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A simulation study on effects of suspended sediment through high riverine discharge on surface river plume and vertical water exchange
Estuarine, Coastal and Shelf Science 228 (2019) 106352
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A simulation study on effects of suspended sediment through high riverine discharge on surface river plume and vertical water exchange
T
Yasuhiro Hoshibaa,∗, Yoshimasa Matsumuraa, Hiroyasu Hasumia, Sachihiko Itoha, Satoshi Nakadab, Keita W. Suzukic a
Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8564, Japan National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba-shi, Ibaraki, 305-8506, Japan c Field Science Education and Research Center, Kyoto University, Nagahama, Maizuru-shi, Kyoto, 625-0086, Japan b
A R T I C LE I N FO
A B S T R A C T
Keywords: 3-D simulation Flood event SSM Interactive effects of SSM on physical processes Hypopycnal plume Tango bay, Japan
Rivers transport freshwater and suspended sediment matter (SSM) from land to coastal seas. In coastal seas termed as regions of freshwater influence (ROFIs), SSM is not only passively transported but also changes the density of ambient water and influences the physical characteristics especially in flood events, when a lot of SSM is supplied to the sea. Although the influence of SSM on the physical field in ROFIs would be significant, interactive physical processes, such as dynamics of river plumes and estuarine circulations, have hardly been investigated for hypopycnal plumes (i.e., the riverine sediment-freshwater is not denser than the seawater). In order to quantitatively estimate the interactive effects of SSM, we employ a non-hydrostatic ocean model with Lagrangian particles, which represents SSM and affects the density and buoyancy of ambient water. We use two experimental settings: (1) realistic simulations of the Tango Bay, Japan under the flooding of September 2013 and (2) idealized simulations for an open-bay ROFI. The former is conducted to assess whether the simulations could reproduce an actual event to some extent. The realistic simulations demonstrate that the choice of parameters such as SSM-particle size and composition is important for coastal simulations of flood events. The latter is conducted to understand the basic physics and to study the quantitative sensitivities of the physical processes to the riverine flux, composition, and particle size of SSM. The idealized simulations demonstrate that a large amount of riverine SSM affects the physical field in ROFIs through the following process: 1) horizontal density differences between nearshore and offshore waters are reduced as apparent density is increased close to the river mouth by riverine SSM, 2) the strength of vertical circulation is weakened by the reduced horizontal density difference, and 3) vertical water exchange between the surface and the subsurface layers decreases. The process in the control case of this study increases the relative amounts of surface freshwater in the river plume by 0.8% of the total riverine freshwater input. Sensitivity experiments with changing the parameters of SSM flux into the river, SSM composition, and SSM-particle diameter show that the percentage can be raised up to 2%. Meanwhile, the above-mentioned processes do not apply to extreme cases of small particle size and enormous SSM input wherein homopycnal (i.e., a similar density between the riverine sediment-freshwater and the ambient seawater) and hyperpycnal (i.e., the density of the riverine sediment-freshwater mixture exceeds the ambient seawater) plumes take place.
1. Introduction Rivers transport freshwater and suspended sediment matter (SSM) such as clay, silt, and sand from land to coastal seas. Riverine buoyant freshwater inputs induce river plumes and estuarine circulations which play an important role in coastal regions through physical, biogeochemical and ecological functions (Hoshiba and Yamanaka, 2013) by inducing exchange and mixing of water masses between the deeper
∗
nutrient-rich layer and the surface layer. The large areas of shelf seas adjacent to estuaries, over which such an influence of riverine freshwater extends, are called regions of freshwater influence (ROFIs) (Simpson, 1997). In ROFIs, the strength and evolution of horizontal movement of riverine plumes (e.g., anticyclonic gyres) and vertical estuarine circulations depend on density difference between river water and seawater. In the scale where the Earth's rotation is effective, buoyant water inputs
Corresponding author. E-mail address: [email protected] (Y. Hoshiba).
https://doi.org/10.1016/j.ecss.2019.106352 Received 6 September 2018; Received in revised form 21 June 2019; Accepted 23 August 2019 Available online 27 August 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.
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of Yura River on its southern end. The river is designated in Japan as a grade-one river due to its economic and other importance. The catchment area is approximately 1882 km2. For the Yura River, a high discharge event was recorded in Sep. 2013 (Fig. 2), which is the largest flood in the river between 1953 and 2016 according to available observed data (Ministry of Land, Infrastructure, Transport and Tourism, Japan). The Tango Bay has simple surrounding conditions: only one river (the Yura River) dominates the freshwater supply to the bay, and the river water is likely to dominate over the heat and precipitation fluxes in affecting the density of surface water in the bay (Itoh et al., 2016). The effects of tides in the Tango Bay are also substantially weak (Unoki, 1993). The model employed in this study is a non-hydrostatic ocean model “kinaco”. The model formulation in detail is described in Matsumura and Hasumi (2008). We also employ a modeling framework for dispersed multiphase flow using a built-in Lagrangian particle-tracking system (Matsumura and Ohshima, 2015) that enables representation of SSM and their effects on the buoyancy of ambient water. In the realistic simulations, we set a realistic topography of the Tango Bay (Fig. 1 (b)), although a few small bays connected to the Tango Bay are treated as land-grids. The seafloor depth varies from 0.1 m in the nearshore area to 91 m for the offshore area. We deal with freshwater and SSM flowing from the southern river mouth into the sea. The northern and eastern edges of the model domain are open boundaries where the total outflow flux is adjusted to the same as that of the inflow from the river discharge in order to conserve the total water mass in the domain. The zonal and meridional grid spacing is 9.0 × 10−4 degree which approximately corresponds to 90 m, and there are 38 vertical levels whose thickness varies from 1 m near the surface to 5 m at the bottom. Note that the thickness of the top-most level is taken to be 5 m, which is an assumed depth of the river estimated from the water level rise observed during the targeted flood event (Ministry of Land, Infrastructure, Transport and Tourism, Japan). We also assume that the top-most 5 m layer is well mixed at the flood event. The horizontal eddy diffusivity and viscosity coefficients are calculated by a Smagorinsky-type sheardependent scheme (Smagorinsky, 1963). The vertical viscosity and diffusion coefficients are calculated by a generic length-scale scheme (GLS; Umlauf and Burchard, 2003). SSM particles are not diffused (i.e., random motion is not added) in the model because we have confirmed that the effects of SSM have not quantitatively changed much, even if the same diffusivity as that for temperature and salinity is adopted as particle random motions (not shown here). Effects of particle-settling overwhelm those of turbulent motions. For tracer advection, we use a high-accuracy scheme “COSMIC-QUICKEST” (Leonard, 1979, 1991; Leonard et al., 1996). As for the freshwater input, we use the observed discharge from the Yura River, (Fig. 2), in Sep. 2013 which includes a period of large discharge over a few days due to heavy rainfall. The freshwater flux of zero-salinity and 22.9 °C is imposed at the river channel area of 2.9 km2 with 325 m wide river mouth. The imposed freshwater temperature is taken from the last data before the discharge event observed in the river on Sep. 11, 2013 (Ministry of Land, Infrastructure, Transport and Tourism, Japan). The hourly wind vector from GPV MSM, which is an operational weather prediction model of Japan Meteorological Agency with a horizontal resolution of 5 km, is used in the simulations. The wind data is spline interpolated onto our model grids. We do not take account of the effect of tides in the Tango Bay as they are mostly weak around the Sea of Japan. The sum of the amplitudes of four major tidal components (M2+S2+K1+O1) is estimated to be 19 cm at most in the Tango Bay (Unoki, 1993). It is reasonable to neglect their effect, particularly during flood events. SSM input is given by an empirical equation for Japanese mountain rivers (Takekawa and Nihei, 2013),
induce anticyclonic gyres. Such anticyclonic gyres sometimes grow seaward and/or expand to the left of seaward (e.g., major Siberian rivers in the Arctic; Weingartner et al., 1999, La Plata River; Matano and Palma, 2010, Ishikari River; Hoshiba and Yamanaka, 2016). “Ballooning” of river plumes occurs when the width of the river mouth is smaller than the internal deformation radius which is controlled by the difference in density between the river water and seawater. Especially, the ballooning tends to occur at floods which lead to a larger deformation radius due to a larger density contrast. There are many previous studies about the physical processes of horizontal anticyclonic gyres (e.g., Kubokawa, 1991; McCreary et al., 1997). Characteristics of their development are classified by non-dimensional parameters such as the Richardson number, the Froude number, and the inlet Kelvin number estimated from density difference between river water and seawater (Schiller and Kourafalou, 2010). Buoyant riverine water also induces vertical circulation, composed of seaward surface flows and subsurface counter-flows with upwelling near the coast (Horner-Devine et al., 2015). The strength of the vertical circulation depends on horizontal buoyancy gradient between river water and coastal seawater. SSM concentration is one of the dominant factors controlling the density of river water. When it is sufficiently large, such as at an extreme flood event, a “hyperpycnal” (i.e., the density of the riverine sediment-freshwater mixture exceeds that of the ambient seawater) river plume is realized (Mulder and Syvitski, 1995). Otherwise, a “hypopycnal” (i.e., the riverine sediment-laden freshwater is less dense than the seawater) plume occurs, which is usual in most coastal areas even during flood events. High riverine discharge events are the major contributors to the global sediment flux to the ocean (Warrick and Milliman, 2003; Dadson et al., 2005; Kao and Milliman, 2008). In spite of the prominent contribution to biogeochemical processes, it is difficult to conduct spatially and temporally fine monitoring of horizontal gyres and vertical circulations accompanied by hyperpycnal and hypopycnal events. This is because flood events are highly episodic, and massive sediment discharge would bury observational instruments (Liu and Lin, 2004; Warrick et al., 2008; Liu et al., 2012). Previous studies show the effects of sediment-laden riverine water on physical processes in hyperpycnal plumes using numerical models and laboratory experiments (Sequeiros et al., 2009; Wang et al., 2011; Chen et al., 2013). However, in the case of more general and typical hypopycnal plumes in ROFIs, the effects of SSM on horizontal plumes and vertical circulations have yet to be sufficiently investigated. In this study, we employ a non-hydrostatic ocean model with a built-in Lagrangian particle tracking system, which is dynamically coupled with continuous water phase and can explicitly represent SSM and their effects on the apparent density of ambient water. The model enables us to quantitatively evaluate the exchange of water between the surface and the subsurface induced by the influence of SSM on the horizontal and vertical motions. We especially focus on the case of hypopycnal plumes during their developing stage initiated by floods. We use two experimental settings: (1) realistic simulations of the Tango Bay, Japan under the flooding of Sep. 2013 and (2) idealized simulations for an open-bay ROFI. The former is conducted to assess whether the simulations could reproduce an actual event to some extent. The latter is conducted to understand the basic physics and to study the quantitative sensitivities of the physical processes to the riverine flux, composition, and particle size of SSM. 2. Model description 2.1. Realistic setting for the Tango Bay The Tango Bay is a gulf-type ROFI opening to the Sea of Japan (Fig. 1 (a)). The width and length of the bay are ∼18 km and ∼21 km, respectively, which is wide enough for the Coriolis force to influence river plumes. Based on the length-width ratio, the bay is classified as an open bay (Watanabe et al., 2017). In the Tango Bay, there is the mouth
SSM RF 1.86 = 36.14 × ( ) A A 2
(1)
Estuarine, Coastal and Shelf Science 228 (2019) 106352
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Fig. 1. (a) Location and topography of Tango Bay and Yura River, Japan. Topographies of the model domain in (b) realistic setting and (c) idealized setting. Contours for water depth are at 10 m intervals.
The size-spectrum of SSM particles are given by a log-normal distribution with a mode at 0.05 mm (the mean diameter for the Yura River during high discharge; Committee on Hydroscience and Hydraulic Engineering, 2014). The composition (density) of particles is 2650 kg/m3, the representative value of Earth's crust, as in previous studies (e.g., Chen et al., 2013). The particles settle down with the rate dependent on their diameter and density, as estimated by Rubey (1933), and are removed from seawater when they reach the bottom except in the river channel area. There is no SSM in the model domain in the initial condition. In order to generate the initial flows and horizontal and vertical density gradients in the Tango Bay, we restore the model temperature, salinity and sea surface height to ocean reanalysis data (JCOPE-T; Varlamov et al., 2015) of Sep. 1, 2013, for 14 days with a 1-h time constant during which the annual mean discharge of Yura River (62.46 m3/s) for 2013 is imposed. The integration is initiated from 15th Sep. and continued for 9 days.
Fig. 2. Introduced river discharge observed at Fukuchiyama located 37 km upstream from the mouth of Yura River, between September 15th to 23rd, 2013 (Ministry of Land, Infrastructure, Transport and Tourism, Japan). Broken line indicates the mean discharge from 2000 to 2013.
where SSM (ton/day) is the mass flux of SSM, RF (m3/s) is the volume flux of river discharge, and A (km2) is the river catchment area. One Lagrangian particle represents 135.5 kg of SSM in the simulations. Introducing a larger number of particles, with a smaller mass represented by each particle, is preferable for more accurate simulation of SSM distribution but requires larger computational resource. The above value is determined by conducting preliminary simulations so that the requirements for accuracy and computational cost are best balanced. The number of input particles to the river area changes hourly from 18 to 144,954 depending on the input SSM (Fig. 7). SSM changes the density of ambient water and therefore influences plumes and circulations while it is passively transported, whereas SSM does not change the ambient water volume in the model as such an effect is negligibly small.
2.2. Idealized setting In the idealized simulations, we set an idealized geography mimicking a gulf-type ROFI, the Tango Bay, Japan (Fig. 1 (c)), on the fplane (i.e., assuming the Coriolis parameter of a fixed latitude, 35.6 °N). Differing from the setting described in Section 2.1, the horizontal grid spacing is taken to be 100 m, 36 vertical levels are set over 80 m depth, and wind forcing is not imposed in the idealized simulations. The vertical viscosity and diffusion coefficients are constant (1.0 × 10−4 m2/s). The initial salinity and temperature throughout the calculated domain are also constant horizontally and have the vertical profile (Fig. S1) of a point near the northeastern boundary in the spunup state of the realistic simulations (Section 2.1). These changes for the 3
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Table 1 Parameters for sensitivity experimentsa. Fluxb 3
Composition (kg/m ) c
Diameter (mm); Settling speed (cm/s) a b c
0.2
0.4
0.6
0.8
1 (CTR)
2
4
6
8
10
1900
2050
2200
2350
2500
2650 (CTR)
2800
2950
3100
3250
0.01; 0.01
0.02; 0.03
0.03; 0.08
0.04; 0.14
0.05; 0.21 (CTR)
0.06; 0.31
0.07; 0.41
0.08; 0.53
0.09; 0.67
0.1; 0.81
In each experiment, one of these three parameters are varied while the other two are fixed at the values in CTR (1, 2650 kg/m3 and 0.05 mm). Unit of flux is normalized by the flux of CTR. Settling speeds are estimated from Rubey (1933), using the diameters and composition (2650 kg/mc).
another area of high concentration is found along the right coast in the simulation, data are missing there in the satellite-derived CDOM. As indicated by these examples, simulation results seem to be very sensitive to the choice of parameters for SSM. However, it is difficult to observe and correctly estimate the parameters especially in a flood event. In order to understand the behavior of flood-discharged SSM and its influences on ROFIs, therefore, it is better to first study sensitivity of physics in ROFIs to uncertain parameters of SSM.
idealized simulations are adopted in order to focus on SSM effects on the physical field, simplify the background conditions and facilitate scientific interpretation. We have confirmed that the effects of SSM have not quantitatively changed much, even if the settings of the vertical viscosity, diffusion, and the initial state are the same as the realistic simulations (not shown here). The model integration with the same setting for SSM particles as in Section 2.1 is referred to as the control case (CTR; Table 1) hereafter. The riverine flux of SSM can be significantly different depending on individual rivers and actual situations. The density of SSM particles varies broadly with the composition of SSM (i.e., from terrestrial light organic matter to heavy inorganic matter). Further, aggregation and deflocculation of SSM, which happen especially when particles experience a change of pH, modify the effective diameter of particles. However, it is very hard to observe specific values for these parameters during a flood event. In order to address the significance of such uncertainties, a number of experiments are conducted to study the sensitivity of the behavior of river plumes and vertical circulation to the SSM-flux into the river area, the particle composition and the SSMparticle diameter (Table 1). The ranges of these three parameters in our sensitivity experiments (Table 1) are determined on the basis of previous studies and reasonable values for actual ROFIs (Mitsusio, 1985; National Astronomical Observatory, 2001; Takekawa and Nihei, 2013). Using the idealized setting, we conduct ten cases for each of these three parameters. The mass represented by one Lagrangian particle is changed along with the magnitude of SSM-flux: from 27.1 kg (0.2 times SSM input case) to 1355.5 kg (10 times SSM input case) in the sensitivity experiments. We also conduct the case where river discharge provides only freshwater (OFW; i.e., excluding SSM) to distinguish the impact of SSM on the physical processes.
3.2. Control case for idealized setting We describe horizontal and vertical flows, salinity and SSM distributions in the idealized simulation. The low salinity water due to the flood spread from the river mouth with time (Fig. 4 (a)). The discharged water turns right due to the earth's rotation and flows out of the northeastern boundary. The low salinity area (≤28.0: inside of black broken line) becomes small with time after the plume area reaches the maximum at 0000 LT (local time) on Sep.17, which is attributable primarily to the variation of river discharge peaking at 0800 LT on Sep.16 (Fig. 2). In the meantime before the plume area reaches the maximum (Sep.16, 1200 LT in Fig. 4 (a)), the discharged water bifurcates into two branches. One is trapped to the coast, with the coast on the right-hand side, as a density current, while the other is slightly deflected to the left side near the river mouth and directed offshore. Such a leftward propagation of plumes mentioned in Introduction would take place at flood events as reported by several previous studies (e.g., McCreary et al., 1997; Matano and Palma, 2010). The evolution of SSM distribution (Fig. 4 (b)) is similar to that of salinity. The flows induced by the river-origin freshwater transport the water of high SSM concentration. The high SSM concentration area (> 16.7 g/m3) is also divided into two: directed offshore and along the coast on the right (Sep.16, 1200 LT in Fig. 4 (b)). Such a bifurcation is consistent with the simulations under the realistic setting (e.g., 17th in Fig. 3 (c)). The high SSM area becomes largest on Sep. 16 of the idealized setting, which differs from the area of low salinity. This is because SSM particles are removed from the surface water by the gravitational settling of particles. Of the two branches of high SSM concentration, only one along the right coast remains until 0000 LT on Sep.17. As the river discharge becomes small, the strong flow directed offshore disappears quickly, but the flow trapped to the right coast lingers. In the last stage, the flow along the coast also weakens (Sep.17, 1200 LT). On the vertical section extending northward from the river mouth, the low salinity of the river plume is distributed in the surface (< 10 m) layer (Fig. 5 (a)). The characteristic of such a thin-surface stratified structure is clearly classified as a hypopycnal plume, in contrast to a hyperpycnal plume diving and moving along the sea floor as an undercurrent (Kassem and Imran, 2001). The stream function of zonally integrated flows shows an estuarine vertical circulation composed of the offshore surface (< 5 m) flow and the subsurface (> 5 m) counterflow with upwelling near the coast. The vertical circulation causes vertical exchange of water between the surface and subsurface layers. The SSM distribution on the same section (Fig. 5 (b)) shows that a large amount of SSM particles discharged with riverine freshwater is transported offshore, and gradually removed from the water column. At
3. Results and discussion 3.1. Realistic simulation (Tango Bay) The realistic simulation adopting the SSM flux, composition and mean diameter of SSM particles defined in Section 2.1 shows a distribution of SSM concentration (Fig. 3 (b)). In-situ observations of SSM were not conducted then, whereas an optical absorption coefficient of chromophoric dissolved organic matter (CDOM) estimated from satellite data collected by the Geostationary Ocean Color Imager (GOCI) are available (Fig. 3 (a)). CDOM is rich in terrestrial plant-derived compound (Hansell, 2013) and has a long residence time in the surface water. So, the distribution of CDOM is expected to be qualitatively similar to that of small size SSM. The simulated SSM concentration is very small compared with the optical absorption coefficient of CDOM, but their spatial distributions have some common features. Another simulation is conducted here with smaller particle diameter (0.02 mm) and lower composition (1900 kg/m3), and the result becomes closer to the satellite-derived CDOM distribution both qualitatively and quantitatively (Fig. 3(c)). For example, the long branch of high concentration area (> 28 mg/l) extending to the offshore and turning to the right on 17th is reproduced in this simulation, and it starts disappearing on 18th both in the simulation and the satellite-derived CDOM. Although 4
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Fig. 3. (a) Surface distribution of chromophoric dissolved organic matter (CDOM) estimated from satellite-derived absorption at 443 nm wavelength (GOCI-COMS). (b) The same as in (a) except for SSM depth-averaged concentration in the top-most 5 m layer by the simulation in which the parameters of SSM-particle diameter and composition are 0.05 mm and 2650 kg/m3 in the realistic model setting for the Tango Bay. (c) The same as in (b) except for the parameters changed to 0.02 mm and 1900 kg/m3, respectively.
0000 LT on Sep.18 (after the flood peak) in CTR, for example, the amount of surface SSM is 1.3% of the total SSM discharge from the river. Mertes and Warrick (2001) estimate that the mass of sediment in surface plumes observed by remote sensing represents only 1–2% of the total sediment output from rivers possibly because most of particles are removed within hours by the settling process.
3.3. Difference between with and without effects of SSM SSM changes the density of water. Its effects are depicted by the change of density in CTR compared with OFW (Fig. 6 (a) and (c)). SSM makes density higher around the river mouth (about 2 km from the river mouth) and lower in the offshore region. It means that the density difference between the nearshore and offshore waters is smaller in CTR 5
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Fig. 4. (a) Salinity with current velocity field vectors drawn at intervals of ten grids in the surface layer (2.5 m depth) of the control case. Black broken lines depict 28.0 salinity value. (b) The same as in (a) except for suspended sediment matter (SSM) depth-averaged concentration in the top-most 5 m layer. Black straight lines show the location of the vertical north-south section detailed in Fig. 3. h
than in OFW. The density difference at 1700 LT on Sep.16, for example, is 4.9 kg/m3 and 6.6 kg/m3 in CTR and OFW, respectively. The strength of vertical circulation depends on the horizontal density difference (Horner-Devine et al., 2015): the vertical circulation is weaker under a smaller density difference. The vertical circulation is actually weaker in CTR than in OFW (Fig. 6 (e)). The total overturning transport at 1500 LT on Sep.16, for instance, is 10,325 m3/s and 11,761 m3/s in CTR and OFW, respectively. The weakened vertical circulation reduces vertical water exchange between the surface and subsurface layers. The amount of surface freshwater originating from the river (Q) is estimated by
Q=
∫ dz∫ dx∫ dy S0S−0 S 0
(2)
where h, S0, and S are the depth of the surface layer base (5 m), the initial salinity at 0000 LT on Sep.15 and salinity for each grid, respectively (modified from Isobe, 2005; Iwanaka and Isobe, 2018). The horizontal integration (in the x-y space) is taken over the whole calculated domain. The freshwater amount is estimated at 0000 LT on Sep.17 when the area of river plume (defined as S ≤ 28.0) is largest, then it is normalized by the total accumulated input of freshwater. In CTR, the relative amounts of freshwater are 64.6% in the sea surface 6
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Fig. 5. (a) Salinity (color) and stream function (contour) along the north-south section at the river mouth indicated by the black straight line in Fig. 2 (Sep.16, 2200 LT and Sep.17, 0000 LT). Contours for stream function are at 3000 m3/s intervals. (b) The same as in (a) except for SSM concentration. The depth of most surface stream function is 0 m and those of salinity and SSM are 2.5 m because the velocity and the salinity values are located on the grids' edge and in the center of grids of the model, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
vertical circulation is weakened by the lessened density difference, and 3) the vertical water exchange between the surface and the subsurface is weakened. The influence of SSM on the reduction of vertical water exchange (i.e., a larger amount of riverine freshwater stays in the surface layer) reaches the maximum within 1 day after the SSM input peak.
layer (≤ 5 m), 24.4% in the sea subsurface layer (> 5 m) and the remaining in the river area. In OFW, those are 63.8% in the surface, 25.2% in the subsurface and the same in the river area. It shows that the vertical exchange of water becomes smaller in CTR, though the difference between CTR and OFW is only 0.8% of the total riverine freshwater input. The time series of difference between CTR and OFW (Fig. 7) shows that the content of riverine freshwater in the surface layer in CTR becomes enhanced (up to approximately 0.8% at 0000 LT on Sep.17) after the SSM discharge starts increasing (up to 5.46 × 106 g/s at 0800 LT on Sep.16). In summary, a large discharge of SSM during a flood event influences the physical fields in ROFIs through the following processes: 1) the water density difference between the nearshore and offshore regions is decreased as SSM raises density near the river mouth, 2) the
3.4. Sensitivity experiments The results of sensitivity experiments (Fig. 8) are consistent with the difference between CTR and OFW, i.e., a higher SSM concentration reduces the exchange of water between the surface and subsurface layers, except for the case of small particle size (0.01 mm in Fig. 8 (c)). The surface freshwater content becomes larger in the cases of larger SSM input (Fig. 8 (a)), higher SSM density (Fig. 8 (b)) and smaller SSM 7
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Fig. 6. (a, b) Horizontal distribution of density differences in the surface layer (2.5 m depth) at 1200 LT on September 16. (c, d) Vertical distribution of density differences along the north-south section at the river mouth indicated by the black line in (a, b). (e, f) The same as in (c, d) except for differences of stream function. The left and right panels show the differences between the control case (CTR) and the case where the river discharge gives freshwater without SSM (OFW) and between the case of 0.1 mm small particle (SP; mentioned in Section 3.3) and OFW, respectively.
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Fig. 7. Time series of SSM flux (left vertical axis) and difference of the freshwater content (right vertical axis) in the surface layer (5 m) between CTR and OFW (blue solid line) and between SP and OFW (broken line). The freshwater difference is normalized by the total accumulated input of freshwater. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
(i.e., the riverine sediment-freshwater is less dense than the seawater). Similarly, over the range of 6–10 times in Fig. 8 (a), the increase of surface freshwater reaches a plateau due to a different reason from hypopycnal cases. Such an extraordinary amount of SSM seems to locally change the physical condition from hypopycnal to hyperpycnal cases, although hyperpycnal situations probably hardly occur in real Japanese high-transparent mountain rivers. Judging from the fact that the particle diameter (0.05 mm) in CTR is larger than the threshold (0.04 mm), we could also suggest that a hypopycnal plume would take place during the targeted flood. The mean diameter value (0.05 mm) was estimated from data in situ, although the observation was carried out in the river, not in the sea out of the river mouth, during the flood. The effects of SSM on the physical field can be roughly classified with the parameter ranges as follows: homopycnal to hyperpycnal over 6-fold SSM-input cases, homopycnal under 0.04 mm diameter cases, and hypopycnal situations in the other cases. The smaller horizontal (vertical) density gradient between the nearshore (surface) and offshore (subsurface) induces the smaller estuarine circulation (larger vertical mixing) due to the shorter (longer) residence of SSM. In the cases of the shorter residence time, most SSM is removed around the river mouth and horizontal density gradient more dominantly influences the physical fields such as estuarine circulations compared with the dominance of vertical density gradient in the longer residence cases. The results are least sensitive to composition among the three parameters changed here. Although we set the thickness of the top-most model level to be 5 m, higher vertical resolution would be desired in more precisely reproducing the physics in ROFIs. We have conducted a case with 1 m vertical grid in the top 5 m, which demonstrates that the plume thickness becomes thinner, from 5 m to 3 m, and that the effects of SSM on the surface freshwater content change by the order of 0.1% (that in CTR is 0.8%). However, the main process (i.e., the influence of SSM diminishes vertical water exchange and augments riverine freshwater in the surface layer) is not qualitatively changed, though the detailed threshold and surface freshwater content values depicted in Fig. 8 would vary with the resolution. Calculating all of the simulations here with the finer surface layer is very difficult due to the shorter time step required by the finer grid. Thus, we employed the 5 m layer thickness in
diameter (Fig. 8 (c)) because these factors function to raise the seawater density near the river mouth and weaken the vertical circulation. For the particle size smaller than a certain threshold (0.04 mm), however, other effects of longer residence of SSM in the surface layer become salient. Assuming a water column of 10 m depth, for example, it takes 32.2 and 0.3 h for SSM particles of 0.01 and 0.1 mm in diameter to be removed, respectively. Below the threshold, the following processes seem to make the situation different from what is mentioned in Section 3.3: 1) SSM spreads over a larger area due to the smaller particle sinking rate, 2) the horizontal density difference between the nearshore and offshore waters becomes less small, 3) the vertical density difference between the surface and subsurface layers is reduced as a consequence of higher density in the whole surface layer due to residual SSM and 4) the freshwater content in the surface layer becomes small due to enhanced vertical mixing. The Richardson number becomes smaller in these cases, which supports the above mechanism. Although it is for a hyperpycnal plume case, Chao (1998) has shown that the salinity in the surface is lower in the case of larger SSM sinking rate. Judging from the sinking rate used in that study, Chao's (1998) result seems to correspond to the cases of small particle size of the present study. Chen et al. (2013) have also suggested that hyperpycnal plumes are unlikely to occur in the cases of fast particle sinking and deposition. This suggestion is consistent with the tendency of our result, i.e., the vertical water mixing is suppressed for larger particle size, though our cases are for hypopycnal plumes. In the case of 0.01 mm small particle (SP; Fig. 6 (b), (d)), the density of the surface layer is larger over the entire domain than in CTR (Fig. 6 (a), (c)) due to the wide-spreading and long-residing SSM. The surface freshwater content in SP (63.6%) is smaller than that in CTR, which is consistent with the above-mentioned mechanism. Comparing the time series of surface freshwater content anomaly (Fig. 7), the evolution of SP substantially differs from that in CTR. After the start of SSM input, the surface freshwater content becomes small in SP until 1800 LT on Sep.16. However, it gradually becomes larger after the minimum and exceeds that of OFW after 1200 LT on Sep.17 due to the removal of SSM from the surface. Consequently, the situation in SP until 1200 LT on Sep.17 is likely to be close to the homopycnal case (i.e., a similar density between the riverine sedimentfreshwater and the ambient seawater), rather than the hypopycnal case 9
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simulations of flood events where a large amount of SSM is discharged from rivers. Idealized simulations were also conducted to understand the effects of SSM on the physical field such as river plumes and estuarine circulations under simplified background conditions. We found that riverine SSM affects the physical fields in the ROFI through the following processes: 1) the horizontal density difference between the river and offshore waters is reduced by the raised density near the river mouth due to high SSM content, 2) the estuarine circulation driven by the density difference is weakened due to the reduced density difference, and 3) the weakened vertical estuarine circulation suppresses the vertical water exchange between the surface and subsurface layers. In order to address the uncertainties in the parameters for controlling SSM, we conducted sensitivity experiments with changing the parameters of SSM flux into the river, SSM composition, and SSM-particle diameter. The obtained response to the change of these parameters is controlled by the above-mentioned processes, except for the cases of small particle diameters. The same processes do not apply to the extreme cases of small particle size and enormous SSM input wherein homopycnal and hyperpycnal plumes take place. Assuming the case where the effects of tides and winds are not essential, we mainly focused on a hypopycnal river plume because it is more general and typical than hyperpycnal plumes. In the case of hyperpycnal plumes, the effect of high SSM discharge should work as an accelerator of vertical exchange between the surface and subsurface layers. As shown by the small particle case of this study, which corresponds to an intermediate case between hypopycnal and hyperpycnal, vertical mixing is enhanced by the SSM effect. Strong tidal forcing and wind mixing effects may also change the patterns of horizontal plumes and vertical circulations. We should investigate the effects of tides and winds, together with hyperpycnal situations in the future. Only the interaction between physical processes and SSM was investigated in the present study, but physical and biogeochemical interactive impacts through processes of river plumes, estuarine circulations, SSM, biological production and so forth should also be elucidated. Highly discharged SSM might significantly change distributions of salinity, nutrients and light limitation for primary production, which may contribute to conditions of phytoplankton growth. We will proceed with further investigations of physical-biogeochemical interactions for comprehensively understanding ROFIs.
Acknowledgments
Fig. 8. Freshwater contents in the surface layer normalized by the total accumulated input of freshwater in sensitivity experiments with respect to (a) SSMflux into the river area, (b) SSM particle composition, and (c) SSM-particle diameter changing. The horizontal axes represent the parameter values and the vertical axes are normalized freshwater contents when the river plumes maximize (Sep.17, 0000 LT). The values of the horizontal axis in (a) SSM-flux are normalized by the flux of CTR.
This study was supported by JSPS/MEXT KAKENHI Grant Numbers JP16K12575, JP26247080, and JP15H05825. River discharge data of the Yura River in September 2013 was obtained from Water Information System by Ministry of Land, Infrastructure, Transport and Tourism, Japan (http://163.49.30.82/cgi-bin/SiteInfo.exe?ID= 306091286605030 and http://163.49.30.82/cgi-bin/SiteInfo.exe?ID= 406091286605070). We deeply thank Biological Oceanography group, Atmosphere and Ocean Research Institute, The University of Tokyo and the Japan Coastal Ocean Predictability Experiment (JCOPE) group of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) for providing the topography data and the reanalysis data in the Tango Bay, respectively. The remote sensing products were downloaded from the website of the Korea Ocean Satellite Center (KOSC). The wind forcing data were collected from the Japan Meteorological Agency and distributed by Research Institute for Sustainable Humanosphere, Kyoto University (http://database.rish.kyoto-u.ac.jp/index-e.html). We are also grateful to Dr. Shota Katsura of Atmosphere and Ocean Research Institute, The University of Tokyo for processing and providing the wind data. The model necessary to reproduce the simulations is available online at http://lmr.aori.u-tokyo.ac.jp/feog/ymatsu/kinaco.git/ (free access).
the present study.
4. Conclusions We employed a non-hydrostatic ocean model with a built-in Lagrangian particle-tracking system, which is dynamically coupled with continuous water phase and can explicitly represent the influence of SSM on the apparent density of water, in order to investigate the effects of freshwater and SSM discharged from a river on horizontal movement of river plumes and vertical estuarine circulation in a flood event of a ROFI. Our main scope is understanding of the dynamics of hypopycnal plumes during their developing stage under a high riverine discharge. Realistic simulations of the Tango Bay, Japan, were conducted to qualitatively compare the modeled results with satellite observations. It is demonstrated that the choice of sensitive parameters, such as SSMparticle diameter, composition and so forth, is important for coastal 10
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Appendix A. Supplementary data
Liu, J.T., Wang, Y., Yang, R.J., Hsu, R.T., Kao, S., Lin, H., Kuo, F.H., 2012. Cyclone‐induced hyperpycnal turbidity currents in a submarine canyon. J. Geophys. Res.: Oceans 117, C04033. https://doi.org/10.1029/2011JC007630. Matano, R.P., Palma, E.D., 2010. The upstream spreading of bottom-trapped plumes. J. Phys. Oceanogr. 40, 1631–1650. Matsumura, Y., Hasumi, H., 2008. A non-hydrostatic ocean model with a scalable multigrid Poisson solver. Ocean Model. 24, 15–28. Matsumura, Y., Ohshima, K.I., 2015. Lagrangian modelling of frazil ice in the ocean. Ann. Glaciol. 56, 373–382. McCreary, J.P., Zhang, S., Shetye, S.R., 1997. Coastal circulations driven by river outflow in a variable‐density 1½‐layer model. J. Geophys. Res.: Oceans 102, 15535–15554. Mertes, L.A., Warrick, J.A., 2001. Measuring flood output from 110 coastal watersheds in California with field measurements and SeaWiFS. Geology 29, 659–662. Mitsusio, T., 1985. Comparison of grain size in the fields of geology, pedology, and soil engineering (in Japanese with English abstract). J. Sedimentol. Soc. Jpn. 22, 117–121. Mulder, T., Syvitski, J.P., 1995. Turbidity currents generated at river mouths during exceptional discharges to the world oceans. J. Geol. 103, 285–299. National Astronomical Observatory, 2001. Chronological Science Tables (In Japanese). Maruzen Co., Japan, pp. 1064. Rubey, W., 1933. Settling velocity of gravel, sand, and silt particles. Am. J. Sci. https:// doi.org/10.2475/ajs.s5-25.148.325. Schiller, R.V., Kourafalou, V.H., 2010. Modeling river plume dynamics with the HYbrid coordinate ocean model. Ocean Model. 33, 101–117. Sequeiros, O.E., Naruse, H., Endo, N., Garcia, M.H., Parker, G., 2009. Experimental study on self‐accelerating turbidity currents. J. Geophys. Res.: Oceans 114, C05025. https://doi.org/10.1029/2008JC005149. Simpson, J., 1997. Physical processes in the ROFI regime. J. Mar. Syst. 12, 3–15. Smagorinsky, J., 1963. General circulation experiments with the primitive equations: I. The basic experiment. Mon. Weather Rev. 91, 99–164. Takekawa, K., Nihei, Y., 2013. Correlation between suspended sediment transport and river discharge in Japan (in Japanese with English abstract). J. Jpn. Soc. Civ. Eng. B2 Coast Eng. 69, I_1221–I_1225. Umlauf, L., Burchard, H., 2003. A generic length-scale equation for geophysical turbulence models. J. Mar. Res. 61, 235–265. Unoki, S., 1993. Engan No Kaiyo Butsurigaku (Physical Oceanography in Coastal Regions). Tokai University Press, Japan, pp. 672 (in Japanese). Varlamov, S.M., Guo, X., Miyama, T., Ichikawa, K., Waseda, T., Miyazawa, Y., 2015. M2 baroclinic tide variability modulated by the ocean circulation south of Japan. J. Geophys. Res.: Oceans 120, 3681–3710. Wang, Y., Wang, H., Bi, N., Yang, Z., 2011. Numerical modeling of hyperpycnal flows in an idealized river mouth. Estuarine. Coast. Shelf Sci. 93, 228–238. Warrick, J.A., Milliman, J.D., 2003. Hyperpycnal sediment discharge from semiarid southern California rivers: implications for coastal sediment budgets. Geology 31, 781–784. Warrick, J.A., Xu, J., Noble, M.A., Lee, H.J., 2008. Rapid formation of hyperpycnal sediment gravity currents offshore of a semi-arid California river. Cont. Shelf Res. 28, 991–1009. Watanabe, K., Kasai, A., Fukuzaki, K., Ueno, M., Yamashita, Y., 2017. Estuarine circulation-driven entrainment of oceanic nutrients fuels coastal phytoplankton in an open coastal system in Japan. Estuar. Coast Shelf Sci. 184, 126–137. Weingartner, T.J., Danielson, S., Sasaki, Y., Pavlov, V., Kulakov, M., 1999. The Siberian Coastal Current: a wind‐and buoyancy‐forced Arctic coastal current. J. Geophys. Res.: Oceans 104, 29697–29713.
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecss.2019.106352. References Chao, S., 1998. Hyperpycnal and buoyant plumes from a sediment‐laden river. J. Geophys. Res.: Oceans 103, 3067–3081. Chen, S., Geyer, W.R., Hsu, T., 2013. A numerical investigation of the dynamics and structure of hyperpycnal river plumes on sloping continental shelves. J. Geophys. Res.: Oceans 118, 2702–2718. Committee on Hydroscience and Hydraulic Engineering, 2014. Final report of damage from flood by Typhoon 18 in 2013 (in Japanese). http://committees.jsce.or.jp/ report/system/files/201309kyoto-siga_fin.pdf, Accessed date: 26 May 2018. Dadson, S., Hovius, N., Pegg, S., Dade, W.B., Horng, M., Chen, H., 2005. Hyperpycnal river flows from an active mountain belt. J. Geophys. Res.: Earth Surf. 110, F04016. https://doi.org/10.1029/2004JF000244. Hansell, D.A., 2013. Recalcitrant dissolved organic carbon fractions. Annu. Rev. Mar. Sci. 5, 421–445. Horner-Devine, A.R., Hetland, R.D., MacDonald, D.G., 2015. Mixing and transport in coastal river plumes. Annu. Rev. Fluid Mech. 47, 569–594. Hoshiba, Y., Yamanaka, Y., 2013. Along-coast shifts of plankton blooms driven by riverine inputs of nutrients and fresh water onto the coastal shelf: a model simulation. J. Oceanogr. 69, 753–767. Hoshiba, Y., Yamanaka, Y., 2016. Simulation of the effects of bottom topography on net primary production induced by riverine input. Cont. Shelf Res. 117, 20–29. Isobe, A., 2005. Ballooning of river-plume bulge and its stabilization by tidal currents. J. Phys. Oceanogr. 35, 2337–2351. Itoh, S., Kasai, A., Takeshige, A., Zenimoto, K., Kimura, S., Suzuki, K.W., Miyake, Y., Funahashi, T., Yamashita, Y., Watanabe, Y., 2016. Circulation and haline structure of a microtidal bay in the Sea of Japan influenced by the winter monsoon and the Tsushima Warm Current. J. Geophys. Res.: Oceans 121, 6331–6350. Iwanaka, Y., Isobe, A., 2018. Tidally induced instability processes suppressing river plume spread in a nonrotating and nonhydrostatic regime. J. Geophys. Res.: Oceans 123, 3545–3562. Kao, S., Milliman, J., 2008. Water and sediment discharge from small mountainous rivers, Taiwan: the roles of lithology, episodic events, and human activities. J. Geol. 116, 431–448. Kassem, A., Imran, J., 2001. Simulation of turbid underflows generated by the plunging of a river. Geology 29, 655–658. Kubokawa, A., 1991. On the behaviour of outflows with low potential vorticity from a sea strait. Tellus A Dyn. Meteorol. Oceanogr. 43, 168–176. Leonard, B.P., 1979. A stable and accurate convective modelling procedure based on quadratic upstream interpolation. Comput. Methods Appl. Mech. Eng. 19, 59–98. Leonard, B.P., 1991. The ULTIMATE conservative difference scheme applied to unsteady one-dimensional advection. Comput. Methods Appl. Mech. Eng. 88, 17–74. Leonard, B.P., Lock, A., MacVean, M., 1996. Conservative explicit unrestricted-time-step multidimensional constancy-preserving advection schemes. Mon. Weather Rev. 124, 2588–2606. Liu, J.T., Lin, H., 2004. Sediment dynamics in a submarine canyon: a case of river–sea interaction. Mar. Geol. 207, 55–81.
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Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss
A simulation study on effects of suspended sediment through high riverine discharge on surface river plume and vertical water exchange
T
Yasuhiro Hoshibaa,∗, Yoshimasa Matsumuraa, Hiroyasu Hasumia, Sachihiko Itoha, Satoshi Nakadab, Keita W. Suzukic a
Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba, 277-8564, Japan National Institute for Environmental Studies, 16-2, Onogawa, Tsukuba-shi, Ibaraki, 305-8506, Japan c Field Science Education and Research Center, Kyoto University, Nagahama, Maizuru-shi, Kyoto, 625-0086, Japan b
A R T I C LE I N FO
A B S T R A C T
Keywords: 3-D simulation Flood event SSM Interactive effects of SSM on physical processes Hypopycnal plume Tango bay, Japan
Rivers transport freshwater and suspended sediment matter (SSM) from land to coastal seas. In coastal seas termed as regions of freshwater influence (ROFIs), SSM is not only passively transported but also changes the density of ambient water and influences the physical characteristics especially in flood events, when a lot of SSM is supplied to the sea. Although the influence of SSM on the physical field in ROFIs would be significant, interactive physical processes, such as dynamics of river plumes and estuarine circulations, have hardly been investigated for hypopycnal plumes (i.e., the riverine sediment-freshwater is not denser than the seawater). In order to quantitatively estimate the interactive effects of SSM, we employ a non-hydrostatic ocean model with Lagrangian particles, which represents SSM and affects the density and buoyancy of ambient water. We use two experimental settings: (1) realistic simulations of the Tango Bay, Japan under the flooding of September 2013 and (2) idealized simulations for an open-bay ROFI. The former is conducted to assess whether the simulations could reproduce an actual event to some extent. The realistic simulations demonstrate that the choice of parameters such as SSM-particle size and composition is important for coastal simulations of flood events. The latter is conducted to understand the basic physics and to study the quantitative sensitivities of the physical processes to the riverine flux, composition, and particle size of SSM. The idealized simulations demonstrate that a large amount of riverine SSM affects the physical field in ROFIs through the following process: 1) horizontal density differences between nearshore and offshore waters are reduced as apparent density is increased close to the river mouth by riverine SSM, 2) the strength of vertical circulation is weakened by the reduced horizontal density difference, and 3) vertical water exchange between the surface and the subsurface layers decreases. The process in the control case of this study increases the relative amounts of surface freshwater in the river plume by 0.8% of the total riverine freshwater input. Sensitivity experiments with changing the parameters of SSM flux into the river, SSM composition, and SSM-particle diameter show that the percentage can be raised up to 2%. Meanwhile, the above-mentioned processes do not apply to extreme cases of small particle size and enormous SSM input wherein homopycnal (i.e., a similar density between the riverine sediment-freshwater and the ambient seawater) and hyperpycnal (i.e., the density of the riverine sediment-freshwater mixture exceeds the ambient seawater) plumes take place.
1. Introduction Rivers transport freshwater and suspended sediment matter (SSM) such as clay, silt, and sand from land to coastal seas. Riverine buoyant freshwater inputs induce river plumes and estuarine circulations which play an important role in coastal regions through physical, biogeochemical and ecological functions (Hoshiba and Yamanaka, 2013) by inducing exchange and mixing of water masses between the deeper
∗
nutrient-rich layer and the surface layer. The large areas of shelf seas adjacent to estuaries, over which such an influence of riverine freshwater extends, are called regions of freshwater influence (ROFIs) (Simpson, 1997). In ROFIs, the strength and evolution of horizontal movement of riverine plumes (e.g., anticyclonic gyres) and vertical estuarine circulations depend on density difference between river water and seawater. In the scale where the Earth's rotation is effective, buoyant water inputs
Corresponding author. E-mail address: [email protected] (Y. Hoshiba).
https://doi.org/10.1016/j.ecss.2019.106352 Received 6 September 2018; Received in revised form 21 June 2019; Accepted 23 August 2019 Available online 27 August 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.
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of Yura River on its southern end. The river is designated in Japan as a grade-one river due to its economic and other importance. The catchment area is approximately 1882 km2. For the Yura River, a high discharge event was recorded in Sep. 2013 (Fig. 2), which is the largest flood in the river between 1953 and 2016 according to available observed data (Ministry of Land, Infrastructure, Transport and Tourism, Japan). The Tango Bay has simple surrounding conditions: only one river (the Yura River) dominates the freshwater supply to the bay, and the river water is likely to dominate over the heat and precipitation fluxes in affecting the density of surface water in the bay (Itoh et al., 2016). The effects of tides in the Tango Bay are also substantially weak (Unoki, 1993). The model employed in this study is a non-hydrostatic ocean model “kinaco”. The model formulation in detail is described in Matsumura and Hasumi (2008). We also employ a modeling framework for dispersed multiphase flow using a built-in Lagrangian particle-tracking system (Matsumura and Ohshima, 2015) that enables representation of SSM and their effects on the buoyancy of ambient water. In the realistic simulations, we set a realistic topography of the Tango Bay (Fig. 1 (b)), although a few small bays connected to the Tango Bay are treated as land-grids. The seafloor depth varies from 0.1 m in the nearshore area to 91 m for the offshore area. We deal with freshwater and SSM flowing from the southern river mouth into the sea. The northern and eastern edges of the model domain are open boundaries where the total outflow flux is adjusted to the same as that of the inflow from the river discharge in order to conserve the total water mass in the domain. The zonal and meridional grid spacing is 9.0 × 10−4 degree which approximately corresponds to 90 m, and there are 38 vertical levels whose thickness varies from 1 m near the surface to 5 m at the bottom. Note that the thickness of the top-most level is taken to be 5 m, which is an assumed depth of the river estimated from the water level rise observed during the targeted flood event (Ministry of Land, Infrastructure, Transport and Tourism, Japan). We also assume that the top-most 5 m layer is well mixed at the flood event. The horizontal eddy diffusivity and viscosity coefficients are calculated by a Smagorinsky-type sheardependent scheme (Smagorinsky, 1963). The vertical viscosity and diffusion coefficients are calculated by a generic length-scale scheme (GLS; Umlauf and Burchard, 2003). SSM particles are not diffused (i.e., random motion is not added) in the model because we have confirmed that the effects of SSM have not quantitatively changed much, even if the same diffusivity as that for temperature and salinity is adopted as particle random motions (not shown here). Effects of particle-settling overwhelm those of turbulent motions. For tracer advection, we use a high-accuracy scheme “COSMIC-QUICKEST” (Leonard, 1979, 1991; Leonard et al., 1996). As for the freshwater input, we use the observed discharge from the Yura River, (Fig. 2), in Sep. 2013 which includes a period of large discharge over a few days due to heavy rainfall. The freshwater flux of zero-salinity and 22.9 °C is imposed at the river channel area of 2.9 km2 with 325 m wide river mouth. The imposed freshwater temperature is taken from the last data before the discharge event observed in the river on Sep. 11, 2013 (Ministry of Land, Infrastructure, Transport and Tourism, Japan). The hourly wind vector from GPV MSM, which is an operational weather prediction model of Japan Meteorological Agency with a horizontal resolution of 5 km, is used in the simulations. The wind data is spline interpolated onto our model grids. We do not take account of the effect of tides in the Tango Bay as they are mostly weak around the Sea of Japan. The sum of the amplitudes of four major tidal components (M2+S2+K1+O1) is estimated to be 19 cm at most in the Tango Bay (Unoki, 1993). It is reasonable to neglect their effect, particularly during flood events. SSM input is given by an empirical equation for Japanese mountain rivers (Takekawa and Nihei, 2013),
induce anticyclonic gyres. Such anticyclonic gyres sometimes grow seaward and/or expand to the left of seaward (e.g., major Siberian rivers in the Arctic; Weingartner et al., 1999, La Plata River; Matano and Palma, 2010, Ishikari River; Hoshiba and Yamanaka, 2016). “Ballooning” of river plumes occurs when the width of the river mouth is smaller than the internal deformation radius which is controlled by the difference in density between the river water and seawater. Especially, the ballooning tends to occur at floods which lead to a larger deformation radius due to a larger density contrast. There are many previous studies about the physical processes of horizontal anticyclonic gyres (e.g., Kubokawa, 1991; McCreary et al., 1997). Characteristics of their development are classified by non-dimensional parameters such as the Richardson number, the Froude number, and the inlet Kelvin number estimated from density difference between river water and seawater (Schiller and Kourafalou, 2010). Buoyant riverine water also induces vertical circulation, composed of seaward surface flows and subsurface counter-flows with upwelling near the coast (Horner-Devine et al., 2015). The strength of the vertical circulation depends on horizontal buoyancy gradient between river water and coastal seawater. SSM concentration is one of the dominant factors controlling the density of river water. When it is sufficiently large, such as at an extreme flood event, a “hyperpycnal” (i.e., the density of the riverine sediment-freshwater mixture exceeds that of the ambient seawater) river plume is realized (Mulder and Syvitski, 1995). Otherwise, a “hypopycnal” (i.e., the riverine sediment-laden freshwater is less dense than the seawater) plume occurs, which is usual in most coastal areas even during flood events. High riverine discharge events are the major contributors to the global sediment flux to the ocean (Warrick and Milliman, 2003; Dadson et al., 2005; Kao and Milliman, 2008). In spite of the prominent contribution to biogeochemical processes, it is difficult to conduct spatially and temporally fine monitoring of horizontal gyres and vertical circulations accompanied by hyperpycnal and hypopycnal events. This is because flood events are highly episodic, and massive sediment discharge would bury observational instruments (Liu and Lin, 2004; Warrick et al., 2008; Liu et al., 2012). Previous studies show the effects of sediment-laden riverine water on physical processes in hyperpycnal plumes using numerical models and laboratory experiments (Sequeiros et al., 2009; Wang et al., 2011; Chen et al., 2013). However, in the case of more general and typical hypopycnal plumes in ROFIs, the effects of SSM on horizontal plumes and vertical circulations have yet to be sufficiently investigated. In this study, we employ a non-hydrostatic ocean model with a built-in Lagrangian particle tracking system, which is dynamically coupled with continuous water phase and can explicitly represent SSM and their effects on the apparent density of ambient water. The model enables us to quantitatively evaluate the exchange of water between the surface and the subsurface induced by the influence of SSM on the horizontal and vertical motions. We especially focus on the case of hypopycnal plumes during their developing stage initiated by floods. We use two experimental settings: (1) realistic simulations of the Tango Bay, Japan under the flooding of Sep. 2013 and (2) idealized simulations for an open-bay ROFI. The former is conducted to assess whether the simulations could reproduce an actual event to some extent. The latter is conducted to understand the basic physics and to study the quantitative sensitivities of the physical processes to the riverine flux, composition, and particle size of SSM. 2. Model description 2.1. Realistic setting for the Tango Bay The Tango Bay is a gulf-type ROFI opening to the Sea of Japan (Fig. 1 (a)). The width and length of the bay are ∼18 km and ∼21 km, respectively, which is wide enough for the Coriolis force to influence river plumes. Based on the length-width ratio, the bay is classified as an open bay (Watanabe et al., 2017). In the Tango Bay, there is the mouth
SSM RF 1.86 = 36.14 × ( ) A A 2
(1)
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Fig. 1. (a) Location and topography of Tango Bay and Yura River, Japan. Topographies of the model domain in (b) realistic setting and (c) idealized setting. Contours for water depth are at 10 m intervals.
The size-spectrum of SSM particles are given by a log-normal distribution with a mode at 0.05 mm (the mean diameter for the Yura River during high discharge; Committee on Hydroscience and Hydraulic Engineering, 2014). The composition (density) of particles is 2650 kg/m3, the representative value of Earth's crust, as in previous studies (e.g., Chen et al., 2013). The particles settle down with the rate dependent on their diameter and density, as estimated by Rubey (1933), and are removed from seawater when they reach the bottom except in the river channel area. There is no SSM in the model domain in the initial condition. In order to generate the initial flows and horizontal and vertical density gradients in the Tango Bay, we restore the model temperature, salinity and sea surface height to ocean reanalysis data (JCOPE-T; Varlamov et al., 2015) of Sep. 1, 2013, for 14 days with a 1-h time constant during which the annual mean discharge of Yura River (62.46 m3/s) for 2013 is imposed. The integration is initiated from 15th Sep. and continued for 9 days.
Fig. 2. Introduced river discharge observed at Fukuchiyama located 37 km upstream from the mouth of Yura River, between September 15th to 23rd, 2013 (Ministry of Land, Infrastructure, Transport and Tourism, Japan). Broken line indicates the mean discharge from 2000 to 2013.
where SSM (ton/day) is the mass flux of SSM, RF (m3/s) is the volume flux of river discharge, and A (km2) is the river catchment area. One Lagrangian particle represents 135.5 kg of SSM in the simulations. Introducing a larger number of particles, with a smaller mass represented by each particle, is preferable for more accurate simulation of SSM distribution but requires larger computational resource. The above value is determined by conducting preliminary simulations so that the requirements for accuracy and computational cost are best balanced. The number of input particles to the river area changes hourly from 18 to 144,954 depending on the input SSM (Fig. 7). SSM changes the density of ambient water and therefore influences plumes and circulations while it is passively transported, whereas SSM does not change the ambient water volume in the model as such an effect is negligibly small.
2.2. Idealized setting In the idealized simulations, we set an idealized geography mimicking a gulf-type ROFI, the Tango Bay, Japan (Fig. 1 (c)), on the fplane (i.e., assuming the Coriolis parameter of a fixed latitude, 35.6 °N). Differing from the setting described in Section 2.1, the horizontal grid spacing is taken to be 100 m, 36 vertical levels are set over 80 m depth, and wind forcing is not imposed in the idealized simulations. The vertical viscosity and diffusion coefficients are constant (1.0 × 10−4 m2/s). The initial salinity and temperature throughout the calculated domain are also constant horizontally and have the vertical profile (Fig. S1) of a point near the northeastern boundary in the spunup state of the realistic simulations (Section 2.1). These changes for the 3
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Table 1 Parameters for sensitivity experimentsa. Fluxb 3
Composition (kg/m ) c
Diameter (mm); Settling speed (cm/s) a b c
0.2
0.4
0.6
0.8
1 (CTR)
2
4
6
8
10
1900
2050
2200
2350
2500
2650 (CTR)
2800
2950
3100
3250
0.01; 0.01
0.02; 0.03
0.03; 0.08
0.04; 0.14
0.05; 0.21 (CTR)
0.06; 0.31
0.07; 0.41
0.08; 0.53
0.09; 0.67
0.1; 0.81
In each experiment, one of these three parameters are varied while the other two are fixed at the values in CTR (1, 2650 kg/m3 and 0.05 mm). Unit of flux is normalized by the flux of CTR. Settling speeds are estimated from Rubey (1933), using the diameters and composition (2650 kg/mc).
another area of high concentration is found along the right coast in the simulation, data are missing there in the satellite-derived CDOM. As indicated by these examples, simulation results seem to be very sensitive to the choice of parameters for SSM. However, it is difficult to observe and correctly estimate the parameters especially in a flood event. In order to understand the behavior of flood-discharged SSM and its influences on ROFIs, therefore, it is better to first study sensitivity of physics in ROFIs to uncertain parameters of SSM.
idealized simulations are adopted in order to focus on SSM effects on the physical field, simplify the background conditions and facilitate scientific interpretation. We have confirmed that the effects of SSM have not quantitatively changed much, even if the settings of the vertical viscosity, diffusion, and the initial state are the same as the realistic simulations (not shown here). The model integration with the same setting for SSM particles as in Section 2.1 is referred to as the control case (CTR; Table 1) hereafter. The riverine flux of SSM can be significantly different depending on individual rivers and actual situations. The density of SSM particles varies broadly with the composition of SSM (i.e., from terrestrial light organic matter to heavy inorganic matter). Further, aggregation and deflocculation of SSM, which happen especially when particles experience a change of pH, modify the effective diameter of particles. However, it is very hard to observe specific values for these parameters during a flood event. In order to address the significance of such uncertainties, a number of experiments are conducted to study the sensitivity of the behavior of river plumes and vertical circulation to the SSM-flux into the river area, the particle composition and the SSMparticle diameter (Table 1). The ranges of these three parameters in our sensitivity experiments (Table 1) are determined on the basis of previous studies and reasonable values for actual ROFIs (Mitsusio, 1985; National Astronomical Observatory, 2001; Takekawa and Nihei, 2013). Using the idealized setting, we conduct ten cases for each of these three parameters. The mass represented by one Lagrangian particle is changed along with the magnitude of SSM-flux: from 27.1 kg (0.2 times SSM input case) to 1355.5 kg (10 times SSM input case) in the sensitivity experiments. We also conduct the case where river discharge provides only freshwater (OFW; i.e., excluding SSM) to distinguish the impact of SSM on the physical processes.
3.2. Control case for idealized setting We describe horizontal and vertical flows, salinity and SSM distributions in the idealized simulation. The low salinity water due to the flood spread from the river mouth with time (Fig. 4 (a)). The discharged water turns right due to the earth's rotation and flows out of the northeastern boundary. The low salinity area (≤28.0: inside of black broken line) becomes small with time after the plume area reaches the maximum at 0000 LT (local time) on Sep.17, which is attributable primarily to the variation of river discharge peaking at 0800 LT on Sep.16 (Fig. 2). In the meantime before the plume area reaches the maximum (Sep.16, 1200 LT in Fig. 4 (a)), the discharged water bifurcates into two branches. One is trapped to the coast, with the coast on the right-hand side, as a density current, while the other is slightly deflected to the left side near the river mouth and directed offshore. Such a leftward propagation of plumes mentioned in Introduction would take place at flood events as reported by several previous studies (e.g., McCreary et al., 1997; Matano and Palma, 2010). The evolution of SSM distribution (Fig. 4 (b)) is similar to that of salinity. The flows induced by the river-origin freshwater transport the water of high SSM concentration. The high SSM concentration area (> 16.7 g/m3) is also divided into two: directed offshore and along the coast on the right (Sep.16, 1200 LT in Fig. 4 (b)). Such a bifurcation is consistent with the simulations under the realistic setting (e.g., 17th in Fig. 3 (c)). The high SSM area becomes largest on Sep. 16 of the idealized setting, which differs from the area of low salinity. This is because SSM particles are removed from the surface water by the gravitational settling of particles. Of the two branches of high SSM concentration, only one along the right coast remains until 0000 LT on Sep.17. As the river discharge becomes small, the strong flow directed offshore disappears quickly, but the flow trapped to the right coast lingers. In the last stage, the flow along the coast also weakens (Sep.17, 1200 LT). On the vertical section extending northward from the river mouth, the low salinity of the river plume is distributed in the surface (< 10 m) layer (Fig. 5 (a)). The characteristic of such a thin-surface stratified structure is clearly classified as a hypopycnal plume, in contrast to a hyperpycnal plume diving and moving along the sea floor as an undercurrent (Kassem and Imran, 2001). The stream function of zonally integrated flows shows an estuarine vertical circulation composed of the offshore surface (< 5 m) flow and the subsurface (> 5 m) counterflow with upwelling near the coast. The vertical circulation causes vertical exchange of water between the surface and subsurface layers. The SSM distribution on the same section (Fig. 5 (b)) shows that a large amount of SSM particles discharged with riverine freshwater is transported offshore, and gradually removed from the water column. At
3. Results and discussion 3.1. Realistic simulation (Tango Bay) The realistic simulation adopting the SSM flux, composition and mean diameter of SSM particles defined in Section 2.1 shows a distribution of SSM concentration (Fig. 3 (b)). In-situ observations of SSM were not conducted then, whereas an optical absorption coefficient of chromophoric dissolved organic matter (CDOM) estimated from satellite data collected by the Geostationary Ocean Color Imager (GOCI) are available (Fig. 3 (a)). CDOM is rich in terrestrial plant-derived compound (Hansell, 2013) and has a long residence time in the surface water. So, the distribution of CDOM is expected to be qualitatively similar to that of small size SSM. The simulated SSM concentration is very small compared with the optical absorption coefficient of CDOM, but their spatial distributions have some common features. Another simulation is conducted here with smaller particle diameter (0.02 mm) and lower composition (1900 kg/m3), and the result becomes closer to the satellite-derived CDOM distribution both qualitatively and quantitatively (Fig. 3(c)). For example, the long branch of high concentration area (> 28 mg/l) extending to the offshore and turning to the right on 17th is reproduced in this simulation, and it starts disappearing on 18th both in the simulation and the satellite-derived CDOM. Although 4
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Fig. 3. (a) Surface distribution of chromophoric dissolved organic matter (CDOM) estimated from satellite-derived absorption at 443 nm wavelength (GOCI-COMS). (b) The same as in (a) except for SSM depth-averaged concentration in the top-most 5 m layer by the simulation in which the parameters of SSM-particle diameter and composition are 0.05 mm and 2650 kg/m3 in the realistic model setting for the Tango Bay. (c) The same as in (b) except for the parameters changed to 0.02 mm and 1900 kg/m3, respectively.
0000 LT on Sep.18 (after the flood peak) in CTR, for example, the amount of surface SSM is 1.3% of the total SSM discharge from the river. Mertes and Warrick (2001) estimate that the mass of sediment in surface plumes observed by remote sensing represents only 1–2% of the total sediment output from rivers possibly because most of particles are removed within hours by the settling process.
3.3. Difference between with and without effects of SSM SSM changes the density of water. Its effects are depicted by the change of density in CTR compared with OFW (Fig. 6 (a) and (c)). SSM makes density higher around the river mouth (about 2 km from the river mouth) and lower in the offshore region. It means that the density difference between the nearshore and offshore waters is smaller in CTR 5
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Fig. 4. (a) Salinity with current velocity field vectors drawn at intervals of ten grids in the surface layer (2.5 m depth) of the control case. Black broken lines depict 28.0 salinity value. (b) The same as in (a) except for suspended sediment matter (SSM) depth-averaged concentration in the top-most 5 m layer. Black straight lines show the location of the vertical north-south section detailed in Fig. 3. h
than in OFW. The density difference at 1700 LT on Sep.16, for example, is 4.9 kg/m3 and 6.6 kg/m3 in CTR and OFW, respectively. The strength of vertical circulation depends on the horizontal density difference (Horner-Devine et al., 2015): the vertical circulation is weaker under a smaller density difference. The vertical circulation is actually weaker in CTR than in OFW (Fig. 6 (e)). The total overturning transport at 1500 LT on Sep.16, for instance, is 10,325 m3/s and 11,761 m3/s in CTR and OFW, respectively. The weakened vertical circulation reduces vertical water exchange between the surface and subsurface layers. The amount of surface freshwater originating from the river (Q) is estimated by
Q=
∫ dz∫ dx∫ dy S0S−0 S 0
(2)
where h, S0, and S are the depth of the surface layer base (5 m), the initial salinity at 0000 LT on Sep.15 and salinity for each grid, respectively (modified from Isobe, 2005; Iwanaka and Isobe, 2018). The horizontal integration (in the x-y space) is taken over the whole calculated domain. The freshwater amount is estimated at 0000 LT on Sep.17 when the area of river plume (defined as S ≤ 28.0) is largest, then it is normalized by the total accumulated input of freshwater. In CTR, the relative amounts of freshwater are 64.6% in the sea surface 6
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Fig. 5. (a) Salinity (color) and stream function (contour) along the north-south section at the river mouth indicated by the black straight line in Fig. 2 (Sep.16, 2200 LT and Sep.17, 0000 LT). Contours for stream function are at 3000 m3/s intervals. (b) The same as in (a) except for SSM concentration. The depth of most surface stream function is 0 m and those of salinity and SSM are 2.5 m because the velocity and the salinity values are located on the grids' edge and in the center of grids of the model, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
vertical circulation is weakened by the lessened density difference, and 3) the vertical water exchange between the surface and the subsurface is weakened. The influence of SSM on the reduction of vertical water exchange (i.e., a larger amount of riverine freshwater stays in the surface layer) reaches the maximum within 1 day after the SSM input peak.
layer (≤ 5 m), 24.4% in the sea subsurface layer (> 5 m) and the remaining in the river area. In OFW, those are 63.8% in the surface, 25.2% in the subsurface and the same in the river area. It shows that the vertical exchange of water becomes smaller in CTR, though the difference between CTR and OFW is only 0.8% of the total riverine freshwater input. The time series of difference between CTR and OFW (Fig. 7) shows that the content of riverine freshwater in the surface layer in CTR becomes enhanced (up to approximately 0.8% at 0000 LT on Sep.17) after the SSM discharge starts increasing (up to 5.46 × 106 g/s at 0800 LT on Sep.16). In summary, a large discharge of SSM during a flood event influences the physical fields in ROFIs through the following processes: 1) the water density difference between the nearshore and offshore regions is decreased as SSM raises density near the river mouth, 2) the
3.4. Sensitivity experiments The results of sensitivity experiments (Fig. 8) are consistent with the difference between CTR and OFW, i.e., a higher SSM concentration reduces the exchange of water between the surface and subsurface layers, except for the case of small particle size (0.01 mm in Fig. 8 (c)). The surface freshwater content becomes larger in the cases of larger SSM input (Fig. 8 (a)), higher SSM density (Fig. 8 (b)) and smaller SSM 7
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Fig. 6. (a, b) Horizontal distribution of density differences in the surface layer (2.5 m depth) at 1200 LT on September 16. (c, d) Vertical distribution of density differences along the north-south section at the river mouth indicated by the black line in (a, b). (e, f) The same as in (c, d) except for differences of stream function. The left and right panels show the differences between the control case (CTR) and the case where the river discharge gives freshwater without SSM (OFW) and between the case of 0.1 mm small particle (SP; mentioned in Section 3.3) and OFW, respectively.
8
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Fig. 7. Time series of SSM flux (left vertical axis) and difference of the freshwater content (right vertical axis) in the surface layer (5 m) between CTR and OFW (blue solid line) and between SP and OFW (broken line). The freshwater difference is normalized by the total accumulated input of freshwater. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
(i.e., the riverine sediment-freshwater is less dense than the seawater). Similarly, over the range of 6–10 times in Fig. 8 (a), the increase of surface freshwater reaches a plateau due to a different reason from hypopycnal cases. Such an extraordinary amount of SSM seems to locally change the physical condition from hypopycnal to hyperpycnal cases, although hyperpycnal situations probably hardly occur in real Japanese high-transparent mountain rivers. Judging from the fact that the particle diameter (0.05 mm) in CTR is larger than the threshold (0.04 mm), we could also suggest that a hypopycnal plume would take place during the targeted flood. The mean diameter value (0.05 mm) was estimated from data in situ, although the observation was carried out in the river, not in the sea out of the river mouth, during the flood. The effects of SSM on the physical field can be roughly classified with the parameter ranges as follows: homopycnal to hyperpycnal over 6-fold SSM-input cases, homopycnal under 0.04 mm diameter cases, and hypopycnal situations in the other cases. The smaller horizontal (vertical) density gradient between the nearshore (surface) and offshore (subsurface) induces the smaller estuarine circulation (larger vertical mixing) due to the shorter (longer) residence of SSM. In the cases of the shorter residence time, most SSM is removed around the river mouth and horizontal density gradient more dominantly influences the physical fields such as estuarine circulations compared with the dominance of vertical density gradient in the longer residence cases. The results are least sensitive to composition among the three parameters changed here. Although we set the thickness of the top-most model level to be 5 m, higher vertical resolution would be desired in more precisely reproducing the physics in ROFIs. We have conducted a case with 1 m vertical grid in the top 5 m, which demonstrates that the plume thickness becomes thinner, from 5 m to 3 m, and that the effects of SSM on the surface freshwater content change by the order of 0.1% (that in CTR is 0.8%). However, the main process (i.e., the influence of SSM diminishes vertical water exchange and augments riverine freshwater in the surface layer) is not qualitatively changed, though the detailed threshold and surface freshwater content values depicted in Fig. 8 would vary with the resolution. Calculating all of the simulations here with the finer surface layer is very difficult due to the shorter time step required by the finer grid. Thus, we employed the 5 m layer thickness in
diameter (Fig. 8 (c)) because these factors function to raise the seawater density near the river mouth and weaken the vertical circulation. For the particle size smaller than a certain threshold (0.04 mm), however, other effects of longer residence of SSM in the surface layer become salient. Assuming a water column of 10 m depth, for example, it takes 32.2 and 0.3 h for SSM particles of 0.01 and 0.1 mm in diameter to be removed, respectively. Below the threshold, the following processes seem to make the situation different from what is mentioned in Section 3.3: 1) SSM spreads over a larger area due to the smaller particle sinking rate, 2) the horizontal density difference between the nearshore and offshore waters becomes less small, 3) the vertical density difference between the surface and subsurface layers is reduced as a consequence of higher density in the whole surface layer due to residual SSM and 4) the freshwater content in the surface layer becomes small due to enhanced vertical mixing. The Richardson number becomes smaller in these cases, which supports the above mechanism. Although it is for a hyperpycnal plume case, Chao (1998) has shown that the salinity in the surface is lower in the case of larger SSM sinking rate. Judging from the sinking rate used in that study, Chao's (1998) result seems to correspond to the cases of small particle size of the present study. Chen et al. (2013) have also suggested that hyperpycnal plumes are unlikely to occur in the cases of fast particle sinking and deposition. This suggestion is consistent with the tendency of our result, i.e., the vertical water mixing is suppressed for larger particle size, though our cases are for hypopycnal plumes. In the case of 0.01 mm small particle (SP; Fig. 6 (b), (d)), the density of the surface layer is larger over the entire domain than in CTR (Fig. 6 (a), (c)) due to the wide-spreading and long-residing SSM. The surface freshwater content in SP (63.6%) is smaller than that in CTR, which is consistent with the above-mentioned mechanism. Comparing the time series of surface freshwater content anomaly (Fig. 7), the evolution of SP substantially differs from that in CTR. After the start of SSM input, the surface freshwater content becomes small in SP until 1800 LT on Sep.16. However, it gradually becomes larger after the minimum and exceeds that of OFW after 1200 LT on Sep.17 due to the removal of SSM from the surface. Consequently, the situation in SP until 1200 LT on Sep.17 is likely to be close to the homopycnal case (i.e., a similar density between the riverine sedimentfreshwater and the ambient seawater), rather than the hypopycnal case 9
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simulations of flood events where a large amount of SSM is discharged from rivers. Idealized simulations were also conducted to understand the effects of SSM on the physical field such as river plumes and estuarine circulations under simplified background conditions. We found that riverine SSM affects the physical fields in the ROFI through the following processes: 1) the horizontal density difference between the river and offshore waters is reduced by the raised density near the river mouth due to high SSM content, 2) the estuarine circulation driven by the density difference is weakened due to the reduced density difference, and 3) the weakened vertical estuarine circulation suppresses the vertical water exchange between the surface and subsurface layers. In order to address the uncertainties in the parameters for controlling SSM, we conducted sensitivity experiments with changing the parameters of SSM flux into the river, SSM composition, and SSM-particle diameter. The obtained response to the change of these parameters is controlled by the above-mentioned processes, except for the cases of small particle diameters. The same processes do not apply to the extreme cases of small particle size and enormous SSM input wherein homopycnal and hyperpycnal plumes take place. Assuming the case where the effects of tides and winds are not essential, we mainly focused on a hypopycnal river plume because it is more general and typical than hyperpycnal plumes. In the case of hyperpycnal plumes, the effect of high SSM discharge should work as an accelerator of vertical exchange between the surface and subsurface layers. As shown by the small particle case of this study, which corresponds to an intermediate case between hypopycnal and hyperpycnal, vertical mixing is enhanced by the SSM effect. Strong tidal forcing and wind mixing effects may also change the patterns of horizontal plumes and vertical circulations. We should investigate the effects of tides and winds, together with hyperpycnal situations in the future. Only the interaction between physical processes and SSM was investigated in the present study, but physical and biogeochemical interactive impacts through processes of river plumes, estuarine circulations, SSM, biological production and so forth should also be elucidated. Highly discharged SSM might significantly change distributions of salinity, nutrients and light limitation for primary production, which may contribute to conditions of phytoplankton growth. We will proceed with further investigations of physical-biogeochemical interactions for comprehensively understanding ROFIs.
Acknowledgments
Fig. 8. Freshwater contents in the surface layer normalized by the total accumulated input of freshwater in sensitivity experiments with respect to (a) SSMflux into the river area, (b) SSM particle composition, and (c) SSM-particle diameter changing. The horizontal axes represent the parameter values and the vertical axes are normalized freshwater contents when the river plumes maximize (Sep.17, 0000 LT). The values of the horizontal axis in (a) SSM-flux are normalized by the flux of CTR.
This study was supported by JSPS/MEXT KAKENHI Grant Numbers JP16K12575, JP26247080, and JP15H05825. River discharge data of the Yura River in September 2013 was obtained from Water Information System by Ministry of Land, Infrastructure, Transport and Tourism, Japan (http://163.49.30.82/cgi-bin/SiteInfo.exe?ID= 306091286605030 and http://163.49.30.82/cgi-bin/SiteInfo.exe?ID= 406091286605070). We deeply thank Biological Oceanography group, Atmosphere and Ocean Research Institute, The University of Tokyo and the Japan Coastal Ocean Predictability Experiment (JCOPE) group of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) for providing the topography data and the reanalysis data in the Tango Bay, respectively. The remote sensing products were downloaded from the website of the Korea Ocean Satellite Center (KOSC). The wind forcing data were collected from the Japan Meteorological Agency and distributed by Research Institute for Sustainable Humanosphere, Kyoto University (http://database.rish.kyoto-u.ac.jp/index-e.html). We are also grateful to Dr. Shota Katsura of Atmosphere and Ocean Research Institute, The University of Tokyo for processing and providing the wind data. The model necessary to reproduce the simulations is available online at http://lmr.aori.u-tokyo.ac.jp/feog/ymatsu/kinaco.git/ (free access).
the present study.
4. Conclusions We employed a non-hydrostatic ocean model with a built-in Lagrangian particle-tracking system, which is dynamically coupled with continuous water phase and can explicitly represent the influence of SSM on the apparent density of water, in order to investigate the effects of freshwater and SSM discharged from a river on horizontal movement of river plumes and vertical estuarine circulation in a flood event of a ROFI. Our main scope is understanding of the dynamics of hypopycnal plumes during their developing stage under a high riverine discharge. Realistic simulations of the Tango Bay, Japan, were conducted to qualitatively compare the modeled results with satellite observations. It is demonstrated that the choice of sensitive parameters, such as SSMparticle diameter, composition and so forth, is important for coastal 10
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Appendix A. Supplementary data
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