Proceedings of the 37th IAHR World Congress August 13 – 18, 2017, Kuala Lumpur, Malaysia
NUMERICAL SIMULATION OF A DEEP-SCOUR HOLE IN A TIDAL RIVER CONFLUENCE USING DELFT 3D (1)
(2)
(3)
WENHONG DAI , AHMED BILAL , QIANCHENG XIE
(4)
& YANYAN ZHAI
(1,2,3)
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China,
[email protected],
[email protected],
[email protected] State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing, China,
[email protected] (1) National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing, China, (4) College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing, China. (1,4)
ABSTRACT River confluences have complex hydrodynamic and sediment transport behavior. Research on the interactions including flow, sedimentation and river bed morphology at confluences has long been neglected to some degree in the past years, especially in the tidal river channel confluences. The deep-scour hole is a standard feature of both fluvial and tidal channel confluences. Numerical simulation of the deep-scour hole at tidal channel confluence has not been carried out before as per authors’ knowledge. In this study, a tidal channel confluence, namely Sanjiangkou zone, is numerically modeled with a focus to simulate hydrodynamics and deep-scour hole propagation. Sanjiangkou zone is in the Zhejiang Province of China, where two upstream rivers, Yao River and Fenghua River, merge into a downstream river called Yong River. Delft-3D code is applied to simulate the deep-scour hole study. A regular orthogonal grid is used to carry out the simulation. Numerical results are compared with observed measurements. The study indicates that Delft3D model is useful for simulating a deep-scour hole at tidal river confluences. Such techniques may also be useful in other similar cases. Keywords: Tidal channel confluences; deep-scour hole; delft 3D; hydrologic modeling; sanjiangkou.
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INTRODUCTION Confluences have complex hydrodynamic and sediment transport behavior in both fluvial as well as tidal channels. Research on the interactions including flow, sedimentation and river bed morphology at confluences has long been neglected to some degree in the past years, especially in the tidal channel confluences. Some studies hypothesized the deep-scour hole, also called deep-hole, with limited field data (Ginsberg and Perillo, 1999, 2004). Ginsberg et al. (2009) analyzed the deep-holes in detail including its genesis and evolution with detailed field investigation. Structures are often built on tidal channels located within or near growing cities. As a result, tidal channel frequently readjusts its cross section as well as hydrodynamic conditions to achieve equilibrium. It becomes more important to gain a better understanding of complex processes such as deep-holes, to reduce any potential risk for population or infrastructure. Numerical modeling of estuarine channels is a powerful tool to study the morphological evolution and hydrodynamic changes. Numerical simulation of the deep-scour hole at tidal channel confluence has not been carried out before as per authors’ knowledge. In this study, a tidal channel confluence, namely Sanjiangkou zone, is numerically modeled using Delft 3D. The primary focus of the study is to simulate hydrodynamics and deep-hole propagation under heavy sedimentation environment (Chen et al., 2013) of Yong River. Sanjiangkou zone is in Ningbo City, Zhejiang Province of China, where two upstream rivers, Yao River and Fenghua River, merge into a downstream river called Yong River 2
STUDY SITE Yong River is one of the eight central water systems in Zhejiang Province in China. It is of vital importance for Ningbo city, which is located on the east coast of Zhejiang Province and the south to the Hangzhou Bay. Yong River has two tributaries, which originates from Fenghua River in the south and Yao River in the north, as shown in Figure 1. These tributaries join in Ningbo City at their confluence zone, called Sanjiangkou, as its name (in Chinese) suggested. The Yong River is at the downstream of Sanjiangkou, and it flows into the East China Sea at Waiyoushan in Zhenhai. The total length of Yong River is 25.6 km from Sanjiangkou to its mouth at the East China Sea. The Yong River is predominantly tidal in nature.
©2017, IAHR. Used with permission / ISSN 1562-6865 (Online) - ISSN 1063-7710 (Print)
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Proceedings of the 37th IAHR World Congress August 13 – 18, 2017, Kuala Lumpur, Malaysia
Figure 1. Location of Sanjiangkou; the confluence formed by Yao River and Fenghua River in Ningbo City. 3
The annual average river runoff of Yongjiang from 1973 to 2006 is 2.912 billion m (Chen et al., 2013), but the sediment transport in the Yong trunk stream is mainly affected by the tides from the coast. Since 1959, water and sediment characteristics in the Yong River have considerably changed due to the construction of water conservancy projects along the river, especially Yao River’s sluice gate, constructed in 1959, which has led to reduced tidal discharge. As a result of the reduced tidal effects, severe deposition within the channel is occurring. After the 1980s, Yong River is affected by human activities, in particular with the increasing building of bridges and wharfs in the area (Chen et al., 2013), which led to severe sediment deposition. The width of Yao River ranges from 230m to 180m, however just before confluence it forms a constricted section where its width first becomes 140m and then gradually it again broadens to be 180m at the apex of confluence. The width of Fenghua River is relatively smooth before confluence. Its width is 152m at WS7 and then smoothly reduces to 110m before again widening itself to become 154m wide at the apex of the junction region. Bed morphology clearly shows a deep-hole (Figure 2) just after the confluence zone. The deepest point of the deep-hole, or only deep hole, is 17.2m below sea level, while bed level around it is approximately 6.5m below sea level. Data used in this study was acquired during a field survey in 2015 for a particular flood season. Figure 3 shows locations of water level recording stations (WS location) and cross-sections where flow was measured, (CS locations). Both suspended sediments and channel bed material were 95 % consisted of cohesive material, i.e. clayey silt with grain size diameter between 0.5 - 10μm.
Figure 2. Bathymetry of confluence zone shows that a deep-hole is present at the confluence zone and extends into the post confluence zone, i.e. Yong River.
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©2017, IAHR. Used with permission / ISSN 1562-6865 (Online) - ISSN 1063-7710 (Print)
Proceedings of the 37th IAHR World Congress August 13 – 18, 2017, Kuala Lumpur, Malaysia
Figure 3. Modeled portion of Sanjiangkou. 3
MODEL SETUP
3.1 Mathematical background Delft 3D code is based on finite difference method and utilizes the approach to solving Navier-Stokes equation with shallow water and Boussinesq assumption (Deltares, 2014). A deleted description of hydrodynamic and transport equations is described by Lesser et al. (2004) and Deltares (2014). However, a brief summary is presented here Delft 3D models the three-dimensional suspended sediment transport using a mass balance, advectiondiffusion equation (Eq. 1) 𝜕𝑐 𝜕 𝜕 𝜕 𝜕 𝜕𝑐 𝜕 𝜕𝑐 𝜕 𝜕𝑐 (𝑢𝑐) + (𝑣𝑐) + [(𝑤 − 𝑤𝑠 )𝑐] − + �𝜀𝑆,𝑥 � − �𝜀𝑆,𝑦 � − �𝜀𝑆,𝑧 � = 0 𝜕𝑡 𝜕𝑥 𝜕𝑦 𝜕𝑧 𝜕𝑥 𝜕𝑥 𝜕𝑦 𝜕𝑦 𝜕𝑧 𝜕𝑧 where, c u,v and w 𝜀𝑆,𝑥 , 𝜀𝑆,𝑦 and 𝜀𝑆,𝑧 𝑤𝑠
[1]
3
is mass concentration of sediment fraction [kg/m ] are longitudinal(x), transversal(y) and vertical(z) components of flow velocity [m/s] 2 are eddy diffusivities of sediment fraction [m /s] is the settling velocity of sediment fraction [m/s]
Suspended sediments are cohesive in nature at the Sanjiangkou. Delft 3D uses well known Partheniades-Krone formulations for calculation of erosion and deposition of cohesive sediments. 𝐸 = 𝑀𝑆�𝜏𝑐𝑤 , 𝜏𝑐𝑟,𝑒 �
where, E M 𝑆�𝜏𝑐𝑤 , 𝜏𝑐𝑟,𝑒 � D 𝑤𝑠 𝑐𝑏 𝑆�𝜏𝑐𝑤 , 𝜏𝑐𝑟,𝑑 � 𝜏𝑐𝑤 𝜏𝑐𝑟,𝑒 𝜏𝑐𝑟,𝑑
𝐷 = 𝑤𝑠 𝑐𝑏 𝑆�𝜏𝑐𝑤 , 𝜏𝑐𝑟,𝑑 �
[2] [3]
is erosion flux [kg m-2 s-1] is erosion parameter [kg m-2 s-1] is erosion step function is deposition flux [kg m-2 s-1] is fall velocity [m s-1] 3 is average sediment concentration in the near bottom computational layer [kg/m ] is deposition step function is maximum bed shear stress due to current and wave [N m-2] is critical erosion shear stress [N m-2] is critical deposition shear stress [N m-2]
Hydrodynamic conditions vary very quickly (in hours), however significant morphological changes occur in months or years. Practically, detailed data for input is not available in most of the cases. One way to tackle ©2017, IAHR. Used with permission / ISSN 1562-6865 (Online) - ISSN 1063-7710 (Print)
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Proceedings of the 37th IAHR World Congress August 13 – 18, 2017, Kuala Lumpur, Malaysia
this problem is by introducing morphological scale factor to accelerate the bed change for each hydrodynamic time step. Lesser et al. (2004) conceptually described it in Eq. 4.
where, ∆𝑡𝑚𝑜𝑟𝑝ℎ𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑀𝑂𝑅 ∆𝑡ℎ𝑦𝑑𝑟𝑜𝑑𝑦𝑛𝑎𝑚𝑖𝑐
∆𝑡𝑚𝑜𝑟𝑝ℎ𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 = 𝑓𝑀𝑂𝑅 ∆𝑡ℎ𝑦𝑑𝑟𝑜𝑑𝑦𝑛𝑎𝑚𝑖𝑐
[4]
is morphological time step is morphological scale factor is hydrodynamic time step
3.2 Calibration A regular orthogonal grid was generated for simulating the deep-hole at the confluence zone. Four layers were used which were skewed towards the bottom to gain more accuracy in the bed change calculations. The model was tested for several different options available for boundary conditions, altering roughness, and grid refinements. Calibration of the model was based on corresponding hourly observed data for spring tide which occurred on interval 2015-06-17 10:00– 2015-06-1816:00, totaling 31 hours. The primary task during the calibration was to adjust roughness, calculation time step, select suitable boundary condition and work on grid refinement so that a reasonable match was found between observed and model-calculated water levels and flows at the end of simulation time. There are several model evaluation techniques currently being used. Four model evaluation parameters, as well as good visual fit, were selected to check model performance. The four model evaluation parameters include i.Nash-Sutcliffe efficiency (NSE) 2 ii.R-Squared (R ) iii.Percent bias (PBIAS) iv.RSME-Stander deviation of observations Ratio (RSR) Table 1. Mathematical expressions, range and satisfactory values (Moriasi et al., 2007) for model evaluation parameters. Optimal Satisfactory Equation Range value NSE
2
R
-∞ to 1
0 to 1
1
1
𝐸 =1−
>0.5
>0.5
PBIAS
-∞ to ∞
0
±25% Streamflow ±55% Sediment
RSR
0 to ∞
0
≤ 0.7
Where O is observed(measured) value P is predicted (calculated) value by the model 𝑂� is average of observed values 𝑃� is average of predicted values n is total number values
𝑅2 = ⎛ ⎝
∑𝑛𝑖=1(𝑂𝑖 − 𝑃𝑖 )2 ∑𝑛𝑖=1(𝑂𝑖 − 𝑂� )2
∑𝑛𝑖=1(𝑂𝑖 − 𝑂� )(𝑃𝑖 − 𝑃� )
�∑𝑛𝑖=1(𝑂𝑖 − 𝑂� )2 �∑𝑛𝑖=1(𝑃𝑖 − 𝑃� )2
∑𝑛𝑖=1(𝑂𝑖 − 𝑃𝑖 ) × 100 𝑃𝐵𝐼𝐴𝑆 = ∑𝑛𝑖=1 𝑂𝑖 �∑𝑛𝑖=1(𝑂𝑖 − 𝑃𝑖 )2 𝑅𝑆𝑅 = �∑𝑛𝑖=1(𝑂𝑖 − 𝑂� )2
[5]
⎞ ⎠
2
[6]
[7] [8]
A main criterion adopted to compare model results with observed values was a graphical match. If the observed and the computed values show a reasonable graphical match, then the model evaluation parameters have been calculated for each location. If at least three of four parameters were within acceptable limits, then model results were considered reasonable. The simulated and observed water levels (at WS2 and WS3) and flows (at CS2 and CS6) are shown in Figure 4. The exact location of CS6 does not come under modeled area of Sanjiangkou. However, as CS6 lied close to WS7, predicted flow values by Delft 3D at WS7 were compared with observed flow values at CS6. 3.3 Validation Validation of the model was done on a neap tide event which occurred on interval 2015-06-24 15:00 – 2015-06-25 22:00. Same principle as used for calibration was adopted to consider a model result as being 636
©2017, IAHR. Used with permission / ISSN 1562-6865 (Online) - ISSN 1063-7710 (Print)
Proceedings of the 37th IAHR World Congress August 13 – 18, 2017, Kuala Lumpur, Malaysia
validated. Validation results are shown in Figure 5. PBIAS for CS6 was more than acceptable. But since a reasonable graphical match and satisfactory value for other three parameters was reached, it was considered acceptable so that this model can be used to analyze the hydraulic and sediment characteristic of Sanjiangkou zone.
Figure 4. Calibration results - Comparison of model results and observed data for a spring tide event (a) WS2, (b) WS3, (c) CS2, and (d) CS6.
Figure 5. Validation results - Comparison of model results and observed data for a spring tide event (a) WS2, (b) WS3, (c) CS2, and (d) CS6. 4
RESULTS After the model was calibrated and validated, it was used to see the channel bed response under current flow and sediment conditions. Morphological scale factor was used to accelerate the erosion and deposition at the channel bed. The scale factor was set to 365 on spring tide data. The results of bed change at the start and three results each after 3-hour break are shown in Figure 6. By analyzing Figure 6, it was visible that all the three rivers were under heavy sedimentation as identified by Chen et al. (2013). The results showed that the maximum depth of the deep-hole changed from an initial 17.2 m below sea level to about 10 m below sea level after one year of development. It may be concluded that under the existing conditions of water, sediment, and river boundary conditions, the depth of the deep-hole has an aggrading tendency. Horizontally, the deephole is approximately oval-shaped, which is gradually extending along the flow direction and narrowing along the width of the river (Figure 6). It is estimated that finally, it will result in a deep groove along the Fenghua River and Yong River.
Figure 6. Morphological changes under spring tide conditions with Morphological scale factor set to 365. ©2017, IAHR. Used with permission / ISSN 1562-6865 (Online) - ISSN 1063-7710 (Print)
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CONCLUSIONS Hydrodynamics and sediment transport at tidal channel confluence follow the complicated pattern and are dependent on many other parameters. Deep-holes are one of the results of complex sediment dynamics. In addition to field studies and physical modeling, numerical simulation offers new opportunities to understand hydro-morphodynamics of a tidal river. Broadly, the aim of the study is to use a numerical model Delft 3D to simulate such a complex environment. Specifically, the purpose of this research is to observe the response of the deep-hole qualitatively under heavily depositive environment as identified by Chen et al. (2013). Delft 3D is successfully used to model the deep-hole at Sanjiangkou confluence. By making use of morphological scale factor, it is confirmed that more sediments are being deposited then being eroded in this system of three rivers. Channel bed level is increasing not only in Yong River but also in Yao and Fenghua Rivers. As also studied by Chen et al. (2013), this aggradation may be linked to the construction of gates upstream of both Yao River and Fenghua River as well as bridges and wharfs built along the reach, which significantly change the natural flow and sediment characteristics. Also, the model predicts that if the situation continues, the deep-hole will be filled slowly and may disappear. ACKNOWLEDGEMENTS The authors are grateful to the Hydrology and Water Resources Survey Bureau of the Lower Yangtze River for proving the data. This research is supported by the NSFC (51479071), the National Key R&D Program of China (2016YFC0402501), the 111 Project (B12032, B17015) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (YS11001). REFERENCES Chen, J., Tang, H.W., Xiao, Y. & Ji, M. (2013). Hydrodynamic Characteristics and Sediment Transport of a Tidal River under Influence of Wading Engineering Groups. China Ocean Engineering, 27(6), 829842. Deltares. (2014). Delft3D-FLOW, User Manual. Delft: Deltares https://oss.deltares.nl/documents/183920/185723/Delft3D-FLOW_User_Manual.pdf. Ginsberg, S.S., Aliotta, S. & Lizasoain, G.O. (2009). Morphodynamics and Seismostratigraphy of a Deep Hole at Tidal Channel Confluence. Geomorphology, 104(3-4), 253-261. Ginsberg, S.S. & Perillo, G.M.E. (1999). Deep-Scour Holes at Tidal Channel Junctions, Bahia Blanca Estuary, Argentina. Marine Geology, 160(1-2), 171-182. Ginsberg, S.S. & Perillo, G.M.E. (2004). Characteristics of Tidal Channels in a Mesotidal Estuary of Argentina. Journal of Coastal Research, 20(2), 489-497. Lesser, G.R., Roelvink, J.A., van Kester, J.A.T.M. & Stelling, G.S. (2004). Development and Validation of a three Dimensional Morphological Model. Coastal Engineering, 51(8-9), 883-915. Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. & Veith, T.L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885-900.
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