A method for identifying driving factors of spatiotemporal evolution of total phosphorus concentration in a lake

Abstract

The application discloses a kind of lake total phosphorus concentration spatio-temporal evolution and driving factor identification method, and the application relates to water environment monitoring technical field, constructs water body, sediment and in and out lake river multi-medium linkage analysis system, carries out multi-scale spatio-temporal evolution analysis and multi-path mechanism quantization, realizes the accurate identification of scene main control driving factor, and the standardization analysis process is formed by verification solidification, the application has the advantages that by constructing water body, sediment and in and out lake river multi-medium linkage full-dimensional analysis system, simultaneously integrate multi-source spatio-temporal data covering full lake area function partition, complete hydrological year cycle, carry out the coupling analysis of multiple time scales and multiple spatial dimensions, break through the industry common defects that the evolution law of total phosphorus concentration is not comprehensive, data is insufficiently representative, and the whole chain variation characteristics cannot be clarified, caused by the fact that the prior art only focuses on single medium of water body, uses scattered point position low-frequency monitoring data.

Description

Technical Field

[0001] This invention relates to the field of water environment monitoring technology, specifically a method for identifying the spatiotemporal evolution of total phosphorus concentration in lakes and its driving factors. Background Technology

[0002] Lakes, as important carriers of freshwater resources and ecosystem units, play an irreplaceable role in watershed water supply, ecological conservation, climate regulation, and biodiversity protection. Total phosphorus (TP) is a key limiting nutrient controlling the eutrophication process of lakes. Its spatiotemporal distribution and dynamic evolution directly determine the water quality, algal proliferation trend, and health level of the aquatic ecosystem, making it a core indicator for surface water environmental quality assessment, eutrophication control, and ecological environmental protection in my country. With the continuous advancement of lake ecological environmental protection and governance in my country, accurately grasping the spatiotemporal distribution pattern and long-term evolution law of total phosphorus concentration in lakes, and scientifically identifying the key influencing factors driving changes in total phosphorus concentration, has significant theoretical and engineering application value for conducting lake pollution source tracing, nutrient load management, refined water environment governance, and ecological restoration plan formulation. Existing technologies for analyzing the spatiotemporal evolution of total phosphorus concentration (TP) in lakes and identifying driving factors mainly rely on routine water quality monitoring at fixed locations combined with basic statistical analysis methods to interpret the characteristics of TP concentration changes and identify relevant influencing factors. However, these technologies have certain limitations. First, existing technologies often focus only on TP data from a single water medium, generally using data from scattered locations and low-frequency monitoring, resulting in insufficient data representativeness and an inability to clarify the entire evolutionary pattern of TP. This leads to poor completeness and accuracy in analyzing the spatiotemporal evolution of TP. Second, existing technologies can only perform surface correlation statistics between driving factors and TP concentration, often employing a unified analysis model across the entire lake and all time periods. This results in inaccurate identification of driving factors and superficial analysis of influencing mechanisms, failing to provide effective support for targeted management of TP and eutrophication control in lakes. Therefore, we propose a method for analyzing the spatiotemporal evolution of TP concentration in lakes and identifying driving factors. Summary of the Invention

[0003] The purpose of this invention is to provide a method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes, the identification method comprising the following steps: Step 1: Delineate the spatiotemporal boundaries of the target lake analysis, spatially covering the functional zones of the entire lake area, temporally covering the continuous and complete hydrological annual cycle, and constructing a basic database that covers multiple media including water bodies, sediments, and rivers flowing into and out of the lake, integrating geospatial data, total phosphorus concentration, and environmental factors. Step 2: Perform multi-source heterogeneous data cleaning, missing value imputation, homogeneous calibration and standardization preprocessing on the basic database to eliminate data errors and dimensional differences, and obtain a standardized dataset that can be directly used for analysis. Step 3: Based on standardized datasets, analyze the evolution of total phosphorus concentration over multiple time scales (year, month, day) to clarify the linkage and response relationships of total phosphorus concentration sequences in multiple media. Step 4: Conduct spatial heterogeneity analysis of total phosphorus concentration in the entire lake area to clarify the spatial distribution pattern, aggregation characteristics and spatial diffusion pathways of total phosphorus concentration; Step 5: Delineate typical scene split datasets for spring, summer, autumn and winter, and use a geographic detector to conduct multi-factor coupled driving analysis for different scenes to identify the main driving factors and mechanisms in different spatiotemporal scenes; Step 6: Construct a multi-media interconnected structural equation model to quantify and analyze the multi-path influence mechanism of total phosphorus concentration changes and clarify the contribution ratio of each influence path; Step 7: Using field measurement data from independent and complete hydrological years that do not overlap with the aforementioned data, verify the accuracy of the identification results of this method. Based on the verification results, optimize the key parameters of the method, and finally fix the entire set of analysis steps to form a reusable and standardized analysis process.

[0005] As a further aspect of the present invention: In step one, the spatiotemporal boundaries of the analysis are clearly defined. The spatial boundary is the catchment area of ​​the target lake's watershed as the outer boundary. Combined with the lake's water function zoning, the distribution of inflow rivers, and land use types, the lake is divided into no less than 5 types of spatial units, including water conservation areas, urban built-up areas, agricultural planting areas, inflow river areas, and lake areas. The temporal boundary is defined as a time series of no less than 5 consecutive complete hydrological years, covering the complete cycle of the lake's high water period, normal water period, and low water period, and synchronously matching special periods such as extreme hydrological events and algal blooms. A multi-dimensional, multi-media basic database covering three major categories of data was constructed. The first category is basic geographic and spatial data, including lake basin DEM digital elevation data, land use type data, water system distribution data, and administrative division and functional zoning data. The second category is multi-media total phosphorus concentration data, including total phosphorus concentration data from in-situ monitoring of lakes, total phosphorus content data from sediment column samples, total phosphorus flux data from cross-sections of rivers flowing into and out of the lake, and total phosphorus flux data from atmospheric deposition. The temporal resolution of the data is no less than that of a month, and the spatial resolution covers all delineated spatial units of the lake area. The third category is environmental factor data, including hydrological and meteorological data, water quality and quantity data, aquatic biological data, and sediment phosphorus content data. All data are matched with the same spatiotemporal resolution as the total phosphorus data.

[0006] As a further aspect of the present invention: In step two, for the multi-dimensional, multi-media basic database constructed in step one, a full-process data calibration and preprocessing is carried out. Data cleaning is completed using logical verification and cross-validation methods to remove duplicate values ​​and logically contradictory values ​​from the data records. Outliers are identified and removed using the 3σ criterion, where values ​​exceeding the sample mean ± 3 times the standard deviation are judged as outliers. For missing values ​​in the data sequence, spatiotemporal coupling interpolation is used to complete the data. Spatially continuous missing data is completed using ordinary kriging interpolation, and temporally continuous missing data is completed using the seasonal autoregressive integral moving average (SARIMA) model to ensure that the data integrity is not less than 98%. Then, the minimum-maximum normalization method is used to standardize environmental factor data of different dimensions and magnitudes to eliminate the interference of dimensional differences on subsequent analysis results. After standardization, the value range of all data is limited to the [0,1] interval, completing the homogeneous calibration of multi-source data. All data are uniformly matched to the same spatiotemporal grid unit to form a standardized dataset that can be directly used for subsequent analysis.

[0007] As a further aspect of the present invention: In step three, based on the obtained standardized dataset, the temporal evolution of total phosphorus concentration at multiple time scales (year, month, day) is analyzed. First, the Mann-Kendall nonparametric trend test method is used to conduct a trend significance test on the total phosphorus concentration sequences of three types of media: water, sediment, and rivers flowing into and out of lakes, and the trend test statistic is calculated. The value is calculated using the following formula: ; in, The Mann-Kendall test statistic is used. For statistics The variance, when | When |>1.96, the sequence was determined to have passed the significance trend test at the 95% confidence level, clarifying the interannual rising / falling trend and significance level of total phosphorus concentration in different media. Then, the continuous wavelet transform method was used to analyze the main period and secondary period characteristics of the total phosphorus concentration sequence, clarifying the periodic fluctuation law of total phosphorus concentration change. Next, the Petitt mutation test method was used to identify the mutation time nodes of the total phosphorus concentration sequence, and the hydrological control events and extreme meteorological events corresponding to the mutation nodes were matched. Finally, the cross wavelet transform method was used to carry out the linkage response analysis of the total phosphorus concentration sequences of water bodies, sediments and rivers flowing into and out of the lake, clarifying the lag response time and correlation coefficient of total phosphorus concentration changes in different media, and fully clarifying the temporal evolution law of total phosphorus concentration in the whole chain.

[0008] As a further aspect of the present invention: In step four, based on the standardized dataset and the results of time evolution, the spatial heterogeneity and evolution characteristics of total phosphorus concentration in the entire lake area are analyzed. First, ordinary Kriging spatial interpolation is used to generate a continuous distribution raster layer of total phosphorus concentration with a spatial resolution of 10m×10m covering the entire lake area based on the measured data of total phosphorus concentration at each monitoring point. This achieves the conversion from discrete point data to continuous spatial surface data, fully presenting the spatial distribution pattern of total phosphorus concentration. Subsequently, global Moran's I spatial autocorrelation analysis is used to examine the spatial clustering characteristics of total phosphorus concentration in the entire lake area. The global Moran's I index calculation formula is: ; in, This represents the total number of spatial units in the lake area. This is a spatial weight matrix constructed based on the Rook adjacency rule. , They are spatial units , Total phosphorus concentration value, The total phosphorus concentration is the average value for the entire lake area. Moran's I index ranges from [-1, 1]. When the total phosphorus concentration is greater than 0 and passes the significance test, it is determined that the total phosphorus concentration exhibits a significant spatial clustering distribution characteristic. Then, the Getis-OrdGi* hotspot analysis method is used to identify high-value hotspots and low-value coldspots of total phosphorus concentration in the entire lake area, clarifying the spatial distribution range and area proportion of high-pollution-risk areas. Finally, combined with the functional zoning of the lake area delineated in step one, the mean, extreme values, and exceedance rates of total phosphorus concentration in different functional zones are statistically analyzed. The interannual and seasonal evolution differences of total phosphorus concentration in different spatial units are analyzed, and the spatial correspondence between pollution input from rivers flowing into the lake and high-value areas of total phosphorus in the lake area is matched to clarify the spatial diffusion path of total phosphorus concentration.

[0009] As a further aspect of the present invention: In step five, multi-factor coupled driving analysis and main control factor identification are carried out in different scenarios. First, typical analysis scenarios for the target lake in spring, summer, autumn, and winter are defined, including four categories: rainy season, dry season, wind and wave disturbance period, and algal bloom period. The rainy season is defined as consecutive months in which the monthly rainfall accounts for 15% or more of the annual rainfall; the dry season is defined as consecutive months in which the monthly rainfall accounts for 3% or less of the annual rainfall; the wind and wave disturbance period is defined as a period of ≥3 consecutive days with a daily average wind speed ≥5m / s; and the algal bloom period is defined as a period of ≥5 consecutive days with a chlorophyll a concentration ≥20μg / L in the water body. The standardized dataset is then split according to the defined scenarios to form independent analysis datasets corresponding to each scenario. A geographic detector model is used to detect driving factors for each scenario's dataset and calculate the driving force of each driving factor. Statistic, The formula for calculating the statistic is: ; in, To determine the number of strata for the driving factors, the natural breakpoint method was used to divide the driving factors into 5 strata. For layer Sample size The total sample size for the study area. For layer The variance of total phosphorus concentration The variance of total phosphorus concentration in the total sample of the study area. The statistic ranges from [0,1]. A two-factor interaction probe is then performed to calculate the interaction result of the two driving factors. Values, comparing before and after the interaction Changes in values ​​clarify the type of synergistic enhancement, nonlinear enhancement, or independent coupling effects among factors, ultimately according to... The driving factors are sorted from largest to smallest, and the top 3 controlling driving factors in each scenario are identified to clarify the core driving factors of total phosphorus concentration changes in different spatiotemporal scenarios.

[0010] As a further aspect of the present invention: In step six, based on the spatiotemporal evolution results obtained in steps three and four, and the key driving factors obtained in step five, a structural equation model (SEM) involving multiple media such as water, sediment, and inflow / outflow rivers is constructed to quantify and analyze the multi-path influence mechanism of total phosphorus concentration changes. First, based on Pearson correlation analysis, sediment total phosphorus content, inflow total phosphorus flux, outflow total phosphorus flux, hydrological and meteorological factors, and bottom sediment environmental factors that are significantly correlated with total phosphorus concentration in the water (P < 0.05) are screened. Variables without significant correlation are eliminated, forming the latent variable and observed variable system of the initial model. The latent variables include external inputs. The study considered endogenous release and hydrological driving factors, with observed variables being measured indices of the corresponding latent variables. An initial structural equation model was then constructed using the maximum likelihood estimation method. The model was tested and optimized using fitness indices, including the chi-square degree-of-freedom ratio (χ² / df), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), and root mean square error (RMSEA). A good model fitness was considered achieved when the chi-square degree-of-freedom ratio / df < 3, GFI > 0.9, AGFI > 0.85, and RMSEA < 0.08. Finally, based on the optimized structural equation model, the direct, indirect, and total effects of each latent variable on the total phosphorus concentration in the water body were quantitatively calculated.

[0011] As a further aspect of the present invention: In step seven, in-situ monitoring data of the entire lake area from an independent typical hydrological year that does not overlap with the time boundary defined in step one is selected as a verification dataset to verify the accuracy and stability of the method. First, the verification dataset is substituted into the full-process analysis steps of the method to output the spatiotemporal evolution law of total phosphorus and the identification results of the main driving factors under different scenarios in the typical year. The identified main driving factor change sequence is fitted with the measured total phosphorus concentration change sequence, and the goodness of fit R² and root mean square error are calculated. ,in The calculation formula is as follows: ; in, To verify the sample size, This is the measured value of total phosphorus concentration. The total phosphorus concentration prediction is obtained based on the fitting of the main control factor, when R² ≥ 0.85 and When the concentration is ≤0.02mg / L, the identification result of this method is considered accurate and reliable. If the threshold is not reached, return to steps two to six to optimize the data preprocessing parameters, the number of model layers, and the scene division threshold until the method meets the accuracy requirements. Finally, solidify the optimized full-process analysis steps into a standardized analysis process, clarify the execution standards, parameter thresholds, model selection specifications, and output requirements for each step, and form a standardized method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes that is traceable, reusable, and scalable.

[0012] Compared with the prior art, the beneficial effects of the present invention by adopting the above technical solution are as follows: 1. This invention constructs a multi-media, multi-dimensional analysis system linking water bodies, sediments, and rivers flowing into and out of the lake. It simultaneously integrates multi-source spatiotemporal data covering the functional zones of the entire lake area and the complete annual hydrological cycle, and conducts coupled analysis at multiple time scales and spatial dimensions. This overcomes the common industry defects of existing technologies that only focus on a single medium, water bodies, and use scattered low-frequency monitoring data, resulting in incomplete analysis of the evolution of total phosphorus concentration, insufficient data representativeness, and inability to clarify the characteristics of changes throughout the entire chain. This invention fully clarifies the evolution of total phosphorus from external input to internal release, accurately grasps the temporal fluctuation characteristics, spatial heterogeneity distribution, and diffusion path of total phosphorus concentration, and lays a comprehensive and reliable technical foundation for the source tracing and driving mechanism analysis of total phosphorus pollution, significantly improving the completeness and accuracy of the analysis of the evolution of total phosphorus. 2. This invention constructs a progressive multi-path influence mechanism quantification and scenario-specific driver factor identification system. First, it clarifies the core influence path of total phosphorus concentration change through a multi-media linkage structural equation model. Then, it conducts multi-factor coupling driver analysis and main control factor identification for different typical scenarios. This overcomes the core defects of existing technologies, which can only conduct surface correlation statistics, uniform analysis of the entire lake and all time periods, resulting in inaccurate identification of driving factors, superficial analysis of influence mechanisms, and analysis results that cannot be adapted to targeted governance needs. It accurately identifies the core driving factors in different spatiotemporal scenarios, clarifies the multi-factor coupling effect, and forms a reusable and scalable standardized analysis process. The analysis results can directly provide accurate scientific support for targeted control of total phosphorus and eutrophication management in lakes, greatly improving the engineering practicality and application value of the method. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the method steps in an embodiment of the present invention. Detailed Implementation

[0014] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0015] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0016] Please see the appendix Figure 1 This invention discloses a method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes, characterized in that the identification method includes the following steps: Step 1: Delineate the spatiotemporal boundaries of the target lake analysis, spatially covering the functional zones of the entire lake area, temporally covering the continuous and complete hydrological annual cycle, and constructing a basic database that covers multiple media including water bodies, sediments, and rivers flowing into and out of the lake, integrating geospatial data, total phosphorus concentration, and environmental factors. Step 2: Perform multi-source heterogeneous data cleaning, missing value imputation, homogeneous calibration and standardization preprocessing on the basic database to eliminate data errors and dimensional differences, and obtain a standardized dataset that can be directly used for analysis. Step 3: Based on standardized datasets, analyze the evolution of total phosphorus concentration over multiple time scales (year, month, day) to clarify the linkage and response relationships of total phosphorus concentration sequences in multiple media. Step 4: Conduct spatial heterogeneity analysis of total phosphorus concentration in the entire lake area to clarify the spatial distribution pattern, aggregation characteristics and spatial diffusion pathways of total phosphorus concentration; Step 5: Delineate typical scene split datasets for spring, summer, autumn and winter, and use a geographic detector to conduct multi-factor coupled driving analysis for different scenes to identify the main driving factors and mechanisms in different spatiotemporal scenes; Step 6: Construct a multi-media interconnected structural equation model to quantify and analyze the multi-path influence mechanism of total phosphorus concentration changes and clarify the contribution ratio of each influence path; Step 7: Using field measurement data from independent and complete hydrological years that do not overlap with the aforementioned data, verify the accuracy of the identification results of this method. Based on the verification results, optimize the key parameters of the method, and finally fix the entire set of analysis steps to form a reusable and standardized analysis process.

[0017] Example 1 This embodiment takes a typical shallow lake in the eastern plains of my country as the research object, and carries out the spatiotemporal evolution of total phosphorus concentration in the lake and the identification of driving factors. The specific implementation steps are as follows: Step 1: Delineate the spatiotemporal boundaries of the target lake analysis. The spatial boundary is the outer boundary of the entire watershed catchment area of ​​the target lake. Combining the lake's water function zoning, the distribution of inflow rivers, water depth gradient, and ecological function zoning, the lake is divided into five spatial units: water source conservation area, urban built-up area, agricultural planting area, inflow river area, and lake body area. A total of 32 in-situ monitoring points are set up throughout the lake to achieve full spatial coverage of the entire lake area. The time boundary is defined as five consecutive complete hydrological years, covering the complete cycle of the target lake's high water period, normal water period, and low water period, and simultaneously matching special periods such as extreme floods in the basin and algal blooms in the lake area; Based on this, a multi-dimensional and multi-media basic database of the target lake is constructed. The first category is basic geographic and spatial data, including 30m resolution DEM digital elevation data of the lake basin, 30m resolution land use type data, water system distribution data, administrative division and water function zoning data. The second category is multi-media total phosphorus concentration data, including monthly in-situ monitoring data of total phosphorus in water bodies at 32 locations, total phosphorus content data of quarterly sediment column samples at 16 locations, and daily total phosphorus flux data of major rivers flowing into and out of the lake. The temporal resolution of the data is no less than that of the month, and the spatial resolution covers all designated lake area spatial units. The third category is environmental factor data, including daily hydro-meteorological data (rainfall, wind speed, water level, water temperature, sunshine duration), annual watershed pollution load data (total point source emissions, agricultural non-point source pollution output), monthly aquatic biological data (chlorophyll a concentration, phytoplankton biomass, submerged plant coverage), and quarterly sediment environmental data (sediment redox potential, organic matter content). All data are matched with the same monthly spatiotemporal resolution as the total phosphorus data, achieving precise alignment of the spatiotemporal dimensions of multi-source data.

[0018] Step 2: For the constructed target lake basic database, conduct full-process data calibration and preprocessing. First, use logical verification and cross-validation to clean the data, removing duplicate and logically contradictory values ​​from the data records. Use the 3σ criterion to identify and remove outliers. In this embodiment, a total of 12 sets of outlier data were removed, with a data removal rate of less than 0.5%. For missing values ​​in the data sequence, a spatiotemporal coupling interpolation method is used to complete them. Spatially continuous missing data is completed using ordinary kriging interpolation, while temporally continuous missing data is completed using the seasonal autoregressive integral moving average (SARIMA) model. After processing, the data completeness reaches 99.2%, which meets the requirement of not less than 98%. Subsequently, the minimum-maximum normalization method was used to standardize the environmental factor data of different dimensions and magnitudes to eliminate the interference of dimensional differences on the subsequent analysis results. After standardization, the value range of all data was limited to the interval [0,1]. Finally, the homogeneity calibration of the multi-source data was completed, and all data were uniformly matched to a 10m×10m spatiotemporal grid cell to form a standardized dataset that can be directly used for subsequent analysis.

[0019] Step 3: Based on the standardized dataset, analyze the temporal evolution of total phosphorus concentration in the target lake at multiple time scales (year, month, day). First, use the Mann-Kendall nonparametric trend test to conduct a trend significance test on the total phosphorus concentration sequences of three media: water, sediment, and rivers flowing into and out of the lake. The calculated trend test statistic Z-value for the total phosphorus concentration sequence of the target lake is -2.37, |Z|>1.96. After passing the significance test at the 95% confidence level, it is determined that the total phosphorus concentration of the target lake showed a significant decreasing trend during the study period. The Z-value for the total phosphorus content sequence of sediment is -1.82, showing a non-significant decreasing trend, and the Z-value for the total phosphorus flux sequence of rivers flowing into the lake is -2.74, showing a highly significant decreasing trend. Step 4: Subsequently, the continuous wavelet transform method was used to analyze and obtain the total phosphorus concentration sequence of the target lake. The main period was 12 months and the secondary period was 6 months, showing a significant seasonal periodic fluctuation pattern. Then, the Pettitt mutation test was used to identify the mutation time nodes of the total phosphorus concentration sequence of the target lake, which highly matched the time of extreme flood events in the basin. Finally, the cross-wavelet transform method was used to conduct a linkage response analysis of the total phosphorus concentration sequence in multiple media. It was found that the lag response time of the change in total phosphorus flux in the inflow river to the total phosphorus concentration in the water body was 15 days, and the lag response time of the change in total phosphorus content in the sediment to the total phosphorus concentration in the water body was 45 days. The correlation coefficients between the two and the total phosphorus concentration in the water body were 0.72 and 0.58, respectively, and both passed the significance test at the 95% confidence level. The temporal evolution law of the total phosphorus concentration in the target lake in the whole chain was fully clarified.

[0020] Based on the standardized dataset and the results of time evolution, the spatial heterogeneity and evolution characteristics of total phosphorus concentration in the entire lake area of ​​the target lake were analyzed. First, ordinary kriging spatial interpolation was used to generate a 10m×10m spatial resolution raster layer of total phosphorus concentration covering the entire lake area based on the measured data of total phosphorus concentration from 32 monitoring points. This achieved the conversion from discrete point data to continuous spatial surface data and fully presented the spatial distribution pattern of total phosphorus concentration in the target lake. Subsequently, global Moran's I spatial autocorrelation analysis was used to test the global Moran's I index of the total phosphorus concentration in the target lake, which was found to be 0.68 (P < 0.01). After a significance test at the 99% confidence level, it was determined that the total phosphorus concentration in the target lake exhibited a significant spatial clustering distribution. Using the Getis-OrdGi* hotspot analysis method, it was found that the high-value hotspots of total phosphorus concentration in the target lake are mainly distributed in the river estuary area, and the high-pollution-risk area accounts for 27.3% of the total lake area. The low-value cold spots are mainly distributed in the central lake area and the ecological conservation area. Finally, combining the delineated functional zones of the lake area, the statistics showed that the average total phosphorus concentration in the river estuary area was the highest, exceeding the Class III standard limit of the "Surface Water Environmental Quality Standard" by 1.3 times, while the average total phosphorus concentration in the ecological conservation area was the lowest. The analysis showed that the annual decrease in total phosphorus concentration in the river estuary area was significantly higher than that in the lake center area. There was a significant spatial correspondence between the pollution input from the rivers flowing into the lake and the high-value area of ​​total phosphorus in the lake area, clarifying the spatial diffusion path of total phosphorus concentration in the target lake.

[0021] Step 5: Based on the results of the influence path analysis, conduct multi-factor coupling-driven analysis and main control factor identification for the target lake in different scenarios. First, delineate four typical analysis scenarios for the target lake in spring, summer, autumn and winter. The four seasons include rainy season, dry season, wind and wave disturbance period and algal bloom period. Complete the scenario delineation according to the scenario division rules, and split the standardized dataset according to the delineated scenarios to form independent analysis datasets corresponding to each scenario. Using a geographic detector model, driving factor detection was carried out on the dataset of each scene, the driving force q statistics of each driving factor were calculated, and then two-factor interaction detection was carried out to clarify the coupling effect between factors. The results showed that the q value of the interaction between total phosphorus flux entering the lake and rainfall reached 0.82, showing a significant synergistic enhancement effect. Finally, the driving factors were sorted from largest to smallest according to their q values, and the top three controlling driving factors in each scenario were identified. The controlling driving factors during the rainy season were total phosphorus flux into the lake, rainfall, and non-point source pollution output. The controlling driving factors during the dry season were total phosphorus content in sediments, water level, and water temperature. The controlling driving factors during the wind and wave disturbance period were wind speed, total phosphorus content in sediments, and water depth. The controlling driving factors during the algal bloom period were water temperature, chlorophyll a concentration, and total phosphorus flux into the lake. The core driving factors of the change in total phosphorus concentration in the target lake under different spatiotemporal scenarios were clarified.

[0022] Step Six: Based on the spatiotemporal evolution results, construct a multi-media structural equation model (SEM) for the target lake to quantify and analyze the multi-path influence mechanism of total phosphorus concentration changes. First, based on Pearson correlation analysis, select 12 indicators that are significantly correlated with the total phosphorus concentration of the target lake (P < 0.05), and remove variables with no significant correlation to form the latent and observed variable system of the initial model. The latent variables include three categories: external input, internal release, and hydrological driving. The observed variables corresponding to external input are total phosphorus flux entering the lake, non-point source pollution output, and total point source emissions. The observed variables corresponding to internal release are total phosphorus content in sediments, redox potential in sediments, and organic matter content. The observed variables corresponding to hydrological driving are rainfall, water level, wind speed, and water temperature. Subsequently, an initial structural equation model was constructed based on the maximum likelihood estimation method. The model was then tested and optimized using the fitness index. After optimization, the chi-square degrees of freedom ratio χ² / df = 2.17, GFI = 0.942, AGFI = 0.896, and RMSEA = 0.062, all of which met the criteria for good fitness. Finally, based on the optimized structural equation model, the total effect of exogenous input on the total phosphorus concentration of the target lake was calculated to be 0.67, the total effect of endogenous release was 0.42, and the total effect of hydrological driving was 0.35. This clarifies that exogenous input is the core pathway affecting the change of total phosphorus concentration in the target lake, and elucidates the entire chain of influence pathway from exogenous input of rivers flowing into the lake to accumulation of total phosphorus in the water body, endogenous release from sediments, and secondary increase of total phosphorus in the water body.

[0023] Step 7: Select in-situ monitoring data of the entire lake area of ​​the target lake in an independent typical hydrological year that does not overlap with the time boundary as a validation dataset to verify the accuracy and stability of this method. Substitute the validation dataset into the full-process analysis steps of this method to output the spatiotemporal evolution law of total phosphorus and the identification results of the main driving factors under different scenarios in the typical year. Fit the identified main factor change sequence with the measured total phosphorus concentration change sequence and calculate the goodness of fit R²=0.912 and the root mean square error RMSE=0.012mg / L. It meets the judgment threshold of R²≥0.85 and RMSE≤0.02mg / L, and the identification results of this method are determined to be accurate and reliable. Ultimately, the optimized full-process analysis steps will be solidified into a standardized analysis process, clarifying the execution standards, parameter thresholds, model selection specifications, and output requirements for each step, thus forming a traceable and reusable standardized method.

[0024] Example 2 This embodiment takes a typical polluted bay of the target lake as the research object, and the specific implementation is as follows: The spatiotemporal boundaries of the target lake bay were defined. The spatial boundary was taken as the outer boundary of the water catchment area of ​​the lake bay, and it was divided into six spatial units: water source conservation area, urban built-up area, agricultural planting area, estuary area, lake area, and aquaculture area. 24 in-situ monitoring points were set up. The time boundary is five consecutive complete hydrological years, covering the entire water period and periods of special events. A multi-dimensional, multi-media basic database of the lake bay is constructed, encompassing basic geospatial data, total phosphorus data from water bodies, sediments, and rivers flowing into and out of the lake, as well as environmental factor data on hydrological and meteorological conditions, pollution loads, aquatic organisms, and sediment characteristics. All data are matched with monthly spatiotemporal resolution to achieve precise alignment of spatiotemporal dimensions.

[0025] Following step two of this invention, data preprocessing is completed, and the data integrity reaches 99.5%. Standardization processing and homogeneity calibration are completed to form a standardized dataset.

[0026] The full-process analysis was completed according to steps three to six of this invention. The results showed that the total phosphorus concentration in the lake bay waters decreased significantly during the study period (Mann-Kendall test Z-value = -2.04, P < 0.05), the main period of total phosphorus concentration was 12 months, the lag response time of total phosphorus flux in the inflow rivers to total phosphorus in the water was 10 days, and the lag response time of total phosphorus in the sediments was 30 days. The total phosphorus concentration showed significant spatial clustering characteristics (global Moran's I = 0.71, P < 0.01), with high-value hotspots concentrated in the inflow river estuary area. The total effect of external input on the total phosphorus concentration in the water was 0.71, which is the core influencing path. The main controlling factors identified by scenario were: total phosphorus flux inflow, rainfall, and non-point source pollution load during the rainy season; total phosphorus content in sediments, water temperature, and water level during the dry season; wind speed, total phosphorus content in sediments, and water depth during the wind and wave disturbance period; and water temperature, chlorophyll a concentration, and total phosphorus flux inflow in the lake during the algal bloom period.

[0027] In-situ monitoring data from independent typical hydrological years were selected as the validation set. The method validation was completed according to step seven of this invention, and the goodness of fit R was calculated. 2 =0.897, RMSE=0.015mg / L, which meets the accuracy judgment threshold of the method of this invention. The method identification results are accurate and reliable, and finally solidified into a standardized analysis process adapted to the scale of lake bays.

[0028] Comparative Example This comparative example uses conventional methods for analyzing total phosphorus concentration in lakes, with the same research subjects as in Example 1 as the main analytical subjects. The specific implementation process is as follows: Monthly total phosphorus concentration data of the target lake at 12 regular monitoring points during the study period were used, excluding sediment and multi-media data of rivers flowing into and out of the lake, and only single-media data processing was carried out; Missing values ​​were filled using a simple linear interpolation method, without performing multi-source data homology calibration and standardization preprocessing. We used simple linear regression to analyze the interannual variation trend of total phosphorus concentration, without conducting multi-timescale periodic, abrupt, and multi-media linkage response analyses. Spatial distribution analysis is conducted using the inverse distance weighted interpolation method, without analyzing spatial clustering characteristics, hotspots, and diffusion paths. Pearson correlation analysis was used to statistically analyze the correlation between driving factors and total phosphorus concentration. No multi-path influence mechanism quantification or scenario-specific analysis was conducted. Driving factors were uniformly identified across the entire lake and at all times. Finally, the same independent typical hydrological year in-situ monitoring data as in Example 1 were used for verification, and the goodness of fit R was calculated. 2 =0.527, RMSE=0.041mg / L, which does not meet the accuracy requirements. Moreover, it can only identify two relevant factors throughout the time period and cannot distinguish the differentiated main driving factors under different scenarios. The analysis results cannot provide accurate support for the targeted governance of the target lake by time period and lake area.

[0029] This invention, through two examples of lakes and bays at different scales, has established a stable, accurate, and scalable method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes. Compared to existing conventional techniques, this method demonstrates significant technical advantages and application value. The verification results show that the goodness of fit R of the method to changes in total phosphorus concentration is high in different application scenarios. 2 All scores reached above 0.89, and the root mean square error (RMSE) was below the threshold requirement of 0.02 mg / L. Compared with existing conventional methods, the fitting accuracy was improved by more than 70%, and the prediction error was reduced by more than 60%, which completely solved the core defects of existing technologies such as insufficient data representativeness, incomplete pattern analysis, and inaccurate identification of driving factors.

[0030] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes, characterized in that, The identification method includes the following steps: Step 1: Delineate the spatiotemporal boundaries of the target lake analysis, spatially covering the functional zones of the entire lake area, temporally covering the continuous and complete hydrological annual cycle, and constructing a basic database that covers multiple media including water bodies, sediments, and rivers flowing into and out of the lake, integrating geospatial data, total phosphorus concentration, and environmental factors. Step 2: Perform multi-source heterogeneous data cleaning, missing value imputation, homogeneous calibration and standardization preprocessing on the basic database to eliminate data errors and dimensional differences, and obtain a standardized dataset that can be directly used for analysis. Step 3: Based on standardized datasets, analyze the evolution of total phosphorus concentration over multiple time scales (year, month, day) to clarify the linkage and response relationships of total phosphorus concentration sequences in multiple media. Step 4: Conduct spatial heterogeneity analysis of total phosphorus concentration in the entire lake area to clarify the spatial distribution pattern, aggregation characteristics and spatial diffusion pathways of total phosphorus concentration; Step 5: Delineate typical scene split datasets for spring, summer, autumn and winter, and use a geographic detector to conduct multi-factor coupled driving analysis for different scenes to identify the main driving factors and mechanisms in different spatiotemporal scenes; Step 6: Construct a multi-media interconnected structural equation model to quantify and analyze the multi-path influence mechanism of total phosphorus concentration changes and clarify the contribution ratio of each influence path; Step 7: Using field measurement data from independent and complete hydrological years that do not overlap with the aforementioned data, verify the accuracy of the identification results of this method. Based on the verification results, optimize the key parameters of the method, and finally fix the entire set of analysis steps to form a reusable and standardized analysis process.

2. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 1, characterized in that: In step one, the spatiotemporal boundaries of the analysis are clearly defined. The spatial boundary is the catchment area of ​​the target lake's watershed as the outer boundary. Combining the lake's water function zoning, the distribution of inflow rivers, and land use types, the lake is divided into no less than 5 types of spatial units, including water conservation areas, urban built-up areas, agricultural planting areas, inflow river areas, and lake areas. The temporal boundary is defined as a time series of no less than 5 consecutive complete hydrological years, covering the complete cycle of the lake's high water period, normal water period, and low water period, and simultaneously matching extreme hydrological events and special periods of algal blooms. A multi-dimensional, multi-media basic database covering three major categories of data was constructed. The first category is basic geographic and spatial data, including lake basin DEM digital elevation data, land use type data, water system distribution data, and administrative division and functional zoning data. The second category is multi-media total phosphorus concentration data, including total phosphorus concentration data from in-situ monitoring of lakes, total phosphorus content data from sediment column samples, total phosphorus flux data from cross-sections of rivers flowing into and out of the lake, and total phosphorus flux data from atmospheric deposition. The temporal resolution of the data is no less than that of a month, and the spatial resolution covers all delineated spatial units of the lake area. The third category is environmental factor data, including hydrological and meteorological data, water quality and quantity data, aquatic biological data, and sediment phosphorus content data. All data are matched with the same spatiotemporal resolution as the total phosphorus data.

3. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 2, characterized in that: In step two, a full-process data calibration and preprocessing is carried out on the multi-dimensional, multi-media basic database constructed in step one. Data cleaning is performed using logical verification and cross-validation to remove duplicate and logically contradictory values ​​from the data records. Outliers are identified and removed using the 3σ criterion, with values ​​exceeding the sample mean ± 3 times the standard deviation being considered outliers. For missing values ​​in the data sequence, spatiotemporal coupling interpolation is used to complete the data. Spatially continuous missing data is completed using ordinary kriging interpolation, while temporally continuous missing data is completed using the seasonal autoregressive integral moving average (SARIMA) model. Then, the minimum-maximum normalization method is used to standardize environmental factor data of different dimensions and magnitudes. After standardization, the value range of all data is limited to the interval [0,1], completing the homogeneity calibration of multi-source data. All data are uniformly matched to the same spatiotemporal grid cell to form a standardized dataset that can be directly used for subsequent analysis.

4. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 3, characterized in that: In step three, based on the obtained standardized dataset, the temporal evolution of total phosphorus concentration at multiple time scales (year, month, day) is analyzed. First, the Mann-Kendall nonparametric trend test method is used to conduct a trend significance test on the total phosphorus concentration sequences of three types of media: water, sediment, and rivers flowing into and out of the lake, and the trend test statistic is calculated. The value is calculated using the following formula: ； in, The Mann-Kendall test statistic is used. For statistics The variance, when | When |>1.96, the sequence was determined to have passed the significance trend test at the 95% confidence level, clarifying the interannual rising / falling trend and significance level of total phosphorus concentration in different media. Then, the continuous wavelet transform method was used to analyze the main period and secondary period characteristics of the total phosphorus concentration sequence, clarifying the periodic fluctuation law of total phosphorus concentration changes. Next, the Petitt mutation test method was used to identify the mutation time nodes of the total phosphorus concentration sequence, and the corresponding watershed hydrological regulation events and extreme meteorological events were matched with the mutation nodes. Finally, the cross wavelet transform method was used to carry out the linkage response analysis of the total phosphorus concentration sequences of water bodies, sediments and rivers flowing into and out of the lake.

5. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 4, characterized in that: In step four, based on the standardized dataset and the results of time evolution, the spatial heterogeneity and evolution characteristics of total phosphorus concentration in the entire lake area are analyzed. First, ordinary kriging spatial interpolation is used to generate a continuous distribution raster layer of total phosphorus concentration with a spatial resolution of 10m×10m covering the entire lake area based on the measured data of total phosphorus concentration at each monitoring point. Then, global Moran's I spatial autocorrelation analysis is used to examine the spatial clustering characteristics of total phosphorus concentration in the entire lake area. The formula for calculating the global Moran's I index is: ； in, This represents the total number of spatial units in the lake area. This is a spatial weight matrix constructed based on the Rook adjacency rule. , They are spatial units , Total phosphorus concentration value, The total phosphorus concentration is the average value for the entire lake area. Moran's I index ranges from [-1, 1]. When the total phosphorus concentration is greater than 0 and passes the significance test, it is determined that the total phosphorus concentration exhibits a significant spatial clustering distribution characteristic. Then, the Getis-OrdGi* hotspot analysis method is used to identify high-value hotspots and low-value coldspots of total phosphorus concentration in the entire lake area, clarifying the spatial distribution range and area proportion of high-pollution-risk areas. Finally, combined with the functional zoning of the lake area delineated in step one, the mean, extreme values, and exceedance rates of total phosphorus concentration in different functional zones are statistically analyzed. The interannual and seasonal evolution differences of total phosphorus concentration in different spatial units are analyzed, and the spatial correspondence between pollution input from rivers flowing into the lake and high-value areas of total phosphorus in the lake area is matched to clarify the spatial diffusion path of total phosphorus concentration.

6. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 5, characterized in that: In step five, multi-factor coupled driving analysis and main control factor identification are carried out in different scenarios. First, the typical analysis scenarios of the target lake in spring, summer, autumn and winter are defined, including four categories: rainy season, dry season, wind and wave disturbance period and algal bloom period. The rainy season is defined as consecutive months in which the monthly rainfall accounts for 15% or more of the annual rainfall. The dry season is defined as consecutive months in which the monthly rainfall accounts for 3% or less of the annual rainfall. The wind and wave disturbance period is defined as a period of ≥3 consecutive days with a daily average wind speed ≥5m / s. The algal bloom period is defined as a period of ≥5 consecutive days with a chlorophyll a concentration ≥20μg / L in the water body. The standardized dataset is split according to the defined scenarios to form independent analysis datasets corresponding to each scenario. Using a geographic detector model, driving factors are detected for each scene's dataset, and the driving force of each factor is calculated. Statistic, The formula for calculating the statistic is: ； in, To determine the number of strata for the driving factors, the natural breakpoint method was used to divide the driving factors into 5 strata. For layer Sample size The total sample size for the study area. For layer The variance of total phosphorus concentration The variance of total phosphorus concentration in the total sample of the study area. The statistic ranges from [0,1]. A two-factor interaction probe is then performed to calculate the interaction result of the two driving factors. Values, comparing before and after the interaction Changes in values ​​clarify the type of synergistic enhancement, nonlinear enhancement, or independent coupling effects among factors, ultimately according to... The driving factors are sorted from largest to smallest, and the top 3 controlling driving factors in each scenario are identified to clarify the core driving factors of total phosphorus concentration changes in different spatiotemporal scenarios.

7. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 6, characterized in that: In step six, based on the spatiotemporal evolution results obtained in steps three and four, and the key driving factors obtained in step five, a structural equation model (SEM) is constructed to link water bodies, sediments, and inflowing and outflowing rivers. This quantifies the multi-path impact mechanism of total phosphorus concentration changes. First, based on Pearson correlation analysis, sediment total phosphorus content, inflow total phosphorus flux, outflow total phosphorus flux, hydrological and meteorological factors, and sediment environmental factors that are significantly correlated with total phosphorus concentration in the water body (P < 0.05) are screened. Variables without significant correlation are then removed, forming the latent and observed variable system of the initial model. The latent variables include exogenous input, endogenous release, and... Hydrologically driven, the observed variables are the measured indices of the corresponding latent variables. Subsequently, an initial structural equation model is constructed based on the maximum likelihood estimation method. The model is tested and optimized using fitness indices, including the chi-square degree-of-freedom ratio (χ² / df), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), and root mean square error (RMSEA). When the chi-square degree-of-freedom ratio (χ² / df) < 3, GFI > 0.9, AGFI > 0.85, and RMSEA < 0.08, the model is considered to have good fitness. Finally, based on the optimized structural equation model, the direct, indirect, and total effects of each latent variable on the total phosphorus concentration in the water body are quantitatively calculated.

8. The method for identifying the spatiotemporal evolution and driving factors of total phosphorus concentration in lakes according to claim 7, characterized in that: In-situ monitoring data from the entire lake area for independent typical hydrological years that do not overlap with the time boundary defined in Step 1 were selected as the validation dataset to verify the accuracy and stability of this method. First, the validation dataset was substituted into the full-process analysis steps of this method, outputting the spatiotemporal evolution of total phosphorus and the identification results of the main driving factors under different scenarios in the typical year. The identified main driving factor change sequences were then fitted with the measured total phosphorus concentration change sequences, and the goodness of fit R² and root mean square error were calculated. ,in The calculation formula is as follows: ； in, To verify the sample size, This is the measured value of total phosphorus concentration. The total phosphorus concentration prediction is obtained based on the fitting of the main control factor, when R² ≥ 0.85 and When the concentration is ≤0.02mg / L, the identification result of this method is considered accurate and reliable. If the threshold is not reached, return to steps two to six to optimize the data preprocessing parameters, the number of model layers, and the scene division threshold until the method meets the accuracy requirements. Finally, solidify the optimized full-process analysis steps into a standardized analysis process, and clarify the execution standards, parameter thresholds, model selection specifications, and output requirements for each step.

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