An engineering prediction method for concentration of suspended sediment in a river channel with complex vegetation

Abstract

The application provides an engineering prediction method for the concentration of suspended sediment in a river channel with complex vegetation, and belongs to the technical field of hydraulics and river dynamics in natural science research. First, the basic monitoring data of the river channel are collected on site, and then the data are processed according to the types of vegetation, and the results of the vertical nonlinear vegetation water resistance area distribution of the full water depth are calculated in sections. The vertical flow velocity distribution results of the full water depth average flow velocity profile are calculated by using the river hydraulic parameters and the measured data of the cross section vertical flow velocity, and the layered flow velocity model is used for the boundary conditions of the vegetation. Combined with the basic data of the river channel, the sediment concentration analytical expression is substituted, and the sediment volume fraction of each water depth point is solved to obtain the predicted value of the sediment concentration. The application can output the vertical distribution results of the full water depth sediment only by relying on the conventional monitoring data on site, and provides a quantifiable prediction method for the river ecological restoration.

Description

Technical Field

[0001] This invention belongs to the field of hydraulics and river dynamics in natural science research, and particularly relates to an engineering prediction method for suspended sediment concentration in river channels with complex vegetation morphology. Background Technology

[0002] In natural river and wetland ecosystems, aquatic vegetation is a vital ecological component, widely distributed in river channels, floodplains, and riparian zones. Vegetation not only provides habitats for aquatic organisms but also significantly alters flow structure, turbulence characteristics, and sediment transport processes, profoundly impacting riverbed evolution, water purification, and ecosystem health. With the large-scale implementation of global river ecological restoration projects, artificial vegetation planting has become a key measure for improving the river environment. In-depth research into the interaction mechanisms of vegetation-flow-sediment has significant theoretical and engineering value.

[0003] Traditional hydraulics and sediment transport theories are primarily based on open channel flow studies in vegetated conditions, and have formed a relatively complete theoretical system. Regarding the vertical distribution of suspended sediment, existing technologies have developed the classic Rouse formula based on turbulent diffusion theory, establishing the relationship between concentration distribution and turbulent characteristics; a gravity theory from an energy balance perspective has been proposed, providing a new perspective for suspended sediment transport studies by dividing the liquid and solid phases and establishing separate energy equations; and a two-phase flow theory has been developed, introducing the characteristic length of suspended sediment and constructing a more unified equation for the vertical distribution of suspended sediment concentration. These theories can all describe sediment distribution patterns well under vegetated conditions, laying an important foundation for river sediment dynamics. However, these technologies have significant limitations when applied to vegetated channels. The introduction of vegetation alters flow resistance, turbulent structure, and energy dissipation mechanisms, leading to significant deviations between traditional model predictions and actual conditions. To address this issue, early technologies often simplified vegetation into a uniform rigid cylindrical array, which, while convenient for analysis, fundamentally differs from the actual vegetation morphology. While subsequent technological improvements have gradually incorporated vegetation stratification assumptions and stochastic displacement models, they are still largely based on the diffusion theory framework and often use vegetation water-blocking area per unit height as a benchmark. Simplifying it to constant or linear changes ignores its inherent nonlinear characteristics along water depth. Real vegetation exhibits complex morphologies such as layered stems and leaves, flexible deformation, and non-uniform distribution, especially for aquatic plants like reeds and cattails. Significant vertical variations directly affect velocity distribution and sediment diffusion. Existing technologies are insufficient in characterizing this aspect, leading to decreased prediction accuracy when vegetation morphology is complex.

[0004] Currently, there is a lack of high-precision prediction methods in ecological river design and restoration that can accurately quantify vegetation morphology characteristics and couple their interactions with water flow and sediment. Therefore, developing a method based on the complex morphology of real vegetation and capable of accurately predicting the vertical distribution of suspended sediment concentration is of great significance for improving the scientific rigor and engineering effectiveness of river ecological restoration. Summary of the Invention

[0005] To address the shortcomings of existing river sediment prediction methods, which simplify the water-blocking area of ​​aquatic vegetation to a constant and ignore the vertical nonlinear morphology of stems and leaves, failing to accurately characterize the coupling effect of vegetation-flow-sediment, and resulting in large prediction errors of suspended sediment concentration in rivers with complex emergent vegetation, making them unsuitable for direct application in ecological restoration engineering design, this invention proposes an engineering prediction method for suspended sediment concentration in rivers with complex vegetation morphology. This invention creatively incorporates the vertical nonlinear water-blocking morphology of two mainstream emergent vegetation types, reeds and broadleaf cattails, into the sediment prediction process, deconstructing the river flow profile in layers and establishing an engineering calculation link between vegetation morphology parameters, flow velocity profiles, and suspended sediment concentration. Without the need for professional modeling and derivation, it can output the vertical distribution results of sediment across the entire water depth based solely on routine on-site monitoring data, providing a high-precision and implementable quantitative prediction method for river ecological restoration and riverbed evolution management.

[0006] This invention provides an engineering method for predicting suspended sediment concentration in river channels with complex vegetation morphology, comprising the following steps: S1, collect basic monitoring data of the river channel on site, which are divided into four categories: river hydraulic parameters, vegetation morphology parameters, sediment inherent characteristics parameters, and measured data of vertical flow velocity at cross sections. S2, based on the collected vegetation morphology characteristic parameters, processes the vegetation types and calculates the vertical nonlinear vegetation flow-blocking area distribution across the entire water depth in segments, outputting the vertical vegetation flow-blocking distribution results. ; S3, with results To stratify the vegetation boundary conditions, measured data of river hydraulic parameters and cross-sectional vertical velocity were used. A stratified velocity model was employed to calculate the time-averaged velocity profile across the entire river depth, and the vertical velocity distribution results were output. ; S4, combined results ,result By substituting the river hydraulic parameters and inherent sediment characteristic parameters into the analytical calculation relationship of sediment concentration, and using the sediment volume fraction at the water surface position as the boundary condition, the sediment volume fraction at each water depth is obtained by solving for the sediment volume fraction at each water depth. This is then converted into the predicted value of the vertical suspended sediment concentration of the entire cross section that can be used in the project.

[0007] Preferably, in step S1, the specific implementation process for collecting basic river monitoring data on-site is as follows: River hydraulic parameters: measured river depth, hydraulic gradient, and average cross-sectional flow; if the hydraulic gradient cannot be measured directly, it is calculated by converting the upstream and downstream water level difference with the river length. Vegetation morphology parameters: Collected from two types of vegetation, reed and broadleaf cattail, including total vegetation height, stem boundary height, plant density, maximum leaf spread height, minimum stem water-blocking width, and maximum leaf water-blocking width; when planting density is missing, it is calculated by measuring the horizontal and vertical planting spacing of the plants. Inherent characteristic parameters of sediment: Collect sediment samples, measure the median particle size and density of sediment using a particle size analyzer, and then calculate the static water settling velocity of particles using an empirical formula for sediment settling velocity. Vertical velocity data of cross sections: Multiple monitoring points are evenly distributed along the water depth, and the instantaneous velocity at each point is collected using an acoustic Doppler current meter. Abnormal pulsations are eliminated by time averaging to obtain the measured velocity at each vertical point.

[0008] Preferably, in step S2, vegetation parameters are processed according to vegetation type, and the vertical water-blocking area distribution is calculated. The specific process is as follows: S21, identify the vegetation type in the river channel, and divide it into two branches: reed-type vegetation and broadleaf cattail-type vegetation. S22. If the vegetation is reed-type, the water-blocking area is calculated separately for each of the following three segments based on water depth: stem segment, leaf segment, and submerged canopy segment. A fixed minimum water-blocking width is used for the stem segment. The water-blocking area of ​​the leaf segment is fitted using a quadratic curve to show a continuously varying vertical water-blocking area. A fixed narrow water-blocking width is used for the submerged canopy segment. All water depth data are integrated to form a continuous vertical water-blocking area distribution result. ; S23. If the vegetation is broadleaf cattail, ignore the vortex interface effect between stem and leaf layers, and directly calculate the water-blocking area across the entire water depth based on the vertical width of the leaves and the plant density, outputting a uniform vertical water-blocking area distribution result. .

[0009] Preferably, the specific process of S3 is as follows: S31. Based on vegetation type, the velocity calculation levels are divided into three layers: reed-type vegetation, leaf layer, stem uniform velocity sub-layer, and stem vortex velocity sub-layer; broadleaf cattail-type vegetation only uses a single layer uniform velocity calculation. S32, Solving for flow velocity in stratified reed-type vegetation: The measured data of river hydraulic parameters and cross-sectional vertical flow velocity include: measured river depth, hydraulic gradient, average cross-sectional flow, and measured flow velocity at each vertical point. Leaf layer: Based on the vertical water-blocking area distribution results The average flow velocity of the leaf layer was calculated by measuring the actual water depth, hydraulic gradient, cross-sectional average flow, and flow velocity at each vertical point. The local flow velocity at each vertical point was then calculated by combining the local water-blocking area at each water depth. Stem uniform velocity sublayer: The stem water-blocking width is constant, and the stable uniform velocity can be directly solved; Stem vortex velocity sublayer: First, calculate the vortex extension width at the leaf-stem interface, determine the influence range of the vortex on the stem layer, and use power series approximation to solve the velocity at each height in the vortex layer; S33, the three-layer flow velocity of reeds and the full-depth flow velocity of broadleaf cattail vegetation are calculated using a layered flow velocity model; the three-layer flow velocity of reeds includes the local point flow velocity of reed leaf layer, the uniform flow velocity of reed stem, and the power series of reed vortex flow velocity sublayer.

[0010] Preferably, in S33, the three-layer flow velocity of reeds includes the local point flow velocity of the reed leaf layer, the uniform flow velocity of the reed stem, and the power series of the reed vortex flow velocity sublayer. Formula for calculating local velocity at points within the reed leaf layer: ; In the formula: H represents the river depth. The vegetation height corresponds to the water depth. leaf layer Local time-averaged flow velocity at height; for( ) Area blocked by high-level vegetation; The average velocity of the blade cross section; ; In the formula: The average velocity of the blade cross section; J is the acceleration due to gravity; J is the hydraulic gradient of the river channel. This represents the vegetation drag coefficient. Planting density; This represents the average water-blocking area of ​​the blade layer; Formula for calculating the uniform flow velocity sublayer on reed stems: ; In the formula: homogeneous sublayer Average flow velocity at height; Minimum fixed water-blocking width for the stem; Formula for calculating the power series of reed vortex velocity sublayers: ; In the formula: vortex sublayer Average flow velocity at height; The underlying reference flow rate; For dimensionless parameters of the vortex, Kármán's constant; The formula for calculating the single-layer flow velocity at full water depth in the broadleaf cattail is as follows: ; In the formula: The mean flow velocity at height z of the cattail vegetation; This represents the vertical water-blocking area of ​​the cattail.

[0011] Preferably, the specific process of S4 is as follows: S41, extract the vegetation water-blocking area and the corresponding height-average flow velocity measurement value for each vertical point at the full water depth, and simultaneously retrieve the inherent characteristic parameters of sediment, including: sediment density, water density, sediment settling velocity, and gravitational acceleration. S42 uses the sediment volume fraction at the water surface as the calculation boundary condition, substitutes the analytical calculation relationship of sediment concentration, and solves the sediment volume fraction at each water depth point; introduces an error correction parameter to offset the calculation deviation caused by the low sediment content approximation. S43 converts the sediment volume fraction at each water depth point into the suspended sediment concentration in the general unit of the engineering site, outputs the complete vertical sediment concentration distribution curve of the entire river section, and completes the quantitative prediction of suspended sediment in vegetated river channels.

[0012] Preferably, in step S42, the vertical volume fraction of sediment is calculated using the following analytical formula: ; In the formula: Let z be the volume fraction of sediment at any water depth z; For water surface Sediment volume fraction; The parameters are used to correct the approximate error for low sand content. The density of the sediment; Density of water; The settling velocity of silt in still water; It is the acceleration due to gravity; The river is deep; The water depth in the river at any location; This represents the vegetation drag coefficient. Planting density; The vertical water-blocking area of ​​the vegetation; The velocity is the longitudinal point velocity.

[0013] Preferably, in S43, the conversion formula for the measured suspended sediment concentration in engineering is as follows: ; In the formula: For general suspended sediment concentration in engineering, the unit is ( S is the volume fraction of sediment obtained by solving S42; This refers to the density of the sediment.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The research object of this invention is closer to the real vegetation morphology, and the water-blocking area per unit height of vegetation is measured. The nonlinear variation along the water depth direction is introduced into the suspended sediment concentration prediction model, overcoming the limitations of traditional methods. The limitations of simplifying to a constant or linear distribution. Obtaining real vegetation (such as reeds) through experiments. By distributing data and constructing a piecewise mathematical model, the prediction accuracy of the model under complex vegetation conditions was significantly improved. (2) Based on the Velikanov gravity theory model, this invention introduces the vegetation additional resistance term, establishes the liquid and solid phase energy balance equations for water flow with vegetation, and forms a new suspended sediment concentration prediction model applicable to river channels with complex vegetation morphology, thus expanding the applicability of traditional sediment dynamics theory. (3) Based on the structural characteristics of emergent vegetation, this invention divides the vertical flow of water into leaf layer, stem layer and vortex sublayer, and establishes flow velocity distribution models for each layer. This can accurately reflect the influence of vegetation morphology on flow velocity profile and provide reliable flow velocity input for suspended sediment concentration model. (4) The input parameters required by this invention (such as vegetation density, water blocking area, flow velocity distribution, etc.) can all be obtained through field investigation or simple flume experiment. The model form is clearly defined and easy to embed into existing river dynamics simulation software, and has good engineering promotion value. Attached Figure Description

[0015] Figure 1 This is a flowchart of the overall process of the present invention.

[0016] Figure 2 The present invention provides a schematic diagram showing the water-silt micro-element as divided into liquid and solid phases and their displacement per unit time.

[0017] Figure 3 This is a schematic diagram of the vertical distribution of the flow velocity in this invention.

[0018] Figure 4 This is a schematic diagram of the artificial reed vegetation of the present invention.

[0019] Figure 5 This is a comparison chart of the measured and calculated values ​​of the flow velocity distribution under operating condition 1 of this invention.

[0020] Figure 6 This is a comparison chart of the measured and calculated values ​​of the flow velocity distribution under working condition 2 of this invention.

[0021] Figure 7This is a comparison chart of the measured and calculated values ​​of the flow velocity distribution under operating condition 3 of this invention.

[0022] Figure 8 This is a comparison chart of the measured and calculated values ​​of the flow velocity distribution under working condition 4 of this invention.

[0023] Figure 9 This is a comparison chart of the measured and calculated values ​​of the flow velocity distribution under operating condition 5 of this invention.

[0024] Figure 10 This is a comparison chart of the measured and calculated values ​​of the flow velocity distribution under operating condition 6 of this invention.

[0025] Figure 11 This is a comparison chart of the measured and calculated values ​​of suspended sediment concentration under working condition 1 of this invention.

[0026] Figure 12 This is a comparison chart of the measured and calculated values ​​of suspended sediment concentration under working condition 2 of this invention.

[0027] Figure 13 This is a comparison chart of the measured and calculated values ​​of suspended sediment concentration under working condition 3 of this invention.

[0028] Figure 14 This is a comparison chart of the measured and calculated values ​​of suspended sediment concentration under working condition 4 of this invention.

[0029] Figure 15 This is a comparison chart of the measured and calculated values ​​of suspended sediment concentration under working condition 5 of this invention.

[0030] Figure 16 This is a comparison chart of the measured and calculated values ​​of suspended sediment concentration under working condition 6 of this invention. Detailed Implementation

[0031] The overall process of this invention is as follows Figure 1 As shown in the figure. The method of the present invention will be further described below with reference to specific embodiments.

[0032] The experiment was conducted using a 12m long and 1m wide circulating variable slope flume at Xi'an University of Technology. The flume bottom slope was adjustable from 0 to 2%, and the flow rate was controlled from 10 to 100 L / s. The model sand used was ( , Silica particles were introduced and cross-validated using data from the Xu and Nepf (2021) broadleaf cattail experiment. The prediction of suspended sediment concentration in river channels using artificial reed vegetation was conducted. In this case, 60cm high artificial reeds were planted in the flume, with a stem dividing height of 17cm. Two plant densities were set: 100 plants / m² for conditions 1-3 and 336 plants / m² for conditions 4-6, with water depths of 0.3m and 0.4m respectively. The entire process from S1 to S4 was executed. Figure 2This is a schematic diagram illustrating the division of the water-silt micro-element into liquid and solid phases and their displacement per unit time. Figure 3 This is a schematic diagram of the vertical distribution of the flow velocity in this invention.

[0033] Example 1: S1 collects basic monitoring data at the cross section with fully developed vegetation (x=6m), collecting four types of parameters: Hydraulic parameters: water depth Measured hydraulic gradient J, average flow velocity of cross section ( ); Vegetation morphology parameters: Total vegetation height Stem dividing height Minimum water-blocking width of stem Maximum water-blocking width of the blade Maximum blade unfolding height Plant density and When vegetation density is missing, it can be calculated by measuring the horizontal and vertical planting spacing of the plants. Sediment parameters: Sediment density Median particle size of sediment Sediment settling speed ; Vertical flow velocity: along Measurement points were set up, and ADV sampled at 25Hz for 360s. After removing abnormal pulsations, the time-averaged flow velocity was obtained. The experimental conditions and parameter statistics are shown in Table 1.

[0034] Table 1 Summary of Experimental Conditions

[0035] S2 The calculation of the vertical water-blocking distribution of reeds is solved in segments according to the three-segment rule. : S21 First, preferred The specific method for establishing the calculation formula is as follows: In emergent vegetation water flow, the water-facing and water-blocking area per unit height of vegetation. It exhibits non-linear variation along the vertical (z) direction, and different vegetation forms correspond to unique characteristics. Distribution curves. To quantify this relationship, this study conducted laboratory flume experiments using artificial reeds, obtaining a series of data. The experimental values. Through analysis... The distribution pattern reveals that its change process can be divided into three typical stages, such as... Figure 4 As shown. In the stem layer ( ), Maintain constant value In the blade layer ( ), It first increases and then decreases along the vertical direction, and at The maximum value is reached at this point. ;when hour, The value decreases as z increases, which is due to the morphological reconstruction of the blades under the action of water flow, such as bending and downstream adjustment; at the top of the canopy ( ), Maintain constancy constant value (like Figure 4 (As shown by the black dot in the middle).

[0036] Through artificial reeds A deeper analysis reveals that, The vertical variation curve can be described and calculated using a mathematical model. Given the maximum water depth of the experiment... The top of the canopy was not actually submerged, therefore this layer was not included in the mathematical model. (Leaf layer) )of The change model is expressed as follows: ; in , , ; In the formula, for The water-facing and water-blocking area per unit height of vegetation. For the maximum water-facing and water-blocking area Corresponding height, Total vegetation height This represents the maximum water-facing and water-blocking area per unit height of vegetation. This represents the minimum water-facing and water-blocking area per unit height of vegetation. Parameter , , , , All of these are closely related to vegetation morphology characteristics, and their specific values ​​depend on the type and structure of the vegetation.

[0037] S22 In this implementation scheme, the specific form is as follows: Stem layer ( ):fixed ; Blade layer ( Substitute the quadratic curve formula and vegetation characteristic parameters to solve the problem. b, c), calculate the water-blocking width at each water depth in centimeters.

[0038] S23 integrates all water depths The numerical results show that the calculated curve fits the measured vegetation projection data from the flume. Figure 4 The water-blocking area of ​​the leaf layer exhibits a non-linear characteristic of first increasing and then decreasing, which is consistent with the bending deformation law of water flow in real reed leaves.

[0039] S3 layered calculation of full-depth flow velocity profile results, plant density under this working condition ( Substituting into the formula for vortex extension width, we get ( The eddies do not extend to the riverbed; the stem layer is divided into a uniform velocity sublayer and a vortex velocity sublayer, such as... Figure 3 As shown: leaf layer ( First, calculate the average water-blocking area of ​​the blade layer. ), substitute ( The formula yields the average velocity of the blade layer, which is then combined with the velocity at various water depths. Convert local flow velocity ( ); Uniform stalk sublayer: ( The velocity is constant, and the steady-state uniform flow rate can be directly calculated. ); Vortex sublayer: Substituting the Reynolds shear stress closure relation and power series solution, solve for the flow velocity at each height within the vortex sublayer. ); By splicing the three velocity layers, a complete vertical velocity profile is obtained. The calculated velocity values ​​are compared with the measured ADV data. Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 As shown, the error between the two is extremely small, and the measured data are shown in Table 2.

[0040] Table 2 Existing experimental flow rates measured data

[0041] S4 Calculate the predicted concentration of suspended sediment at full water depth S41 Extract the corresponding ( for each water depth) , ), retrieve fixed parameters for water and sediment; The process of establishing the prediction formula for suspended sediment concentration in S42 is as follows: Step 1: For sediment-laden water flows containing vegetation, the water body is divided into liquid and solid phases. Figure 2 The forces acting on infinitesimal elements were analyzed. Based on the law of conservation of energy, the energy conservation of the solid and liquid phases of the sediment-laden water flow per unit volume was considered, and energy balance equations for the liquid and solid phases were established. (1) (2) In the formula: Let z be the volume fraction of sediment at any water depth z; The density of the sediment; Density of water; The settling velocity of silt in still water; It is the acceleration due to gravity; The river is deep; The water depth in the river at any location; This represents the vegetation drag coefficient. Planting density; The vertical water-blocking area of ​​the vegetation; Where is the longitudinal point velocity; is the hydraulic gradient; For longitudinal Reynolds stress ( In equation (1), the term on the left side represents the energy provided by the clear water portion during the flow of sediment-laden water from high head to low head per unit time; the first term on the right side of equation (1) represents the energy consumed by the clear water portion per unit volume per unit time to overcome flow resistance; the second term on the right side of equation (1) represents the energy consumed by the clear water portion per unit volume per unit time to maintain sediment suspension; the term on the left side of equation (2) represents the energy provided by the sediment portion during the flow of sediment-laden water from high head to low head per unit time; the term on the right side of equation (2) represents the energy consumed by the sediment portion per unit volume per unit time to overcome resistance.

[0042] Step 2 involves refining the energy balance equation established for water flow without vegetation by introducing an energy loss term caused by the additional resistance of vegetation, and then re-establishing the energy balance equation for water and sediment in vegetated water flow. This specifically includes the following process: Velikanov's (1954) energy balance equation was established in vegetated channels and did not consider energy loss caused by the resistance of vegetation. Huai et al. (2010), through force analysis of micro-elements within the vegetation layer of submerged vegetated water flow, derived an expression for calculating the energy loss per unit volume of water flow per unit time caused by the additional resistance of vegetation: (3) In the formula For vegetation density, Number of vegetation The water-blocking area of ​​a single vegetation unit is the water-blocking area of ​​a single vegetation unit at a unit height. Add the energy loss term caused by the additional resistance of vegetation per unit volume of water flow per unit time to equations (1) and (2) (equation (3)): (4) (5) Equations (7) and (8) can be simplified to: (6) (7) Substituting equation (10) into equation (9), equation (9) can be further simplified to obtain: (8) For equation (8) in arrive Numerical integration is performed within the range where z is... We can obtain: (9) In equation (9), since Negligible, after ignoring the second term on the right, the above equation can be simplified to: (10) Differentiate both sides of equation (10) with respect to z: (11) The first term on the right side of equation (6) Split into and We can obtain: (12) Substituting equation (11) into equation (12), equation (12) can be further simplified to: (13) Similarly, for water flows with low sediment concentration, it can be approximated as... , Then equation (13) can be simplified to: (14) By adjusting equation (14) from the water depth ( Integrating up to the water surface H, we obtain the sediment concentration at any height. The analytical solution is: (15) in For the water surface ( The suspended sediment volume fraction, This is the correction factor approximation The parameters introduced due to the resulting errors.

[0043] S43 with water surface ( ) Measured sediment volume fraction ( Substituting these as boundary conditions into the analytical formula for sediment concentration, the volume fraction is solved for each water depth. , introduce ( Correcting the approximate error for low sediment content; through Convert to commonly used concentration units in engineering, output sediment distribution curves for the entire water depth, and compare the predicted values ​​with the measured SSC of ADV acoustic backscattering (SSC). Figure 11 , Figure 12 , Figure 13 , Figure 14 , Figure 15 , Figure 16 The measured suspended sediment concentration data are shown in Table 3. The overall average relative error (MRE) of the 6 groups of reed conditions in this example is 12.5%, which meets the accuracy requirements for river ecological monitoring and restoration design.

[0044] Table 3 Existing experimental sediment concentrations measured data

[0045] Example 2: Prediction of suspended sediment concentration in river channels using broadleaf cattail vegetation: This case study uses flume test data published by Xu and Nepf (2021). The vegetation was broadleaf cattail, with a plant height of 38 cm and a planting density of ( Set up two sets of water depth conditions (H=0.35m) and (H=0.2m), and execute the complete S1~S4 process: S1 collects basic monitoring data for cattails: Hydraulic parameters: water depth 0.35m, average cross-sectional velocity ( ); Operating condition 8: water depth 0.2m, average cross-sectional velocity ( ); Vegetation morphology parameters: No obvious stem-leaf stratification vortex interface. , ; Sediment parameters: Sediment density Median particle size of sediment Sediment settling speed ; Vertical flow velocity: The average flow velocity at each height was collected by an optical backscattering sensor in layers.

[0046] S2 calculates the vertical water-blocking distribution of Typha latifolia. The vortex interface effect between stem and leaf layers is ignored, and the total water depth is calculated directly in segments based on the vertical width of the leaves and the plant density. The output shows the continuous vertical water blockage distribution.

[0047] S3 calculates the full-depth velocity profile; for cattails, no stratification is required, only single-layer velocity calculation is performed, based on hydraulic gradient and local ( The average flow velocity at each water depth is directly calculated, and the complete flow velocity profile is output. The data is shown in Table 4.

[0048] S4 calculates the predicted suspended sediment concentration by substituting water and sediment parameters, flow velocity, and obstructing area into the sediment concentration analytical formula to calculate the dimensionless concentration at each water depth. Comparison with measured data shows that the average relative error (MRE) for the two sets of cattail conditions is 14.1%, and the vertical distribution trend of the concentration perfectly matches the measured values. The data is shown in Table 5. On-site implementation instructions: When conducting sediment concentration prediction in natural river channels, the entire S1~S4 process is executed: monitoring points are set up at typical river cross-sections; all basic parameters for S1 are collected using a depth gauge, ADV current meter, laser particle size analyzer, and vegetation quadrat survey; vegetation elevation images are taken, and the data for each water depth is extracted in batches using image processing software. The system automatically generates water obstruction distribution results; all parameters are entered into Excel or commercial river dynamics simulation software, which contains all the analytical calculation formulas of this invention and automatically outputs flow velocity profile results and vertical distribution curves of suspended sediment concentration across the entire water depth in batches; the output sediment distribution results can be directly used for river siltation risk prediction, ecological restoration vegetation layout optimization, and boundary condition assignment for numerical simulation of riverbed evolution.

[0049] The data used to verify the suspended sediment concentration prediction model (15) of this invention are 6 sets of working conditions from a self-designed experimental dataset and a total of 8 sets of data obtained by Xu and Nepf (2021) in a circulating variable slope flume. The specific experimental working conditions and measured experimental data are shown in Tables 3, 4 and 5.

[0050] in, Because of water depth, For vegetation height, To adjust the length of the water flow, For the density of sediment, For sediment settling speed, The maximum width of the vegetation. Minimum width of vegetation, The vegetation drag coefficient. The average velocity of the water flow cross section in the blade layer. This represents the depth of eddy current intrusion.

[0051] Table 4 Experimental flow rates by Xu and Nepf (2021) measured data

[0052] Table 5. Sediment Concentration in Experiments by Xu and Nepf (2021) measured data

[0053] The average relative error is chosen as the criterion for judging the reliability of the formula; the smaller the average relative error, the more reliable it is. The specific expression is as follows: ; The velocity distribution and suspended sediment concentration results of the six sets of data in the existing experimental dataset and the flume data of Xu and Nepf (2021) were calculated using Equation (15). (See attached figures.) Figures 5 to 16 The average relative errors calculated from the existing experimental data (6 sets) and the experimental data (2 sets) from Xu and Nepf (2021) were 12.5% ​​and 14.1%, respectively, indicating that the model has high accuracy in predicting suspended sediment concentration.

[0054] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0055] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for predicting suspended sediment concentration in river channels with complex vegetation morphology, characterized in that, The process includes the following: S1, collect basic monitoring data of the river channel on site, which are divided into four categories: river hydraulic parameters, vegetation morphology parameters, sediment inherent characteristics parameters, and measured data of vertical flow velocity at cross sections. S2, based on the collected vegetation morphology characteristic parameters, processes the vegetation types and calculates the vertical nonlinear vegetation flow-blocking area distribution across the entire water depth in segments, outputting the vertical vegetation flow-blocking distribution results. ; S3, with results To stratify the vegetation boundary conditions, measured data of river hydraulic parameters and cross-sectional vertical velocity were used. A stratified velocity model was employed to calculate the time-averaged velocity profile across the entire river depth, and the vertical velocity distribution results were output. ; S4, combined with results ,result By substituting the river hydraulic parameters and inherent sediment characteristic parameters into the analytical calculation relationship of sediment concentration, and using the sediment volume fraction at the water surface position as the boundary condition, the sediment volume fraction at each water depth is obtained by solving for the sediment volume fraction at each water depth. This is then converted into the predicted value of the vertical suspended sediment concentration of the entire cross section that can be used in the project.

2. The method for predicting suspended sediment concentration in river channels with complex vegetation morphology as described in claim 1, characterized in that: In S1, the specific implementation process for collecting basic river monitoring data on-site is as follows: River hydraulic parameters: measured river depth, hydraulic gradient, and average cross-sectional flow; if the hydraulic gradient cannot be measured directly, it is calculated by converting the upstream and downstream water level difference with the river length. Vegetation morphology parameters: Collected from two types of vegetation, reed and broadleaf cattail, including total vegetation height, stem boundary height, plant density, maximum leaf spread height, minimum stem water-blocking width, and maximum leaf water-blocking width; when planting density is missing, it is calculated by measuring the horizontal and vertical planting spacing of the plants. Inherent characteristic parameters of sediment: Collect sediment samples, measure the median particle size and density of sediment using a particle size analyzer, and then calculate the static water settling velocity of particles using an empirical formula for sediment settling velocity. Vertical velocity data of cross sections: Multiple monitoring points are evenly distributed along the water depth, and the instantaneous velocity at each point is collected using an acoustic Doppler current meter. Abnormal pulsations are eliminated by time averaging to obtain the measured velocity at each vertical point.

3. The method for predicting suspended sediment concentration in river channels with complex vegetation morphology as described in claim 1, characterized in that: In step S2, vegetation parameters are processed according to vegetation type, and the vertical water-blocking area distribution is calculated. The specific process is as follows: S21, identify the vegetation type in the river channel, and divide it into two branches: reed-type vegetation and broadleaf cattail-type vegetation. S22. If the vegetation is reed-type, the water-blocking area is calculated separately for each of the following three segments based on water depth: stem segment, leaf segment, and submerged canopy segment. A fixed minimum water-blocking width is used for the stem segment. The water-blocking area of ​​the leaf segment is fitted using a quadratic curve to show a continuously varying vertical water-blocking area. A fixed narrow water-blocking width is used for the submerged canopy segment. All water depth data are integrated to form a continuous vertical water-blocking area distribution result. ; S23. If the vegetation is broadleaf cattail, ignore the vortex interface effect between stem and leaf layers, and directly calculate the water-blocking area across the entire water depth based on the vertical width of the leaves and the plant density, outputting a uniform vertical water-blocking area distribution result. .

4. The method for predicting suspended sediment concentration in river channels with complex vegetation morphology as described in claim 4, characterized in that: The specific process of S3 is as follows: S31. Based on vegetation type, the velocity calculation levels are divided into three layers: reed-type vegetation, leaf layer, stem uniform velocity sub-layer, and stem vortex velocity sub-layer; broadleaf cattail-type vegetation only uses a single layer uniform velocity calculation. S32, Solving for flow velocity in stratified reed-type vegetation: The measured data of river hydraulic parameters and cross-sectional vertical flow velocity include: measured river depth, hydraulic gradient, average cross-sectional flow, and measured flow velocity at each vertical point. Leaf layer: Based on the vertical water-blocking area distribution results The average flow velocity of the leaf layer was calculated by measuring the actual water depth, hydraulic gradient, cross-sectional average flow, and flow velocity at each vertical point. The local flow velocity at each vertical point was then calculated by combining the local water-blocking area at each water depth. Stem uniform velocity sublayer: The stem water-blocking width is constant, and the stable uniform velocity can be directly solved; Stem vortex velocity sublayer: First, calculate the vortex extension width at the leaf-stem interface, determine the influence range of the vortex on the stem layer, and use power series approximation to solve the velocity at each height in the vortex layer; S33, the three-layer flow velocity of reeds and the full-depth flow velocity of broadleaf cattail vegetation are calculated using a layered flow velocity model; the three-layer flow velocity of reeds includes the local point flow velocity of reed leaf layer, the uniform flow velocity of reed stem, and the power series of reed vortex flow velocity sublayer.

5. The method for predicting suspended sediment concentration in river channels with complex vegetation morphology as described in claim 4, characterized in that: The specific process of S4 is as follows: S41, extract the vegetation water-blocking area and the corresponding height-average flow velocity measurement value for each vertical point at the full water depth, and simultaneously retrieve the inherent characteristic parameters of sediment, including: sediment density, water density, sediment settling velocity, and gravitational acceleration. S42 uses the sediment volume fraction at the water surface as the calculation boundary condition, substitutes the analytical calculation relationship of sediment concentration, and solves the sediment volume fraction at each water depth point; introduces an error correction parameter to offset the calculation deviation caused by the low sediment content approximation. S43 converts the sediment volume fraction at each water depth point into the suspended sediment concentration in the general unit of the engineering site, outputs the complete vertical sediment concentration distribution curve of the entire river section, and completes the quantitative prediction of suspended sediment in vegetated river channels.

6. The method for predicting suspended sediment concentration in river channels with complex vegetation morphology as described in claim 5, characterized in that: In S42, the vertical volume fraction of sediment is calculated using the following analytical formula: ； In the formula: Let z be the volume fraction of sediment at any water depth z; For water surface Sediment volume fraction; The parameters are used to correct the approximate error for low sand content. The density of the sediment; The density of the water body; The settling velocity of silt in still water is [missing information]. It is the acceleration due to gravity; The river is deep; The water depth in the river at any location; This represents the vegetation drag coefficient. Planting density; The vertical water-blocking area of ​​the vegetation; The velocity is the longitudinal point velocity.

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