Treatment method for the influence of lake heat storage without water temperature profile observation on evaporation

Abstract

An embodiment of the present invention relates to a method for dealing with the influence of lake heat storage on evaporation in the absence of water temperature profile observations, including: determining a first energy balance formula, a second energy balance formula, and a third energy balance formula according to whether lake heat storage is considered and whether the inherent energy non-closure problem of the eddy covariance (EC) system is considered; determining the first to third latent heat fluxes of a first model, the fourth to sixth latent heat fluxes of a second model, and the first to third evaporation amounts of a third model according to the first to third energy balance formulas, and determining the seventh to ninth latent heat fluxes after processing the first to third evaporation amounts; evaluating the first to ninth latent heat fluxes based on the big data of the latent heat fluxes observed by EC, and determining a target model from the first model to the third model; calculating the lake heat storage and its ratio to the latent heat flux through the target model and the net radiation flux, sensible heat flux, and latent heat flux observed by EC, and determining the influence of the lake heat storage on evaporation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method for dealing with the influence of lake heat storage on evaporation in the absence of water temperature profile observations. Background Art

[0002] Lakes are important water resources and play an important role in water supply, ecosystem services, and biodiversity conservation. For the water balance equation of alpine lakes, the water input mainly comes from lake surface precipitation and surface runoff, supplemented by glacier meltwater, and lake evaporation is the main water output. Lake hydrodynamic model simulations show that the global lake evaporation has increased significantly in recent decades. The increase in lake evaporation will affect the regional climate and environment. For example, it will cool the surface air temperature, enhance the downwind precipitation, lower the lake water level, and reduce the lake area. Therefore, it is essential to explore the changes in lake evaporation and its impact on the environment.

[0003] Lake evaporation can be directly observed and obtained through Eddy Covariance (EC) technology and evaporators (multiplied by an evaporation conversion coefficient). In addition, it is usually estimated using models such as Penman (PE), Priestley-Taylor (PT), and Penman-monteith (PM). These models combine energy terms and aerodynamic terms and use conventional meteorological data to estimate evaporation. However, due to the differences in model initial conditions and driving data, especially due to the lack of lake water temperature profile observations and the neglect of lake heat storage G, there is still uncertainty in the estimation of lake evaporation.

[0004] Lake heat storage plays an important role in regulating lake evaporation and its changes. However, due to the limited observations of water temperature profiles for calculating lake heat storage, the research on the influence of lake heat storage on lake evaporation is not sufficient, and it is difficult to accurately quantify the influence of lake heat storage on lake evaporation. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for dealing with the influence of lake heat storage on evaporation in the absence of water temperature profile observations to solve the problems existing in the prior art.

[0006] To achieve the above purpose, the present invention provides a method for dealing with the influence of lake heat storage on evaporation in the absence of water temperature profile observations, including:

[0007] Determine the first energy balance formula, the second energy balance formula, and the third energy balance formula according to whether the lake heat storage is considered and whether the energy non-closure inherent in the eddy covariance (EC) system is considered; among them, the first energy balance formula does not consider the lake heat storage, the second energy balance formula considers the lake heat storage, and the third energy balance formula considers the lake heat storage and the energy non-closure inherent in the EC system;

[0008] Determine the first latent heat flux, the second latent heat flux, and the third latent heat flux of the first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence;

[0009] Determine the fourth latent heat flux, the fifth latent heat flux, and the sixth latent heat flux of the second model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence;

[0010] Determine the first evaporation amount, the second evaporation amount, and the third evaporation amount of the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence;

[0011] Process the first evaporation amount, the second evaporation amount, and the third evaporation amount respectively to determine the seventh latent heat flux, the eighth latent heat flux, and the ninth latent heat flux;

[0012] Evaluate the first to ninth latent heat fluxes based on the latent heat flux observed by the eddy covariance (EC) method, and determine the target model from the first model to the third model;

[0013] Calculate the lake heat storage and the ratio of the lake heat storage to the latent heat flux through the target model and the net radiation flux, sensible heat flux, and latent heat flux observed by the EC method, and determine the influence of the lake heat storage on evaporation.

[0014] In a possible implementation manner, the determination of the first latent heat flux, the second latent heat flux, and the third latent heat flux of the first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence specifically includes:

[0015] Substitute the first energy balance formula \(G = 0\) into the first model Determine the first latent heat flux; where \(\Delta\) is the slope of the saturation vapor pressure - temperature curve, \(\gamma\) is the psychrometric constant, \(R\) n is the net radiation based on the EC system observation, \(f(u)\) is the wind function, \(f(u)=0.26\times(0.5 + 0.536\times u)\), \(e\) s is the saturation vapor pressure at the air temperature, \(e\) a is the actual vapor pressure;

[0016] Substitute the second energy balance formula \(R\)n –G = H0 + LE0 is substituted into the first model Determine the second latent heat flux; where, H0 is the sensible heat flux observed based on the EC system, and LE0 is the latent heat flux observed based on the EC system;

[0017] Substitute the third energy balance formula R n –G = a×(H0 + LE0) + b into the first model Determine the third latent heat flux, where a and b are regression fitting coefficients respectively.

[0018] In a possible implementation, the determination of the fourth latent heat flux, the fifth latent heat flux, and the sixth latent heat flux of the second model successively under the first energy balance formula, the second energy balance formula, and the third energy balance formula specifically includes:

[0019] Substitute the first energy balance formula G = 0 into the second model Determine the fourth latent heat flux; where, Δ is the slope of the saturation vapor pressure - temperature curve, γ is the psychrometric constant, R n is the net radiation observed by the EC system, and α is a constant;

[0020] Substitute the second energy balance formula R n –G = H0 + LE0 into the second model Determine the fifth latent heat flux; where, H0 is the sensible heat flux observed based on the EC system, and LE0 is the latent heat flux observed based on the EC system;

[0021] Substitute the third energy balance formula R n –G = a×(H0 + LE0) + b into the second model Determine the sixth latent heat flux; where a and b are regression fitting coefficients respectively.

[0022] In a possible implementation, the determination of the first evaporation amount, the second evaporation amount, and the third evaporation amount of the third model successively under the first energy balance formula, the second energy balance formula, and the third energy balance formula specifically includes:

[0023] Substitute the first energy balance formula G = 0 into the third model Determine the first evaporation amount of the third model; where, Δ is the slope of the saturation vapor pressure - temperature curve, R n is the net radiation observed based on the EC system, γ is the psychrometric constant, f(u) is the wind function, f(u) = 0.26×(0.5 + 0.536×u), u is the horizontal wind speed at a height of 2 m above the water surface, e s is the saturation vapor pressure at the air temperature, e a is the actual vapor pressure, T ais the air temperature;

[0024] In the second energy balance formula R n – G = H0 + LE0, determine the second evaporation amount of the third model according to where H0 is the sensible heat flux based on EC observations, and LE0 is the latent heat flux based on EC system observations;

[0025] In the third energy balance formula R n – G = a × (H0 + LE0) + b, determine the third evaporation amount of the third model according to

[0026] In a possible implementation, the calculation method of the third energy balance formula is:

[0027] In the third energy balance formula R n – G = a × (H0 + LE0) + b, according to the sensible heat flux H0 based on EC observations, the latent heat flux LE0 based on EC observations, and the net radiation R based on EC observations n , substitute them into the first model, the second model, and the third model respectively to obtain multiple lake heat storages G;

[0028] According to the sensible heat flux H0, latent heat flux LE0, and net radiation R based on EC system observations n , and the lake heat storage G, fit to obtain a and b.

[0029] In a possible implementation, according to calculate the slope of the saturation vapor pressure - temperature curve; where Δ is the slope of the saturation vapor pressure - temperature curve, and T a is the air temperature;

[0030] According to calculate the psychrometric constant; where γ is the psychrometric constant, c p is the specific heat of dry air, P is the atmospheric pressure, ε is the molar mass ratio of moist air to dry air, and L is the latent heat of vaporization;

[0031] According to calculate the saturation vapor pressure at the air temperature, where e s is the saturation vapor pressure at the air temperature.

[0032] In a possible implementation, based on the latent heat flux observed by eddy covariance EC, evaluate the first to ninth latent heat fluxes, and determine the target model from the first model to the third model, specifically including:

[0033] ​Calculate nine sets of statistical indicators based on the latent heat fluxes of the first model, the second model, and the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula respectively, as well as the latent heat flux observed based on the EC system;

[0034] Determine the target model from the first model to the third model according to the nine sets of statistical indicators.

[0035] In a possible implementation, the set of statistical indicators includes the correlation coefficient, the coefficient of determination, the root mean square error, and the mean absolute error. The specific process of determining the target model from the first model to the third model according to the nine sets of statistical indicators includes:

[0036] According to Determine the correlation coefficient; where r is the correlation coefficient, X i is the latent heat flux observed based on the EC system, X' i is the latent heat flux obtained from the model, n is the number of samples, i is a corresponding sample, is the average value of the latent heat flux observed based on the EC system, is the average value of the latent heat flux obtained from the model;

[0037] According to Determine the coefficient of determination; where R 2 is the coefficient of determination;

[0038] According to Determine the root mean square error; where RMSE is the root mean square error;

[0039] According to Determine the mean absolute error; where MAE is the mean absolute error;

[0040] Determine the target model according to the correlation coefficient, the coefficient of determination, the root mean square error, and the mean absolute error in the nine sets of statistical indicators.

[0041] In a possible implementation, the specific process of calculating the lake heat storage and the ratio of the lake heat storage to the latent heat flux through the target model and the net radiation flux, sensible heat flux, and latent heat flux observed by EC, and determining the influence of the lake heat storage on evaporation includes:

[0042] Calculate the target lake heat storage according to the energy balance equation corresponding to the target model and the net radiation flux observed by EC and the sensible heat flux observed by EC;

[0043] Determine the influence of the lake heat storage on lake evaporation according to the ratio of the target lake heat storage to the latent heat flux observed by EC.

[0044] In a second aspect, the present invention provides a device, including a memory and a processor, where the memory is used to store a program, and the processor is used to execute the method according to any one of the first aspect.

[0045] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it executes the method according to any one of the first aspect.

[0046] By applying the method for dealing with the influence of lake heat storage on evaporation without water temperature profile observation provided by the embodiments of the present invention, the lake heat storage is estimated by using the energy balance equation and the water and heat fluxes observed by the eddy covariance (EC) system, and the energy non-closure problem in the EC system is mainly solved based on the linear relationship between the estimated (Rn–G) by the model and the observed (H+LE) by the EC. On this basis, according to the final target model, the influence of lake heat storage on lake evaporation is quantified. This application fills the blank of quantifying the influence of lake heat storage on the evaporation of alpine deep lakes without water temperature profile observation, highlights the importance of integrating multi-source evaporation observations and multi-model simulations to estimate lake evaporation, and improves the understanding of the water and heat exchange process in alpine deep lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic flowchart of the method for dealing with the influence of lake heat storage on evaporation without water temperature profile observation provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Figure 1 It is a schematic flowchart of the method for dealing with the influence of lake heat storage on evaporation without water temperature profile observation provided by the embodiments of the present invention. The main processing object of this application is the application of alpine lakes because the regional differentiation of water level and water volume changes in alpine lakes is significant. The following combines Figure 1 , and the technical solutions of the present invention will be described with specific embodiments. As Figure 1 shown, this application includes the following steps:

[0050] Step 110: Determine the first energy balance formula, the second energy balance formula, and the third energy balance formula according to whether the lake heat storage is considered and whether the inherent energy non-closure problem of the eddy covariance (EC) system related to turbulence is considered.

[0051] Among them, the first energy balance formula does not consider the lake heat storage, the second energy balance formula considers the lake heat storage, and the third energy balance formula considers the lake heat storage and the inherent energy non-closure of the EC system.

[0052] Specifically, in order to explore the influence of lake heat storage on lake evaporation, the influence degree of lake heat storage on lake evaporation can be discussed through three energy balance formulas, as follows:

[0053] First: Ignore the influence of lake heat storage on lake evaporation, that is, G = 0; which is equivalent to the first energy equation being G = 0; where G represents the lake heat storage.

[0054] Second: Consider the influence of lake heat storage on lake evaporation. According to the second energy balance equation R n –G = H0 + LE0, this type of model uses the H0 + LE0 observed by EC to replace (R n –G) in the model. Where R n represents the net radiation flux observed by EC.

[0055] Third: The influence of lake heat storage on lake evaporation, and consider the inherent energy non-closure problem of the EC system. Through the third energy balance equation R n –G = a × (H0 + LE0) + b. In the formula, a and b are the regression coefficients of the linear regression between (R n –G) and (H0 + LE0). Where H0 is the sensible heat flux observed by EC, and LE0 is the latent heat flux observed by EC.

[0056] As for how to determine a and b, first substitute the H0, LE0, and Rn observed by EC and various meteorological variables (such as air temperature, actual water vapor pressure, wind speed, etc.) into the first, second, and third models respectively, so as to inversely calculate (Rn - G) of the first, second, and third models; then linearly fit the calculated (Rn - G) with the (H0 + LE0) observed by EC to obtain the values of a and b, so as to obtain the specific expressions of the first, second, and third models under the third energy balance formula. Subsequently, the fitted (Rn - G) can be substituted into the first, second, and third models to calculate the latent heat fluxes corresponding to the first model to the second model under the first to third energy balance formulas, and the evaporation amounts of the third model under the first to third energy balance formulas, and then convert them into latent heat fluxes.

[0057] Step 120, determine the first latent heat flux, the second latent heat flux, and the third latent heat flux of the first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence;

[0058] Specifically, substitute the first energy balance formula G = 0 into the first model Determine the first latent heat flux; where Δ is the slope of the saturation vapor pressure - temperature curve, γ is the psychrometric constant, f(u) is the wind function, f(u) = 0.26×(0.5 + 0.536×u), e s is the saturation vapor pressure at the air temperature, e a is the actual vapor pressure;

[0059] where the horizontal wind speed u, the actual vapor pressure e a , the air temperature T a and other meteorological variables are directly observed by the eddy covariance system EC. The net radiation flux Rn, the sensible heat flux H0, and the latent heat flux LE0 are indirectly calculated from variables such as the three - dimensional wind speed, ultrasonic virtual temperature, and water vapor and carbon dioxide concentrations observed by EC.

[0060] According to calculate the slope of the saturation vapor pressure - temperature curve; where T a is the air temperature; According to calculate the psychrometric constant; c p is the specific heat of dry air, P is the atmospheric pressure, ε is the molar mass ratio of moist air to dry air, and L is the latent heat of vaporization; According to calculate the saturation vapor pressure e at the air temperature s .

[0061] For EC observations, there may be cases of missing data. The interpolation scheme for missing data is as follows: (1) When there are missing data at half - hour intervals within a day, the average value of the remaining half - hour interval data of that day is used as the daily value data of that day; (2) When there are missing measurements for a certain day, the average value of the data of the two days before and after that day is used as the daily value data of that day; (3) When there are missing data for two consecutive days or more, the linear interpolation method is used to fill in the missing data.

[0062] Substitute the second energy balance formula R n – G = H0 + LE0 into the first model to determine the second latent heat flux;

[0063] Substitute the third energy balance formula R n – G = a×(H0 + LE0)+b into the first model to determine the third latent heat flux.

[0064] Step 130, determine the fourth latent heat flux, the fifth latent heat flux, and the sixth latent heat flux of the second model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence;

[0065] Specifically, the second model can estimate the evaporation amount without observing the aerodynamic term. Substitute the first energy balance formula G = 0 into the second model Determine the fourth latent heat flux; α is a constant, generally taking a default value, and the default value can be 1.26;

[0066] Substitute the second energy balance formula R n –G = H0 + LE0 into the second model Determine the fifth latent heat flux;

[0067] Substitute the third energy balance formula R n –G = a×(H0 + LE0) + b into the second model Determine the sixth latent heat flux.

[0068] Step 140, determine the first evaporation amount, the second evaporation amount, and the third evaporation amount of the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence;

[0069] Specifically, the third model is an effective tool for calculating daily and monthly reference evapotranspiration using local or basin-scale conventional meteorological data, such as T a , RH, P, u, and sunshine hours, without the need for calibration of the regional wind speed function.

[0070] Substitute the first energy balance formula G = 0 into the third model Determine the first evaporation amount of the third model;

[0071] In the second energy balance formula R n –G = H0 + LE0, according to Determine the second evaporation amount of the third model; in the third energy balance formula R n –G = a×(H0 + LE0) + b, according to Determine the third evaporation amount of the third model.

[0072] After steps 120 - 140, the variants of the first model to the third model formed are shown in Table 1:

[0073]

[0074] Table 1

[0075] Among them, H0 on the right side of the formula in Table 1 represents the sensible heat flux observed by the current EC, and LE0 represents the latent heat flux observed by the current EC. LE on the left side of the formula represents the latent heat flux calculated by the model, and E represents the evaporation amount calculated by the model.

[0076] Step 150, process the first evaporation amount, the second evaporation amount, and the third evaporation amount respectively to determine the seventh latent heat flux, the eighth latent heat flux, and the ninth latent heat flux;

[0077] Specifically, the evaporation process (transport of water vapor) is accompanied by the release of energy. Therefore, latent heat flux and evaporation are two expressions of evaporation. Latent heat flux is the energy expression of evaporation, and evaporation is the water quantity expression of evaporation. The two variables can be mutually converted through unit conversion. That is, the evaporation multiplied by the latent heat of vaporization (L or λ) gives the latent heat flux.

[0078] Step 160: Based on the latent heat flux observed by eddy covariance (EC), evaluate the first to ninth latent heat fluxes, and determine the target model from the first to third models.

[0079] Among them, the preset statistical indicators include correlation coefficient, determination coefficient, root mean square error, and mean absolute error. In this application, nine groups of statistical indicators are calculated successively according to the latent heat fluxes of the first model, the second model, and the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula respectively, and the latent heat flux observed by EC; then, according to the nine groups of statistical indicators, the target model is determined from the first to third models.

[0080] Specifically, according to Determine the correlation coefficient; where r is the correlation coefficient, X i is the latent heat flux observed by EC, X' i is the latent heat flux obtained by the model, n is the number of samples, i is a corresponding sample, is the average value of the latent heat flux observed by EC, is the average value of the latent heat flux obtained by the model; in actual calculation, the large data of the latent heat flux observed by EC within a preset time period can be averaged to obtain the average value of the latent heat flux observed by EC;

[0081] According to Determine the determination coefficient; where R 2 is the determination coefficient;

[0082] According to Determine the root mean square error; where RMSE is the root mean square error;

[0083] According to Determine the mean absolute error; where MAE is the mean absolute error;

[0084] Determine the target model according to the correlation coefficient, determination coefficient, root mean square error, and mean absolute error in the nine groups of statistical indicators.

[0085] Specifically, r represents the linear correlation between the lake evaporation observed by EC and the model estimation. R 2 represents the fitting degree between the evaporation estimated value and the observed value. RMSE and MAE represent the average error magnitude between the evaporation estimated value and the observed value. r or R 2The higher the value, the lower the RMSE or MAE value, indicating a higher estimation accuracy of lake evaporation.

[0086] Furthermore, according to C v = σ / μ to calculate the coefficient of variation, C v can be used to describe the fluctuation amplitude of any time series. In this application, it can be the time series of latent heat flux observed by EC, or the latent heat flux simulated by the model, or the evaporation, or the time series of any meteorological variable observed, etc. The standard deviation and mean correspond to the standard deviation and mean of the variables in the corresponding time series. The coefficient of variation is used to indicate the fluctuation amplitude of the time series; where σ and μ are the standard deviation and mean obtained by the model respectively; here the coefficient of variation is only used as a statistical indicator to describe the fluctuation amplitude of the variable time series. When the fluctuation amplitude of the time series of the latent heat flux simulated by the model is similar to that of the latent heat flux time series observed by EC, it indicates a better model simulation effect.

[0087] Step 170, calculate the lake heat storage and the ratio of the lake heat storage to the latent heat flux through the target model and the net radiation flux, sensible heat flux, and latent heat flux observed by EC, and determine the influence of the lake heat storage on evaporation.

[0088] Specifically, calculate the target lake heat storage according to the energy balance equation corresponding to the target model and the net radiation flux and sensible heat flux observed by EC;

[0089] Determine the influence of lake heat storage on lake evaporation according to the ratio of the target lake heat storage to the latent heat flux observed by EC.

[0090] Among them, the target lake heat storage refers to the lake heat storage calculated by substituting the net radiation flux and sensible heat flux observed by EC into the energy balance equation corresponding to the target model. Thus, in this application, the ratio of the lake heat storage to the latent heat flux observed by EC can be used as one parameter, and the target lake heat storage can be used as another parameter to judge the influence of the lake heat storage on evaporation from these two parameters, realizing the quantification of lake evaporation.

[0091] In an example, in the warm season, a large amount of heat is stored in the lake water body and released in the form of evaporation in the cold season. By comparing the simulated evaporation by the model, the observed values of the evaporator, and the observed values of EC, the influence of G on lake evaporation is divided into two aspects. On the one hand, G reduces the magnitude of lake evaporation. In the second model class and the third model considering G, G mainly warms the lake water body during the observation period, so there is not enough heat to heat the atmosphere, resulting in a decrease in lake evaporation. It can be explained by the lake energy balance equation R n –G = H + LE, that is, LE = R n–H–G. In addition, G increases the lake evaporation in the cold season (autumn and winter), while decreases it in the warm season (summer). This is attributed to the different roles of the lake in the cold and warm seasons, that is, in the warm season, the lake acts as a heat sink to heat the lake water body; in the cold season, the lake acts as a heat source to heat the atmosphere in the form of evaporation.

[0092] On the other hand, there is a significant phase difference or peak asynchrony between the peak of net radiation and the peak of evaporation in alpine lakes, with the former being 4 - 6 months earlier than the latter. The peak lag phenomenon of lake evaporation can be attributed to the redistribution effect of net radiant energy between heating the water body in the warm season and heating the atmosphere in the form of evaporation in the cold season.

[0093] By applying the method for dealing with the influence of lake heat storage on evaporation provided by the embodiment of the present invention in the absence of water temperature profile observations, the lake heat storage is estimated by using the energy balance equation and the water - heat fluxes observed by EC, and the energy non - closure problem in the EC system is mainly solved based on the linear relationship between the estimated (Rn–G) by the model and the observed (H0 + LE0) by EC. On this basis, according to the final target model, the influence of lake heat storage on lake evaporation is quantified. This application fills the gap in quantifying the influence of lake heat storage on the evaporation of alpine deep lakes lacking water temperature profile observations, highlights the importance of integrating multi - source evaporation observations and multi - model simulations to estimate lake evaporation, and improves the understanding of the water - heat exchange process in alpine deep lakes.

[0094] The second embodiment of the invention provides a device, including a memory and a processor. The memory is used to store programs and can be connected to the processor through a bus. The memory can be a non - volatile memory, such as a hard disk drive and a flash memory, and stores software programs and device drivers. The software program can execute various functions of the above - mentioned method provided by the embodiment of the present invention; the device driver can be a network and interface driver. The processor is used to execute the software program, and when the software program is executed, it can implement the method provided by the first embodiment of the present invention.

[0095] The third embodiment of the present invention provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the method provided by the first embodiment of the present invention.

[0096] The fourth embodiment of the present invention provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method provided by the first embodiment of the present invention.

[0097] Those skilled in the art should further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered as exceeding the scope of the present invention.

[0098] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0099] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for dealing with the influence of lake heat storage on evaporation in the absence of water temperature profile observations, characterized in that The method includes: Determine a first energy balance formula, a second energy balance formula, and a third energy balance formula according to whether the lake heat storage is considered and whether the inherent energy non-closure problem of the eddy covariance (EC) system is considered; wherein, the first energy balance formula does not consider the lake heat storage, the second energy balance formula considers the lake heat storage, and the third energy balance formula considers the lake heat storage and the inherent energy non-closure of the EC system; Determine the first latent heat flux, the second latent heat flux, and the third latent heat flux of the first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence; Determine the fourth latent heat flux, the fifth latent heat flux, and the sixth latent heat flux of the second model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence; Determine the first evaporation amount, the second evaporation amount, and the third evaporation amount of the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence; Process the first evaporation amount, the second evaporation amount, and the third evaporation amount respectively to determine the seventh latent heat flux, the eighth latent heat flux, and the ninth latent heat flux; Evaluate the first to ninth latent heat fluxes based on the latent heat flux observed by the eddy covariance (EC) method, and determine the target model from the first model to the third model; Calculate the lake heat storage and the ratio of the lake heat storage to the latent heat flux through the net radiation flux, sensible heat flux, and latent heat flux observed by the target model and the EC method, and determine the influence of the lake heat storage on evaporation; Wherein, the determination of the first latent heat flux, the second latent heat flux, and the third latent heat flux of the first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence specifically includes: Substitute the first energy balance formula G = 0 into the first model , and determine the first latent heat flux; where, Δ is the slope of the saturation vapor pressure-temperature curve, γ is the psychrometric constant, R n is the net radiation observed based on the EC system, f(u) is the wind function, f(u) = 0.26×(0.5 + 0.536×u), e s is the saturation vapor pressure at the air temperature, e a is the actual vapor pressure; Substitute the second energy balance formula R n – G = H0 + LE0 into the first model , and determine the second latent heat flux; where H0 is the sensible heat flux observed based on the EC system, and LE0 is the latent heat flux observed based on the EC system; Substitute the third energy balance formula R n –G = a×(H0 + LE0)+b into the first model to determine the third latent heat flux, where a and b are regression fitting coefficients respectively.

2. The method according to claim 1, wherein The determination of the fourth latent heat flux, the fifth latent heat flux, and the sixth latent heat flux of the second model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence specifically includes: Substitute the first energy balance formula G = 0 into the second model , and determine the fourth latent heat flux; where Δ is the slope of the saturation vapor pressure - temperature curve, γ is the psychrometric constant, R n is the net radiation observed by the EC system, and α is a constant; Substitute the second energy balance formula R n – G = H0 + LE0 into the second model , and determine the fifth latent heat flux; where H0 is the sensible heat flux observed based on the EC system, and LE0 is the latent heat flux observed based on the EC system; Substitute the third energy balance formula R n –G = a×(H0 + LE0)+b into the second model to determine the sixth latent heat flux; where a and b are regression fitting coefficients respectively.

3. The method according to claim 1, characterized in that, The determination of the first evaporation amount, the second evaporation amount, and the third evaporation amount of the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence specifically includes: Substitute the first energy balance formula G = 0 into the third model , and determine the first evaporation rate of the third model; where, Δ is the slope of the saturation vapor pressure - temperature curve, R n is the net radiation observed based on the EC system, γ is the psychrometric constant, f(u) is the wind function, f(u) = 0.26×(0.5 + 0.536×u), u is the horizontal wind speed at a height of 2 m above the water surface, e s is the saturation vapor pressure at the air temperature, e a is the actual vapor pressure, T a is the air temperature; In the second energy balance formula R n – G = H0 + LE0, according to determine the second evaporation amount of the third model; where H0 is the sensible heat flux observed based on EC, and LE0 is the latent heat flux observed based on the EC system; In the third energy balance formula R n –G = a×(H0 + LE0) + b, according to determine the third evaporation amount of the third model.

4. The method according to any one of claims 2-3, characterized in that The calculation method of the third energy balance formula is: The third energy balance formula R n – G = a × (H0 + LE0) + b, where according to the sensible heat flux H0 observed based on EC, the latent heat flux LE0 observed based on EC, and the net radiation R observed based on EC n , substitute them into the first model, the second model, and the third model respectively to obtain the heat storage G of multiple lakes; Based on the sensible heat flux H0, latent heat flux LE0 and net radiation R observed by the EC system n , and the lake heat storage G, a and b are obtained by fitting.

5. The method according to any one of claims 2 or 3, wherein According to calculate the slope of the saturation vapor pressure-temperature curve; where Δ is the slope of the saturation vapor pressure-temperature curve, and T a is the air temperature; According to calculate the wet-dry constant; where γ is the wet-dry constant, c p is the specific heat of dry air, P is the atmospheric pressure, ε is the molar mass ratio of moist air to dry air, and L is the latent heat of vaporization; According to calculate the saturated water vapor pressure at the air temperature, where e s is the saturated water vapor pressure at the air temperature.

6. The method according to claim 1, characterized in that Evaluating the first to ninth latent heat fluxes based on the latent heat flux observed by the eddy covariance (EC) method, and determining the target model from the first model to the third model specifically includes: Calculate nine sets of statistical indicators in sequence according to the latent heat fluxes of the first model, the second model, and the third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula respectively, and the latent heat flux observed based on the EC system; Determine the target model from the first model to the third model according to the nine sets of statistical indicators.

7. The method according to claim 6, wherein The statistical indicators include the correlation coefficient, the determination coefficient, the root mean square error, and the mean absolute error. The determination of the target model from the first model to the third model according to the nine sets of statistical indicators specifically includes: According to , determine the correlation coefficient; where is the correlation coefficient,[[]] is the latent heat flux observed based on the EC system,[[]] is the latent heat flux obtained from the model, n is the number of samples, and i is a corresponding sample,[[]] is the average value of the latent heat flux observed based on the EC system,[[]] is the average value of the latent heat flux obtained from the model; According to determine the coefficient of determination; where is the coefficient of determination; According to the root mean square error is determined; where RMSE is the root mean square error According to determine the mean absolute error; wherein, MAE is the mean absolute error; Determine the target model according to the correlation coefficient, determination coefficient, root mean square error, and mean absolute error in nine groups of statistical indicators.

8. The method according to claim 1, wherein Calculating the lake heat storage and the ratio of the lake heat storage to the latent heat flux through the net radiation flux, sensible heat flux, and latent heat flux observed by the target model and EC, and determining the influence of the lake heat storage on evaporation specifically includes: Calculate the target lake heat storage according to the energy balance equation corresponding to the target model and the net radiation flux observed by EC and the sensible heat flux observed by EC; Determine the influence of lake heat storage on lake evaporation according to the ratio of the target lake heat storage to the latent heat flux observed by EC.

9. A processing device for the impact of lake heat storage on evaporation without water temperature profile observation, comprising a memory and a processor, characterized in that, The memory is used to store programs, and the processor is used to execute the method according to any one of claims 1-8.

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