Improved method of output-inflow coefficient model for load estimation in large river basins

Abstract

The present invention discloses a method for improving the load estimation of the output-into-river coefficient model in a large watershed. A nested sub-basin is set for the measured watershed, a Latin hypercube sampling method is used to initialize the population, and a genetic algorithm is used in combination with relevant data of the nested watershed to solve the optimized watershed pollution equation to calculate the land use output coefficient of different sub-basins; the surface runoff factor, soil water leaching factor, landscape interception factor and soil erosion factor of the corresponding watershed are coupled to calibrate the output coefficient of pollution sources in different spatial units. At the same time, the ratio of the product of the rainfall erosivity factor and the terrain influence factor to the product of the cost distance factor and the vegetation coverage factor is used to calibrate the river inlet coefficient of pollution sources in different spatial units; a large watershed non-point source pollution load estimation model is constructed based on the calibrated output coefficient and river inlet coefficient, and the pollutant output and river inlet under various land use types are calculated to improve the accuracy of the estimation of non-point source pollution in large-scale watersheds.

Description

Technical Field

[0001] The invention relates to the field of measurement technology, in particular to a method for estimating the load of a large river basin by improving an output-inflow coefficient model. Background Art

[0002] When estimating the pollution load in a large river basin, the corresponding pollutant output coefficient must be determined according to the specific land use type, hydrology, topography, etc. of the local area. The existing classic output coefficient model mainly considers the impact of different land use types, rural population, socioeconomic conditions and different fertilization methods on non-point source pollution; the classic output coefficient model is improved from different angles. For example: the sedimentation emission coefficient is introduced to construct an erosion-scaled total phosphorus (TP) output coefficient model; the key influencing factors such as rainfall factors, topography, surface runoff, groundwater runoff and interception factors are incorporated to improve the output coefficient model of the upper reaches of the Yellow River; for different land use types, based on the output coefficient method, the pollution load coefficient is introduced, and a mathematical model for calculating the non-point source nitrogen and phosphorus load in the upper reaches of the Three Gorges Reservoir is established.

[0003] In some existing technologies, when constructing the output coefficient model, the relationship between factors such as rainfall, slope, soil characteristics, and land use and the output coefficient is further considered. For example, based on the main framework of driving factor-transmission factor-infiltration factor-interception factor, key influencing factors such as rainfall factor, topography, surface runoff, groundwater runoff, and interception factor are incorporated to improve the output coefficient model of the upper mainstream area of ​​the Yellow River;

[0004] However, most of the improved output coefficient models are studied on small watersheds, while there are relatively few output coefficient simulation studies on non-point source pollution loads in large watersheds such as the Yellow River Basin. Therefore, there is an urgent need for an output-inlet coefficient model to realize the application of large watershed load estimation. Summary of the invention

[0005] The purpose of the present invention is to provide a method for estimating the load of a large river basin by improving the output-inflow coefficient model, which can effectively solve the problems existing in the above-mentioned prior art.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solution: a method for estimating the load of a large river basin by improving the output-inflow coefficient model, comprising the following steps:

[0007] For the nested sub-basins of the watershed to be tested, the Latin hypercube sampling method is used to initialize the population, and the optimized pollution production equation is solved by using the genetic algorithm in the nested watershed data to calculate the land use output coefficients of different sub-basins;

[0008] Calculate and couple the surface runoff factor, soil water leaching factor, landscape interception factor and soil erosion factor of the corresponding watershed to calibrate the output coefficients of pollution sources in different spatial units;

[0009] The ratio of the product of rainfall erosivity factor and terrain influence factor to the product of cost distance factor and vegetation coverage factor is used to calibrate the river entry coefficient of pollution sources in different spatial units.

[0010] A large-basin non-point source pollution distribution model is constructed through the calibrated output coefficient and the calibrated river inflow coefficient, and the pollutant output and river inflow under various land use types are calculated.

[0011] Preferably, the land use is divided into n types, and the output coefficients of different land use types are , where i represents the pollutant type and j represents the land use type; m ( ) small watersheds, according to the mass conservation principle of pollutant input and output in the watershed, the corresponding pollutant output equation can be established for each small watershed:

[0012] ;

[0013] in, is the annual load of the ith pollutant in the basin; is the annual load of point source pollution; is the annual load of the i-th pollutant generated by rural and urban populations and livestock and poultry farming; is the output coefficient of pollutant i on the jth land use type in the watershed; is the area of ​​the jth land use type in the basin; is the amount of the i-th pollutant input by rainfall;

[0014] Genetic algorithm is used to optimize the pollution equation. For pollutant i, m pollution equations are established for m small watersheds respectively, thus forming a set of equations:

[0015] ;

[0016] in, is the annual average concentration of the i-th pollutant in the t-th small watershed; is the annual runoff of the tth small watershed; is the loss coefficient of the i-th pollutant in the t-th small watershed; For the The average monitoring concentration of the pollutant at the outlet of the t-th small watershed during the dry season; is the total flow at the outlet of the t-th small watershed during the dry season; is the dry season time of the tth small watershed.

[0017] Preferably, the pollution sources include total phosphorus pollution sources and total nitrogen pollution sources.

[0018] Preferably, the output coefficient of total nitrogen pollution sources in different spatial units is calibrated by the ratio of the normalized values ​​of surface runoff factor and soil water leaching factor to the landscape interception factor:

[0019] ;

[0020] The output coefficients of total phosphorus pollution sources in different spatial units are calibrated by the ratio of the normalized values ​​of surface runoff factor and soil erosion factor to the landscape interception factor:

[0021] ;

[0022] in, and is the improved output coefficient, RI is the surface runoff factor, LI is the soil water leaching factor, K is the soil erosion factor, LII is the landscape interception factor, EC N With EC P It is the result of using genetic algorithm to obtain the original output coefficients in the nested watershed.

[0023] Preferably, the rainfall erosivity factor , terrain influence factors and cost distance factor is proportional to the amount of pollutants, while the vegetation coverage factor Inversely proportional to the amount of pollutants; for different land use types, the rainfall erosivity factor and terrain factors Product and cost distance factor Vegetation coverage factor The ratio of the product of is used as the weight factor of total nitrogen and total phosphorus to calibrate the river entry coefficient of pollution sources in different spatial units;

[0024] Calibration of river inflow coefficients of total nitrogen pollution sources in different spatial units :

[0025] ;

[0026] Calibration of river inflow coefficients of total phosphorus pollution sources in different spatial units :

[0027] ;

[0028] in, is the concentrated inflow coefficient of total nitrogen into the river, are the centralized river inflow coefficients of total phosphorus, dimensionless, with values ​​of 0.3 and 0.2 respectively.

[0029] Preferably, the pollution factor CI, slope factor S, and slope length factor L are reclassified, and these factors are combined with the cost distance to the mainstream of the Yellow River. The cost distance factor is constructed by taking the logarithm of the cost distance. : ,in, , , Represent the results of each factor after reclassification.

[0030] Preferably, the pollution factor CI is: ;

[0031] Slope factor S:

[0032] ;

[0033] Slope length factor L:

[0034]

[0035] in, It is the cumulative water catchment parameter at a certain point in the basin; and are the slope and slope length extracted from the digital elevation model, is the slope length index, and its value varies with the slope changes with the changes.

[0036] Preferably, the estimation of non-point source pollution in a large river basin and the distribution model of pollution source areas are:

[0037] ;

[0038] is the total amount of pollutant i entering the river; For the improved or The output coefficient of; i is the type of pollutant; is the area of ​​the jth land use type in the basin; is the output coefficient of pollutant i on the jth land use type in the basin, is the number of livestock and poultry slaughtered or the population, is the amount of the i-th pollutant input by rainfall, For the improved or The river entry coefficient.

[0039] Beneficial effects: The present invention improves the output coefficient by calculating and coupling the surface runoff factor, soil water leaching factor, landscape interception factor and soil erosion factor of the corresponding watershed, making it more portable and easy to obtain data. It also introduces factors such as rainfall erosion, terrain influence and cost distance, and constructs the cost distance factor by taking the logarithm of the cost distance, which effectively reduces the relative error of the total nitrogen and total phosphorus entering the river caused by land use types, and significantly improves the accuracy of estimating the amount of non-point source pollution entering the river in large watersheds. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0041] In the attached picture:

[0042] Figure 1 It is a framework diagram of an output-into-river model for improving output coefficients and input coefficients of the present invention;

[0043] Figure 2 The spatial distribution of total nitrogen and total phosphorus output and river inflow coefficients of the present invention (a: initial TN output coefficient; b: initial TP output coefficient; c, improved TN output coefficient; d: improved TP output coefficient; e: improved TN river inflow coefficient);

[0044] Figure 3 This is the distribution map of urban and rural population and their surface sources in the present invention; a: urban population; d: rural population, c: urban total nitrogen production, d: rural total nitrogen production; e: urban total phosphorus production; f: rural total phosphorus production;

[0045] Figure 4 It is the livestock and poultry breeding output and TN and TP emissions of the present invention; a: the number of livestock and poultry, b: total nitrogen pollution production; c: total phosphorus pollution production;

[0046] Figure 5 It is a comparison chart of the output coefficients of the improved EC-ICM and the pollution generation equation of the present invention;

[0047] Figure 6 This is a comparison chart of the surface sources generated by land use, population and livestock and poultry in the present invention; a: land use type pollution discharge, b: urban and rural population pollution discharge, c: livestock and poultry breeding pollution discharge, d: total nitrogen and total phosphorus pollution discharge in the entire basin);

[0048] Figure 7 It is the total nitrogen and total phosphorus pollution zoning map of the present invention. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are described below in conjunction with the drawings in the embodiments of the present invention. The terms used in the implementation mode of the present invention are only used to explain the specific embodiments of the present invention, and are not intended to limit the present invention. The embodiments of the present application are described below in conjunction with the drawings.

[0050] Taking the Yellow River Basin as an example, the pollution sources are total phosphorus pollution sources and total nitrogen TP pollution sources TN as examples:

[0051] Example: Reference Figure 1 As shown, taking the counties and districts in the nine provinces in the Yellow River Basin as units, the two indicators of total nitrogen and total phosphorus were selected from 2021 to 2023 as the evaluation objects of 432 counties and districts in the Yellow River Basin; the output coefficient-into-river coefficient nonpoint source model (EC-ICM) consists of two parts: the improved output coefficient (EC) and the into-river coefficient (IC).

[0052] The output coefficient was improved as follows: Genetic algorithm and Latin hypercube sampling were used to calculate the output coefficient of different land use types, and the ratio of the normalized value of surface runoff (RI) and soil water leaching (LI) or soil erosion factor (K) (Norm (RI+LI)) to the landscape interception factor (LII) was used as the weight factor to improve the output coefficient (EC);

[0053] Where: Output coefficients for calibrating total nitrogen pollution sources in different spatial units:

[0054] ;

[0055] Expands to:

[0056] ;

[0057] Calibrate the output coefficients of total phosphorus pollution sources in different spatial units:

[0058] ;

[0059] Expands to:

[0060] ;

[0061] and is the improved output coefficient, EC N With EC P It is the result of using the genetic algorithm to obtain the original output coefficients in the nested watershed;

[0062] The improvement of the river inflow coefficient is as follows: , terrain influence factors and cost distance factor is proportional to the amount of pollutants, while the vegetation coverage factor Inversely proportional to the amount of pollutants; for different land use types, the rainfall erosivity factor and terrain factors Product, and cost distance factor and vegetation coverage factor The ratio of the products is used as the weight factor of total nitrogen and total phosphorus to calibrate the river entry coefficients of pollution sources in different spatial units and realize their spatial representation.

[0063] Among them: Calibration of the river inflow coefficient of total nitrogen pollution sources in different spatial units :

[0064] ;

[0065] Calibration of river inflow coefficients of total phosphorus pollution sources in different spatial units :

[0066] ;

[0067] The pollution factor CI, slope factor S, and slope length factor L are reclassified, and these factors are combined with the cost distance to the mainstream of the Yellow River. The cost distance factor is constructed by taking the logarithm of the cost distance. : ,in, , , Represent the results of each factor after reclassification.

[0068] Pollution factor CI: ;

[0069] Slope factor S:

[0070] ;

[0071] Slope length factor L:

[0072] ;

[0073] in, It is the cumulative water catchment parameter at a certain point in the basin; and are the slope and slope length extracted from the digital elevation model, is the slope length index, and its value varies with the slope changes due to changes in

[0074] Based on the above: The estimation of non-point source pollution in large watersheds and the pollution source area distribution model (EC-ICM model) are:

[0075] ;

[0076] is the total amount of pollutant i entering the river; For the improved or The output coefficient of; i is the type of pollutant; is the area of ​​the jth land use type in the basin; is the output coefficient of pollutant i on the jth land use type in the basin, is the number of livestock and poultry slaughtered or the population, is the amount of the i-th pollutant input by rainfall, For the improved or The river entry coefficient;

[0077] For the total amount of total nitrogen pollutants entering the river:

[0078] ;

[0079] For the total amount of total phosphorus pollutants entering the river:

[0080] .

[0081] The pollutant concentration measured data C (mg / L) and the monthly average runoff Q (m2) of the Luokou hydrological station in the mainstream of the Yellow River Basin from 2021 to 2023 provided by the Yellow River Network were used. 3 ), estimate the pollutant flux at the outlet of the Yellow River Basin (Eq. );

[0082] The traditional ECM and improved EC-ICM models were used to estimate the TN and TP loads in the Yellow River Basin, and the errors between them and the measured values ​​at the outlet of the basin were determined. Simulated value with model The relative error (%), verifying the accuracy of the constructed TN and TP output coefficient models.

[0083] ;

[0084] ;

[0085] The Latin hypercube sampling method was used to initialize the population-different land use types in the nested sub-basins of the Yellow River Basin. The population size was set to 4000, the number of iterations was 2000, the maximum number of iterations was 200, and the initial ranges of the crossover rate and mutation rate were set to (0.5, 0.9) and (0.1, 0.6). The relative error between the true value and the predicted value of total nitrogen or total phosphorus was used as the objective function, and the genetic algorithm was used to optimize the pollution production equations to obtain the output coefficients of different land use types in the nested basins such as KNH, XHY, BYGL, TG, XY and LK:

[0086] The land use is divided into n types, and the output coefficients of different land use types are , where i represents the pollutant type and j represents the land use type; m ( ) small watersheds, according to the mass conservation principle of pollutant input and output in the watershed, the corresponding pollutant output equation can be established for each small watershed:

[0087] ;

[0088] in, is the annual load of the ith pollutant in the basin; is the annual load of point source pollution; is the annual load of the i-th pollutant generated by rural and urban populations and livestock and poultry farming; is the output coefficient of pollutant i on the jth land use type in the watershed; is the area of ​​the jth land use type in the basin; is the amount of the i-th pollutant input by rainfall;

[0089] Genetic algorithm is used to optimize the pollution equation. For pollutant i, m pollution equations are established for m small watersheds respectively, thus forming a set of equations:

[0090] ;

[0091] in, is the annual average concentration of the i-th pollutant in the t-th small watershed; is the annual runoff of the tth small watershed; is the loss coefficient of the i-th pollutant in the t-th small watershed; For the The average monitoring concentration of the pollutant at the outlet of the t-th small watershed during the dry season; is the total flow at the outlet of the t-th small watershed during the dry season; is the dry season time of the tth small watershed.

[0092] There are 6 types of land use in the Yellow River Basin (paddy fields and dry land are combined into cultivated land), namely cultivated land, forest land, grassland, urban land, rural settlements and others; there are 9 types of sub-basins, namely TNH, XHY, BYGL, WZJX, SHHK, TG, LM, XY and LK;

[0093] The output coefficients of different land use types after the genetic algorithm optimizes the pollution generation equation are shown in the following table:

[0094] <![CDATA[Indicator (kg / km 2 )]]> arable land woodland grassland Urban land Rural settlements other TN 333.23 166.63 233.40 266.50 233.30 166.83 TP 6.50 1.75 5.90 3.55 3.50 4.50

[0095] The output coefficient error (Re) at the nested watershed scale is shown in the following table:

[0096] Nested subbasins KNH XHY BYGL TG XY LK LK′ TN 31.58% 48.50% 27.51% 20.22% 43.19 18.55% 13.12% Nested subbasins SHHK Wlq LM TG XY LK LK′ TP 16.28% 5.41% 10.65% 2.93% 24.80% 6.92% 0.23%

[0097] The statistical results of total nitrogen or total phosphorus load at LK in the Yellow River Basin were 72739.01 and 920.53 tons, respectively. The simulated values ​​(LK′) using the improved EC-ICM were 63194.4218 and 918.37 tons, respectively, with relative errors of 13.12% and 0.23%, which were significantly better than the results obtained based on the genetic algorithm in the nested basin (LK), that is, the TN and TP loads were 59244.77 and 860.97 tons, with relative errors of 18.55% and 13.12%, respectively; after the improved EC-ICM model, the simulation relative error of TN was reduced from 18.55% in the nested basin of the pollution equation to 13.12%, and the relative error of TP was reduced from 6.92% to 0.23%.

[0098] The results show that the EC-ICM model, which takes into account processes such as rainfall runoff, soil water leaching, landscape interception, soil erosion and topography, has a strong simulation ability and is suitable for estimating total nitrogen and total phosphorus loads in large-scale watersheds with scarce data.

[0099] In addition, the improved output-inflow coefficient model (EC-ICM) in the present invention adopts the calculation unit division method of the traditional output coefficient model, converts the layers such as surface runoff factor, soil water leaching factor, landscape interception factor and cost distance factor into raster layers with an accuracy of 30 meters, and then generates the spatial distribution map of total nitrogen and total phosphorus inflow into the river per unit pixel through the raster calculator. The results show that its performance is better than the traditional centralized output coefficient model.

[0100] refer to Figure 2 As shown in the figure, the spatial distribution of total nitrogen and total phosphorus output and inflow coefficients, in which a: initial TN output coefficient; b: initial TP output coefficient; c: improved TN output coefficient; d: improved TP output coefficient; e: improved TN inflow coefficient; among which, the initial output coefficient is the result calculated by the nested watershed equation group of the genetic algorithm mentioned above;

[0101] The initial output coefficients of different land use types obtained by the algorithm optimization equation group are a numerical value for each land use type, and the spatial variability is low. In contrast, the output coefficients and river inflow coefficients of the improved EC-ICM model have a wider range of variation, and the coefficient distribution related to TN has a clear dividing line in space. The grassland area is 48.47% of the entire basin area, and the initial output coefficients of total nitrogen and total phosphorus are 233.40, respectively. and 5.90 The second largest area is cultivated land, accounting for 24.68% of the entire basin area, and the initial output coefficients of total nitrogen and total phosphorus are 332.23 respectively. and 5.90 , forest land accounts for 13.33% of the entire basin area, and and 1.75 .

[0102] according to Figure 2 The initial output coefficients, improved output coefficients and improved inflow coefficients of total nitrogen and total phosphorus are shown in Figure 1. The improved output and inflow coefficients of total nitrogen and total phosphorus have greater spatial variability than the initial output coefficients. Figure 2 From the bar graph of (c), we can see that the weight of the improved total nitrogen output coefficient (EC) is mainly concentrated between 0 and 3.58, with a maximum value of 4.27, a minimum value of 0, and an average value of 1.00. Different types of underlying surface landscape vegetation have an effective interception, retention, and degradation effect on non-point source pollutants entering the water body, resulting in the EC change of TN increasing from northwest to southeast. The EC with higher coefficients is mainly concentrated in the hilly gully area and downstream areas of the middle reaches of the Yellow River. This is mainly because the annual average water erosion and wind erosion intensity in the hilly gully area is higher, and the downstream North China Plain is greatly affected by rainfall erosion. The EC of TP has no obvious spatial difference in the entire basin. On the contrary, Figure 2 (d) The bar graph shows that the weight of TP inflow coefficient is relatively evenly distributed in the basin, mainly concentrated between 0 and 1.70, with a maximum value of 5.46, a minimum value of 0, and an average value of 0.7. Figure 2 e) is mainly concentrated between 0 and 4.1, with a maximum value of 23.94 and an average value of 1.00. The improved inflow coefficient (IC) takes into account the rainfall erosivity factor ( )、Terrain influence factor( )、Vegetation coverage( ) and the cost distance factor ( ); Compared with the traditional centralized model, it can better reflect the spatial differences in the generation of different pollutants; IC is significantly affected by topography and vegetation cover. In areas with large terrain undulations or large slopes, erosion is more intense, so the larger values ​​of the river entry coefficient are concentrated in the upper reaches of the Yellow River.

[0103] Based on the above: the improved EC-ICM has a wider range of pollutant load variation, and the TN distribution has a clear dividing line; the EC is smaller in the sub-basins controlled by BYGL, SHHK and WZJX, while the spatial differences in other basins are more significant, especially in the sub-basins controlled by TNH and LK.

[0104] Therefore, compared with the traditional centralized model, considering the terrain influence factor ( )、Vegetation coverage( ) and the cost distance factor ( ), the improvement of the river entry coefficient by these factors can better reflect the spatial differences in the generation of different pollutants.

[0105] Estimation and verification of non-point source pollution load:

[0106] Non-point source pollution load of urban and rural residents:

[0107] Based on the population data of the counties and districts through which the Yellow River flows, combined with the living output coefficients of rural and urban residents, the following table is shown:

[0108] index <![CDATA[Urban population (t·10,000 people -1 ·a -1 )]]> <![CDATA[(t·ten thousand people of rural population -1 ·a -1 )]]> <![CDATA[Large livestock (t·(10 4 ca) -1 ·a -1 )]]> <![CDATA[Pig(t·(10 4 ca) -1 ·a -1 )]]> <![CDATA[Sheep(t·(10 4 ca) -1 ·a -1 )]]> <![CDATA[Poultry (t·(10 4 ca) -1 ·a -1 ) <!-- 8 -->]]> EC_TP 2.5 1.6 2.2 1.4 0.45 0.05 EC_TN 24.8 15.8 102.1 7.4 4 0.4 IC_TP 0.1 0.1 0.2179 0.1417 0.045 0.0054 IC_TN 0.1 0.1 0.2179 0.1417 0.045 0.0054

[0109] refer to Figure 3 As shown in Figure 2, the total load of TN and TP in rural and urban life in the Yellow River Basin was estimated; the number of counties controlled by the TNH, XHY, BYGL, LM, SHHK, WJZX, TG, XY, and LK sub-basins in the Yellow River Basin are 20, 56, 24, 44, 9, 25, 112, 37, and 105, respectively.

[0110] In general, the trend of rural population change is basically the same as that of urban population, but the number is slightly less than that of urban population. This is because with the development of urbanization, the urbanization rate of the national permanent population has reached 66.16%. In addition, due to the influence of administrative divisions, policies or economic development, the population of counties in the downstream sub-basin is relatively large, resulting in significant spatial differences in the population distribution of the entire basin.

[0111] Non-point source pollution load of livestock and poultry farming:

[0112] refer to Figure 4As shown in the figure, from the sub-basin level, the output of large livestock and sheep is evenly distributed in each sub-basin, with standard deviations of 0.07 and 0.06 respectively; the output of large livestock and sheep in the XHY sub-basin is the highest, 1.5415 million and 11.0370 million respectively, contributing 24.47% to TN; followed by the BYGL sub-basin, including 992,300 large livestock (contributing 14.96% to TP) and 7.8154 million sheep; the contribution rate of livestock and poultry in the two sub-basins of XHY and BYGL is 33.20%; except for TNH and XY, the output of sheep in other sub-basins is between 5.1 and 11 million, and the annual average nitrogen emission of sheep in these sub-basins is 8.89 times that of phosphorus; the output of pigs is unevenly distributed in the sub-basins, with a standard deviation of 0.12. The LK sub-basin has the highest pig output, which is 12.9577 million heads, and its total contribution rate to the entire basin is 36.33%; followed by the TG sub-basin, with an average annual output of 9.9743 million heads, and a total contribution rate of 27.97% to the entire basin; the pig and poultry output in the TNH sub-basin only account for 0.3% of the total basin, and the contribution rate to pollutants is extremely low; the contribution rate of pigs to the basin in other sub-basins is between 2.80% and 9.10%; the distribution of poultry output in the sub-basin is the most uneven, with a standard deviation of 0.19. The LK sub-basin has the highest poultry output, which is 373.3870 million heads, and its total contribution rate to the entire basin is 58.92%; followed by the TG sub-basin, where sheep contribute 23.00% to the entire basin.

[0113] In general, large livestock and sheep in the LK and TG sub-basins contributed the most to the basin discharge, reaching 40.20% and 45.34%, respectively, while pigs and poultry in the TG and BYGL sub-basins contributed the most to the basin discharge, reaching 64.30% and 81.92%, respectively.

[0114] Estimation of non-point source pollution emissions from land use:

[0115] refer to Figure 5 As shown, the total nitrogen and total phosphorus loads of land use calculated by the improved output-inflow coefficient model (EC-ICM) and the pollution production equation optimized by the genetic algorithm are shown; the improved EC-ICM reduces the total nitrogen load of the entire basin, while the total phosphorus load increases; for total nitrogen, the total nitrogen discharge calculated by the improved TN output coefficient model is 6016.10 tons less than that of the model before the improvement; from the perspective of different land use types, the loads of cultivated land, forest land, urban construction land and rural settlements have all increased, among which the load of forest land has increased the most, reaching 649.87 tons; while the loads on grassland and other land use types have slightly decreased, by 4975.58 and 1927.96 tons respectively;

[0116] For total phosphorus, the improved output coefficient model calculated that the total nitrogen discharge was 105.77 tons higher than that of the model before the improvement; the total phosphorus load increased in each land use type, among which grassland had the largest increase of 51.77 tons, accounting for 48.94% of the total phosphorus increase; followed by cultivated land, with TP increasing by 35.65 tons, with a contribution rate of 33.70%; overall, the improved EC-ICM model reduced the TN load of the entire basin, while increasing the TP load.

[0117] Among the land use types in the Yellow River Basin, forest land, cultivated land and grassland account for 48.47%, 24.68% and 13.22% of the total area of ​​the basin, respectively, and a total of 86.37% of the total area; the area of ​​urban land and rural settlements is relatively small, accounting for only 2.80% of the total area of ​​the basin, and other types account for less than 10.85%. At the level of land use types, grassland produces the most pollution, with TN and TP entering the river of 24203.81 and 327.73 tons respectively, and the contribution rates to TN and TP are 46.70% and 54.10% respectively; followed by cultivated land, which produces TN and TP entering the river of 20114.87 and 261.79 tons respectively, and the contribution rates to total nitrogen and total phosphorus are 38.19% and 31.12% respectively. Forest land can dilute TN, dissolved phosphorus and large pollution particles. Its TN and TP are 5388.89 and 37.52 tons respectively, with a total contribution rate of 4.36%. The contribution rate of pollutants entering rivers from rural settlements is about twice that of urban land. The contribution rates of TN are 2.24% and 1.45% respectively, and the contribution rates of TP are 0.91% and 0.51%.

[0118] In summary, cultivated land, grassland, population and livestock and poultry breeding activities in the Yellow River Basin have different degrees of impact on non-point source (NPS) pollution. Grassland and cultivated land are the main sources of NPS pollution, while forest land has the lowest EC. It is recommended to carry out land reclamation and afforestation to reduce pollutants discharged into water bodies.

[0119] Calculation of three types of non-point source loads into rivers:

[0120] Based on the data on the output of urban and rural population, large livestock, sheep, pigs and poultry summarized in the statistical yearbook, and combined with the output and river inflow coefficients in the output coefficient table of rural and urban residents, the river inflow of TN and TP was calculated. The results are as follows: Figure 6As shown. Land use change is one of the main driving factors of hydrological change, so the land use type in the Yellow River Basin is the main source of TN non-point source pollution, with a contribution rate of 57.15%. At present, TN non-point source pollution in the Yellow River Basin is mainly concentrated on cultivated land, with a contribution rate of 38.19% in terms of land use. For TP inflow into the river, urban population is the main source of pollution, followed by livestock and poultry, with contribution rates of 38.70% and 31.07% respectively. Since the largest source of total phosphorus pollution in the Taihu Lake Basin is rural domestic sewage, while in the Yellow River Basin it is urban population; this difference is mainly due to the high level of urbanization in the Yellow River Basin, especially in the middle and lower reaches of the Yellow River, the number of urban population is much larger than the number of rural population, so the urban population has become the main source of TP in the Yellow River Basin.

[0121] With the increase of urbanization rate, the urban population in the Yellow River Basin has exceeded the rural population; the total amount of total nitrogen and total phosphorus pollution loads entering the river generated by the rural population in different sub-basins is similar to that of the urban population. At the same time, the pollutant output coefficient generated by the urban population is higher than that of the rural population ( Figure 6 (b)), so the amount of pollutants discharged and discharged into the river in urban areas is higher than that in rural areas; in the TG and LK sub-basins with large cultivated land areas and large populations, the TP discharged into the river by the urban population is 109.93 tons and 8595 tons, and the TN discharged into the river is 5452.74 tons and 4263.03 tons, respectively. In the XHY sub-basin, the TN discharged into the river by the urban population is 2216.79 tons, and the TP discharged into the river is 44.69 tons; the population in the XY sub-basin is about half of that in the XHY sub-basin, and its TN discharged into the river is 1254.84 tons, and its TP discharged into the river is 25.30 tons.

[0122] In the Yellow River Basin, the TP input into the river from livestock and poultry farming is 316.75 tons, and the TN input into the river is 20146.31 tons, of which pigs and poultry contribute the most; pigs produce the largest amount of TN input into the river, accounting for about 29.95% of the TN in the Yellow River Basin, which may be because the output coefficient of pigs is much higher than that of sheep and poultry; the number of large livestock slaughtered in the Yellow River Basin is 17.66% of that of pigs, the number of sheep slaughtered is 1.60 times that of pigs, and the number of poultry slaughtered is 17.78 times that of pigs; therefore, the total nitrogen and total phosphorus generated by livestock and poultry farming mainly come from pigs and poultry (see Figure 6(c)). The sub-basins with the most serious pollutant inflows are mainly TG and LK. The livestock and poultry in these two sub-basins contribute 31.55% to the TN inflow and 55.07% to the TP inflow. In the TG sub-basin, the total nitrogen inflows from large livestock, pigs, sheep and poultry were 2023.00, 1429.70, 2644.95 and 142.97 tons, respectively, and the TP inflows were 43.59, 160.84, 500.40 and 17.87 tons, respectively. In the LK sub-basin, the TN inflows from large livestock, pigs, sheep and poultry were 1102.66, 945.08, 1748.39 and 94.51 tons, respectively, and the TP inflows were 23.76, 106.32, 330.78 and 11.81 tons, respectively.

[0123] Depend on Figure 6 (d) It can be seen that the order of TN and total phosphorus inflow into the river in the sub-basins from high to low is: TG, LK, XHY, LM, XY, BYGL, downstream of WZJX and SHHK; the total phosphorus and total nitrogen inflow into the river in the entire Yellow River basin are 3291.93 and 86416.15 tons, respectively. The total phosphorus inflow into the river is much smaller than the total nitrogen, accounting for 3.81% of the total nitrogen inflow into the river. However, there are significant differences in the amount of pollutants generated by population, livestock and poultry and land use types in different sub-basins; the main sources of total phosphorus pollution are cultivated land and grassland, and the total phosphorus inflow into the river is concentrated in the TNH sub-basin, about 102.15 tons, because the counties controlled by the TNH sub-basin are mainly grassland, with high soil erodibility and large slope, resulting in a high inflow coefficient; followed by sub-basins with large total phosphorus inflows, including TG (99.93 tons), LM (95.04 tons), XHY (94.88 tons) and BYGL (65.40 tons); the total phosphorus inflows into the river in XY, the downstream of WZJX and SHHK are not much different, ranging from 28.16 to 36.72 tons. The contribution of TN in different sub-basins and different land use types is similar to that of total phosphorus.

[0124] Taking the county as the unit, the pollution load of each county is calculated based on the amount of water entering the river caused by land use, livestock and poultry breeding, and population ( Figure 7 ), the pollution source intensity is divided into five intervals: low pollution area, relatively low pollution area, medium pollution area, relatively high pollution area, and high pollution area using the natural crack point method. From the perspective of total nitrogen pollution intensity, the seriously polluted areas are XY, TG, and LK, the moderately polluted area is XHY, and the total nitrogen pollution in other sub-basin control areas is not serious. The total nitrogen of total phosphorus pollution is similar in different regions, and the seriously polluted areas are XY, TG, and LK. The total nitrogen pollution level in some counties is very high and relatively concentrated. In general, the total nitrogen and total phosphorus pollution zoning is extremely similar, and the counties and districts where high pollution areas are concentrated are also consistent.

[0125] The results show that: first, non-point source emissions in the Yellow River Basin vary significantly among counties, and pollution is most serious in the downstream area; pigs and poultry contribute the most in livestock and poultry breeding, cultivated land and grassland contribute the most in land use, and non-point source pollution generated by urban population is greater than that of rural population. Second, the improved model can be well applied to the Yellow River Basin, involving rainfall, topography, leaching, landscape interception, soil erosion, vegetation cover, distance factors, etc., taking into account the three-dimensional landform heterogeneity of the Yellow River Basin. The improved model better estimates the amount of non-point source generation and the amount of inflow into the river, which helps to solve the problem that ECM is not suitable for areas with diverse and complex terrain or large-scale areas.

[0126] The above data show that cultivated land and grassland are the main sources of pollution among land use types, among which the total nitrogen and total phosphorus produced by cultivated land account for 38.19% and 42.63% of the total pollution load respectively, while the total nitrogen and total phosphorus produced by grassland account for 31.12% and 53.31% of the total pollution load respectively.

[0127] Overall, although the amount of total nitrogen entering the river is much greater than that of total phosphorus, the change trends of the two inflows are similar in each sub-basin. The amount of pollutants entering the river in the SHHK, downstream of WZJX and BYGL sub-basins is relatively low, while TG and LK are sub-basins with relatively high amounts of pollutants entering the river. The amount of pollutants entering the river from different land use types, livestock and poultry breeding and population in these areas is relatively high.

[0128] refer to Figure 7 As shown, pollution zoning: taking the counties of the nine provinces in the Yellow River Basin as units, the pollution source intensity of each county is calculated based on the amount of water entering the river caused by land use, livestock and poultry breeding, and population; the pollution source intensity is divided into five intervals using the natural breaking point method: low pollution area, relatively low pollution area, medium pollution area, relatively high pollution area, and high pollution area.

[0129] From the perspective of total nitrogen pollution intensity, the seriously polluted areas are concentrated in XY, TG and LK, the moderately polluted area is XHY, and the total nitrogen pollution in other sub-basin control areas is relatively light; the total nitrogen pollution level in some counties is very high and relatively concentrated, and the regional distribution of total phosphorus pollution is similar to that of total nitrogen, and the seriously polluted sub-basins are also XY, TG and LK. In general, the distribution of total nitrogen and total phosphorus pollution is very similar, and the high pollution areas are concentrated in the same counties.

[0130] The improved output-into-river coefficient model (EC-ICM) in the present invention estimates the output of pollutants in the basin by using relatively easy-to-obtain economic data, land use conditions, meteorological data and other data, thereby avoiding the problem of a large amount of data support required for physical model modeling and simulation, and requiring fewer parameters; the improved model more accurately estimates the amount of non-point source pollution generated and the amount of pollution entering the river, which helps to solve the application limitations of the traditional output coefficient model (ECM) model in areas with diverse and complex terrain or large-scale areas.

[0131] Based on the above non-point source pollution results, the following can be done in terms of basin pollution control: For cultivated land and grassland, which are the main non-point source pollution sources, the amount of pollutants entering the river should be reduced and agricultural pollution control should be strengthened by implementing necessary ecological restoration and vegetation cover measures such as restoration of degraded forests and continuation of closed-mountain afforestation. At the same time, strict control should be exercised over the sewage management of agricultural fertilization and livestock and poultry breeding to reduce the emission of TN and TP. In the urbanized areas of the middle and lower reaches of the Yellow River, the construction and operation management of sewage treatment facilities should be strengthened to reduce the pollution of water bodies by domestic sewage. According to the results of the risk zoning of non-point source pollutants, a zoning control strategy should be formulated, and high-risk areas should be treated first. The Yellow River Basin is mainly affected by domestic sewage, livestock and poultry breeding wastewater, and agricultural non-point source pollution. Therefore, work should be carried out from the control of urban domestic pollution, the development of livestock and poultry breeding pollution control, and the reduction of rural agricultural non-point source pollution. Regionally differentiated pollution control measures should be taken to improve the pertinence and efficiency of basin management, thereby improving the surface water environment quality of the Yellow River Basin.

[0132] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. Improve the method of output-inflow coefficient model for estimating load in large river basins, characterized by: The steps include: For the nested sub-basins of the watershed to be tested, the Latin hypercube sampling method is used to initialize the population, and the optimized pollution production equation is solved by using the genetic algorithm in the nested watershed data to calculate the land use output coefficients of different sub-basins; Calculate and couple the surface runoff factor, soil water leaching factor, landscape interception factor and soil erosion factor of the corresponding watershed to calibrate the output coefficients of pollution sources in different spatial units; Pollution sources include total phosphorus pollution sources and total nitrogen pollution sources; The output coefficients of total nitrogen pollution sources in different spatial units are calibrated by the ratio of the normalized values ​​of surface runoff factor and soil water leaching factor to the landscape interception factor: ； The output coefficients of total phosphorus pollution sources in different spatial units are calibrated by the ratio of the normalized values ​​of surface runoff factor and soil erosion factor to the landscape interception factor: ； in, and is the improved output coefficient, RI is the surface runoff factor, LI is the soil water leaching factor, K is the soil erosion factor, LII is the landscape interception factor, EC N and EC P It is the result of using genetic algorithm to obtain the original output coefficients in the nested watershed; The ratio of the product of rainfall erosivity factor and terrain influence factor to the product of cost distance factor and vegetation coverage factor is used to calibrate the river entry coefficient of pollution sources in different spatial units; Calibration of river inflow coefficients of total nitrogen pollution sources in different spatial units : ； Calibration of river inflow coefficients of total phosphorus pollution sources in different spatial units : ； in, is the concentrated inflow coefficient of total nitrogen into the river, is the concentrated inflow coefficient of total phosphorus into the river, Rainfall erosivity factor, The terrain influence factor, Cost distance factor, is the vegetation coverage factor; The calibrated output coefficient and the calibrated river inflow coefficient are used to construct the estimation of non-point source pollution in large watersheds and the pollution source area distribution model, and the pollutant output and river inflow under various land use types are calculated; The rough estimate of non-point source pollution in large watersheds and the distribution model of pollution source areas are as follows: ； in, is the total amount of pollutant i entering the river; For the improved or The output coefficient of; i is the type of pollutant; is the area of ​​the jth land use type in the basin; is the output coefficient of pollutant i on the jth land use type in the basin, is the number of livestock and poultry slaughtered or the population, is the amount of the i-th pollutant input by rainfall, For the improved or The river entry coefficient.

2. The method for estimating the load of a large river basin using the improved output-inflow coefficient model according to claim 1 is characterized by: Genetic algorithm is used to optimize the pollution equation. For pollutant i, m pollution equations are established for m small watersheds respectively, thus forming a set of equations: ； in, is the annual average concentration of the i-th pollutant in the t-th small watershed; is the annual runoff of the tth small watershed; is the loss coefficient of the i-th pollutant in the t-th small watershed; is the average monitoring concentration of the ith pollutant at the outlet of the tth small watershed during the dry season; is the total flow at the outlet of the t-th small watershed during the dry season; is the dry season time of the tth small watershed.

3. The method for estimating the load of a large river basin using the improved output-inflow coefficient model according to claim 1 is characterized by: The pollution factor CI, slope factor S, and slope length factor L are reclassified, and these factors are combined with the cost distance to the mainstream of the Yellow River. The cost distance factor is constructed by taking the logarithm of the cost distance. : ,in, , , Represent the results of each factor after reclassification.

4. The method for estimating the load of a large river basin using the improved output-inflow coefficient model according to claim 3 is characterized by: Pollution factor CI: ; Slope factor S: ； Slope length factor L: ； in, It is the cumulative water catchment parameter at a certain point in the basin; and are the slope and slope length extracted from the digital elevation model, is the slope length index, and its value varies with the slope changes with the changes.

Images