Method and system for calculating vegetation carbon sequestration in artificial rain enhancement operation, and electronic device

Abstract

This invention provides a method, system, and electronic equipment for calculating vegetation carbon sequestration during artificial rain enhancement operations. The method includes: acquiring raw meteorological observation data of the assessment area to obtain daily average temperature and daily precipitation datasets; obtaining raster datasets of daily average temperature and daily precipitation for the target period in the assessment area, with consistent spatial resolution; obtaining the average biological temperature and total precipitation for the target year based on the raster data of daily average temperature and daily precipitation; calculating the artificial rain enhancement amount for the target year using an index threshold method, and obtaining the natural precipitation amount by the difference between the total precipitation and the artificial rain enhancement amount for the target year; obtaining the NPP (Natural Precipitation Percentage) under the total precipitation and natural precipitation for the target year using the Zhou Guangsheng-Zhang Xinshi model, and obtaining the NPP of artificial rain enhancement based on the difference between the two; and calculating the total vegetation carbon sequestration amount based on the NPP of artificial rain enhancement, spatial resolution, and preset coefficients.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of ecological meteorology and carbon sequestration accounting, and in particular to a method, system and electronic equipment for calculating vegetation carbon sequestration in artificial rain enhancement operations. Background Technology

[0002] With the intensification of global climate change, the construction of ecological carbon sinks has become an important tool for achieving the "dual carbon" goals. Artificial rain enhancement, as a key technology for precise regulation of the atmospheric water cycle, can effectively alleviate regional vegetation water stress, promote vegetation photosynthesis and biomass accumulation, and thus enhance vegetation carbon sequestration capacity. It has been widely applied in various ecological projects such as forest restoration, grassland management, and desertification control. Against this backdrop, the precise quantification of the vegetation carbon sequestration effect brought about by artificial rain enhancement operations has become a major component of its ecological benefit evaluation and a key support for the construction of ecological carbon sinks using weather modification technology.

[0003] Currently, the estimation of vegetation carbon sequestration by artificial rain enhancement is mostly based on measured or remote sensing inversion data of vegetation net primary productivity (NPP), using traditional statistical methods that compare the operational area with the control area or the time series before and after the operation. However, these methods have significant technical limitations. On the one hand, changes in vegetation NPP are driven by multiple factors such as temperature, light, soil fertility, and human activities. Traditional statistical methods cannot effectively separate the independent contribution of precipitation increment to NPP changes, which can easily lead to significant biases in carbon sequestration assessment results. On the other hand, existing technologies have not yet established a quantitative response relationship between artificial rain enhancement, NPP increment, and carbon sequestration increment, and cannot achieve a mechanistic and chain-like accounting of the effect of artificial rain enhancement operations to ecological carbon sequestration benefits, resulting in assessment results lacking sufficient theoretical and mechanistic support.

[0004] In arid and semi-arid regions sensitive to precipitation, rainfall is a major factor restricting vegetation growth and carbon sequestration, making the ecological carbon sequestration benefits of artificial rain enhancement particularly prominent and of great significance to the construction of ecological carbon sinks in these regions. However, existing assessment methods fail to effectively combine the mechanistic advantages of NPP estimation models, and cannot establish a quantitative correlation between increased precipitation and enhanced vegetation carbon sequestration, thus failing to meet the needs for regionalized and refined assessment of the ecological benefits of artificial rain enhancement. Summary of the Invention

[0005] This invention provides a method for calculating vegetation carbon sequestration during artificial rain enhancement operations, comprising: acquiring raw meteorological observation data of the assessment area to obtain daily average temperature and daily precipitation datasets; obtaining raster datasets of the daily average temperature and daily precipitation within a target period in the assessment area, wherein the raster datasets have consistent spatial resolution; obtaining the average biological temperature and total precipitation of the target year based on the raster data of the daily average temperature and daily precipitation; calculating the amount of artificial rain enhancement for the target year using an index threshold method, and obtaining the natural precipitation by the difference between the total precipitation of the target year and the amount of artificial rain enhancement for the target year; obtaining the net primary productivity of vegetation under the total precipitation and natural precipitation of the target year using the Zhou Guangsheng-Zhang Xinshi model, and obtaining the net primary productivity of vegetation for artificial rain enhancement based on the difference between the two; and calculating the total amount of vegetation carbon sequestration by artificial rain enhancement based on the net primary productivity of vegetation for artificial rain enhancement, the spatial resolution, and a preset coefficient.

[0006] According to one embodiment of the present invention, obtaining the average biological temperature of the target year includes: confirming that the daily average temperature is greater than 30° or lower than 0°, and assigning the daily average temperature a value of 0; calculating the average biological temperature of the target year based on the sum of the daily average temperatures within the target year and the total number of days in the target year.

[0007] According to one embodiment of the present invention, the step of calculating the artificial rain enhancement amount for the target year using the index threshold method includes: screening operational parameters based on existing artificial rain enhancement summary results to construct an artificial rain enhancement rate index threshold library; obtaining operational information of the operation to be evaluated; determining the catalyst dispensing location based on the operational information of the operation to be evaluated, and matching and binding the catalyst location with geospatial information; analyzing the rationality of the operational information of the operation to be evaluated by referring to a rationality judgment index library; merging and deduplicating overlapping impact areas by simulating the spatial range of the operation's impact using a catalyst transport and diffusion model and a three-dimensional wind field, and outputting the operation's impact duration and hourly impact area product; matching the operational information of the operation to be evaluated with the artificial rain enhancement rate index threshold library according to the hourly impact area product and the operation's impact duration to obtain the artificial rain enhancement index threshold of the operation to be evaluated; and calculating the hourly rain enhancement grid product, the horizontal distribution of rain enhancement at any time period, the hourly rain enhancement, and the rain enhancement at any time period using the index threshold method based on the artificial rain enhancement index threshold.

[0008] According to one embodiment of the present invention, the step of calculating the artificial rainfall enhancement amount for the target year using the index threshold method further includes: according to the region, cloud conditions, and operation mode corresponding to the operation to be evaluated, matching and obtaining the artificial rainfall enhancement index threshold corresponding to the operation to be evaluated based on the artificial rainfall enhancement rate index threshold library; based on the artificial rainfall enhancement index threshold, using the hourly impact area product, calculating the hourly impact area of ​​the region corresponding to the operation to be evaluated and the hourly average precipitation that spatiotemporally matches the hourly impact area; calculating the hourly rainfall enhancement amount and the rainfall enhancement amount for any time period using the index threshold method; and summarizing the time period data corresponding to the target year from the rainfall enhancement amount for any time period to obtain the artificial rainfall enhancement amount for the target year.

[0009] According to one embodiment of the present invention, the Zhou Guangsheng-Zhang Xinshi model is as follows: ; ; ; in, Indicates net primary productivity of vegetation. K The unit conversion factor is represented by RDI, which represents radiative dryness, and P represents total precipitation or natural precipitation. RDI Indicates radiation dryness. PER Indicates the possible evapotranspiration rate.

[0010] According to one embodiment of the present invention, the calculation of the total carbon sequestration by vegetation under artificial rain enhancement based on the net primary productivity of vegetation, the spatial resolution, and a preset coefficient specifically includes: calculating the actual land surface area corresponding to each raster cell based on the spatial resolution; calculating the vegetation carbon sequestration of each cell based on the increase in net primary productivity of vegetation in the raster data of vegetation under artificial rain enhancement and the actual land surface area corresponding to the cell; summing up the vegetation carbon sequestration of all raster cells and multiplying it by the preset coefficient to obtain the total carbon sequestration by vegetation under artificial rain enhancement in the evaluation area.

[0011] According to one embodiment of the present invention, obtaining the raster dataset of the daily average temperature and the daily precipitation within the target time period of the assessment area includes: performing spatial interpolation processing on the daily average temperature and the daily precipitation using the Kriging interpolation method.

[0012] According to one embodiment of the present invention, the method further includes preprocessing after acquiring the original meteorological observation data and before obtaining the daily average temperature and daily precipitation dataset, specifically including: confirming that there are outliers in the daily average temperature and removing the outliers; confirming that there are negative values ​​in the daily precipitation and removing or setting the daily precipitation to zero; confirming that the daily precipitation is a trace amount and assigning the daily precipitation to a preset trace amount value; and using interpolation to fill in the missing values ​​of the daily average temperature and the daily precipitation.

[0013] This invention also provides a vegetation carbon sequestration calculation system for artificial rain enhancement operations, comprising: a meteorological data acquisition module for acquiring raw meteorological observation data of the assessment area to obtain daily average temperature and daily precipitation datasets; a raster data generation module for obtaining a raster dataset of the daily average temperature and daily precipitation within a target period in the assessment area, wherein the raster datasets have consistent spatial resolution; an annual parameter calculation module for obtaining the average biological temperature and total precipitation for the target year based on the raster data of the daily average temperature and daily precipitation; and an artificial rain enhancement amount calculation module. The module is used to calculate the artificial rainfall amount for the target year using the index threshold method, and obtain the natural precipitation by the difference between the total precipitation of the target year and the artificial rainfall amount for the target year; the vegetation net primary productivity calculation module is used to obtain the vegetation net primary productivity under the total precipitation and natural precipitation of the target year using the Zhou Guangsheng-Zhang Xinshi model, and obtain the vegetation net primary productivity of artificial rainfall based on the difference between the two; the total carbon sequestration calculation module is used to calculate the total carbon sequestration of vegetation by artificial rainfall based on the vegetation net primary productivity of artificial rainfall, the spatial resolution and preset coefficients.

[0014] The present invention also provides an electronic device, including 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 steps of the method for calculating vegetation carbon sequestration in artificial rain enhancement operations as described above.

[0015] The present invention provides a method for calculating vegetation carbon sequestration during artificial rain enhancement operations. This method effectively solves the technical problems of insufficient accuracy and lack of mechanistic support in the assessment of vegetation carbon sequestration during artificial rain enhancement in existing technologies. By organically integrating standardized meteorological data processing, accurate calculation of artificial rain enhancement volume, and NPP estimation models, a complete quantitative accounting system for vegetation carbon sequestration during artificial rain enhancement is formed, significantly improving the scientific rigor and accuracy of the ecological benefit assessment of artificial rain enhancement. This method simulates and calculates NPP under two scenarios: total precipitation and natural precipitation. The difference between the two calculation results represents the NPP increase brought about by artificial rain enhancement. It can effectively eliminate the interference of other environmental factors such as temperature, light, and soil fertility on vegetation growth, accurately isolate the independent contribution of artificial rain enhancement to vegetation carbon sequestration, and make the calculation results of vegetation carbon sequestration during artificial rain enhancement more consistent with the actual ecological environment.

[0016] The method utilizes spatial interpolation technology to generate a meteorological raster dataset covering the entire assessment area with consistent spatial resolution. It performs pixel-by-pixel calculations and accumulations based on raster cells, fully considering the spatial distribution differences of meteorological factors and vegetation growth status. This enables refined spatial accounting of carbon sequestration by vegetation in artificial rain enhancement, adapting to the accounting needs of assessment areas with different scales and terrain features, significantly improving the method's applicability and accuracy. Simultaneously, the method deeply couples the index threshold method with the NPP estimation model, establishing a complete quantitative conversion process from artificial rain enhancement amount accounting to NPP increment calculation and then to vegetation carbon sequestration total conversion. This achieves a chain-like accounting from the effects of artificial rain enhancement operations to ecological carbon sequestration benefits, allowing the assessment of vegetation carbon sequestration by artificial rain enhancement to be based on ecological mechanisms through quantitative calculations. This provides sufficient theoretical support for the assessment results and changes the traditional assessment method that relies on statistical comparisons. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the method for calculating vegetation carbon sequestration in artificial rain enhancement operations provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the process for obtaining the average biological temperature of a target year, provided by the present invention.

[0020] Figure 3 This is one of the flowcharts provided by the present invention for calculating the amount of artificial rain enhancement for a target year using the index threshold method.

[0021] Figure 4 This is the second flowchart of the process for calculating the amount of artificial rain enhancement for a target year using the index threshold method provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the process for calculating the total carbon sequestration of vegetation by artificial rain enhancement based on the net primary productivity and spatial resolution of vegetation and preset coefficients provided by the present invention.

[0023] Figure 6 This is a schematic diagram of the preprocessing flow provided by the present invention.

[0024] Figure 7 This is a block diagram of the vegetation carbon sequestration calculation system for artificial rain enhancement operations provided by the present invention.

[0025] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0026] Figure label: 100: Vegetation carbon sequestration calculation system for artificial rain enhancement operations; 110: Meteorological data acquisition module; 120: Raster data generation module; 130: Annual parameter calculation module; 140: Artificial rain enhancement amount accounting module; 150: Vegetation net primary productivity calculation module; 160: Total carbon sequestration calculation module; 810: Processor; 820: Communication interface; 830: Memory; 840: Communication bus. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] The following is combined with Figures 1 to 8 This invention describes the method, system, and electronic equipment for calculating vegetation carbon sequestration in artificial rain enhancement operations.

[0029] Figure 1 This is a flowchart illustrating the method for calculating vegetation carbon sequestration in artificial rain enhancement operations provided by the present invention, as shown below. Figure 1As shown, the method includes the following steps: In step S100, the original meteorological observation data of the evaluation area is acquired to obtain a dataset of daily average temperature and daily precipitation; in step S200, a raster dataset of daily average temperature and daily precipitation within the target period of the evaluation area is obtained, with consistent spatial resolution of the raster dataset; in step S300, the average biological temperature and total precipitation of the target year are obtained based on the raster data of daily average temperature and daily precipitation; in step S400, the artificial rain enhancement amount for the target year is calculated using the index threshold method, and the natural precipitation is obtained by the difference between the total precipitation of the target year and the artificial rain enhancement amount for the target year; the net primary productivity of vegetation under the total precipitation and natural precipitation of the target year is obtained using the Zhou Guangsheng-Zhang Xinshi model, and the net primary productivity of vegetation under artificial rain enhancement is obtained based on the difference between the two; in step S500, the total carbon sequestration of vegetation under artificial rain enhancement is calculated based on the net primary productivity of vegetation under artificial rain enhancement, spatial resolution, and preset coefficients.

[0030] Specifically, this method is based on the acquisition and processing of meteorological data, and gradually completes parameter calculation, artificial rain enhancement calculation, vegetation net primary productivity difference calculation, and total carbon sequestration calculation. The specific calculations and implementation logic of each step are interconnected, forming a closed-loop accounting system. In step S100, the original meteorological observation data of the assessment area is first acquired. Systematic preprocessing operations are carried out on the original data. When abnormal values ​​of daily average temperature are confirmed, abnormal daily average temperatures are removed. When negative values ​​of daily precipitation are confirmed, they are removed or assigned a value of zero. If the daily precipitation is trace, it is assigned a preset trace value. At the same time, interpolation is used to fill in the missing values ​​of daily average temperature and daily precipitation. Through quality control and validity verification of the whole process, a standardized and normalized dataset of daily average temperature and daily precipitation is obtained, providing accurate and reliable basic data support for all subsequent calculation steps.

[0031] In step S200, based on the preprocessed daily average temperature and daily precipitation datasets, spatial interpolation is performed on the two types of meteorological data. Specifically, Kriging interpolation is used to perform the interpolation calculation, generating a daily average temperature raster dataset and a daily precipitation raster dataset that can fully cover the entire assessment area. At the same time, it is ensured that the spatial resolution of the two types of raster datasets remains consistent, so that data of different meteorological elements can be processed on a unified geographic spatial scale. This fully adapts to the differentiated analysis needs of meteorological factors and vegetation growth in geographic space, laying a spatial data foundation for subsequent refined calculations at the raster scale.

[0032] In step S300, based on raster data of daily average temperature and daily precipitation with uniform spatial resolution, the average biotemperature and total precipitation for the target year are calculated. These two parameters are the foundational parameters for subsequent NPP calculations. When calculating the average biotemperature, a threshold filtering process is first applied to the daily average temperature, assigning a value of 0 to daily average temperatures greater than 30℃ or lower than 0℃. Then, the threshold-processed daily average temperatures for the target year are summed, and the average value is calculated by combining this with the total number of days in the target year. Specifically, this is done using the formula... Implementation, in which This is the preprocessed daily average temperature data. n The total number of days in the target year; when calculating total precipitation, the formula is used. Implementation, in which For daily rainfall, n Given the total number of days in the target year, the daily precipitation corresponding to the total number of days in the target year is added up sequentially to obtain the total precipitation for the target year.

[0033] In step S400, the target annual artificial rain enhancement amount is first accurately calculated using the index threshold method. Based on existing artificial rain enhancement results, operational parameters are screened to construct an artificial rain enhancement rate index threshold library. Simultaneously, operational information for the operations to be evaluated is obtained. Based on this information, the catalyst dispersal location is determined, and the location is matched and bound to geospatial information. Then, referring to a rationality judgment index library, the rationality of the operational information is analyzed. Subsequently, the impact spatial range is simulated using a catalyst transport and diffusion model and a three-dimensional wind field. The simulated overlapping impact areas are merged and deduplicated, outputting the operation impact duration and hourly impact area products. Based on the hourly impact area products... To assess the duration of the impact of the operation, the operation information is matched with the artificial rain enhancement rate threshold database to obtain the artificial rain enhancement index threshold for the operation. Simultaneously, considering the region, cloud conditions, and operation method of the operation, a refined matching of the artificial rain enhancement index threshold is achieved. Based on the matched artificial rain enhancement index threshold, the hourly impact area product is used to calculate the hourly impact area of ​​the region corresponding to the operation and the hourly average precipitation that matches the hourly impact area in time and space. The index threshold method is used to calculate the hourly rain enhancement and the rain enhancement for any time period. Finally, the time period data corresponding to the target year from the rain enhancement for any time period are summarized to obtain the artificial rain enhancement amount for the target year. Subtracting the calculated artificial rain enhancement amount for the target year from the total precipitation for the target year yields the natural precipitation for the target year. The total precipitation and natural precipitation are then used as input parameters and substituted into the Zhou Guangsheng-Zhang Xinshi model to calculate the NPP under two precipitation scenarios. The calculation of this model requires the sequential derivation of multiple parameters, starting with formulas... Calculate the possible evapotranspiration rate, where The average biological temperature, P The total precipitation or natural precipitation is then calculated using the formula. The radiation dryness degree is calculated using the formula. Calculate NPP, where K This is a unit conversion factor; the reference value is 50. RDI Radiation dryness, P This refers to either total precipitation or natural precipitation. After calculating the NPP under total precipitation and natural precipitation respectively, the difference between the two values ​​is obtained, and this difference is the NPP brought about by artificial rain enhancement.

[0034] In step S500, based on the calculated NPP of artificial rain enhancement and the spatial resolution of the raster dataset, the total carbon sequestration by vegetation in the assessment area through artificial rain enhancement is calculated. First, based on a unified spatial resolution and geospatial scale conversion rules, the actual land surface area corresponding to each raster cell is calculated. Then, the NPP increment of each cell in the NPP raster data of artificial rain enhancement is extracted and multiplied by the actual land surface area of ​​the corresponding cell to obtain the vegetation carbon sequestration of each raster cell. Finally, the vegetation carbon sequestration of all raster cells in the assessment area is accumulated and summarized to complete the numerical integration of the entire area. The preset coefficient is 0.96. This coefficient is determined by consulting relevant literature on ecological carbon sink accounting and combining the actual conversion law of vegetation carbon sequestration in the assessment area. It is used to correct the loss and deviation in the process of converting the net primary productivity increment of vegetation into actual carbon sequestration. Finally, the accumulated result of vegetation carbon sequestration of all raster cells is multiplied by 0.96 to obtain the total vegetation carbon sequestration of artificial rain enhancement in the entire assessment area. This achieves a complete calculation from raster-scale refined accounting to regional-scale total summation, ensuring that the total carbon sequestration result is more consistent with the actual situation of vegetation carbon sequestration in the assessment area.

[0035] Figure 2 This is a schematic diagram of the process for obtaining the average biological temperature of a target year, provided by the present invention. Figure 2 As shown, according to an embodiment of the present invention, obtaining the average biological temperature of a target year includes: in steps S311 to S314, confirming that the daily average temperature is greater than 30° or lower than 0°, and assigning the daily average temperature a value of 0; in step S315, calculating the average biological temperature of the target year based on the sum of the daily average temperatures in the target year and the total number of days in the target year.

[0036] Specifically, mean biological temperature is a fundamental parameter for subsequent NPP calculations. Figure 2 The demonstrated workflow is a refined breakdown of the target annual average biological temperature calculation step in the overall method. This workflow relies on a raster dataset of daily average temperatures in the assessment area for calculation, closely adhering to the temperature response patterns of vegetation growth throughout the process to ensure the ecological rationality of the calculation results. In steps S311 to S314, threshold determination and numerical processing are performed on a pixel-by-pixel basis for the daily average temperature raster data in the assessment area. Considering the physiological characteristics of vegetation growth, photosynthesis and biomass accumulation outside the temperature range of 0℃ to 30℃ have no effective contribution. Therefore, the daily average temperature of each raster unit is checked one by one. If the daily average temperature value in the raster is greater than 30℃ or lower than 0℃, the daily average temperature of that raster is uniformly assigned a value of 0. If the daily average temperature in the raster is within the effective range of 0℃ to 30℃, the original temperature value remains unchanged. This processing process needs to cover all raster units in the assessment area, completing the effective value screening of daily average temperatures for the entire area without omission, ensuring that the temperature data participating in subsequent calculations are all effective data that actually contribute to vegetation growth. After completing the temperature threshold processing for the entire region and all time periods, step S315 proceeds to the final calculation of the target year's average biological temperature. First, all daily average temperature raster data within the target year, after threshold processing, are summed across the entire region. The summation object is the processed temperature value of all raster cells in the assessment area for each day of the target year, yielding the effective temperature sum for the target year. Then, this effective temperature sum is divided by the total number of days in the target year, and the average biological temperature of the assessment area for the target year is calculated using the formula. Implementation, in which The data consists of daily average temperatures after preprocessing and threshold filtering. n The total number of days in the target year. The entire calculation process strictly follows the professional calculation requirements for biological temperature in NPP estimation, and also relies on raster data to complete the refined spatial calculation of the assessment area. This avoids the problem of ignoring spatial differences in the overall mean calculation, making the obtained average biological temperature more consistent with the actual meteorological and ecological environment conditions of the assessment area. This provides reliable temperature parameter support for the subsequent accurate calculation of radiation aridity and NPP, and also makes the basic parameters of the entire artificial rain enhancement vegetation carbon sequestration accounting system more scientific.

[0037] According to an embodiment of the present invention, obtaining the total precipitation of a target year includes: the total precipitation of the target year is the sum of the daily precipitation of the total number of days in the target year.

[0038] Specifically, total precipitation is a hydrological parameter used in subsequent NPP calculations. Its calculation relies entirely on a daily precipitation raster dataset with consistent spatial resolution within the assessment area, maintaining the same spatial scale and temporal range as the mean biotemperature calculation to ensure compatibility of various fundamental parameters in subsequent operations. This calculation is based on preprocessed and spatially interpolated daily precipitation raster data. At this stage, the daily precipitation raster dataset fully covers the entire assessment area, and its spatial resolution is completely consistent with the daily mean temperature raster dataset. The daily precipitation data for each raster cell has undergone validity checks and normalization, including negative value removal, assignment of trace precipitation values, and missing value imputation, providing standardized raster data support for accurate total precipitation calculation. During the calculation, for the target year's time range, a time-series summation of daily precipitation is performed on each raster cell within the assessment area. Based on the total number of days in the target year, the precipitation values ​​for each day within the target year are summed sequentially, using the formula... Implementation, in which For the daily precipitation of the grid cells, n The total number of days in the target year. This accumulation process is carried out independently for all grid cells in the assessment area. Each grid cell completes the full summation of daily precipitation within its own target year, ultimately forming a raster dataset of total precipitation for the target year covering the assessment area. This is not done by using regional station averages, but rather by fully preserving the spatial distribution differences of precipitation. This ensures that the total annual precipitation of each grid cell more closely matches the actual spatial distribution characteristics of precipitation in the assessment area, avoiding the spatial information loss caused by averaging station data. The resulting raster data of total precipitation for the target year serves as the direct basis for subsequent calculations of natural precipitation and is also the main input parameter for calculating NPP in the Zhou Guangsheng-Zhang Xinshi model. Its accurate spatial data characteristics lay a crucial hydrological data foundation for subsequent raster-scale differentiated calculations of NPP and refined calculations of vegetation carbon sequestration in artificial rain enhancement.

[0039] It should be noted that the calculation of average biological temperature and total precipitation in this technical solution is based on an annual time dimension. The subsequent calculation of net primary productivity of vegetation and total carbon sequestration by artificial rain enhancement vegetation are all derived based on annual scale parameters. Therefore, only the annual scale carbon sequestration results can be output, and the daily or monthly scale carbon sequestration calculation cannot be achieved.

[0040] Figure 3 This is one of the flowcharts illustrating the process of calculating the target year's artificial rain enhancement amount using the index threshold method provided by this invention. For example... Figure 3As shown, according to an embodiment of the present invention, the artificial rain enhancement amount for the target year is calculated using the index threshold method, including: in step S411, screening operational parameters based on existing artificial rain enhancement summary results to construct an artificial rain enhancement rate index threshold library; obtaining operational information of the operation to be evaluated; in step S412, determining the catalyst seeding location based on the operational information of the operation to be evaluated, and matching and binding the catalyst location with geospatial information; in step S413, analyzing the rationality of the operational information of the operation to be evaluated by referring to the rationality judgment index library; in step S414... In step 414, the influence space of the operation is simulated by the catalyst transport and diffusion model and the three-dimensional wind field. The overlapping influence areas are merged and deduplicated, and the operation influence duration and hourly influence area products are output. In step S415, the operation information of the operation to be evaluated is matched with the artificial rain enhancement rate index threshold library according to the hourly influence area products and operation influence duration to obtain the artificial rain enhancement index threshold of the operation to be evaluated. Based on the artificial rain enhancement index threshold, the index threshold method is used to calculate the hourly rain enhancement grid product, the horizontal distribution of rain enhancement at any time period, the hourly rain enhancement, and the rain enhancement at any time period.

[0041] Specifically, the index threshold method is the main method for calculating the rainfall increase from artificial rain enhancement operations. Figure 3The demonstrated process is a practical breakdown of the calculation procedure for this method. The entire process revolves around the construction of an operational parameter library, operational information processing, impact zone simulation, threshold matching, and rainfall increase calculation, forming a standardized calculation link for operational impact zones and rainfall increase. This provides refined spatiotemporal data support for the subsequent aggregation of artificial rain enhancement amounts for the target year. The implementation of each step maintains scale matching with the geospatial and meteorological data of the assessment area, ensuring the accuracy and scientific rigor of the rainfall increase calculation. In step S411, based on the summarized results of existing artificial rain enhancement operations, various operational parameters strongly correlated with rainfall enhancement effects are selected. These parameters cover key dimensions such as the region where the operation was carried out, cloud conditions, and operational methods. Based on these selected parameters, an artificial rain enhancement rate index threshold library is constructed, ensuring that the threshold data in the library is highly adapted to the actual operational scenario. Simultaneously, complete operational information for the operation to be evaluated is comprehensively acquired, including the location, time, and amount of catalyst dissemination, as well as the corresponding cloud conditions and region, providing complete and fundamental operational data support for subsequent location binding, rationality analysis, and threshold matching. In step S412, based on the acquired operational information of the operation to be evaluated, the relevant data of the catalyst application location are accurately extracted. Then, the catalyst location is precisely matched and bound to the geospatial information of the evaluation area, associating the coordinates of the catalyst location with the raster geospatial data of the evaluation area. This ensures the catalyst location is accurately positioned within the raster cells of the evaluation area, achieving a precise correspondence between the operational location and geospatial location. This provides an accurate spatial benchmark for the spatial range of the subsequent simulated operational impact, ensuring that the spatial location of the simulated impact area is consistent with the actual operational situation. In step S413, referring to a pre-built rationality judgment index library, a comprehensive rationality analysis is conducted on the operational information of the operation to be evaluated, which includes rationality judgment standards for dimensions such as operational time, cloud conditions, catalyst quantity, and operational area. Each dimension of the operational information is verified according to these standards, eliminating invalid information that clearly does not conform to operational specifications. This ensures the validity and rationality of the operational information used in subsequent calculations, avoiding deviations in rainfall calculation caused by distorted operational information from the data source, making the subsequent impact area simulation and rainfall calculation more closely resemble the actual operational scenario.In step S414, based on the geospatially bound catalyst location and the operational information after rationality analysis, a catalyst transport and diffusion model and three-dimensional wind field data are introduced. Combining the physical properties of the catalyst and meteorological elements such as wind speed, direction, and height in the three-dimensional wind field, the transport and diffusion process of the catalyst in the atmosphere is simulated, thereby accurately simulating the spatial range of the operational impact of the operation to be evaluated. During the simulation, if there are multiple operational stages or different time periods with overlapping impact areas in space, the overlapping impact areas are merged and deduplicated to eliminate duplicate calculation areas in space, ensuring the uniqueness and accuracy of the impact area range. Finally, the output shows the operational impact duration, which reflects the time dimension of the operational impact, and the hourly impact area product, which reflects the spatial dimension. This product is presented in raster data form and has the same spatial resolution as the meteorological raster data of the evaluation area, providing spatiotemporally matched impact area data for the subsequent refined calculation of rainfall. In step S415, the output hourly impact area product and operation impact duration are used as matching conditions. The operation information of the operation to be evaluated is accurately matched with the pre-constructed artificial rain enhancement rate index threshold library. Artificial rain enhancement index thresholds that highly match the scenario and spatiotemporal conditions of the operation to be evaluated are selected from the library. Then, based on the artificial rain enhancement index threshold, the index threshold method is used to carry out refined calculation of rainfall. First, the hourly rainfall enhancement grid product presented in the form of raster data is calculated. Then, based on the grid product, the horizontal distribution of rainfall enhancement in any time period that can intuitively reflect the spatial distribution characteristics of rainfall enhancement is generated. At the same time, the numerical results of hourly rainfall enhancement and rainfall enhancement in any time period are statistically obtained. Various rainfall enhancement products include both spatiotemporally refined raster data and the corresponding time period summary numerical data, which provides complete and accurate basic data for the subsequent further aggregation of rainfall enhancement in any time period into the target year's artificial rain enhancement amount. This ensures that the calculation of the target year's artificial rain enhancement amount can be completed based on refined hourly data, improving the accuracy of the overall calculation results.

[0042] Figure 4 This is the second schematic diagram of the process for calculating the target year's artificial rain enhancement amount using the index threshold method provided by this invention. For example... Figure 4As shown, according to an embodiment of the present invention, the method of calculating the artificial rainfall enhancement amount for a target year using the index threshold method further includes: in step S416, according to the region, cloud conditions and operation mode corresponding to the operation to be evaluated, the artificial rainfall enhancement index threshold corresponding to the operation to be evaluated is obtained by matching based on the artificial rainfall enhancement rate index threshold library; in step S417, based on the artificial rainfall enhancement index threshold, using the hourly impact area product, the hourly impact area of ​​the region corresponding to the operation to be evaluated and the hourly average precipitation that is spatiotemporally matched with the hourly impact area are calculated, and the hourly rainfall enhancement amount and the rainfall enhancement amount for any time period are calculated using the index threshold method; in step S418, the time period data corresponding to the target year in the rainfall enhancement amount for any time period are summarized to obtain the artificial rainfall enhancement amount for the target year.

[0043] Specifically, Figure 4 The demonstrated workflow is a further refined supplement to the threshold method for calculating artificial rainfall enhancement, focusing on precise threshold matching, spatiotemporal coupling calculation of rainfall enhancement, and annual total aggregation. Figure 3 The processes complement each other, further improving the accuracy of artificial rain enhancement calculations for the target year through multi-dimensional scenario adaptation and precise spatiotemporal calculations, ensuring that rain enhancement data can be accurately linked to subsequent natural precipitation and NPP calculations. In step S416, for the specific implementation scenario of the operation to be evaluated, three key dimensions are selected: region, cloud conditions, and operation method. The combination characteristics of these three dimensions are used as the matching basis for precise retrieval in a pre-built artificial rain enhancement rate index threshold library. The artificial rain enhancement rate index threshold library has been classified, stored, and indexed according to the climate characteristics of different regions, the macro and micro characteristics of different cloud conditions, and the catalytic efficacy of different operation methods. Through multi-dimensional combination matching, the threshold deviation caused by single-dimensional matching can be avoided, ensuring that the final matched artificial rain enhancement index threshold is highly consistent with the actual scenario of the operation to be evaluated, providing parameter support for subsequent rain enhancement calculations that are close to the actual operation results.

[0044] In step S417, the precise artificial rain enhancement index threshold obtained in step S416 is used as the calculation basis, making full use of... Figure 3 The hourly impact area product output by the workflow is used for spatiotemporal coupling calculation of rainfall enhancement. First, based on the hourly impact area product, the impact area of ​​artificial rain enhancement operations within each hourly segment is extracted, and the corresponding hourly impact area is calculated to ensure consistency between the area calculation and the spatial resolution of the raster dataset. Simultaneously, hourly average precipitation that perfectly matches the hourly impact area in both time and space is selected, achieving a precise spatiotemporal correspondence between the impact area range and precipitation data, avoiding calculation errors caused by temporal or spatial mismatches. Based on this, the index threshold method is used to calculate the hourly rainfall enhancement, specifically through the formula... To achieve, among which, For the first i Hourly rainfall increase m This represents the number of non-intersecting raster cells in the merged influence area for that hour. This represents the hourly average precipitation in a single affected area. For the area of ​​a single affected zone, The rainfall increase rate is matched to a single affected area. After the hourly rainfall increase is quantitatively calculated using this formula, the rainfall increase in different hourly segments is accumulated according to the actual assessment needs, so as to obtain the rainfall increase result for any time period. This calculation process relies entirely on refined data at the raster scale to ensure the accuracy of rainfall increase in both spatiotemporal dimensions. It can reflect the hourly rainfall increase dynamics and also meet the aggregation needs of different time periods.

[0045] In step S418, to meet the accounting requirements of the target year, the rainfall increase data for any time period obtained in step S417 is filtered and summarized in terms of time dimension. From all rainfall increase results for any time period, rainfall increase data whose time range falls entirely within the target year is selected, while data for time periods exceeding the target year or partially spanning multiple years are removed, ensuring that the data included in the summary are all rainfall increases generated by artificial rain enhancement operations within the target year. Subsequently, the rainfall increases for each time period within the selected target year are summed to finally obtain the total artificial rainfall increase for the target year. This step realizes a closed-loop accounting from hourly and arbitrary time period rainfall increases to the annual total, allowing the accounting results of artificial rainfall increases to be directly linked to the calculation of the difference between the total precipitation and natural precipitation in the target year in the claims. This provides accurate and standardized hydrological parameters for the subsequent dual-scenario simulation of NPP, ensuring that every link in the entire artificial rain enhancement vegetation carbon sequestration accounting system can achieve accurate data transmission and efficient adaptation.

[0046] According to an embodiment of the present invention, the Zhou Guangsheng-Zhang Xinshi model is as follows: ; ; ; in, Indicates net primary productivity of vegetation. K Indicates the unit conversion factor. RDI Indicates radiation dryness. P Total precipitation or natural precipitation RDI Indicates radiation dryness. PER Indicates the possible evapotranspiration rate.

[0047] Specifically, the Zhou Guangsheng-Zhang Xinshi model achieves the quantitative conversion from meteorological factors to vegetation net primary productivity through multi-parameter step-by-step derivation. The three formulas support each other and are closely connected with the data processing steps mentioned above, ensuring the coherence of the calculation logic and the rationality of the results.

[0048] possible evapotranspiration rate PER The calculation is a fundamental part of the model, aiming to quantify and assess the water supply and demand relationship in the region. This calculation requires the target year's average biological temperature and corresponding precipitation as inputs. The average biological temperature is first thresholded by applying a threshold to the daily average air temperature, assigning values ​​greater than 30℃ or below 0℃ to 0℃. Then, it is obtained by averaging the sum of the treated temperatures and the total number of days within the target year, in °C. Precipitation is the target year's total precipitation or natural precipitation, which must be selected based on the specific calculation scenario, in millimeters (mm). In the formula, 58.93 is an empirical coefficient fitted through long-term ecological observation, used to convert the temperature-to-precipitation ratio into a probable evapotranspiration rate with practical physical meaning. PER The larger the value, the more significant the imbalance between regional water supply and demand, providing a key basis for subsequent calculations of radiation aridity.

[0049] Radiation dryness RDI The calculation of the undertaking PER The results aim to quantify the limiting effect of climate aridity on vegetation growth. In the calculations, 0.629, 0.237, and 0.00313 are empirical coefficients fitted from a large amount of observational data to ensure accuracy. RDI It can accurately reflect different PER The dry climate characteristics under these conditions. RDI It is positively correlated with the degree of regional aridity, when PER When the imbalance between water supply and demand intensifies, the situation worsens. RDI A rise in water levels indicates a drier climate and more pronounced water-limited vegetation growth; conversely, a fall in water levels indicates a decrease in vegetation growth. RDI The water level decreases, making the moisture conditions more suitable for vegetation growth. Through this process, PER The linear relationship is transformed RDI The nonlinear relationship is more in line with the complex influence of actual climate on vegetation productivity.

[0050] The calculation of net primary productivity (NPP) is the output of the model, integrating multiple factors such as temperature, precipitation, and climate aridity to quantify the net productivity of vegetation under specific conditions. The unit conversion factor K used in the calculation has a reference value of 50 and is a dimensionless parameter used to convert the model results into units commonly used in the ecological field. The net primary productivity of vegetation is expressed in grams of carbon per square meter per year (gC / m²). 2 •a) Ensure the standardization of numerical measurements. RDIThe squared term is used to amplify the nonlinear effect of climate aridity on NPP, reflecting the significant limitation of arid conditions on vegetation productivity; the fractional part is the precipitation use efficiency adjustment term, which combines precipitation and... RDI Quantifying the ability of vegetation to utilize precipitation under different degrees of aridity, when RDI When the moisture content is low, the fractional value is relatively large, indicating that precipitation can be used efficiently. RDI Increasing aridity leads to a decrease in the fractional value, reflecting a decline in precipitation use efficiency; the exponential term represents aridity attenuation, where 9.87 and 6.25 are empirical coefficients. RDI The square of the equation ensures that the attenuation effect increases nonlinearly with increasing dryness. RDI The larger the index, the smaller the value, and the stronger the attenuation effect on NPP, which directly reflects the reduction in vegetation productivity under extremely dry conditions.

[0051] Throughout the model calculation process, all parameters are derived from the standardized meteorological data and annual parameter calculation results mentioned above. By substituting the target annual total precipitation and natural precipitation into the data, the net primary productivity of vegetation under the two precipitation scenarios can be obtained. The difference between the two values ​​represents the increase in net primary productivity of vegetation brought about by artificial rain enhancement, providing input for the subsequent calculation of the total carbon sequestration of vegetation by artificial rain enhancement. This process spans the entire technical chain from meteorological data processing to the quantification of ecological benefits.

[0052] Figure 5 This is a schematic diagram illustrating the process of calculating the total carbon sequestration by vegetation through artificial rain enhancement, based on the net primary productivity and spatial resolution of vegetation and preset coefficients provided by this invention. Figure 5 As shown, according to an embodiment of the present invention, the total amount of carbon sequestration by vegetation during artificial rain enhancement is calculated based on the net primary productivity of vegetation and spatial resolution. Specifically, the calculation includes: in step S501, calculating the actual land surface area corresponding to each raster cell based on the spatial resolution; in step S502, calculating the amount of carbon sequestration by vegetation in each cell based on the increase in net primary productivity of vegetation in the raster data of net primary productivity of vegetation during artificial rain enhancement and the actual land surface area corresponding to the cell; and in step S503, summing up the amount of carbon sequestration by vegetation in all raster cells and multiplying it by a preset coefficient to obtain the total amount of carbon sequestration by vegetation during artificial rain enhancement in the evaluation area.

[0053] Specifically, Figure 5The demonstrated process is the final key step in calculating the total carbon sequestration of vegetation through artificial rain enhancement. It focuses on the refined calculation of carbon sequestration at the raster scale and the aggregation of the total amount in the region. Through the logical progression of pixel area calculation, single pixel carbon sequestration calculation, and total accumulation of the entire region, the increase in NPP brought about by artificial rain enhancement is transformed into an intuitive and quantitative result of total carbon sequestration. This ensures the scientific nature of the calculation process and the accuracy of the results. Each step maintains scale consistency with the raster data system mentioned above, achieving seamless data integration.

[0054] Based on the unified spatial resolution of the raster dataset, the actual land area corresponding to each raster cell is calculated. Spatial resolution characterizes the actual land area represented by each cell in the raster data. If the spatial resolution is a meter-level square (e.g., 1000m × 1000m), the actual land area of ​​a single raster cell is calculated using the formula... The calculations are in square meters. This calculation needs to be performed independently for all raster cells within the assessment area to ensure that the surface area of ​​each cell accurately corresponds to the actual geographic spatial extent, avoiding the impact of scale conversion errors on subsequent carbon sequestration calculations. Through this process, a quantitative correlation is established between abstract raster data and actual surface space, providing fundamental spatial scale parameters for the accurate calculation of carbon sequestration per cell, allowing carbon sequestration calculations to align with the geographic spatial distribution characteristics of the assessment area.

[0055] Based on the actual land surface area of ​​each individual raster cell, and combined with the NPP raster data from artificial rain enhancement, the vegetation carbon sequestration of a single cell is calculated. In the NPP raster data from artificial rain enhancement, the value of each cell represents the NPP increase brought about by artificial rain enhancement within that spatial unit, expressed in grams of carbon per square meter per year. Since the increase in NPP directly corresponds to the increase in net vegetation carbon sequestration, the vegetation carbon sequestration of a single raster cell can be obtained by multiplying the NPP increase of each cell by the actual land surface area corresponding to that cell. The calculation formula is as follows: , in This represents the amount of carbon sequestration by vegetation in a single pixel. This represents the NPP increment for artificial rain enhancement in this pixel. This represents the actual surface area of ​​the pixel. This calculation achieves a direct conversion from vegetation productivity increment to carbon sequestration, and is based on independent calculation of raster cells, fully preserving the differences in carbon sequestration across geospatial areas and avoiding the loss of detail caused by overall regional averaging.

[0056] A comprehensive summation of vegetation carbon sequestration for all raster pixels within the assessment area is performed. A preset coefficient of 0.96 is used, determined by consulting relevant literature on ecological carbon sequestration accounting and considering the actual conversion patterns of vegetation carbon sequestration in the assessment area. This coefficient is used to correct for losses and deviations in the conversion process from net primary productivity increment to actual carbon sequestration. The carbon sequestration values ​​of each pixel must be summed up without omission, and then the summation result is multiplied by 0.96. The summation process must cover all raster pixels in the assessment area, summing up the carbon sequestration values ​​of each pixel without omission. The formula for calculating the total vegetation carbon sequestration from artificial rain enhancement in the assessment area is as follows: , in, To assess the total number of raster cells within the area, and to meet practical measurement habits, the accumulated total carbon fixation amount needs to be converted to tons of carbon, with a conversion factor of 1 ton of carbon equal to 10. 6 The carbon sequestration is measured in tonnes, ultimately yielding the total carbon sequestration by vegetation in the assessment area through artificial rain enhancement. This process completes a closed loop from refined calculation at the grid scale to total integration at the regional scale, enabling the calculation results to directly serve practical applications such as the ecological benefit assessment of artificial rain enhancement and regional carbon sink accounting, providing intuitive and reliable quantitative basis for relevant decision-making.

[0057] According to an embodiment of the present invention, obtaining a raster dataset of daily average temperature and daily precipitation within a target time period for an evaluation area includes: performing spatial interpolation processing on the daily average temperature and daily precipitation using the Kriging interpolation method.

[0058] Specifically, Kriging interpolation is used for spatial interpolation to convert discrete meteorological station data into a spatially continuous raster dataset with uniform resolution. This fills in the gaps in meteorological data in areas not covered by stations, while preserving the spatial distribution characteristics of meteorological factors, providing fundamental data support for subsequent refined calculations at the raster scale. This interpolation method is particularly well-suited to the spatial distribution patterns of meteorological data such as temperature and precipitation, and can fully utilize the spatial correlation of data to improve interpolation accuracy, making the generated raster data more closely reflect the actual meteorological conditions of the assessment area.

[0059] Interpolation processing uses preprocessed daily average temperature and daily precipitation datasets as input. Both types of data have undergone normalization processes such as outlier removal and missing value imputation, ensuring data quality that meets interpolation requirements. During implementation, a unified spatial resolution for the raster dataset is first defined. This resolution is determined based on the size of the assessment area, terrain complexity, and required computational accuracy, ensuring that subsequent temperature and precipitation raster data can be matched and processed at the same spatial scale. Subsequently, interpolation operations are performed separately for daily average temperature and daily precipitation: first, spatial structure analysis is conducted on station data for each target time period, constructing a variogram to quantify the spatial variability of the data, such as the rate of spatial change of precipitation data in mountainous and plain areas, and the gradient characteristics of temperature data with latitude or altitude; then, based on the spatial correlation revealed by the variogram, Kriging interpolation is used to accurately estimate the meteorological values ​​of all raster cells within the assessment area, ensuring that each raster obtains a unique corresponding daily average temperature or daily precipitation data.

[0060] For the interpolation of daily average temperature, the influence of topographic factors such as latitude and altitude on the spatial distribution of temperature is fully considered. These influencing factors are integrated through a variogram function, ensuring that the interpolation results reflect the three-dimensional distribution differences of temperature within the assessment area. For the interpolation of daily precipitation, the focus is on the spatial distribution of precipitation caused by topographic relief and water vapor transport paths, avoiding interpolation bias caused by neglecting the influence of local topography. After interpolation, two types of raster datasets that fully cover the assessment area are generated. The spatial resolution of the daily average temperature raster dataset and the daily precipitation raster dataset are strictly consistent, ensuring that subsequent operations such as the calculation of average biotemperature and the accumulation of total precipitation based on raster data can be carried out smoothly at a unified spatial scale. This provides a key guarantee for the spatial consistency and accuracy of the entire artificial rain enhancement vegetation carbon sequestration accounting system.

[0061] Figure 6 This is a block diagram of the vegetation carbon sequestration calculation system for artificial rain enhancement operations provided by the present invention. Figure 6 As shown, the method also includes preprocessing after acquiring the original meteorological observation data and before obtaining the daily average temperature and daily precipitation datasets. Specifically, this includes: in steps S101 to S102, confirming the existence of outliers in the daily average temperature and removing the outliers; in steps S103 to S104, confirming the existence of negative values ​​in the daily precipitation and removing or assigning the daily precipitation value to zero; in steps S105 to S106, confirming that the daily precipitation is a trace amount and assigning the daily precipitation value to a preset trace amount value; and in step S107, using interpolation to fill in the missing values ​​of the daily average temperature and daily precipitation.

[0062] Specifically, Figure 6The preprocessing workflow demonstrated is a crucial step in ensuring the quality of meteorological data. Its purpose is to systematically verify and standardize the raw meteorological observation data, eliminating invalid data, unifying data standards, and filling data gaps. This ensures that the subsequently generated daily average temperature and daily precipitation datasets are accurate and reliable, providing high-quality foundational data support for the entire accounting system. This workflow follows closely after the acquisition of the raw meteorological observation data, encompassing the entire process of data screening, correction, and supplementation. Each step is progressive and addresses different types of data issues in a targeted manner.

[0063] In steps S101 and S102, the focus is on identifying and removing outliers in the daily average temperature. Outliers typically refer to values ​​that exceed the reasonable temperature range under the climatic background of the assessment area, possibly caused by factors such as observation equipment malfunction or recording errors. By combining historical climate data of the assessment area to set a reasonable temperature threshold range, the daily average temperature of each meteorological station is verified one by one. If a value exceeds this reasonable range, it is identified as an outlier and removed. This process can prevent outlier data from interfering with subsequent steps such as the calculation of average biological temperature, ensuring that the temperature data can accurately reflect the actual temperature conditions of the assessment area, and laying an accurate foundation for the calculation of temperature-based vegetation growth parameters.

[0064] In steps S103 and S104, a specific processing method is used to address the issue of negative values ​​in daily precipitation. Precipitation, as a physical quantity characterizing the amount of rainfall, inherently possesses a non-negative value; negative values ​​are mostly caused by errors in observation or data transmission. During processing, all daily precipitation data must be thoroughly checked. If negative values ​​are found, two processing methods can be chosen based on the actual data: obviously erroneous extreme negative values ​​are directly discarded; for tiny negative values ​​close to zero, to ensure the integrity of the data sequence, they can be assigned a value of zero. This processing ensures that the precipitation data conforms to physical meaning and avoids the impact of unreasonable values ​​on the accuracy of subsequent calculations such as total precipitation accumulation and artificial rain enhancement calculations.

[0065] In steps S105 and S106, the trace precipitation in the daily precipitation data is standardized. Trace precipitation refers to extremely small observation records with precipitation amounts less than 0.1 mm. If the original values ​​of this type of data are directly retained, the data may fluctuate too much due to differences in observation accuracy, affecting the stability of subsequent spatial interpolation and quantification calculations. By uniformly assigning all trace precipitation values ​​to a preset trace value, the trace precipitation data is standardized and unified. This preserves the information about precipitation occurrence while avoiding the interference of data dispersion on the calculation results, improving the consistency and reliability of the precipitation dataset. The preset trace value is determined based on meteorological observation standards and calculation accuracy requirements, such as 0.05 mm.

[0066] In step S107, interpolation is used to fill in the missing values ​​for daily average temperature and daily precipitation. The original meteorological observation data may have gaps in some periods due to equipment maintenance, severe weather, or other factors. Directly retaining these gaps would lead to incomplete data, affecting the continuity of rasterization and subsequent parameter calculations. When filling in the gaps, appropriate interpolation methods such as linear interpolation or neighboring station interpolation can be selected based on the data gap situation. Based on valid data before and after the missing period or synchronous observation data from nearby stations, the missing values ​​are scientifically estimated and filled in, ensuring the integrity and continuity of the daily average temperature and daily precipitation dataset. This guarantees that all subsequent calculations can be carried out smoothly based on complete and standardized data, improving the accuracy of the entire artificial rain enhancement vegetation carbon sequestration calculation results from the source.

[0067] The present invention also provides a system for calculating vegetation carbon sequestration in artificial rain enhancement operations. Figure 7 This is a block diagram of the vegetation carbon sequestration calculation system for artificial rain enhancement operations provided by the present invention. Figure 7 As shown, the vegetation carbon sequestration calculation system 100 for artificial rain enhancement operations provided by the present invention includes: a meteorological data acquisition module 110, used to acquire raw meteorological observation data of the assessment area to obtain daily average temperature and daily precipitation datasets; a raster data generation module 120, used to obtain raster datasets of daily average temperature and daily precipitation in the assessment area during the target period, wherein the spatial resolution of the raster datasets is consistent; an annual parameter calculation module 130, used to obtain the average biological temperature and total precipitation of the target year based on the raster data of daily average temperature and daily precipitation; and artificial rain enhancement volume. The calculation module 140 is used to calculate the artificial rainfall amount for the target year using the index threshold method, and obtain the natural rainfall amount by the difference between the total precipitation and the artificial rainfall amount for the target year; the vegetation net primary productivity calculation module 150 is used to obtain the vegetation net primary productivity under the total precipitation and natural precipitation for the target year using the Zhou Guangsheng-Zhang Xinshi model, and obtain the vegetation net primary productivity of artificial rainfall based on the difference between the two; the total carbon sequestration calculation module 160 is used to calculate the total carbon sequestration of vegetation by artificial rainfall based on the vegetation net primary productivity, spatial resolution and preset coefficients.

[0068] The vegetation carbon sequestration calculation system for artificial rain enhancement operations provided by this invention has each functional module precisely corresponding to the steps of the above-mentioned vegetation carbon sequestration calculation method for artificial rain enhancement operations. The main logic, technical principles, calculation rules and parameter requirements are completely consistent and can be used for mutual reference.

[0069] Specifically, the steps in the meteorological data acquisition module for acquiring raw meteorological observation data of the assessment area and obtaining daily average temperature and daily precipitation datasets correspond to the steps in the raster data generation module for obtaining raster datasets with consistent spatial resolution within the target time period of the assessment area; the steps in the annual parameter calculation module for obtaining the target annual average biological temperature and total precipitation based on raster data correspond to the steps in the artificial rain enhancement calculation module for calculating artificial rain enhancement using the index threshold method and deriving natural precipitation correspond to the steps in the NPP calculation module for calculating dual-scenario NPP using the Zhou Guangsheng-Zhang Xinshi model and obtaining the difference correspond to the steps in the total carbon sequestration calculation module for calculating total carbon sequestration based on NPP and spatial resolution from artificial rain enhancement.

[0070] The specific implementation details, operational logic, and data processing requirements of each module of the system have been elaborated in detail in the specific implementation of the above methods, and will not be repeated here. Please refer directly to the relevant descriptions in the method section.

[0071] Figure 8 A schematic diagram of the physical structure of an electronic device provided by the present invention is illustrated, such as... Figure 8 As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other through the communications bus 840. The processor 810 can call logic instructions in the memory 830 to execute a method for calculating vegetation carbon sequestration in artificial rain enhancement operations. This method includes: acquiring raw meteorological observation data of the assessment area to obtain daily average temperature and daily precipitation datasets; obtaining a raster dataset of daily average temperature and daily precipitation for the target period in the assessment area, with consistent spatial resolution; obtaining the average biological temperature and total precipitation for the target year based on the raster data of daily average temperature and daily precipitation; calculating the amount of artificial rain enhancement for the target year using an index threshold method, and obtaining natural precipitation by the difference between the total precipitation and the amount of artificial rain enhancement for the target year; obtaining the net primary productivity of vegetation under the total precipitation and natural precipitation for the target year using the Zhou Guangsheng-Zhang Xinshi model, and obtaining the net primary productivity of vegetation for artificial rain enhancement based on the difference between the two; and calculating the total amount of vegetation carbon sequestration by artificial rain enhancement based on the net primary productivity of vegetation, spatial resolution, and preset coefficients.

[0072] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating vegetation carbon sequestration during artificial rain enhancement operations, characterized in that, include: Obtain raw meteorological observation data of the assessment area to obtain daily average temperature and daily precipitation datasets; Obtain a raster dataset of the daily average temperature and the daily precipitation for the target time period in the assessment area, wherein the raster dataset has a consistent spatial resolution; The average biological temperature and total precipitation for the target year are obtained based on the raster data of the daily average temperature and the daily precipitation. The artificial rainfall enhancement amount for the target year is calculated using the index threshold method, and the natural rainfall amount is obtained by the difference between the total precipitation of the target year and the artificial rainfall enhancement amount for the target year. The net primary productivity of vegetation under the target annual total precipitation and natural precipitation was obtained using the Zhou Guangsheng-Zhang Xinshi model, and the net primary productivity of vegetation under artificial rain enhancement was obtained based on the difference between the two. The total carbon sequestration of vegetation by artificial rain enhancement is calculated based on the net primary productivity of the vegetation, the spatial resolution, and the preset coefficient.

2. The method according to claim 1, characterized in that, The average biological temperature for the target year includes: If the daily average temperature is confirmed to be greater than 30° or lower than 0°, the daily average temperature is assigned a value of 0. The average biological temperature of the target year is calculated based on the sum of the daily average temperatures within the target year and the total number of days in the target year.

3. The method according to claim 1, characterized in that, The calculation of the target year's artificial rain enhancement amount using the index threshold method includes: Based on the existing results of artificial rain enhancement, operational parameters were selected, and a threshold library of artificial rain enhancement rate indicators was constructed; operational information of the operations to be evaluated was obtained. Based on the operation information of the operation to be evaluated, the catalytic location for catalyst application is determined, and the catalytic location is matched and bound with geospatial information; The rationality of the work information of the work to be evaluated is analyzed by referring to the rationality judgment index library; By using a catalyst transport and diffusion model and a three-dimensional wind field simulation to measure the spatial range of the operational impact, overlapping impact areas are merged and deduplicated, and the duration of the operational impact and the product of the impact area per hour are output. Based on the hourly impact area products and the duration of the operation, the operation information of the operation to be evaluated is matched with the artificial rain enhancement rate index threshold library to obtain the artificial rain enhancement index threshold of the operation to be evaluated; based on the artificial rain enhancement index threshold, the hourly rainfall grid product, the horizontal distribution of rainfall in any time period, the hourly rainfall, and the rainfall in any time period are calculated using the index threshold method.

4. The method according to claim 3, characterized in that, The calculation of the target year's artificial rain enhancement amount using the index threshold method further includes: Based on the region, cloud conditions, and operation method corresponding to the operation to be evaluated, the artificial rain enhancement index threshold corresponding to the operation to be evaluated is obtained by matching based on the artificial rain enhancement rate index threshold library. Based on the artificial rain enhancement index threshold, using the hourly impact area product, the hourly impact area of ​​the region corresponding to the operation to be evaluated and the hourly average precipitation that is spatiotemporally matched with the hourly impact area are calculated. The index threshold method is used to calculate the hourly rain enhancement amount and the rain enhancement amount for any time period. The artificial rainfall amount for the target year is obtained by summing up the rainfall increase data for any given time period.

5. The method according to claim 1, characterized in that, The Zhou Guangsheng-Zhang Xinshi model is as follows: ； ； ； in, Indicates net primary productivity of vegetation. K The unit conversion factor is represented by RDI, which represents radiative dryness, and P represents total precipitation or natural precipitation. RDI Indicates radiation dryness. PER Indicates the possible evapotranspiration rate.

6. The method according to claim 1, characterized in that, The calculation of total carbon sequestration by vegetation through artificial rain enhancement, based on the net primary productivity of vegetation, the spatial resolution, and a preset coefficient, specifically includes: Based on the spatial resolution, calculate the actual land surface area corresponding to each raster cell; The vegetation carbon sequestration of a pixel is calculated based on the vegetation net primary productivity increment of each pixel in the vegetation net primary productivity raster data of the artificial rain enhancement and the actual land surface area corresponding to the pixel. The total amount of vegetation carbon sequestration in the evaluation area is obtained by summing up the amount of vegetation carbon sequestration in all grid cells and multiplying it by the preset coefficient.

7. The method according to claim 1, characterized in that, The raster dataset for obtaining the daily average temperature and daily precipitation in the target time period of the assessment area includes: The daily average temperature and daily precipitation are spatially interpolated using the Kriging interpolation method.

8. The method according to claim 1, characterized in that, The method further includes preprocessing after acquiring the original meteorological observation data and before obtaining the daily average temperature and daily precipitation dataset, specifically including: If an outlier is found in the daily average temperature, the outlier daily average temperature will be removed. If a negative value is found in the daily precipitation, the daily precipitation will be removed or assigned a value of zero. Confirm that the daily precipitation is a trace amount, and assign the daily precipitation a preset trace value; The missing values ​​of the daily average temperature and the daily precipitation are filled in using interpolation.

9. A system for calculating vegetation carbon sequestration during artificial rain enhancement operations, characterized in that, include: The meteorological data acquisition module is used to acquire raw meteorological observation data of the assessment area and obtain daily average temperature and daily precipitation datasets. A raster data generation module is used to obtain a raster dataset of the daily average temperature and the daily precipitation in the target time period of the assessment area, wherein the raster dataset has a consistent spatial resolution. The annual parameter calculation module is used to obtain the average biological temperature and total precipitation of the target year based on the raster data of the daily average temperature and the daily precipitation. The artificial rainfall calculation module is used to calculate the artificial rainfall amount for the target year using the index threshold method, and obtain the natural rainfall amount by the difference between the total precipitation of the target year and the artificial rainfall amount for the target year. The vegetation net primary productivity calculation module is used to obtain the vegetation net primary productivity under the target annual total precipitation and natural precipitation using the Zhou Guangsheng-Zhang Xinshi model, and to obtain the vegetation net primary productivity of artificial rain enhancement based on the difference between the two. The total carbon sequestration calculation module is used to calculate the total carbon sequestration of vegetation by artificial rain enhancement based on the net primary productivity of vegetation, the spatial resolution, and preset coefficients.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for calculating vegetation carbon sequestration in artificial rain enhancement operations as described in any one of claims 1 to 8.

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