Method for analyzing influence of climatic change and human activity on space-time evolution of water resource

Abstract

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

Description

Technical Field

[0001] This invention belongs to the field of hydrology, and in particular relates to the impact of climate change and human activities on the spatiotemporal evolution of water resources and analytical methods. Background Technology

[0002] Global change is a common problem facing all countries in the world today. It encompasses both climate change, such as global warming, and the various impacts of human activities on the human environment. Against this backdrop, significant changes in the global water cycle and water resources have attracted widespread attention from hydrologists and resource allocators. Water resources, as one of the key material resources for human survival and development, are the source of life. With the deteriorating state of water resources, water issues have become a crucial factor in the socio-economic development of many countries and regions. Therefore, studying the evolution of water resources and its influencing factors provides a theoretical basis for the rational development and utilization of water resources, thereby promoting my country's sustainable resource development and ecological civilization construction.

[0003] Chinese hydrologists have made significant progress in water resource research, from initially using trend analysis and correlation analysis to perform preliminary calculations of the spatiotemporal variations of water resources and their influencing factors. Later, they employed hydrological models such as distribution models to differentiate the impacts of climate change and human activities on water resource evolution under the dual water cycle. However, quantitative analysis of these influencing factors remains lacking. Furthermore, because the impacts of climate change and human activities on water resources are not isolated—climate change is one cause of intensified human activities, and human activities are another cause of climate change—multicollinearity between these two explanatory variables is inevitable. Therefore, it is crucial to quantitatively calculate the contribution of different influencing factors while reducing the correlation between the two factors. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an analysis method for the impact of climate change and human activities on the spatiotemporal evolution of water resources. Under the premise of reducing the correlation between climate change and human activities, the contribution of different influencing factors is quantitatively calculated.

[0005] To achieve the above objectives, this invention provides a method for analyzing the impacts of climate change and human activities on the spatiotemporal evolution of water resources, including:

[0006] Obtain meteorological, land use, soil, socio-economic, and water resource information for the area to be tested;

[0007] Trend analysis is performed on the water resource information to obtain the spatiotemporal evolution patterns of the water resource information;

[0008] The spatiotemporal evolution pattern is analyzed from three aspects: meteorological information, land information, and socioeconomic information, to obtain the influencing factors of the spatiotemporal evolution of water resources.

[0009] Obtain the correlation between the influencing factors and the spatiotemporal evolution patterns, and complete the analysis of the impact of climate change and human activities on the spatiotemporal evolution of water resources.

[0010] Optionally, the spatiotemporal evolution patterns of the water resource information obtained include:

[0011] By combining the water resource information with the Mann-Kendall rank correlation test, the trend change information of the water resource information is obtained;

[0012] By combining the water resource information with Theil-Sen Median trend analysis, the overall change information of the water resource information can be obtained;

[0013] Based on the trend change information and overall change information, the spatiotemporal evolution pattern of the water resources information is obtained.

[0014] Optionally, the influencing factors of the spatiotemporal evolution of water resources include: numerical influencing factors and character influencing factors.

[0015] Optionally, the characteristic is that obtaining the correlation between the influencing factors and the spatiotemporal evolution law includes:

[0016] The character-type influencing factors are digitized to obtain a digitized matrix of character-type influencing factors;

[0017] Based on the aforementioned digitized matrix, a regression analysis model is constructed;

[0018] Based on the regression analysis model, the correlation between the influencing factors and the spatiotemporal evolution pattern is obtained.

[0019] Optionally, obtaining the digitized matrix of the character-type influencing factors includes:

[0020] The test area is divided into different units based on the different influencing factors of the characters mentioned above;

[0021] Based on the aforementioned unit, statistical values ​​are obtained;

[0022] Based on the statistics, obtain the digitized matrix of the character-type influencing factors.

[0023] Optionally, the statistics include: the average value of water resources, the geometric mean of water resources, the median value of water resources, the average standard deviation of water resources, the extreme values ​​of water resources, the skewness of water resources, and the kurtosis of water resources.

[0024] Optionally, the regression analysis model is:

[0025] y = β0 + β1x1 + β2x2 + ... + β p x p +ε

[0026] Where p is the number of explanatory variables, β0 is a constant, β is the regression coefficient, y is the water resource variable consisting of water resource information, the explanatory variables consist of three aspects of influencing factors consisting of meteorological information, land information, and socio-economic information, and ε is the random error.

[0027] Optionally, based on the regression analysis model, obtaining the correlation between the influencing factors and the spatiotemporal evolution pattern includes:

[0028] Use one dependent variable and several explanatory variables from three aspects as a set of input data;

[0029] The explanatory variable is input into the regression analysis model, and several explanatory variables are introduced into the regression analysis model one by one, and the explanatory variables are tested one by one;

[0030] Based on the test results, obtain the results of stepwise regression analysis;

[0031] Combine the other explained variables with several explanatory variables from the three aspects to form other input data sets, and perform stepwise regression analysis according to the above steps;

[0032] Based on the regression analysis results, a correlation model between the influencing factors and the spatiotemporal evolution law is constructed.

[0033] Based on the aforementioned model, the degree of correlation between the influencing factors and the spatiotemporal evolution pattern is obtained.

[0034] Optionally, the relevant model is:

[0035]

[0036] Where CPCC is the comprehensive partial correlation coefficient, i is the explanatory variable, including three aspects of influencing factors: meteorological information, land information, and socioeconomic information; j is the dependent variable, including water resource information; n is the number of dependent variables; and R0 is the total number of dependent variables. ij This is the multiple correlation coefficient.

[0037] Optionally, performing individual tests on the variables includes:

[0038] Construct the test statistic;

[0039] Determine whether to introduce variables based on test statistics;

[0040] Before performing individual tests on the variables, the following steps are included:

[0041] Determine the level of inspection;

[0042] Based on the level of test, determine the inclusion and exclusion of variables.

[0043] Compared with the prior art, the present invention has the following advantages and technical effects:

[0044] This invention, based on acquired hydrological, meteorological, and socioeconomic data of the target area, analyzes the significant spatiotemporal evolution trends and overall changes of water resources in the target area using a combination of Mann-Kendall rank correlation test and Theil-Sen Median trend analysis. Furthermore, a multivariate statistical stepwise regression analysis model is employed to study the influencing factors of water resource evolution in the target area. The stepwise regression analysis reduces multicollinearity among climate change and human activity-related influencing factors, improving computational accuracy and providing better theoretical support for the scientific and rational utilization of local water resources, thereby promoting sustainable water resource development. Attached Figure Description

[0045] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0046] Figure 1 This is a flowchart illustrating the impact of climate change and human activities on the spatiotemporal evolution of water resources and the analysis method according to an embodiment of the present invention. Detailed Implementation

[0047] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0048] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0049] Example:

[0050] like Figure 1 As shown, this embodiment provides methods for analyzing the impacts of climate change and human activities on the spatiotemporal evolution of water resources, including:

[0051] (1) Obtain meteorological data for Hebei Province: long-term precipitation, average temperature, daily maximum temperature, daily minimum temperature, average sunshine, and average humidity; water resources information: long-term total surface water resources, total groundwater resources, total water resources, and river runoff in Hebei Province; land use and soil data; socio-economic data: water consumption of industrial, agricultural, commercial, and tertiary industries; distribution of primary, secondary, and tertiary industries in the area to be measured; and urban and rural population data.

[0052] (2) The total surface water resources in the water resources composition of Hebei Province obtained in step (1) were analyzed for significance and trend using the Mann-Kendall rank correlation test. The specific calculation steps are as follows:

[0053] ① Given the null hypothesis H0: The total amount of surface water resources over a long period of time has not shown an upward or downward trend;

[0054] ②Construction quantity S:

[0055]

[0056] Where: x1, x2, ..., x n This represents a long-term surface water resource series; n is the sample size of the long-term series; sign() is the sign function, and its expression is:

[0057]

[0058] When n > 10, S basically follows a normal distribution, and its standard quantization is:

[0059]

[0060] ③ Given a significance level α, the critical value Z can be found from the standard normal distribution table. α / 2 At that time, |Z|>Z α / 2 At this point, we reject the null hypothesis H0, meaning that at the α confidence level, the long-term total surface water resources series exhibits a significant upward or downward trend. A positive α indicates an upward or increasing trend, while a negative α indicates a downward or decreasing trend. Based on experience, we choose a significance level of α = 0.05, and the critical value Z can be found in the standard normal distribution table. α / 2 =1.96, meaning that when |Z|>1.96, it indicates that the sequence has passed the significance test at the 5% confidence level, indicating that the sequence shows a significant increasing or decreasing trend at the 5% confidence level. Otherwise, the null hypothesis is accepted, indicating that the sequence does not show a significant changing trend.

[0061] (3) The Theil-Sen Median trend analysis is then used to analyze the overall change in the long-term total surface water resources series. This method uses the β value to characterize the degree of change in the entire series. The specific calculation steps are as follows:

[0062]

[0063] Where: x1, x2, ..., x n This represents a long-term surface water resources series; n is the sample size of the long-term series; Median() represents the median function; β is the median of all adjacent combinations of the rate of change in this time series, which eliminates the influence of individual extreme values ​​in the series on the estimation results, thus obtaining the magnitude of the series' change. When β>0, the series has an upward trend, and vice versa; the larger |β| is, the greater the degree of increase or decrease in the series.

[0064] (4) The above-mentioned long-term series of the total surface water resources in Hebei Province was used as an example to conduct trend analysis. Other water resources overview indicators, including the total groundwater resources, total water resources, and river runoff, were also analyzed according to the above method to obtain the spatiotemporal distribution evolution law of water resources in Hebei Province.

[0065] (5) Based on the water cycle structure of water inflow and water use in Hebei Province, analyze the causes of the spatiotemporal evolution of water resources and obtain the influencing factors of water resource evolution, including climate change influencing factors: precipitation, temperature, and sunshine, and human activity influencing factors: land use and cover changes, water consumption of industries such as industry, agriculture and commerce, changes in urban and rural population, and distribution of economic structure of various industries.

[0066] (6) The influencing factors obtained in step (5) are classified into numerical and character types. Character-type influencing factors include land use and cover change in Hebei Province and the distribution of economic structures of various industries. Numerical influencing factors include precipitation, temperature, sunshine variation, water consumption of industries such as industry, agriculture, and commerce, and changes in urban and rural population. Based on the two character-type influencing factors of land use and cover change and the distribution of economic structures of various industries, the area to be measured is divided into different statistical units. Basic statistics are obtained for different statistical units. Basic statistics refer to the statistics used to characterize the total surface water resources, total groundwater resources, and other water resources under different statistical units, including: average value, geometric mean, median value, average standard deviation, extreme value, skewness, and kurtosis. In this embodiment, the basic statistics represent the average value of the total surface water resources and total groundwater resources in different statistical units, and finally the matrix digitization of character-type influencing factors is completed.

[0067] (7) The method for obtaining the correlation between the spatiotemporal evolution of water resources in Hebei Province and influencing factors is a multivariate statistical stepwise regression model. The basic idea is as follows: Based on the matrix digitization results, a regression equation is constructed, and explanatory variables are introduced into the regression equation one by one. After each explanatory variable is introduced, the selected variables are tested individually. When an introduced variable becomes insignificant due to the introduction of later variables, it is removed. Introducing a variable or removing a variable from the regression equation is one step in the stepwise regression. An F-test is performed at each step to ensure that the equation contains only significant variables before each new variable is introduced. This process is repeated until no significant explanatory variables are selected into the regression equation, and no insignificant explanatory variables are removed. Finally, based on the stepwise regression analysis results, the multiple correlation coefficient of each influencing factor with different water resource quantities is obtained to characterize the correlation between the explanatory and explained variables. The specific calculation process of this method is as follows:

[0068] ① The general form of a multiple linear stepwise regression model:

[0069] y = β0 + β1x1 + β2x2 + ... + β p x p +ε

[0070] In the formula: p: number of explanatory variables; β0: constant; β j (j=1,2,…,p): Regression coefficients; y: Explained variable, including total surface water resources, total groundwater resources, total water resources, and river runoff in Hebei Province; x: Explained variables, including precipitation, average temperature, daily maximum temperature, daily minimum temperature, average sunshine, average humidity, land use and soil cover, water consumption of industrial, agricultural, commercial and tertiary industries, distribution of primary, secondary and tertiary industries in the area under test, and urban and rural population; ε: Random error.

[0071] ② For n sets of experimental data (x 11 ,x 12 ,…,x 1p ,y1),(x 21 ,x 22 ,…,x 2p ,y2),…,(x n1 ,x n2 ,…,x np ,y n The above equation can be written in the form of a system of equations:

[0072]

[0073] In the formula: p: number of explanatory variables; β0: constant; β j(j = 1, 2, ..., p): regression coefficients; y: dependent variable; n: number of dependent variables, which are 4 in this embodiment, namely total surface water resources, total groundwater resources, total water resources, and river runoff. The reason for selecting these four quantities is that they can comprehensively represent the water resources situation in Hebei Province.

[0074] ③ If (m-1) independent variables have already been introduced into the equation, consider introducing another variable X. j Let X be introduced. j The regression sum of squares for the equation after the initial equation (containing m independent variables) is an SS regression, and the residuals are SS residuals; the previous equation contained (m-1) independent variables (excluding X). j If the regression sum of squares of the equation is SS regression (-j), then X j The partial regression sum of squares is U = SS, and the test statistic is:

[0075]

[0076] ④F j Obey F a (1, nm-1) distribution, if F j If X > F(1, nm-1), then X j Select it into the equation; otherwise, do not select it.

[0077] ⑤ Before performing stepwise regression, the significance level should be determined first to serve as the standard for introducing or removing variables. The significance level can be determined according to the specific circumstances; generally, the F-value can be set at an α level of 0.05, 0.10, or 0.20. For the inclusion and exclusion levels of the regression equation, it is often chosen that αinclude ≤ αexclude. Choosing different F-values ​​(or α levels) may result in inconsistent regression equations; different F-values ​​(or α values) can be used for adjustment.

[0078] ⑥ Based on the regression analysis results in ⑤, the non-significant explanatory variables that were removed were obtained through the explanatory variables contained in the regression equation. At the same time, the judgment criteria for each introduction and removal were obtained based on the F test value.

[0079] ⑦ Based on the multiple correlation coefficient obtained in ⑤, calculate the Comprehensive Partial Correlation Coefficient (CPCC) using the formula. The magnitude of the comprehensive partial correlation coefficient is used as the basis for judging the contribution of influencing factors; that is, the larger the comprehensive partial correlation coefficient, the greater the contribution, and vice versa. The specific calculation formula is as follows:

[0080]

[0081] Wherein, CPCC is the Comprehensive Partial Correlation Coefficient, i represents the influencing factors of water resource spatiotemporal evolution, including precipitation, temperature, light intensity variation, land use and cover variation, water consumption in industrial, agricultural and tertiary industries, and population change in Hebei Province; j represents the total surface water resources, total groundwater resources, total water resources, and river runoff, which characterize the water resource status; R ij The multiple correlation coefficient is calculated by summing the absolute values ​​of the multiple correlation coefficients. Each influencing factor has a multiple correlation coefficient with four elements, reflecting the degree of correlation with the corresponding measured values. The comprehensive partial correlation coefficient reflects the comprehensive correlation of the influencing factors with the four water resource status indicators. By comparing the comprehensive partial correlation coefficients of each influencing factor, the primary and secondary influencing factors can be distinguished.

[0082] This embodiment discloses the impacts of climate change and human activities on the spatiotemporal evolution of water resources and its analytical methods. Based on the acquired hydrological and meteorological information and socioeconomic data of the area under test, two methods, Mann-Kendall rank correlation test and Theil-Sen Median trend analysis, are used in combination to analyze the significant trends and overall changes in the spatiotemporal evolution of water resources in the area under test. Furthermore, a multivariate statistical stepwise regression analysis model is used to study the influencing factors of water resource evolution in the area under test. The characteristics of stepwise regression analysis reduce the multicollinearity among the influencing factors related to climate change and human activities, improving the accuracy of calculations and providing better theoretical support for the scientific and rational utilization of local water resources, thereby promoting the sustainable development of water resources.

[0083] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. Impact of climate change and human activities on the spatio-temporal evolution of water resources and analysis method, characterized in that, The method comprises the following steps: obtaining meteorological information, land information, social and economic information and water resource information of a to-be-tested region; performing trend analysis on the water resource information to obtain a spatio-temporal evolution rule of the water resource information; performing attribution analysis on the spatio-temporal evolution rule from the meteorological information, the land information and the social and economic information to obtain an influencing factor of the spatio-temporal evolution of the water resource; obtaining a correlation degree of the influencing factor and the spatio-temporal evolution rule to complete analysis of the spatio-temporal evolution of the water resource under the influence of climate change and human activities.

2. The method according to claim 1, wherein, The step of obtaining the spatio-temporal evolution rule of the water resource information comprises the following steps: obtaining trend change information of the water resource information by using the water resource information and a Mann-Kendall rank correlation test; obtaining overall change information of the water resource information by using the water resource information and a Theil-Sen Median trend analysis; obtaining the spatio-temporal evolution rule of the water resource information based on the trend change information and the overall change information.

3. The method according to claim 1, wherein, The influencing factor of the spatio-temporal evolution of the water resource comprises a digital influencing factor and a character influencing factor.

4. The method according to claim 3, wherein, The step of obtaining the correlation degree of the influencing factor and the spatio-temporal evolution rule comprises the following steps: digitizing the character influencing factor to obtain a digitized matrix of the character influencing factor; constructing a regression analysis model based on the digitized matrix; obtaining the correlation degree of the influencing factor and the spatio-temporal evolution rule based on the regression analysis model.

5. The method for analyzing the influence of climate change and human activities on the spatio-temporal evolution of water resources according to claim 4, characterized in that, The step of obtaining the digitized matrix of the character influencing factor comprises the following steps: dividing the to-be-tested region into different units based on different character influencing factors; obtaining a statistical quantity based on the units; obtaining the digitized matrix of the character influencing factor based on the statistical quantity.

6. The method for analyzing the influence of climate change and human activities on the spatio-temporal evolution of water resources according to claim 5, characterized in that, The statistical quantity comprises an average value of the water resource, a geometric mean value of the water resource, a median value of the water resource, an average standard deviation of the water resource, an extreme value of the water resource, a skewness of the water resource and a kurtosis of the water resource.

7. The method for analyzing the influence of climate change and human activities on the spatio-temporal evolution of water resources according to claim 4, characterized in that, The regression analysis model is as follows: y = β0+ β1x1+ β2x2+... + β p x p + ε wherein p is the number of explanatory variables, β0 is a constant, β is a regression coefficient, y is a water resource variable composed of to-be-explained variables including the water resource information, x is an influencing factor variable composed of three aspects of explanatory variables including the meteorological information, the land information and the social and economic information, and ε is a random error. 8.The method of claim 4, wherein, The step of obtaining the correlation degree of the influencing factor and the spatio-temporal evolution rule based on the regression analysis model comprises the following steps: taking one to-be-explained variable and several explanatory variables of the three aspects as a group of input data; inputting the to-be-explained variable into the regression analysis model, introducing the several explanatory variables into the regression analysis model one by one, and testing the explanatory variables one by one; obtaining a stepwise regression analysis result based on a test result; performing stepwise regression analysis on other input data groups composed of other to-be-explained variables and several explanatory variables of the three aspects according to the above steps; constructing a correlation model of the influencing factor and the spatio-temporal evolution rule based on the regression analysis result; obtaining the correlation degree of the influencing factor and the spatio-temporal evolution rule based on the correlation model.

9. The method for analyzing the influence of climate change and human activities on the spatio-temporal evolution of water resources according to claim 8, characterized in that, The correlation model is as follows: Wherein, CPCC is the comprehensive partial correlation coefficient, i is the influence factor variable of three aspects including the meteorological information, the land information and the social economic information, j is the water resource variable including the water resource information, n is the number of the explained variable, R ij is the multiple correlation coefficient.

10. The method for analyzing the influence of climate change and human activities on the spatio-temporal evolution of water resources according to claim 9, characterized in that, The step of testing the variables one by one comprises the following steps: constructing a test statistic; determining whether to introduce a variable based on a test statistic; the method further comprising, prior to the step of testing the variables one-by-one: determining a test level; based on the test level, determining introduction and rejection of the variable.

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