Lake surface evaporation processing method based on dynamic conversion coefficient of multiple models of evaporators

Abstract

Embodiments of the present application relate to a lake surface evaporation processing method based on dynamic conversion coefficients of multiple types of evaporators, comprising: obtaining multiple first, second and third evaporator observed evaporation amounts in a preset time period respectively; calculating first and second conversion coefficients on a daily scale in a first period; calculating first and second conversion coefficients on a monthly scale in the first period according to the first and second conversion coefficients on the daily scale in the first period; calculating first and second conversion coefficients on a monthly scale in a second period; determining a target coefficient; calculating a first lake surface evaporation amount set corresponding to the current time according to the first conversion coefficient on the monthly scale corresponding to the period and the first evaporator observed evaporation amount at the current time; calculating a second lake surface evaporation amount set corresponding to the current time; and evaluating the first and second daily, monthly and annual lake surface evaporation amounts according to a set index and the third evaporator observed evaporation amount at the current time to determine data finally representing the lake surface evaporation amount.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for processing lake surface evaporation based on dynamic conversion coefficients of multiple evaporator models. Background Technology

[0002] Lake surface evaporation (LSE) is a crucial component of lake water and energy balance, and accurate LSE estimation is essential for the scientific water resource management of alpine lakes on the Tibetan Plateau under the background of global warming. However, the scarcity of hydrological and meteorological observation data and limited local model parameterization hinder the accuracy of LSE estimation, especially during lake freezing periods. Summary of the Invention

[0003] The purpose of this invention is to address the deficiencies in existing technologies by providing a method and apparatus for processing lake surface evaporation based on dynamic conversion coefficients of multiple evaporator models, thereby solving the problems existing in the prior art.

[0004] To achieve the above objectives, this invention provides a method for processing lake surface evaporation based on dynamic conversion coefficients for multiple evaporator models, comprising:

[0005] The observed evaporation amounts of the first evaporator, the second evaporator, and the third evaporator are obtained within a preset time period. The observed evaporation amounts of the first evaporator include the observed evaporation amounts of the first period and the second period, the observed evaporation amounts of the second evaporator include the observed evaporation amounts of the first period, and the observed evaporation amounts of the third evaporator include the observed evaporation amounts of the first period.

[0006] Based on the observed evaporation from the first evaporator and the observed evaporation from the third evaporator, calculate the first conversion factor on the daily scale for the first period.

[0007] Based on the observed evaporation from the second evaporator and the observed evaporation from the third evaporator, calculate the second conversion factor on the daily scale for the first period.

[0008] Based on the first and second conversion factors on the daily scale of the first period, calculate the first and second conversion factors on the monthly scale of the first period respectively;

[0009] Based on the observed evaporation of the first evaporator, the observed evaporation of the second evaporator, and the observed evaporation of the third evaporator at the second period monthly scale, the first conversion factor and the second conversion factor at the second period monthly scale are calculated respectively.

[0010] The target coefficient is determined based on the first and second conversion factors on the daily scale in the first period, and the first and second conversion factors on the monthly scale in the first and second periods, respectively.

[0011] When the first and second conversion factors on the monthly scale are used as target factors, the period in which the current time falls is determined.

[0012] Based on the first conversion factor on the monthly scale corresponding to the period and the first evaporator observation evaporation at the current time, calculate the first lake surface evaporation set corresponding to the current time; wherein, the first lake surface evaporation set includes the lake surface evaporation of the first day, the lake surface evaporation of the first month, and the lake surface evaporation of the first year;

[0013] Based on the second conversion factor on the monthly scale corresponding to the period and the second evaporator observation evaporation at the current time, calculate the second lake surface evaporation set corresponding to the current time; wherein, the second lake surface evaporation set includes the lake surface evaporation of the second day, the lake surface evaporation of the second month, and the lake surface evaporation of the second year;

[0014] Based on the set indicators and the evaporation observed by the third evaporator at the current time, the evaporation of the lake surface on the first day, the lake surface evaporation in the first month, and the lake surface evaporation in the first year, as well as the evaporation of the lake surface on the second day, the lake surface evaporation in the second month, and the lake surface evaporation in the second year, are evaluated to determine the final data representing the lake surface evaporation.

[0015] In one possible implementation, a first conversion factor on a daily scale for a first period is calculated based on the observed evaporation rates of the first evaporator and the third evaporator, specifically including:

[0016] The first conversion factor is calculated daily based on the ratio of the daily observed evaporation of the third evaporator to the corresponding observed evaporation of the first evaporator.

[0017] Within a preset time period, the average of the first conversion factor for the same date each year is calculated to obtain the first conversion factor for each day on a daily scale.

[0018] In one possible implementation, calculating the second conversion factor on a daily scale for the first period based on the observed evaporation from the second evaporator and the observed evaporation from the third evaporator specifically includes:

[0019] The second conversion factor is calculated daily based on the ratio of the daily observed evaporation amount of the third evaporator to the corresponding observed evaporation amount of the second evaporator.

[0020] Within a preset time period, the average of multiple second conversion factors for the same date each year is calculated to obtain the second conversion factor for each day on a daily scale.

[0021] In one possible implementation, the step of calculating the first and second conversion factors on the monthly scale based on the first and second conversion factors on the daily scale of the first period specifically includes:

[0022] Within a preset time period, the average of multiple first conversion factors on a daily scale within the same month of each year is calculated to obtain the first conversion factor on a monthly scale for each month.

[0023] Within a preset timeframe, the average of multiple second conversion factors on a daily scale within the same month of each year is calculated to obtain the second conversion factor on a monthly scale for each month; and,

[0024] Based on the observed evaporation rates of the first, second, and third evaporators at the second-period monthly scale, the calculation of the first and second conversion factors at the second-period monthly scale specifically includes:

[0025] The first conversion factor on the monthly scale of the second period is calculated based on the ratio of the evaporation observed by the third evaporator on the monthly scale to the evaporation observed by the first evaporator on the monthly scale.

[0026] The second conversion factor on the second-period monthly scale is calculated based on the ratio of the observed evaporation of the third evaporator on the second-period monthly scale to the observed evaporation of the first evaporator on the second-period monthly scale.

[0027] In one possible implementation, determining the target coefficient based on the first and second conversion factors on a daily scale in the first period, and the first and second conversion factors on a monthly scale in the first and second periods respectively, specifically includes:

[0028] Within a preset time period, determine the first change range of the first conversion factor on the adjacent daily scale, the second change range of the second conversion factor on the adjacent daily scale, the third change range of the first conversion factor on the adjacent monthly scale, and the fourth change range of the second conversion factor on the adjacent monthly scale.

[0029] Based on the first change magnitude and the third change magnitude, the first conversion factor on the monthly scale is determined as one of the target factors;

[0030] Based on the second and fourth magnitudes of change, the second conversion factor on a monthly scale is determined to be another of the target factors.

[0031] In one possible implementation, calculating the first lake surface evaporation set corresponding to the current time based on the first conversion factor on the monthly scale corresponding to the period and the first evaporator observation evaporation at the current time specifically includes:

[0032] The lake surface evaporation on the first day is calculated by multiplying the first conversion factor on the monthly scale of the current month with the first evaporator observation evaporation at the current time.

[0033] The lake surface evaporation for the first month is calculated by multiplying the first conversion factor on the monthly scale of the current month with the first evaporator observation evaporation for the current month.

[0034] Based on the current year, add up the lake's evaporation in the first month of each month within that year to obtain the first year's evaporation; and...

[0035] Based on the second reduction factor on the monthly scale corresponding to the aforementioned period and the second evaporator observation evaporation at the current time, the calculation of the second lake surface evaporation set corresponding to the current time specifically includes:

[0036] The lake surface evaporation on the second day is calculated by multiplying the second conversion factor on the monthly scale of the current month with the second evaporator observation evaporation at the current time.

[0037] The lake surface evaporation for the second month is calculated by multiplying the second conversion factor on the monthly scale of the current month with the second evaporator observation evaporation for the current month.

[0038] Based on the current year, add up the evaporation of the lake surface in the second month of each month within that year to obtain the second annual evaporation of the lake surface.

[0039] In one possible implementation, the evaluation of the lake surface evaporation on the first day, the first month, and the first year, as well as the lake surface evaporation on the second day, the second month, and the second year, based on set indicators and the evaporation observed by the third evaporator at the current time, to determine the final data representing the lake surface evaporation, specifically includes:

[0040] The determination coefficients, root mean square errors, average deviation errors, and average absolute errors of the lake surface evaporation on the first day, the first month, and the first year, respectively, are compared with the evaporation observed by the third evaporator to obtain the first result.

[0041] The determination coefficients, root mean square errors, average deviation errors, and average absolute errors of the lake surface evaporation on the second day, the second month, and the second year, respectively, compared with the evaporation observed by the third evaporator, are calculated to obtain the second result.

[0042] Based on the first and second results, the final data representing lake surface evaporation is determined; wherein, on daily and monthly scales, the coefficient of determination of the second result is greater than that of the first result, and the root mean square error, mean deviation error, and mean absolute error of the second result are less than those of the first result; on an annual scale, the coefficient of determination of the first result is greater than that of the second result, and the root mean square error, mean deviation error, and mean absolute error of the first result are less than those of the second result; to obtain high-resolution lake surface evaporation for the entire year, in the first period, the lake surface evaporation of the second day is used as the final data representing lake surface evaporation, and in the second period, the lake surface evaporation of the first day is used as the final data representing lake surface evaporation.

[0043] In one possible implementation, the method further includes:

[0044] Acquire meteorological elements on a daily scale; the meteorological elements include daily average air temperature, daily average lake surface water temperature, daily precipitation, daily actual water vapor pressure, daily average relative humidity, daily saturated water vapor pressure difference, and daily water temperature difference;

[0045] The daily average air temperature, daily average lake surface water temperature, daily precipitation, daily actual water vapor pressure, daily average relative humidity, daily saturated water vapor pressure difference, and daily water temperature difference are used as independent variables. The lake surface evaporation on the second day of the first period and the lake surface evaporation on the first day of the second period are used as initial dependent variables. The independent variables and initial dependent variables are divided into training sets and test sets, and then fed into a random forest regression model (RFRM). The parameters of the RFRM are adjusted, and the RFRM is trained to obtain the trained RFRM.

[0046] Substitute the independent variables of the test set into the trained RFRM to obtain the target dependent variable;

[0047] The target dependent variable is compared with the evaporation observed by the third evaporator on a daily scale to determine whether the trained RFRM meets the requirements.

[0048] When the trained RFRM meets the requirements, based on the output of the RFRM, determine the degree of influence of each meteorological element on the second day's lake surface evaporation in the first period combined with the first day's lake surface evaporation in the second period.

[0049] In one possible implementation, according to Calculate the daily saturated vapor pressure; the e s The daily saturated vapor pressure, T a The average daily temperature;

[0050] according to Calculate the daily average relative humidity; e a To actually observe water vapor pressure;

[0051] According to VPD=e s -e a Calculate the daily saturated vapor pressure difference;

[0052] The daily water-temperature difference is obtained by comparing the daily average lake surface water temperature with the daily average air temperature.

[0053] In a second aspect, the present invention provides an apparatus including a memory and a processor, the memory being used to store a program and the processor being used to execute any of the methods described in the first aspect.

[0054] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs any of the methods described in the first aspect.

[0055] By applying the lake surface evaporation processing method based on dynamic conversion factors of multiple evaporator models provided in this invention, accurate LSE quantification is achieved using multi-source daily evaporation observations and dynamic conversion factors. This enables the quantification of daily evaporation of alpine lakes throughout the year, including both non-freezing and freezing periods, with high accuracy. Furthermore, this application can quantify meteorological factors affecting the daily evaporation of alpine lakes and further verifies the accuracy of the daily lake surface evaporation obtained through the conversion factors using RFRM. Attached Figure Description

[0056] Figure 1 This is one of the schematic diagrams of a lake surface evaporation treatment method based on dynamic conversion coefficients of multiple evaporator models provided in an embodiment of the present invention.

[0057] Figure 2 for Figure 1 Flowchart for step 150;

[0058] Figure 3 for Figure 1 Flowchart for step 170;

[0059] Figure 4 for Figure 1 Flowchart for step 180;

[0060] Figure 5 for Figure 1 Flowchart for step 190;

[0061] Figure 6 This is the second schematic diagram of the process for processing lake surface evaporation based on dynamic conversion coefficients of multiple evaporator models, provided in an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0063] Figure 1 This is one of the flowcharts illustrating a method for treating lake evaporation based on dynamic conversion coefficients of multiple evaporator models, provided in an embodiment of the present invention. The application of this method primarily targets high-altitude lakes, as these lakes have both frozen and unfrozen periods, and the evaporation during the frozen period is difficult to estimate. The following is a combination of... Figure 1 The technical solution of the present invention will be described with reference to specific embodiments. Figure 1 As shown, this application includes the following steps:

[0064] Step 100: Obtain the observed evaporation amounts of the first evaporator, the second evaporator, and the third evaporator within a preset time period; the observed evaporation amounts of the first evaporator include the observed evaporation amounts of the first period and the second period, the observed evaporation amounts of the second evaporator include the observed evaporation amounts of the first period, and the observed evaporation amounts of the third evaporator include the observed evaporation amounts of the first period.

[0065] In this system, the first evaporator is an evaporating dish, such as a 20cm evaporating dish; the second evaporator is an E601 evaporator; and the third evaporator is an evaporation pool, such as a 20㎡ evaporation pool. The first period is the non-freezing period, and the second period is the freezing period. The evaporation rate observed by the first evaporator can be the daily evaporation rate observed through it. The water level change in the 20cm evaporating dish during the non-freezing period and the weight change in the 20cm evaporating dish during the freezing period can be represented by E. -20cm E on the daytime scale -20cm Observations are conducted year-round. Correspondingly, the evaporation rate observed by the second evaporator can be the daily evaporation rate observed through the second evaporator, specifically during the non-freezing period. The evaporation rate observed by the third evaporator can also be the daily evaporation rate observed through the third evaporator, again during the non-freezing period. The daily evaporation rate of the E601 evaporating dish and the 20㎡ evaporating pool can be obtained using the probe method. Furthermore, since the E601 evaporating dish and the 20㎡ evaporating pool are typically inoperable during freezing periods, therefore, E... -E601 and E -20m 2 Daily evaporation is only available during non-freezing periods.

[0066] Step 110: Calculate the first conversion factor on the daily scale of the first period based on the evaporation observed by the first evaporator and the evaporation observed by the third evaporator.

[0067] Specifically, based on the evaporation observed from the first evaporator and the evaporation observed from the third evaporator, the first conversion factor on the daily scale for the first period is calculated, including:

[0068] The first conversion factor is calculated daily based on the ratio of the daily observed evaporation of the third evaporator to the corresponding observed evaporation of the first evaporator.

[0069] Within a preset time period, the average of the first conversion factor for the same date each year is calculated to obtain the first conversion factor for each day on a daily scale.

[0070] The preset duration can be set as needed, such as 10 years. For example, for a specific day during the non-freezing period, such as May 20th, to calculate the first conversion factor of May 20th on a daily scale for the 10 years from 2009 to 2018, the first conversion factor of May 20th for each of these 10 years can be calculated, and then the average of the 10 first conversion factors can be taken to obtain the first conversion factor of May 20th on a daily scale.

[0071] Step 120: Calculate the second conversion factor on the daily scale for the first period based on the evaporation observed by the second evaporator and the evaporation observed by the third evaporator.

[0072] The calculation of the second conversion factor on a daily scale for the first period, based on the evaporation observed by the second and third evaporators, specifically includes:

[0073] The second conversion factor is calculated daily based on the ratio of the daily observed evaporation amount of the third evaporator to the corresponding observed evaporation amount of the second evaporator.

[0074] Within a preset time period, the average of multiple second conversion factors for the same date each year is calculated to obtain the second conversion factor for each day on a daily scale.

[0075] For example, to calculate the second conversion factor of May 20th on a daily scale for a specific day outside the freezing period, such as May 20th, over the 10 years from 2009 to 2018, we can calculate the second conversion factor for each May 20th in those 10 years, and then take the average of the 10 second conversion factors to obtain the second conversion factor of May 20th on a daily scale.

[0076] Step 130: Calculate the first and second conversion factors on the monthly scale of the first period based on the first and second conversion factors on the daily scale of the first period, respectively.

[0077] Specifically, within a preset time period, the average of multiple first conversion factors on a daily scale within the same month of each year in the first period is calculated to obtain the first conversion factor on a monthly scale for each month in the first period.

[0078] Within a preset time period, the average of multiple second conversion factors on a daily scale is calculated for the same month of each year in the first period, thus obtaining the second conversion factor on a monthly scale for each month in the first period.

[0079] Specifically, to calculate the first conversion factor for May 2009, you can take the average of the first conversion factors for each day in May on a daily scale to obtain the first conversion factor for May on a monthly scale.

[0080] Step 140: Based on the observed evaporation of the first evaporator, the observed evaporation of the second evaporator, and the observed evaporation of the third evaporator at the monthly scale of the second period, calculate the first conversion factor and the second conversion factor at the monthly scale of the second period, respectively.

[0081] Specifically, the first evaporator observed evaporation on a monthly scale in the second period refers to the first evaporator observed evaporation for each month in the second period. This can be achieved by summing the daily first evaporator observed evaporations to obtain the monthly first evaporator observed evaporation. The second and third evaporator observed evaporations on a monthly scale in the second period can be obtained using either a weighing method or a probe method.

[0082] The first conversion factor at the monthly scale for the second period can be calculated based on the ratio of the observed evaporation of the third evaporator to the observed evaporation of the first evaporator at the monthly scale. The second conversion factor at the monthly scale for the second period can be calculated based on the ratio of the observed evaporation of the third evaporator to the observed evaporation of the second evaporator at the monthly scale.

[0083] Step 150: Determine the target coefficient based on the first and second conversion factors on the daily scale of the first period, and the first and second conversion factors on the monthly scale of the first and second periods, respectively.

[0084] Specifically, such as Figure 2 As shown, step 150 includes the following:

[0085] Step 1501: Determine the first change range of the first conversion factor on the adjacent daily scale, the second change range of the second conversion factor on the adjacent daily scale, the third change range of the first conversion factor on the adjacent monthly scale, and the fourth change range of the second conversion factor on the adjacent monthly scale within a preset time period.

[0086] Step 1502: Based on the first and third change magnitudes, determine the first conversion factor on the monthly scale as one of the target factors;

[0087] Step 1503: Based on the second and fourth change magnitudes, determine the second conversion factor on the monthly scale as another of the target factors.

[0088] Specifically, there are two target coefficients. The first change range and the third change range within a preset time period can be compared to determine if the third change range is smaller than the first change range, or if the average change range of the third change range is smaller than the average change range of the first change range. Thus, the first conversion coefficient on the monthly scale is determined as one of the target coefficients, and correspondingly, the second conversion coefficient on the monthly scale is determined as the other target coefficient.

[0089] Step 160: When the first and second conversion factors on the monthly scale are used as the target factors, determine the period in which the current time falls;

[0090] Specifically, to obtain the evaporation rate observed by the evaporator at the current time, one can first determine whether the current time is in the first period or the second period. If it is in the first period, subsequent calculations can be performed using the first and second conversion factors on the monthly scale corresponding to the first period; if it is in the second period, subsequent calculations can be performed using the first and second conversion factors on the monthly scale corresponding to the second period.

[0091] Step 170: Calculate the first lake surface evaporation set corresponding to the current time based on the first conversion factor on the monthly scale corresponding to the period and the first evaporator observation evaporation at the current time; wherein, the first lake surface evaporation set includes the lake surface evaporation on the first day, the lake surface evaporation on the first month, and the lake surface evaporation on the first year;

[0092] Specifically, such as Figure 3 As shown, step 170 includes the following steps:

[0093] Step 1701: Calculate the lake surface evaporation on the first day by multiplying the first conversion factor on the monthly scale of the month in which the current time is located with the first evaporator observation evaporation at the current time.

[0094] Step 1702: Calculate the lake surface evaporation for the first month by multiplying the first conversion factor on the monthly scale of the current month with the first evaporator observation evaporation for the current month.

[0095] Step 1703: Based on the current year, add up the evaporation of the lake surface in the first month of each month in that year to obtain the annual evaporation of the first lake surface.

[0096] Therefore, by using the above three steps, the daily, monthly, and annual lake surface evaporation rates corresponding to the current time can be calculated. The first, second, and third steps are used here to distinguish them from the subsequent calculations using the second conversion factor.

[0097] Step 180: Calculate the second lake surface evaporation set corresponding to the current time based on the second conversion factor on the monthly scale corresponding to the period and the second evaporator observation evaporation at the current time; wherein, the second lake surface evaporation set includes the lake surface evaporation on the second day, the lake surface evaporation on the second month, and the lake surface evaporation on the second year;

[0098] Specifically, such as Figure 4 As shown, step 180 includes the following steps:

[0099] Step 1801: Calculate the lake surface evaporation on the second day by multiplying the second conversion factor on the monthly scale of the current month with the second evaporator observation evaporation at the current time.

[0100] Step 1802: Calculate the lake surface evaporation for the second month by multiplying the second conversion factor on the monthly scale of the current month with the second evaporator observation evaporation for the current month.

[0101] Step 1803: Based on the current year, add up the evaporation of the lake surface in the second month of each month within that year to obtain the second annual evaporation of the lake surface.

[0102] Step 190: Based on the set indicators and the evaporation observed by the third evaporator at the current time, evaluate the lake surface evaporation on the first day, the lake surface evaporation in the first month, and the lake surface evaporation in the first year, as well as the lake surface evaporation on the second day, the lake surface evaporation in the second month, and the lake surface evaporation in the second year, and determine the final data representing the lake surface evaporation.

[0103] Specifically, such as Figure 5 As shown, step 190 includes the following steps:

[0104] Step 1901: Calculate the determination coefficient, root mean square error, average deviation error, and average absolute error of the lake surface evaporation on the first day, the lake surface evaporation in the first month, and the lake surface evaporation in the first year, respectively, relative to the evaporation observed by the third evaporator, and obtain the first result;

[0105] Step 1902: Calculate the determination coefficient, root mean square error, average deviation error, and average absolute error of the lake surface evaporation on the second day, the lake surface evaporation in the second month, and the lake surface evaporation in the second year, respectively, relative to the evaporation observed by the third evaporator, and obtain the second result;

[0106] Step 1903: Based on the first and second results, determine the final data representing the lake surface evaporation.

[0107] The formulas for calculating the coefficient of determination (R²), root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) are shown below:

[0108]

[0109] Where, x i The evaporation observed by the third evaporator is xi', which is the daily lake surface evaporation sequence composed of the lake surface evaporation on the second day of the first period and the lake surface evaporation on the first day of the second period. To observe the average evaporation rate, by The calculation shows that n is the total number of samples.

[0110] For example, when xi' represents the lake surface evaporation on the first day, we can obtain n lake surface evaporation values ​​for the first day. Then, we can obtain the daily evaporation values ​​observed by the third evaporator for these n dates. We can then calculate a set of coefficients of determination, root mean square (RMS), mean deviation error, and mean absolute error. Correspondingly, we can obtain a set of coefficients of determination, RMS, mean deviation error, and mean absolute error for the lake surface evaporation in the first month, the first year, the second day, the second month, and the second year. Thus, we can determine the final data representing the lake surface evaporation. A higher R² and lower RMSE, MAE, and MBE indicate better accuracy in estimating LSE.

[0111] Therefore, based on the above four evaluation parameters, it can be determined that on daily and monthly scales, the coefficient of determination of the second result is greater than that of the first result, and the root mean square error, mean deviation error, and mean absolute error of the second result are less than those of the first result. On an annual scale, the coefficient of determination of the first result is greater than that of the second result, and the root mean square error, mean deviation error, and mean absolute error of the first result are less than those of the second result. Since this application aims to obtain high-resolution lake surface evaporation over the entire year, the smaller the time scale, the higher the resolution of the obtained lake surface evaporation. Therefore, this application can use the lake surface evaporation of the second day as the final data representing lake surface evaporation in the first period, and use the lake surface evaporation of the first day as the final data representing lake surface evaporation in the second period.

[0112] For example, in one instance, the lake surface evaporation estimated using the second conversion factor is more accurate on daily and monthly scales; while on an annual scale, the lake surface evaporation estimated using the first conversion factor is more accurate. However, since the primary objective of this application is to obtain high-resolution (daily scale) lake surface evaporation throughout the entire year (including both frozen and unfrozen periods), it can be determined here that when daily observations are available for E-E601, the lake surface evaporation for the second day is estimated using the second conversion factor, while when there are no daily observations for E-E601, the lake surface evaporation for the corresponding first day is estimated using the first conversion factor. By combining the two methods, full-time daily-scale lake surface evaporation can be obtained.

[0113] Furthermore, such as Figure 6 As shown, this application also includes:

[0114] Step 610: Obtain meteorological elements on a daily scale; meteorological elements include daily average air temperature, daily average lake surface water temperature, daily precipitation, daily actual water vapor pressure, daily average relative humidity, daily saturated water vapor pressure difference, and daily water temperature difference.

[0115] Among them, according to Calculate the daily saturated vapor pressure; e s T is the daily saturated vapor pressure. a The average daily temperature;

[0116] according to Calculate the daily average relative humidity; e a To actually observe water vapor pressure;

[0117] According to VPD=e s -e a Calculate the daily saturated vapor pressure difference;

[0118] The daily water-temperature difference is obtained by comparing the daily average lake surface water temperature with the daily average air temperature.

[0119] Step 620: The daily average air temperature, daily average lake surface water temperature, daily precipitation, daily actual water vapor pressure, daily average relative humidity, daily saturated water vapor pressure difference, and daily water temperature difference are used as independent variables. The lake surface evaporation on the second day of the first period and the lake surface evaporation on the first day of the second period are used as initial dependent variables. The independent variables and initial dependent variables are divided into training sets and test sets, and then fed into the Random Forest Regression (RFRM) model. The parameters of the RFRM are adjusted, and the RFRM is trained to obtain the trained RFRM.

[0120] Step 630: Substitute the independent variables of the test set into the trained RFRM to obtain the target dependent variable;

[0121] Step 640: Compare the target dependent variable with the evaporation observed by the third evaporator on a daily scale to determine whether the trained RFRM meets the requirements;

[0122] Step 650: When the trained RFRM meets the requirements, based on the output of the RFRM, determine the degree of influence of each meteorological element on the second day's lake surface evaporation in the first period combined with the first day's lake surface evaporation in the second period.

[0123] Among them, the lake surface evaporation on the second day of the first period and the lake surface evaporation on the first day of the second period constitute a complete daily lake surface evaporation sequence within the preset time period.

[0124] Specifically, in this application, 70% of the data samples are used as the training set and 30% as the test set. The 70% of the samples are used to train the regression relationship between daily lake surface evaporation and different meteorological factors. The parameters of the RFRM are continuously adjusted, such as the number of nodes and leaves; for example, the number of decision trees and the minimum number of leaf nodes are set to 1000 and 2 respectively, to train the RFRM and obtain the trained RFRM. Based on this, the independent variables of the remaining 30% of the data are substituted into the trained RFRM to obtain the target dependent variable. This target dependent variable is then compared and validated with the daily evaporation observed by the third evaporator. The results show that the R², RMSE, MAE, and MBE of the target dependent variable compared to the daily evaporation observed by the third evaporator are 0.38, 0.85 mm / d, 0.64 mm / d, and 0.0308 mm / d, respectively. This indicates that the LSE predicted by the trained RFRM has high accuracy. Therefore, based on the trained RFRM, the relative importance of each meteorological factor in different meteorological elements to the daily lake surface evaporation can be directly output. The relative importance can also be called the degree of influence, which can be expressed by specific values. The larger the value, the greater the degree of influence.

[0125] Furthermore, this application may also:

[0126] The meteorological elements and the final estimated lake surface evaporation on the second day of the first period were combined with the lake surface evaporation on the first day of the second period. Specifically, the data preprocessing and analysis were carried out using the coefficient of variation (Cv), Pearson correlation analysis, and moving average filtering.

[0127] Cv describes the dispersion of a variable; a higher value indicates greater fluctuation over time. σ and μ represent the standard deviation and mean, respectively. Cv can be used to analyze the fluctuations of meteorological elements (such as Ta, Tw, and RH), evaporation, and daily and monthly conversion factors.

[0128]

[0129] Pearson correlation analysis measures the linear correlation between two variables. The closer the correlation coefficient (r) is to 1 or -1, the stronger the linear relationship between the two variables. p < 0.05, p < 0.01, and p < 0.001 indicate a significant correlation between the variables at the 0.05, 0.01, and 0.001 levels, respectively. This application primarily uses Pearson correlation analysis to examine the correlation between daily meteorological elements and LSE.

[0130] Moving average filtering typically smooths data by applying an arithmetic or weighted average to the data sequence over a moving window. Specifically, this method slides the data sequence across a fixed window of length L. During this process, older data before the window is removed, and new data after the window is added, with each slide sampling interval. Simultaneously, the L data points within each moving window are arithmetically averaged, resulting in a filtered dataset. This method was applied to raw daily meteorological data to better reveal their interannual trends. Considering the significant seasonal variations in meteorological elements, the moving window and averaging step size were set to 365 days and 1, respectively.

[0131] We can further divide the nodal purity increase index of each predictor variable by the sum of the nodal purity increase indices of all predictor variables. Then, convert the results to percentages to visually compare the relative importance of different meteorological elements to LSE changes.

[0132] Further analysis of the interannual trends of air temperature, actual vapor pressure, saturated vapor pressure, and evaporation reveals that a significant decrease in air temperature from 2009 to 2018 led to a decrease in saturated vapor pressure (which is positively correlated with air temperature), while actual vapor pressure increased. Consequently, the saturated vapor pressure differential (VPD) (i.e., saturated vapor pressure es - actual vapor pressure ea) decreased. Evaporation showed a non-significant decrease from 2009 to 2018. This indicates that the increase in actual vapor pressure ea and the decrease in saturated vapor pressure es due to the decrease in air temperature (Ta) are the main factors causing the decrease in saturated vapor pressure differential (VPD), which in turn led to a downward trend in daily lake surface evaporation (LSE).

[0133] Consistent with the results of simple Pearson linear correlation analysis, the saturated vapor pressure difference (VPD) plays the most dominant role in the daily variation of lake surface evaporation (contribution rate of 26.2%), followed by the water-air temperature difference (contribution rate of 24.3%), while precipitation has the least impact (contribution rate of less than 10%).

[0134] Therefore, this application quantifies the key meteorological factors affecting high-altitude lakes and further verifies the accuracy of daily lake surface evaporation obtained through the conversion factor using RFRM. By applying the lake surface evaporation processing method based on dynamic conversion factors of multiple evaporator models provided in this invention embodiment, during the non-freezing period, LSE can be calculated using the monthly evaporation conversion factor (E). -20m2 / E -E601 Multiply by daily E -E601 This can be used to estimate well; during the freezing period, the monthly conversion factor (E) can be used. -20m 2 / E -20cm Multiply by daily E -20cm The estimated daily LSE shows a slight decrease, but significant fluctuations, mainly controlled by a decrease in the saturated vapor pressure difference. This is a result of the combined effect of an increase in actual vapor pressure and a decrease in saturated vapor pressure. This application uses multi-source daily evaporation observations and dynamic conversion factors to quantify the daily evaporation of alpine lakes throughout the year, including both the non-freezing and freezing periods, with high accuracy. Furthermore, this application can quantify the meteorological factors affecting the daily evaporation of alpine lakes and further verifies the accuracy of the daily lake surface evaporation estimated based on dynamic conversion factors through RFRM.

[0135] Embodiment 2 of the invention provides a device including a memory and a processor. The memory is used to store a program and can be connected to the processor via a bus. The memory can be a non-volatile memory, such as a hard disk drive or flash memory. The memory stores a software program and a device driver. The software program is capable of performing various functions of the methods provided in the embodiments of the invention. The device driver can be a network and interface driver. The processor is used to execute the software program, which, when executed, can implement the methods provided in Embodiment 1 of the invention.

[0136] Embodiment 3 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 in Embodiment 1 of the present invention.

[0137] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method provided in Embodiment 1 of the present invention.

[0138] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0139] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main 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 known in the art.

[0140] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment 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 within the scope of protection of the present invention.

Claims

1. A method for processing lake surface evaporation based on dynamic conversion coefficients for multiple evaporator models, characterized in that, The method includes: The observed evaporation amounts of the first evaporator, the second evaporator, and the third evaporator are obtained within a preset time period. The observed evaporation amounts of the first evaporator include the observed evaporation amounts of the first period and the second period, the observed evaporation amounts of the second evaporator include the observed evaporation amounts of the first period, and the observed evaporation amounts of the third evaporator include the observed evaporation amounts of the first period. Based on the observed evaporation from the first evaporator and the observed evaporation from the third evaporator, calculate the first conversion factor on the daily scale for the first period. Based on the observed evaporation from the second evaporator and the observed evaporation from the third evaporator, calculate the second conversion factor on the daily scale for the first period. Based on the first and second conversion factors on the daily scale of the first period, calculate the first and second conversion factors on the monthly scale of the first period respectively; Based on the observed evaporation of the first evaporator, the observed evaporation of the second evaporator, and the observed evaporation of the third evaporator at the second period monthly scale, the first conversion factor and the second conversion factor at the second period monthly scale are calculated respectively. The target coefficient is determined based on the first and second conversion factors on the daily scale in the first period, and the first and second conversion factors on the monthly scale in the first and second periods, respectively. When the first and second conversion factors on the monthly scale are used as target factors, the period in which the current time falls is determined. Based on the first conversion factor on the monthly scale corresponding to the period and the first evaporator observation evaporation at the current time, calculate the first lake surface evaporation set corresponding to the current time; wherein, the first lake surface evaporation set includes the lake surface evaporation of the first day, the lake surface evaporation of the first month, and the lake surface evaporation of the first year; Based on the second conversion factor on the monthly scale corresponding to the period and the second evaporator observation evaporation at the current time, calculate the second lake surface evaporation set corresponding to the current time; wherein, the second lake surface evaporation set includes the lake surface evaporation of the second day, the lake surface evaporation of the second month, and the lake surface evaporation of the second year; Based on the set indicators and the evaporation observed by the third evaporator at the current time, the evaporation of the lake surface on the first day, the lake surface evaporation in the first month, and the lake surface evaporation in the first year, as well as the evaporation of the lake surface on the second day, the lake surface evaporation in the second month, and the lake surface evaporation in the second year, are evaluated to determine the final data representing the lake surface evaporation.

2. The method according to claim 1, characterized in that, Based on the observed evaporation rates of the first evaporator and the third evaporator, a first conversion factor on a daily scale for the first period is calculated, specifically including: The first conversion factor is calculated daily based on the ratio of the daily observed evaporation of the third evaporator to the corresponding observed evaporation of the first evaporator. Within a preset time period, the average of the first conversion factor for the same date each year is calculated to obtain the first conversion factor for each day on a daily scale.

3. The method according to claim 1, characterized in that, The calculation of the second conversion factor on a daily scale for the first period based on the evaporation observed by the second evaporator and the evaporation observed by the third evaporator specifically includes: The second conversion factor is calculated daily based on the ratio of the daily observed evaporation amount of the third evaporator to the corresponding observed evaporation amount of the second evaporator. Within a preset time period, the average of multiple second conversion factors for the same date each year is calculated to obtain the second conversion factor for each day on a daily scale.

4. The method according to claim 1, characterized in that, The calculation of the first and second conversion factors on the monthly scale based on the first and second conversion factors on the daily scale of the first period specifically includes: Within a preset time period, the average of multiple first conversion factors on a daily scale within the same month of each year is calculated to obtain the first conversion factor on a monthly scale for each month. Within a preset timeframe, the average of multiple second conversion factors on a daily scale within the same month of each year is calculated to obtain the second conversion factor on a monthly scale for each month; and, Based on the observed evaporation rates of the first, second, and third evaporators at the second-period monthly scale, the calculation of the first and second conversion factors at the second-period monthly scale specifically includes: The first conversion factor on the monthly scale of the second period is calculated based on the ratio of the evaporation observed by the third evaporator on the monthly scale to the evaporation observed by the first evaporator on the monthly scale. The second conversion factor on the second-period monthly scale is calculated based on the ratio of the observed evaporation of the third evaporator on the second-period monthly scale to the observed evaporation of the first evaporator on the second-period monthly scale.

5. The method according to claim 1, characterized in that, The determination of the target coefficient based on the first and second conversion factors on a daily scale in the first period, and the first and second conversion factors on a monthly scale in the first and second periods respectively, specifically includes: Within a preset time period, determine the first change range of the first conversion factor on the adjacent daily scale, the second change range of the second conversion factor on the adjacent daily scale, the third change range of the first conversion factor on the adjacent monthly scale, and the fourth change range of the second conversion factor on the adjacent monthly scale. Based on the first change magnitude and the third change magnitude, the first conversion factor on the monthly scale is determined as one of the target factors; Based on the second and fourth magnitudes of change, the second conversion factor on a monthly scale is determined to be another of the target factors.

6. The method according to claim 1, characterized in that, The step of calculating the first lake surface evaporation set corresponding to the current time based on the first conversion factor on the monthly scale corresponding to the period and the first evaporator observation evaporation at the current time specifically includes: The lake surface evaporation on the first day is calculated by multiplying the first conversion factor on the monthly scale of the current month with the first evaporator observation evaporation at the current time. The lake surface evaporation for the first month is calculated by multiplying the first conversion factor on the monthly scale of the current month with the first evaporator observation evaporation for the current month. Based on the current year, add up the lake's evaporation in the first month of each month within that year to obtain the first year's evaporation; and... Based on the second reduction factor on the monthly scale corresponding to the aforementioned period and the second evaporator observation evaporation at the current time, the calculation of the second lake surface evaporation set corresponding to the current time specifically includes: The lake surface evaporation on the second day is calculated by multiplying the second conversion factor on the monthly scale of the current month with the second evaporator observation evaporation at the current time. The lake surface evaporation for the second month is calculated by multiplying the second conversion factor on the monthly scale of the current month with the second evaporator observation evaporation for the current month. Based on the current year, add up the evaporation of the lake surface in the second month of each month within that year to obtain the second annual evaporation of the lake surface.

7. The method according to claim 1, characterized in that, The process of evaluating the lake surface evaporation on the first day, the first month, and the first year, as well as the lake surface evaporation on the second day, the second month, and the second year, based on the set indicators and the evaporation observed by the third evaporator at the current time, and determining the final data representing the lake surface evaporation, specifically includes: The determination coefficients, root mean square errors, average deviation errors, and average absolute errors of the lake surface evaporation on the first day, the first month, and the first year, respectively, are compared with the evaporation observed by the third evaporator to obtain the first result. The determination coefficients, root mean square errors, average deviation errors, and average absolute errors of the lake surface evaporation on the second day, the second month, and the second year, respectively, compared with the evaporation observed by the third evaporator, are calculated to obtain the second result. Based on the first and second results, the final data representing lake surface evaporation is determined; wherein, on daily and monthly scales, the coefficient of determination of the second result is greater than that of the first result, and the root mean square error, mean deviation error, and mean absolute error of the second result are less than those of the first result; on an annual scale, the coefficient of determination of the first result is greater than that of the second result, and the root mean square error, mean deviation error, and mean absolute error of the first result are less than those of the second result; to obtain high-resolution lake surface evaporation for the entire year, in the first period, the lake surface evaporation of the second day is used as the final data representing lake surface evaporation, and in the second period, the lake surface evaporation of the first day is used as the final data representing lake surface evaporation.

8. The method according to claim 7, characterized in that, The method further includes: Acquire meteorological elements on a daily scale; the meteorological elements include daily average air temperature, daily average lake surface water temperature, daily precipitation, daily actual water vapor pressure, daily average relative humidity, daily saturated water vapor pressure difference, and daily water temperature difference; The daily average air temperature, daily average lake surface water temperature, daily precipitation, daily actual water vapor pressure, daily average relative humidity, daily saturated water vapor pressure difference, and daily water temperature difference are used as independent variables. The lake surface evaporation on the second day of the first period and the lake surface evaporation on the first day of the second period are used as initial dependent variables. The independent variables and initial dependent variables are divided into training sets and test sets, and then fed into the Random Forest Regression Model (RFRM). The parameters of the RFRM are adjusted, and the RFRM is trained to obtain the trained RFRM. Substitute the independent variables of the test set into the trained RFRM to obtain the target dependent variable; The target dependent variable is compared with the evaporation observed by the third evaporator on the diurnal scale to determine whether the trained RFRM meets the requirements. When the trained RFRM meets the requirements, based on the output of the RFRM, determine the degree of influence of each meteorological element on the second day's lake surface evaporation in the first period combined with the first day's lake surface evaporation in the second period.

9. The method according to claim 8, characterized in that, according to Calculate the daily saturated vapor pressure; the e s The daily saturated vapor pressure, T a The average daily temperature; according to Calculate the daily average relative humidity; e a To actually observe water vapor pressure; According to VPD=e s -e a Calculate the daily saturated vapor pressure difference; The daily water-temperature difference is obtained by comparing the daily average lake surface water temperature with the daily average air temperature.

10. A device comprising a memory and a processor, characterized in that, The memory is used to store a program, and the processor is used to execute the method according to any one of claims 1-9.

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