CN108009562B - Method for identifying characteristic spatial variability of hydrology and water resource - Google Patents
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Abstract
The invention relates to the field of hydrology and water resources, in particular to a method for identifying the variability of characteristic space of hydrology and water resources. The method comprises the following steps: s1, collecting synchronous actual measurement water level data of hydrological stations of a river network area and carrying out grid layout on the river network area; s2, combining the collected data and grid layout, calculating a water level half-variation function in each direction, and researching the variability of the water level in each direction; and S3, superposing the half-variation functions of different variation ranges by using a model superposition method to form a new registration structure half-variation function model, conveniently obtaining the half-variation function value between any two points in the area according to the registration structure half-variation function model, and estimating the water level of a point to be estimated in the area to be researched. The method has the advantages of low calculation complexity, high calculation efficiency, high prediction accuracy and simple operation, and can be used for carrying out spatial variability analysis on the water level characteristics of the river network area and carrying out spatial point estimation simulation on the water level in the network river area.
Description
Technical Field
The invention relates to the field of hydrology and water resources, in particular to a method for identifying the variability of characteristic space of hydrology and water resources.
Background
Due to the superposition influence of multiple factors such as human activities, climate change and the like, the natural environment of the river network area is greatly changed, which is mainly reflected in the variation of the geomorphic characteristics of hydrology and rivers. The water level of the network river area is a regional variable which changes along with the space position, and has randomness and certain structuredness. In flood control, disaster reduction, and water resource development and utilization management, water level is one of the most important hydrological features. However, the water level data measured by the existing hydrological station network can not meet the requirements of the production department, because the density of the station network is always limited, and the water level value actually needed to be known may not be on the station or even in the station network.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for identifying the spatial variability of the characteristics of hydrologic water resources, which can be used for carrying out spatial variability analysis on the water level characteristics of a river network area and carrying out spatial point estimation simulation on the water level in the network river area.
In order to solve the problems, the technical scheme provided by the invention is as follows: a method for identifying spatial variability of characteristics of hydrology and water resources comprises the following steps:
s1, collecting synchronous actual measurement water level data of hydrological stations of a river network area and carrying out grid layout on the river network area;
s2, combining the collected data and grid layout, calculating a water level half-variation function in each direction, and researching the variability of the water level in each direction;
and S3, superposing the half-variation functions of different variation ranges by using a model superposition method to form a new registration structure half-variation function model, conveniently obtaining the half-variation function value between any two points in the area according to the registration structure half-variation function model, and estimating the water level of a point to be estimated in the area to be researched.
Further, the step S2 includes:
s21, calculating a test half variation function gamma*(h)：
By calculating [ Z (x) ]i)-Z(xi+h)]2To calculate gamma by means of the arithmetic mean of*(h) (ii) a Wherein h is the distance vector, N (h) is the number of experimental data pairs separated by the vector h, Z (x)i) And Z (x)i+ h) is a point x spaced h apart in the x directioniAnd (x)i+ h) observed value;
s22, calculating to obtain pairs of h and gamma*(h) Value, make h-gamma*(h) Experimental hemivariogram;
and S23, fitting the experimental semi-variable function by using a proper theoretical semi-variable function model on the basis of the experimental semi-variable function, and analyzing the spatial variability of the water level characteristic by using the fitted model.
Further, the step S23 includes:
s231, selecting a spherical model, wherein the general formula of the spherical model is as follows:
wherein, C0Is the gold lump constant; (C)0+ C) is the base station value; c is called arch height; a is a variable range;
and S232, when h is more than 0 and less than or equal to a, fitting the spherical model half-variation function graph by using a weighted polynomial regression method.
Further, the step of S3 includes:
s31, constructing theoretical half-variation functions in the x direction and the y direction, wherein,
the theoretical half-variogram in the x-direction is:
the theoretical half-variogram in the y-direction is:
s32, for hyThe coordinates are linearly transformed to h'yIs then, γy(hy) Can be changed to maintain the original base station value, but change the range and gammay(hy) Is one intermediate half variant function gamma 'with the same variable range'y(h'y)；
S33. preparing gamma'y(h'y) Viewed as a homogeneous structure gamma in all directionsx(hx) Is then h 'again'yAnd (3) superposing the nesting structure of the semi-variation function of the other spherical model in the direction to obtain a nesting result model:
γ(h')＝γ0(h'0)+γ1(h'x)+γ2(h'y)；
according to the registration structure semi-variation function model, the semi-variation function value between any two points in the region can be obtained, and the model is used as a system estimation value which can be expanded to be a point to be estimated in the research region.
Furthermore, the grid layout is set according to the size of the river network area, the site density and the distribution condition.
Compared with the prior art, the beneficial effects are: the method for identifying the spatial variability of the characteristics of the hydrological water resources, provided by the invention, has the advantages of lower calculation complexity, high calculation efficiency, high prediction accuracy and simplicity in operation, and can be used for carrying out spatial variability analysis on the water level characteristics of the river network area and carrying out spatial point estimation simulation on the water level in the network river area.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a contour map of water level in the river network of the pearl river delta at 7, 16, 13 of 1999, wherein the number in the figure represents the water level and the unit is m.
Fig. 3 is a contour map of water level in the river network of the pearl river delta at 7, 22, 6 of 1999, wherein the number in the figure represents the water level and the unit is m.
FIG. 4 shows parameters of a registered structural model according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a semivariogram of a model of a water level registration structure of a network river of the pearl river delta in the embodiment of the present invention.
FIG. 6 is the result of curve estimation of the theoretical model of the hemivariation function of the network region of the Yangtze river delta at 7, 16 and 13 of 1999.
FIG. 7 is the result of curve estimation of the theoretical model of the hemivariation function of the network region of the Yangtze river delta at 7, 22, 6 of 1999.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
As shown in fig. 1, a method for identifying spatial variability of characteristics of hydrology and water resources includes the following steps:
acquiring synchronous actual measurement water level data of hydrological stations in a river network area and carrying out grid layout on the river network area; the grid layout is set according to the size of the river network area, the site density and the distribution condition.
Combining the collected data and the grid layout, calculating a water level semi-variation function in each direction, and researching the variability of the water level in each direction;
1. calculating the half-variation function gamma of the experiment*(h)：
By calculating [ Z (x) ]i)-Z(xi+h)]2To calculate gamma by means of the arithmetic mean of*(h) (ii) a Wherein h is the distance vector, N (h) is the number of experimental data pairs separated by the vector h, Z (x)i) And Z (x)i+ h) is a point x spaced h apart in the x directioniAnd (x)i+ h) observed value;
2. calculating to obtain pairs of h and gamma*(h) Value, make h-gamma*(h) Experimental hemivariogram;
3. fitting the experimental semi-variable function by using a proper theoretical semi-variable function model on the basis of the experimental semi-variable function, and analyzing the spatial variability of the water level characteristic by using the fitted model; in the invention, a spherical model is selected, and the general formula of the spherical model is as follows:
wherein, C0Is the gold lump constant; (C)0+ C) is the base station value; c is called arch height; a is a variable range;
since h is 0, γ (h) is 0; when h is greater than a, gamma (h) ═ C0+ C (a constant) is simple, and the invention only discusses the fitting problem when h is more than 0 and less than or equal to a. This time is:
this is an incomplete cubic polynomial of h, and a weighted polynomial regression can be used to fit the spherical model hemivariogram.
And thirdly, superposing the half-variation functions of different variation ranges by using a model superposition method to form a new registration structure half-variation function model, conveniently obtaining the half-variation function value between any two points in the region according to the registration structure half-variation function model, and estimating the water level of the point to be estimated in the region to be researched.
1. Constructing theoretical half-variogram functions in the x-direction and the y-direction, wherein,
the theoretical half-variogram in the x-direction is:
the theoretical half-variogram in the y-direction is:
2. to hyThe coordinates are linearly transformed to h'yIs then, γy(hy) Can be changed to maintain the original base station value, but change the range and gammay(hy) Is one intermediate half variant function gamma 'with the same variable range'y(h'y)；
3. Prepared from gamma'y(h'y) Viewed as a homogeneous structure gamma in all directionsx(hx) Is then h 'again'yAnd (3) superposing the nesting structure of the semi-variation function of the other spherical model in the direction to obtain a nesting result model:
γ(h')＝γ0(h'0)+γ1(h'x)+γ2(h'y)；
according to the registration structure semi-variation function model, the semi-variation function value between any two points in the region can be obtained, and the model is used as a system estimation value which can be expanded to be a point to be estimated in the research region.
Example 1
The present invention will be described in further detail with reference to the drawings and the network river area of the pearl river delta as an example, but the present invention should not be construed as being limited by the examples.
Step 1, collecting synchronous actual measurement water level data of hydrological stations in a river network area and carrying out grid layout on the river network area. In the invention, 51 multiplied by 51 grids are arranged in the river network area of the Zhujiang Delta, and the grid distance is 2 km; the net river has 66 hydrological stations.
And 2, combining the collected data and the grid layout, calculating the water level half-variation function in each direction, and researching the variability of the water level in each direction. Semivariogram functions in the north-south direction, east-west direction, southeast 45 ° direction and southwest 45 ° direction were calculated for the water levels of the river network of the pearl river delta at 16 th 13 of 7 th month and 22 th 6 of 7 th month in 1999, respectively. The water level spatial interpolation contour maps of fig. 2 and 3 reflect the variation characteristics of the variation functions in different directions, so that the water level is continuously changed in space; however, the changes in the southeast 45-degree direction and the south-north direction are greater than the changes in the east-west direction and the southwest 45-degree direction, which shows that the water level variability in the southeast 45-degree direction and the south-north direction is less than the water level variability in the east-west direction and the southwest 45-degree direction, and the base station values are different in the four directions, which reflects that the water levels are distributed in a band shape in the rivernet area of the Zhujiang delta river.
And 3, superposing the half-variation functions of different ranges by using a model superposition method to form a new registration structure half-variation function model, conveniently obtaining the half-variation function value between any two points in the region according to the registration structure half-variation function model, and estimating the water level of a point to be estimated in the region to be researched.
FIG. 4 is a graph of the half-variance function of the registered structure model, and FIG. 5 is a graph of the registered structure model parameters (1999.07.16.13) of the present invention. According to the registration structure semi-variation function model, the semi-variation function value between any two points in the region can be conveniently obtained, and the model can be expanded to be the system estimation value of the point to be estimated in the research region according to the model.
As can be seen from fig. 4 and 5, the change range of the water level of the zhuang delta network in the rich water period is 13.02km, which indicates that the direct correlation of the spatial change of the water level is basically disappeared beyond two points which are separated by 13.02km, which is consistent with the fact that the correlation of the water level is greatly reduced before and after the water system branching is about 10km in the zhuang delta network river region. The water level base value corresponding to the variable range of 13.02km is 6.18m2The maximum variation function value reflects the strength of the inter-water level variability; the lump coefficient is 0.045, which is caused by a water level measurement error, a continuous change in water level, and the like.
The theoretical half-variogram model constructed in this case can be used for the Kriging estimation value, therefore, for the water level of each known point, the Kriging method is used to estimate the points by using a plurality of known points around the points, at this time, the point is assumed to be unknown, then the error between the estimation value and the known value of each point is calculated, the estimation error is minimized to be the objective function, and the parameters of the model are optimized. The method utilizes a half-variation function theoretical model curve to estimate 15 stations in 66 hydrological stations in the Zhujiang Delta river network area, and the comprehensive result is shown in figures 6 and 7.
The calculation results of fig. 6 and 7 show that if a large number of known points are present around the estimated point, the effect of estimation by the kriging method is good, and the relative error is all below 1%. Therefore, according to the spatial variation principle, the method for estimating the water level of the point by using the kriging method is feasible, rigorous, high in precision and capable of giving estimation errors.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (2)
1. A method for identifying the spatial variability of characteristics of hydrology and water resources is characterized by comprising the following steps:
s1, collecting synchronous actual measurement water level data of hydrological stations of a river network area and carrying out grid layout on the river network area;
s2, combining the collected data and grid layout, calculating a water level half-variation function in each direction, and researching the variability of the water level in each direction; the step S2 includes:
s21, calculating a test half variation function gamma*(h)：
By calculating [ Z (x) ]i)-Z(xi+h)]2To calculate gamma by means of the arithmetic mean of*(h) (ii) a Wherein h is the distance vector, N (h) is the number of experimental data pairs separated by the vector h, Z (x)i) And Z (x)i+ h) is a point x spaced h apart in the x directioniAnd (x)i+ h) observed value;
s22, calculating to obtain pairs of h and gamma*(h) Value, make h-gamma*(h) Experimental hemivariogram;
s23, fitting the experimental semi-variable function by using a proper theoretical semi-variable function model on the basis of the experimental semi-variable function, and analyzing the water level characteristic space variability by using the fitted model; the step S23 includes:
s231, selecting a spherical model, wherein the general formula of the spherical model is as follows:
wherein, C0Is the gold lump constant; (C)0+ C) is the base station value; c is called arch height; a is a variable range;
s232, when h is more than 0 and less than or equal to a, a weighted polynomial regression method is selected to fit the semi-variation function graph of the spherical model;
s3, superposing the half-variation functions of different variation ranges by using a model superposition method to form a new registration structure half-variation function model, conveniently obtaining the half-variation function value between any two points in the area according to the registration structure half-variation function model, and estimating the water level of a point to be estimated in the area to be researched; the step S3 includes:
s31, constructing theoretical half-variation functions in the x direction and the y direction, wherein,
the theoretical half-variogram in the x-direction is:
the theoretical half-variogram in the y-direction is:
wherein (C)0+C1) Base station value in x direction; c1Referred to as the arch height in the x-direction; alpha is alpha1Is a variation in the x direction; (C)0+C2) Base station value in y direction; c2Referred to as the y-direction arch height; alpha is alpha2Is a variation in the y direction;
s32, for hyThe coordinates are linearly transformed to h'yIs then, γy(hy) Can be changed to maintain the original base station value, but change the range and gammay(hy) Is one intermediate half variant function gamma 'with the same variable range'y(h'y)；
S33. preparing gamma'y(h'y) Viewed as a homogeneous structure gamma in all directionsx(hx) Is then h 'again'yAnd (3) superposing the nesting structure of the semi-variation function of the other spherical model in the direction to obtain a nesting result model:
γ(h')＝γ0(h'0)+γ1(h'x)+γ2(h'y)；
according to the registration structure semi-variation function model, the semi-variation function value between any two points in the region can be obtained, and the model is used as a system estimation value which can be expanded to be a point to be estimated in the research region.
2. The method for recognition of spatial variability of characteristics of hydrology and water resources according to claim 1, wherein the grid layout is set according to river network area size and site density and distribution.
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