نشریه پژوهش های مهندسی آب ایران
پیاپی 1 (زمستان 1400)

One of the most important issues in modeling wetland basins is estimating water balance by simulating its hydrological components, For which, various software was developed. DYRESM model was applied to indicate the water quality conditions of the Torogh Dam reservoir (KHAYAMI et al., 2009) and Fifteen-Khordad dam reservoir (ETEMAD et al., 2009) by examining the temporal changes of temperature, salinity, wind speed and dissolved oxygen parameters. Using MIKE SHE model, water balance in Paya Indah Wetlands (PIW) watershed in Malaysian was estimated (Rahim et al., 2012). CE-QUAL-W2 2D model was used for thermal and oxygen modeling in the Sefidrood dam reservoir (Taheri et al., 2014). Modeling of water level changes due to breakwaters in Anzali International Wetland was investigated using MIKE 21 numerical model (Juston and Kadlec, 2019).In this research, a qualitative and quantitative two-dimensional model of Bamdej wetland is developed using MIKE 21 software to operate and manage the wetland for the first time. The model is calibrated and validated using measured data over a one-year period. Finally, changes in hydrodynamic parameters of water depth and velocity in x and y directions and qualitative parameters such as temperature, dissolved oxygen, nitrate, ammonia are analyzed.

Bamdej wetland is located between 48˚ 33’ to 48 ˚ 39’ longitude and between 31˚ 41’ to 31˚ 47’ latitude in Khuzestan province which is far from northwestern Ahvaz city about 40 kilometers.The two-dimensional model, conducted on Bamdej wetland using the MIKE 21 model, is an unsteady model which applies laterally averaged depth-(in X- and Y-direction), revealed by equations 1-4. The hydrodynamic and quality parameters used in this study were shown in ‎Table 1. Using field surveying data initial values of Manning’s roughness for the river bed and the flood plain of the wetland were chosen. ‎Fig. 3 shows the points at which flow and pollution resources were considered. Then, the model results were validated by changing the initial values of the roughness. Finally, boundary conditions, ‎Table 4, were applied and run. The parameters related to determining the time step of the calculations were considered according to ‎Table 5.

Taking into account different values ​​of the Manning coefficient, the final values ​​of the Manning roughness coefficient are equal to 0.04 for the riverbed and 0.06 for the right and left floodplains. Due to the limited data available in the hydrodynamic model, only the roughness coefficient and the pollutant transfer model, only the scattering coefficient parameter (as the most important parameters) were calibrated. For calibrating the roughness coefficient, the most agreement was made between the computational and observational zones of the wetland.The model outputs include the mentioned hydrodynamic and qualitative parameters during one year of simulation time with 2.5 hours. Run times for a year take about 36 hours, and since wet and dry seasons are experienced during the year, an acceptable methodology can be enumerated. We applied ArcGIS software for analysis and zoning of variables. the results are presented only for a limited time in ‎Fig. 4 to 11.The amount of water depth in Bamdej wetland is mostly less than one meter, although it reaches about 2 meters and even up to 2.6 meters in some places. The average depth velocity varies between 0.05 to 0.2 m / s in Bamadj wetland (‎Fig. 3 and 4). Comparing depth magnitudes (‎Fig. 4) and the amounts of water quality parameters (‎Fig. 5 to ‎11) throughout Bamdej wetland revealed that where the depth is greater the values of quality parameters are higher, consistent with the results of previous research.The high concentration of dissolved oxygen in the lagoon reaches up to 5 mg / l, but high levels occur in the main bed. The same is true of biochemical oxygen demand values. However, the maximum ​biochemical oxygen concentration (2 mg / l) is less than the dissolved oxygen concentration.Ammonia concentration varies between 1 and 3; however, the slightest changes and high values ​​ observe only in a few points among the qualitative parameters.In general, the results showed that the simulation accuracy is about one percent, concluded that the prepared two-dimensional and unsteady hydrodynamic model has good accuracy and can calculate the flow parameters and the lagoon area with acceptable accuracy.

Hydrodynamic and quality maps of the wetland procured by two-dimensional modeling of hydrodynamic and quality of Bamdej wetland, developed in this research, show the proper Manning's roughness coefficients for the river and flood plain are 0.04 and 0.06, respectively. Furthermore, the hydrodynamic and quality model is accurate and could precisely calculate flow parameters and wetland zones.

An interesting procedure to evaluate the system management policies, including water distribution networks, is to use performance indicators, referring to parameters governing the system performance, including field monitoring, how it works, operation status (Kliseh et al., 2018). In general, two indicators of reliability and resilience are considered in the operation of the water distribution network in urban and rural areas. Hydraulic reliability refers to the continuous supply of a certain amount of water with the required pressures at the desired time and place (Mazaherizadeh et al., 2019) and resilience as the ability of a system or community to survive, absorb and adapt to risk and to protect yourself from the effects of hazards is defined as quickly and efficiently as possible (UNISDR, 2018).Resilience index has been applied to assess the hydraulic needs of the network (Pandit et al., 2012), to evaluate the urban water network infrastructure against earthquakes (Alavi et al., 2018), to optimize the network Water distribution (Reca et al., 2017; Mazaherizadeh et al., 2019), and simultaneous pipe failure assumptions (Gheisi and Naser, 2014; Atashi et al., 2020).In this study, the efficiency of the distribution network in the case of integrating several reservoirs and discrete was investigated and compared using the Resilience index. The distribution network was analyzed based on two scenarios: 1) sufficient flow in the network and 2) essential reversibility of the system.

The water distribution network of Surat, India, with a resilience index of 0.396, can supply water to only 56% of its population (Popawala & Shah, 2011). The area covered by six reservoirs (‎Fig. 1) was selected to evaluate the city's water distribution network. The resilience index of the distribution network was calculated for both cases of the connected and discrete reservoirs. In the current situation, the reservoirs are connected through transmission lines, and the maximum water level of all reservoirs is the same. ‎Fig. 2 shows the hourly fluctuations of consumption patterns.WaterGEMS software, an upgraded version of WaterCAD software, was used for modeling. This software evaluates the water supply network based on hydraulic conditions and examines the pressure and flow velocity at the node points. This software has more flexibility than other software and also provides the possibility of interacting with other software.To build the model, first, the physic of the distribution network was simulated and the nodal elevations and consumption amounts were allocated. According to the reservoir output pattern and maximum daily and hourly coefficient, consumer consumption pattern was calculated based on the output flow analysis and assigned to the nodes related to each reservoir. Finally, the hydraulic model of the grid was dynamically fabricated for maximum daily consumption, and calibrated for points with very high or shallow pressures over four months at different time intervals.

‎Fig. 3 shows the outlet pressure ranges of the hydraulic model. In the case of maximum consumption, it changes from 26 to 60 meters in most cases in the current situation. Ten percent of water distribution network nodes in the study area have pressures below 26 meters and more than 60 meters. The most important reason for the equilibrium pressure of the site is the reservoirs connection and the integrity of the water distribution network system throughout the area under study. Also, a minimal number of nodes in the minimum consumption state have pressure above 60 meters affected by night patterns for pressure breakers in the study area.‎Table 2 shows the pressure in the water distribution network in the state of maximum consumption in integrated and discrete tanks. The rate of nodes with hydraulic pressure in the defined range in the case of connected tanks is higher than that of unconnected tanks.‎Table 3 represents the network resilience index (SI), the number of points outside the desired pressure range (26 to 60 meters of water pressure) during peak hours and minimum consumption, and the ratio of this number of points to all points. The resilience index of the network in the state of integrated reservoirs (0.87) is higher than the state of non-interconnection (0.79) because happening a failure, the interconnected reservoirs help adjacent areas in the matter of water supply. In general, the status of the network is considered acceptable.

A continuous and separate hydraulic model of the study water distribution network was prepared. The results show that the integration of adjacent reservoirs has a positive effect on improving the resilience of the distribution network; in other words, at different times of the day and night, depending on the volume of the tank and the consumption pattern of the connected tanks, it helps to supply water to the adjacent related areas. Due to the complexity of connecting distribution network lines in neighboring regions and the change of consumption pattern at different hours, the degree of dependence of nodes to the relevant reservoir varies during the day and night.

The primary source of water supply in Dasht-e Hengam is groundwater; therefore, it is necessary to study the qualitative and quantitative status of groundwater resources in this area due to its high agricultural use. Groundwater researchers have used mathematical models, MODFLOW and MT3DMS, to simulate groundwater and evaluate their efficiency (Sadeghi Goghari, 2013; Paliz, 2014; Yousefi et al., 2014; Abbasi, 2014).Simulation of groundwater resources shows that droughts and wetlands have little effect on groundwater in Sarvestan plain (Torshizi et al., 2015), and the main reason for declining aquifers Is the uncontrolled extraction of wells from the groundwater aquifer (Torshizi et al., 2015; Jabbari et al., 2015; Runama and Jafari, 2017). In addition, the level of pollution has increased in some plains (Deymehkar, 2016; Fasihi and Zare Abyaneh, 2018; Shahnavaz, 2019). For improving the quality of aquifers, aquifer feeding (Ghafari et al., 2020), a suitable hydraulic system (Ghafari et al., 2020), and (or) reactive-nano-iron barriers have been used (Divya et al., 2020).In this research, with the help of two powerful specialized tools of water resources, MODFLOW and MT3DMS particle transfer model, the plain aquifer during the simulation and the quantitative and qualitative status of groundwater is investigated.

Dasht-e Hengam, with approximately ​​525 square kilometers, is located in 76 kilometers of Qirokarzin city and 220 kilometers south of Shiraz city. Geological conditions, alluvial sediment status, location of available groundwater resources, and observation wells are plain alluvial aquifers (Regional Water Company of Fars, 2012). ‎Fig. 1 shows the geographical location of Hengam Plain (Regional Water Company of Fars, 2012), and ‎Table 1 shows the monthly distribution of rainfall in the plain. Data from ten observation wells in the groundwater aquifer, ‎Fig. 2, were used to calibrate quantitative and qualitative models of the plain. The amount of feed from the surface includes returned water from agricultural and drinking wells, from the river (runoff), drainage, annual rainfall at the aquifer level, and sewage, the information of which is listed in (Regional Water Company of Fars, 2012). Piezometers per month were first interpolated in GIS software and then transferred to the model. The model network cell size for all possible observation wells in the area with a cell size of 150 m was considered. A series of pilot points (Figure 3) were designed at the range level for more accurate and faster calibration.

By calibrating the model over 36 months, the optimal hydraulic conductivity value with an average of 17.3 and a standard deviation of 24.9 m/day was obtained (‎Fig. 5). The highest hydraulic conductivity coefficient is related to the northern and northwestern parts of the plain, and the lowest coefficient occurs at the highest accumulation of exploitation wells. Also, the amount of specific storativity varies between 0.00167 and 0.636, with an average of 0.103 and a standard deviation of 0.117.The slight difference between the computational and observational data, ‎Fig. 6 to ‎Fig. 9, indicates desired modeling in the scope of the study. The correlation between computational and observational values ​​(‎Fig. 10, typically shown for the simulation month 60) also shows this.The quantitative model, MODFLOW, shows that the aquifer volume decreases daily during the 36-month modeling period (‎Fig. 11 and ‎Fig. 12); as a result, the amount of groundwater discharge from the boundary of the plain vanishes due to falling groundwater levels. Also, the volume of groundwater inflow is about 24.4 Mm3/year, the recharge into the aquifer from the surface is approximately 10.97 Mm3/year, discharge from alluvial groundwater and complex formation (often wells) is about 39.43 Mm3/year, and the volume of output due to natural drainage is almost 0.546 Mm3/year. The aquifer storage volume is 12.1, and the reservoir withdrawal volume is about 13.5 Mm3/year (‎Fig. 13); thus, the reservoir deficit is 1.36 Mm3/year.‎Fig. 14 shows the groundwater level of the Hengam aquifer in the last step of the 36 months, which, compared to the beginning of the period, indicates a drop in water after three years of groundwater abstraction. ‎Fig. 15 shows that the water level decreased by about 4.5 m during the simulation period, equivalent to an average annual drop of about 1.5 m.MT3DMS package and chemical quality data related to 10 agricultural wells were used to prepare a qualitative model. Information about the location of sampling wells and initial concentration of qualitative parameters in the aquifer in ‎Table 4 and the calibrated value of qualitative parameters are presented in ‎Table 5 for the last calibration step. The distribution of TDS in Figures 19 to 21 shows that the contamination is constantly expanding during these three years, and ‎Fig. 22 shows that as the groundwater level decreases, the TDS contamination increases during the simulation period.

In this study, using MODFLOW and MT3DMS, the quantitative and qualitative status of Hengam plain was investigated. The groundwater flow model created the slightest statistical deviation on the optimization parameters according to the automated calibration and validation approach. The results showed the nonlinear trend of groundwater level sagging with the final year conditions in an optimistic manner.

Breakwaters are one of the first offshore structures to create calming ponds for mooring ships. Numerous studies have been conducted in numerical modeling of sediment transport and the evolution of shore morphology against coastal waves and currents (Sverdrup and Munk, 1947; Gelsi et al., 1957; Phillips, 1957; Miles, 1957; Hasselmann,1974).Examining the effects of coastal structures on coastal sediment transport demonstrates that the construction of ports or breakwaters causes coastal erosion and sediment accumulation and distribution (El-Asmar and White, 2002; Bohlolei et al., 2005; Tang et al., 2017; Sakhaee and Khalili, 2020).Anzali wetland, one of Iran's most sensitive aquatic ecosystems, is connected to Anzali Port tranquility basin through several Rogas. Due to the lack of wharves' capacity, decision-makers provide a program to develop this port. ‎Fig. 1 shows the port of Anzali before and after development. Simultaneously with the construction of new port breakwaters, changes were observed in the wetland conditions and sediment accumulation at the wetland margin.In this paper, simulation of waves and water flow and sediment in Bandar Anzali area before and after port development investigates the effect of new Anzali breakwaters on the daily bed change rate and sediment concentrations increase using MIKE 21 software.

MIKE 21, a subset of MIKE software, is a comprehensive system for modeling open and two-dimensional flows in which fluid flow layering can be neglected.We took the information related to depth measurement, hydrodynamic, sediment, border conditions (‎Fig. 2), and Wind (‎Fig. 3) to provide the model. The hydrographic maps belong to 1) November 2007 (before the development of Bandar Anzali) and 2) February 2017 (after development). The solution area was divided into a network. Then, the second hydrographic file was fed to the Mesh Generator module of the MIKE 21 SW module. Comparing the height of waves of this model with the height of the buoy waves and the waves of the previous stage in each trial and error led to selecting network dimensions, ‎Fig. 4. Hereafter, it was modified for the state of before development. Subsequently, it was altered for stage 1, before development.The MIKE 21 Spectral Waves SW madule were used to simulate the waves, and the non-cohesive sediment transport sub-model was used to model sediment flow. Simulation was run for both stages.

‎Fig. 8-(a) shows that the daily bed change (DBC) rate for before development in most areas of the port basin is less than 0.001 m/day, and only a tiny part of the Nahange-Bozorg Roga experiences larger values. It takes values greater than 0.002 and 0.003 m/day in the center of Nahang Roga. At the junction of Pir-Bazar Roga and Sousar Ruga, the DBC rate is more than 0.003 m/day. Also, at the intersection of the four rivers, a minor part has a value of more than 0.003 m/day. However, Shanbe-Bazar Roga did not illustrate a significant amount of DBC.‎Fig. 8-(b) illustrates that the rate of DBC after development in most areas of the port basin is about 0.001 m/day, and only the southern borders and a part of Shanbe-Bazar Roga the rate values ​​are more than 0.002 m/day; however, even 0.004 m/day. In a large area of the intersection of Pir-Bazar Roga and Sousar Roga, the rate takes a litter change of more than 0.002 and 0.004 m/day. Also, along with all four rivers outlets, there is a length with a rate amount of more than 0.002 m/day. Furthermore, in a tiny part of Shanbe-Bazar Roga, the amount rate of DBC is more than 0.002 m/day.Comparing ‎Fig. 9-(a) and b shows that the concentration of suspended sediments (CSS) after development in most areas of the port basin is about 1.5 g/m3, and only the southern borders and a part of Shanbe-Bazar Roga show larger values, which is more significant than 0.8 g/m3 before development. However, in the eastern part of the study area and the region where Shanbe-Bazar Roga connects to the tranquility -basin, CSS is about 3 g/m3. The Pir Bazar and the Nahange-Bozorg do not report significant amounts of CSS, and for most of the length of Nahang Roga, CSS is about 4 g/m3. The maximum rate of CSS occurs in 100 meters length of Sousar Roga, along Which CSS has reached 13 g/m3, and at last 1 meter of that, it shows the amount of 15 g/m3.

Areas that have experienced a change in DBC in post-development conditions compared to port pre-development are 1) a small area of Shanbe-Bazar Roga, 2) the junction of Nahang Roga and Nahang-e-Bozorg Roga, 3) the connection of Sousar and Pir-Bazar Roga, and 4) Southern region, the intersection of Nahang, Pir-Bazar, and Sousar Rogas.In terms of sediment concentration, an increase was reported along 300 meters of Sousar Roga.The new breakwaters are the only difference between pre-and-post development modeling of the Anzali port basin; thus, they can effectively change flow transfer from the lagoon to the sea. However, since the difference in the DBC and CSS value rate is low, the construction of new breakwaters can not be the leading cause of the wetland destruction process during the past years, and other factors affecting this water area need to be considered.

The scouring mechanism at the abutment is very similar to the pier of a bridge; such that the decrease in current intensity near the upstream of the abutment, a vertical pressure gradient is created, this pressure gradient moves downwards to become the initial vortex, the size of which expands with the development of the scour cavity (Melville, B.W. 1997).The bridge pier near the abutment causes deepening of the scour near the support (Hong, S. 2005). However, the scouring depth is determined prominently by the scouring of the abutment and that this value may be greater than the scouring depth of a pedestal alone (Nyarko, K., & Ettema, R. 2011; Karami et al., 2017).The length of the abutment has an essential effect on its scouring depth, such that by doubling the length of the abutment, the scouring increases by about 18 times and decreases with decreasing abutment distance (Arab and Zomordian 1393; Anjum Rooz et al., 2017).In the present study, we applied FLOW-3D to build a numerical model to investigate the effect of changing the piers' distance from the abutment on the scour depth and scour rate around the two piers.

Flow-3D is one of the most potent applications in computational fluid dynamics. This research makes applications to solve the mean Navier-Stokes equations of time (Reynolds equations) using the finite volume method under a rectangular grid on the base of the Eulerian theory.The flow continuity equation is written as Equation 1. FLOW-3D calculates the load transport of each sediment type, including bedload transport, separately. FLOW-3D calculates the load transport of each sediment type, including bedload transport, separately. The dimensionless form of the bed load transfer rate in terms of n is written by Equation 2, in which dn is obtained from Equation 3.We used the laboratory results obtained from the research conducted by Hosseini et al. (2016) to validate the numerical model and adopted the study undertaken by Khosronejad et al. (2012) to model the pier scour. A rectangular channel with a length of 6 meters, a width of 1.21 meters, and a height of 0.45 meters; was used to model the bridge pier and abutment. ‎Fig. 2 shows the rectangular channel a pier and abutment placed in, and Table one describes the model configuration.

For the three-dimensional simulation of flow, the geometry of the field was discretized into a fine mesh. ‎Fig. 2 shows the boundary conditions assigned to the numerical model. Then, three turbulence models, K-ɛ, RNG, and LES have been investigated to compare the maximum scour depth in the rectangular channel for a flow rate of 0.052 m3/s, and their results are shown in Table 3.The results indicate that the LES turbulence model can estimate the maximum scour depth for the bridge pier and the abutment. Although the LES turbulence model takes more time to run, it is more accurate and presents a scouring pattern similar to the following laboratory sample justifying the computational costs. The maximum scour depth at the bridge abutment estimated by the LES model is about 74% of the measured value, while its maximum scour depth measured at the bridge's pier is 87%.‎Figs 3 and ‎4 illustrate the bed changes around the abutment and the bridge pier for the numerical and laboratory model. Comparing the bed changes and the location of maximum scour depth for the numerical model of pier and abutment and results of laboratory models shows that the numerical model has a good capability in simulating bed changes and local scour around each of the abutment structures and bridge pier.Comparison of scour time development resulting from numerical models around the abutment and bridge pier with laboratory models, ‎Fig. 6, shows that 70% of the maximum scour depth in the abutment area occurs in 20% of the initial scour time, which is relatively consistent for both numerical and laboratory models.Examination of substrate changes, ‎Figs. 7-‎11, shows that a deep scouring depth occurred for the abutment and at the upper-right edge of the forehead. And by reducing the pier distance from the abutment in the models, the maximum scours depth around the pier, and abutment structures have undergone significant changes.

Comparing the scour pattern and the location at which the maximum scour-depth for pier and abutment resulted by the numerical model with experimental, shows that the numerical model procured using FLOW-3D can well simulate bed changes and local scour around the bridge pier and abutment. Furthermore, increasing the relative velocity of the flow, strengthening the eddy currents around the pier and abutment, consequently arising the scour depth; as the relative velocity increases by 10%, the scour depth increases by about 50%. Moreover, when the distance from the base to the abutment decreases, the scouring depth around the rectangular abutment increases.

Scouring is the erosion around structures that occurs due to complex eddy currents and creates a pit around the supporting foundations of structures (Melville, 1997). Depth of scour plays a vital role in 1) the degree of the potential destruction of flow around the structure and 2) designing the dimensions of the foundation of structures in the path of water flow. Therefore, determining the mathematical relationship between scour depth and affecting parameters (Barbhuiya and Dey, 2004) and understanding the scour mechanism around bridge piers, and accurately estimating scour depth for more and better protection of bridge piers against this phenomenon (Breusers et al ., 1997) is of great importance. Numerous comprehensive studies have been performed since 1953 to identify the effect of various factors on scouring rate and determine its estimation methods (Laursen and Toch, 1953; Shen et al., 1966; Garde and Ranga-Raju, 1985; Melville, 1995).To date, researchers have studied the effect of geometric and hydraulic parameters (Sturm and Sadiq, 1996; Kouchakzadeh and Townsend, 2000; Jordanova and James, 2003) and effective vegetation Young et al., 1993; Amir et al., 2018) on scouring, but the simultaneous effect of changing the length of abutments and vegetation on the amount of middle pier scour has been less considered. Therefore, in this study, the amount of scour around the middle pier of the bridge in compound sections in case of simultaneous change of abutment length and vegetation has been investigated.

Normalized parameters significantly deepen the understanding of various physical phenomena and allow the results of limited experiments to be applied to different situations. In this study, the maximum scour depth  was selected as the primary variable and expressed as equation ‏(1) in terms of effective parameters. Then, considering the parameters  as iterative variables, using Buckingham p-theorem, equation ‏(1) was written as a dimensionless relation ‏(2). Because the amount of channel slope, unit weight of sediments, uniformity coefficient and average diameter of materials, velocity and depth of flow, and length of bridge pier are constant in all tests; normalized parameters  and Froude number were removed. By simplifying and combining other parameters, dimensionless relations for degree of scour depth and volume were obtained as equations ‏(3) and ‏(4).A trapezoidal laboratory channel made at the Water Research Institute, ‎Fig. 1, was used for the experiments. The central pier of the bridge around which the scour was examined has a square cross-section with sides of 4 cm. Abutments on both sides of the channel were provided in 8, 16, and 24 cm sizes. Vegetation was studied using non-submerged cylinders in diameter and length of 1 and 20 cm respectively applied in rows in areas of ​​1, 2, and 4% of the floodplain area. ‎Fig. 2 shows the image of the laboratory channel. Experiments were performed in two time intervals of 3 and 8 hours to obtain equilibrium time and maximum scouring.

A total of 9 experiments were performed in which the depth and volume of scouring were determined. To name each experiment, the vegetation and abutment length were marked respectively with V and A. For example, V%1A80 refers to an experiment with 1% vegetation and an abutment of 80 mm length.‎Fig. 3 shows the changes in the longitudinal profile of the bed after scouring in different conditions of vegetation and the length of the abutment. Increase in the abutment length increased the height of material accumulation in all experiments. Increasing the percentage of vegetation leads to an increase in the volume of material accumulation in all samples due to increased bed roughness and turbulence of the flow and enlargement of the scour cavity. Also, it is observed that with increasing the percentage of vegetation and the length of the abutment, the length of scouring (after the hole in the direction of flow) increases.‎Fig. 4 shows that the depth and volume of scouring increases with increasing vegetation percentage. The highest scour volume occurred in 4% vegetation (the densest vegetation condition studied), which results in 9.2 cm and 7031 cm3 for experiments V%4A160 and V%4A240, respectively. The lowest scour depth and volume values were related to 1% vegetation and V%1A80 test, which are 4.1 cm and 766 cm3, respectively. Increasing the vegetation from 1 to 2% and from 2 to 4%, the scour depth at the pier of the bridge (with length and width 4 cm and 8 cm abutment length) increases by 14 and 57%, respectively, i.e., the scour volume, respectively increases 3.2. And 1.9 times.As the relative length of the abutment increases, the rate of deflection and the intensity of the flow in the main channel increase, after which the amount of scouring around the middle pier increases. As the percentage of vegetation in the floodplain becomes denser, the bed roughness increases, and the flow rate in the main channel increases, causing more scouring around the middle pier of the bridge (‎Figs. 5 ‎and 6).

This study investigates the effect of flood vegetation and abutment length on the scouring depth and volume in a compound channel. This study shows that with increasing the percentage of vegetation, the scour depth at the pier of the bridge increases, and with increasing the length of the bridge abutment, the depth and volume of the scour increases. By the way, vegetation and abutment length percentages are the most influential parameters in creating scour depth and volume in which the former has more effect.