A method for calculating ecosystem water and heat fluxes based on daily flux variance
Through the daily flux variance method, multi-station data are used to establish a linear relationship between temperature standard deviation and friction wind speed. Combined with the energy balance equation, the problem of flux variance method relying on atmospheric stability is solved, and low-cost ecosystem water and heat flux calculation is achieved, which is suitable for long-term observations such as meteorological stations.
Patent Information
- Application Number
- CN202511084151.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-04
- Publication Date
- 2025-09-26
- Estimated Expiration
- 2045-08-04
AI Technical Summary
The existing flux variance method relies on atmospheric stability parameters when calculating ecosystem water and heat flux and lacks universality. It is difficult to obtain accurate sensible heat flux and latent heat flux on a long time scale, and the equipment and maintenance costs are high.
The daily flux variance method is used to establish a linear relationship between the product of temperature standard deviation and friction wind speed and sensible heat flux through multi-station data. The water and heat flux of the ecosystem is calculated by combining low-frequency observed temperature and energy balance equation, avoiding the use of atmospheric stability parameters.
It realizes the calculation of water and heat flux independent of the eddy covariance system on a daily scale, reduces equipment and maintenance costs, and obtains universal parameters of flux variance, which is suitable for large-area and long-term observations.
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Figure CN120578863B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of ecosystem water and heat flux calculation, and in particular to a method for calculating ecosystem water and heat flux based on daily-scale flux variance. Background Art
[0002] Surface heat flux (sensible and latent) is a crucial parameter for understanding interactions between land and atmosphere, as well as mass and energy transfer processes. Accurately estimating surface heat flux is crucial for studying water management and energy allocation in ecosystems, particularly agricultural ecosystems.
[0003] The eddy covariance method is recognized as a standard measurement method for directly observing sensible heat flux and latent heat flux. However, this technology has disadvantages such as expensive equipment, high station construction and maintenance costs, and complex data post-processing, which leads to limited observation sites.
[0004] The flux variance method indirectly calculates sensible and latent heat fluxes based on the Monin-Obukhov similarity theory. Calculations require only the standard deviation of high-frequency scalar observations (temperature and water vapor). Consequently, the method requires simple equipment, low maintenance costs, and relatively simple data processing, making it suitable for widespread adoption. While the flux variance method has been widely used to calculate water and heat fluxes in various ecosystems, several challenges remain. Calculating sensible and latent heat fluxes using the flux variance method requires first clarifying the similarity relationship between the flux and the variance of temperature or water vapor and obtaining similarity parameters. Establishing this similarity relationship relies on atmospheric stability parameters, which are primarily derived from three-dimensional ultrasonic observations. Therefore, the current flux variance method for calculating sensible and latent heat fluxes cannot completely escape the eddy covariance system, and the similarity parameters used in the flux variance method are not universally applicable. In addition, some studies have found that only under unstable conditions do the flux and observed scalar variances have a good similarity relationship. Currently, most of the research on the flux variance method also focuses on unstable atmospheric conditions, which makes it difficult for this method to obtain sensible heat flux and latent heat flux on a long time scale.
[0005] Therefore, it is of great practical significance to develop a method that can avoid the use of atmospheric stability parameters and obtain universal parameters of flux variance to calculate ecosystem water and heat fluxes. Summary of the Invention
[0006] In view of the above shortcomings of the prior art, the present invention provides a method for calculating ecosystem water and heat flux based on daily flux variance.
[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:
[0008] The method for calculating ecosystem water and heat flux based on daily flux variance includes the following steps:
[0009] S1. Collection of multi-station flux and meteorological data;
[0010] S2. Perform step S2-1 or step S2-2;
[0011] S2-1. Use multi-station data to establish a linear relationship between the product of the daytime and nighttime temperature standard deviations and friction wind speed and the sensible heat flux observed by eddy covariance. Combine the linear parameters, temperature standard deviation, and friction wind speed to obtain the daytime and nighttime sensible heat flux of a single station, and then calculate the daily scale sensible heat flux. ;
[0012] S2-2. Use multi-station data to directly establish a linear relationship between the product of temperature standard deviation and friction wind speed and sensible heat flux. Combine the linear parameters, temperature standard deviation and friction wind speed to directly calculate the daily sensible heat flux of a single station. ;
[0013] S3. Calculate the latent heat flux using the following formula:
[0014]
[0015] Where, is the daily latent heat flux at a single site, is the daily net radiation of a single site, is the daily soil heat flux at a single site.
[0016] Furthermore, in step S1, flux data, meteorological data and other basic data are collected from multiple ecosystem sites with simple canopy structures and uniform underlying surfaces.
[0017] Furthermore, the flux data includes sensible heat flux and latent heat flux.
[0018] Furthermore, the meteorological data are meteorological half-hourly scale data, including soil heat flux, net radiation, air temperature, wind speed and air density.
[0019] Furthermore, other basic data include instrument observation altitude and underlying surface canopy height.
[0020] Furthermore, step S2-1 specifically includes the following steps:
[0021] S2-11. On a half-hourly scale, the sensible heat flux and meteorological data from multi-station eddy covariance observations are divided into daytime and nighttime groups based on net radiation. The net radiation of the daytime group is R n >0, net radiation of night group R n <0, and calculate the average;
[0022] S2-12. Based on the data from all stations, establish the linear relationship between the product of the daytime and nighttime temperature standard deviations and the frictional wind speed and the sensible heat flux, and obtain the linear parameters. The formula is as follows:
[0023]
[0024]
[0025] In the formula, the subscript and represent the average values of each variable during the day and night, is the average sensible heat flux observed during the day (W m -2 ), is the average sensible heat flux of nighttime eddy covariance observations, is the average air density during the day (kg m -3 ), is the average air density at night, is the specific heat capacity at constant pressure (J kg -1 K -1 ), and are the linear parameters for daytime and nighttime, is the mean standard deviation of low-frequency temperature observed in the daytime microclimate system (K), is the mean standard deviation of low-frequency temperature observed in the nighttime microclimate system, is the average friction wind speed obtained based on the wind speed profile formula under neutral stratification during the day (ms -1 ), It is the average friction wind speed at night obtained based on the wind speed profile formula under neutral stratification.
[0026] S2-13, linear parameters and Substitute the following formulas to calculate the average daytime and nighttime sensible heat fluxes at a single station, and then calculate the daily sensible heat flux based on the duration ratio:
[0027]
[0028]
[0029]
[0030] Where, and are the average sensible heat fluxes calculated for the day and night at a single station, is the daily sensible heat flux at a single site, and are the ratios of daytime and nighttime duration, Indicates different sites; is the average daytime air density of a single site (kg m -3 ), is the average nighttime air density of a single station, is the mean standard deviation of low-frequency temperature observed in the daytime microclimate system at a single station (K), is the mean standard deviation of low-frequency temperature observed in the nighttime microclimate system at a single station, is the average friction wind speed (ms) obtained based on the wind speed profile formula under neutral stratification during the daytime at a single station. -1 ), It is the average friction wind speed at a single station at night obtained based on the wind speed profile formula under neutral stratification.
[0031] Furthermore, in step S2-12, the sensible heat flux of the eddy covariance observation is pre-measured. Perform energy balance closure correction.
[0032] Furthermore, the sensible heat flux of eddy covariance observations Perform energy balance closure correction as follows:
[0033] Assuming the Bowen ratio is accurate, the remaining energy term is redistributed to the sensible heat flux and latent heat flux using the Bowen ratio:
[0034]
[0035] Where, For Bowenbi, is the sensible heat flux, is the latent heat flux;
[0036]
[0037] Where, is the latent heat flux after energy closure correction, is the net radiation, is the soil heat flux;
[0038]
[0039] Where, is the sensible heat flux after energy closure correction.
[0040] Furthermore, in step S2-12, the friction wind speed , calculated based on the wind speed profile formula under neutral stratification:
[0041]
[0042] Where, is the mean wind speed observed for the microclimate system; is the von Karman constant; is the observation height; is zero plane displacement; is the roughness length.
[0043] Furthermore, step S2-2 specifically includes the following steps:
[0044] S2-21. Establish a linear relationship between the product of the daily temperature standard deviation and the friction wind speed and the sensible heat flux to obtain the linear parameters. The formula is as follows:
[0045]
[0046] Where, subscript a represents the daily average value, is the daily scale linear parameter;
[0047] is the daily average sensible heat flux from eddy covariance observations, is the average daytime air density (kgm -3 ), is the daily average low-frequency temperature standard deviation of the microclimate system observations, is the daily average friction wind speed obtained based on the wind speed profile formula under neutral stratification;
[0048] S2-22. Substitute the linear parameter C into the following formula to calculate the daily sensible heat flux at a single station:
[0049]
[0050] Where, is the daily sensible heat flux at a single site, is the daily average daytime air density at a single site (kg m -3 ), is the standard deviation of the low-frequency temperature observed in the microclimate system at a single station on a daily basis, It is the friction wind speed of a single site averaged daily based on the wind speed profile formula under neutral stratification.
[0051] Compared with existing technologies, the present invention has the following advantages: based on the traditional flux variance method, the present invention upgrades the half-hourly scale to the daily scale, combines low-frequency temperature observations to estimate sensible heat flux, and then combines the energy balance equation to calculate latent heat flux. The advantages of the improved flux variance method are: it avoids the use of atmospheric stability parameters, that is, there is no need to consider atmospheric stability issues on the daily scale, ensuring that the method is completely independent of the eddy covariance system, greatly reducing equipment and maintenance costs. At the same time, the calculation principle of daily-scale flux variance is simple, and universal flux variance parameters can be obtained based on multi-station data, making it suitable for large-scale promotion at sites such as meteorological stations that require long-term observations. BRIEF DESCRIPTION OF THE DRAWINGS
[0052] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.
[0053] Figure 1 The flowchart of the method for calculating ecosystem water and heat flux based on daily flux variance of the present invention. DETAILED DESCRIPTION
[0054] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.
[0055] Example 1: In some embodiments, please refer to the accompanying drawings of the specification. Figure 1 The method for calculating ecosystem water and heat flux based on daily flux variance includes the following steps:
[0056] S1. Collect flux data, meteorological data, and other basic data from multiple ecosystem sites with simple canopy structures and relatively uniform underlying surfaces;
[0057] Flux data include half-hourly sensible and latent heat flux data. Meteorological data are half-hourly data, including soil heat flux, net radiation, temperature, wind speed, and air density. Other basic data include instrument observation altitude and underlying canopy height. Flux data were quality-controlled in advance, and no interpolation was performed to ensure the accuracy of the calculation results.
[0058] S2-1. Use multi-station data to establish a linear relationship between the product of the daytime and nighttime temperature standard deviations and frictional wind speeds and the sensible heat flux observed by eddy covariance. Combine the linear parameters, temperature standard deviations, and frictional wind speeds to obtain the daytime and nighttime sensible heat fluxes of a single station, and then calculate the daily-scale sensible heat flux. Figure 1 M1 in the code specifically includes the following steps:
[0059] S2-11. On a half-hourly scale, the sensible heat flux and meteorological data from multi-station eddy covariance observations are divided into daytime and nighttime groups based on net radiation. The net radiation of the daytime group is R n >0, net radiation of night group R n <0, and calculate the average;
[0060] S2-12. Based on the data from all stations, a linear relationship is established between the product of the daytime and nighttime temperature standard deviations and the frictional wind speed and the sensible heat flux, and then a universal linear parameter is obtained. The formula is as follows:
[0061]
[0062]
[0063] In the formula, the subscript and represent the average values of each variable during the day and night, is the average sensible heat flux observed during the day (W m -2 ), is the average sensible heat flux of nighttime eddy covariance observations, is the average air density during the day (kg m -3 ), is the average air density at night, is the specific heat capacity at constant pressure (J kg -1 K -1 ), and are the linear parameters for daytime and nighttime, is the mean standard deviation of low-frequency temperature observed in the daytime microclimate system (K), is the mean standard deviation of low-frequency temperature observed in the nighttime microclimate system, is the average friction wind speed obtained based on the wind speed profile formula under neutral stratification during the day (ms -1 ), It is the average friction wind speed at night obtained based on the wind speed profile formula under neutral stratification.
[0064] In step S2-12, due to the energy non-closure problem of eddy covariance observation, the sensible heat flux of eddy covariance observation is pre-calculated. Perform energy balance closure correction. Specifically, it is usually assumed that the Bowen ratio is accurate, and the remaining energy term is redistributed to the sensible heat flux and latent heat flux using the Bowen ratio:
[0065]
[0066] Where, For Bowenbi, is the sensible heat flux, is the latent heat flux;
[0067]
[0068] Where, is the latent heat flux after energy closure correction, is the net radiation, is the soil heat flux;
[0069]
[0070] Where, is the sensible heat flux after energy closure correction.
[0071] In step S2-12, the friction wind speed , calculated based on the wind speed profile formula under neutral stratification:
[0072]
[0073] Where, is the average wind speed observed in the microclimate system (ms -1 ); is the von Karman constant (0.4); is the observation height; For zero plane displacement, take 2 / 3 of the canopy height according to the empirical relationship; is the roughness length, which is taken as 0.1 times of the canopy height according to the empirical relationship.
[0074] S2-13, linear parameters based on single-site data and universality, linear parameters and Substitute the following formulas to calculate the average daytime and nighttime sensible heat fluxes at a single station, and then calculate the daily sensible heat flux based on the duration ratio:
[0075]
[0076]
[0077]
[0078] Where, and are the average sensible heat fluxes calculated for the day and night at a single station, is the daily sensible heat flux at a single site, and are the ratios of daytime and nighttime duration, Indicates different sites; is the average daytime air density of a single site (kg m -3 ), is the average nighttime air density of a single station, is the mean standard deviation of low-frequency temperature observed in the daytime microclimate system at a single station (K), is the mean standard deviation of low-frequency temperature observed in the nighttime microclimate system at a single station, is the average friction wind speed (ms) obtained based on the wind speed profile formula under neutral stratification during the daytime at a single station. -1 ), It is the average friction wind speed at a single station at night obtained based on the wind speed profile formula under neutral stratification.
[0079] S3. Calculate the latent heat flux using the following formula:
[0080]
[0081] Where, is the daily latent heat flux at a single site, is the daily net radiation of a single site, is the daily soil heat flux at a single site.
[0082] Example 2: A method for calculating ecosystem water and heat flux based on daily flux variance, comprising the following steps:
[0083] S1. Same as step S1 in embodiment 1. Further, the half-hourly flux and meteorological data corresponding to the date with a valid data ratio greater than 80% are selected to calculate the daily average value.
[0084] S2-2 is similar to S2-1, except that this method uses multi-station data to directly establish a linear relationship between the product of temperature standard deviation and friction wind speed and sensible heat flux, and combines the linear parameters, temperature standard deviation and friction wind speed to directly calculate the daily sensible heat flux of a single station. Figure 1 The M2 in the code specifically includes the following steps:
[0085] S2-21. Establish a linear relationship between the product of the daily temperature standard deviation and the friction wind speed and the sensible heat flux to obtain the linear parameters. The formula is as follows:
[0086]
[0087] Where, subscript a represents the daily average value, is the daily scale linear parameter; is the daily average sensible heat flux from eddy covariance observations, is the average daytime air density (kg m -3 ), is the daily average low-frequency temperature standard deviation of the microclimate system observations, is the daily average friction wind speed obtained based on the wind speed profile formula under neutral stratification.
[0088] S2-22. Substitute the linear parameter C into the following formula to calculate the daily sensible heat flux at a single station:
[0089]
[0090] Where, is the daily sensible heat flux at a single site, is the daily average daytime air density at a single site (kg m -3 ), is the standard deviation of the low-frequency temperature observed in the microclimate system at a single station on a daily basis, It is the friction wind speed of a single site averaged daily based on the wind speed profile formula under neutral stratification.
[0091] S3: Same as step S3 in embodiment 1.
[0092] This method, based on the traditional flux variance method, upgrades the half-hourly scale to a daily one. It also combines low-frequency temperature observations to estimate sensible heat flux, and then uses the energy balance equation to calculate latent heat flux. The advantages of this improved flux variance method include avoiding the use of atmospheric stability parameters, eliminating the need to consider atmospheric stability issues on the daily scale. This ensures the method's complete independence from the eddy covariance system, significantly reducing equipment and maintenance costs. Furthermore, the calculation principle for daily-scale flux variance is simple, and universal flux variance parameters can be obtained based on multi-site data, making it suitable for large-scale deployment at sites such as meteorological stations that require long-term observations.
[0093] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.
Claims
1. A method for calculating ecosystem water and heat flux based on daily flux variance, characterized in that: The following steps are involved: S1. Collection of multi-station flux and meteorological data; S2. Perform step S2-1 or step S2-2; S2-1. Use multi-station data to establish a linear relationship between the product of the daytime and nighttime temperature standard deviations and friction wind speed and the sensible heat flux observed by eddy covariance. Combine the linear parameters, temperature standard deviation, and friction wind speed to obtain the daytime and nighttime sensible heat flux of a single station, and then calculate the daily scale sensible heat flux. ; The specific steps include: S2-11. On a half-hourly scale, the sensible heat flux and meteorological data from multi-station eddy covariance observations are divided into daytime and nighttime groups based on net radiation. The net radiation of the daytime group is R n >0, net radiation of night group R n <0, and calculate the average; S2-12. Based on the data from all stations, establish the linear relationship between the product of the daytime and nighttime temperature standard deviations and the frictional wind speed and the sensible heat flux, and obtain the linear parameters. The formula is as follows: ； ； In the formula, the subscript and represent the average values of each variable during the day and night, is the average value of the sensible heat flux from the daytime eddy covariance observation, is the average sensible heat flux of nighttime eddy covariance observations, is the average air density during the day, is the average air density at night, is the specific heat capacity at constant pressure, and are the linear parameters for daytime and nighttime, is the mean standard deviation of low-frequency temperature observed in the daytime microclimate system, is the mean standard deviation of low-frequency temperature observed in the nighttime microclimate system, is the average friction wind speed obtained based on the wind speed profile formula under neutral stratification during the day, is the average friction wind speed at night obtained based on the wind speed profile formula under neutral stratification; S2-13, linear parameters and Substitute the following formulas to calculate the average daytime and nighttime sensible heat fluxes at a single station, and then calculate the daily sensible heat flux based on the duration ratio: ； ； ； Where, and are the average sensible heat fluxes calculated for the day and night at a single station, is the daily sensible heat flux at a single site, and are the ratios of daytime and nighttime duration, Indicates different sites; is the average daytime air density of a single station, is the average nighttime air density of a single station, is the mean standard deviation of low-frequency temperature observed in the daytime microclimate system at a single station, is the mean standard deviation of low-frequency temperature observed in the nighttime microclimate system at a single station, is the average friction wind speed at a single site during the daytime based on the wind speed profile formula under neutral stratification, is the average friction wind speed at a single station at night obtained based on the wind speed profile formula under neutral stratification; S2-2. Use multi-station data to directly establish a linear relationship between the product of temperature standard deviation and friction wind speed and sensible heat flux. Combine the linear parameters, temperature standard deviation and friction wind speed to directly calculate the daily sensible heat flux of a single station. ; Specifically including the following steps: S2-21. Establish a linear relationship between the product of the daily temperature standard deviation and the friction wind speed and the sensible heat flux to obtain the linear parameters. The formula is as follows: ； Where, subscript a represents the daily average value, is the daily scale linear parameter; is the daily average sensible heat flux from eddy covariance observations, is the average daytime air density, is the daily average low-frequency temperature standard deviation of the microclimate system observations, is the daily average friction wind speed obtained based on the wind speed profile formula under neutral stratification; S2-22. Substitute the linear parameter C into the following formula to calculate the daily sensible heat flux at a single station: ； Where, is the daily sensible heat flux at a single site, is the daily average daytime air density at a single station, is the standard deviation of the low-frequency temperature observed in the microclimate system at a single station on a daily basis, is the friction wind speed obtained based on the wind speed profile formula under neutral stratification for the daily average of a single station; S3. Calculate the latent heat flux using the following formula: ； Where, is the daily latent heat flux at a single site, is the daily net radiation of a single site, is the daily soil heat flux at a single site.
2. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 1, characterized in that: In step S1, flux data, meteorological data and other basic data are collected from multiple ecosystem sites with simple canopy structures and uniform underlying surfaces.
3. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 2, characterized in that: The flux data includes sensible heat flux and latent heat flux.
4. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 2, characterized in that: Meteorological data are half-hourly data, including soil heat flux, net radiation, air temperature, wind speed and air density.
5. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 2, characterized in that: Other basic data include instrument observation height and underlying surface canopy height.
6. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 5, characterized in that: In step S2-12, the sensible heat flux of the eddy covariance observation is pre-measured. Perform energy balance closure correction.
7. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 1, characterized in that: Sensible heat flux from eddy covariance observations Perform energy balance closure correction as follows: Assuming the Bowen ratio is accurate, the remaining energy term is redistributed to the sensible heat flux and latent heat flux using the Bowen ratio: ； Where, For Bowenbi, is the sensible heat flux, is the latent heat flux; ； Where, is the latent heat flux after energy closure correction, is the net radiation, is the soil heat flux; ； Where, is the sensible heat flux after energy closure correction.
8. The method for calculating ecosystem water and heat flux based on daily flux variance according to claim 1, characterized in that: In step S2-12, the friction wind speed , calculated based on the wind speed profile formula under neutral stratification: ； Where, is the mean wind speed observed for the microclimate system; is the von Karman constant; is the observation height; is zero plane displacement; is the roughness length.