidal sand ridge environment. More experimental information is required to accurately determine the erosion risk for different sediment characteristics and flow conditions. The model is a good starting point to investigate the occurrence of large-scale morphological features.