A quantitative method for the negative externalities of multi-objective operation of reservoir groups on water ecology

Abstract

The present invention discloses a quantitative method for the negative externalities caused by multi-objective dispatching of a reservoir group on water ecology, comprising the following steps: S1, flow data acquisition: obtaining the natural flow process of the reservoir and the flow process after the reservoir dispatching; S2, flow data processing: interpolation and replacement of missing data, abnormal values ​​and mutation points of the natural flow process and the flow process after the reservoir dispatching; S3, negative externality determination: based on the natural flow sequence and the flow sequence after the reservoir dispatching of the same time period and time step, the difference ratio of each data point is calculated, and the average difference ratio of the data point is calculated according to the sum of the difference ratios of the data points, and the average difference ratio of the data points is used as an evaluation index of the negative externalities of water ecology. The advantages are: it is possible to more conveniently evaluate the impact of multi-objective dispatching of a reservoir group on water ecology, better balance the conflict between water resource development and water ecology, and promote the sustainable development of the ecological environment.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir group ecological regulation, and in particular to a quantitative method for negative externalities of reservoir group multi-objective regulation on water ecology. Background Art

[0002] The multi-objective dispatching of a reservoir group comprehensively considers flood control, power generation, navigation, water supply and ecological needs. The realization of each goal often affects the realization of other goals. For example, for water ecology or aquatic fish, the natural flow process without human intervention is the best environment and the most conducive living environment for the reproduction of aquatic organisms. However, the goals of power generation, navigation, flood control, water supply and other goals of the reservoir group will change the natural runoff process to varying degrees, thereby affecting the water ecological environment. The traditional method of calculating the negative externalities of multiple goals for a certain goal requires calculating the values ​​of the negative externalities of different goals for a certain goal separately, and summing them up to obtain the final total negative externalities. However, for the multi-objective dispatching of a reservoir group, since there is still a coupling and mutual feedback relationship between multiple goals, the change of one goal will affect the realization of the benefits of multiple other goals. Therefore, it is difficult to calculate the negative externalities of a single goal to the water ecological goal. Summary of the invention

[0003] The purpose of the present invention is to provide a quantitative method for the negative externalities caused by multi-objective scheduling of a reservoir group on water ecology, thereby solving the above-mentioned problems existing in the prior art.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A quantitative method for the negative externalities of multi-objective operation of a reservoir group on water ecology includes the following steps:

[0006] S1. Traffic data acquisition:

[0007] Obtain the natural flow process of the reservoir and the flow process after reservoir regulation;

[0008] S2. Traffic data processing:

[0009] Interpolate and replace missing data, outliers and mutation points in natural flow processes and flow processes after reservoir regulation;

[0010] S3. Negative externalities determination:

[0011] Based on the natural flow series and the flow series after reservoir scheduling in the same time period and time step, the difference ratio of each data point is calculated, and the average difference ratio of the data points is calculated according to the sum of the difference ratios of the data points. The average difference ratio of the data points is used as an evaluation indicator of negative externalities of water ecology.

[0012] Preferably, step S3 specifically includes the following contents:

[0013] S31. Based on the natural flow process after data processing and the flow process after reservoir regulation, obtain the natural flow sequence and the flow sequence after reservoir regulation in the same time period and time step, which are recorded as Q_natural() and Q_current() respectively; each sequence has N data;

[0014] S32. For each data point, the absolute value of the difference between the natural flow and the flow after reservoir regulation is calculated, and then divided by the natural flow to obtain the difference ratio of each data point; the calculation formula is,

[0015]

[0016] Among them, R i is the difference ratio of the i-th data point, 1≤i≤N; Q natural,t is the natural flow data at time t; Q current,t is the flow data after reservoir regulation at time t;

[0017] S33, based on the difference ratios of all data points, the sum of the difference ratios of the data points is calculated, and then divided by the total number of data points to obtain the average difference ratio of the data points; the calculation formula is,

[0018]

[0019] Where R is the average difference ratio of the data points;

[0020] S34. The average difference ratio R of data points is used as an evaluation index of negative externalities of water ecology; R ranges from 0 to 1. The smaller the R value, the smaller the negative externalities of water ecology; conversely, the greater the negative externalities of water ecology.

[0021] Preferably, step S1 specifically includes the following contents:

[0022] S11. Obtain the natural flow process without human intervention by constructing a distributed hydrological model or using mathematical and statistical methods;

[0023] S12. Obtain the flow process after reservoir scheduling by means of historical data query and real-time data observation.

[0024] Preferably, in step S11, a distributed hydrological model is constructed to obtain a natural flow process without human intervention, which specifically includes the following contents:

[0025] S111, constructing a distributed hydrological model multi-factor integrated database module to provide a basis for the construction of the hydrological model, wherein the module includes a basic hydrological database and a water conservancy project database of the target basin;

[0026] The historical precipitation and temperature data of the target basin were obtained based on the China precipitation and temperature 0.25° grid data set; the meteorological data of the target basin were obtained based on the NCEP-FNL reanalysis data; the river network and surface elevation data of the target basin were obtained based on the HydroSHEDs data set; the soil texture parameters of the target basin were obtained based on the World Soil Database; the land use data of the target basin were obtained based on the very high resolution radiometer; and a variety of fusion algorithms were combined to scale the obtained data up and down to the same spatial and temporal resolution to build a basic hydrological database covering the target basin;

[0027] Collect basic data of water conservancy projects in the target basin, including basic engineering characteristic data of reservoirs, dispatching diagrams, and dispatching regulations, and construct a water conservancy project database for the target basin based on the data;

[0028] S112. Input the data from the basic hydrological database and water conservancy project database of the target basin into the distributed hydrological model to construct a distributed hydrological model of the target area; calibrate the confluence parameters of the distributed hydrological model of the target area based on the measured runoff of the hydrological station, and verify the flow data obtained by simulating the distributed hydrological model of the target area, requiring that the flow certainty coefficient of the simulated flow at the daily scale is greater than the preset threshold; finally, obtain the natural flow process without human intervention based on the calibrated distributed hydrological model of the target area.

[0029] Preferably, in step S111, the target area is monitored in real time and its historical data is integrated and transmitted to the database to achieve rolling update of the database.

[0030] Preferably, step S2 specifically includes the following contents:

[0031] S21. For the missing data in the flow process, the linear interpolation method is used to process it to ensure the continuity of the runoff data;

[0032] S22. For outliers and mutation points in flow process data, linear interpolation method is used to replace them to improve data rationality.

[0033] The beneficial effects of the present invention are as follows: 1. The method of the present invention is based on the basic judgment that the natural flow process without human intervention is the best aquatic habitat. It uses the degree of change in natural flow caused by multi-objective scheduling of a reservoir group as a quantitative measurement method for the negative externalities caused by multi-objective scheduling on water ecology, solving the problem that the negative externalities caused by multi-objective scheduling of a reservoir group on water ecology are difficult to quantify. 2. The method of the present invention can more conveniently evaluate the impact of multi-objective scheduling of a reservoir group on water ecology, can better balance the conflict between water resource development and water ecology, and promote the sustainable development of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of a quantification method in an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the natural flow and measured flow of a certain river basin in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] Embodiment 1

[0038] like Figure 1 As shown, in this embodiment, a method for quantifying the negative externalities caused by multi-objective scheduling of a reservoir group on the water ecology is provided, which is used to quantitatively evaluate the negative externalities caused by the multi-objective scheduling of the reservoir group on the water ecological environment. The method of the present invention overcomes the limitations existing in the prior art, that is, it is difficult to accurately evaluate the total negative impact of multiple objectives on the water ecology under the circumstances of multi-objective interaction and complex coupling. The present invention proposes a more accurate and reasonable way to evaluate the impact of multi-objective scheduling on the water ecological environment by quantifying the degree of change in the natural flow process caused by the scheduling of a reservoir group.

[0039] The method of the present invention is based on the basic judgment that the natural flow process without human intervention is the best aquatic habitat. First, the natural flow process without human intervention must be obtained. The natural flow must be calculated by building a hydrological model and other methods. Then, the flow process after the reservoir group is dispatched is collected, and the data is cleaned and processed. Finally, by comparing the natural flow and the flow after reservoir dispatch, the degree of change in the natural flow process caused by the reservoir group dispatch is quantitatively analyzed, and it is used as the negative externality of multi-objective dispatch on water ecology. The quantitative method specifically includes the following three parts:

[0040] 1. Traffic data acquisition:

[0041] Obtain the natural flow process of the reservoir and the flow process after the reservoir is dispatched. Specifically,

[0042] 1.1. Obtaining the natural flow process: Obtaining the natural flow process without human intervention by constructing a distributed hydrological model or using mathematical statistics methods;

[0043] 1.2. Obtain the flow process after reservoir scheduling: Obtain the flow process after reservoir scheduling through historical data query and real-time data observation.

[0044] 2. Traffic data processing:

[0045] Interpolation and replacement of missing data, outliers and mutation points in natural flow processes and flow processes after reservoir regulation. Specifically,

[0046] 2.1. Interpolation processing: For the missing data in the flow process, the linear interpolation method is used to ensure the continuity of the runoff data.

[0047] 2.2. Replacement processing: For abnormal values ​​(such as negative values) and mutation points in the flow process data, linear interpolation method is used to replace them to improve data rationality.

[0048] 3. Determination of negative externalities:

[0049] Based on the natural flow sequence and the flow sequence after reservoir regulation in the same time period and time step, the difference ratio of each data point is calculated, and the average difference ratio of the data point is calculated based on the sum of the difference ratios of the data points. The average difference ratio of the data points is used as an evaluation indicator of negative externalities of water ecology. Specifically,

[0050] 3.1. Based on the natural flow process after data processing and the flow process after reservoir regulation, obtain the natural flow sequence and the flow sequence after reservoir regulation in the same time period and time step, which are recorded as Q_natural() and Q_current() respectively; there are N data in each sequence;

[0051] 3.2. For each data point, calculate the absolute value of the difference between the natural flow and the flow after reservoir regulation, and then divide it by the natural flow to obtain the difference ratio of each data point; the calculation formula is,

[0052]

[0053] Among them, R i is the difference ratio of the i-th data point, 1≤i≤N; Q natural,t is the natural flow data at time t; Q current,t is the flow data after reservoir regulation at time t;

[0054] 3.3. Based on the difference ratios of all data points, the sum of the difference ratios of the data points is calculated, and then divided by the total number of data points to obtain the average difference ratio of the data points; the calculation formula is,

[0055]

[0056] Where R is the average difference ratio of the data points;

[0057] 3.4. The average difference ratio R of data points is used as an evaluation index of negative externalities of water ecology; R ranges from 0 to 1. The smaller the R value, the smaller the negative externalities of water ecology; conversely, the greater the negative externalities of water ecology.

[0058] Embodiment 2

[0059] In this embodiment, according to the steps described in the method, a certain river basin is selected as a case to evaluate the negative externalities of multi-objective scheduling of a reservoir group on water ecology.

[0060] 1. Traffic data acquisition:

[0061] 1.1. Process of obtaining natural traffic

[0062] (1) Construct a distributed hydrological model multi-factor comprehensive database module, including a basic hydrological database and a water conservancy project database, including:

[0063] a. Basic hydrological database: The historical precipitation and temperature data of the basin are obtained based on the China precipitation and temperature 0.25° grid data set; the meteorological data of the basin, such as air pressure, radiation, specific humidity, and wind speed, are obtained based on the NCEP-FNL reanalysis data; the river network and surface elevation (DEM) data of the basin are obtained based on the HydroSHEDs data set; the soil texture parameters of the basin are obtained based on the World Soil Database (HWSD); the land use data of the basin are obtained based on the Very High Resolution Radiometer (AVHRR); and a variety of fusion algorithms are combined to scale the above data up and down to the same spatiotemporal resolution to construct a basic hydrological database covering a certain basin.

[0064] b. Water conservancy project database: Collect the basic data of regional water conservancy projects to form a water conservancy project database. The basic data includes the basic engineering characteristic data of the reservoir (characteristic storage capacity, characteristic water level, water level storage capacity curve, discharge curve, etc.), dispatching diagram, dispatching regulations, and the water conservancy project database of the basin is constructed based on this, providing a basis for the construction of the hydrological model.

[0065] In this embodiment, real-time monitoring and acquisition and historical data are integrated in the target area and transmitted to the database to achieve rolling update of the database.

[0066] (2) Construct a distributed hydrological model of the basin, including:

[0067] a. Input data such as DEM, meteorological drivers, vegetation cover and soil texture into the distributed hydrological model to construct a distributed hydrological model for the area.

[0068] b. Calibrate the runoff parameters of the distributed hydrological model based on the measured runoff at the hydrological station.

[0069] c. Verify the flow data obtained by distributed hydrological model simulation. The flow certainty coefficient of daily scale simulation is required to be greater than 0.8.

[0070] d. Finally, the distributed hydrological model after calibration is used to obtain the natural flow process of the basin without human intervention.

[0071] 1.2. Obtaining the flow process after reservoir group dispatching

[0072] By querying historical data and observing real-time data, the observed flow data after reservoir scheduling can be obtained.

[0073] 2. Traffic data processing:

[0074] 2.1. Interpolation processing: For the missing data in the flow process data, the linear interpolation method is used to ensure the continuity of the runoff data.

[0075] 2.2. Interpolation processing: For the missing data in the flow process data, linear interpolation method is used to ensure the continuity of runoff data.

[0076] 3. Determination of negative externalities:

[0077] 3.1. Obtain the natural flow sequence and observed flow sequence of the same time period and time step, and record them as: Q_natural() and Q_current(), where each sequence has 21 data, and the data at time t are recorded as Q natural,t and Q current,t , natural flow and measured flow are shown in Figure 2 .

[0078] 3.2 For each data point, calculate the absolute value of the difference between the natural flow and the measured flow, and then divide it by the natural flow to obtain the difference ratio R i is 4.66.

[0079] 3.3. Calculate the sum of all difference ratios and divide it by the total number of data points (21) to get the average difference ratio R of 0.221.

[0080] 3.4. Taking R as an indicator for evaluating the negative externalities of water ecology, the negative externalities of water ecology is 0.221.

[0081] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0082] The present invention provides a quantitative method for the negative externalities caused by multi-objective scheduling of a reservoir group on water ecology. The method of the present invention is based on the basic judgment that the natural flow process without human intervention is the best aquatic habitat. The degree of change of the natural flow caused by multi-objective scheduling of a reservoir group is used as a quantitative measurement method for the negative externalities caused by multi-objective scheduling on water ecology, which solves the problem that the negative externalities caused by multi-objective scheduling of a reservoir group on water ecology are difficult to quantify. The method of the present invention can more conveniently evaluate the impact of multi-objective scheduling of a reservoir group on water ecology, can better balance the conflict between water resource development and water ecology, and promote the sustainable development of the ecological environment.

[0083] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A quantitative method for the negative externalities of multi-objective operation of a reservoir group on water ecology, characterized by: The following steps are included: S1. Traffic data acquisition: Obtain the natural flow process of the reservoir and the flow process after the reservoir is dispatched; Step S1 specifically includes the following contents: S11, by constructing a distributed hydrological model or a mathematical statistics method, obtaining a natural flow process without human intervention; Step S11 obtains a natural flow process without human intervention by constructing a distributed hydrological model, specifically including the following contents: S111, constructing a distributed hydrological model multi-factor integrated database module to provide a basis for the construction of the hydrological model, wherein the module includes a basic hydrological database and a water conservancy project database of the target basin; The historical precipitation and temperature data of the target basin were obtained based on the China precipitation and temperature 0.25° grid data set; the meteorological data of the target basin were obtained based on the NCEP-FNL reanalysis data; the river network and surface elevation data of the target basin were obtained based on the HydroSHEDs data set; the soil texture parameters of the target basin were obtained based on the World Soil Database; and the land use data of the target basin were obtained based on the very high resolution radiometer. Combine multiple fusion algorithms to upscale and downscale the acquired data to the same spatial and temporal resolution, and build a basic hydrological database covering the target basin; Collect basic data of water conservancy projects in the target basin, including basic engineering characteristic data of reservoirs, dispatching diagrams, and dispatching regulations, and construct a water conservancy project database for the target basin based on the data; S112, input the data in the basic hydrological database and water conservancy project database of the target basin into the distributed hydrological model to construct a distributed hydrological model of the target area; calibrate the confluence parameters of the distributed hydrological model of the target area based on the measured runoff of the hydrological station, and verify the flow data obtained by simulating the distributed hydrological model of the target area, requiring that the flow certainty coefficient of the simulated flow at the daily scale is greater than a preset threshold; finally, obtain the natural flow process without human intervention based on the calibrated distributed hydrological model of the target area; S12. Obtain the flow process after reservoir dispatch by means of historical data query and real-time data observation; S2. Traffic data processing: Interpolate and replace missing data, outliers and mutation points in natural flow processes and flow processes after reservoir regulation; S3. Negative externalities determination: Based on the natural flow series and the flow series after reservoir regulation in the same time period and time step, the difference ratio of each data point is calculated, and the average difference ratio of the data point is calculated according to the sum of the difference ratios of the data points. The average difference ratio of the data points is used as an evaluation indicator of negative externalities of water ecology. Step S3 specifically includes the following contents: S31. Based on the natural flow process after data processing and the flow process after reservoir regulation, obtain the natural flow sequence and the flow sequence after reservoir regulation in the same time period and time step, which are recorded as Q_natural() and Q_current() respectively; each sequence has N data; S32. For each data point, the absolute value of the difference between the natural flow and the flow after reservoir regulation is calculated, and then divided by the natural flow to obtain the difference ratio of each data point; the calculation formula is, Among them, R i is the difference ratio of the i-th data point, 1≤i≤N; Q natural,t is the natural flow data at time t; Q current,t is the flow data after reservoir regulation at time t; S33, based on the difference ratios of all data points, the sum of the difference ratios of the data points is calculated, and then divided by the total number of data points to obtain the average difference ratio of the data points; the calculation formula is, Where R is the average difference ratio of the data points; S34. The average difference ratio R of data points is used as an evaluation index of negative externalities of water ecology; R ranges from 0 to 1. The smaller the R value, the smaller the negative externalities of water ecology; conversely, the greater the negative externalities of water ecology.

2. The quantitative method for the negative externalities of multi-objective regulation of reservoir groups on water ecology according to claim 1 is characterized by: In step S111, the target area is monitored in real time and its historical data is integrated and transmitted to the database to achieve rolling update of the database.

3. The quantitative method for the negative externalities of multi-objective regulation of reservoir groups on water ecology according to claim 1 is characterized by: Step S2 specifically includes the following contents: S21. For the missing data in the flow process, the linear interpolation method is used to process it to ensure the continuity of the runoff data; S22. For outliers and mutation points in flow process data, linear interpolation method is used to replace them to improve data rationality.

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