Precipitation space interpolation method, device and system, and storage medium

Abstract

The invention discloses a precipitation space interpolation method, device and system and a storage medium. The precipitation space interpolation method comprises the following steps: S1, obtaining precipitation data of a target area; s2, pre-processing the rainfall data of the target area; and S3, carrying out Anuspin spatial interpolation on the preprocessed rainfall data of different time scales. The technical scheme of the invention is suitable for spatial interpolation of meteorological factors in mountainous regions, and the precision is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorology, and in particular relates to a precipitation spatial interpolation method and device, a system, and a storage medium. Background Art

[0002] Precipitation is one of the most important meteorological elements and an important parameter in the research process of meteorology, hydrology, vegetation and ecology. Detailed precipitation data, especially digital precipitation, is the most important setting parameter to support various ecosystem models and runoff simulations. It is also an important index for climate change research. How to obtain high-precision raster data has become one of the key issues in climate change research. With the development of geography and the advancement of GIS (Geographic Information System) technology, spatial interpolation methods have emerged. Spatial interpolation is based on a certain mathematical method to use known meteorological point data to infer its unknown meteorological point data, thereby converting vector data into raster data. Common meteorological interpolation methods include Kriging, Inverse Distance Weighting (IDW) and Spline. At present, there are many studies on meteorological element interpolation methods. The ordinary Kriging method is usually used for temperature interpolation. However, the use of precipitation interpolation methods has always been in the selection stage, and there is no definite standard model. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a precipitation spatial interpolation method and device, system, and storage medium, which are suitable for spatial interpolation of meteorological factors in mountainous areas and have relatively high accuracy.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A precipitation spatial interpolation method, comprising:

[0006] Step S1, obtaining precipitation data of a target area;

[0007] Step S2, preprocessing the precipitation data of the target area;

[0008] Step S3: Perform Anusplin spatial interpolation on the preprocessed precipitation data at different time scales.

[0009] Preferably, preprocessing the precipitation data of the target area includes: removing discontinuous, redundant and erroneous data from the precipitation data, and performing consistency check on the precipitation data.

[0010] Preferably, a plane smoothing spline function is used to analyze and interpolate the preprocessed precipitation data at different time scales.

[0011] The present invention also provides a precipitation spatial interpolation device, comprising:

[0012] An acquisition module is used to obtain precipitation data of a target area;

[0013] A preprocessing module is used to preprocess the precipitation data of the target area;

[0014] The interpolation module is used to perform Anusplin spatial interpolation on the preprocessed precipitation data of different time scales.

[0015] Preferably, preprocessing the precipitation data of the target area includes: removing discontinuous, redundant and erroneous data from the precipitation data, and performing consistency check on the precipitation data.

[0016] Preferably, a plane smoothing spline function is used to analyze and interpolate the preprocessed precipitation data at different time scales.

[0017] An embodiment of the present invention further provides a precipitation spatial interpolation system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a precipitation spatial interpolation method when executed by the processor.

[0018] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes the precipitation spatial interpolation method when running.

[0019] The present invention uses the Anusplin interpolation method to interpolate precipitation in mountainous areas, and obtains an accurate, high-precision and high-resolution precipitation raster dataset in mountainous areas, which plays an important basic role in further scientific research on soil moisture conditions, reservoir scheduling and biomass estimation in mountainous areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0021] Figure 1 This is a flow chart of the precipitation spatial interpolation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Embodiment 1:

[0025] like Figure 1 As shown, an embodiment of the present invention provides a precipitation spatial interpolation method, including:

[0026] Step S1, obtaining precipitation data of a target area;

[0027] Step S2, preprocessing the precipitation data of the target area;

[0028] Step S3: Perform Anusplin spatial interpolation on the preprocessed precipitation data at different time scales.

[0029] As an implementation manner of an embodiment of the present invention, preprocessing the precipitation data of the target area includes: removing discontinuous, redundant and erroneous data from the precipitation data, and performing consistency check on the precipitation data.

[0030] As an implementation method of the embodiment of the present invention, a plane smoothing spline function is used to analyze and interpolate preprocessed precipitation data of different time scales.

[0031] Anusplin is based on the interpolation theory of ordinary thin disk and local thin disk spline functions. Local thin disk smoothing spline is an extension of the thin disk smoothing spline prototype. In addition to the usual spline independent variables, it allows the introduction of linear covariate sub-models, such as the correlation between temperature and altitude, and between evaporation and water vapor pressure difference.

[0032] The theoretical model of local thin disk smoothing splines is expressed as follows:

[0033] Z i =f(x i )+b T y i +e i (i=1,…,N)…(1)

[0034] Among them, z i is the dependent variable at point i in space; x i is the d-dimensional spline independent variable; f is the estimated value of xi Unknown smooth function; y i is a p-dimensional independent covariate; b is y i The p-dimensional coefficient; e i is a random error of the independent variable with an expected value of 0.

[0035] When the second term, i.e., the covariate (p = 0), is missing in formula (1), the model is simplified to an ordinary thin disk smooth spline; when the first independent variable is missing, the model becomes a multiple linear regression (this situation is not allowed in Anusplin). When interpolating precipitation, a three-variable local thin disk smooth spline function is used (longitude and latitude are independent variables, and altitude is a covariate), and the spline degree is set to 2.

[0036] Embodiment 2:

[0037] An embodiment of the present invention provides a precipitation spatial interpolation method, comprising:

[0038] Step 1: Collect precipitation data. The precipitation data comes from the 12-month data from 1959 to 2015 from the Shaanxi Meteorological Bureau, 31 meteorological stations, including 22 on the southern slope of Qinling Mountains and 9 on the northern slope.

[0039] Step 2: Preprocess the data: remove discontinuous, redundant and erroneous data, and perform consistency test on the data. The standard skewness coefficient analysis was performed on the original data. When the skewness coefficient is greater than 0, it indicates that the precipitation change tends to be more positive and presents a positive skewed distribution; when the skewness coefficient is equal to 0, it presents a normal distribution; when it is less than 0, it indicates that the precipitation change presents a negative skewed distribution. There are many methods for normality test. The W test (Shapiro-Wilk test) is suitable for small samples (n≤10), and the Kolmogorov-Smirnov test method is used. The test results show that the annual precipitation data in the Qinling Mountains from 1959 to 2015 conform to the normal distribution (p>0.05) (Table 1), so there is no need to transform the original precipitation data.

[0040] The DEM data was resampled to a resolution of 500m×500m according to research needs.

[0041] Table 1

[0042]

[0043] Step 3: Interpolate the data spatially: interpolate the precipitation data at different time scales. The interpolation results are shown in the figure. Based on the Anusplin method, the spatial distribution of the annual and seasonal average precipitation grids in the Qinling area from 1959 to 2015 was obtained, with a grid resolution of 500m×500m. Figure 1Spatial analysis shows that the average annual precipitation in the Qinling Mountains over the past 57 years ranges from 545.44 to 1155.46 mm, with an average rainfall of 824.76 mm. The spatial distribution generally shows a distribution of more abundant precipitation in the south and less precipitation in the north, with more precipitation in the southwest. The average annual precipitation on the south slope over the past 57 years ranges from 601.97 to 1155.46 mm, with an average rainfall of 847.37 mm; while the precipitation on the north slope ranges from 545.44 to 1061.84 mm, with an average rainfall of 737.25 mm, that is, the precipitation on the south slope is significantly more than that on the north slope, about 110.12 mm. The average precipitation in the four seasons of the Qinling Mountains is in the following order: summer (403.76 mm) > autumn (237.26 mm) > spring (169.11 mm) > winter (25.62 mm), and the precipitation on the south slope is greater than that on the north slope. The Qinling Mountains are the 800 mm dividing line, with an average annual rainfall of less than 800 mm on the north slope and more than 800 mm on the south slope. The rainfall obtained by the embodiment of the present invention is basically consistent with the research results of predecessors.

[0044] Step 4: Accuracy verification:

[0045] (1) Error test of low- and medium-altitude sample points. Since most of the meteorological stations in the Qinling Mountains are distributed in low- and medium-altitude areas of 400 to 1000 m, in order to further test the accuracy of interpolation, three observation stations that did not participate in the interpolation from 2013 to 2015 (Chenggu, Danfeng on the south slope and Huayin on the north slope) were used as sample points, and error analysis was performed with the corresponding sample points in the grid map. The error is expressed by RMSE and MAE. Table 2 shows the monthly error between the two. The results show that the error in summer and autumn is greater than that in spring and winter. The reason for this phenomenon may be that the Qinling Mountains have abundant rainfall and heavy rain in summer and autumn, with more uncertainties, and the winter and spring are cold and dry with limited rainfall. From the standard deviation results (Table 3), it can be seen that Huayin County has the most stable precipitation in the past three years, and Danfeng County has the most unstable; the precipitation standard deviation is basically within 20 mm.

[0046] Table 2

[0047]

[0048] Table 3

[0049] Site Altitude / m Precipitation in 2013 / mm Precipitation in 2014 / mm Precipitation in 2015 / mm Precipitation sum / mm Huayin 351.3 11.78 10.20 7.63 29.60 Chenggu 486.4 14.76 15.55 18.32 48.63 Danfeng 581.4 20.64 16.56 19.18 56.38

[0050] (2) Error test of high mountain sample points. For a long time, due to the scarcity of high mountain stations in Qinling Mountains, there is no station observation data. On the other hand, due to the high mountains, complex geographical environment and sensitive response of forest line areas to climate change in Qinling Mountains, in order to further verify the accuracy of precipitation interpolation in Qinling Mountains, the meteorological data of the past three years from two medium and high altitude sample points (Red River Valley and Taiyang Highway) in Taibai Mountain that did not participate in the interpolation were selected for verification. As shown in Tables 4 and 5, the error in summer and autumn is greater than that in spring and winter. This result is consistent with the conclusion of the low altitude error test. The standard deviation of annual precipitation from 2013 to 2015 is basically within 30 mm.

[0051] Through self-checking, error checking of medium and low sample points, and error checking of high mountain sample points, it is shown that the error is within the range and the raster dataset is relatively scientific.

[0052] Table 4

[0053]

[0054] Table 5

[0055]

[0056] Step 5. Comparison of different interpolation methods: To further illustrate the interpolation accuracy of Anusplin, two interpolation methods commonly used in meteorology were selected for horizontal comparative analysis. The two interpolation methods were ordinary kriging interpolation and inverse distance weighted method. The 57-year average annual precipitation in the Qinling Mountains was spatially interpolated, and then the measured values ​​and interpolation values ​​of 29 stations of the three interpolation results were correlated. The results showed that the precision of precipitation results based on ordinary kriging, inverse distance weighted method and Anusplin interpolation methods was different. Anusplin has the best accuracy, with a correlation of 0.99; the correlations of the other two methods are both 0.91. The 57-year average annual precipitation in the Qinling Mountains based on the three interpolation methods is abundant in the south and less in the north. Ankang and Hanzhong in southern Shaanxi have high precipitation due to latitude, topography and altitude, while the precipitation in the northern foot of the Qinling Mountains and Shangluo in the east is less. The spatial distribution of the Anusplin method is obviously smoother than that of the other two methods, and it more clearly expresses the distribution of precipitation in the Qinling Mountains, while the interpolation results of the other two methods all show "bull's eyes". This shows that the Anusplin interpolation method is more suitable for precipitation interpolation in the Qinling Mountains.

[0057] Step 6: Interpolation result presentation:

[0058] The distribution of annual and seasonal precipitation change rates in the Qinling region over the past 57 years was obtained by using the univariate linear regression method and ArcGIS 10.3. From 1959 to 2015, the annual average precipitation change rate in the Qinling region was in the range of -3.02 to 0.83 mm / 10 years, with the largest change rate in Shangluo in the eastern Qinling region, reaching 0.83 mm / 10 years; the areas with an increasing trend in precipitation change were Lueyang, Shiquan and Shangnan on the southern slope of the Qinling Mountains, with an average altitude of 811 m; the areas with a decreasing trend in precipitation change were Taibai Mountain, the main peak of the Qinling Mountains, and Ankang on the southern slope of the Qinling Mountains, with an average altitude of 1177 m. The change rates in spring, summer, autumn and winter were -0.73 to 0.65 mm, -2.73 to 2.50 mm, -2.13 to 3.69 mm and -0.20 to 1.02 mm, respectively. On the 57-year scale, the increasing and decreasing trends did not pass the significance test.

[0059] Embodiment 3:

[0060] The embodiment of the present invention further provides a precipitation spatial interpolation device, comprising:

[0061] An acquisition module is used to obtain precipitation data of a target area;

[0062] A preprocessing module is used to preprocess the precipitation data of the target area;

[0063] The interpolation module is used to perform Anusplin spatial interpolation on the preprocessed precipitation data of different time scales.

[0064] As an implementation manner of an embodiment of the present invention, preprocessing the precipitation data of the target area includes: removing discontinuous, redundant and erroneous data from the precipitation data, and performing consistency check on the precipitation data.

[0065] As an implementation method of the embodiment of the present invention, a plane smoothing spline function is used to analyze and interpolate preprocessed precipitation data of different time scales.

[0066] Embodiment 4:

[0067] An embodiment of the present invention further provides a precipitation spatial interpolation system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a precipitation spatial interpolation method when executed by the processor.

[0068] Embodiment 5:

[0069] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and the computer program executes the precipitation spatial interpolation method when running.

[0070] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A precipitation spatial interpolation method, characterized in that: include: Step S1, obtaining precipitation data of a target area; Step S2, preprocessing the precipitation data of the target area; Step S3: Perform Anusplin spatial interpolation on the preprocessed precipitation data at different time scales.

2. The precipitation spatial interpolation method according to claim 1, characterized in that: Preprocessing of precipitation data in the target area includes: removing discontinuous, redundant and erroneous data from the precipitation data, and performing consistency checks on the precipitation data.

3. The precipitation spatial interpolation method according to claim 2, characterized in that: Plane smoothing spline functions are used to analyze and interpolate preprocessed precipitation data at different time scales.

4. A precipitation spatial interpolation device, characterized in that: include: An acquisition module is used to obtain precipitation data of a target area; A preprocessing module is used to preprocess the precipitation data of the target area; The interpolation module is used to perform Anusplin spatial interpolation on the preprocessed precipitation data of different time scales.

5. The precipitation spatial interpolation device according to claim 4, characterized in that: Preprocessing of precipitation data in the target area includes: removing discontinuous, redundant and erroneous data from the precipitation data, and performing consistency checks on the precipitation data.

6. The precipitation spatial interpolation device according to claim 5, characterized in that: Plane smoothing spline functions are used to analyze and interpolate preprocessed precipitation data at different time scales.

7. A precipitation spatial interpolation system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the precipitation spatial interpolation method according to any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program executes the precipitation spatial interpolation method according to any one of claims 1 to 3 when running.

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