CN117669793B - Rainfall frequency estimation method and device for combined satellite and site data - Google Patents
Rainfall frequency estimation method and device for combined satellite and site data Download PDFInfo
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Abstract
The invention relates to a rainfall frequency estimation method combining satellite and site data, which comprises the steps of dividing a consistent area by using two factors of a annual maximum daily rainfall sequence and a annual maximum rainfall process through a multi-element linear moment method, and compared with the current advanced regional linear moment method based on a single weather factor of annual extreme rainfall, the rainfall characteristic of the consistent area can be better reflected, so that the regional result is more reasonable and reliable, the rainfall frequency estimation value based on a satellite gridding product and the rainfall frequency estimation value based on the rainfall site data are subjected to data fusion, and not only can rainfall frequency estimation values with high resolution and higher accuracy be provided for rainfall-free sites or sites in rare areas, but also more specific and accurate rainfall frequency estimation value space distribution of a research area can be obtained. The rainfall frequency estimation method of the combined satellite and site data constructed by the invention can also provide a certain scientific reference for calculating rainfall frequency by utilizing satellite data in other areas.
Description
Technical Field
The invention relates to the technical field of hydrological weather related in flood control and disaster reduction, in particular to a rainfall frequency estimation method and device combining satellite and site data.
Background
At present, under the background of global warming, natural disasters such as extreme rainstorm and flood in China frequently occur, wherein flood is one of the most serious threats to the economic development of China society and the life and property safety of people. How to strengthen the early warning of flood disasters and how to scientifically conduct flood control planning and design is an important problem of the current flood control and disaster reduction work.
The design flood calculated according to the storm data by utilizing hydrologic frequency calculation is one of important bases of flood control design standards in China. The most advanced frequency calculation method internationally is to combine the linear moment method and the regional analysis method. The linear moment may provide unbiased, robust parameter estimates in frequency calculation, while the regional analysis rules may utilize the regional global hydrographic information. Based on the method, not only can rainfall frequency estimation values with higher accuracy and precision be obtained, but also the spatial distribution of heavy rain intensity under different frequencies can be obtained.
However, at present, this frequency calculation method is more applied to site data. For areas with sparse sites and uneven distribution, the obtained rainfall frequency estimated value spatial distribution can not meet the requirements of engineering design and area flood control planning because the rainfall frequency estimated value spatial distribution contains less heavy rain data.
Disclosure of Invention
The invention aims to at least solve one of the defects in the prior art and provides a rainfall frequency estimation method and device combining satellite and site data.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
Specifically, a rainfall frequency estimation method combining satellite and site data is provided, which comprises the following steps:
Step 110, acquiring satellite data of a target research area, screening and controlling the satellite data, and obtaining the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process required by frequency calculation, thereby obtaining the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall sequence of each grid point;
Step 120, performing hydrological weather consistent area division on a target research area based on the satellite data to obtain a plurality of primary sub-areas which are preliminarily divided, and a plurality of secondary sub-areas which are further divided into the primary sub-areas;
130, verifying dissonance of the consistent area of the secondary subarea, and adjusting dissonance points of the secondary subarea with the dissonance points to obtain an adjusted secondary subarea, namely the consistent area;
140, performing optimal distribution line type selection on the consistent area to obtain an optimal distribution function corresponding to the consistent area;
Step 150, determining regional frequency factors of the consistent area based on the optimal distribution function, and calculating rainfall frequency estimation values of any grid points contained in the consistent area based on the regional frequency factors;
step 160, acquiring site data of each rainfall site in a target research area, constructing a annual maximum daily rainfall sequence of each rainfall site and a corresponding annual maximum storm process total rainfall sequence based on the site data, and repeatedly executing steps 120 to 150 to obtain rainfall frequency estimated values of different rainfall sites;
And 170, carrying out data fusion on the rainfall frequency estimated value of the grid point and the rainfall frequency estimated value of the rainfall station when the recurrence period is T in the target research area through a linear regression model, and obtaining the rainfall frequency estimated value when the recurrence period is T after the correction of the target research area.
Further, in step 110, the satellite data includes satellite gridding daily rainfall and daily average air temperature products within the latitude and longitude range of the target research area, and latitude and longitude information of each grid point, and quality control needs to satisfy the principles of representativeness, reliability and consistency required by frequency calculation.
Further, in particular, step 120 includes the following,
Step 121, selecting month-by-month historical average value data of daily rainfall and daily average air temperature of each grid point as input variables, and primarily dividing the grid points of a target research area through fuzzy C-means clustering to obtain a first-level subarea;
step 122, calculating a multi-element linear moment dispersion coefficient tau 2[12],τ2[12] of each grid point by using a annual maximum daily rainfall sequence X 1 and a annual maximum heavy rain process total rainfall sequence X 2 of each grid point in the divided first-level subarea, wherein the calculation formula is as follows:
And/>
Wherein,For the variable X (j), j=1, 2, the kth linear moment coefficient, in particular, defines:
λ2[ij]＝2Cov[Xi,Fj(Xj)]
λ3[ij]＝6Cov{Xi,[Fj(Xj)-1/2]2}
Where i, j=1, 2 and defines F j (), j=1, 2 is the distribution function of the variable X j,
The primary subregion is subdivided into a plurality of secondary subregions according to the statistical characteristic identity of τ 2[12], so that the heterogeneity check index H ||.|| <1 of each secondary subregion, the calculation formula of the heterogeneity index H ||.|| is as follows:
In the method, in the process of the invention,
Wherein the method comprises the steps ofFor the linear moment covariance coefficient matrix of lattice point i, define
N i is the effective year length of the satellite daily rainfall data of the ith grid point in the subarea, and II A II is defined as a new standard of a matrix A,A t is the transposed matrix of matrix a.
Further, in particular, step 130 includes the following,
Assuming N lattice points in the secondary subarea, calculating a second-order linear moment coefficient matrix of each lattice point iThird-order linear moment coefficient matrix/>Fourth-order linear moment coefficient matrix/>Forming a matrix
And (3) making:
When D i is larger than a critical value corresponding to the number N (N is larger than or equal to 5) of grid points in the consistent area, the grid points are considered to be incoordination grid points;
When the dissonance lattice points exist in the secondary subarea, an analysis and verification result of the dissonance lattice points is obtained, if the analysis and verification result passes, the analysis and verification result is reserved in the primary secondary subarea, and if the analysis and verification result does not pass, the primary secondary subarea is removed.
Further, in particular, step 140 includes,
Step 141, assuming that N grid points exist in the secondary subarea, wherein the length of the annual maximum daily rainfall sequence of the ith grid point is N i, decomposing the annual maximum daily rainfall sequence of the ith grid point into a common component and a personalized component, wherein the personalized component is the average value of the annual maximum daily rainfall sequence of the ith grid point, removing the average value of the annual maximum daily rainfall sequence of the ith grid point to obtain a common component reflecting regional commonality, and calculating a single grid point sample linear moment deviation coefficient t (i) and a sample linear moment bias coefficient by utilizing the common component of each grid pointSample linear moment kurtosis coefficient/>Weighted average is carried out according to the sequence length of each lattice point to obtain a regional average linear moment dispersion coefficient t R and a bias coefficient/>And kurtosis coefficient/>
And 142, determining the optimal distribution function of each secondary partition from the generalized logic cliff distribution, generalized extremum distribution, generalized normal distribution, generalized pareto distribution and pearson III type distribution of the three parameters by utilizing Monte Carlo simulation test according to the relation between the regional average linear moment coefficient and the probability distribution function parameter.
Further, in particular, step 150 includes,
Based on the optimal distribution function of the jth consistent area, the frequency estimation value of the jth consistent area when the reproduction period is T can be determined, namely the regional frequency factor q T,j of the consistent area;
Determining a rainfall frequency estimated value Q T,i,j of the ith grid point in the jth consistent area when the reproduction period is T according to the following steps:
In the method, in the process of the invention, Is the historical average of the maximum daily rainfall of the ith grid point year in the jth consistent area.
Further, in particular, step 160 includes,
The site data of each rainfall site comprises longitude and latitude, elevation and moving condition of each site, and historical daily rainfall and daily average air temperature data of the site.
Further, in particular, the process of data fusion in step 170 includes,
Step 171, assuming that n rainfall stations are shared in the target research area, P g is a station rainfall frequency estimation value sequence when a reproduction period consisting of n rainfall stations is T, and P s is a corresponding grid rainfall frequency estimation value sequence. The linear regression equation is assumed as follows:
Pg＝A×Ps+B
Wherein A and B are regression parameters,
Step 172, estimating by a least square method to obtain a regression equation of the following form:
In the method, in the process of the invention, And/>Mean square error of site precipitation frequency estimation value sequence and corresponding grid point precipitation frequency estimation value sequence respectively,/>And/>The average values of the site precipitation frequency estimated value sequence and the grid point precipitation frequency estimated value sequence are respectively, r is a correlation coefficient, and the calculation formula is as follows:
Regression coefficients can thus be obtained:
Step 173, performing significance test on the regression coefficient r, and under the level of the confidence coefficient alpha=5%, searching a critical value r α from a correlation coefficient test table according to the number n of stations, and when |r| > r α, turning to step 174;
Step 174, taking the grid point precipitation frequency estimated value of the whole target research area as an independent variable P s ′, and introducing the grid point precipitation frequency estimated value into P g ′＝A×Ps ′ +B, wherein the calculated P g ′ is the precipitation frequency estimated value when the reproduction period after the correction of the target research area is T.
The invention also provides a rainfall frequency estimation device combining satellite and site data, which comprises:
The data acquisition module is used for acquiring satellite data of a target research area, screening the satellite data and controlling the quality to obtain the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process required by the frequency calculation, thereby obtaining the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall sequence of each grid point;
the regional division module is used for carrying out hydrological consistent regional division on the target research region based on the satellite data to obtain a plurality of primary sub-regions which are preliminarily divided and a plurality of secondary sub-regions which are used for further dividing the primary sub-regions;
The dissonance verification module is used for carrying out consistent area dissonance verification on the secondary subarea, carrying out dissonance point adjustment on the secondary subarea with the dissonance points, and obtaining an adjusted secondary subarea, namely a consistent area;
The optimal distribution function calculation module is used for carrying out optimal distribution line type selection on the consistent area to obtain an optimal distribution function corresponding to the consistent area;
the grid point rainfall frequency estimation value calculation module is used for determining regional frequency factors of the consistent area based on the optimal distribution function and calculating rainfall frequency estimation values of any grid points contained in the consistent area based on the regional frequency factors;
The rainfall station rainfall frequency estimation value calculation module is used for acquiring station information of each rainfall station in a target research area, constructing a annual maximum daily rainfall sequence of each rainfall station and a corresponding annual maximum storm process total rainfall sequence based on the station information, and then repeatedly operating the area division module, the dissonance verification module, the optimal distribution function calculation module and the lattice rainfall frequency estimation value calculation module to obtain rainfall frequency estimation values of different rainfall stations;
And the rainfall frequency estimation module is used for carrying out data fusion on the rainfall frequency estimation value of the grid point of the target research area at the reproduction period T and the rainfall frequency estimation value of the rainfall station through a linear regression model to obtain the rainfall frequency estimation value of the target research area at the reproduction period T after correction.
The invention provides a rainfall frequency estimation method combining satellite and site data, which has the following beneficial effects compared with the prior art:
1. The invention adopts a multi-element linear moment method when carrying out uniform division. Compared with the current advanced regional linear moment method based on the annual extreme rainfall single meteorological element, the method uses two elements of the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall, and can better reflect the storm characteristics of the consistent region, so that the zoning result is more reasonable and reliable.
2. According to the invention, the rainfall frequency estimated value based on the satellite gridding product and the rainfall frequency estimated value based on rainfall site data are subjected to data fusion, so that the rainfall frequency estimated value with high resolution and higher accuracy can be provided for a rainfall-free site or a site rare area, and the more specific and accurate rainfall frequency estimated value space distribution of a research area can be obtained.
3. The construction method can provide a certain scientific reference for calculating rainfall frequency by using satellite data in other areas.
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The above and other features of the present disclosure will become more apparent from the detailed description of the embodiments illustrated in the accompanying drawings, in which like reference numerals designate like or similar elements, and which, as will be apparent to those of ordinary skill in the art, are merely some examples of the present disclosure, from which other drawings may be made without inventive effort, wherein:
FIG. 1 is a flow chart of a method for estimating rainfall frequency of combined satellite and site data according to the present invention.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
Referring to fig. 1, embodiment 1 of the present invention provides a rainfall frequency estimation method combining satellite and site data, comprising the following steps:
Step 110, acquiring satellite data of a target research area, screening the satellite data and controlling the quality to obtain the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process required by the frequency calculation, thereby obtaining the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall sequence of each grid point;
Step 120, performing hydrological weather consistent area division on a target research area based on the satellite data to obtain a plurality of primary sub-areas which are preliminarily divided, and a plurality of secondary sub-areas which are further divided into the primary sub-areas;
130, verifying dissonance of the secondary subarea in a consistent area, and adjusting the dissonance points of the secondary subarea with the dissonance points to obtain an adjusted secondary subarea, namely a consistent area;
140, performing optimal distribution line type selection on the consistent area to obtain an optimal distribution function corresponding to the consistent area;
Step 150, determining regional frequency factors of the consistent area based on the optimal distribution function, and calculating rainfall frequency estimation values of any grid points contained in the consistent area based on the regional frequency factors; the calculated regional frequency factor is overlapped with rainfall individual components of any grid point contained in the consistent region to obtain a rainfall frequency estimated value of the grid point, wherein the rainfall individual components refer to an average value of a maximum daily rainfall sequence of the grid point.
Step 160, acquiring site data of each rainfall site in a target research area, constructing a annual maximum daily rainfall sequence of each rainfall site and a corresponding annual maximum storm process total rainfall sequence based on the site data, and repeatedly executing steps 120 to 150 to obtain rainfall frequency estimated values of different rainfall sites;
And 170, carrying out data fusion on the rainfall frequency estimated value of the grid point and the rainfall frequency estimated value of the rainfall station when the recurrence period is T in the target research area through a linear regression model, and obtaining the rainfall frequency estimated value when the recurrence period is T after the correction of the target research area.
In step 110, the satellite data includes satellite gridding daily rainfall and daily average air temperature products within the latitude and longitude range of the target research area, and latitude and longitude information of each grid point, so that quality control needs to satisfy the principles of representativeness, reliability and consistency required by frequency calculation.
In step 110, satellite gridding daily rainfall and daily average air temperature products in the longitude and latitude range of the research area are collected, and longitude and latitude information of each grid point is collected. Selecting the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process, respectively obtaining a maximum daily rainfall sequence and a total rainfall sequence in a maximum storm process;
Quality control includes checking whether the historical annual maximum daily rainfall series data of the grid points has specific values, whether the effective data length exceeds 20 years, whether the effective data are from the same overall distribution, and the like. Namely, the principle of representativeness, reliability, consistency and the like required by frequency calculation is satisfied.
As a preferred embodiment of the present invention, step 120 specifically includes the following,
Step 121, carrying out preliminary partitioning on a research area according to meteorological similarity, selecting month-by-month historical average value data of daily rainfall and daily average air temperature of each grid point as input variables, and carrying out preliminary partitioning on the grid points of a target research area through fuzzy C-means clustering to obtain a first-level sub-area;
step 122, calculating a multi-element linear moment dispersion coefficient tau 2[12],τ2[12] of each grid point by using a annual maximum daily rainfall sequence X 1 and a annual maximum heavy rain process total rainfall sequence X 2 of each grid point in the divided first-level subarea, wherein the calculation formula is as follows:
And/>
Wherein,For the variable X (j), j=1, 2, the kth linear moment coefficient, in particular, defines:
λ2[ij]＝2Cov[Xi,Fj(Xj)]
λ3[ij]＝6Cov{Xi,[Fj(Xj)-1/2]2}
Where i, j=1, 2 and defines F j (), j=1, 2 is the distribution function of the variable X j,
The primary subregion is subdivided into a plurality of secondary subregions according to the statistical characteristic identity of τ 2[12], so that the heterogeneity check index H ||.|| <1 of each secondary subregion, the calculation formula of the heterogeneity index H ||.|| is as follows:
In the method, in the process of the invention,
Wherein the method comprises the steps ofFor the linear moment covariance coefficient matrix of lattice point i, define
N i is the effective year length of the satellite daily rainfall data in the satellite data of the ith grid point in the subarea, and II is defined as a new standard of a matrix A,A t is the transposed matrix of matrix a.
As a preferred embodiment of the present invention, specifically, step 130 includes the following,
Assuming N lattice points in the secondary subarea, calculating a second-order linear moment coefficient matrix of each lattice point iThird-order linear moment coefficient matrix/>Fourth-order linear moment coefficient matrix/>Forming a matrix
And (3) making:
When D i is larger than the critical value corresponding to the number N (N is larger than or equal to 5) of grid points in the consistent area, the grid points are considered to be dissonance grid points, wherein the critical value corresponding to N is expressed as the following table I:
| Number of grid points in region | D i critical value | Number of grid points in region | D i critical value |
| 5 | 1.333 | 11 | 2.632 |
| 6 | 1.648 | 12 | 2.757 |
| 7 | 1.917 | 13 | 2.869 |
| 8 | 2.140 | 14 | 2.971 |
| 9 | 2.329 | ≥15 | 3 |
| 10 | 2.491 |
A first table;
When the dissonance lattice points exist in the secondary subareas, an analysis and verification result of the dissonance lattice points is obtained, if the analysis and verification result passes, the analysis and verification result is reserved in the primary secondary subareas, if the analysis and verification result does not pass, the primary secondary subareas are removed, and the processing mode of the dissonance lattice points can be considered to be adjusted to other areas or independent subareas; if the dissonance of the grid is considered to be caused by an extreme weather event and the extremum rainfall data is verified to be a true value, the grid is kept in the current area.
As a preferred embodiment of the present invention, step 140 specifically comprises,
Step 141, assuming that N grid points exist in the secondary subarea, wherein the length of the annual maximum daily rainfall sequence of the ith grid point is N i, decomposing the annual maximum daily rainfall sequence of the ith grid point into a common component and a personalized component, wherein the personalized component is the average value of the annual maximum daily rainfall sequence of the ith grid point, removing the average value of the annual maximum daily rainfall sequence of the ith grid point to obtain a common component reflecting regional commonality, and calculating a single grid point sample linear moment deviation coefficient t (i) and a sample linear moment bias coefficient by utilizing the common component of each grid pointSample linear moment kurtosis coefficient/>Weighted average is carried out according to the sequence length of each lattice point to obtain a regional average linear moment dispersion coefficient t R and a bias coefficient/>And kurtosis coefficient/>
Step 142, determining the optimal distribution function of each secondary partition from the generalized logic cliff distribution, generalized extremum distribution, generalized normal distribution, generalized pareto distribution and pearson III type distribution of the three parameters by utilizing Monte Carlo simulation test according to the relation between the regional average linear moment coefficient and the probability distribution function parameters,
Specifically, the calculation process is as follows,
For the divided secondary subareas, assuming a certain distribution line type, simulation is performed N sim times by using monte carlo simulation, and the length of each lattice simulation data series is required to be the same as the length of the lattice actual measurement data series. For the mth simulation result, the regional average linear moment kurtosis coefficientThe deviations of (2) are as follows:
the standard deviation of the corresponding analog kurtosis coefficient is:
the statistic Z DIST of the goodness-of-fit test criterion is defined as:
In the method, in the process of the invention, Is the kurtosis coefficient of the hypothetical distribution function.
If the statistic of simulation is |Z DIST | is less than or equal to 1.64, the fitting result is considered acceptable. And the smaller the Z DIST is, the better the fitting degree is.
As a preferred embodiment of the present invention, step 150 comprises,
Based on the optimal distribution function of the jth consistent area, the frequency estimation value of the jth consistent area when the reproduction period is T can be determined, namely the regional frequency factor q T,j of the consistent area, which reflects the rainfall characteristic shared in the consistent area;
Determining a rainfall frequency estimated value Q T,i,j of the ith grid point in the jth consistent area when the reproduction period is T according to the following steps:
In the method, in the process of the invention, Is the historical average of the maximum daily rainfall of the ith grid point year in the jth consistent area.
As a preferred embodiment of the present invention, step 160 comprises,
The site data of each rainfall site comprises longitude and latitude, elevation and moving condition of each site, and historical daily rainfall and daily average air temperature data of the site.
In the preferred embodiment, in step 160, for the rainfall sites in the study area, longitude and latitude, elevation and movement conditions of each site, as well as historical daily rainfall and daily average air temperature data of the site are collected. And (3) constructing a maximum annual rainfall sequence of each station and a total rainfall sequence of the corresponding maximum annual rainfall process, and repeating the steps S2 to S5 after finishing the quality control of the maximum annual rainfall sequence, so as to obtain rainfall estimated values of each station under different frequencies.
As a preferred embodiment of the present invention, specifically, the process of data fusion in step 170 includes,
Step 171, assuming that n rainfall stations are shared in the target research area, P g is a station rainfall frequency estimation value sequence when a reproduction period consisting of n rainfall stations is T, and P s is a corresponding grid rainfall frequency estimation value sequence. The linear regression equation is assumed as follows:
Pg＝A×Ps+B
Wherein A and B are regression parameters,
Step 172, estimating by a least square method to obtain a regression equation of the following form:
In the method, in the process of the invention, And/>Mean square error of site precipitation frequency estimation value sequence and corresponding grid point precipitation frequency estimation value sequence respectively,/>And/>The average values of the site precipitation frequency estimated value sequence and the grid point precipitation frequency estimated value sequence are respectively, r is a correlation coefficient, and the calculation formula is as follows:
Regression coefficients can thus be obtained:
Step 173, performing significance test on the regression coefficient r, and under the level of the confidence coefficient alpha=5%, searching a critical value r α from a correlation coefficient test table according to the number n of stations, and when |r| > r α, turning to step 174;
Step 174, taking the grid point precipitation frequency estimated value of the whole target research area as an independent variable P s ′, and introducing the grid point precipitation frequency estimated value into P g ′＝A×Ps ′ +B, wherein the calculated P g ′ is the precipitation frequency estimated value when the reproduction period after the correction of the target research area is T.
The invention also provides a rainfall frequency estimation device combining satellite and site data, which comprises:
The data acquisition module is used for acquiring satellite data of a target research area, screening the satellite data and controlling the quality to obtain the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process required by the frequency calculation, thereby obtaining the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall sequence of each grid point;
the regional division module is used for carrying out hydrological consistent regional division on the target research region based on the satellite data to obtain a plurality of primary sub-regions which are preliminarily divided and a plurality of secondary sub-regions which are used for further dividing the primary sub-regions;
The dissonance verification module is used for carrying out consistent area dissonance verification on the secondary subarea, carrying out dissonance point adjustment on the secondary subarea with the dissonance points, and obtaining an adjusted secondary subarea, namely a consistent area;
The optimal distribution function calculation module is used for carrying out optimal distribution line type selection on the consistent area to obtain an optimal distribution function corresponding to the consistent area;
the grid point rainfall frequency estimation value calculation module is used for determining regional frequency factors of the consistent area based on the optimal distribution function and calculating rainfall frequency estimation values of any grid points contained in the consistent area based on the regional frequency factors;
The calculated regional frequency factor is overlapped with rainfall individual components of any grid point contained in the consistent region to obtain a rainfall frequency estimated value of the grid point, wherein the rainfall individual components refer to an average value of a maximum daily rainfall sequence of the grid point.
The rainfall station rainfall frequency estimation value calculation module is used for acquiring station information of each rainfall station in a target research area, constructing a annual maximum daily rainfall sequence of each rainfall station and a corresponding annual maximum storm process total rainfall sequence based on the station information, and then repeatedly operating the area division module, the dissonance verification module, the optimal distribution function calculation module and the lattice rainfall frequency estimation value calculation module to obtain rainfall frequency estimation values of different rainfall stations;
And the rainfall frequency estimation module is used for carrying out data fusion on the rainfall frequency estimation value of the grid point of the target research area at the reproduction period T and the rainfall frequency estimation value of the rainfall station through a linear regression model to obtain the rainfall frequency estimation value of the target research area at the reproduction period T after correction.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
While the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiments or any particular embodiment, but is to be construed as providing broad interpretation of such claims by reference to the appended claims in view of the prior art so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing description of the invention has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the invention that may not be presently contemplated, may represent an equivalent modification of the invention.
The present invention is not limited to the above embodiments, but is merely preferred embodiments of the present invention, and the present invention should be construed as being limited to the above embodiments as long as the technical effects of the present invention are achieved by the same means. Various modifications and variations are possible in the technical solution and/or in the embodiments within the scope of the invention.
Claims (6)
1. The rainfall frequency estimation method combining satellite and site data is characterized by comprising the following steps:
Step 110, acquiring satellite data of a target research area, screening the satellite data and controlling the quality to obtain the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process required by the frequency calculation, thereby obtaining the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall sequence of each grid point;
Step 120, performing hydrological weather consistent area division on a target research area based on the satellite data to obtain a plurality of primary sub-areas which are preliminarily divided, and a plurality of secondary sub-areas which are further divided into the primary sub-areas;
130, verifying dissonance of the consistent area of the secondary subarea, and adjusting dissonance points of the secondary subarea with the dissonance points to obtain an adjusted secondary subarea, namely the consistent area;
140, performing optimal distribution line type selection on the consistent area to obtain an optimal distribution function corresponding to the consistent area;
Step 150, determining regional frequency factors of the consistent area based on the optimal distribution function, and calculating rainfall frequency estimation values of any grid points contained in the consistent area based on the regional frequency factors;
step 160, acquiring site data of each rainfall site in a target research area, constructing a annual maximum daily rainfall sequence of each rainfall site and a corresponding annual maximum storm process total rainfall sequence based on the site data, and repeatedly executing steps 120 to 150 to obtain rainfall frequency estimated values of different rainfall sites;
Step 170, carrying out data fusion on the frequency estimated value of the grid point of the target research area at the reproduction period T and the rainfall frequency estimated value of the rainfall station through a linear regression model to obtain a rainfall frequency estimated value of the target research area at the reproduction period T after correction;
specifically, step 120 includes the following,
Step 121, selecting month-by-month historical average value data of daily rainfall and daily average air temperature of each grid point as input variables, and primarily dividing the grid points of a target research area through fuzzy C-means clustering to obtain a first-level subarea;
step 122, calculating a multi-element linear moment dispersion coefficient tau 2[12],τ2[12] of each grid point by using a annual maximum daily rainfall sequence X 1 and a annual maximum heavy rain process total rainfall sequence X 2 of each grid point in the divided first-level subarea, wherein the calculation formula is as follows:
And/>
Wherein,For the variable X (j), j=1, 2, the kth linear moment coefficient, in particular, defines:
λ2[ij]＝2Cov[Xi,Fj(Xj)]
λ3[ij]＝6Cov{Xi,[Fj(Xj)-1/2]2}
Where i, j=1, 2 and defines F j (), j=1, 2 is the distribution function of the variable X j,
The primary subregion is subdivided into a plurality of secondary subregions according to the statistical characteristic identity of τ 2[12], so that the heterogeneity check index H ||.|| <1 of each secondary subregion, the calculation formula of the heterogeneity index H ||.|| is as follows:
In the method, in the process of the invention,
Wherein the method comprises the steps ofFor the linear moment covariance coefficient matrix of lattice point i, define
N i is the effective year length of the satellite daily rainfall data in the satellite data of the ith grid point in the subarea, and is defined as a new standard of the matrix A,A t is the transposed matrix of matrix a;
specifically, step 130 includes the following,
Assuming N lattice points in the secondary subarea, calculating a second-order linear moment coefficient matrix of each lattice point iThird-order linear moment coefficient matrix/>Fourth-order linear moment coefficient matrix/>Forming a matrix
And (3) making:
when D i is larger than a critical value corresponding to the number N of grid points in the consistent area, N is more than or equal to 5, and the grid points are regarded as incoordination grid points;
When the dissonance lattice points exist in the secondary subarea, an analysis and verification result of the dissonance lattice points is obtained, if the analysis and verification result passes, the analysis and verification result is reserved in the primary secondary subarea, and if the analysis and verification result does not pass, the primary secondary subarea is removed;
Specifically, the process of data fusion in step 170 includes,
Step 171, assuming that n rainfall stations are shared in the target research area, P g is a station rainfall frequency estimation value sequence when a reproduction period formed by the n rainfall stations is T, P s is a corresponding lattice rainfall frequency estimation value sequence, and assuming that a linear regression equation is as follows:
Pg＝A×Ps+B
Wherein A and B are regression parameters,
Step 172, estimating by a least square method to obtain a regression equation of the following form:
In the method, in the process of the invention, And/>Mean square error of site precipitation frequency estimation value sequence and corresponding grid point precipitation frequency estimation value sequence respectively,/>And/>The average values of the site precipitation frequency estimated value sequence and the grid point precipitation frequency estimated value sequence are respectively, r is a correlation coefficient, and the calculation formula is as follows:
Regression coefficients can thus be obtained:
Step 173, performing significance test on the regression coefficient r, and under the level of the confidence coefficient alpha=5%, searching a critical value r α from a correlation coefficient test table according to the number n of stations, and when |r| > r α, turning to step 174;
Step 174, taking the grid point precipitation frequency estimated value of the whole target research area as an independent variable P s ' into P g'＝A×Ps ' +B, and obtaining a calculated P g ' which is the precipitation frequency estimated value when the corrected reproduction period of the target research area is T.
2. The method of claim 1, wherein in step 110, the satellite data includes satellite gridding daily rainfall and daily average air temperature products within a longitude and latitude range of a target research area, and longitude and latitude information of each grid point, and quality control is required to satisfy the principles of representativeness, reliability and consistency required by frequency calculation.
3. The method of estimating a rainfall frequency of joint satellite and station data of claim 1 wherein step 140 comprises,
Step 141, assuming that N grid points exist in the secondary subarea, wherein the length of the annual maximum daily rainfall sequence of the ith grid point is N i, decomposing the annual maximum daily rainfall sequence of the ith grid point into a common component and a personalized component, wherein the personalized component is the average value of the annual maximum daily rainfall sequence of the ith grid point, removing the average value of the annual maximum daily rainfall sequence of the ith grid point to obtain a common component reflecting regional commonality, and calculating a single grid point sample linear moment deviation coefficient t (i) and a sample linear moment bias coefficient by utilizing the common component of each grid pointSample linear moment kurtosis coefficient/>Weighted average is carried out according to the sequence length of each lattice point to obtain a regional average linear moment dispersion coefficient t R and a bias coefficient/>And kurtosis coefficient/>
And 142, determining the optimal distribution function of each secondary partition from the generalized logic cliff distribution, generalized extremum distribution, generalized normal distribution, generalized pareto distribution and pearson III type distribution of the three parameters by utilizing Monte Carlo simulation test according to the relation between the regional average linear moment coefficient and the probability distribution function parameter.
4. The method of estimating a rainfall frequency of joint satellite and station data of claim 1 wherein step 150 comprises,
Based on the optimal distribution function of the jth consistent area, the frequency estimation value of the jth consistent area when the reproduction period is T can be determined, namely the regional frequency factor q T,j of the consistent area;
Determining a rainfall frequency estimated value Q T,i,j of the ith grid point in the jth consistent area when the reproduction period is T according to the following steps:
In the method, in the process of the invention, Is the historical average of the maximum daily rainfall of the ith grid point year in the jth consistent area.
5. The method of estimating a rainfall frequency of joint satellite and station data of claim 1 wherein step 160 comprises,
The site data of each rainfall site comprises longitude and latitude, elevation and moving condition of each site, and historical daily rainfall and daily average air temperature data of the site.
6. A rainfall frequency estimation device combining satellite and site data, comprising:
The data acquisition module is used for acquiring satellite data of a target research area, screening the satellite data and controlling the quality to obtain the annual maximum daily rainfall of each grid point and the total rainfall of the corresponding storm process required by the frequency calculation, thereby obtaining the annual maximum daily rainfall sequence and the annual maximum storm process total rainfall sequence of each grid point;
the regional division module is used for carrying out hydrological consistent regional division on the target research region based on the satellite data to obtain a plurality of primary sub-regions which are preliminarily divided and a plurality of secondary sub-regions which are used for further dividing the primary sub-regions;
The dissonance verification module is used for carrying out consistent area dissonance verification on the secondary subarea, carrying out dissonance point adjustment on the secondary subarea with the dissonance points, and obtaining an adjusted secondary subarea, namely a consistent area;
The optimal distribution function calculation module is used for carrying out optimal distribution line type selection on the consistent area to obtain an optimal distribution function corresponding to the consistent area;
the grid point rainfall frequency estimation value calculation module is used for determining regional frequency factors of the consistent area based on the optimal distribution function and calculating rainfall frequency estimation values of any grid points contained in the consistent area based on the regional frequency factors;
The rainfall station rainfall frequency estimation value calculation module is used for acquiring station information of each rainfall station in a target research area, constructing a annual maximum daily rainfall sequence of each rainfall station and a corresponding annual maximum storm process total rainfall sequence based on the station information, and then repeatedly operating the area division module, the dissonance verification module, the optimal distribution function calculation module and the lattice rainfall frequency estimation value calculation module to obtain rainfall frequency estimation values of different rainfall stations;
The rainfall frequency estimation module is used for carrying out data fusion on the rainfall frequency estimation value of the grid point of the target research area at the reproduction period T and the rainfall frequency estimation value of the rainfall station through a linear regression model to obtain the rainfall frequency estimation value of the target research area at the reproduction period T after correction;
Specifically, the operation process of the area dividing module includes the following,
Step 121, selecting month-by-month historical average value data of daily rainfall and daily average air temperature of each grid point as input variables, and primarily dividing the grid points of a target research area through fuzzy C-means clustering to obtain a first-level subarea;
step 122, calculating a multi-element linear moment dispersion coefficient tau 2[12],τ2[12] of each grid point by using a annual maximum daily rainfall sequence X 1 and a annual maximum heavy rain process total rainfall sequence X 2 of each grid point in the divided first-level subarea, wherein the calculation formula is as follows:
And/>
Wherein,For the variable X (j), j=1, 2, the kth linear moment coefficient, in particular, defines:
λ2[ij]＝2Cov[Xi,Fj(Xj)]
λ3[ij]＝6Cov{Xi,[Fj(Xj)-1/2]2}
Where i, j=1, 2 and defines F j (), j=1, 2 is the distribution function of the variable X j,
The primary subregion is subdivided into a plurality of secondary subregions according to the statistical characteristic identity of τ 2[12], so that the heterogeneity check index H ||.|| <1 of each secondary subregion, the calculation formula of the heterogeneity index H ||.|| is as follows:
In the method, in the process of the invention,
Wherein the method comprises the steps ofFor the linear moment covariance coefficient matrix of lattice point i, define
N i is the effective year length of the satellite daily rainfall data in the satellite data of the ith grid point in the subarea, and is defined as a new standard of the matrix A,A t is the transposed matrix of matrix a;
in particular, the operation of the dissatisfaction verification module includes,
Assuming N lattice points in the secondary subarea, calculating a second-order linear moment coefficient matrix of each lattice point iThird-order linear moment coefficient matrix/>Fourth-order linear moment coefficient matrix/>Forming a matrix
And (3) making:
when D i is larger than a critical value corresponding to the number N of grid points in the consistent area, N is more than or equal to 5, and the grid points are regarded as incoordination grid points;
When the dissonance lattice points exist in the secondary subarea, an analysis and verification result of the dissonance lattice points is obtained, if the analysis and verification result passes, the analysis and verification result is reserved in the primary secondary subarea, and if the analysis and verification result does not pass, the primary secondary subarea is removed;
In particular, the process of data fusion includes,
Step 171, assuming that n rainfall stations are shared in the target research area, P g is a station rainfall frequency estimation value sequence when a reproduction period formed by the n rainfall stations is T, P s is a corresponding lattice rainfall frequency estimation value sequence, and assuming that a linear regression equation is as follows:
Pg＝A×Ps+B
Wherein A and B are regression parameters,
Step 172, estimating by a least square method to obtain a regression equation of the following form:
In the method, in the process of the invention, And/>Mean square error of site precipitation frequency estimation value sequence and corresponding grid point precipitation frequency estimation value sequence respectively,/>And/>The average values of the site precipitation frequency estimated value sequence and the grid point precipitation frequency estimated value sequence are respectively, r is a correlation coefficient, and the calculation formula is as follows:
Regression coefficients can thus be obtained:
Step 173, performing significance test on the regression coefficient r, and under the level of the confidence coefficient alpha=5%, searching a critical value r α from a correlation coefficient test table according to the number n of stations, and when |r| > r α, turning to step 174;
Step 174, taking the grid point precipitation frequency estimated value of the whole target research area as an independent variable P s ' into P g'＝A×Ps ' +B, and obtaining a calculated P g ' which is the precipitation frequency estimated value when the corrected reproduction period of the target research area is T.
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