Method for improving lake water ecological space resolution and precision

Abstract

The application discloses a method for improving lake water ecological space resolution and precision, and relates to the field of resource environment remote sensing; the method comprises the following steps: firstly, according to research and analysis problems, investigating water environment and water ecology indexes of water area and collecting historical data, determining focused key indexes of water environment and water ecology and corresponding associated indexes; secondly, performing spatial variation degree calculation on each index, dividing different levels according to index variation degree, and performing spatial grid unit division on each type of index according to water environment and water ecology index space-time precision requirements; finally, performing numerical simulation calculation on each index by using a model, so that different types of indexes in the numerical simulation calculation can well depict spatial pattern states and changes of corresponding elements of water environment and water ecology, and the numerical simulation calculation can greatly reduce system storage resource consumption and calculation amount, and further achieve intelligent management of water environment and water ecology numerical simulation research.

Description

Technical Field

[0001] This invention relates to the field of resource and environmental remote sensing, specifically a method for improving the spatial resolution and accuracy of lake water ecology. Background Technology

[0002] Aquatic environment and aquatic ecology are crucial foundations for the survival and development of human society and economy. Earth's surface waters provide the basic water sources, various aquatic products, and recreational spaces necessary for human production and daily life, while also providing habitats and breeding grounds for various aquatic organisms. Therefore, the aquatic environment and aquatic ecology have always been important aspects of human survival and development. People have devoted significant human, material, and financial resources to extensive computational work to understand the state, changes, and quality of the aquatic environment and aquatic ecology, providing a basis for the scientific and rational development and utilization of these resources.

[0003] On the other hand, with the improvement of human society's economic and technological levels, the requirements for understanding and managing the water environment and water ecology are also constantly increasing. Pollutant total quantity control has evolved from annual total quantity control to daily total quantity control, and the control area has expanded from large-scale watershed control to control of small watersheds, sub-watersheds, and control units. In particular, the current management model for water environment and water ecology governance emphasizes precise and efficient pollution control, which inevitably places higher demands on the accuracy of understanding the spatiotemporal changes in the water environment and water ecology, as well as the accuracy of the driving factors of these changes. For numerical simulations, the spatial resolution and accuracy of the models must also be continuously improved. This poses a huge challenge to the computational cost and computational workload of numerical simulations. How to improve spatial resolution while minimizing the rapid increase in computational cost has become an issue that cannot be ignored and urgently needs to be addressed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for improving the spatial resolution and accuracy of lake aquatic ecosystems, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides a method for improving the spatial resolution and accuracy of lake aquatic ecosystems. Addressing current requirements for water environment and aquatic ecosystem protection, management, and comprehensive governance, this method enables a more precise and systematic understanding and mastery of aquatic environment and aquatic ecosystem indicators. Given the spatiotemporal evolution characteristics and key driving factors, this method meets the need to further improve the spatial resolution, accuracy, and efficiency of numerical simulations of the water environment and aquatic ecosystems. Simultaneously, it reduces the spatial step size of the computational grid cells in the numerical simulation, thereby decreasing the overhead on computer cache resources and the rapid increase in computational load. This paper creatively proposes classifying water environment and water ecology indicators into different levels based on their spatial and temporal variability. According to the level of the indicator, its spatial variation is represented and described using a grid of appropriate scale. Specifically, indicators with small spatial variability are described using a grid with a relatively large spatial step size, while indicators with large spatial variability are described using a grid with a relatively small spatial step size. By significantly improving the simulation accuracy of indicators with large spatial variability, the overall accuracy of water environment and water ecology numerical simulation is improved, meeting the needs of water environment and water ecology protection, management, and comprehensive governance. This approach only slightly increases the computational cost and numerical simulation workload, thus effectively resolving the aforementioned contradictions. The method includes the following steps:

[0006] S1. Investigate water environment and water ecology indicators of the water area and collect historical data;

[0007] S2. Calculate the spatial and temporal variability of each indicator;

[0008] S3. Sort and classify the indicators according to their degree of variability;

[0009] S4. Based on the spatiotemporal accuracy requirements of water environment and water ecology indicators, spatial grid units are divided for various indicators.

[0010] S5. Perform numerical simulation calculations on each indicator;

[0011] S6. Analyze the numerical simulation results to verify the model.

[0012] In S1, the indicators include key indicators and related indicators. First, based on the research and analysis questions, key indicators for the aquatic environment and aquatic ecology (total phosphorus, total nitrogen, and chlorophyll a in eutrophic lakes) are identified. Second, related indicators that have an important role or influence on the key indicators are analyzed. These related indicators are retrieved from hydrodynamic indicators (waves, lake currents, water level, water temperature, and density), water quality indicators, sediment indicators (ammonia nitrogen, nitrate nitrogen, total nitrogen, dissolved total nitrogen, total nitrogen, whole phosphate phosphorus, total phosphorus, dissolved oxygen, permanganate index, total phosphorus and total nitrogen in sediments, and the content of phosphorus and nitrogen in interstitial water), and aquatic ecosystem indicators (phytoplankton biomass, zooplankton biomass, benthic biomass, and aquatic plant biomass). Finally, after identifying the key indicators and their related indicators, relevant data and historical data are collected, and the current values ​​of the indicators are observed.

[0013] Using the above technical solution, the key indicators to be collected, i.e. the corresponding related indicators, are determined based on the research and analysis problem, and detailed parameter information of these indicators is collected.

[0014] In S2, calculating the spatial and temporal variability of hydrodynamic, water environment, and water ecology indicators requires analyzing the spatial or temporal variability of key and related indicators. When performing spatial variability analysis, the indicators are first dimensionless. This is done by dividing the indicator value at each point in space by the maximum value of the indicator in space, reducing the dimensionless value to a range between 0 and 1. Then, the difference between the dimensionless indicators at different spatial points is calculated, and the absolute value of the ratio of this difference to the distance between any two points is calculated, forming a spatial variation dataset. Based on this, pairwise comparisons of the data in the dataset are used to identify the maximum, minimum, average, and median values ​​of the variation dataset. One or more linear combinations of these values ​​are then used to calculate the comprehensive variability of the indicators. The steps are as follows:

[0015] S201. Obtain the number and types of indicators of interest, and count the number of points with values, coordinates and specific values ​​of each indicator in the research and analysis space.

[0016] S202. Find the maximum, minimum and average values ​​of each indicator in the research and analysis space;

[0017] S203. Perform dimensionless processing on the indicator by dividing the indicator value at each point in space by the maximum value of the indicator in space, thus reducing the indicator to a dimensionless value between 0 and 1. The calculation formula is as follows:

[0018]

[0019] In the formula, Let j be the observed value of the j-th index at the i-th point in the analysis space. Let be the normalized value of the j-th indicator at the i-th point in the research and analysis space;

[0020] S204. Then, calculate the difference between the dimensionless index and different spatial points, and calculate the absolute value of the ratio of the index to the distance between the two points to form a spatial variation dataset. The calculation formula is as follows:

[0021]

[0022] In the formula, Let j be the normalized value of the j-th index at the i-th point in the analysis space. Let j be the normalized value of the j-th index at the k-th point in the analysis space. To study and analyze the distance between two points i and k in space;

[0023] S205. By comparing each pair of data in the dataset, identify the maximum, minimum, average, and median values ​​of the changing dataset. Calculate the spatial variability of the indicator using one or more linear combinations of these values, as shown in the following formula:

[0024]

[0025] In the formula, Let represent the spatial variability of the j-th indicator, where j is the indicator number or time number of the research and analysis focus, and i is the spatial point number where the indicator j has a value. Let j be the number of points with values ​​in the research and analysis space for the j-th indicator. To study and analyze the degree of variation of the j-th indicator of interest at the i-th spatial point;

[0026] S206. The calculation method for time variability is the same as that for spatial variability. To calculate time variability, parameters can be substituted into the spatial variability calculation expression to obtain the time variability.

[0027] Using the above technical solution, key indicators and related indicators are used as parameters, and the corresponding spatial and temporal variability is calculated through dimensionless methods, difference methods, and absolute values.

[0028] In S3, different indicators are divided into ranges according to their spatial variability values, classifying water environment and water ecology indicators into different levels: Level 1, Level 2, and Level 3. First, the variability of different indicators is arranged in ascending order. The indicators with the smallest and largest variability are identified. Then, the variability of these two indicators is substituted into the formula to calculate the endpoint values ​​of each level's interval. The formula is as follows:

[0029]

[0030]

[0031] In the formula, C represents a single interval value, and N represents the number of division levels. For maximum variability, R represents the minimum variability, and R is the variability interval. The range is for level 1 variability. The range is a level 2 variability interval. The range represents the Nth level of variability.

[0032] Using the above technical solution, all indicators are sorted and calculated to obtain the interval values ​​for each level. If a value falls within a certain interval, it is considered to be at the corresponding level.

[0033] In S4, the spatial grid unit division rule is as follows: the size of the cell is gradually reduced through continuous division. The downscaling grid spatial unit subdivision is further subdivided based on the upscaling grid spatial unit subdivision, ensuring that the downscaling grid unit does not cross different units of the upscaling grid. The spatial grid unit division method is as follows: based on the spatiotemporal accuracy requirements of water environment and water ecology indicators, the first-level indicators are divided into spatial grid units; after the division, the second-level indicators are divided into grid units based on the first-level indicator grid unit division and the spatiotemporal accuracy requirements of the second-level water environment and water ecology indicators; after the division, the third-level indicators are divided into grid units based on the second-level indicator grid unit division and the spatiotemporal accuracy requirements of the third-level water environment and water ecology indicators. The second-level grid unit division is directly based on the first-level grid unit division, and the third-level grid unit division is directly based on the second-level grid unit division. Each first-level grid unit contains the same number of second-level grid units, each second-level grid unit contains the same number of third-level grid units, and grid units of the same level are all the same size. The method of dividing from second-level grid units to third-level grid units is the same as the method of dividing second-level grid units based on first-level grid units.

[0034] The above technical solution enables the gradual subdivision of spatial grid units according to different levels of indicators, ensuring that the size of grid units at each level is the same.

[0035] In S5, different levels of indicators are coupled during the numerical simulation process. The calculation of index integration in upscaling grids, index integration in downscaling grids, and the conversion of downscaling grid index values ​​to upscaling grid values ​​are performed alternately. The calculation of the index value for the next time step is divided into two cases: 1. When using the current index value of an upscaling grid cell, the index value of the current scale grid can be used directly; 2. When using the current index value of a downscaling grid cell, the arithmetic mean of the current index values ​​of all downscaling grid cells contained in the upscaling grid cell containing the current-size grid cell is used. The numerical simulation calculation refers to using the existing Ecotaihu model to calculate the hydrodynamic, water quality, and aquatic ecological indicators of the lake. The calculation steps are as follows:

[0036] S501. Based on the monitoring and collection of observation data of water environment and water ecology indicators, generate the initial values, boundary conditions and external functions of the corresponding level network units of Class I and above indicators;

[0037] S502. First, calculate the highest level indicators of water environment and water ecology: take the indicator value of the low level grid point corresponding to the high level grid point as the indicator value of the high level grid point, and use the conventional single-scale grid point value to simulate and calculate to generate the indicator value of the next time step.

[0038] S503. After the next time step index value of all grid points of all high-level indicators is calculated, the low-level indicators are calculated: collect all high-level indicators contained in the low-level grid cells where the high-level indicators are located, and use mathematical average, geometric average and weighted average methods to calculate the current value and the next time step index value of the low-level indicators.

[0039] S504. After all low-level grid points have been calculated, determine whether all level indicators have been calculated. If the result is no, proceed to step S503. If the result is yes, proceed to step S505.

[0040] S505. Determine whether the set calculation time has been reached. If the result is no, proceed to step S502. If the result is yes, directly output the numerical simulation calculation result.

[0041] The Ecotaihu model is constructed for the water environment and aquatic ecology of Taihu Lake. This model includes hydrodynamic, water quality, and aquatic ecological indicators of Taihu Lake. The hydrodynamic model of the Ecotaihu model includes four indicators: water surface shift and three-dimensional flow velocity; the water quality model includes ten indicators: dissolved oxygen, suspended solids, permanganate index, total nitrogen, total phosphorus, ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, whole phosphate phosphorus, and chlorophyll a; the aquatic ecological model includes nine indicators: phytoplankton biomass, phytoplankton nitrogen, phytoplankton phosphorus, zooplankton biomass, zooplankton nitrogen, zooplankton phosphorus, aquatic plant biomass, aquatic plant nitrogen, and aquatic plant phosphorus.

[0042] The above technical solution utilizes the existing Ecotaihu model to achieve numerical simulation calculations of hydrodynamic, water quality, and aquatic ecological indicators.

[0043] In S6, a general model error and verification method is used to analyze the differences and accuracy between numerical simulation results and measured results, thereby verifying the model; a general numerical simulation method is used to analyze the effectiveness of numerical experimental scenario control schemes, and to answer questions about water environment and water ecology protection, regulation and comprehensive management.

[0044] By employing the above technical solutions and using common model error and verification methods, the differences and accuracy between numerical simulation results and measured results are analyzed, thereby verifying the accuracy of the model.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0046] 1. This invention classifies all indicators into levels and gradually refines the spatial grid units according to different indicator levels, thereby improving the spatial resolution of core indicators for water environment and water ecology management.

[0047] 2. This invention achieves collaborative numerical simulation calculation of multiple water environment and water ecology indicators under different spatial resolution grids by alternating calculations of different levels of indicators, thereby reducing the consumption of computing system storage resources and the amount of numerical simulation calculations. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0049] Figure 1 This is a flowchart illustrating a method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to the present invention.

[0050] Figure 2 This is a schematic diagram of grid-level division for a method of improving the spatial resolution and accuracy of lake water ecology according to the present invention. Detailed Implementation

[0051] 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 embodiments of the present invention, and not all embodiments. 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.

[0052] Please see Figure 1This invention provides a method to improve the spatial resolution and accuracy of lake aquatic ecosystems. Addressing current requirements for water environment and aquatic ecosystem protection, management, and comprehensive governance, it enables a more precise and systematic understanding and mastery of aquatic environment and aquatic ecosystem indicators. Given the spatiotemporal evolution characteristics and key driving factors, it meets the need to further improve the spatial resolution, accuracy, and efficiency of numerical simulations of the aquatic environment and aquatic ecosystems. Simultaneously, it reduces the spatial step size of the computational grid cells in the numerical simulation, thereby decreasing the overhead on computer cache resources and the rapid increase in computational load. This paper creatively proposes classifying water environment and water ecology indicators into different levels based on their spatial and temporal variability. According to the level of the indicator, its spatial variation is represented and described using a grid of appropriate scale. Specifically, indicators with small spatial variability are described using a grid with a relatively large spatial step size, while indicators with large spatial variability are described using a grid with a relatively small spatial step size. By significantly improving the simulation accuracy of indicators with large spatial variability, the overall accuracy of water environment and water ecology numerical simulation is improved, meeting the needs of water environment and water ecology protection, management, and comprehensive governance. This approach only slightly increases the computational cost and numerical simulation workload, thus effectively resolving the aforementioned contradictions. The method includes the following steps:

[0053] S1. Investigate water environment and water ecology indicators of the water area and collect historical data;

[0054] S2. Calculate the spatial and temporal variability of each indicator;

[0055] S3. Sort and classify the indicators according to their degree of variability;

[0056] S4. Based on the spatiotemporal accuracy requirements of water environment and water ecology indicators, spatial grid units are divided for various indicators.

[0057] S5. Perform numerical simulation calculations on each indicator;

[0058] S6. Analyze the numerical simulation results to verify the model.

[0059] In S1, the indicators include key indicators and related indicators. First, based on the research and analysis questions, key indicators focusing on the aquatic environment and aquatic ecology are identified (total phosphorus, total nitrogen, and chlorophyll a in eutrophic lakes). Second, related indicators that have an important role or influence on the key indicators are analyzed. These related indicators are retrieved from hydrodynamic indicators (waves, lake currents, water level, water temperature, and density), water quality indicators, sediment indicators (ammonia nitrogen, nitrate nitrogen, total nitrogen, dissolved total nitrogen, total nitrogen, whole phosphate phosphorus, total phosphorus, dissolved oxygen, permanganate index, total phosphorus and total nitrogen in sediments, and the content of phosphorus and nitrogen in interstitial water), and aquatic ecosystem indicators (phytoplankton biomass, zooplankton biomass, benthic biomass, and aquatic plant biomass). Finally, after identifying the key indicators and their related indicators, relevant data and historical data are collected, and the current values ​​of the indicators are observed.

[0060] Based on the research and analysis of the problem, identify the key indicators to be collected, namely the corresponding related indicators, and collect detailed parameter information for these indicators.

[0061] In S2, calculating the spatial and temporal variability of hydrodynamic, water environment, and water ecology indicators requires analyzing the spatial or temporal variability of key and related indicators. When performing spatial variability analysis, the indicators are first dimensionless. This is done by dividing the indicator value at each point in space by the maximum value of the indicator in space, reducing the dimensionless value to a range between 0 and 1. Then, the difference between the dimensionless indicators at different spatial points is calculated, and the absolute value of the ratio of this difference to the distance between any two points is calculated, forming a spatial variation dataset. Based on this, pairwise comparisons of the data in the dataset are used to identify the maximum, minimum, average, and median values ​​of the variation dataset. One or more linear combinations of these values ​​are then used to calculate the comprehensive variability of the indicators. The steps are as follows:

[0062] S201. Obtain the number and types of indicators of interest, and count the number of points with values, coordinates and specific values ​​of each indicator in the research and analysis space.

[0063] S202. Find the maximum, minimum and average values ​​of each indicator in the research and analysis space;

[0064] S203. Perform dimensionless processing on the indicator by dividing the indicator value at each point in space by the maximum value of the indicator in space, thus reducing the indicator to a dimensionless value between 0 and 1. The calculation formula is as follows:

[0065]

[0066] In the formula, Let j be the observed value of the j-th index at the i-th point in the analysis space. Let be the normalized value of the j-th indicator at the i-th point in the research and analysis space;

[0067] S204. Then, calculate the difference between the dimensionless index and different spatial points, and calculate the absolute value of the ratio of the index to the distance between the two points to form a spatial variation dataset. The calculation formula is as follows:

[0068]

[0069] In the formula, Let j be the normalized value of the j-th index at the i-th point in the analysis space. Let j be the normalized value of the j-th index at the k-th point in the analysis space. To study and analyze the distance between two points i and k in space;

[0070] S205. By comparing each pair of data in the dataset, identify the maximum, minimum, average, and median values ​​of the changing dataset. Calculate the spatial variability of the indicator using one or more linear combinations of these values, as shown in the following formula:

[0071]

[0072] In the formula, Let represent the spatial variability of the j-th indicator, where j is the indicator number or time number of the research and analysis focus, and i is the spatial point number where the indicator j has a value. Let j be the number of points with values ​​in the research and analysis space for the j-th indicator. To study and analyze the degree of variation of the j-th indicator of interest at the i-th spatial point;

[0073] S206. The calculation method for time variability is the same as that for spatial variability. To calculate time variability, parameters can be substituted into the spatial variability calculation expression to obtain the time variability.

[0074] Using key and related indicators as parameters, the corresponding spatial and temporal variability is calculated through dimensionless methods, difference methods, and absolute values.

[0075] In S3, different indicators are divided into ranges according to their spatial variability values, classifying water environment and water ecology indicators into different levels: Level 1, Level 2, and Level 3. First, the variability of different indicators is arranged in ascending order. The indicators with the smallest and largest variability are identified. Then, the variability of these two indicators is substituted into the formula to calculate the endpoint values ​​of each level's interval. The formula is as follows:

[0076]

[0077]

[0078] In the formula, C represents a single interval value, and N represents the number of division levels. For maximum variability, R represents the minimum variability, and R is the variability interval. The range is for level 1 variability. The range is a level 2 variability interval. The range represents the Nth level of variability.

[0079] All indicators are sorted and calculated to obtain the range values ​​for each level. If a value falls within a range, it is considered to be at the corresponding level.

[0080] In S4, please refer to Figure 2 The spatial grid unit division rule is as follows: the size of the cells is gradually reduced through continuous division. Downscaling of the spatial grid units is further subdivided based on upscaling of the spatial grid units, ensuring that downscaling units do not cross different units of the upscaling grid. The spatial grid unit division method is as follows: First-level indicators are divided into spatial grid units based on the spatiotemporal accuracy requirements of water environment and water ecology indicators. After this division, second-level indicators are further divided into grid units based on the first-level indicator grid units and the spatiotemporal accuracy requirements of second-level water environment and water ecology indicators. Third-level indicators are further divided into grid units based on the second-level indicator grid units and the spatiotemporal accuracy requirements of third-level water environment and water ecology indicators. Second-level grid unit division is directly based on first-level grid unit division, and third-level grid unit division is directly based on second-level grid unit division. Each first-level grid unit contains the same number of second-level grid units, and each second-level grid unit contains the same number of third-level grid units. Grid units of the same level are all the same size. The method for dividing from second-level to third-level grid units is the same as the method for dividing from first-level to second-level grid units.

[0081] The spatial grid cells are progressively subdivided according to different levels of indicators to ensure that the size of the grid cells at each level is the same.

[0082] In S5, different levels of indicators are coupled during the numerical simulation process. The calculation of index integration in upscaling grids, index integration in downscaling grids, and the conversion of downscaling grid index values ​​to upscaling grid values ​​are performed alternately. The calculation of the index value for the next time step is divided into two cases: 1. When using the current index value of an upscaling grid cell, the index value of the current scale grid can be used directly; 2. When using the current index value of a downscaling grid cell, the arithmetic mean of the current index values ​​of all downscaling grid cells contained in the upscaling grid cell containing the current-size grid cell is used. Numerical simulation calculation refers to using the existing Ecotaihu model to calculate the hydrodynamic, water quality, and aquatic ecological indicators of the lake. The calculation steps are as follows:

[0083] S501. Based on the monitoring and collection of observation data of water environment and water ecology indicators, generate the initial values, boundary conditions and external functions of the corresponding level network units of Class I and above indicators;

[0084] S502. First, calculate the highest level indicators of water environment and water ecology: take the indicator value of the low level grid point corresponding to the high level grid point as the indicator value of the high level grid point, and use the conventional single-scale grid point value to simulate and calculate to generate the indicator value of the next time step.

[0085] S503. After the next time step index value of all grid points of all high-level indicators is calculated, the low-level indicators are calculated: collect all high-level indicators contained in the low-level grid cells where the high-level indicators are located, and use mathematical average, geometric average and weighted average methods to calculate the current value and the next time step index value of the low-level indicators.

[0086] S504. After all low-level grid points have been calculated, determine whether all level indicators have been calculated. If the result is no, proceed to step S503. If the result is yes, proceed to step S505.

[0087] S505. Determine whether the set calculation time has been reached. If the result is no, proceed to step S502. If the result is yes, directly output the numerical simulation calculation result.

[0088] The Ecotaihu model is constructed for the water environment and aquatic ecology of Taihu Lake. This model includes hydrodynamic, water quality, and aquatic ecological indicators of Taihu Lake. The hydrodynamic model of the Ecotaihu model includes four indicators: water surface shift and three-dimensional flow velocity; the water quality model includes ten indicators: dissolved oxygen, suspended solids, permanganate index, total nitrogen, total phosphorus, ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, whole phosphate phosphorus, and chlorophyll a; the aquatic ecological model includes nine indicators: phytoplankton biomass, phytoplankton nitrogen, phytoplankton phosphorus, zooplankton biomass, zooplankton nitrogen, zooplankton phosphorus, aquatic plant biomass, aquatic plant nitrogen, and aquatic plant phosphorus.

[0089] The existing Ecotaihu model is used to perform numerical simulation calculations of hydrodynamic, water quality, and aquatic ecological indicators.

[0090] In S6, a general model error and verification method is used to analyze the differences and accuracy between numerical simulation results and measured results, thereby verifying the model; a general numerical simulation method is used to analyze the effectiveness of numerical experimental scenario control schemes, and to answer questions about water environment and water ecology protection, regulation and comprehensive management.

[0091] A general model error and verification method is used to analyze the differences and accuracy between numerical simulation results and experimental results, thereby verifying the accuracy of the model.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0093] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for improving the spatial resolution and accuracy of lake aquatic ecosystems, characterized in that... The method includes the following steps: S1. Investigate water environment and water ecology indicators of the water area and collect historical data; S2. Calculate the spatial and temporal variability of each indicator; S3. Sort and classify the indicators according to their degree of variability; S4. Based on the spatiotemporal accuracy requirements of water environment and water ecology indicators, spatial grid units are divided for various indicators. S5. Perform numerical simulation calculations on each indicator; S6. Analyze the numerical simulation results to verify the model; In S4, the spatial grid unit division method is as follows: First-level indicators are divided into spatial grid units based on the spatiotemporal accuracy requirements of water environment and water ecology indicators. After this division, second-level indicators are further divided into grid units based on the first-level indicator grid units and the spatiotemporal accuracy requirements of second-level water environment and water ecology indicators. Third-level indicators are further divided into grid units based on the second-level indicator grid units and the spatiotemporal accuracy requirements of third-level water environment and water ecology indicators. Second-level grid unit division is directly performed on top of the first-level grid units, and third-level grid unit division is performed directly on top of the second-level grid units. Each first-level grid unit contains the same number of second-level grid units, each second-level grid unit contains the same number of third-level grid units, and grid units of the same level are all the same size. The method for dividing from second-level grid units to third-level grid units is the same as the method for dividing from first-level grid units to second-level grid units. In S5, the numerical simulation calculation refers to using the existing Ecotaihu model to calculate the values ​​of lake hydrodynamic, water quality, and aquatic ecological indicators; the calculation steps are as follows: S501. Based on the monitoring and collection of observation data of water environment and water ecology indicators, generate the initial values, boundary conditions and external functions of the corresponding level network units of Class I and above indicators; S502. Calculate the highest level indicators for water environment and water ecology: Take the indicator value of the low level grid point corresponding to the high level grid point as the indicator value of the high level grid point, and use the conventional single-scale grid point value to perform simulation calculation to generate the indicator value for the next time step. S503. After the next time step index value of all grid points of all high-level indicators is calculated, the low-level indicators are calculated: collect all high-level indicators contained in the low-level grid cells where the high-level indicators are located, and use mathematical average, geometric average and weighted average methods to calculate the current value and the next time step index value of the low-level indicators. S504. After all low-level grid points have been calculated, determine whether all level indicators have been calculated. If the result is no, proceed to step S503. If the result is yes, proceed to step S505. S505. Determine whether the set calculation time has been reached. If the result is no, proceed to step S502. If the result is yes, directly output the numerical simulation calculation result.

2. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that: In S1, the indicators include key indicators and related indicators. First, based on the research and analysis questions, key indicators focusing on the water environment and water ecology are determined. Second, related indicators that have an important role or influence on the key indicators are analyzed. Related indicators are retrieved from hydrodynamic indicators, water quality indicators, sediment indicators, and water ecosystem indicators. Finally, after determining the key indicators and their related indicators, data on relevant indicators are collected, and the current status values ​​of the indicators are observed.

3. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that, In S2, the steps for calculating the spatial and temporal variability of each indicator are as follows: S201. Obtain the number and types of indicators of interest, and count the number of points with values, coordinates and specific values ​​of each indicator in the research and analysis space. S202. Find the maximum, minimum and average values ​​of each indicator in the research and analysis space; S203. Perform dimensionless processing on the indicator by dividing the indicator value at each point in space by the maximum value of the indicator in space, thus reducing the indicator to a dimensionless value between 0 and 1. The calculation formula is as follows: ； In the formula, Let j be the observed value of the j-th index at the i-th point in the analysis space. Let be the normalized value of the j-th indicator at the i-th point in the research and analysis space; S204. Then, calculate the difference between the dimensionless index and different spatial points, and calculate the absolute value of the ratio of the index to the distance between the two points to form a spatial variation dataset. The calculation formula is as follows: ； In the formula, Let j be the normalized value of the j-th index at the i-th point in the analysis space. Let j be the normalized value of the j-th index at the k-th point in the analysis space. To study and analyze the distance between two points i and k in space; S205. By comparing each pair of data in the dataset, identify the maximum, minimum, average, and median values ​​of the changing dataset. Calculate the spatial variability of the indicator using one or more linear combinations of these values, as shown in the following formula: ； In the formula, Let represent the spatial variability of the j-th indicator, where j is the indicator number or time number of the research and analysis focus, and i is the spatial point number where the indicator j has a value. Let j be the number of points with values ​​in the research and analysis space for the j-th indicator. To study and analyze the degree of variation of the j-th indicator of interest at the i-th spatial point; S206. The calculation method for time variability is the same as that for spatial variability. To calculate time variability, parameters can be substituted into the spatial variability calculation expression to obtain the time variability.

4. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that: In S3, all indicators are divided into two or more levels according to their spatial variability. Different grid scales are used when calculating the integral of the next state from the current state. The higher the indicator level, the smaller the grid scale is used.

5. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that: In S3, different indicators are divided into ranges according to their spatial variability values, classifying water environment and water ecology indicators into different levels: Level 1, Level 2, and Level 3. First, the variability of different indicators is arranged in ascending order. The indicators with the smallest and largest variability are identified. Then, the variability of these two indicators is substituted into the formula to calculate the endpoint values ​​of each level's interval. The formula is as follows: ； In the formula, C represents a single interval value, and N represents the number of division levels. For maximum variability, R represents the minimum variability, and R is the variability interval. The range is for level 1 variability. The range is a level 2 variability interval. The range represents the Nth level of variability.

6. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that, In S4, the rule for dividing spatial grid cells is as follows: the size of the cells is gradually reduced by continuously dividing the grid. The descaled grid spatial cell subdivision is further subdivided on the basis of the upscaled grid spatial cell subdivision to ensure that the descaled grid cells do not cross different cells of the upscaled grid.

7. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that: In S5, different levels of indices are coupled during the numerical simulation process. The calculation of index integration for upscaling grids, the calculation of index integration for downscaling grids, and the calculation of converting downscaling grid index values ​​to upscaling grid values ​​are performed alternately. The calculation of index values ​​for the next time step is divided into two cases: a. When using the current index value of upscaling grid cells, the index value of the current scale grid can be used directly; b. When using the current index value of downscaling grid cells, the arithmetic mean of the current index values ​​of all downscaling grid cells contained in the upscaling grid cell in which the current size grid cell is located is used.

8. The method for improving the spatial resolution and accuracy of lake aquatic ecosystems according to claim 1, characterized in that: In S6, a general model error and verification method is used to analyze the differences and accuracy between numerical simulation results and measured results, thereby verifying the model; a general numerical simulation method is used to analyze the effectiveness of numerical experimental scenario control schemes, and to answer questions on water environment and water ecology protection, regulation and comprehensive management.

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