Method and system for determining river dynamic roughness based on vegetation index and hydraulic parameters

Abstract

The application discloses a river dynamic roughness determination method and system based on a vegetation index and a hydraulic parameter, and relates to the technical field of river channel hydrodynamic simulation. The method comprises the following steps: setting a roughness analysis section in an experimental vegetation section and acquiring data; calculating the Manning roughness coefficient of the roughness analysis section per hour; calculating the monthly Manning roughness coefficient and the NDVI value of the experimental vegetation section; constructing a Manning roughness coefficient calculation formula; acquiring the data of a target river channel to be analyzed, calculating the monthly Manning roughness coefficient of each vegetation area and non-vegetation area, and simulating the water flow condition of the target river channel by using a two-dimensional hydrodynamic model. The application can realize real-time feedback and accurate quantification of the roughness with plant growth, comprehensively improve the adaptability, accuracy and reliability of hydrodynamic simulation in a complex environment, and thus effectively support fine management of water resources in a new era, scientific early warning of extreme flood and waterlogging events and sustainable protection of a river basin ecosystem.

Description

Technical Field

[0001] This application relates to the field of river hydrodynamic simulation technology, and in particular to a method and system for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters. Background Technology

[0002] In hydrodynamic numerical simulation, the Manning roughness coefficient is a core parameter that controls flow resistance, determines velocity distribution, inundation range, and flow capacity. The accuracy of its value directly affects the model's simulation accuracy of river hydrological processes, and plays a key role, especially in ecological flow assessment, flood risk prediction, and river management decision-making.

[0003] Currently, in engineering practice, the initial roughness values ​​of riverbeds and floodplains are generally determined by consulting roughness coefficient tables based on land use types, followed by model calibration using measured hydrological data. Existing technologies, such as the invention patent with publication number CN120176630A, disclose a method for determining the water level-discharge relationship of a river cross-section. This method first obtains the parameter information of the river cross-section, then generates a river cross-section map based on the parameter information; determines the characteristic parameters of the river cross-section based on the river cross-section map; then determines the Manning roughness coefficient for different river cross-sections; and finally determines the water level-discharge relationship based on the characteristic parameters and the Manning roughness coefficient. However, this table lookup method has significant limitations and is difficult to apply to the dynamic simulation needs of seasonally interrupted river channels.

[0004] (1) Ignoring the spatiotemporal dynamics of vegetation growth: Traditional methods often set a fixed roughness based on the vegetation cover state (such as tall herbs or shrubs) during the flood season, without considering the continuous process of vegetation succession from low and sparse to high and lush during the growing season (spring to autumn). In seasonal rivers, vegetation height, density, and biomass change significantly with hydrological rhythms, resulting in monthly dynamic fluctuations in riverbed roughness. Existing methods cannot capture such non-steady-state responses, causing systematic biases in the simulation results.

[0005] (2) Insufficient characterization of spatial heterogeneity: Seasonal rivers often span complex land use patterns, and the width of the river channel can vary by several times within a year. The vegetation patches are scattered and have blurred boundaries. Relying solely on a limited number of hydrological stations for overall calibration makes it difficult to assign detailed parameters to each vegetation patch, resulting in parameter distortion caused by "representing the whole area with a point".

[0006] (3) Qualitative and empirical parameter determination methods: Existing table lookup methods lack quantitative basis and have not established a physical correlation model between roughness and vegetation physiological indicators or remote sensing vegetation indices, resulting in strong subjectivity and poor portability of parameter assignment, making it difficult to extend to areas without measured data. The needs for seasonal river channel annual restoration of flow and ecological base flow protection are becoming increasingly urgent, and there is an urgent need for a dynamic roughness determination method that can dynamically respond to the vegetation growth process, is driven by remote sensing data, and has spatial universality, so as to achieve high-precision simulation of river flow processes. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for determining the dynamic roughness of rivers based on vegetation indices and hydraulic parameters. This method can dynamically calculate the Manning roughness coefficient of different regions and time periods of a river by combining the temporal variation characteristics of vegetation growth with the normalized difference vegetation index (NDVI) value obtained from remote sensing inversion and the measured hydraulic geometric parameters (such as water depth and channel width). This significantly improves the simulation accuracy of two-dimensional hydrodynamic models for river flow processes (including flow velocity, water depth, inundation range, and flow capacity). It provides scientific, quantifiable, and scalable technical support for the ecological flow regulation of seasonal rivers, the assessment of the entire river channel, the planning of floodplain management, flood risk early warning, and the refined management of water resources.

[0008] To achieve the above objectives, this application provides the following solution:

[0009] This invention provides a method for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters, comprising:

[0010] Select experimental vegetation sections in the target river channel vegetation area and set up several roughness analysis sections; obtain continuous 24-hour data and multi-band remote sensing data for each roughness analysis section;

[0011] Calculate the Manning roughness coefficient for each roughness analysis section hourly;

[0012] Calculate the monthly Manning roughness coefficient, channel width, and water depth of the experimental vegetation section;

[0013] Calculate the monthly NDVI value of the experimental vegetation based on multi-band remote sensing data;

[0014] Construct a formula for calculating the Manning roughness coefficient of the target river channel vegetation zone;

[0015] Obtain actual data of the target river channel, and use the Manning roughness coefficient calculation formula to calculate the monthly Manning roughness coefficient for each vegetation zone during the analysis period; query the roughness coefficient table to determine the monthly Manning roughness coefficient for the non-vegetated area of ​​the target river channel.

[0016] A two-dimensional hydrodynamic model of the target river channel is constructed to simulate the water flow conditions of the target river channel during the period to be analyzed.

[0017] Furthermore, the roughness analysis sections are set at equal intervals.

[0018] Furthermore, the Manning roughness coefficient for each roughness analysis section is calculated hourly using the Manning formula.

[0019] Furthermore, the calculation of the monthly Manning roughness coefficient, channel width, and water depth of the experimental vegetation section is specifically as follows: the hourly Manning roughness coefficient of each roughness analysis section on each measurement day is averaged to obtain the monthly Manning roughness coefficient of the corresponding roughness analysis section for each month; the monthly Manning roughness coefficient of all roughness analysis sections in each experimental vegetation section for the same month is averaged to obtain the monthly Manning roughness coefficient of the experimental vegetation section for each month; the monthly channel width and water depth of the experimental vegetation section are calculated in the same manner.

[0020] Furthermore, the calculation of the monthly NDVI value of the experimental vegetation based on multi-band remote sensing data specifically involves: extracting the near-infrared reflectance (NIR) and red chromatic reflectance (Red) from the multi-band remote sensing data, and calculating the NDVI value using the following formula:

[0021] ;

[0022] Furthermore, the specific formula for calculating the Manning roughness coefficient is as follows:

[0023] ;

[0024] In the formula, W is the width of the river channel, H is the water depth, and a, b, c, and d are fitting coefficients.

[0025] Furthermore, the acquisition of actual data of the target river channel and the calculation of the monthly Manning roughness coefficient for each vegetation zone during the analysis period using the Manning roughness coefficient calculation formula are specifically as follows: Based on land use data, all vegetation zones of the target river channel are determined; based on multi-band remote sensing data, the NDVI values ​​of all vegetation zones of the target river channel for each month during the analysis period are calculated, and the river channel inundation range and river channel width for each month during the analysis period are extracted; based on the river channel inundation range and topographic data of all vegetation zones of the target river channel, the water depth of all vegetation zones of the target river channel is determined; based on the NDVI values, river channel width, and water depth of all vegetation zones of the target river channel, the monthly Manning roughness coefficient for each vegetation zone is calculated using the Manning roughness coefficient calculation formula.

[0026] Furthermore, the method for determining the water depth of all vegetation zones in the target river channel is as follows: based on the inundation range of all vegetation zones in the target river channel, draw the inundation boundary line of each vegetation zone; randomly select a point on the inundation boundary line of each vegetation zone as the inundation boundary point of that vegetation zone, and determine the elevation of the inundation boundary point of that vegetation zone in combination with the topographic data of the target river channel; within the inundation range of each vegetation zone, take the minimum value of the topographic data as the riverbed elevation of that vegetation zone; calculate the difference between the elevation of the inundation boundary point of each vegetation zone and the riverbed elevation as the water depth of each vegetation zone.

[0027] Secondly, the present invention provides a system for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters, comprising the following modules:

[0028] The first acquisition module is used to select experimental vegetation sections in the target river vegetation area and set up several roughness analysis sections; acquire continuous 24-hour data and multi-band remote sensing data of each roughness analysis section.

[0029] The first calculation module calculates the hourly Manning roughness coefficient of each roughness analysis section; calculates the monthly Manning roughness coefficient, channel width, and water depth of the experimental vegetation section; and calculates the monthly NDVI value of the experimental vegetation based on multi-band remote sensing data.

[0030] The formula construction module is used to construct the calculation formula for the Manning roughness coefficient of the target river channel vegetation area;

[0031] The second acquisition module is used to acquire actual data of the target river channel;

[0032] The second calculation module is used to calculate the monthly Manning roughness coefficient for each vegetation zone during the analysis period based on the data obtained by the second acquisition module and using the Manning roughness coefficient calculation formula; and to query the roughness coefficient table to determine the monthly Manning roughness coefficient of the non-vegetated area of ​​the target river channel.

[0033] The model simulation module is used to construct a two-dimensional hydrodynamic model of the target river channel and simulate the water flow conditions of the target river channel during the period to be analyzed.

[0034] The beneficial effects of this invention are:

[0035] This invention proposes a method and system for determining the dynamic roughness of rivers based on vegetation indices and hydraulic parameters, which solves the three major bottlenecks of the traditional fixed roughness lookup method in seasonal river hydrodynamic simulation: (1) A dynamic response model of “NDVI-water depth-width-Manning roughness” is constructed, which breaks through the limitations of relying solely on a single vegetation index or empirical lookup table, and realizes the quantitative characterization of the synergistic effect of the roughness coefficient on the seasonal variation of vegetation growth and hydrological rhythm; (2) Through remote sensing inversion and fusion of topographic data, the monthly inundation boundary, water depth distribution and river width of each vegetation patch are accurately extracted, overcoming the spatial parameter distortion caused by “point-to-surface”, and realizing the spatial dynamic assignment of the whole area; (3) Subjective empirical assignment is abandoned, and a multivariate regression formula driven by measured data is established, so that the roughness coefficient has physical interpretability and regional portability, significantly improving the generalization ability of the hydrodynamic model in areas without hydrological stations. This invention can automatically adapt to the entire process of river flow interruption-resumption-flooding-receding throughout the year, which helps to improve the accuracy of flood evolution simulation, the reliability of ecological baseflow estimation, and the accuracy of inundation range prediction. It provides high-precision, quantifiable, and scalable technical support for river ecological restoration, ecological flow regulation, river and lake connectivity engineering design, and smart water conservancy decision-making. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of a method for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters.

[0037] Figure 2 A comparative schematic diagram of cross-sectional flow simulation results for key sections of the target river provided in the embodiments of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] This example uses a section of the Xiliao River main channel in Tongliao City as an example. This section of the river is approximately 10 km long, with dense reeds forming a part of the urban landscape. Currently, the Xiliao River is a seasonal river; during the spring flood (March) and summer flood (September), the flow rate in this section is high, exceeding 10 m³ / h. 3 / s; however, at other times, upstream water flow is very low, and this section of the river relies on water diversion projects to ensure flow, with a flow rate of less than 1m³ / s; 3 Accurate simulation of the hydrodynamic processes in this river section is of great significance for urban flood control, disaster reduction, and ecological restoration.

[0040] The accompanying diagram details a method for determining the dynamic roughness of a river based on vegetation indices and hydraulic parameters, comprising the following steps:

[0041] S1. Select an experimental vegetation section within any vegetation zone of the target river channel. Set up several roughness analysis cross-sections within this section. In this embodiment, the experimental vegetation section is 600m long, with a roughness analysis cross-section set every 200 meters, for a total of four cross-sections. Manually measure the river channel cross-section shape and gradient at each roughness analysis cross-section. The river channel cross-section is trapezoidal, with a bottom elevation of 176.48-176.30m and a gradient of 0.03%. On a clear day each month during the Sentinel2 satellite's transit over the river channel in 2024, select one day as the measurement day. Obtain hourly cross-sectional flow, hourly water depth, and hourly river channel width for 24 consecutive hours on each measurement day. Simultaneously, acquire multi-band remote sensing data for the corresponding measurement day for the experimental vegetation section.

[0042] S2. Based on the river cross-sectional shape and hourly water depth, calculate the hourly water-passing area and hourly hydraulic radius of each roughness analysis section. Based on the hourly cross-sectional flow, hourly water-passing area, hourly hydraulic radius and river gradient, calculate the hourly Manning roughness coefficient of each roughness analysis section using the Manning formula. The calculation formula is as follows.

[0043] S3. The hourly Manning roughness coefficients for each measurement day at each roughness analysis section are averaged to obtain the monthly Manning roughness coefficient for that section. Similarly, the monthly Manning roughness coefficients for all roughness analysis sections in each experimental vegetation segment are averaged to obtain the monthly Manning roughness coefficient for that segment. Specifically, in this embodiment, calculations show that the monthly Manning roughness coefficient for the experimental vegetation segment exhibits significant seasonal variations; for example, the Manning roughness coefficient is 0.25 in August (summer) and 0.018 in March (spring). The river width and water depth for each month in the experimental vegetation segment are calculated in the same manner.

[0044] S4. Calculate the NDVI value for each measurement day based on the multi-band remote sensing data of each measurement day in the experimental vegetation section, and use it as the NDVI value for the corresponding month of the experimental vegetation section.

[0045] Specifically, in this embodiment, the near-infrared reflectance (NIR) (Band 8) and red chromatic reflectance (Red) (Band 4) within the experimental vegetation zone are extracted from Sentinel2 multi-band remote sensing data, and the NDVI value is calculated using the following formula:

[0046] ;

[0047] The calculated NDVI values ​​for each month of 2024 in the experimental vegetation zone are shown in the table below. It can be seen that the NDVI values ​​do indeed change with the seasons, being higher in summer and lower in other seasons.

[0048] Table 1 shows the NDVI values ​​of the experimental vegetation sections in different months.

[0049]

[0050] S5. Based on the monthly Manning roughness coefficient, NDVI value, channel width, and water depth of each month in the experimental vegetation section, the following formula for calculating the Manning roughness coefficient of the target channel vegetation area is constructed through data fitting:

[0051] .

[0052] S6. Acquire Sentinel2 multiband remote sensing data, hourly cross-sectional flow data of the upper boundary of the target river channel during the summer flood season (July-October 2025), land use data, and topographic data of the target river channel during the summer flood season of 2025. Based on the land use data, determine all vegetation zones of the target river channel; based on the near-infrared reflectance (NIR) (Band8) and red chromatic reflectance (Red) (Band4) of the Sentinel2 multiband remote sensing data, calculate the NDVI values ​​of all vegetation zones of the target river channel for each month during the summer flood season of 2025, and extract the inundation range and width of the river channel for each month during the summer flood season of 2025 for all vegetation zones; based on the inundation range and topographic data of all vegetation zones of the target river channel, determine the water depth of all vegetation zones of the target river channel; based on the NDVI values, river width, and water depth of all vegetation zones of the target river channel, calculate the monthly Manning roughness coefficient for each vegetation zone using the Manning roughness coefficient calculation formula. Based on the land use data of the target river channel, the roughness coefficient table is consulted to determine the monthly Manning roughness coefficient of the non-vegetated area of ​​the target river channel.

[0053] The method for determining the water depth of all vegetation zones in the target river channel is as follows: based on the inundation range of all vegetation zones in the target river channel, draw the inundation boundary line of each vegetation zone; randomly select a point on the inundation boundary line of each vegetation zone as the inundation boundary point of that vegetation zone, and determine the elevation of the inundation boundary point of that vegetation zone in combination with the topographic data of the target river channel; within the inundation range of each vegetation zone, take the minimum value of the topographic data as the riverbed elevation of that vegetation zone; calculate the difference between the elevation of the inundation boundary point of each vegetation zone and the riverbed elevation as the water depth of each vegetation zone.

[0054] S7. Based on the topographic data of the target river channel, the monthly Manning roughness coefficient of all vegetated and non-vegetated areas of the target river channel, and the hourly cross-sectional flow data of the upper boundary of the target river channel during the summer flood season of 2025, a two-dimensional hydrodynamic model of the target river channel is constructed to simulate the water flow of the target river channel during the period to be analyzed.

[0055] Specifically, in this embodiment, the Bluekenue grid drawing program is used to draw the computational grid of the target river channel, and the topographic data of the target river channel is interpolated to the grid nodes to form the topographic grid of the target river channel; the monthly Manning roughness coefficients of all vegetated and non-vegetated areas of the target river channel are interpolated to the grid nodes to form the Manning roughness coefficient grid of the target river channel; the hourly cross-sectional flow data of the upper boundary of the target river channel during the summer flood season of 2025 are used as the upper boundary condition of the two-dimensional hydrodynamic model; the Telemac2D two-dimensional hydrodynamic model is used to simulate the changes in velocity, water level, and flow at key cross-sections of each computational grid node during the summer flood season of 2025.

[0056] The two-dimensional hydrodynamic model is as follows:

[0057] Continuity equation:

[0058] ;

[0059] Momentum equation:

[0060] ;

[0061] .

[0062] For a key cross-section in the middle reaches of the target river in this embodiment, during the summer flood season of 2025, the predicted effects of the measured cross-sectional flow, this embodiment (i.e., this technical solution), and the traditional solution (determining the roughness of the vegetation area by consulting the roughness coefficient table) were compared. For example... Figure 2 As shown. Because this embodiment better considers the current growth status of vegetation, water depth and channel width, the Manning roughness coefficient is more accurate and better reflects the obstructive effect of vegetation on water flow. Therefore, compared with the traditional scheme, the simulated cross-sectional flow rate in this embodiment is closer to the measured cross-sectional flow rate.

[0063] This embodiment also provides a system for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters, including the following modules:

[0064] The first acquisition module is used to select an experimental vegetation section in any vegetation area of ​​the target river channel, and set up several roughness analysis sections in the experimental vegetation section; select a measurement day each month to measure and obtain continuous 24-hour data of each roughness analysis section, including river channel cross-section shape, river channel gradient, hourly cross-sectional flow, hourly water depth, hourly river channel width, and acquire multi-band remote sensing data of the experimental vegetation section corresponding to the measurement day.

[0065] The first calculation module is used to calculate the hourly water-passing area and hourly hydraulic radius of each roughness analysis section based on the river cross-section shape and hourly water depth; to calculate the hourly Manning roughness coefficient of each roughness analysis section based on the hourly cross-sectional flow, hourly water-passing area, hourly hydraulic radius and river gradient; to calculate the monthly Manning roughness coefficient, river width and water depth of the experimental vegetation section; and to calculate the NDVI value of each measurement day based on the multi-band remote sensing data of each measurement day of the experimental vegetation section, which is used as the corresponding monthly NDVI value of the experimental vegetation section.

[0066] The formula construction module is used to construct the formula for calculating the Manning roughness coefficient of the target river vegetation area by data fitting based on the monthly Manning roughness coefficient, NDVI value, river width and water depth of the experimental vegetation section in each month.

[0067] The second acquisition module is used to acquire land use data, topographic data, multi-band remote sensing data of the target river channel, and hourly cross-sectional flow data of the upper boundary for the period to be analyzed.

[0068] The second calculation module is used to calculate the monthly Manning roughness coefficient for each vegetation zone during the analysis period based on the data obtained by the second acquisition module and using the Manning roughness coefficient calculation formula; and to determine the monthly Manning roughness coefficient for the non-vegetated area of ​​the target river by querying the roughness coefficient table according to the land use data of the target river.

[0069] The model simulation module is used to construct a two-dimensional hydrodynamic model of the target river based on the topographic data of the target river channel, the monthly Manning roughness coefficients of all vegetated and non-vegetated areas of the target river channel, and the hourly cross-sectional flow data of the upper boundary of the target river channel during the period to be analyzed, so as to simulate the water flow of the target river channel during the period to be analyzed.

[0070] The above description represents a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters, characterized in that, include: Select experimental vegetation sections in the target river channel vegetation area and set up several roughness analysis sections; Acquire continuous 24-hour data and multi-band remote sensing data for each roughness analysis section; Based on the hourly cross-sectional flow rate, hourly water flow area, hourly hydraulic radius and channel gradient, the Manning formula is used to calculate the hourly Manning roughness coefficient of each roughness analysis section. The monthly Manning roughness coefficient, channel width, and water depth of the experimental vegetation section were calculated. Specifically, the hourly Manning roughness coefficients of each roughness analysis section on each measurement day were averaged to obtain the monthly Manning roughness coefficient for each corresponding roughness analysis section. The monthly Manning roughness coefficients of all roughness analysis sections in each experimental vegetation section for the same month were averaged to obtain the monthly Manning roughness coefficient for each month of the experimental vegetation section. The monthly channel width and water depth of the experimental vegetation section were calculated in the same manner. Calculate the monthly NDVI value of the experimental vegetation based on multi-band remote sensing data; Based on the monthly Manning roughness coefficient, NDVI value, channel width, and water depth of the experimental vegetation section, a calculation formula for the Manning roughness coefficient of the target channel vegetation area was constructed through data fitting. The specific calculation formula for the Manning roughness coefficient is as follows: ； In the formula, W is the width of the river channel, H is the water depth, and a, b, c, and d are fitting coefficients; Obtain actual data of the target river channel, and use the Manning roughness coefficient calculation formula for the vegetation zone of the target river channel to calculate the monthly Manning roughness coefficient for each vegetation zone during the period to be analyzed.

2. The method for determining river dynamic roughness based on vegetation index and hydraulic parameters according to claim 1, characterized in that, The roughness analysis sections are set at equal intervals.

3. The method for determining river dynamic roughness based on vegetation index and hydraulic parameters according to claim 1, characterized in that, The calculation of the monthly NDVI value of the experimental vegetation based on multi-band remote sensing data specifically involves: extracting the near-infrared reflectance (NIR) and red chromatic reflectance (Red) from the multi-band remote sensing data, and calculating the NDVI value using the following formula: 。 4. The method for determining river dynamic roughness based on vegetation index and hydraulic parameters according to claim 1, characterized in that, The process of acquiring actual data of the target river channel and calculating the monthly Manning roughness coefficient for each vegetation zone during the analysis period using the Manning roughness coefficient calculation formula involves: determining all vegetation zones of the target river channel based on land use data; calculating the NDVI values ​​for each month of the analysis period for all vegetation zones of the target river channel based on multi-band remote sensing data, and extracting the river channel inundation range and river channel width for each month of the analysis period for all vegetation zones; and determining the water depth of all vegetation zones of the target river channel based on the river channel inundation range and the topographic data of the target river channel. Based on the NDVI values, channel width, and water depth of all vegetation zones in the target river channel, the monthly Manning roughness coefficient for each vegetation zone is calculated using the Manning roughness coefficient calculation formula.

5. The method for determining river dynamic roughness based on vegetation index and hydraulic parameters according to claim 4, characterized in that, The method for determining the water depth of all vegetation zones in the target river channel is as follows: based on the inundation range of all vegetation zones in the target river channel, draw the inundation boundary line of each vegetation zone; randomly select a point on the inundation boundary line of each vegetation zone as the inundation boundary point of that vegetation zone, and determine the elevation of the inundation boundary point of that vegetation zone in combination with the topographic data of the target river channel; within the inundation range of each vegetation zone, take the minimum value of the topographic data as the riverbed elevation of that vegetation zone; calculate the difference between the elevation of the inundation boundary point of each vegetation zone and the riverbed elevation as the water depth of each vegetation zone.

6. A system for determining the dynamic roughness of a river based on vegetation index and hydraulic parameters, comprising the following modules: The first acquisition module is used to select experimental vegetation sections in the target river vegetation area and set up several roughness analysis sections; acquire continuous 24-hour data and multi-band remote sensing data of each roughness analysis section. The first calculation module calculates the hourly Manning roughness coefficient of each roughness analysis section using the Manning formula, based on the hourly cross-sectional flow, hourly water flow area, hourly hydraulic radius and river gradient. The monthly Manning roughness coefficient, channel width, and water depth of the experimental vegetation section were calculated. The monthly NDVI value of the experimental vegetation was calculated based on multi-band remote sensing data. Specifically, the calculation of the monthly Manning roughness coefficient, channel width, and water depth of the experimental vegetation section involved: averaging the hourly Manning roughness coefficients for each measurement day at each roughness analysis section as the monthly Manning roughness coefficient for that section; averaging the monthly Manning roughness coefficients for the same month at all roughness analysis sections within each experimental vegetation section as the monthly Manning roughness coefficient for that section; and calculating the monthly channel width and water depth of the experimental vegetation section in the same manner. The formula construction module is used to construct a formula for calculating the Manning roughness coefficient of the target river vegetation area based on the monthly Manning roughness coefficient, NDVI value, channel width, and water depth of the experimental vegetation section in each month through data fitting; the specific formula for calculating the Manning roughness coefficient is as follows: ； In the formula, W is the width of the river channel, H is the water depth, and a, b, c, and d are fitting coefficients; The second acquisition module is used to acquire actual data of the target river channel; The second calculation module is used to calculate the monthly Manning roughness coefficient of each vegetation zone for the period to be analyzed based on the data obtained by the second acquisition module and using the Manning roughness coefficient calculation formula for the target river vegetation zone.

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