Method and system for predicting total primary productivity of ecological system under drought stress

Abstract

The invention provides a method and system for predicting the total primary productivity of an ecological system under drought stress, and the method comprises the steps: coupling a nonlinear response and a hydrothermal carbon physical mechanism under the drought stress; and an important reference basis with high operability is provided for globally and regionally predicting and evaluating a response mechanism of future drought evolution to an ecological system carbon sink function under a climate change scene.

Description

Technical Field

[0001] The invention relates to the technical field of ecohydrological prediction, and in particular to a method and system for predicting the total primary productivity of an ecosystem under drought stress. Background Art

[0002] In previous studies, considering the various physical causes of drought in meteorology, hydrology, agriculture and socio-economics, scholars at home and abroad proposed a variety of different drought indices, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index, Palmer Drought Severity Index (PDSI), Standardized Runoff Index (SRI), etc., in order to quantitatively characterize the degree of water deficit. However, each drought index mainly considers one or more meteorological and hydrological elements, and cannot fully describe the intrinsic physical characteristics of drought events. With the rapid development of satellite telemetry and data inversion technology in recent years, quantitative observation products based on GRACE and GRACE-FO gravity satellites not only provide a wider coverage and higher spatiotemporal resolution, but also effectively make up for the lack of ground station network deployment, which provides strong data support for drought monitoring in areas without data. Therefore, scholars began to use GRACE / GRACE-FO satellite inversion data for drought assessment and found that the Terrestrial water storage anomaly-drought severity index (TWSA-DSI) based on terrestrial water storage anomalies can not only effectively monitor drought events, but also capture the spatiotemporal changes of surface fluxes on a large scale.

[0003] Drought is one of the most important stressors affecting the carbon sink function of terrestrial ecosystems at regional and global scales. Domestic and foreign scholars have used satellite and flux station data to evaluate the impact of drought on vegetation carbon budget and found that extreme drought caused a significant decrease in gross primary productivity (GPP) and net ecosystem productivity (NEP). Some scholars have proposed that the weakening of ecosystem diversity and carbon sink capacity in the Amazon rainforest in recent years is related to the intensification of drought. Global temperature and saturated water vapor pressure difference may continue to grow, causing more frequent and more intense heat and drought compound disasters, and have a significant impact on GPP. After suffering from severe climate disasters, plants usually recover slowly due to slowed growth, irreversible damage to hydraulic conductivity, and depletion of carbon reserves; this delayed recovery will increase the vulnerability to continuous climate disasters, thus affecting the carbon sink of terrestrial ecosystems. The world is currently experiencing climate change dominated by "warming", and the response of ecosystem carbon sinks to climate warming has significant temporal and spatial heterogeneity, and its changes are affected by atmospheric water vapor capacity, relative humidity and atmospheric stability. Especially under drought stress, the delayed response of different vegetation types to drought under climatic conditions has seasonal and nonlinear response characteristics. However, there are great uncertainties in the global gross primary productivity (GPP) and long-term change trends simulated by various methods, which restricts the estimation of terrestrial carbon budget and the understanding of mutual feedback with drought. Although scholars at home and abroad have gradually paid attention to the impact of drought on ecosystem productivity, there is no literature that comprehensively considers the dynamic response characteristics of terrestrial ecosystem carbon budget under future drought stress, and combines emergent constraints to predict the gross primary productivity of ecosystems under future drought stress. Summary of the invention

[0004] The present invention provides a method and system for predicting total primary productivity of an ecosystem under drought stress, which are used to solve the defect in the prior art that the nonlinear response characteristics of carbon budget variables under drought stress are not considered in the future carbon flux prediction.

[0005] In a first aspect, the present invention provides a method for predicting the total primary productivity of an ecosystem under drought stress, comprising: Acquire multi-source data sets including carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data, and Earth system model output in the study area; The Clausius-Clapeyron thermodynamic equation was used to determine the saturated vapor pressure deficit, relative humidity, and wet-bulb temperature, and the drought characteristics in the study area were extracted based on the run theory and TWSA-DSI: Based on the water and heat factors and drought index TWSA-DSI, combined with the convergent cross-mapping function, the causal relationship between the water and heat stress factors, drought indicators and ecosystem GPP is obtained, and the key water and heat factors affecting GPP are extracted; According to the key water and heat factors affecting GPP and the drought characteristics, the key factors derived from ERA5 reanalysis data and the GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model, and the water and heat factors under different scenarios output by the Earth system model are combined to predict the GPP data under different scenarios in the future; The TWSA-DSI is obtained by combining the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model. The emergence constraint model is constructed based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress.

[0006] According to a method for predicting total primary productivity of an ecosystem under drought stress provided by the present invention, a multi-source data set including carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data and earth system model output is obtained in a study area, including: The quality control and bias correction of several global flux tower observation data provided by the FLUXNET 2015 dataset were performed, and the soil moisture content was interpolated using the marginal distribution method; Respectively converting the atmospheric reanalysis data and the MODIS satellite inversion data into daily-scale data with a preset spatial resolution and monthly-scale data with a preset spatial resolution; The three GRACE / GRACE-FO gravity satellite data sets in the gravity satellite inversion data are interpolated to the preset scale space grid, so that the land water storage anomaly data is consistent with the spatial resolution of the variables extracted by ERA5, and the average of each product is taken in each time step to obtain the monthly scale data set of land water storage anomaly; Five global climate model datasets released by the sixth phase of the International Coupled Model Intercomparison Project (CMIP6) were used.

[0007] According to a method for predicting total primary productivity of an ecosystem under drought stress provided by the present invention, the Clausius-Clapeyron thermodynamic equation is used to determine the saturated water vapor pressure deficit, relative humidity and wet bulb temperature, and the drought characteristics in the study area are extracted based on the run theory and TWSA-DSI, including: The saturated water vapor pressure was determined using the Clausius-Clapeyron thermodynamic equation:

[0008] in, and are the first integral constant and the second integral constant, which are 273.16 K and 611 Pa respectively; Lv is the latent heat of vaporization constant, which is 2.5×106 J kg-1; Rv is the water vapor gas constant, which is 461 J kg-1 K-1; is the saturated vapor pressure deficit, T is the air temperature; The temperature T 2m and dew point temperature T dew Substituting into the Clausius-Clapeyron thermodynamic equation, we get the near-saturation vapor pressure deficit ; The temperature at each grid point ( T 2m ) and dew point temperature ( T dew ) are substituted as input variables into the Clausius-Clapeyron thermodynamic equation to calculate the relative humidity of each grid point ; Based on the air temperature and relative humidity data, calculate the wet bulb temperature at each grid point: Where RH is the monthly average relative humidity, T 2m is the monthly mean temperature, T w is the estimated wet bulb temperature, atan is the inverse tangent function; Extraction of abnormal data of land water storage :

[0009] in, is the jth month of the i-th year TWSA data, and The jth month in the study period TWSA The mean and standard deviation of According to the run theory, the drought frequency, duration and intensity of the period corresponding to the TWSA-DSI being less than the preset drought threshold are determined.

[0010] According to a method for predicting the total primary productivity of an ecosystem under drought stress provided by the present invention, based on water and heat factors and drought index TWSA-DSI, combined with a convergent cross-mapping function, the causal relationship between the water and heat stress factors, drought index and ecosystem GPP is obtained, and key water and heat factors affecting GPP are extracted, including: The hydrothermal factor and the drought index are used as variables X of the convergent cross-mapping function, and GPP is used as variable Y of the convergent cross-mapping function; Constructing a coupled logic differential equation of the convergent cross-mapping function, and determining the intrinsic growth rate of variable X, the intrinsic growth rate of variable Y, the mutual dynamic influence factor of the variables, and the time step of the coupled logic differential equation; The cross-mapping skill coefficients of the variable X and the variable Y are calculated to satisfy the cross-mapping skill coefficients corresponding to the preset convergence conditions, and the key hydrothermal factors affecting GPP are extracted.

[0011] According to a method for predicting the gross primary productivity of an ecosystem under drought stress provided by the present invention, based on the key water and heat factors affecting GPP and the drought characteristics, the key factors of the ERA5 reanalysis data and the GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model, and the water and heat factors under different scenarios output by the earth system model are combined to predict the GPP data under different scenarios in the future, including: Extracting several driving factors from ERA5 reanalysis data, terrestrial water storage data inverted by GRACE gravity satellite and GPP data inverted by MODIS, and taking the driving factors with the top number of preset positions among the several driving factors as key water and heat factors; Determine the key hydrothermal factors corresponding to the current moment and several moments before the current moment as input variables of the long short-term memory machine learning model, and train to obtain a GPP prediction model; The hydrothermal factors, TWSA-DSI and drought characteristics under different scenarios output by the Earth system model are used to drive the GPP prediction model to obtain GPP data under different future scenarios.

[0012] According to a method for predicting the total primary productivity of an ecosystem under drought stress provided by the present invention, the future drought index TWSA-DSI is obtained by combining the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model. An emergent constraint model is constructed based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress, including: Based on the temperature and relative humidity data of each global climate model historical period, combined with the wet-bulb temperature of each grid point in the study area, the linear regression method was used to obtain the trend of annual average wet-bulb temperature; The spatial sliding window method is used to obtain the multi-grid point annual average wet-bulb temperature change trend data under 5 global climate models, and the first emergent constraint sub-model is constructed based on the multi-grid point annual average wet-bulb temperature change trend data, the GPP data under different future scenarios, the first constraint parameter and the second constraint parameter; A spatial sliding window method is used to obtain multi-grid TWSA-DSI under five global climate models, and a second emergent constraint sub-model is constructed based on the multi-grid TWSA-DSI, the GPP data under different future scenarios, the third constraint parameter and the fourth constraint parameter; Solving the first constraint parameter, the second constraint parameter, the third constraint parameter and the fourth constraint parameter based on the least square method, and obtaining the emergence constraint model from the first emergence constraint sub-model and the second emergence constraint sub-model; The emergence constraint model is driven under different future scenarios to obtain the GPP prediction results under drought stress under different scenarios.

[0013] In a second aspect, the present invention further provides a system for predicting total primary productivity of an ecosystem under drought stress, comprising: The acquisition module is used to obtain carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data and multi-source data sets output by the Earth system model in the study area; The first extraction module is used to determine the saturated water vapor pressure deficit, relative humidity and wet bulb temperature using the Clausius-Clapeyron thermodynamic equation, and extract the drought characteristics in the study area based on the run theory and TWSA-DSI: The second extraction module is used to obtain the causal relationship between the water and heat stress factors, drought indicators and the gross primary productivity (GPP) of the ecosystem based on the water and heat factors and the drought indicator TWSA-DSI, combined with the convergent cross-mapping function, and extract the key water and heat factors affecting the GPP; A prediction module is used to construct a long-short term memory machine learning model based on the key water and heat factors affecting GPP and the drought characteristics, using the key factors derived from the fifth generation global climate reanalysis data set ERA5 reanalysis data and the GPP data inverted by MODIS, and combining the water and heat factors under different scenarios output by the earth system model to predict the GPP data under different scenarios in the future; The constraint module is used to combine the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model to obtain the future drought index TWSA-DSI, and construct an emergent constraint model based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress.

[0014] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting the total primary productivity of an ecosystem under drought stress as described in any one of the above is implemented.

[0015] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the prediction of the total primary productivity of an ecosystem under drought stress as described in any of the above.

[0016] The method and system for predicting the total primary productivity of an ecosystem under drought stress provided by the present invention, by coupling the nonlinear response and hydrothermal carbon physical mechanism under drought stress, provide an important and highly operational reference basis for global and regional prediction and evaluation of the response mechanism of future drought evolution to the carbon sink function of the ecosystem under climate change scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a schematic flow chart of a method for predicting total primary productivity of an ecosystem under drought stress provided by the present invention; Figure 2 It is a schematic diagram of the run theory provided by the present invention; Figure 3 It is a schematic diagram of the convergent cross-mapping technology provided by the present invention; Figure 4 It is a schematic diagram of the empty window sliding technology provided by the present invention; Figure 5 It is a schematic diagram of the structure of the method for predicting the total primary productivity of an ecosystem under drought stress provided by the present invention; Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Figure 1 : is a schematic diagram of a method for predicting total primary productivity of an ecosystem under drought stress provided by an embodiment of the present invention, such as Figure 1 As shown, including: Step 100: Obtain a multi-source data set of carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data, and Earth system model output in the study area; Step 200: Using the Clausius-Clapeyron thermodynamic equation to determine the saturated vapor pressure deficit, relative humidity and wet bulb temperature, the drought characteristics in the study area are extracted based on the run theory and TWSA-DSI: Step 300: Based on the water and heat factors and the drought index TWSA-DSI, combined with the convergent cross-mapping function, the causal relationship between the water and heat stress factors, drought index and ecosystem GPP is obtained, and the key water and heat factors affecting GPP are extracted; Step 400: Based on the key water and heat factors affecting GPP and the drought characteristics, the key factors derived from the ERA5 reanalysis data and the GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model, and the water and heat factors under different scenarios output by the earth system model are combined to predict the GPP data under different scenarios in the future; Step 500: Combine the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model to obtain TWSA-DSI, and construct an emergent constraint model based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress.

[0021] Specifically, the method for predicting the total primary productivity of an ecosystem under drought stress proposed in the embodiment of the present invention comprises the following steps: Step 1: Obtain carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data, and multi-source data sets output by the Earth system model in the selected study area, including gridded terrestrial water storage, gross primary productivity (GPP), and water and heat stress factors (temperature, soil moisture, radiation, precipitation, etc.); Step 2: The Clausius-Clapeyron thermodynamic equation is used to determine the saturated water vapor pressure deficit, relative humidity and wet bulb temperature. Based on the run theory and drought index TWSA-DSI, the drought characteristics in the study area are extracted, including frequency, duration and intensity. Step 3: Based on the water and heat factors and drought index TWSA-DSI extracted in Steps 1 and 2, combined with the Convergent Cross Mapping (CCM) function, evaluate the causal relationship between these long series of water and heat factors and drought indicators and the gross primary productivity (GPP) of the ecosystem, and then extract the key water and heat factors that affect GPP; Step 4: Based on step 3, the key water and heat factors affecting GPP and the drought characteristics are determined, and the key factors from the ERA5 reanalysis data since 2000 and the GPP data inverted by MODIS are used to build a long-short-term memory machine learning model, combined with the water and heat factors under different scenarios output by the Earth system model, to predict the GPP data under different scenarios in the future; Step 5: Combine the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model to deduce the future drought index TWSA-DSI; based on the GPP data set predicted in step 4, construct an emergence constraint model to reduce the uncertainty of the prediction of gross primary productivity GPP under future drought stress.

[0022] By coupling the nonlinear response and hydrothermal carbon physical mechanism under drought stress, the present invention provides an important and highly operational reference for global and regional prediction and assessment of the response mechanism of future drought evolution to the carbon sink function of ecosystems under climate change scenarios.

[0023] Based on the above embodiment, step 100 includes: Step 1.1, collect carbon flux observation data in the study area, use the global M flux tower observation data provided by the FLUXNET 2015 dataset, and select variables including soil moisture (including all soil layers), temperature, precipitation, radiation, relative humidity and GPP. The above data have been strictly quality controlled and bias corrected, and the missing values ​​of soil moisture are interpolated using the marginal distribution (MD) method. In this embodiment, M is 212.

[0024] For each grid point in the study area, daily precipitation, near-ground 2m air temperature, 2m dew point temperature, soil moisture content, shortwave radiation and other water and heat factors in the study area during 1985-2020 were collected from the fifth-generation atmospheric reanalysis dataset (ERA5) of the European Centre for Medium-Range Weather Forecasts, and the above variables were converted into daily-scale data with a spatial resolution of 0.5°×0.5°.

[0025] This example uses the global gridded GPP product from 2000 to 2019 in the study area based on MODIS data and light use efficiency theory inversion. This dataset uses the most advanced vegetation index gap filling and smoothing algorithms and separate processing of C3 / C4 photosynthesis pathways, and has better performance when verifying in situ GPP estimation. This example converts the above variables into monthly scale data with a spatial resolution of 0.5°×0.5°.

[0026] In step 1.2, the three GRACE / GRACE-FO gravity satellite datasets were interpolated to a 0.5° × 0.5° spatial grid to make the spatial resolution of the variables extracted from the TWSA data and the ERA5 reanalysis data consistent. The average of each product was taken in each time step to finally obtain the TWSA monthly dataset.

[0027] The Earth's gravity field information observed by the GRACE / GRACE-FO satellites can be used to invert terrestrial water storage. The Potsdam Geoscience Center in Germany, the Jet Propulsion Laboratory (JPL) of the California Institute of Technology, the Center for Space Research at University of Texas, Austin (CSR) and the Goddard Space Flight Center (GSFC) of NASA are responsible for data interpretation and have released global gravity field output data on a monthly scale. In this embodiment, the latest mascon (mass concentration) products released by JPL, CSR and GSFC are used at the same time to output a long sequence of TWSA (Terrestrial water storage anomaly) data sets. In order to consider the uncertainties that may be caused by different data products, the three sets of GRACE gravity satellite data are interpolated to a 0.5°×0.5° spatial grid, and the average of each product is taken in each time step, and finally the TWSA monthly scale data set covering the study area from 2002 to the present is obtained.

[0028] Step 1.3, to predict future climate scenarios, the five global climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) released recently in the sixth phase of the International Coupled Model Inter-comparison Project (CMIP6) are used. Compared with the earlier version (CMIP5), CMIP6 uses the matrix framework of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). This example uses meteorological data output by the models of three emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5). The output variables of the five global climate models include data sets such as monthly temperature, relative humidity, precipitation, soil moisture, radiation and carbon flux. The period from 1985 to 2014 (30 years in total) was selected as the historical period. In order to predict the meteorological characteristics under future climate change scenarios, the last 30-year window of this century (2071-2100) was set as the future period.

[0029] The global hydrological model H08 is further used to drive the above five global climate models to obtain monthly land water storage data, covering the historical period and the future period. Among them, the H08 model and related driving methods are conventional technologies in this field.

[0030] Based on the above embodiment, step 200 includes: Step 2.1, determine the saturation vapor pressure deficit, relative humidity and wet bulb temperature using the Clausius-Clapeyron thermodynamic equation; The Clausius-Clapeyron thermodynamic equation can quantitatively describe the saturated water vapor pressure Nonlinear relationship with temperature T: (1) in, and are the first and second integral constants, which are 273.16 K and 611 Pa respectively; L v is the latent heat of vaporization constant, taken as 2.5×106 J kg-1; R v is the gas constant for water vapor, taken as 461 J kg-1 K-1; is the saturated vapor pressure deficit and T is the air temperature.

[0031] The dew point temperature represents the temperature at which the air is cooled to water vapor saturation under the condition of constant water vapor content and air pressure. Substituting it into the Clausius-Clapeyron equation can measure the actual water vapor pressure. T 2m ) and dew point temperature ( T dew ) are substituted into formula (1) to deduce the near-saturation vapor pressure deficit .

[0032] Furthermore, the temperature at each grid point ( T 2m ) and dew point temperature ( T dew ) are substituted into the Clausius-Clapeyron thermodynamic equation as input variables to calculate the relative humidity of each grid point ( RH ), specifically .

[0033] Furthermore, based on the air temperature and relative humidity data, the wet bulb temperature of each grid point is calculated using the following formula: (2) Where: RH is the monthly average relative humidity, T 2m is the monthly mean temperature, T wis the wet-bulb temperature to be derived, and atan is the inverse tangent function.

[0034] Step 2.2, using the terrestrial water storage TWSA data inverted by gravity satellites, the drought index TWSA-DSI is extracted, and further based on the run theory, the drought characteristics in the study area are extracted, including frequency, duration, and intensity.

[0035] Drought Index TWSA-DSI It is a dimensionless normalized water storage anomaly index that is spatially comparable across different hydroclimatic regions. TWSA-DSI The calculation formula is as follows: (3) Where: Represents the jth month of the i-th year TWSA data, and The jth month in the study period TWSA The mean and standard deviation of .

[0036] This embodiment uses TWSA-DSI<-0.8 to identify drought, and uses the run theory to determine that when the abnormal land water storage drought index TWSA-DSI is less than the preset drought threshold, the annual drought frequency, duration and intensity of the corresponding period are extracted. Figure 2 As shown, a schematic diagram of run theory is given.

[0037] Based on the above embodiment, step 300 includes: Eight hydrothermal factors, including soil moisture (including all soil layers), air temperature, precipitation, evaporation, radiation, relative humidity, saturated vapor pressure deficit, and drought index TWSA-DSI obtained in step 2, were selected as independent variables based on the flux station observation data obtained in step 1, and carbon flux GPP was selected as the dependent variable. The convergent cross-mapping technique was used to detect the causal relationship path between hydrothermal factors and carbon flux, and to identify the key hydrothermal influencing factors of carbon flux GPP.

[0038] CCM is derived from the phase space reconstruction theory and is an effective method for analyzing causal relationships and false correlations in multivariate time series of nonlinear dynamic systems. It is suitable for time series data analysis of complex ecosystems and can identify whether there is a nonlinear causal relationship between variables. Based on Takens theory and its extension theorem, the shadow manifold M is determined using observable time series. X and M Y Since variables X and Y are dynamically coupled, M X and M Y The points on the graph correspond to each other in time, representing the correlation between X and Y. The main algorithm is based on the nearest neighbor prediction method, which involves tracking the forward trajectory of nearby points in the lagged coordinate embedding.X Point M on Y The points on the graph are predicted, and the causal relationship is reflected according to the prediction ability. Then, by changing the length of the time series L, different cross-mapping skills ρ are calculated, and the trend graph of its change with the increase of L is obtained to observe whether it converges. The convergence of CCM refers to the property that the correlation coefficient between the predicted value and the observed value (cross-mapping skill ρ) increases with the increase of the time series length L of the constructed sample, and remains stable after reaching the extreme value. Its model can be represented by two coupled logical difference equations: (4) (5) in and is the time step, and represent the intrinsic growth rates of the variables, and Variables For variables Dynamic impact and variables For variables dynamic impact.

[0039] In this embodiment, variable X represents water and heat factors (air temperature, soil moisture, radiation, precipitation, saturated vapor pressure deficit, relative humidity, wet bulb temperature and drought index TWSA-DSI calculated in step 2 observed by FLUX flux), and variable Y represents gross primary productivity GPP based on FLUX flux observation. The cross-mapping skill ρ is defined as the correlation coefficient between variables X and Y. If ρ increases with the length of the time series and there is convergence, the bidirectional causal effect of X and Y can be inferred, and then the main influencing factors of X or Y can be judged by the high or low ρ value that remains stable after converging to the extreme value, thereby extracting the top 8 key water and heat factors affecting GPP. Figure 3 As shown, a schematic diagram of the convergent cross-mapping technique is given.

[0040] Based on the above embodiment, step 400 includes: Step 4.1, based on step 3, determine the key water and heat factors affecting GPP and the drought characteristics, use the key factors derived from ERA5 reanalysis data since 2000 and the GPP data inverted by MODIS to construct a relationship model between the driving factors and GPP.

[0041] The key hydrothermal factors affecting GPP determined in step 3 can be used as explanatory variables; further use ERA5 reanalysis data, GRACE gravity satellite inverted land water storage data and MODIS inverted GPP data to extract driving factors, including temperature, saturated water vapor pressure deficit, precipitation, soil moisture, shortwave radiation, relative humidity, evaporation, drought frequency, duration, and intensity (a total of 10 driving factors), and match the annual drought characteristics with the monthly scale key hydrothermal factors, that is, the monthly drought characteristics of the same year are set to the same value, and the first 8 key hydrothermal factors are selected as input variables. The input data of the machine learning model are all MODIS satellite inversion period (2000-2019), and the time lag effect of each driving factor on GPP is considered. For each month, the driving factors of this month and the previous 1-3 months are selected. For this embodiment, there are 8×4=32 hydrothermal factors as model inputs for training long short-term memory machine learning models.

[0042] The constructed long short-term memory machine learning model is used to simulate the GPP data since 2000, which is expressed as: (6) in, represents the total primary productivity simulated by machine learning at time t, represents the input variable at time t, represents the input variable at time t-1, represents the input variable at time t-2, represents the input variable at time t-3; F represents the long short-term memory machine learning model.

[0043] Step 4.2, based on the key factors selected in step 3 and the GPP prediction model constructed in step 4.1, the long series of key hydrothermal factors and terrestrial water reserves under different scenarios output by the Earth system model are used, combined with step 2 to extract the drought index TWSA-DSI for different future scenarios, and further combined with the run theory to obtain the drought frequency, duration, and intensity under different future scenarios, driving the long-term and short-term machine learning model established in step 4.1 to predict the long series of GPP data sets under different future scenarios.

[0044] Based on the above embodiment, step 500 includes: Step 5.1: Based on the temperature and relative humidity data of each global climate model historical period, the wet-bulb temperature of each grid point in the study area is calculated in combination with step 2, and the linear regression method is used to deduce the changing trend of the annual average wet-bulb temperature.

[0045] Furthermore, the air temperature of the ERA5 dataset and the relative humidity data derived in step 2 are used to deduce the wet-bulb temperature of each grid point during 1985-2014, and its changing trend is calculated.

[0046] Step 5.2, such as Figure 4 As shown in Figure 2, for each grid point, the spatial sliding window method is used to search for the grid point and its neighboring grid points, a total of 25 grid points. For each grid point, the trend of the annual average wet-bulb temperature in the historical period under each global climate model is calculated (denoted as Twet_Trend ). Further, the data of 25 grid points in 5 global climate models were integrated to obtain 125 sets of historical annual average wet-bulb temperature change trends and the future period predictions in step 4. The pairing combination of , constructs the constraint model 1 as follows: (7) Where: a1 and b1 represent the parameters of the emergence constraint model 1, Twet_Trend is the trend of wet bulb temperature; In addition, considering the stress effect of drought on the total primary productivity of the ecosystem, the drought index TWSA-DSI for the historical and future periods is obtained based on the long series of terrestrial water storage sequences output by the Earth system model. For each grid point, the change trend of the historical average annual drought index TWSA-DSI under each global climate model is calculated (denoted as TWSADSI_Trend ). Furthermore, the data of 25 grid points in 5 global climate models were integrated to obtain 125 sets of historical average annual drought index TWSA-DSI change trends and the future period predicted by step 4. The pairing combination of , constructs the constraint model 2 as follows: (8) Where: a2 and b2 represent the parameters of the emergence constraint model 2, TWSADSI-Trend is the trend of drought index TWSA-DSI. Figure 4 As shown, a schematic diagram of the empty window sliding technology is given.

[0047] Step 5.3, using the least squares method to solve the parameters a1, a2, b1 and b2 of the emergence constraint model; based on the future terrestrial water storage data predicted by the H08 model, combined with step 2, the drought index TWSA-DSI is extracted. In this embodiment, TWSA-DSI<-0.8 is used to identify drought, and then the GPP anomaly of the future drought month is extracted, which is recorded as the predicted result of the total primary productivity under drought stress in the future period. .

[0048] Based on the reanalysis data of the ERA5 dataset, the changing trend of GPP anomalies under drought stress during 1985-2014 was deduced, and the trend term was substituted into the constructed emergence constraint model, as follows: (9) Where: It represents the predicted results of total primary productivity under drought stress in the future period after correction; Twet_Trend is the changing trend of the annual average wet-bulb temperature at this grid point obtained based on step 2; TWSADSI-Trend is the trend of the annual average drought index TWSA-DSI obtained in step 2 at this grid point; parameters a1, a2, b1 and b2 come from the parameters determined by the emergence constraint models one and two.

[0049] Step 5.4, construct the emergence constraint model under different future scenarios, and finally obtain the prediction results of total primary productivity under drought stress under three different scenarios in the future.

[0050] The total primary productivity prediction system of an ecosystem under drought stress provided by the present invention is described below. The total primary productivity prediction system of an ecosystem under drought stress described below and the total primary productivity prediction method of an ecosystem under drought stress described above can be referenced to each other.

[0051] Figure 5 is a schematic diagram of the structure of a method for predicting total primary productivity of an ecosystem under drought stress provided by an embodiment of the present invention. Figure 5 As shown, it includes: an acquisition module 51, a first extraction module 52, a second extraction module 53, a prediction module 54 and a constraint module 55, wherein: The acquisition module 51 is used to obtain carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data and multi-source data sets output by the earth system model in the study area; the first extraction module 52 is used to determine the saturated water vapor pressure deficit, relative humidity and wet bulb temperature using the Clausius-Clapeyron thermodynamic equation, and extract the drought characteristics in the study area based on the run theory and TWSA-DSI: the second extraction module 53 is used to obtain the causal relationship between the water and heat stress factors, drought indicators and the gross primary productivity (GPP) of the ecosystem based on the water and heat factors and the drought index TWSA-DSI, combined with the convergent cross-mapping function, and extract the key water and heat factors affecting GPP. Factors; the prediction module 54 is used to construct a long-short-term memory machine learning model based on the key water and heat factors affecting GPP, using the key factors derived from the fifth-generation global climate reanalysis data set ERA5 reanalysis data and the GPP data inverted by MODIS, and combining the water and heat factors under different scenarios output by the earth system model to predict the GPP data under different scenarios in the future; the constraint module 55 is used to combine the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model to obtain the future drought index TWSA-DSI, and construct an emergent constraint model based on the GPP data under different scenarios in the future to reduce the uncertainty of GPP prediction under future drought stress.

[0052] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630 and a communications bus 640, wherein the processor 610, the communications interface 620 and the memory 630 communicate with each other through the communications bus 640. The processor 610 may call the logic instructions in the memory 630 to execute a method for predicting the total primary productivity of an ecosystem under drought stress, the method comprising: obtaining carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data and a multi-source data set output by an earth system model in the study area; using the Clausius-Clapeyron thermodynamic equation to determine the saturated water vapor pressure deficit, relative humidity and wet bulb temperature, and extracting drought characteristics in the study area based on the run theory and TWSA-DSI: based on the water and heat factor and the drought index TWSA-DSI, combined with the convergent cross-mapping function, obtaining the water and heat stress factor, The causal relationship between drought indicators and ecosystem GPP is established, and the key water and heat factors affecting GPP are extracted; based on the key water and heat factors affecting GPP, the key factors derived from the ERA5 reanalysis data and the GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model, and the water and heat factors under different scenarios output by the earth system model are combined to predict the GPP data under different scenarios in the future; the TWSA-DSI is obtained by combining the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model, and an emergence constraint model is constructed based on the GPP data under different scenarios in the future to reduce the uncertainty of GPP prediction under future drought stress.

[0053] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0054] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the method for predicting the total primary productivity of an ecosystem under drought stress provided by the above methods, the method comprising: obtaining carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data, and a multi-source data set output by an earth system model in a study area; using the Clausius-Clapeyron thermodynamic equation to determine the saturated water vapor pressure deficit, relative humidity, and wet-bulb temperature, extracting drought characteristics in the study area based on run theory and TWSA-DSI: based on water and heat factors and drought index TWSA-DSI, combined with convergence A cross-mapping function is used to obtain the causal relationship between the water and heat stress factors, drought indicators and ecosystem GPP, and to extract the key water and heat factors affecting GPP; based on the key water and heat factors affecting GPP, the key factors derived from the ERA5 reanalysis data and the GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model, and the water and heat factors under different scenarios output by the earth system model are combined to predict the GPP data under different scenarios in the future; the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model are combined to obtain TWSA-DSI, and an emergence constraint model is constructed based on the GPP data under different scenarios in the future to reduce the uncertainty of GPP prediction under future drought stress.

[0055] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0056] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0057] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the total primary productivity of an ecosystem under drought stress, characterized in that: include: Acquire multi-source data sets including carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data, and Earth system model output in the study area; The Clausius-Clapeyron thermodynamic equation was used to determine the saturated vapor pressure deficit, relative humidity and wet bulb temperature, and the drought characteristics in the study area were extracted based on the run theory and the abnormal drought index of terrestrial water storage - DSI: Based on the water and heat factors and drought index TWSA-DSI, combined with the convergent cross-mapping function, the causal relationship between the water and heat stress factors, drought indicators and the gross primary productivity (GPP) of the ecosystem is obtained, and the key water and heat factors affecting GPP are extracted; According to the key water and heat factors affecting GPP and the drought characteristics, the key factors derived from the fifth-generation global climate reanalysis data set ERA5 reanalysis data and the GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model, combined with the water and heat factors under different scenarios output by the earth system model, to predict the GPP data under different scenarios in the future; The future drought index TWSA-DSI is obtained by combining the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model. The emergence constraint model is constructed based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress.

2. The method for predicting total primary productivity of an ecosystem under drought stress according to claim 1, characterized in that: Acquire multi-source data sets including carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data and Earth system model output in the study area, including: The quality control and bias correction of several global flux tower observation data provided by the FLUXNET 2015 dataset were performed, and the soil moisture content was interpolated using the marginal distribution method; Respectively converting the atmospheric reanalysis data and the MODIS satellite inversion data into daily-scale data with a preset spatial resolution and monthly-scale data with a preset spatial resolution; The three GRACE / GRACE-FO gravity satellite data sets in the gravity satellite inversion data are interpolated to the preset scale space grid, so that the land water storage anomaly data is consistent with the spatial resolution of the variables extracted by ERA5, and the average of each product is taken in each time step to obtain the monthly land water storage anomaly data set; Five global climate model datasets released by the sixth phase of the International Coupled Model Intercomparison Project (CMIP6) were used.

3. The method for predicting total primary productivity of an ecosystem under drought stress according to claim 1, characterized in that: The Clausius-Clapeyron thermodynamic equation was used to determine the saturated vapor pressure deficit, relative humidity and wet bulb temperature. The drought characteristics in the study area were extracted based on the run theory and TWSA-DSI, including: The saturated water vapor pressure was determined using the Clausius-Clapeyron thermodynamic equation: in, and are the first integral constant and the second integral constant, which are 273.16 K and 611 Pa respectively; Lv is the latent heat of vaporization constant, which is 2.5×106 J kg-1; Rv is the water vapor gas constant, which is 461 J kg-1 K-1; is the saturated vapor pressure deficit, T is the air temperature; The temperature T 2m and dew point temperature T dew Substituting into the Clausius-Clapeyron thermodynamic equation, we get the near-saturation vapor pressure deficit ; The temperature at each grid point ( T 2m ) and dew point temperature ( T dew ) are substituted as input variables into the Clausius-Clapeyron thermodynamic equation to calculate the relative humidity of each grid point ; Based on the air temperature and relative humidity data, calculate the wet bulb temperature at each grid point: Where RH is the monthly average relative humidity, T 2m is the monthly mean temperature, T w is the estimated wet bulb temperature, atan is the inverse tangent function; Extraction of abnormal data of land water storage : in, is the jth month of the i-th year TWSA data, and The jth month in the study period TWSA The mean and standard deviation of According to the run theory, the drought frequency, duration and intensity of the period corresponding to the TWSA-DSI being less than the preset drought threshold are determined.

4. The method for predicting total primary productivity of an ecosystem under drought stress according to claim 1, characterized in that: Based on the water and heat factors and drought index TWSA-DSI, combined with the convergent cross-mapping function, the causal relationship between the water and heat stress factors, drought indicators and ecosystem GPP is obtained, and the key water and heat factors affecting GPP are extracted, including: The hydrothermal factor and the drought index TWSA-DSI are used as variables X of the convergent cross-mapping function, and GPP is used as variable Y of the convergent cross-mapping function; Constructing a coupled logic differential equation of the convergent cross-mapping function, and determining the intrinsic growth rate of variable X, the intrinsic growth rate of variable Y, the mutual dynamic influence factor of the variables, and the time step of the coupled logic differential equation; The cross-mapping skill coefficients of the variable X and the variable Y are calculated to satisfy the cross-mapping skill coefficients corresponding to the preset convergence conditions, and the key hydrothermal factors affecting GPP are extracted.

5. The method for predicting total primary productivity of an ecosystem under drought stress according to claim 1, characterized in that: According to the key water and heat factors affecting GPP and the drought characteristics, the key factors of ERA5 reanalysis data and GPP data inverted by MODIS are used to construct a long-short-term memory machine learning model. Combined with the water and heat factors under different scenarios output by the Earth system model, the GPP data under different scenarios in the future are predicted, including: Extracting several driving factors from ERA5 reanalysis data, terrestrial water storage data inverted by GRACE gravity satellite and GPP data inverted by MODIS, and taking the driving factors with the top number of preset positions among the several driving factors as key water and heat factors; Determine the key hydrothermal factors corresponding to the current moment and several moments before the current moment as input variables of the long short-term memory machine learning model, and train to obtain a GPP prediction model; The hydrothermal factors, TWSA-DSI and drought characteristics under different scenarios output by the Earth system model are used to drive the GPP prediction model to obtain GPP data under different future scenarios.

6. The method for predicting total primary productivity of an ecosystem under drought stress according to claim 1, characterized in that: Combining the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model, the future drought index TWSA-DSI is obtained. The emergence constraint model is constructed based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress, including: Based on the temperature and relative humidity data of each global climate model historical period, combined with the wet-bulb temperature of each grid point in the study area, the linear regression method was used to obtain the trend of annual average wet-bulb temperature; The spatial sliding window method is used to obtain the multi-grid point annual average wet-bulb temperature change trend data under 5 global climate models, and the first emergent constraint sub-model is constructed based on the multi-grid point annual average wet-bulb temperature change trend data, the GPP data under different future scenarios, the first constraint parameter and the second constraint parameter; A spatial sliding window method is used to obtain multi-grid TWSA-DSI under five global climate models, and a second emergent constraint sub-model is constructed based on the multi-grid TWSA-DSI, the GPP data under different future scenarios, the third constraint parameter and the fourth constraint parameter; Solving the first constraint parameter, the second constraint parameter, the third constraint parameter and the fourth constraint parameter based on the least square method, and obtaining the emergence constraint model from the first emergence constraint sub-model and the second emergence constraint sub-model; The emergence constraint model is driven under different future scenarios to obtain the GPP prediction results under drought stress under different scenarios.

7. A system for predicting total primary productivity of ecosystems under drought stress, characterized in that: include: The acquisition module is used to obtain carbon flux observation data, atmospheric reanalysis data, MODIS satellite inversion data, gravity satellite inversion data and multi-source data sets output by the Earth system model in the study area; The first extraction module is used to determine the saturated water vapor pressure deficit, relative humidity and wet bulb temperature using the Clausius-Clapeyron thermodynamic equation, and extract the drought characteristics in the study area based on the run theory and TWSA-DSI: The second extraction module is used to obtain the causal relationship between the water and heat stress factors, drought indicators and the gross primary productivity (GPP) of the ecosystem based on the water and heat factors and the drought indicator TWSA-DSI, combined with the convergent cross-mapping function, and extract the key water and heat factors affecting the GPP; A prediction module is used to construct a long-short term memory machine learning model based on the key water and heat factors affecting GPP and the drought characteristics, using the key factors derived from the fifth generation global climate reanalysis data set ERA5 reanalysis data and the GPP data inverted by MODIS, and combining the water and heat factors under different scenarios output by the earth system model to predict the GPP data under different scenarios in the future; The constraint module is used to combine the wet-bulb temperature and terrestrial water storage TWS data of each grid point in the study area output by the global climate model to obtain the future drought index TWSA-DSI, and construct an emergent constraint model based on the GPP data under different future scenarios to reduce the uncertainty of GPP prediction under future drought stress.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for predicting the total primary productivity of an ecosystem under drought stress as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the total primary productivity of an ecosystem under drought stress as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the total primary productivity of an ecosystem under drought stress as claimed in any one of claims 1 to 6 is implemented.

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