Water diversion project runoff abundance and dryness compensation evaluation method and system based on Copula function

Abstract

The invention relates to the technical field of water resource allocation, in particular to a Copula function-based runoff abundance and dryness compensation evaluation method and system for a water diversion project. The method comprises the following steps of: acquiring long-time sequence actually-measured runoff data of representative hydrometric stations of a water source region and a water receiving region of the water diversion project; constructing a Copula function-based runoff abundance and dryness encounter probability model of the water diversion project, and fitting parameters by using a mixed evolution MCMC algorithm based on Bayesian analysis; using 37.5% and 62.5% of cumulative frequencies as runoff runoff-runoff runoff-runoff runoff-runoff runoff-runoff runoff-runoff runoff-runoff runoff-runoff runoff-runoff runoff According to the method, the magnitude of the runoff abundance and dryness compensation of the water diversion project can be quantitatively evaluated by taking the probability of abundance and dryness synchronization, abundance and dryness asynchronization, favorable water diversion and unfavorable water diversion as runoff abundance and dryness compensation evaluation indexes, and a conclusion whether the abundance and dryness compensation exists or not is accurately obtained.

Description

Technical Field

[0001] The present application relates to the technical field of water resources allocation, and particularly to a method and system for evaluating the runoff abundance and drought compensation of water diversion and regulation projects based on the Copula function. Background Art

[0002] Water diversion and regulation projects are an important part of the national water network construction and the main method to solve the uneven spatio-temporal distribution of water resources. Quantitatively evaluating the runoff abundance and drought compensation between the water source area and the water receiving area of a water diversion and regulation project can provide an important reference for the planning and demonstration of the water diversion and regulation project.

[0003] Currently, certain research progress has been made in the analysis methods of runoff abundance and drought encounters in the field of water resources allocation. The first type of method is to statistically analyze the runoff abundance and drought frequencies in the water source area and the water receiving area, and then analyze the runoff abundance and drought conditions between regions. The second type is to fit the marginal distributions using the runoff series in the water source area and the water receiving area, obtain the joint distribution based on the Copula connection function, calculate the encounter probabilities in different combinations of abundance-normal-drought scenarios, and then calculate indicators such as ecological flow, available water volume, or water supply assurance rate to provide a reference for water resources allocation and optimal operation. Currently, regarding the evaluation method of runoff abundance and drought compensation for water diversion and regulation projects, a systematic evaluation index system has not been established, and it is impossible to directly obtain a conclusion on whether there is abundance and drought compensation between the water source area and the water receiving area. Summary of the Invention

[0004] To achieve the above object, the present application provides the following technical solutions:

[0005] According to the first aspect of the present invention, the present invention claims to protect a method for evaluating the runoff abundance and drought compensation of a water diversion and regulation project based on the Copula function, including the following steps:

[0006] Step S1: Obtain long-term series runoff data of the water source area to be evaluated and the water receiving area to be evaluated in different periods;

[0007] Step S2: Construct a runoff abundance and drought encounter probability model of the water diversion and regulation project based on the Copula function according to the long-term series runoff data, fit the parameters of the Copula function, and select the optimal model;

[0008] Step S3: Calculate the encounter probabilities of the abundance and drought scenarios of the water source area to be evaluated and the water receiving area to be evaluated in different periods based on the critical values for dividing the cumulative frequency runoff abundance and drought levels;

[0009] Step S4: Use the combined probabilities of different abundance and drought scenario pairs as the evaluation indicators for runoff abundance and drought compensation, quantitatively evaluate the size of runoff abundance and drought compensation of the water diversion and regulation project in different periods, and determine whether there is abundance and drought compensation.

[0010] Further, in step S1, the long-term series runoff data in different periods includes:

[0011] The sequential runoff data for the hydrological year, flood season, and dry season are the measured flow or water volume sequences at a hydrological station, and the sequential runoff data are continuous and have a length of not less than 30 years.

[0012] Further, step S2 specifically includes:

[0013] Step S21: Calculate the correlation coefficients of the runoff series in different periods for the water source area to be evaluated and the water receiving area to be evaluated respectively, and conduct a significance test, and screen out those that meet the requirements for constructing a runoff abundance and drought encounter probability model based on the Copula function;

[0014] Step S22: Based on the marginal distribution function, use the least squares method for parameter estimation, calculate the goodness-of-fit evaluation index to select the optimal marginal distribution, and draw the fitting effect diagram of its optimal marginal distribution;

[0015] Step S23: Based on the Copula function, use the hybrid evolutionary MCMC algorithm based on Bayesian analysis to fit the function parameters, and select the optimal Copula function through the goodness-of-fit evaluation index, and draw the joint non-exceedance cumulative probability contour map.

[0016] Further, step S3 is specifically:

[0017] Step S31: Select the cumulative frequency runoff abundance and drought grades of dry-flat and flat-abundant as the critical values for the runoff abundance and drought grade division, and determine the value ranges in dry years, normal years, and wet years for the given flow / volume;

[0018] Step S32: If the runoff sequences of the water source area to be evaluated and the water receiving area to be evaluated are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, obtain the abundance and drought scenario pairs and encounter probabilities of the water source area to be evaluated and the water receiving area to be evaluated.

[0019] Further, step S4 is specifically:

[0020] Step S41: The probability P of abundance and drought synchronization 丰枯同步 is the sum of the probabilities when the abundance and drought scenario pairs are abundant-abundant, flat-flat, and dry-dry;

[0021] The probability P of abundance and drought asynchrony 丰枯异步 is the sum of the probabilities when the abundance and drought scenario pairs are abundant-flat, abundant-dry, flat-dry, flat-abundant, dry-flat, and dry-abundant;

[0022] The probability P of abundance and drought synchronization 丰枯同步 and the probability P of abundance and drought asynchrony 丰枯异步 sum to 1;

[0023] Step S42: The probability P of favorable water transfer 有利调水The probability sum for the scenarios of abundant-dry, abundant-abundant, abundant-normal, normal-normal, and normal-dry;

[0024] The probability P of unfavorable water diversion 不利调水 The probability sum for the scenarios of normal-abundant, dry-abundant, dry-normal, and dry-dry;

[0025] The probability P of favorable water diversion 有利调水 And the probability P of unfavorable water diversion 不利调水 Sum to 1;

[0026] Step S43: Use P 丰枯异步 And P 有利调水 To quantitatively evaluate the runoff compensation of the water diversion project. If P 丰枯异步 > 50% and P 有利调水 > 50%, then there is runoff compensation between the water source area to be evaluated and the water receiving area to be evaluated; otherwise, there is no such compensation.

[0027] According to the second aspect of the present invention, the present invention claims protection for a runoff compensation evaluation system for water diversion projects based on the Copula function, including:

[0028] A data acquisition module that obtains long-term series runoff data of the water source area to be evaluated and the water receiving area to be evaluated at different times;

[0029] A model optimization module that constructs a runoff encounter probability model for water diversion projects based on the Copula function according to the long-term series runoff data, fits the parameters of the Copula function, and selects the optimal model;

[0030] A scenario encounter evaluation module that calculates the encounter probability of the runoff abundance and drought scenarios of the water source area to be evaluated and the water receiving area to be evaluated at different times based on the critical values of the cumulative frequency runoff abundance and drought levels;

[0031] A compensation evaluation module that uses the combined probabilities of different runoff abundance and drought scenarios as runoff compensation evaluation indicators to quantitatively evaluate the runoff compensation of the water diversion project at different times and determine whether there is runoff compensation.

[0032] Furthermore, the data acquisition module further includes:

[0033] The series runoff data of hydrological years, flood seasons, and dry seasons are the measured flow or water volume series of hydrological stations, and the series runoff data are continuous and the length is not less than 30 years.

[0034] Furthermore, the model optimization module specifically includes:

[0035] Calculate the correlation coefficients of the runoff series in the water source area and water receiving area to be evaluated in different periods respectively, and conduct a significance test to screen out those that meet the requirements for constructing a runoff abundance and drought encounter probability model based on the Copula function;

[0036] Based on the marginal distribution function, use the least squares method for parameter estimation, calculate the goodness-of-fit evaluation index to select the optimal marginal distribution, and draw the fitting effect diagram of its optimal marginal distribution;

[0037] Based on the Copula function, use the hybrid evolutionary MCMC algorithm based on Bayesian analysis to fit the function parameters, select the optimal Copula function through the goodness-of-fit evaluation index, and draw the joint non-exceedance cumulative probability contour map.

[0038] Furthermore, the scenario encounter evaluation module specifically includes:

[0039] Select the cumulative frequency runoff abundance and drought levels of dry-normal and normal-abundant as the critical values for runoff abundance and drought level division, and determine the value ranges in dry years, normal years, and wet years for a given flow / quantity of water;

[0040] If the runoff sequences of the water source area and water receiving area to be evaluated are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, calculate the abundance and drought scenarios and encounter probabilities of the water source area and water receiving area to be evaluated.

[0041] Furthermore, the compensation evaluation module specifically includes:

[0042] The probability P of abundance and drought synchronization 丰枯同步 Is the sum of the probabilities when the abundance and drought scenarios are abundant-abundant, normal-normal, and dry-dry;

[0043] The probability P of abundance and drought asynchrony 丰枯异步 Is the sum of the probabilities when the abundance and drought scenarios are abundant-normal, abundant-dry, normal-dry, normal-abundant, dry-normal, and dry-abundant;

[0044] The probability P of abundance and drought synchronization 丰枯同步 And the probability P of abundance and drought asynchrony 丰枯异步 The sum is 1;

[0045] The probability P of favorable water transfer 有利调水 Is the sum of the probabilities when the abundance and drought scenarios are abundant-abundant, abundant-normal, abundant-dry, normal-normal, and normal-dry;

[0046] The probability P of unfavorable water transfer 不利调水 Is the sum of the probabilities when the abundance and drought scenarios are normal-abundant, dry-abundant, dry-normal, and dry-dry;

[0047] The probability P of favorable water transfer 有利调水 And the probability P of unfavorable water transfer不利调水 The sum is 1;

[0048] Adopt P 丰枯异步 and P 有利调水 to quantitatively evaluate the runoff abundance and drought compensation of the water diversion project. If P 丰枯异步 > 50% and P 有利调水 > 50%, then there is runoff abundance and drought compensation between the water source area to be evaluated and the water receiving area to be evaluated, otherwise there is no such compensation.

[0049] This application relates to the technical field of water resources allocation, and particularly to a method and system for evaluating the runoff abundance and drought compensation of a water diversion project based on the Copula function. By obtaining the measured runoff data of the representative hydrological stations in the water source area and the water receiving area of the water diversion project for a long time series; constructing a runoff abundance and drought encounter probability model of the water diversion project based on the Copula function, and using a hybrid evolutionary MCMC algorithm based on Bayesian analysis to fit the parameters; calculating the abundance and drought encounter probability with the cumulative frequencies of 37.5% and 62.5% as the critical values for dividing the runoff drought-flat and flat-abundant grades; using the probabilities of abundance and drought synchronization, abundance and drought asynchrony, favorable water diversion and unfavorable water diversion as the evaluation indicators for runoff abundance and drought compensation, the present invention can quantitatively evaluate the size of the runoff abundance and drought compensation of the water diversion project and accurately draw a conclusion on whether there is abundance and drought compensation. Description of the Drawings

[0050] Figure 1 is a flowchart of a method for evaluating the runoff abundance and drought compensation of a water diversion project based on the Copula function, which is requested to be protected in the embodiments of this application;

[0051] Figure 2 is a structural module diagram of a system for evaluating the runoff abundance and drought compensation of a water diversion project based on the Copula function, which is requested to be protected in the embodiments of this application. Detailed Embodiments

[0052] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0053] The terms "first", "second", and "third" in this application are only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative positional relationship, movement conditions, etc. between components in a specific posture (as shown in the drawings). If the specific posture changes, then the directional indications will also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0054] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0055] According to the first embodiment of the present invention, the present invention claims a method for evaluating the runoff abundance and drought compensation of water diversion projects based on the Copula function, referring to Figure 1 , including the following steps:

[0056] Step S1: Obtain long-term series runoff data of the water source area to be evaluated and the water receiving area to be evaluated in different periods;

[0057] Step S2: Construct a runoff abundance and drought encounter probability model of the water diversion project based on the Copula function according to the long-term series runoff data, fit the parameters of the Copula function, and select the optimal model;

[0058] Step S3: Calculate the encounter probability of the abundance and drought scenarios of the water source area to be evaluated and the water receiving area to be evaluated in different periods with the critical value of the cumulative frequency runoff abundance and drought grade division;

[0059] Step S4: Use the combined probability of different abundance and drought scenario pairs as the evaluation index for runoff abundance and drought compensation, quantitatively evaluate the size of the runoff abundance and drought compensation of the water diversion project in different periods, and determine whether there is abundance and drought compensation.

[0060] Further, in step S1, the long-term series runoff data in different periods includes:

[0061] The series runoff data in the hydrological year, flood season, and dry season are the measured flow or water volume series of the hydrological station. The series runoff data is continuous and has a length of not less than 30 years.

[0062] Further, step S2 specifically includes:

[0063] Step S21: Calculate the correlation coefficients of the runoff series in different periods for the water source area to be evaluated and the water receiving area to be evaluated respectively, and conduct a significance test on them to screen out those that meet the requirements for constructing a runoff abundance and drought encounter probability model based on the Copula function;

[0064] Step S22: Based on the marginal distribution function, use the least squares method for parameter estimation, calculate the goodness-of-fit evaluation index to select the optimal marginal distribution, and draw the fitting effect diagram of its optimal marginal distribution;

[0065] Step S23: Based on the Copula function, use the hybrid evolutionary MCMC algorithm based on Bayesian analysis to fit the function parameters, select the optimal Copula function through the goodness-of-fit evaluation index, and draw the joint non-exceedance cumulative probability contour map.

[0066] Among them, in this embodiment, the marginal distribution function includes at least 18 marginal distribution functions such as Beta, Birnbaum-Saunders, Exponential, Extreme-Value, Gamma, Generalized-Extreme-Value, Generalized-Pareto, Inverse-Gaussian, Logistic, Loglogistic, Lognormal, Nakagami, Normal, Rayleigh, Rician, t-Location-Scale, Weibull, and P-Ⅲ;

[0067] The goodness-of-fit evaluation index includes at least goodness-of-fit evaluation indexes such as NLogL, AIC, BIC, M_L, AIC, BIC, RMSE, and NSE;

[0068] Copula functions include at least 26 Copula functions such as Gaussian, t, Clayton, Frank, Gumbel, Independence, AMH, Joe, FGM, Gumbel - Barnett, Plackett, Cuadras - Auge, Raftery, Shih - Louis, Linear - Spearman, Cubic, Burr, Nelsen, Galambos, Marshal - Olkin, Fischer - Hinzmann, Roch - Alegre, Fischer - Kock, BB1, BB5, and Tawn.

[0069] The calculation methods of goodness - of - fit evaluation indicators such as NLogL, AIC, and BIC are as follows:

[0070]

[0071] Among them, y i is the empirical frequency value, is the fitted probability value, is the estimated value of the standard deviation of the simulation error. Here, n is the sample size. The smaller the NLogL, the better the fitting effect.

[0072]

[0073] In the formula, D is the number of parameters of the model. The smaller the AIC, the better the model simulation.

[0074]

[0075] Similar to AIC, the smaller the BIC, the better the model simulation.

[0076] The Copula functions used in this embodiment are shown in Table 1:

[0077] Table 1 Mathematical Descriptions and Parameter Value Ranges of Copula Functions

[0078]

[0079]

[0080]

[0081] Furthermore, step S3 is specifically as follows:

[0082] Step S31: Select the cumulative frequency runoff dry - normal and normal - wet grades as the critical values for runoff dry - wet grade division, and determine the value ranges in dry years, normal years, and wet years for the given flow / quantity of water.

[0083] Step S32: If the runoff sequences of the water source area to be evaluated and the water receiving area to be evaluated are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, obtain the dry-wet scenario pairs and encounter probabilities of the water source area to be evaluated and the water receiving area to be evaluated.

[0084] Among them, in this embodiment, P k = 37.5% (dry-average) and P f = 62.5% (average-wet) cumulative frequencies are selected as the critical values for runoff dry-wet grade division. Assume that P k corresponds to the flow / quantity of water x k , P f corresponds to the flow / quantity of water x f . Then, for a given flow / quantity of water x i , its value ranges in dry years, average years, and wet years are x i ≤ x k , x k ≤ x i ≤ x f and x i ≥ x f .

[0085] The 9 dry-wet scenario pairs and their encounter probabilities can be obtained by the calculation method shown in Table 2.

[0086] Table 2 Calculation method of dry-wet scenario pairs and their encounter probabilities for runoff

[0087]

[0088]

[0089] In the above table, x k , x f represent the critical water quantities for dry-average and average-wet grade divisions of the water source area to be evaluated, y k , y f represent the critical water quantities for dry-average and average-wet grade divisions of the water receiving area to be evaluated, u k , u f represent the cumulative frequencies for dry-average and average-wet grade divisions of the water source area to be evaluated, and v k , v f represent the cumulative frequencies for dry-average and average-wet grade divisions of the water receiving area to be evaluated.

[0090] Furthermore, step S4 is specifically as follows:

[0091] Step S41: The probability P 丰枯同步 of dry-wet synchronization is the sum of the probabilities when the dry-wet scenario pairs are wet-wet, average-average, and dry-dry;

[0092] Probability P of asynchronous abundance and drought 丰枯异步 It is the sum of probabilities when the abundance-drought scenarios are abundance-normal, abundance-drought, normal-drought, normal-abundance, drought-normal, and drought-abundance;

[0093] Probability P of synchronous abundance and drought 丰枯同步 And the probability P of asynchronous abundance and drought 丰枯异步 The sum is 1;

[0094] Step S42: Probability P of beneficial water diversion 有利调水 It is the sum of probabilities when the abundance-drought scenarios are abundance-abundance, abundance-normal, abundance-drought, normal-normal, and normal-drought;

[0095] Probability P of adverse water diversion 不利调水 It is the sum of probabilities when the abundance-drought scenarios are normal-abundance, drought-abundance, drought-normal, and drought-drought;

[0096] The probability P of the beneficial water diversion 有利调水 And the probability P of the adverse water diversion 不利调水 The sum is 1;

[0097] Step S43: Adopt P 丰枯异步 And P 有利调水 To quantitatively evaluate the runoff compensation of the water diversion project by their magnitudes. If P 丰枯异步 > 50% and P 有利调水 > 50%, then there is runoff compensation between the water source area to be evaluated and the water receiving area to be evaluated; otherwise, there is no such compensation.

[0098] Among them, in this embodiment, the probability of synchronous abundance and drought is P 丰枯同步 = P(X ≤ x k , Y ≤ y k ) + P(x k ≤ X ≤ x f , y k ≤ Y ≤ y f ) + P(X ≥ x f , Y ≥ y k ), and the probability of asynchronous abundance and drought is P 丰枯异步 = P(X ≤ x k , y k ≤ Y ≤ y f ) + P(X ≤ x k , Y ≥ y f ) + P(x k ≤ X ≤ x f , Y ≤ y k ) + P(x k ≤ X ≤ x f , Y ≤ y k ) + P(X ≥ x f , Y ≤ y k) + P(X ≥ x f , y k ≤ Y ≤ y f ), it should satisfy P 丰枯同步 + P 丰枯异步 = 1.

[0099] Step S42: The probability of beneficial water diversion is P 有利调水 = P(X ≥ x f , Y ≥ y k ) + P(X ≥ x f , y k ≤ Y ≤ y f ) + P(X ≥ x f , Y ≤ y k ) + P(x k ≤ X ≤ x f , y k ≤ Y ≤ y f ) + P(x k ≤ X ≤ x f , Y ≤ y k ), the probability of adverse water diversion is P 不利调水 = P(x k ≤ X ≤ x f , Y ≤ y k ) + P(X ≤ x k , Y ≥ y f ) + P(X ≤ x k , y k ≤ Y ≤ y f ) + P(X ≤ x k , Y ≤ y k ), it should satisfy P 有利调水 + P 不利调水 = 1.

[0100] Step S43: The runoff abundance and deficit compensation of the water diversion project can be quantitatively evaluated by the magnitudes of P 丰枯异步 and P 有利调水 . If P 丰枯异步 > 50% and P 有利调水 > 50%, then there is runoff abundance and deficit compensation between the water source area and the water receiving area; otherwise, there is no.

[0101] According to the second embodiment of the present invention, the present invention claims a runoff abundance and deficit compensation evaluation system for a water diversion project based on the Copula function. Referring to Figure 2 , it includes:

[0102] Data acquisition module, which acquires long-term time series runoff data of the water source area to be evaluated and the water receiving area to be evaluated in different periods;

[0103] The model optimization module constructs a runoff abundance and drought encounter probability model for water diversion projects based on the Copula function according to the long-term series runoff data, fits the parameters of the Copula function, and selects the optimal model.

[0104] The scenario encounter assessment module calculates the encounter probabilities of the abundance and drought scenarios of the water source area and the water receiving area to be evaluated in different periods using the critical values divided by the cumulative frequency of runoff abundance and drought levels.

[0105] The compensation assessment module uses the combined probabilities of different abundance and drought scenario pairs as the runoff abundance and drought compensation assessment indicators, quantitatively evaluates the size of runoff abundance and drought compensation of the water diversion project in different periods, and determines whether there is abundance and drought compensation.

[0106] Furthermore, the data acquisition module further includes:

[0107] The sequential runoff data for hydrological years, flood seasons, and dry seasons are the measured flow or water volume sequences of hydrological stations, and the sequential runoff data are continuous and have a length of not less than 30 years.

[0108] Furthermore, the model optimization module specifically includes:

[0109] Calculate the correlation coefficients of the runoff series of the water source area and the water receiving area to be evaluated in different periods respectively, and conduct a significance test on them to screen out those that meet the requirements for constructing a runoff abundance and drought encounter probability model based on the Copula function.

[0110] Based on the marginal distribution function, use the least squares method for parameter estimation, calculate the goodness-of-fit evaluation index to select the optimal marginal distribution, and draw the fitting effect diagram of its optimal marginal distribution.

[0111] Based on the Copula function, use the hybrid evolutionary MCMC algorithm based on Bayesian analysis to fit the function parameters, select the optimal Copula function through the goodness-of-fit evaluation index, and draw the joint non-exceedance cumulative probability contour map.

[0112] Furthermore, the scenario encounter assessment module specifically includes:

[0113] Select the cumulative frequency runoff abundance and drought levels of dry-flat and flat-abundant as the critical values for runoff abundance and drought level division, and determine the value ranges in dry years, normal years, and wet years for a given flow / volume.

[0114] If the runoff sequences of the water source area and the water receiving area to be evaluated are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, calculate the abundance and drought scenario pairs and encounter probabilities of the water source area and the water receiving area to be evaluated.

[0115] Furthermore, the compensation assessment module specifically includes:

[0116] The probability P of synchronous abundance and drought 丰枯同步 is the sum of the probabilities when the abundance-drought scenarios are abundance-abundance, normal-normal, and drought-drought;

[0117] The probability P of asynchronous abundance and drought 丰枯异步 is the sum of the probabilities when the abundance-drought scenarios are abundance-normal, abundance-drought, normal-drought, normal-abundance, drought-normal, and drought-abundance;

[0118] The probability P of synchronous abundance and drought 丰枯同步 and the probability P of asynchronous abundance and drought 丰枯异步 sum to 1;

[0119] The probability P of favorable water diversion 有利调水 is the sum of the probabilities when the abundance-drought scenarios are abundance-abundance, abundance-normal, abundance-drought, normal-normal, and normal-drought;

[0120] The probability P of unfavorable water diversion 不利调水 is the sum of the probabilities when the abundance-drought scenarios are normal-abundance, drought-abundance, drought-normal, and drought-drought;

[0121] The aforementioned probability P of favorable water diversion 有利调水 and the probability P of unfavorable water diversion 不利调水 sum to 1;

[0122] Using the magnitudes of P 丰枯异步 and P 有利调水 to quantitatively evaluate the magnitude of the runoff abundance-drought compensation of the water diversion project. If P 丰枯异步 > 50% and P 有利调水 > 50%, then there is runoff abundance-drought compensation between the water source area to be evaluated and the water receiving area to be evaluated; otherwise, there is no such compensation.

[0123] In a specific embodiment of the present invention, taking the Shigu Hydrological Station in the water source area of the Middle Route Project of Yunnan Water Diversion and the Zhonghe Hydrological Station in the Kunming water receiving area as examples, a probability model of runoff abundance-drought encounter for the water diversion project based on the Copula function is established to quantitatively evaluate the magnitude of runoff abundance-drought compensation between the water source area and the water receiving area, and a conclusion on whether there is abundance-drought compensation is obtained.

[0124] In this embodiment, the monthly runoff (flow) data of the Shigu Hydrological Station in the water source area of the Middle Route Project of Yunnan Water Diversion and the Zhonghe Hydrological Station in the Kunming water receiving area from 1967 to 2019 are obtained. Further, the data are divided into flow sequences on three time scales: hydrological year (June to May of the following year), flood season (June to October), and dry season (November to May of the following year). The lengths of the hydrological year and dry season sequences are 53 years, and the length of the flood season sequence is 52 years, meeting the requirements that the runoff sequences are continuous and the length is not less than 30 years.

[0125] Calculate the Kendall rank correlation coefficient, Spearman rank correlation coefficient, and Pearson linear correlation coefficient of the runoff series in the hydrological year, flood season, and dry season for the water source area and water receiving area of the water diversion project respectively, and conduct a significance test with a confidence level of 0.05. When the P-value of at least one correlation coefficient is less than 0.05, the runoff series is not independent, meeting the requirements for constructing a runoff abundance and drought encounter probability model based on the Copula function.

[0126] The Kendall rank correlation coefficient, Spearman rank correlation coefficient, Pearson linear correlation coefficient, and their significance test results of the flow rate series of Shigu Hydrological Station in the water source area and Zhonghe Hydrological Station in the Kunming water receiving area in the hydrological year, flood season, and dry season are shown in Table 3. It can be seen that the P-values of the significance tests are all less than 0.05, indicating that the correlation coefficients are statistically significant, and the flow rate series between the two hydrological stations is not independent, meeting the requirements for constructing a runoff abundance and drought encounter probability model based on the Copula function.

[0127] Table 3 Correlation of flow rate series between Shigu Hydrological Station in the water source area and Zhonghe Hydrological Station in the Kunming water receiving area

[0128] Correlation coefficient type Magnitude of correlation coefficient P value Kendall rank correlation coefficient 0.53 0.0001 Spearman rank correlation coefficient 0.70 0.0001 Pearson linear correlation coefficient 0.91 0.0001

[0129] Based on 18 marginal distribution functions such as Beta, Birnbaum-Saunders, Exponential, Extreme-Value, Gamma, Generalized-Extreme-Value, Generalized-Pareto, Inverse-Gaussian, Logistic, Loglogistic, Lognormal, Nakagami, Normal, Rayleigh, Rician, t-Location-Scale, Weibull, and P-Ⅲ, use the least squares method for parameter estimation, calculate goodness-of-fit evaluation indicators such as NLogL, AIC, and BIC to select the optimal marginal distribution, and draw the fitting effect diagram of its optimal marginal distribution.

[0130] The least squares method is used to fit 18 marginal distribution functions. The evaluation indicators such as NLogL, AIC, and BIC of the optimal marginal distribution and its parameters are shown in Table 4. The fitting effect of the marginal distribution in the optimal marginal distribution fitting diagram is relatively good.

[0131] Table 4 Goodness-of-fit evaluation indicators and their parameters of the optimal marginal distribution

[0132]

[0133] Construct a runoff abundance and drought encounter probability model based on the Copula function using the flow data of the water source area and the water receiving area in the hydrological year, flood season and dry season. Use the hybrid evolutionary MCMC method based on Bayesian analysis to fit the parameters of 26 Copula functions. The optimal Copula function and its parameters selected according to the goodness-of-fit evaluation indicators such as M_L, AIC, BIC, RMSE and NSE are shown in Table 5. It can be seen that the goodness of fit is good and the parameter values are relatively reliable.

[0134] Table 5 Goodness-of-fit evaluation indicators and parameters of the optimal Copula function

[0135]

[0136]

[0137] Select the cumulative frequencies of P k = 37.5% (dry - normal) and P f = 62.5% (normal - wet) as the critical values for dividing the runoff abundance and drought grades. Assume that the flow / quantity of water corresponding to P k is x k , and the flow / quantity of water corresponding to P f is x f . Then for the given flow / quantity of water x i , its value ranges in dry years, normal years and wet years are x i ≤x k , x k ≤x i ≤x f and x i ≥x f .

[0138] If the runoff sequences of two stations are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, then the 9 abundance and drought encounter scenarios and their encounter probabilities between the water source area and the water receiving area can be calculated.

[0139] Taking the cumulative frequencies of 37.5% and 62.5% as the critical values for dividing the dry - normal and normal - wet grades of runoff, calculate the encounter probabilities of dry - dry, dry - normal, dry - wet, normal - dry, normal - normal, normal - wet, wet - dry, wet - normal and wet - wet between the water source area and the water receiving area of the water diversion project in the hydrological year, flood season and dry season as shown in Table 6.

[0140] Table 6 Runoff abundance and drought encounter probabilities between the water source area and the water receiving area (%)

[0141] Time Dry - dry Dry - normal Dry - wet Normal - dry Normal - normal Normal - wet Wet - dry Wet - normal Wet - wet Hydrological year 15.69 9.73 12.08 9.73 6.39 8.88 12.08 8.88 16.54 Flood season 16.38 9.34 11.78 9.34 6.33 9.34 11.78 9.34 16.38 Dry season 9.78 10.20 17.51 10.20 6.50 8.30 17.51 8.30 11.69

[0142] The runoff abundance and deficit compensation indexes such as asynchronous abundance and deficit, synchronous abundance and deficit, favorable water diversion, and unfavorable water diversion between the water source area and the water receiving area in hydrological years, flood seasons, and dry seasons are shown in Table 7. It can be seen that the probability of asynchronous abundance and deficit is greater than that of synchronous abundance and deficit, and the probability of favorable water diversion is greater than that of unfavorable water diversion on three time scales. It can be obtained that there is runoff abundance and deficit compensation between the water source area of the Middle Route Project of Yunnan Water Diversion and the water receiving area in Kunming. This conclusion can provide support for the necessity and rationality of the planning and demonstration of water diversion projects.

[0143] Table 7 Runoff Abundance and Deficit Compensation Evaluation Indexes between Water Source Area and Water Receiving Area (%)

[0144]

[0145]

[0146] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0147] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. The above is only the implementation manner of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, is equally included in the patent protection scope of the present application.

[0148] The specific implementation manners of the invention have been described in detail above, but they are only examples. The present application is not limited to the specific implementation manners described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application. Therefore, equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principles of the present application should all be covered within the scope of the present application.

Claims

1. A method for evaluating the runoff compensation of water diversion and regulation projects based on Copula function, characterized in that: The following steps are involved: Step S1: Obtain long-term runoff data of the water source area to be assessed and the water receiving area to be assessed at different periods; Step S2: constructing a probability model of runoff abundance and drought for water diversion and regulation projects based on the Copula function according to the long-term runoff data, fitting the parameters of the Copula function, and selecting the optimal model; Step S3: Calculate the probability of encountering the flood and drought scenarios of the water source area to be assessed and the water receiving area to be assessed in different periods based on the critical value of the cumulative frequency runoff flood and drought level division; Step S4: Based on the combined probability of different flood-drought scenario pairs as the runoff flood-drought compensation evaluation index, quantitatively evaluate the runoff flood-drought compensation size of the water diversion and regulation project in different periods to determine whether it has flood-drought compensation.

2. A method for evaluating the runoff compensation of water diversion and regulation projects based on Copula function as claimed in claim 1, characterized in that: In step S1, the long-term runoff data of different periods include: The serial runoff data of the hydrological year, flood season and dry season are the measured flow or water volume sequences of the hydrological stations. The serial runoff data are continuous and the length is not less than 30 years.

3. A method for evaluating the runoff compensation of water diversion and regulation projects based on Copula function as claimed in claim 1, characterized in that: Step S2 specifically includes: Step S21: Calculate the correlation coefficients of the runoff series of the water source area to be evaluated and the water receiving area to be evaluated at different periods, and perform significance tests to screen out the ones that meet the requirements of building a runoff flood and drought encounter probability model based on the Copula function; Step S22: Based on the marginal distribution function, the least square method is used to perform parameter estimation, the goodness of fit evaluation index is calculated to select the optimal marginal distribution, and the optimal marginal distribution fitting effect diagram is drawn; Step S23: Based on the Copula function, the function parameters are fitted using the hybrid evolutionary MCMC algorithm based on Bayesian analysis, the optimal Copula function is selected through the goodness of fit evaluation index, and a contour map of the joint non-exceeding cumulative probability is drawn.

4. A method for evaluating the runoff compensation of water diversion and regulation projects based on Copula function as claimed in claim 1, characterized in that: Step S3 is specifically as follows: Step S31: Selecting the cumulative frequency runoff level of dry-normal and normal-high as the critical value for runoff level classification, and for a given flow / water volume, determining the value range in dry years, normal years and high years; Step S32: If the runoff sequences of the water source area to be evaluated and the water receiving area to be evaluated are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, the wet and dry scenario pairs and the encounter probabilities of the water source area to be evaluated and the water receiving area to be evaluated are obtained.

5. A method for evaluating the runoff compensation of water diversion and regulation projects based on Copula function as claimed in claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Probability P of synchronization between flood and drought 丰枯同步 is the probability and probability of the scenarios of rich-rich, average-average, and poor-poor; The probability of asynchronous flood and drought is P 丰枯异步 is the probability sum of the scenarios of abundant and scarce: abundant-average, abundant-dry, average-dry, average-abundant, dry-average, and dry-abundant; The probability of synchronous flood and drought is P 丰枯同步 The probability P of being asynchronous with the abundance and scarcity 丰枯异步 The sum is 1; Step S42: Probability P of favorable water transfer 有利调水 is the probability sum of the scenarios of abundance-abundance, abundance-average, abundance-abundance, average-average, and average-abundance; Probability of unfavorable water diversion P 不利调水 is the probability sum of the wet-dry scenarios for normal-wealthy, dry-wealthy, dry-normal, and dry-dry; The probability P of favorable water transfer 有利调水 The probability of unfavorable water diversion P 不利调水 The sum is 1; Step S43: Using P 丰枯异步 and P 有利调水 The size of the runoff compensation of the water diversion project is quantitatively evaluated. If P 丰枯异步 >50% and P 有利调水 >50%, there is runoff compensation between the water source area to be assessed and the receiving area to be assessed, otherwise there is no such compensation.

6. A water diversion and regulation project runoff compensation evaluation system based on Copula function, characterized in that: include: The data acquisition module obtains long-term runoff data of the water source area to be evaluated and the water receiving area to be evaluated at different periods; A model optimization module, which constructs a probability model of runoff abundance and drought for water diversion and regulation projects based on the Copula function according to the long-term runoff data, fits the parameters of the Copula function, and selects the optimal model; A scenario encounter assessment module calculates the probability of encountering the flood and drought scenarios of the water source area to be assessed and the water receiving area to be assessed at different periods based on the critical value of the cumulative frequency runoff flood and drought level division; The compensation assessment module uses the combined probability of different flood and drought scenario pairs as the runoff flood and drought compensation assessment index to quantitatively evaluate the runoff flood and drought compensation of the water diversion and regulation project in different periods and determine whether it has flood and drought compensation properties.

7. A water diversion and regulation project runoff compensation assessment system based on Copula function as claimed in claim 6, characterized in that: The data acquisition module also includes: The serial runoff data of the hydrological year, flood season and dry season are the measured flow or water volume sequences of the hydrological stations. The serial runoff data are continuous and the length is not less than 30 years.

8. A water diversion and regulation project runoff compensation assessment system based on Copula function as claimed in claim 6, characterized in that: The model optimization module specifically includes: The correlation coefficients of the runoff series in different periods of the water source area and the receiving area to be evaluated are calculated respectively, and the significance test is carried out to screen out the requirements for building the runoff encounter probability model based on the Copula function; Based on the marginal distribution function, the least square method is used to estimate the parameters, the goodness of fit evaluation index is calculated to select the optimal marginal distribution, and the optimal marginal distribution fitting effect diagram is drawn; Based on the Copula function, the hybrid evolutionary MCMC algorithm based on Bayesian analysis was used to fit the function parameters. The optimal Copula function was selected through the goodness of fit evaluation index, and the contour map of the joint non-exceeding cumulative probability was drawn.

9. A water diversion and regulation project runoff compensation assessment system based on Copula function as claimed in claim 6, characterized in that: The scenario encounter assessment module specifically includes: The cumulative frequency runoff level is selected as dry-normal and normal-high as the critical value for runoff level classification. For a given flow / water volume, the value range in dry years, normal years and high years is determined. If the runoff sequences of the water source area to be evaluated and the water receiving area to be evaluated are X and Y respectively, the corresponding marginal distribution functions are u and v, and the optimal Copula function is C, the wet and dry scenario pairs and the encounter probability of the water source area to be evaluated and the water receiving area to be evaluated are calculated.

10. A water diversion and regulation project runoff compensation assessment system based on Copula function as claimed in claim 6, characterized in that: The compensation assessment module specifically includes: The probability of synchronous flood and drought is P 丰枯同步 is the probability and probability of the scenarios of rich-rich, average-average, and poor-poor; The probability of asynchronous flood and drought is P 丰枯异步 is the probability sum of the scenarios of abundant and scarce: abundant-average, abundant-dry, average-dry, average-abundant, dry-average, and dry-abundant; The probability of synchronous flood and drought is P 丰枯同步 The probability P of being asynchronous with the abundance and scarcity 丰枯异步 The sum is 1; The probability of favorable water transfer P 有利调水 is the probability sum of the scenarios of abundance-abundance, abundance-average, abundance-abundance, average-average, and average-abundance; Probability of unfavorable water diversion P 不利调水 is the sum of the probabilities of the flood-dry scenario pairs: normal-flood, dry-flood, dry-normal, and dry-dry; The probability P of favorable water transfer 有利调水 The probability of unfavorable water diversion P 不利调水 The sum is 1; Using P 丰枯异步 and P 有利调水 The size of the runoff compensation of the water diversion project is quantitatively evaluated. If P 丰枯异步 >50% and P 有利调水 >50%, there is runoff compensation between the water source area to be assessed and the receiving area to be assessed, otherwise there is no such compensation.

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