Basin water and soil conservation vegetation synergy layout method and system and electronic equipment

Abstract

The invention provides a drainage basin water and soil conservation vegetation synergy layout method and system and electronic equipment, and the method comprises the steps: calculating a sediment connectivity index for a target drainage basin in a grid-by-grid manner based on a preset sediment connectivity index model, and combining the sediment connectivity indexes of all the vegetation coverage grids to form a sediment connectivity index spatial distribution diagram of the vegetation coverage of the drainage basin, and spatially superposing the sediment connectivity index spatial distribution diagram with the sediment connectivity index spatial distribution diagram under the bare land scene of the drainage basin to generate a sediment connectivity index difference distribution diagram. According to the difference value of the sediment connectivity indexes, the vegetation configuration priority order of each grid is determined, and vegetation is arranged according to the vegetation configuration priority order of the grids, so that under the condition that the total number of vegetation configuration is the same or the existing vegetation coverage number of a watershed is not changed, the vegetation can be quickly and accurately configured, and the vegetation can be quickly and accurately distributed. And the drainage basin water and soil conservation function is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water and soil conservation, and particularly relates to a watershed water and soil conservation vegetation synergistic layout method and system and an electronic device. BACKGROUND

[0002] Vegetation plays an important role in inhibiting hydraulic erosion through interception of rainfall by the aboveground canopy, slowing runoff by surface coverage, and soil fixation and infiltration by underground roots. Since this century, global greening has been significant, and vegetation coverage area and quality have been greatly improved. However, due to the carrying capacity of water and soil resources, in most ecologically fragile areas, the potential for improving ecology by continuously increasing the amount of vegetation is very limited. In addition, with the simultaneous growth of vegetation coverage, the water and sediment regulation effect of vegetation shows a law of diminishing marginal benefit. At the same time, as an important component and unit of the landscape, in addition to the type and quantity, the spatial distribution and pattern of vegetation patches are also closely related to water and sediment production and transport, and the influence tends to be more significant as the spatial scale expands. Therefore, under the background of the increasing global climate change and the increasingly scarce water and soil resources, optimizing the spatial layout of vegetation has become an important way to further improve its erosion and sediment reduction effect from the perspectives of efficiency and potential.

[0003] A watershed is the basic unit of hydrological response and water and soil loss prevention, and is also the main unit of ecological management such as vegetation restoration. In practice, a targeted vegetation configuration optimization adjustment scheme needs to be quickly proposed based on the actual vegetation growth and distribution status of the watershed, while maintaining the existing vegetation coverage rate, and it is necessary to clarify where to add vegetation and where to reduce the original vegetation, so as to improve the overall vegetation water and soil conservation function of the watershed. Early vegetation optimization configuration research was mainly limited to patch (community) scale, and the principle of "suitable tree for suitable place" was used to guide the optimization configuration of vegetation type, density, structure and management mode, taking topography, soil, water and heat characteristics as constraints. Some studies also used indoor simulated rainfall or water flushing tests to explore the runoff and sediment reduction benefits under different vegetation spatial configuration modes, so as to select the optimal vegetation spatial configuration mode. With the continuous development of hydrological models, models such as InVEST (Integrated Valuation of Ecosystem Services and Trade offs), Cellular Automata (CA) and SWAT (Soil and Water Assessment Tool) are widely used to simulate the ecological effects of water and soil conservation under different vegetation distribution and configuration scenarios, so as to select the vegetation configuration mode with the best ecological effect and clarify the priority configuration area, thereby providing a certain degree of scientific basis for the actual development of watershed vegetation spatial optimization configuration. However, the existing technology cannot achieve rapid and accurate water and soil conservation vegetation optimization configuration. SUMMARY

[0004] This invention provides a method, system, and electronic device for optimizing vegetation layout in watersheds to improve soil and water conservation, addressing the shortcomings of existing technologies that cannot achieve rapid and accurate optimization of vegetation configuration for soil and water conservation. It can enhance the soil and water conservation function of watershed vegetation through rapid and accurate optimization of vegetation configuration, without changing the total number of vegetation configurations or the existing vegetation coverage of the watershed. The technical solution proposed by this invention is as follows: In a first aspect, the present invention provides a method for optimizing vegetation layout for watershed soil and water conservation, comprising: Obtain a preset sediment connectivity index model. Based on the preset sediment connectivity index model, calculate the sediment connectivity index of each grid cell in the target watershed when the grid cell is covered by vegetation. Combine the sediment connectivity indices of all grid cells to form a spatial distribution map of sediment connectivity index of vegetation cover in the watershed. Obtain the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed, and spatially overlay it with the spatial distribution map of sediment connectivity index under vegetation cover of the watershed to generate a sediment connectivity index difference distribution map. Based on the distribution map of sediment connectivity index difference, the priority order of vegetation configuration for each grid is determined according to the sediment connectivity index difference from large to small, and vegetation is deployed according to the priority order of vegetation configuration.

[0005] Optionally, the step of obtaining a spatial distribution map of sediment connectivity index under a bare land scenario in the target watershed, and spatially overlaying it with a spatial distribution map of sediment connectivity index under vegetation cover in the watershed to generate a sediment connectivity index difference distribution map, includes: Obtain a spatial distribution map of sediment connectivity index under bare land scenario in the target watershed; For each grid cell within the target watershed, the difference between the sediment connectivity index of the grid cell under individual vegetation cover and the sediment connectivity index of the corresponding grid cell under bare land scenario is calculated to obtain the sediment connectivity index difference value. A sediment connectivity index difference distribution map is generated based on the sediment connectivity index difference of all grids.

[0006] Optionally, the step of determining the vegetation configuration priority order of each grid cell based on the sediment connectivity index difference distribution map, in descending order of sediment connectivity index difference, and deploying vegetation according to the vegetation configuration priority order includes: All grids within the target watershed are sorted, and their vegetation configuration priority is determined by the difference in sediment connectivity index from largest to smallest. According to the vegetation configuration priority, vegetation is first deployed in the areas with the higher priority.

[0007] Optionally, the method further includes: Obtain the priority order of vegetation configuration for each grid cell in the target watershed under different characteristic years; For each vegetation type, the vegetation configuration priority order of all grids of that vegetation type is accumulated to obtain the total priority order; for each characteristic year, the average priority order is determined based on the total priority order and the number of grids corresponding to each vegetation type in that characteristic year. The vegetation configuration function level for each characteristic year is determined by ranking the average priority from smallest to largest.

[0008] Optionally, the method further includes: Obtain actual land use scenarios and runoff and sediment transport in characteristic years under the same rainfall; By fitting the average priority order and runoff volume under each characteristic year, a first regression model on runoff volume and average priority order is obtained; By fitting the average priority order and sediment transport volume under each characteristic year, a second regression model on sediment transport volume and average priority order is obtained; Obtain the determination coefficients of the first regression model and the second regression model, and evaluate the effectiveness of vegetation layout based on the determination coefficients of the two regression models.

[0009] Secondly, the present invention also provides a watershed soil and water conservation vegetation enhancement layout system, comprising the following modules: The data processing module is used to obtain a preset sediment connectivity index model, and based on the preset sediment connectivity index model, calculate the sediment connectivity index of each grid cell in the target watershed when the grid cell is covered by vegetation, and combine the sediment connectivity indices of all grid cells to form a spatial distribution map of sediment connectivity index of watershed vegetation cover. The spatial overlay module is used to obtain the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed, and to spatially overlay it with the spatial distribution map of sediment connectivity index under vegetation cover of the watershed to generate a sediment connectivity index difference distribution map, thereby obtaining the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed. The order determination module is used to determine the priority order of vegetation configuration for each grid cell based on the distribution map of sediment connectivity index difference, in descending order of sediment connectivity index difference, and to deploy vegetation according to the priority order of vegetation configuration.

[0010] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the watershed soil and water conservation vegetation enhancement layout method as described in the first aspect above.

[0011] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the watershed soil and water conservation vegetation enhancement layout method as described in the first aspect above.

[0012] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the watershed soil and water conservation vegetation enhancement layout method as described in the first aspect above.

[0013] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows: This invention provides a method, system, and electronic equipment for enhancing vegetation layout in watershed soil and water conservation. By dividing the watershed into multiple grids, it enables refined analysis of each grid within the watershed. This grid-scale analysis can more accurately capture the effects of vegetation configuration at different locations within the watershed. By individually changing the vegetation cover state of each grid and calculating its sediment connectivity index, the specific impact of vegetation placement in each grid on the watershed's sediment connectivity can be quantified. This simulation comprehensively considers the effectiveness of vegetation configuration at different locations within the watershed, providing rich basic data for selecting the optimal configuration strategy. By determining the difference in sediment connectivity index under vegetation cover and bare land scenarios, and combining them to generate a spatial distribution map of the sediment connectivity index, it provides the changes in sediment connectivity under different grids, providing a scientific basis for selecting the optimal vegetation configuration scheme. By comparing the difference in sediment connectivity index of vegetation placement in different grids, the priority order of vegetation configuration is determined according to the order of the difference from largest to smallest. By planting vegetation in areas with a high priority in vegetation configuration, the water and soil conservation function of the vegetation in the watershed can be improved without changing the vegetation coverage rate of the watershed.

[0014] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the watershed soil and water conservation vegetation enhancement layout method provided by the present invention.

[0018] Figure 2This is a schematic diagram of the framework for determining the priority order of watershed vegetation configuration provided by the present invention.

[0019] Figure 3 This is a schematic diagram of the priority order distribution of vegetation configuration in a single grid in a certain watershed, provided by the present invention.

[0020] Figure 4 This is a schematic diagram illustrating the simulation results of annual runoff and annual sediment load of a watershed under different characteristic years, provided by the present invention; wherein, Figure 4 (a) in the figure is a schematic diagram of the simulation results of annual runoff in different characteristic years; Figure 4 (b) in the figure is a schematic diagram of the simulation results of annual sediment transport in different characteristic years.

[0021] Figure 5 This is a schematic diagram illustrating the fitting relationship between the average priority order of vegetation patches in a watershed under different characteristic years and simulated runoff and sediment transport, provided by the present invention. Figure 5 (a) and (b) in the figure are the fitting relationship between the average priority order and the simulated average runoff and average sediment transport in different characteristic years.

[0022] Figure 6 This is a schematic diagram of the watershed soil and water conservation vegetation enhancement layout system provided by the present invention.

[0023] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] Existing studies on the optimization of vegetation patterns at the watershed scale mostly employ hydrological models to simulate different land use scenarios, thereby selecting vegetation patterns that can effectively perform soil and water conservation functions. However, no method has yet been found to determine the priority order of vegetation configuration with the goal of erosion control and sediment reduction. This makes it impossible to rapidly and accurately optimize and adjust vegetation patches to improve the soil and water conservation function of watershed vegetation under the same vegetation cover conditions. To address these issues, this invention proposes a method, system, and electronic equipment for enhancing the layout of vegetation for soil and water conservation in watersheds. This method is primarily used to rapidly optimize the configuration of watershed vegetation patches to improve soil and water conservation functions while maintaining a constant watershed vegetation cover. The method is then rigorously evaluated using current land use scenarios.

[0026] Reference Figure 1 As shown, the watershed soil and water conservation vegetation enhancement layout method provided by the present invention includes: S110. Obtain a preset sediment connectivity index model. Based on the preset sediment connectivity index model, calculate the sediment connectivity index of each grid cell in the target watershed when the grid cell is individually covered by vegetation. Combine the sediment connectivity indices of all grid cells to form a spatial distribution map of the sediment connectivity index of the watershed vegetation cover.

[0027] The target watershed was divided into multiple grids, each grid was individually vegetated, while the remaining grids were left bare. (Refer to...) Figure 2 As shown, vegetation (such as trees, shrubs, or grasslands) is sequentially placed in one grid cell for all grid cells within the watershed, while the remaining grid cells are set to bare ground, generating various single-grid vegetation cover scenarios (i.e., Figure 2 (Scenarios 1-4 in the example). If there are 1000 grids in the watershed, vegetation is placed in one grid at a time in grid order, and the remaining grids are bare land, thus obtaining 1000 vegetation cover scenarios.

[0028] A pre-defined sediment connectivity index model was used to calculate the sediment connectivity index of the grid cells under each vegetation cover scenario. In each scenario, the sediment connectivity index of the vegetation cover grid cells (i.e., the grid cells with vegetation) was taken as the final sediment connectivity value for that grid cell. The sediment connectivity indices of all vegetation cover grid cells were combined to form a spatial distribution map of the sediment connectivity index of the watershed vegetation cover. The sediment connectivity index IC... ZQ The ratio of the upslope component to the downslope component is used for calculation. The upslope component incorporates the effective catchment area (Ar) that takes into account the impact of land use, while the downslope component uses the runoff velocity factor from the SEDD (sediment delivery distributed model).

[0029] Specifically, assuming the watershed is divided into multiple grids (e.g., 1000 grids), vegetation (e.g., trees, shrubs, or grasslands) is sequentially planted on individual grids, while the remaining grids remain bare. This generates 1000 vegetation cover scenarios, the same number as the number of grids. By individually changing the vegetation cover of each grid, the impact of vegetation placement on watershed sediment connectivity is quantified. For each vegetation cover scenario, an improved sediment connectivity index model is used to calculate the sediment connectivity index for each grid within the watershed. This improved model considers the confluence of upslope land use changes and their impact on downslope erosion and sediment yield, thus more accurately reflecting the sediment connectivity of grid units. 1000 sets of spatial distribution maps of watershed sediment connectivity indices are obtained, each corresponding to a vegetation cover scenario. Under each scenario, the sediment connectivity index of the vegetated grid is used as the final sediment connectivity value for that grid. The final sediment connectivity values ​​of all vegetation grids are combined to form a new spatial distribution raster map of the watershed vegetation-sediment connectivity index. This map reflects the sediment connectivity characteristics of each grid when vegetation is deployed individually, providing a basis for subsequent comparisons.

[0030] S120. Obtain the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed, and spatially overlay it with the spatial distribution map of sediment connectivity index under vegetation cover of the watershed to generate a sediment connectivity index difference distribution map.

[0031] A bare land scenario (without vegetation cover) was set up in the same watershed. The sediment connectivity index of each grid cell was calculated, and a spatial distribution map of the sediment connectivity index under the bare land scenario was generated as the baseline scenario. The difference in sediment connectivity index was obtained by subtracting the sediment connectivity index of the corresponding grid cell under the bare land scenario from the sediment connectivity index of each grid cell when it was covered by vegetation.

[0032] The process of obtaining a spatial distribution map of sediment connectivity index under a bare land scenario in the target watershed, and spatially overlaying it with a spatial distribution map of sediment connectivity index under vegetation cover in the watershed to generate a sediment connectivity index difference distribution map, includes: Obtain the spatial distribution map of sediment connectivity index under the bare land scenario in the target watershed; for each grid cell in the target watershed, calculate the difference between the sediment connectivity index of the grid cell with individual vegetation cover and the sediment connectivity index of the corresponding grid cell under the bare land scenario, and obtain the sediment connectivity index difference value; generate a sediment connectivity index difference value distribution map based on the sediment connectivity index difference values ​​of all grid cells.

[0033] Specifically, the spatial distribution map of sediment connectivity index under watershed vegetation is spatially overlaid with the spatial distribution map of sediment connectivity index under bare land scenario. By comparing the sediment connectivity index under vegetation cover scenario and bare land scenario, the impact of vegetation on sediment connectivity is quantified. For each grid cell, the sediment connectivity index under vegetation cover scenario (i.e., Figure 2 IC in SI_1、 IC S2_2、 IC S3_3、 IC S4_4 ) and sediment connectivity index under bare land scenario (i.e. Figure 2 IC in LD_1 IC LD_2 IC LD_3 IC LD_4 The difference between the sediment connectivity index and the sediment connectivity index is used to obtain the difference ∆IC. ZQ (Right now Figure 2 IC in SI_1-LD_1 IC S2_2-LD_2 IC S3_3-LD_3 IC S4_4-LD_4 Based on the sediment connectivity index difference values ​​under all scenarios, a sediment connectivity index difference distribution map is generated. The larger the sediment connectivity index difference value, the more significant the effect of the grid vegetation cover on reducing sediment connectivity.

[0034] S130. Based on the distribution map of sediment connectivity index difference, determine the priority order of vegetation configuration for each grid according to the sediment connectivity index difference from large to small, and deploy vegetation according to the priority order of vegetation configuration.

[0035] All grid cells within the target watershed are sorted, and their vegetation placement priorities are determined by the difference in sediment connectivity index (∆IC) from largest to smallest. Vegetation is then placed in areas with higher vegetation placement priorities. Specifically, based on the difference in sediment connectivity index (∆IC)... ZQ The value is used to sort all grids within the watershed according to the ∆IC value. ZQ The priority order for vegetation configuration is determined by ranking the values ​​from largest to smallest. Raster cells with larger sediment connectivity index differences are prioritized for vegetation configuration. For example, using ArcGIS's raster-to-point tool, the ∆IC values ​​of the sediment connectivity index difference distribution map can be converted. ZQ The values ​​are sorted from largest to smallest, and the sequential number represents the priority order of vegetation configuration for the corresponding grid. Vegetation is prioritized in areas with higher priority orders, thereby enhancing the watershed's soil and water conservation function while maintaining the existing vegetation coverage.

[0036] The watershed soil and water conservation vegetation enhancement layout method provided by this invention achieves refined analysis of each grid within the watershed by dividing the watershed into multiple grids. This grid-level analysis can more accurately capture the vegetation configuration requirements at different locations within the watershed. By individually changing the vegetation cover state of each grid and calculating its sediment connectivity index, the specific impact of vegetation placement in each grid on the watershed's sediment connectivity can be quantified. By setting a single grid vegetation cover scenario, a vegetation cover scenario with the same number of grids is generated. This simulation can comprehensively consider the possibilities of vegetation configuration at different locations within the watershed, providing rich basic data for selecting the optimal configuration strategy. By determining the difference in sediment connectivity index under vegetation cover scenario and bare land scenario, and combining them to generate a spatial distribution map of the sediment connectivity index difference, it provides the changes in sediment connectivity under different vegetation cover scenarios, providing a scientific basis for selecting the optimal vegetation configuration scheme. By comparing the differences in sediment connectivity indices among different vegetation cover grids, and determining the priority order of vegetation configuration accordingly, water and soil conservation vegetation is prioritized for placement in areas with higher priority order, thereby maximizing the water and soil conservation function of the watershed while maintaining the existing vegetation coverage.

[0037] The aforementioned preset sediment connectivity index model in the method of this invention employs an improved sediment connectivity index (IC). ZQ The model comprehensively considers the impact of upslope land use change confluence on downslope erosion and sediment yield, and can more accurately reflect the sediment connectivity of the grid.

[0038] In some embodiments, researchers (Liu et al., 2022) have incorporated three factors—rainfall erosivity factor (R), soil erodibility factor (K), and soil and water conservation measures factor (P)—from the RUSLE (Revised Universal Soil Loss Equation) into the upslope component, proposing an improved IC algorithm (IC). LS The IC algorithm selected in this invention is based on IC... LS Based on the existing IC algorithm, this paper proposes an improved sediment connectivity index model by introducing an effective catchment area (Ar) that takes into account the impact of land use in the upslope component and replacing the runoff velocity factor (v) of the SEDD model in the downslope component.

[0039] The specific improvement methods are as follows: ① Improvement based on the upslope component of the effective runoff area considering land use impact: In the upslope component of IC, the effective runoff area (Ar) that takes into account the impact of land use is used instead of the theoretical runoff area based on the flow direction accumulation of the Digital Elevation Model (DEM) in the original algorithm, so as to reflect the impact of upslope land use changes on runoff and thus on downslope erosion and sediment yield.

[0040] ② Improved downslope component reflecting the physical processes of sediment transport: In the downslope component of the IC model, the runoff velocity factor (v) from the Sediment Delivery Ratio (SDR) formula in the SEDD model is selected to replace the weighting factor in the original algorithm. This makes the improved downslope component completely consistent with the sediment transport time (t) formula in the SDR calculation, attempting to reflect the ease or probability of transport by using the time cost of sediment transporting downward into the channel or the nearest depositional zone. Since the determination of sediment transport time (t) in the SDR formula considers factors such as land use, confluence length, and Manning roughness, its physical meaning of describing the time of sediment transport downward is clearer and conforms to the definition of sediment connectivity. Therefore, the improved sediment connectivity index will enhance the description and response capabilities of sediment transport processes and establish an organic relationship with other commonly used important indicators.

[0041] The improved sediment connectivity index model is as follows: in, The sediment connectivity index, ranging from [-∞, +∞], indicates better sediment connectivity and stronger sediment production and transport capacity; D up D dn These represent the uphill and downhill components, respectively; s i The slope (m / m) is used; to avoid distortion of the calculation results, the values ​​need to be corrected to the range of 0.005-1; w i A represents the weighting factor of the i-th grid in the watershed digital elevation model, reflecting the combined influence of factors such as rainfall, soil, vegetation, and soil and water conservation measures; ri The effective catchment area (m²) of the i-th grid 2 ), taking into account the land use / cover impacts of uphill slopes; d i v represents the confluence path length (m) from the i-th grid to the nearest channel or sedimentation zone, affecting the distance and time of sediment transport; i is the runoff velocity factor of the i-th grid, which affects the velocity and efficiency of sediment transport; This represents the total number of grid cells.

[0042] Calculate the uphill component D based on the effective catchment area and weighting factor. upBased on the confluence path length and runoff velocity factor, the downhill component D is calculated. dn Substituting the upslope and downslope components into the formula, the sediment connectivity index is calculated. It should be noted that the sediment connectivity index calculation method in the bare land scenario also adopts the improved sediment connectivity index model described above.

[0043] This invention improves the sediment connectivity index model by considering the impact of upslope land use changes on runoff and consequently on downslope erosion and sediment yield. This allows for a more accurate quantification of sediment connectivity within grid cells, providing a scientific basis for optimal vegetation configuration. The model incorporates parameters reflecting the physical processes of sediment transport (such as runoff velocity factors), making the sediment connectivity index more accurately reflect the actual situation of sediment transport and improving the model's accuracy and reliability. Through grid-level analysis, the model can capture differences in sediment connectivity at different locations within the watershed, providing refined guidance for optimal vegetation configuration. This helps prioritize vegetation placement in key areas, improving soil and water conservation benefits. Based on the calculation results of the sediment connectivity index, the priority order of vegetation configuration in different grid cells within the watershed can be determined, providing decision support for optimal vegetation configuration.

[0044] In some embodiments, the effective runoff area (Ar) in the upslope component is obtained via C## programming based on ASCII format data transferred from land use and flow direction raster. The effective runoff area is calculated and determined as follows: S210. Obtain land use raster data, wherein the land use raster data includes the land use type of each raster.

[0045] Land use type data for each grid within the watershed is obtained through remote sensing image interpretation and geographic information system processing. This data is stored in grid form, with each grid representing a small area within the watershed and labeled with the land use type (such as forest, grassland, cultivated land, etc.) of that area.

[0046] S220. Calculate the runoff contribution rate of each grid cell based on the runoff generation coefficient corresponding to the land use type, and multiply it by the grid cell area to obtain the runoff contribution area.

[0047] Based on the runoff generation characteristics of different land use types, a runoff generation coefficient is assigned to each land use type. The runoff generation coefficient reflects the ability of that land type to generate runoff under rainfall conditions. For each grid cell, the catchment area contribution rate of that grid cell is calculated based on the runoff generation coefficient corresponding to its land use type. The contribution rate indicates the degree to which that grid cell contributes to the downslope catchment area under rainfall conditions.

[0048] in, The contribution rate of the catchment area of ​​grid n; The runoff coefficient corresponding to the land use type within grid n; This represents the runoff coefficient for fallow land.

[0049] S230. Based on the cumulative convergence contribution area of ​​all grids within the uphill convergence path, the effective convergence area is obtained.

[0050] Using digital elevation model (DEM) data, the runoff direction of each grid within the watershed was determined. The runoff direction represents the path of water flow from higher to lower elevations. For each grid, the runoff contribution area of ​​all grids along its uphill runoff path was accumulated. This accumulated value is the effective runoff area (A) of that grid. ri Finally, the effective catchment area of ​​each grid is converted from ASCII format data to Grid format data using ArcGIS for subsequent calculations and visualization.

[0051] in, The effective catchment area (m²) of the i-th grid 2 The result is obtained by accumulating the effective confluence contribution surfaces of all grids flowing into the i-th grid. Let n be the number of grid cells within the uphill confluence path of the i-th grid cell; for the n grid cells flowing into the i-th grid cell, The uphill confluence area (m²) of grid n calculated based on the confluence direction. 2 D is the side length (m) of the grid cell.

[0052] This invention, by introducing an effective runoff area that considers land use influences, improves the sediment connectivity index model, enabling it to more accurately reflect the impact of upslope land use types on downslope runoff and erosion-induced sediment yield. This helps to fully consider the differences in land use types in vegetation optimization, improving the scientific rigor and effectiveness of configuration strategies. The calculation of the effective runoff area is a crucial component of the sediment connectivity index model. By calculating the effective runoff area more accurately, the sediment connectivity index's ability to describe sediment transport potential can be enhanced, improving the model's accuracy and reliability.

[0053] Effective runoff area calculations are performed at the raster level, supporting refined raster-level analysis. Raster-level analysis captures the differences in runoff characteristics at different locations within the watershed, providing more detailed guidance for optimal vegetation configuration. Based on the calculated effective runoff area, the priority order of vegetation configuration in different raster areas within the watershed can be determined more scientifically. This helps to prioritize vegetation configuration in key locations (such as areas with large runoff areas and high erosion risk) during vegetation optimization, thereby enhancing soil and water conservation functions. By optimizing vegetation configuration strategies, soil erosion within the watershed can be effectively reduced, promoting ecological restoration and soil and water conservation in the watershed.

[0054] In some embodiments, for the weighting factor w in the uphill component i Determined by the following formula: in, These represent the average rainfall erosivity factor, average soil erosibility factor, average vegetation cover and management factor, and average soil and water conservation measures factor for the upslope runoff area of ​​the i-th grid.

[0055] The average rainfall erosivity factor, average soil erosibility factor, average vegetation cover and management factor, and average soil and water conservation measures factor values ​​were obtained by averaging the rainfall erosivity factor, soil erosibility factor, vegetation cover and management factor, and soil and water conservation measures factor values ​​across all grids in the upslope catchment area. Factor values ​​(R, K) outside the range of 0 to 1 need to be standardized.

[0056] Soil erodibility factor K was calculated using the EPIC model:

[0057] Wherein, SAN, SIL, and CLA represent the soil's sand content, silt content, and clay content (%), respectively; SOC represents the soil's organic matter content (%); SN1 = 1 - SAN / 100. SN1 is the proportion of non-sand particles in the soil.

[0058] The vegetation cover and management factor C and the soil and water conservation measures factor P are assigned values ​​based on the land use type of each grid, and adjusted in conjunction with regional characteristics and management measures. The vegetation cover and management factor C reflects the impact of different vegetation covers and management methods on soil erosion, with a value ranging from 0 to 1, where 0 represents complete protection (e.g., dense forests or water bodies) and 1 represents no protection (e.g., bare land). When assigning values, existing literature or databases are referenced, such as the USLE / RUSLE handbook or regional soil erosion studies, to assign typical C values ​​to each land use type. For example, the C value for evergreen forest is 0.001 to 0.01, for farmland (e.g., corn or wheat) it ranges from 0.15 to 0.3, for grassland it is between 0.01 and 0.1, while the C value for bare land is as high as 0.8 to 1.0. This is because bare land has almost no vegetation cover, and the soil is directly exposed to rainfall and runoff, making it extremely susceptible to erosion.

[0059] The soil and water conservation measure factor P is used to quantify the inhibitory effect of soil and water conservation measures on soil erosion. Its value ranges from 0 to 1, where 0 indicates that the measures completely prevent erosion (e.g., terraces), and 1 indicates no measures (e.g., downhill cultivation). The value should be assigned based on the implicit soil and water conservation measures in the land use type (e.g., terraces, contour cultivation, or grassland buffer zones). For example, the P value for downhill cultivation without measures is 1.0, the P value for contour cultivation ranges from 0.5 to 0.8, the P value for terraces is as low as 0.1 to 0.3, and the P value for grassland buffer zones is between 0.3 and 0.6. For non-erosion areas (e.g., water bodies), the P value can be directly assigned as 0.

[0060] In some embodiments, the average rainfall erosivity factor value of the upslope runoff area is obtained by averaging the annual rainfall erosivity factors of all grids in the upslope runoff area. The annual rainfall erosivity factor is calculated by dividing the year into 24 half-months using daily rainfall data, and the results are summed to obtain the annual rainfall erosivity factor. Specifically, the annual rainfall erosivity factor is determined in the following manner: S310. Obtain daily rainfall data and divide the year into several half-months. Calculate the rainfall erosivity for each half-month using the following formula. Collect daily rainfall data for each grid within the watershed or uphill catchment area. Divide the year into multiple half-months (typically 24 half-months, i.e., each half-month is a calculation unit) to more finely reflect the seasonal variations in rainfall erosivity. For each half-month, calculate the rainfall erosivity for that half-month.

[0061] S320. By summing the erosivity of rainfall over all half-month periods, we obtain the annual rainfall erosivity. ; S330. The annual rainfall erosivity is normalized by the natural logarithm to obtain the annual rainfall erosivity factor; Where, R j The rainfall erosivity in the j-th half-month (MJ·mm·hm) -2 ·h -1 ·a -1 ); n is the number of days of erosive rainfall within half a month; α is a correction parameter, which takes different values ​​depending on the month (e.g., 0.3957 for May-September, and 0.3101 for October to April of the following year); P j,k P represents the daily rainfall of the k-th erosive rainfall event within the j-th half-month; when there is no erosive rainfall in that half-month, P... j,k The value is assigned to 0; R is the annual rainfall erosivity (MJ·mm·hm). -2 ·h -1 ); j represents a sequence for each half-month; ;R t The annual rainfall erosivity factor is the result of natural logarithmic standardization of annual rainfall erosivity. and These represent the maximum and minimum annual rainfall erosivity, respectively.

[0062] Erosive rainfall refers to rainfall exceeding a certain threshold, such as 10 mm. The average annual rainfall erosivity factor of the upslope runoff area is obtained by averaging the annual rainfall erosivity factor of all grids within the upslope runoff area.

[0063] This invention divides the year into multiple half-months for calculation, enabling a more precise reflection of the seasonal variations in rainfall erosivity and improving calculation accuracy. It considers only the contribution of erosive rainfall (rainfall exceeding a certain threshold) to soil erosion, eliminating the interference of non-erosive rainfall and making the calculation results closer to reality. The annual rainfall erosivity is normalized using the natural logarithm, eliminating the influence of dimensions and numerical magnitude, making rainfall erosivity factors comparable across different watersheds and years. By calculating the average rainfall erosivity factor value in upslope runoff areas, the contribution of upslope runoff areas to soil erosion can be more accurately assessed, providing a scientific basis for optimal vegetation configuration. By comprehensively considering factors such as seasonal variations in rainfall, the influence of erosive rainfall, and standardization, the accuracy and reliability of the improved sediment connectivity index model are enhanced, making vegetation optimization strategies more scientific and effective.

[0064] In some embodiments, in the downslope component of the IC model, the runoff velocity factor (v) included in the Sediment Delivery Ratio (SDR) formula of the SEDD model is selected to replace the weighting factor in the original algorithm. This makes the improved downslope component completely consistent with the sediment transport time (t) formula in the SDR calculation, attempting to reflect the ease or probability of transport by using the time cost of sediment transport to the channel or the nearest depositional area. Since the determination of sediment transport time (t) in the SDR formula considers factors such as land use, confluence length, and Manning roughness, its physical meaning of describing the time of sediment transport is clearer and conforms to the definition of sediment connectivity. Therefore, the improved sediment connectivity index model will enhance its ability to describe and respond to sediment transport processes and establish an organic relationship with other commonly used important indicators. The runoff velocity factor is determined by the following formula: Where, k i The hindrance coefficient is the coefficient of the i-th grid cell; the hindrance coefficient is determined based on the Manning roughness coefficient corresponding to the land use type.

[0065] First, based on the land use type of the i-th grid cell, find the corresponding Manning roughness value. Manning roughness is a parameter describing the resistance encountered by water flow when passing through different land cover types; different land use types (such as woodland, grassland, cultivated land, bare land, etc.) have different Manning roughness values. Convert the found Manning roughness value into a resistance coefficient; the conversion method can be referred to the description in existing technology. The resistance coefficient reflects the ease or difficulty of water flow passing through that land use type.

[0066] Using digital elevation model data, measure or calculate the slope (s) of the i-th grid. i The greater the gradient, the faster the water flow velocity generally is. The drag coefficient (k) i ) and slope (s i Substituting these values ​​into the calculation, we obtain the internal flow velocity factor (v) of the i-th grid. i This factor comprehensively considers the impact of land use type on water flow resistance and the impact of surface slope on water flow velocity.

[0067] The runoff velocity factor calculation of this invention fully considers the influence of land use type and surface slope on water flow velocity, and can more accurately reflect the actual water flow velocity, providing a more reliable basis for the calculation of the sediment connectivity index. Sediment Connectivity Index (IC) ZQIn the calculation of the runoff velocity factor, the downslope component is closely related to the physical processes of sediment transport. Accurate calculation of the runoff velocity factor enhances the description and response capability of the sediment connectivity index to sediment transport processes, improving the index's accuracy and reliability. By accurately calculating the runoff velocity factor, the sediment transport potential of different grid cells can be assessed more scientifically, providing strong support for optimal vegetation configuration. The flexibility and adaptability of the runoff velocity factor calculation method allow for adjustments to the Manning roughness value or consideration of other influencing factors based on the actual conditions of different watersheds and regions, enabling the improved sediment connectivity index model to better adapt to various complex environmental conditions.

[0068] This invention is based on the changes in sediment connectivity index under different vegetation cover scenarios. Its aim is to preferentially deploy vegetation patches in areas where they can better inhibit erosion and reduce sediment, thereby achieving a more efficient distribution pattern of the same amount of vegetation within the watershed. The specific implementation steps and logical principles are as follows (…). Figure 2 ): ① Selection and Data Preparation of Sediment Connectivity Index. Prepare the necessary basic data for the watershed, including rainfall, soil, and topography. To ensure compatibility between different raster data types during calculation, all data are resampled to the same resolution. Determine the total number of raster cells based on the watershed area and DEM resolution. Calculate the sediment connectivity index distribution raster map for a bare-land scenario using the improved sediment connectivity index model.

[0069] ② Calculation of sediment connectivity across different grid cells. Specific operations include: Step 1: Generate multiple vegetation cover scenarios. Specifically, set up a single-grid vegetation cover scenario (i.e., Figure 2 Scenario 1-4 in the example: if there are 1000 grids in the watershed, vegetation is placed in one grid at a time in the order of the grids, and the remaining grids are bare land, thus obtaining 1000 vegetation cover scenarios.

[0070] Step 2: Calculate the sediment connectivity index IC under different vegetation cover scenarios. ZQ Specifically, the spatial distribution of sediment connectivity index for 1000 vegetation cover scenarios was calculated in batches.

[0071] Step 3: Assemble ICs for vegetation cover grids under different scenarios ZQ Specifically, the sediment connectivity index of the vegetation cover grid under each scenario is used as the final sediment connectivity value of the corresponding grid, and they are combined to form a new spatial distribution grid map of watershed sediment connectivity index; Step 4: Determine the IC under the combined vegetation cover scenario compared to the bare ground scenario. ZQ The difference ∆IC ZQSpecifically, the combined spatial distribution raster map of the watershed sediment connectivity index is spatially overlaid with the spatial distribution raster map of the watershed sediment connectivity index under the scenario of all bare land, and the difference in sediment connectivity index between the two (∆IC) is calculated. ZQ This generates a raster map showing the distribution of the sediment connectivity index difference.

[0072] Step 5: Based on ∆IC ZQ Size determines the priority of vegetation configuration for each grid cell. ∆IC of different grid cells in the watershed. ZQ The numerical values ​​reflect the sediment reduction potential of vegetation configuration compared to bare land. Therefore, the priority order of vegetation configuration is determined by ranking the values ​​from largest to smallest, ∆IC. ZQ A larger value indicates a greater sediment yield potential and a greater sediment reduction potential for vegetation placement within that raster location, making it more suitable for priority vegetation placement. Specifically, the raster-to-point tool in ArcGIS is used to convert the watershed's ∆IC value. ZQ The values ​​in the raster map are sorted from largest to smallest, and the sequential numbering represents the priority order of vegetation configuration for the corresponding raster.

[0073] In some embodiments, the method further includes: S410. Obtain the vegetation configuration priority order of each grid in the target watershed under different characteristic years; for each vegetation type, accumulate the vegetation configuration priority order of all grids with that vegetation type to obtain the total priority order. The vegetation layout method for enhancing soil and water conservation in watersheds, provided by this invention, is used to deploy vegetation in a watershed, obtaining the vegetation type and corresponding vegetation configuration priority order for each grid cell in the watershed. For each vegetation type (such as forest land in Table 1), the vegetation configuration priority orders of all grid cells with that vegetation type are accumulated to obtain the total priority order (such as the total priority order of forest land in Table 1). Simultaneously, for each vegetation type, the number of grid cells with that vegetation type is obtained. The number of grid cells reflects the distribution range of that vegetation type within the watershed.

[0074] S420. For each characteristic year, determine the average priority order based on the total priority order and the number of grids corresponding to each vegetation type under that characteristic year.

[0075] For a given characteristic year, assuming there are n vegetation types, the total priority order of the i-th vegetation type is P. i The number of grid cells is N i The average priority order of this characteristic year. for: S430. Determine the vegetation configuration function level for each characteristic year in ascending order of average priority.

[0076] Based on the average priority order of each characteristic year calculated, the vegetation configuration functional levels are sorted in ascending order.

[0077] In some embodiments, the method further includes: S440. Obtain the actual land use scenario and the average runoff and average sediment transport in characteristic years under the same rainfall.

[0078] S450. Fit the average priority order and average runoff for each characteristic year to obtain the first regression model of average runoff and average priority order; fit the average priority order and average sediment transport for each characteristic year to obtain the second regression model of average sediment transport and average priority order.

[0079] Use statistical software (such as SPSS, R language, etc.) to perform regression analysis and solve for the regression coefficients.

[0080] The fitting process for the second regression model is similar to that described above for fitting the average priority order and average runoff. First, a scatter plot of the average priority order and average sediment load is plotted to observe the data distribution trend and select a regression model. Then, statistical software is used to perform regression analysis, solve for the regression coefficients, and obtain the second regression model.

[0081] S460. Obtain the determination coefficients of the first regression model and the second regression model, and evaluate the effectiveness of vegetation layout based on the determination coefficients of the two regression models.

[0082] This invention establishes a regression model to link vegetation layout (characterized by average priority order) with hydrological effects (average runoff and average sediment transport), thereby quantifying the relationship between the two. A high coefficient of determination indicates a strong correlation between the vegetation configuration priority order determined by this method and actual hydrological effects (such as runoff and sediment transport), suggesting that the vegetation layout can effectively achieve soil and water conservation goals.

[0083] This invention uses a classical hydrological model to simulate runoff and sediment transport under actual land use scenarios in a watershed under annual rainfall conditions, and performs regression analysis with the determined average priority order of the watershed vegetation cover grid to verify the effectiveness of the vegetation patch optimization configuration method. The specific steps are as follows: Using ArcGIS, we extracted the vegetation configuration priority order of watershed vegetation cover rasters (woodland or grassland) under different characteristic years and calculated the average priority order. We then sorted the vegetation patches according to the average priority order. The higher the ranking, the higher the overall functional level of erosion and sediment reduction in the corresponding land use scenario, and the stronger the potential sediment reduction effect. We used a classical hydrological model to simulate watershed runoff and sediment transport under the same rainfall in different characteristic years. We then performed regression analysis on the vegetation configuration priority order corresponding to the land use scenario in each characteristic year and the ranking of simulated watershed runoff and sediment transport to evaluate the effectiveness of the vegetation patch optimization configuration method.

[0084] The following example, using the vegetation layout process for soil and water conservation in a certain watershed, illustrates the aforementioned method for enhancing the effectiveness of vegetation layout in soil and water conservation within a watershed: First, in the arid and semi-arid region of the Loess Plateau, a specific watershed was selected, and topographic, soil, and land use data, as well as all meteorological and hydrological data from the time meteorological, runoff, and sediment transport observations up to 2020, were collected. The total number of grid cells was determined based on the watershed area and DEM resolution, and a single-grid vegetation cover scenario was set. An improved sediment connectivity index model was used to calculate the sediment connectivity index of vegetation cover for each grid cell, and these were combined to form a spatial distribution map of the sediment connectivity index of the watershed vegetation cover. This spatial distribution map was then spatially overlaid with the spatial distribution map of the watershed sediment connectivity index under the bare land scenario, and the difference between the two was calculated. The vegetation configuration priority was determined according to the difference from largest to smallest. Secondly, the SWAT model was used to simulate the watershed runoff and sediment transport under actual land use scenarios and the same rainfall conditions (2001-2020) in 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. Then, regression analysis was performed on the average vegetation priority order corresponding to the land use scenarios in each characteristic year and the simulated average runoff and sediment transport order to evaluate the effectiveness of the vegetation patch optimization method.

[0085] Based on the ΔIC of this watershed ZQ The distribution, sorted from largest to smallest, yielded a priority distribution map of vegetation configuration in the watershed (e.g., Figure 3 As shown in the figure, the vegetation configuration priority order of each grid is unique and definite. It can be seen that the vegetation configuration priority order of the watershed slope is relatively high, the vegetation configuration priority order of the watershed and other parts is relatively low, and the vegetation configuration priority order of the watershed and other parts is in the middle.

[0086] Reference Figure 4 As shown, the annual runoff and annual sediment load in the characteristic years of this watershed both show a continuous decreasing trend, with the annual runoff in each characteristic year ranging from 0 to 600,000 m³. 3 The highest annual runoff was recorded in 1985, reaching 214,400 m³.3 The lowest was in 2020, at 163,800 m³. 3 The annual sediment transport volume varies from 0 to 60,000 tons in different years, and its variation is basically consistent with the annual runoff. The annual sediment transport volume was the highest in 1985, reaching 20,800 tons, and the lowest in 2020, at only 10,900 tons. Figure 4 (a) in the figure is a schematic diagram of the simulation results of annual runoff in different characteristic years; Figure 4 (b) in the figure is a schematic diagram of the simulation results of annual sediment transport in different characteristic years.

[0087] Based on the calculated IC for different characteristic years of the watershed ZQ The average priority order was calculated by taking the annual runoff and annual sediment load simulated by the SWAT model and calculating the average priority order of vegetation configuration in the watershed vegetation cover grid (woodland or grassland) under different characteristic years. The vegetation configuration function level of the corresponding land use scenario was then determined by the average priority order from small to large. That is, the smaller the average priority order, the higher the function level of the vegetation configuration in the corresponding land use pattern, and the more conducive it is to erosion prevention and sediment reduction. The calculation results of the above method are shown in Table 1. Taking 1985 as an example, the average priority order = (20833508 + 4417361) / (3042 + 645) = 6847.

[0088] Table 1

[0089] The overall functional level of the land use scenario in this watershed was lowest in 1985 (highest value) and highest in 2020 (lowest value). Furthermore, the ranking of functional levels among different characteristic years was consistent with the ranking of average runoff and average sediment transport for the corresponding years. Moreover, there was a significant linear relationship (P<0.01) between the average priority order of vegetation cover grids in different characteristic years and the average runoff and average sediment transport for the corresponding years. Figure 5 As shown in (a) and (b) in the figure. This demonstrates that the overall functional level of land use can reflect its erosion and sediment reduction capacity, and also proves the effectiveness of the watershed soil and water conservation vegetation enhancement layout of the present invention. Figure 5 (a) and (b) in the figure are the fitting relationship between the average priority order and the simulated average runoff and average sediment transport in different characteristic years.

[0090] This invention utilizes an improved sediment connectivity index model to determine the priority order of different grid vegetation configurations within a watershed, enabling rapid and efficient vegetation configuration aimed at enhancing soil and water conservation. Furthermore, it employs a classical hydrological model to simulate annual watershed runoff and annual sediment load under actual land use scenarios in different characteristic years. Regression analysis is then performed between the average priority order corresponding to each characteristic year and the simulated average runoff and average sediment load rankings. This verifies and evaluates the watershed soil and water conservation vegetation efficiency layout method of this invention. The method is generally simple to apply and yields good results.

[0091] The following describes the watershed soil and water conservation vegetation enhancement layout system provided by the present invention. The watershed soil and water conservation vegetation enhancement layout system described below can be referred to in correspondence with the watershed soil and water conservation vegetation enhancement layout method described above.

[0092] The watershed soil and water conservation vegetation enhancement layout system provided by this invention refers to... Figure 6 As shown, it includes: The data processing module 510 is used to obtain a preset sediment connectivity index model, and based on the preset sediment connectivity index model, calculate the sediment connectivity index of each grid cell in the target watershed when the grid cell is covered by vegetation, and combine the sediment connectivity indices of all grid cells to form a spatial distribution map of sediment connectivity index of vegetation cover in the watershed. The spatial overlay module 520 is used to obtain the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed, and to spatially overlay it with the spatial distribution map of sediment connectivity index under vegetation cover of the watershed to generate a sediment connectivity index difference distribution map. The order determination module 530 is used to determine the priority order of vegetation configuration for each grid cell based on the sediment connectivity index difference distribution map, in descending order of sediment connectivity index difference, and to deploy vegetation according to the priority order of vegetation configuration.

[0093] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute a watershed soil and water conservation vegetation enhancement layout method.

[0094] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the watershed soil and water conservation vegetation enhancement layout method provided by the above methods.

[0096] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the watershed soil and water conservation vegetation enhancement layout method provided by the above methods.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for enhancing vegetation layout in watershed soil and water conservation, characterized in that, include: Obtain a preset sediment connectivity index model. Based on the preset sediment connectivity index model, calculate the sediment connectivity index of each grid cell in the target watershed when the grid cell is covered by vegetation. Combine the sediment connectivity indices of all grid cells to form a spatial distribution map of sediment connectivity index of vegetation cover in the watershed. Obtain the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed, and spatially overlay it with the spatial distribution map of sediment connectivity index under vegetation cover of the watershed to generate a sediment connectivity index difference distribution map. Based on the distribution map of sediment connectivity index difference, the priority order of vegetation configuration for each grid is determined according to the sediment connectivity index difference from large to small, and vegetation is deployed according to the priority order of vegetation configuration.

2. The watershed soil and water conservation vegetation enhancement layout method according to claim 1, characterized in that, The process of obtaining a spatial distribution map of sediment connectivity index under a bare land scenario in the target watershed, and spatially overlaying it with a spatial distribution map of sediment connectivity index under vegetation cover in the watershed to generate a sediment connectivity index difference distribution map, includes: Obtain a spatial distribution map of sediment connectivity index under bare land scenario in the target watershed; For each grid cell within the target watershed, the difference between the sediment connectivity index of the grid cell under individual vegetation cover and the sediment connectivity index of the corresponding grid cell under bare land scenario is calculated to obtain the sediment connectivity index difference value. A sediment connectivity index difference distribution map is generated based on the sediment connectivity index difference of all grids.

3. The watershed soil and water conservation vegetation enhancement layout method according to claim 1, characterized in that, The method involves determining the vegetation configuration priority order for each grid cell based on the sediment connectivity index difference distribution map, from largest to smallest, and then deploying vegetation according to the priority order, including: All grids within the target watershed are sorted, and their vegetation configuration priority is determined by the difference in sediment connectivity index from largest to smallest. According to the vegetation configuration priority, vegetation is first deployed in the areas with the higher priority.

4. The watershed soil and water conservation vegetation enhancement layout method according to claim 1, characterized in that, The method further includes: Obtain the priority order of vegetation configuration for each grid cell in the target watershed under different characteristic years; For each vegetation type, the total priority order is obtained by accumulating the vegetation configuration priority order of all grids of that vegetation type. For each characteristic year, the average priority order is determined based on the total priority order and the number of grids corresponding to each vegetation type in that characteristic year. The vegetation configuration function level for each characteristic year is determined by ranking the average priority from smallest to largest.

5. The watershed soil and water conservation vegetation enhancement layout method according to claim 4, characterized in that, The method further includes: Obtain actual land use scenarios and runoff and sediment transport in characteristic years under the same rainfall; By fitting the average priority order and runoff volume under each characteristic year, a first regression model for runoff volume and average priority order is obtained; By fitting the average priority order and sediment transport volume under each characteristic year, a second regression model on sediment transport volume and average priority order is obtained; Obtain the determination coefficients of the first regression model and the second regression model, and evaluate the effectiveness of vegetation layout based on the determination coefficients of the two regression models.

6. A watershed soil and water conservation vegetation enhancement layout system, characterized in that, include: The data processing module is used to obtain a preset sediment connectivity index model, calculate the sediment connectivity index of each grid cell when the grid cell is covered by vegetation, and combine the sediment connectivity indices of all grid cells to form a spatial distribution map of sediment connectivity index of watershed vegetation cover. The spatial overlay module is used to obtain the spatial distribution map of sediment connectivity index under the bare land scenario of the target watershed, and to spatially overlay it with the spatial distribution map of sediment connectivity index under vegetation cover of the watershed to generate a sediment connectivity index difference distribution map. The order determination module is used to determine the priority order of vegetation configuration for each grid cell based on the distribution map of sediment connectivity index difference, in descending order of sediment connectivity index difference, and to deploy vegetation according to the priority order of vegetation configuration.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the watershed soil and water conservation vegetation enhancement layout method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the watershed soil and water conservation vegetation enhancement layout method as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the watershed soil and water conservation vegetation enhancement layout method as described in any one of claims 1 to 5.

Images