CN116681262A - Water resource optimal configuration method based on remote sensing data - Google Patents
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Abstract
The invention relates to a water resource optimal allocation method based on remote sensing data, and belongs to the technical field of water resource optimal allocation. Firstly, extracting ecological vegetation coverage conditions of a research area by using remote sensing image data; extracting vegetation transpiration data in a research area PML_V2, calculating a mean value of the vegetation transpiration data in the research area for many years, taking the obtained ecological vegetation spatial distribution condition as a frame, extracting a mean value of the vegetation transpiration data in a corresponding grid for many years, and obtaining a mean spatial distribution map of vegetation transpiration in the research area for many years; calculating the minimum ecological water requirement and the optimal ecological water requirement of the ecological vegetation in the research area according to the average spatial distribution map of the vegetation transpiration for years; constructing a water resource optimal configuration model: the method comprises the steps of solving an objective by an objective function and a condition constraint. The invention considers the space variability of ecological water demand more reasonably, not only can effectively reduce the investment of manpower and material resources, but also can realize the effective protection of ecological vegetation and improve the water resource management efficiency.
Description
Technical Field
The invention relates to a water resource optimal allocation method based on remote sensing data, and belongs to the technical field of water resource optimal allocation.
Background
The optimal allocation of water resources refers to scientific allocation of limited, different forms of water resources among users through engineering and non-engineering measures in a specific river basin or region according to effective, fair and sustainable principles. The optimal allocation of the water resources is the basis for realizing reasonable development and utilization of the water resources, and is the fundamental guarantee of sustainable utilization of the water resources. At present, a mathematical optimization model is an effective means for realizing water resource optimization configuration, and more mathematical algorithms are applied to the water resource optimization model, and uncertainty methods such as dynamic planning, interval planning, fuzzy mathematical planning and the like are widely applied. In the process of constructing the optimization model, some system parameters in the optimization model may be more complex, but most of parameters in the optimization model at present adopt a point-to-plane mode, and spatial variability of the parameters is not considered. As patent CN 112465206A discloses a water resource optimizing configuration and optimizing result evaluation feedback method, collecting monitoring data and regional population data; establishing a water resource bearing capacity index system; confirming index weight of the evaluation model, establishing a correlation function, and constructing a water resource bearing capacity evaluation model; the method comprises the steps of taking total water available for regional water resources as a core, taking maximized social and economic benefits and maximized unit system ecological benefits of each department as optimization targets, taking water available for each water source and water required by each department as constraint conditions, constructing a water resource optimal configuration model, and solving the model to obtain a regional water resource optimal configuration scheme. However, the patent does not consider the spatial variability of the ecological water demand, or use point values to calculate the model.
At present, with the development of remote sensing technology, space remote sensing technology is utilized to observe a large area with space-time continuity, continuous data can be acquired from remote sensing observation even if the number of observation sites is small, and the utilization of remote sensing technology and the development of a geographic information system enable more space information to be acquired.
Disclosure of Invention
The invention aims to provide a water resource optimal allocation method based on remote sensing data, aiming at areas with outstanding water resource supply and demand contradiction, the method can be combined with remote sensing basic monitoring data, and the scientific water resource optimal allocation can be realized by utilizing the actual evaporation of the ecological vegetation in an unmanned aerial vehicle or satellite remote sensing data observation research area and then calculating and acquiring the minimum and optimal ecological water demand in the research area.
The technical scheme adopted by the invention is as follows:
a water resource optimal configuration method based on remote sensing data comprises the following steps:
s1, extracting ecological vegetation coverage conditions of a research area by using remote sensing image data;
s2, extracting vegetation transpiration data in a research area PML_V2 by utilizing a PML_V2 land evapotranspiration and total primary productivity data set, calculating a multi-year vegetation transpiration data average value in the research area, and extracting a multi-year vegetation transpiration data average value in a corresponding grid by taking the ecological vegetation space distribution situation obtained in the step S1 as a frame to obtain a vegetation transpiration multi-year average spatial distribution map in the research area;
s3, calculating the minimum ecological water requirement and the optimal ecological water requirement of the ecological vegetation in the research area according to the average spatial distribution map of the vegetation transpiration for a plurality of years;
s4, constructing a water resource optimal configuration model: the method comprises the following steps of objective function and condition constraint, adopting 95% confidence interval numbers of annual water resource gazettes to represent the minimum and maximum water demand of agriculture, life and industry in a research area, and establishing a double-objective optimization model by combining the minimum ecological water demand and the optimum ecological water demand of each county in the research area estimated by remote sensing in the step S3:
1) The economic benefit is maximum
，
Wherein: f (F) 1 Is economic benefit, unit element; i is each county (i=1, 2, …) within the study area; j is water for each user, including industrial, agricultural, domestic and ecological water, unit m 3 ；B ij For i subareas j water use benefit coefficients, X ij The water supply amount of j water supply targets (users) in i subareas is represented by the number of intervals; wherein the lowest ecological water requirement and the most suitable ecological water requirement are the interval number (upper and lower limits) of the ecological water in the objective function;
2) Minimum water deficiency
，
Wherein: f (F) 2 For water shortage, the regional water shortage degree is used for expressing social benefit, W ijmax Maximum water demand for j water supply targets (users) in i subareas, X ij The water supply amount of j water supply targets (users) in i subareas is represented by the number of intervals;
converting the double-target model into a single-target model by adopting a weighted minimum deviation method to solve the double-target model;
constraints include a water availability limit, a water availability limit for each water user, and a non-negative constraint.
In the method, the ecological vegetation coverage in step S1 mainly extracts three ecological vegetation types including low coverage vegetation, high coverage vegetation and woodland, wherein the low coverage grassland mainly includes two types of attributes of 130 (grassland), 11 (herbaceous coverage), the high coverage grassland mainly includes four types of attributes of 120 (shrub), 121 (evergreen shrub), 122 (deciduous shrub), 12 (tree or shrub coverage (orchard)), and the woodland mainly includes 10 types of attributes of 51 (evergreen broadleaf forest), 52 (evergreen broadleaf forest), 61 (evergreen broadleaf forest), 62 (enclosed broadleaf forest), 71 (evergreen needle forest), 72 (evergreen needle forest), 81 (broadleaf needle forest), 82 (enclosed needle forest), 91 (evergreen mixed forest) and 92 (enclosed mixed forest). The attribute classification adopts the existing 30 m global fine earth surface coverage classification product in 2020 as a reference.
The pml_v2 land evaporation and total primary productivity dataset described in step S2 is downloaded from the national tibetan plateau data center. The calculation method of the average value of the annual vegetation transpiration data in the research area comprises the following steps: and extracting vegetation transpiration data in a study area PML_V2, overlapping Ec day by day in ArcGis software to obtain spatial distribution data of the vegetation transpiration of the study area for years, overlapping the spatial data of the vegetation transpiration of many years one by one, and then averaging to obtain an average value of the vegetation transpiration data of the study area for many years.
The minimum ecological water requirement and the optimum ecological water requirement in the step S3 are calculated according to the following formula:
，
，
i includes each county within the study area for each partition (i=1, 2, …); k is various ecological vegetation (k=1, 2, 3) including three ecological vegetation of low coverage vegetation and high coverage vegetation and woodland; ET (electric T) imin The lowest ecological water demand is i subareas; a is that ik Planting a quilt coverage area for the partition k of the i; ET (electric T) ikmin 10% quantiles of the transpiration quantity of k plants in the i partition are sequenced from small to large; ET (electric T) ikmax 90% quantile number for sorting transpiration amount of k plants in i subareas from small to large, ET imax Is the most suitable ecological water demand.
In the step S4, a weighted minimum deviation method is adopted to convert the double-target model into a single-target model for solving, and the specific formula is as follows:
，
wherein: f (F) 1 ，F 2 Respectively two objective functions in the optimization model; f (F) 1 max ，F 1 min ，F 2 max ，F 2 min Respectively the maximum value and the minimum value of the objective function; omega 1 ，ω 2 Respectively of two purposesThe weight of the scalar function, here taken as 0.5,0.5.
The constraint conditions in step S4 are specifically:
1) Limited water supply
，
Wherein: w (W) Public use Is commonly owned water, namely yellow water and river water; w (W) Independent and independent Is water unique to itself, namely surface water and underground water;
2) Water supply limit for each water user
，
Wherein: w (W) ijmin Minimum water demand for j water supply targets for i zones; w (W) ijmax The maximum water demand of j water supply targets is i subareas;
3) Non-negative constraints
。
The beneficial effects of the invention are as follows:
(1) And the remote sensing data is adopted to analyze the ecological water demand, so that the traditional artificial estimation method is replaced, and the accuracy of ecological water demand estimation is improved.
(2) The ecological water demand in the model is no longer in the form of point values, the minimum ecological water demand and the optimal ecological water demand of each subarea in the research area are no longer the same, and the space variability of the ecological water demand is more reasonably considered.
(3) Not only can the investment of manpower and material resources be effectively reduced, but also the effective protection of ecological vegetation can be realized, and the water resource management efficiency is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
Further description will be provided below in connection with specific embodiments and exemplary examples.
A water resource optimal configuration method based on remote sensing data comprises the following steps:
s1, extracting ecological vegetation coverage conditions of a research area by using remote sensing image data:
the dynamic monitoring product is covered on the fine ground surface 30 m by using the space information innovation institute of China academy of sciences to obtain vegetation coverage conditions of a research area, and the data are downloaded from the space information innovation institute of China academy of sciences. The data are processed in ArcGIS software, and the ecological vegetation coverage conditions of a research area are extracted through tools such as mask extraction, attribute extraction and the like, so that three ecological vegetation types including low-coverage vegetation, high-coverage vegetation and woodland are mainly extracted, wherein the low-coverage grassland mainly comprises two types of attributes 130 and 11, the high-coverage grassland mainly comprises four types of attributes 120, 121, 122 and 12, and the woodland mainly comprises 10 types of attributes 51, 52, 61, 62, 71, 72, 81, 82, 91 and 92.
S2, extracting vegetation transpiration data in a research area PML_V2 by utilizing a PML_V2 land evapotranspiration and total primary productivity data set, calculating a multi-year vegetation transpiration data average value in the research area, and extracting a multi-year vegetation transpiration data average value in a corresponding grid by taking the ecological vegetation space distribution situation obtained in the step S1 as a frame to obtain a vegetation transpiration multi-year average spatial distribution map in the research area:
and selecting proper remote sensing data, and further acquiring the spatial distribution condition of vegetation actual evaporation. The water-carbon coupled land evaporation and total primary productivity dataset of the selected area pml_v2, pml_v2 (China), includes 5 elements of total primary productivity (gross primary product, GPP), vegetation transpiration (vegetation transpiration, ec), soil evaporation (Es), canopy interception evaporation (vaporization of intercepted rainfall, ei) and water and ice and snow evaporation (et_water). The data format is TIFF, and the space-time resolution is 1 day, 500 meters. Compared with the global version, the simulation precision of the PML_V2 (China) product in China is greatly improved, and the data are downloaded from the national Qinghai-Tibet plateau data center.
And extracting vegetation transpiration data in the PML_V2 of the research area, overlapping Ec day by day in ArcGis software to obtain vegetation transpiration spatial distribution data of the research area for years, and averaging the vegetation transpiration data of the research area for years. And (3) taking the space distribution condition of the ecological vegetation as a frame, extracting the average value of vegetation transpiration data in the corresponding grid, and obtaining an average space distribution map of vegetation transpiration in the research area for years.
S3, calculating the minimum ecological water requirement and the optimal ecological water requirement of the ecological vegetation in the research area according to the average spatial distribution map of the vegetation transpiration for years:
the ArcGis software is adopted to extract the transpiration values of all grids in three ecological vegetation distribution areas in each county in a research area respectively, the values are ordered from small to large in Excel respectively, and the minimum ecological water demand and the optimal ecological water demand of each county are calculated according to the following calculation formula:
，
，
i is each county within each sector (i=1, 2, …) pack study area; k is various ecological vegetation (k=1, 2, 3) including three ecological vegetation of low coverage vegetation and high coverage vegetation and woodland; ET (electric T) imin The lowest ecological water demand is i subareas; a is that ik Planting a quilt coverage area for the partition k of the i; ET (electric T) ikmin 10% quantiles of the transpiration quantity of k plants in the i partition are sequenced from small to large; ET (electric T) ikmax 90% quantile number for sorting transpiration amount of k plants in i subareas from small to large, ET imax Is the most suitable ecological water demand.
S4, constructing a water resource optimization configuration model, wherein the water resource optimization configuration model comprises an objective function and a condition constraint:
(1) Objective function
In order to accurately reflect the ecological water demand conditions of different counties in a research area and maximize economic benefit, a water resource optimization configuration based on remote sensing estimation of ecological water demand is established, the minimum and maximum water demand of agriculture, life and industry in the research area is represented by 95% confidence interval numbers of the past year water resource gazette, and a double-target optimization model is established by combining the minimum and maximum ecological water demand of each county in the research area estimated by remote sensing:
1) The economic benefit is maximum
，
Wherein: f (F) 1 Is economic benefit and primary; i is each county (i=1, 2, …) within the study area; j is water for each user, including industrial, agricultural, domestic and ecological water, m 3 ；B ij The water benefit coefficient of j water users in i subareas; x is X ij The water supply amount of j water supply targets (users) in i subareas is represented by the number of intervals; wherein the lowest ecological water requirement and the most suitable ecological water requirement are the interval number (upper and lower limits) of the ecological water in the objective function;
2) Minimum water deficiency
，
Wherein: f (F) 2 For water shortage, the regional water shortage degree is used for expressing social benefit, W ijmax The maximum water demand of j water supply targets in the i subareas.
(2) The target model is constrained by the following conditions:
1) Limited water supply
，
Wherein: w (W) Public use Is commonly owned water, namely yellow water and river water; w (W) Independent and independent Is water unique to itself, namely surface water and underground water;
2) Water supply limit for each water user
，
Wherein: w (W) ijmin Minimum water demand for j water supply targets for i zones; w (W) ijmax The most suitable for j water supply targets in i subareasLarge water demand;
3) Non-negative constraints
。
The model solving method comprises the following steps: and converting the double-target model into a single-target model by adopting a weighted minimum deviation method to solve, wherein the specific formula is as follows:
，
wherein: f (F) 1 ，F 2 Respectively two objective functions in the optimization model; f (F) 1 max ，F 1 min ，F 2 max ，F 2 min Respectively the maximum value and the minimum value of the objective function; omega 1 ，ω 2 The weights of the two objective functions are taken here as 0.5, respectively.
Examples: taking the optimal allocation of the city water resource of the Weifang as an example, the optimal allocation of the city water resource of the Weifang is carried out.
Firstly, the land classification data of the Weifang city is downloaded, and tools such as reclassification, mask extraction and the like are used in ArcGis to obtain the spatial distribution condition of three ecological vegetation including low coverage vegetation, high coverage vegetation and woodland in the Weifang city.
And downloading the water-carbon coupled land evaporation distribution and total primary productivity data set of the PML_V2 of the Weifang city by adopting ArcGis software, respectively overlapping Ec day by day in ArcGis according to the period of the year to obtain the vegetation transpiration space distribution condition of each year of the Weifang city, overlapping a plurality of years of data layers in ArcGis, and obtaining an average value to obtain the space distribution data of the average value of the vegetation transpiration years of the Weifang city.
Continuously processing in ArcGis, respectively extracting the transpiration values of three ecological vegetation in each county, respectively sequencing the values from small to large, and calculating the minimum ecological water demand and the optimal ecological water demand of each county, wherein the calculation formula is as follows:
，
，
i is each subarea (i=1, 2, …, 9) comprising 9 counties of prefecture, qingzhou city, all cities, shou city, anchu city, high density city, changyi city, lin_264; k is various ecological vegetation (k=1, 2, 3) including three ecological vegetation of low coverage vegetation and high coverage vegetation and woodland; ET (electric T) imin The lowest ecological water demand is i subareas; a is that ik Planting a quilt coverage area for the partition k of the i; ET (electric T) ikmin 10% quantiles of the transpiration quantity of k plants in the i partition are sequenced from small to large; ET (electric T) ikmax 90% quantile number for sorting transpiration amount of k plants in i subareas from small to large, ET imax Is the most suitable ecological water demand.
The calculated minimum ecological water requirement and the optimal ecological water requirement of each county are brought into an optimization model, and the optimization configuration model is as follows:
(1) Objective function
In order to accurately reflect the ecological water demand conditions of different counties in a research area and maximize economic benefit, a water resource optimization configuration based on remote sensing estimation of ecological water demand is established, the minimum and maximum water demand of agriculture, life and industry in the research area is represented by 95% confidence interval numbers of the past year water resource gazette, and a double-target optimization model is established by combining the minimum and maximum ecological water demand of each county in the research area estimated by remote sensing:
1) The economic benefit is maximum
（1），
Wherein: f (F) 1 Is economic benefit and primary; i is nine sectorial counties (i=1, 2, …) of the district of interest, qingzhou city, zhugu city, shou city, anchu city, high density city, changyi city, lin_264; j is water for each user, including industrial, agricultural, domestic and ecological water, m 3 ；B ij Partitioning j for iWater benefit coefficient, X of water user ij Water supply amounts for j kinds of water supply targets are i-zone.
2) Minimum water deficiency
（2） ，
Wherein: f (F) 2 For water shortage, the regional water shortage degree is used for expressing social benefit, W ijmax The maximum water demand of j water supply targets in the i subareas.
(2) Model targets are constrained by:
1) Limited water supply
（3），
Wherein: w (W) Public use Is commonly owned water, namely yellow water and river water; w (W) Independent and independent Is water unique to itself, namely surface water and underground water;
2) Water supply limit for each water user
（4），
Wherein: w (W) ijmin Minimum water demand for j water supply targets for i zones; w (W) ijmax The maximum water demand of j water supply targets is i subareas;
3) Non-negative constraints
（5）。
And converting the double-target model into a single-target model by adopting a weighted minimum deviation method to solve, wherein the specific formula is as follows:
（6），
wherein: f (F) 1 ，F 2 Respectively two objective functions in the optimization model; f (F) 1 max ，F 1 min ，F 2 max ，F 2 min Respectively the maximum value and the minimum value of the objective function; omega 1 ，ω 2 The weights of the two objective functions are taken here as 0.5, respectively.
The minimum ecological water demand and the maximum ecological water demand estimated by remote sensing are taken as the upper limit and the lower limit of the ecological water demand of the city of the Weifang in the model, the solution is carried out in Lingo and Excel, and formulas (1) (2) (3) (4) (5) (6) are written and operated in the Lingo in the form of codes, so that the results are shown in the following table:
(unit: ten thousand m) 3 ）。
Claims (7)
1. The water resource optimizing configuration method based on remote sensing data is characterized by comprising the following steps:
s1, extracting ecological vegetation coverage conditions of a research area by using remote sensing image data;
s2, extracting vegetation transpiration data in a research area PML_V2 by utilizing a PML_V2 land evapotranspiration and total primary productivity data set, calculating a multi-year vegetation transpiration data average value in the research area, and extracting a multi-year vegetation transpiration data average value in a corresponding grid by taking the ecological vegetation space distribution situation obtained in the step S1 as a frame to obtain a vegetation transpiration multi-year average spatial distribution map in the research area;
s3, calculating the minimum ecological water requirement and the optimal ecological water requirement of the ecological vegetation in the research area according to the average spatial distribution map of the vegetation transpiration for a plurality of years;
s4, constructing a water resource optimal configuration model: the method comprises the following steps of objective function and condition constraint, adopting 95% confidence interval numbers of annual water resource gazettes to represent the minimum and maximum water demand of agriculture, life and industry in a research area, and establishing a double-objective optimization model by combining the minimum ecological water demand and the optimum ecological water demand of each county in the research area estimated by remote sensing in the step S3:
1) The economic benefit is maximum
，
Wherein: f (F) 1 Is economic benefit, unit element; i is each county (i=1, 2, …) within each study area; j is water for each user, including industrial, agricultural, domestic and ecological water, unit m 3 ；B ij For i subareas j water use benefit coefficients, X ij The water supply quantity of j water supply targets in i subareas is expressed by the number of intervals; wherein the minimum ecological water requirement and the most suitable ecological water requirement are the interval number of ecological water in the objective function;
2) Minimum water deficiency
，
Wherein: f (F) 2 For water shortage, the regional water shortage degree is used for expressing social benefit, W ijmax Maximum water demand for j water supply targets in i subareas, X ij The water supply quantity of j water supply targets in i subareas is expressed by the number of intervals;
converting the double-target model into a single-target model by adopting a weighted minimum deviation method to solve the double-target model;
constraints include a water availability limit, a water availability limit for each water user, and a non-negative constraint.
2. The water resource optimal allocation method based on remote sensing data according to claim 1, wherein the ecological vegetation coverage condition in the step S1 mainly extracts three ecological vegetation of low coverage vegetation, high coverage vegetation and woodland.
3. The method for optimizing water resource allocation based on remote sensing data according to claim 1, wherein the pml_v2 land evaporation and total primary productivity dataset of step S2 is downloaded from a national Qinghai-Tibet plateau data center.
4. The water resource optimal allocation method based on remote sensing data as claimed in claim 1, wherein the calculation method of the average value of the years vegetation transpiration data of the research area in step S2 is as follows: and extracting vegetation transpiration data in a study area PML-V2, overlapping Ec day by day in ArcGis software to obtain vegetation transpiration spatial distribution data of the study area for years, overlapping the spatial data for years one by one, and averaging to obtain an average value of vegetation transpiration data of the study area for years.
5. The water resource optimizing configuration method based on remote sensing data according to claim 1, wherein the minimum ecological water requirement and the optimum ecological water requirement in the step S3 are calculated as follows:
，
，
i is each county within each sector (i=1, 2, …) pack study area; k is various ecological vegetation (k=1, 2, 3) including three ecological vegetation of low coverage vegetation and high coverage vegetation and woodland; ET (electric T) imin The lowest ecological water demand is i subareas; a is that ik Planting a quilt coverage area for the partition k of the i; ET (electric T) ikmin 10% quantiles of the transpiration quantity of k plants in the i partition are sequenced from small to large; ET (electric T) ikmax 90% quantile number for sorting transpiration amount of k plants in i subareas from small to large, ET imax Is the most suitable ecological water demand.
6. The water resource optimization configuration method based on remote sensing data according to claim 1, wherein in step S4, a weighted minimum deviation method is adopted to convert a double-target model into a single-target model for solving, and the specific formula is as follows:
，
wherein: f (F) 1 ，F 2 Respectively two objective functions in the optimization model; f (F) 1 max ，F 1 min ，F 2 max ，F 2 min Respectively the maximum value and the minimum value of the objective function; omega 1 ，ω 2 The weights of the two objective functions are taken here as 0.5, respectively.
7. The water resource optimizing configuration method based on remote sensing data as claimed in claim 1, wherein the constraint condition in the step S4 is specifically:
1) Limited water supply
，
Wherein: w (W) Public use Is commonly owned water, namely yellow water and river water; w (W) Independent and independent Is water unique to itself, namely surface water and underground water;
2) Water supply limit for each water user
，
Wherein: w (W) ijmin Minimum water demand for j water supply targets for i zones; w (W) ijmax The maximum water demand of j water supply targets is i subareas;
3) Non-negative constraints
。
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