Distributed storage and hypersonic dynamic combination model construction method based on water and soil conservation measures

Abstract

The invention belongs to the technical field of watershed hydrological process simulation research, particularly relates to a distributed storage and hypersonic dynamic combination model construction method based on water and soil conservation measures, and solves the technical problems that an existing loess plateau region hydrological model is low in simulation precision and poor in applicability. Firstly, a research area is subdivided into a plurality of uniform grids through uniform grid division, and each grid represents different underlying surface features; the method comprises the following steps: carrying out an observation test on a typical field slope of a loess plateau to obtain observation data under different water and soil conservation measures, and determining a model parameter range in each grid under the water and soil conservation measures. According to the method, the rainfall-runoff process in each grid is linked with each other by combining a one-dimensional motion wave equation to form a complete distributed storage hyperdynamic combined model, the model structure can dynamically reflect runoff production mechanisms of different grid units, and the prediction capability of the model is improved.

Description

Technical Field

[0001] The invention relates to the technical field of basin hydrological process simulation research, and in particular to a method for constructing a distributed storage-excess dynamic combination model based on soil and water conservation measures. Background Art

[0002] The Loess Plateau is an important ecological barrier in my country and an important region for achieving ecological protection and high-quality development in the Yellow River Basin. In recent years, under the combined influence of climate change and human activities, the implementation of engineering measures such as terraces, forest and grass measures, and agronomic cultivation measures have changed parameters such as canopy interception, surface depression flow storage, soil infiltration, surface albedo, and underlying surface hydrothermal characteristics, resulting in significant changes in slope infiltration, water storage, and runoff generation mechanisms to a certain extent. The traditional over-infiltration runoff model has certain limitations in describing the runoff generation mechanism of the Loess Plateau, especially in arid and semi-arid areas. Factors such as the spatiotemporal variability of rainfall, the diversity of soil types, and the complexity of land cover make water cycle simulation particularly difficult. These factors together lead to the complexity of the construction of hydrological models in the Loess Plateau, posing a challenge to regional water resources management and ecological protection.

[0003] Model simulation is an important means to study the hydrological process in the Loess Plateau. However, looking at the existing runoff models at home and abroad, although the empirical models have low requirements for parameters and input data, they still lack a detailed description of the slope runoff process; although the physical model can significantly improve the simulation accuracy of the slope runoff process, the existing model parameters require many, the technical requirements are complex, and the regional restrictions are large, making it difficult to simply transplant and apply it in the typical loess area of ​​Shanxi, although it has a wide range of applications in the field of hydrological simulation.

[0004] In order to improve the accuracy of the hydrological model construction in the Loess Plateau, researchers have made a lot of attempts and efforts. However, most of the existing studies have focused on the impact of climate change and land use change on the water cycle, and relatively little attention has been paid to other factors that may affect the runoff process, such as soil type, topography, etc. This has led to a lack of comprehensive and in-depth consideration when constructing the hydrological model in the Loess Plateau, which has further affected the simulation accuracy and applicability of the model. Summary of the invention

[0005] In order to overcome the technical defects of low simulation accuracy and poor applicability of existing hydrological models in the Loess Plateau, the present invention provides a method for constructing a distributed super-storage dynamic combination model based on soil and water conservation measures.

[0006] The present invention provides a method for constructing a distributed super storage dynamic combination model based on soil and water conservation measures, the steps of which are:

[0007] S1. Divide the slope grid cells and determine the flow direction of each grid cell:

[0008] Select drones equipped with high-resolution cameras or laser radar equipment (LiDAR) to obtain digital elevation models (DEMs) of typical field slopes on the Loess Plateau; Based on the size of typical field slopes on the Loess Plateau, use ArcGIS software to divide them into multiple grid cells of the same size and perform flow direction analysis on them to determine the flow direction of each grid cell; obtain the grid number, grid width and flow direction of each grid cell;

[0009] S2. Determine the range of model runoff parameters:

[0010] Based on the field slope test, the mini infiltration meter (Mini Disk) was used to measure the soil period infiltration rate under different soil and water conservation measures, including different vegetation types, different plant growth periods and different initial soil moisture conditions. The Horton infiltration equation was used to perform nonlinear fitting to determine the soil period infiltration rate under different scenarios. The parameters that need to be determined in the Horton infiltration equation include: initial infiltration rate f0, stable infiltration rate f c and empirical parameter k; then determine the maximum interception capacity under different measures; conduct ring knife soil sampling on typical field slopes on the Loess Plateau, and use micro-infiltrator to determine the range of soil field water holding capacity and soil saturated water content parameters under different soil types; determine the parameter range of water flow velocity v under different soil and water conservation measures based on field slope dye tracer test, and use Manning's formula to deduce the parameter range of roughness coefficient n; convert the initial infiltration rate f0 and stable infiltration rate f c , the empirical parameter k, the maximum interception capacity, the soil field water holding capacity, the soil saturated water content, and the parameter ranges of the roughness coefficient n under different soil and water conservation measures are assigned to each grid obtained in S1 to determine the input parameter range of each grid model;

[0011] S3. Construction of flow generation module:

[0012] The runoff module includes interception module, evapotranspiration module, infiltration module, surface runoff module, and subsoil flow and underground runoff module;

[0013] S31, the interception module uses the method in the Gash model to calculate the interception amount of each grid at different time periods; the maximum interception amount is defined as:

[0014] ΔI m (t)=0.935+0.498*LAI(t)-0.00575*LAI(t) 2 The intercepted rainfall depth and intercepted evaporation are defined as:

[0015]

[0016] ΔE I(t) = min(ΔI c (t),ΔE(t))

[0017] Wherein, the correction coefficient η(t) = 0.04*LAI(t);

[0018] Net rainfall is defined as:

[0019] ΔP n (t)=max(0,ΔP(t)-ΔI c (t))

[0020] The initial retention at the next moment is defined as:

[0021] ΔI0(t+1)=max(0,ΔI0(t)+ΔI c (t)-ΔE I (t))

[0022] Among them, ΔP(t) is the rainfall at time t, LAI(t) is the leaf area index at time t, ΔI0(t=0) is the initial interception when t is 0, assuming that the initial interception at the initial moment is 0, ΔI0(t+1) is the initial interception at the next moment, ΔE(t) is the potential evaporation at time t, ΔP n (t) is the net rainfall at time t, ΔE I (t) is the intercepted evaporation at time t;

[0023] S32, evapotranspiration module adopts sequential evaporation, and the potential evapotranspiration first meets the evaporation of the intercepted water surface, then meets the transpiration of plants, and finally meets the evaporation of the surface soil;

[0024] Plant transpiration is defined as:

[0025]

[0026] Soil evaporation is defined as:

[0027] ΔE s (t) = ΔE(t) - ΔE I (t)-ΔE P (t)

[0028] Where, ΔE I (t) is the evaporation of the intercepted water surface at time t; ΔE(t) is the potential evaporation at time t, LAI(t) is the leaf area index at time t, ΔE P (t) is the transpiration of vegetation at time t, ΔE s (t) is the soil evaporation at time t;

[0029] S33, the infiltration module uses the Horton infiltration model to calculate the infiltration volume during each period, and the soil module uses a double-layer calculation;

[0030] The infiltration rate equation is defined as:

[0031] f(t)=f c +(f0-f)e -kt

[0032] The time infiltration equation is defined as:

[0033]

[0034] ΔF(t)=F((t-1)+1)-F(t-1)

[0035] The water availability factor is defined as:

[0036]

[0037] The surface soil infiltration equation is defined as:

[0038]

[0039] Where f0 is the initial infiltration rate; f c is the stable infiltration rate; k is an empirical parameter; β is the grid unit slope, Δx(t) is the water supply at time t, ΔF c (t) is the infiltration amount of the upper soil at time t;

[0040] S34, surface runoff module considers the calculation of super-seepage surface runoff and saturated surface runoff:

[0041] Surface runoff is defined as:

[0042]

[0043] The initial interception amount of soil and water conservation measures at the next moment is defined as:

[0044]

[0045] The upper soil moisture content is defined as:

[0046]

[0047] Among them, ΔS0(t) is the initial water content of the upper soil at time t, S a is the saturated water content of the upper soil layer, ΔR n (t) is the net rainfall at time t, ΔF c (t) is the infiltration amount of the upper soil layer at time t, ΔC0(t) is the initial interception amount of the water conservation measures at time t, ΔC0(t+1) is the initial interception amount of the water conservation measures at the next moment, C m is the maximum interception capacity of water conservation measures at time t, ΔS c(t) is the initial moisture content of the upper soil at time t, ΔR c (t) is the surface runoff at time t;

[0048] S35, soil flow and underground runoff module considers dividing the soil vadose zone into two layers for soil flow calculation, and underground runoff is the outflow from the soil bottom holes after stable infiltration;

[0049] The infiltration rate from the upper soil layer to the lower soil layer is defined as:

[0050]

[0051] The initial moisture content of the upper soil at the next moment is defined as:

[0052]

[0053] The amount of gravity water produced by the subsoil is defined as:

[0054]

[0055] The initial soil moisture content of the lower layer at the next moment is defined as:

[0056]

[0057] The soil flow is defined as:

[0058] ΔP s (t) = δ1·ΔG(t)·sinβ

[0059] Groundwater runoff is defined as:

[0060] ΔR g (t) = δ2·ΔG(t)

[0061] Total runoff is defined as:

[0062] ΔR T (t) = ΔR c +ΔR s +ΔR g

[0063] Among them, ΔS c (t) is the initial moisture content of the upper soil at time t, S h is the field water holding capacity of the upper soil layer, S a is the saturated water content of the upper soil, ΔW0(t) is the initial water content of the lower soil at time t, W h is the field water holding capacity of the lower soil, δ1 is the lateral drainage coefficient of soil midstream, β is the grid unit slope, δ2 is the lateral drainage coefficient of underground runoff, ΔF d(t) is the infiltration amount of the upper soil to the lower soil at time t, ΔS0(t) is the initial soil moisture content of the upper soil at time t, ΔG(t) is the gravity water content of the lower soil at time t, and ΔR s (t) is the soil flow at time t, ΔR g (t) is the underground runoff at time t, ΔR T (t) is the total runoff at time t;

[0064] S4. Construction of confluence module:

[0065] The slope confluence of each grid surface adopts the one-dimensional motion wave equation combined with the Manning formula to construct the relationship between the flow generation and outflow of the grid unit. In the one-dimensional motion wave equation, the inertia term and the pressure term are ignored, and the friction resistance ratio S is considered. f and the slope S0, and considers that S f =S0, so the wave equation of motion is defined as:

[0066]

[0067] S f -S0=0

[0068] The water depth is defined according to the Manning formula:

[0069]

[0070] Establish the partial differential equations for runoff and outlet flow:

[0071]

[0072] Among them, h is the runoff depth, q is the grid unit flow, Q is the grid unit outflow, S0 is the grid slope, and n is the roughness coefficient under different soil and water conservation measures;

[0073] S5. Construction of dynamic combination model of distributed storage overload on the Loess Plateau:

[0074] S51, based on the runoff generation module and the runoff confluence module obtained in steps S3 and S4, a distributed storage super dynamic combination model of the Loess Plateau is established, the parameter ranges determined in step S2 are input into the distributed storage super dynamic combination model of the Loess Plateau to obtain simulated runoff, the simulated runoff is compared with the actual observed runoff, and the determination coefficient R is used to determine the runoff. 2 The Nash efficiency coefficient NSE is used to evaluate whether the simulated runoff is consistent with the actual observed runoff;

[0075] Coefficient of determination (R 2 ) is calculated as follows:

[0076]

[0077] The Nash efficiency coefficient (NSE) calculation formula is as follows:

[0078]

[0079] Among them, Q o To observe the runoff, is the average value of observed runoff, Q s To simulate runoff, is the average value of simulated runoff, n is the total number of runoff observations;

[0080] S52, introduce genetic algorithm into the dynamic combination model of distributed storage super in the Loess Plateau, and establish the genetic algorithm objective function, where the objective function is the deterministic coefficient R 2 The values ​​of the coefficient of certainty NSE tend to 1, and the model runoff parameters are optimized according to the objective function;

[0081] S53. Determine the calibration parameter results of the Loess Plateau distributed storage-superdynamic combination model according to the established objective function, and finally obtain the Loess Plateau distributed storage-superdynamic combination model with optimal parameters.

[0082] The method of the present invention first divides the study area into multiple uniform grids by uniform grid division. Each grid represents an area with different hydrological characteristics. This method helps to improve the spatial resolution of the model, so that the hydrological simulation can more finely reflect the underlying surface characteristics of the slope unit. Uniform grid division can effectively reduce the model error caused by regional differences and provide a basis for subsequent parameterization.

[0083] Secondly, field observation tests were conducted in the study area to obtain measured data on the effects of different soil and water conservation measures (such as tree planting, fish scale pits, terraces, etc.) on water infiltration, evaporation, runoff, etc. These data were used to determine the model parameter range under soil and water conservation measures in each grid. Parameterization based on measured data can improve the accuracy and reliability of the model and ensure that the model can adapt to actual conditions.

[0084] In the method described in the present invention, a grid-based distributed storage-over-dynamic combination model is constructed, which includes the over-infiltration and "shallow storage-full" runoff generation mechanisms, and combines the one-dimensional motion wave equation to interconnect the rainfall-runoff processes in each grid to form a complete distributed storage-over-dynamic combination model.

[0085] The working principle of the distributed super-saturation dynamic combination model includes the over-infiltration runoff mechanism and the "shallow storage" runoff mechanism. The over-infiltration runoff mechanism refers to the excess water produced when the rainfall exceeds the infiltration capacity of the surface soil. This part of water will form over-infiltration surface runoff on the soil surface. In the full-saturation runoff mechanism, saturated surface runoff refers to the free-flowing water generated by the temporary saturated zone formed in the relatively impermeable layer of the soil vadose zone when the soil moisture exceeds the field water holding capacity. It gradually rises to the surface to form supersaturated surface runoff. In the arid and semi-arid areas of the Loess Plateau, the vadose zone is thicker, and the temporary saturated zone may only occur in the soil surface. The runoff mechanism is the "shallow storage" runoff mechanism. The structure of the distributed super-saturation dynamic combination model can dynamically reflect the runoff mechanism of units in different grids, and has higher prediction ability and application value.

[0086] Compared with the prior art, the technical solution provided by the present invention has the following technical effects: the present invention proposes a method for constructing a distributed excess storage dynamic combination model based on soil and water conservation measures. The method solves the technical problems of low simulation accuracy and poor applicability of existing hydrological models in the Loess Plateau region through uniform grid division, slope test data acquisition and distributed excess storage dynamic combination model construction. The present invention firstly divides the study area into multiple small grids by uniform grid division, and each grid may represent different underlying surface characteristics. This division method improves the spatial resolution of the model, so that the hydrological simulation can more finely reflect the underlying surface characteristics of the slope, and effectively reduce the model error caused by regional differences; the present invention conducts field observation experiments in the study area to obtain the model measured data under different soil and water conservation measures, and uses these data to determine the parameter range of soil and water conservation measures in each grid. Through field experiments, the influence of soil and water conservation measures on water infiltration, evaporation, runoff, etc. is determined, providing real and reliable parameters for the model, and the parameterization based on measured data improves the accuracy and reliability of the model; the distributed storage-super dynamic combination model constructed by the present invention introduces the super-infiltration and "shallow storage" runoff generation mechanism, and combines the one-dimensional motion wave equation to make the rainfall-runoff process in each grid interconnected, forming a complete distributed storage-super dynamic combination model, and this model structure can dynamically reflect the runoff generation mechanism of different grid units, and improve the prediction ability of the model. The distributed storage-super dynamic combination model can calculate and predict the hydrological process in different regions in real time, and has higher prediction ability and application value. In arid and semi-arid areas such as the Loess Plateau, this mechanism can more accurately reflect soil moisture changes, which is helpful to formulate effective soil and water conservation strategies; through this method, the runoff characteristics of the Loess Plateau can be more accurately described, and the simulation accuracy and applicability of the model can be improved. The establishment of this model will provide a scientific basis and technical support for soil and water conservation work in the Loess Plateau, and will have significant social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0088] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0089] Figure 1 It is a structural schematic diagram of a method for constructing a distributed super storage dynamic combination model based on soil and water conservation measures in a certain embodiment of the present invention. DETAILED DESCRIPTION

[0090] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0091] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all of the embodiments.

[0092] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0093] In one embodiment, Figure 1 As shown, a method for constructing a distributed super storage dynamic combination model based on soil and water conservation measures is disclosed, and the steps are:

[0094] S1. Divide the slope grid cells and determine the flow direction of each grid cell:

[0095] Select drones equipped with high-resolution cameras or laser radar equipment (LiDAR) to obtain digital elevation models (DEMs) of typical field slopes on the Loess Plateau; Based on the size of typical field slopes on the Loess Plateau, use ArcGIS software to divide them into multiple grid cells of the same size and perform flow direction analysis on them to determine the flow direction of each grid cell; obtain the grid number, grid width and flow direction of each grid cell;

[0096] S2. Determine the range of model runoff parameters:

[0097] Based on the field slope test, the mini infiltration meter (Mini Disk) was used to measure the soil period infiltration rate under different soil and water conservation measures, including different vegetation types, different plant growth periods and different initial soil moisture conditions. The Horton infiltration equation was used to perform nonlinear fitting to determine the soil period infiltration rate under different scenarios. The parameters that need to be determined in the Horton infiltration equation include: initial infiltration rate f0, stable infiltration rate f c and empirical parameter k; then determine the maximum interception capacity under different measures; conduct ring knife soil sampling on typical field slopes on the Loess Plateau, and use micro-infiltrator to determine the range of soil field water holding capacity and soil saturated water content parameters under different soil types; determine the parameter range of water flow velocity v under different soil and water conservation measures based on field slope dye tracer test, and use Manning's formula to deduce the parameter range of roughness coefficient n; convert the initial infiltration rate f0 and stable infiltration rate f c , the empirical parameter k, the maximum interception capacity, the soil field water holding capacity, the soil saturated water content, and the parameter ranges of the roughness coefficient n under different soil and water conservation measures are assigned to each grid obtained in S1 to determine the input parameter range of each grid model;

[0098] S3. Construction of flow generation module:

[0099] The runoff module includes interception module, evapotranspiration module, infiltration module, surface runoff module, and subsoil flow and underground runoff module;

[0100] S31, the interception module uses the method in the Gash model to calculate the interception amount of each grid at different time periods; the maximum interception amount is defined as:

[0101] ΔI m (t)=0.935+0.498*LAI(t)-0.00575*LAI(t) 2

[0102] The intercepted rainfall depth and intercepted evaporation are defined as:

[0103]

[0104] ΔE I (t) = min(ΔI c (t),ΔE(t))

[0105] Wherein, the correction coefficient η(t) = 0.04*LAI(t);

[0106] Net rainfall is defined as:

[0107] ΔP n (t)=max(0,ΔP(t)-ΔI c (t))

[0108] The initial retention at the next moment is defined as:

[0109] ΔI0(t+1)=max(0,ΔI0(t)+ΔI c (t)-ΔE I (t))

[0110] Among them, ΔP(t) is the rainfall at time t, LAI(t) is the leaf area index at time t, ΔI0(t=0) is the initial interception when t is 0, assuming that the initial interception at the initial moment is 0, ΔI0(t+1) is the initial interception at the next moment, ΔE(t) is the potential evaporation at time t, ΔP n (Δ) is the net rainfall at time t, ΔE I (t) is the intercepted evaporation at time t;

[0111] S32, evapotranspiration module adopts sequential evaporation, and the potential evapotranspiration first meets the evaporation of the intercepted water surface, then meets the transpiration of plants, and finally meets the evaporation of the surface soil;

[0112] Plant transpiration is defined as:

[0113]

[0114] Soil evaporation is defined as:

[0115] ΔE s (t) = ΔE(t) - ΔE I (t)-ΔE P (t)

[0116] Where, ΔE I (t) is the evaporation of the intercepted water surface at time t; ΔE(t) is the potential evaporation at time t, LAI(t) is the leaf area index at time t, ΔE P (t) is the transpiration of vegetation at time t, ΔE s (t) is the soil evaporation at time t;

[0117] S33, the infiltration module uses the Horton infiltration model to calculate the infiltration volume during each period, and the soil module uses a double-layer calculation;

[0118] The infiltration rate equation is defined as:

[0119] f(t)=f c +(f0-f)e -kt

[0120] The time infiltration equation is defined as:

[0121]

[0122] ΔF(t)=F((t-1)+1)-F(t-1)

[0123] The water availability factor is defined as:

[0124]

[0125] The surface soil infiltration equation is defined as:

[0126]

[0127] Where f0 is the initial infiltration rate; f c is the stable infiltration rate; k is an empirical parameter; β is the grid unit slope, Δx(t) is the water supply at time t, ΔF c (t) is the infiltration amount of the upper soil at time t;

[0128] S34, surface runoff module considers the calculation of super-seepage surface runoff and saturated surface runoff:

[0129] Surface runoff is defined as:

[0130]

[0131] The initial interception amount of soil and water conservation measures at the next moment is defined as:

[0132]

[0133] The upper soil moisture content is defined as:

[0134]

[0135] Among them, ΔS0(t) is the initial water content of the upper soil at time t, S a is the saturated water content of the upper soil layer, ΔP n (t) is the net rainfall at time t, ΔF c (t) is the infiltration amount of the upper soil layer at time t, ΔC0(t) is the initial interception amount of the water conservation measures at time t, ΔC0(t+1) is the initial interception amount of the water conservation measures at the next moment, C m is the maximum interception capacity of water conservation measures at time t, ΔS c (t) is the initial moisture content of the upper soil at time t, ΔR c (t) is the surface runoff at time t;

[0136] S35, soil flow and underground runoff module considers dividing the soil vadose zone into two layers for soil flow calculation, and underground runoff is the outflow from the soil bottom holes after stable infiltration;

[0137] The infiltration rate from the upper soil layer to the lower soil layer is defined as:

[0138]

[0139] The initial moisture content of the upper soil at the next moment is defined as:

[0140]

[0141] The amount of gravity water produced by the subsoil is defined as:

[0142]

[0143] The initial soil moisture content of the lower layer at the next moment is defined as:

[0144]

[0145] The soil flow is defined as:

[0146] ΔP s (t) = δ1·ΔG(t)·sinβ

[0147] Groundwater runoff is defined as:

[0148] ΔR g (t) = δ2·ΔG(t)

[0149] Total runoff is defined as:

[0150] ΔR T (t) = ΔR c +ΔR s +ΔR g

[0151] Among them, ΔS c (t) is the initial moisture content of the upper soil at time t, S h is the field water holding capacity of the upper soil layer, S a is the saturated water content of the upper soil, ΔW0(t) is the initial water content of the lower soil at time t, W h is the field water holding capacity of the lower soil, δ1 is the lateral drainage coefficient of soil midstream, β is the grid unit slope, δ2 is the lateral drainage coefficient of underground runoff, ΔF d (t) is the infiltration amount of the upper soil to the lower soil at time t, ΔS0(t) is the initial soil moisture content of the upper soil at time t, ΔG(t) is the gravity water content of the lower soil at time t, and ΔR s (t) is the soil flow at time t, ΔR g (t) is the underground runoff at time t, ΔR T (t) is the total runoff at time t;

[0152] S4. Construction of confluence module:

[0153] The slope confluence of each grid surface adopts the one-dimensional motion wave equation combined with the Manning formula to construct the relationship between the flow generation and outflow of the grid unit. In the one-dimensional motion wave equation, the inertia term and the pressure term are ignored, and the friction resistance ratio S is considered. f and the slope S0, and considers that S f =S0, so the wave equation of motion is defined as:

[0154]

[0155] S f -S0=0

[0156] The water depth is defined according to the Manning formula:

[0157]

[0158] Establish the partial differential equations for runoff and outlet flow:

[0159]

[0160] Among them, h is the runoff depth, q is the grid unit flow, Q is the grid unit outflow, S0 is the grid slope, and n is the roughness coefficient under different soil and water conservation measures;

[0161] S5. Construction of the distributed storage-excess dynamic combined model (SIM-RFMOD) on the Loess Plateau:

[0162] S51, based on the runoff generation module and the runoff confluence module obtained in steps S3 and S4, a distributed storage super dynamic combination model of the Loess Plateau is established, the parameter ranges determined in step S2 are input into the distributed storage super dynamic combination model of the Loess Plateau to obtain simulated runoff, the simulated runoff is compared with the actual observed runoff, and the determination coefficient R is used to determine the runoff. 2 The Nash efficiency coefficient NSE is used to evaluate whether the simulated runoff is consistent with the actual observed runoff;

[0163] Coefficient of determination (R 2 ) is calculated as follows:

[0164]

[0165] The Nash efficiency coefficient (NSE) calculation formula is as follows:

[0166]

[0167] Among them, Q o To observe the runoff, is the average value of observed runoff, Q s To simulate runoff, is the average value of simulated runoff, n is the total number of runoff observations;

[0168] S52, introduce genetic algorithm into the dynamic combination model of distributed storage super in the Loess Plateau, and establish the genetic algorithm objective function, where the objective function is the deterministic coefficient R 2 The values ​​of the coefficient of certainty NSE tend to 1, and the model runoff parameters are optimized according to the objective function;

[0169] S53. Determine the calibration parameter results of the Loess Plateau distributed storage-superdynamic combination model according to the established objective function, and finally obtain the Loess Plateau distributed storage-superdynamic combination model with optimal parameters.

[0170] The above is only a specific implementation of the present invention, which enables those skilled in the art to understand or implement the present invention. Although detailed descriptions are given with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A method for constructing a distributed storage dynamic combination model based on soil and water conservation measures, characterized in that: The steps are: S1. Divide the slope grid cells and determine the flow direction of each grid cell: Select a drone equipped with a high-resolution camera or lidar device to obtain a digital elevation model of a typical field slope on the Loess Plateau; divide the typical field slope on the Loess Plateau into multiple grid cells of the same size based on their size and perform flow direction analysis to determine the flow direction of each grid cell; obtain the grid number, grid width and flow direction of each grid cell; S2. Determine the range of model runoff parameters: Based on the field slope test, the soil period infiltration rate under different soil and water conservation measures, including different vegetation types, different plant growth periods and different initial soil moisture conditions, was measured using a micro-infiltration meter. The Horton infiltration equation was used to perform nonlinear fitting to determine the soil period infiltration rate under different scenarios. The parameters that need to be determined in the Horton infiltration equation include: initial infiltration rate f0, stable infiltration rate f c and empirical parameter k; then determine the maximum interception capacity under different measures; conduct ring knife soil sampling on typical field slopes on the Loess Plateau, and use micro-infiltrator to determine the range of soil field water holding capacity and soil saturated water content parameters under different soil types; determine the parameter range of water flow velocity v under different soil and water conservation measures based on field slope dye tracer test, and use Manning's formula to deduce the parameter range of roughness coefficient n; convert the initial infiltration rate f0 and stable infiltration rate f c , the empirical parameter k, the maximum interception capacity, the soil field water holding capacity, the soil saturated water content, and the parameter ranges of the roughness coefficient n under different soil and water conservation measures are assigned to each grid obtained in S1 to determine the input parameter range of each grid model; S3. Construction of flow generation module: The runoff module includes interception module, evapotranspiration module, infiltration module, surface runoff module, and subsoil flow and underground runoff module; S31, the interception module uses the method in the Gash model to calculate the interception amount of each grid at different time periods; The maximum retention is defined as: ΔI m (t)=0.935+0.498*LAI(t)-0.00575*LAI(t) 2 The intercepted rainfall depth and intercepted evaporation are defined as: D.E. I (t)=min(ΔI c (t),ΔE(t)) Wherein, the correction coefficient η(t) = 0.04*LAI(t); Net rainfall is defined as: ΔP n (t)=max(0,ΔP(t)-ΔI c (t)) The initial retention at the next moment is defined as: ΔI0(t+1)=max(0,ΔI0(t)+ΔI c (t)-ΔE I (t)) Among them, ΔP(t) is the rainfall at time t, LAI(t) is the leaf area index at time t, ΔI0(t=0) is the initial interception when t is 0, assuming that the initial interception at the initial moment is 0, ΔI0(t+1) is the initial interception at the next moment, ΔE(t) is the potential evaporation at time t, ΔP n (t) is the net rainfall at time t, ΔE I (t) is the intercepted evaporation at time t; S32, evapotranspiration module adopts sequential evaporation, and the potential evapotranspiration first meets the evaporation of the intercepted water surface, then meets the transpiration of plants, and finally meets the evaporation of the surface soil; Plant transpiration is defined as: Soil evaporation is defined as: ΔE s (t)=ΔE(t)-ΔE I (t)-ΔE P (t) Where, ΔE I (t) is the evaporation of the intercepted water surface at time t; ΔE(t) is the potential evaporation at time t, LAI(t) is the leaf area index at time t, ΔE P (t) is the transpiration of vegetation at time t, ΔE s (t) is the soil evaporation at time t; S33, the infiltration module uses the Horton infiltration model to calculate the infiltration volume during each period, and the soil module uses a double-layer calculation; The infiltration rate equation is defined as: f(t)=f c +(f0-fc)e -kt The time infiltration equation is defined as: ΔF(t)=F((t-1)+1)-F(t-1) The water availability factor is defined as: The surface soil infiltration equation is defined as: Where f0 is the initial infiltration rate; f c is the stable infiltration rate; k is an empirical parameter; β is the grid unit slope, Δx(t) is the water supply at time t, ΔF c (t) is the infiltration amount of the upper soil at time t; S34, surface runoff module considers the calculation of super-seepage surface runoff and saturated surface runoff: Surface runoff is defined as: The initial interception amount of soil and water conservation measures at the next moment is defined as: The upper soil moisture content is defined as: Among them, ΔS0(t) is the initial water content of the upper soil at time t, S a is the saturated water content of the upper soil layer, ΔP n (t) is the net rainfall at time t, ΔF c (t) is the infiltration amount of the upper soil layer at time t, ΔC0(t) is the initial interception amount of the water conservation measures at time t, ΔC0(t+1) is the initial interception amount of the water conservation measures at the next moment, C m is the maximum interception capacity of water conservation measures at time t, ΔS c (t) is the initial moisture content of the upper soil at time t, ΔR c (t) is the surface runoff at time t; S35, soil flow and underground runoff module considers dividing the soil vadose zone into two layers for soil flow calculation, and underground runoff is the outflow from the soil bottom holes after stable infiltration; The infiltration rate from the upper soil layer to the lower soil layer is defined as: The initial moisture content of the upper soil at the next moment is defined as: The amount of gravity water produced by the subsoil is defined as: The initial soil moisture content of the lower layer at the next moment is defined as: The soil flow is defined as: ΔR s (t)=δ1·ΔG(t)·sinβ Groundwater runoff is defined as: ΔR g (t)=δ2·ΔG(t) Total runoff is defined as: ΔR T (t)=ΔR c +ΔR s +ΔR g Among them, ΔS c (t) is the initial moisture content of the upper soil at time t, S h is the field water holding capacity of the upper soil layer, S a is the saturated water content of the upper soil, ΔW0(t) is the initial water content of the lower soil at time t, W h is the field water holding capacity of the lower soil, δ1 is the lateral drainage coefficient of soil midstream, β is the grid unit slope, δ2 is the lateral drainage coefficient of underground runoff, ΔF d (t) is the infiltration amount of the upper soil to the lower soil at time t, ΔS0(t) is the initial soil moisture content of the upper soil at time t, ΔG(t) is the gravity water content of the lower soil at time t, and ΔR s (t) is the soil flow at time t, ΔR g (t) is the underground runoff at time t, ΔR T (t) is the total runoff at time t; S4. Construction of confluence module: The slope confluence of each grid surface adopts the one-dimensional motion wave equation combined with the Manning formula to construct the relationship between the flow generation and outflow of the grid unit. In the one-dimensional motion wave equation, the inertia term and the pressure term are ignored, and the friction resistance ratio S is considered. f and the slope S0, and considers that S f =S0, so the wave equation of motion is defined as: S f -S0=0 The water depth is defined according to the Manning formula: Establish the partial differential equations for runoff and outlet flow: Among them, h is the runoff depth, q is the grid unit flow, Q is the grid unit outflow, S0 is the grid slope, and n is the roughness coefficient under different soil and water conservation measures; S5. Construction of dynamic combination model of distributed storage overload on the Loess Plateau: S51, based on the runoff generation module and the runoff confluence module obtained in steps S3 and S4, a distributed storage super dynamic combination model of the Loess Plateau is established, the parameter ranges determined in step S2 are input into the distributed storage super dynamic combination model of the Loess Plateau to obtain simulated runoff, the simulated runoff is compared with the actual observed runoff, and the determination coefficient R is used to determine the runoff. 2 The Nash efficiency coefficient NSE is used to evaluate whether the simulated runoff is consistent with the actual observed runoff; Coefficient of determination (R 2 ) is calculated as follows: The Nash efficiency coefficient (NSE) calculation formula is as follows: Among them, Q o To observe the runoff, is the average value of observed runoff, Q s To simulate runoff, is the average value of simulated runoff, n is the total number of runoff observations; S52, introduce genetic algorithm into the dynamic combination model of distributed storage super in the Loess Plateau, and establish the genetic algorithm objective function, where the objective function is the deterministic coefficient R 2 The values ​​of the coefficient of certainty NSE tend to 1, and the model runoff parameters are optimized according to the objective function; S53. Determine the calibration parameter results of the Loess Plateau distributed storage-superdynamic combination model according to the established objective function, and finally obtain the Loess Plateau distributed storage-superdynamic combination model with optimal parameters.

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