A method for deriving soil water storage capacity parameters considering underlying surface geographical characteristics

Abstract

A soil water storage capacity parameter derivation method considering the underlying surface geographical characteristics comprises the following steps: Step 1: Select several typical watersheds with relatively rich rainfall runoff observation data and obtain the soil water storage capacity parameter W in each typical watershed. M Step 2: Count the underlying geographical characteristics of each typical watershed and screen for those factors that significantly influence the soil water storage capacity parameters. Step 3: Based on the screened underlying geographical characteristics, fit the relationship function F between the underlying geographical characteristics and the soil water storage capacity parameters. Step 4: Extract the underlying geographical characteristics of the watersheds with no data surrounding the typical watersheds and use the relationship function F to derive the soil water storage capacity parameters within the watersheds with no data, thereby achieving spatial distribution derivation of the parameters. This method has the advantages of a stable and reliable data source, high computational efficiency, and objective and reasonable results, and is therefore worthy of promotion.

Description

Technical Field

[0001] The present invention relates to the field of hydrological technology, and in particular to a method for deducing soil water storage capacity parameters taking into account underlying surface geographical characteristics. Background Art

[0002] Small mountainous watersheds are often located in remote areas, making it difficult to deploy a sufficient number of hydrological monitoring stations to monitor multiple factors such as water level and flow. It is also difficult to obtain sufficient hydrological observation data in each small mountainous watershed to formulate flood forecast model parameter schemes. As a result, the flood simulation accuracy of small mountainous watersheds without data is usually low, which is not conducive to the widespread development of rapid flood forecasting in remote mountainous watersheds and poses a huge hidden danger to flood control safety.

[0003] Currently, the construction of flood forecast model parameter schemes for small watersheds in mountainous areas with no data is typically done by transplanting parameters from adjacent watersheds. This method involves finding a watershed with a known parameter scheme near the data-free area and aligning the parameter scheme for the data-free area with the known parameter scheme. However, the underlying surface characteristics of different watersheds inevitably vary, and this simple parameter transplantation method ignores the underlying surface geographic features that influence the parameters. Therefore, it is difficult to improve the accuracy of flood forecasts for a large number of small watersheds in mountainous areas, hindering the improvement of flood forecasting capabilities in mountainous watersheds.

[0004] In view of the above shortcomings, how to adopt an economical, effective and scientifically reasonable method in the vast mountainous small watersheds to realize the deduction of the spatial distribution of soil water storage capacity parameters taking into account the geographical characteristics of the underlying surface of the watershed, and support the scientific forecast of floods in the data-free mountainous watersheds, is the problem that needs to be solved. Summary of the Invention

[0005] In order to avoid the above problems, a method for deriving soil water storage capacity parameters taking into account the geographical characteristics of the underlying surface is provided. It has the advantages of stable and reliable data source, high calculation efficiency, and objective and reasonable results, and is worthy of promotion.

[0006] The present invention provides a method for deriving soil water storage capacity parameters taking into account the geographical characteristics of the underlying surface, comprising the following steps:

[0007] Step 1: Select several typical watersheds with relatively rich rainfall runoff observation data and obtain the soil water storage capacity parameter W in each typical watershed. M ;

[0008] Step 2: Count the underlying surface geographical characteristic factors of each typical watershed and screen out the underlying surface geographical characteristic factors that have a significant impact on soil water storage capacity parameters;

[0009] Step 3: Based on the selected underlying surface geographical characteristic factors, a relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters is obtained by fitting;

[0010] Step 4: Extract the underlying surface geographic characteristic factors of the data-free basins around the typical basin, and use the relationship function F to obtain the soil water storage capacity parameters in the data-free basin to achieve the spatial distribution deduction of the parameters.

[0011] Preferably, the underlying surface geographical characteristic factors include: the average slope S of the watershed c , soil thickness S t , sand content S d , Powder content S s , clay content S y .

[0012] Preferably, step 1 specifically includes:

[0013] 1.1 Extract water systems based on digital elevation data and identify main river channels;

[0014] 1.2 Select several typical watersheds with abundant rainfall and runoff observation data around the main river channels;

[0015] 1.3 Use intelligent optimization methods to calibrate the soil water storage capacity parameters in each typical watershed.

[0016] Preferably, step 1.2 is specifically as follows: conduct a preliminary investigation of rainfall, the distribution of the hydrological observation station network and the length of the water-rainfall observation data sequence, select several typical river basins with hourly water-rainfall observation data and a data sequence length of more than 3 years, and each typical river basin is evenly distributed along the main river channel from upstream to downstream and on both sides of the main river channel.

[0017] Preferably, step 1.3 is specifically as follows: constructing a full-storage runoff model in a typical river basin, and using a cooperative optimization algorithm to carry out parameter optimization calibration of the full-storage runoff model in each typical river basin based on three years of water and rainfall observation data, to obtain the soil water storage capacity parameters in each typical river basin.

[0018] Preferably, step 2 specifically includes:

[0019] 2.1 Statistics of underlying surface characteristic factors of each typical watershed: Average slope S of the watershed c , soil thickness S t , sand content S d , Powder content S s , clay content S y ;

[0020]

[0021] Where S i 、S t，i 、S d，i 、S s，i 、S y，iare the slope, soil thickness, sand content, silt content, and clay content of each grid cell in the watershed; N is the number of grid cells in the watershed;

[0022] 2.2 Calculate the correlation coefficient γ between the soil water storage capacity parameters and the underlying surface characteristic factors in a typical watershed:

[0023]

[0024] Where: W represents the water storage capacity of the watershed soil M and θ i The correlation coefficient between i Represents variable S c 、S t 、S d 、S s 、S y , i is the number of the variable; E is the mathematical expectation calculation function;

[0025] 2.3 The absolute values ​​of are ranked from large to small, and the top three underlying surface characteristics of the watershed are selected as the main factors affecting the soil water storage capacity parameters.

[0026] Preferably, step 3 specifically includes:

[0027] 3.1 Preliminary fitting of the relationship function f1 between the three main underlying surface geographical characteristics and soil water storage capacity parameters:

[0028] Where: These are the three main underlying surface geographical characteristics factors that affect soil water storage capacity;

[0029] 3.2 Use function f1 to calculate the soil water storage capacity parameters in a typical watershed. Sort the calculated soil water storage capacity parameters from small to large to obtain the sequence LC. Sort the optimized soil water storage capacity parameters from small to large to obtain the sequence LE:

[0030] LC={W M，1 , W M，2 …W M，k …W M，K},

[0031] LE={W M，1 , W M，2 …W M，r …W M，K};

[0032] Where: W M，k represents a typical watershed numbered k, where k ranges from 1 to K and K is the number of typical watersheds; W M，rRepresents a typical watershed numbered r, where r ranges from 1 to K;

[0033] 3.3 If the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE correspond to the same typical watershed, that is, any k is the same as r, representing the same typical watershed, then the function f1 is determined as the relationship function F between the underlying surface geographical characteristic factor and the soil water storage capacity parameter;

[0034] If the soil water storage capacity parameters in the same order in sequence LC and sequence LE correspond to different typical watersheds, the sequences LE′ and LC′ are adjusted to obtain the corresponding sequences, and the operations from steps 2.1 to 3.1 are repeated until the function f1′ is determined to be the relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters.

[0035] Preferably, the method for obtaining the sequences LE′ and LC′ is as follows: a heavy rainfall process with a long drought in the early stage is selected, with rainfall P as the vertical axis and runoff R as the horizontal axis, a P-R scatter plot is plotted for each time period, and the intercept V on the vertical axis of a straight line passing through the P-R scatter points and with a slope of 1 is found. The soil water storage capacity parameters in LE and LC are readjusted in order from small to large according to the V value to obtain LE′ and LC′, so that the order of the soil water storage capacity parameters of each typical watershed in the two sequences is the same.

[0036] Compared with existing technologies, the present invention has the following beneficial effects: The present invention provides a method for inferring the spatial distribution of soil water storage capacity parameters that considers the geographic characteristics of the watershed's underlying surface. Based on the physical factors that influence soil water storage capacity parameters in small mountain watersheds, it combines intelligent algorithms to achieve optimal calibration of soil water storage capacity parameters within the watershed, rationally quantifying the impact of the watershed's underlying surface geographic characteristics on soil water storage capacity parameters. This ensures the accuracy and reliability of the calculation results while simultaneously resolving the difficult problem of inferring the spatial distribution of soil water storage capacity parameters that considers the geographic characteristics of the watershed's underlying surface. Furthermore, this method primarily utilizes a watershed digital elevation model and intelligent algorithms, resulting in a stable and reliable data source and clear functional relationships between variables in the method. This facilitates the inference of the spatial distribution of soil water storage capacity parameters that considers the geographic characteristics of the watershed's underlying surface, while ensuring the objectivity and rationality of the results. This can further promote the in-depth development of digital hydrology and the rapid improvement of flood control capabilities in mountainous areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of a method for deriving soil water storage capacity parameters taking into account underlying surface geographical characteristics according to a preferred embodiment of the present invention;

[0038] Figure 2 A digital elevation of a certain interval in a preferred embodiment of the present invention;

[0039] Figure 3A water system and typical river basin distribution in a certain area of ​​a preferred embodiment of the present invention;

[0040] Figure 4 The parameter calibration results of a typical watershed in a certain interval of a preferred embodiment of the present invention;

[0041] Figure 5 The slope of each sub-basin in a certain interval of a preferred embodiment of the present invention;

[0042] Figure 6 The soil thickness distribution in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0043] Figure 7 The distribution of sand content in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0044] Figure 8 The distribution of powder content in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0045] Figure 9 The distribution of clay content in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0046] Figure 10 is the average slope of each sub-basin in a certain interval in a preferred embodiment of the present invention;

[0047] Figure 11 is the average soil thickness in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0048] Figure 12 is the average sand content in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0049] Figure 13 is the average particle content in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0050] Figure 14 is the average clay content in each sub-basin of a certain interval in a preferred embodiment of the present invention;

[0051] Figure 15 This is a P-R scatter diagram diagram of a preferred embodiment of the present invention;

[0052] Figure 16 This is the spatial distribution of soil water storage capacity parameters in a certain interval according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0053] This embodiment provides a method for deriving soil water storage capacity parameters that takes into account the underlying surface geographical characteristics, including the following steps:

[0054] Step 1: Select several typical watersheds with abundant rainfall and runoff observation data, and use the intelligent optimization method to calibrate the soil water storage capacity parameters in each typical watershed. The specific steps are as follows:

[0055] 1.1 Extract water systems based on digital elevation data and identify main river channels;

[0056] 1.2 Select several typical watersheds with abundant rainfall and runoff observation data around the main river channels;

[0057] A preliminary investigation was conducted on the distribution of rainfall and hydrological observation station network and the length of water and rainfall observation data series. Several typical river basins with hourly water and rainfall observation data and data series length of more than 3 years were selected. Each typical river basin was evenly distributed along the main river channel from upstream to downstream and on both sides of the main river channel.

[0058] 1.3 Using intelligent optimization methods to calibrate the soil water storage capacity parameters in each typical watershed;

[0059] A full storage runoff model was constructed in a typical river basin. Based on three years of water and rainfall observation data, the cooperative optimization algorithm (CSA) was used to optimize and calibrate the parameters of the full storage runoff model in each typical river basin, as well as the soil water storage capacity parameters in each typical river basin.

[0060] Step 2: Obtain the average slope S of each typical watershed by statistics. c , soil thickness S t , sand content S d , Powder content S s , clay content S y The underlying surface geographical characteristic factors such as the following are screened to obtain the underlying surface geographical characteristic factors that have a significant impact on soil water storage capacity parameters. The specific steps are as follows:

[0061] 2.1 Statistics of the average slope S of each typical watershed c , soil thickness S t , sand content S d , Powder content S s , clay content S y Equal underlying surface characteristic factors;

[0062]

[0063] Where: S i 、S t，i 、S d，i 、S s，i 、S y，i are the slope, soil thickness, sand content, silt content, and clay content of each grid cell in the watershed; N is the number of grid cells in the watershed;

[0064] 2.2 Calculate the correlation coefficient γ between soil water storage capacity parameters and underlying surface characteristic factors in a typical watershed;

[0065] Calculate soil water storage capacity parameter W M Correlation coefficient between the underlying surface characteristic factors:

[0066]

[0067] Where: W represents the water storage capacity of the watershed soil M and θ i The correlation coefficient between i Stands for S c 、S t 、S d 、S y Equal variables, i is the variable number; E is the mathematical expectation calculation function.

[0068] 2.3 Screening the underlying surface geographical characteristics that have a significant impact on soil water storage capacity parameters;

[0069] Will The absolute values ​​of are ranked from large to small, and the top three underlying surface characteristics of the watershed are selected as the main factors affecting the soil water storage capacity parameters.

[0070] Step 3: Based on the soil water storage capacity parameters in typical watersheds and the selected underlying surface geographical characteristic factors, the relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters is fitted. The specific steps are as follows:

[0071] 3.1 Preliminary fitting yields the relationship function f1 between the underlying surface geographical characteristic factors and the soil water storage capacity parameters:

[0072] Where: They are the three main factors affecting soil water storage capacity. The function form of f1 is generally a three-variable linear equation.

[0073] 3.2 Use function f1 to calculate the soil water storage capacity parameters in a typical watershed, sort the calculated soil water storage capacity parameters from small to large to obtain the sequence LC, and sort the soil water storage capacity parameters obtained by optimization and calibration from small to large to obtain the sequence LE;

[0074] LC={W M，1 , W M，2 …W M，k …W M，K},

[0075] LE={W M，1 , W M，2 …W M，r…W M，K};

[0076] Where: W M，k represents a typical watershed numbered k, where k ranges from 1 to K and K is the number of typical watersheds; W M，r Represents a typical watershed numbered r, where r ranges from 1 to K.

[0077] If the typical watershed corresponding to the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE is the same, that is, any k is the same as r, indicating the same typical watershed, then the function f1 is determined as the relationship function F between the underlying surface geographical characteristic factor and the soil water storage capacity parameter;

[0078] If the typical watersheds corresponding to the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE are different, the sequences LE′ and LC′ are adjusted to obtain the sequences, and the operations between steps 2.1 to 3.1 are repeated to determine the function f1′ at this time as the relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters.

[0079] The method for obtaining the sequences LE′ and LC′ is as follows:

[0080] A heavy rainfall process with a long drought in the early stage was selected. With rainfall P as the vertical axis and runoff R as the horizontal axis, a P-R scatter plot was plotted for each time period. The intercept V of the straight line passing through the P-R scatter points and with a slope of 1 on the vertical axis was found. The soil water storage capacity parameters in LE and LC were readjusted in order from small to large according to the V value to obtain LE′ and LC′, so that the order of the soil water storage capacity parameters of each typical watershed in the two sequences was the same.

[0081] Step 4: Extract the underlying surface geographic characteristics of several watersheds with no data around the typical watershed, and use the relationship function F to estimate the soil water storage capacity parameters in the watersheds with no data, and deduce the spatial distribution of the parameters. The specific steps are as follows:

[0082] 4.1 According to the method in step 2.1, extract the underlying surface geographical characteristic factors of several watersheds without data around the typical watershed;

[0083] 4.2 Using the relationship function F between the underlying surface geographic characteristic factors and the soil water storage capacity parameters determined in step 3, the underlying surface geographic characteristic factors are used as input to calculate the soil water storage capacity parameters in each data-free basin and realize the spatial distribution deduction of the parameters.

[0084] Taking a certain interval basin in the upper reaches of the Yangtze River as an example, this basin has a controlled area of ​​approximately 1 million square kilometers, of which the main catchment area is 55,907 square kilometers. This interval is prone to heavy rain in the upper reaches of the Yangtze River. Floods caused by heavy rain are frequent, intense, and of high magnitude, and are influenced by topography and landforms, which have a significant impact on the flood control safety and operation and scheduling of reservoirs in the region. Taking this interval as an example, this paper uses basins A, B, C, D, E, F, G, H, I, and J as typical basins with abundant measured data. Applying the proposed method, we deduce the spatial distribution of soil water storage capacity parameters, taking into account the geographic characteristics of the basin's underlying surface.

[0085] A method for inferring the spatial distribution of soil water storage capacity parameters taking into account the geographical characteristics of the underlying surface of a watershed comprises the following steps:

[0086] Step 1: Select several typical watersheds with abundant rainfall and runoff observation data, and use the intelligent optimization method to calibrate the soil water storage capacity parameters in each typical watershed. The specific steps are as follows:

[0087] 1.1 Based on digital elevation data (such as Figure 2 ) Extract water systems and identify main river channels;

[0088] 1.2 Select several typical basins with abundant rainfall and runoff observation data around the main river. Figure 3 ;

[0089] A preliminary investigation was conducted on the distribution of rainfall and hydrological observation station network and the length of water and rainfall observation data series. Several typical river basins with hourly water and rainfall observation data and data series length of more than 3 years were selected. Each typical river basin was evenly distributed along the main river channel from upstream to downstream and on both sides of the main river channel.

[0090] The typical basins selected in this example are: A, B, C, D, E, F, G, H, I, J, etc. The typical basin distribution is shown in Figure 3 .

[0091] 1.3 Using intelligent optimization methods to calibrate the soil water storage capacity parameters in each typical watershed;

[0092] A full-storage runoff model was constructed in a typical watershed. Based on three years of water and rainfall observation data, the cooperative optimization algorithm (CSA) was used to optimize and calibrate the parameters of the full-storage runoff model in each typical watershed. The soil water storage capacity parameters in each typical watershed are shown in Figure 2. Figure 4 .

[0093] Step 2: Obtain the average slope S of each typical watershed by statistics. c , soil thickness S t , sand content S d , Powder content Ss , clay content S y The underlying surface geographical characteristic factors such as the following are screened to obtain the underlying surface geographical characteristic factors that have a significant impact on soil water storage capacity parameters. The specific steps are as follows:

[0094] 2.1 Statistics of the average slope S of each typical watershed c , soil thickness S t , sand content S d , Powder content S s , clay content S y Equal underlying surface characteristic factors;

[0095]

[0096]

[0097] Where: S i 、S t，i 、S d，i 、S s，i 、S y，i are the slope, soil thickness, sand content, silt content, and clay content of each grid cell in the basin; N is the number of grid cells in the basin; the slope, soil thickness, sand content, and clay content of each grid cell in each sub-basin of a certain interval are shown in Figure 2. Figure 5 、 6 , 7, 8, 9; the average slope, average soil thickness, average sand content, average silt content, and average clay content in each sub-basin of a certain interval are shown in Figure 2. Figure 10 、 11 , 12, 13, 14.

[0098] 2.2 Calculate the correlation coefficient γ between soil water storage capacity parameters and underlying surface characteristic factors in a typical watershed;

[0099] Calculate soil water storage capacity parameter W M Correlation coefficient between the underlying surface characteristic factors:

[0100]

[0101] Where: W represents the water storage capacity of the watershed soil M and θ i The correlation coefficient between i Represents ΔE, S w 、S c 、S t 、S d 、S y Equal variables, i is the variable number; E is the mathematical expectation calculation function.

[0102] 2.3 Screening the underlying surface geographical characteristics that have a significant impact on soil water storage capacity parameters;

[0103] Will The absolute values ​​of are ranked from large to small, and the top three underlying surface characteristics of the watershed are selected as the main factors affecting the soil water storage capacity parameters.

[0104] In this embodiment, the soil water storage capacity parameter W is calculated in a certain interval. M The correlation coefficient between the underlying surface characteristic factor is:

[0105] Soil thickness Sand content Powder content Clay content slope 0.66 0.62 -0.18 -0.16 -0.53

[0106] Therefore, in this embodiment, a certain interval affects the soil water storage capacity parameter W M The main underlying surface characteristics of the basin are: soil thickness, sand content and slope.

[0107] Step 3: Based on the soil water storage capacity parameters in typical watersheds and the selected underlying surface geographical characteristic factors, the relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters is fitted. The specific steps are as follows:

[0108] 3.1 Preliminary fitting yields the relationship function f1 between the underlying surface geographical characteristic factors and the soil water storage capacity parameters:

[0109] Where: They are the three main factors affecting soil water storage capacity, which in this case are soil thickness, sand content and slope. The function form of f1 is generally a three-variable linear equation.

[0110] 3.2 Use function f1 to calculate the soil water storage capacity parameters in a typical watershed, sort the calculated soil water storage capacity parameters from small to large to obtain the sequence LC, and sort the soil water storage capacity parameters obtained by optimization and calibration from small to large to obtain the sequence LE;

[0111] LC={W M，1 , W M，2 …W M，k …W M，K},

[0112] LE={W M，1 , W M，2 …W M，r …W M，K};

[0113] Where: W M，k represents a typical watershed numbered k, where k ranges from 1 to K and K is the number of typical watersheds; W M，r Represents a typical watershed numbered r, where r ranges from 1 to K.

[0114] If the typical watershed corresponding to the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE is the same, that is, any k is the same as r, indicating the same typical watershed, then the function f1 is determined as the relationship function F between the underlying surface geographical characteristic factor and the soil water storage capacity parameter;

[0115] If the typical watersheds corresponding to the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE are different, the sequences LE′ and LC′ are adjusted to obtain the sequences, and the operations between steps 2.1 to 3.1 are repeated to determine the function f1′ at this time as the relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters.

[0116] The method for obtaining the sequences LE′ and LC′ is as follows:

[0117] A heavy rainfall process with a long drought period was selected, with rainfall P as the vertical axis and runoff R as the horizontal axis, and a P-R scatter plot was drawn for each time period. Figure 15 , find the intercept V of the straight line on the vertical axis that passes through the P~R scatter points and has a slope of 1, and readjust the order of the soil water storage capacity parameters in LE and LC in the order of V value from small to large to obtain LE′ and LC′, so that the order of the soil water storage capacity parameters of each typical watershed in the two sequences is the same.

[0118] In this example, the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE correspond to the same typical watershed, and the final fitting relationship function F is:

[0119] W M =-0.2433×S c +0.0129×S t +0.0363×S d .

[0120] Step 4: Extract the underlying surface geographic characteristics of several watersheds with no data around the typical watershed, and use the relationship function F to estimate the soil water storage capacity parameters in the watersheds with no data, and deduce the spatial distribution of the parameters. The specific steps are as follows:

[0121] 4.1 According to the method in step 2.1, extract the underlying surface geographical characteristic factors of several watersheds without data around the typical watershed;

[0122] 4.2 Using the relationship function F between the underlying surface geographic characteristic factors and the soil water storage capacity parameters determined in step 3, the underlying surface geographic characteristic factors are used as input to calculate the soil water storage capacity parameters in each data-free basin, and to achieve the spatial distribution deduction of the parameters, see Figure 16 .

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deriving soil water storage capacity parameters taking into account the geographical characteristics of the underlying surface, characterized in that: The steps include: Step 1: Select several typical watersheds with abundant rainfall runoff observation data and obtain the soil water storage capacity parameter W in each typical watershed. M ; Step 2: Count the underlying surface geographical characteristic factors of each typical watershed and screen out the underlying surface geographical characteristic factors that affect the soil water storage capacity parameters; Step 3: Based on the selected underlying surface geographical characteristic factors, a relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters is obtained by fitting; Step 4: Extract the underlying surface geographical characteristic factors of the watershed without data around the typical watershed, and use the relationship function F to obtain the soil water storage capacity parameters in the watershed without data to achieve the spatial distribution deduction of the parameters; the underlying surface geographical characteristic factors include: the average slope S of the watershed c , soil thickness S t , sand content S d , Powder content S s , clay content S y ; Step 1 specifically includes: 1.1 Extract water systems based on digital elevation data and identify main river channels; 1.2 Select several typical watersheds with abundant rainfall and runoff observation data around the main river channels; 1.3 Using intelligent optimization methods to calibrate the soil water storage capacity parameters in each typical watershed; Step 1.2 specifically involves conducting a survey of rainfall, the distribution of hydrological observation stations, and the length of water and rainfall observation data series. Select several typical river basins with hourly water and rainfall observation data and a data series length of more than three years. Each typical river basin should be evenly distributed along the main river channel from upstream to downstream and on both sides of the main river channel. Step 2 specifically includes: 2.1 Statistics of underlying surface characteristic factors of each typical watershed: Average slope S of the watershed c , soil thickness S t , sand content S d , Powder content S s , clay content S y ; Where S i 、S t,i 、S d,i 、S s,i 、S y,i are the slope, soil thickness, sand content, silt content, and clay content of each grid cell in the watershed; N is the number of grid cells in the watershed; 2.2 Calculate the correlation coefficient γ between the soil water storage capacity parameters and the underlying surface characteristic factors in a typical watershed: Where: W represents the water storage capacity of the watershed soil M and θ i The correlation coefficient between i Represents variable S c 、S t 、S d 、S s 、S y , i is the number of the variable; E is the mathematical expectation calculation function; 2.3 The absolute values ​​of the watershed are sorted from large to small, and the top three watershed underlying surface characteristics are selected as the main factors affecting the soil water storage capacity parameters; Step 3 specifically includes: 3.1 Preliminary fitting of the relationship function f1 between the three main underlying surface geographical characteristics and soil water storage capacity parameters: Where: These are the three main underlying surface geographical characteristics factors that affect soil water storage capacity; 3.2 Use function f1 to calculate the soil water storage capacity parameters in a typical watershed. Sort the calculated soil water storage capacity parameters from small to large to obtain the sequence LC. Sort the optimized soil water storage capacity parameters from small to large to obtain the sequence LE: LC={W M,1 ,W M,2 …W M,k …W M,K }, LE={W M,1 ,W M,2 …W M,r …W M,K }; Where: W M,k represents a typical watershed numbered k, where k ranges from 1 to K and K is the number of typical watersheds; W M,r Represents a typical watershed numbered r, where r ranges from 1 to K; 3.3 If the soil water storage capacity parameters in the same order in the sequence LC and the sequence LE correspond to the same typical watershed, that is, any k is the same as r, representing the same typical watershed, then the function f1 is determined as the relationship function F between the underlying surface geographical characteristic factor and the soil water storage capacity parameter; If the soil water storage capacity parameters in the same order in sequence LC and sequence LE correspond to different typical watersheds, the sequences LE′ and LC′ are adjusted to obtain the corresponding sequences, and the operations from steps 2.1 to 3.1 are repeated until the function f1′ is determined to be the relationship function F between the underlying surface geographical characteristic factors and the soil water storage capacity parameters.

2. The method for deriving soil water storage capacity parameters taking into account the underlying surface geographical characteristics as claimed in claim 1, characterized in that: Step 1.3 is as follows: construct a full-storage runoff model in a typical watershed, and use a cooperative optimization algorithm to optimize and calibrate the parameters of the full-storage runoff model in each typical watershed based on three years of water and rainfall observation data to obtain the soil water storage capacity parameters in each typical watershed.

3. The method for deriving soil water storage capacity parameters taking into account the underlying surface geographical characteristics as claimed in claim 1, characterized in that: The method for obtaining the sequences LE′ and LC′ is as follows: select a heavy rainfall process with a long drought in the early stage, use rainfall P as the vertical axis and runoff R as the horizontal axis, plot a P-R scatter plot for each time period, find the intercept V on the vertical axis of the straight line passing through the P-R scatter points and with a slope of 1, and readjust the order of the soil water storage capacity parameters in LE and LC in order from small to large V values ​​to obtain LW′ and LC′, so that the order of the soil water storage capacity parameters of each typical watershed in the two sequences is the same.

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