Tea garden carbon reserve dynamic monitoring and predicting method based on meteorological data

Abstract

The invention relates to the technical field of plant ecological carbon sink measurement and calculation, in particular to a tea garden carbon reserve dynamic monitoring and prediction method based on meteorological data, a typical sample plot in a tea garden is selected, meteorological data and basic carbon reserve of the typical sample plot are acquired, and the meteorological data comprise real-time data and forecast data; establishing dynamic monitoring, and performing heat damage identification and freeze damage identification on real-time data; establishing a carbon reserve dynamic correction model; a carbon reserve prediction model is established, and when heat damage and / or freezing damage occur in prediction data, the carbon reserve dynamic correction model is used for prediction to obtain a prediction result; according to the method, the damage of high-temperature stress to the tea trees has a gradual superposition characteristic, high-temperature inhibits photosynthesis, so that calculation is performed in a summation form, cold waves increase respiratory consumption, and the damage mainly depends on the intensity peak value and the cooling rate of a single event; by providing more accurate dynamic measurement and calculation as the supplement of static measurement and calculation, the change condition of the carbon reserves of the tea garden can be reflected in time.

Description

Technical Field

[0001] The present invention relates to the technical field of plant ecological carbon sink measurement and calculation, and in particular to a method for dynamic monitoring and prediction of carbon storage in a tea garden based on meteorological data. Background Art

[0002] Tea garden carbon sequestration is a crucial component of agricultural and forestry carbon sequestration in my country's tea-growing regions. Current tea garden carbon sequestration measurement and monitoring standards are based on a fixed method (static calculation), treating the tea tree as a fixed production machine. This method fails to consider that the tea tree itself is a living organism and is affected by weather fluctuations. While this method is effective in normal weather conditions, when tea trees are mature and have a stable production cycle, high temperatures and cold snaps can inhibit tea plant activity. Therefore, a dynamic tea garden carbon storage monitoring and prediction method based on meteorological data is needed to account for the physiological stresses caused by extreme weather on tea trees, which in turn affect agricultural carbon sequestration. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for dynamically monitoring and predicting carbon reserves in tea gardens based on meteorological data, which can detect the physiological stress of tea trees caused by extreme weather and thus affect agricultural carbon sequestration.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data, comprising: S1. Select a typical plot in the tea garden and obtain meteorological data and basic carbon storage of the typical plot , meteorological data includes real-time data and forecast data; S2. Establish dynamic monitoring and identify heat and frost damage based on real-time data. Heat damage is considered to have occurred when the average daily temperature is >30°C and the relative humidity is <60% for three consecutive days. Frost damage is considered to have occurred when the temperature drops by ≥8°C or the minimum daily temperature is ≤-5°C within 24 hours. When heat damage occurs, conduct heat damage HHSI monitoring: HHSI= × (1- ); When frost damage occurs, carry out frost damage CSSI monitoring: CSSI= + ; Where n is the total number of days of a single heat injury, and i is the i-th day in n; is the maximum temperature on day i, is the minimum relative humidity on day i; The temperature drop in 24 hours, is the lowest temperature; S3. Establish a dynamic correction model for carbon storage ; = × in, is the thermal damage carbon loss coefficient, is the freezing carbon loss coefficient, is the slope of heat stress response, is the slope of the freezing stress response; S4. Establish a carbon storage prediction model and use the carbon storage dynamic correction model when the forecast data shows the occurrence of heat damage and / or frost damage Make predictions to obtain prediction results.

[0005] Preferably, the carbon storage dynamic correction model is used when the forecast data is consistent with the occurrence of heat damage and / or frost damage. Make predictions and get prediction results , = - .

[0006] Preferably, the basic carbon reserve = + + ; in, is tea tree biomass carbon, is soil organic carbon, is the litter carbon storage.

[0007] Preferably, the tea plant biomass carbon includes branches, leaves and roots of the tea plant.

[0008] Preferably, the soil organic carbon is in the 0–30 cm soil layer.

[0009] Preferably, a temperature and humidity sensor is provided in the typical sample plot, and the real-time data is provided by the temperature and humidity sensor; When the heat damage identification and frost damage identification of the real-time data are detected to be inconsistent with the heat damage identification and frost damage identification of the forecast data, the predicted results shall be corrected based on the heat damage identification and frost damage identification of the real-time data.

[0010] Preferably, a statistical period is from when heat damage or freezing damage occurs to when the heat damage or freezing damage is completed.

[0011] Preferably, when heat damage occurs for the first time, the calibration is performed manually. 、 ; When frost damage occurs for the first time, it is manually measured and calibrated 、 .

[0012] Preferably, the 、 、 Determined by the tea tree variety.

[0013] Preferably, when the tea tree variety is the mature Huacha No. 1, =0.15, =0.8, =0.1, =0.6.

[0014] The beneficial effect of the present invention is that: by identifying heat damage and freezing damage, the damage of high temperature stress to tea trees is gradually superimposed. High temperature inhibits photosynthesis and is therefore calculated in the form of summation, while cold waves increase respiratory consumption. The damage mainly depends on the intensity peak of a single event (such as the lowest temperature). ) and cooling rate (|ΔT|) rather than accumulation of duration, there is no need to sum them up; by providing more accurate dynamic measurements as a supplement to static measurements, it can timely reflect changes in tea garden carbon storage, that is, statistics on carbon losses when heat damage and frost damage occur, which can reduce the measurement error of traditional static carbon storage and provide real-time data support for tea garden managers; and through the use of two types of meteorological data, real-time data and forecast data, real-time data can facilitate calibration when heat damage and frost damage occur, and can correct forecast data to ensure the accuracy of dynamic measurements; while forecast data can realize carbon sink prediction, which is helpful to formulate more forward-looking tea garden carbon management strategies, such as reasonably arranging picking time, adjusting fertilization plans, etc., to achieve the goal of reducing emissions and increasing carbon sinks in the tea garden ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of a method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0016] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0017] Please refer to Figure 1 A method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data includes: S1. Select a typical plot in the tea garden and obtain meteorological data and basic carbon storage of the typical plot , meteorological data includes real-time data and forecast data; S2. Establish dynamic monitoring and identify heat and frost damage based on real-time data. Heat damage is considered to have occurred when the average daily temperature is >30°C and the relative humidity is <60% for three consecutive days. Frost damage is considered to have occurred when the temperature drops by ≥8°C or the minimum daily temperature is ≤-5°C within 24 hours. When heat damage occurs, conduct heat damage HHSI monitoring: HHSI= × (1- ); When frost damage occurs, carry out frost damage CSSI monitoring: CSSI= + ; Where n is the total number of days of a single heat injury, and i is the i-th day in n; is the maximum temperature on day i, is the minimum relative humidity on day i; The temperature drop in 24 hours, is the lowest temperature; S3. Establish a dynamic correction model for carbon storage ; = × in, is the thermal damage carbon loss coefficient, is the freezing carbon loss coefficient, is the slope of heat stress response, is the slope of the freezing stress response; S4. Establish a carbon storage prediction model and use the carbon storage dynamic correction model when the forecast data shows the occurrence of heat damage and / or frost damage Make predictions to obtain prediction results.

[0018] From the above description, we can know that through the identification of heat damage and frost damage, the damage of high temperature stress to tea trees is gradually superimposed. High temperature inhibits photosynthesis, so it is calculated in the form of summation, while cold waves increase respiratory consumption. The damage mainly depends on the intensity peak of a single event (such as the lowest temperature). ) and cooling rate (|ΔT|) rather than accumulation of duration, there is no need to sum them up; by providing more accurate dynamic measurements as a supplement to static measurements, it can timely reflect changes in tea garden carbon storage, that is, statistics on carbon losses when heat damage and frost damage occur, which can reduce the measurement error of traditional static carbon storage and provide real-time data support for tea garden managers; and through the use of two types of meteorological data, real-time data and forecast data, real-time data can facilitate calibration when heat damage and frost damage occur, and can correct forecast data to ensure the accuracy of dynamic measurements; while forecast data can realize carbon sink prediction, which is helpful to formulate more forward-looking tea garden carbon management strategies, such as reasonably arranging picking time, adjusting fertilization plans, etc., to achieve the goal of reducing emissions and increasing carbon sinks in the tea garden ecosystem.

[0019] Furthermore, when the forecast data show the occurrence of heat damage and / or frost damage, the carbon storage dynamic correction model is used. Make predictions and get prediction results , = - .

[0020] Furthermore, the basic carbon reserve = + + ; in, is tea tree biomass carbon, is soil organic carbon, Litter carbon storage.

[0021] Furthermore, the tea tree biomass carbon includes branches, leaves and roots of the tea tree.

[0022] Furthermore, the soil organic carbon is the 0–30 cm soil layer.

[0023] Furthermore, a temperature and humidity sensor is provided in the typical sample plot, and real-time data is provided by the temperature and humidity sensor; When the heat damage identification and frost damage identification of the real-time data are detected to be inconsistent with the heat damage identification and frost damage identification of the forecast data, the predicted results shall be corrected based on the heat damage identification and frost damage identification of the real-time data.

[0024] From the above description, it can be seen that by setting up temperature and humidity sensors in the tea garden, the accuracy of the data can be guaranteed, and inaccurate measurements caused by discrepancies between weather forecasts and actual data can be avoided.

[0025] Furthermore, a statistical period is from when heat damage or frost damage occurs to when the heat damage or frost damage is completed.

[0026] From the above description, we can see that by using freezing damage and heat damage as the dividing points, the carbon storage dynamic correction model can be Avoid calculation errors caused by the superposition of multiple heat damage and frost damage, and the conditions of each frost damage and heat damage may be different. If it is simply superimposed, it will easily lead to errors in carbon sink calculations.

[0027] Furthermore, when heat damage occurs for the first time, it is manually measured and calibrated. 、 ; When frost damage occurs for the first time, it is manually measured and calibrated 、 .

[0028] From the above description, we can see that through manual calibration 、 、 , which can improve the accuracy of correction and calibrate in sequence. The same type of tea trees can also use the data synchronously without repeated calibration.

[0029] Furthermore, the 、 、 Determined by the tea tree variety.

[0030] Furthermore, when the tea tree variety is the mature Huacha No. 1, =0.15, =0.8, =0.1, =0.6.

[0031] Example 1 A method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data, comprising: S1. Select a typical plot in the tea garden, where temperature and humidity sensors are installed; obtain meteorological data (including real-time data and forecast data) and basic carbon storage data of the typical plot. = + + ; in, is the tea tree biomass carbon (including branches, leaves, and roots), is soil organic carbon (0–30 cm soil layer), is the litter carbon storage.

[0032] S2. Establish dynamic monitoring and identify heat and frost damage based on real-time data. Heat damage is considered to have occurred when the average daily temperature is >30°C and the relative humidity is <60% for three consecutive days. Frost damage is considered to have occurred when the temperature drops by ≥8°C or the minimum daily temperature is ≤-5°C within 24 hours. When heat damage occurs, conduct heat damage HHSI monitoring: HHSI= × (1- ); When frost damage occurs, carry out frost damage CSSI monitoring: CSSI= + ; Where n is the total number of days of a single heat injury, and i is the i-th day in n; is the maximum temperature on day i, is the minimum relative humidity on day i; The temperature drop in 24 hours, is the lowest temperature; To quantify the contribution of cooling rate to osmotic stress in tea plants; Calculate critical damage.

[0033] S3. Establish a dynamic correction model for carbon storage ; = × in, is the thermal damage carbon loss coefficient, is the freezing carbon loss coefficient, is the slope of heat stress response, is the response slope of freezing stress; when heat damage occurs for the first time, it is manually measured and calibrated 、 ; When frost damage occurs for the first time, it is manually measured and calibrated 、 . 、 、 Determined by the tea tree variety.

[0034] S4. Establish a carbon storage prediction model and use the carbon storage dynamic correction model when the forecast data shows the occurrence of heat damage and / or frost damage Make predictions and get prediction results , = - When the heat damage identification and frost damage identification of the real-time data are found to be inconsistent with the heat damage identification and frost damage identification of the forecast data, the predicted results shall be corrected based on the heat damage identification and frost damage identification of the real-time data.

[0035] Example 2 A method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data is provided. The same aspects as those in Example 1 are omitted for clarity. When the tea tree variety is Huacha No. 1 in its mature stage, =0.15, =0.8, =0.1, =0.6.

[0036] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data, characterized in that: include: S1. Select a typical plot in the tea garden and obtain meteorological data and basic carbon storage of the typical plot , meteorological data includes real-time data and forecast data; S2. Establish dynamic monitoring and identify heat and frost damage based on real-time data. Heat damage is considered to have occurred when the average daily temperature is >30°C and the relative humidity is <60% for three consecutive days. Frost damage is considered to have occurred when the temperature drops by ≥8°C or the minimum daily temperature is ≤-5°C within 24 hours. When heat damage occurs, conduct heat damage HHSI monitoring: HHSI= × (1- ); When frost damage occurs, carry out frost damage CSSI monitoring: CSSI= + ; Where n is the total number of days of a single heat injury, and i is the i-th day in n; is the maximum temperature on day i, is the minimum relative humidity on day i; The temperature drop in 24 hours, is the lowest temperature; S3. Establish a dynamic correction model for carbon storage ; = × in, is the thermal damage carbon loss coefficient, is the freezing carbon loss coefficient, is the slope of heat stress response, is the slope of the freezing stress response; S4. Establish a carbon storage prediction model and use the carbon storage dynamic correction model when the forecast data shows the occurrence of heat damage and / or frost damage Make predictions to obtain prediction results.

2. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 1, characterized in that: When the forecast data shows the occurrence of heat damage and / or frost damage, the carbon storage dynamic correction model is used. Make predictions and get prediction results , = - .

3. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 1, characterized in that: The basic carbon stock = + + ; in, is tea tree biomass carbon, is soil organic carbon, Litter carbon storage.

4. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 3 is characterized in that: The tea plant biomass carbon includes branches, leaves and roots of the tea plant.

5. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 3 is characterized in that: The soil organic carbon refers to the 0–30 cm soil layer.

6. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 1, characterized in that: A temperature and humidity sensor is provided in the typical sample plot, and real-time data is provided by the temperature and humidity sensor; When the heat damage identification and frost damage identification of the real-time data are detected to be inconsistent with the heat damage identification and frost damage identification of the forecast data, the predicted results shall be corrected based on the heat damage identification and frost damage identification of the real-time data.

7. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 1, characterized in that: A statistical period is from the time when heat damage or frost damage occurs to the time when the heat damage or frost damage is completed.

8. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 1, characterized in that: When heat damage occurs for the first time, it is manually measured and calibrated 、 ; When frost damage occurs for the first time, it is manually measured and calibrated 、 .

9. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 8, characterized in that: described 、 、 Determined by the tea tree variety.

10. The method for dynamic monitoring and prediction of carbon storage in tea gardens based on meteorological data according to claim 8, characterized in that: When the tea tree variety is Huacha No. 1 in its mature stage, =0.15, =0.8, =0.1, =0.6.

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