Method for treating influence of lake heat reserves lacking water temperature profile observation on evaporation

Abstract

The embodiment of the invention relates to a method for processing the influence of lake heat reserves lacking water temperature profile observation on evaporation, and the method comprises the steps: determining a first energy balance formula, a second energy balance formula and a third energy balance formula according to whether the lake heat reserves are considered or not and whether the inherent energy non-closing problem of a vortex motion related EC system is considered or not; determining first to third latent heat fluxes of the first model, fourth to sixth latent heat fluxes of the second model and first to third evaporation capacities of the third model according to first to third energy balance formulas, and determining seventh to ninth latent heat fluxes after processing the first to third evaporation capacities; evaluating the first to ninth latent heat fluxes based on the big data of the latent heat fluxes observed by the EC, and determining a target model from the first model to the third model; according to the target model and net radiation flux, sensible heat flux and latent heat flux observed by the EC, the lake heat reserve and the ratio of the lake heat reserve to the latent heat flux are calculated, and the influence of the lake heat reserve on evaporation is determined.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a method for processing the influence of heat reserves of lakes on evaporation in the absence of water temperature profile observation. 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, 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 global lake evaporation has increased significantly in recent decades. Increased lake evaporation will affect regional climate and environment, for example, cooling surface air temperature, enhancing downwind precipitation, lowering lake water levels and reducing lake area. Therefore, it is essential to explore changes in lake evaporation and its impact on the environment.

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

[0004] Lake heat storage plays an important role in regulating lake evaporation and its changes. However, due to the limited observational data of water temperature profiles for calculating lake heat storage, the impact of lake heat storage on lake evaporation has not been fully studied, making it difficult to accurately quantify the impact 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 processing the influence of heat reserve of lakes on evaporation in the absence of water temperature profile observation in order to solve the problems existing in the prior art in view of the defects existing in the prior art.

[0006] To achieve the above object, the present invention provides a method for processing the influence of heat storage on evaporation of lakes lacking water temperature profile observation, comprising:

[0007] According to whether the lake heat reserve is considered and whether the inherent energy non-closure problem of the eddy-correlated EC system is considered, the first energy balance formula, the second energy balance formula and the third energy balance formula are determined; wherein the first energy balance formula does not consider the lake heat reserve, the second energy balance formula considers the lake heat reserve, and the third energy balance formula considers the lake heat reserve and the inherent energy non-closure problem of the EC system;

[0008] Determining a first latent heat flux, a second latent heat flux, and a third latent heat flux of a first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula, respectively;

[0009] determining a fourth latent heat flux, a fifth latent heat flux, and a 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, respectively;

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

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

[0012] Based on the latent heat flux observed by eddy covariance EC, the first to ninth latent heat fluxes are evaluated and a target model is determined from the first model to the third model;

[0013] The heat reserve of the lake and the ratio of the heat reserve of the lake to the latent heat flux are calculated by using the target model and the net radiation flux, sensible heat flux and latent heat flux observed by EC, and the influence of the heat reserve of the lake on evaporation is determined.

[0014] In a possible implementation, the determining, in sequence, 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 specifically includes:

[0015] 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 saturated water vapor pressure-temperature curve, γ is the dry-wet constant, and 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 saturated water vapor pressure at air temperature, e a is the actual water vapor pressure;

[0016] The second energy balance formula Rn –G=H0+LE0Substitute into the first model Determine a second latent heat flux; wherein H0 is the sensible heat flux based on the EC system observation, and LE0 is the latent heat flux based on the EC system observation;

[0017] The third energy balance formula R n –G=a×(H0+LE0)+b, substitute 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 determining, in sequence, under the first energy balance formula, the second energy balance formula, and the third energy balance formula, the fourth latent heat flux, the fifth latent heat flux, and the sixth latent heat flux of the second model specifically include:

[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 saturated water vapor pressure-temperature curve, γ is the dry-wet constant, and R n is the net radiation observed by the EC system, α is a constant;

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

[0021] The third energy balance formula R n –G=a×(H0+LE0)+bSubstitute 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 determining, in sequence, 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 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 saturated water vapor pressure-temperature curve, R n is the net radiation observed based on the EC system, γ is the dry and wet 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 saturated water vapor pressure at air temperature, e a is the actual water vapor pressure, T ais the air temperature;

[0024] In the second energy balance formula R n –G=H0+LE0, according to Determine the second evaporation amount of the third model; wherein 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, according to A third evaporation amount for the third model is determined.

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

[0027] The third energy balance formula R n –G=a×(H0+LE0)+b, based on the sensible heat flux H0 based on EC observation, the latent heat flux LE0 based on EC observation, and the net radiation R based on EC observation n , respectively substituted into the first model, the second model and the third model, and the heat storage G of multiple lakes was obtained;

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

[0029] In one possible implementation, according to Calculate the slope of the saturated water vapor pressure-temperature curve; where Δ is the slope of the saturated water vapor pressure-temperature curve, T a is the air temperature;

[0030] according to Calculate the wet and dry constant; where γ is the wet and 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;

[0031] according to Calculate the saturated water vapor pressure at air temperature, where e s is the saturated water vapor pressure at air temperature.

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

[0033] Nine groups of statistical indicators are calculated 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, and the latent heat flux based on the observation of the EC system;

[0034] According to the nine groups of statistical indicators, a target model is determined from the first model to the third model.

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

[0036] according to Determine the correlation coefficient; where r is the correlation coefficient, X i is the latent heat flux based on EC system observation, 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 latent heat flux based on EC system observations, is the average value of latent heat flux obtained by 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] The target model is determined based on the correlation coefficient, determination coefficient, root mean square error and mean absolute error among the nine groups of statistical indicators.

[0041] In a possible implementation, the net radiation flux, sensible heat flux and latent heat flux observed by the target model and EC are used to calculate the lake heat reserve and the ratio of the lake heat reserve to the latent heat flux, and determine the influence of the lake heat reserve on evaporation, specifically including:

[0042] The heat storage of the target lake is calculated based on the energy balance equation corresponding to the target model, the net radiation flux observed by EC, and the sensible heat flux observed by EC;

[0043] The effect of lake heat storage on lake evaporation is determined based on the ratio of the target lake heat storage and the latent heat flux observed by EC.

[0044] In a second aspect, the present invention provides a device including a memory and a processor, wherein the memory is used to store programs, and the processor is used to execute any method described in the first aspect.

[0045] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, any method described in the first aspect is performed.

[0046] By applying the method for processing the impact of lake heat reserves on evaporation in the absence of water temperature profile observations provided in an embodiment of the present invention, the energy balance equation and the water heat flux observed by EC are used to estimate the lake heat reserves, and the problem of energy non-closure in the EC system is solved based on the linear relationship between the model-estimated (Rn-G) and the EC-observed (H+LE). On this basis, according to the final target model, the impact of lake heat reserves on lake evaporation is quantified. This application fills the gap in quantifying the impact of lake heat reserves on the evaporation of high-altitude deep lakes that lack 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 high-altitude deep lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic flow chart of a method for processing the impact of lake heat reserves on evaporation in the absence of water temperature profile observations provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme 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 part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Figure 1 A schematic diagram of a method for processing the effect of heat storage on evaporation in a lake lacking water temperature profile observations provided in an embodiment of the present invention. The processing object of this application is mainly applied to alpine lakes, because the water level and water volume changes in alpine lakes vary significantly in different regions. Figure 1 , the technical solution of the present invention is described with specific embodiments. Figure 1 As shown, this application includes the following steps:

[0050] Step 110, determining the first energy balance formula, the second energy balance formula and the third energy balance formula according to whether the lake heat reserve is considered and whether the inherent energy non-closure problem of the eddy-correlated EC system is considered.

[0051] Among them, the first energy balance formula does not take into account the lake heat reserve, the second energy balance formula takes into account the lake heat reserve, and the third energy balance formula takes into account the lake heat reserve and the inherent energy non-closure of the EC system.

[0052] Specifically, in order to explore the impact of lake heat storage on lake evaporation, three energy balance formulas can be used to discuss the impact of lake heat storage on lake evaporation, such as the following three:

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

[0054] Second: Considering the impact of lake heat storage on lake evaporation, according to the second energy balance equation R n –G=H0+LE0, this type of model replaces the (R n –G). Among them, R n represents the net radiation flux observed by EC.

[0055] Third: The impact of lake heat storage on lake evaporation, and considering the inherent energy non-closure problem of the EC system, the third energy balance equation R n –G=a×(H0+LE0)+b. In the formula, a and b are (R n =The regression coefficient of the linear regression between (H0 – 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 (e.g., air temperature, actual water vapor pressure, wind speed, etc.) into the first, second and third models respectively, and then inversely calculate the (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, and thus 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 flux corresponding to the first model to the second model under the first to third energy balance formula, and the evaporation of the third model under the first to third energy balance formula, and then convert it into latent heat flux.

[0057] Step 120, determining a first latent heat flux, a second latent heat flux, and a 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, respectively;

[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 saturated water vapor pressure-temperature curve, γ is the dry-wet constant, f(u) is the wind function, f(u) = 0.26 × (0.5 + 0.536 × u), e s is the saturated water vapor pressure at air temperature, e a is the actual water vapor pressure;

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

[0060] according to Calculate the slope of the saturated water vapor pressure-temperature curve; where T a is the air temperature; Calculate the wet and dry constant; c p is the specific heat of dry air, P is the atmospheric pressure, ε is the molar mass ratio of wet air to dry air, and L is the latent heat of vaporization; according to Calculate the saturated water vapor pressure e at air temperature s .

[0061] For EC observations, there may be missing data. The interpolation scheme for missing data is as follows: (1) When there are missing data in the half-hour interval within a day, the average value of the remaining half-hour interval data of the day is used as the daily value data of that day; (2) When there are missing data on 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 or more consecutive days, the missing data are filled by linear interpolation method.

[0062] The second energy balance formula R n –G=H0+LE0Substitute into the first model determining a second latent heat flux;

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

[0064] Step 130, determining a fourth latent heat flux, a fifth latent heat flux, and a 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, respectively;

[0065] Specifically, the second model can estimate the evaporation without observing the aerodynamic term. Substituting 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, which may be 1.26;

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

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

[0068] Step 140, determining a first evaporation amount, a second evaporation amount, and a third evaporation amount of a 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 uses conventional meteorological data at the local or basin scale, such as T a , RH, P,u, and sunshine hours, an effective tool for calculating daily and monthly reference evapotranspiration without the need for calibration as a function of regional wind speed.

[0070] Substitute the first energy balance formula G=0 into the third model determining a first evaporation amount of a 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 A third evaporation amount for the third model is determined.

[0072] After steps 120-140, the variants of the first model to the third model 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 calculated by the model.

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

[0077] Specifically, the process of evaporation (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 amount evaporated. The two variables can be converted to each other by converting units. That is, the evaporation amount is multiplied by the latent heat of vaporization (L or λ) to obtain the latent heat flux.

[0078] Step 160 , evaluating the first to ninth latent heat fluxes based on the latent heat flux observed by the eddy covariance EC, and determining a target model from the first model to the third model;

[0079] Among them, the preset statistical indicators include correlation coefficient, determination coefficient, root mean square error and mean absolute error. The present application calculates nine sets of statistical indicators according to the latent heat flux under the first energy balance formula, the second energy balance formula and the third energy balance formula of the first model, the second model and the third model respectively, and the latent heat flux observed by EC; and then determines the target model from the first model to the third model according to the nine sets of statistical indicators.

[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 latent heat flux observed by EC, is the average value of the latent heat flux obtained by the model; in actual calculation, the big 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 coefficient of determination; where R 2 is the coefficient of determination;

[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] The target model is determined based on the correlation coefficient, determination coefficient, root mean square error and mean absolute error among the nine groups of statistical indicators.

[0085] Specifically, r represents the linear correlation between EC observations and model-estimated lake evaporation. 2 Represents the degree of fit between the evaporation estimate and the observed value. RMSE and MAE represent the average error between the evaporation estimate and the observed value. r or R 2The higher the value, the lower the RMSE or MAE value, indicating a higher accuracy in estimating lake evaporation.

[0086] Furthermore, according to C v =σ / μ to calculate the coefficient of variation, C v It 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, the latent heat flux simulated by the model, evaporation, the time series of observing any meteorological variables, etc. The standard deviation and mean correspond to the standard deviation and mean of the variables in the time series. The coefficient of variation is used to indicate the fluctuation amplitude of the time series; wherein 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 latent heat flux simulated by the model is close to the fluctuation amplitude of the time series of latent heat flux observed by EC, it indicates that the model simulation effect is better.

[0087] Step 170, calculate the lake heat reserve and the ratio of the lake heat reserve 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, and determine the impact of the lake heat reserve on evaporation.

[0088] Specifically, the heat storage of the target lake is calculated based on the energy balance equation corresponding to the target model and the net radiation flux and sensible heat flux observed by EC;

[0089] The effect of lake heat storage on lake evaporation was determined based on the ratio of the target lake heat storage to the latent heat flux observed by EC.

[0090] The target lake heat reserve refers to the lake heat reserve calculated by substituting the net radiation flux and sensible heat flux observed by EC into the energy balance equation corresponding to the target model. Therefore, the present application can use the ratio of the lake heat reserve to the latent heat flux observed by EC as a parameter, and the target lake heat reserve as another parameter, and use these two parameters to determine the impact of the lake heat reserve on evaporation, thereby realizing the quantification of lake evaporation.

[0091] In one example, a large amount of heat is stored in the lake water during the warm season and released in the form of evaporation during the cold season. The impact of G on lake evaporation will be divided into two aspects by comparing the model simulated evaporation, evaporator observations and EC observations. On the one hand, G reduces the amount of lake evaporation. In the second model class and the third model considering G, G mainly warms the lake water during the observation period, so there is no more heat to heat the atmosphere, resulting in a decrease in lake evaporation. It can be expressed by the lake energy balance equation R n – G=H+LE, that is, LE=R n–H–G. In addition, G increases lake evaporation in the cold season (autumn and winter) and reduces lake evaporation in the warm season (summer). This is attributed to the different roles of lakes in the cold and warm seasons, that is, in the warm season, lakes act as heat sinks to heat the lake water body; in the cold season, lakes act as heat sources 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 occurring 4–6 months earlier than the latter. The peak hysteresis of lake evaporation can be attributed to the redistribution of net radiation 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 processing the impact of lake heat reserves on evaporation in the absence of water temperature profile observations provided in an embodiment of the present invention, the energy balance equation and the water heat flux observed by EC are used to estimate the lake heat reserves, and the linear relationship between the model-estimated (Rn–G) and the EC-observed (H0+LE0) is used to focus on solving the energy non-closure problem in the EC system. On this basis, according to the final target model, the impact of lake heat reserves on lake evaporation is quantified. This application fills the gap in quantifying the impact of lake heat reserves on the evaporation of high-altitude deep lakes that lack 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 high-altitude deep lakes.

[0094] Embodiment 2 of the invention provides a device, including a memory and a processor, wherein the memory is used to store a program, and the memory can be connected to the processor via a bus. The memory can be a non-volatile memory, such as a hard disk drive and a flash memory, and the memory stores a software program and a device driver. The software program can perform various functions of the above method provided in 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, the method provided in embodiment 1 of the present invention can be implemented.

[0095] Embodiment 3 of the present invention provides a computer program product including instructions. When the computer program product is run on a computer, the computer executes the method provided in Embodiment 1 of the present invention.

[0096] Embodiment 4 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, the method provided in Embodiment 1 of the present invention is implemented.

[0097] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0098] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0099] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing the effect of heat storage on evaporation in lakes lacking water temperature profile observations, characterized in that: The method comprises: According to whether the lake heat reserve is considered and whether the inherent energy non-closure problem of the eddy-correlated EC system is considered, the first energy balance formula, the second energy balance formula and the third energy balance formula are determined; wherein the first energy balance formula does not consider the lake heat reserve, the second energy balance formula considers the lake heat reserve, and the third energy balance formula considers the lake heat reserve and the inherent energy non-closure problem of the EC system; Determining a first latent heat flux, a second latent heat flux, and a third latent heat flux of a first model under the first energy balance formula, the second energy balance formula, and the third energy balance formula, respectively; determining a fourth latent heat flux, a fifth latent heat flux, and a 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, respectively; determining a first evaporation amount, a second evaporation amount, and a third evaporation amount of a third model under the first energy balance formula, the second energy balance formula, and the third energy balance formula in sequence; processing the first evaporation amount, the second evaporation amount, and the third evaporation amount respectively to determine a seventh latent heat flux, an eighth latent heat flux, and a ninth latent heat flux; Based on the latent heat flux observed by eddy covariance EC, the first to ninth latent heat fluxes are evaluated and the target model is determined from the first model to the third model; The heat reserve of the lake and the ratio of the heat reserve of the lake to the latent heat flux are calculated by using the target model and the net radiation flux, sensible heat flux and latent heat flux observed by EC, and the influence of the heat reserve of the lake on evaporation is determined.

2. The method according to claim 1, characterized in that The determining, in sequence, 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 specifically includes: 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 saturated water vapor pressure-temperature curve, γ is the dry-wet constant, and 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 saturated water vapor pressure at air temperature, e a is the actual water vapor pressure; The second energy balance formula R n –G=H0+LE0Substitute into the first model Determine a second latent heat flux; wherein H0 is the sensible heat flux based on the EC system observation, and LE0 is the latent heat flux based on the EC system observation; The third energy balance formula R n –G=a×(H0+LE0)+b, substitute into the first model Determine the third latent heat flux, where a and b are regression fitting coefficients, respectively.

3. The method according to claim 1, characterized in that The determining, in sequence, 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 specifically includes: 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 saturated water vapor pressure-temperature curve, γ is the dry-wet constant, and R n is the net radiation observed by the EC system, α is a constant; The second energy balance formula R n –G=H0+LE0 is substituted into the second model Determine a fifth latent heat flux; wherein H0 is the sensible heat flux based on the EC system observation, and LE0 is the latent heat flux based on the EC system observation; The third energy balance formula R n –G=a×(H0+LE0)+bSubstitute into the second model Determine the sixth latent heat flux; where a and b are regression fitting coefficients, respectively.

4. The method according to claim 1, characterized in that: The determining, in sequence, 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 specifically includes: 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 saturated water vapor pressure-temperature curve, R n is the net radiation observed based on the EC system, γ is the dry and wet 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 saturated water vapor pressure at air temperature, e a is the actual water 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; wherein H0 is the sensible heat flux based on EC observations, and LE0 is the latent heat flux based on EC system observations; In the third energy balance formula R n –G=a×(H0+LE0)+b, according to A third evaporation amount for the third model is determined.

5. The method according to any one of claims 2 to 4, 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, based on the sensible heat flux H0 based on EC observation, the latent heat flux LE0 based on EC observation, and the net radiation R based on EC observation n , respectively substituted into the first model, the second model and the third model, and the heat storage G of multiple lakes was obtained; According to the sensible heat flux H0, latent heat flux LE0 and net radiation R based on the EC system observations n , and the lake heat storage G, are fitted to obtain a and b.

6. The method according to any one of claims 2 or 4, characterized in that: according to Calculate the slope of the saturated water vapor pressure-temperature curve; where Δ is the slope of the saturated water vapor pressure-temperature curve, T a is the air temperature; according to Calculate the wet and dry constant; where γ is the wet and 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 air temperature, where e s is the saturated water vapor pressure at air temperature.

7. The method according to claim 1, characterized in that Based on the latent heat flux observed by eddy covariance EC, the first to ninth latent heat fluxes are evaluated, and the target model is determined from the first model to the third model, specifically including: Nine groups of statistical indicators are calculated 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, and the latent heat flux based on the observation of the EC system; According to the nine groups of statistical indicators, a target model is determined from the first model to the third model.

8. The method according to claim 7, characterized in that The set of statistical indicators includes a correlation coefficient, a determination coefficient, a root mean square error and a mean absolute error, and determining 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 r is the correlation coefficient, X j is the latent heat flux based on EC system observation, 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 latent heat flux based on EC system observations, is the average value of latent heat flux obtained by the model; according to Determine the coefficient of determination; where R 2 is the coefficient of determination; according to Determine the root mean square error; where RMSE is the root mean square error; according to Determine the mean absolute error; where MAE is the mean absolute error; The target model is determined based on the correlation coefficient, determination coefficient, root mean square error and mean absolute error among the nine groups of statistical indicators.

9. The method according to claim 1, characterized in that: The net radiation flux, sensible heat flux and latent heat flux observed by the target model and EC are used to calculate the lake heat reserve and the ratio of the lake heat reserve to the latent heat flux, and determine the influence of the lake heat reserve on evaporation. Specifically, the method includes: The heat storage of the target lake is calculated based on the energy balance equation corresponding to the target model, the net radiation flux observed by EC, and the sensible heat flux observed by EC; The effect of lake heat storage on lake evaporation is determined based on the ratio of the target lake heat storage and the latent heat flux observed by EC.

10. A device 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 to 9.

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