Method and device for estimating potential evapotranspiration in a target area

Abstract

The present invention provides a method and device for estimating potential evapotranspiration in a target area, relating to the technical field of hydrometeorology. The method includes: estimating the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature in the target area; calculating a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively; establishing a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value; solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; and estimating the potential evapotranspiration in the target area by using the multi-source data potential evapotranspiration estimation model. The present invention can not only effectively improve the estimation accuracy of potential evapotranspiration in the target area, but also improve the applicability and generality in different target areas.

Description

Background Art

[0002] Potential Evapotranspiration (PET) is one of the important parameters characterizing the hydrological cycle. Its changes are directly related to various atmospheric factors and affect hydrological, meteorological, and climatic variables related to the sustainability of natural ecosystems. At the same time, PET is one of the sensitive parameters for calculating actual evapotranspiration, soil moisture, and various drought monitoring indices (such as the standardized precipitation evapotranspiration index and the Palmer drought index). Accurately evaluating PET helps to further understand the hydrological cycle and improve the estimation accuracy of related parameters.

[0003] Currently, there are many types of traditional potential evapotranspiration generation methods, including direct measurement methods and empirical models. Empirical models are divided into temperature methods, radiation methods, mass transfer methods, and comprehensive methods according to the variables required for calculation. Among various types of empirical models, the most widely used are the Thornthwaite (TH) and the Penman-Monteith (PM) model recommended by the Food and Agriculture Organization of the United Nations.

[0004] The TH model only considers the influence of temperature and latitude, is simple to calculate but does not consider the influence of factors such as wind speed, solar radiation, and humidity, resulting in low calculation accuracy, and there is often an underestimation phenomenon in arid and semi-arid regions, while it will be overestimated in humid regions. The PM model fully considers the energy balance and water vapor diffusion theory, and has a high accuracy in obtaining PET, and is usually used as a reference for verifying other methods, but the calculation is relatively complex, and the required meteorological data is difficult to obtain in some regions. Summary of the Invention

[0005] To overcome the problems existing in the related art, the present invention provides a method and device for estimating potential evapotranspiration in a target area.

[0006] According to the first aspect of the embodiments of the present invention, a method for estimating potential evapotranspiration in a target area is provided, and the method includes:

[0007] Estimate the zenith total delay based on the pressure, precipitable water in the atmosphere, and weighted average temperature of the atmosphere in the target area;

[0008] Calculate a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively;

[0009] Establish a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value;

[0010] Solve the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model;

[0011] Estimate the potential evapotranspiration of the target area using the multi-source data potential evapotranspiration estimation model.

[0012] In some exemplary embodiments of the present invention, based on the foregoing solution, estimating the zenith total delay based on the pressure, precipitable water vapor, and weighted mean temperature of the atmosphere in the target area includes:

[0013] Calculate the zenith dry delay using the pressure in the target area;

[0014] Calculate the zenith wet delay using the precipitable water vapor and the weighted mean temperature of the atmosphere;

[0015] Estimate the zenith total delay according to the zenith dry delay and the zenith wet delay.

[0016] In some exemplary embodiments of the present invention, based on the foregoing solution, estimate the zenith total delay according to the zenith dry delay and the zenith wet delay including:

[0017]

[0018] wherein, represents the zenith dry delay and , is the surface pressure of the target area, and are the latitude and ellipsoidal height of the grid point, respectively; represents the zenith wet delay and , is the precipitable water vapor, , , are constant values, is the weighted mean temperature of the atmosphere.

[0019] In some exemplary embodiments of the present invention, based on the foregoing solution, the hybrid potential evapotranspiration fusion model includes:

[0020]

[0021] wherein, and represent the first estimated value and the second estimated value, respectively, are the longitude, latitude, height, and monthly average temperature at the station, respectively; are the longitude, latitude, height, and monthly average temperature at the grid point, respectively, represents the zenith total delay at the station, represents the zenith total delay at the grid point, is the model coefficient.

[0022] In some exemplary embodiments of the present invention, based on the foregoing solution, resolving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model includes:

[0023] Calculating the posterior residual value of the posterior residual value of the first estimated value and the posterior residual value of the second estimated value according to the potential evapotranspiration of the station and grid point and the modeling data matrix;

[0024] Calculating the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value respectively according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value;

[0025] Calculating the aggregated variance according to the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value;

[0026] Calculating the chi-square distribution value of the hybrid potential evapotranspiration fusion model according to the aggregated variance;

[0027] Determining the model coefficients of the hybrid potential evapotranspiration fusion model according to the chi-square distribution value;

[0028] Substituting the model coefficients into the hybrid potential evapotranspiration fusion model to obtain the multi-source data potential evapotranspiration estimation model.

[0029] In some exemplary embodiments of the present invention, based on the foregoing solution, the calculation method of the posterior residual value of the posterior residual value of the first estimated value and / or the posterior residual value of the second estimated value is:

[0030]

[0031] Wherein, is the posterior residual value, is the input matrix of the grid point and the station, that is, the modeling data matrix and , are the longitude, latitude, altitude and monthly average temperature at the station respectively; are the longitude, latitude, altitude and monthly average temperature at the grid point respectively, represents the total zenith delay at the station, represents the total zenith delay at the grid point, is the fusion coefficient matrix of the first estimated value and the second estimated value, is the fusion matrix of the first estimated value and the second estimated value and , and represent the first estimated value and the second estimated value respectively.

[0032] In some exemplary embodiments of the present invention, based on the foregoing solution, the calculation method of the posterior unit weight variance of the first estimated value and / or the posterior unit weight variance of the second estimated value is as follows:

[0033]

[0034] Wherein, is the posterior unit weight variance, is the posterior residual value, is the weight, is the number of points of the site or grid point, is the rank of the matrix, is a substitution parameter and .

[0035] In some exemplary embodiments of the present invention, based on the foregoing solution, according to the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value, calculating the aggregated variance includes:

[0036]

[0037] Wherein, is the aggregated variance, is the number of points of the site or grid point, represents the number of points of the site when represents the number of points of the grid point when; is the posterior unit weight variance of the posterior unit weight variance of the first estimated value or the second estimated value, is the number of potential evapotranspiration calculation models.

[0038] According to the second aspect of the embodiments of the present invention, there is provided a device, including:

[0039] A first estimation module, configured to estimate the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature of the target area;

[0040] A second estimation module, configured to calculate a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively;

[0041] A model establishment module, configured to establish a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value;

[0042] A model solution module, configured to solve the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model;

[0043] A potential evapotranspiration estimation module for estimating the potential evapotranspiration of a target area using a multi-source data potential evapotranspiration estimation model.

[0044] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including: a processor; and a memory having computer-readable instructions stored thereon, and when the computer-readable instructions are executed by the processor, the target area potential evapotranspiration estimation method in the first aspect is implemented.

[0045] According to a fourth aspect of an embodiment of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the target area potential evapotranspiration estimation method in the first aspect is implemented.

[0046] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0047] In the embodiments of the present invention, by combining pressure, precipitable water in the atmosphere, and weighted average temperature of the atmosphere to estimate the zenith total delay, and comprehensively using different potential evapotranspiration calculation models to establish a hybrid potential evapotranspiration fusion model and a multi-source data potential evapotranspiration estimation model, it is possible to fully consider the influence of various factors on potential evapotranspiration, not only effectively improving the estimation accuracy of potential evapotranspiration in the target area, but also improving the applicability and generality in different target areas.

[0048] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings herein are incorporated into the specification and form a part of the present invention, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0050] Figure 1 A schematic diagram of a system architecture showing an exemplary application environment of a target area potential evapotranspiration estimation method and apparatus to which the embodiments of the present invention can be applied;

[0051] Figure 2 A schematic flow chart showing the process of a target area potential evapotranspiration estimation method according to some embodiments of the present invention;

[0052] Figure 3 A schematic diagram showing a target area potential evapotranspiration estimation apparatus according to some embodiments of the present invention;

[0053] Figure 4 A schematic diagram showing the structure of a computer system of an electronic device according to some embodiments of the present invention;

[0054] Figure 5 A schematic diagram of a computer-readable storage medium according to some embodiments of the present invention is schematically shown. Detailed implementation manners

[0055] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0056] The terms used in the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "the", and "said" used in the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0057] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0058] Figure 1 A schematic diagram of a system architecture of an exemplary application environment of a potential evapotranspiration estimation method and device for a target area to which embodiments of the present invention can be applied is shown.

[0059] As Figure 1 shown, the system architecture 100 may include one or more of terminal devices such as a desktop computer 101, a portable computer 102, a smart phone 103, etc., a network 104, and a server 105. The network 104 is a medium for providing a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc. The terminal device may be various electronic devices having data processing functions, and a display screen is provided on the electronic device for presenting three-dimensional spatial information of target structural feature points or individual independent pipelines to the user, including but not limited to the above-mentioned desktop computer, portable computer, smart phone, etc. It should be understood, Figure 1The numbers of the terminal devices, networks, and servers therein are merely illustrative. According to actual requirements, there can be any number of terminal devices, networks, and servers. For example, the server 105 can be a sub-server cluster composed of multiple sub-servers, etc.

[0060] The method for estimating the potential evapotranspiration of the target area provided by the embodiments of the present invention can generally be executed by a terminal device. Correspondingly, the device for estimating the potential evapotranspiration of the target area is generally set in the terminal device. However, those skilled in the art can easily understand that the method for estimating the potential evapotranspiration of the target area provided by the embodiments of the present invention can also be executed by the server 105. Correspondingly, the device for estimating the potential evapotranspiration of the target area can also be set in the server 105. No special limitation is made in this exemplary embodiment.

[0061] In addition, it should be understood that the method for estimating the potential evapotranspiration of the target area in the embodiments of the present invention can be configured as a software module. In some implementation scenarios, the solution for estimating the potential evapotranspiration of the target area of the present invention can be deployed independently to realize the construction and generation of three-dimensional spatial information corresponding to different types of independent pipelines. In other implementation scenarios, the solution for estimating the potential evapotranspiration of the target area of the present invention can be deployed in other software as a functional module of the software. For example, it can be deployed in the analysis software of underground pipelines. The present invention does not make special restrictions on the application manner of the method for estimating the potential evapotranspiration of the target area.

[0062] Next, the embodiments of the present invention will be described in detail.

[0063] As Figure 2 shown, Figure 2 is a flowchart of estimating the potential evapotranspiration of a target area shown by the present invention according to an exemplary embodiment, including the following steps:

[0064] S210: Estimate the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature of the target area;

[0065] S220: Calculate the first estimated value and the second estimated value of the target area by using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively;

[0066] S230: Establish a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value;

[0067] S240: Solve the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model;

[0068] S250: Estimate the potential evapotranspiration of the target area by using the multi-source data potential evapotranspiration estimation model.

[0069] In the embodiments of the present invention, by combining pressure, precipitable water in the atmosphere, and weighted mean temperature of the atmosphere to estimate the total zenith delay, and comprehensively using different potential evapotranspiration calculation models to establish a hybrid potential evapotranspiration fusion model and a multi-source data potential evapotranspiration estimation model, it is possible to fully consider the influence of various factors on potential evapotranspiration, not only effectively improving the estimation accuracy of potential evapotranspiration in the target area, but also enhancing the applicability and generality in different target areas.

[0070] In S210, based on the pressure, precipitable water in the atmosphere, and weighted mean temperature of the atmosphere in the target area, the total zenith delay is estimated.

[0071] The total zenith delay consists of and The dry zenith delay component accounts for ninety percent or more of the total atmospheric zenith delay, while the wet zenith delay component caused by atmospheric water vapor only accounts for about ten percent. Therefore, the atmospheric water vapor content can be deduced from the wet zenith delay component. Based on the dry zenith delay model, by combining the station location information, geodetic elevation value, and barometric observation data, the dry zenith delay can be accurately calculated . Then, a relevant weighted mean temperature model of the atmosphere is established to calculate the weighted mean temperature of the atmosphere, and the wet zenith delay is obtained by using the precipitable water in the atmosphere and the weighted mean temperature data , so as to sum the dry zenith delay and the wet zenith delay to obtain the total zenith delay caused by atmospheric water vapor in the meteorological station observation data .

[0072] Therefore, estimating the total zenith delay based on the pressure, precipitable water in the atmosphere, and weighted mean temperature of the atmosphere in the target area includes:

[0073] Calculating the dry zenith delay by using the pressure in the target area;

[0074] Calculating the wet zenith delay by using the precipitable water in the atmosphere and the weighted mean temperature of the atmosphere;

[0075] Estimating the total zenith delay according to the dry zenith delay and the wet zenith delay.

[0076] The dry zenith delay can be accurately obtained through the pressure in the target area and the Saastamoinen model, and the expression is:

[0077]

[0078] where is the surface pressure in the target area, and are the latitude and ellipsoidal height of the grid point respectively.

[0079] Zenith wet delay Obtained by using the atmospheric precipitable water and the weighted mean temperature data of the atmosphere and through an empirical formula:

[0080]

[0081] Wherein, is the atmospheric precipitable water, , , are constant values, is the weighted mean temperature of the atmosphere.

[0082] In the present invention, Based on an improved model (Global Pressure and Temperature 2 Wet, GPT2w), and the performance of this model is better than other models. It mainly uses the data and ellipsoidal height data of the grid points of the Chinese Global Geodetic Observation System to correct the of GPT2w. The corrected model takes into account the specific characteristics and conditions of the Chinese region.

[0083] After that, according to the zenith dry delay and the above-mentioned zenith wet delay, the zenith total delay is estimated including:

[0084]

[0085] In S220, the first estimated value and the second estimated value of the target area are calculated by using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively.

[0086] The present invention does not specifically limit the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model. In some embodiments, the first potential evapotranspiration calculation model can be one of the Penman-Monteith Model, Horton Model, FAO-56 Two-Source Model, and Blaney-Criddle Model. The second potential evapotranspiration calculation model can be the same as or different from the first potential evapotranspiration calculation model. Of course, in different situations, the second potential evapotranspiration calculation model can also be one of the above models. The specific choice of model depends on data availability, computing resources, required accuracy, and specific requirements of the application scenario. In practice, it may also be necessary to correct and validate the model based on actual monitoring data to ensure the accuracy of the prediction.

[0087] On this basis, the present invention fully considers the monthly average temperature and location of the target area to form a heat index, a heat index coefficient, and a correction coefficient, thereby determining the first potential evapotranspiration calculation model, and then obtaining a first estimated value based on the first potential evapotranspiration calculation model. Specifically, it includes:

[0088] Calculate the heat index of the target area based on the monthly average temperature of the target area;

[0089] Calculate the heat index coefficient according to the heat index;

[0090] Calculate the correction coefficient of the target area based on the latitude and solar declination of the target area;

[0091] Obtain the first potential evapotranspiration calculation model according to the heat index, the heat index coefficient, and the correction coefficient;

[0092] Obtain the first estimated value according to the temperature data of the target area in the current month and the first potential evapotranspiration calculation model.

[0093] Obviously, the first potential evapotranspiration calculation model provided by the present invention takes into detailed consideration the comprehensive effects of temperature, geographical latitude, and seasonality on potential evapotranspiration, so as to be able to more accurately quantify and estimate the potential evapotranspiration of the target area.

[0094] On this basis, those skilled in the art can calculate the heat index of the target area in any way based on the average temperature of the target area, and the present invention does not make specific limitations. In some embodiments, the average temperature can be made proportional to the heat index or a heat stress index can be designed, etc., to determine the calculation method of the heat index.

[0095] In some exemplary embodiments of the present invention, the heat index of the target area can be calculated by the following method:

[0096]

[0097] Wherein, is the monthly average temperature of the i th month, with the unit of °C; is the heat index of 12 months.

[0098] On this basis, the heat index coefficient can be calculated by the following method:

[0099]

[0100] Wherein, is the heat index coefficient.

[0101] In addition, in some embodiments, the latitude and solar declination of the target area can be directly obtained from data or calculated by some methods. On this basis, the correction coefficient of the target area can be directly calculated based on the latitude and solar declination of the target area, or the maximum sunshine hours can be calculated based on the latitude and solar declination of the target area, and then the correction coefficient can be calculated through the maximum sunshine hours and the number of days in a month. As an exemplary embodiment of the present invention, optionally, the correction coefficient of latitude and month can be calculated by the following method:

[0102]

[0103] Wherein, represents the correction coefficient, represents the maximum sunshine hours of the target area and , represents pi, represents the angle at which the sun rises per hour and , represents the arccosine function, represents the tangent function, represents the latitude of the target area, represents the solar declination of the target area and , represents the sine function, represents the average Julian day in a month, represents the number of days in a month.

[0104] Based on this, according to the heat index, the heat index coefficient and the correction coefficient, the first potential evapotranspiration calculation model can be expressed as:

[0105]

[0106] In the formula, is the potential evapotranspiration calculated using the first potential evapotranspiration calculation model (i.e., the first estimated value, PET), T is the monthly temperature data within the preset range of the target area, I is the heat index, m is the heat index coefficient, is the correction coefficient. Since the air dynamic term is ignored when calculating PET using the first potential evapotranspiration calculation model, only the influence of the heat factor on PET is considered. However, PET is determined by multiple meteorological factors. Therefore, the first potential evapotranspiration calculation model cannot obtain high-precision PET for the region.

[0107] Therefore, in the exemplary embodiment provided by the present invention, the second potential evapotranspiration calculation model can be established in the following manner:

[0108] That is, taking the assumed crop canopy of the target area as the standard, considering the influence of multiple influencing factors on the potential evapotranspiration process, a second potential evapotranspiration calculation model is established; among them, the multiple influencing factors include radiation, temperature, vapor pressure, and wind speed.

[0109] On this basis, according to the air pressure of the target area, the temperature data of the current month, the temperature data of the previous month, and the second potential evapotranspiration calculation model, a second estimated value is obtained.

[0110] The second potential evapotranspiration calculation model takes the assumed crop canopy as the standard and fully considers the influence of multiple factors such as radiation, temperature, vapor pressure, and wind speed on the potential evapotranspiration process. The calculation process is relatively complex. Therefore, the specific calculation formula of the second potential evapotranspiration calculation model is as follows:

[0111]

[0112] In the formula, is the potential evapotranspiration calculated using the second potential evapotranspiration calculation model (i.e., the second estimated value, PET), is the net radiation at the crop surface; is the soil heat flux; is the humidity constant; and respectively represent the monthly average air temperature and wind speed within the preset range of the target area; is the saturation water vapor pressure; is the actual water vapor pressure; is the slope of the vapor pressure curve; the constants 900 and 0.34 are the standard crop coefficient and the standard crop wind coefficient, respectively.

[0113] Among them, the calculation method of the saturation water vapor pressure is specifically:

[0114]

[0115] The calculation method of the actual water vapor pressure is specifically as follows:

[0116]

[0117] and respectively represent the maximum temperature and the minimum temperature; is the dew point temperature.

[0118] Humidity constant can be calculated through the air pressure The calculation formula is as follows:

[0119]

[0120] Soil heat flux can be calculated through the average temperatures of the target month and the previous month. The specific calculation formula is as follows:

[0121]

[0122] and respectively refer to the i th month and the monthly average air temperature of the previous month ( i -1).

[0123] In S230, according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value, a hybrid potential evapotranspiration fusion model is established.

[0124] Based on the above solution, when the first potential evapotranspiration calculation model is the TH model and the second potential evapotranspiration calculation model is the PM model. Since the TH model has low accuracy but requires fewer parameters and can obtain areal PET, while the PM model has high accuracy but requires a large amount of meteorological data and the PET value at any location cannot be obtained. Therefore, to integrate the advantages of the TH and PM models and obtain a PET value with high accuracy and high spatial resolution, the present invention comprehensively considers the influence of geographical location and the correlation between TH / PM_PET and T / ZTD, and uses a multivariate linear polynomial function to construct a fusion model. The specific calculation formula is:

[0125]

[0126] where and respectively represent the first estimated value and the second estimated value, are respectively the longitude, latitude, altitude and monthly average temperature at the station; They are the longitude, latitude, altitude, and monthly average temperature at the grid points, respectively. It represents the total zenith delay at the station. It represents the total zenith delay at the grid points. They are the model coefficients.

[0127] In S240, the hybrid potential evapotranspiration fusion model is solved to obtain a multi-source data potential evapotranspiration estimation model.

[0128] The PET calculated by the TH model and the PM model cannot simply divide their weights in equal proportion due to differences in data sources, calculation methods, and accuracies. Therefore, a reasonable weight ratio needs to be determined first when solving the hybrid potential evapotranspiration fusion model.

[0129] The present invention does not specifically limit the determination method of the weight ratio, and the weight ratio can be determined by methods such as experimental comparison method, statistical analysis method, and machine learning method.

[0130] In some embodiments, when determining the weight ratio by the experimental comparison method, multiple target areas with different climate characteristics and geographical conditions can be selected as test areas first; then, the hybrid potential evapotranspiration fusion model is solved with different weight ratio combinations in each test area to obtain a multi-source data potential evapotranspiration estimation model; after that, the estimation results are compared with the actual observed data in this area to evaluate the accuracy of the estimation model under different weight ratio combinations; finally, according to the accuracy evaluation results, a more reasonable weight ratio range under different climate and geographical conditions is determined.

[0131] In other embodiments, when using the statistical analysis method to determine the weight ratio, a large amount of historical data can be collected first, including PET data calculated by the TH model and the PM model respectively, and the corresponding actual potential evapotranspiration observed data; then, the data is statistically analyzed, such as calculating the deviation degree between the estimation results of the TH model and the PM model and the actual value in different situations; by analyzing the distribution of the deviations, the relative reliability of the two models under different conditions is determined; finally, the weight ratio is determined according to the relative reliability, and a larger weight is assigned to the model with higher reliability.

[0132] It can be seen that whether it is the experimental comparison method or the statistical analysis method, a large amount of data needs to be processed, which brings certain challenges to practical applications.

[0133] To solve this problem, the solution of the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model includes:

[0134] According to the potential evapotranspiration at the station and grid points and the modeling data matrix, calculate the posterior residual value of the posterior residual value of the first estimated value and the posterior residual value of the second estimated value.

[0135] Calculate the posterior variance of unit weight of the first estimated value and the posterior variance of unit weight of the second estimated value respectively according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value;

[0136] Calculate the pooled variance according to the posterior variance of unit weight of the first estimated value and the posterior variance of unit weight of the second estimated value;

[0137] Calculate the chi-square distribution value of the hybrid potential evapotranspiration fusion model according to the pooled variance;

[0138] Determine the model coefficients of the hybrid potential evapotranspiration fusion model according to the chi-square distribution value;

[0139] Substitute the model coefficients into the hybrid potential evapotranspiration fusion model to obtain the multi-source data potential evapotranspiration estimation model.

[0140] The posterior variance of unit weight of the first estimated value and the posterior variance of unit weight of the second estimated value can be calculated in the same way:

[0141]

[0142] where, is the posterior residual value, is the input matrix of grid points and stations, that is, the modeling data matrix and , are the longitude, latitude, altitude and monthly average temperature at the station respectively; are the longitude, latitude, altitude and monthly average temperature at the grid point respectively, represents the total zenith delay at the station, represents the total zenith delay at the grid point, is the fusion coefficient matrix of the first estimated value and the second estimated value, is the fusion matrix of the first estimated value and the second estimated value and , and represent the first estimated value and the second estimated value respectively.

[0143] After obtaining the posterior residual values of different data sources, let , and be the weights of grid points and stations respectively. On this basis, the posterior variance of unit weight of the first estimated value and the posterior variance of unit weight of the second estimated value can be obtained in the same way:

[0144]

[0145] where, is the a posteriori variance of unit weight, is the a posteriori residual value, is the weight, is the number of points of the site or grid point, is the rank of the matrix, is a surrogate parameter and .

[0146] It is found through calculation that the weights of PET from different sources are not equal. Therefore, calculate the pooled variance of the two variance components :

[0147]

[0148] wherein, is the pooled variance, is the number of points of the site or grid point, represents the number of points of the site when represents the number of points of the grid point when; is the a posteriori variance of unit weight of the first estimated value or the a posteriori variance of unit weight of the a posteriori variance of the second estimated value, is the number of potential evapotranspiration calculation models.

[0149] Calculate the chi-square distribution value of the mixed potential evapotranspiration fusion model according to the said pooled variance including:

[0150]

[0151] wherein, is the number of potential evapotranspiration calculation models, is the number of points of the site or grid point, represents the number of points of the site when represents the number of points of the grid point when; is the pooled variance, is the a posteriori variance of unit weight of the first estimated value or the a posteriori variance of unit weight of the a posteriori variance of the second estimated value.

[0152] The degrees of freedom and significance level of the chi-square distribution are 2 and 0.1 respectively, is 0.02. On this basis, determining the model coefficients of the mixed potential evapotranspiration fusion model according to the said chi-square distribution value includes:

[0153] Judge whether the chi-square distribution value of the hybrid potential evapotranspiration fusion model is less than or equal to the significance level value of the chi-square distribution. If so, it is considered that the variance of unit weight of the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model are statistically equal, and output the current weight as the optimal weight; otherwise, update the weight and recalculate the posterior variance of unit weight.

[0154] According to the optimal weight, obtain the fusion coefficient matrix of the first estimated value and the second estimated value :

[0155]

[0156] where is the fusion weight diagonal matrix of the optimal weights of the grid points and the stations.

[0157] The calculation method for updating the weight is:

[0158]

[0159] where is the weight of the hybrid potential evapotranspiration fusion model, is the number of iterations, is the summary variance of the hybrid potential evapotranspiration fusion model, is an arbitrary value and .

[0160] Here, when updating, is updated simultaneously .

[0161] In S250, use the multi-source data potential evapotranspiration estimation model to estimate the potential evapotranspiration of the target area.

[0162] Calculate the location information of PET and the temperature and zenith total delay data, and then the PET at this point can be calculated. The specific calculation formula is as follows:

[0163] .

[0164] The present invention can effectively solve the defect that the existing TH model requires fewer parameters to calculate PET, can obtain areal data but has low accuracy, while the PM model has high accuracy but requires a large number of meteorological parameters and is usually at the station scale.

[0165] According to the second aspect of the embodiments of the present invention, there is also provided an apparatus for estimating potential evapotranspiration of a target area. Referring to Figure 3 shown, the apparatus 300 for estimating potential evapotranspiration of the target area includes:

[0166] The first estimation module 310 is configured to estimate the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature of the target area;

[0167] The second estimation module 320 is configured to calculate a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively;

[0168] The model establishment module 330 is configured to establish a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value;

[0169] The model solution module 340 is configured to solve the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model;

[0170] The potential evapotranspiration estimation module 350 is configured to estimate the potential evapotranspiration of the target area by using the multi-source data potential evapotranspiration estimation model.

[0171] In an exemplary embodiment of the present invention, based on the foregoing solution, the model solution module 340 may further include:

[0172] The posterior residual calculation sub-module is configured to calculate the posterior residual value of the first estimated value and the posterior residual value of the second estimated value according to the potential evapotranspiration of the site and grid points and the modeling data matrix;

[0173] The posterior unit weight variance calculation sub-module is configured to calculate the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value respectively according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value;

[0174] The summary variance calculation sub-module is configured to calculate the summary variance according to the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value;

[0175] The chi-square distribution calculation sub-module is configured to calculate the chi-square distribution value of the hybrid potential evapotranspiration fusion model according to the summary variance;

[0176] The model coefficient determination sub-module is configured to determine the model coefficient of the hybrid potential evapotranspiration fusion model according to the chi-square distribution value;

[0177] The model determination sub-module is configured to substitute the model coefficient into the hybrid potential evapotranspiration fusion model to obtain the multi-source data potential evapotranspiration estimation model.

[0178] It should be noted that although several modules and sub - modules of the potential evapotranspiration estimation device for the target area are mentioned in the above - detailed description, this division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above - described modules or sub - modules can be embodied in one module or unit. Conversely, the features and functions of one module or sub - module described above can be further divided and embodied by multiple modules or sub - modules.

[0179] In addition, in an exemplary embodiment of the present invention, an electronic device capable of implementing the above - described potential evapotranspiration estimation method for the target area is also provided.

[0180] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "system" here.

[0181] Next, refer to Figure 4 to describe the electronic device 400 according to this embodiment of the present invention. Figure 4 The illustrated electronic device 400 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0182] As Figure 4 shown, the electronic device 400 is presented in the form of a general - purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one of the above - mentioned processing units 410, at least one of the above - mentioned storage units 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0183] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present invention described in the above - mentioned "exemplary method" part of the present invention. For example, the processing unit 410 can execute as Figure 2As shown in S210, estimate the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature in the target area; S220, calculate the first estimated value and the second estimated value of the target area using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively; S230, establish a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value; S240, solve the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; S250, estimate the potential evapotranspiration of the target area using the multi-source data potential evapotranspiration estimation model.

[0184] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 421 and / or a cache storage unit 422, and may further include a read-only storage unit (ROM) 423.

[0185] The storage unit 420 may also include a program / utilities 424 having a set (at least one) of program modules 425. Such program modules 425 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0186] The bus 430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0187] The electronic device 400 may also communicate with one or more external devices 470 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or may communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be through an input / output (I / O) interface 450. And, the electronic device 400 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 460. As shown in the figure, the network adapter 460 communicates with other modules of the electronic device 400 through the bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0188] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or in a manner that combines software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0189] In an exemplary embodiment of the present invention, a computer-readable storage medium is further provided, on which a program product capable of implementing the above method of the present invention is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present invention.

[0190] Refer to Figure 5 As shown, a program product 500 for implementing the above-mentioned potential evapotranspiration estimation method in the target area according to an embodiment of the present invention is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In the present invention, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0191] The program product can adopt any combination of one or more readable storage media. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access storage unit (RAM), a read-only storage unit (ROM), an erasable programmable read-only storage unit (EPROM or flash memory), an optical fiber, a portable compact disc read-only storage unit (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0192] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or, it can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0193] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed, for example, synchronously or asynchronously in multiple modules.

[0194] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0195] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include known common knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the claims.

[0196] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for estimating potential evapotranspiration in a target area, characterized in that, The method for estimating the potential evapotranspiration in the target area includes: Estimating the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature in the target area; Calculating a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively; Establishing a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value; Solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; Estimating the potential evapotranspiration in the target area by using the multi-source data potential evapotranspiration estimation model; The hybrid potential evapotranspiration fusion model includes: Among them, and represent the first estimated value and the second estimated value respectively, which are the longitude, latitude, altitude and monthly average temperature at the station respectively; which are the longitude, latitude, altitude and monthly average temperature at the grid point respectively, represents the total zenith delay at the station, represents the total zenith delay at the grid point, is the model coefficient; Solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model includes: Calculating the posterior residual value of the posterior residual value of the first estimated value and the posterior residual value of the second estimated value according to the potential evapotranspiration of the site and grid points and the modeling data matrix; Calculating the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value respectively according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value; Calculating the summary variance according to the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value; Calculating the chi-square distribution value of the hybrid potential evapotranspiration fusion model according to the summary variance; Determining the model coefficients of the hybrid potential evapotranspiration fusion model according to the chi-square distribution value; Substituting the model coefficients into the hybrid potential evapotranspiration fusion model to obtain the multi-source data potential evapotranspiration estimation model.

2. The potential evapotranspiration estimation method for the target area according to claim 1, characterized in that, Estimating the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature in the target area includes: Calculating the zenith dry delay by using the pressure in the target area; Calculating the zenith wet delay by using the atmospheric precipitable water and the atmospheric weighted average temperature; Estimating the zenith total delay according to the zenith dry delay and the zenith wet delay.

3. The potential evapotranspiration estimation method for the target area according to claim 2, wherein Estimate the zenith total delay according to the zenith hydrostatic delay and the zenith wet delay including: Among them, represents the zenith hydrostatic delay and , is the surface pressure of the target area, and are the latitude and ellipsoidal height of the grid point respectively; represents the zenith wet delay and , is the precipitable water vapor, , , are constant values, is the weighted mean temperature of the atmosphere.

4. The potential evapotranspiration estimation method for the target area according to claim 1, wherein The calculation method of the posterior residual value of the posterior residual value of the first estimated value and / or the posterior residual value of the second estimated value is: wherein, is the posterior residual value, is the input matrix of grid points and stations, that is, the modeling data matrix and , are the longitude, latitude, altitude and monthly average temperature at the station respectively; are the longitude, latitude, altitude and monthly average temperature at the grid point respectively, represents the total zenith delay at the station, represents the total zenith delay at the grid point, is the fusion coefficient matrix of the first estimated value and the second estimated value, is the fusion matrix of the first estimated value and the second estimated value and , and represent the first estimated value and the second estimated value respectively.

5. The potential evapotranspiration estimation method for the target area according to claim 1, wherein The calculation method of the posterior unit weight variance of the first estimated value and / or the posterior unit weight variance of the second estimated value is: wherein, is the posterior unit weight variance, is the posterior residual value, is the weight, is the number of points of the site or grid point, is the rank of the matrix, is the substitution parameter and .

6. The method for estimating potential evapotranspiration in a target area according to any one of claims 1-5, characterized in that, Calculating the summary variance according to the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value includes: Among them, is the aggregated variance, is the number of points at the station or grid point, represents the number of points at the station when represents the number of points at the grid point when; is the posterior unit weight variance of the first estimated value or the posterior unit weight variance of the second estimated value, is the number of potential evapotranspiration calculation models.

7. An apparatus for estimating the potential evapotranspiration of a target area based on the method according to any one of claims 1-6, characterized in that, The device includes: A first estimation module for estimating the zenith total delay based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature in the target area; A second estimation module for calculating a first estimated value and a second estimated value of the target area by using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively; A model establishment module for establishing a hybrid potential evapotranspiration fusion model according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value; A model solution module for solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; A potential evapotranspiration estimation module for estimating the potential evapotranspiration of a target area using a multi-source data-based potential evapotranspiration estimation model.

8. An electronic device, characterized in that, Including: A processor; And A memory storing computer-readable instructions that, when executed by the processor, implement the method for estimating the potential evapotranspiration of a target area as described in any one of claims 1 to 6.

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