Method and device for evaluating influence of drought and heat wave events on total primary productivity of vegetation

Abstract

The invention relates to the technical field of ecology and environment, in particular to a method and device for evaluating the influence of drought and heat wave events on vegetation total primary productivity, and the method comprises the steps: constructing original drive data and typical meteorological year data according to meteorological data and underlying surface data, establishing a Noah-MP land surface mode to simulate vegetation total primary productivity and each ecosystem variable under original and typical meteorological year scenes, and calculating meteorological drought, agricultural drought and heat wave thresholds under the original scene so as to identify drought and heat wave events; and constructing a time-varying most probable function of the vegetation total primary productivity and each ecosystem variable according to the drought heat wave event, so as to compare the difference between the vegetation total primary productivity and each ecosystem variable in the original and typical weather year scenarios, and analyze the influence of the drought heat wave event on the vegetation total primary productivity under different driving factors. Therefore, the problems that in the prior art, interaction of various variables is not fully considered, and a physical mechanism is lacked are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecology and environment, and particularly relates to a method and device for evaluating the impact of drought and heatwave events on the gross primary productivity of vegetation. Background Art

[0002] In the context of climate change, drought and heatwaves are among the most frequent and widespread natural disasters. Drought can inhibit the photosynthesis of vegetation, leading to a decrease in the carbon sequestration of vegetation. Vegetation is an important carbon sink in the terrestrial ecosystem, and the gross primary productivity of vegetation is an important indicator for measuring the carbon flux entering the ecosystem, which is of great significance for achieving the "dual carbon" goal. Past studies have shown that the gross primary productivity of vegetation decreases under drought and heatwave conditions, but the current response mechanism of the gross primary productivity of vegetation to drought and heatwaves is still unclear, threatening the sustainable development of the ecosystem. To address the above challenges, there is an urgent need to strengthen the research on the impact of drought and heatwave events on the gross primary productivity of vegetation under climate change.

[0003] Some scholars have evaluated the sensitivity of the gross primary productivity of vegetation to the vapor pressure deficit and soil moisture. However, the gross primary productivity of vegetation is a complex biochemical process, which is jointly affected by various environmental variables such as air temperature, shortwave radiation, wind speed, precipitation, and soil moisture. Considering that drought and heatwaves will have complex and long-term effects on the ecosystem, the current impact of drought and heatwaves on the sensitivity of the gross primary productivity of vegetation is still unclear.

[0004] Currently, the research on the response of vegetation to drought and heatwaves mostly uses statistical methods such as partial correlation analysis or machine learning. These methods can characterize the correlation between the gross primary productivity of vegetation and ecosystem variables, but do not fully consider the interaction of multiple variables and lack physical mechanisms. Summary of the Invention

[0005] The present invention provides a method and device for evaluating the impact of drought and heatwave events on the gross primary productivity of vegetation to solve the defects in the prior art that do not fully consider the interaction of multiple variables and lack physical mechanisms.

[0006] An embodiment of the first aspect of the present invention provides a method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation, including the following steps: obtaining meteorological data and underlying surface data of the target study area, and constructing original driving data and typical meteorological year data based on the meteorological data and the underlying surface data; establishing a Noah-MP land surface model according to the original driving data and the typical meteorological year data, and using the Noah-MP land surface model to simulate the total primary productivity of vegetation and various ecosystem variables in the original scenario and the typical meteorological year scenario; calculating meteorological drought, agricultural drought, and heatwave thresholds in the original scenario according to the original driving data and the typical meteorological year data, and identifying drought and heatwave events according to the meteorological drought, agricultural drought, and heatwave thresholds in the original scenario; constructing a time-varying most likely function of the total primary productivity of vegetation and the various ecosystem variables according to the drought and heatwave events; comparing the differences in the total primary productivity of vegetation and the various ecosystem variables between the original scenario and the typical meteorological year scenario according to the time-varying most likely function to obtain a difference comparison result, and analyzing the impact degree of the drought and heatwave events on the total primary productivity of vegetation under different driving factors according to the difference comparison result.

[0007] Optionally, the meteorological data is air temperature data, specific humidity data, wind speed data, downward shortwave radiation data, downward longwave radiation data, precipitation data, and surface air pressure data from the ERA5 dataset;

[0008] The underlying surface data includes land use data from the MODIS dataset, soil type data from the HWSD dataset, and elevation data from the ASTER GDEM dataset.

[0009] Optionally, the constructing of the original driving data and the typical meteorological year data according to the meteorological data and the underlying surface data includes:

[0010] Interpolating the meteorological driving data and elevation data into a preset resolution grid to obtain a first grid dataset;

[0011] Resampling the underlying surface data to the preset resolution grid to obtain a second grid dataset;

[0012] Constructing the original driving data according to the first grid dataset and the second grid dataset;

[0013] Comparing the closeness of the probability density function of the selected month's meteorological data in the meteorological driving data to the probability density function of the long-time series to determine the typical meteorological year data.

[0014] Optionally, calculating meteorological drought, agricultural drought, and heatwave thresholds in the original scenario based on the original driving data and the typical meteorological year data, and identifying drought and heatwave events based on the meteorological drought, agricultural drought, and heatwave thresholds in the original scenario, includes:

[0015] Constructing a meteorological drought sequence and an agricultural drought sequence using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data;

[0016] Based on the run theory, respectively identifying the meteorological drought sequence and the agricultural drought sequence to obtain drought events;

[0017] Sorting the temperature data in the meteorological data to select the monthly temperature exceeding a preset quantile as the heatwave event threshold, and determining the temperature months exceeding the heatwave event threshold as heatwave events;

[0018] When the drought event and the heatwave event occur simultaneously in a month, it is defined as the drought and heatwave event.

[0019] Optionally, the constructing a meteorological drought sequence and an agricultural drought sequence using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data includes:

[0020] Using the gamma probability density function to fit the precipitation data to obtain the precipitation frequency distribution, and using the gamma probability density function to fit the soil moisture data to obtain the soil moisture frequency distribution;

[0021] Estimating the first shape parameter and the first scale parameter in the precipitation frequency distribution according to the precipitation data in the original driving data to clarify the precipitation frequency distribution, and estimating the second shape parameter and the second scale parameter in the soil moisture frequency distribution according to the soil moisture data in the typical meteorological year data to clarify the soil moisture frequency distribution;

[0022] Solving the first probability that the random precipitation variable event is less than the preset precipitation event according to the precipitation frequency distribution, and solving the second probability that the random soil moisture variable event is less than the preset soil moisture event according to the soil moisture frequency distribution;

[0023] Respectively performing normal standardization processing on the first probability and the second probability to obtain the meteorological drought sequence and the agricultural drought sequence.

[0024] Optionally, constructing the time-varying most probable functions of the total primary productivity of vegetation and the various ecosystem variables based on the drought and heatwave events includes:

[0025] Taking the precipitation data, soil moisture data, air temperature data, wind speed data, latent heat flux data, shortwave radiation data, and total vegetation primary productivity data in the drought and heatwave event as covariates;

[0026] Based on the Copula function and the principle of maximum joint probability density, using the covariates to construct a time-varying most-probable function of the total vegetation primary productivity and the various ecosystem variables.

[0027] An embodiment of the second aspect of the present invention provides an apparatus for evaluating the impact of drought and heatwave events on total vegetation primary productivity, including: a first construction module for obtaining meteorological data and underlying surface data of a target study area, and constructing original driving data and typical meteorological year data according to the meteorological data and the underlying surface data; a simulation module for establishing a Noah-MP land surface model according to the original driving data and the typical meteorological year data, and using the Noah-MP land surface model to simulate the total vegetation primary productivity and various ecosystem variables in the original scenario and the typical meteorological year scenario; an identification module for calculating meteorological drought, agricultural drought, and heatwave thresholds in the original scenario according to the original driving data and the typical meteorological year data, and identifying drought and heatwave events according to the meteorological drought, agricultural drought, and heatwave thresholds in the original scenario; a second construction module for constructing a time-varying most-probable function of the total vegetation primary productivity and the various ecosystem variables according to the drought and heatwave events; an analysis module for comparing the differences in the total vegetation primary productivity and various ecosystem variables between the original scenario and the typical meteorological year scenario according to the time-varying most-probable function to obtain a difference comparison result, and analyzing the impact degree of the drought and heatwave events on the total vegetation primary productivity under different driving factors according to the difference comparison result.

[0028] An embodiment of the third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the method for evaluating the impact of drought and heatwave events on total vegetation primary productivity as described in the above embodiment.

[0029] An embodiment of the fourth aspect of the present invention provides a computer program product, and when the computer program / instructions are executed by a processor, the method for evaluating the impact of drought and heatwave events on total vegetation primary productivity as described above is implemented.

[0030] An embodiment of the fifth aspect of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the method for evaluating the impact of drought and heatwave events on total vegetation primary productivity as described above is implemented.

[0031] The method and device for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation proposed in the embodiments of the present invention combine a land surface model, a typical meteorological year method, and a multivariate analysis method to analyze the impact of drought and heatwave events on the total primary productivity of regional vegetation, and can obtain the changes in the total primary productivity of vegetation and other ecosystem factors under different drought and heatwave events, providing an important reference basis for the calculation and evaluation of carbon emissions and carbon absorption under climate change, and providing engineering reference value for achieving the goals of carbon peak and carbon neutrality.

[0032] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 is a flowchart of a method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation provided by an embodiment of the present invention;

[0035] Figure 2 is a schematic diagram of the run theory provided by an embodiment of the present invention;

[0036] Figure 3 is a block schematic diagram of a device for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation provided by an embodiment of the present invention;

[0037] Figure 4 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0039] The method and device for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation according to the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0040] Figure 1 is a schematic flow diagram of a method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation provided by an embodiment of the present invention.

[0041] As Figure 1 shown, the method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation includes the following steps:

[0042] In step S101, meteorological data and underlying surface data of the target study area are obtained, and original driving data and typical meteorological year data are constructed based on the meteorological data and the underlying surface data.

[0043] In some embodiments, the meteorological data are air temperature data, specific humidity data, wind speed data, downward shortwave radiation data, downward longwave radiation data, precipitation data, and surface air pressure data from the ERA5 dataset;

[0044] The underlying surface data includes land use data from the MODIS dataset, soil type data from the HWSD dataset, and elevation data from the ASTER GDEM dataset.

[0045] Specifically, the meteorological data are 2m air temperature data, specific humidity data, 2m wind speed data, downward shortwave radiation data, downward longwave radiation data, precipitation data, and surface air pressure data from the ERA5 dataset. Among them, ERA5 is the fifth-generation global reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), with high spatio-temporal resolution and long-time coverage. Its temporal resolution is hourly, the spatial resolution is about 31km, and the time range covers from 1950 to the present. ERA5 contains about 240 meteorological and climate variables, covering multiple fields such as the atmosphere, land, and ocean, such as air temperature, precipitation, wind speed, soil moisture, etc., and is widely used in fields such as climate research, weather analysis, hydrological simulation, and environmental science.

[0046] The underlying surface data includes land use data from the MODIS dataset, soil type data from the HWSD dataset, and elevation data from the ASTER GDEM dataset. Among them, the MODIS (Moderate Resolution Imaging Spectroradiometer) land use data is a global surface remote sensing data product obtained based on the Moderate Resolution Imaging Spectroradiometer provided by the National Aeronautics and Space Administration (NASA) of the United States. This data is obtained through two satellites (Terra and Aqua), and has the capabilities of global coverage, high temporal resolution, and multi-spectral observation. The MODIS land use data provides various surface cover types at a spatial resolution of 500 meters to 1 kilometer, including farmland, forest, grassland, wetland, urban area, bare land, etc. Commonly used data products include MCD12Q1 (annual land cover type) and dynamic land cover products. This data is widely used in fields such as climate change, ecosystem research, hydrological model construction, and land use change analysis, providing important support for global environmental and resource management. The HWSD (Harmonized World Soil Database) is a globally unified soil database jointly developed by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA), providing high-resolution global soil property and classification information. The HWSD dataset combines soil survey data and digital soil maps from multiple regions and countries, with a resolution of approximately 1 kilometer. Its core data includes physical and chemical properties such as soil organic matter content, soil texture (proportion of sand, silt, and clay), soil depth, soil pH value, and bulk density, which are uniformly standardized according to the FAO-90 and USDA classification systems. The HWSD is widely used in agriculture, ecosystem modeling, water resource management, and climate change research, providing important data support for global soil resource assessment and sustainable management. The ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model) is a high-resolution global digital elevation model dataset jointly developed by the Ministry of Economy, Trade and Industry (METI) of Japan and the National Aeronautics and Space Administration (NASA) of the United States. The ASTER GDEM is generated based on the stereo image data of the ASTER sensor carried by the Terra satellite, with a resolution of 30 meters, and its coverage almost covers all land areas on the Earth's surface. The ASTER GDEM is one of the important elevation datasets currently widely used in terrain and environmental research.

[0047] In some embodiments, constructing the original driving data and typical meteorological year data according to the meteorological data and the underlying surface data includes:

[0048] Interpolate the meteorological driving data and elevation data into a preset resolution grid to obtain the first grid dataset;

[0049] Resample the underlying surface data to a preset resolution grid to obtain the second grid dataset;

[0050] Construct the original driving data based on the first grid dataset and the second grid dataset;

[0051] Compare the closeness of the probability density function of the meteorological data in the selected month in the meteorological driving data with the probability density function of the long-time series to determine the typical meteorological year data.

[0052] In the actual execution process, interpolate the meteorological driving data and elevation data into a preset resolution grid through bilinear interpolation technology to obtain the first grid dataset; resample the land use data and soil type data to a preset resolution grid through the mode resampling technology to obtain the second grid dataset; construct the original driving data based on the first grid dataset and the second grid dataset.

[0053] Furthermore, the typical meteorological year method is used to process the meteorological driving data into typical meteorological year data. The specific method is to compare the closeness of the probability density function of the meteorological data in the selected month with the probability density function of the long-time series, including:

[0054]

[0055] where cdf(x) is the cumulative distribution value at x, n is the total number, and k is the coefficient.

[0056] In step S102, establish the Noah-MP land surface model based on the original driving data and the typical meteorological year data, and use the Noah-MP land surface model to simulate the gross primary productivity of vegetation and various ecosystem variables under the original scenario and the typical meteorological year scenario.

[0057] In the actual execution process, establish the Noah-MP land surface model based on the original driving data and the typical meteorological year data to simulate the gross primary productivity of vegetation and other ecosystem variables under the original and typical scenarios. Among them, the Noah-MP land surface model is the Noah-MP v5.0 land surface model with a dynamic vegetation scheme, and it simulates the soil moisture and the gross primary productivity of vegetation under the original scenario and the typical meteorological year scenario.

[0058] It should be noted that the Noah-MP (Noah Land Surface Model with Multi-Parameterization) model is a new generation of land surface model improved from the Noah land surface model. It has multi-parameterization options and can flexibly simulate surface hydrology, energy exchange, and ecological processes. Its main features include dynamic vegetation simulation, improved interaction between groundwater and surface water, fine multi-layer soil moisture simulation, and complex energy balance calculation. Noah-MP is commonly used in climate research, hydrological simulation, ecosystem analysis, and extreme event assessment, providing a powerful tool for the study of land-atmosphere interaction and surface processes.

[0059] In step S103, calculate the meteorological drought, agricultural drought, and heatwave thresholds under the original scenario based on the original driving data and typical meteorological year data, and identify drought and heatwave events according to the meteorological drought, agricultural drought, and heatwave thresholds under the original scenario.

[0060] In some embodiments, calculating the meteorological drought, agricultural drought, and heatwave thresholds under the original scenario based on the original driving data and typical meteorological year data, and identifying drought and heatwave events according to the meteorological drought, agricultural drought, and heatwave thresholds under the original scenario includes:

[0061] Construct a meteorological drought sequence and an agricultural drought sequence using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data;

[0062] Based on the run theory, identify the meteorological drought sequence and the agricultural drought sequence respectively to obtain drought events;

[0063] Sort the air temperature data in the meteorological data to select the monthly air temperature exceeding the preset quantile as the heatwave event threshold, and determine the air temperature months exceeding the heatwave event threshold as heatwave events;

[0064] When drought events and heatwave events occur simultaneously in a month, it is defined as a drought and heatwave event.

[0065] In some embodiments, constructing a meteorological drought sequence and an agricultural drought sequence using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data includes:

[0066] Use the gamma probability density function to fit the precipitation data to obtain the precipitation frequency distribution, and use the gamma probability density function to fit the soil moisture data to obtain the soil moisture frequency distribution;

[0067] Estimate the first shape parameter and the first scale parameter in the precipitation amount frequency distribution based on the precipitation data in the original driving data to clarify the precipitation amount frequency distribution, and estimate the second shape parameter and the second scale parameter in the soil moisture frequency distribution based on the soil moisture data in the typical meteorological year data to clarify the soil moisture frequency distribution;

[0068] Solve the first probability that the random precipitation variable event is less than the preset precipitation event according to the precipitation amount frequency distribution, and solve the second probability that the random soil moisture variable event is less than the preset soil moisture event according to the soil moisture frequency distribution;

[0069] Perform normal standardization processing on the first probability and the second probability respectively to obtain the meteorological drought sequence and the agricultural drought sequence.

[0070] In the actual execution process, construct the meteorological drought sequence (SPI-3) using the precipitation data in the original driving data, and its calculation process is as follows:

[0071] Use the gamma probability density function Γ(x) to fit the precipitation amount frequency distribution of the research site:

[0072]

[0073] where α is the first shape parameter (α>0), β is the second scale parameter (β>0), and x is the precipitation amount (x>0).

[0074]

[0075] In the formula, Γ(x) is the gamma function.

[0076] Estimate the first shape parameter α and the first scale parameter β based on the precipitation data in the original driving data:

[0077]

[0078] where is the estimated value of the shape parameter α, is the estimated value of the scale parameter β, n is the number of precipitation amount values, is the mean precipitation amount on the time scale under study, x i is the precipitation data sample, and A is the measure value of the skewness of the distribution.

[0079] After reconfirming the parameters in the precipitation amount frequency distribution, for the precipitation amount x0 of a certain year, the first probability of the event that the random variable x is less than x0 can be obtained as:

[0080]

[0081] The probability of the event when the precipitation amount is 0 is estimated by the following formula:

[0082] F(x = 0)= m / n (7)

[0083] Where m is the number of samples with a precipitation of 0; n is the total number of samples.

[0084] Perform normal standardization on the first probability, that is:

[0085]

[0086] Approximate solution gives:

[0087]

[0088] Where: F is the probability obtained by solving equation (6) or (7); and when F < 0.5, F = 1.0 - F, S = 1; when F ≤ 0.5, S = -1. When SPI < -0.5, it is considered that a meteorological drought event has occurred.

[0089] Similarly, use the soil moisture data in the typical meteorological year data to construct an agricultural drought sequence (SSMI - 3), the calculation method of which is the same as that of the meteorological drought sequence (SPI - 3). Fit the soil moisture data with a gamma probability density function, estimate the second shape parameter and the second scale parameter according to the soil moisture data in the typical meteorological year data, determine the soil moisture frequency distribution according to the second shape parameter and the second scale parameter, solve the second probability that the random soil moisture variable event is less than the preset soil moisture event according to the soil moisture frequency distribution, perform normal standardization on the second probability to obtain the agricultural drought sequence (SSMI - 3), and when SSMI < -0.5, it is considered that an agricultural drought event has occurred.

[0090] Furthermore, as Figure 2 shown, use the run - length theory to identify drought events for the meteorological drought sequence and the agricultural drought sequence respectively. Sort the temperature data, select the monthly temperature exceeding the 90th percentile as the threshold for heatwave events, and determine the months with temperatures exceeding the corresponding temperature threshold as heatwave events. When a month simultaneously experiences meteorological drought, agricultural drought and heatwave events, it is defined as a drought - heatwave event.

[0091] In step S104, construct a time - varying most - likely function of the total primary productivity of vegetation and each ecosystem variable according to the drought - heatwave event.

[0092] In some embodiments, constructing a time - varying most - likely function of the total primary productivity of vegetation and each ecosystem variable according to the drought - heatwave event includes:

[0093] Take the precipitation data, soil moisture data, air temperature data, wind speed data, latent heat flux data, shortwave radiation data, and total primary productivity data of vegetation in drought and heatwave events as covariates;

[0094] Based on the Copula function and the principle of maximum joint probability density, construct the time-varying most likely function of the total primary productivity of vegetation and each ecosystem variable using the covariates.

[0095] In the actual execution process, use the precipitation, soil moisture, air temperature, wind speed, latent heat flux, shortwave radiation data, and total primary productivity data of vegetation during drought and heatwave events in the original scenario and the typical meteorological year scenario as covariates, and construct the time-varying most likely function based on the Copula function respectively.

[0096] In step S105, compare the differences between the total primary productivity of vegetation and each ecosystem variable in the original scenario and the typical meteorological year scenario according to the time-varying most likely function to obtain the difference comparison result, and analyze the influence degree of drought and heatwave events on the total primary productivity of vegetation under different driving factors according to the difference comparison result.

[0097] In the actual execution process, construct a combined scenario that optimizes multiple variables based on the principle of maximum joint probability density according to the time-varying most likely function to compare the optimal combination differences between the total primary productivity of vegetation and other variables in different scenarios, obtain the difference comparison result, and obtain the influence of drought and heatwave events on the total primary productivity of vegetation according to the differences of the Copula function in different scenarios, where the combined scenario that optimizes multiple variables based on the principle of maximum joint probability density is as follows:

[0098]

[0099] Among them, (p*, sm*, t*, U*, lh*, rs*, gpp*) are the optimal value combinations with the maximum joint probability density between precipitation, soil moisture, air temperature, wind speed, latent heat flux, shortwave radiation data, and total primary productivity of vegetation; T AND is the joint return period, representing the average recurrence event of 7 variables reaching a specific value; F Xi represents the cumulative distribution function (CDF) of the i-th variable (such as precipitation, soil moisture, etc.); C(F P , F SM , F T , F U , F LH , F Rs , F GPP ) is a 7-dimensional Copula function that captures the joint dependence relationship of 7 variables; f X is the marginal density function of the X variable (such as precipitation, soil moisture, etc.); F Xis the cumulative density function of the X variable (such as precipitation, soil moisture, etc.); E is the time length of the research period.

[0100] In summary, according to the method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation proposed in the embodiments of the present invention, by combining a land surface model, a typical meteorological year method, and a multivariate analysis method to analyze the impact of drought and heatwave events on the total primary productivity of regional vegetation, the changes in the total primary productivity of vegetation and other ecosystem factors under different drought and heatwave events can be obtained, which can provide an important reference basis for the calculation and evaluation of carbon emissions and carbon absorption under climate change, and provide engineering reference value for achieving the goals of carbon peak and carbon neutrality.

[0101] Next, a device for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation proposed in the embodiments of the present invention will be described with reference to the accompanying drawings.

[0102] Figure 3 is a block diagram of the device for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation according to the embodiments of the present invention.

[0103] As Figure 3 shown, the device 30 for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation includes: a first construction module 301, a simulation module 302, an identification module 303, a second construction module 304, and an analysis module 305.

[0104] Among them, the first construction module 301 is used to obtain meteorological data and underlying surface data of the target research area, and construct original driving data and typical meteorological year data according to the meteorological data and underlying surface data. The simulation module 302 is used to establish a Noah-MP land surface model according to the original driving data and typical meteorological year data, and use the Noah-MP land surface model to simulate the total primary productivity of vegetation and various ecosystem variables under the original scenario and the typical meteorological year scenario. The identification module 303 is used to calculate the meteorological drought, agricultural drought, and heatwave thresholds under the original scenario according to the original driving data and typical meteorological year data, and identify drought and heatwave events according to the meteorological drought, agricultural drought, and heatwave thresholds under the original scenario. The second construction module 304 is used to construct a time-varying most likely function of the total primary productivity of vegetation and various ecosystem variables according to the drought and heatwave events. The analysis module 305 is used to compare the differences in the total primary productivity of vegetation and various ecosystem variables between the original scenario and the typical meteorological year scenario according to the time-varying most likely function to obtain a difference comparison result, and analyze the impact degree of drought and heatwave events on the total primary productivity of vegetation under different driving factors according to the difference comparison result.

[0105] In some embodiments, the meteorological data is temperature data, specific humidity data, wind speed data, downward shortwave radiation data, downward longwave radiation data, precipitation data, and surface air pressure data from the ERA5 dataset;

[0106] The underlying surface data includes land use data from the MODIS dataset, soil type data from the HWSD dataset, and elevation data from the ASTER GDEM dataset.

[0107] In some embodiments, the first construction module 301 includes:

[0108] An interpolation unit for interpolating meteorological driving data and elevation data into a preset resolution grid to obtain a first grid dataset;

[0109] A resampling unit for resampling the underlying surface data into a preset resolution grid to obtain a second grid dataset;

[0110] A construction data unit for constructing original driving data based on the first grid dataset and the second grid dataset;

[0111] A comparison unit for comparing the closeness of the probability density function of the meteorological data in the selected month in the meteorological driving data with the probability density function of the long time series to determine the typical meteorological year data.

[0112] In some embodiments, the identification module 303 includes:

[0113] A construction sequence unit for constructing a meteorological drought sequence and an agricultural drought sequence using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data;

[0114] An identification unit for respectively identifying the meteorological drought sequence and the agricultural drought sequence based on the run theory to obtain drought events;

[0115] A sorting unit for sorting the temperature data in the meteorological data to select the monthly temperature exceeding the preset quantile as the heatwave event threshold, and determining the temperature months exceeding the heatwave event threshold as heatwave events;

[0116] A defining event unit for defining as a drought heatwave event when a drought event and a heatwave event occur simultaneously in a month.

[0117] In some embodiments, the construction sequence unit includes:

[0118] A fitting subunit for fitting the precipitation data using a gamma probability density function to obtain a precipitation frequency distribution, and fitting the soil moisture data using a gamma probability density function to obtain a soil moisture frequency distribution;

[0119] An estimator subunit, configured to estimate a first shape parameter and a first scale parameter in a precipitation amount frequency distribution according to precipitation data in original driving data to clarify the precipitation amount frequency distribution, and estimate a second shape parameter and a second scale parameter in a soil moisture frequency distribution according to soil moisture data in typical meteorological year data to clarify the soil moisture frequency distribution;

[0120] A solver subunit, configured to solve a first probability that a random precipitation variable event is less than a preset precipitation event according to the precipitation amount frequency distribution, and solve a second probability that a random soil moisture variable event is less than a preset soil moisture event according to the soil moisture frequency distribution;

[0121] A processor subunit, configured to perform normal standardization processing on the first probability and the second probability respectively to obtain a meteorological drought sequence and an agricultural drought sequence.

[0122] In some embodiments, the second construction module 304 includes:

[0123] A variable definition unit, configured to use precipitation data, soil moisture data, air temperature data, wind speed data, latent heat flux data, shortwave radiation data, and vegetation gross primary productivity data in a drought heatwave event as covariates;

[0124] A function construction unit, configured to construct a time-varying most probable function between vegetation gross primary productivity and each ecosystem variable based on the Copula function and the principle of maximum joint probability density using the covariates.

[0125] It should be noted that the foregoing explanation of the embodiments of the method for evaluating the impact of a drought heatwave event on vegetation gross primary productivity also applies to the device for evaluating the impact of a drought heatwave event on vegetation gross primary productivity in this embodiment, and will not be elaborated here.

[0126] The device for evaluating the impact of a drought heatwave event on vegetation gross primary productivity according to an embodiment of the present invention combines a land surface model, a typical meteorological year method, and a multivariate analysis method to analyze the impact of a drought heatwave event on the regional vegetation gross primary productivity, and can obtain the changes in vegetation gross primary productivity and other ecosystem factors under different drought heatwave events, which can provide an important reference basis for the calculation and evaluation of carbon emissions and carbon absorption under climate change, and provide an engineering reference value for achieving the goals of carbon peak and carbon neutrality.

[0127] Figure 4 The structure diagram of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0128] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0129] When the processor 402 executes the program, it implements the method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation provided in the above embodiments.

[0130] Furthermore, the electronic device further includes:

[0131] A communication interface 403 for communication between the memory 401 and the processor 402.

[0132] A memory 401 for storing a computer program that can run on the processor 402.

[0133] The memory 401 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0134] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0135] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other through an internal interface.

[0136] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0137] The embodiments of the present invention also provide a computer program product, and when the computer program / instructions are executed by a processor, the method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation as described above is implemented.

[0138] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above method for evaluating the impact of drought and heatwave events on the total primary productivity of vegetation is implemented.

[0139] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0140] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0141] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0142] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0143] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0144] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0145] In addition, each functional unit in various embodiments of the present invention may be integrated into a processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0146] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation, characterized in that: The following steps are involved: Acquire meteorological data and underlying surface data of the target study area, and construct original driving data and typical meteorological year data based on the meteorological data and the underlying surface data; A Noah-MP land surface model is established based on the original driving data and the typical meteorological year data, and the Noah-MP land surface model is used to simulate the total primary productivity of vegetation and various ecosystem variables under the original scenario and the typical meteorological year scenario; Calculating meteorological drought, agricultural drought and heat wave thresholds under the original scenario based on the original driving data and the typical meteorological year data, and identifying drought and heat wave events based on the meteorological drought, agricultural drought and heat wave thresholds under the original scenario; Constructing a time-varying most likely function of the total primary productivity of vegetation and each ecosystem variable according to the drought and heat wave event; The differences between the total primary productivity of vegetation and each ecosystem variable under the original scenario and the typical meteorological year scenario are compared according to the time-varying most likely function to obtain a difference comparison result, and the degree of influence of the drought and heat wave event on the total primary productivity of vegetation under different driving factors is analyzed according to the difference comparison result.

2. The method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation according to claim 1, characterized in that: The meteorological data are temperature data, specific humidity data, wind speed data, downward shortwave radiation data, downward longwave radiation data, precipitation data and surface pressure data from the ERA5 data set; The underlying surface data include land use data from the MODIS dataset, soil type data from the HWSD dataset, and elevation data from the ASTER GDEM dataset.

3. The method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation according to claim 1, characterized in that: The constructing of original driving data and typical meteorological year data according to the meteorological data and the underlying surface data includes: Interpolating the meteorological driving data and the elevation data into a preset resolution grid to obtain a first grid data set; Resampling the underlying surface data to the preset resolution grid to obtain a second grid data set; constructing the original driving data according to the first raster dataset and the second raster dataset; The typical meteorological year data is determined by comparing the proximity of the probability density function of the meteorological data of the selected month in the meteorological driving data with the probability density function of the long time series.

4. The method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation according to claim 1, characterized in that: The calculating the meteorological drought, agricultural drought and heat wave thresholds under the original scenario according to the original driving data and the typical meteorological year data, and identifying drought and heat wave events according to the meteorological drought, agricultural drought and heat wave thresholds under the original scenario, comprises: Using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data to construct a meteorological drought sequence and an agricultural drought sequence; Based on the run theory, the meteorological drought sequence and the agricultural drought sequence are respectively identified to obtain drought events; Sorting the temperature data in the meteorological data to select the monthly temperature exceeding a preset quantile as a heat wave event threshold, and selecting the temperature month exceeding the heat wave event threshold to determine as a heat wave event; When the drought event and the heat wave event occur simultaneously in one month, it is defined as the drought heat wave event.

5. The method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation according to claim 4, characterized in that: The method of constructing a meteorological drought sequence and an agricultural drought sequence by using the precipitation data in the original driving data and the soil moisture data in the typical meteorological year data comprises: Fitting the precipitation data with a gamma probability density function to obtain a precipitation frequency distribution, and fitting the soil moisture data with a gamma probability density function to obtain a soil moisture frequency distribution; Estimate a first shape parameter and a first scale parameter in the precipitation frequency distribution according to the precipitation data in the original driving data to clarify the precipitation frequency distribution, and estimate a second shape parameter and a second scale parameter in the soil moisture frequency distribution according to the soil moisture data in the typical meteorological year data to clarify the soil moisture frequency distribution; Solving a first probability that a random precipitation variable event is less than a preset precipitation event according to the precipitation frequency distribution, and solving a second probability that a random soil moisture variable event is less than a preset soil moisture event according to the soil moisture frequency distribution; The first probability and the second probability are respectively subjected to normal standardization processing to obtain the meteorological drought sequence and the agricultural drought sequence.

6. The method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation according to claim 1, characterized in that: The step of constructing the time-varying most likely function of the total primary productivity of vegetation and each ecosystem variable according to the drought and heat wave event comprises: The precipitation data, soil moisture data, temperature data, wind speed data, latent heat flux data, shortwave radiation data and vegetation gross primary productivity data in the drought heat wave event are used as covariates; Based on the Copula function and the principle of maximum joint probability density, the covariate is used to construct the time-varying most likely function of the total primary productivity of the vegetation and the various ecosystem variables.

7. A device for evaluating the impact of drought and heat wave events on the total primary productivity of vegetation, characterized in that: include: The first construction module is used to obtain meteorological data and underlying surface data of the target study area, and construct original driving data and typical meteorological year data according to the meteorological data and the underlying surface data; A simulation module, for establishing a Noah-MP land surface model according to the original driving data and the typical meteorological year data, and using the Noah-MP land surface model to simulate the total primary productivity of vegetation and various ecosystem variables under the original scenario and the typical meteorological year scenario; an identification module, for calculating meteorological drought, agricultural drought and heat wave thresholds under the original scenario according to the original driving data and the typical meteorological year data, and identifying drought and heat wave events according to the meteorological drought, agricultural drought and heat wave thresholds under the original scenario; A second construction module is used to construct a time-varying most likely function of the total primary productivity of vegetation and each ecosystem variable according to the drought and heat wave event; An analysis module is used to compare the differences between the total primary productivity of vegetation and each ecosystem variable under the original scenario and the typical meteorological year scenario according to the time-varying most likely function to obtain a difference comparison result, and analyze the impact of the drought and heat wave event on the total primary productivity of vegetation under different driving factors according to the difference comparison result.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for assessing the impact of drought and heat wave events on the gross primary productivity of vegetation as described in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the method for evaluating the impact of drought and heat wave events on the gross primary productivity of vegetation as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for evaluating the impact of drought and heat wave events on the total primary productivity of vegetation as described in any one of claims 1 to 6.

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