A method, device and storage medium for surface runoff concentration calculation

Abstract

The present invention discloses a method, device and storage medium for surface runoff concentration calculation. The surface runoff concentration calculation method includes: obtaining medium and low-resolution DEM data and high-resolution land cover data of a basin, and calculating the farmland proportion of each grid cell; obtaining the ridge height data, and calculating the ridge interception capacity of each grid cell in the basin; determining the ridge interception amount of each grid within a time period and the cumulative ridge interception amount at the end of the time period based on iterative operation; constructing a grid-based Muskingum concentration calculation method considering ridge interception, and performing iterative operation according to the calculation order of the grids to obtain the surface runoff outflows of each grid at the end of the time period, wherein the surface runoff outflow of the last grid is the surface runoff at the basin outlet. The present invention realizes the refined simulation of the surface runoff concentration process affected by the ridge interception effect at both the time and space levels, and improves the accuracy of flood simulation and forecasting.

Description

Technical Field

[0001] The present invention belongs to the field of hydrological forecasting, and particularly relates to a surface runoff concentration calculation method considering the interception effect of ridge on surface runoff. Background Art

[0002] Flood forecasting is an important non-engineering measure for flood control and disaster reduction and water resources management. Accurate flood forecasting is of great significance for reducing disaster losses and optimizing water conservancy dispatching. Distributed hydrological models can more precisely simulate hydrological processes by considering the spatial heterogeneity of the underlying surface, and have become an important tool for flood forecasting. In addition, the heterogeneity of farmland spatial distribution (such as the size and shape of fields, ridge layout, etc.) significantly affects the generation and concentration processes of surface runoff. As a common geomorphic feature in farmland, ridges have the effect of intercepting surface runoff, which will change the flow path and concentration time, thus affecting the flood process at the basin outlet. In the North China Plain of China, ridge interception is one of the important factors for the significant reduction of river runoff in the plain. However, existing distributed hydrological models (such as SWAT, HEC-HMS, grid Xin'anjiang model, etc.) usually regard farmland as a homogeneous unit, ignore the interception effect of ridges, or only approximately process it through rough parameterization methods, and fail to fully describe this influence spatially, resulting in insufficient flood forecasting accuracy. Although existing studies (such as the patent "A high spatio-temporal resolution paddy field runoff prediction method based on remote sensing and water balance") have begun to focus on the hydrological effects of ridges, these studies are mostly limited to the runoff generation link, only considering the influence of ridges on the runoff generation amount of the current grid, and failing to capture the interception effect of ridges on the surface runoff from upstream grids during the concentration process, and unable to achieve refined simulation of the concentration process. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a surface runoff concentration calculation method, device and storage medium that can effectively improve the accuracy of flood simulation and forecasting.

[0004] To achieve the above object, the present invention specifically adopts the following technical solutions:

[0005] The present invention first provides a concentration calculation method, characterized by including the following steps:

[0006] Step 1, obtain medium and low-resolution DEM data and high-resolution land cover data of the basin, take each grid cell of the DEM as a calculation unit, and calculate the farmland proportion of each grid cell;

[0007] Step 2, obtain ridge height data, and calculate the ridge interception capacity of each grid cell in the basin based on the farmland proportion and ridge height of the grid cell;

[0008] Step 3, estimate the initial ridge interception amount of each grid cell based on soil moisture data;

[0009] Step 4: Based on the ridge interception capacity and the initial ridge interception volume of each grid cell in the basin, determine the ridge interception volume of each grid during the time period and the cumulative ridge interception volume at the end of the time period through iterative operations.

[0010] Step 5: Based on the ridge interception volume during the time period, construct a rasterized Muskingum flow concentration calculation method considering ridge interception; perform iterative operations according to the calculation order of the grids to obtain the overland runoff discharge of each grid at the end of the time period, where the overland runoff discharge of the last grid is the overland runoff at the basin outlet.

[0011] Ridge interception capacity: The ridge interception capacity characterizes the interception ability of the ridge for overland runoff. Assuming that the ridge height within the grid is consistent, the ridge interception capacity is positively correlated with the proportion of farmland within the grid. During the overland flow concentration process, when the cumulative overland inflow of the grid is greater than the ridge interception capacity, the excess part forms the overland runoff discharge and participates in the overland flow concentration calculation of the adjacent downstream grid; when the cumulative overland inflow of the grid is less than the ridge interception capacity, the current grid will not form an effective overland runoff discharge.

[0012] In Step 2, the specific algorithm for calculating the ridge interception capacity of each grid calculation unit in the basin is as follows: In the formula, is the ridge interception capacity of the th grid, in mm; is the proportion of farmland in the th grid; is the ridge height, in mm.

[0013] In Step 1, the proportion of farmland in the grid is obtained from medium- and low-resolution DEM data and high-resolution land cover data: In the formula, is the grid area, in km 2 ; is the area of farmland in the th grid, in km 2 .

[0014] Initial ridge interception volume: The amount of overland water intercepted by the ridge in the grid cell before a flood occurs, which can also be regarded as the accumulated water volume.

[0015] In Step 3, the specific algorithm for estimating the initial ridge interception volume of each grid cell is as follows: In the formula, is the Initial ridge interception volume of each grid, mm; and are the initial soil water content and saturated water content of the th grid, respectively; is the conversion coefficient, generally taken as 0.1 - 0.2. The initial soil water content can be obtained through continuous simulation of the hydrological model or inversion of remote sensing images.

[0016] In step 4, the ridge interception volume of each grid within the time period is solved by the following formula: In the formula, is the ridge interception volume of the th grid within the current time period, represents the total inflow of surface runoff of the th grid at the end of the current time period (i.e., at the moment), including the surface runoff outflows from all adjacent upstream grids plus the surface runoff generated by the current grid; is the time step; is the cumulative ridge interception volume of the th grid at the beginning of the current time period (i.e., at the moment). The specific algorithm is: In the formula, is the ridge interception volume of the th grid from the first time period (from the 0 moment to the moment) to the previous time period (from the moment to the moment). At this time, the cumulative ridge interception volume of each grid at the end of the time period can be expressed as: In the formula, is the cumulative ridge interception volume of the th grid at the end of the current time period.

[0017] In step 5, the specific algorithm for the surface runoff outflow of each grid at the end of the time period is: In the formula, and represent the surface runoff outflows of the th grid at the end of the current time period (i.e., at the moment) and at the beginning of the current time period (i.e., at the moment), respectively; and represent the surface runoff outflows of the the moment) and the beginning of the current time period (i.e., the moment) the total inflow of surface runoff of the grid, including the surface runoff outflows of all adjacent upstream grids plus the surface runoff generated by the current grid; and are respectively the ridge interception amounts of the th grid within the current time period and the previous time period; is the time step; is the grid area; and are surface runoff concentration parameters, obtained by calibrating parameters according to the measured flood process; , and are respectively functions of and , and the sum of the three is 1. The calculation order of the grids is determined by the upstream and downstream relationship. The calculation order of the uppermost upstream grid is 1, and the calculation order of the grid at the basin outlet is the largest. The iterative calculation starts from the grid with the calculation order of 1 and ends at the grid at the basin outlet. Therefore, the surface runoff outflow of the last grid is the surface runoff at the basin outlet.

[0018] The present invention also provides a surface runoff concentration calculation device, including a processor and a memory; a program or instruction is stored in the memory, and the program or instruction is loaded and executed by the processor to implement the steps of the surface runoff concentration calculation.

[0019] The present invention also provides a computer-readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the surface runoff concentration calculation method are implemented.

[0020] Advantages of the present invention:

[0021] The present invention considers a method for calculating the surface runoff concentration considering the influence of ridge interception. First, medium and low-resolution DEM data and high-resolution land cover data of the basin are obtained. Each grid cell of the DEM is used as a calculation unit to calculate the proportion of farmland in each grid cell. Then, the ridge height data is obtained, and based on the proportion of farmland in the grid cell and the ridge height, the ridge interception capacity of each grid cell in the basin is calculated. After that, based on the soil moisture data, the initial ridge interception amount of each grid cell is estimated. According to the ridge interception capacity and the initial ridge interception amount of each grid cell in the basin, the ridge interception amount of each grid during the time period and the cumulative ridge interception amount at the end of the time period are determined based on iterative operations. Finally, based on the ridge interception amount during the time period, a rasterized Muskingum concentration calculation method considering ridge interception is constructed. Through iterative operations according to the calculation order of the grids, the surface runoff outflows of each grid at the end of the time period are obtained, and the surface runoff outflow of the last grid is the surface runoff at the basin outlet. The present invention proposes a method for calculating the sub-flood concentration of grid cells based on the ridge interception amount, which considers the influence of ridge interception on slope concentration, solves the problem that the existing concentration calculation methods cannot well reflect the ridge interception effect, and can effectively improve the accuracy of flood simulation and forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 FIG. is a schematic flow chart of the surface runoff concentration calculation method considering ridge interception provided by the present invention;

[0023] Figure 2 FIG. is the ridge interception capacity of each grid in the basin in a specific embodiment;

[0024] Figure 3 FIG. is the relative error of the peak flow of the simulation of 12 flood events by the grid Xin'anjiang - surface and underground dual artificial regulation distributed hydrological model in a specific embodiment;

[0025] Figure 4 FIG. is the simulation results of a typical flood event in a specific embodiment with and without considering ridge interception, (a) the original surface runoff concentration calculation method, (b) the surface runoff concentration calculation method considering ridge interception. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The present invention will be further described below with reference to the drawings and specific embodiments.

[0027] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] Embodiment 1

[0029] As Figure 1As shown in the figure, the present invention provides a method for calculating surface runoff concentration based on a physical mechanism and considering ridge interception at the grid scale, including the following steps:

[0030] Step 1: Obtain medium and low-resolution DEM data and high-resolution land cover data of the basin. Taking each grid cell of the DEM as a calculation unit, calculate the proportion of farmland in each grid cell. The specific steps include:

[0031] Step 11: Obtain medium and low-resolution DEM data of the basin. Taking each grid cell of the DEM as a calculation unit;

[0032] Step 12: Obtain high-resolution land cover data of the basin and calculate the proportion of farmland in each grid cell: In the formula, is the proportion of farmland in the th grid; is the grid area, km 2 ; is the area of farmland in the th grid, km 2 .

[0033] Based on the 1 km spatial resolution DEM data of China and the 30 m spatial resolution China Multi-temporal Land Cover Dataset (CNLUCC), extract the DEM data and the land cover data in 2010 of the Qingshui River Basin in the Daqing River System of the Haihe River. Taking each grid cell of the DEM as a calculation unit, calculate the proportion of farmland in each grid cell.

[0034] Step 2: Obtain the ridge height data. Based on the proportion of farmland in the grid cell and the ridge height, calculate the ridge interception capacity of each grid cell in the basin. The specific steps include:

[0035] Step 21: Obtain the ridge height data and determine the ridge height with the highest occurrence frequency as the ridge height of the basin;

[0036] Step 22: Based on the proportion of farmland in the grid cell and the ridge height, calculate the ridge interception capacity of each grid cell in the basin: In the formula, is the ridge interception capacity of the th grid, mm; is the ridge height, mm.

[0037] There is literature indicating that through field research, the height of the field ridges in the North China Plain is usually between 8 and 16 cm, and the frequency of field ridges around 10 cm is the highest. Therefore, the height of the field ridge is 10 cm. Combining the proportion of farmland in the grid cell, calculate the field ridge interception capacity of each grid cell in the Qingshui River Basin, as Figure 2 shown.

[0038] Step 3: Based on the soil moisture data, estimate the initial field ridge interception volume of each grid cell: In the formula, is the initial field ridge interception volume of the th grid, in mm; and are the initial soil water content and saturated water content of the th grid respectively; is the conversion coefficient, generally taking 0.1 - 0.2. The initial soil water content can be obtained through continuous simulation of the hydrological model or inversion of remote sensing images.

[0039] Obtain the initial soil water content of each grid in the Qingshui River Basin before the flood through daily continuous simulation of the grid Xin'anjiang - surface - subsurface double artificial regulation and storage distributed hydrological model, and estimate the initial field ridge interception volume of each grid.

[0040] Step 4: Based on the field ridge interception capacity and initial field ridge interception volume of each grid cell in the basin, determine the field ridge interception volume of each grid within the time period and the cumulative field ridge interception volume at the end of the time period through iterative operation. The specific steps include:

[0041] Step 41: Based on the field ridge interception capacity and initial field ridge interception volume of each grid cell in the basin, determine the field ridge interception volume of each grid within the time period through iterative operation: In the formula, is the field ridge interception volume of the th grid within the current time period, represents the total surface runoff inflow volume of the th grid at the end of the current time period (i.e., at time), including the surface runoff outflow volume of all adjacent upstream grids plus the surface runoff generated by the current grid; is the time step; is the cumulative field ridge interception volume of the th grid at the beginning of the current time period (i.e., at time), which can be expressed as: In the formula, is the The amount of ridge interception in each time period of a grid from the first time period (from time 0 to time) to the previous time period (from time to time).

[0042] Step 42, determine the cumulative ridge interception at the end of the time period: In the formula, is the cumulative ridge interception of the th grid at the end of the current time period.

[0043] According to the ridge interception capacity and the initial ridge interception amount estimated for each grid unit in the Qingshui River Basin, combined with the runoff generation module of the grid Xin'anjiang-surface and underground double artificial regulation and storage distributed hydrological model (GXAJ-DAR), the ridge interception amount of each grid in each time period and the cumulative ridge interception amount at the end of the time period are determined based on iterative calculations.

[0044] Step 5, based on the ridge interception amount in the time period, construct a grid-based Muskingum flow concentration calculation method considering ridge interception; perform iterative calculations according to the calculation order of the grids to obtain the surface runoff outflow of each grid at the end of the time period, where the surface runoff outflow of the last grid is the surface runoff at the basin outlet. When the cumulative surface runoff inflow of the grid is greater than the ridge interception capacity, the excess part forms the surface runoff outflow and participates in the surface runoff concentration calculation of the adjacent downstream grids; when the cumulative surface runoff inflow of the grid is less than the ridge interception capacity, the current grid will not form an effective surface runoff outflow: In the formula, and respectively represent the surface runoff outflow of the th grid at the end of the current time period (i.e., time) and at the beginning of the current time period (i.e., time); and respectively represent the total surface runoff inflow of the th grid at the end of the current time period (i.e., time) and at the beginning of the current time period (i.e., time), including the surface runoff outflow of all adjacent upstream grids plus the surface runoff generated by the current grid; and are respectively the ridge interception amounts of the th grid in the current time period and the previous time period; is the time step; is the grid area; and is the surface runoff concentration parameter, which is obtained by calibrating parameters according to the measured flood process; and and are respectively and functions, and the sum of the three is 1; the calculation order of the grids is determined by the upstream and downstream relationship. The calculation order of the uppermost grid is 1, and the calculation order of the grid at the basin outlet is the largest. The iterative operation starts from the grid with the calculation order of 1 and ends at the grid at the basin outlet. Therefore, the surface runoff out - flow of the last grid is the surface runoff at the basin outlet.

[0045] The grid Xin'anjiang - dual artificial regulation distributed hydrological model for surface and underground (GXAJ - DAR) showed good performance in simulating 12 floods in the Qingshui River Basin from 1980 to 2002. The relative errors of the peak flow are as Figure 3 shown. The passing rate of the peak flow is 58.3%. For the 5 floods with poor simulation effects, the GXAJ - DAR model significantly underestimated the peak flow. However, between 2008 and 2019, the peak flow at the basin outlet was less than 10 m 3 / s. Especially when the rainfall amount and rainfall intensity were both greater than the historical rainstorm process, the measured peak flow was only one - tenth of the historical flood. It can be obtained from the literature that this is caused by the interception of the ridge. Therefore, a typical rainstorm flood in the Qingshui River Basin in 2013 was selected. Based on the calibrated GXAJ - DAR model, the original runoff concentration calculation methods of subsurface flow, groundwater flow and channel flow were retained, and the original and the surface runoff concentration calculation method considering the interception of the ridge were respectively used for simulation. The simulation results are as Figure 4 shown. Figure 4 It shows that the original GXAJ - DAR model far overestimated the peak flow, while the surface runoff concentration calculation method considering the interception of the ridge effectively reduced the peak flow, and the relative error of the peak flow was 15.30%.

[0046] This embodiment provides a surface runoff concentration calculation device, including a processor and a memory; the memory stores programs or instructions, and the programs or instructions are loaded and executed by the processor to implement the steps of the surface runoff concentration calculation method in the above - mentioned embodiment.

[0047] This embodiment provides a computer - readable storage medium, on which programs or instructions are stored, and when the programs or instructions are executed by a processor, the steps of the surface runoff concentration calculation method in the above - mentioned embodiment are implemented.

[0048] The above - mentioned is only the preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art should regard the scope of protection of the present invention as including several improvements and optimizations made without departing from the concept of the present invention.

Claims

1. A method for ground runoff concentration calculation, characterized in that, It includes the following steps: Step 1: Obtain the medium- and low-resolution DEM data and high-resolution land cover data of the basin. Taking each grid cell of the DEM as a calculation unit, calculate the proportion of farmland in each grid cell. Step 2: Obtain the ridge height data. Based on the proportion of farmland in the grid cell and the ridge height, calculate the ridge interception capacity of each grid cell in the basin. Step 3: Based on the soil moisture data, estimate the initial ridge interception amount of each grid cell. Step 4: According to the ridge interception capacity and the initial ridge interception amount of each grid cell in the basin, determine the ridge interception amount of each grid during the time period and the cumulative ridge interception amount at the end of the time period based on iterative operations. Step 5: Based on the ridge interception amount during the time period, construct a rasterized Muskingum runoff concentration calculation method considering ridge interception; perform iterative operations according to the calculation order of the grids to obtain the surface runoff outflows of each grid at the end of the time period, where the surface runoff outflow of the last grid is the surface runoff at the basin outlet. In step 5, iterative calculations are performed according to the calculation order of the grids, and the surface runoff outflows of each grid at the end of the time period are obtained as follows: In the formula, and respectively represent the moment and the moment of the surface runoff outflows of the th grid; and respectively represent the moment and the moment of the total surface runoff inflows of the th grid, including the surface runoff outflows of all adjacent upstream grids plus the surface runoff generated by the current grid; and are respectively the corresponding time period at the moment and the ridge interception amounts of the th grid within the corresponding time period at the moment; is the time step; is the grid area; , and are respectively and functions, and the sum of the three is 1; Function , and are respectively: In the formula, and are surface runoff concentration parameters, which are obtained by calibrating parameters according to the measured flood process.

2. The ground runoff confluence calculation method according to claim 1, characterized in that, The ridge interception volume within the current period Obtained by solving the following formula: In the formula, is the ridge interception capacity of the th grid; is the cumulative ridge interception volume of the th grid at the beginning of the current period; At this time, the cumulative interception volume of the ridge of the th grid at the end of the current period is expressed as: .

3. The surface runoff concentration calculation method according to claim 2, wherein, At the beginning of the current period, the cumulative interception volume of the ridge of the th grid is obtained by solving the following formula: In the formula, is the initial ridge interception volume of the th grid, is the ridge interception volume of the th grid in each period from the first period to the previous period, where the first period is from the 0 moment to the moment, and the previous period is from the moment to the moment.

4. The ground runoff concentration calculation method according to claim 3, characterized in that The initial ridge interception volume of the th grid is obtained by solving the following formula: In the formula, and are respectively the initial soil water content and saturated water content of the th grid; is the conversion coefficient, taking 0.1 - 0.

2.

5. The surface runoff concentration calculation method according to claim 4, characterized in that, The ridge interception capacity of the th grid is calculated as follows: In the formula, is the proportion of farmland in the th grid; is the ridge height.

6. The surface runoff concentration calculation method according to claim 5, characterized in that The proportion of farmland in the th grid is expressed as: where is the area of farmland in the th grid. ​ 7. A ground runoff concentration calculation device, characterized in that, It includes a processor and a memory; programs or instructions are stored in the memory, and the programs or instructions are loaded and executed by the processor to implement the steps of the surface runoff concentration calculation method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, Programs or instructions are stored on the readable storage medium, and when the programs or instructions are executed by the processor, the steps of the surface runoff concentration calculation method as described in any one of claims 1 to 6 are implemented.

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