Basin management measure space configuration optimization method

Abstract

The invention discloses a drainage basin management measure space configuration optimization method, and relates to the technical field of ecological environment. The method comprises the following steps: construction and applicability analysis of a watershed non-point source model, description and screening of BMPs, and sensitivity analysis of NSGA-II parameters. An innovative optimization framework is provided and used for identifying and developing the optimal BMPs combination on the watershed scale, the framework considers the weights of the TN and the TP, the main target is to improve the comprehensive reduction rate to the maximum extent by coupling the TN and the TP, and the total cost related to implementation of 14 BMPs is reduced to the maximum extent. Three optimal BMP solutions based on Pareto optimum are obtained through simulation by using the framework; wherein the fusion of the NSGA-II and the BMPs database is derived from the output of the SWAT model after calibration and verification driven by local data, so that the optimization process is greatly accelerated, the calculation complexity is reduced, and the optimal BMPs combination is steadily sought, so that the nitrogen and phosphorus reduction rate is improved to the maximum extent, and the total cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of ecological environment technology, and in particular to a method for optimizing the spatial configuration of watershed management measures. Background Art

[0002] Currently, non-point source pollution has become the primary source of global water pollution. The excessive water quality factor (total nitrogen) in rivers and lakes mainly comes from the contribution of agricultural non-point sources. Although a large number of studies have been conducted on the total amount of water pollutants, most researchers only focus on specific aspects and lack systematic research to support the implementation of the total amount of water pollutants control system. This has led to the lack of a complete set of technical methods for the total amount of water pollutants control, resulting in insufficient practicality of the research methods.

[0003] However, in the absence of an innovative optimization framework and comprehensive reduction rate measurement method, it is difficult to determine the optimal BMP combination at the watershed scale, resulting in uncertainty in pollutant reduction effects and total cost control. At the same time, failure to consider the maximum reduction rates of TN and TP under coupling conditions may lead to failure to achieve the most effective pollutant control. Without analyzing multiple BMP combination schemes, it may not be possible to fully understand the pollutant control needs and feasibility of agricultural industries at different scales, resulting in incomplete localized solutions. At the same time, the lack of a trade-off model framework that balances total cost and pollution reduction efficiency may make it difficult to achieve the optimal economic solution while ensuring environmental and social benefits. In response to the above problems, the inventors proposed a spatial configuration optimization method for watershed management measures to solve the above problems. Summary of the invention

[0004] In order to solve the problems that, in the current environment, there is a lack of innovative optimization frameworks and comprehensive reduction rate measurement methods, which makes it difficult to determine the optimal basin-scale BMP combination, which may lead to uncertainty in pollutant control effects and total costs; at the same time, the maximum reduction rates of TN and TP under coupling conditions are not considered and there is a lack of analysis of multiple BMP combination schemes, which may lead to a failure to fully understand the pollutant control needs and feasibility of the agricultural industry and make it difficult to achieve an optimal balance between the environment, society and economy; the purpose of the present invention is to provide a method for optimizing the spatial configuration of watershed management measures.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solution: a method for optimizing the spatial configuration of watershed management measures, comprising the following steps:

[0006] S1. Construction and applicability analysis of watershed non-point sources: key points of SWAT model construction, calculation and prediction ability evaluation method of SWAT model for total nitrogen and total phosphorus, and performance evaluation index of SWAT model and its significance;

[0007] S2. Description and screening of BMPs: Combination diversity of different BMPs on cultivated land and methods for determining pollutant loads and sub-watershed weights;

[0008] S3. Sensitivity analysis of NSGA-II: Application of NSGA-II method in basin-scale BMPs configuration and potential limitations and improvement opportunities.

[0009] Preferably, the key points of SWAT model construction in S1 are as follows: SWAT model construction includes watershed delineation, hydrological response unit delineation, meteorological data input, SWAT database generation, and model calibration and validation. SWAT2012 divides the watershed according to DEM and then divides it into sub-watersheds and hydrological response units according to the homogeneity of land use, soil, and slope;

[0010] Calculation and prediction ability evaluation method of total nitrogen and total phosphorus in SWAT model in S1: In the SWAT model, the calculation formulas of total nitrogen and total phosphorus are calculated according to specific equations (calculation formulas 1.1-1.2 for total nitrogen and total phosphorus). In order to evaluate the prediction ability of the model, three objective functions were selected: Nash-Sutcliffe efficiency (NSE) coefficient, determination coefficient (R 2 ) and percentage bias (PBIAS). When the NSE and R2 values ​​are greater than 0.50, the PBIAS value of runoff remains between ±25%, and the TN and TP values ​​remain between ±70%, the model prediction can be considered to be relatively accurate;

[0011] TN=ORGN+NSURQ+LAT_Q_NO3+GWNO3 (1.1);

[0012] TP = ORGP + SOLP + SEDP (1.2);

[0013] In the formula: ORGN is organic nitrogen output, kg / ha; NSURQ is surface flow nitrate output, kg / ha; LAT_Q_NO3 is lateral flow nitrate output, kg / ha; GWNO3 is ground flow nitrate output, kg / ha; ORGP is organic phosphorus output, kg / ha; SOLP is soluble phosphorus output, kg / ha; SEDP is particulate inorganic phosphorus output, kg / ha.

[0014] The performance evaluation indicators of the SWAT model in S1 and their significance: The performance of the SWAT model can be evaluated by NSE, R 2 and PBIAS values ​​to evaluate the coefficient of determination (R 2 ) and percentage bias (PBIAS), when NSE and R 2When the value is greater than 0.50, the PBIAS value of runoff is kept within ±25%, and the TN and TP values ​​are kept within ±70%. The performance of the SWAT model is determined by NSE, R 2 and PBIAS value evaluation, as shown in equations 1.3-1.5:

[0015]

[0016] Where: O i is the observed value at the i-th moment; P i is the simulation value at the i-th moment; is the mean of the observed values; is the mean of the simulated values ​​and n is the total number of observations.

[0017] Preferably, the combination diversity of different BMPs on cultivated land in S2: 14 BMPs were implemented on cultivated land, including CT, ST, PT, RC, CH, FR10%, FR20%, FR30%, VFS1, VFS3, VFS6, VFS15, VFS30, GFG15°, GFG25°, RFG15°, and RFG25° (as shown in Table 1);

[0018] Table 1 Best management practice scenario settings

[0019]

[0020]

[0021] Tillage management, vegetation buffer strips, nutrient management, and landscape management can take 0-4 types in each sub-watershed (0≤j≤4), so each sub-watershed may have up to 720 (6×6×4×5) BMPs. For example, in a watershed with 30 sub-watersheds, there may be up to 2.16×104 (5×6×4×5×30) BMPs.

[0022] Method for determining pollutant load and sub-basin weights in S2: The entropy weight method is used to determine the weights of TN and TP, and the TOPS IS technology is combined to consider multiple evaluation factors to determine the weights of sub-basins, taking into account the different reduction rates of different sub-basins.

[0023] Preferably, the NSGA-II method in S3 is applied to the configuration of BMPs at the watershed scale:

[0024] Sensitive parameter values ​​of NSGA-II

[0025]

[0026] Although many studies on basin-specific management measures have used the dual objectives of cost-effectiveness to optimize total cost and reduction effect, they mainly focus on optimizing the use of single pollutants. By combining AHP and TOPSIS methods, a comprehensive reduction rate is proposed. This method is more in line with the pollution status in the basin and allocates corresponding BMPs in an equivalent manner. The NSGA-II method is used to determine multiple combinations of BMPs for all sub-basins. Although this provides a cost solution for the configuration of BMPs at the basin scale, it ignores the configuration focused on key source areas. Generally speaking, key source areas are the priority implementation sites of BMPs, which can improve the efficiency of different BMPs in reducing non-point pollution and achieve economic costs. The differences in the research scales also provide a reasonable explanation. This study utilized sub-basins composed of numerous hydrological response units, which require small-scale exploration. The proposed method can be effectively expanded and applied to other basins for priority BMPs for non-point source pollution control. Although the BMPs configuration spans the entire basin, it can provide decision makers with a trade-off between total cost and emission reduction efficiency. Integrating stakeholder interest indexes into the optimization process can form a comprehensive decision-making method that carefully addresses local economic needs.

[0027] Potential limitations and improvement opportunities in S3: The default operator value settings and the SWAT calibration process may affect the accuracy and result uncertainty of BMPs, while the limitations of the BMPs database may also affect the selection of optimal BMPs, suggesting that opportunities for improvement exist in these aspects.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. An innovative optimization framework is proposed in this invention to identify and develop the best BMP combination at the watershed scale. The main goal of this framework is to maximize the comprehensive reduction rate by coupling TN and TP and minimize the total cost associated with the implementation of 14 BMPs. The framework is evenly divided into three optimal BMP solutions located at the Pareto optimal. This invention introduces a new framework to measure the comprehensive reduction rate of each sub-basin, which is achieved by implementing AHP and TOPSIS. The model takes into account the weights of TN and TP, and considers ten different aspects such as sub-basin area, total TP, and TN and sediment; the fusion of NSGA-II and BMPs database, derived from the output of the SWAT model after local data-driven calibration and verification, not only greatly accelerates the optimization process and reduces the computational complexity, but also robustly seeks the best BMPs combination to maximize nitrogen and phosphorus reduction rates and reduce total costs;

[0030] 2. The present invention analyzes four categories of measures, namely, different farming methods, fertilizer reduction, interception process and landscape measures, at different scales (sub-basins and hydrological response units) in the selected watershed, and randomly combines these measures in the entire watershed to obtain different combinations of measures under the premise of meeting the total cost and pollutant reduction efficiency, thus providing a trade-off model framework for balancing environmental well-being and social benefits, which is widely applicable to areas where agricultural industries are concentrated and non-point source pollution control is urgently needed;

[0031] 3. In the present invention, by generating multiple optimal measures combination schemes, three optimal schemes of medium, low and high costs are set according to the total investment, and their reduction effects are ranked as low cost < medium cost < high cost. Specifically, a higher reduction rate corresponds to more BMPs implemented in the sub-basin. Therefore, it is of vital importance to use a suitable multi-objective optimization algorithm to determine the optimal location of BMPs in the Jinghe River Basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a schematic diagram of the propulsion relationship between SWAT and BMPs of the present invention;

[0034] Figure 2 It is a schematic diagram of the technical route of the present invention;

[0035] Figure 3 A schematic diagram of the BMPs genome in the chromosome of the present invention;

[0036] Figure 4 This is the spatial distribution of total nitrogen and total phosphorus reduction effects in the Jinghe River Basin. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1-3 As shown, the present invention provides a method for optimizing the spatial configuration of watershed management measures, comprising the following steps:

[0039] S1. Construction and applicability analysis of watershed non-point source model: key points of SWAT model construction, calculation and prediction ability evaluation method of SWAT model for total nitrogen and total phosphorus, and performance evaluation index of SWAT model and its significance;

[0040] S2. Description and screening of BMPs: Combination diversity of different BMPs on cultivated land and methods for determining pollutant loads and sub-watershed weights;

[0041] S3. Sensitivity analysis of NSGA-II: Application of NSGA-II method in basin-scale BMPs configuration and potential limitations and improvement opportunities;

[0042] Key points of SWAT model construction in S1: SWAT model construction includes watershed delineation, hydrological response unit delineation, meteorological data input, SWAT database generation, and model calibration and validation. SWAT2012 divides the watershed according to DEM, and then divides it into sub-watersheds and hydrological response units according to the homogeneity of land use, soil, and slope;

[0043] Calculation and prediction ability evaluation method of total nitrogen and total phosphorus in SWAT model in S1: In the SWAT model, the calculation formulas of total nitrogen and total phosphorus are calculated according to specific equations (calculation formulas 1.1-1.2 for total nitrogen and total phosphorus). In order to evaluate the prediction ability of the model, three objective functions were selected: Nash-Sutcliffe efficiency (NSE) coefficient, determination coefficient (R 2 ) and percentage bias (PBIAS). When the NSE and R2 values ​​are greater than 0.50, the PBIAS value of runoff remains between ±25%, and the TN and TP values ​​remain between ±70%, the model prediction can be considered to be relatively accurate;

[0044] TN=ORGN+NSURQ+LAT_Q_NO3+GWNO3 (1.1);

[0045] TP = ORGP + SOLP + SEDP (1.2);

[0046] The performance evaluation indicators of the SWAT model in S1 and their significance: The performance of the SWAT model can be evaluated by NSE, R 2 and PBIAS values ​​to evaluate the coefficient of determination (R 2 ) and percentage bias (PBIAS), when NSE and R 2 When the value is greater than 0.50, the PBIAS value of runoff is kept within ±25%, and the TN and TP values ​​are kept within ±70%. The performance of the SWAT model is determined by NSE, R 2 and PBIAS values, as shown in Equations 1.3-1.5:

[0047]

[0048]

[0049] Combination diversity of different BMPs on cultivated land in S2: 14 BMPs were implemented on cultivated land, including CT, ST, PT, RC, CH, FR10%, FR20%, FR30%, VFS1, VFS3, VFS6, VFS15, VFS30, GFG15°, GFG25°, RFG15°, and RFG25° (Table 1);

[0050] Table 1 Best management practice scenario settings

[0051]

[0052]

[0053] Tillage management, vegetation buffer strips, nutrient management, and landscape management can take 0-4 types in each sub-watershed (0≤j≤4), so each sub-watershed may have up to 720 (6×6×4×5) BMPs. For example, in a watershed with 30 sub-watersheds, there may be up to 2.16×104 (5×6×4×5×30) BMPs.

[0054] Method for determining pollutant load and sub-basin weights in S2: The entropy weight method is used to determine the weights of TN and TP, and the TOPS IS technology is combined to consider multiple evaluation factors to determine the weights of sub-basins, taking into account the different reduction rates of different sub-basins;

[0055] Application of NSGA-II method in S3 for basin-scale BMPs configuration:

[0056] Sensitive parameter values ​​of NSGA-II

[0057]

[0058] Although many studies on basin-specific management measures have used the dual objectives of cost-effectiveness to optimize total cost and reduction effect, they mainly focus on optimizing the use of single pollutants. A comprehensive reduction rate is proposed by combining AHP and TOPSIS methods. This method is more in line with the pollution status in the basin and allocates corresponding BMPs in an equivalent manner. The NSGA-II method is used to determine multiple combinations of BMPs for all sub-basins. Although this provides a cost solution for the configuration of BMPs at the basin scale, it ignores the configuration focused on key source areas. Generally speaking, key source areas are the priority implementation sites of BMPs, which can improve the efficiency of different BMPs in reducing non-point pollution and achieve economic costs. The differences in the research scales also provide a reasonable explanation. This study utilized sub-basins composed of numerous hydrological response units, which require small-scale exploration. The proposed method can be effectively expanded and applied to other basins for priority BMPs for non-point source pollution control. Although the BMPs configuration spans the entire basin, it can provide decision makers with a trade-off between total cost and emission reduction efficiency. Integrating stakeholder interest indexes into the optimization process can form a comprehensive decision-making method that carefully addresses local economic needs.

[0059] Potential limitations and improvement opportunities in S3: The default operator value settings and the SWAT calibration process may affect the accuracy and result uncertainty of BMPs, while the limitations of the BMPs database may also affect the selection of optimal BMPs, suggesting that opportunities for improvement exist in these aspects.

[0060] Although a large number of studies have been conducted on BMPs configuration, most of them only focus on specific aspects such as single or multiple BMPs efficiency screening. There is a lack of systematic research on the spatial optimization configuration of BMPs in the basin that comprehensively considers the trade-off between abatement efficiency and measure costs. This has resulted in a lack of a complete set of technical methods for total water pollutant control, resulting in insufficient practicality of the research methods. To remedy this deficiency, we combined the research results of previous studies and conducted a study on the Jinghe River Basin. Our goal is to reduce the total amount of water environmental pollution at different scales within the range of minimum cost and maximum reduction efficiency, and to conduct a trade-off analysis of water quality management goals. By establishing a regional total allocation and reduction measures management system, our goal is to provide a comprehensive solution for total water pollutant control. Through such a systematic study, we will ensure that we provide practical solutions to address non-point source pollution and minimize the impact on the water environment.

[0061] The implementation of watershed BMPs requires reasonable simulation and evaluation, which usually involves watershed models. The Soil and Water Assessment Tool (SWAT) has played an important role in evaluating the optimization of surface pollution source control management measures such as surface pollution, agricultural control management, and tillage management measures, and has good applicability. It has been shown that incorporating the distributed watershed model SWAT into the optimization framework can promote optimal solutions. For example, the successful coupling of SWAT and NSGA-II in the BMPs optimization model has been applied to different watersheds. It has been proven that the coupling method is conducive to the spatial optimization of BMPs, provides cost-effective options for decision makers, and improves the effectiveness of protection measures. Nevertheless, accurate BMPs configuration and optimization using NSGA-II brings some challenges: (1) due to the neglect of the optimal placement of agricultural BMPs and the trade-offs between multiple objectives, potential solutions may be overlooked, thus introducing uncertainty in the search for the best optimization, and (2) the different efficiencies of different prevention and control measures in reducing pollutant loads complicate the complexity of integrating and optimizing individual measures for multi-objective optimization, thus bringing practical challenges to the use of best management practices.

[0062] The spatial configuration of BMPs in the watershed was analyzed by combining the multi-objective optimization algorithms of NSGA-II and SWAT. This study simultaneously solved the combined reduction rate and multi-objective functions under multiple constraints. The main research objectives are:

[0063] (1) Develop a new method to solve the combined reduction rate of TN and TP, which is integrated to solve multiple objective functions and BMPs constraints;

[0064] (2) Construct a cohesive optimization framework to propose effective solutions by coupling SWAT and NSGA-II to select the best combination of measures while meeting economic and cost constraints. The main technical roadmap is as follows: Figure 2 shown.

[0065] Application scenario: The spatial configuration optimization method of watershed management measures considering the trade-off between cost and abatement efficiency can be extended to other watersheds to determine appropriate BMPs. The method considers the interests of stakeholders from the perspective of total cost and abatement effect, thereby ensuring a comprehensive and inclusive decision-making process. Jinghe River is a secondary tributary of the Yellow River and the largest major tributary of the Weihe River. It originates from the eastern foot of Liupan Mountain in Jingyuan County, Ningxia Hui Autonomous Region. The river traverses three provinces (autonomous regions) of Ningxia, Gansu and Shaanxi, forming the Jinghe River Basin with a basin area of ​​45,421 kilometers. Before 2018, the water pollution level in the Jingcun section was serious, with Class IV water accounting for 42.9% and Class V water accounting for 57.1% from 2011 to 2017. Non-point source pollution such as pesticides, fertilizers, and livestock has been identified as the main cause of exceeding the standard.

[0066] In order to intuitively represent the geographical locations of various BMPs, the spatial distribution of the most cost-effective BMPs is proposed. Finally, the distribution and arrangement of the optimal BMPs in the Jinghe River Basin are visualized.

[0067] The final result of the scheme is to generate multiple optimal measures and set three optimal schemes of medium, low and high costs according to the total investment. Their reduction effects are ranked as low cost < medium cost < high cost. Specifically, higher reduction rates correspond to more BMPs implemented in the sub-basin. Therefore, it is of vital importance to use a suitable multi-objective optimization algorithm to determine the optimal spatial location of BMPs in the Jinghe River Basin.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing spatial configuration of watershed management measures, characterized in that: The following steps are involved: S1. Construction and applicability analysis of watershed non-point source model: key points of SWAT model construction, evaluation method of SWAT model's total nitrogen and total phosphorus simulation and prediction capabilities, and SWAT model performance evaluation indicators and their significance; S2. Description and screening of BMPs: Combination diversity of different BMPs on cultivated land and methods for determining pollutant loads and sub-watershed weights; S3. Sensitivity analysis of NSGA-II parameters: Application of NSGA-II method in basin-scale BMPs configuration and potential limitations and improvement opportunities.

2. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: Key points of SWAT model construction in S1: SWAT model construction includes watershed delineation, hydrological response unit delineation, meteorological data input, SWAT database generation, and model calibration and validation. SWAT2012 divides the watershed according to DEM, and then divides it into sub-watersheds and hydrological response units according to the homogeneity of land use, soil, and slope.

3. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: Calculation of total nitrogen and total phosphorus and evaluation of the prediction ability of the SWAT model in S1: In the SWAT model, the formulas for total nitrogen and total phosphorus are calculated according to specific equations. In order to evaluate the prediction ability of the model, three objective functions were selected: Nash-Sutcliffe efficiency coefficient, determination coefficient (R 2 ) and percentage bias (PBIAS), when NSE and R 2 When the value is greater than 0.50, the PBIAS value of runoff remains between ±25%, and the TN and TP values ​​remain between ±70%, the model prediction is considered to be relatively accurate; TN=ORGN+NSURQ+LAT_Q_NO3+GWNO3; TP=ORGP+SOLP+SEDP.

4. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: The performance evaluation indicators of the SWAT model in S1 and their significance: The performance of the SWAT model can be evaluated by NSE, R 2 The performance of the SWAT model is evaluated by NSE, R and PBIAS values, coefficient of determination and percentage deviation. When NSE and R are greater than 0.50, the PBIAS value of runoff remains within ±25%, and the TN and TP values ​​remain within ±70%. 2 and PBIAS value; 5. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: Combination diversity of different BMPs on cultivated land in S2: 14 BMPs were implemented on cultivated land, including CT, ST, PT, RC, CH, FR10%, FR20%, FR30%, VFS1, VFS3, VFS6, VFS15, VFS30, GFG15°, GFG25°, RFG15°, and RFG25°; Table 1 Best management practice scenario settings Tillage management, vegetated buffers, nutrient management, and landscape management can take 0-4 types in each sub-watershed, so there may be up to 720 BMPs in each sub-watershed.

6. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: Method for determining pollutant load and sub-basin weights in S2: The entropy weight method is used to determine the weights of TN and TP, and the TOPSIS technology is combined to consider multiple evaluation factors to determine the weights of sub-basins, taking into account the different reduction rates of different sub-basins.

7. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: Application of NSGA-II method in S3 for basin-scale BMPs configuration: Sensitive parameter values ​​of NSGA-II By combining the AHP and TOPSIS methods, a comprehensive reduction rate is proposed. This method is more in line with the pollution status in the basin and allocates the corresponding BMPs in an equivalent manner. The NSGA-II method is used to determine the various combinations of BMPs in all sub-basins. The basin-scale BMPs configuration provides a cost solution, but it ignores the configuration focused on key source areas. Key source areas are the priority implementation sites of BMPs, which can improve the efficiency of different BMPs in reducing non-point source pollution and maximize economic benefits. This study used sub-basins composed of many hydrological response units for BMPs configuration. The proposed method can be effectively promoted and applied to other basins for priority BMPs configuration for non-point source pollution control. Although the BMPs configuration spans the entire basin, it can provide decision makers with a trade-off between total cost and emission reduction efficiency. Integrating the stakeholder interest index into the optimization process can form a comprehensive decision-making method to effectively address local economic needs.

8. A method for optimizing spatial configuration of watershed management measures according to claim 1, characterized in that: Potential limitations and improvement opportunities in S3: The default operator value settings and the SWAT calibration process may affect the accuracy and result uncertainty of BMPs, while the limitations of the BMPs database may also affect the selection of optimal BMPs, suggesting that opportunities for improvement exist in these aspects.

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