Regional Water Resources Accessibility Evaluation Method Based on River Network Accessibility and Water Resources Quantity

Abstract

The present invention discloses a method for evaluating the regional water resource accessibility based on river network accessibility and water resource quantity. Step 1: Collect topographic, river network, precipitation, and water network project data of the area to be analyzed. Step 2: Determine the evaluation indicators for river network accessibility. According to the evaluation requirements, determine the evaluation scale, divide the evaluation area into spatial grids, and calculate the evaluation indicator values on the spatial grids. Step 3: Normalize the evaluation indicators. Step 4: Determine the river network accessibility through factor analysis. Step 5: Combine the river network accessibility and water resource quantity to evaluate and analyze the regional water resource accessibility. By establishing an evaluation model, the present invention comprehensively considers the river network accessibility and water resource quantity, evaluates the regional water resource accessibility, clarifies the independent influence of the river network layout or water resource quantity on water resource accessibility, enables the actual role of water network projects to be clearly expressed, is conducive to enhancing the understanding of water networks, and improving the planning level of water network governance and water network projects.

Description

Technical Field

[0001] The present invention relates to the field of river network evaluation and management, and provides a method for evaluating regional water resource accessibility based on water network accessibility and water resource quantity. Background Art

[0002] Water resource accessibility is a commonly used indicator to describe the availability and convenience of regional water resources. Specifically, it refers to the degree of difficulty in obtaining water from the river network at any point within the region. Water resource accessibility is the key to determining water intake volume and water intake efficiency. Therefore, it is of great significance to explore the impact of water network projects on regional water resource accessibility and its spatial distribution, for understanding the role of water network projects and further optimizing the water network pattern. Currently, research on water resource accessibility mainly calculates accessibility by weighted calculation of indicators such as water intake distance, elevation difference, runoff, land use, and river connectivity through methods such as analytic hierarchy process, fuzzy comprehensive evaluation, and TOPSIS. The selection of indicators has limitations, the combination method is greatly affected by subjective factors, and the accessibility evaluation results lack clear practical significance. In regions with dense river networks but scarce water resources, due to low water accessibility, additional water can be obtained through water diversion projects to improve water accessibility; in regions with rich water resources, due to the lack of river networks as transportation channels, the accessibility is low, and the accessibility needs to be improved through the construction of water network projects. Previous accessibility evaluations mixed river network characteristic indicators and runoff expressions, making it difficult to identify the key factors affecting water resource accessibility. Summary of the Invention

[0003] Based on the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for evaluating regional water resource accessibility based on river network accessibility and water resource quantity, aiming to establish a water resource accessibility evaluation system including two dimensions of river network accessibility and water resource quantity, clarify the impact of water network project construction, and enhance the comprehensive understanding of the water network pattern by the basin management department. Therefore, innovative methods are needed to construct a water resource accessibility evaluation model. By combining water resource quantity on the basis of determining river network accessibility through factor analysis, the above problems can be well solved. The water resource accessibility evaluation model established based on this has important significance.

[0004] The present invention provides a method for evaluating regional water resource accessibility based on river network accessibility and water resource quantity, including:

[0005] Collecting topographic features, river networks, precipitation, water network projects, and water diversion volume data of the area to be evaluated, and obtaining spatial sample points from the area to be evaluated;

[0006] Selecting topographic indicators, river spatial structure indicators, and river network topological structure indicators as river network accessibility evaluation indicators, and extracting the values of various evaluation indicators within the spatial sample points, and taking the mean value of various evaluation indicators as sample data;

[0007] The normalized evaluation indicators are combined into a sample matrix X, and the covariance matrix S of the sample matrix is calculated. After determining the appropriate evaluation indicators, an orthogonal factor model of the sample matrix is established, and the expression is:

[0008] X = μ + AF + ε

[0009] The parameters in the above formula satisfy

[0010] In the formula, F = (F1, F2, …, F m ) T is the set of common factors of the sample matrix X, m is the number of common factors, μ = (μ1, μ2, …, μ K ) T is the mean of the sample matrix X, ε = (ε1, ε2, ε3, …, ε K ) T is the set of specific factors of the sample matrix X, mE(ε) is to find the mean of the variable ε, var(ε) is to find the covariance of each element in the variable ε, is a diagonal matrix, is the variance of the Kth variable, I is the covariance matrix of the common factors, cov(F, ε) is to find the covariance between the variable F and the variable ε, A = (a Km ) K×m is the factor loading matrix of the sample matrix X, cov(X, F) = A, C - D = AA T , A T is the transposed factor loading matrix, D is the covariance matrix of the specific factors; A and D are obtained through the following process:

[0011] i) Decompose the covariance matrix S of the sample matrix, and the expression is:

[0012]

[0013] In the formula, λ1, λ2,... λ K are the eigenvalues of the covariance matrix, and λ1 ≥ λ2 ≥ … ≥ λ K ≥ 0, l1, l2, …, l K are the corresponding eigenvectors, T represents transpose;

[0014] ii) Estimate the relevant parameters by the principal component method, determine the number m of common factors, and thus obtain the variance estimate of the factor loading matrix and the variance of the specific factors, and the expression is:

[0015]

[0016] In the formula, s KK is the diagonal element of the sample covariance matrix S, diag(BB T) is the matrix B and the transposed matrix B T 's diagonal matrix;

[0017] Perform an orthogonal rotation of the common factor with the largest variance to obtain the rotated factor score matrix Z. The expression is:

[0018]

[0019] In the formula, Γ is an orthogonal matrix, and the rotated factor orthogonal model is: X = μ + AΓZ + ε;

[0020] Through the variance contribution of the common factor Weighted calculation of the river network accessibility H. The expression is:

[0021]

[0022] In the formula, Z m is the element of the rotated factor score matrix Z;

[0023] Combining the river network accessibility and the natural water resources volume, the average annual runoff R of the catchment area is used as an estimate of the natural water resources volume to evaluate the water resources accessibility W of the area to be evaluated. The expression is;

[0024] W = H·R

[0025] In the formula, R is the average annual runoff of the catchment area, and H is the river network accessibility.

[0026] In some embodiments, formally define the terrain index d K , the river spatial structure index z K and the river network topological structure index g K , and the expression is:

[0027] d 1,2,...,K1 = [d 1,2,...,K1,1 , d 1,2,... , K1,2 , …, d 1,2,...,K1,N

[0028] z 1,2,...,K2 = [z 1,2,...,K2,1 , z 1,2,...,K,2 , …, z 1,2,...,K2,N

[0029] g 1,2,...,K3 = [g 1,2,...,K3,1 , g 1,2,...,K,2 , …, g 1,2,...,K3,N

[0030] In the formula, N is the number of samples, d 1,2,...,K1 is the terrain index value of the K1th evaluation index of all samples, z 1,2,...,K2 ​​​The value of the river spatial structure index, which is the K2th evaluation index for all samples, is g 1,2,...,K3 The value of the river network topological structure index, which is the K3th evaluation index for all samples.

[0031] In some embodiments, the determination of suitable evaluation indices further includes performing a KMO test and a Bartlett sphericity test on the samples to determine the suitable evaluation indices.

[0032] In some embodiments, the parts with large covariance eigenvalues are determined as common factors, and the number of common factors m is set.

[0033] In some embodiments, the positive or negative sign of the connection symbol in the expression of the river network accessibility H depends on the actual positive or negative meaning of the common factor. If it has a positive meaning, the connection symbol is +, and if it has a negative meaning, the connection symbol is −.

[0034] In some embodiments, the expression for the average annual runoff of the catchment area is:

[0035] R = A r ·P·α

[0036] In the formula, R is the average annual runoff of the catchment area, A r is the catchment area, P is the average annual precipitation of the catchment area, and α is the runoff coefficient of the catchment area.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] 1) By establishing an evaluation model, comprehensively considering the river network accessibility and water resource quantity, evaluating the regional water resource accessibility, clarifying the independent influence of the river network layout or water resource quantity on water resource accessibility, and clearly expressing the actual role of the water network project construction, which is beneficial to enhancing the understanding of the water network and improving the planning level of water network governance and water network projects;

[0039] 2) Introducing common factors, calculating the river network accessibility by weighted variance contribution of common factors, and more effectively improving the influence degree and pertinence of dominant factors in the evaluation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the regional water resource accessibility evaluation method based on river network accessibility and water resource quantity of the present invention;

[0041] Figure 2 is a schematic diagram of the specific process for determining river network accessibility by factor analysis method;

[0042] Figure 3 is a spatial distribution diagram of river network accessibility;

[0043] Figure 4 is the spatial distribution map of water resource quantity;

[0044] Figure 5 is the spatial distribution map of water resource accessibility. Specific implementation manners

[0045] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] The technical solution of the present invention will be described below in conjunction with specific embodiments and the accompanying drawings.

[0047] As Figure 1 shown, the regional water resource accessibility evaluation method based on river network accessibility and water resource quantity of the present invention has the following specific process:

[0048] Step 1, collect topographic features, river network, precipitation, water network project and water transfer volume data of the area to be evaluated; specifically, the collected data are regional digital elevation data, 2019 river line vector data, 2003 - 2018 rainfall station precipitation data, South-to-North Water Diversion Middle Route Project vector data, 2014 - 2021 South-to-North Water Diversion Middle Route Project gate water diversion data;

[0049] Step 2, select and calculate river network accessibility evaluation indicators, and the specific steps are as follows:

[0050] Step 2-1, determine the evaluation scale as 1km * 1km according to the evaluation cost, divide the area to be evaluated into equal-area square grids in ArcGIS according to the evaluation scale, and use them as spatial sample points;

[0051] Step 2-2, select three types of indicators including topographic indicators, river spatial structure indicators and river network topological structure indicators as evaluation indicators, collect various indicators included in each grid, calculate the mean value of each type of indicator respectively, and use the mean value as sample data; according to previous studies, the indicators characterizing topography mainly include elevation, relief, slope, distance, etc., the indicators describing the river spatial structure mainly include river network density, box dimension, bifurcation ratio, river curvature, etc., and the indicators describing the topological structure of the river network mainly include the number of nodes, edge point ratio, degree, closeness centrality, betweenness centrality, clustering coefficient, average path length, etc. Select indicators with spatial distribution characteristics and large spatial differentiation in the study area. Specifically, select water intake distance (Distance), elevation difference (Elevation), slope (Slope) as topographic indicators, river network density (Density), box dimension (D f ) as river spatial structure indicators, degree (Degree), closeness centrality (CC), betweenness centrality (BC) as river network topological structure indicators;

[0052] Step 2-3, formally define the topographic indicator dK , the river spatial structure index z K and the river network topological structure index g K , and the expressions are as follows:

[0053] d 1,2,...,K1 = [d 1,2,...,K,1 , d 1,2,...,K,2 , …, d 1,2,...,K1,N

[0054] z 1,2,...,K2 = [z 1,2,...,K,1 , z 1,2,...,K,2 , …, z 1,2,...,K2,N

[0055] g 1,2,...,K3 = [g 1,2,...,K,1 , g 1,2,...,K,2 , …, g 1,2,...,K3,N

[0056] In the formula, N is the number of samples, which is used as the sample index here, N = 1, 2, …, d 1,2,...,K1 is the value of the river network topological structure index of the K1th evaluation index, z 1,2,...,K2 is the value of the river spatial structure index of the K2th evaluation index for all samples, g 1,2,...,K3 is the value of the river network topological structure index of the K3th evaluation index for all samples; among them, the calculation unit of the river spatial structure index is a 10km * 10km grid, the calculation result of the river network topology is a linear index, which is extended to the entire study area through kernel density estimation, and K1 + K2 + K3 = K;

[0057] Step 3, normalize the evaluation index, and the general formula for normalization is as follows:

[0058]

[0059] In the formula, x is the normalized value of the evaluation index, T is the initial value of the evaluation index, T max is the initial maximum value of the evaluation index; T min is the initial minimum value of the evaluation index;

[0060] Step 4, determine the river network accessibility through factor analysis, as Figure 2 shown, and the specific treatment is as follows:

[0061] Step 4-1, combine the normalized evaluation indexes into a sample matrix X, and the expression is:

[0062]

[0063] In the formula, x 1,K ,... x N,K ​​​The 1st to the Kth evaluation indicators of the 1st to the Nth samples after normalization;

[0064] Calculate the covariance matrix S of the sample matrix, and the expression is as follows:

[0065]

[0066] In the formula, s KK is an element in the covariance matrix;

[0067] Step 4-2: Conduct the KMO test and Bartlett spherical test on the samples to test the suitable evaluation indicators;

[0068] The general KMO test expression followed is:

[0069]

[0070] In the formula, represents the correlation coefficient between two elements i and j to be tested, represents the partial correlation coefficient between two elements i and j to be tested.

[0071] The general Bartlett spherical test expression followed is:

[0072] (1) Assume that the covariance matrices of all samples are equal (spherical):

[0073] (2) Calculate the evaluation index statistic χ 2 , and the expression is:

[0074]

[0075] In the formula, |S| is the determinant of the overall covariance matrix;

[0076] (3) Calculate the degrees of freedom df, and the expression is:

[0077]

[0078] In the formula, K is the number of evaluation indicators;

[0079] (4) Conduct the hypothesis test in (1): According to the evaluation index statistic χ 2 and the degrees of freedom df, look up the corresponding critical value from the chi-square distribution table. If the calculated χ 2 > critical value, reject the original hypothesis; otherwise, do not reject the original hypothesis.

[0080] From this, it is inferred that in the present invention, when the sample satisfies the KMO test coefficient KMO > 0.6 and the significance level coefficient p < 0.05, it is determined that the factor analysis result of the evaluation indicators is suitable;

[0081] Step 4-3, after determining that the evaluation index is suitable, establish the orthogonal factor model X of the sample matrix X, and the expression is:

[0082] X = μ + AF + ε

[0083] The parameters in the above formula satisfy

[0084] In the formula, F = (F1, F2, …, F m ) T is the set of common factors of the sample matrix X, m is the number of common factors, μ = (μ1, μ2, …, μ K ) T is the mean of the sample matrix X, ε = (ε1, ε2, ε3, …, ε K ) T is the set of specific factors of the sample matrix X, E(ε) is to find the mean of the variable ε, var(ε) is to find the covariance of each element in the variable ε, is a diagonal matrix (K×K), is the variance of the Kth variable, I is the covariance matrix of the common factors, which is an identity matrix (m×m), cov(F, ε) represents the covariance between the variable F and the variable ε, A = (a Km ) K×m is the factor loading matrix (K×m) of the sample matrix X. From the model conditions, it can be deduced that cov(X, F) = A, S - D = AA T , A T is the transposed factor loading matrix, D is the covariance matrix of the specific factors (K×K); where A and D are unknown and are obtained through the following process:

[0085] i. Estimate the covariance of the common factors by the following principal component method, determine the part with larger covariance eigenvalues as the common factors, and set the number of common factors m;

[0086] ii. Decompose the covariance matrix S of the sample matrix, and the expression is:

[0087]

[0088] In the formula, λ1, λ2, … λ K are the eigenvalues of the covariance matrix, and λ1 ≥ λ2 ≥ … ≥ λ K ≥ 0, l1, l2, …, l K are the corresponding eigenvectors, i.e., the error term, T represents the transpose;

[0089] Introduce a common factor \(m\) that approximately represents the principal components of the original variables. \(m\) is the part with larger eigenvalues, so that the variance of the factor loading matrix and the variance estimate of the specific factor can be obtained. The expression is:

[0090]

[0091] In the formula, \(s\) KK is the diagonal element of the sample covariance matrix \(S\), and \(\text{diag}(BB\) T ) is the diagonal matrix of matrix \(B\) and its transpose matrix \(B\) T ;

[0092] Through the variance contribution j of the common factor \(F\) it reflects the total influence of the common factor \(F\) j on all variables and is an index to measure the relative importance of the common factor. The formula for the variance contribution j of the common factor \(F\) is as follows:

[0093]

[0094] Among them, the common factor refers to the common factors shared by the original variables, and the specific factor refers to the factor that only appears in the corresponding single original variable;

[0095] Step 4-4: Perform an orthogonal rotation of the common factor with the largest variance, so that the squares of the factor loadings are transformed towards the two poles of 0 and 1 by column, in order to highlight the practical significance of each common factor. Perform an orthogonal rotation on the factor model \(X = \mu+AF+\varepsilon\). The expression is:

[0096]

[0097] In the formula, \(\Gamma\) is an orthogonal matrix, and the rotated factor orthogonal model is \(X = \mu+AVZ+\varepsilon\), where \(Z\) is the rotated factor score matrix (\(m\times1\));

[0098] Step 4-5: Calculate the river network accessibility \(H\) by weighted calculation through the variance contribution of the common factor. The expression is:

[0099]

[0100] In the formula, \(Z\) m is the element of the rotated factor score matrix \(Z\);

[0101] The positive or negative of the connection symbol depends on the actual positive or negative significance of the common factor. If it is of positive significance, the connection symbol is +, and if it is of negative significance, the connection symbol is -;

[0102] As shown in Table 1 and Table 2, the characteristic roots, factor loadings, and variance contributions before and after the rotation of the orthogonal model are presented. According to the eigenvalue being greater than 1, three common factors are selected, and the cumulative variance contribution is 79.708%.

[0103] Table 1

[0104]

[0105]

[0106] Table 2

[0107]

[0108] It can be seen from the factor loadings after rotation that the loadings of factor 1 degree, CCY, and BCY are relatively large, reflecting the impact of river network connectivity on accessibility; the loadings of factor 2 density, box dimension, and distance are relatively large, reflecting the impact of the ease of water acquisition on accessibility; the loadings of factor 3 elevation difference and Slope are relatively large, reflecting the impact of terrain on accessibility. Each common factor has a relatively clear practical significance; factor 1 and factor 2 have a positive impact on accessibility, and the connection symbol is positive, while factor 3 has a negative impact on accessibility, and the connection symbol is negative. The calculation results are as Figure 3 shown.

[0109] Step 5: Combine the river network accessibility and water resource quantity to conduct an evaluation and analysis of the water resource accessibility in the area to be evaluated;

[0110] Step 5-1: Estimate the natural water resource quantity. The annual average runoff of the catchment area is calculated using the runoff coefficient method, and the expression is:

[0111] R = A r ·P·α

[0112] where R is the runoff of the catchment area, A r is the area of the catchment area, P is the annual average precipitation of the catchment area, and α is the runoff coefficient of the catchment area;

[0113] Among them, the annual average runoff R of the catchment area over the years is used as the estimated value of the natural water resource quantity;

[0114] Step 5-2: Statistically calculate the externally transferred water quantity within the area to be evaluated, and add it to the natural water resource quantity to determine the actual water resource quantity in the area to be evaluated, including externally transferred water quantity + natural water resource quantity, as Figure 4 shown;

[0115] Step 5-3: Combine the river network accessibility and the natural water resource quantity. The annual average runoff R of the catchment area is used as the estimated value of the natural water resource quantity to evaluate the water resource accessibility W of the area to be evaluated;

[0116] W = H·R

[0117] , such as Figure 5 shown.

[0118] In summary, the present invention comprehensively considers the river network accessibility and water resource quantity, evaluates the regional water resource accessibility by establishing an evaluation model, clarifies the independent influence of the river network layout or water resource quantity on water resource accessibility, enables the actual role of the water network project construction to be clearly expressed, is conducive to enhancing the understanding of the water network, and improving the planning level of water network governance and water network projects.

[0119] The implementation steps of the regional water resource accessibility evaluation method based on river network accessibility and water resource quantity involved in the above specific embodiments are only used to help understand the specific method and core idea of the present invention. There will be changes in the specific implementation manner and application scope, and it is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent replacements, improvements or changes made based on the inventive concept of this application should be included within the protection scope of the present invention.

Claims

1. A regional water resource accessibility evaluation method based on river network accessibility and water resource quantity, characterized in that, Including: Collecting topographic and geomorphic features, river networks, precipitation, water network projects, and water transfer volume data of the area to be evaluated, and obtaining spatial sample points from the area to be evaluated; Selecting topographic indicators, river spatial structure indicators, and river network topological structure indicators as river network accessibility evaluation indicators, extracting the values of various evaluation indicators within the spatial sample points, and taking the mean values of various evaluation indicators as sample data; Combining the normalized evaluation indicators into a sample matrix X, calculating the covariance matrix S of the sample matrix, and after determining the suitable evaluation indicators, establishing an orthogonal factor model of the sample matrix X, the expression of which is: X = μ + AF + ε The parameters of the above formula satisfy where \(F=(F_1,F_2,\cdots,F m ) T is the set of common factors of the sample matrix \(X\), \(m\) is the number of common factors, \(\mu = (\mu_1,\mu_2,\cdots,\mu K ) T is the mean of the sample matrix \(X\), \(\varepsilon = (\varepsilon_1,\varepsilon_2,\varepsilon_3,\cdots,\varepsilon K ) T is the set of specific factors of the sample matrix \(X\), \(E(\varepsilon)\) is to find the mean of the variable \(\varepsilon\), \(var(\varepsilon)\) is to find the covariance of each element in the variable \(\varepsilon\), is a diagonal matrix, is the variance of the \(K\)th variable, \(I\) is the covariance matrix of the common factors, \(cov(F,\varepsilon)\) is to find the covariance between the variable \(F\) and the variable \(\varepsilon\), \(A=(a Km ) K×m is the factor loading matrix of the sample matrix \(X\), \(cov(X,F)=A\), \(S - D = AA T \), \(A T is the transposed matrix of the factor loading, \(D\) is the covariance matrix of the specific factors; Obtaining A and D through the following process: i) Decomposing the covariance matrix S of the sample matrix, the expression of which is: where λ1, λ2,... λ K are the eigenvalues of the covariance matrix, and λ1 ≥ λ2 ≥ … ≥ λ K ≥ 0, and l1, l2, …, l K are the corresponding eigenvectors, T represents the transpose; ii) Estimating relevant parameters by the principal component method, determining the number m of common factors, and thus obtaining the variance estimation of the factor loading matrix and the variance estimation of the specific factor, the expression of which is: where s KK is the diagonal element of the sample covariance matrix S, and diag(BB T ) is the diagonal matrix of matrix B and its transpose matrix B T ; Performing orthogonal rotation on the common factor with the largest variance to obtain the rotated factor score matrix Z, the expression of which is: In the formula, Γ is an orthogonal matrix, and the rotated factor orthogonal model is: X = μ + AΓZ + ε; Through the contribution of common factor variance The weighted calculation of river network accessibility H is expressed as: where Z m is an element of the rotated factor score matrix Z; Combining the river network accessibility and the natural water resources volume, taking the average annual runoff R of the catchment area as the estimated value of the natural water resources volume, and evaluating the water resources accessibility W of the area to be evaluated, the expression of which is: W = H·R In the formula, R is the average annual runoff of the catchment area, and H is the river network accessibility.

2. The regional water resource accessibility evaluation method based on river network accessibility and water resource quantity according to claim 1, characterized in that Formal definition of terrain index d K , river spatial structure index z K and river network topological structure index g K , the expression is: d 1,2,...,K1 = [d 1,2,...,K1,1 , d 1,2,...,K1,2 , …, d 1,2,...,K1,N ​ z 1,2,...,K2 = [z 1,2,...,K2,1 , z 1,2,...,K,2 , …, z 1,2,...,K2,N ​ g 1,2,...,K3 = [g 1,2,...,K3,1 , g 1,2,...,K3,2 , …, g 1,2,...,K3,N ​ where N is the number of samples, and d 1,2,...,K1 is the topographic index value of the K1th evaluation index for all samples, and z 1,2,...,K2 is the river spatial structure index value of the K2th evaluation index for all samples, and g 1,2,...,K3 is the river network topological structure index value of the K3th evaluation index for all samples.

3. The regional water resource accessibility evaluation method based on river network accessibility and water resource quantity as described in claim 1, characterized in that, The determination of suitable evaluation indicators further includes performing KMO test and Bartlett spherical test on the samples to determine the suitable evaluation indicators.

4. The regional water resource accessibility evaluation method based on river network accessibility and water resource quantity according to claim 1, characterized in that Determining the part with large covariance eigenvalues as the common factors and setting the number m of common factors.

5. The regional water resource accessibility evaluation method based on river network accessibility and water resource quantity according to claim 1, characterized in that The positive or negative sign of the connection symbol in the expression of the river network accessibility H depends on the actual positive or negative meaning of the common factors. If it is a positive meaning, the connection symbol is +, and if it is a negative meaning, the connection symbol is -.

6. The regional water resource accessibility evaluation method based on river network accessibility and water resource quantity according to claim 1, characterized in that, The expression of the average annual runoff of the catchment area is: R = A r ·P·α where R is the average annual runoff of the catchment area, A r is the catchment area, P is the average annual precipitation of the catchment area, and α is the runoff coefficient of the catchment area.

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