CN117312746B - Rainfall erosion force calculation method based on satellite rainfall data - Google Patents
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Abstract
The invention discloses a rainfall erosion force calculation method based on satellite rainfall data, which comprises the following steps: step 1, acquiring data; step 2, calculating rainfall erosion force; step 3, calculating a correction coefficient; and 4, correcting the result. According to the method, on the basis of satellite precipitation data, the rainfall erosion force data is corrected by combining correction coefficients of different types of sub-climate zones, so that the calculation deviation of the rainfall erosion force caused by the accuracy problem of the satellite precipitation data is reduced; the accuracy of the rainfall erosion force calculation result is improved.
Description
Technical Field
The invention belongs to the technical field of rainfall erosion, and particularly relates to a rainfall erosion force calculation method based on satellite rainfall data.
Background
Under the global climate change background, the hydraulic erosion in the global scope presents an aggravation trend, accurately characterizes rainfall erosion force distribution conditions, is beneficial to knowing the degree of influence of the hydraulic erosion on different areas, and provides references for land utilization planning and management of various countries. The accurate rainfall data is obtained on the premise of calculating rainfall erosion force, and the current rainfall measurement is mainly realized by weather stations, radars, satellites and the like distributed in all the world. The rainfall erosion force distribution condition in the small-scale range can be accurately depicted by means of spatial interpolation and other methods based on rainfall data observed by a large number of weather stations, and the rainfall erosion force distribution condition has the characteristics of high precision and the like. However, in areas where weather stations are sparsely distributed, relying on spatial interpolation methods for rainfall estimation will reduce the accuracy of the calculation results. The distribution of weather stations is limited by natural geographical conditions and economic conditions of each country, and global coverage is difficult to achieve. Therefore, rainfall erosion force data obtained by interpolation of the traditional sites is limited by site distribution density, and rainfall erosion force distribution conditions cannot be accurately reflected on a large scale. With the development of science and technology, satellite precipitation data provides possibility for rainfall monitoring on a larger scale, the data has the advantages of larger space range, implementation observation and the like, the defects of site data are overcome, the satellite precipitation data is limited by a technical method for estimating rainfall capacity, and researches show that the satellite precipitation data is smaller for a strong rainfall estimated value, and further the rainfall erosion value calculated on the whole is smaller. In addition, the evaluation standards of the erosion rainfall events in the previous research are different, so that the error of the rainfall erosion force calculation result is larger. Therefore, how to improve the accuracy of the rainfall erosion force calculation result is a technical problem to be solved currently.
Disclosure of Invention
The invention aims to provide a rainfall erosion force calculation method based on satellite rainfall data, so as to solve the technical problems.
The invention is realized by the following technical scheme:
the invention provides a rainfall erosion force calculation method based on satellite precipitation data, which comprises the following steps:
step 1, acquiring data: acquiring satellite precipitation data, site interpolation rainfall erosion data and weather zone data;
step 2, calculating rainfall erosion force: judging whether the single rainfall event is an aggressive rainfall event according to the acquired satellite rainfall data, if so, calculating rainfall erosion force of the aggressive rainfall event, and further calculating to obtain annual average rainfall erosion force data;
step 3, calculating a correction coefficient: carrying out unitary linear regression analysis on the annual average rainfall erosion data obtained in the step 2 and the site interpolation rainfall erosion data obtained in the step 1 on weather zone data, obtaining a correction coefficient through calculation, and carrying out reliability verification on the correction coefficient;
step 4, correcting the result: and (3) correcting the annual average rainfall erosion data obtained in the step (2) according to the correction coefficient obtained in the step (3) to obtain corrected rainfall erosion data.
Further, the climate zone data in step 1 includes a plurality of different types of sub-climate zone data, which are respectively: tropical climate zone data; climate zone data of the semiarid grasslands; desert climate zone data; marine climate zone data; data of the Mediterranean climate zone; subtropical zone wet climate zone data; data of temperate continental wetting climate zones; temperate zone monsoon climate zone data.
Further, in the step 2, the specific process of determining whether the single rainfall event is an aggressive rainfall event is: the determination is made according to the following conditions:
(1) Total amount of single rainfall P Total (S) Exceeding 12.7mm;
(2) Rainfall P of single rainfall within any 30min 30 Exceeding 6.35mm;
(3) Rainfall P in 6 hours if continuous rainfall 6h If the number of the continuous rainfall events is not more than 1.27mm, dividing the continuous rainfall events into two rainfall events from the 6 th place, and judging by using the conditions (1) and (2) respectively;
a rainfall event meeting one of the above conditions is determined to be an aggressive rainfall event.
Further, the calculation formula of the rainfall erosion force in the step 2 is as follows:
wherein R represents rainfall erosion force, and the unit is MJ mm h -1 ha -1 The method comprises the steps of carrying out a first treatment on the surface of the E represents the total kinetic energy of rainfall, and the unit is MJ ha -1 ；I 30 Represents the maximum rainfall intensity within 30min, and the unit is mm h -1 The method comprises the steps of carrying out a first treatment on the surface of the n represents the number of aggressive rainfall events;
the calculation formula of the rainfall total kinetic energy E is as follows:
wherein k represents dividing a single-field erosive rainfall event into k segments; v r Rainfall for the r segment; e, e r The unit of rainfall kinetic energy of the r segment is MJ ha -1 mm -1 ；
E, according to the rainfall energy calculation formula in RUSLE2 r The calculation formula is as follows:
wherein i is r The intensity of rainfall of the r segment is expressed in mm h -1 The method comprises the steps of carrying out a first treatment on the surface of the e is a natural constant; the annual average rainfall erosion force is calculated as follows:
wherein R is m Is annual average rainfall erosion force, and is expressed as MJ mm h -1 ha -1 yr -1 The method comprises the steps of carrying out a first treatment on the surface of the m is the year, and m is more than or equal to 2.
Further, the specific calculation process of the correction coefficient in the step 3 is as follows:
reading the annual average rainfall erosion force data obtained in the step 2 and the site interpolation rainfall erosion force data obtained in the step 1, and respectively cutting the annual average rainfall erosion force data obtained by calculation and the site interpolation rainfall erosion force data according to different types of sub-climate zone data; carrying out unitary linear regression analysis on different rainfall erosion force data of the same sub-climate zone type, selecting annual average rainfall erosion force data as an independent variable x, and carrying out regression analysis on site interpolation rainfall erosion force data as a dependent variable y, wherein a unitary linear regression equation is as follows:
y＝θ 0 +θ 1 x (5)
wherein θ 0 Is an intercept term; θ 1 Is a correction coefficient;
calculating and obtaining theta according to least square method 0 And theta 1 The calculation process is as follows:
there are q sets of sample points: (x) a ，y a )，a＝1，…，q；
For each x a All have a linear model predictive valueThe formula is:
linear model predictive valueAnd true value y a The difference between them, called prediction error->The calculation formula is as follows:
θ is adjusted by the following equation (8) 0 And theta 1 To take the value of (a) so that the prediction errorMinimum:
finally obtaining the correction coefficient theta of the sub-climate zone type 1 ；
The specific process for verifying the reliability of the correction coefficient comprises the following steps:
calculating the determination coefficient R of the unitary linear regression equation 2 ：
Wherein SSR is regression square sum; SST is the sum of the total squares; y is i Is a true value;is a predicted value; />Is the average value of y;
when R is 2 Above 0.5, the correction factor is verified.
Further, the specific process of correcting the annual average rainfall erosion data obtained in the step 2 according to the correction coefficient obtained in the step 3 in the step 4 is as follows: on the basis of the calculated annual average rainfall erosion data, the numerical value on each point position is multiplied by a corresponding correction coefficient according to different sub-climate zone types to obtain corrected rainfall erosion data results on different sub-climate zones, then the rainfall erosion correction results of different sub-climate zones are spliced, and finally a corrected global rainfall erosion calculation result is obtained.
The beneficial effects of the invention are as follows: according to the method, on the basis of satellite precipitation data, the rainfall erosion force data is corrected by combining correction coefficients of different types of sub-climate zones, so that the calculation deviation of the rainfall erosion force caused by the accuracy problem of the satellite precipitation data is reduced; the accuracy of the rainfall erosion force calculation result is improved.
The invention is described in further detail below with reference to the drawings and the detailed description.
Drawings
FIG. 1 is a schematic flow chart of the method of the invention;
FIG. 2 is a graphical representation of the results of a unitary linear regression analysis over different sub-climate zone types in example one.
Detailed Description
The invention provides a rainfall erosion force calculation method based on satellite rainfall data, which is shown in fig. 1 and comprises the following steps:
step 1, acquiring data: and acquiring satellite precipitation data, site interpolation rainfall erosion force data and weather zone data.
Satellite precipitation data is Level 3 (Final Run) precipitation data of a GPM satellite obtained through downloading in the U.S. space agency (NASA); site interpolation rainfall erosion force data is worldwide downloaded through the European soil data center (ESDAC)Site interpolation rainfall erosion force data; climate zone data is downloaded through national earth system science data center-Geiger climate zone data; the climate zone data comprises a plurality of different types of sub-climate zone data, which are respectively: tropical climate zone data; climate zone data of the semiarid grasslands; desert climate zone data; marine climate zone data; data of the Mediterranean climate zone; subtropical zone wet climate zone data; data of temperate continental wetting climate zones; temperate zone monsoon climate zone data.
Step 2, calculating rainfall erosion force: judging whether the single rainfall event is an aggressive rainfall event according to the acquired satellite rainfall data, and if so, calculating rainfall erosion force of the aggressive rainfall event, so as to obtain annual average rainfall erosion force data.
Firstly, calculating to obtain the total amount P of single rainfall according to the acquired satellite rainfall data Total (S) Rainfall P within any 30min of single rainfall 30 Then, whether the single rainfall event is an aggressive rainfall event is judged according to the following conditions:
(1) Total amount of single rainfall P Total (S) Exceeding 12.7mm;
(2) Rainfall P of single rainfall within any 30min 30 Exceeding 6.35mm;
(3) Rainfall P in 6 hours if continuous rainfall 6h And if the number of the continuous rainfall events is not more than 1.27mm, dividing the continuous rainfall events into two rainfall events from the 6 th place, and judging by using the conditions (1) and (2) respectively.
And judging the rainfall event meeting one of the conditions as an aggressive rainfall event, and calculating rainfall erosion force of the aggressive rainfall event.
The calculation formula of rainfall erosion force R is:
wherein R representsRainfall erosion force in MJ mm h -1 ha -1 The method comprises the steps of carrying out a first treatment on the surface of the E represents the total kinetic energy of rainfall, and the unit is MJ ha -1 ；I 30 Represents the maximum rainfall intensity within 30min, and the unit is mm h -1 The method comprises the steps of carrying out a first treatment on the surface of the n represents the number of aggressive rainfall events; i 30 And comparing the rainfall intensity of each 30min in the aggressive rainfall of the field one by one, and screening the obtained maximum rainfall intensity within 30 min.
The calculation formula of the rainfall total kinetic energy E is as follows:
wherein k represents dividing a single-field erosive rainfall event into k segments; v r Rainfall for the r segment; e, e r The unit of rainfall kinetic energy of the r segment is MJ ha -1 mm -1 。
E, according to the rainfall energy calculation formula in RUSLE2 r The calculation formula is as follows:
wherein i is r The intensity of rainfall of the r segment is expressed in mm h -1 The method comprises the steps of carrying out a first treatment on the surface of the e is a natural constant.
Further, according to the rainfall erosion force R, calculating the annual average rainfall erosion force R m The formula of (2) is:
wherein R is m Is annual average rainfall erosion force, and is expressed as MJ mm h -1 ha -1 yr -1 The method comprises the steps of carrying out a first treatment on the surface of the m is the year, and m is more than or equal to 2.
Step 3, calculating a correction coefficient: and (3) carrying out unitary linear regression analysis on the annual average rainfall erosion data obtained in the step (2) and the site interpolation rainfall erosion data obtained in the step (1) on weather zone data, obtaining a correction coefficient through calculation, and carrying out reliability verification on the correction coefficient.
Outputting the calculated annual average rainfall erosion force data into an ASCII file by using R language, then reading the ASCII file and the site interpolation rainfall erosion force data obtained in the step 1 by using ArcGIS software, and cutting the calculated annual average rainfall erosion force data and the site interpolation rainfall erosion force data respectively according to different types of sub-climate zone data; and then, performing unitary linear regression analysis on different rainfall erosion force data of the same sub-climate zone type.
The annual average rainfall erosion force data is selected as an independent variable x, the site interpolation rainfall erosion force data is selected as a dependent variable y, regression analysis is carried out, and a unitary linear regression equation is as follows:
y＝θ 0 +θ 1 x (5)
wherein θ 0 Is an intercept term; θ 1 Is a correction coefficient.
θ 0 And theta 1 For undetermined coefficient, calculating and obtaining theta according to least square method 0 And theta 1 The calculation process is as follows:
there are q sets of sample points: (x) a ，y a )，a＝1，…，q；
For each x a All have a linear model predictive valueThe formula is as follows:
linear model predictive valueAnd true value y a The difference between them, called prediction error->Its calculation formulaThe formula is:
in order to make the predicted valueAnd true value y a More closely, it is necessary to make the prediction error +.>Smaller, θ is adjusted by the following equation (8) 0 And theta 1 To the value of (1) so that the prediction error +.>Minimum:
θ determined by calculation according to formula 0 And theta 1 I.e. the correction coefficient theta of the sub-climate zone type is obtained 1 。
To verify the reliability of the calculated correction coefficients, the determination coefficient R of the unitary linear regression equation model needs to be calculated 2 ，R 2 The size is characterized by: the model can be interpreted as how much the independent variable causes a change in the dependent variable. I.e. for example R 2 0.7, it can be interpreted that 70% of the model is independent, resulting in a change in the dependent variable. In general, when R 2 Above 0.5, the unitary linear regression results are considered to be good, i.e., pass the reliability verification.
Determining the coefficient R 2 The calculation formula of (2) is as follows:
wherein SSR is regression square sum; SST is the sum of the total squares; y is i Is a true value;is a predicted value; />Is the y average value.
According to the calculation method, correction coefficients on different sub-climate zone types are calculated in sequence.
Step 4, correcting the result: and (3) correcting the annual average rainfall erosion data obtained in the step (2) according to the correction coefficient obtained in the step (3) to obtain corrected rainfall erosion data.
On the basis of the calculated annual average rainfall erosion data, the numerical value on each point position is multiplied by a corresponding correction coefficient according to different sub-climate zone types to obtain corrected rainfall erosion data results on different sub-climate zones, and then the corrected results of the rainfall erosion in different sub-climate zones are spliced by ArcGIS software to obtain corrected global rainfall erosion calculation results.
Example 1
The present embodiment is a specific application example of the above method.
The satellite precipitation data selected by the embodiment is GPM satellite precipitation data in 2001-2020 with the time precision of 30min and the space precision of 0.1 degrees; calculating to obtain annual average rainfall erosion data of 20 years worldwide according to the steps of the method; site interpolation rainfall erosion data is global site interpolation rainfall erosion data downloaded by the european soil data center (esac); climate zone data is downloaded through national earth system science data centerGeiger climate zone data.
Cutting annual average rainfall erosion force data and site interpolation rainfall erosion force data according to eight sub-climate zone data in ArcGIS respectively, wherein the annual average rainfall erosion force data and the site interpolation rainfall erosion force data are respectively: a, tropical climate; bs, semiarid grassland climate; bw, desert climate; cf, marine climate; cs, mediterranean climate; cw, subtropical humid climate; df, temperate continental moist climate; dw, temperate zone monsoon climate.
And (3) carrying out unitary linear regression analysis on the annual average rainfall erosion data and the site interpolation rainfall erosion data on different sub-climate zone types, and sequentially calculating correction coefficients on the different sub-climate zone types, wherein the result is shown in figure 2. The present embodiment uses lm (y x-1) functions in the R language, thereby rejecting the intercept θ 0 Obtaining a unitary linear regression equation passing through the origin: y=θ 1 *x，θ 1 Namely, the correction coefficient calculated in this embodiment. Determining the coefficient R 2 The result is shown in table 1, which is calculated by using the ggplot2 tool in the R language.
TABLE 1
According to the calculation, finally obtaining the regression result, regression equation and determination coefficient R of the unitary linear regression equation model shown in FIG. 2 2 。
And correcting the annual average rainfall erosion data of 20 years worldwide according to the calculated correction coefficient to obtain corrected rainfall erosion data, and using ArcGIS software to map to obtain a corrected annual average rainfall erosion data distribution map of 20 years worldwide.
In order to verify the superiority of the results of the method of the present invention, after the corrected annual average rainfall erosion data distribution map of 20 years is obtained in this embodiment, the calculation result (GPM) of this embodiment, the site interpolation rainfall erosion data result (gloreta) and the average value and standard deviation of the rainfall erosion data result (CMORPH) calculated based on satellite precipitation data are checked by using ArcGIS software, meanwhile, the GPM result and the CMORPH result are subtracted from the gloreta result respectively by using ArcGIS software, finally, the GPM and the CMORPH are subjected to correlation analysis with gloreta in R language respectively, and Spearman (Spearman) correlation analysis is selected, and the calculation formula is as follows:
wherein, beta is Spearman rank correlation coefficient; s is the number of objects; d, d i The difference between ranks of the corresponding variables, that is, the difference between the positions (grades) of the paired variables after the two variables are respectively ordered.
Beta is the rank correlation coefficient result of this embodiment, and the result is shown in table 2.
TABLE 2
As can be seen from table 2, compared with the rainfall erosion force data result obtained by the previous calculation based on satellite precipitation data, the rainfall erosion force data result obtained by the method is closer to the site interpolation rainfall erosion force data result, and the accuracy is higher.
Finally, it should be noted that the above description is only for the purpose of illustrating the technical solution of the present invention and not for the purpose of limiting the same, and that although the present invention has been described in detail with reference to the preferred arrangement, it will be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention.
Claims (5)
1. A method for calculating rainfall erosion force based on satellite precipitation data, the method comprising the steps of:
step 1, acquiring data: acquiring satellite precipitation data, site interpolation rainfall erosion data and weather zone data;
step 2, calculating rainfall erosion force: judging whether the single rainfall event is an aggressive rainfall event according to the acquired satellite rainfall data, if so, calculating rainfall erosion force of the aggressive rainfall event, and further calculating to obtain annual average rainfall erosion force data;
step 3, calculating a correction coefficient: carrying out unitary linear regression analysis on the annual average rainfall erosion data obtained in the step 2 and the site interpolation rainfall erosion data obtained in the step 1 on weather zone data, obtaining a correction coefficient through calculation, and carrying out reliability verification on the correction coefficient;
step 4, correcting the result: correcting the annual average rainfall erosion data obtained in the step 2 according to the correction coefficient obtained in the step 3 to obtain corrected rainfall erosion data;
the calculation formula of the rainfall erosion force in the step 2 is as follows:
wherein R represents rainfall erosion force, and the unit is MJ mm h -1 ha -1 The method comprises the steps of carrying out a first treatment on the surface of the E represents the total kinetic energy of rainfall, and the unit is MJ ha -1 ；I 30 Represents the maximum rainfall intensity within 30min, and the unit is mm h -1 The method comprises the steps of carrying out a first treatment on the surface of the n represents the number of aggressive rainfall events;
the calculation formula of the rainfall total kinetic energy E is as follows:
wherein k represents dividing a single-field erosive rainfall event into k segments; v r Rainfall for the r segment; e, e r The unit of rainfall kinetic energy of the r segment is MJ ha -1 mm -1 ；
E, according to the rainfall energy calculation formula in RUSLE2 r The calculation formula is as follows:
wherein i is r The intensity of rainfall of the r segment is expressed in mm h -1 The method comprises the steps of carrying out a first treatment on the surface of the e is a natural constant;
the annual average rainfall erosion force is calculated as follows:
wherein R is m Is annual average rainfall erosion force, and is expressed as MJ mm h -1 ha -1 yr -1 The method comprises the steps of carrying out a first treatment on the surface of the m is the year, and m is more than or equal to 2.
2. The method for calculating rainfall erosion force based on satellite precipitation data according to claim 1, wherein the weather zone data in step 1 comprises a plurality of different types of sub-weather zone data, respectively: tropical climate zone data; climate zone data of the semiarid grasslands; desert climate zone data; marine climate zone data; data of the Mediterranean climate zone; subtropical zone wet climate zone data; data of temperate continental wetting climate zones; temperate zone monsoon climate zone data.
3. The method for calculating rainfall erosion force based on satellite rainfall data according to claim 1, wherein the specific process of determining whether the single-site rainfall event is an erosive rainfall event in step 2 is as follows: the determination is made according to the following conditions:
(1) Total amount of single rainfall P Total (S) Exceeding 12.7mm;
(2) Rainfall P of single rainfall within any 30min 30 Exceeding 6.35mm;
(3) Rainfall P in 6 hours if continuous rainfall 6h If the number of the continuous rainfall events is not more than 1.27mm, dividing the continuous rainfall events into two rainfall events from the 6 th place, and judging by using the conditions (1) and (2) respectively;
a rainfall event meeting one of the above conditions is determined to be an aggressive rainfall event.
4. The method for calculating rainfall erosion force based on satellite rainfall data according to claim 2, wherein the specific calculation process of the correction coefficient in step 3 is as follows:
reading the annual average rainfall erosion force data obtained in the step 2 and the site interpolation rainfall erosion force data obtained in the step 1, and respectively cutting the annual average rainfall erosion force data obtained by calculation and the site interpolation rainfall erosion force data according to different types of sub-climate zone data; carrying out unitary linear regression analysis on different rainfall erosion force data of the same sub-climate zone type, selecting annual average rainfall erosion force data as an independent variable x, and carrying out regression analysis on site interpolation rainfall erosion force data as a dependent variable y, wherein a unitary linear regression equation is as follows:
y＝θ 0 +θ 1 x (5)
wherein θ 0 Is an intercept term; θ 1 Is a correction coefficient;
calculating and obtaining theta according to least square method 0 And theta 1 The calculation process is as follows:
there are q sets of sample points: (x) a ，y a )，a＝1，…，q；
For each x a All have a linear model predictive valueThe formula is:
linear model predictive valueAnd true value y a The difference between them, called prediction error->The calculation formula is as follows:
θ is adjusted by the following equation (8) 0 And theta 1 To take the value of (a) so that the prediction errorMinimum:
finally obtaining the correction coefficient theta of the sub-climate zone type 1 ；
The specific process for verifying the reliability of the correction coefficient comprises the following steps:
calculating the determination coefficient R of the unitary linear regression equation 2 ：
Wherein SSR is regression square sum; SST is the sum of the total squares; y is i Is a true value;is a predicted value; />Is the average value of y;
when R is 2 Above 0.5, the correction factor is verified.
5. The method for calculating rainfall erosion force based on satellite rainfall data according to claim 2, wherein the specific process of correcting the annual average rainfall erosion force data obtained in step 2 according to the correction coefficient obtained in step 3 in step 4 is as follows: on the basis of the calculated annual average rainfall erosion data, the numerical value on each point position is multiplied by a corresponding correction coefficient according to different sub-climate zone types to obtain corrected rainfall erosion data results on different sub-climate zones, then the rainfall erosion correction results of different sub-climate zones are spliced, and finally a corrected global rainfall erosion calculation result is obtained.
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| WO2020209432A1 (en) * | 2019-04-12 | 2020-10-15 | 대한민국(행정안전부 국립재난안전연구원장) | Rainfall simulator calibration system, and rainfall simulator calibration method |
| CN110296942A (en) * | 2019-05-21 | 2019-10-01 | 华中农业大学 | A kind of dynamic assessment method of vegetative coverage object element |
| CN112666070A (en) * | 2020-12-29 | 2021-04-16 | 重庆师范大学 | Soil erosion calculation method |
| CN115510377A (en) * | 2022-11-23 | 2022-12-23 | 水利部交通运输部国家能源局南京水利科学研究院 | Soil erosion intensity estimation method, system and storage medium |
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