Greywater footprint calculation method based on Budyko hypothesis and SWAT model

Abstract

The invention discloses a grey water footprint calculation method based on the Budyko hypothesis and the SWAT model, which specifically comprises the following operation steps: obtaining basic data, constructing a SWAT model, performing verification, obtaining an optimal SWAT model, performing a trend test on the annual runoff of a sub-basin by using the Mann-Kendall trend test, and drawing a trend graph, determining the year of annual runoff mutation in the trend graph by using the Pettit mutation method, calculating the natural background concentration of pollutants in the sub-basin where a hydrological station is located, and calculating the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located. The invention solves the problem in the prior art that the influence of the natural background concentration of pollutants on the quantitative water quality load is not considered when calculating the grey water footprint.

Description

Technical Field

[0001] The invention belongs to the technical field of water resource carrying capacity assessment, and in particular relates to a grey water footprint calculation method based on the Budyko hypothesis and the SWAT model. Background Art

[0002] Against the backdrop of increasingly severe climate change, the impact of human activities on agricultural non-point source pollution has become increasingly significant, which not only threatens human life and production. Temperature fluctuations and changes in rainfall patterns, as direct consequences of climate change, have indirectly exacerbated the problem of non-point source pollution. The direct impact of human activities, such as changes in land use types, further strengthens this connection, which makes the pollution problem of the water environment more complicated by reshaping the underlying surface characteristics and the hydrological process of the basin. The drastic changes in climate change have prompted changes in land use patterns, and the diversity of underlying surface properties has led to differences in climatic factors such as rainfall and temperature. These factors work together to make the severity of the entire problem more prominent; in 2002, Professor Arjen Hoekstra proposed the concept of water footprint to reveal the pressure of human occupation of water resources, which has become an important technical method for assessing water resources and water environment. The gray water footprint represents the amount of purified water required to dilute pollutants emitted by human activities to environmental quality standards, which can be used to quantify the water pollution load caused by human activities. Although some grey water footprint quantification methods have been proposed, they fail to consider the impact of the natural background concentration of pollutants on the quantification of water quality loads. The natural background concentration in river water quality parameters is usually considered to be zero. This practice ignores the weak pollution that may exist in the water body in its natural state, leading to an underestimation of the actual pollution load and failing to fully assess the impact of human activities on water quality deterioration. Clarifying the natural background concentration of water quality parameters has a significant impact on the accurate calculation of the grey water footprint of agricultural non-point source pollutants. Therefore, it is urgent to propose a grey water footprint calculation method that takes into account the natural background concentration of pollutants in water bodies, so as to more accurately assess the impact of human activities on water quality and formulate more effective water quality protection strategies. Summary of the invention

[0003] The purpose of the present invention is to provide a grey water footprint calculation method based on the Budyko hypothesis and the SWAT model, which solves the problem in the prior art that the natural background concentration of pollutants does not take into account the quantitative impact of water quality load when calculating grey water footprint.

[0004] The technical solution adopted by the present invention is a grey water footprint calculation method based on the Budyko hypothesis and the SWAT model, and the specific steps are as follows:

[0005] S1. Obtain basic data, build a SWAT model, and verify the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section to obtain the optimal SWAT model;

[0006] S2. Use the optimal SWAT model to output the monthly runoff of the sub-basin where the hydrological station is located, calculate the annual runoff of the sub-basin, use the Mann-Kendall trend test to perform a trend test on the annual runoff of the sub-basin, and draw a trend graph;

[0007] S3. Use the Pettit mutation method to determine the annual runoff mutation year in the trend chart, and calculate the contribution rate of human activities to the change of annual runoff in the sub-basin based on the mutation year;

[0008] S4. Based on the contribution rate, determine the basin where the hydrological station is located with the smallest impact of human activities on the change of annual runoff, and calculate the natural background concentration of pollutants in the sub-basin where the hydrological station is located;

[0009] S5. Calculate the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located;

[0010] Step S4 is specifically as follows:

[0011] Compare the multiple contribution rates obtained in step S3, select the sub-basin where the hydrological station with the lowest contribution rate is located, output the ammonia nitrogen load of the sub-basin where the hydrological station with the smallest impact on annual runoff is located based on the optimal SWAT model obtained in step S1, and use the ammonia nitrogen load of the sub-basin where the hydrological station with the smallest impact on annual runoff is located and the actual runoff of the sub-basin where the hydrological station is located to calculate the natural background concentration of pollutants in the water body of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0012] (9)

[0013] Where: is the pollutant load in the sub-basin where human activities have the least impact on runoff, is the actual runoff of the sub-basin, is the natural background concentration of the pollutant in the sub-watershed;

[0014] Step S5 is specifically as follows:

[0015] The grey water footprint of the sub-basin where the hydrological station with the least impact of human activities on annual runoff is calculated based on the natural background concentration of pollutants in the water body of the sub-basin where the hydrological station with the least impact of human activities on annual runoff is located. The calculation method is as follows:

[0016] (10)

[0017] Where: L is the pollution index load, unit is kg, C max , i is the maximum limit of pollutants, in mg / L. is the natural background concentration of the pollutant in the sub-basin.

[0018] The present invention is also characterized in that:

[0019] The basic data in step S1 include spatial data and attribute data. The spatial data are DEM elevation data, soil and land use spatial distribution data, and the attribute data are meteorological data, soil and land use type data, and agricultural management measures data.

[0020] In step S1, the SWAT model includes a runoff generation and convergence submodule, a hydrological process submodule, and a pollution load submodule; the verification of the SWAT model is divided into hydrological verification and water quality verification.

[0021] In step S1, the SWAT model is verified based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section. The specific operation is:

[0022] By directly connecting the SWAT model output results with the SWAT-CUP calibration tool, the runoff parameters in the SWAT model output parameters are first calibrated and verified using the runoff sensitive parameters, and the calibration is performed from upstream to downstream. The calibration period and verification period of the SWAT model are calculated according to the calculation formula of the correlation coefficient R² and the Nash coefficient Ens.

[0023] First, the hydrological process of the SWAT model is calibrated and verified using the hydrological data of the sub-basin where the hydrological station is located. During the calibration and verification period, the SWAT model meets the requirements of the correlation coefficient R² not less than 0.6 and the Nash coefficient Ens not less than 0.5;

[0024] The water quality data of the water quality monitoring section over the years are then used to calibrate and verify the water quality process of the SWAT model. When the correlation coefficient R² is not less than 0.6 and the Nash coefficient Ens is not less than 0.5 during the calibration and verification period, the optimal SWAT model is obtained;

[0025] The calculation methods of correlation coefficient and Nash coefficient are as follows:

[0026] (1)

[0027] (2)

[0028] Where: Q o t and Q s t are the measured and simulated values ​​of the t-th runoff, and are the measured average and simulated average of runoff during the simulation period, respectively.

[0029] The calculation method of the sub-basin annual runoff in step S2 is as follows:

[0030] (3)

[0031] Where: Q is the average monthly flow in the basin at the hydrological station; For time; F is the drainage area of ​​the hydrological station basin.

[0032] Step S3 is specifically as follows:

[0033] S301: Use the Penman-Monteith formula modified by the Food and Agriculture Organization of the United Nations to calculate the long-term potential evapotranspiration of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0034] (4)

[0035] Where: is the long-term potential evapotranspiration of the sub-basin where the hydrological station is located, in mm / d. is the slope of the saturated water vapor pressure curve, in KPa / ℃, is the net solar radiation, in MJ m -2 d -1 , is the soil heat flux, in MJ m -2 d -1 , is the dry and wet constant, the unit is KPa / ℃, is the wind speed at a height of 2m, in m / s, is the average temperature in °C, is the average saturated water vapor pressure, in KPa, is the actual water vapor pressure, in KPa, is the saturated water vapor pressure difference, in Kpa;

[0036] S302: Using the long-term potential evapotranspiration and water-heat coupling balance equation of the sub-basin where the hydrological station is located, calculate the underlying surface parameters of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0037] (5)

[0038] Where: is the rainfall in mm, is the actual evapotranspiration, in mm, The underlying surface parameters of the sub-basin where the hydrological station is located;

[0039] S303: Calculate the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located by using the long-term potential evapotranspiration of the sub-basin where the hydrological station is located and the underlying surface parameters of the sub-basin where the hydrological station is located , the calculation method is as follows:

[0040] (6)

[0041] S304: Using the underlying surface parameters of the sub-basin where the hydrological station is located and the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located, calculate the change in annual runoff in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows:

[0042] (7)

[0043] Where: is the runoff depth in mm, The change of annual runoff under the influence of human activities, The changes in the underlying surface parameters of the sub-basin where the hydrological station is located;

[0044] S305: The contribution rate of human activities to the change of annual runoff is calculated by using the change of annual runoff and runoff depth in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows:

[0045] (8)

[0046] The beneficial effects of the present invention are as follows: the grey water footprint calculation method based on the Budyko hypothesis and the SWAT model proposed in the present invention calculates the contribution rate of human activities to the annual runoff change, selects the sub-basin with the smallest impact of human activities on the annual runoff, and calculates the natural background concentration of nutrients in the sub-basin with the smallest impact of human activities on the annual runoff, and uses the natural background concentration to correct the grey water footprint calculation method, thereby solving the problem in the prior art that the grey water footprint calculation does not consider the quantitative impact of the natural background concentration of pollutants on the water quality load, and improves the calculation accuracy of the grey water footprint. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the grey water footprint estimation method based on the Budyko hypothesis and mechanism model of the present invention;

[0048] Figure 2 The geographical location map of the confluence of Fenghe River and Weihe River in Example 1;

[0049] Figure 3 It is the result diagram of monthly runoff simulation of Wushan Station in Example 1;

[0050] Figure 4 It is the result diagram of monthly runoff simulation at Qinan Station in Example 1;

[0051] Figure 5 This is a graph of the monthly runoff simulation results of Qianyang Station in Example 1;

[0052] Figure 6 This is a graph of the monthly runoff simulation results of Linjia Village Station in Example 1;

[0053] Figure 7 It is the result diagram of monthly runoff simulation of Xianyang Station in Example 1;

[0054] Figure 8 This is a simulation result diagram of the average monthly ammonia nitrogen in Tianshui Niubei in Example 1;

[0055] Fig. 9 It is a schematic diagram of the change trend and mutation year of the runoff in the Wushan Station basin from 2000 to 2022 in Example 1;

[0056] Fig.10 It is a schematic diagram of the change trend and mutation year of the runoff in the Qinan Station basin from 2000 to 2022 in Example 1;

[0057] Fig.11 It is a schematic diagram of the change trend and mutation year of the runoff in the Qianyang Station Basin from 2000 to 2022 in Example 1;

[0058] Fig.12 It is a schematic diagram of the change trend and mutation year of the runoff in the Linjia Village Station basin from 2000 to 2022 in Example 1;

[0059] Fig.13 It is a schematic diagram of the change trend and mutation year of the runoff in the Xianyang station basin from 2000 to 2022 in Example 1; DETAILED DESCRIPTION

[0060] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] The present invention provides a grey water footprint calculation method based on the Budyko hypothesis and the SWAT model, which is specifically implemented according to the following steps:

[0062] S1. Obtain basic data, build a SWAT model, and verify the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section to obtain the optimal SWAT model;

[0063] The basic data in step S1 includes spatial data and attribute data, the spatial data is digital elevation model (DEM) data, soil and land use spatial distribution data, and the attribute data is meteorological data, soil and land use type data, and agricultural management measures data;

[0064] Digital elevation model data, soil and land use spatial distribution maps must use a unified projection coordinate system. This method uses the "WGS_1984_UTM_Zone_49N" projection coordinate system based on the geographical location of the study area;

[0065] The hydrological data of the sub-basin where the hydrological station is located over the years are the water volume and pollutant load of each sub-basin input to the river. The collection method is to obtain from the China National Climate Center, the National Hydrological and Water Resources Data Center, the World Meteorological Organization (WMO), and the government's open data platform; the basic data obtained are used to build a hydrological and water quality model. Based on the runoff and river confluence parts in the SWAT model, the water volume and pollutant load of each sub-basin input to the river can be simulated. The hydrological process submodule divides the sub-basin and generates the sub-basin outlet to simulate and calculate the long-term series hydrological cycle process of each sub-basin. The pollution load submodule in the SWAT model is used to simulate the transport and migration process of pollutants from the river network to the basin outlet, and the pollution load of the sub-basin is calculated.

[0066] By directly connecting the SWAT model output results with the SWAT hydrological model calibration (SWAT-CUP) tool, the runoff parameters in the SWAT model output parameters are first calibrated and verified using the runoff sensitive parameters, and the calibration is performed from upstream to downstream. The calibration period and verification period of the SWAT model are calculated according to the calculation formula of the correlation coefficient R² and the Nash coefficient Ens.

[0067] First, the hydrological process of the SWAT model is calibrated and verified using the hydrological data of the sub-basin where the hydrological station is located. During the calibration and verification period, the SWAT model meets the requirements of the correlation coefficient R² not less than 0.6 and the Nash coefficient Ens not less than 0.5;

[0068] The water quality data of the water quality monitoring sections over the years were then used to calibrate and verify the water quality process of the SWAT model. The optimal SWAT model was obtained when the correlation coefficient R² was not less than 0.6 and the Nash coefficient Ens was not less than 0.5 during the calibration and verification period.

[0069] The calculation formulas for the correlation coefficient and the Nash coefficient are:

[0070] (1)

[0071] (2)

[0072] Where: Q o t and Q s t are the measured and simulated values ​​of the t-th runoff, and are the measured average and simulated average of runoff during the simulation period, respectively.

[0073] S2. Use the optimal SWAT model to output the monthly runoff of the sub-basin where the hydrological station is located, calculate the annual runoff of the sub-basin, use the Mann-Kendall trend test to perform a trend test on the annual runoff of the sub-basin, and draw a trend graph;

[0074] The positive and negative Z values ​​are used to determine whether there is a significant change trend in the annual runoff of the sub-basin where the hydrological station is located. If the Z value is greater than zero, it means that the annual runoff has a significant growth trend during the study period. Otherwise, there is a significant decreasing trend. If the annual runoff has a significant change trend and mutation during the study period, the mutation can be attributed to the changes in annual rainfall, potential evapotranspiration and basin underlying surface parameters. The P value is used to determine whether the change trend of the annual runoff is significant. If P is less than 0.05, it means that the annual runoff is significant in the corresponding change trend. If P is greater than 0.05, it is considered that the change trend of the annual runoff is not significant.

[0075] S3. Use the Pettit mutation method to determine the annual runoff mutation year in the trend chart, and calculate the contribution rate of human activities to the change of annual runoff in the sub-basin based on the mutation year;

[0076] Specifically, the time series data are obtained from the annual runoff trend chart; the time series data are divided into two parts, assuming that the mutation point is in the kth year, the data of the first k years are one group, and the data of the remaining years are another group; the rank sum of the two groups of data is calculated; for each possible mutation point k, the corresponding Pettit statistic is calculated; for each possible mutation point, its corresponding significance level is calculated to determine the critical value of the Pettit statistic at a given significance level; the Pettit statistics of all possible mutation points are compared with the critical value. If the Pettit statistic of a certain point exceeds the critical value, the point is considered to be a significant mutation point, that is, the mutation year.

[0077] Step S3 is as follows:

[0078] S301: Use the Penman-Monteith formula modified by the Food and Agriculture Organization of the United Nations to calculate the long-term potential evapotranspiration of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0079] (3)

[0080] Where: is the long-term potential evapotranspiration of the sub-basin where the hydrological station is located, in mm / d. is the slope of the saturated water vapor pressure curve, in KPa / ℃, is the net solar radiation, in MJ m -2d -1 , is the soil heat flux, in MJ m -2 d -1 , is the dry and wet constant, the unit is KPa / ℃, is the wind speed at a height of 2m, in m / s, is the average temperature in °C, is the average saturated water vapor pressure, in KPa, is the actual water vapor pressure, in KPa, is the saturated water vapor pressure difference, in KPa;

[0081] S302: Using the long-term potential evapotranspiration and water-heat coupling balance equation of the sub-basin where the hydrological station is located, calculate the underlying surface parameters of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0082] (4)

[0083] Where: is the rainfall in mm, is the actual evapotranspiration, in mm, The underlying surface parameters of the sub-basin where the hydrological station is located;

[0084] S303: Calculate the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located by using the long-term potential evapotranspiration of the sub-basin where the hydrological station is located and the underlying surface parameters of the sub-basin where the hydrological station is located , the calculation method is as follows:

[0085] (5)

[0086] S304: Using the underlying surface parameters of the sub-basin where the hydrological station is located and the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located, calculate the change in annual runoff in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows:

[0087] (6)

[0088] In formula (6): is the runoff depth in mm, The change of annual runoff under the influence of human activities, The changes in the underlying surface parameters of the sub-basin where the hydrological station is located;

[0089] S305: The contribution rate of human activities to the change of annual runoff is calculated by using the change of annual runoff and runoff depth in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows:

[0090] (7)

[0091] S4. Based on the contribution rate, determine the basin where the hydrological station with the smallest impact of human activities on the annual runoff change is located, and calculate the natural background concentration of pollutants in the sub-basin where the hydrological station is located;

[0092] Step S4 is specifically as follows:

[0093] Compare the multiple contribution rates obtained in S3, select the sub-basin where the hydrological station with the lowest contribution rate is located, and output the ammonia nitrogen load of the sub-basin where the hydrological station with the smallest impact on annual runoff is located based on the optimal SWAT model constructed in step S1. Use the ammonia nitrogen load of the sub-basin where the hydrological station with the smallest impact on annual runoff is located and the actual runoff of the sub-basin where the hydrological station is located to calculate the natural background concentration of pollutants in the water body of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0094] (8)

[0095] Where: is the pollutant load in the sub-basin where human activities have the least impact on runoff, is the actual runoff of the sub-basin, is the natural background concentration of the pollutant in the sub-basin.

[0096] S5. Calculate the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located.

[0097] In step S5, the specific process is as follows:

[0098] The grey water footprint of the sub-basin where the hydrological station with the least impact of human activities on annual runoff is calculated based on the natural background concentration of pollutants in the water body of the sub-basin where the hydrological station with the least impact of human activities on annual runoff is located. The calculation method is as follows:

[0099] (9)

[0100] Where: L is the pollution index load, unit is kg, C max is the maximum limit of pollutants, in mg / L. is the natural background concentration of the pollutant in the sub-basin.

[0101] Example 1

[0102] The Wei River is the largest tributary of the Yellow River and the main source of water for people's lives and production in Gansu and Guanzhong Plain. The Feng River is a first-level tributary on the right bank of the Wei River. It flows into the Wei River in Xianyang City, Shaanxi Province. The agricultural industry in the basin is well developed before the confluence. The water pollution of the Wei River is mainly caused by nutrients such as nitrogen and phosphorus remaining in agricultural production, which enter the river with the soil under the drive of rainfall runoff. The Wei River and the Feng River flow through many cities and counties (districts). The water environment conditions will affect the growth of crops along the coast and people's life and production. The water environment quality should be taken seriously by water resource managers. Therefore, this method selects the confluence basin of the Wei River and the Feng River to calculate the gray water footprint of the basin. The hydrological stations involved include 5 hydrological stations in Wushan, Qin'an, Qianyang, Linjia Village, and Xianyang, as well as 1 Tianshui Niubei surface water monitoring section, with a time span of 2000-2022.

[0103] S1. Obtain basic data, build a SWAT model, and verify the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section to obtain the optimal SWAT model;

[0104] Using the acquired spatial data and attribute data, a soil and water assessment model SWAT (Soil and Water Assessment Tool) for typical river confluence areas is established. Based on the runoff and river confluence parts of the SWAT model, the amount of water input into the river channel and the pollutant load of each sub-basin can be simulated. The hydrological process submodule divides the sub-basins and generates sub-basin outlets, and sets the outlets of the hydrological station basin to simulate and calculate the long-term hydrological cycle process of each sub-basin. The pollution load submodule in the SWAT model is used to simulate the transport and migration of pollutants from the river network to the basin outlet, and the pollution load of the sub-basin is calculated. Finally, a two-year warm-up period is set, and the model is run and output.

[0105] In this embodiment, five hydrological stations, namely Wushan, Qinan, Qianyang, Linjiacun, and Xianyang, and one Tianshui Niubei surface water monitoring section are selected. Figure 2 As shown in the figure, Xianyang Station is located downstream of Wushan Station, Qinan Station, Qianyang Station and Linjia Village Station. Therefore, when calibrating the water volume, Wushan, Qianyang, Qinan and Linjia Village must be calibrated first before the water volume of Xianyang Station can be calibrated. Through the above process, the fitting results of the simulated values ​​of the monthly runoff of each hydrological station and the measured flow value are as follows: Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 As shown in the figure, the correlation coefficient and Nash coefficient of each hydrological station during the calibration period and the verification period are much greater than the minimum requirement of the objective function, indicating that the SWAT model has good applicability in simulating the hydrological process in the study area. Figure 8 Shown are the simulation results of the ammonia nitrogen load model at the Tianshui Niubei water quality monitoring section.

[0106] S2. Use the optimal SWAT model to output the simulated value of the hydrological data of the sub-basin where the hydrological station is located, fit the simulated value with the corresponding hydrological data of previous years, output the monthly runoff of the sub-basin, calculate the annual runoff of the sub-basin, use the Marm-Kendall trend test to perform a trend test on the annual runoff of the sub-basin, and draw a trend graph;

[0107] (1)

[0108] Where: Q is the average monthly flow in the basin at the hydrological station; For time; F is the drainage area of ​​the hydrological station basin.

[0109] S3. Use the Pettit mutation method to determine the annual runoff mutation year in the trend diagram, and calculate the contribution rate of human activities to the change of annual runoff in the sub-basin based on the mutation year;

[0110] S4. Based on the contribution rate, determine the basin where the hydrological station is located with the smallest impact of human activities on the change of annual runoff, and calculate the natural background concentration of pollutants in the sub-basin where the hydrological station is located;

[0111] The runoff trend and mutation years of each hydrological station in the sub-basin of the confluence of the Weihe River and Fenghe River from 2000 to 2022 are as follows Fig. 9 , Fig.10 , Fig.11 , Fig.12 , Fig.13 It can be seen that the annual runoff of the sub-basins where the hydrological stations are located has an increasing trend during the study period. Except for the Qianyang Basin where P is greater than 0.05 and the growth trend is not significant, the P values ​​of the Wushan Basin, Qin'an Basin, Linjia Village Basin and Xianyang Basin are all less than 0.05, indicating that the annual runoff has a significant growth trend during the study period. Therefore, the Qianyang Basin is not considered in the discussion of the impact of human activities on annual runoff changes.

[0112] First, obtain the basic daily meteorological data required to calculate potential evapotranspiration and convert it into the slope of the saturated water vapor pressure curve. , net solar radiation , soil heat flux , wet and dry constant , wind speed at 2m height , average temperature , average saturated vapor pressure , Actual vapor pressure, Saturated Vapor Pressure Deficit Input into the Penman-Monteith formula modified by the Food and Agriculture Organization of the United Nations to calculate the long-term potential evapotranspiration of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0113] (2)

[0114] Where: is the long-term potential evapotranspiration of the sub-basin where the hydrological station is located, in mm / d. is the slope of the saturated water vapor pressure curve, in KPa / ℃, is the net solar radiation, in MJ m -2 d -1 , is the soil heat flux, in MJ m -2 d -1 , is the dry and wet constant, the unit is KPa / ℃, is the wind speed at a height of 2m, in m / s, is the average temperature in °C, is the average saturated water vapor pressure, in KPa, is the actual water vapor pressure, in KPa, is the saturated water vapor pressure difference, in KPa.

[0115] The long-term potential evapotranspiration and water-heat coupling balance equation of the sub-basin where the hydrological station is located are used to calculate the underlying surface parameters of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0116] (3)

[0117] Where: is the runoff depth in mm, is the rainfall in mm, is the actual evapotranspiration, in mm, It is the underlying surface parameter of the sub-basin where the hydrological station is located.

[0118] The elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located is calculated using the long-term potential evapotranspiration of the sub-basin where the hydrological station is located and the underlying surface parameters of the sub-basin where the hydrological station is located. , the calculation method is as follows:

[0119] (4)

[0120] The change of annual runoff in the sub-basin where the hydrological station is located under the influence of human activities is calculated by using the underlying surface parameters of the sub-basin where the hydrological station is located and the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located. The calculation method is as follows:

[0121] (5)

[0122] Where: The change of annual runoff under the influence of human activities, It is the change of underlying surface parameters of the sub-basin where the hydrological station is located.

[0123] The contribution rate of human activities to the change of annual runoff is calculated by using the change of annual runoff and runoff depth in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows:

[0124] (6)

[0125] Table 1 shows the variation characteristics of hydrological and underlying surface parameters in the sub-basin of the hydrological station.

[0126] Table 1 Variation characteristics of hydrological and underlying surface parameters in sub-basins of hydrological stations

[0127]

[0128] The changes in annual runoff in the basin where the hydrological station is located under the influence of human activities are calculated using the underlying surface parameters of the sub-basin where the hydrological station is located and the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located. The contribution rate of human activities to the change in annual runoff in the confluence of the Weihe River and Fenghe River is calculated using the changes in annual runoff and runoff depth under the influence of human activities. The contribution rate of human activities to the change in annual runoff in the confluence of the Weihe River and Fenghe River is shown in Table 2.

[0129] Table 2 Contribution of activities in the confluence of the Weihe River and Fenghe River to the change of annual runoff

[0130]

[0131] It can be seen that human activities in the Wushan Station basin have the greatest impact on the change of annual runoff, with a contribution rate of 29.1%. Human activities in the Qin'an basin have an inhibitory effect on the growth trend of annual runoff, with a contribution rate of -0.8%. Therefore, the ammonia nitrogen load in this basin is used to calculate the natural background concentration in the study area.

[0132] S5. Calculate the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located.

[0133] Embodiment 2:

[0134] The grey water footprint calculation method of the present invention is based on the Budyko hypothesis and the SWAT model, and the specific steps are as follows:

[0135] S1. Obtain basic data, build a SWAT model, and verify the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section from 2000 to 2022 to obtain the optimal SWAT model;

[0136] S2. Use the optimal SWAT model to output the monthly runoff of the sub-basin where the hydrological station is located, calculate the annual runoff of the sub-basin, use the Mann-Kendall trend test to perform a trend test on the annual runoff of the sub-basin, and draw a trend graph;

[0137] S3. Use the Pettit mutation method to determine the annual runoff mutation year in the trend chart, and calculate the contribution rate of human activities to the change of annual runoff in the sub-basin based on the mutation year;

[0138] S4. Based on the contribution rate, determine the basin where the hydrological station with the smallest impact of human activities on the annual runoff change is located, and calculate the natural background concentration of pollutants in the sub-basin where the hydrological station is located;

[0139] S5. Calculate the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located.

[0140] Embodiment 3:

[0141] The grey water footprint calculation method of the present invention is based on the Budyko hypothesis and the SWAT model, and the specific steps are as follows:

[0142] S1. Obtain basic data, build a SWAT model, and verify the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section from 2000 to 2022 to obtain the optimal SWAT model;

[0143] S2. Use the optimal SWAT model to output the monthly runoff of the sub-basins where the Wushan, Qin'an, Qianyang, Linjiacun, and Xianyang hydrological stations are located from 2000 to 2022, calculate the annual runoff of the sub-basins, use the Mann-Kendall trend test to conduct a trend test on the annual runoff of the sub-basins, and draw a trend chart;

[0144] S3. Use the Pettit mutation method to determine the annual runoff mutation year in the trend chart, and calculate the contribution rate of human activities to the change of annual runoff in the sub-basin based on the mutation year;

[0145] S4. Based on the contribution rate, determine the basin where the hydrological station with the smallest impact of human activities on the annual runoff change is located, and calculate the natural background concentration of pollutants in the sub-basin where the hydrological station is located;

[0146] S5. Calculate the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located.

Claims

1. Grey water footprint calculation method based on Budyko hypothesis and SWAT model, characterized in that: The specific steps are as follows: S1. Obtain basic data, build a SWAT model, and verify the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section to obtain the optimal SWAT model; S2. Use the optimal SWAT model to output the monthly runoff of the sub-basin where the hydrological station is located, calculate the annual runoff of the sub-basin, use the Mann-Kendall trend test to perform a trend test on the annual runoff of the sub-basin, and draw a trend graph; S3. Use the Pettit mutation method to determine the annual runoff mutation year in the trend chart, and calculate the contribution rate of human activities to the change of annual runoff in the sub-basin based on the mutation year; S4. Based on the contribution rate, determine the basin where the hydrological station with the smallest impact of human activities on the annual runoff change is located, and calculate the natural background concentration of pollutants in the sub-basin where the hydrological station is located; The step S4 is specifically as follows: Compare the multiple contribution rates obtained in step S3, select the sub-basin where the hydrological station with the lowest contribution rate is located, output the ammonia nitrogen load of the sub-basin where the hydrological station with the smallest impact on annual runoff is located based on the optimal SWAT model obtained in step S1, and use the ammonia nitrogen load of the sub-basin where the hydrological station with the smallest impact on annual runoff is located and the actual runoff of the sub-basin where the hydrological station is located to calculate the natural background concentration of pollutants in the water body of the sub-basin where the hydrological station is located. The calculation method is as follows: （9） Where: is the pollutant load in the sub-basin where human activities have the least impact on runoff, is the actual runoff of the sub-basin, is the natural background concentration of the pollutant in the sub-watershed; S5. Calculate the grey water footprint of pollutants in the sub-basin where the hydrological station is located based on the natural background concentration of pollutants in the sub-basin where the hydrological station is located; The step S5 is specifically as follows: The grey water footprint of the sub-basin where the hydrological station with the least impact of human activities on annual runoff is calculated based on the natural background concentration of pollutants in the water body of the sub-basin where the hydrological station with the least impact of human activities on annual runoff is located. The calculation method is as follows: （10） Where: L is the pollution index load, unit is kg, is the maximum limit of pollutants, in mg / L. is the natural background concentration of the pollutant in the sub-basin.

2. The grey water footprint calculation method based on the Budyko hypothesis and the SWAT model according to claim 1, characterized in that: The basic data in step S1 include spatial data and attribute data, the spatial data are DEM elevation data, soil and land use spatial distribution data, and the attribute data are meteorological data, soil and land use type data, and agricultural management measures data.

3. The grey water footprint calculation method based on the Budyko hypothesis and the SWAT model according to claim 2, characterized in that: The SWAT model in step S1 includes a runoff generation and convergence submodule, a hydrological process submodule, and a pollution load submodule; the verification of the SWAT model is divided into a hydrological verification and a water quality verification.

4. The grey water footprint calculation method based on the Budyko hypothesis and the SWAT model according to claim 3, characterized in that: The specific operation of verifying the SWAT model based on the historical hydrological data of the sub-basin where the hydrological station is located and the historical water quality data of the water quality monitoring section in step S1 is as follows: By directly connecting the SWAT model output results with the SWAT-CUP calibration tool, the runoff parameters in the SWAT model output parameters are first calibrated and verified using the runoff sensitive parameters, and the calibration is performed from upstream to downstream. The calibration period and verification period of the SWAT model are calculated according to the calculation formula of the correlation coefficient R² and the Nash coefficient Ens. First, the hydrological process of the SWAT model is calibrated and verified using the hydrological data of the sub-basin where the hydrological station is located. During the calibration and verification period, the SWAT model meets the requirements of the correlation coefficient R² not less than 0.6 and the Nash coefficient Ens not less than 0.5; The water quality data of the water quality monitoring section over the years are then used to calibrate and verify the water quality process of the SWAT model. When the correlation coefficient R² is not less than 0.6 and the Nash coefficient Ens is not less than 0.5 during the calibration and verification period, the optimal SWAT model is obtained; The calculation methods of correlation coefficient and Nash coefficient are as follows: （1） （2） Where: Q o t and Q s t are the measured and simulated values ​​of the t-th runoff, and are the measured average and simulated average of runoff during the simulation period, respectively.

5. The grey water footprint calculation method based on the Budyko hypothesis and the SWAT model according to claim 4, characterized in that: The calculation method of the sub-basin annual runoff in step S2 is as follows: （3） Where: Q is the average monthly flow in the basin at the hydrological station; For time; F is the drainage area of ​​the hydrological station basin.

6. The grey water footprint calculation method based on the Budyko hypothesis and the SWAT model according to claim 5, characterized in that: The step S3 is specifically as follows: S301: Use the Penman-Monteith formula modified by the Food and Agriculture Organization of the United Nations to calculate the long-term potential evapotranspiration of the sub-basin where the hydrological station is located. The calculation method is as follows: （4） Where: is the long-term potential evapotranspiration of the sub-basin where the hydrological station is located, in mm / d. is the slope of the saturated water vapor pressure curve, in KPa / ℃, is the net solar radiation, in MJ m -2 d -1 , is the soil heat flux, in MJ m -2 d -1 , is the dry and wet constant, the unit is KPa / ℃, is the wind speed at a height of 2m, in m / s, is the average temperature in °C, is the average saturated water vapor pressure, in KPa, is the actual water vapor pressure, in KPa, is the saturated water vapor pressure difference, in Kpa; S302: Using the long-term potential evapotranspiration and water-heat coupling balance equation of the sub-basin where the hydrological station is located, calculate the underlying surface parameters of the sub-basin where the hydrological station is located. The calculation method is as follows: （5） Where: is the rainfall in mm, is the actual evapotranspiration, in mm, The underlying surface parameters of the sub-basin where the hydrological station is located; S303: Calculate the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located by using the long-term potential evapotranspiration of the sub-basin where the hydrological station is located and the underlying surface parameters of the sub-basin where the hydrological station is located , the calculation method is as follows: （6） S304: Using the underlying surface parameters of the sub-basin where the hydrological station is located and the elastic coefficient of the underlying surface parameters of the sub-basin where the hydrological station is located, calculate the change in annual runoff in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows: （7） Where: is the runoff depth in mm, The change of annual runoff under the influence of human activities, The changes in the underlying surface parameters of the sub-basin where the hydrological station is located; S305: The contribution rate of human activities to the change of annual runoff is calculated by using the change of annual runoff and runoff depth in the sub-basin where the hydrological station is located under the influence of human activities. The calculation method is as follows: （8）。

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