A watershed-scale LID spatial layout method based on confluence paths

Abstract

This invention discloses a method for spatial deployment of LID (Limited Area Identification) based on confluence paths, comprising the following steps: S1. Dividing sub-catchments; S2. Constructing an evaluation system for the benefits of LID deployment in sub-catchments; S3. Analyzing the benefits of LID deployment based on confluence paths; S4. Verifying its rationality. Compared with existing technologies, this method provides precise LID deployment location information, rather than the traditional LID area proportion. This method is applicable to various spatial scales from small plots to watersheds. By combining index evaluation with numerical simulation, it can quickly obtain the spatial deployment location of LIDs and calculate their hydrological benefits.

Description

Technical Field

[0001] This invention belongs to the technical field of Low Impact Development (LID) facilities and their hydrological effects, specifically relating to a watershed-scale LID spatial deployment method based on confluence paths. Background Technology

[0002] As a relatively independent hydrological unit, a watershed's rainwater runoff characteristics and ecological functions are of great significance to regional hydrological cycles and environmental protection. The scientific deployment of Light-Induced Discharge (LID) facilities can effectively mitigate the impacts of human activities and disaster-causing topography on the watershed's hydrological cycle, reduce total runoff and peak flow, alleviate flood control pressure on downstream channels, and simultaneously promote the efficient use of rainwater resources and improve the watershed's ecological environment. The spatial deployment of LID facilities is a multi-objective, multi-scale combined optimization problem involving type, quantity, and location, requiring comprehensive consideration of the watershed's topographic features, current land use, rainfall intensity, and socio-economic factors.

[0003] In recent years, numerous studies have been conducted by scholars both domestically and internationally regarding the optimal deployment of Light-Induced Drainage (LID) facilities. However, previous research has used the deployment area of ​​LID facilities as the basis for spatial distribution to simulate their regulatory effects on the hydrological cycle. This approach can only provide the deployment ratio within a specific sub-catchment area and cannot accurately determine the deployment and simulation of LID facilities. Therefore, developing a multi-objective optimization framework based on runoff paths and its hydrological benefit assessment can accurately identify suitable deployment locations for LID facilities, which has significant theoretical and practical value for optimizing their spatial deployment and simulating their hydrological benefits. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a watershed-scale LID spatial layout method based on confluence paths.

[0005] The watershed-scale LID spatial deployment method based on confluence paths of the present invention includes the following steps:

[0006] S1. Divide the watershed into sub-catchments;

[0007] S2. Construct an evaluation system for the benefits of LID deployment in sub-catchment areas;

[0008] S3. LID deployment benefit analysis based on confluence path;

[0009] S4. Reasonableness verification.

[0010] The process of dividing the watershed into sub-catchments includes the following steps:

[0011] S101. Collect spatial and attribute data of the study area;

[0012] S102. Data preprocessing;

[0013] S103. Sub-basin delineation;

[0014] S104. Output the results.

[0015] The S2 construction of the LID deployment benefit evaluation system for the sub-catchment area requires the calculation of hydrological index, land use index, landscape ecological pattern index and techno-economic index.

[0016] The hydrological indices include elevation stress index, slope stress index, topographic humidity stress index, and undulation stress index; the land use index includes the proportion of impervious area and the proportion of building area; the landscape ecological pattern index includes the number and shape of patches; and the techno-economic index is composed of the unit cost of different LIDs.

[0017] S3 includes the following steps:

[0018] S301. Calculate the cumulative runoff within the watershed and generate a raster layer of runoff paths;

[0019] S302. Generate multiple straight lines perpendicular to the confluence path and extract the evaluation indices on the confluence path;

[0020] S303. Construct a LID deployment benefit evaluation system based on the confluence path and identify sensitive points on the confluence path;

[0021] S304. Based on the identified sensitive points along the confluence path, establish a multi-objective optimization model for LID facilities that considers both hydrological benefits and engineering costs, and determine the location, type, and proportion of LIDs along the confluence path.

[0022] S305. Model Output.

[0023] The formula for the LID deployment benefit evaluation system based on the merging path is as follows:

[0024] LBI(p i ) = w1·ESCRI(p i )+w2·SSCRI(p i )+w3·TWSCRI(p i )+w4·RSCRI(p i )

[0025] In the formula, LBI is the LID deployment benefit evaluation index, assuming the pixel sequence along the confluence path is P={p1,p2,...,p n}, where p i Represents the i-th pixel, ESCRI(p i SSCRI (p) is the elevation stress rate of change index along the confluence path. iTWSCRI(p) represents the slope stress rate of change index along the confluence path. i RSCRI(p) represents the rate of change of topographic humidity stress along the confluence path. i ) represents the rate of change of undulating stress along the confluence path, and w1 represents the ESCRI(p i The weights of SSCRI(p) are w2 and w2 is the weight of SSCRI(p). i The weights of TWSCRI(p) are w3, where w3 is the weight of TWSCRI(p). i The weights of ) are w4, where w4 is the weight of RSCRI(p). i The weight of ).

[0026] The formula for calculating the elevation stress rate of change index is as follows:

[0027]

[0028] In the formula, ESI(p i ) is the elevation stress index of the i-th pixel, ESI(p) i-1 ) is the elevation stress index of the (i-1)th pixel, d(p i ,p i-1 ) is the spatial distance between the i-th pixel and the (i-1)-th pixel.

[0029] The multi-objective optimization model is as follows:

[0030] Max Z = α1·H - α2·C + α3·S

[0031] Where Z is the objective function, H is the total hydrological benefit, C is the total project cost, S is the total landscape benefit, and α1, α2, and α3 are the weights of hydrological benefit, project cost, and landscape benefit, respectively, and α1+α2+α3=1.

[0032] The formula for calculating the total hydrological benefits is as follows:

[0033]

[0034] In the formula, H represents the total hydrological benefit, and LBI i Let x be the LBI value of the i-th sensitive point. i Let x be the decision variable, representing whether to deploy LID facilities at the i-th sensitive point (x). i =1 indicates deployment, x i =0 indicates no deployment), h i The hydrological benefit coefficient for deploying LID facilities at the i-th sensitive point.

[0035] The rationality verification of S4 refers to simulating the hydrological benefits of LID at different locations by improving the LID setting scheme in SWMM; verifying the hydrological benefits of both to ultimately determine the reasonable LID deployment location, LID deployment type, and LID deployment ratio.

[0036] The beneficial effects of this invention are that it proposes a spatial deployment method for LIDs based on confluence paths. Compared with previous technologies, this method provides accurate LID deployment location information, rather than the traditional LID area proportion. This method is applicable to various spatial scales from small plots to watersheds. By combining index evaluation with numerical simulation, it can quickly obtain the spatial deployment location of LIDs and calculate their hydrological benefits. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the watershed-scale LID spatial layout method based on the confluence path of the present invention.

[0038] Figure 2 This is a topographical diagram of the present invention.

[0039] Figure 3 This is a schematic diagram of land use classification according to the present invention.

[0040] Figure 4 This is a schematic diagram of the sub-catchment area division of the present invention.

[0041] Figure 5 Therefore Figure 4 A schematic diagram of the confluence path and vertical line, taking the S0 sub-basin as an example.

[0042] Figure 6 Therefore Figure 4 A schematic diagram of the confluence path and sensitive points, taking the S0 sub-basin as an example.

[0043] Figure 7 This is a schematic diagram comparing the flood peak results of LID deployment using the method described in this application and LID deployment using conventional methods. Detailed Implementation

[0044] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0045] like Figures 1-7 As shown, the watershed-scale LID spatial deployment method based on confluence paths of the present invention includes the following steps:

[0046] S1. Divide the watershed into sub-catchments;

[0047] S101. Collect spatial and attribute data of the study area;

[0048] (1) Spatial data.

[0049] High-precision digital elevation model (DEM), high-precision land use data (LULC), and soil data.

[0050] (2) Attribute data.

[0051] Ecological and environmental data include the distribution of nature reserves, vegetation coverage, and ecologically sensitive areas.

[0052] Economic and technical data: Collect information on the construction and maintenance costs of LID facilities, as well as land development restrictions.

[0053] S102. Data preprocessing;

[0054] (1) Convert all data into raster data, crop to the study area, and unify the resolution.

[0055] (2) Process the DEM using hydrological analysis tools (such as PyFLWDir and TauDEM) to generate the following necessary data:

[0056] Flow Direction Grid (FDR): Represents the direction of water flow in each cell.

[0057] FlowAccumulation Grid: Represents the cumulative flow through each cell.

[0058] Flow direction matrix: PyFLWDir uses the flow direction matrix as the core input data to define the direction of water flow for each cell.

[0059] If a flow direction matrix (FDR) is not readily available, it can be extracted from the DEM using the following steps:

[0060] 1) Depression Filling: Eliminate depressions in the digital elevation model to ensure smooth water flow.

[0061] 2) Flow Direction Calculation: Based on the terrain gradient, determine which neighboring cell the water flows from from each cell.

[0062] 3) Generate flow accumulation map: Calculate the catchment area of ​​each cell to identify high flow areas.

[0063] 4) According to the D8 algorithm, connect the pixels with high cumulative flow to obtain the confluence routes in the study area, and then determine the flow direction matrix (FDR).

[0064] S103. Sub-basin delineation;

[0065] (1) Sub-basin division logic

[0066] PyFLWDir performs sub-basin partitioning according to the following process:

[0067] Upstream tracing: Tracing upstream from the specified exit point along the flow matrix, marking all upstream cells connected to the exit point.

[0068] Independent mask generation: Repeat the above steps to generate an independent sub-basin mask for each exit point.

[0069] (2) Determine the sub-basin division points

[0070] Automatic confluence point identification: Based on the flow accumulation grid, high-flow cells are identified, which typically correspond to the main confluence points or outlets of the river.

[0071] Manually set control points: You can also manually specify the exit point according to actual needs to further control the scope of the sub-basin division.

[0072] S104. Output the results.

[0073] The data processing results include:

[0074] Raster mask: Marks the cells corresponding to each sub-watershed for further spatial analysis.

[0075] Boundary vectors: Boundary data of sub-basins, which can be exported as GIS file formats (such as Shapefile) for visualization or analysis.

[0076] Hydrological characteristics statistics: Key indices such as area, slope, aspect, slope variation, and topographic ruggedness of each sub-basin are used for hydrological research or management decisions.

[0077] S2. Construct an evaluation system for the benefits of LID deployment in sub-catchment areas;

[0078] Constructing an evaluation system for LID Allocation (ESLA) for sub-catchment areas requires calculating the Hydrology Index (HI), Landcover Index (LCI), Landscape Index (LSI), and Techno-economic Index (TEI).

[0079] The hydrological indices include the elevation stress index, slope stress index, topographic humidity stress index, and undulation stress index; the land use index includes the proportion of impervious area and the proportion of building area; the landscape ecological pattern index includes the number and shape of patches; and the techno-economic index is composed of the unit cost of different LIDs.

[0080] 1. Calculate the hydrological index.

[0081] (1) Elevation Stress Index (ESI)

[0082] Elevation Stress Index (E i The elevation stress index (ESSI) is a standardized index calculated based on digital elevation data (DEM) to measure the degree of deviation of the elevation of a given pixel from the overall elevation range of the study area. The Elevation Stress Index can help quantify the impact of elevation on ecology, flooding, or other environmental factors.

[0083] Elevation Stress Index Formula:

[0084]

[0085] In the formula, H i : Current pixel elevation value; H min : The lowest elevation value of the study area; H max : The highest elevation value in the study area.

[0086] explain:

[0087] When E i When E = 0, it indicates that the pixel is at the lowest elevation; when E i When E = 1, it indicates that the pixel is at its highest elevation. i The value is between [0,1], representing the standardized result of the elevation.

[0088] In ArcGIS, the elevation stress index can be calculated using the following steps: First, use the Zonal Statisticsas Table tool to obtain the minimum and maximum values ​​of the Digital Elevation Model (DEM); then, use the Raster Calculator tool to calculate it according to the formula mentioned above. The output is the elevation stress index raster, which reflects the relative degree of elevation stress.

[0089] (2) Slope Stress Index (SSI)

[0090] Slope Stress Index (S iThe slope stress index is a standardized index calculated based on slope data, used to measure the degree of deviation of the slope of a given pixel from the overall slope range of the study area. The slope stress index can help quantify the impact of slope on runoff, soil erosion, and other topographically related environmental factors.

[0091] Slope stress index formula:

[0092]

[0093] In the formula, θ i θ: The slope value of the current pixel. min : Minimum slope value of the study area; θ max : The highest slope value in the study area.

[0094] explain:

[0095] When S i When S = 0, it indicates that the pixel is at the lowest slope; when S i When S = 1, it indicates that the pixel is at the highest slope. i The value is between [0,1], representing the standardized result of the slope.

[0096] In ArcGIS, the slope stress index can be calculated through the following steps: First, use the Slope tool to generate a slope raster; then use the Zonal Statistics as Table tool to obtain the minimum and maximum values ​​of the slope raster; finally, use the Raster Calculator tool to calculate the slope stress index according to the above formula. The output result is the slope stress index raster, which reflects the relative stress level of the slope.

[0097] (3) Terrain Humidity Stress Index (TWSI)

[0098] Topographic Wetness Stress Index (T i The wetland index (WTI) is calculated based on catchment area and slope, and is used to measure the degree to which topographic conditions affect wetland humidity or soil moisture. This index is widely used in hydrological analysis to predict wetland distribution, precipitation retention, and areas with potential flood risk.

[0099] Formula for topographic humidity stress index:

[0100]

[0101] In the formula, A i : The upstream catchment area of ​​the current pixel; β i : The slope value (in radians) of the current cell.

[0102] explain:

[0103] T i A higher value indicates a region with higher humidity (such as low-lying areas or wetlands); T i A lower value indicates a less humid area (such as a steep slope or high ground).

[0104] The steps for calculating the topographic humidity stress index in ArcGIS are as follows: First, use the Flow Accumulation tool to generate a catchment area raster; second, use the Slope tool to generate a slope raster and convert the slope units to radians; finally, use the Raster Calculator tool to calculate according to the formula to generate the topographic humidity stress index raster.

[0105] (4) Rising Stress Index (RSI)

[0106] ReliefStress Index (R) i The elevation standard deviation (PED) is an index calculated based on the elevation standard deviation to reflect the impact of terrain undulation on the geographical environment. This index can be used to assess the complexity or ruggedness of the terrain.

[0107] Formula for fluctuating stress index:

[0108]

[0109] In the formula, σ i σ: Standard deviation of elevation of the current pixel; min σ represents the minimum standard deviation of elevation in the study area. max Standard deviation of the highest elevation in the study area.

[0110] explain:

[0111] When R i When R = 0, it indicates that the terrain undulation is minimal; when R = 0, it indicates that the terrain undulation i When R = 1, it indicates the maximum topographic relief. i The value is between [0,1], representing the standardized result of the degree of fluctuation.

[0112] In ArcGIS, the undulation stress index can be calculated through the following steps: First, use the Focal Statistics tool to calculate the local standard deviation of the elevation raster; then use the Zonal Statistics as Table tool to obtain the minimum and maximum values ​​of the local standard deviation; finally, use the Raster Calculator tool to calculate according to the formula, and the output result is the undulation stress index raster, which reflects the relative stress level of terrain undulation.

[0113] 2. Calculate the land use index (LCIs).

[0114] (1) Percentage of impermeable area (PIA)

[0115] The Percentage of Impervious Area (PIA) refers to the proportion of impervious surfaces (such as buildings, roads, and parking lots) within a given area (e.g., a sub-catchment area, an urban area). It is an important indicator in hydrological analysis and urban planning, used to assess a region's surface runoff, rainwater infiltration capacity, and urbanization level.

[0116] Calculation formula:

[0117] Calculation steps:

[0118] 1) Determine the scope of the sub-catchment area: Clarify the boundary of the sub-catchment area to be calculated.

[0119] 2) Obtain the total area of ​​the sub-catchment: Calculate the total area of ​​the sub-catchment (usually in square meters or hectares) using Geographic Information System (GIS) or remote sensing data.

[0120] 3) Extracting the impermeable area: The impermeable area includes the area of ​​impermeable surfaces such as buildings, roads, and parking lots.

[0121] Using land use data, remote sensing imagery, or GIS tools, extract the area of ​​all impermeable surfaces within the sub-catchment area.

[0122] 4) Calculate the percentage of impervious area: Divide the impervious area by the total area of ​​the sub-catchment area, and then multiply by 100% to obtain the percentage of impervious area (PIA).

[0123] (2) Building Area Percentage (PBA)

[0124] The percentage of building area (PBA) refers to the proportion of the area occupied by buildings within a specific area (such as a sub-catchment or urban block). It is an important indicator for measuring the degree of urbanization and land use intensity, and is commonly used in urban planning, hydrological analysis, and environmental impact assessment.

[0125] Calculation formula:

[0126] 3. Calculate the landscape ecological pattern index (LSIs).

[0127] (1) Number of patches (PN)

[0128] Patch number (PN) is an important index in landscape ecology, used to describe the number of patches (i.e., areas with similar land use or vegetation types; in this application, it specifically refers to designated areas where LID (Land Use Indicator) needs to be assigned) in a landscape. It is one of the key indicators for assessing the degree of landscape fragmentation and spatial heterogeneity.

[0129] Calculation formula: PN = Total number of patches in the landscape

[0130] Calculation steps:

[0131] 1) Determine the study area: Clarify the scope of the landscape to be analyzed.

[0132] 2) Obtain land use / cover data: Use remote sensing imagery, land use maps, or GIS data to obtain land use / cover classification data for the study area.

[0133] 3) Identify patches: Use GIS software (such as ArcGIS, QGIS) or landscape ecology software (such as FRAGSTATS) to identify patches based on land use / cover classification data.

[0134] Patch identification is usually based on classification results, grouping areas of the same type into a single patch.

[0135] 4) Count the number of patches: The total number of all patches in the landscape is called the number of patches (PN).

[0136] (2) Shape Index (SI)

[0137] The Shape Index (SI) is an important index in landscape ecology used to describe the shape complexity of patches in a landscape. It reflects the degree to which the boundary shape of a patch deviates from a regular geometric shape (such as a circle or square), and is one of the important indicators for assessing landscape spatial structure and ecological processes.

[0138] Calculation formula:

[0139] Where: Patch perimeter: the total length of the patch boundary; Patch area: the total area of ​​the patch.

[0140] Calculation steps:

[0141] 1) Determine the study area: Clarify the scope of the landscape to be analyzed.

[0142] 2) Obtain land use / cover data: Use remote sensing imagery, land use maps, or GIS data to obtain land use / cover classification data for the study area.

[0143] 3) Identify patches: Use GIS software (such as ArcGIS, QGIS) or landscape ecology software (such as FRAGSTATS) to identify patches based on land use / cover classification data.

[0144] 4) Calculate the perimeter and area of ​​each patch: Use GIS tools to calculate the perimeter and area of ​​each patch.

[0145] 5) Calculate the shape index: Calculate the shape index (SI) for each patch according to the formula.

[0146] 6) Statistical analysis: Calculate the average, maximum, minimum and other statistical measures of the shape index of all patches in the landscape.

[0147] 4. Calculate the Technology and Economic Index (TEI) (UC)

[0148] The techno-economic index is composed of the unit cost (UC) for different Low Impact Development (LID) measures, used to assess the economic costs of various LID measures. LID measures include: rain gardens, permeable paving, green roofs, and vegetated buffer zones. Unit cost: refers to the construction and maintenance cost of an LID measure per unit area or per unit volume (usually expressed in RMB / square meter or RMB / cubic meter).

[0149] Calculation formula:

[0150] Among them: Total cost: includes construction cost (such as materials and construction) and operation and maintenance cost (such as maintenance and management); Scale: can be the area (such as square meters) or volume (such as cubic meters) of the LID measures.

[0151] Calculation steps:

[0152] 1) Determine the type of LID measure: Identify the type of LID measure that needs to be calculated (such as rain gardens, permeable paving, green roofs, etc.).

[0153] 2) Collect cost data: Construction costs include material costs, construction costs, equipment costs, etc.; Operation and maintenance costs include maintenance fees, management fees, energy consumption, etc. Data sources can include project budgets, historical data, market research, or literature.

[0154] 3) Determine the scale of LID measures: Calculate the area or volume of LID measures.

[0155] 4) Calculate the total cost: Add the construction cost and the operation and maintenance cost to get the total cost of LID measures.

[0156] 5) Calculate the unit cost: Divide the total cost by the scale of the LID measures to obtain the unit cost (UC).

[0157] S3. LID deployment benefit analysis based on confluence path;

[0158] S301. Calculate the cumulative runoff within the watershed and generate a raster layer of runoff paths;

[0159] S302. Generate multiple straight lines perpendicular to the confluence path. The spacing between these perpendicular lines can be calculated (generally 1 km). 2 In the sub-catchment area (with a vertical spacing of 30m), each evaluation index on the confluence path is extracted.

[0160] The specific indices are as follows:

[0161] (1) Elevation stress change rate index along the confluence path

[0162] The Elevation Stress Change Rate Index (ESCRI) along a confluence path is defined based on the Elevation Stress Index (ESI) and the characteristics of the confluence path. The ESCRI aims to quantify the rate of change of the Elevation Stress Index along the confluence path, thereby reflecting the spatial trend of elevation stress.

[0163] The Elevation Stress Change Rate Index (ESCRI) along a confluence path refers to the rate of change of the elevation stress index (ESI) of each pixel along the confluence path relative to its upstream pixels. It reflects the spatial rate of change of elevation stress along the confluence path and can be used to analyze the dynamic impact of elevation on hydrological, ecological, or other environmental processes.

[0164] Suppose the pixel sequence along the confluence path is P = {p1, p2, ..., p...} n}, where p i Represents the i-th pixel, ESI(p) i ) represents the elevation stress index of the i-th pixel.

[0165] The elevation stress rate of change index (ESCRI) along the confluence path is then... i ) is defined as:

[0166]

[0167] Where: ESI(p i ) is the elevation stress index of the i-th pixel;

[0168] ESI(p i-1 ) is the elevation stress index of the (i-1)th pixel (upstream pixel);

[0169] d(p i ,p i-1 ) is the spatial distance (usually the cell size) between the i-th cell and the (i-1)-th cell.

[0170] (2) Slope stress change rate index along the confluence path

[0171] The Slope Stress Change Rate Index (SSCRI) along a confluence path is defined based on the Slope Stress Index (SSI) and the characteristics of the confluence path. It is used to quantify the rate of change of the Slope Stress Index along the confluence path, thereby reflecting the dynamic impact of slope on runoff, soil erosion, and other topographically related environmental factors.

[0172] The Slope Stress Rate of Change Index (SSCRI) along a confluence path refers to the rate of change of the Slope Stress Index (SSI) of each pixel along the confluence path relative to its upstream pixels. It reflects the spatial rate of change of slope stress along the confluence path and can be used to analyze the dynamic impact of slope on hydrological processes, soil erosion, or other environmental processes.

[0173] Suppose the pixel sequence along the confluence path is P = {p1, p2, ..., p...} n}, where p i SSI(p) represents the i-th pixel. i ) represents the slope stress index of the i-th pixel.

[0174] The slope stress rate of change index (SSCRI) along the confluence path is then... i ) is defined as:

[0175]

[0176] Where: SSI(p i ) is the slope stress index of the i-th pixel.

[0177] SSI(p i-1 ) is the slope stress index of the (i-1)th pixel (upstream pixel).

[0178] d(p i ,p i-1 ) is the spatial distance (usually the cell size) between the i-th cell and the (i-1)-th cell.

[0179] (3) Rate of change of topographic humidity stress along the confluence path

[0180] The Topographic Wetness Stress Change Rate Index (TWSCRI) along a confluence path is defined based on the Topographic Wetness Stress Index (TWSI) and the characteristics of the confluence path. It is used to quantify the rate of change of the Topographic Wetness Stress Index along the confluence path, thereby reflecting the dynamic impact of topographic moisture on humidity, soil moisture, and other hydrological processes.

[0181] The Terrain Humidity Stress Variation Rate Index (TWSCRI) along a confluence path refers to the rate of change of the Terrain Humidity Stress Index (TWSI) of each pixel along the confluence path relative to its upstream pixels. It reflects the spatial rate of change of terrain humidity stress along the confluence path and can be used to analyze the dynamic impact of terrain humidity on hydrological processes, wetland distribution, or other environmental processes.

[0182] Suppose the pixel sequence along the confluence path is P = {p1, p2, ..., p...} n}, where p i TWSI(p) represents the i-th pixel. i ) represents the topographic humidity stress index of the i-th pixel.

[0183] The topographic humidity stress rate of change index (TWSCRI) along the confluence path is then... i ) is defined as:

[0184]

[0185] Where: TWSI(p i ) is the topographic humidity stress index of the i-th pixel.

[0186] TWSI(p i-1 ) is the topographic humidity stress index of the (i-1)th pixel (upstream pixel).

[0187] d(p i ,p i-1 ) is the spatial distance (usually the cell size) between the i-th cell and the (i-1)-th cell.

[0188] (4) The rate of change of undulation stress along the confluence path

[0189] The Relief Stress Change Rate Index (RSCRI) along a confluence path is defined based on the Relief Stress Index (RSI) and the characteristics of the confluence path. It is used to quantify the rate of change of the Relief Stress Index along the confluence path, thereby reflecting the dynamic impact of topographic relief on the geographical environment.

[0190] The Rate of Change of Topographic Stress Along Confluence Path (RSCRI) refers to the rate of change of the Topographic Stress Index (RSI) of each pixel along the confluence path relative to its upstream pixel. It reflects the spatial rate of change of topographic undulation stress along the confluence path and can be used to analyze the dynamic impact of topographic undulation on the geographic environment, hydrological processes, or other environmental processes.

[0191] Suppose the pixel sequence along the confluence path is P = {p1, p2, ..., p...} n}, where p i Represents the i-th cell, RSI(p) i ) represents the fluctuation stress index of the i-th pixel.

[0192] The rate of change of undulation stress along the confluence path is expressed as RSCRI (p i ) is defined as:

[0193]

[0194] Where: RSI(p i ) is the fluctuation stress index of the i-th pixel.

[0195] RSI(p i-1 ) is the fluctuation stress index of the (i-1)th pixel (upstream pixel).

[0196] d(p i ,p i-1 ) is the spatial distance (usually the cell size) between the i-th cell and the (i-1)-th cell.

[0197] S303. Construct a LID deployment benefit evaluation system based on the confluence path and identify sensitive points on the confluence path;

[0198] (1) Construct a LID deployment benefit evaluation system based on the confluence path.

[0199] The evaluation indices along the convergence path are combined into a single LID deployment benefit evaluation index (LID BenefitIndex, LBI), as shown in the following formula:

[0200] LBI(p i ) = w1·ESCRI(p i )+w2·SSCRI(p i )+w3·TWSCRI(p i )+w4·RSCRI(p i )

[0201] In the formula, LBI is the LID deployment benefit evaluation index, assuming the pixel sequence along the confluence path is P={p1,p2,...,p n}, where p i Represents the i-th pixel, ESCRI(p i SSCRI (p) is the elevation stress rate of change index along the confluence path. i TWSCRI(p) represents the slope stress rate of change index along the confluence path. i RSCRI(p) represents the rate of change of topographic humidity stress along the confluence path. i ) represents the rate of change of undulating stress along the confluence path, and w1 represents the ESCRI(p i The weights of SSCRI(p) are w2 and w2 is the weight of SSCRI(p). i The weights of TWSCRI(p) are w3, where w3 is the weight of TWSCRI(p). i The weights of ) are w4, where w4 is the weight of RSCRI(p). i The weight of ).

[0202] w1, w2, w3, and w4 are determined based on the specific needs of the study area (for example, the weight of ESCRI can be increased in areas with high flood risk, and the weight of SSCRI can be increased in areas with severe soil erosion).

[0203] LBI(p i The higher the value, the higher the LID deployment benefit in that area.

[0204] (2) Identify sensitive points on the confluence path

[0205] Setting a threshold: Based on the characteristics of the study area, set a threshold for sensitive point identification for the LBI value. For example:

[0206] High-efficiency sensitive point: LBI > T high

[0207] Thresholds can be determined using statistical methods (such as the mean plus standard deviation) or expert experience. For example:

[0208] T high =T mean +T ST

[0209] In the formula, T high T is the threshold for LBI. mean and T ST LBI(p) i The mean and variance of ).

[0210] Identify sensitive points: Check each pixel along the confluence path and mark the pixels that meet the above conditions as sensitive points.

[0211] Generate sensitive point layers: Convert the identified sensitive points into raster or vector layers for easy visualization.

[0212] S304. Based on the identified sensitive points along the confluence path, establish a multi-objective optimization model for LID facilities that considers both hydrological benefits and engineering costs, and determine the location, type, and proportion of LIDs along the confluence path.

[0213] Based on the identified sensitive points along the confluence path, a multi-objective optimization model for LID facilities, considering both hydrological benefits and engineering costs, can be established. This model can help determine the optimal location for LID facilities to achieve the dual objectives of maximizing hydrological benefits and minimizing engineering costs.

[0214] The multi-objective optimization model is as follows:

[0215] Max Z = α1·H - α2·C + α3·S

[0216] Where Z is the objective function, H is the total hydrological benefit, C is the total project cost, S is the total landscape benefit, and α1, α2, and α3 are the weights of hydrological benefit, project cost, and landscape benefit, respectively, and α1+α2+α3=1.

[0217] The following are the detailed steps and methods for building this model:

[0218] (1) Optimization Objective

[0219] Maximizing hydrological benefits: By deploying LID (Liquid Infiltration) facilities, runoff can be reduced to the maximum extent, infiltration can be increased, and flood risk can be reduced, i.e., the runoff reduction rate can be maximized.

[0220] Minimize project costs: While meeting hydrological benefits, reduce the construction and maintenance costs of LID facilities as much as possible, i.e., minimize expenses.

[0221] Optimization of landscape effect: By deploying LID facilities, the aesthetics of the area are improved, and the ecological and cultural landscape value is enhanced, that is, the aesthetic effect is optimized.

[0222] (2) Objective function

[0223] 1) Hydrological benefit objectives:

[0224]

[0225] Where: H represents total hydrological benefits; LBI i x is the LBI value of the i-th sensitive point; i Let x be the decision variable, representing whether to deploy LID facilities at the i-th sensitive point (x). i =1 indicates deployment, x i =0 indicates no deployment); h iThe hydrological benefit coefficient for deploying LID facilities at the i-th sensitive point.

[0226] 2) Project cost target:

[0227]

[0228] Where: C represents the total project cost; C i The engineering cost of deploying LID facilities for the i-th sensitive point.

[0229] 3) Landscape benefit objectives:

[0230]

[0231] Where: S represents the total landscape benefit; S i Landscape score for deploying LID facilities at the i-th sensitive point.

[0232] (3) Constraints

[0233] 1) Computational constraints:

[0234]

[0235] Where B represents the available budget.

[0236] 2) Land use constraints: Some sensitive locations may not be able to deploy LID facilities due to land use restrictions (such as buildings, roads, etc.).

[0237] 3) Soil conditions constraints: Some sensitive locations may not be suitable for the deployment of specific types of LID facilities due to poor soil permeability or other reasons.

[0238] 4) LID facility quantity constraint: The total number of LID facilities deployed shall not exceed a certain number.

[0239] (4) Model Solving

[0240] Multi-objective optimization methods:

[0241] The Pareto optimal solution set is obtained by solving the model using multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.).

[0242] The Pareto optimal solution set represents the optimal solution that balances hydrological benefits, engineering costs, and landscape benefits.

[0243] Weighting method:

[0244] The multi-objective problem is transformed into a single-objective problem, and the optimal solution is obtained by assigning different weights to hydrological benefits, engineering costs, and landscape benefits.

[0245] Constraint handling:

[0246] During the optimization process, ensure that the solution satisfies all constraints.

[0247] S305. Model Output.

[0248] Optimal deployment plan: Determine which sensitive points to deploy LID facilities and the types of LID facilities to deploy.

[0249] Hydrological benefits, engineering cost and landscape benefits: Calculate the total hydrological benefits, total engineering cost and total landscape benefits under the optimal layout scheme.

[0250] Sensitivity analysis: Analyze the model's sensitivity to input parameters (such as budget, LID facility type, etc.).

[0251] S4. Reasonableness verification.

[0252] Based on the LID deployment locations determined in the previous step, the hydrological benefits of LID at different locations are simulated by improving the LID setting scheme in SWMM; the hydrological benefits of the two are mutually verified, and finally, reasonable LID deployment locations, LID deployment types, and LID deployment ratios are determined.

[0253] Appendix Figure 7 This diagram illustrates a comparison of flood peak values ​​when LIDs are deployed using the method described in this application and when LIDs are deployed using conventional methods. In the diagram, river_LID_old represents the runoff hydrograph at the watershed outlet after LIDs are deployed using the previous method. river_LID_new represents the runoff hydrograph at the watershed outlet optimized using the method described in this application.

[0254] Although the above embodiments have been shown and described, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made to the above embodiments by those skilled in the art are within the protection scope of the present invention.

Claims

1. A watershed-scale LID spatial layout method based on confluence paths, characterized in that, Includes the following steps: S1. Divide the watershed into sub-catchments; S2. To construct an evaluation system for the LID deployment benefits of sub-catchment areas, it is necessary to calculate hydrological indices, land use indices, landscape ecological pattern indices, and techno-economic indices; hydrological indices include elevation stress index, slope stress index, topographic humidity stress index, and undulation stress index; S3. LID Deployment Benefit Analysis Based on Convergence Path: Includes the following steps: S301. Calculate the cumulative runoff within the watershed and generate a runoff path raster layer; S302. Generate multiple straight lines perpendicular to the confluence path and extract the evaluation indices on the confluence path; S303. Construct a LID deployment benefit evaluation system based on the confluence path and identify sensitive points on the confluence path; The formula for the LID deployment benefit evaluation system based on the merging path is as follows: In the formula, LBI is the LID deployment benefit evaluation index, assuming the pixel sequence along the confluence path is... ,in Indicates the first One pixel, The elevation stress change rate index along the confluence path, The slope stress change rate index is the index along the confluence path. The index represents the rate of change of topographic humidity stress along the confluence path. The exponent of the rate of change of undulation stress along the confluence path. for The weight, for The weight, for The weight, for The weights; S304. Based on the identified sensitive points along the confluence path, establish a multi-objective optimization model for LID facilities that considers "hydrological benefits to engineering costs" to determine the location, type, and proportion of LID facilities along the confluence path. S305. Model Output; S4. Reasonableness verification.

2. The watershed-scale LID spatial layout method based on confluence paths according to claim 1, characterized in that, The process of dividing the watershed into sub-catchments includes the following steps: S101. Collect spatial and attribute data of the study area; S102. Data preprocessing; S103. Sub-basin delineation; S104. Output the results.

3. The watershed-scale LID spatial layout method based on confluence paths according to claim 1, characterized in that, The land use index includes the proportion of impervious area and the proportion of building area; the landscape ecological pattern index includes the number and shape index of patches; the techno-economic index is composed of the unit cost of different LIDs.

4. The watershed-scale LID spatial layout method based on confluence paths according to claim 1, characterized in that, The formula for calculating the elevation stress rate of change index is as follows: In the formula, It is the first The elevation stress index of a single pixel It is the first The elevation stress index of a single pixel It is the first The pixel and the first The spatial distance between pixels.

5. The watershed-scale LID spatial layout method based on confluence paths according to claim 1, characterized in that, The multi-objective optimization model is as follows: In the formula, Z is the objective function, H is the total hydrological benefit, C is the total project cost, and S is the total landscape benefit. The weights for hydrological benefits, engineering costs, and landscape benefits are respectively given. .

6. The watershed-scale LID spatial layout method based on confluence paths according to claim 5, characterized in that, The formula for calculating the total hydrological benefits is as follows: In the formula, For overall hydrological benefits, For the first LBI value of a sensitive point Let be the decision variable, indicating whether or not it is in the th... LID facilities are deployed at each sensitive point. Indicates deployment, This indicates that no deployment will be made. For the first The hydrological benefit coefficient of deploying LID facilities at each sensitive point.

7. The watershed-scale LID spatial layout method based on confluence paths according to claim 1, characterized in that, The rationality verification of S4 refers to simulating the hydrological benefits of LIDs at different locations by improving the LID setting scheme in SWMM; cross-verifying the hydrological benefits of LIDs set up according to the methods of S1-S3 and the conventional method, and finally determining the reasonable LID setting location, LID setting type, and LID setting ratio.

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