Ecological flow threshold dynamic determination method considering hydrological situation change and medium

Abstract

The invention discloses an ecological flow threshold value dynamic determination method considering hydrological situation changes and a medium, and belongs to the technical field of hydrology and water resource planning. The method comprises the following steps: S1, collecting daily scale long sequence runoff volume data of a hydrometric station, and carrying out quality inspection on the data; s2, calculating each hydrological situation index year by year according to the daily runoff condition; s3, determining an empirical distribution condition of each hydrological situation index by adopting a kernel density estimation mode, and calculating a hydrological situation index value corresponding to a main quantile; s4, running a genetic algorithm by taking an annual flow minimum value as a target function, taking daily runoff as a decision variable and taking a hydrological situation variation allowable interval and water balance as constraint conditions; and S5, checking the convergence of the algorithm, and determining a daily ecological flow threshold. According to the invention, a more accurate ecological flow threshold can be obtained.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydrology and water resources planning, and particularly relates to a method for dynamically determining an ecological flow threshold considering hydrological regime changes and a medium. BACKGROUND

[0002] Ecological flow refers to the water flow condition required to maintain the health and stability of the ecological system of rivers, lakes, wetlands and other water areas, including the key characteristics such as water quantity, timing, duration, and variation rhythm. With the increasing frequency of human activities such as water conservancy construction, the natural water flow pattern is severely disturbed, leading to problems such as river drying up, wetland shrinking, and biodiversity decline. Scientific determination of ecological flow is to find out the minimum water requirement for maintaining the normal operation of the ecological system, to provide a basis for water resources planning and management, water conservancy operation, and to achieve a win-win situation of economic development and ecological protection.

[0003] From the perspective of habitat protection, reasonable determination of ecological flow is crucial to maintaining the ecological system of water areas. Appropriate water flow conditions can provide necessary living environment for various aquatic organisms, including suitable water depth, flow rate, water quality and other elements. For example, maintaining basic river water quantity can prevent aquatic organisms from dying due to drought, and periodic large flow can help shape diverse riverbed morphology, forming deep pools and shoals and providing habitats for various organisms. At the same time, reasonable water flow can also drive sediment movement and maintain riverbed stability, creating a good living space for benthic organisms. By scientifically evaluating the survival needs of important species, and then determining reasonable ecological flow, it is an effective way to protect the integrity of the ecological system of water areas, and an important guarantee for the sustainable use of water resources.

[0004] Currently, the main methods for determining ecological flow include hydrological index method, habitat simulation method, overall analysis method, and hydrodynamic-ecological coupling model method. The current ecological flow determination technology has the following shortcomings: [1] Ecological flow constraints are biased towards static, ignoring the time difference of ecological needs; [2] The hydrological variation requirements for habitat quality are not considered, lacking necessary pulse stimulation and flow continuity constraints; [3] Lack of ecological environment change constraints to ensure that the hydrological regime changes within a reasonable range. SUMMARY

[0005] The present application aims to at least partially solve one of the above-mentioned technical problems in the related art.

[0006] To this end, the present application aims to provide a method for dynamically determining an ecological flow threshold considering hydrological regime changes and a medium, which can consider the hydrological variation requirements for habitat quality and obtain more accurate ecological flow threshold.

[0007] In order to solve the above technical problems, the present application is implemented as follows: The embodiment of the present application provides a method for dynamically determining an ecological flow threshold value by considering hydrological regime variation, and the method comprises the following steps: S1, collecting daily scale long sequence runoff data of a hydrological station, and performing quality inspection on the data; S2, calculating each hydrological regime index year by year according to daily runoff; S3, determining the empirical distribution of each hydrological regime index by using kernel density estimation, and calculating the hydrological regime index value corresponding to the main quantile; S4: taking the minimum annual runoff as a target function, taking daily runoff as a decision variable, taking the allowable interval of hydrological regime variation and water balance as a constraint condition, and running a genetic algorithm; S5: checking the convergence of the algorithm, and determining the daily ecological flow threshold value.

[0008] In addition, the method for dynamically determining an ecological flow threshold value by considering hydrological regime variation according to the present application can further have the following additional technical features: In some embodiments, step S1 comprises: S1.1: obtaining long sequence daily scale runoff data of the hydrological station for more than 30 years by means including consulting a hydrological yearbook and a basin committee website; S1.2: performing consistency inspection on the runoff data to ensure the reliability, consistency and representativeness of the original input data.

[0009] In some embodiments, the consistency inspection is performed by using a double mass curve method and / or an MK mutation inspection method.

[0010] In some embodiments, the hydrological regime index in step S2 comprises daily runoff, annual maximum 3-day, 7-day, 30-day and 90-day runoff, annual minimum 3-day, 7-day, 30-day and 90-day runoff, annual low flow pulse duration and annual high flow pulse duration. The low flow pulse is a daily value lower than 25% frequency, and the high flow pulse is a daily value higher than 75% frequency.

[0011] In some embodiments, the hydrological regime index is obtained year by year by using a Python programming method in step S2.

[0012] In some embodiments, step S3 comprises: S3.1: for the annual hydrological regime index sequence, a polynomial kernel function density estimation method is used to estimate the cumulative probability density thereof; S3.2: using the hydrological regime value corresponding to the inverse cumulative probability density function as the upper and lower limits of the hydrological regime variation, the empirical distribution is calculated.

[0013] In some embodiments, step S4 comprises: S4.1: Establish an optimization mathematical model with the minimum average annual runoff as the objective function, and the hydrological regime change interval and the water balance limit as the constraint condition; S4.2: Based on Python language, define the optimization problem by using the optimization library Pymoo, establish a genetic algorithm, set parameters including population size, iteration number, crossover rate and mutation rate, and solve the optimization problem.

[0014] In some embodiments, step S5 comprises: S5.1: Determine the convergence of the algorithm by checking the curve of the objective function with the iteration number; S5.2: After convergence, export the final variable result as the ecological flow threshold.

[0015] In some embodiments, the main quantile in step S3 includes 20% and 80%.

[0016] The embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the ecological flow threshold dynamic determination method considering hydrological regime change.

[0017] Compared with the prior art, the application has at least the following beneficial effects: In the embodiment of the application, the ecological flow threshold dynamic determination method considering hydrological regime change dynamically optimizes the daily scale ecological flow by using a genetic algorithm, breaks through the limitations of the traditional fixed threshold method, and makes the flow distribution more in line with the natural fluctuation characteristics of the river; In the embodiment of the application, the ecological flow threshold dynamic determination method considering hydrological regime change quantifies the 20%-80% quantile interval of the hydrological regime index based on kernel density estimation, scientifically defines the allowable range of ecological flow, minimizes water resource consumption while ensuring the stability of the ecological system; In the embodiment of the application, the ecological flow threshold dynamic determination method considering hydrological regime change automatically solves the global optimal solution by using an intelligent optimization algorithm, which avoids the blindness of manual trial calculation and realizes ecological protection and efficient use of water resources.

[0018] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flowchart of the ecological flow threshold dynamic determination method considering hydrological regime change disclosed in an embodiment of the application is shown. Figure 2 Convergence of the iterative calculation algorithm is disclosed for an embodiment of the present application; Figure 3 Specific ecological flow threshold conditions determined for a hydrological station are disclosed for an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be apparently and completely described in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0021] The embodiments of the present application will be described in detail below in combination with the drawings and specific embodiments and application scenarios thereof.

[0022] Referring to FIG. 1, Figure 1 In some embodiments of the present application, a dynamic ecological flow threshold determination method considering hydrological regime change is provided. The method provides a daily-scale ecological flow determination framework based on genetic algorithm considering the range of hydrological change indicators. The framework analyzes and calculates hydrological regime sequences such as average flow, maximum several-day flow and pulse duration through historical hydrological data, and determines the allowable interval of hydrological regime change through probabilistic analysis. Then, according to the allowable interval of hydrological regime change, the minimum annual discharge value is set and genetic algorithm is deployed to solve the daily dynamic ecological flow, so as to determine the daily ecological flow threshold.

[0023] The steps of the dynamic ecological flow threshold determination method considering hydrological regime change provided by the present application include: Step 1: Collect daily-scale long sequence (30 years are appropriate) runoff (runoff depth) data of a hydrological station, and perform data quality inspection; Step 2: Calculate each hydrological regime indicator year by year according to daily runoff; Step 3: Determine the empirical distribution of each hydrological regime indicator by kernel density estimation, and calculate the hydrological regime indicator values corresponding to 20% and 80% quantiles; Step 4: Deploy and run genetic algorithm with the minimum annual flow as the objective function, daily runoff as the decision variable, hydrological regime change allowable interval and water balance as the constraint conditions; Step 5: Check the convergence of the algorithm, and determine the daily ecological flow threshold.

[0024] In some embodiments of the application, in step 1, daily scale long sequence (30 years are appropriate) runoff (runoff depth) data of hydrological stations are collected, and data quality inspection is carried out, as follows: Step 1.1: Obtain 30-year (and above) long sequence daily scale runoff data of reliable hydrological stations by consulting hydrological yearbooks, basin committee websites and other ways.

[0025] Step 1.2: Select suitable methods such as double cumulative curve method and MK mutation test for consistency test to ensure the reliability, consistency and representativeness of the original input data.

[0026] In some embodiments of the application, in step 2, according to the daily runoff, each year, the hydrological regime indicators are calculated, as follows: Step 2.1: The selected hydrological regime indicators include daily runoff, annual maximum 3-day, 7-day, 30-day and 90-day flow, annual minimum 3-day, 7-day, 30-day and 90-day flow, annual low flow pulse duration, and annual high flow pulse duration. Low pulse is defined as daily value below 25% frequency, and high pulse is defined as daily value above 75% frequency; the ecological significance is shown in Table 1 Step 2.2: Use Python programming to obtain hydrological regime indicators year by year for subsequent edge distribution estimation.

[0027] Table 1 Ecological significance of hydrological regime indicators

[0028] In some embodiments of the application, in step 3, the empirical distribution of each hydrological regime indicator is determined by using kernel density estimation, and the hydrological regime indicator values corresponding to 20% and 80% quantiles are calculated. Specifically as follows: Step 3.1: For the annual hydrological regime indicator sequence, the cumulative probability density is estimated by using polynomial kernel function density estimation, as shown in equations 1-2: (1) (2) Where, is the estimated probability density function of hydrological regime indicator X at x, n is the sample size, K(x) is the kernel function, h is the bandwidth, and F(x) is the cumulative probability density function of hydrological regime indicator X at x.

[0029] Step 3.2: Set 20% and 80% quantiles, and use the corresponding hydrological regime values of inverse cumulative probability density function as the upper and lower limits of hydrological regime variation, as shown in equations 3-4 (3) (4) in, as well as These are the lower and upper bounds of the hydrological situation index X, respectively, and icdf is the cumulative probability density function. inverse function .

[0030] In some embodiments of the present invention, step 4 uses the minimum annual flow as the objective function, daily runoff as the decision variable, and the allowable range of hydrological changes and water balance as constraints to deploy and run a genetic algorithm. Specifically: Step 4.1: Using the minimization of average annual runoff as the objective function, and taking the hydrological situation change interval and water balance constraints as constraints, establish the optimization mathematical model as shown in Equation 5: (5) (6) in, Let t be the traffic volume in the t-th time period. Let N be the value of the hydrological situation index. The inflow value at time t. Let t be the water volume at time t-1. The ecological outflow at time t cannot exceed the sum of the inflow and the stored water volume at time t.

[0031] Step 4.2: Based on the Python language, use the Pymoo optimization library to define the optimization problem, establish a genetic algorithm, and set parameters such as population size, number of iterations, crossover rate, and mutation rate to solve the optimization problem.

[0032] In some embodiments of the present invention, step 5 checks the convergence of the algorithm to determine the daily ecological flow threshold, as follows: Step 5.1: Determine the convergence of the algorithm by examining the curve of the objective function changing with the number of iterations.

[0033] Step 5.2: Derive the final variable result as the ecological flow threshold.

[0034] Example 1:

[0035] In this embodiment, the steps of the method for dynamically determining the ecological flow threshold considering changes in hydrological conditions include: Step 1: Collect daily long-term runoff (runoff depth) data from a hydrological station for the period 1980-2010 and perform data quality checks; specific steps include: Step 1.1: Obtain reliable 30-year long-sequence daily runoff data from hydrological stations by consulting the river basin committee's website or other means.

[0036] Step 1.2, after the three nature review, the data reliability, consistency and representativeness meet the requirements.

[0037] Step 2, according to the daily runoff condition, the hydrological regime index is calculated year by year.

[0038] Step 2.1, the selected hydrological regime index includes daily runoff, annual maximum 3-day, 7-day, 30-day, 90-day flow, annual minimum 3-day, 7-day, 30-day, 90-day flow, annual low flow pulse duration, annual high flow pulse duration.

[0039] Step 2.2, the hydrological regime index is obtained year by year by using Python programming, for subsequent edge distribution estimation.

[0040] Step 3, the empirical distribution of the hydrological regime index is determined by using kernel density estimation, and the hydrological regime index values corresponding to 20% and 80% quantile are calculated. The results are shown in Table 2: Table 2 Hydrological index allowable upper and lower limit

[0041] Step 4, taking the annual minimum flow as the objective function, taking the daily runoff as the decision variable, taking the allowable range of hydrological regime change and water balance as the constraint condition, setting the population number to 5000, the iteration number to 1000, the crossover rate to 0.6, deploying and running the genetic algorithm.

[0042] Step 5, check the convergence of the algorithm, and determine the daily ecological flow threshold. Figure 2 The objective function value converges at about 800 generations, and the result can be used as the ecological flow threshold. The specific ecological flow condition is shown in Figure 3 .

[0043] The parts of the application not described in detail can refer to the prior art or be the known technology of those skilled in the art, and the embodiment is not limited thereto, and will not be described in detail here.

[0044] The embodiments of the application are described above in combination with the drawings, but the application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative but not limiting, and those skilled in the art can make many forms under the inspiration of the application without departing from the purpose of the application and the scope protected by the claims, which all belong to the protection of the application.

Claims

1. A method for dynamically determining an ecological flow threshold value considering hydrological regime variation, characterized in that, The method comprises: S1, collecting hydrological station daily scale long sequence runoff data, and performing data quality inspection; S2, according to the daily runoff, the annual calculation of each hydrological regime index; S3, the empirical distribution of each hydrological regime index is determined by using kernel density estimation, and the hydrological regime index value corresponding to the main quantile is calculated; S4: taking the minimum annual runoff as the objective function, taking the daily runoff as the decision variable, taking the hydrological regime change interval and water balance as the constraint condition, and running the genetic algorithm; S5: check the convergence of the algorithm, and determine the daily ecological flow threshold. 2.The method of claim 1, wherein, Step S1 includes: S1.1: obtain the long sequence daily scale runoff data of the hydrological station through the method including consulting the hydrological yearbook, the website of the basin committee, etc. S1.2: consistency test is performed on the runoff data to ensure the reliability, consistency and representativeness of the original input data. 3.The method of claim 2, wherein, The consistency test method is double cumulative curve method and / or MK mutation test method. 4.The method according to claim 1, wherein, The hydrological regime index in step S2 includes: daily runoff, annual maximum 3-day, 7-day, 30-day and 90-day flow, annual minimum 3-day, 7-day, 30-day and 90-day flow, annual low flow pulse duration and annual high flow pulse duration; The low flow pulse is the daily value lower than 25% frequency, and the high flow pulse is the daily value higher than 75% frequency. 5.The method of claim 1, wherein, In step S2, the hydrological regime index is obtained year by year by using Python programming. 6.The method of claim 1, wherein, Step S3 includes: S3.1: for the annual hydrological regime index sequence, the cumulative probability density is estimated by using polynomial kernel function density estimation; S3.2: the hydrological regime value corresponding to the inverse cumulative probability density function is used as the upper and lower limits of the hydrological regime change, and the empirical distribution is calculated.

7. The method of claim 1, wherein the method further comprises: Step S4 includes: S4.1: an optimization mathematical model is established by taking the minimization of average annual runoff as the objective function, and taking the hydrological regime change interval and water balance restriction as the constraint condition; S4.2: based on Python language, the optimization problem is defined by using optimization library Pymoo, the genetic algorithm is established, the parameters including population size, iteration number, crossover rate and mutation rate are set, and the optimization problem is solved. 8.The method according to claim 1, wherein, Step S5 includes: S5.1: the convergence of the algorithm is judged by checking the curve of the objective function with the iteration number; S5.2: after the convergence is completed, the final variable result is derived as the ecological flow threshold. 9.The method of claim 1, wherein, The main quantile in step S3 includes 20% and 80%.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the ecological flow threshold dynamic determination method considering the hydrological regime change in any one of claims 1-9.

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