Method and medium for dynamically determining ecological flow threshold considering hydrological regime variation

Abstract

This invention discloses a method and medium for dynamically determining ecological flow thresholds considering changes in hydrological conditions, belonging to the field of hydrology and water resources planning technology. The method includes: S1, collecting daily long-series runoff data from hydrological stations and performing data quality checks; S2, calculating various hydrological condition indicators annually based on daily runoff; S3, determining the empirical distribution of each hydrological condition indicator using kernel density estimation and calculating the hydrological condition indicator values ​​corresponding to the major quantiles; S4, running a genetic algorithm with the minimum annual flow as the objective function, daily runoff as the decision variable, and the allowable range of hydrological condition changes and water balance as constraints; S5, checking the convergence of the algorithm and determining the daily ecological flow threshold. This invention can obtain more accurate ecological flow thresholds.

Description

Technical Field

[0001] This invention belongs to the field of hydrology and water resources planning technology, specifically relating to a method and medium for dynamically determining ecological flow thresholds that take into account changes in hydrological conditions. Background Technology

[0002] Ecological flow refers to the water flow conditions required to maintain the health and stability of aquatic ecosystems such as rivers, lakes, and wetlands. It includes key characteristics such as water volume, timing of occurrence, duration, and rate of change. With increasingly frequent human activities such as water conservancy projects, natural water flow patterns have been severely disrupted, leading to problems such as river drying up, wetland shrinkage, and decreased biodiversity in many areas. Scientifically determining ecological flow aims to identify the minimum water volume required to maintain the normal operation of ecosystems, providing a basis for water resource planning and management, and the scheduling and operation of water conservancy projects, ultimately achieving a win-win situation for economic development and ecological protection.

[0003] From a habitat protection perspective, rationally determining ecological flow is crucial for maintaining aquatic ecosystems. Appropriate water flow conditions provide the necessary living environment for various aquatic organisms, including suitable water depth, flow velocity, and water quality. For example, maintaining a basic river volume can prevent aquatic organisms from dying due to drought, while periodically larger flows help shape diverse riverbed morphologies, forming different habitats such as deep pools and shallows, providing habitats for various organisms. At the same time, reasonable water flow can also promote sediment transport, maintain riverbed stability, and create favorable living space for benthic organisms. Assessing the survival needs of important species through scientific methods, and then determining reasonable ecological flow, is an effective way to protect the integrity of aquatic ecosystems and an important guarantee for achieving sustainable water resource utilization.

[0004] The main methods for determining ecological flow at present include: hydrological index method, habitat simulation method, overall analysis method and hydrodynamic-ecological coupling model method. The current technology for determining ecological flow has the following shortcomings: [1] Ecological flow constraints are biased towards static, ignoring the time-varying differences in ecological needs; [2] It does not consider the hydrological change requirements for habitat quality, and lacks necessary impulse stimulation and flow continuity constraints; [3] It lacks constraints on ecological and environmental changes to ensure that hydrological changes are within a reasonable range. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the aforementioned related technologies.

[0006] Therefore, the purpose of this invention is to provide a method and medium for dynamically determining ecological flow thresholds that takes into account changes in hydrological conditions, which can take into account the needs of hydrological changes in habitat quality and obtain more accurate ecological flow thresholds.

[0007] To solve the above-mentioned technical problems, the present invention is implemented as follows:

[0008] This invention provides a method for dynamically determining ecological flow thresholds considering changes in hydrological conditions, the method comprising:

[0009] S1. Collect daily long-series runoff data from hydrological stations and perform data quality checks;

[0010] S2. Calculate various hydrological indicators annually based on daily runoff data;

[0011] S3. The empirical distribution of each hydrological situation index is determined by kernel density estimation, and the hydrological situation index values ​​corresponding to the main quantiles are calculated.

[0012] S4: With 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, run the genetic algorithm.

[0013] S5: Check the convergence of the algorithm and determine the daily ecological flow threshold.

[0014] In addition, the method for dynamically determining the ecological flow threshold considering changes in hydrological conditions according to the present invention may also have the following additional technical features:

[0015] In some of these embodiments, step S1 includes:

[0016] S1.1: Obtain long-term daily runoff data of hydrological stations for more than 30 years through methods including consulting hydrological yearbooks and river basin committee websites;

[0017] S1.2: Perform a consistency check on the runoff data to ensure the reliability, consistency, and representativeness of the original input data.

[0018] In some of these implementations, the consistency test is performed using the double cumulative curve method and / or the MK mutation test.

[0019] In some of these implementations, the hydrological situation indicators in step S2 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.

[0020] Low flow pulses are daily values ​​with a frequency below 25%, and high flow pulses are daily values ​​with a frequency above 75%.

[0021] In some implementations, step S2 uses Python programming to obtain hydrological situation indicators year by year.

[0022] In some of these embodiments, step S3 includes:

[0023] S3.1: For the annual hydrological situation index series, the cumulative probability density is estimated by using the polynomial kernel function density estimation method;

[0024] S3.2: Calculate the empirical distribution by using the hydrological situation value corresponding to the inverse cumulative probability density function as the upper and lower limits of hydrological situation changes.

[0025] In some of these implementations, step S4 includes:

[0026] S4.1: Establish an optimization mathematical model with the objective function of minimizing the average annual runoff and the constraints of the hydrological situation change range and water balance limit.

[0027] S4.2: Based on the Python language, use the Pymoo optimization library to define the optimization problem, establish a genetic algorithm, and set parameters including population size, number of iterations, crossover rate and mutation rate to solve the optimization problem.

[0028] In some of these implementations, step S5 includes:

[0029] S5.1: Determine the convergence of the algorithm by examining the curve of the objective function changing with the number of iterations;

[0030] S5.2: After convergence, derive the final variable result as the ecological flow threshold.

[0031] In some of these implementations, the major quantiles in step S3 include 20% and 80%.

[0032] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for dynamically determining ecological flow thresholds considering changes in hydrological conditions as described in any of the preceding claims.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] In this embodiment of the invention, the provided method for dynamically determining the ecological flow threshold considering changes in hydrological conditions dynamically optimizes the daily-scale ecological flow through a genetic algorithm, breaking through the limitations of the traditional fixed threshold method and making the flow allocation more in line with the natural fluctuation characteristics of the river.

[0035] In this embodiment of the invention, the provided method for dynamically determining the ecological flow threshold considering changes in hydrological conditions scientifically defines the permissible range of ecological flow based on the 20%-80% quantile range of hydrological condition indicators quantified by kernel density estimation, thereby minimizing water resource consumption while ensuring the stability of the ecosystem.

[0036] In this embodiment of the invention, the method for dynamically determining the ecological flow threshold considering changes in hydrological conditions uses an intelligent optimization algorithm to automatically solve for the global optimal solution, which avoids the blindness of manual trial calculations and achieves both ecological protection and efficient utilization of water resources.

[0037] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method for dynamically determining ecological flow thresholds that takes into account changes in hydrological conditions, as disclosed in an embodiment of the present invention.

[0039] Figure 2 This describes the convergence of the algorithm during iterative computation, as disclosed in one embodiment of the present invention.

[0040] Figure 3 This is a specific ecological flow threshold determined by a hydrological station as disclosed in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and specific examples and application scenarios.

[0043] Please see Figure 1 As shown, in some embodiments of the present invention, a method for dynamically determining ecological flow thresholds considering hydrological situation changes is provided. This method provides a daily-scale ecological flow determination framework based on a genetic algorithm that considers the range of hydrological change indicators. This framework calculates hydrological situation sequences such as average flow, maximum daily flow, and pulse duration through historical hydrological data analysis, and determines the permissible range of hydrological situation changes through probabilistic analysis. Subsequently, based on the permissible range of hydrological situation changes, a minimum annual average discharge value is set, and a genetic algorithm is deployed to solve for daily dynamic ecological flow, thereby determining the daily ecological flow threshold.

[0044] The steps of the method for dynamically determining the ecological flow threshold considering changes in hydrological conditions proposed in this invention include:

[0045] Step 1: Collect daily-scale long-term (preferably 30 years) runoff (runoff depth) data from hydrological stations and perform data quality checks;

[0046] Step 2: Calculate various hydrological indicators year by year based on daily runoff data;

[0047] Step 3: Use kernel density estimation to determine the empirical distribution of each hydrological condition index, and calculate the hydrological condition index values ​​corresponding to the 20th and 80th quantiles;

[0048] Step 4: Deploy and run the genetic algorithm with 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.

[0049] Step 5: Check the convergence of the algorithm and determine the daily ecological flow threshold.

[0050] In some embodiments of the present invention, step 1 involves collecting daily-scale long-series (preferably 30 years) runoff (runoff depth) data from hydrological stations and performing data quality checks, as detailed below:

[0051] Step 1.1: Obtain reliable 30-year (or longer) long-sequence daily runoff data from hydrological stations by consulting hydrological yearbooks, river basin commission websites, etc.

[0052] Step 1.2: Select appropriate methods such as the double cumulative curve method and the MK mutation test to perform consistency checks to ensure the reliability, consistency and representativeness of the original input data.

[0053] In some embodiments of the present invention, step 2 involves calculating various hydrological indicators annually based on daily runoff, as detailed below:

[0054] Step 2.1 The selected hydrological indicators include daily runoff, annual maximum 3-day, 7-day, 30-day, and 90-day flow rates, annual minimum 3-day, 7-day, 30-day, and 90-day flow rates, annual low-flow pulse duration, and annual high-flow pulse duration. A low pulse is defined as a daily value with a frequency below 25%, while a high pulse refers to a daily value with a frequency above 75%. Their ecological significance is shown in Table 1.

[0055] Step 2.2 Use Python programming to obtain hydrological situation indicators year by year for subsequent marginal distribution estimation.

[0056] Table 1. Ecological significance of hydrological situation indicators

[0057]

[0058] In some embodiments of the present invention, step 3 uses kernel density estimation to determine the empirical distribution of various hydrological condition indicators, and calculates the hydrological condition indicator values ​​corresponding to the 20th and 80th quantiles. Specifically:

[0059] Step 3.1: For the annual hydrological condition index series, the cumulative probability density is estimated using the multinomial kernel function density estimation method, as shown in Equation 1-2:

[0060] (1)

[0061] (2)

[0062] in, Let be the probability density function estimated by the hydrological condition index X at x, n be the sample size, K(x) be the kernel function, h be the bandwidth, and F(x) be the cumulative probability density function of the hydrological condition index X at x.

[0063] Step 3.2: Set the 20% and 80% quantiles, and use the hydrological situation values ​​corresponding to the inverse cumulative probability density function as the upper and lower limits of hydrological situation changes, as shown in Equation 3-4.

[0064] (3)

[0065] (4)

[0066] 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 .

[0067] 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:

[0068] 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:

[0069] (5)

[0070] (6)

[0071] 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.

[0072] 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.

[0073] In some embodiments of the present invention, step 5 checks the convergence of the algorithm to determine the daily ecological flow threshold, as follows:

[0074] Step 5.1: Determine the convergence of the algorithm by examining the curve of the objective function changing with the number of iterations.

[0075] Step 5.2: Derive the final variable result as the ecological flow threshold.

[0076] Example 1:

[0077] In this embodiment, the steps of the method for dynamically determining the ecological flow threshold considering changes in hydrological conditions include:

[0078] 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:

[0079] Step 1.1: Obtain 30-year long-sequence daily runoff data from reliable hydrological stations by consulting the river basin committee's website or other means.

[0080] Step 1.2: After the three-dimensional review, the data meets the requirements for reliability, consistency and representativeness.

[0081] Step 2: Calculate various hydrological indicators year by year based on daily runoff.

[0082] Step 2.1: Selected hydrological 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.

[0083] Step 2.2: Use Python programming to obtain hydrological situation indicators year by year for subsequent marginal distribution estimation.

[0084] Step 3: The empirical distribution of each hydrological condition index was determined using kernel density estimation, and the values ​​of the hydrological condition indices corresponding to the 20th and 80th quantiles were calculated. The results are shown in Table 2.

[0085] Table 2 Permissible Upper and Lower Limits of Hydrological Indicators

[0086]

[0087] Step 4: With 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, set the population size to 5000, the number of iterations to 1000, and the crossover rate to 0.6, deploy and run the genetic algorithm.

[0088] Step 5: Check the convergence of the algorithm and determine the daily ecological flow threshold. Figure 2 The objective function value converges around 800 generations, and the result can be used as an ecological flow threshold. Specific ecological flow details are as follows: Figure 3 As shown.

[0089] Any part of this invention not described in detail can be referred to in the prior art or in the art known to those skilled in the art. This embodiment does not limit such part and will not describe it in detail here.

[0090] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

Claims

1. A method for dynamically determining ecological flow thresholds considering changes in hydrological conditions, characterized in that, The method includes: S1. Collect daily long-series runoff data from hydrological stations and perform data quality checks; S2. Calculate various hydrological indicators annually based on daily runoff data; S3. The empirical distribution of each hydrological condition index is determined using kernel density estimation, and the hydrological condition index values ​​corresponding to the principal quantiles are calculated; the principal quantiles include 20% and 80%. S4: With 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, run the genetic algorithm. S5: Check the convergence of the algorithm and determine the daily ecological flow threshold; Step S1 includes: S1.1: Obtain long-term daily runoff data of hydrological stations for more than 30 years through methods including consulting hydrological yearbooks and river basin committee websites; S1.2: Perform a consistency check on the runoff data to ensure the reliability, consistency, and representativeness of the original input data; The consistency test methods are the double cumulative curve method and / or the MK mutation test method; The hydrological situation indicators in step S2 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; Step S4 includes: S4.1: Establish an optimization mathematical model with the objective function of minimizing the average annual runoff and the constraints of the hydrological situation change range and water balance limit. S4.2: Based on the Python language, use the Pymoo optimization library to define the optimization problem, establish a genetic algorithm, and set parameters including population size, number of iterations, crossover rate and mutation rate to solve the optimization problem; Step S5 includes: S5.1: Determine the convergence of the algorithm by examining the curve of the objective function changing with the number of iterations; S5.2: After convergence, derive the final variable result as the ecological flow threshold.

2. The method for dynamically determining the ecological flow threshold considering changes in hydrological conditions according to claim 1, characterized in that, Low flow pulses are daily values ​​with a frequency below 25%, and high flow pulses are daily values ​​with a frequency above 75%.

3. The method for dynamically determining the ecological flow threshold considering changes in hydrological conditions according to claim 1, characterized in that, In step S2, hydrological situation indicators are obtained year by year using Python programming.

4. The method for dynamically determining the ecological flow threshold considering changes in hydrological conditions according to claim 1, characterized in that, Step S3 includes: S3.1: For the annual hydrological situation index series, the cumulative probability density is estimated by using the polynomial kernel function density estimation method; S3.2: Calculate the empirical distribution by using the hydrological situation value corresponding to the inverse cumulative probability density function as the upper and lower limits of hydrological situation changes.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method for dynamically determining the ecological flow threshold considering changes in hydrological conditions as described in any one of claims 1-4.

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