KR101379332B1 - Method and apparatus for estimating roughness coefficient using field data of natural and vegetated rivers - Google Patents

Abstract

The present invention is an illuminometer using natural field and vegetation stream field measurement data to analyze the roughness coefficient by using field data composed of natural riverbed and vegetation material for analysis on the flow resistance of the river. A method and apparatus for calculating numbers.
The method of estimating the roughness coefficient using the field measurement data of the natural river and the vegetation stream of the present invention includes: a first step of inputting field measurement data of the natural river and the vegetation stream into an input module; A second step of the illuminance coefficient calculating module calculating an illuminance coefficient for the natural river and the vegetation stream from the field measurement data inputted through the input module; A third step of analyzing, by the roughness coefficient analysis module, the roughness coefficients of the natural and vegetation streams calculated in the second step; And a fourth step of outputting respective roughness coefficients analyzed by the roughness coefficient output module.

Description

Method and Apparatus for Estimating Roughness Coefficient Using Field and Field Measurement Data of Natural and Vegetative Rivers {METHOD AND APPARATUS FOR ESTIMATING ROUGHNESS COEFFICIENT USING FIELD DATA OF NATURAL AND VEGETATED RIVERS}

The present invention relates to a method and apparatus for estimating roughness coefficient, and more specifically, to calculate roughness coefficient by using field data composed of natural riverbed and vegetation material of vegetation stream for analysis of flow resistance of river. The present invention relates to a method and apparatus for estimating roughness coefficients using on-site data of natural and vegetation streams analyzed.

Resistant to flow in natural rivers consisting mainly of sand and gravel, or vegetation streams in which vegetation such as grass, shrubs, and trees are scattered in flood plains and dikes. It causes the change of flow velocity depending on vegetation and bed data on the overrun. These phenomena are being studied as a major task in hydraulic engineering. The flow resistance can be classified into surface friction of the surface, drag which is a resistance due to shape, wave resistance due to surface distortion, and abnormal state of flow due to local acceleration.

The resistance characteristics of vegetation in rivers are closely related to flow conditions and vegetation structure, which have various distributions depending on the topographical characteristics of rivers, the location of waterway sections, and the degree and extent of distribution of vegetation species.

The relationship between friction slope and flow rate in a stream is governed by vegetation and stream material resistance to flow, and resistance to stream flow is caused by primitive variability such as friction, bed irregularities, vegetation bed contours, channel alignments, etc. do.

Shear stresses generated by interactions between streams and riverbeds in rivers are the basis for flow analysis and hydraulic stability analysis. In rivers, roughness coefficients are largely classified into vegetation and channel shape and bed data. Roughness coefficients based on riverbed data are mainly determined using empirical formulas obtained through field measurements and model experiments.

In addition, the vegetation of river banks and floodplains ensures the stability of waterways and banks by inhibiting erosion, and serves as habitats for aquatic animals, while it is necessary to predict them hydraulically since it causes disturbance of water due to flow resistance. There is.

Regarding the technique for analyzing the flow of rivers, Korean Patent Laid-Open Publication No. 10-2010-0044371 relates to a method and apparatus for numerical analysis of river fluctuations in natural rivers, which occur in natural streams in which linear and curved channel shapes are mixed. Techniques for accurately simulating bed changes that can occur under various conditions are disclosed.

However, such a conventional technique is a technique for simulating river bed fluctuations that may occur in a river, and a method of estimating roughness coefficient in consideration of vegetation characteristics cannot be expected. There is also a limit to understanding the underlying variability of all resistance to flow in vegetation streams.

The present invention has been devised to solve the problems described above, by deriving the roughness coefficient of the stream flow through the field measurement data, the roughness coefficient using the field measurement data of natural rivers and vegetation rivers that can be usefully used in manual practice To provide a method and apparatus for calculating

In addition, the present invention analyzes the roughness coefficients for the riverbed data and vegetation data, suggests the distribution range for each type, and provides on-site data of natural and vegetation streams that can derive the roughness coefficient relational function as a function of flow rate and friction inclination. An object of the present invention is to provide a method and a device for calculating the roughness coefficient using the.

In addition, the present invention is a method of estimating roughness coefficients using field measurement data of natural and vegetation rivers that can be usefully used for manual practice by deriving a dimensionless shear rate equation as a function of relative diving ratio in natural and vegetation streams. And for the provision of the device.

However, the object of the present invention is not limited to the above-mentioned objects, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, the method of calculating the roughness coefficient using the field measurement data of natural rivers and vegetation streams according to the present invention, the first step of inputting the field measurement data of natural rivers and vegetation streams to the input module; A second step of the illuminance coefficient calculating module calculating an illuminance coefficient for the natural river and the vegetation stream from the field measurement data inputted through the input module; A third step of analyzing, by the roughness coefficient analysis module, the roughness coefficients of the natural and vegetation streams calculated in the second step; And a fourth step of outputting respective roughness coefficients analyzed by the roughness coefficient output module.

Method for calculating the roughness coefficient using the field measurement data according to the present invention, the field measurement data of the natural river is the riverbed data for sand, gravel, pebbles and amber stones, the field measurement data of the vegetation river is herbal, shrubs and trees Characterized in that the vegetation data for.

The method of estimating roughness coefficient using on-site actual measurement data according to the present invention includes a range of minimum and maximum values for flow rate, friction inclination, median particle size, average flow rate and depth for sand, gravel, pebbles and amber stones. Characterized in that made.

According to the present invention, a method of estimating roughness coefficient using field measurement data includes a range of minimum and maximum values for flow rate, friction slope, median particle size, average flow rate, and depth for the herbaceous plants, shrubs, and trees. It is characterized by.

In the method of calculating the roughness coefficient using field measurement data according to the present invention, the roughness coefficient of the natural river in the second step may be calculated using the following relational expression.

,

,

Where Q is the flow rate (㎥ / s), S _f is the friction slope, α _nat is a constant, and β _nat is the exponent.

The method of calculating the roughness coefficient using field measurement data according to the present invention is characterized in that in the third step, the roughness coefficient of the natural river is analyzed using a box-whisker quartile analysis method.

In the method of estimating the roughness coefficient using field measurement data according to the present invention, in the third step, the roughness coefficient of the natural river calculated in the second step and the parameter for the roughness coefficient (nQ ^1/8 / S _f ^1/6 ).

In the method of calculating the roughness coefficient using the field measurement data according to the present invention, the roughness coefficient of the vegetation stream in the second step is calculated using the following relational expression.

Where Q is the flow rate (m3 / s), S _f is the friction gradient, α _veg is a constant, and β _veg is the exponent.

The method of calculating the roughness coefficient using the field measurement data according to the present invention is characterized in that after the fourth step, a dimensionless shear rate for the relative diving ratio of the field measurement data is calculated using a regression analysis method.

Apparatus for estimating roughness coefficients using on-site data of natural and vegetation rivers according to the present invention is a device for estimating roughness coefficients using on-site data of natural and vegetation streams, including sand, gravel, pebble and A data input module for inputting field data including amber data on amber stones and vegetation data on herbs, shrubs and trees of vegetation streams; An illuminance coefficient calculation module for calculating an illuminance coefficient for the field measurement data inputted to the input module; An illuminance coefficient analysis module for analyzing the illuminance coefficient calculated by the illuminance coefficient calculation module; And an illuminance coefficient output module for outputting an illuminance coefficient analyzed by the illuminance coefficient analysis module.

Apparatus for estimating roughness coefficient using field measurement data according to the present invention further includes a dimensionless shear rate calculating module for calculating a dimensionless shear rate for the relative diving ratio of the field measurement data using a regression analysis method. It is done.

According to the method and apparatus for estimating the roughness coefficient using the field measurement data of the natural river and the vegetation stream of the present invention, the roughness coefficient of the stream flow is derived through the field measurement data, and the roughness coefficients for the riverbed data and the vegetation data are analyzed. By presenting the distribution range for each type and deriving a roughness coefficient relation function as a function of flow rate and friction inclination, there is an advantage that it can be usefully used in manual practice.

In addition, the present invention has the advantage that it can be usefully used in manual practice by inducing a dimensionless shear rate equation as a function of relative diving ratio in natural and vegetation streams.

1 is a block diagram showing an apparatus for estimating roughness coefficients using field measurement data of natural and vegetation streams according to the present invention.
2 is a flowchart illustrating a method of calculating roughness coefficients using field measurement data of natural and vegetation streams according to the present invention.
3 is a view showing an experimental example of the relationship analysis of the flow rate-roughness coefficient of the natural river induced by the regression analysis method according to the present invention.
4 is a view showing an experimental example of the relationship analysis of the friction gradient roughness coefficient of the natural river induced by the regression analysis method according to the present invention.
Figure 5 is an exemplary view showing the analysis of the roughness coefficient for the riverbed data of the natural river of the present invention.
Figure 6 is an exemplary view showing the analysis of parameters for riverbed data of natural rivers of the present invention.
7 is a view showing an experimental example of the relationship analysis of the flow rate-roughness coefficient of the vegetation stream induced by the curve fitting method according to the present invention.
8 is a diagram illustrating an experimental example of a relationship analysis of a flow rate-roughness coefficient of a vegetation stream induced by a regression analysis method according to the present invention.
9 is a diagram illustrating an experimental example of a relationship analysis of friction inclination-roughness coefficients of vegetation streams induced by a regression analysis method according to the present invention.
Figure 10 is an exemplary view showing the analysis of the roughness coefficient for vegetation data of the vegetation stream of the present invention.
11 is an exemplary view showing the analysis of the parameters for the vegetation data of the vegetation stream of the present invention.
11 is an exemplary view showing the relationship between the shear rate ratio to the relative diving ratio in the riverbed data of the natural river of the present invention.
12 is an exemplary diagram showing the relationship between the shear rate ratio to the relative diving ratio in the vegetation data of the vegetation stream of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a detailed description of preferred embodiments of the present invention will be given with reference to the accompanying drawings. In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Embodiments in accordance with the concepts of the present invention can make various changes and have various forms, so that specific embodiments are illustrated in the drawings and described in detail in this specification or application. It is to be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms of disclosure, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, the terms "comprises ",or" having ", or the like, specify that there is a stated feature, number, step, operation, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

1 is a block diagram showing an apparatus for estimating roughness coefficients using field measurement data of natural and vegetation streams according to the present invention, and FIG. 2 is a diagram showing the roughness coefficients using field measurement data of natural and vegetation streams according to the present invention. It is a flowchart which shows a calculation method.

Referring to the drawings, the apparatus 100 for estimating the roughness coefficient using field measurement data of natural rivers and vegetation streams includes a data input module 110, a roughness coefficient calculating module 120, a roughness coefficient analyzing module 130, and a relative Diving ratio US shear rate ratio calculation module 140 and roughness coefficient output module 150 may be included.

The data input module 110 receives field measurement data including riverbed data consisting of sand, gravel, pebbles, and amber stones of natural rivers, and vegetation data consisting of herbs, shrubs, and trees of vegetation streams (S101). The bed data consists of a range of minimum and maximum values for flow, friction slope, median diameter, average velocity and depth for sand, gravel, pebbles and amber stones, and vegetation data for flow, friction slope for herbaceous plants, shrubs and trees. It can consist of a range of minimum and maximum values for the median diameter, average velocity and depth.

The illuminance coefficient calculation module 120 may calculate an illuminance coefficient for the field measurement data of the natural river and the vegetation stream inputted to the data input module 110 (S102). In the illuminance coefficient calculation module 120, the illuminance coefficient may be derived from the following illuminance coefficient relational expression (Equation 1) for the field measurement data of the natural river.

[Formula 1]

,

,

Where Q is the flow rate (m3 / s), S _f is the friction gradient, α _nat is a constant, and β _nat is the exponent.

In addition, the illuminance coefficient calculation module 120 may derive the illuminance coefficient from the field measurement data of the vegetation stream through the following illuminance coefficient relational expression (Equation 2).

[Formula 2]

,

,

(Where Q is the flow rate (m 3 / s), S _f is the friction slope, α _veg is a constant, β _veg is an index)

The roughness coefficient analysis module 130 analyzes the roughness coefficient calculated by the roughness coefficient calculating module 120 through the respective roughness coefficient relational expressions (S103). Roughness coefficient analysis module 130, in order to facilitate understanding of the variability of the data, through the regression analysis and Box-Whisker quartile analysis, roughness coefficients for natural and vegetation streams the distribution of the intermediate characteristic in the roughness coefficient variable _{^{(nQ 1/8 / S f}} 1/6) can be analyzed. The data analyzed through the illuminance coefficient analysis module 130 is output through the illuminance coefficient output module 150 (S104).

The box-whisker method uses a box to display statistically analyzed data, with the bottom and top of the box always positioned at the first and third quarters, with 2 / in the middle of the box. Place the median, which is the quartile. The maximum and minimum values are at the bottom and top. Such analytical methods not only facilitate the statistical analysis of many data, but also facilitate the identification of data variability.

In addition, in the dimensionless shear rate calculation module 140 of the roughness coefficient calculating device 100 of the present invention, it is possible to calculate the relationship between the relative diving ratio and the shear rate ratio for the bed data and the vegetation data by using a regression analysis method.

Field measurement data of natural rivers and vegetation streams according to the present invention consisted of 2,614 pieces of data including 1,875 riverbed data and 739 vegetation data. These data were used to analyze the distribution of Manning's roughness coefficients for flow rate and friction inclination, to derive relations, and to derive relative diving ratio and shear velocity ratio relations.

Riverbed data are classified into sand, gravel, cobble, and boulder, and vegetation data are classified into grass, shrub, and tree. As shown in Table 1, these bed data and vegetation data are presented in the ranges of the minimum and maximum values of flow rate, friction slope, median diameter, average flow velocity, and depth.

[Table 1]

In the experimental example of the present invention using field measured data shown in Table 1, as well as the friction slope (S _f) and flow rate (Q) of the Manning roughness number (n), parameters _{^{(nQ 1/8 / S f}} 1 ^{/ 6} ) and the relationship between relative diving ratio and shear rate ratio (manning-Strickler relation) (h / d _s- V / u _* ), respectively.

In particular, the roughness coefficient (n) and the parameters are performed by box-whisker quartile analysis, and the quartile (%) analysis shows the minimum value of the top 1% of the target data as the bottom horizontal line. The median and above and below it are the top 25% and 75% values, representing the maximum value with 100% of the top horizontal line.

In addition, the dimensionless shear rate uses the following equation (Equation 3) to derive the roughness coefficient (n).

[Equation 3]

인 전단속도이고, ρ는 물의 밀도이다.)
(here,

Is the shear rate at which ρ is the density of the water.)

The dimensionless shear rate can be approximated by the following equation (Equation 4) at the hydraulically rough boundary without bed deformation, and the flow resistance of streams with rougher bed data than sand.

[Formula 4]

Here, k _s ' ≒ 3d ₉₀ or k _s ' ≒ 6d ₅₀ can be approximated, where k _s ' is the particle roughness and d ₅₀ and d ₉₀ correspond to 50% and 90% of the weight percent of the particles. the particle diameter, R _h represents a hydraulic radius.

Dimensional shear rate can be applied to a wide range of data of relative depth ratio (h / d ₅₀ ) for practical purposes, which can be approximated using the following equation (Equation 5).

[Formula 5]

Moreover, Manning - in the case of streaming cluster power function equation is n = 0.064d ₅₀ ^1/6 roughness meter number with respect to the center of the particle diameter d ₅₀ (Manning-Strickler) may be used in the following equation (Equation 6).

[Formula 6]

Therefore, the relational expression (Equation 4) at the hydraulically rough boundary can be used as the following relational expression (Equation 7).

[Equation 7]

Experimental Example 1 Roughness Coefficient of Natural River (n)

1,875 riverbeds of natural streams consist of sand, gravel, pebbles, and amber stones. Using these data, Manning's roughness coefficient relation can be derived by regression analysis as a function of flow rate Q and friction slope S _f , as shown in FIGS. 3 and 4.

와

인 멱함수 형으로 유도된다. 여기서, α_nat, β_nat는 자연하천의 매닝 조도계수 관계식에 대한 상수와 지수를 나타낸다.The roughness coefficient relation of manning in natural rivers is represented by the function of flow rate Q (m 3 / s) and friction slope S _f as shown in Table 2.

Wow

It is derived from the power function form. Where α _nat and β _nat represent constants and exponents for the Manning roughness coefficient relation of natural rivers.

[Table 2]

As shown in Table 2, the roughness coefficient derived from four bed data shows that the smaller the particle size, the smaller the coefficient of crystal coefficient. This may not be effective in terms of statistics because the distribution of hydraulic volume is quite large.However, the relationship between amber and pebble, except sand, is given by the planned flood volume due to precipitation and runoff, or by water slope or friction slope. In the state, it can be usefully used for calculating the roughness coefficient.

5 and 6 the box Manning roughness number (n) and the parameter _{^{(nQ 1/8 / S f}} 1/6) to 1,875 of data of the respective natural river - represents a whisker analysis results, the table the results of this analysis 3 is shown in more detail.

[Table 3]

As shown in Table 3, the box-whisker analysis results in natural river data for roughness coefficient (n) of FIG. The data for amber 0.30.3 and 53 amber stones were 0.023 to 0.444, and the data for 1,875 were 0.004 to 0.444, respectively.

Further, also in the light meter 6 (n) and the parameter (nQ ^1/8 / S _f ^1/6) as compared to the analysis results, the ratio of the average value and the upper / middle value (3/4) value / upper (1 These mean values were 1.3, 1.9, 1.6, and 4.3 at the ratio of / 4).

Experimental Example 2 Roughness Coefficient of Vegetation River (n)

The 739 vegetational data of vegetation streams consist of herbaceous plants, shrubs and trees. Using these data, Manning's roughness coefficient relation can be derived by regression analysis as a function of flow rate Q and friction slope S _f , as shown in FIGS. 7 and 8.

와

인 멱함수 형으로 유도된다. 여기서, α_veg, β_veg는 식생하천에 대한 매닝 조도계수 관계식의 상수와 지수를 각각 나타낸다.In the vegetation stream, Manning's roughness coefficient relational equation is expressed as Table 4, where the flow rate Q (m 3 / s) and the friction slope S _f are functions

Wow

It is derived from the power function form. Where α _veg and β _veg represent constants and exponents of Manning roughness coefficients for vegetation streams, respectively.

[Table 4]

As shown in Table 4, the roughness coefficient derived from the three vegetation data shows that the smaller the species, the smaller the coefficient of determination. This may be less effective in terms of statistics because of the large distribution range of hydraulics, but these relations may be used to calculate roughness coefficients given the planned flood volume due to precipitation and runoff, or in determining waterway or friction slopes in manual practice. It can be useful.

9 and 10 the box Manning roughness number (n) and the parameter _{^{(nQ 1/8 / S f}} 1/6) to 739 of data each vegetation rivers - represents a whisker analysis results, the table the results of this analysis 5 is shown in more detail.

[Table 5]

In the vegetation stream data of the roughness coefficient (n) of FIG. 9, the box-whisker analysis results are as shown in Table 5, and 0.012 to 0.250 for 281 herbaceous materials, 0.016 to 0.250 for 150 shrubs, and 0.018 for 308 trees. In the total of ˜0.310 and 739 data, it was 0.015 to 0.310.

Figure 10 roughness meter of the (n) and the parameter _{^{(nQ 1/8 / S f}} 1/6) as compared to the analysis results, the average value of the ratio and the upper / middle value (3/4) for the value / upper (1/4 In the ratio of), these mean values were 1.1, 1.6 and 1.1, 1.6, and showed the same ratio.

[Relationship of Dimensionless Shear Rate for Relative Diving Ratio]

11 and 12 are exemplary diagrams derived through a regression analysis of the relationship between the non-dimensional shear rate (V / v _* ) of the relative diving ratio (h / d ₅₀ ) of each of the natural and vegetation stream.

로 유도되었고, 도 12에 나타낸 바와 같이, 식생하천에서의 매닝-스트리클러 관계식은 절편이 5보다 약간 큰 5.3, 기울기는 1/6보다 약간 작은 1/8.3을 갖는

로 각각 유도되었다. 또한, 자연하천과 식생하천에서 상대 잠수비(h/d₅₀)에 대한 무차원 전단속도(V/v_*)와의 관계식은 실용 목적 및 수리학적 거친 경계인 식보다 우수한 값을 갖는

로 유도되었다.
As shown in FIG. 11, the Manning-Strickler relation in natural streams has an intercept of 5.4 with the intercept being slightly larger than 5, and the slope being 1/10 slightly smaller than 1/6.

As shown in FIG. 12, the Manning-Strickler relationship in vegetation streams has 5.3 with the intercepts slightly larger than 5 and the slope with 1 / 8.3 slightly less than 1/6.

To each. In addition, the relationship between the dimensionless shear rate (V / v _* ) for the relative diving ratio (h / d ₅₀ ) in natural and vegetation streams is better than that for practical purposes and hydraulic rough boundaries.

Was induced.

[Experiment result]

The data of natural rivers composed of sand, gravel, pebbles, and amber stones and the vegetation rivers composed of herbs, shrubs, and trees were analyzed to have manning roughness coefficients (n) as shown in Table 6. .

TABLE 6

Manning roughness coefficients (f) for these data range from 0.004 to 0.151 for 179 sands of natural rivers, 0.008 to 0.250 for 992 gravels, 0.05 to 0.327 for 651 pebble and 0.023 to 0.444 for 53 amber stones. And 1,875 data were 0.004 ~ 0.444.

Also, in vegetation streams, 0.011∼0.250 in 281 herbaceous materials, 0.016∼0.250 in 150 shrubs, 0.018∼0.310 in 308 arboretums, and 0.015∼0.310 in 739 total data.

Table 7 shows the box-whisker analysis of natural and vegetation streams.

[Table 7]

As shown in Table 7, the mean / median manning roughness coefficients and parameters, and the upper (3/4) and upper (1/4) quartile averages, differ slightly from natural rivers to 1.3, 1.9, 1.6, and 4.3. In vegetation streams, it is found that 1.3, 1.7, 1.1, and 1.6 are not significantly different.

As described above, according to the present invention, it is possible to derive the roughness coefficient of the stream flow through the field measurement data of the natural river and the vegetation stream, and also present the distribution range for each type by analyzing the roughness coefficients of the river data and the vegetation data. By deriving the roughness coefficient relation function as a function of flow rate and friction inclination, it can be usefully used for manual work.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. will be. Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.

100: roughness coefficient calculation device 110: data input module
120: roughness coefficient calculation module 130: roughness coefficient analysis module
140: dimensionless shear rate calculation module
150: roughness coefficient output module

Claims (19)

In the method of estimating roughness coefficient by using field measurement data of natural river and vegetation river,
Riverbed data consisting of the minimum and maximum ranges for the flow rate, friction slope, median particle diameter, average velocity, and depth of sand, gravel, pebble and amber stone of the natural stream, and for herbs, shrubs and trees of the vegetation stream A first step of inputting field measurement data including vegetation data including a range of minimum and maximum values for flow rate, friction slope, median diameter, average flow rate, and depth;
A second step of the illuminance coefficient calculating module calculating the illuminance coefficients for the natural river and the vegetation stream from the field measurement data inputted through the input module;
A third step of analyzing the roughness coefficients of the natural and vegetation streams calculated in the second step by using a roughness coefficient analysis module using a Box-Whisker quartile method; And
And a fourth step of outputting the respective roughness coefficients analyzed by the roughness coefficient output module.
In the second step, when calculating the roughness coefficient of vegetation stream, the roughness coefficient calculating module is represented by

Where Q is the flow rate (m3 / s), S _f is the friction gradient, α _veg is a constant, and β _veg is the exponent.
To calculate the roughness coefficient of the vegetation stream,
When calculating the roughness coefficient of natural river, the roughness coefficient calculating module is expressed as follows.

,

Where Q is the flow rate (㎥ / s), S _f is the friction slope, α _nat is a constant, and β _nat is the exponent.
Calculation method of roughness coefficient using field measurement data, characterized in that to calculate the roughness coefficient of the natural river using.
delete delete delete delete delete delete The method of claim 1,
The third step is analyzed by using the roughness coefficient of the natural river calculated in the second step and the parameter (nQ ^1/8 / S _f ^1/6 ) for this roughness coefficient Calculation method of roughness coefficient using
delete delete The method of claim 1,
The third step is analyzed by using the roughness coefficient of the vegetation stream calculated in the second step and the parameter (nQ ^1/8 / S _f ^1/6 ) for this roughness coefficient. Calculation method of roughness coefficient using
The method of claim 1,
And after the fourth step, calculating the dimensionless shear rate for the relative diving ratio of the field measurement data using a regression analysis method.
Apparatus for Estimating Roughness Coefficient Using Field Measurement Data of Natural River and Vegetation River,
Riverbed data consisting of the minimum and maximum ranges of flow, sand, gravel, pebble and amber stones in natural streams, frictional slopes, median particle diameters, average flow rates, and depths, and flow rates for vegetation, grasses and trees, A data input module for inputting field measurement data including vegetation data consisting of a range of minimum and maximum values for friction inclination, median particle size, average flow velocity, and depth;
An illuminance coefficient calculation module for calculating an illuminance coefficient of a natural river and a vegetation stream with respect to the field measurement data inputted to the data input module;
An illuminance coefficient analysis module analyzing an illuminance coefficient calculated through the illuminance coefficient calculation module using a box-whisker quartile method; And
And an illuminance coefficient output module configured to output an illuminance coefficient analyzed by the illuminance coefficient analysis module.
The illuminance coefficient calculation module,
The following relation when calculating the roughness coefficient of vegetation stream

Where Q is the flow rate (m3 / s), S _f is the friction gradient, α _veg is a constant, and β _veg is the exponent.
To calculate the roughness coefficient of the vegetation stream,
The following relation for calculating the roughness coefficient of natural rivers

,

Where Q is the flow rate (㎥ / s), S _f is the friction slope, α _nat is a constant, and β _nat is the exponent.
Estimate the roughness coefficient using the actual field data, characterized in that to calculate the roughness coefficient of the natural river using.
delete delete delete delete delete 14. The method of claim 13,
And a dimensionless shear rate calculating module for calculating a dimensionless shear rate for the relative diving ratio of the field measurement data using a regression analysis method.

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