CN115221687B - Numerical simulation method for influence of coal mining on river runoff - Google Patents
Numerical simulation method for influence of coal mining on river runoff Download PDFInfo
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Abstract
The invention relates to a numerical simulation method for influence of coal mining on river runoff, and belongs to the field of hydrologic technology analysis. The invention collects the data needed by the WEP distributed hydrological model and performs space spreading; collecting runoff data of hydrologic stations in a simulated flow field, setting a periodic rate and a verification period, and carrying out parameter calibration and verification on a WEP model; adding a coal mining module into the WEP model, simulating the influence of coal mining, and calibrating coal mine parameters; based on the rated WEP model, different situations are set, the runoff under different situations of coal mining, climate change and social water is output, and the influence analysis of different factors on the runoff is realized. According to the invention, through different situation settings, the relation between driving factors such as climate change, coal mining, social water and the like and runoff is analyzed, and a reference basis is provided for evaluating the contribution of the runoff change and the ecological protection and repair of a river.
Description
Technical Field
The invention belongs to the field of hydrologic technology analysis, and particularly relates to a numerical simulation method for influence of coal mining on river runoff.
Background
Coal mining has a great influence on river runoff around the coal mine. The influence of the existing coal exploitation on river runoff is generally obtained by adopting a statistical method, namely analyzing rainfall-runoff correlation under the conditions of coal exploitation and no coal exploitation. However, the influence of coal mining on river runoff is a dynamic and complex process, and meanwhile, the influence of coal mines and other human activities on river runoff cannot be accurately identified in a river basin with strong human activities. According to the method, the coal mine generalized model which dynamically changes along with time and the exploitation amount is constructed through generalizing the influence and indirect influence of coal exploitation on the diameter of river runoffs, so that an analysis result has a physical basis, and the defects of the traditional method are overcome.
Disclosure of Invention
First, the technical problem to be solved
The invention aims to provide a numerical simulation method for influence of coal mining on river runoff, so as to solve the problem that the influence of coal mine and other human activities on the river runoff cannot be accurately identified by the existing method.
(II) technical scheme
In order to solve the technical problems, the invention provides a numerical simulation method for influence of coal mining on river runoff, which comprises the following steps:
s1, collecting data required by an input WEP distributed hydrological model, and performing space spreading;
s2, collecting runoff data of hydrologic stations in the simulated flow field, setting a periodic rate and a verification period, and carrying out parameter calibration and verification on the WEP model;
s3, adding a coal mining module into the WEP model, simulating the influence of coal mining, and calibrating coal mine parameters;
s4, setting different scenes based on the calibrated WEP model, outputting the runoff under different situations of coal mining, climate change and social water consumption, and realizing the influence analysis of different factors on the runoff.
Further, in the step S1, the collected data includes meteorological data, land utilization data, soil type data, vegetation type data, DEM data, hydrologic station data, and social water data.
Further, in the step S1, the geographic information system software ARCGIS is used to fill in the depression, flow direction, flow rate, and extract the river network of the DEM data, and then extract the drainage basin range of the research area, and divide the sub-drainage basin and the equal altitude zone.
Further, in the step S1, meteorological data of a meteorological site is obtained, including daily precipitation data, daily air temperature data, daily humidity data, daily sunlight data and daily wind speed data, and the meteorological data is spread into a sub-river basin of a research area by using a VB program, and a 'dat' format required by WEP model input is formed;
the soil type data are obtained through a Chinese soil resource database;
land utilization data and vegetation type data are obtained through satellite remote sensing image interpretation, and are processed into ASCII code formats by ARCGIS software;
hydrologic station data comes from watershed hydrologic yearbook;
social water data is obtained through water resource gazettes and spread to the contour bands according to water types.
Further, the step S2 specifically includes the following steps:
s21, the selectivity is periodic and the verification period are carried out, the runoff data of the hydrologic station are actually measured as objects, all input data files in the step S1 are input, and the runoff is simulated through a WEP model; comparing the simulated diameter value obtained based on the WEP output of the distributed hydrological model with the actual measurement value;
s22, taking the Nash efficiency coefficient, the relative error and the correlation coefficient as evaluation indexes of the WEP simulation effect of the distributed hydrological model, and calculating an evaluation index result through the simulation diameter current value and the actual measurement value;
s23, according to the evaluation index result, the model parameters are adjusted, and when preset conditions are met, the model simulation is considered to be relatively reasonable, namely, the calibration process of the distributed hydrological model WEP is completed, and the model is considered to be applicable to subsequent simulation analysis.
Further, the preset conditions are: the Nash efficiency coefficient is larger than 0.7, the relative error is smaller than 10%, and the correlation coefficient is larger than 0.6.
Further, the step S3 specifically includes the following steps:
s31, adding a coal mining module into a WEP model, collecting coal mine data, and setting related parameters and correction coefficients;
dividing the goaf into x parts from near to far from the mine, intersecting the image layer with the image layer of the computing unit of the WEP model to obtain the affected area proportion of each computing unit, and weighting according to the inverse square distance of the goaf radius to obtain the comprehensive influence coefficient of the coal mine on the computing units, wherein the expression is as follows:
in which I cij The comprehensive influence coefficient of the coal mine I to the computing unit J is represented, x represents the number of the goaf of the coal mine I divided into different parts, S jk Represents the area of the j-number computing unit affected by the k-th part of the goaf of the coal mine i, S cj Representing the area, ω of the calculation unit j k Representing the weight of the inverse distance weighting, r k Representing the radius of the k-th partial goaf;
q in sj Indicating the water storage capacity in the goaf formed by the coal mine in the j-number calculating unit, n indicating the influence on the calculating unitNumber of coal mines A i Represents annual capacity of coal mine No. i, C i Correction coefficient representing water storage capacity of i-type coal mine goaf, v ij The weight of the comprehensive influence coefficient of the coal mine I to the computing unit j is represented, and m is the number of computing units influenced by the coal mine I; i cij The comprehensive influence coefficient of the coal mine I to the computing unit j is obtained;
s32, the expression of the coal mine drainage is as follows:
d in s Represents the coal mine drainage in a certain calculation unit, l represents the number of coal mines in the calculation unit, T i Represents the ton coal drainage coefficient of the No. i coal mine, namely the ratio of the drainage amount of the coal mine to the coal exploitation amount, A i Represents annual production capacity of coal mine No. i, R i The recycling coefficient of the coal mine drainage quantity of the number i is represented;
s33, simulating rapid infiltration and seepage in the goaf by increasing the vertical and lateral hydraulic conductivity coefficients in the model, wherein the expression is as follows:
wherein n represents the number of coal mines affecting a certain calculation unit, K' s Represents the corrected vertical water conservancy conductivity coefficient, K 'in the calculation unit' u Representing corrected lateral water conservancy conductivity coefficient, K in the calculation unit s Representing the vertical water conservancy conductivity coefficient, K before correction in the calculation unit u Representing the lateral water conservancy conductivity coefficient before correction in the calculating unit; k (K) sc And K uc Is the correction coefficient of the vertical water conservancy conductivity coefficient and the lateral water conservancy conductivity coefficient in model simulation, I ci Indicating the coal mine number i to the calculation sheetThe comprehensive influence coefficient of the element is calculated by adopting a formula (1) after j is fixed;
s34, leading A into the coal mining module i 、S jk 、S cj 、r k 、K s 、K u Data, calculate I cij 、Q sj 、D s 、K' s 、K' u Data, then calibrating C by comparing with measured runoff data i 、R i 、K sc 、K uc Parameters.
Further, the coal mine data includes location, extent, annual production of the coal mine and goaf extent of coal mine influence.
Further, in the step S4, the difference between the measured base flow in the change period and the reference period reduction flow includes four parts, one of which is a precipitation change influencing part, the other of which is an artificial water using influencing part, the other of which is a coal mining part, and the other of which is an underlying surface change influencing part, and the expression is as follows:
RC＝CC+WC+CM+LC (8)
wherein RC represents the absolute change amount of the base flow under the comprehensive influence of each driving factor, CC represents the base flow change amount under the climate change condition, WC represents the base flow change amount under the social water change condition, CM represents the base flow change amount under the coal mining change condition, and LC represents the base flow change amount under the land use change condition.
Further, the step S4 specifically includes:
s41, for a natural scene 1, a WEP model is established, no water is added, parameters are calibrated according to historical reduction runoff data Q0, and then the runoff quantity Q1 under the condition that no water is taken in a natural climate is obtained;
s42, setting a comparison scenario for the climate change scenario 2, and replacing the climate data in the verification period with the climate data with regular rate without adding water, so as to obtain the runoff quantity Q2 under the climate change-free water taking scenario;
s43, for the social water scenario 3, adding the manual water taking data into the scenario 1 model to obtain a simulated runoff quantity Q3, wherein Q3 is the runoff quantity under the action of climate change and manual water taking factor change;
s44, for land use scenario 4, in scenario 2, replacing land use data with regular rate, and obtaining runoff quantity Q4 under the situation of no climate change and no land use;
s45, adding a coal mining module into the scene 3 to obtain the water for manual extraction and the runoff quantity Q5 under the influence of coal mining;
s46, calculating contribution amounts of different driving factors to the base flow change:
RC＝Q 5 -Q 1
CC＝Q 1 -Q 2
WC＝Q 3 -Q 1
LC＝Q 1 -Q 4
CM＝Q 5 -Q 1
s47, calculating the contribution rate of different driving factors to runoff change:
(III) beneficial effects
The invention provides a numerical simulation method for influence of coal mining on river runoff, which has the following advantages:
1. the invention is based on the WEP distributed hydrological model, generalizes coal mining, realizes dynamic simulation of the influence of the coal mining process on runoffs, and is not limited to the simulation of the coal mining influence by adjusting the basic parameters of the model.
2. The invention improves the WEP output structure of the distributed hydrologic model, so that the model can simulate the influence of coal mining on runoff, the simulation result has a hydrologic cycle physical basis, and the defects of the traditional and numerical statistical methods are overcome.
3. According to the invention, through different situation settings, the relation between driving factors such as climate change, coal mining, social water and the like and runoff is analyzed, and a reference basis is provided for evaluating the contribution of the runoff change and the ecological protection and repair of a river.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic representation of the coal mine impact of the present invention.
Detailed Description
To make the objects, contents and advantages of the present invention more apparent, the following detailed description of the present invention will be given with reference to the accompanying drawings and examples.
The invention realizes simulation of influence of coal mining on river runoff in a river basin based on a WEP distributed hydrological model, and analyzes contribution rate of each driving factor to runoff change based on a physical mechanism through different scene settings.
The invention is based on a WEP distributed hydrologic model with a physical mechanism, which can link each element process of water circulation to carry out detailed simulation, describes the hydrologic problem of a river basin from the dynamic mechanism of the water circulation, and estimates model parameters according to the physical properties of a water moving medium. After the influence on coal mining is generalized, the change of the bedding surface of the river basin and the production confluence change rule under the condition of coal mining can be analyzed, and further the contribution of coal mining and other driving factors to runoff change is obtained.
As shown in fig. 1, a numerical simulation method for influence of coal mining on river runoff comprises the following steps:
s1, collecting data required by the WEP distributed hydrologic model and performing spatial spreading.
S2, collecting runoff data of hydrologic stations in the simulated flow field, setting a periodic rate and a verification period, and carrying out parameter calibration and verification on the WEP model.
S3, adding a coal mining module into the WEP model, simulating the influence of coal mining, and calibrating coal mine parameters.
S4, setting different scenes based on the calibrated WEP model, outputting the runoff under different situations of coal mining, climate change and social water consumption, and realizing the influence analysis of different factors on the runoff.
In step S1 of the present embodiment, the collected data includes meteorological data, land use data, soil type data, vegetation type data, DEM data, hydrologic station data, social water usage data, and the like.
In step S1, after the steps of filling, flowing, extracting river network and the like are carried out on DEM data by using geographic information system software ARCGIS, extracting the scope of a study area, and dividing sub-drainage areas and equal altitude areas.
Meteorological data of a meteorological site are acquired, wherein the meteorological data comprise daily precipitation data, daily air temperature data, daily humidity data, daily sunshine data and daily wind speed data, and are spread into a sub-river basin of a research area by using a VB program, and a 'dat' format required by WEP model input is formed.
The soil type data are obtained through a Chinese soil resource database; land utilization data and vegetation type data are obtained through satellite remote sensing image interpretation, and are processed into ASCII code formats by ARCGIS software.
The hydrologic station data come from the hydrologic annual survey of the watershed, and the social water data are obtained through water resource gazettes and are spread to the contour band according to the water type.
The S2 in this embodiment specifically is:
s21, selecting a periodic period and a verification period, taking the measured hydrologic station runoff data as objects, inputting all input data files in the step S1, and simulating the runoff through a WEP model. And comparing the simulation diameter value obtained based on the WEP output of the distributed hydrological model with the actual measurement value.
S22, taking characteristic values such as Nash efficiency coefficient, relative error and correlation coefficient as evaluation indexes of the WEP simulation effect of the distributed hydrological model, and calculating an evaluation index result through the simulation diameter current value and the actual measurement value.
S23, adjusting model parameters according to the evaluation index result, and when preset conditions are met: the Nash efficiency coefficient is larger than 0.7, the relative error is smaller than 10%, and the correlation coefficient is larger than 0.6, namely the model simulation is considered to be relatively reasonable, namely the calibration process of the distributed hydrologic model WEP is completed, and the model simulation method can be used for subsequent simulation analysis.
The S3 in this embodiment specifically is:
the influence of coal mining on the underlying surface mainly forms a goaf and a fracture zone, the fracture zone can cause the speed of underground underwater seepage and seepage to be obviously increased, and the goaf can store a large amount of leaked water, so that the underground water level is reduced and the runoff is reduced.
S31, adding a coal mining module into the WEP model, collecting related coal mine data including the position, the range, the annual mining amount, the goaf range influenced by the coal mine and the like of the coal mine, and setting related parameters and correction coefficients. In order to embody the influence that the closer the computing unit is to the mine is influenced by the coal mine, the goaf is divided into x parts from the near to the far according to the distance from the mine, the image layer is intersected with the computing unit image layer of the WEP model, the affected area proportion of each computing unit can be obtained, then the comprehensive influence coefficient of the coal mine on the computing units can be obtained by weighting according to the square inverse distance of the goaf radius, and the expression is as follows:
in which I cij The comprehensive influence coefficient of the coal mine I to the computing unit J is represented, x represents the number of the goaf of the coal mine I divided into different parts, S jk Represents the area (km) of the j-number computing unit affected by the k-th portion of the goaf of the coal mine i 2 )，S cj Representing the area (km) of the calculation unit j 2 )，ω k Representing the weight of the inverse distance weighting, r k Representing the k-th partial goaf radius (km).
Q in sj Represents the water storage capacity in the goaf formed by the coal mine in the j-number calculating unit, n represents the number of the coal mine affecting the calculating unit, A i Represents annual capacity (ten thousand tons) of coal mine No. i, C i Correction coefficient representing water storage capacity of i-type coal mine goaf, v ij And (3) representing the weight of the comprehensive influence coefficient of the coal mine I on the computing unit j, wherein m is the number of computing units influenced by the coal mine I. I cij The comprehensive influence coefficient of the coal mine I to the computing unit j is obtained;
s32, the expression of the coal mine drainage is as follows:
d in s Represents the coal mine drainage (ten thousand tons) in a certain calculation unit, l represents the number of coal mines in the calculation unit, T i Represents the ton coal drainage coefficient of the No. i coal mine, namely the ratio of the drainage amount of the coal mine to the coal exploitation amount, A i Represents annual capacity (ten thousand tons) of coal mine No. i, R i And the recycling coefficient of the coal mine drainage quantity of the number i is shown.
S33, simulating rapid infiltration and seepage in the goaf by increasing the vertical and lateral hydraulic conductivity coefficients in the model, wherein the expression is as follows:
wherein n represents the number of coal mines affecting a certain calculation unit, K' s Representing the corrected vertical water conservancy conductivity coefficient (m/d), K 'in the calculation unit' u Representing corrected lateral water conservancy conductivity (m/d), K in the calculation unit s Representing the vertical water conservancy conductivity coefficient (m/d) and K before correction in the calculation unit u Representing the lateral hydraulic conductivity (m/d) before correction in the calculation unit. K (K) sc And K uc Is the correction coefficient of the vertical water conservancy conductivity coefficient and the lateral water conservancy conductivity coefficient in model simulation, I ci And (3) representing the comprehensive influence coefficient of the coal mine I on the calculation unit, and calculating by adopting a formula (1).
S34, leading A into the coal mining module i 、S jk 、S cj 、r k 、K s 、K u Data, calculate I cij 、Q sj 、D s 、K' s 、K' u Equal data and then calibrating C by comparing with measured runoff data i 、R i 、K sc 、K uc And the like.
In step S4, when the influence of climate change and human activity is studied, when the data sequence of the reference period and the human activity period is long, the discrete quantization result is less affected by accident, the long-sequence change characteristic is embodied, the difference between the actually measured base flow of the change period and the reduced flow of the reference period can be considered to comprise four parts, one part is a precipitation change influence part, the other part is a manually-used water influence part, the other part is a coal mining part, the other part is an underlying surface change influence part, and the expression is as follows:
RC＝CC+WC+CM+LC (8)
wherein RC represents the absolute change amount of the base flow under the comprehensive influence of each driving factor, CC represents the base flow change amount under the climate change condition, WC represents the base flow change amount under the social water change condition, CM represents the base flow change amount under the coal mining change condition, and LC represents the base flow change amount under the land use change condition.
The S4 in this embodiment specifically is:
s41, for the natural scene 1, a WEP model is built, no water is added, parameters are calibrated according to historical restored runoff data Q0, and then the runoff quantity Q1 under the condition that no water is taken in the natural climate is obtained
S42, setting a comparison scenario for the climate change scenario 2, replacing the climate data in the verification period with the climate data with regular rate without adding water, and obtaining the runoff quantity Q2 under the climate change-free water taking scenario
S43, for the social water scenario 3, the manual water taking data are added into the scenario 1 model, and the simulated runoff quantity Q3 is obtained, wherein the simulated runoff quantity Q3 is the runoff quantity under the action of climate change and manual water taking factor change.
S44, in the land use scenario 4, land use data is replaced with land use data having a regular rate in scenario 2, and the traffic flow Q4 in the land use scenario without climate change is obtained.
S45, adding a coal mining module in the scene 3 for the influence of coal mining to obtain the manually-taken water and the runoff quantity Q5 under the influence of coal mining.
S46, calculating contribution amounts of different driving factors to the base flow change:
RC＝Q 5 -Q 1
CC＝Q 1 -Q 2
WC＝Q 3 -Q 1
LC＝Q 1 -Q 4
CM＝Q 5 -Q 1
s47, calculating the contribution rate of different driving factors to runoff change:
the invention has the following advantages:
1. the invention is based on the WEP distributed hydrological model, generalizes coal mining, realizes dynamic simulation of the influence of the coal mining process on runoffs, and is not limited to the simulation of the coal mining influence by adjusting the basic parameters of the model.
2. The invention improves the WEP output structure of the distributed hydrologic model, so that the model can simulate the influence of coal mining on runoff, the simulation result has a hydrologic cycle physical basis, and the defects of the traditional and numerical statistical methods are overcome.
3. According to the invention, through different situation settings, the relation between driving factors such as climate change, coal mining, social water and the like and runoff is analyzed, and a reference basis is provided for evaluating the contribution of the runoff change and the ecological protection and repair of a river.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (7)
1. A numerical simulation method for influence of coal exploitation on river runoff is characterized by comprising the following steps:
s1, collecting data required by an input WEP distributed hydrological model, and performing space spreading;
s2, collecting runoff data of hydrologic stations in the simulated flow field, setting a periodic rate and a verification period, and carrying out parameter calibration and verification on the WEP model;
s3, adding a coal mining module into the WEP model, simulating the influence of coal mining, and calibrating coal mine parameters;
s4, setting different situations based on the calibrated WEP model, outputting the runoff under different situations of coal mining, climate change and social water consumption, and realizing the influence analysis of different factors on the runoff;
in the step S1, the collected data includes meteorological data, land utilization data, soil type data, vegetation type data, DEM data, hydrological station data and social water data;
the step S2 specifically includes the following steps:
s21, the selectivity is periodic and the verification period are carried out, the runoff data of the hydrologic station are actually measured as objects, all input data files in the step S1 are input, and the runoff is simulated through a WEP model; comparing the simulated diameter value obtained based on the WEP output of the distributed hydrological model with the actual measurement value;
s22, taking the Nash efficiency coefficient, the relative error and the correlation coefficient as evaluation indexes of the WEP simulation effect of the distributed hydrological model, and calculating an evaluation index result through the simulation diameter current value and the actual measurement value;
s23, according to the evaluation index result, the model parameters are adjusted, when preset conditions are met, the model simulation is considered to be relatively reasonable, namely, the calibration process of the distributed hydrological model WEP is completed, and the model is considered to be applicable to subsequent simulation analysis;
the step S3 specifically includes the following steps:
s31, adding a coal mining module into a WEP model, collecting coal mine data, and setting related parameters and correction coefficients;
dividing the goaf into x parts from near to far from the mine, intersecting the image layer with the image layer of the computing unit of the WEP model to obtain the affected area proportion of each computing unit, and weighting according to the inverse square distance of the goaf radius to obtain the comprehensive influence coefficient of the coal mine on the computing units, wherein the expression is as follows:
in which I cij The comprehensive influence coefficient of the coal mine I to the computing unit J is represented, x represents the number of the goaf of the coal mine I divided into different parts, S jk Represents the area of the j-number computing unit affected by the k-th part of the goaf of the coal mine i, S cj Representing the area, ω of the calculation unit j k Representing the weight of the inverse distance weighting, r k Representing the radius of the k-th partial goaf;
q in sj Represents the water storage capacity in the goaf formed by the coal mine in the j-number calculating unit, n represents the number of the coal mine affecting the calculating unit, A i Represents annual capacity of coal mine No. i, C i Correction coefficient representing water storage capacity of i-type coal mine goaf, v ij The weight of the comprehensive influence coefficient of the coal mine I to the computing unit j is represented, and m is the number of computing units influenced by the coal mine I; i cij The comprehensive influence coefficient of the coal mine I to the computing unit j is obtained;
s32, the expression of the coal mine drainage is as follows:
d in s Represents the coal mine drainage in a certain calculation unit, l represents the number of coal mines in the calculation unit, T i Represents the ton coal drainage coefficient of the No. i coal mine, namely the ratio of the drainage amount of the coal mine to the coal exploitation amount, A i Year of coal mine of i numberCapacity, R i The recycling coefficient of the coal mine drainage quantity of the number i is represented;
s33, simulating rapid infiltration and seepage in the goaf by increasing the vertical and lateral hydraulic conductivity coefficients in the model, wherein the expression is as follows:
wherein n represents the number of coal mines affecting a certain calculation unit, K' s Represents the corrected vertical water conservancy conductivity coefficient, K 'in the calculation unit' u Representing corrected lateral water conservancy conductivity coefficient, K in the calculation unit s Representing the vertical water conservancy conductivity coefficient, K before correction in the calculation unit u Representing the lateral water conservancy conductivity coefficient before correction in the calculating unit; k (K) sc And K uc Is the correction coefficient of the vertical water conservancy conductivity coefficient and the lateral water conservancy conductivity coefficient in model simulation, I ci The comprehensive influence coefficient of the coal mine I on the calculation unit is represented, and the calculation is carried out by adopting a formula (1) after j is fixed;
s34, leading A into the coal mining module i 、S jk 、S cj 、r k 、K s 、K u Data, calculate I cij 、Q sj 、D s 、K' s 、K' u Data, then calibrating C by comparing with measured runoff data i 、R i 、K sc 、K uc Parameters.
2. The method for numerical simulation of influence of coal mining on river runoff according to claim 1, wherein in the step S1, the geographical information system software ARCGIS is used to fill, flow and extract river network for DEM data, and then the scope of the study area is extracted, and the sub-river basin and the contour zone are divided.
3. The method for simulating the numerical simulation of the influence of coal mining on river runoff according to claim 2, wherein in the step S1, meteorological data of a meteorological site are obtained, including daily precipitation data, daily air temperature data, daily humidity data, daily sunlight data and daily wind speed data, and the data are spread into a subbasin of a research area by using a VB program to form a ". Dat" format required by WEP model input;
the soil type data are obtained through a Chinese soil resource database;
land utilization data and vegetation type data are obtained through satellite remote sensing image interpretation, and are processed into ASCII code formats by ARCGIS software;
hydrologic station data comes from watershed hydrologic yearbook;
social water data is obtained through water resource gazettes and spread to the contour bands according to water types.
4. The numerical simulation method of influence of coal mining on river runoff as claimed in claim 1, wherein the preset conditions are: the Nash efficiency coefficient is larger than 0.7, the relative error is smaller than 10%, and the correlation coefficient is larger than 0.6.
5. The method of numerical modeling of the impact of coal mining on river runoff of claim 1, wherein the coal mine data includes location, extent, annual production of coal mine and goaf extent of coal mine impact.
6. The method for numerical simulation of influence of coal mining on river runoff according to claim 1, wherein in the step S4, the difference between the measured base flow rate in the change period and the reduced flow rate in the reference period includes four parts, one of which is a precipitation change influencing part, the other of which is an artificial water using influencing part, the other of which is a coal mining part, and the other of which is an underlying change influencing part, and the expression is:
RC＝CC+WC+CM+LC (8)
wherein RC represents the absolute change amount of the base flow under the comprehensive influence of each driving factor, CC represents the base flow change amount under the climate change condition, WC represents the base flow change amount under the social water change condition, CM represents the base flow change amount under the coal mining change condition, and LC represents the base flow change amount under the land use change condition.
7. The method for numerically simulating the influence of coal mining on river runoff as set forth in claim 6, wherein said step S4 specifically includes:
s41, for a natural scene 1, a WEP model is established, no water is added, parameters are calibrated according to historical reduction runoff data Q0, and then the runoff quantity Q1 under the condition that no water is taken in a natural climate is obtained;
s42, setting a comparison scenario for the climate change scenario 2, and replacing the climate data in the verification period with the climate data with regular rate without adding water, so as to obtain the runoff quantity Q2 under the climate change-free water taking scenario;
s43, for the social water scenario 3, adding the manual water taking data into the scenario 1 model to obtain a simulated runoff quantity Q3, wherein Q3 is the runoff quantity under the action of climate change and manual water taking factor change;
s44, for the scene 4, in the scene 2, replacing land utilization data with regular rate to obtain the runoff quantity Q4 under the scene without climate change and land utilization;
s45, adding a coal mining module into the scene 3 to obtain the water for manual extraction and the runoff quantity Q5 under the influence of coal mining;
s46, calculating contribution amounts of different driving factors to the base flow change:
RC＝Q 5 -Q 1
CC＝Q 1 -Q 2
WC＝Q 3 -Q 1
LC＝Q 2 -Q 4
CM＝Q 5 -Q 3
s47, calculating the contribution rate of different driving factors to runoff change:
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