Distributed soil water storage capacity parameter iteration method based on spatial distribution function

Abstract

The present invention discloses a distributed soil water storage capacity parameter iteration method based on spatial distribution function, which estimates soil hydrological properties based on soil physical characteristics of the underlying surface of the watershed; calculates the spatial distribution of the prior soil water storage capacity parameters in the watershed based on the soil hydrological properties and soil thickness; extracts the spatial distribution of the length of the upstream water flow path in the watershed in combination with the water system network in the watershed; constructs a parameter adjustment coefficient spatial distribution function based on the spatial distribution of the length of the upstream water flow path in the watershed and the spatial distribution of the prior soil water storage capacity parameters; uses the parameter adjustment coefficient spatial distribution function to adjust the spatial distribution of the prior soil water storage capacity parameters, and obtains an optimized spatial distribution of the soil water storage capacity parameters. The present invention not only ensures the accuracy and reliability of the calculation results, but also solves the problem of estimating the spatial distribution of soil water storage capacity parameters in a distributed model.

Description

Technical Field

[0001] The invention relates to a distributed soil water storage capacity parameter iteration method, in particular to a distributed soil water storage capacity parameter iteration optimization method based on a spatial distribution function, and belongs to the technical field of hydrology. Background Art

[0002] Traditional lumped hydrological models usually treat the watershed as a whole, making it difficult to quantitatively simulate the dynamic process of the spatiotemporal distribution of soil water within the watershed. However, with the development of technologies based on remote sensing, geographic information, and digital watersheds, the grid digital elevation model (DEM) that uses numerical matrices to describe surface elevation changes has gradually matured and has been widely used. Specifically, based on digital elevation models or distributed hydrological models, the watershed is generally divided into several calculation units, and the runoff calculations performed in different grid units can achieve distributed simulation of the runoff process in the watershed. However, how to consider the spatial distribution differences of water storage capacity parameters in each grid unit and quantify the impact of the underlying surface characteristics of the watershed on the soil water storage capacity parameters is a key and difficult issue to ensure the simulation accuracy of the distributed model hydrological process and improve the level of refined simulation of the watershed runoff process.

[0003] Generally speaking, in order to promote the development of spatial distribution estimation of distributed soil water storage capacity parameters and improve the simulation accuracy of distributed rainfall-runoff processes, a more scientific method is needed to optimize and adjust the distributed soil water storage capacity parameters. When optimizing and adjusting the distributed soil water storage capacity parameters, there may be the following problems: first, how to reasonably apply the basin underlying surface characteristics to the distributed model parameter optimization; second, in current practical applications, the spatial distribution adjustment of soil water storage capacity parameters is usually multiplied by the same coefficient for all grid cells in the basin. However, the soil water storage capacity parameter values ​​near the ridge with the longest water flow path or the river with the shortest water flow path are often easier to determine in advance. If they are multiplied by a coefficient, it is easy to cause unreasonable spatial distribution of parameters, which in turn reduces the simulation accuracy of distributed rainfall-runoff processes to a certain extent and restricts the refinement level of rainfall-runoff process simulation. Based on the above two points, it can be seen that simply using the same adjustment coefficient to optimize the spatial distribution of soil water storage capacity parameters in the basin hinders the improvement of the simulation accuracy of distributed hydrological processes. Summary of the invention

[0004] In order to solve the problems in the above-mentioned background technology, the present invention proposes a distributed soil water storage capacity parameter iteration method based on spatial distribution function. The method can reasonably quantify the role of basin underlying surface characteristics and improve the rationality of soil water storage capacity parameter distribution estimation. In addition, the method has the advantages of stable and reliable data source, high calculation efficiency, and objective and reasonable results. It is conducive to the reasonable estimation of distributed soil water storage capacity parameters and is worthy of promotion.

[0005] The present invention specifically adopts the following technical scheme: a distributed soil water storage capacity parameter iteration method based on a spatial distribution function, the distributed soil water storage capacity parameter iteration method comprising the following steps:

[0006] Step 1, estimating soil hydrological properties based on soil physical properties of the watershed underlying surface;

[0007] Step 2: Based on soil hydrological properties and soil thickness, calculate the spatial distribution of prior soil water storage capacity parameters in the basin. wm ;

[0008] Step 3: Combine the water network in the basin and extract the spatial distribution Raster of the upstream water flow path length in the basin Lh ;

[0009] Step 4: Raster based on the spatial distribution of the length of the upstream water flow path Lh and spatial distribution of prior soil water storage capacity parameters Raster wm , construct the parameter adjustment coefficient spatial distribution function F LW ;

[0010] Step 5: Using rainfall as input, simulate the typical flood process using the basin hydrological model combined with the prior soil water storage capacity parameters, compare the simulation results with the measured process, and calculate the water volume difference Δ Q , based on the water volume difference Δ of the simulation results Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution Raster of prior soil water storage capacity parameters wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm .

[0011] As a further technical solution of the present invention, the step 1 specifically includes the following steps:

[0012] Step 1.1, divide the watershed into a number of orthogonal grid cells based on digital elevation data, and extract the watershed boundaries and flow directions;

[0013] Step 1.2, extracting the soil physical properties in the watershed in combination with the watershed boundary, wherein the soil physical properties include the spatial distribution of sand content, the spatial distribution of silt content and the spatial distribution of clay content;

[0014] Step 1.3, based on the soil physical properties of the basin underlying surface, estimate the soil hydrological properties, where the soil hydrological properties include soil field water holding capacity ρ f and the wilting coefficient ρ wp .

[0015] As a further technical solution of the present invention, step 2 specifically includes: for a single grid cell, the prior soil water storage capacity Cell in the grid cell can be calculated according to the following formula: wm :

[0016] Cell wm =SoilT×(ρ f -ρ wp )

[0017] Where: SoilT is the soil thickness;

[0018] According to this formula, all grid cells in the basin are traversed to calculate the spatial distribution of the prior soil water storage capacity parameters in the basin. wm .

[0019] As a further technical solution of the present invention, step 3 is to start from the grid unit of the river channel in the basin, search upstream for the grid unit of the upstream water, and calculate the length of the upstream water flow path, which specifically includes:

[0020] Step 3.1, calculate the length CL that the water needs to flow through when passing through a single grid cell based on the flow direction:

[0021]

[0022] Where: Csize is the side length of the grid cell, that is, the resolution of the digital elevation data; dir is the flow direction of the grid cell, that is, the direction of the water flow in the grid cell. When dir is equal to 1, 2, 3, 4, 5, 6, 7, and 8, the flow directions of the grid cells are east, southeast, south, southwest, west, northwest, north, and northeast respectively;

[0023] Step 3.2: Calculate the upstream water flow path length HL of the grid cell according to the following formula:

[0024]

[0025] Where: N is the total number of grid cells that the water flow in each grid cell passes through when it moves to the river grid cell; i is the number of the grid cell that the water flow passes through when it moves to the grid cell, from 1 to N; CLi It is the length that water needs to flow when passing through the grid cell numbered i.

[0026] Step 3.3, traverse all grid cells in the basin to obtain the spatial distribution Raster of the upstream water flow path length in the basin Lh .

[0027] As a further technical solution of the present invention, step 4 specifically includes the following steps:

[0028] Step 4.1, with the flow path length as the horizontal axis and the prior soil water storage capacity parameter as the vertical axis, a scatter plot is drawn between the flow path length and the prior soil water storage capacity parameter, and the scatter points are displayed in different colors according to the probability density value based on the probability density value, the darker the color, the higher the probability density;

[0029] Step 4.2, calculate the proportional coefficient δ of the parameter high probability density distribution area:

[0030]

[0031] Where: UL ∈ is the maximum flow path length of the scattered points with probability density higher than ∈; DL ∈ is the minimum flow path length of the scattered points with probability density higher than ∈; HL max is the maximum flow path length value in the basin;

[0032] Step 4.3: Construct parameter adjustment coefficient spatial distribution function F LW :

[0033] γ=0.5 1 / δ -(0.5-μ) 1 / δ

[0034] Where: γ is the parameter adjustment coefficient of the grid unit; μ is the flow path length coefficient of the grid unit, which is calculated by HL / HL max Calculated.

[0035] As a further technical solution of the present invention, the step 5 specifically includes the following steps:

[0036] Step 5.1, using rainfall as input, simulate the typical rainfall flood process using the basin hydrological model combined with the prior soil water storage capacity parameters, and compare the simulation results with the measured process to calculate the water volume difference Δ Q ;

[0037] Δ Q =Q cal -Q obs

[0038] Where: Qcal is the simulated water volume; Q obs is the observed water volume;

[0039] Step 5.2: Based on the water volume difference Δ of the simulation results Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of prior soil water storage capacity parameters Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm ;

[0040]

[0041] δ′=δ+δ×α

[0042] γ′ k =0.5 1 / δ′ -(0.5-μ k ) 1 / δ′

[0043] If Δ Q If the value of is greater than 0, then: WM′ k =WM k -WM k ×γ k '

[0044] If Δ Q If the value of is less than 0, then: WM′ k =WM k +WM k ×γ k '

[0045] Where: α is Δ Q and Q obs δ′ is the parameter calculated based on the proportional coefficient; γ k ′ is the parameter adjustment coefficient of the grid cell numbered k; WM k is the water storage capacity parameter of the grid unit numbered k; WM′ k is the water storage capacity parameter of the grid unit numbered k after adjustment;

[0046] If the value of α is less than the trial threshold α 阈值 , then stop the trial calculation and set WM′ k As the final result of the trial calculation, the trial calculation threshold α is generally set according to the "Water Regime Forecast Specifications". 阈值 The value is set to 0.2, and all grid cells in the basin are traversed to obtain the optimized spatial distribution of soil water storage capacity parameters Raster′ wm .

[0047] Beneficial effects of the present invention:

[0048] 1) Based on the physical factors that affect soil water storage capacity parameters, the present invention quantifies the influence of underlying surface characteristics on soil water storage capacity parameters, and then proposes a distributed soil water storage capacity parameter iterative optimization method based on spatial distribution function;

[0049] 2) The distributed soil water storage capacity parameter iterative optimization method not only ensures the accuracy and reliability of the calculation results, but also solves the problem of estimating the spatial distribution of soil water storage capacity parameters in the distributed model. In addition, this method mainly uses the digital elevation model of the watershed, and the data source is stable and reliable. The functional relationship between the variables in the method is clear, which is conducive to the rapid and automatic estimation of soil water storage capacity parameters. At the same time, it ensures the objectivity and rationality of the results, which can further promote the in-depth development of digital hydrology and distributed models. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0051] Figure 2 It is a schematic diagram of the elevation distribution grid of the Chen River Basin of the present invention;

[0052] Figure 3 It is a schematic diagram of the spatial distribution of the flow direction of the grid unit in the watershed of the present invention;

[0053] Figure 4 is a schematic diagram of the spatial distribution of sand content in a watershed of the present invention;

[0054] Figure 5 It is a schematic diagram of the spatial distribution of powder content in the watershed of the present invention;

[0055] Figure 6 It is a schematic diagram of the spatial distribution of clay content in the watershed of the present invention;

[0056] Figure 7 It is a schematic diagram of spatial distribution of soil field water holding capacity in a watershed of the present invention;

[0057] Figure 8 It is a schematic diagram of spatial distribution of wilting coefficient in a watershed of the present invention;

[0058] Fig. 9 It is a schematic diagram of the spatial distribution of a priori soil water storage capacity parameters in the watershed of the present invention;

[0059] Fig.10 It is a schematic diagram of the flow direction diagram of the grid unit in the watershed of the present invention;

[0060] Fig.11 It is a schematic diagram of the spatial distribution of the length of the upstream water flow path in the basin of the present invention;

[0061] Fig.12 Schematic diagram of flow path length-parameter scatter points of the present invention;

[0062] Fig.13 It is a schematic diagram of the spatial distribution function of the parameter adjustment coefficient of the present invention;

[0063] Fig.14 Schematic diagram of spatial distribution of soil water storage capacity parameters after optimization of the present invention;

[0064] Fig.15 It is a schematic diagram comparing the flood process calculated by the present invention and the flood process actually measured.

[0065] Specific implementation methods

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Embodiment 1: Figure 1 As shown, this embodiment provides a distributed soil water storage capacity parameter iteration method based on a spatial distribution function, and the distributed soil water storage capacity parameter iteration method includes the following steps:

[0068] First: Estimate soil hydrological properties based on the soil physical characteristics of the basin's underlying surface.

[0069] 1) Based on the digital elevation data, the watershed is divided into several orthogonal grid cells, and the watershed boundaries and flow directions (such as Figure 3 shown);

[0070] 2) Combined with the watershed boundary extraction, the soil physical characteristics in the watershed are obtained, where the soil physical characteristics include the spatial distribution of sand content, the spatial distribution of silt content and the spatial distribution of clay content;

[0071] 3) Based on the soil physical properties of the basin's underlying surface, the soil hydrological properties are estimated, including the soil field water holding capacity ρ f and the wilting coefficient ρ wp .

[0072] Soil field water holding capacity ρ f and the wilting coefficient ρ wp The specific calculation is as follows:

[0073] δ γ =-0.024×G S +0.487×G C +0.006×GOm +0.005×G S ×G Om -0.013×G C ×G Om +0.068×G S ×G C +0.031

[0074] r wp =ρ γ +10.14×ρ γ -0.024

[0075] r ε =0.278×G S +0.034×G C +0.022×G Om -0.018×G S ×G Om -0.027×G C ×G Om -0.584×G S ×G C +0.078

[0076] r σ =ρ ε +(0.636×ρ ε -0.107)

[0077] r μ =-0.251×G S +0.195×G C +0.011×G Om +0.006×G S ×G Om -0.027×G C ×G Om +0.452×G S ×G C +0.299

[0078] r τ =ρ μ +1.283×r μ ] -0.374×ρ μ -0.015

[0079] r s =ρ τ +r σ -0.097×G S +0.043

[0080] r f =1-((1-p s)×1.2)

[0081] Where: G S , G C and G Om are sand content, clay content and organic matter content, respectively, in mass percentage; ρ wp , f are wilting coefficient and field water holding capacity, which are mass percentages respectively; the other variables are intermediate variables for calculation.

[0082] Second: Based on soil hydrological properties and soil thickness, the spatial distribution of prior soil water storage capacity parameters in the basin is calculated. wm .

[0083] For a single grid cell, the prior soil water storage capacity in the grid cell can be calculated according to the following formula: wm :

[0084] Cell wm =SoilT×(ρ f -ρ wp )

[0085] Where: SoilT is the soil thickness;

[0086] According to this formula, all grid cells in the basin are traversed to calculate the spatial distribution of the prior soil water storage capacity parameters in the basin. wm .

[0087] Third: Combine the water network in the basin to extract the spatial distribution of the upstream water flow path length in the basin Raster Lh Taking the river grid unit in the basin as the starting point, search for the upstream water grid unit in the upstream direction and calculate the length of the upstream water flow path.

[0088] 1) Calculate the length CL that water needs to flow through a single grid cell based on the flow direction:

[0089]

[0090] Where Csize is the side length of the grid cell, that is, the resolution of the digital elevation data; dir is the flow direction of the grid cell, that is, the direction of the water flow in the grid cell. When dir is equal to 1, 2, 3, 4, 5, 6, 7, and 8, the flow directions of the grid cells are east, southeast, south, southwest, west, northwest, north, and northeast respectively;

[0091] 2) Calculate the upstream water flow path length HL of the grid unit according to the following formula:

[0092]

[0093] Where: N is the total number of grid cells that the water flow in each grid cell passes through when it moves to the river grid cell; i is the number of the grid cell that the water flow passes through when it moves to the grid cell, from 1 to N; CL i It is the length that water needs to flow when passing through the grid cell numbered i.

[0094] 3) Traverse all grid cells in the basin to obtain the spatial distribution of the upstream water flow path length in the basin Raster Lh .

[0095] Fourth: Raster based on the spatial distribution of the length of the upstream water flow path Lh and spatial distribution of prior soil water storage capacity parameters Raster wm , construct the parameter adjustment coefficient spatial distribution function F LW .

[0096] 1) With the flow path length as the horizontal axis and the prior soil water storage capacity parameter as the vertical axis, a scatter plot is drawn between the flow path length and the prior soil water storage capacity parameter, and the scatter points are displayed in different colors according to the probability density value based on the probability density value, the darker the color, the higher the probability density;

[0097] 2) Calculate the proportional coefficient δ of the parameter high probability density distribution area:

[0098]

[0099] Where: UL ∈ is the maximum flow path length of the scattered points with probability density higher than ∈, DL ∈ is the minimum flow path length of the scattered points with probability density higher than ∈, HL max is the maximum flow path length value in the basin;

[0100] 3) Construct parameter adjustment coefficient spatial distribution function F LW :

[0101] γ=0.5 1 / δ -(0.5-μ) 1 / δ

[0102] Where: γ is the parameter adjustment coefficient of the grid unit, μ is the flow path length coefficient of the grid unit, which is calculated by HL / HL max Calculated.

[0103] Fifth: Using rainfall as input, the typical flood process is simulated using the basin hydrological model combined with the prior soil water storage capacity parameters, and the simulation results are compared with the measured process to calculate the water volume difference Δ of the simulation results Q , based on the water volume difference Δ of the simulation results Q, using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of the prior soil water storage capacity parameter Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm .

[0104] 1) Taking rainfall as input, the typical rainfall flood process is simulated using the basin hydrological model combined with the prior soil water storage capacity parameters, and the simulation results are compared with the measured process to calculate the water volume difference Δ of the simulation results Q :

[0105] Δ Q =Q FaG -Q obs

[0106] Where: Q cal is the simulated water volume, Q obs is the observed amount of water.

[0107] 2) Water volume difference Δ based on simulation results Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of prior soil water storage capacity parameters Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm :

[0108]

[0109] δ′=δ+δ×α

[0110] γ k ′=0.5 1 / δ′ -(0.5-μ k ) 1 / δ′

[0111] If Δ Q If the value of is greater than 0, then: WM′ k =WM k -WM k ×γ k '

[0112] If Δ Q If the value of is less than 0, then: WM′ k =WM k +WM k ×γ k '

[0113] Where: α is Δ Q and Q obsThe proportional coefficient between them, δ′ is the parameter calculated according to the proportional coefficient, γ k ′ is the parameter adjustment coefficient of the grid unit numbered k, WM k is the water storage capacity parameter of the grid unit numbered k, WM′ k is the water storage capacity parameter of the grid unit numbered k after adjustment.

[0114] If the value of α is less than the trial threshold α 阈值 Then stop the trial calculation and set WM′ k As the final result of the trial calculation, the trial calculation threshold α is generally set according to the "Water Regime Forecast Specifications". 阈值 The value is set to 0.2, and all grid cells in the basin are traversed to obtain the optimized spatial distribution of soil water storage capacity parameters Raster′ wm .

[0115] In Example 2, taking the Chenhe River Basin in Shaanxi Province as an example, a distributed soil water storage capacity parameter iterative optimization method based on spatial distribution function is applied to estimate the spatial distribution of soil water storage capacity parameters in the basin. Taking the 20160507 flood as an example, the flood simulation effects before and after parameter adjustment are compared.

[0116] Step 1: Divide the watershed into several orthogonal grid cells based on digital elevation data (such as Figure 2 The side length of the grid unit is 90m, and the watershed boundary and flow direction (such as Figure 3 shown);

[0117] Step 2: Combined with the watershed boundary extraction, the soil physical properties in the watershed are obtained. The soil physical properties mainly include the spatial distribution of sand content (such as Figure 4 as shown), spatial distribution of powder content (as shown Figure 5 ) and the spatial distribution of clay content (as shown in Figure 6 shown);

[0118] Step 3: Based on the soil physical properties of the basin's underlying surface, estimate the soil hydrological properties, which include soil field water holding capacity ρ f ( Figure 7 ) and the wilting coefficient ρ wp ( Figure 8 ), as follows:

[0119] ρ γ =-0.024×G S +0.487×G C +0.006×G Om +0.005×G S ×G Om -0.013×G C ×GOm +0.068×G S ×G C +0.031

[0120] ρ wp =ρ γ +10.14×ρ γ -0.024

[0121] ρ ε =0.278×G S +0.034×G C +0.022×G Om -0.018×G S ×G Om -0.027×G C ×G Om -0.584×G S ×G C +0.078

[0122] ρ σ =ρ ε +(0.636×ρ ε -0.107)

[0123] ρ μ =-0.251×G S +0.195×G C +0.011×G Om +0.006×G S ×G Om -0.027×G C ×G Om +0.452×G S ×G C +0.299

[0124] ρ τ =ρ μ +1.283×ρ μ 2 -0.374×ρ μ -0.015

[0125] ρ s =ρ τ +ρ σ -0.097×G S +0.043

[0126] ρ f =1-((1-ρ s )×1.2)

[0127] Where: G S 、G C and GOm are sand content, clay content and organic matter content, respectively, in mass percentage; ρ wp , f are wilting coefficient and field water holding capacity, which are mass percentages respectively; the other variables are intermediate variables for calculation.

[0128] Step 4: Based on soil hydrological properties and soil thickness, calculate the spatial distribution of prior soil water storage capacity parameters in the basin Raster wm (like Fig. 9 For a single grid cell, the prior soil water storage capacity in the grid cell can be calculated according to the following formula: wm :

[0129] Cell wm =SoilT×(ρ f -ρ wp )

[0130] Where: SoilT is the soil thickness;

[0131] According to this method, all grid cells in the basin are traversed to calculate the spatial distribution of the prior soil water storage capacity parameters in the basin. wm .

[0132] Step 5: Combine the water network in the basin and extract the spatial distribution Raster of the upstream water flow path length in the basin Lh , starting from the river grid unit in the basin, search for the upstream water grid unit upstream and calculate the length of the upstream water flow path.

[0133] 1) Calculate the length CL that water needs to flow through a single grid cell based on the flow direction:

[0134]

[0135] Where: Csize is the side length of the grid cell, that is, the resolution of the digital elevation data, which is 90m; dir is the flow direction of the grid cell (such as Fig.10 When dir is 1, 2, 3, 4, 5, 6, 7, or 8, the flow directions of the grid cells are east, southeast, south, southwest, west, northwest, north, and northeast, respectively.

[0136] 2) Calculate the upstream water flow path length HL of the grid unit according to the following formula:

[0137]

[0138] Where: N is the total number of grid cells that the water flow in each grid cell passes through when it moves to the river grid cell; i is the number of the grid cell that the water flow passes through when it moves to the grid cell, from 1 to N; CL i is the length that water needs to flow through when passing through the grid unit numbered i;

[0139] 3) Traverse all grid cells in the basin to obtain the spatial distribution of the upstream water flow path length in the basin Raster Lh (like Fig.11 As shown in Figure 2, it is calculated that the maximum upstream water flow path length in the Chen River Basin is about 5 km.

[0140] Step 6: Raster based on watershed slope length Lh and spatial distribution of prior soil water storage capacity parameters Raster wm , construct the parameter adjustment coefficient spatial distribution function F LW (like Fig.12 The specific steps are as follows:

[0141] 1) With the flow path length as the horizontal axis and the prior soil water storage capacity parameter as the vertical axis, a scatter plot is drawn between the flow path length and the prior soil water storage capacity parameter. Based on the probability density that can reflect the degree of clustering of the scattered points, the scattered points are displayed in different colors according to the probability density value. The darker the color, the higher the probability density (e.g. Fig.12 shown);

[0142] 2) Calculate the proportional coefficient δ of the parameter high probability density distribution area;

[0143]

[0144] Where: UL ∈ is the maximum flow path length of the scattered points with probability density higher than ∈, DL ∈ is the minimum flow path length of the scattered points with probability density higher than ∈, HL max is the maximum flow path length value in the basin;

[0145] In the Chen River basin, set the value of ∈ to 3e -5 , get UL ∈ and DL ∈ The values ​​are 0.2km and 3.3km respectively, and the calculated value of δ is 0.62;

[0146] 3) Construct parameter adjustment coefficient spatial distribution function F LW (like Fig.13 As shown in Figure 2), in the Chenhe River Basin, the constructed parameter adjustment coefficient spatial distribution function is:

[0147] γ=0.5 1 / 0.62 -(0.5-μ)1 / 0.62

[0148] Where: γ is the parameter adjustment coefficient of the grid unit, μ is the flow path length coefficient of the grid unit, which is calculated by HL / HL max Calculated;

[0149] Step 7: Calculate the water volume difference Δ between the hydrological model simulation results and the measured data Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of prior soil water storage capacity parameters Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm (like Fig.14 shown).

[0150] 1) Taking the rainfall of the 20160507 flood as input, the total rainfall value is 27mm, and the typical rainfall flood process is simulated using the basin hydrological model combined with the prior soil water storage capacity parameters, and the simulation results are compared with the measured process (such as Fig.15 Calculate the water volume difference Δ of the simulation results Q :

[0151] Δ Q =Q cal -Q obs

[0152] Where: Q cal is the simulated water volume, Q obs is the observed water volume;

[0153] 2) Water volume difference Δ based on simulation results Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of prior soil water storage capacity parameters Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm :

[0154]

[0155] δ′=δ+δ×α

[0156] γ k ′=0.5 1 / δ′ -(0.5-μ k ) 1 / δ′

[0157] If Δ Q If the value of is greater than 0, then: WM′ k =WM k -WM k×γ k '

[0158] If Δ Q If the value of is less than 0, then: WM′ k =WM k +WM k ×γ k '

[0159] Where: α is Δ Q and Q obs The proportional coefficient between them, δ′ is the parameter calculated according to the proportional coefficient, γ k ′ is the parameter adjustment coefficient of the grid unit numbered k, WM k is the water storage capacity parameter of the grid unit numbered k, WM′ k is the water storage capacity parameter of the grid unit numbered k after adjustment.

[0160] If the value of α is less than the trial threshold α 阈值 , then stop the trial calculation and set WM′ k As the final result of the trial calculation, the trial calculation threshold α is generally set according to the "Water Regime Forecast Specifications". 阈值 The value is set to 0.2, and all grid cells in the basin are traversed to obtain the optimized spatial distribution of soil water storage capacity parameters Raster′ wm .

[0161] The optimized water storage capacity parameters are obtained by statistics, making the water volume difference Δ Q It was reduced from 15.7% to 7.9%, further improving the accuracy of flood simulation.

[0162] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0163] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A distributed soil water storage capacity parameter iteration method based on spatial distribution function, characterized in that: The distributed soil water storage capacity parameter iteration method comprises the following steps: Step 1, estimating soil hydrological properties based on soil physical properties of the watershed underlying surface; Step 2: Based on soil hydrological properties and soil thickness, calculate the spatial distribution of prior soil water storage capacity parameters in the basin. wm ; Step 3: Combine the water network in the basin and extract the spatial distribution Raster of the upstream water flow path length in the basin Lh ; Step 4: Raster based on the spatial distribution of the length of the upstream water flow path Lh and spatial distribution of prior soil water storage capacity parameters Raster wm , construct the parameter adjustment coefficient spatial distribution function F LW ; The step 4 specifically includes: Step 4.1, with the flow path length as the horizontal axis and the prior soil water storage capacity parameter as the vertical axis, a scatter plot is drawn between the flow path length and the prior soil water storage capacity parameter, and the scatter points are displayed in different colors according to the probability density value based on the probability density value, the darker the color, the higher the probability density; Step 4.2, calculate the proportional coefficient δ of the parameter high probability density distribution area: Where: UL ∈ is the maximum flow path length of the scattered points with probability density higher than ∈; DL ∈ is the minimum flow path length of the scattered points with probability density higher than ∈; HL max is the maximum flow path length value in the basin; Step 4.3: Construct parameter adjustment coefficient spatial distribution function F LW : γ=0.5 1 / δ -(0.5-m) 1 / δ Where: γ is the parameter adjustment coefficient of the grid unit; μ is the flow path length coefficient of the grid unit, which is calculated by HL / HL max Calculated; Step 5: Using rainfall as input, simulate the typical flood process using the basin hydrological model combined with the prior soil water storage capacity parameters, compare the simulation results with the measured process, and calculate the water volume difference Δ Q , based on the water volume difference Δ of the simulation results Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of prior soil water storage capacity parameters Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm .

2. The distributed soil water storage capacity parameter iteration method according to claim 1 is characterized in that: The step 1 specifically includes: Step 1.1, divide the watershed into a number of orthogonal grid cells based on digital elevation data, and extract the watershed boundaries and flow directions; Step 1.2, extracting the soil physical properties in the watershed in combination with the watershed boundary, wherein the soil physical properties include the spatial distribution of sand content, the spatial distribution of silt content and the spatial distribution of clay content; Step 1.3: Based on the soil physical properties of the watershed underlying surface, the soil hydrological properties are estimated, including the soil field water holding capacity ρ f and the wilting coefficient ρ wp .

3. The distributed soil water storage capacity parameter iteration method according to claim 1 is characterized in that: The step 2 specifically includes: For a single grid cell, the prior soil water storage capacity in the grid cell is calculated according to the following formula: wm : Cell wm =SoilT×(ρ f -r wp ) Where: SoilT is the soil thickness; And, according to this formula, all grid cells in the basin are traversed to calculate the spatial distribution of the prior soil water storage capacity parameters in the basin. wm .

4. The distributed soil water storage capacity parameter iteration method according to claim 3 is characterized in that: In the step 3: starting from the grid unit of the river in the basin, searching upstream water grid units in the reverse direction, and calculating the length of the upstream water flow path; Step 3.1, calculate the length CL that the water needs to flow through when passing through a single grid cell based on the flow direction: Where: Csize is the side length of the grid cell, that is, the resolution of the digital elevation data; dir is the flow direction of the grid cell, that is, the direction of the water flow in the grid cell. When dir is equal to 1, 2, 3, 4, 5, 6, 7, and 8, the flow directions of the grid cells are east, southeast, south, southwest, west, northwest, north, and northeast respectively; Step 3.2: Calculate the upstream water flow path length HL of the grid cell according to the following formula: Where: N is the total number of grid cells that the water flow in each grid cell passes through when it moves to the river grid cell; i is the number of the grid cell that the water flow passes through when it moves to the grid cell, from 1 to N; CL i is the length that water needs to flow through when passing through the grid unit numbered i; Step 3.3, traverse all grid cells in the basin to obtain the spatial distribution Raster of the upstream water flow path length in the basin Lh .

5. The distributed soil water storage capacity parameter iteration method according to claim 1, characterized in that: The step 5 specifically includes: Step 5.1, using rainfall as input, simulate the typical rainfall flood process using the basin hydrological model combined with the prior soil water storage capacity parameters, and compare the simulation results with the measured process to calculate the water volume difference Δ Q : Δ Q =Q call -Q obs Where: Q cal is the simulated water volume; Q obs is the observed water volume; Step 5.2: Based on the water volume difference Δ of the simulation results Q , using the parameter adjustment coefficient space distribution function F LW Realize the spatial distribution of prior soil water storage capacity parameters Raster wm The optimized spatial distribution of soil water storage capacity parameters Raster′ is obtained by adjusting wm ; δ′=δ+δ×α c′ k =0.5 1 / δ′ -(0.5-m k ) 1 / δ′ If Δ Q If the value of is greater than 0, then: WM′ k =WM k -WM k ×γ k ' If Δ Q If the value of is less than 0, then: WM′ k =WM k +WM k ×γ k ' Where: α is Δ Q and Q obs δ′ is the parameter calculated based on the proportional coefficient; γ k ′ is the parameter adjustment coefficient of the grid cell numbered k; WM k is the water storage capacity parameter of the grid unit numbered k; WM′ k is the water storage capacity parameter of the grid unit numbered k after adjustment; If the value of α is less than the trial threshold α 阈值 , then stop the trial calculation and set WM′ k As the final result of the trial calculation, according to the "Water Regime Forecast Specifications", the trial calculation threshold α 阈值 The value is set to 0.2, and all grid cells in the basin are traversed to obtain the optimized spatial distribution of soil water storage capacity parameters Raster′ wm .

Images