River course roughness determination method and system based on month and flow level distribution

Abstract

The invention provides a river course roughness determination method and system based on month and flow level distribution. The method comprises the following steps: collecting research river reach hydrology and river course topographic data; determining the monthly flow range within the year of the upstream control section of the research river reach, and dividing each monthly flow to obtain different flow levels; obtaining an upstream control section time period-flow process set and a downstream control section time period-water level process set, establishing a one-dimensional hydrodynamic model, and performing hydrological situation simulation on the research river reach; and calculating to obtain the river course roughness of the research river reach under different flow levels in different months in the year, obtaining the river course roughness of the research river reach corresponding to each flow level in each month in the year, and quickly determining the river course roughness of the research river reach according to the month and flow process in the simulation situation. According to the method, powerful support can be provided for rapid and accurate determination of the river course roughness during hydrological water environment element simulation of the research river reach, and then reliable water safety guarantee is provided for flood control of drainage basins and areas, water environment simulation and the like.

Description

Technical Field

[0001] The invention belongs to the technical field of water environment protection and river dynamics, and in particular relates to a method and system for determining river channel roughness based on month and flow level distribution. Background Art

[0002] The channel roughness is a comprehensive parameter that reflects the resistance of the river channel to the river. It is affected by meteorological conditions, river channel topography, shore vegetation, riverbed texture and other factors. It is also related to river morphology (gradient, etc.) and water flow characteristics (fluctuation, etc.). Especially for mountain rivers, within a certain flow level range, when the flow is large, the water volume in the river channel and the flooding range are large, the resistance to water flow movement in the river channel is enhanced, which is specifically reflected in the larger channel roughness. On the contrary, when the flow is small, the channel roughness is small. In river hydraulic calculation, the channel roughness is an important and sensitive hydraulic parameter. Its value has a great influence on the simulation results of hydrological and water environment elements. The runoff of mountain rivers varies greatly within a day or a year, and the channel roughness also varies greatly under different situations. Therefore, it is of great significance to scientifically and reasonably determine the channel roughness of mountain rivers. At present, the commonly used methods for determining the channel roughness are mainly empirical method, trial and error method and inversion algorithm.

[0003] The empirical method mainly obtains the roughness by looking up tables based on the river channel topography, riverbed material composition, etc., or determines the roughness by referring to the research results of similar basins or regions. The river channel roughness obtained by the empirical method often takes into account insufficient factors, has a limited scope of application, and cannot accurately reflect the roughness of the river section. The trial and error method generally preliminarily determines the roughness range based on experience, and then constructs the corresponding numerical model. The roughness is selected within the determined roughness range for simulation and trial calculation until the water level difference between the simulated value and the actual value meets the calculation accuracy requirements. To derive the river channel roughness by trial and error method, it is necessary to construct a suitable numerical model, and then select flow calculation conditions similar to the actual working conditions. By changing the roughness, manual debugging is continuously performed. The calculation amount and workload are large, and there are high requirements for the hydrological conditions required for the calculation. When the actual working conditions or river channel characteristics change, the river channel roughness needs to be re-determined.

[0004] The inversion algorithm is currently the most widely used, most efficient and most accurate method for determining the roughness of a river channel. It usually uses the roughness along the river channel as the decision variable, and the minimum difference between the simulated water level and the actual water level as the objective function, and uses an intelligent optimization algorithm to perform a single-objective optimization solution. With the rapid improvement of computer performance and in-depth research by relevant experts and scholars, intelligent optimization algorithms such as dynamic programming algorithms and simulated annealing algorithms have been successively applied to determine the roughness of a river channel. At the same time, considering that the roughness is affected by flow level, river vegetation, etc., some scholars have also conducted relevant research on determining the roughness of a river channel by layer and segment.

[0005] With regard to the existing technology, the method for determining the roughness of a river channel still has certain shortcomings. First, the simulated working conditions and the actual calculated working conditions need to be basically consistent in terms of flow level, and the use of simulated working conditions to determine the roughness of a river channel requires calibration and verification using multiple sets of data with the same flow level in order to be representative and typical. Secondly, the roughness of a river channel is not only related to the flow level of the river channel, but also to the month. That is, at the same flow level before and after the flood season, the roughness of the river channel will be different in different months (mainly reflected in the process of rising and falling water in the river channel), and the roughness of the river channel needs to be determined separately for different months. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention proposes a method and system for determining the channel roughness of mountainous rivers based on the distribution of months and flow levels when determining the channel roughness of mountainous rivers. The present invention can scientifically, reasonably, effectively and quickly determine the channel roughness under different flow levels in different months, providing reliable support for further accurate river hydrological and water environment simulation.

[0007] According to a first aspect of the present invention, a method for determining the channel roughness of a mountain river based on month and flow level distribution is provided, comprising:

[0008] Step S1: Collect hydrological and river topographic data of the study river section;

[0009] Step S2: Determine the annual monthly flow range of the upstream control section of the study river section based on the collected hydrological data, and divide each monthly flow to obtain different flow levels;

[0010] Step S3: for the monthly flow level of the upstream control section of the study river section, multiple groups of upstream control section time periods-flow processes within the flow level range are screened to form an upstream control section time period-flow process set, and correspondingly obtain a tributary port station time period-flow process set and a downstream control section time period-water level process set, establish a one-dimensional hydrodynamic model, and simulate the hydrological situation of the study river section;

[0011] Step S4: construct a single-objective optimization model to calculate the river roughness at different flow levels in different months of the year in the study river section;

[0012] Repeat steps S3 to S4 to obtain the channel roughness of the study river section corresponding to each flow level in each month of the year, and quickly determine the channel roughness of the study river section according to the month and flow process in the simulation scenario.

[0013] Based on the above technical solution, the present invention can also make the following improvements.

[0014] Optionally, the hydrological and river channel topographic data of the study river section include: long-series hydrological data of upstream and downstream control hydrological stations and tributary hydrological stations, measured topographic data of the river channel of the study river section, and river channel roughness value data of similar areas.

[0015] Optionally, the monthly flow range of the upstream control section of the study river section determined based on the collected hydrological data includes:

[0016] According to the collected hydrological data, the study river section has fewer tributaries and smaller flow. The average daily flow of the upstream control section and the average daily water level of the downstream control section in the dry season of the study river section in recent years were screened to determine the flow range of the upstream control section in the dry season of the study river section in recent years.

[0017] Optionally, dividing the monthly traffic to obtain different traffic levels includes:

[0018] Based on the hydrological data collected in recent years, the upper and lower limits of the flow rate of the upstream control section of the study river section are determined, that is, the maximum flow rate Q of the upstream control section max and minimum flow Q min , and the maximum flow Q in month i (i=1,2,...,12) i,max and minimum flow Q i,min ;

[0019] The monthly flow level of the upstream control section of the study river section is obtained, starting from the month with the smaller flow range, and is determined comprehensively based on the size of the flow range of that month and its proportion in the flow range of the upstream control section, the length of the study river section, and the law of flow process changes to form a flow level set for that month. Finally, the flow level set for each month of the year is obtained, that is, the flow level set for month i.

[0020] Optionally, the step of establishing a one-dimensional hydrodynamic model and simulating the hydrological situation of the studied river section includes:

[0021] A one-dimensional hydrodynamic model is established to form a set of upstream control section time period-flow process, a set of tributary mouth station time period-flow process and a set of downstream control section time period-water level process, thereby obtaining the simulated water level process of the downstream control section.

[0022] Optionally, the process of obtaining the simulated water level of the downstream control section includes:

[0023] For the jth flow level in month i, in the long series of hydrological data collected in month i, the lth group of upstream control section flow processes at the jth flow level are screened out to form a time period-flow process set of the jth flow level of the upstream control section, and the lth group of tributary mouth station flow processes and downstream control section water level processes at the jth flow level in month i under the corresponding scenario are obtained to form a tributary mouth station time period-flow process set and a downstream control section time period-water level process set.

[0024] Optionally, the single-objective optimization model is constructed to calculate the river roughness of the study river section at different flow levels in different months of the year, including:

[0025] A single-objective optimization model was constructed using a genetic algorithm. The flow rate at the upstream control section of the study river section and the inflow rate of the tributaries were used as the basis to simulate the water level at the downstream control section. The objective function was constructed by minimizing the sum of squares of the errors between the simulated water level and the measured water level at the downstream control section. The channel roughness of the study river section corresponding to a certain flow level in a certain month was calculated, and a three-dimensional scatter plot of month-flow level-channel roughness was drawn. Finally, the channel roughness of the study river section at different flow levels in different months of the year was obtained.

[0026] Optionally, for determining the channel roughness at the jth flow level in month i, the objective function of the single-objective optimization model is expressed as:

[0027]

[0028] In the formula, x i,j is the river roughness at the jth flow level in month i; z m,n and z m,m is the measured water level and simulated water level of the downstream control section at the kth flow level of the upstream control section of the lth group; α l is the proportion of the simulation results of the first group, α 1 +α 2 +...+α m =1.

[0029] Optionally, the method of quickly determining the channel roughness of the study river section according to the month and flow process in the simulation scenario includes:

[0030] Quickly determine the range of river roughness values ​​for the study section based on the months and flow processes in the simulation scenario;

[0031] According to the flow level range and month in the simulation scenario, as well as the channel roughness results of the study river section corresponding to different flow levels in different months, linear interpolation is used to quickly determine the channel roughness in the simulation scenario based on the average flow at the upper and lower boundaries of the flow level range.

[0032] According to a second aspect of the present invention, there is provided a system for determining the channel roughness of a mountain river based on month and flow level distribution, comprising:

[0033] The data collection module is used to collect hydrological and river topographic data of the study river section, including: long-series hydrological data of upstream and downstream control stations and tributary checkpoints, measured topographic data of the study river section, and river roughness data of similar areas;

[0034] The flow level division module is used to determine the annual monthly flow range of the upstream control section of the study river section based on the collected hydrological data, and divide each monthly flow to obtain different flow levels;

[0035] The hydrological situation simulation module is used to select multiple groups of upstream control section time periods and flow processes within the flow level range of the upstream control section of the study river section for the flow level of a certain month, form an upstream control section time period and flow process set, and obtain the corresponding tributary port station time period and flow process set and the downstream control section time period and water level process set, establish a one-dimensional hydrodynamic model, and simulate the hydrological situation of the study river section;

[0036] The channel roughness determination module uses a single-objective optimization model constructed by genetic algorithm to determine the channel roughness of the study river section under different flow level scenarios in different months. At the same time, it quickly determines the channel roughness of the study river section according to the month and flow process under the simulation scenario.

[0037] Technical effects and advantages of the present invention:

[0038] The present invention proposes a method and system for determining the roughness of a mountain river channel based on the distribution of months and flow levels. Based on the shortcomings of the existing methods for determining the roughness of a river channel, the method scientifically defines the flow levels of different months according to the basic conditions such as the hydrological characteristics, water system characteristics, and topographic conditions of the river section under study, combined with the flow process of the river section under study in different months. By coupling a one-dimensional hydrodynamic model and a single-objective optimization model, a genetic algorithm is used to solve the roughness of the river channel corresponding to different flow levels in different months in the river section under study, and a three-dimensional scatter plot of month-flow level-river channel roughness is drawn, which can provide strong support for the rapid and accurate determination of the roughness of the river channel when simulating the hydrological and water environment elements in the subsequent river section under study, and thus provide reliable water security for flood control and water environment simulation in the basin and region. It has the characteristics of rapid screening and accurate determination.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1A flow chart of a method for determining the roughness of a mountain river channel based on month and flow level distribution provided by an embodiment of the present invention;

[0041] Figure 2 The average daily flow rate of the upstream control section and the average daily water level of the downstream control section in March during the dry season in the past five years provided by the embodiment of the present invention;

[0042] Figure 3 The flow level of the upstream control section in March during the dry season in the past five years provided by the embodiment of the present invention is (989m 3 / s,1500m 3 / s) time period-flow process set and downstream control section time period-water level process set diagram;

[0043] Figure 4 A schematic diagram of a basic flow chart of a genetic algorithm provided by an embodiment of the present invention;

[0044] Figure 5 The embodiment of the present invention provides the channel roughness of the study river section at the upstream control section in March under different flow level scenarios. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] It is understandable that, based on the defects in the background technology, when determining the roughness of a mountain river channel, the embodiment of the present invention proposes a method for determining the roughness of a mountain river channel based on the month and flow level distribution, as follows: Figure 1 As shown, the method comprises the following steps:

[0047] Step 1: Collect hydrological and river topographic data of the study river section;

[0048] The collected hydrological and river channel topographic data of the study river section include: long series hydrological data such as flow, water level, flow velocity, etc. of upstream and downstream control hydrological stations and tributary hydrological stations, measured topographic data of the study river section, and river channel roughness value data of similar areas. Among them, the measured topographic data of the study river section includes underwater measured elevation data of the river channel; the river channel roughness value data of similar areas includes river channel roughness value data of upstream areas, adjacent basins or similar topographic and geomorphic features.

[0049] Step 2: Determine the monthly flow range of the upstream control section of the study river section according to the collected hydrological data, and divide each monthly flow to obtain different flow levels;

[0050] Specifically, the monthly flow range of the upstream control section of the study river section determined based on the collected hydrological data includes:

[0051] According to the collected hydrological data, the study section has fewer tributaries and smaller flow, and the average daily flow of the upstream control section and the average daily water level of the downstream control section in March during the dry season in the past five years were selected. Figure 2 As shown in the figure, the flow range of the upstream control section of the study river in March during the dry season in the past five years is [989m 3 / s,2500m 3 / s].

[0052] Considering the flow range in March of the study section and its proportion of the annual flow, the length of the study section, and the flow process change law of the upstream control section, the flow in March is divided into three flow levels, namely [989m 3 / s,1500m 3 / s), [1500m 3 / s,2000m 3 / s), [2000m 3 / s,2500m 3 / s].

[0053] For river channels in mountainous areas, the river topography generally does not change significantly due to siltation, development and utilization, so the length of the hydrological data series is generally the last three to five years.

[0054] The monthly traffic is divided into different traffic levels, including:

[0055] Determine the monthly flow range, including: determine the upper and lower limits of the flow of the upstream control section of the study river section based on the collected hydrological data in recent years, that is, the maximum flow Q of the upstream control section max and minimum flow Q min , and the maximum flow Q in month i (i=1,2,...,12) i,max and minimum flow Q i,min ;

[0056] Scientifically and reasonably divide the flow range, including: obtaining the monthly flow level of the upstream control section of the study river section, starting from the month with the smallest flow range, and comprehensively determining the flow level set of the month based on the size of the flow range of the month and the proportion of the flow range in the upstream control section, the length of the study river section, the law of flow process change, etc., and finally obtaining the flow level set of each month of the year, that is, the flow level set of month i, which is expressed as

[0057] {[Q i,min ,Q i,1 ), [Q i,1 ,Q i,2 )......[Q i,j-1 ,Q i,max ]},(j=1,2,...n)

[0058] Where j is the number of flow levels in the upstream control section in month i, Q i,j-1 represents the lower limit of the flow rate of the jth flow level in month i, and also the upper limit of the flow rate of the j-1th flow level in that month. i,min =Q i,0 ,Q i,max =Q i,j .

[0059] Step 3: For the monthly flow level of the upstream control section of the study river section, multiple groups of upstream control section time periods-flow processes within this flow level range are selected to form an upstream control section time period-flow process set, and the corresponding tributary port station time period-flow process set and downstream control section time period-water level process set are obtained to establish a one-dimensional hydrodynamic model and simulate the hydrological situation of the study river section;

[0060] Specifically consider the flow level in March [989m 3 / s,1500m 3 / s), filter the upstream control section time period-flow process set and the downstream control section time period-water level process set within this flow level range, that is, the upstream control section flow process and the downstream control section water level process in the two time periods from March 19 to 31 of the first year and from March 8 to 24 of the second year are as follows: Figure 3 shown.

[0061] A one-dimensional hydrodynamic model is established to form the upstream control section time period-flow process set, the tributary mouth station time period-flow process set and the downstream control section time period-water level process set. The main work contents include:

[0062] For the jth flow level in month i [Q i,j-1 ,Q i,j ), in the i-th month of the long series of hydrological data collected, the flow process of the l-th group of upstream control sections at the j-th flow level is selected, that is, [(t i,j,l,1 ,q 上i,j,l,1 ),(t i,j,l,2 ,q 上i,j,l,2 ),...,(t i,j,l,k ,q 上i,j,l,k )], and form the time period-flow process set of the jth flow level of the upstream control section. Among them, t i,j,l,k and q上i,j,l,k They represent the corresponding flow at the kth time and the kth time in the flow process of the jth flow level and the lth group of the upstream control section in month i. Similarly, the flow process of the tributary mouth station and the water level process of the downstream control section at the jth flow level and the lth group of the tributary in month i under the corresponding scenario are obtained, respectively [(t i,j,l,1 ,q 支i,j,l,1 ),(t i,j,l,2 ,q 支i,j,l,2 ),...,(t i,j,l,k ,q 支i,j,l,k )] and [(t i,j,l,1 ,z 支i,j,l,1 ),(t i,j,l,2 ,z 支i,j,l,2 ),...,(t i,j,l,k ,z 支i,j,l,k )], and form a set of flow process at the tributary mouth station time period and a set of water level process at the downstream control section time period.

[0063] In this embodiment, the one-dimensional hydrodynamic model can be described by the Saint-Venant equations, whose continuity equation and motion equation are respectively:

[0064]

[0065] Where: Z is the water level, m; Q is the flow, m 3 / s; A is the cross-sectional area of ​​water flow, m 2 ; B is the width of the water surface, m; L q is the lateral inflow per unit river length, m 2 / s; g is the acceleration due to gravity, m / s 2 ; x is the distance along the river, m; S f is the hydraulic gradient.

[0066] Step 4: Construct a single-objective optimization model to calculate the channel roughness of the study river section corresponding to a certain flow level in a certain month;

[0067] The single-objective optimization model is constructed to calculate the river roughness of the research river section corresponding to a certain flow level in a certain month, specifically including:

[0068] Genetic algorithm is used to build a single-objective optimization model. The basic process of genetic algorithm is as follows: Figure 4 As shown. Based on the flow of the upstream control section and the inflow of the tributaries in the study section, the water level of the downstream control section is simulated, and the objective function is constructed by minimizing the sum of squares of the errors between the simulated water level and the measured water level in the downstream control section. The river roughness of the study section corresponding to a certain flow level in a certain month is calculated.

[0069] For determining the channel roughness at the jth flow level in month i, the objective function of the single-objective optimization model can be described as:

[0070]

[0071] In the formula, x i,j is the river roughness at the jth flow level in month i; z m,n and z' m,m is the measured water level and simulated water level of the downstream control section at the kth flow level of the upstream control section of the lth group; α l is the proportion of the simulation results of the first group, α 1 +α 2 +...+α m =1.

[0072] To further improve the computational efficiency and simulation accuracy, the constraints can be described as:

[0073] Constraints on river channel roughness value:

[0074] x min ≤x i,j ≤x max

[0075] In the formula, x min and x max They are the minimum and maximum values ​​of the river roughness, respectively, which are determined based on historical river roughness values ​​and river roughness value data in similar areas.

[0076] Error constraints between measured and simulated water levels at the downstream control section:

[0077] abs(z l,k -z l ' ,k )≤θ i,j

[0078] In the formula, θ i,j It is the water level error limit of the downstream control section at the jth flow level in month i, which is determined according to the size and range of the flow level of the upstream control section and the length of the studied river section.

[0079] In this embodiment, the one-dimensional hydrodynamic model and the single-objective optimization model are coupled, and the intelligent optimization algorithm genetic algorithm is used to solve the flow level in March [989m 3 / s,1500m 3 / s) situation.

[0080] Set the weights of the two groups of simulation results to be the same, that is, α 1 =α 2 =0.5,

[0081] Referring to previous research results and roughness value data of similar areas, the initial value range of river roughness is set to [0.035, 0.07]. At the same time, it is determined that the water level error of the downstream control section does not exceed 0.2m.

[0082] The model was used to calculate the flow level in March [989m 3 / s,1500m 3 / s) is 0.04512.

[0083] In addition, for other months, a single-objective optimization model was constructed using a genetic algorithm to obtain the channel roughness of the study river section corresponding to each flow level in each month of the year, and a three-dimensional scatter plot of month-flow level-channel roughness was drawn, that is, the channel roughness of the study river section at different flow levels in different months of the year. This can provide strong support for the rapid and accurate determination of the channel roughness when simulating the hydrological and water environment elements of the study river section in the subsequent period.

[0084] Step 5, repeat steps 3 to 4 to obtain the channel roughness of the study river section corresponding to each flow level in each month of the year. On this basis, and according to the month and flow process in the simulation scenario, quickly determine the channel roughness of the study river section.

[0085] The method of quickly determining the channel roughness of the study river section according to the month and flow process under the simulation scenario includes:

[0086] Quickly determine the range of river roughness values ​​for the study section based on the months and flow processes in the simulation scenario;

[0087] According to the flow level range and month in the simulation scenario, as well as the channel roughness results of the study river section corresponding to different flow levels in different months, linear interpolation is used to quickly determine the channel roughness in the simulation scenario based on the average flow at the upper and lower boundaries of the flow level range.

[0088]

[0089] Where: x i,模拟 is the river roughness under the simulated scenario in month i, q i,模拟 is the average flow rate of the upper and lower boundaries in the simulation scenario in month i, q i,j 、x i,j are the average upper and lower boundary flow and river roughness of the jth flow level in month i, respectively, i,j+1 、x i,j+1 are the average upper and lower boundary flows and the river roughness of the j+1th flow level in month i, respectively.

[0090] In summary, the above steps 3 and 4 are adopted, such as Figure 5 The figure shows the channel roughness of the upstream control section under different flow levels in March. Figure 5As shown, the flow level in March is calculated as [1500m 3 / s,2000m 3 / s) and [2000m 3 / s,2500m 3 / s) were 0.05327 and 0.05722 respectively. Repeat steps 2 to 4 to calculate the channel roughness of the study section under different flow levels in the remaining 11 months.

[0091] To quickly determine the March [1000m 3 / s,2000m 3 / s) flow level scenario, the channel roughness of the river section is studied, then according to the flow level [989m 3 / s,1500m 3 / s) and [1500m 3 / s,2000m 3 / s) and the corresponding river roughness, we can calculate the March [1000m 3 / s,2000m 3 / s) flow level scenario, the channel roughness of the studied river section is 0.04924.

[0092] According to a second aspect of the present invention, there is provided a system for determining river channel roughness based on month and flow level distribution, comprising:

[0093] The data collection module is used to collect hydrological and river topographic data of the study river section, including: long-series hydrological data of upstream and downstream control stations and tributary checkpoints, measured topographic data of the study river section, and river roughness data of similar areas;

[0094] The flow level division module is used to determine the annual monthly flow range of the upstream control section of the study river section based on the collected hydrological data, and divide each monthly flow to obtain different flow levels;

[0095] The hydrological situation simulation module is used to select multiple groups of upstream control section time periods and flow processes within the flow level range of the upstream control section of the study river section for the flow level of a certain month, form an upstream control section time period and flow process set, and obtain the corresponding tributary port station time period and flow process set and the downstream control section time period and water level process set, establish a one-dimensional hydrodynamic model, and simulate the hydrological situation of the study river section;

[0096] The channel roughness determination module uses a single-objective optimization model constructed by genetic algorithm to determine the channel roughness of the study river section under different flow level scenarios in different months. At the same time, it quickly determines the channel roughness of the study river section according to the month and flow process under the simulation scenario.

[0097] It can be understood that the river channel roughness determination system based on month and flow level distribution provided by the present invention corresponds to the river channel roughness determination method based on month and flow level distribution provided by the aforementioned embodiments. The relevant technical features of the river channel roughness determination system based on month and flow level distribution can refer to the relevant technical features of the river channel roughness determination method based on month and flow level distribution, which will not be repeated here.

[0098] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0099] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining river channel roughness based on month and flow level distribution, characterized in that: The following steps are involved: Step S1: Collect hydrological and river topographic data of the study river section; Step S2: Determine the annual monthly flow range of the upstream control section of the study river section based on the collected hydrological data, and divide each monthly flow to obtain different flow levels; Step S3: for the monthly flow level of the upstream control section of the study river section, multiple groups of upstream control section time periods-flow processes within the flow level range are screened to form an upstream control section time period-flow process set, and correspondingly obtain a tributary port station time period-flow process set and a downstream control section time period-water level process set, establish a one-dimensional hydrodynamic model, and simulate the hydrological situation of the study river section; Step S4: construct a single-objective optimization model to calculate the river roughness at different flow levels in different months of the year in the study river section; Repeat steps S3 to S4 to obtain the channel roughness of the study river section corresponding to each flow level in each month of the year, and quickly determine the channel roughness of the study river section according to the month and flow process in the simulation scenario.

2. A method for determining river channel roughness based on month and flow level distribution according to claim 1, characterized in that: The hydrological and river channel topographic data of the study river section include: long-series hydrological data of upstream and downstream control hydrological stations and tributary mouth hydrological stations, measured topographic data of the study river section, and river channel roughness value data in similar areas.

3. A method for determining river channel roughness based on month and flow level distribution according to claim 1, characterized in that: The monthly flow range of the upstream control section of the study river section determined based on the collected hydrological data includes: According to the collected hydrological data, the study river section has fewer tributaries and smaller flow. The average daily flow of the upstream control section and the average daily water level of the downstream control section in the dry season of the study river section in recent years were screened to determine the flow range of the upstream control section in the dry season of the study river section in recent years.

4. A method for determining river channel roughness based on month and flow level distribution according to claim 3, characterized in that: The monthly traffic is divided into different traffic levels, including: Based on the hydrological data collected in recent years, the upper and lower limits of the flow rate of the upstream control section of the study river section are determined, that is, the maximum flow rate Q of the upstream control section max and minimum flow Q min , and the maximum flow Q in month i (i=1,2,...,12) i,max and minimum flow Q i,min ; According to the monthly flow level of the upstream control section of the study river section, starting from the month with the smaller flow range, and based on the size of the flow range of that month and its proportion in the flow range of the upstream control section, the length of the study river section, and the law of flow process changes, the flow level set for that month is formed, and finally the flow level set for each month of the year is obtained, that is, the flow level set for month i.

5. A method for determining river channel roughness based on month and flow level distribution according to claim 1, characterized in that: The establishment of a one-dimensional hydrodynamic model to simulate the hydrological situation of the study river section includes: A one-dimensional hydrodynamic model is established to form a set of upstream control section time period-flow process, a set of tributary mouth station time period-flow process and a set of downstream control section time period-water level process, thereby obtaining the simulated water level process of the downstream control section.

6. A method for determining river channel roughness based on month and flow level distribution according to claim 5, characterized in that: The process of obtaining the simulated water level of the downstream control section includes: For the jth flow level in month i, in the long series of hydrological data collected in month i, the lth group of upstream control section flow processes at the jth flow level are screened out to form a time period-flow process set of the jth flow level of the upstream control section, and the lth group of tributary mouth station flow processes and downstream control section water level processes at the jth flow level in month i under the corresponding scenario are obtained to form a tributary mouth station time period-flow process set and a downstream control section time period-water level process set.

7. A method for determining river channel roughness based on month and flow level distribution according to claim 1, characterized in that: The single-objective optimization model is constructed to calculate the river roughness under different flow levels in different months of the year in the study section, including: A single-objective optimization model was constructed using a genetic algorithm. The flow rate at the upstream control section of the study river section and the inflow rate of the tributaries were used as the basis to simulate the water level at the downstream control section. The objective function was constructed by minimizing the sum of squares of the errors between the simulated water level and the measured water level at the downstream control section. The channel roughness of the study river section corresponding to a certain flow level in a certain month was calculated, and a three-dimensional scatter plot of month-flow level-channel roughness was drawn. Finally, the channel roughness of the study river section at different flow levels in different months of the year was obtained.

8. A method for determining river channel roughness based on month and flow level distribution according to claim 7, characterized in that: For determining the river roughness at the jth flow level in month i, the objective function of the single-objective optimization model is expressed as: In the formula, x i,j is the river roughness at the jth flow level in month i; z m,n and z' m,m is the measured water level and simulated water level of the downstream control section at the kth flow level of the upstream control section of the lth group; α l is the proportion of the simulation results of the first group, α1+α2+...+α m =1.

9. A method for determining river channel roughness based on month and flow level distribution according to claim 1, characterized in that: The method of quickly determining the channel roughness of the study river section according to the month and flow process under the simulation scenario includes: Quickly determine the range of river roughness values ​​for the study section based on the months and flow processes in the simulation scenario; According to the flow level range and month in the simulation scenario, as well as the channel roughness results of the study river section corresponding to different flow levels in different months, linear interpolation is used to quickly determine the channel roughness in the simulation scenario based on the average flow at the upper and lower boundaries of the flow level range.

10. A system for determining river channel roughness based on month and flow level distribution, characterized in that: include: The data collection module is used to collect hydrological and river topographic data of the study river section, including: long-series hydrological data of upstream and downstream control stations and tributary checkpoints, measured topographic data of the study river section, and river roughness data of similar areas; The flow level division module is used to determine the annual monthly flow range of the upstream control section of the study river section based on the collected hydrological data, and divide each monthly flow to obtain different flow levels; The hydrological situation simulation module is used to select multiple groups of upstream control section time periods and flow processes within the flow level range of the upstream control section of the study river section for the flow level of a certain month, form an upstream control section time period and flow process set, and obtain the corresponding tributary port station time period and flow process set and the downstream control section time period and water level process set, establish a one-dimensional hydrodynamic model, and simulate the hydrological situation of the study river section; The channel roughness determination module uses a single-objective optimization model constructed by genetic algorithm to determine the channel roughness of the study river section under different flow level scenarios in different months. At the same time, it quickly determines the channel roughness of the study river section according to the month and flow process under the simulation scenario.

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