CN113032993A - Evaluation method for measuring influence of land utilization on watershed non-point source pollution migration - Google Patents

Abstract

The invention relates to an evaluation method for measuring influence of land utilization on non-point source pollution migration of a drainage basin, which comprises the following steps of: step 1: constructing a basic database; step 2: classifying the soil of the research basin according to the hydrologic soil type division standard; and step 3: assigning CN values to different land types of the research basin; and 4, step 4: calculating a dynamic action coefficient; and 5: calculating a resistance action coefficient; step 6: and calculating the land utilization influence coefficient. The method is suitable for small watersheds which lack hydrological monitoring data, and has the advantages of simplifying the complex ecological process and effectively identifying the key area causing pollution to the water body, thereby selecting a control means in a targeted manner and effectively reducing the risk to the aquatic ecosystem with lower cost.

Description

Evaluation method for measuring influence of land utilization on watershed non-point source pollution migration

Technical Field

The invention relates to an evaluation method for measuring influence of land utilization on non-point source pollution migration of a drainage basin.

Background

Land use changes are one of the important factors affecting non-point source pollution migration. The small watershed of the semi-urbanized area has strong sensitivity to the interference of human activities, particularly to the change of land utilization modes, thereby causing obvious non-point source pollution change. Most studies evaluate the degree of non-point source pollution from the perspective of tracing, hydrological processes, influencing factors, etc. of the non-point source pollution in order to reduce the negative effects. Many studies have considered increasing forest land and establishing river bank vegetation buffers as the best option for suppressing non-point source pollution. However, in which plots are added woodland or vegetation buffers? How much non-point source pollution is relieved? These problems are urgently needed for further research. Control of non-point source pollution through land use/cover type and spatial distribution tuning and optimization is a more economical and effective strategy. Therefore, the space-time distribution and river-entering path of the non-point source pollution are quickly identified, the influence degree of land utilization on the non-point source pollution is analyzed, the river-entering coefficient of the river basin non-point source pollution is reduced by reasonably planning the spatial layout of the green land and the like, and a research foundation is laid for providing targeted land management, regional space planning and river basin ecological environment protection strategies.

In recent years, distributed hydrological models are widely applied, however, the models (such as SWAT and the like) are limited by data acquisition in terms of parameter calibration and the like, and the bottleneck of scale effect cannot be crossed between sub-watersheds. The method has the advantages of simplicity, easiness in use and strong practicability. However, this type of model is limited by the failure to study the mechanisms of surface runoff migration processes and pollutant transport. The Chen Li roof and the like provide a 'source' and 'sink' landscape concept and theory applicable to non-point source pollution, and combine the influences of various factors such as distance, land utilization type, terrain, soil, rainfall characteristics and the like to construct a non-point source pollution river-entering coefficient which is used as a measurement index of the river-entering capacity of pollutants of each grid unit. Although the river entering coefficient is influenced by various factors, the climate and terrain factors of a watershed are relatively stable. In the past, researches show that the land utilization type mainly influences the resistance action coefficient, if the river entering coefficient is adjusted to reduce the river entering flux of pollutants, the process of transferring the pollutants from the land to the water body can be controlled by optimizing the spatial layout of the land utilization and reasonably configuring the land type on the premise of determining the river entering path of the pollutants.

Disclosure of Invention

An evaluation method for measuring influence of land utilization on river basin non-point source pollution migration is provided, so that a non-point source pollution river entering coefficient is reduced by optimizing land utilization/coverage types of key areas in a river basin. Therefore, the invention adopts the following specific technical scheme:

an evaluation method for measuring influence of land utilization on non-point source pollution migration of a drainage basin can comprise the following steps:

step 1: constructing a basic database, and acquiring land utilization data, digital elevations, river basin boundaries, river networks, rainfall data and soil attribute data in a research river basin;

step 2: classifying the soil of the research basin according to the hydrological soil type standard;

and step 3: combining the hydrological soil type and the soil coverage type, and endowing CN values to different land types of the research basin according to the runoff Curve Number (CN) division standard;

and 4, step 4: rasterizing the research basin, and respectively calculating the dynamic action coefficient of each grid unit according to the surface net runoff depth and the secondary rainfall in the basin;

and 5: respectively calculating the resistance action coefficient of each grid unit according to the Manning roughness coefficient of different land utilization types in the drainage basin;

step 6: and deducing an influence model of land utilization change on the river basin non-point source pollution migration based on the non-point source pollution river entering coefficient model, and calculating a land utilization influence coefficient.

Further, step 1 comprises:

step 1.1: converting all the spatial data into a unified projection coordinate system and a reference ellipsoid;

step 1.2: dividing sub-watersheds based on the digital elevation data and the river network data;

step 1.3: and extracting and correcting the land use type map based on the remote sensing image.

Further, the calculation formula of step 4 is as follows:

S＝(25400/CN)-254，

in the formula, d is a dynamic action coefficient, Q is surface net runoff depth (mm), P is sub rainfall (mm), S is maximum retention, namely maximum saturated water storage capacity (mm), and lambda is an initial retention coefficient.

Further, the calculation formula of step 5 is: the coefficient of resistance to action is 0.6 to the power of the Mannich coefficient.

Further, the calculation formula of the river entry coefficient of the non-point source pollution in the step 6 is as follows:

wherein a is the river coefficient of the target grid, c₁、c₂Is constant, m is the number of other grids flowing into the target grid, n is the number of grid units which the target grid needs to pass through to flow into the water outlet or enter the receiving water body, d_iCoefficient of action of power for the ith grid to flow into the target grid, b_jIs the resistance coefficient of the target grid flowing through the jth grid, theta_iGradient, θ, of the ith grid into the target grid_jGradient, L, for the target grid flowing through the jth grid_iThe distance of the grid cell from the target cell for the ith inflow grid.

By adopting the technical scheme, the invention has the beneficial effects that: the method is suitable for small watersheds which lack hydrological monitoring data, and has the advantages of simplifying the complex ecological process and effectively identifying the key area causing pollution to the water body, thereby selecting control means (land management, urban space planning and the like) in a targeted manner and effectively reducing the risk to the aquatic ecosystem with lower cost.

Drawings

To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.

FIG. 1 is a flow chart of an evaluation method for measuring the influence of land use on the migration of non-point source pollution in a drainage basin according to the invention;

FIG. 2 is a digital elevation map of a research basin;

FIG. 3 is a map of the soil utilization of a research basin;

FIG. 4 is a slope plot for a research basin;

FIG. 5 is a river map of a research basin;

FIG. 6 is a flow diagram of a research basin;

FIG. 7 is a flow chart of a research basin;

FIG. 8 is a sub-watershed map of a research watershed;

FIG. 9 is a graph of CN values for a research basin;

FIG. 10 is a plot of the kinetic coefficients of action for a particular study basin;

FIG. 11 is a plot of the resistance coefficient of action for a study basin;

FIG. 12 is a rasterized surface water flow diagram;

FIG. 13 is a land use impact coefficient profile for a research watershed;

FIG. 14 is a graph of the degree of influence of land utilization in a research watershed on non-point source pollution land-water migration;

FIG. 15 is a land use scenario simulation of a research watershed;

fig. 16 is a comparison graph of results in a scenario simulation of a certain research basin.

Detailed Description

The invention will now be further described with reference to the accompanying drawings and detailed description.

Referring to fig. 1, an evaluation method for measuring the influence of land use on the migration of non-point source pollution in a drainage basin is described. The method of the present invention will be described in detail below by taking a specific research basin as an example. The method comprises the following steps:

step 1: and constructing a basic database. Constructing a basic database, acquiring digital elevation data (shown in figure 2), land utilization data (shown in figure 3), gradient (shown in figure 4), river channel (shown in figure 5), flow direction (shown in figure 6) and flow (shown in figure 7) in a research flow domain, and converting all spatial data into a uniform projection coordinate system and a reference ellipsoid; dividing the study area into 24 sub-watersheds based on digital elevation data and hydrological data (fig. 8); and extracting and correcting the land use type map based on the remote sensing image.

Step 2: the soil was classified according to the relevant hydrologic soil type criteria. The soil was classified according to hydrological soil types classified by the United States Department of Agriculture (USDA) based on soil size fraction composition (table 1). Wherein, the A-type soil has the following characteristics: the soil infiltration rate is high, and the soil has lower runoff potential when being completely wet; water freely spreads through the soil; the clay content is less than 10% and the sand or gravel content exceeds 90%. Soil of type B: the soil has moderate infiltration rate and moderate and low runoff potential when being fully wetted; water transmission through the soil is unimpeded; typically containing 10% to 20% clay and 50% to 90% sand. Soil of type C: the soil has low infiltration rate and has a critical layer for draining water downwards. Soil of class D: even when fully wet, the soil has very low permeability, including permanently high groundwater levels of swelling clays and soils. The hydrological soil types of the research basin mainly comprise A-type soil and B-type soil.

TABLE 1 hydrological soil types classified based on soil texture

Type of soil	Texture of soil
		A	Sandy soil, sandy soil, sandy loam
B	Loam, silt or silt loam
		C	Sandy clay loam
D	Clay loam, silt clay, clay

And step 3: the CN value is calculated. And (3) assigning CN values to different land types of the research area according to the definition of the USDA on the CN values of different hydrological soil coverage complexes by combining factors such as the hydrological soil types and the soil coverage types (figure 9). Specifically, the soil humidity condition (AMC) in the early stage was evaluated based on the total rainfall 5 days before rainfall, and the CN value was adjusted. The early soil wettability classifications are shown in table 2.

TABLE 2 early soil wettability (AMC) classification

The correction calculation of the CN value is shown in formulas 1-3, and the CN values of different land types are shown in Table 3.

To CN_ⅡAnd (5) correcting:

where slp is the average slope of the sub-basin.

TABLE 3 CN values for different land types

Note: 2/crop residue coverage is only applicable when the annual residue occupies around 5% of the surface area.

The 3/hydrological conditions are based on a combination of factors affecting penetration and runoff, including (a) density and crown of the vegetation area, (b) annual cover capacity, (c) amount of grass or closed-feeding fruiting pods, (d) percentage of land surface residue coverage, and (e) degree of land surface toughness.

4/Difference: ground coverage was less than 50% or grazing heavily without coverage.

Medium: the ground coverage was 50% to 75% and there was no excessive grazing.

Well: ground coverage was over 75%, with mild or only occasional grazing.

5/difference: the ground coverage is less than 50%.

Medium: the ground coverage is 50% to 75%.

Well: the ground coverage rate exceeds 75%.

The CN value of 6/display was calculated for an area covered by 50% forest and 50% grass (pasture). Other combinations of conditions may be calculated from CN values for forests and pastures.

7/difference: withering and defoliation, small trees, shrubs, etc. are damaged by excessive grazing or regular burning.

Medium: forests were used for grazing, but were not destroyed by combustion, and some withered and fallen leaves covered the soil.

Well: the forest is prevented from being damaged by grazing and the like, and residues such as withering, leaf falling and the like fully cover the soil.

And 4, step 4: and calculating the dynamic action coefficient d. The study area was subjected to rasterization processing, divided into 30m × 30m grids, and the dynamic action coefficient of each grid cell was calculated. The specific calculation formula is as follows:

it is generally assumed that I_aλ S, where λ is 0.2,

S＝(25400/CN)-254 (7)

in the formula, d is a dynamic action coefficient; q is the surface net runoff depth (mm); p is the secondary rainfall (mm); i is_aThe loss amount is the initial retention amount of soil after rainfall, namely the initial loss amount (mm), and is generated by vegetation interception, soil infiltration, filling and water storage and the like; s is the maximum retention, namely the maximum saturated water storage capacity (mm).

And acquiring the real-time rain condition information published by a water conservancy information network, and capturing rainfall data on the day of rainfall and 5 days before the rainfall. And (4) collecting rainfall information of secondary rainfall of 30 rainfall monitoring stations around the research area, and performing idw spatial interpolation on the rainfall. The mean dynamic effect coefficient graph in 2019 is shown in FIG. 10.

And 5: and calculating a resistance action coefficient. The study area was subjected to rasterization processing, divided into 30m × 30m grids, and the resistance action coefficient of each grid cell was calculated. Here, the coefficient of resistance action is defined as the coefficient of Mannich roughness to the power of 0.6. The Mannich roughness coefficients for different land use types are shown in Table 4. The resistance effect coefficient graph in 2019 is shown in FIG. 11.

TABLE 4 Manning roughness coefficient for different land utilization

Type of land use	Coefficient of Mannich roughness
		Grass land	0.259
Woodlands	0.4
		Cultivation of land	0.1
Land for water area and water conservancy facilities	0.03
		Land for urban construction	0.094
Rural residential site	0.053
		Unused land	0.05

Step 6: and calculating a land utilization influence coefficient k. And calculating a land utilization influence coefficient k of each grid unit by using Python based on parameters such as water flow direction, water flow, gradient, river channel, dynamic action coefficient, resistance action coefficient and the like. The specific process is as follows:

1. river coefficient model, see equation 8:

in the formula, a is the river entering coefficient of the target grid; c. C₁、c₂Is a constant; m is the number of other grids flowing into the target grid; n is the number of grid units which the target grid needs to pass through when flowing into a water outlet or entering a receiving water body; d_iThe dynamic action coefficient of the ith grid which is the inflow target grid; b_jA resistance effect coefficient for the target grid flowing through the jth grid; theta_iThe slope of the ith grid being the inflow target grid; theta_jThe gradient of the target grid flowing through the jth grid; l is_iThe distance of the grid cell from the target cell for the ith inflow grid.

Calculating the water flow direction by using a D8 one-way flow algorithm, 2⁰1 stands for right, 2¹2 stands for right down, 2²4 stands for down, 2³8 stands for lower left, 2⁴16 for left, 2⁵32 stands for upper left, 2⁶64 stands for upwards, 2⁷128 represents right up. The length 1 of the grid cell is taken as the unit length of the water flow direction 1, 4, 16 and 64, and the diagonal hypotenuses of the grid cell

The water flow direction is 2, 8, 32, 128 units.

2. Derivation of an influence model of land use change on non-point source pollution migration:

as shown in FIG. 12, assume that the water flow path on the earth's surface is defined by grid g₁₃Flows into the grid g in sequence₂₃、g₃₂、g₄₃、g₅₄Finally flows into the water body g₅₅. Grid g₁₃Has a river entry coefficient of₁₃Grid g₂₃Has a river entry coefficient of₂₃Grid g₁₃And grid g₂₃The sum of the river entering coefficients (a)₁₃+a₂₃) Can be regarded as a target grid g₃₂Coefficient of resistance action (b)₃₂) Is a linear relation function of variables. That is, a unit change in land use type for a particular target grid will cause a corresponding change in the river coefficient for a unit of pollutant. We define this as the land use impact coefficient (k), with a larger value of k indicating a higher degree of impact of the grid on the river coefficient. Therefore, by adjusting the k value, the land use space distribution of the sensitive area in the river basin is optimized, and the condition that pollutants enter the river is relieved.

The derivation process is shown in formula (9-18):

is provided with

The above formula can be represented as

a₂₃＝-k₂₃·b₃₂+z₂₃ (13)

In the same way, there are

a₁₃＝-k₁₃·b₃₂+z₁₃ (14)

Then, a₁₃+a₂₃＝-(k₁₃+k₂₃)·b₃₂+z₁₃+z₂₃ (15)

Is derived from_p＝-K_p·b_p+Z_p (16)

A_p＝a₁+a₂+…+a_q (17)

K_p＝k₁+k₂+…+k_q (18)

In the formula, A_pIs the sum of the river coefficients of the upstream grids flowing into the target grid p; z_pAs a function of the dynamic coefficient of action of the upstream grid and the resistance coefficient of action of the downstream grid; b_pThe resistance action coefficient corresponding to the pth target grid; q is the number of upstream grids flowing into the target grid p; k_pThe influence coefficient for land utilization is dimensionless.

For a certain research basin, the distribution of land use influence coefficients in 2019 is shown in fig. 13, and the influence degree of land use on non-point source pollution migration is shown in fig. 14.

In order to verify the feasibility of the invention, Pearson correlation analysis is carried out on the monitored water quality indexes (TN and TP) and the non-point source pollution river-entering coefficient. The results show that: TN and river entry coefficient show obvious positive correlation, and the correlation coefficient r is 0.755, and p is 0.000< 0.05. The TP has positive correlation with the river entry coefficient, the correlation coefficient r is 0.522, and p is 0.001< 0.05.

In order to illustrate the advantages of the evaluation method for measuring influence of land utilization on river basin non-point source pollution migration, the land utilization types and the spatial distribution in the high influence area of the river basin land utilization influence coefficient (k) and the periphery of the river main stream are optimized on the basis of researching the land utilization types of the river basin in 2019, the specific gravity of the reduction of the non-point source pollution river entering coefficient (a) in the river basin is calculated, and the remission degree of pollutants entering the river in the river basin is compared with that in 2019. Respectively making 90 m buffer zones for k value high-influence zone and river main stream, modifying land type in the buffer zones, and setting the buffer zones as vegetation buffer zones (using forest and grass land/irrigation land) when the land type is in non-urbanized zoneGrass-dominated); when in the urbanized area, the rushing area is set as a green mosaic (mainly grassland). The scene simulation is shown in fig. 15. The results show that: a buffer zone of 90 meters is arranged along the high influence zone, and the area of the buffer zone is 0.71km in total²The river coefficient of non-point source pollution decreased by 0.58% in the full flow field (fig. 16). A buffer zone with the length of 90 meters is arranged along the river main stream, and the area of the buffer zone is 1.16km in total²The river coefficient of non-point source pollution is reduced by 0.21% in the whole flow field. The research shows that: compared with a buffer zone made of a river main stream, the method optimizes the land type and the spatial distribution in the k-value high-influence zone, and has higher river entry coefficient reduction rate.

While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. An evaluation method for measuring influence of land utilization on non-point source pollution migration of a drainage basin is characterized by comprising the following steps:

step 1: constructing a basic database, and acquiring land utilization data, digital elevations, river basin boundaries, river networks, rainfall data and soil attribute data in a research river basin;

step 2: classifying the soil of the research basin according to the hydrologic soil type division standard;

and step 3: combining the hydrological soil type and the soil coverage type, and endowing CN values to different land types of the research basin according to the runoff Curve Number (CN) division standard;

and 4, step 4: rasterizing the research basin, and respectively calculating the dynamic action coefficient of each grid unit according to the surface net runoff depth and the secondary rainfall in the basin;

and 5: respectively calculating the resistance action coefficient of each grid unit according to the Manning roughness coefficient of different land utilization types in the drainage basin;

step 6: and deducing an influence model of land utilization change on the river basin non-point source pollution migration based on the non-point source pollution river entering coefficient model, and calculating a land utilization influence coefficient.

2. The method of claim 1, wherein step 1 comprises:

step 1.1: converting all the spatial data into a unified projection coordinate system and a reference ellipsoid;

step 1.2: dividing sub-watersheds based on the digital elevation data and the river network data;

step 1.3: and extracting and correcting the land use type map based on the remote sensing image.

3. The method of claim 1, wherein the calculation formula of step 4 is as follows:

S＝(25400/CN)-254，

in the formula, d is a dynamic action coefficient, Q is surface net runoff depth (mm), P is sub rainfall (mm), S is maximum retention, namely maximum saturated water storage capacity (mm), and lambda is an initial retention coefficient.

4. The method of claim 1, wherein the calculation formula of step 5 is: the coefficient of resistance to action is 0.6 to the power of the Mannich coefficient.

5. The method of claim 1, wherein the non-point source pollution river-entering coefficient in step 6 is calculated according to the following formula:

wherein a is the river coefficient of the target grid, c₁、c₂Is constant, m is the number of other grids flowing into the target grid, n is the number of grid units which the target grid needs to pass through to flow into the water outlet or enter the receiving water body, d_iCoefficient of action of power for the ith grid to flow into the target grid, b_jIs the resistance coefficient of the target grid flowing through the jth grid, theta_iGradient, θ, of the ith grid into the target grid_jGradient, L, for the target grid flowing through the jth grid_iThe distance of the grid cell from the target cell for the ith inflow grid.

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