Monthly dynamic accounting method and system for watershed water environment capacity

Abstract

The invention discloses a monthly dynamic accounting method and system for watershed water environment capacity, and belongs to the technical field of water environment capacity accounting. The method comprises the following steps: firstly, establishing a pollution source-section dynamic response relationship through a one-dimensional river network water quality model to realize refined space-time simulation of a pollutant migration and transformation process; converting the water quality standard into a dynamic constraint condition by adopting a linear programming algorithm driven by a one-dimensional model; and finally, a water quality target-oriented dynamic optimization model is constructed, the water environment capacity is converted into space-time variable regulation and control parameters through monthly accounting, and dynamic matching of pollution load distribution and the water environment capacity is realized. According to the method, the monthly-scale hydrological water quality change is brought into an accounting framework for the first time, the total pollutant amount control target is decomposed through a monthly dynamic accounting method, and the contradiction between the regional pollution discharge demand and the section water quality standard reaching requirement is effectively coordinated. Compared with the prior art, a traditional static calculation mode is broken through, and the problem that the space-time adaptability of the basin water environment capacity is insufficient is solved.

Description

Technical Field

[0001] This invention belongs to the field of water environment capacity accounting technology, and relates to a method and system for monthly dynamic accounting of watershed water environment capacity. Background Technology

[0002] Water environmental capacity is defined as the maximum pollution load that a specific area can withstand within a given water quality target framework. As a core decision parameter for watershed pollution load allocation, the scientific validity of water environmental capacity directly impacts the accuracy and effectiveness of water environment management. In existing technological systems, deterministic modeling methods are limited by static hydrological and water quality assumptions. While one-dimensional steady-state models (such as the Thomas analytical formula method) have computational efficiency advantages, they can only characterize pollution load thresholds under constant flow conditions, making them unsuitable for managing monthly hydrological fluctuations and dynamic changes in pollution sources. Model trial-and-error methods consume significant computational resources during iterative optimization, leading to efficiency bottlenecks in practical applications. Furthermore, system optimization methods based on the TMDL framework suffer from a structural misalignment between their total allocation orientation and my country's water quality target management requirements. Regarding parameter uncertainty, methods such as probabilistic dilution models and grey system theory, while introducing stochastic analysis frameworks, are limited by the universality of probability distribution assumptions and the technical complexity of fuzzy quantification methods. Although two-dimensional / three-dimensional dynamic models can characterize complex hydrodynamic processes, their high parameter calibration costs and computational complexity hinder their application in management practice.

[0003] Overall, existing technologies still have significant shortcomings in terms of applicability, dynamism, and uncertainty quantification across different scenarios. This invention innovatively constructs a three-in-one technical system of "model-driven, dynamic constraints, and optimized regulation": First, a dynamic response matrix of pollution sources and cross-sections is established through a one-dimensional river network water quality model, breaking through the limitations of traditional static control sections and achieving a refined spatiotemporal characterization of pollutant migration and transformation processes. Then, monthly hydrological and water quality change characteristics are innovatively transformed into dynamic constraints, and a model-driven linear programming algorithm is used to construct a dynamic optimization model oriented towards water quality goals, upgrading water environmental capacity from a single static threshold to a regulation parameter with spatiotemporal resolution. This method establishes a dynamic balance mechanism between pollution discharge demand and water quality compliance at the watershed scale by dynamically decomposing the total pollutant control target on a monthly basis. It inherits the computational efficiency of traditional one-dimensional models and integrates the spatiotemporal adaptability advantages of dynamic simulation, significantly improving the scientific decision-making level and management implementation efficiency of pollution load allocation schemes. This accounting framework, which dynamically couples monthly hydrological fluctuations with water quality standards, has significant innovative value in watershed-level refined management and pollution prevention and control practices. Summary of the Invention

[0004] The purpose of this invention is to solve the aforementioned technical problems and provide a method and system for monthly dynamic calculation of watershed water environment capacity. It employs a linear programming method based on the GRMS one-dimensional river network water quality model to determine the water environment capacity of the lower reaches of the Yanhe River. This overcomes the capacity distortion defects caused by existing technologies relying on fixed thresholds or empirical formulas. To achieve the above objectives, this invention mainly provides the following technical solutions:

[0005] First, embodiments of the present invention provide a method for monthly dynamic calculation of watershed water environment capacity.

[0006] Step 1: Using the GRMS one-dimensional river network water quality model, establish the dynamic relationship between the pollution discharge volume of the sewage outlet and the water quality of each key monitoring section.

[0007] The GRMS one-dimensional river network water quality model uses a one-dimensional convection-diffusion equation to describe pollutant transport, including basic convection-diffusion calculations and eutrophication calculations considering biochemical reactions. The specific form of the one-dimensional convection-diffusion equation for river network water quality control is as follows:

[0008]

[0009] Where Q represents the flow rate, measured in meters (m). 3 / s; A is the cross-sectional area of ​​the river channel, in m². 2 C represents the concentration of pollutants transported by the water flow, in g / m³. 3 E is the longitudinal diffusion coefficient; t is time (seconds); x is distance (meters); S c The term represents the attenuation term related to pollutant concentration; S represents the pollutant runoff source term per unit time and per unit river length, in g / m³. 3 ·s.

[0010] The time term is discretized as follows:

[0011]

[0012] In the formula, n represents the current time step, and n+1 represents the next time step; A i This represents the cross-sectional area of ​​water passage in unit i, in m². 2 C i This represents the pollutant concentration in unit i, in g / m³. 3 ; △t represents the time calculation step, in seconds.

[0013] Spatial discretization is performed using the grid-centered finite volume method (FVM), dividing the one-dimensional channel into equidistant or non-equidistant cells. Variables within each cell (such as concentration C) are treated as mean values. The solution is obtained by accumulating fluxes at the cell interfaces. The convection term is discretized as follows:

[0014]

[0015] In the formula, △x i This indicates the unit length, in meters (m). and These represent the volumetric flux of water flow at the left and right interfaces, respectively, in m³. 3 / s; and These represent the pollutant concentrations at the left and right interfaces, respectively, in g / m³. 3 .

[0016] The diffusion term is discretized as follows:

[0017]

[0018] In the formula, △x i+1 / 2 Δx represents the distance between element i and element i+1, in meters; i-1 / 2 The longitudinal diffusion coefficient represents the distance between element i and element i-1, in meters. The water depth in unit i is represented in meters (m); u * This indicates the frictional velocity, expressed in m / s. g represents the acceleration due to gravity, and its unit is g / s². 2 R represents the hydraulic radius, defined as the ratio of the flow area to the wetted perimeter, with units of m. S represents the channel bottom slope (dimensionless), which is the elevation drop per unit length.

[0019] Step 2: Set the upper limit of pollutant discharge as the objective function, and use the water quality standards of the monitoring sections as constraints. Calculate the maximum pollution load that each monitoring section can withstand, i.e., the water environment capacity, using the linear programming solution method.

[0020] Objective function:

[0021]

[0022] Constraints:

[0023]

[0024] In the formula, i is the sewage outlet number; n is the number of sewage outlets; j is the control section number; m is the number of water quality sections; a ij Let x be the water quality response coefficient of the i-th discharge outlet to the j-th control section; i is the allowable discharge capacity of the i-th discharge outlet, in mg / L; W is the water environmental capacity, in g / s; C j0 To control the background water quality concentration at the cross-section, the unit is mg / L; C j To control the target water quality concentration at the cross-section, the unit is mg / L; Q iThe amount of pollutants discharged from the sewage outlet, in cubic meters (m³). 3 / s.

[0025] Step 3: Calculate the water environment capacity of the lower reaches of the Yanhe River by constraining the linear equation, obtain the maximum total discharge load in the region, and calculate the water environment capacity of total nitrogen (TN) and total phosphorus (TP) in the lower reaches of the Yanhe River.

[0026] Furthermore, the calculation of water environmental capacity requires clear prerequisites such as design hydrological conditions, sewage outlet location, water quality control sections, and the water quality standards to be implemented.

[0027] (1) Setting water quality targets

[0028] According to national or local water function zoning requirements, different monitoring sections within the watershed must meet the corresponding water quality category standards. For typical monitoring sections, key pollutant indicators such as total nitrogen (TN) and total phosphorus (TP) are selected, and concentration limits are set according to Class IV and Class III surface water standards respectively to ensure that the model calculation results meet the water quality management needs of different functional zones.

[0029] (2) Generalization method of cross-section and sewage outlet

[0030] For watersheds with dendritic drainage patterns, the model construction prioritizes simplifying the main channel structure, incorporating major tributaries into the system through lateral confluence. Based on pollution source distribution characteristics, upstream non-point source pollution inputs are generalized as Non-point Source Input 1, while downstream tributary pollution inputs are categorized according to river segment location as Non-point Source Input 2, Non-point Source Input 3, and so on. Point source discharge outlets are integrated based on spatial distribution characteristics, corresponding to pollution load inputs from different river segments. The main channel segment lengths are rationally divided according to actual hydrological characteristics, providing a spatial scale basis for subsequent pollutant migration simulations.

[0031] (3) Design flow selection principles

[0032] Referring to the recommended methods in the "National Guidelines for Calculating Water Environmental Capacity" and considering the hydrological characteristics of the basin during the dry season, the monthly average flow rate of typical dry years was selected as the unfavorable design condition. By analyzing the monthly flow rate data of the past ten years during the dry season, and using a flow series with a guarantee rate of not less than 90%, a dynamic flow boundary reflecting seasonal hydrological fluctuations was constructed to accurately assess the spatiotemporal variation patterns of water environmental capacity.

[0033] (4) Determination of initial water quality parameters

[0034] The initial state of the model was set based on measured water quality data from typical monitoring sections, selecting the concentrations of ammonia nitrogen, total nitrogen, and total phosphorus at the beginning of the dry season as initial conditions. Hydrological parameters were recorded simultaneously during data acquisition to ensure that the initial values ​​reflected the background levels of pollutants in the basin during the dry season, providing a reliable starting point for simulating pollutant migration and transformation.

[0035] (5) Boundary condition construction method

[0036] The upstream and downstream boundary conditions of the model are driven by measured hydrological and water quality data, combined with the results of regional pollution source surveys to determine the intensity of non-point source inputs. By analyzing the spatial distribution characteristics of pollution sources, point source discharge outlets are grouped into control units to establish an input boundary system reflecting the spatiotemporal characteristics of watershed pollution load. This effectively integrates multi-source data and improves the model's response accuracy to complex pollution inputs.

[0037] (6) Model building and parameter setting

[0038] One-dimensional water quality modeling software was used for simulation. River centerline data was imported to characterize the spatial pattern of the river system, and river cross-sectional parameters and hydrological and water quality time series were input to construct a dynamic model. The computation time step was set to the minute level to ensure numerical stability, and the output results were integrated on a daily scale. The simulation period covered the complete hydrological process of a typical dry year. The water depth threshold was set to balance computational efficiency and physical realism, ensuring the model's applicability under low flow conditions. This modeling framework is adaptable to the hydrological and water quality characteristics of different watersheds and has good regional scalability.

[0039] Furthermore, in step 3, a positive calculation result indicates the remaining water environmental capacity; a negative result indicates no water environmental capacity, requiring measures to reduce the pollution load. Details are as follows:

[0040] Environmental capacity (WEC) = Water body's self-purification capacity - Pollutant input

[0041] Self-cleaning capacity = Q·(C 允许 -C 本底 )+K·C 本底 (7)

[0042] Where: river flow (Q), pollutant degradation rate (K), and allowable concentration limit (C) 允许 ), background concentration (C) 本底 ).

[0043] ① Positive number (WEC>0): There is remaining water environmental capacity, that is, the self-purification capacity of the water body is greater than the current amount of pollutants input, and there is still room to accommodate additional pollutant discharge.

[0044] Remaining capacity = Self-cleaning capacity - L 排放 >0(8)

[0045] ② Negative number (WEC<0): No water environmental capacity, that is, the amount of pollutants input has exceeded the water body's self-purification capacity, and the water body cannot reduce the pollution concentration to below the allowable limit through its own action. Measures need to be taken to reduce emissions or strengthen treatment.

[0046] Overload capacity = Self-cleaning capacity - L排放 <0(9)

[0047] Second, this embodiment of the invention also provides a monthly dynamic accounting system for watershed water environment capacity. The system includes: one or more processors; a memory for storing one or more programs; the processors are configured to execute program instructions stored in the memory, and the program instructions execute the above-described monthly dynamic accounting method for watershed water environment capacity when they are executed.

[0048] Third, this embodiment of the invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by one or more processors, implements the above-described method for monthly dynamic calculation of watershed water environment capacity.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] (1) Spatiotemporal dynamic adaptability is significantly improved

[0051] Existing technical limitations: Traditional static accounting methods cannot accurately reflect the impact of seasonal hydrological changes (such as fluctuations in flow and self-purification capacity) on water environmental capacity, leading to a disconnect between the accounting results and the actual environmental carrying capacity, which can easily cause problems of over-control or under-control.

[0052] Advantages of this invention: By establishing a one-dimensional river network water quality model and a monthly dynamic accounting mechanism, this invention achieves, for the first time, a spatiotemporal correlation analysis between pollution discharge and water quality at key monitoring sections. For example, in Case 1, the monthly water environment capacity accounting of the lower reaches of the Yanhe River, this invention can dynamically adjust the water environment capacity accounting results according to the hydrological conditions of different months (such as the wet season and the dry season), making the water quality management objectives more closely aligned with the dynamic characteristics of the actual environmental carrying capacity and avoiding the biases caused by static accounting methods.

[0053] (2) Enhanced multi-pollutant synergistic optimization capability

[0054] Limitations of existing technologies: Traditional methods often calculate based on a single pollutant, making it difficult to comprehensively consider the synergistic effects of multiple pollutants, resulting in a lack of systematic and scientific management measures.

[0055] Advantages of this invention: Through dynamic modeling and multi-pollutant synergistic optimization technology, this invention can simultaneously consider the impact of multiple pollutants on water quality, achieving comprehensive pollutant management. For example, in the water environment capacity accounting after implementing non-point source pollution control practices (BMPs) in Case 2, this invention can accurately assess the synergistic emission reduction effects of different BMPs on multiple pollutants (such as nitrogen and phosphorus), providing strong support for developing more scientific and effective water quality management measures.

[0056] (3) Enhanced spatial refinement analysis capabilities

[0057] Existing technical limitations: Traditional methods typically use regional averages or simplified models for calculation, which makes it difficult to accurately reflect the differences in water environmental capacity in different areas within the watershed, resulting in a lack of targeted and precise management measures.

[0058] Advantages of this invention: Through spatial fine-grained analysis technology, this invention can accurately identify the water environment capacity characteristics of different areas within a watershed, providing a basis for formulating differentiated water quality management measures. For example, in Case 1, the monthly water environment capacity calculation of the lower reaches of the Yanhe River, this invention can perform fine-grained calculations of the water environment capacity for different river sections and cross-sections, providing data support for formulating more precise water quality management plans.

[0059] (4) Timeliness and decision support capabilities have been significantly improved.

[0060] Existing technological shortcomings: Traditional methods have long calculation cycles and slow data updates, making it difficult to meet the timeliness requirements of water quality management, resulting in management measures lagging behind changes in water quality.

[0061] Advantages of this invention: Through dynamic modeling and real-time data update mechanisms, this invention can significantly improve the timeliness of water environment capacity accounting, providing timely and accurate decision support for water quality management. For example, in the water environment capacity accounting after the implementation of non-point source pollution control (BMPs) in Case 2, this invention can quickly assess the changes in water environment capacity after the implementation of BMPs, providing a scientific basis for timely adjustment of management measures.

[0062] (5) Innovative technology pathways provide a scientific basis for management.

[0063] Existing technological shortcomings: Traditional methods lack a systematic technical approach, making it difficult to form a closed-loop management system, resulting in poor water quality management.

[0064] Advantages of this invention: By providing an innovative technical approach of "monthly response, precise cross-section analysis, and coordinated measures," this invention enables the formation of a complete closed-loop management system from data collection and model calculation to the formulation of management measures. This technical approach not only improves the scientific and systematic nature of water quality management but also enhances the pertinence and effectiveness of management measures.

[0065] In summary, this invention significantly improves the timeliness and decision support capabilities of watershed water environment capacity accounting through dynamic modeling, multi-pollutant synergistic optimization, and spatial refinement analysis. It addresses the shortcomings of traditional static calculation models in terms of spatiotemporal adaptability, multi-pollutant synergistic optimization, and spatial refinement analysis, providing more scientific, accurate, and effective technical support for water quality target management. Attached Figure Description

[0066] Figure 1A framework for a monthly dynamic accounting method for watershed water environment capacity;

[0067] Figure 2 This shows the distribution of cross-sections within the watershed.

[0068] Figure 3 A generalized simulation diagram of water quality in the lower reaches of the Yanhe River;

[0069] Figure 4 Comparison of simulated and measured water quality values ​​at the Zhujiagou section;

[0070] Figure 5 Comparison of simulated and measured water quality values ​​at the Yanjiatan section;

[0071] Figure 6 Monthly trends in actual water environmental capacity of total nitrogen and total phosphorus in the lower reaches of the Yanhe River. Detailed Implementation

[0072] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0073] Reference Figure 1 The present invention provides a method for monthly dynamic calculation of watershed water environment capacity, the specific steps of which are as follows:

[0074] Example 1. Monthly water environment capacity calculation of the lower reaches of the Yanhe River

[0075] 1.1 Calculation process of water environmental capacity

[0076] Water environmental capacity refers to the maximum pollutant load that a specific water body can bear under the premise of meeting the established water quality functional zoning requirements. Compared with traditional empirical estimation methods, linear programming based on water environment mathematical models has become the mainstream technical approach in current research due to its accurate quantitative analysis capabilities, flexible boundary condition adaptability, and wide applicability to various water body types. This study innovatively constructs a composite calculation system combining the GRMS model and linear programming, aiming to scientifically calculate the water environmental carrying capacity threshold of the downstream section of the Yanhe River Basin.

[0077] The computational system follows a two-stage modeling process: the first stage establishes a hydrodynamic-water quality coupling model to analyze the nonlinear relationship between the emission intensity of pollution sources and the water quality response of control sections, and obtains the pollution transmission coefficient matrix of each discharge unit to the target water quality section; the second stage takes maximizing the total amount of pollution emissions as the objective function and the environmental standard limit of the water quality control section as the constraint condition to construct a linear optimization model, and finally solves for the maximum pollution load that meets the water quality safety threshold, which is the environmental capacity value of the target water area.

[0078] According to the technical framework of this study, the calculation of water environmental capacity requires four core elements: ① clearly defining the water quality functional zoning standards corresponding to each control section; ② accurately locating the spatial distribution of pollution source discharge outlets; ③ scientifically deploying a network system of water quality monitoring sections; and ④ determining typical hydrological design conditions (such as 90% guaranteed flow rate during the dry season). These basic parameters together constitute the boundary condition system of the mathematical model, directly affecting the reliability and applicability of the capacity calculation results.

[0079] (1) Basis for setting water quality targets

[0080] According to the water quality function positioning of the Yanhe River Basin in the "Shaanxi Province Water Function Zoning", the Zhujia Gou monitoring section implements the Class IV surface water standard, with a total nitrogen (TN) concentration limit of 1.5 mg / L and a total phosphorus (TP) concentration limit of 0.3 mg / L; the Yanjia Tan control section needs to meet the Class III surface water quality requirements, corresponding to the pollution load control standard of TN ≤ 1.0 mg / L and TP ≤ 0.2 mg / L. These standard limits serve as water quality constraints and constitute the benchmark thresholds for subsequent capacity calculations.

[0081] (2) Spatial generalization of monitoring sections and pollution sources

[0082] like Figure 2 and Figure 3 As shown, a dendritic drainage topology model including the main channel and major tributaries was constructed for the study area. Through spatial generalization, the main channel was divided into two control units: Zhujia Gou-Ganguyi (interval length 28 km) and Ganguyi-Yanjiatan (interval length 84 km). Pollution input from tributaries was integrated through lateral confluence. The pollution source system was generalized as follows:

[0083] • Non-point source systems:

[0084] Non-point source input 1: Flow and water quality simulation results of all confluence units in the SWAT model rch file of the upstream section of Zhujiagou;

[0085] Non-point source input 2: The lateral input of SWAT model 13, 14 and 17-24 in the Zhujiagou-Ganguyi section;

[0086] Non-point source input 3: Collect the pollution load of SWAT model 33-35, 37, 39 and 41 in the Ganguyi-Yanjiatan section.

[0087] Point source system:

[0088] Point source discharge outlet 1: Generalized centralized sewage discharge unit upstream of Zhujiagou section;

[0089] Point source discharge outlet 2: Integrate all point source discharge units along the main channel of the lower reaches of the Yanhe River.

[0090] (3) Determination of hydrological design conditions

[0091] In accordance with the requirements of the "National Technical Guidelines for Water Environment Capacity Calculation", the average monthly flow during the dry season with a 90% guarantee rate is adopted as the design benchmark. Taking into account the hydrological cycle characteristics of the Yanhe River Basin, the SWAT monthly-scale simulated flow sequence for the dry year of 2016 is finally selected (as shown in Table 1). This design reflects both the extreme hydraulic conditions during the dry season and the seasonal fluctuations of the hydrological process, ensuring that the capacity calculation results can characterize the pollution load threshold under the annual minimum dilution self-purification capacity scenario.

[0092] Table 1 Monthly average flow rate (m³) at each control section 3 / s)

[0093]

[0094]

[0095] (4) Initial water quality conditions

[0096] The initial conditions for the model were based on measured water quality data from January 1, 2016 (the initial simulation time). Field monitoring revealed initial concentrations of ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP) at the Zhujiagou section to be 1.5 mg / L, 12.0 mg / L, and 0.34 mg / L, respectively; while at the Yanjiatan section, the corresponding initial concentrations were 1.3 mg / L (NH3-N), 11.5 mg / L (TN), and 0.28 mg / L (TP). These initial values ​​were selected based on a complete simulation cycle of a dry year using the SWAT model, effectively reflecting the initial pollution load accumulation status of the watershed's water environment system.

[0097] (5) Boundary conditions

[0098] The model boundary conditions employ a dual-end control strategy: the upstream and downstream boundaries use the Zhujiagou and Yanjiatan monitoring sections as control nodes, respectively. The flow process lines of river segments 24 and 41 output by the SWAT model are used as hydrodynamic boundary conditions, while the measured water quality time-series data of the corresponding sections are simultaneously input as concentration boundary conditions. The input data for non-point source pollution loads are detailed in Table 2, and their spatiotemporal distribution characteristics have been calibrated using the sub-basin scale simulation results of the SWAT model. The spatial generalization of the point source emission system is based on the Shaanxi Province Pollution Source Statistical Database. After multi-source data fusion processing, the spatial locations and pollution load characteristics of two typical point source emission units (e.g., ...) are finally determined. Figure 3 As shown in Table 3, this method of constructing boundary conditions using multi-source data coupling can effectively improve the analytical accuracy of the model for watershed pollution transport processes.

[0099] Table 2 Non-point source inputs

[0100]

[0101] Table 3 Point Source Emissions

[0102]

[0103] (6) Model Simulation Settings

[0104] Using the domestically developed GRMS one-dimensional water quality cloud model software from Guiren Technology, the river centerline was first imported to represent the river's location, followed by the import of the river cross-section, flow rate, and water quality time series. The calculation step time was set to 1 minute, and the output time step was set to 1 day. The model calculation period was from January 1, 2016 to December 31, 2016. The minimum water depth was set to 0.001 m, and the maximum water depth was set to 10 m.

[0105] (7) Simulation of water quality response coefficient

[0106] The water quality response coefficients of TN and TP at each discharge outlet to the cross section are shown in Table 4.

[0107] Table 4 Water quality response coefficients of each discharge outlet to the cross-section

[0108]

[0109] 1.2 Water quality model validation

[0110] The model was validated using measured data from 2016. This was achieved by comparing the simulated and observed concentrations of NH3-N, TN, and TP at the Zhujia Gou and Yanjia Tan sections (see...). Figure 4 , Figure 5The results show that the model can effectively reproduce the spatiotemporal evolution characteristics of pollutant concentrations, and the simulated values ​​of key pollution indicators are in high agreement with the measured values. Error analysis indicates that the relative error range of the simulation results is mainly within ±18%, which meets the accuracy requirements for water environment simulation. This verifies the rationality of the model parameter calibration and boundary condition settings, and provides reliable technical support for subsequent water environment capacity calculations.

[0111] 1.3 Calculation Results and Analysis of Water Environmental Capacity

[0112] A linear programming constraint model was used to calculate the water environmental capacity of the lower reaches of the Yanhe River. By analyzing the dynamic balance between regional pollution load and the water body's self-purification capacity, the maximum allowable discharge thresholds for nitrogen (TN) and phosphorus (TP) pollutants in the lower reaches of the Yanhe River were determined. The calculation results show that when the amount of pollutants entering the river exceeds the environmental capacity, negative values ​​represent the amount of pollution load that needs to be reduced; when the water body has a pollution carrying capacity, positive values ​​reflect the acceptable increase in pollutants. The monthly dynamic calculation results of nitrogen and phosphorus pollution capacity in the lower reaches of the Yanhe River are detailed in Table 5. Figure 6 The dynamic capacity curve is shown.

[0113] Table 5. Calculation results of water environment capacity for each section of the lower reaches of the Yanhe River (unit: tons / month)

[0114]

[0115] Based on Table 5 and Figure 6 Analysis of dynamic capacity assessment results shows that nitrogen pollution in the lower reaches of the Yanhe River exhibits a continuous overload characteristic throughout the year. The annual total nitrogen load in the Zhujia Gou-Ganguyi and Ganguyi-Yanjiatan sections exceeds the capacity threshold by 544.147 tons and 541.308 tons, respectively. The spatiotemporal distribution of pollution load shows a significant inverse gradient, with pollution pressure significantly intensifying during the dry season. In November alone, the required reduction amount reaches 94.27 tons and 78.266 tons. Conversely, during the wet season, due to runoff increasing to 15-20 times that of the dry season, the total nitrogen reduction requirement in June drops sharply to the range of 5.334-18.698 tons, a decrease of more than 90% compared to the winter peak. This significant seasonal difference indicates a strong correlation between runoff dilution effect and pollution load, especially in winter when the ecological base flow is low, the significant decline in the water body's self-purification capacity leads to a prominent pollutant accumulation effect.

[0116] Regarding phosphorus pollution control, capacity assessment results show differentiated characteristics: the annual total phosphorus capacity in the Zhujia Gou-Ganguyi section still has a surplus of 9.496 tons, while the Ganguyi-Yanjiatan section maintains only a buffer space of 0.99 tons. The total phosphorus exceedances in November during the dry season were 1.125 tons and 0.564 tons respectively, reflecting the severity of phosphorus pollution control under low flow conditions. Spatially, the phosphorus capacity advantage in the upstream section mainly stems from the natural purification process during pollutant migration and transformation. However, the implementation of Class III water quality standards (30% stricter than Class IV standards) in the downstream Yanjiatan section significantly compresses its capacity threshold. This hierarchical management mechanism of water quality targets has a decisive impact on capacity allocation.

[0117] The monthly-scale dynamic assessment system constructed in this invention achieves three technological breakthroughs: First, it establishes a coupled response model of pollution load and ecological flow, quantifying the amplification effect of nitrogen pollution risk caused by insufficient baseflow in winter (the overload in November accounts for 20% of the annual total); second, it innovatively applies the water quality response coefficient matrix to analyze the contribution of pollution sources, accurately identifying that the direct impact of point source discharge outlet 1 on the nitrogen load of the Zhujiagou section reaches 21%; third, it forms a capacity time-series change map, providing data support for formulating a "dry season reduction" pollution control strategy—more than 90% of the pollution load can be absorbed through runoff regulation during the wet season in June, while strict total discharge control is required during the dry season. This dynamic relationship reveals the synergistic effect of ecological flow guarantee and pollution load regulation, providing a quantitative basis for formulating a "water-based pollution control" watershed management strategy.

[0118] Example 2. Water environmental capacity calculation after implementing area source BMPs

[0119] This case study systematically evaluated the effectiveness of 16 agricultural best practices in controlling non-point source pollution in the Yanhe River Basin using a multi-scenario simulation method. Based on the simulation framework constructed using the SWAT watershed non-point source pollution model, Table 6 quantifies the contribution of different management measures to the reduction of nutrient load from agricultural non-point source pollution in key source areas, providing a scientific basis for the optimal allocation of watershed-scale pollution control measures.

[0120] Table 6 Pollution Reduction Effects of Management Measures

[0121]

[0122] A linear programming optimization model was used to dynamically assess the water environment capacity of the lower reaches of the Yanhe River basin, based on the water quality response index system constructed in Case Study 1. With a significant improvement in upstream water quality and effective reduction in non-point source nitrogen and phosphorus pollution loads in various tributaries, the water environment capacity parameters of the basin were recalculated by constructing a water quality-quantity coupled analysis framework. The assessment results can provide a scientific basis for optimizing the management and control of regional nitrogen and phosphorus pollution loads. Detailed assessment data are shown in Table 7.

[0123] Table 7 Calculation results of water environment capacity in the lower reaches of the Yanhe River (unit: tons / year)

[0124]

[0125] Based on SWAT model simulation results, it was found that none of the 16 existing management measures in the Yanhe River Basin could effectively control the total nitrogen (TN) pollution load in the downstream area (Table 7). Among them, Scenario 14 (stubble cover + 2km grassed waterway + conversion of farmland to forest at angles above 25°) showed the best treatment efficiency, reducing the TN exceedance in the Zhujia Gou-Gangu Yi and Gangu Yi-Yanjia Tan sections to 44.525 t / a and 41.486 t / a, respectively. However, these still significantly exceeded the water environment carrying capacity threshold, indicating that nitrogen pollution in the basin was already in a state of severe overload. It is worth noting that the total phosphorus (TP) pollution load achieved compliance control under all management scenarios. The remaining water environment capacity corresponding to Scenario 14 was 69.443 t / a and 65.661 t / a, respectively. Although there was some environmental capacity, the values ​​were relatively small, suggesting the need for continuous optimization of phosphorus input control. Spatial heterogeneity analysis showed that the TN exceedance in the Zhujiagou-Ganguyi section was 12.7% higher than that in the downstream section, while the TP remaining capacity was 8.3% lower, highlighting the spatiotemporal differentiation of pollution characteristics in the basin.

[0126] Based on the above findings, this study innovatively constructs a three-in-one composite governance model of "agronomy-ecology-engineering": (1) In the Zhujiagou-Ganguyi section, which suffers from severe nitrogen overload, the terracing transformation of sloping farmland (reduction efficiency of 38.2%) and the construction of ecological buffer zones (interception efficiency of 45.6%) are integrated; (2) For downstream areas with limited phosphorus capacity, a precision fertilization intelligent management and control system (which can reduce non-point source input by 27.4%) and riverbank wetland restoration projects are implemented (the phosphorus deposition rate is increased to 1.8 kg / (hm)). 2 •a)). By establishing a dynamic balance model of pollution load and water environment capacity, the combined measures were quantitatively verified to improve the reduction efficiency of total nitrogen (TN) by 32%-58% compared with single measures, while maintaining the effective environmental capacity of total phosphorus (TP). This zoned governance strategy based on the migration and transformation process of pollutants not only breaks through the limitations of the traditional planar governance model, but also constructs a technical paradigm for pollution prevention and control in the Loess Plateau agricultural watershed by combining model prediction and empirical analysis. It provides a replicable engineering practice solution for achieving the water quality compliance target of the "Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin".

[0127] This invention also provides a monthly dynamic accounting system for watershed water environment capacity, comprising:

[0128] It includes: one or more processors; a memory for storing one or more programs; the processors are configured to execute program instructions stored in the memory, which, when executed, perform the above-described method for monthly dynamic calculation of watershed water environment capacity.

[0129] This invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by one or more processors, implements the above-described method for monthly dynamic calculation of watershed water environment capacity.

[0130] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0131] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0132] In addition, the memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0133] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0138] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0139] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0140] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, the phrase "comprising an element qualified by..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0141] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Furthermore, although some specific terms are used in this specification, these terms are merely for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A method for monthly dynamic accounting of watershed water environment capacity, characterized in that, The method includes the following steps: Step 1: Using a one-dimensional river network water quality model, establish the dynamic relationship between the pollution discharge volume of the sewage outlet and the water quality of each key monitoring section. Step 2: Set the upper limit of pollutant discharge as the objective function, and use the water quality standards of the monitoring sections as constraints. Incorporate monthly hydrological and water quality changes into the accounting framework, and calculate the maximum pollution load that each monitoring section can withstand through linear programming to obtain the water environment capacity. Step 3: Calculate the water environment capacity of the lower reaches of the Yanhe River using linear programming equations and their constraints, obtain the maximum total discharge load in the region, and calculate the water environment capacity of total nitrogen and total phosphorus in the lower reaches of the Yanhe River.

2. The method for monthly dynamic accounting of watershed water environment capacity according to claim 1, characterized in that, The calculation of water environment capacity requires specifying the design hydrological conditions, the location of the sewage outlet, the water quality control section, and the water quality standards to be implemented.

3. The method for monthly dynamic accounting of watershed water environment capacity according to claim 1, characterized in that, In step 3, if the calculation result is positive, it represents the remaining water environmental capacity; if the result is negative, there is no water environmental capacity, and measures need to be taken to reduce the pollution load.

4. A monthly dynamic accounting system for watershed water environment capacity, characterized in that, include: It includes: one or more processors; a memory for storing one or more programs; the processors are configured to execute program instructions stored in the memory, which, when executed, perform the monthly dynamic accounting method for watershed water environment capacity as described in any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by one or more processors, implements the monthly dynamic accounting method for watershed water environment capacity as described in any one of claims 1 to 3.

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