Method and device for estimating potential evapotranspiration of target area

Abstract

The invention provides a target area potential evapotranspiration estimation method and device, and relates to the technical field of hydro meteorology, and the method comprises the steps: estimating zenith total delay based on the pressure intensity of a target area, the atmospheric precipitable water amount and the atmospheric weighted average temperature; respectively utilizing the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model to calculate a first estimated value and a second estimated value of the target area; establishing a mixed potential evapotranspiration fusion model according to the function relationship between the zenith total delay and the first estimated value and the second estimated value; resolving the mixed potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; and estimating the potential evapotranspiration of the target area by using the multi-source data potential evapotranspiration estimation model. The method not only can effectively improve the estimation precision of the potential evapotranspiration of the target region, but also can improve the applicability and universality in different target regions.

Description

Background Art

[0002] Potential evapotranspiration (PET) is one of the important parameters that characterize the hydrological cycle. Its changes are directly related to various atmospheric factors and affect hydrological, meteorological and climate 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). Accurate evaluation of PET helps to further understand the hydrological cycle and improve the accuracy of estimation of related parameters.

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

[0004] The TH model only considers the effects of temperature and latitude, and the calculation is simple, but it does not consider the influence of factors such as wind speed, solar radiation, and humidity, resulting in low calculation accuracy. It is often underestimated in arid and semi-arid areas, and overestimated in humid areas. The PM model fully considers the energy balance and water vapor diffusion theory, and obtains PET with high accuracy. It is usually used as a reference to verify other methods, but the calculation is relatively complex, and the required meteorological data is difficult to obtain in some areas. Summary of the invention

[0005] In order 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 a first aspect of an embodiment of the present invention, a method for estimating potential evapotranspiration in a target area is provided, the method comprising: Estimate the total zenith delay based on the pressure, atmospheric precipitable water and atmospheric weighted mean temperature over the target area; Calculate a first estimated value and a second estimated value of the target area using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively; Establishing a hybrid potential evapotranspiration fusion model according to a 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; The potential evapotranspiration estimation model based on multi-source data is used to estimate the potential evapotranspiration in the target area.

[0007] In some example embodiments of the present invention, based on the above scheme, based on the pressure of the target area, the atmospheric precipitable water and the atmospheric weighted average temperature, estimating the zenith total delay includes: calculating the zenith dry delay using the pressure in the target area; Calculating zenith wet delay using the atmospheric precipitable water and the atmospheric weighted mean temperature; The zenith total delay is estimated based on the zenith dry delay and the zenith wet delay.

[0008] In some exemplary embodiments of the present invention, based on the above scheme, the zenith total delay is estimated according to the zenith dry delay and the zenith wet delay. include: in, represents the zenith dry delay and , is the surface pressure of the target area, and are the latitude and ellipsoid height of the grid point respectively; represents the zenith wet delay and , is the atmospheric precipitable water, , , is a constant value, is the weighted mean temperature of the atmosphere.

[0009] In some exemplary embodiments of the present invention, based on the above scheme, the hybrid potential evapotranspiration fusion model includes: in, and denote the first estimated value and the second estimated value respectively, are the longitude, latitude, altitude and monthly mean temperature of the station respectively; are the longitude, latitude, altitude and monthly mean temperature of the grid points, represents the total zenith delay at the site, represents the total zenith delay at the grid point, is the model coefficient.

[0010] In some exemplary embodiments of the present invention, based on the above scheme, solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model includes: Calculating a posteriori residual values ​​of the posterior residual values ​​of the first estimated value and a posteriori residual values ​​of the second estimated value based on the potential evapotranspiration of the stations and grid points and the modeling data matrix; Calculating a posterior unit weight variance of the first estimated value and a posterior unit weight variance of the second estimated value according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value respectively; Calculating a summary variance based on the a posteriori unit weighted variance of the first estimate and the a posteriori unit weighted variance of the second estimate; Calculating a chi-square distribution value of the mixed potential evapotranspiration fusion model according to the summary variance; Determining the model coefficient of the mixed potential evapotranspiration fusion model according to the chi-square distribution value; Substituting the model coefficients into the mixed potential evapotranspiration fusion model, the multi-source data potential evapotranspiration estimation model is obtained.

[0011] In some example embodiments of the present invention, based on the above scheme, 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 are calculated as follows: in, is the posterior residual value, is the input matrix of grid points and stations, i.e. the modeling data matrix and , are the longitude, latitude, altitude and monthly mean temperature of the station respectively; are the longitude, latitude, altitude and monthly mean temperature of the grid points, represents the total zenith delay at the site, 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 estimate and the second estimate and , and Represent the first estimated value and the second estimated value respectively.

[0012] In some example embodiments of the present invention, based on the above scheme, the a posteriori unit weight variance of the first estimated value and / or the a posteriori unit weight variance of the second estimated value is calculated as follows: in, is the posterior unit weight variance, is the posterior residual value, is the weight, is the number of sites or grid points, is the rank of the matrix, is a substitute parameter and .

[0013] In some example embodiments of the present invention, based on the foregoing solution, calculating the summary variance according to the a posteriori unit weight variance of the first estimated value and the a posteriori unit weight variance of the second estimated value includes: in, is the aggregate variance, is the number of sites or grid points, When is represented as the number of points of the site, When is represented as the number of grid points; is the a posteriori unit weight variance of the first estimate or the a posteriori unit weight variance of the second estimate, The number of models used to calculate potential evapotranspiration.

[0014] According to a second aspect of an embodiment of the present invention, there is provided an apparatus, including: A first estimation module is used to estimate the total zenith delay based on the pressure, atmospheric precipitable water and atmospheric weighted average temperature of the target area; A second estimation module is used to calculate a first estimation value and a second estimation value of the target area using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively; A model building module, used for building 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 solving module, used for solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; The potential evapotranspiration estimation module is used to estimate the potential evapotranspiration of the target area using the multi-source data potential evapotranspiration estimation model.

[0015] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for estimating potential evapotranspiration in a target area in the first aspect is implemented.

[0016] According to a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for estimating potential evapotranspiration in a target area in the first aspect is implemented.

[0017] The technical solution provided by the embodiments of the present invention may have the following beneficial effects: In the embodiment of the present invention, the total zenith delay is estimated by combining pressure, atmospheric precipitable water and atmospheric weighted mean temperature, and different potential evapotranspiration calculation models are comprehensively utilized to establish a hybrid potential evapotranspiration fusion model and a multi-source data potential evapotranspiration estimation model, which can fully consider the influence of various factors on potential evapotranspiration, and can not only effectively improve the estimation accuracy of potential evapotranspiration in the target area, but also improve the applicability and versatility in different target areas.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0020] Figure 1 A schematic diagram showing a system architecture of an exemplary application environment of a method and device for estimating potential evapotranspiration in a target area to which an embodiment of the present invention can be applied; Figure 2 A schematic diagram of a process flow of a method for estimating potential evapotranspiration in a target area according to some embodiments of the present invention is schematically shown; Figure 3 A schematic diagram of a target area potential evapotranspiration estimation device according to some embodiments of the present invention is schematically shown; Figure 4 A schematic diagram schematically shows a structure of a computer system of an electronic device according to some embodiments of the present invention; Figure 5 A schematic diagram of a computer-readable storage medium according to some embodiments of the present invention is schematically shown. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0022] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0023] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these 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 "at the time of" or "when" or "in response to determining".

[0024] Figure 1 A schematic diagram of a system architecture of an exemplary application environment in which a method and apparatus for estimating potential evapotranspiration in a target area according to an embodiment of the present invention can be applied is shown.

[0025] like Figure 1 As shown, the system architecture 100 may include one or more terminal devices such as a desktop computer 101, a portable computer 102, a smart phone 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables, etc. The terminal device may be any electronic device with data processing capabilities, which has a display screen for displaying target structure feature points or three-dimensional spatial information of each independent pipeline to the user, including but not limited to the above-mentioned desktop computers, portable computers, smart phones, etc. It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a sub-server cluster composed of multiple sub-servers.

[0026] The target area potential evapotranspiration estimation method provided in the embodiment of the present invention can generally be executed by a terminal device, and accordingly, the target area potential evapotranspiration estimation device is generally set in the terminal device. However, it is easy for those skilled in the art to understand that the target area potential evapotranspiration estimation method provided in the embodiment of the present invention can also be executed by the server 105, and accordingly, the target area potential evapotranspiration estimation device can also be set in the server 105, which is not particularly limited in this exemplary embodiment.

[0027] In addition, it should be understood that the target area potential evapotranspiration estimation method of the embodiment of the present invention can be configured as a software module. In some implementation scenarios, the target area potential evapotranspiration estimation scheme of the present invention can be deployed separately to realize the construction and generation of three-dimensional spatial information corresponding to different types of independent pipelines. In other implementation scenarios, the target area potential evapotranspiration estimation scheme of the present invention can be deployed in other software as a functional module of the software, such as deployed in the analysis software of underground pipelines. The present invention does not place any special restrictions on the application of the target area potential evapotranspiration estimation method.

[0028] Next, the embodiments of the present invention are described in detail.

[0029] like Figure 2 As shown, Figure 2 The present invention is a flowchart of estimating potential evapotranspiration in a target area according to an exemplary embodiment, comprising the following steps: S210: Estimate the total zenith delay based on the pressure, atmospheric precipitable water and atmospheric weighted mean temperature of the target area; S220: Calculating a first estimated value and a second estimated value of the target area using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively; S230: establishing a hybrid potential evapotranspiration fusion model according to a functional relationship between the zenith total delay and the first estimated value and the second estimated value; S240: Solving 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.

[0030] In the embodiment of the present invention, the total zenith delay is estimated by combining pressure, atmospheric precipitable water and atmospheric weighted mean temperature, and different potential evapotranspiration calculation models are comprehensively utilized to establish a hybrid potential evapotranspiration fusion model and a multi-source data potential evapotranspiration estimation model, which can fully consider the influence of various factors on potential evapotranspiration, and can not only effectively improve the estimation accuracy of potential evapotranspiration in the target area, but also improve the applicability and versatility in different target areas.

[0031] In S210 , the zenith total delay is estimated based on the pressure, atmospheric precipitable water, and atmospheric weighted average temperature of the target area.

[0032] The total zenith delay is given by and The zenith dry delay component accounts for 90% or even more of the total atmospheric zenith delay, while the zenith wet delay component caused by atmospheric water vapor accounts for only about 10%. Therefore, the atmospheric water vapor content can be inferred based on the zenith wet delay component. Based on the zenith dry delay model combined with the station location information, geodetic elevation values ​​and air pressure observation data, the zenith dry delay can be accurately calculated. Then, the relevant atmospheric weighted average temperature model is established to calculate the atmospheric weighted average temperature, and then the atmospheric precipitable water and atmospheric weighted average temperature data are used to obtain the zenith wet delay , and then the total zenith delay caused by atmospheric water vapor in the meteorological station observation data is obtained by summing the zenith dry delay and the zenith wet delay. .

[0033] Therefore, based on the pressure, atmospheric precipitable water and atmospheric weighted mean temperature over the target area, the total zenith delay is estimated to include: calculating the zenith dry delay using the pressure in the target area; Calculating zenith wet delay using the atmospheric precipitable water and the atmospheric weighted mean temperature; The zenith total delay is estimated based on the zenith dry delay and the zenith wet delay.

[0034] Zenith Dry Delay The pressure in the target area can be accurately obtained by using the Saastamoinen model, as follows: in, is the surface pressure of the target area, and are the latitude and ellipsoid height of the grid point, respectively.

[0035] Zenith Wet Delay The atmospheric precipitable water content and atmospheric weighted average temperature data are used and calculated using the empirical formula: in, is the atmospheric precipitable water, , , is a constant value, is the weighted mean temperature of the atmosphere.

[0036] In the present invention, Based on improved The model is calculated using the Global Tropospheric Empirical Model (Global Pressure and Temperature 2 Wet, GPT2w), which outperforms other models and mainly uses the grid points of the China Global Earth Observation System. Data and ellipsoid height data for GPT2w The revised model takes into account the specific characteristics and conditions of the Chinese region.

[0037] Then, the zenith total delay is estimated based on the zenith dry delay and the zenith wet delay include: In S220 , a first estimated value and a second estimated value of the target area are calculated using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model, respectively.

[0038] 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 may be one of the Penman-Monteith Model, the Horton Model, the FAO-56 Two-Source Model, and the Blaney-Criddle Model. The second potential evapotranspiration calculation model may be the same as the first potential evapotranspiration calculation model, or may be different from the first potential evapotranspiration calculation model. Of course, in different cases, the second potential evapotranspiration calculation model may also be one of the above models. The specific choice of model depends on the availability of data, computing resources, required accuracy, and specific requirements of the application scenario. In practice, it may also be necessary to calibrate and verify the model based on actual monitoring data to ensure the accuracy of the prediction.

[0039] 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 a first potential evapotranspiration calculation model, and then obtaining a first estimated value based on the first potential evapotranspiration calculation model, specifically, including: Calculating a heat index for the target area based on the monthly average temperature of the target area; Calculating a thermal index coefficient according to the thermal index; Calculating a correction factor for the target region based on the latitude of the target region and the solar declination; Obtaining a first potential evapotranspiration calculation model according to the thermal index, the thermal index coefficient and the correction coefficient; A first estimated value is obtained based on the temperature data of the target area in that month and the first potential evapotranspiration calculation model.

[0040] Obviously, the first potential evapotranspiration calculation model provided by the present invention considers in detail the comprehensive impact of temperature, geographical latitude and seasonality on potential evapotranspiration, thereby being able to more accurately quantify and estimate the potential evapotranspiration of the target area.

[0041] On this basis, those skilled in the art can calculate the thermal index of the target area in any manner 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 thermal index or a thermal pressure index can be designed to determine the calculation method of the thermal index.

[0042] In some exemplary embodiments of the present invention, the heat index of the target area may be calculated in the following manner: In the formula, For the i Monthly mean temperature for the month, in °C; The heat index for the 12 months.

[0043] On this basis, the thermal index coefficient is calculated as follows: In the formula, is the thermal index coefficient.

[0044] In addition, in some embodiments, the latitude and solar declination of the target area can be directly obtained through data, or calculated in some way. On this basis, the correction coefficient of the target area can be calculated directly 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 is calculated by the maximum sunshine hours and the number of days in a month. As an example embodiment of the present invention, optionally, the correction coefficient of latitude and month is calculated in the following way: In the formula, represents the correction factor, represents the maximum sunshine hours in the target area and , represents pi, represents the angle of the sun rising every hour and , represents the arccosine function, represents the tangent function, Indicates the latitude of the target area, represents the solar declination of the target area and , represents the sine function, represents the mean Julian day of the month, Indicates the number of days in a month.

[0045] Based on this, according to the thermal index, the thermal index coefficient and the correction coefficient, the first potential evapotranspiration calculation model can be expressed as: In the formula, is the potential evapotranspiration calculated using the first potential evapotranspiration calculation model (i.e., the first estimate, PET), T is the monthly temperature data within the preset range of the target area, I is the heat index, m is the thermal index coefficient, is the correction coefficient. Since the aerodynamic term is ignored when calculating PET using the first potential evapotranspiration calculation model, and only the influence of the thermal factor on PET is considered, but PET is determined by a variety of meteorological factors, the first potential evapotranspiration calculation model cannot obtain high-precision regional PET.

[0046] Therefore, in the exemplary embodiment provided by the present invention, the second potential evapotranspiration calculation model can be established in the following manner: That is, taking the hypothetical crop canopy of the target area as the standard and considering the influence of various influencing factors on the potential evapotranspiration process, a second potential evapotranspiration calculation model is established; wherein the various influencing factors include radiation, temperature, water vapor pressure and wind speed.

[0047] On this basis, a second estimated value is obtained 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.

[0048] 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, water 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: In the formula, is the potential evapotranspiration (i.e., the second estimated value, PET) calculated using the second potential evapotranspiration calculation model. is the net radiation on the crop surface; is the soil heat flux; is the humidity constant; and Respectively represent the monthly average temperature and wind speed within the preset range of the target area; is the saturated 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.

[0049] The saturated water vapor pressure The specific calculation method is: The actual water vapor pressure is calculated as follows: and Represent the maximum temperature and the minimum temperature respectively; is the dew point temperature.

[0050] Humidity constant Through air pressure Calculation, the calculation formula is as follows: Soil heat flux It can be calculated by the average temperature of the target month and the previous month. The specific calculation formula is as follows: and Respectively refer to i month and the previous month ( i -1) of the monthly average temperature.

[0051] In S230, a hybrid potential evapotranspiration fusion model is established according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value.

[0052] Based on the above scheme, 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 planar PET, while the PM model has high accuracy but requires a large amount of meteorological data, it is impossible to obtain PET values ​​at any location. Therefore, in order to integrate the advantages of the TH and PM models and obtain high-precision and high-spatial-resolution PET values, the present invention comprehensively considers the influence of geographical location and the correlation between TH / PM_PET and T / ZTD, and uses a multivariate first-order polynomial function to construct a fusion model, and its specific calculation formula is: in, and denote the first estimated value and the second estimated value respectively, are the longitude, latitude, altitude and monthly mean temperature of the station respectively; are the longitude, latitude, altitude and monthly mean temperature of the grid points, represents the total zenith delay at the site, represents the total zenith delay at the grid point, is the model coefficient.

[0053] In S240, the hybrid potential evapotranspiration fusion model is solved to obtain a multi-source data potential evapotranspiration estimation model.

[0054] The PET calculated by the TH model and the PM model cannot be simply divided in equal proportions due to differences in data sources, calculation methods, and accuracy. Therefore, a reasonable weight ratio needs to be determined before solving the mixed potential evapotranspiration fusion model.

[0055] The present invention does not specifically limit the method for determining the weight ratio, and the weight ratio can be determined by using experimental comparison method, statistical analysis method, machine learning method, etc.

[0056] In some embodiments, when the weight ratio is determined by the experimental comparison method, a plurality of target areas with different climatic characteristics and geographical conditions can be first selected as test areas; then, the mixed potential evapotranspiration fusion model is solved using different weight ratio combinations in each test area to obtain a multi-source data potential evapotranspiration estimation model; then, the estimation results are compared with the actual observation data of the area to evaluate the accuracy of the estimation model under different weight ratio combinations; finally, based on the accuracy evaluation results, a more reasonable weight ratio range under different climatic and geographical conditions is determined.

[0057] In other embodiments, when the weight ratio is determined by statistical analysis, a large amount of historical data can be first collected, including PET data calculated by the TH model and the PM model, respectively, and the corresponding actual potential evapotranspiration observation data; then the data is statistically analyzed, for example, the degree of deviation between the estimated results of the TH model and the PM model and the actual value is calculated under different conditions; 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 based on the relative reliability, and the model with higher reliability is assigned a larger weight.

[0058] It can be seen that both the experimental comparison method and the statistical analysis method require processing a large amount of data, which brings certain challenges to practical applications.

[0059] In order to solve this problem, the present invention solves the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model including: Calculating a posteriori residual values ​​of the posterior residual values ​​of the first estimated value and a posteriori residual values ​​of the second estimated value based on the potential evapotranspiration of the stations and grid points and the modeling data matrix; Calculating a posterior unit weight variance of the first estimated value and a posterior unit weight variance of the second estimated value according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value respectively; Calculating a summary variance based on the a posteriori unit weighted variance of the first estimate and the a posteriori unit weighted variance of the second estimate; Calculating a chi-square distribution value of the mixed potential evapotranspiration fusion model according to the summary variance; Determining the model coefficient of the mixed potential evapotranspiration fusion model according to the chi-square distribution value; Substituting the model coefficients into the mixed potential evapotranspiration fusion model, the multi-source data potential evapotranspiration estimation model is obtained.

[0060] The a posteriori unit weighted variance of the first estimate and the a posteriori unit weighted variance of the second estimate can be calculated in the same way: in, is the posterior residual value, is the input matrix of grid points and stations, i.e. the modeling data matrix and , are the longitude, latitude, altitude and monthly mean temperature of the station respectively; are the longitude, latitude, altitude and monthly mean temperature of the grid points, represents the total zenith delay at the site, 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 estimate and the second estimate and , and Represent the first estimated value and the second estimated value respectively.

[0061] After obtaining the posterior residual values ​​of different data sources, let , and are the weights of the grid points and the stations, respectively. Based on this, the a posteriori unit weight variance of the first estimate and the a posteriori unit weight variance of the second estimate can be obtained in the same way: in, is the posterior unit weight variance, is the posterior residual value, is the weight, is the number of sites or grid points, is the rank of the matrix, is a substitute parameter and .

[0062] The calculation found that the weights of PET from different sources are not equal. Therefore, the summary variance of the two variance components is calculated : in, is the aggregate variance, is the number of sites or grid points, When is represented as the number of points of the site, When is represented as the number of grid points; is the a posteriori unit weight variance of the first estimate or the a posteriori unit weight variance of the second estimate, The number of models used to calculate potential evapotranspiration.

[0063] Based on the summary variance, the chi-square distribution value of the mixed potential evapotranspiration fusion model is calculated. include: in, is the number of potential evapotranspiration calculation models, is the number of sites or grid points, When is represented as the number of points of the site, When is represented as the number of grid points; is the aggregate variance, is the a posteriori unit weight variance of the first estimate or the a posteriori unit weight variance of the second estimate.

[0064] The degrees of freedom and significance level of the chi-square distribution are 2 and 0.1 respectively. On this basis, the model coefficients of the mixed potential evapotranspiration fusion model are determined according to the chi-square distribution value, including: Determine 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, the unit weight variances of the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model are considered to be statistically equal, and the current weight is output as the optimal weight; otherwise, the posterior unit weight variance is recalculated after the weight is updated; According to the optimal weight, the fusion coefficient matrix of the first estimate and the second estimate is obtained. : in, is the diagonal matrix of fusion weights of the optimal weights of grid points and sites.

[0065] The updated weights are calculated as: in, is the weight of the mixed latent evapotranspiration fusion model, is the number of iterations, is the summary variance of the mixed latent evapotranspiration fusion model, is any value and .

[0066] here, When updating, both of.

[0067] In S250, the potential evapotranspiration of the target area is estimated using a multi-source data potential evapotranspiration estimation model.

[0068] By calculating the location information, temperature, and total zenith delay data of the PET, the PET at that point can be calculated. The specific calculation formula is as follows: .

[0069] The present invention can effectively solve the defects that the existing TH model needs few parameters to calculate PET and can obtain planar data but has low accuracy, while the PM model has high accuracy but requires a large number of meteorological parameters, which are usually at the site scale.

[0070] According to a second aspect of an embodiment of the present invention, a device for estimating potential evapotranspiration in a target area is also provided, referring to Figure 3 As shown, the target area potential evapotranspiration estimation device 300 includes: A first estimation module 310 is used to estimate the zenith total delay based on the pressure, atmospheric precipitable water and atmospheric weighted average temperature of the target area; A second estimation module 320, for calculating a first estimation value and a second estimation value of a target area using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively; A model building module 330, configured to build a hybrid potential evapotranspiration fusion model according to a functional relationship between the zenith total delay and the first estimated value and the second estimated value; A model solving module 340 is used to solve the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; The potential evapotranspiration estimation module 350 is used to estimate the potential evapotranspiration of the target area using a multi-source data potential evapotranspiration estimation model.

[0071] In an exemplary embodiment of the present invention, based on the above solution, the model solving module 340 may further include: A posterior residual calculation submodule, for 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 the grid point and the modeling data matrix; a posterior unit weight variance calculation submodule, used to calculate the posterior unit weight variance of the first estimated value and the posterior unit weight variance of the second estimated value according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value, respectively; a summary variance calculation submodule, configured to calculate a summary variance based on the a posteriori unit weighted variance of the first estimated value and the a posteriori unit weighted variance of the second estimated value; A chi-square distribution calculation submodule, used to calculate the chi-square distribution value of the mixed potential evapotranspiration fusion model according to the summary variance; A model coefficient determination submodule, used to determine the model coefficient of the mixed potential evapotranspiration fusion model according to the chi-square distribution value; The model determination submodule is used to substitute the model coefficients into the hybrid potential evapotranspiration fusion model to obtain the multi-source data potential evapotranspiration estimation model.

[0072] It should be noted that although several modules and submodules of the target area potential evapotranspiration estimation device are mentioned in the above detailed description, this division is not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more modules or submodules described above can be embodied in one module or unit. Conversely, the features and functions of one module or submodule described above can be further divided into multiple modules or submodules to be embodied.

[0073] In addition, in an exemplary embodiment of the present invention, an electronic device capable of implementing the above-mentioned method for estimating potential evapotranspiration in a target area is also provided.

[0074] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0075] Refer to the following Figure 4 An electronic device 400 according to such an embodiment of the present invention is described. Figure 4 The electronic device 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0076] like Figure 4As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: the at least one processing unit 410, the at least one storage unit 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0077] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 performs the steps of various exemplary embodiments of the present invention described in the above “Exemplary Method” section. For example, the processing unit 410 can perform the following steps: Figure 2 In S210 shown in the figure, based on the pressure, atmospheric precipitable water and atmospheric weighted average temperature of the target area, the zenith total delay is estimated; in S220, the first estimated value and the second estimated value of the target area are calculated using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively; in S230, a hybrid potential evapotranspiration fusion model is established according to the functional relationship between the zenith total delay and the first estimated value and the second estimated value; in S240, the hybrid potential evapotranspiration fusion model is solved to obtain a multi-source data potential evapotranspiration estimation model; in S250, the potential evapotranspiration of the target area is estimated using the multi-source data potential evapotranspiration estimation model.

[0078] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0079] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0080] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0081] The electronic device 400 may also communicate with one or more external devices 470 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 through a 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.

[0082] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment 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, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present invention.

[0083] In an exemplary embodiment of the present invention, a computer-readable storage medium is also 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 a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps of various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present invention.

[0084] refer to Figure 5 As shown, a program product 500 for implementing the above-mentioned target area potential evapotranspiration estimation method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be 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 containing or storing a program, and the program can be used by or in combination with an instruction execution system, an apparatus or a device.

[0085] The program product may employ any combination of one or more readable storage media. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory unit (RAM), a read-only memory unit (ROM), an erasable programmable read-only memory unit (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory unit (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0086] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user 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 may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0087] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0088] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment 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, and includes 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 embodiment of the present invention.

[0089] Other embodiments of the invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. The invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0090] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for estimating potential evapotranspiration in a target area, characterized in that: The methods for estimating potential evapotranspiration in the target area include: Estimate the total zenith delay based on the pressure, atmospheric precipitable water and atmospheric weighted mean temperature over the target area; Calculate a first estimated value and a second estimated value of the target area using a first potential evapotranspiration calculation model and a second potential evapotranspiration calculation model respectively; Establishing a hybrid potential evapotranspiration fusion model according to a 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; The potential evapotranspiration estimation model based on multi-source data is used to estimate the potential evapotranspiration in the target area.

2. The method for estimating potential evapotranspiration in a target area according to claim 1, characterized in that: Based on the pressure, atmospheric precipitable water and atmospheric weighted mean temperature of the target area, the total zenith delay is estimated by: calculating the zenith dry delay using the pressure in the target area; Calculating zenith wet delay using the atmospheric precipitable water and the atmospheric weighted mean temperature; The zenith total delay is estimated based on the zenith dry delay and the zenith wet delay.

3. The method for estimating potential evapotranspiration in a target area according to claim 2, characterized in that: Estimate the zenith total delay based on the zenith dry delay and the zenith wet delay include: in, represents the zenith dry delay and , is the surface pressure in the target area, and are the latitude and ellipsoid height of the grid point respectively; represents the zenith wet delay and , is the atmospheric precipitable water, , , is a constant value, is the weighted mean temperature of the atmosphere.

4. The method for estimating potential evapotranspiration in a target area according to claim 1, characterized in that: The hybrid potential evapotranspiration fusion model includes: in, and denote the first estimated value and the second estimated value respectively, are the longitude, latitude, altitude and monthly mean temperature of the station respectively; are the longitude, latitude, altitude and monthly mean temperature of the grid points, represents the total zenith delay at the site, represents the total zenith delay at the grid point, is the model coefficient.

5. The method for estimating potential evapotranspiration in a target area according to claim 1, characterized in that: The hybrid potential evapotranspiration fusion model is solved to obtain a multi-source data potential evapotranspiration estimation model including: Calculating a posteriori residual values ​​of the posterior residual values ​​of the first estimated value and a posteriori residual values ​​of the second estimated value based on the potential evapotranspiration of the stations and grid points and the modeling data matrix; Calculating a posterior unit weight variance of the first estimated value and a posterior unit weight variance of the second estimated value according to the posterior residual value of the first estimated value and the posterior residual value of the second estimated value respectively; Calculating a summary variance based on the a posteriori unit weighted variance of the first estimate and the a posteriori unit weighted variance of the second estimate; Calculating a chi-square distribution value of the mixed potential evapotranspiration fusion model according to the summary variance; Determining the model coefficient of the mixed potential evapotranspiration fusion model according to the chi-square distribution value; Substituting the model coefficients into the mixed potential evapotranspiration fusion model, the multi-source data potential evapotranspiration estimation model is obtained.

6. The method for estimating potential evapotranspiration in a target area according to claim 5, characterized in that: 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 are calculated as follows: in, is the posterior residual value, is the input matrix of grid points and stations, i.e. the modeling data matrix and , are the longitude, latitude, altitude and monthly mean temperature of the station respectively; are the longitude, latitude, altitude and monthly mean temperature of the grid points, represents the total zenith delay at the site, 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 estimate and the second estimate and , and Represent the first estimated value and the second estimated value respectively.

7. The method for estimating potential evapotranspiration in a target area according to claim 5, characterized in that: The posterior unit weight variance of the first estimated value and / or the posterior unit weight variance of the second estimated value are calculated as follows: in, is the posterior unit weight variance, is the posterior residual value, is the weight, is the number of sites or grid points, is the rank of the matrix, is a substitute parameter and .

8. The method for estimating potential evapotranspiration in a target area according to any one of claims 5 to 7, characterized in that: Calculating the aggregate variance based on the a posteriori unit weighted variance of the first estimated value and the a posteriori unit weighted variance of the second estimated value includes: in, is the aggregate variance, is the number of sites or grid points, When is represented as the number of points of the site, When is represented as the number of grid points; is the a posteriori unit weight variance of the first estimate or the a posteriori unit weight variance of the second estimate, The number of models used to calculate potential evapotranspiration.

9. A device based on the method for estimating potential evapotranspiration in a target area according to any one of claims 1 to 8, characterized in that: The device comprises: A first estimation module is used to estimate the total zenith delay based on the pressure, atmospheric precipitable water and atmospheric weighted average temperature of the target area; A second estimation module is used to calculate a first estimation value and a second estimation value of the target area using the first potential evapotranspiration calculation model and the second potential evapotranspiration calculation model respectively; A model building module, used for building 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 solving module, used for solving the hybrid potential evapotranspiration fusion model to obtain a multi-source data potential evapotranspiration estimation model; The potential evapotranspiration estimation module is used to estimate the potential evapotranspiration of the target area using the multi-source data potential evapotranspiration estimation model.

10. An electronic device, characterized in that: include: processor; as well as A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method for estimating potential evapotranspiration in a target area according to any one of claims 1 to 8.

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