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Numerical Simulation of Cohesive Sediment Transport in Jakarta Bay

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Numerical Simulation of Cohesive Sediment Transport in Jakarta Bay International Journal of Remote Sensing and Earth Sciences Vol. 6: 65-76 (2009) © IReSES NUMERICAL SIMULATION OF COHESIVE SEDIMENT TRANSPORT IN JAKARTA BAY I. M. Radjawane1 And F. Riandini2 Abstract. The 3D-numerical model has been applied to simulate the current circulation and cohesive sediment transport in the Jakarta Bay, Indonesia. Sediment load comes from 3 river mouths i.e. Angke River, Karang River, and Ancol River. The model was simulated to analyze the effect of tidal current and river discharge. A constant westerly and easterly wind was used as input of the model to see the influence of monsoonal season. The numerical results showed that the tidal current flows from east to western part of the bay during ebb tide and vice versa during flood tide. The surface current circulation was dominantly influenced by the tidal current compared with the wind and river discharge effects. High turbidity level was found near the river mouths with the range of 50 to 100 mg/l. This high sediment concentration was caused by the effect of sediment load from the river upstream. In the offshore area of the bay the sediment concentration decreases up to 10 mg/l. The movement of sediments followed the current circulations. During the flood tide, the sediment concentration from the mouth of Angke River moved to the western part of the bay. Model simulated for increasing the river discharge into two times showed that the sediment distributed to the offshore direction two times longer compare with the normal debit. The transport of sediment from the Angke and Karang Rivers to the offshore area reached > 6 km, while it just reached + 2,5 km from the Ancol River . Keywords: 3D-numerical model, Cohesive sediment, Effect of tidal current and river discharge 1. Introduction trough Java Sea and resulting the co- Jakarta Bay is located at western side of oscillation tides in the central part of Java northern part of Java Island, a shallow bay Sea. The monsoon wind system prevails with an average depth of about 15 m, an over the Java Sea that consists of the area of 514 km2, and a shoreline about 72 northwest monsoon during December to km long. The bay receives highly polluted February, southeast monsoon during June water from 19 rivers that run through the to August, and two transitional monsoons. Jakarta Metropolitan Area (JMA) and There area several rivers, such as the poured out into the Java Sea (UNESCO, Citarum, Ciliwung, Angke, Ancol, and 2000). Karang Rivers. The physical processes affected the dynamics of sedimentation and The current circulation in this bay is erosion in this area. Bathymetry and controlled mainly by the three physical coastline change is one issue in JMA. Wind processes, i.e. tides, wind and river direction and river discharges are seasonal discharges (Koropitan, et. al., 2009 and based data. Rachmayani, 2004). Tides are dominated by the diurnal components, especially the K1 component coming from Flores Sea and ________________________________________________ 1 Oceanography Department, Faculty of Earth Sciences and Technology, Bandung Institute of Technology. E-mail: [email protected] 2 Research Institute of Water Resources and Development, Bandung, Indonesia. 65 Radjawane and Riandini Further, this area becomes worse and simulate the cohesive sediment transport, causes the sea floor and coastline the ECOMSED model was improved by surrounding the Jakarta Bay changed due applying flocculation and consolidation to the anthropogenic effect, i.e. human processes, resulting in the following three activities as reported from UNESCO specific module developments. (2000). Large scale sand extraction started Suspended Sediment Module with harbour dredging in the Jakarta Bay The transport of suspended sediment is area. The dredged material was generally described by the following advection- dumped elsewhere in the bay and diffusion equation: continuation of sand extraction for building ∂cccc∂∂∂∂∂∂∂ started on a small scale and was carried out +−()()uwcAiisδ 3 = H + A H + K H (1) ∂∂txi ∂ x112233 ∂ x ∂ x ∂ x ∂ x ∂ x manually in the 1970s and has intensified yearly in order to provide construction where, c: concentration of the suspended materials. Mangrove area has been sediment, ui: velocity components. AH: the transformed into reclamation area for horizontal diffusivity and KH: the vertical luxury residences, commercial activities, eddy diffusivity. and industrial zones. Based on comparison At the water surface, zs, the net satellite image between 1971 and 2004, sediment flux is zero. At the sediment- there are 80% of land use changes from water interface, zb, the flux is estimated by vegetative area into urban area and almost the rates of erosion and deposition, Ferosion at the coastal area (Koropitan, 2009). and Fdeposition. The bottom boundary A comprehensive study to understand condition can therefore be formulated as the spatial and temporal distribution of follows: ∂c cohesive sediments in this bay due to the ()uwcKis− −= H 0 and ∂x3 main physical processes is needed. In this xZ3 = s study, the monsoon effect, tides, and river ∂c uwcK−− = E discharges are simulated into a ()is H bc, (2) ∂x3 hydrodynamic model to get a more clear xZ3 = b understanding concerning with current with EFb, c=+ erosion F deposition . circulation in this bay. The dynamic of The erosion rate is represented by sedimentation and erosion processes is Partheneiades’s formulation (Partheniades, simulated with a sediment transport model. 1965) as: Several scenarios based on generating τ b forces of the current circulation are FMerosion = −1 (3) τ conducted to understand the variation and e dynamics of seasonal distribution. where, M is a positive empirical erosion parameter,τ is the bed shear stress, τ is the 2. Model Descriptions b e critical shear stress for erosion, is a A three-dimensional finite difference function of the concentration of the top bed model system for hydrodynamic and layer, which itself is given by the state of cohesive sediment transport, ECOMSED consolidation. The erosion coefficient M (HydroQual, 2002), is used in this may also be function of the concentration. simulation. This model solves the Navier- The deposition rate is calculated according Stokes equations with free surface to Krone’s formula (Krone, 1962): boundary conditions and the advection- F = Pwc (4) diffusion equations of the temperature, deposition d s salinity, and any other variable. To 66 International Journal of Remote Sensing and Earth Sciences Vol. 6, 2009 Numerical Simulation Of Cohesive Sediment Transport where Pd is the probability for deposition 2.2.2. A Model for Turbulence-Induced described by, Flocculation τ b A complete flocculation model should Pd =−1 (5) τ d include both aggregation and floc breakup where τd is the critical shear stress for processes. By assuming a simultaneous deposition. acting of aggregation and breakup, the differential equation for the flocculation of Flocculation Module cohesive sediment under the influence of 2.2.1. Relation between Settling Velocity turbulent shear, is described with respect to and Floc Size the number concentration, N, as: ∂∂NN()11−−φφ* p ∂ ∂ ( ) Winterwerp (1998) developed three- + +−uwNDiiδ 3, sr −() s +Γ= T ∂∂tx()12.5 +φ ∂ x ∂ x dimensional Eulerian model of the iii(8) p −−kGDNkGDDDN'1()φ 32 +qq+ 1 − 2 evolution of the settling velocity of fine- ABp* () grained cohesive sediment in turbulent where, Ds : molecular diffusivity, open channel flow: ΓT :turbulent diffusivity, G : the square root nf −1 α ()ρρsw− g 3−nf D of turbulent dissipation rate divided by wDsr= p 0.687 (6) 18βµ 1+ 0.15Re p molecular dynamic viscosity, δi3 : Kronecker’s delta. The parameters k’A and which D is the actual floc size, Dp is the diameter of the primary particle, and n is kB are defined as follows (e.g. Winterwerp, f 1998): fractal dimension for sediment particles. α q 3 − p µ and β are coefficients depending on the kee'Acd= π kaeD= 2 and Bbp spherity of the particles, and Re is the Fy particle Reynolds number. where ec, ed, and eb are efficiency Mud flocs seldom settle as individual parameters for collision, diffusion, and particles. When their concentration breakup respectively. µ is the dynamic becomes high enough, the settling flocs viscosity of the suspension, Fy is the start to hinder each other in their strength of the mud flocs, and numbers of movement, generally known as hindered power, p and q are to be established settling. The effective settling velocity ws empirically. The definition and relation of in suspension of cohesive sediment the number concentration, N, and the mass affected by the process of hindered settling concentration, c, and the volumetric defined as: concentration φ are: 3−n f 3 c D ()11−−φφ* ()p φ = fs ND = (9) ww= ρs Dp ssr12.5+ φ (7) where f s is the shape factor. where, φ the volumetric concentration of The aggregation and floc breakup terms are set to zero at the water surface the flocs (φ=c/cgel), cgel is the gelling concentration at which a space-filling and at the horizontal water-bed interface. network forms, and c is the sediment Hence the boundary conditions lead: ∂N concentration by mass. The factor (1-φ*) ()()uwNDis− −+Γ sT =0 and accounts for the return-flow effect. c/c ∂x gel 3 xZ= can exceed unity in a consolidating fluid 3 s ∂N mud layer. ()()uwNDis−−+Γ= sT E bN, (10) ∂x3 xZ3 = b International Journal of Remote Sensing and Earth Sciences Vol. 6, 2009 67 Radjawane and Riandini The source-sink term Eb,N represents the Data used for this research are input for exchange with the bed and is modeled with hydrodynamic and sediment transport classical formula of Partheniades and models. The data are: bathymetry, water Krone, so that the water-bed exchange level, salinity, and temperature, and formulation is consistent with the one for concentration of total suspended solid.
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