Calculation method of spatial distribution of sediment content in watershed runoff based on time series DEM

Abstract

The invention discloses a method for calculating the spatial distribution of sediment content in watershed runoff based on a time series DEM, which relates to the fields of soil science and soil and water conservation science and technology. The method adopts a multi-flow direction algorithm under the constraint of the mass conservation principle to simulate the sediment transport path, calculates the spatial distribution of the sediment transport rate, and adopts a multi-flow direction algorithm to calculate the spatial distribution of the runoff. Finally, the spatial distribution of the sediment content in the watershed is calculated, which can improve the monitoring accuracy of the sediment content, makes up for the shortcoming that only the sediment content of some points in the watershed can be monitored in the past, and can be used for monitoring soil and water loss in the watershed, so as to evaluate the ecological environment of the region and provide an important reference for ecological environment governance.

Description

Technical Field

[0001] The invention relates to the field of soil science and soil and water conservation science and technology, and in particular to a method for calculating the spatial distribution of sediment content in watershed runoff based on a time series DEM. Background Art

[0002] The sediment transport process is an important research topic in soil science and soil and water conservation science. Basin erosion and sediment transport greatly affects the migration of soil, nutrients, and pollutants, resulting in serious consequences such as reduced soil fertility in areas of soil erosion, pollution of agricultural production, and damage to the regional ecological environment. Therefore, the precise simulation of the sediment transport process has important scientific and application value.

[0003] Sediment transport rate and sediment content are two important indicators in the sediment transport process. Therefore, the refined simulation of the sediment transport process requires the refined simulation of sediment transport rate and sediment content. As a natural geographical entity, the watershed has become a key experimental unit for engineering measures such as water and sand management. Previous researchers have modeled and studied the mechanism of watershed erosion and sediment production based on the watershed as a unit. In the study, the watershed was treated as a "black box", and only the results or averages of a few points were used to replace the overall runoff and sediment production results. The sediment transport process within the watershed was ignored, and there was a lack of research on the simulation of the sediment transport process.

[0004] In recent years, with the development of technologies such as drone photogrammetry and radar measurement, it has become increasingly convenient and quick to obtain high-precision terrain data. Many scholars have begun to use high-precision terrain data to calculate terrain changes, using pixel units and flow direction algorithms to simulate the runoff sediment transport path of the basin and simulate the spatial distribution of sediment transport rate. However, no scholar has used the flow direction algorithm to perform a detailed simulation of the sediment content distribution within the basin. Therefore, it is urgent to combine hydrological data such as sediment transport rate, catchment diameter flow, and internal flow production in the basin to calculate the spatial distribution of sediment content. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, comprising the following steps:

[0006] S1. Obtain the original DEM and hydrological data of multiple measurement periods of time series observation in the study area. The hydrological data include average rainfall intensity, rainfall duration, average soil bulk density, runoff duration, and measured runoff at the catchment;

[0007] S2, performing depression-filling processing on the original DEM data to obtain the DEM data after depression-filling;

[0008] S3. For each measurement period, the original DEM data of adjacent periods are subtracted to obtain the terrain change amount of the DEM data, which is further multiplied by the average soil bulk density to obtain the terrain change mass;

[0009] S4. Based on the DEM data after filling, the multi-flow direction algorithm under the constraint of the mass conservation principle is used to calculate the sediment flow direction and the sediment transport volume in the corresponding direction, and the sediment transport rate data is obtained based on the runoff duration data and sediment transport volume data;

[0010] S5. Calculate the water infiltration rate in the watershed based on the measured runoff and estimated total flow at the catchment;

[0011] S6. According to the water infiltration rate, a multi-flow direction algorithm is used to calculate the runoff data of each pixel;

[0012] S7. Calculate the spatial distribution data of sediment content based on the sediment transport rate data obtained in step S4 and the runoff data obtained in step S6.

[0013] The technical solution further defined in the present invention is:

[0014] Furthermore, in step S1, the original DEM data is a two-dimensional array of M×N. The DEM data of a measurement period includes multiple central pixels. One central pixel corresponds to 8 neighboring pixels adjacent to the central pixel. The domain pixels are shown in the following formula:

[0015]

[0016] Among them, the r i Row r j The elements of the column DEM(r i ,r j ) represents the elevation value at the center pixel; use B k Represents the central pixel (r i ,r j )'s neighboring pixel (r p ,r q ), that is, K-connected region, K=8.

[0017] As described above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S2, depressions in the original DEM data are identified. If the elevation of a certain pixel is lower than the surrounding 8 pixels, it is identified as a depression; for each depression, the depression depth of each pixel is calculated, and the depression is filled by increasing the elevation value of the depression point to the highest elevation value of the surrounding neighborhood;

[0018] The depth of the depression for each pixel is calculated by subtracting the pixel's elevation value from the highest value of the surrounding neighborhood:

[0019] ,

[0020] and Not hollow,

[0021] Among them, D(r i ,r j ) represents the central pixel (r i ,r j ) depth of the depression, DEM(r i ,r j ) represents the original central pixel (r i ,r j ) elevation value, DEM(r p ,r q ) represents the original neighborhood pixel (r p ,r q ), B k Represents the neighborhood pixel B k A collection of, (M, N) respectively represents the values ​​of the two-dimensional array of the original DEM data.

[0022] As mentioned above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S2, the value of the depression depth is used to increase the elevation of the depression, as shown in the following formula:

[0023]

[0024] Among them, H filled (r i ,r j ) represents the central pixel (r i ,r j ) is the height value after filling, H(r i ,r j ) represents the central pixel (r i ,r j ) is the original elevation value before filling, D(r i ,r j ) represents the central pixel (r i ,r j ) is the depth of the depression at .

[0025] As described above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S3, raster calculation is used to subtract the original DEM data of the previous measurement period from the original DEM data of the next measurement period to calculate the terrain change between the original DEM data of adjacent periods; according to the terrain change, the terrain change quality is obtained through the pre-acquired DEM resolution and average soil bulk density, as shown in the following formula:

[0026]

[0027] in, It represents the quality of terrain change at the rth DEM pixel in the nth measurement period; It represents the topographic change at the rth DEM pixel in the nth measurement period, which is obtained by subtracting the original DEM data of two measurement periods; x represents the pre-acquired DEM resolution; represents the average soil bulk density and p represents the porosity.

[0028] As mentioned above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S4, a multi-flow direction algorithm under the constraint of the mass conservation principle is used to perform path allocation, and the sediment transport from the central pixel to the neighboring pixel is obtained. The sediment transport rate data is obtained based on the runoff duration data and the sediment transport data, as shown in the following formula:

[0029]

[0030] in, represents the sediment transport rate of the central pixel to the kth neighboring pixel r during the nth measurement period; k represents the serial number of the neighboring pixel adjacent to the rth central pixel; tanS knr Indicates slope, superscript To control the flow to disperse or concentrate the index, When it is 1, it indicates multiple flow directions. When it is infinite, it indicates a single flow direction; L knr It represents the weighting factor of the contour length of the kth neighboring pixel in the nth measurement period; Indicates the quality of terrain change at the rth central pixel in the nth measurement period; Q nr It represents the total amount of sediment transported from the neighboring pixels to the rth central pixel. ; T knr It represents the amount of sediment transported from the kth neighborhood pixel to the rth central pixel during the nth measurement period.

[0031] As described above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S4, when the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is higher than the rth central pixel, L knr =0; when the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is lower than the rth central pixel and is located in the horizontal or vertical direction, ; When the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is lower than the rth central pixel and is located in the diagonal direction, .

[0032] As mentioned above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S5, it is assumed that the infiltration rate at each location is consistent, and the infiltration rate expression of water flow in the watershed is as follows:

[0033]

[0034] Among them, r n represents the infiltration rate of water flow in the basin during the nth measurement period; It represents the measured runoff at the water catchment; It represents the estimated total flow in the basin, and the calculation method is as follows:

[0035]

[0036] Among them, R represents the average rainfall intensity, t R represents the duration of rainfall, S c Represents the watershed area.

[0037] As described above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S6, the calculation formula for runoff data of each pixel is as follows:

[0038]

[0039] Among them, Qs knr represents the runoff volume transported from the rth central pixel to the kth neighboring pixel adjacent to the central pixel in the nth measurement period; r n represents the infiltration rate of water flow in the basin during the nth measurement period; Qs nr It represents the total runoff volume transported from the neighborhood pixels to the rth central pixel. ;Ts knr Qs represents the runoff volume transported from the kth neighboring pixel to the rth central pixel during the nth measurement period; r It represents the flow rate caused by rainfall on the rth central pixel in the nth measurement period.

[0040] As described above, in the method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, in step S7, the calculation formula for the spatial distribution data of sediment content is as follows:

[0041]

[0042] Among them, C nr It represents the sediment content at the rth central pixel in the nth measurement period; Qs represents the sediment transport rate of the central pixel to the kth neighboring pixel r during the nth measurement period; knr It represents the runoff volume transported from the central pixel to the kth neighboring pixel r during the nth measurement period.

[0043] The beneficial effects of the present invention are:

[0044] In the present invention, based on the principle of conservation of mass, a multi-flow direction algorithm is used to simulate the sediment transport path to obtain the spatial distribution of the sediment transport rate; the water flow transport path is then simulated by the multi-flow direction algorithm to obtain the spatial distribution of the runoff, and the spatial distribution of the sediment content is obtained by calculation, which can improve the monitoring accuracy of the sediment content and be used for monitoring soil and water loss in the basin, so as to evaluate the ecological environment of the region and provide an important reference for ecological environment governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0046] Figure 2 This is a schematic diagram of the original DEM data of the sample area in 9 measurement periods in the embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the topographic change of the sample area in the embodiment of the present invention;

[0048] Figure 4 A schematic diagram of sediment flow direction and water flow distribution in an embodiment of the present invention;

[0049] Figure 5 It is a schematic diagram of the simulation result of the sediment transport rate in the embodiment of the present invention;

[0050] Figure 6 It is a schematic diagram of simulation results of runoff in an embodiment of the present invention;

[0051] Figure 7 This is a schematic diagram of the simulation results of the spatial distribution of sediment content at each time series in the sample area in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] This embodiment provides a method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM. Figure 1 As shown, the following steps are included:

[0053] S1. Obtain the original digital elevation model (DEM) and hydrological data of the study area for 9 measurement periods. The hydrological data include average rainfall intensity, rainfall duration, average soil bulk density, runoff duration, and measured runoff at the catchment; Figure 2 As shown in the figure, the original DEM data of the nine measurement periods are sorted in chronological order. In this example, the projected area of ​​the sample area is 31.49m 2 The average soil bulk density is 1.39 g / cm 3 The original DEM data used has a resolution of 10 mm.

[0054] The original DEM data is a two-dimensional array of M×N, where M is 894 and N is 596. The DEM data of a measurement period includes multiple central pixels, and one central pixel corresponds to 8 neighboring pixels adjacent to the central pixel. The domain pixels are shown in the following formula:

[0055]

[0056] Among them, the r i Row r j The elements of the column DEM(r i ,r j ) represents the elevation value at the center pixel; use B k Represents the central pixel (r i ,r j )'s neighboring pixel (r p ,r q ), that is, K-connected region, K=8.

[0057] S2. Perform depression-filling processing on the original DEM data to fill the holes in the watershed and obtain the filled DEM data; identify the depressions in the original DEM data. If the elevation of a pixel is lower than the surrounding 8 pixels, it is identified as a depression; for each depression, calculate the depression depth of each pixel, and fill the depression by increasing the elevation value of the depression point to the highest elevation value of the surrounding neighborhood.

[0058] The depth of the depression for each pixel is calculated by subtracting the pixel's elevation value from the highest value of the surrounding neighborhood:

[0059] ,

[0060] and Not hollow,

[0061] Among them, D(r i ,r j ) represents the central pixel (r i ,r j ) depth of the depression, DEM(r i ,r j ) represents the original central pixel (r i ,r j ) elevation value, DEM(r p ,r q ) represents the original neighborhood pixel (r p ,r q ), B k Represents the neighborhood pixel B k A collection of , (M, N) represent the values ​​of the two-dimensional array of the original DEM data.

[0062] Use the value of the swale depth to raise the swale elevation as shown in the following formula:

[0063]

[0064] Among them, H filled (r i ,r j ) represents the central pixel (r i ,r j ) is the height value after filling, H(r i ,r j ) represents the central pixel (r i ,r j ) is the original elevation value before filling, D(r i ,r j ) represents the central pixel (r i ,r j ) is the depth of the depression at .

[0065] S3. For each measurement period, the original DEM data of adjacent periods are subtracted to obtain the following Figure 3 The terrain change amount of the 8-period sample area shown in the figure is further multiplied by the average soil bulk density to obtain the terrain change quality; using raster calculation, the original DEM data of the previous measurement period is subtracted from the original DEM data of the next measurement period to calculate the terrain change amount between the original DEM data of adjacent periods; according to the terrain change amount, the terrain change quality is obtained through the pre-acquired DEM resolution and average soil bulk density, as shown in the following formula:

[0066]

[0067] in, It represents the quality of terrain change at the rth DEM pixel in the nth measurement period; It represents the topographic change at the rth DEM pixel in the nth measurement period, which is obtained by subtracting the original DEM data of two measurement periods; x represents the pre-acquired DEM resolution; represents the average soil bulk density, and p represents the porosity. According to the above expression, by traversing the entire DEM, the terrain change quality of each pixel in the DEM can be obtained.

[0068] The expression of the center pixel number r is as follows:

[0069]

[0070] Among them, r i is the row number of the center pixel r, r j is the column number of the center pixel r, and N is the number of columns in DEM.

[0071] S4. Based on the DEM data after filling, the multi-flow direction algorithm under the constraint of the mass conservation principle is used to calculate the sediment flow direction and the sediment transport volume in the corresponding direction, and the sediment transport rate data is obtained based on the runoff duration data and sediment transport volume data. The specific steps are as follows:

[0072] In the nth measurement period, the total amount of sediment Q transported from the neighboring pixels to the central pixel r is nr and the sediment yield of the current central pixel r, that is, the terrain change quality of the central pixel , will be based on the amount of sediment distribution d knr The sediment transported to the surrounding downstream neighboring pixels, that is, the sediment transported to the central pixel, is ; Based on the principle of mass conservation, the sediment transport rate of the central pixel should not be negative, so It should not be a negative value, so it is necessary to make real-time judgments during sediment transport. Is it a negative value? If If it is a negative value, the sediment transport capacity of this group is set to 0.

[0073] Therefore, a multi-flow algorithm under the constraint of mass conservation principle is used for path allocation, such as Figure 4 As shown, the sediment transport from the central pixel to the neighboring pixels is obtained, and the sediment transport rate data is obtained based on the runoff duration data and sediment transport data, as shown in the following formula:

[0074]

[0075] in, represents the sediment transport rate of the central pixel to the kth neighboring pixel r during the nth measurement period; k represents the serial number of the neighboring pixel adjacent to the rth central pixel; tanS knr Indicates slope, superscript To control the flow to disperse or concentrate the index, When it is 1, it indicates multiple flow directions. When it is infinite, it indicates a single flow direction; L knr It represents the weighting factor of the contour length of the kth neighboring pixel in the nth measurement period; Indicates the quality of terrain change at the rth central pixel in the nth measurement period; Q nr It represents the total amount of sediment transported from the neighboring pixels to the rth central pixel. ; T knr It represents the amount of sediment transported from the kth neighborhood pixel to the rth central pixel during the nth measurement period.

[0076] For the contour length weighting factor L knr , when the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is higher than the rth central pixel, L knr=0; when the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is lower than the rth central pixel and is located in the horizontal or vertical direction, ; When the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is lower than the rth central pixel and is located in the diagonal direction, .

[0077] The amount of sediment T knr The steps to obtain are: take the neighborhood pixel k as the central pixel, and the central pixel r as the neighborhood pixel to simulate sediment transport, and distribute the total amount of sediment received by the neighborhood pixel k according to the water flow distribution amount, and the amount allocated to the central pixel r is T knr The central pixel receives T from each neighboring pixel. knr The sum is the total amount of sediment transported from the neighboring pixels to the central pixel. nr ; Then we can get the sediment transport rate from the central pixel to each neighboring pixel. The simulation results of sediment transport rate spatial distribution are shown in the figure below: Figure 5 shown.

[0078] S5. Calculate the water infiltration rate in the basin based on the measured runoff and estimated total flow at the catchment. Assuming that the infiltration rate at each location is the same, the water infiltration rate in the basin is expressed as follows:

[0079]

[0080] Among them, r n represents the infiltration rate of water flow in the basin during the nth measurement period; It represents the measured runoff at the water catchment; It represents the estimated total flow in the basin, and the calculation method is as follows:

[0081]

[0082] Among them, R represents the average rainfall intensity, t R represents the duration of rainfall, S c Represents the watershed area.

[0083] S6. Based on the total flow, flow direction and infiltration rate of the water flow in the basin, the multi-flow direction algorithm is used to calculate the runoff data of each pixel, and then the runoff volume transported from the central pixel to each neighboring pixel is obtained. The simulation results of the spatial distribution of runoff volume are shown in the figure below. Figure 6 shown.

[0084] The formula for calculating runoff data is as follows:

[0085]

[0086] Among them, Qs knrrepresents the runoff volume transported from the rth central pixel to the kth neighboring pixel adjacent to the central pixel in the nth measurement period; r n represents the infiltration rate of water flow in the basin during the nth measurement period; Qs nr It represents the total runoff volume transported from the neighboring pixels to the rth central pixel. ;Ts knr Qs represents the runoff volume transported from the kth neighboring pixel to the rth central pixel during the nth measurement period; r It represents the flow rate caused by rainfall on the rth central pixel in the nth measurement period.

[0087] S7, according to the sediment transport rate data obtained in step S4 and the runoff data obtained in step S6, the sediment content spatial distribution data is calculated, and then the sediment content spatial distribution simulation result diagram is obtained, such as Figure 7 shown.

[0088] The calculation formula for sand content data is as follows:

[0089]

[0090] Among them, C nr It represents the sediment content at the rth central pixel in the nth measurement period; Qs represents the sediment transport rate of the central pixel to the kth neighboring pixel r during the nth measurement period; knr It represents the runoff volume transported from the central pixel to the kth neighboring pixel r during the nth measurement period.

[0091] To sum up, this embodiment uses a multi-flow direction algorithm under the constraint of the mass conservation principle to simulate the sediment transport path, calculates the spatial distribution of the sediment transport rate, and uses a multi-flow direction algorithm to calculate the spatial distribution of the runoff. Finally, the spatial distribution of the sediment content in the basin is calculated, which makes up for the shortcoming that only the sediment content of some points in the basin could be monitored in the past. It can be used to monitor soil erosion in the basin, evaluate the ecological environment of the region, and provide an important reference for ecological environmental governance.

[0092] In addition to the above embodiments, the present invention may also have other implementation modes. Any technical solution formed by equivalent replacement or equivalent transformation falls within the protection scope required by the present invention.

Claims

1. A method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM, characterized by: The following steps are involved: S1. Obtain the original DEM and hydrological data of multiple measurement periods of time series observation in the study area. The hydrological data include average rainfall intensity, rainfall duration, average soil bulk density, runoff duration, and measured runoff at the catchment; S2, performing depression-filling processing on the original DEM data to obtain the DEM data after depression-filling; S3. For each measurement period, the original DEM data of adjacent periods are subtracted to obtain the terrain change amount of the DEM data, which is further multiplied by the average soil bulk density to obtain the terrain change mass; S4. Based on the DEM data after filling, the multi-flow direction algorithm under the constraint of the mass conservation principle is used to calculate the sediment flow direction and the sediment transport volume in the corresponding direction, and the sediment transport rate data is obtained based on the runoff duration data and sediment transport volume data; S5. Calculate the water infiltration rate in the watershed based on the measured runoff and estimated total flow at the catchment; S6. According to the water infiltration rate, a multi-flow direction algorithm is used to calculate the runoff data of each pixel; S7, calculating the spatial distribution data of sediment content according to the sediment transport rate data obtained in step S4 and the runoff data obtained in step S6; In step S3, grid calculation is used to subtract the original DEM data of the previous measurement period from the original DEM data of the next measurement period to calculate the terrain change between the original DEM data of adjacent periods; according to the terrain change, the terrain change quality is obtained through the pre-acquired DEM resolution and average soil bulk density, as shown in the following formula: in, It represents the quality of terrain change at the rth DEM pixel in the nth measurement period; It represents the topographic change at the rth DEM pixel in the nth measurement period, which is obtained by subtracting the original DEM data of two measurement periods; x represents the pre-acquired DEM resolution; represents the average soil bulk density, and p represents the porosity; In step S4, a multi-flow direction algorithm under the constraint of the mass conservation principle is used to perform path allocation to obtain the sediment transport from the central pixel to the neighboring pixels, and the sediment transport rate data is obtained based on the runoff duration data and sediment transport data, as shown in the following formula: in, represents the sediment transport rate of the central pixel to the kth neighboring pixel r during the nth measurement period; k represents the serial number of the neighboring pixel adjacent to the rth central pixel; tanS knr Indicates slope, superscript To control the flow to disperse or concentrate the index, When it is 1, it indicates multiple flow directions. When it is infinite, it indicates a single flow direction; L knr It represents the weighting factor of the contour length of the kth neighboring pixel in the nth measurement period; Indicates the quality of terrain change at the rth central pixel in the nth measurement period; Q nr It represents the total amount of sediment transported from the neighboring pixels to the rth central pixel. ; T knr It represents the amount of sediment transported from the kth neighboring pixel to the rth central pixel during the nth measurement period; In step S5, assuming that the infiltration rate at each location is the same, the infiltration rate of water flow in the basin is expressed as follows: Among them, r n represents the infiltration rate of water flow in the basin during the nth measurement period; It represents the measured runoff at the water catchment; It represents the estimated total flow in the basin, and the calculation method is as follows: Among them, R represents the average rainfall intensity, t R represents the duration of rainfall, S c represents the watershed area; In step S6, the runoff data calculation formula for each pixel is as follows: Among them, Qs knr represents the runoff volume transported from the rth central pixel to the kth neighboring pixel adjacent to the central pixel in the nth measurement period; r n represents the infiltration rate of water flow in the basin during the nth measurement period; Qs nr It represents the total runoff volume transported from the neighboring pixels to the rth central pixel. ;Ts knr Qs represents the runoff volume transported from the kth neighboring pixel to the rth central pixel during the nth measurement period; r It represents the flow rate generated by rainfall on the rth central pixel in the nth measurement period; In step S7, the calculation formula for the spatial distribution data of the sediment content is as follows: Among them, C nr It represents the sediment content at the rth central pixel in the nth measurement period; Qs represents the sediment transport rate of the central pixel to the kth neighboring pixel r during the nth measurement period; knr It represents the runoff volume transported from the central pixel to the kth neighboring pixel r during the nth measurement period.

2. The method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM according to claim 1 is characterized by: In step S1, the original DEM data is a two-dimensional array of M×N. The DEM data of a measurement period includes multiple central pixels. One central pixel corresponds to 8 neighboring pixels adjacent to the central pixel. The domain pixels are shown in the following formula: Among them, the r i Row r j The elements of the column DEM(r i ,r j ) represents the elevation value at the center pixel; use B k Represents the central pixel (r i ,r j )'s neighboring pixel (r p ,r q ), that is, K-connected region, K=8.

3. The method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM according to claim 1 is characterized in that: In step S2, depressions in the original DEM data are identified. If the elevation of a certain pixel is lower than the surrounding eight pixels, it is identified as a depression. For each depression, the depression depth of each pixel is calculated, and the depression is filled by increasing the elevation value of the depression point to the highest elevation value of the surrounding neighborhood. The depth of the depression for each pixel is calculated by subtracting the pixel's elevation value from the highest value of the surrounding neighborhood: ， and Not empty, Among them, D(r i ,r j ) represents the central pixel (r i ,r j ) depth of the depression, DEM(r i ,r j ) represents the original central pixel (r i ,r j ) elevation value, DEM(r p ,r q ) represents the original neighborhood pixel (r p ,r q ), B k Represents the neighborhood pixel B k A collection of, (M, N) respectively represents the values ​​of the two-dimensional array of the original DEM data.

4. The method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM according to claim 3 is characterized by: In step S2, the depth of the depression is used to increase the elevation of the depression, as shown in the following formula: Among them, H filled (r i ,r j ) represents the central pixel (r i ,r j ) is the height value after filling, H(r i ,r j ) represents the central pixel (r i ,r j ) is the original elevation value before filling, D(r i ,r j ) represents the central pixel (r i ,r j ) is the depth of the depression at .

5. The method for calculating the spatial distribution of sediment content in watershed runoff based on time series DEM according to claim 1 is characterized in that: In step S4, when the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is higher than the rth central pixel, L knr =0; when the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is lower than the rth central pixel and is located in the horizontal or vertical direction, ; When the kth neighboring pixel adjacent to the rth central pixel in the nth measurement period is lower than the rth central pixel and is located in the diagonal direction, .

Images