Climate-driven downscaling evaluation method for carbon emission of forest marsh

Abstract

The invention belongs to the technical field of environmental science, and provides a climate-driven forest marsh carbon emission downscaling evaluation method, which comprises the following steps of: obtaining a day-by-day scale air temperature data set and a space remote sensing interpretation identification data set in a research area range; constructing a spatial-temporal scale carbon flux driving factor system of the research area; calculating the spatial-temporal scale carbon flux of the research area through a carbon flux climate-driven model of a least square method in a machine learning algorithm; calculating corresponding forest marsh types and time scale carbon flux, integrating a time range and a space area, and counting and outputting annual carbon emissions of the research area at different time scales; according to the method, the regional climate space-time sequence is used as a forest carbon emission evaluation driving factor, the problem of coupling scale of micro-viewpoint observation and macroclimate in a forest carbon emission chemical relationship is solved based on forest type scale micrometeorological monitoring and a mapping relationship between the forest type scale micrometeorological monitoring and the large-scale climate, and forest type level carbon emission climate downscaling evaluation is realized.

Description

Technical Field

[0001] The invention belongs to the field of environmental science and technology, and specifically is a climate-driven downscaling assessment method for forest swamp carbon emissions. Background Art

[0002] The climate sensitivity of forest ecosystems and their huge accumulated carbon pools are of great strategic significance for building and exerting the carbon sink function of terrestrial ecosystems in China and even the world. Facing the national "dual carbon" goals, it is extremely important and urgent to scientifically understand and accurately assess forest carbon sources / sinks.

[0003] The existing carbon source / sink and carbon budget assessment methods mainly include the sample site inventory method, eddy covariance method and model simulation method.

[0004] The data based on sample plots are accurate and reliable, and can be accurate to the forest type scale, but field surveys are time-consuming and labor-intensive; the eddy covariance method, based on the principles of micrometeorology, can achieve long-term continuous positioning of carbon flux and observe the CO2 exchange between ecosystems and the atmosphere, but its radiation range and number of sites are limited. It is more used for the response of ecosystem-scale carbon cycle to climate change and cannot be accurate to the forest type scale; model simulations based on carbon cycle processes and mechanisms have complex structures and large uncertainties. They are mainly used at regional and global macro scales and are not suitable for small and medium-scale carbon source / sink assessments.

[0005] In summary, there is an urgent need for a high-precision and efficient carbon emission quantification method that can achieve downscaling to forest type level with less time and effort, so as to provide technical support for the efficient implementation of the "carbon peak" and "carbon neutrality" goals. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a climate-driven forest swamp carbon emission downscaling assessment method to solve the problems raised in the background technology.

[0007] A climate-driven downscaling assessment method for forest swamp carbon emissions includes the following steps:

[0008] S100, obtain daily temperature data set and spatial remote sensing interpretation and identification data set within the study area;

[0009] S200, based on the daily temperature data set, construct a spatiotemporal carbon flux driving factor system for the study area;

[0010] S300, based on the spatiotemporal carbon flux driving factor system, the spatiotemporal carbon flux in the study area is calculated by using the carbon flux climate driving model of the least squares method in the machine learning algorithm;

[0011] S400: The carbon flux corresponding to the forest swamp type and time scale is calculated through the S300 model, the time range and spatial area are integrated, and the annual carbon emissions at different time scales in the study area are statistically output.

[0012] Preferably, the temperature data set is a meteorological ordered observation value, expressed as:

[0013] {x i,j}

[0014] Where i represents the month of the assessment base year, ranging from 1 to 12; j represents the date of each month of the base year, ranging from 1 to 31;

[0015] The spatial remote sensing interpretation and identification data set is expressed as:

[0016] S=S F +S G +S C

[0017] Where S represents the total area of ​​the study area in hectares; S F Represents the forest swamp area in hectares; S G Indicates the area of ​​shrub swamp in hectares; S C Indicates the area of ​​herbaceous swamps in hectares.

[0018] Preferably, the driving factor system comprises a background factor and a response factor, and both the background factor and the response factor include three time scales: year, growing season, and non-growing season, wherein the year scale is January to December, the growing season is May to September, and the non-growing season is October to April; the carbon flux background factors at different time scales are the same, and the background factor expression is:

[0019]

[0020] Where N is the number of months in the time scale T. The value of N is 12 for the annual scale, 5 for the growing season, and 7 for the non-growing season. 5a is the average of the highest temperatures on the 5th to 9th, 15th to 19th, and 25th to 29th of each month at each time scale; x 3a is the average of the maximum temperature of the 7th to 9th, 17th to 19th, and 27th to 29th day of each month at each time scale; x 1a is the average of the highest temperatures on the 9th, 19th and 29th of each month at each time scale; x 1b It is the average of the minimum temperature on the 9th, 19th and 29th of each month at each time scale;

[0021] The carbon flux response factors at different time scales are different, among which the annual scale response factor (x 5m , j3) is expressed as:

[0022]

[0023] Among them, x 5m is the average of the 5-day average temperature values ​​from 5th to 9th, 15th to 19th, and 25th to 29th of each month; j3 is the average of the 3-day cumulative values ​​of the daily average temperature from 7th to 9th, 17th to 19th, and 27th to 29th of each month;

[0024] The response factors of growing season scale and non-growing season scale (x 3b , j5) are the same and are expressed as:

[0025]

[0026] Among them, x 3b It is the average of the three-day minimum temperatures from 7th to 9th, 17th to 19th, and 27th to 29th of each month; j5 is the average of the five-day cumulative values ​​of the average daily temperature from 5th to 9th, 15th to 19th, and 25th to 29th of each month.

[0027] Preferably, the method for constructing the carbon flux climate driven model includes:

[0028] The functional relationship was constructed using a regression model built based on least squares fitting;

[0029] Based on the forest type scale, carbon emission flux and micro-meteorological monitoring are collected synchronously at a frequency of 3 times per month and 36 times per year, and a carbon emission-driven micro-meteorological factor system is constructed through screening;

[0030] A multivariate linear regression model was established with carbon emission flux as the dependent variable and microclimate factor system as the independent variable. The spatial dimensions were forest swamp, shrub swamp, and herbaceous swamp, and the temporal dimensions were year, growing season, non-growing season, spring, summer, autumn, and winter.

[0031] Using micro-meteorological factors as a medium, establish correlation coefficients between each micro-meteorological factor and large-scale climate factors;

[0032] Construct the functional relationship between climate and carbon emissions at the forest landscape scale and conduct a climate-scale assessment of carbon emissions.

[0033] Preferably, the annual scale carbon flux climate driven model is:

[0034]

[0035] Among them, f YF (x), f YG (x), f YC (x) are the annual carbon emission fluxes of forest swamp, shrub swamp and herbaceous swamp, respectively, in mg / m2 / h;

[0036] The growing season scale carbon flux climate driven model is:

[0037]

[0038] Among them, f SF (x), f SG (x), f SC (x) are the carbon emission fluxes at the growing season scale of forest swamp, shrub swamp, and herbaceous swamp, respectively, in mg / m2 / h;

[0039] The non-growing season scale carbon flux climate driving model is:

[0040]

[0041] In the formula, f NF (x), f NG (x), f NC (x) are the carbon emission fluxes at the non-growing season scale of forest swamp, shrub swamp and herbaceous swamp, respectively, in mg / m2 / h.

[0042] Preferably, the S300 model is used to calculate the carbon flux corresponding to the forest swamp type and time scale, integrate the time range and spatial area, and statistically output the annual carbon emissions at different time scales in the study area. The function expression is:

[0043]

[0044] Among them, C Y , C S , C N They are annual carbon emissions at the annual scale, growing season scale, and non-growing season scale, in tons / year; 0.0876 is the unit conversion factor.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention shows that climate has become an important driving factor of forest carbon source and sink due to the extreme sensitivity of forest ecosystem carbon balance, its spatiotemporal heterogeneity and continuity genes, and is a key response element in many ecosystem model studies. Regional climate spatiotemporal series are used as driving factors for forest carbon emission assessment. Based on micrometeorological monitoring at the forest type scale and its mapping relationship with larger-scale climate, the coupling scale problem of micro-point observations and macro-climate in the quantitative relationship of forest carbon emissions is solved. At the same time, machine learning algorithm technology is integrated to break through the previous complicated measurement experimental analysis and complex model estimation process, and realize climate downscaling assessment of carbon emissions at the forest type level. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1A flow chart of the climate-driven downscaling assessment method for forest swamp carbon emissions of the present invention;

[0048] Figure 2 This is a comparison chart of the actual carbon flux measurement in the experimental example and the simulation of the present invention;

[0049] Figure 3 It is a scatter plot of the correlation between the actual measurement of carbon flux in the experimental example and the simulation of the present invention. DETAILED DESCRIPTION

[0050] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0051] As attached Figure 1 As shown:

[0052] Embodiment: The present invention provides a climate-driven downscaling assessment method for forest swamp carbon emissions, comprising the following steps:

[0053] S100, obtain daily temperature data set and spatial remote sensing interpretation and identification data set within the study area;

[0054] S200, based on the daily temperature data set, construct a spatiotemporal carbon flux driving factor system for the study area;

[0055] S300, based on the spatiotemporal carbon flux driving factor system, the spatiotemporal carbon flux in the study area is calculated by using the carbon flux climate driving model of the least squares method in the machine learning algorithm;

[0056] S400: The carbon flux corresponding to the forest swamp type and time scale is calculated through the S300 model, the time range and spatial area are integrated, and the annual carbon emissions at different time scales in the study area are statistically output.

[0057] The temperature data set is a meteorological ordered observation value, expressed as:

[0058] {x i,j}

[0059] Where i represents the month of the assessment base year, ranging from 1 to 12; j represents the date of each month of the base year, ranging from 1 to 31;

[0060] The spatial remote sensing interpretation and identification dataset is represented as:

[0061] S=S F +S G +S C

[0062] Where S represents the total area of ​​the study area in hectares; S FRepresents the forest swamp area in hectares; S G Indicates the area of ​​shrub swamp in hectares; S C Indicates the area of ​​herbaceous swamps in hectares.

[0063] The driving factor system includes background factors and response factors. Both the background factors and the response factors include three time scales: year, growing season, and non-growing season. The year scale is from January to December, the growing season is from May to September, and the non-growing season is from October to April. The carbon flux background factors at different time scales are the same, and the background factor expression is:

[0064]

[0065] Where N is the number of months in the time scale T. The value of N is 12 for the annual scale, 5 for the growing season, and 7 for the non-growing season. 5a is the average of the highest temperatures on the 5th to 9th, 15th to 19th, and 25th to 29th of each month at each time scale; x 3a is the average of the maximum temperature of the 7th to 9th, 17th to 19th, and 27th to 29th day of each month at each time scale; x 1a is the average of the highest temperatures on the 9th, 19th and 29th of each month at each time scale; x 1b It is the average of the minimum temperature on the 9th, 19th and 29th of each month at each time scale;

[0066] The carbon flux response factors at different time scales are different, among which the annual scale response factor (x 5m , j3) is expressed as:

[0067]

[0068] Among them, x 5m is the average of the 5-day average temperature values ​​from 5th to 9th, 15th to 19th, and 25th to 29th of each month; j3 is the average of the 3-day cumulative values ​​of the daily average temperature from 7th to 9th, 17th to 19th, and 27th to 29th of each month;

[0069] The response factors of growing season scale and non-growing season scale (x 3b , j5) are the same and are expressed as:

[0070]

[0071] Among them, x 3b It is the average of the three-day minimum temperatures from 7th to 9th, 17th to 19th, and 27th to 29th of each month; j5 is the average of the five-day cumulative values ​​of the average daily temperature from 5th to 9th, 15th to 19th, and 25th to 29th of each month.

[0072] The model construction method in S300 is based on

[0073] The functional relationship is constructed based on the regression model constructed by the least squares fitting method. First, based on the forest type scale, the annual carbon emission flux and micrometeorological monitoring are collected simultaneously at a frequency of 3 times per month and 36 times per year. The carbon emission driven micrometeorological factor system is constructed through screening, and then a multivariate linear regression model with carbon emission flux as the dependent variable and micrometeorological factor system as the independent variable is established. The spatial dimensions are forest swamp, shrub swamp, and herbaceous swamp, and the time dimensions are year, growing season, non-growing season, spring, summer, autumn, and winter. Secondly, with micrometeorological factors as the medium, the correlation coefficient between each micrometeorological factor and the large-scale climate factor is established. Finally, the functional relationship between climate and carbon emissions at the forest landscape scale is constructed to realize the climate scale assessment of carbon emissions.

[0074] The annual scale carbon flux climate driving model is:

[0075]

[0076] Among them, f YF (x), f YG (x), f YC (x) are the annual carbon emission fluxes of forest swamp, shrub swamp and herbaceous swamp, respectively, in mg / m2 / h;

[0077] The climate-driven model of carbon flux at the growing season scale is:

[0078]

[0079] Among them, f SF (x), f SG (x), f SC (x) are the carbon emission fluxes at the growing season scale of forest swamp, shrub swamp, and herbaceous swamp, respectively, in mg / m2 / h;

[0080] The climate-driven model of carbon flux on the non-growing season scale is:

[0081]

[0082] In the formula, f NF (x), f NG (x), f NC (x) are the carbon emission fluxes at the non-growing season scale of forest swamp, shrub swamp and herbaceous swamp, respectively, in mg / m2 / h.

[0083] The S300 model is used to calculate the carbon flux corresponding to the forest swamp type and time scale, integrate the time range and spatial area, and statistically output the annual carbon emissions at different time scales in the study area. The function expression is:

[0084]

[0085] Among them, C Y , C S , C N They are annual carbon emissions at the annual scale, growing season scale, and non-growing season scale, in tons / year; 0.0876 is the unit conversion factor.

[0086] The significance of the relationship between the independent variables and dependent variables of the annual scale carbon emission climate driving model of forest swamps, shrub swamps and herbaceous swamps all reached the 0.001 level; the significance of the relationship between the independent variables and dependent variables of the growing season scale herbaceous swamp carbon emission climate model reached the 0.005 level, and the others reached the 0.001 level; the significance of the relationship between the independent variables and dependent variables of the non-growing season scale shrub swamp carbon emission climate model reached the 0.005 level, and the others reached the 0.001 level.

[0087] As attached Figure 2 To Attachment Figure 3 As shown:

[0088] Experimental example: The field monitoring data of carbon emissions in 2023 and 2024 at the Weihe Forest Farm in Zhangguangcai Mountains was used as the actual measurement, and the carbon emission values ​​of forest swamps, shrub swamps, and herbaceous swamps at different time scales in the Weihe Forest Farm in 2023 and 2024 estimated by the carbon emission climate-driven model method of the present invention were verified and compared. Based on the daily-scale meteorological interannual data set (2023 and 2024) within the scope of the Weihe Forest Farm in Zhangguangcai Mountains, the present invention constructs a carbon emission flux climate factor system, and simulates and calculates the carbon emission flux through the climate-driven model method. For comparison, only the data of 2023, 2024 and the growing season scale are displayed and explained. The comparison results between the measured values ​​and the simulated values ​​of the present invention are shown in Table 1:

[0089] Table 1 Comparison of carbon flux measured on annual and growing season scales with simulations of the present invention

[0090]

[0091] Combine the data in Table 1 to draw emission graphs for 2023, 2024 and the growing season.

[0092] The carbon flux measured on the annual and growing season scales and the simulation of the present invention are as follows Figure 2 As shown. Figure 2 It can be seen that the carbon fluxes simulated in 2023 and 2024 and the growing season are close to the measured data of the field plots, and there is no significant difference between the measured and simulated data (P>0.05, Figure 2 ). The correlation between the actual carbon flux measurement and the simulation of the present invention is as follows Figure 3 The fitting degree R of the annual and growing season scale carbon fluxes simulated by the present invention and the measured values ​​of field plots is 2They are 0.9627 and 0.9898 respectively, which can reflect the real situation of carbon flux and ensure the accuracy requirements of model simulation results. Figure 3 shown.

[0093] Importantly, it should be noted that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible without substantially departing from the novel teachings and advantages of the subject matter described in this application. Without departing from the scope of the present invention, other substitutions, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments. Therefore, the present invention is not limited to a specific embodiment, but extends to a variety of modifications still falling within the scope of the appended claims.

[0094] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0095] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but for those of ordinary skill having the benefit of this disclosure, without undue experimentation, the development effort will be a routine task of design, fabrication, and production.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A climate-driven downscaling assessment method for forest swamp carbon emissions, characterized in that: The following steps are involved: S100, obtain daily temperature data set and spatial remote sensing interpretation and identification data set within the study area; S200, based on the daily temperature data set, construct a spatiotemporal carbon flux driving factor system for the study area; S300, based on the spatiotemporal carbon flux driving factor system, the spatiotemporal carbon flux in the study area is calculated by using the carbon flux climate driving model of the least squares method in the machine learning algorithm; S400: The carbon flux corresponding to the forest swamp type and time scale is calculated through the S300 model, the time range and spatial area are integrated, and the annual carbon emissions at different time scales in the study area are statistically output.

2. A climate-driven downscaling assessment method for forest swamp carbon emissions as claimed in claim 1, characterized in that: The temperature dataset is a meteorological ordered observation value, expressed as: {x i,j} Where i represents the month of the assessment base year, ranging from 1 to 12; j represents the date of each month of the base year, ranging from 1 to 31; The spatial remote sensing interpretation and identification data set is expressed as: S=S F +S G +S C Where S represents the total area of ​​the study area in hectares; S F Represents the forest swamp area in hectares; S G Indicates the area of ​​shrub swamp in hectares; S C It indicates the area of ​​herbaceous swamp in hectares.

3. A climate-driven downscaling assessment method for forest swamp carbon emissions as claimed in claim 1, characterized in that: The driving factor system includes background factors and response factors. Both the background factors and the response factors include three time scales: year, growing season, and non-growing season. The year scale is from January to December, the growing season is from May to September, and the non-growing season is from October to April. The carbon flux background factors at different time scales are the same, and the background factor expression is: Where N is the number of months in the time scale T. The value of N is 12 for the annual scale, 5 for the growing season, and 7 for the non-growing season. 5a is the average of the highest temperatures on the 5th to 9th, 15th to 19th, and 25th to 29th of each month at each time scale; x 3a is the average of the maximum temperature of the 7th to 9th, 17th to 19th, and 27th to 29th day of each month at each time scale; x 1a is the average of the highest temperatures on the 9th, 19th and 29th of each month at each time scale; x 1b It is the average of the minimum temperature on the 9th, 19th and 29th of each month at each time scale; The carbon flux response factors at different time scales are different, among which the annual scale response factor (x 5m , j3) is expressed as: Among them, x 5m is the average of the 5-day average temperature values ​​from 5th to 9th, 15th to 19th, and 25th to 29th of each month; j3 is the average of the 3-day cumulative values ​​of the daily average temperature from 7th to 9th, 17th to 19th, and 27th to 29th of each month; The response factors of growing season scale and non-growing season scale (x 3b , j5) are the same and are expressed as: Among them, x 3b It is the average of the three-day minimum temperatures from 7th to 9th, 17th to 19th, and 27th to 29th of each month; j5 is the average of the five-day cumulative values ​​of the average daily temperature from 5th to 9th, 15th to 19th, and 25th to 29th of each month.

4. A climate-driven downscaling assessment method for forest swamp carbon emissions as claimed in claim 1, characterized in that: The method for constructing the carbon flux climate driven model includes: The functional relationship was constructed using a regression model built based on least squares fitting; Based on the forest type scale, carbon emission flux and micro-meteorological monitoring are collected synchronously at a frequency of 3 times per month and 36 times per year, and a carbon emission-driven micro-meteorological factor system is constructed through screening; A multivariate linear regression model was established with carbon emission flux as the dependent variable and microclimate factor system as the independent variable. The spatial dimensions were forest swamp, shrub swamp, and herbaceous swamp, and the temporal dimensions were year, growing season, non-growing season, spring, summer, autumn, and winter. Using micro-meteorological factors as a medium, establish correlation coefficients between each micro-meteorological factor and large-scale climate factors; Construct the functional relationship between climate and carbon emissions at the forest landscape scale and conduct a climate-scale assessment of carbon emissions.

5. A climate-driven downscaling assessment method for forest swamp carbon emissions as claimed in claim 4, characterized in that: The annual scale carbon flux climate driving model is: Among them, f YF (x), f YG (x), f YC (x) are the annual carbon emission fluxes of forest swamp, shrub swamp and herbaceous swamp, respectively, in mg / m2 / h; The growing season scale carbon flux climate driven model is: Among them, f SF (x), f SG (x), f SC (x) are the carbon emission fluxes at the growing season scale of forest swamp, shrub swamp, and herbaceous swamp, respectively, in mg / m2 / h; The non-growing season scale carbon flux climate driving model is: In the formula, f NF (x), f NG (x), f NC (x) are the carbon emission fluxes at the non-growing season scale of forest swamp, shrub swamp and herbaceous swamp, respectively, in mg / m2 / h.

6. A climate-driven downscaling assessment method for forest swamp carbon emissions as claimed in claim 1, characterized in that: The S300 model is used to calculate the carbon flux corresponding to the forest swamp type and time scale, integrate the time range and spatial area, and statistically output the annual carbon emissions of the study area at different time scales. The function expression is: Among them, C Y , C S , C N They are annual carbon emissions at the annual scale, growing season scale, and non-growing season scale, in tons / year; 0.0876 is the unit conversion factor.

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