Influence factor segmentation calculation method based on river source area runoff

Abstract

The invention discloses an influence factor segmentation calculation method based on runoff in a river source area. The calculation method comprises the following steps: determining a representative hydrological station for runoff segmentation; based on the representative hydrometric station, analyzing an influence factor of the target research area; calculating the contribution rates of different influence factors to the runoff by using a dominance analysis method, and taking the contribution rates of the different influence factors as the segmentation basis of the runoff; and multiplying the contribution rates of different influence factors by the long-sequence runoff monthly data, thereby completing the segmentation of the runoff volume. According to the method, the influence degree analysis of each key element in the runoff forming process of the river source area can be improved, the influence of climate change and human activity on the runoff mode can be better revealed, a more reasonable and effective management strategy can be formulated, the flood disaster risk is reduced, the ecological safety is guaranteed, and the accuracy of runoff prediction is enhanced; and a scientific basis is provided for water resource management and ecological protection.

Description

Technical Field

[0001] The present invention relates to a method for calculating the segmentation of runoff impact factors, specifically a method for calculating the segmentation of runoff impact factors based on the runoff in the source area of the river, and belongs to the technical field of runoff prediction. Background Art

[0002] In the fields of water resources management, hydrological research, and environmental protection, accurately evaluating the influencing factors of runoff in the source area of the river is of great significance for understanding the water cycle process within the basin, predicting flood disasters, and ensuring ecological security.

[0003] In the prior art, runoff analysis methods often rely on empirical formulas or simple statistical models. Although traditional runoff analysis methods are easy to operate, they have great limitations in dealing with complex and changeable natural environments and are difficult to comprehensively reflect all the factors affecting runoff formation and their interaction mechanisms. For example, while considering the contribution degrees of factors such as precipitation, evaporation, and soil type to runoff, it is also necessary to take into account the uncertainty brought by frequent extreme weather events to the runoff pattern under the background of climate change. Summary of the Invention

[0004] The technical solution of the present invention aims at the technical problem of the overly single solution of the prior art and provides a solution significantly different from the prior art. Specifically, the purpose of the present invention is to solve the above-mentioned drawbacks existing in the prior art, and a method for calculating the segmentation of runoff impact factors based on the runoff in the source area of the river is proposed to effectively quantify the influence degrees of key elements such as rainfall, temperature change, land use, and cover change during the runoff formation process, thereby improving the prediction accuracy of runoff, and at the same time, it can also provide an important basis for formulating reasonable and effective water resources management and ecological protection strategies.

[0005] To achieve the above object, the present invention adopts the following technical solution: A method for calculating the segmentation of runoff impact factors based on the runoff in the source area of the river, and this method for calculating the segmentation of runoff impact factors includes the following steps:

[0006] S1. For the target research area, determine the representative hydrological station for runoff segmentation, and obtain the long-term monthly runoff data of the representative hydrological station;

[0007] S2. Based on the representative hydrological station, analyze the impact factors of the target research area, where the impact factors include but are not limited to precipitation, temperature, evaporation, soil moisture, and vegetation cover;

[0008] S3. Use the dominant analysis method to calculate the contribution rates of different impact factors to runoff, and use the contribution rates corresponding to different impact factors as the basis for runoff segmentation;

[0009] S4. Multiply the contribution rates of different impact factors by the long-term monthly runoff data respectively, thereby completing the segmentation of runoff.

[0010] As a further solution of the present invention: The specific steps of S1 are as follows:

[0011] For the representative hydrological station corresponding to the target research area, select the long-term monthly runoff data of the representative hydrological station, and construct a long-term runoff data set Q(i, j), where i is the year and j is the month.

[0012] As a further solution of the present invention: The specific steps of S2 are as follows:

[0013] S21. Select the influencing factors that have an impact on runoff changes, and construct a runoff influencing factor data set A(k, i, j), where k is the type of influencing factor, i is the year, and j is the month;

[0014] S22. Calculate the correlation coefficient through the runoff influencing factor data set A(k, i, j) and the long-term runoff data set Q(i, j) to obtain the influencing factor correlation coefficient data set R(k, i, j), and find the maximum influencing factor data set C(k, j) that affects the runoff in the j-th month from R(k, i, j);

[0015] Among them, the Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient through the runoff influencing factor data set A(k, i, j) and the long-term runoff data set Q(i, j). The formula is as follows:

[0016]

[0017] In the formula, n represents the sample size; X ii represents the i-th observation value of variable X; Y ii represents the i-th observation value of variable Y; represents the sample mean of variable X; represents the sample mean of variable Y.

[0018] As a further solution of the present invention: The specific steps of S3 are as follows:

[0019] S31. Fit the regression full model of the maximum influencing factor data set C(k, j) and runoff y = b1x1 + b2x2 + … + b k x k , and derive 2 k x k -1 sub-models k from the regression full model y = b1x1 + b2x2 + … + b and calculate the goodness-of-fit coefficient R 2 of the influencing factors in different sub-models. The goodness-of-fit coefficient R 2 is the contribution rate β;

[0020] S32. Calculate the total average contribution rate of different influencing factors, and the calculation formula is:

[0021]

[0022] Thus, the matrix β(j, k) of the contribution rate of the k-th influencing factor in the j-th month can be obtained.

[0023] As a further solution of the present invention: The specific steps of S4 are:

[0024] Multiply the contribution rates of different influencing factors by the long-sequence runoff data respectively, and the formula for completing the segmentation of the runoff is:

[0025] E(i, j) = β(j, k) × Q(i, j)

[0026] In the formula, E is the runoff affected by the k-th factor.

[0027] As a further solution of the present invention: Step S31 specifically includes:

[0028] S311. When there is 1 variable in the sub-model, sub-models are obtained. When there are 2 variables in the sub-model, sub-models are obtained... When there are k variables in the sub-model, sub-models are obtained, that is, 2 k -1 sub-models are obtained;

[0029] S312. If there are four influencing factors on the runoff, namely x1, x2, x3, and x4, the equation of the regression full model including these four influencing factors is y = b1x1 + b2x2 + b3x3 + b4x4;

[0030] S313. Calculate the goodness-of-fit coefficient R of the regression full model after adding different influencing factors to the sub-model without the influencing factor itself 2 ; where In the formula, TSS is the total sum of squared deviations, RSS is the regression sum of squares, and the goodness-of-fit coefficient R 2 is the contribution rate β.

[0031] The beneficial effects of the present invention are: The present invention can significantly improve the analysis of the influence degree of key elements such as rainfall, temperature change, land use, and cover change in the runoff formation process in the Jiangyuan region, can better reveal the influence of climate change and human activities on the runoff pattern, helps to formulate more reasonable and effective management strategies, reduce the risk of flood disasters, ensure ecological security, enhance the accuracy of runoff prediction, and provide a scientific basis for water resource management and ecological protection. Brief Description of the Drawings

[0032] Figure 1 Schematic flowchart of an embodiment of the present invention;

[0033] Figure 2 Schematic diagram of the correlation coefficient between monthly runoff and influencing factors at a certain station in an embodiment of the present invention;

[0034] Figure 3 Schematic diagram of the proportion of snowmelt runoff above a certain station to the runoff of the station in an embodiment of the present invention. Detailed implementation manners

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1, as Figure 1 shown, this embodiment provides a method for calculating the division of influencing factors based on runoff in the Jiangyuan region. The method for calculating the division of influencing factors includes the following steps:

[0037] First: Determine the representative hydrological station for runoff division. For the target research area, determine the representative hydrological station for runoff division and obtain the long-term monthly runoff data of the representative hydrological station.

[0038] For the representative hydrological station corresponding to the target research area, select the long-term monthly runoff data of the representative hydrological station and construct a long-term runoff data set Q(i, j), where i is the year and j is the month.

[0039] Second: Based on the representative hydrological station, analyze the influencing factors of the target research area. The influencing factors include, but are not limited to, precipitation, temperature, evaporation, soil moisture, and vegetation cover.

[0040] By analyzing and studying the influencing factors of runoff, the correlation between different influencing factors and runoff can be comprehensively understood, and through specific analysis, the influence degree of each influencing factor on runoff at different time and space scales can be revealed. Specifically, it includes:

[0041] 1) Select the influencing factors that have an effect on runoff changes and construct a runoff influencing factor data set A(k, i, j), where k is the type of influencing factor, i is the year, and j is the month;

[0042] 2) Calculate the correlation coefficient through the runoff impact factor dataset A(k, i, j) and the long-term runoff dataset Q(i, j) to obtain the impact factor correlation coefficient dataset R(k, i, j), and find the maximum impact factor dataset C(k, j) that affects the runoff in the j-th month from R(k, i, j).

[0043] Among them, the Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient through the runoff impact factor dataset A(k, i, j) and the long-term runoff dataset Q(i, j). The formula is as follows:

[0044]

[0045] In the formula, n represents the sample size; X ii represents the i-th observation value of variable X; Y ii represents the i-th observation value of variable Y; represents the sample mean of variable X; represents the sample mean of variable Y.

[0046] The Pearson correlation coefficient eliminates the influence of dimension through standardization, thus objectively reflecting the linear correlation strength between variables.

[0047] Third: Use the dominance analysis method to calculate the contribution rate of different impact factors to runoff, and use the contribution rates corresponding to different impact factors as the basis for dividing runoff. By calculating the contribution rate of different impact factors to runoff, the role of each impact factor in the runoff formation process can be clarified.

[0048] Among them, the dominance analysis method is a commonly used method for determining the contribution degree of different independent variables to the variance of the dependent variable in a multiple linear regression model. The core idea of this method is to decompose the total variance of the dependent variable and determine the contribution of each dependent variable to the total variance, and then evaluate their relative importance. For the specific implementation method and principle, see the literature: Xie Baoguo, Long Lirong. Dominance Analysis Method and Its Application [J]. Psychological Science, 2006, (04): 922 - 925. DOI: 10.16719 / j.cnki.1671 - 6981.2006.04.038.

[0049] 1) Fit the regression full model of the maximum impact factor dataset C(k, j) and runoff y = b1x1 + b2x2 + … + b k x k , and derive 2 k x k -1 sub-models k from the regression full model y = b1x1 + b2x2 + … + b and calculate the goodness-of-fit coefficient R 2 of the impact factor in different sub-models;

[0050] 11) When there is 1 variable in the sub-model, sub-models are obtained. When there are 2 variables in the sub-model, sub-models are obtained… When there are k variables in the sub-model, sub-models are obtained, that is, 2 k -1 sub-models are obtained;

[0051] 12) If there are four impact factors on runoff, namely x1, x2, x3, and x4, the equation of the regression full model containing these four impact factors is y = b1x1 + b2x2 + b3x3 + b4x4;

[0052] 13) By calculating the coefficient of determination R of the regression full model obtained after adding different impact factors to the sub-model without the impact factor itself 2 ; where In the formula, TSS is the total sum of squared deviations, RSS is the regression sum of squares, then the coefficient of determination R 2 is the contribution rate β.

[0053] 2) Calculate the total average contribution rate of different impact factors, and the calculation formula is:

[0054]

[0055] Thus, the matrix β(j,k) of the contribution rate of the k-th impact factor in the j-th month can be obtained.

[0056] Fourth: Multiply the contribution rates of different impact factors by the long-sequence runoff dataset respectively, thereby completing the segmentation of runoff. The reason is that the contribution rates of different impact factors can be regarded as the runoff proportion of different impact factors.

[0057] The formula for multiplying the contribution rates of different impact factors by the long-sequence runoff dataset respectively is:

[0058] E(i,j) = β(j,k) × Q(i,j).

[0059] In the formula, E is the runoff affected by the k-th impact factor.

[0060] In the existing technical system, runoff analysis methods usually rely on empirical formulas or simple statistical models. However, in the natural environment, the factors affecting runoff formation are diverse and complex, and there are complex interaction mechanisms among various factors. Traditional methods are difficult to comprehensively and accurately reflect all these impact factors and their interactions, lacking in the mechanism and interpretability of runoff changes, which is not conducive to the understanding and prediction of runoff. Therefore, dividing runoff according to meteorological impact factors can more scientifically and comprehensively understand the principles and mechanisms of runoff changes.

[0061] Example 2: In this example, a certain hydrological station in the Jinsha River Basin is taken as the representative hydrological station.

[0062] Step 1: Select the monthly runoff data from 1979 to 2019 as the runoff data. The factor sample length is 41 years from 1979 to 2019, meeting the sample size requirement.

[0063] Step 2: Through the previous research on 130 circulation indices of the National Climate Center (including 88 atmospheric circulation indices, 26 sea surface temperature indices, and 16 other types of climate indices), it is considered that rainfall, runoff, snow depth, evaporation, and soil moisture content are important factors affecting the runoff of a certain hydrological station. For the above 5 important variables, monthly data for 43 years from 1978 to 2020 are taken (taking one year before and after the runoff data of a certain hydrological station is convenient for calculating the leading and lagging correlations between factors and runoff), and a 5×43×12 impact factor array is constructed.

[0064] Step 3: Through the correlation coefficient results between runoff and each factor, it is found that the months with the largest correlation coefficients between the above factors and the runoff of a certain hydrological station are mainly the same period or the previous month, that is, these factors mainly have a significant impact on the runoff of the Zhimenda Station in the same period or one month in advance. Therefore, it is considered that the factors in the same period and the previous month are the key impact month factors for the runoff of a certain hydrological station, and a statistical table of the correlation coefficients between the monthly runoff of a certain hydrological station and each factor in the same period or one month in advance is given, as Figure 2 shown;

[0065] Step 4: Based on the relative importance analysis of the multi-factor linear regression equation, the monthly variation of the proportion of snowmelt in the runoff of a certain hydrological station is obtained, as Figure 3 shown. It can be seen that for a certain hydrological station, the flow generated by snowmelt from January to February is very small, the proportion of snowmelt runoff increases significantly in April, reaches the peak of snowmelt in May, the proportion of snowmelt decreases from June, mainly because the summer rainfall increases compared with the winter and spring seasons, the proportion of snowmelt decreases significantly after October, and the proportion of snowmelt runoff in November - December to the Zhimenda Station can be ignored.

[0066] In summary, a method for calculating the division of impact factors based on the runoff in the source area of the river proposed by the present invention fully considers the complexity of the runoff composition, uses the contribution degree of different impact factors to the runoff to complete the division of the runoff composition, deepens the physical understanding and interpretability of the runoff change, and provides a theoretical support for the reasons for the runoff change in the source area of the river.

[0067] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0068] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for calculating the impact factor segmentation based on the runoff in Jiangyuan District, characterized in that The above-mentioned impact factor segmentation calculation method includes the following steps: S1. For the target research area, determine the representative hydrological station for runoff segmentation, and obtain the long-term monthly runoff data of the representative hydrological station; S2. Based on the representative hydrological station, analyze the impact factors of the target research area, where the impact factors include but are not limited to precipitation, temperature, evaporation, soil moisture, and vegetation cover; S3. Use the dominance analysis method to calculate the contribution rate of different impact factors to runoff, and use the contribution rate corresponding to different impact factors as the basis for runoff segmentation; S4. Multiply the contribution rates of different impact factors by the long-term monthly runoff data respectively, so as to complete the segmentation of runoff.

2. The impact factor segmentation calculation method according to claim 1, characterized in that The specific steps of S1 are as follows: For the representative hydrological station corresponding to the target research area, select the long-term monthly runoff data of the representative hydrological station, and construct a long-term runoff data set Q(i,j), where i is the year and j is the month.

3. The impact factor segmentation calculation method according to claim 2, characterized in that The specific steps of S2 are as follows: S21. Select the impact factors that have an effect on runoff change, and construct a runoff impact factor data set A(k,i,j), where k is the type of impact factor, i is the year, and j is the month; S22. Calculate the correlation coefficient through the runoff impact factor data set A(k,i,j) and the long-term runoff data set Q(i,j) to obtain the impact factor correlation coefficient data set R(k,i,j), and find the maximum impact factor data set C(k,j) that affects the runoff in the j-th month from R(k,i,j); Among them, the Pearson correlation coefficient calculation formula is used to calculate the correlation coefficient through the runoff impact factor data set A(k,i,j) and the long-term runoff data set Q(i,j), and the formula is: Where n represents the sample size; X ii represents the i-th observation of variable X; Y ii represents the i-th observation of variable Y; represents the sample mean of variable X; represents the sample mean of variable Y.

4. The impact factor segmentation calculation method according to claim 3, wherein The specific steps of S3 are as follows: S31. Fit the regression full model of the maximum influence factor dataset C(k,j) and runoff, y = b1x1 + b2x2 + … + b k x k , and derive 2 k x k -1 sub-models through the regression full model y = b1x1 + b2x2 + … + b k -1 sub-models and calculate the goodness-of-fit coefficient R of the influence factor in different sub-models 2 . The goodness-of-fit coefficient R 2 is the contribution rate β; S32. Calculate the total average contribution rate of different impact factors, and the calculation formula is: Thus, the matrix β(j,k) of the contribution rate of the k-th impact factor in the j-th month can be obtained.

5. The impact factor segmentation calculation method according to claim 4, wherein The specific steps of S4 are as follows: The formula for multiplying the contribution rates of different impact factors by the long-term runoff data respectively to complete the segmentation of runoff volume is: E(i,j) = β(j,k) × Q(i,j) In the formula, E is the runoff affected by the k-th factor.

6. The impact factor segmentation calculation method according to claim 4, wherein The specific content of S31 includes: S311. When there is 1 variable in the sub-model, obtain sub-models. When there are 2 variables in the sub-model, obtain sub-models... When there are k variables in the sub-model, obtain sub-models, that is, obtain 2 k - 1 sub-models; S312. If there are four impact factors on runoff, namely x1, x2, x3, and x4, the equation of the regression full model including these four impact factors is y = b1x1 + b2x2 + b3x3 + b4x4; S313. Calculate the coefficient of determination R of the regression full model obtained by adding different influencing factors to the sub-model without the influencing factor itself 2 ; where In the formula, TSS is the total sum of squared deviations, RSS is the regression sum of squares, and the coefficient of determination R 2 is the contribution rate β.

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