Method for evaluating influence of vegetation on rainfall, electronic equipment and storage medium

Abstract

The invention provides a method for evaluating the influence of vegetation on rainfall, electronic equipment and a storage medium, and can be applied to the technical field of meteorological big data processing and hydrology and meteorology. The method comprises the following steps: preprocessing remote sensing and meteorological grid monitoring data of a target area to obtain a multi-source data set of the target area; according to the type of meteorological data in the meteorological data set, constructing a multi-stage driving variable representing meteorological change characteristics; utilizing the vegetation mask data set to construct a vegetation adjustment variable, and constructing a plurality of interaction variables representing interaction information between the vegetation adjustment variable and the multi-stage driving variable; constructing a multi-stage driving path model representing rainfall response variables by using the plurality of interaction variables, the reference path effect coefficient of the non-vegetation region and the adjustment effect coefficient of the vegetation region relative to the non-vegetation region; and performing parameter fitting on the multi-stage driving path model by using the multi-source data set, and determining an evaluation result of the vegetation influence rainfall response variables in the target area by using a parameter fitting result.

Description

Technical Field

[0001] This application relates to the fields of meteorological big data processing and hydrometeorology, specifically to a method for assessing the impact of vegetation on precipitation, electronic equipment, and storage media. Background Technology

[0002] Extreme precipitation events, as extreme weather events within the context of global climate change, have a significant impact on terrestrial ecosystems and socio-economic development. Vegetation, as a core link between soil and atmosphere, plays a crucial regulatory role in the triggering and evolution of extreme precipitation by altering surface water cycling, energy balance, and dynamic roughness through a "biological pump" mechanism. Current meteorological and hydrological analysis techniques primarily rely on satellite remote sensing monitoring, meteorological station network observations, and numerical climate model simulations to reveal the feedback relationship between vegetation cover evolution and the spatiotemporal patterns of precipitation.

[0003] Existing methods for assessing the impact of vegetation on precipitation (especially extreme precipitation) mainly include numerical simulation sensitivity experiments and statistical correlation analysis. However, these existing methods have problems such as being unable to quantitatively decouple complex causal physical paths, having difficulty handling high collinearity among atmospheric variables, and lacking the ability to quantify the moderating effect of vegetation. Summary of the Invention

[0004] In view of the above problems, this application provides a method, electronic device and storage medium for improving the assessment of the impact of a type of vegetation on precipitation, in order to solve at least one of the above problems.

[0005] The first aspect of this application provides a method for assessing the impact of vegetation on precipitation, comprising: preprocessing remote sensing and meteorological raster monitoring data of a target area to obtain a multi-source dataset of the target area, wherein the multi-source dataset includes a meteorological dataset and a vegetation mask dataset; constructing multi-level driving variables characterizing the meteorological change characteristics of the target area based on the type of meteorological data in the meteorological dataset; constructing vegetation moderating variables using the vegetation mask dataset, and constructing multiple interaction variables characterizing the interaction information between the vegetation moderating variables and the multi-level driving variables; constructing a multi-level driving path model characterizing precipitation response variables using multiple interaction variables, the baseline path effect coefficient of non-vegetated areas in the target area, and the moderating effect coefficient of vegetated areas relative to non-vegetated areas in the target area, wherein the precipitation response variables are used to represent precipitation information in the target area; performing parameter fitting processing on the multi-source dataset on the multi-level driving path model, and using the parameter fitting results to determine the assessment results of the vegetation impact on precipitation response variables in the target area.

[0006] According to embodiments of this application, the preprocessing of remote sensing and meteorological raster monitoring data of the target area to obtain a multi-source dataset of the target area includes: uniformly processing the remote sensing and meteorological raster monitoring data of the target area according to a preset resolution grid to obtain remote sensing and meteorological raster monitoring data with a standard grid; performing spatial interpolation processing on the resampled data in the remote sensing and meteorological raster monitoring data with a standard grid to obtain remote sensing and meteorological raster monitoring data with a unified spatial unit; performing temporal aggregation processing on the remote sensing and meteorological raster monitoring data with a unified spatial unit to obtain remote sensing and meteorological raster monitoring data with the same spatiotemporal scale; and performing standardization processing based on the same physical dimensions on the continuously changing data in the remote sensing and meteorological raster monitoring data with the same spatiotemporal scale to obtain a multi-source dataset.

[0007] According to an embodiment of this application, the above-mentioned construction of vegetation regulation variables using vegetation mask dataset includes: constructing a vegetation binary region variable based on the vegetation mask threshold of the vegetation mask dataset; and using the vegetation binary region variable to assign values ​​to the data spatial units of the vegetation mask dataset to obtain vegetation regulation variables characterizing whether the data spatial units belong to the vegetation area.

[0008] According to embodiments of this application, the above-mentioned construction of multiple interaction variables characterizing the interaction information between vegetation moderating variables and multi-level driving variables includes: constructing a surface water-vegetation interaction variable between surface water variables and vegetation moderating variables in multi-level driving variables; constructing an evapotranspiration-vegetation interaction variable between evapotranspiration variables and vegetation moderating variables in multi-level driving variables; constructing an entire-layer precipitable water vapor-vegetation interaction variable between entire-layer precipitable water vapor variables and vegetation moderating variables in multi-level driving variables; constructing an atmospheric lower-layer or lower-layer specific humidity-vegetation interaction variable between atmospheric lower-layer or lower-layer specific humidity variables and vegetation moderating variables in multi-level driving variables; constructing a horizontal pressure gradient-vegetation interaction variable between horizontal pressure gradient variables and vegetation moderating variables in multi-level driving variables; and constructing a wind field-vegetation interaction variable between wind field variables and vegetation moderating variables in multi-level driving variables.

[0009] According to embodiments of this application, the above-mentioned multi-level driving path model for constructing a precipitation response variable using multiple interaction variables, the baseline path effect coefficient of the non-vegetated area in the target region, and the moderating effect coefficient of the vegetated area relative to the non-vegetated area in the target region includes: constructing a first path equation for a evapotranspiration variable using surface water variables, surface water-vegetation interaction variables, and vegetation moderating variables; constructing a second path equation for a specific humidity variable in the lower atmosphere or lower layer using evapotranspiration variables, evapotranspiration-vegetation interaction variables, and vegetation moderating variables; and constructing a second path equation for a specific humidity variable in the lower atmosphere or lower layer using horizontal pressure gradient variables, horizontal pressure gradient-vegetation interaction variables, and vegetation moderating variables. A third path equation representing wind field variables is constructed using moderating variables. A fourth path equation representing whole-layer precipitable water vapor variables is constructed using specific humidity variables in the lower atmosphere, specific humidity-vegetation interaction variables in the lower atmosphere, and vegetation moderating variables. The first, second, third, and fourth path equations are simultaneously transformed to obtain a multi-level driving path equation representing the precipitation response variable driven by vegetation moderating variables. The baseline path effect coefficient, moderating effect coefficient, random error term, main effect coefficient of the vegetation zone, and intercept term are added to the multi-level driving path equation to obtain a multi-level driving path model.

[0010] According to the embodiments of this application, when the vegetation regulation variable in the multi-level driving path model is equal to a first preset value, the multi-level driving path model represents the baseline interaction relationship between the non-vegetated area and the multi-level driving variable; wherein, when the vegetation regulation variable in the multi-level driving path model is equal to a second preset value, the multi-level driving path model represents the actual interaction relationship between the vegetated area and the multi-level driving variable.

[0011] According to embodiments of this application, the above-mentioned parameter fitting processing of a multi-level driving path model using multi-source datasets includes: performing parameter fitting processing on the multi-level driving path model using multi-source datasets to obtain parameter fitting results on different driving paths; when the independent variable on the current driving path is a multi-level driving variable, determining that the parameter fitting result is the fitting result of the baseline path effect coefficient in the non-vegetated area; when the independent variable on the current driving path is an interaction variable, determining that the parameter fitting result is the weighted fitting result of the moderating effect coefficient in the vegetated area and the baseline path effect coefficient in the non-vegetated area; when the independent variable on the current driving path is a vegetation moderating variable, determining that the parameter fitting result is the fitting result of the main effect coefficient in the vegetated area.

[0012] According to embodiments of this application, the evaluation results of determining the vegetation impact on precipitation response variables in the target area using parameter fitting results include: when the parameter fitting results indicate the presence of interaction variables in the multi-level driving path model, determining the target path equation where the interaction variables are located; when the parameter fitting result of the moderating effect coefficient is greater than a threshold, determining that the vegetation area has an enhancing effect on precipitation response variables on the driving path represented by the target path equation; and when the parameter fitting result of the moderating effect coefficient is less than or equal to a threshold, determining that the vegetation area has a weakening effect on precipitation response variables on the driving path represented by the target path equation.

[0013] The second aspect of this application provides an apparatus for assessing the impact of vegetation on precipitation, comprising: a multi-source dataset acquisition module for preprocessing remote sensing and meteorological raster monitoring data of a target area to obtain a multi-source dataset of the target area, wherein the multi-source dataset includes a meteorological dataset and a vegetation mask dataset; a multi-level driving variable construction module for constructing multi-level driving variables characterizing the meteorological change characteristics of the target area based on the type of meteorological data in the meteorological dataset; an interaction variable construction module for constructing vegetation moderating variables using the vegetation mask dataset and constructing multiple interaction variables characterizing the interaction information between the vegetation moderating variables and the multi-level driving variables; a driving path model construction module for constructing a multi-level driving path model characterizing precipitation response variables using multiple interaction variables, the baseline path effect coefficient of non-vegetated areas in the target area, and the moderating effect coefficient of vegetated areas relative to non-vegetated areas in the target area, wherein the precipitation response variables are used to represent precipitation information in the target area; and an assessment module for performing parameter fitting processing on the multi-level driving path model using the multi-source dataset and determining the assessment result of the vegetation impact on precipitation response variables in the target area using the parameter fitting results.

[0014] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0015] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0016] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0017] The vegetation impact assessment method provided in this application, through the construction of a multi-level driving path model, can systematically characterize the order of action and transmission mechanism of different physical processes in the formation of extreme precipitation. It can not only determine "which factors are important" but also clarify "the pathways through which these factors influence extreme precipitation," thus significantly improving the ability to explain mechanisms. By introducing vegetation as a moderating variable, it simultaneously characterizes the baseline driving effect and the regional moderating effect within the same multi-level driving path model, without the need for physical sample splitting. This ensures the comparability of model parameters between vegetated and non-vegetated areas, thereby directly identifying regional differences within a unified model framework and analyzing... The results are more stable and interpretable. By introducing and identifying the moderating effect parameters in the parameter fitting results, the enhancing or weakening effect of vegetation on each driving path in the formation of extreme precipitation can be quantitatively identified, thereby revealing the physical driving mechanism behind regional differences and realizing the transformation of regional differences from "phenomenon description" to "mechanism quantification". In addition, the continuous driving variables in remote sensing and meteorological grid monitoring data are standardized to put the parameters of different driving paths on a uniform scale, so that the relative contributions of different physical processes to the formation of extreme precipitation can be directly compared, and the driving path results can be directly used for driving contribution ranking and mechanism comparison analysis. Attached Figure Description

[0018] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0019] Figure 1 An application scenario diagram of the method for assessing the impact of vegetation on precipitation according to an embodiment of this application is shown.

[0020] Figure 2 A flowchart is shown for an assessment method of the impact of vegetation on precipitation according to an embodiment of this application.

[0021] Figure 3 A structural block diagram of an apparatus for assessing the impact of vegetation on precipitation according to an embodiment of this application is shown.

[0022] Figure 4 A block diagram of an electronic device suitable for implementing an assessment method for the impact of vegetation on precipitation, according to an embodiment of this application, is shown. Detailed Implementation

[0023] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0026] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0027] In existing technologies, the main methods for assessing the impact of vegetation on precipitation include numerical simulation sensitivity experiments and statistical correlation analysis. Numerical simulation typically utilizes regional climate models such as Weather Research and Forecasting (WRF) or Regional Climate Models (RegCM) to compare precipitation output differences between "vegetated" and "non-vegetated" or "deforestation" scenarios by modifying land use type or vegetation cover parameters, thereby inferring the contribution of vegetation to precipitation. Statistical analysis methods often employ multiple linear regression or partial correlation analysis to establish a functional relationship between vegetation greenness indices and precipitation indices. For example, some studies have used long-series satellite Normalized Difference Vegetation Index (NDVI) data and extreme precipitation frequencies to create correlation maps, attempting to isolate background climate factors and quantitatively assess the explanatory power of vegetation on precipitation changes.

[0028] However, the aforementioned existing technical solutions have significant technical bottlenecks and shortcomings in practical applications. First, most existing numerical simulation and linear regression methods are "black box" or "single-layer" models, unable to quantitatively decompose the complete physical causal chain from "soil moisture—vegetation transpiration—atmospheric water vapor transport—extreme precipitation triggering," making it difficult to identify in which intermediate link vegetation plays a key regulatory role. Second, atmospheric physical variables (such as specific humidity and total atmospheric water vapor) generally exhibit high collinearity (i.e., mutual causal relationships), and traditional regression models cannot handle the complex causal relationships nested between independent variables, easily leading to serious biases in the estimation of driving coefficients (i.e., the estimation of physical driving path weights). Furthermore, existing solutions typically adopt a sample segmentation and comparison strategy, often separating vegetated and non-vegetated areas for independent research, lacking a technical means to identify significant differences in driving paths between vegetated and non-vegetated areas through interaction term (i.e., statistical test) moderating effect analysis within a full sample framework. Finally, existing technologies are relatively lacking in characterization of dynamic factors, often neglecting the feedback of vegetation to local horizontal pressure gradients and its moderating effect on atmospheric circulation dynamics, resulting in a lack of comprehensiveness in the analysis of extreme precipitation mechanisms.

[0029] To address at least one of the existing problems, this application provides a method, electronic device, and storage medium for assessing the impact of vegetation on precipitation. Based on multi-source remote sensing data and reanalysis data, a multi-level driving path is constructed, which includes surface moisture, evapotranspiration, atmospheric water vapor, dynamic conditions, and extreme precipitation. Vegetation masking data is introduced as a moderating variable. By constructing a multi-level driving path model with interaction terms (i.e., interaction variables), the enhancement or weakening effect of vegetated areas on each driving path relative to non-vegetated areas can be quantitatively identified without splitting the samples.

[0030] Figure 1 An application scenario diagram of the method for assessing the impact of vegetation on precipitation according to an embodiment of this application is shown.

[0031] like Figure 1 As shown, the application scenario 100 according to this embodiment may include scenarios such as meteorological big data processing and hydrometeorology. Network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0032] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0033] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0034] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0035] It should be noted that the vegetation impact assessment method provided in this application embodiment can generally be executed by server 105. Correspondingly, the vegetation impact assessment device provided in this application embodiment can generally be located in server 105. The vegetation impact assessment method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the vegetation impact assessment device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0036] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0037] The following will be based on Figure 1 The scenario described herein is illustrated in detail with reference to other accompanying drawings, which provide a method for assessing the impact of vegetation on precipitation according to the disclosed embodiments.

[0038] Figure 2 A flowchart is shown for an assessment method of the impact of vegetation on precipitation according to an embodiment of this application.

[0039] like Figure 2 As shown, the method for assessing the impact of vegetation on precipitation in this embodiment includes operations S210 to S250.

[0040] In operation S210, the remote sensing and meteorological grid monitoring data of the target area are preprocessed to obtain a multi-source dataset of the target area.

[0041] The multi-source datasets include meteorological datasets and vegetation mask datasets.

[0042] Acquire extreme precipitation data, surface moisture data, evapotranspiration data, atmospheric water vapor data, dynamic condition data, and vegetation masking data of the target area (i.e., the study area). Perform spatial unification, temporal unification, quality control, and standardization processing on the multi-source data to establish a multi-source dataset (i.e., sample dataset) under a unified spatial grid and temporal scale.

[0043] Extreme precipitation in this application refers to a low-probability heavy precipitation event in a specific region (such as the target area or study area in this application) and within a specific time period, where the precipitation amount far exceeds the normal level. Its determination is typically based on statistical methods, using the high percentile (e.g., 90%, 95%, or 99%) of historical precipitation data as a threshold. In the embodiments or implementations of this application, extreme precipitation refers to the total precipitation of all precipitation days within a given year whose daily precipitation exceeds the 95th percentile threshold of the corresponding daily precipitation in the baseline period, expressed in millimeters (mm). The 95th percentile threshold is calculated based on the precipitation distribution of precipitation days (precipitation ≥ 1 mm) within the baseline period. In this application, the precipitation response variable (i.e., R95p) is used to characterize the intensity or frequency of extreme precipitation.

[0044] In operation S220, multi-level driving variables characterizing the meteorological change features of the target area are constructed based on the type of meteorological data in the meteorological dataset.

[0045] The multi-level driving variables include surface water variables (i.e., variables representing water bodies or soil moisture existing on the Earth's land surface), evapotranspiration variables (i.e., variables representing the total amount of surface water converted into water vapor and entering the atmosphere through processes such as soil evaporation and vegetation transpiration), atmospheric water vapor variables (i.e., variables representing the water vapor content in the atmosphere), and aerodynamic variables (including horizontal pressure gradient variables (i.e., variables representing the pressure difference per unit horizontal distance) and wind field variables (i.e., variables representing the vector distribution of wind in space)). The type of multi-level driving variables is determined based on the type of meteorological data in the meteorological dataset.

[0046] In operation S230, vegetation regulation variables are constructed using the vegetation mask dataset, and multiple interaction variables are constructed to represent the interaction information between the vegetation regulation variables and the multi-level driving variables.

[0047] Interaction variables are used to characterize the moderating effect of vegetation on driving pathways.

[0048] In operation S240, a multi-level driving path model characterizing precipitation response variables is constructed using multiple interaction variables, the baseline path effect coefficient of the non-vegetated area in the target region, and the moderating effect coefficient of the vegetated area relative to the non-vegetated area in the target region. The precipitation response variables are used to represent precipitation information in the target region.

[0049] Since multi-level driving variables include surface moisture, evapotranspiration, atmospheric water vapor, and aerodynamic variables, multi-level driving path models can describe the causal path relationships between surface moisture, evapotranspiration, atmospheric water vapor, dynamic conditions, and extreme precipitation, while simultaneously characterizing the moderating effect of vegetation.

[0050] In the S250 operation, the multi-source dataset is used to perform parameter fitting on the multi-level driving path model, and the parameter fitting results are used to determine the evaluation results of the vegetation impact on precipitation response variables in the target area.

[0051] The values ​​of each parameter or coefficient in the multi-level driving path model are determined based on the parameter fitting results. The enhancement or weakening effect of vegetation on each driving path is determined based on the fitting results of the moderating effect parameters. The results of the extreme precipitation driving path difference identification are output.

[0052] The vegetation impact assessment method provided in this application, through the construction of a multi-level driving path model, can systematically characterize the order of action and transmission mechanism of different physical processes in the formation of extreme precipitation. It can not only determine "which factors are important" but also clarify "the pathways through which these factors influence extreme precipitation," thus significantly improving the ability to explain mechanisms. By introducing vegetation as a moderating variable, it simultaneously characterizes the baseline driving effect and the regional moderating effect within the same multi-level driving path model, without the need for physical sample splitting. This ensures the comparability of model parameters between vegetated and non-vegetated areas, thereby directly identifying regional differences within a unified model framework and analyzing... The results are more stable and interpretable. By introducing and identifying the moderating effect parameters in the parameter fitting results, the enhancing or weakening effect of vegetation on each driving path in the formation of extreme precipitation can be quantitatively identified, thereby revealing the physical driving mechanism behind regional differences and realizing the transformation of regional differences from "phenomenon description" to "mechanism quantification". In addition, the continuous driving variables in remote sensing and meteorological grid monitoring data are standardized to put the parameters of different driving paths on a uniform scale, so that the relative contributions of different physical processes to the formation of extreme precipitation can be directly compared, and the driving path results can be directly used for driving contribution ranking and mechanism comparison analysis.

[0053] According to embodiments of this application, the preprocessing of remote sensing and meteorological raster monitoring data of the target area to obtain a multi-source dataset of the target area includes: uniformly processing the remote sensing and meteorological raster monitoring data of the target area according to a preset resolution grid to obtain remote sensing and meteorological raster monitoring data with a standard grid; performing spatial interpolation processing on the resampled data in the remote sensing and meteorological raster monitoring data with a standard grid to obtain remote sensing and meteorological raster monitoring data with a unified spatial unit; performing temporal aggregation processing on the remote sensing and meteorological raster monitoring data with a unified spatial unit to obtain remote sensing and meteorological raster monitoring data with the same spatiotemporal scale; and performing standardization processing based on the same physical dimensions on the continuously changing data in the remote sensing and meteorological raster monitoring data with the same spatiotemporal scale to obtain a multi-source dataset.

[0054] The embodiments described above in this application eliminate spatial scale differences between different sensors or data sources by unifying grid resolution and spatial interpolation processing, ensuring that all grid cells are aligned under a unified spatial framework, significantly enhancing the spatial comparability and overlay analysis capabilities of cross-source data; unifying high-frequency, asynchronous observation data (such as hourly meteorological data and daily remote sensing data) to the same spatiotemporal scale through time aggregation operations; and making different physical properties comparable through standardization processing based on the same physical dimensions.

[0055] The process of obtaining multi-source datasets provided in this application will be further described in detail below through specific implementation methods.

[0056] Acquiring precipitation data for the target area: Precipitation data (in this application embodiment or implementation, precipitation data refers to extreme precipitation data) can be obtained from an annual extreme precipitation index dataset, preferably in the Network Common Data Form (NetCDF) format. The precipitation response variable selected in this application is R95p, which is used to characterize the cumulative amount of extreme precipitation exceeding the historical percentile threshold, and the unit can be millimeters per year (mm / yr).

[0057] Obtaining surface moisture data: Surface moisture data is used to characterize the surface's ability to supply water to evapotranspiration processes. The surface moisture variable is denoted as... Soil moisture content data, such as shallow soil moisture content variables, are preferred. .

[0058] Acquiring evapotranspiration data: Evapotranspiration data is used to characterize the intensity of water vapor transport from land to the atmosphere. The evapotranspiration variable is denoted as... .

[0059] Acquiring atmospheric water vapor data: Atmospheric water vapor state data includes the specific humidity variable of the lower atmosphere or lower layer. (That is, a variable representing the ratio of the mass of water vapor in the lower atmosphere or moist air to the total mass of air, where the lower atmosphere refers to the layer of the Earth's atmosphere closest to the ground, and the lower atmosphere refers to the Earth's atmosphere below 25 kilometers in altitude) and the total precipitable water vapor volume. (That is, the variable representing the total amount of precipitable water vapor in the entire atmospheric layer). Among them, the specific humidity variable is used to characterize the water vapor content in the local air, and the total precipitable water vapor variable is used to characterize the total amount of water vapor in the entire atmospheric column.

[0060] Acquiring aerodynamic data: Aerodynamic data includes horizontal pressure gradient variables. Wind field variables Among them, the wind field variable can preferably be the meridional wind or the wind component related to water vapor transport; the pressure gradient variable is used to characterize atmospheric dynamic forcing.

[0061] According to an embodiment of this application, the above-mentioned construction of vegetation regulation variables using vegetation mask dataset includes: constructing a vegetation binary region variable based on the vegetation mask threshold of the vegetation mask dataset; and using the vegetation binary region variable to assign values ​​to the data spatial units of the vegetation mask dataset to obtain vegetation regulation variables characterizing whether the data spatial units belong to the vegetation area.

[0062] Obtaining vegetation masking data: Vegetation masking data can be derived from land use raster data. Constructing vegetation moderating variables based on masking thresholds. As shown in formula (1):

[0063] (1).

[0064] Based on vegetation masking data, vegetation adjustment variables are assigned to each spatial unit. Vegetation condition variables Used to characterize whether a spatial unit belongs to a vegetated area.

[0065] In another embodiment of this application, land cover type variables, vegetation structure parameter variables, ecological function indicators, climate and geographical zoning variables, and continuous ecological environment gradient variables can be used to replace vegetation adjustment variables; all of the above-mentioned alternative variables can be used to construct interaction terms to characterize the spatial differences of driving paths.

[0066] Temporal and spatial unification processing is performed on remote sensing and meteorological grid monitoring data: data from different sources are unified to the same spatial reference system and a unified grid resolution. Preferably, the raw data is unified to a regular grid, for example... Resolution grid. Spatial interpolation is performed on the data requiring resampling to obtain variable values ​​on a unified spatial unit. Simultaneously, data with different temporal resolutions are uniformly converted to annual-scale data. For data with hourly, daily, or monthly temporal resolutions, time aggregation is performed using annual average, annual cumulative, or annual statistical methods. For extreme precipitation indicators, annual-scale extreme precipitation indicators can also be obtained from statistics of all precipitation events within the year.

[0067] Standardization processing is performed on continuously changing data in remote sensing and meteorological grid monitoring data: Due to the different physical dimensions of different driving variables, continuous variables are standardized to ensure the comparability of path coefficients. The standardization is shown in formula (2):

[0068] (2).

[0069] in, For standardized continuous driving variables, For variables The mean, For continuous driving variables The standard deviation. In the embodiments or implementations of this application, the continuous driving variables that need to be standardized are surface moisture variable sw, evapotranspiration variable e, horizontal pressure gradient variable spg, specific humidity variable of the lower atmosphere or lower layer q, total precipitable water vapor variable tcw, wind field variable u, and precipitation response variable R95p.

[0070] According to embodiments of this application, the above-mentioned construction of multiple interaction variables characterizing the interaction information between vegetation moderating variables and multi-level driving variables includes: constructing a surface water-vegetation interaction variable between surface water variables and vegetation moderating variables in multi-level driving variables; constructing an evapotranspiration-vegetation interaction variable between evapotranspiration variables and vegetation moderating variables in multi-level driving variables; constructing an entire-layer precipitable water vapor-vegetation interaction variable between entire-layer precipitable water vapor variables and vegetation moderating variables in multi-level driving variables; constructing an atmospheric lower-layer or lower-layer specific humidity-vegetation interaction variable between atmospheric lower-layer or lower-layer specific humidity variables and vegetation moderating variables in multi-level driving variables; constructing a horizontal pressure gradient-vegetation interaction variable between horizontal pressure gradient variables and vegetation moderating variables in multi-level driving variables; and constructing a wind field-vegetation interaction variable between wind field variables and vegetation moderating variables in multi-level driving variables.

[0071] The embodiments described above in this application significantly enhance the physical mechanism representation and model performance of precipitation prediction in meteorology by constructing six types of interaction variables between vegetation moderating variables and multi-level driving variables: interaction variables such as surface water-vegetation and evapotranspiration-vegetation directly embed the feedback process of vegetation transpiration on water vapor transport, enabling the model to capture the complete water vapor cycle chain of "vegetation growth → enhanced evapotranspiration → increased specific humidity in the lower layer → increased precipitable water in the entire layer → convection triggering"; interaction variables such as horizontal pressure gradient-vegetation and wind field-vegetation effectively characterize the regulatory effect of vegetation roughness on the dynamic structure of the boundary layer; through the construction of interaction variables, the model can extract high-dimensional physical information from low- and medium-resolution remote sensing and reanalysis data. In addition, by treating vegetation as a "moderating factor" rather than a passive input, precipitation prediction shifts from "black box statistics" to "mechanism-driven".

[0072] To identify the regulatory effect of vegetation on each driving pathway, an interaction term between continuous driving and regulating variables was constructed.

[0073] For any continuous driving variable The corresponding interaction item (i.e., interaction variable) is defined as shown in formula (3):

[0074] (3).

[0075] in, This represents the continuous driving variables before standardization. These are standardized, continuous driving variables; This represents a vegetation moderating variable, which is a binary moderating variable.

[0076] In the embodiments or implementations of this application, the following interaction items are constructed: ;in, This represents the surface water-vegetation interaction variable. This represents the evapotranspiration-vegetation interaction variable; This represents the horizontal pressure gradient-vegetation interaction variable; This represents the specific humidity-vegetation interaction variable in the lower atmosphere or lower troposphere; This represents the interaction variable between precipitable water vapor and vegetation in the entire layer; This represents the wind field-vegetation interaction variable; the interaction term above is used to indicate whether the intensity of the effect of a certain driving variable in the vegetated area changes relative to the non-vegetated area.

[0077] In another embodiment of this application, the interaction variables are not limited to the interaction between vegetation regulation variables and driving variables. They can also be constructed as interaction variables between driving variables, multiple interaction variables between driving variables, regulation variables and time variables, composite interaction variables between different physical processes, and multi-scale / hierarchical interaction variables. Through multi-level interaction terms, the regulation mechanism of multi-process coupling in the formation of extreme precipitation can be characterized.

[0078] The aforementioned interaction variables are in the form of the product of the driving variable and the vegetation mask variable. In another embodiment of this application, the interaction variables can be constructed in the form of a polynomial of the driving variable and the vegetation mask variable, in the form of a nonlinear transformation, in the form of a piecewise function or a threshold function, or in the form of an implicitly nested model structure, etc. As long as the interaction variables can characterize the regulatory effect of different regions on the driving process, they are all within the protection scope of this application.

[0079] According to embodiments of this application, the above-mentioned multi-level driving path model for constructing a precipitation response variable using multiple interaction variables, the baseline path effect coefficient of the non-vegetated area in the target region, and the moderating effect coefficient of the vegetated area relative to the non-vegetated area in the target region includes: constructing a first path equation for a evapotranspiration variable using surface water variables, surface water-vegetation interaction variables, and vegetation moderating variables; constructing a second path equation for a specific humidity variable in the lower atmosphere or lower layer using evapotranspiration variables, evapotranspiration-vegetation interaction variables, and vegetation moderating variables; and constructing a second path equation for a specific humidity variable in the lower atmosphere or lower layer using horizontal pressure gradient variables, horizontal pressure gradient-vegetation interaction variables, and vegetation moderating variables. A third path equation representing wind field variables is constructed using moderating variables. A fourth path equation representing whole-layer precipitable water vapor variables is constructed using specific humidity variables in the lower atmosphere, specific humidity-vegetation interaction variables in the lower atmosphere, and vegetation moderating variables. The first, second, third, and fourth path equations are simultaneously transformed to obtain a multi-level driving path equation representing the precipitation response variable driven by vegetation moderating variables. The baseline path effect coefficient, moderating effect coefficient, random error term, main effect coefficient of the vegetation zone, and intercept term are added to the multi-level driving path equation to obtain a multi-level driving path model.

[0080] The above embodiments of this application can directly capture the effect of vegetation transpiration on the near-surface water vapor content by constructing interaction variables such as surface water-vegetation and evapotranspiration-vegetation; the interaction terms of wind field-vegetation and horizontal pressure gradient-vegetation can effectively characterize the inhibitory and enhancing effects of the vegetation canopy on boundary layer wind speed and turbulence structure, thereby affecting the vertical mixing efficiency of water vapor and improving the accuracy of identifying the dynamic conditions for precipitation formation.

[0081] The process of obtaining the multi-level driving path model provided in this application will be further explained in detail below through specific implementation methods.

[0082] A multi-stage driving path equation representing the precipitation response variable R95p is constructed, as shown in equations (4) to (8):

[0083] (4).

[0084] (5).

[0085] (6).

[0086] (7).

[0087] (8).

[0088] Formula (4) is the first path equation, formula (5) is the second path equation, formula (6) is the third path equation, formula (7) is the fourth path equation, and formula (8) is the multi-level driving path equation. The above path equations sequentially represent: the control effect of surface water on evapotranspiration, the transport effect of evapotranspiration on lower-level water vapor, the influence of water vapor accumulation on the overall water vapor state, the direct influence of dynamic conditions on the cumulative amount of extreme precipitation, and the moderating effect of vegetation on the intensity of each path.

[0089] Unified expression of benchmark effect and moderating effect: For any path relationship, the preferred expression can be uniformly represented as shown in formula (9):

[0090] (9).

[0091] in For a certain explained variable For standardized driving variables, Represents the intercept term. The baseline path coefficient for non-vegetated areas The moderating effect coefficient of the vegetated area relative to the non-vegetated area. The main effect of the vegetation zone itself For random error terms. The above formula (8) is improved by adding the baseline path coefficient, the moderating effect coefficient, etc., as shown in formula (9) to obtain a multi-level driving path model.

[0092] The aforementioned multi-level driving path model constructs a multi-level driving path system of "land surface-atmosphere-extreme precipitation"; uses vegetation area as a moderating variable rather than a sample grouping variable; and achieves comparability of path coefficients based on standardized driving variables.

[0093] In another embodiment of this application, the multi-level driving path model shown in formula (9) can be replaced by the following models: structural equation model, generalized linear model, mixed effects model, machine learning model with embedded interaction structure, and Bayesian hierarchical model; the interaction effect can be expressed in parametric form or structural form in the above models.

[0094] According to the embodiments of this application, when the vegetation regulation variable in the multi-level driving path model is equal to a first preset value, the multi-level driving path model represents the baseline interaction relationship between the non-vegetated area and the multi-level driving variable; wherein, when the vegetation regulation variable in the multi-level driving path model is equal to a second preset value, the multi-level driving path model represents the actual interaction relationship between the vegetated area and the multi-level driving variable.

[0095] In the above formula (9), when When the first preset value is reached, the multi-level drive path model degenerates into the following expression: This indicates the baseline relationship in the non-vegetated area. When When the second preset value is reached, the multi-level drive path model becomes the following expression: This indicates the actual interaction within the vegetation zone.

[0096] According to embodiments of this application, the above-mentioned parameter fitting processing of a multi-level driving path model using multi-source datasets includes: performing parameter fitting processing on the multi-level driving path model using multi-source datasets to obtain parameter fitting results on different driving paths; when the independent variable on the current driving path is a multi-level driving variable, determining that the parameter fitting result is the fitting result of the baseline path effect coefficient in the non-vegetated area; when the independent variable on the current driving path is an interaction variable, determining that the parameter fitting result is the weighted fitting result of the moderating effect coefficient in the vegetated area and the baseline path effect coefficient in the non-vegetated area; when the independent variable on the current driving path is a vegetation moderating variable, determining that the parameter fitting result is the fitting result of the main effect coefficient in the vegetated area.

[0097] The multi-level driving path model is fitted using structural equation modeling to obtain parameter estimation results for each path. The parameter estimation results include at least the path coefficient estimates. Standard error Significance test statistic, significance probability value .

[0098] All path parameters are categorized and extracted: if the path independent variable is a normal continuous driving variable... The corresponding parameter is the main effect parameter, i.e., the baseline coefficient for non-vegetated areas; if the path independent variable is an interaction term... Then the corresponding parameter is the interaction effect parameter, that is, the difference coefficient between vegetated and non-vegetated areas; if the path independent variable is a vegetation moderating variable. The corresponding parameter is the regional main effect parameter of the vegetation zone.

[0099] According to embodiments of this application, the evaluation results of determining the vegetation impact on precipitation response variables in the target area using parameter fitting results include: when the parameter fitting results indicate the presence of interaction variables in the multi-level driving path model, determining the target path equation where the interaction variables are located; when the parameter fitting result of the moderating effect coefficient is greater than a threshold, determining that the vegetation area has an enhancing effect on precipitation response variables on the driving path represented by the target path equation; and when the parameter fitting result of the moderating effect coefficient is less than or equal to a threshold, determining that the vegetation area has a weakening effect on precipitation response variables on the driving path represented by the target path equation.

[0100] The following detailed description of the assessment process of the impact of vegetation on precipitation provided in the embodiments of this application will be further explained through specific implementation methods.

[0101] For any path with interaction terms, the total path coefficient in the vegetation zone This can be expressed as shown in formula (10):

[0102] (10).

[0103] in, For the baseline effect of non-vegetated areas, This is due to the regulatory effect of vegetation.

[0104] Formula (10) can be used to further compare the differences in actual driving intensity of the same path in vegetated and non-vegetated areas.

[0105] Direction determination of the moderating effect coefficient: based on the moderating effect coefficient corresponding to the interaction variable. The sign of the parameter fitting results is used to determine the direction of adjustment of the corresponding driving path in the vegetation area:

[0106] when When, it indicates the driving path corresponding to vegetation enhancement; when When, it indicates the driving path corresponding to vegetation weakening.

[0107] After determining the assessment results of the impact of vegetation on precipitation, the results of precipitation driving path difference identification are output, including: the baseline coefficient of each path in the non-vegetated area, the moderating difference coefficient of each path in the vegetated area, the total path coefficient of each path in the vegetated area, the significance results of each path, and the conclusion that each path "enhances", "weakens" or "has no significant difference".

[0108] Simultaneously, a driving path network diagram can be drawn: where the main effect path represents the baseline driving relationship in the non-vegetated area, and the interaction term path represents the direction and intensity of the difference between the vegetated area and the non-vegetated area. This visualization result provides an intuitive view of the regulatory structure of vegetation on the formation mechanism of extreme precipitation.

[0109] Based on the aforementioned method for assessing the impact of vegetation on precipitation, this application also provides an apparatus for assessing the impact of vegetation on precipitation. The following will be combined with... Figure 3 The device is described in detail.

[0110] Figure 3 A structural block diagram of an apparatus for assessing the impact of vegetation on precipitation according to an embodiment of this application is shown.

[0111] like Figure 3 As shown, the vegetation impact assessment device 300 of this embodiment includes a multi-source dataset acquisition module 310, a multi-level driving variable construction module 320, an interaction variable construction module 330, a driving path model construction module 340, and an assessment module 350.

[0112] The multi-source dataset acquisition module 310 is used to preprocess the remote sensing and meteorological raster monitoring data of the target area to obtain a multi-source dataset of the target area, wherein the multi-source dataset includes a meteorological dataset and a vegetation mask dataset; in one embodiment, the multi-source dataset acquisition module 310 can be used to perform the operation S210 described above, which will not be repeated here.

[0113] The multi-level driving variable construction module 320 is used to construct multi-level driving variables that characterize the meteorological change features of the target area based on the type of meteorological data in the meteorological dataset. In one embodiment, the multi-level driving variable construction module 320 can be used to perform the operation S220 described above, which will not be repeated here.

[0114] The interaction variable construction module 330 is used to construct vegetation regulation variables using the vegetation mask dataset, and to construct multiple interaction variables that characterize the interaction information between the vegetation regulation variables and the multi-level driving variables. In one embodiment, the interaction variable construction module 330 can be used to perform the operation S230 described above, which will not be repeated here.

[0115] The driving path model construction module 340 is used to construct a multi-level driving path model characterizing precipitation response variables using multiple interaction variables, the baseline path effect coefficient of the non-vegetated area in the target area, and the moderating effect coefficient of the vegetated area relative to the non-vegetated area in the target area. The precipitation response variables are used to represent precipitation information in the target area. In one embodiment, the driving path model construction module 340 can be used to perform the operation S240 described above, which will not be repeated here.

[0116] The evaluation module 350 is used to perform parameter fitting processing on the multi-level driving path model using a multi-source dataset, and to determine the evaluation results of the vegetation impact precipitation response variables in the target area using the parameter fitting results. In one embodiment, the evaluation module 350 can be used to perform the operation S250 described above, which will not be repeated here.

[0117] According to embodiments of this application, any multiple modules among the multi-source dataset acquisition module 310, multi-level driving variable construction module 320, interaction variable construction module 330, driving path model construction module 340, and evaluation module 350 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this application, at least one of the multi-source dataset acquisition module 310, multi-level driving variable construction module 320, interaction variable construction module 330, driving path model construction module 340, and evaluation module 350 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the multi-source dataset acquisition module 310, multi-level driving variable construction module 320, interaction variable construction module 330, driving path model construction module 340, and evaluation module 350 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0118] Figure 4 A block diagram of an electronic device suitable for implementing an assessment method for the impact of vegetation on precipitation, according to an embodiment of this application, is shown.

[0119] like Figure 4 As shown, an electronic device 400 according to an embodiment of this application includes a processor 401, which can perform various appropriate actions and processes according to a program stored in ROM 402 (Read-Only Memory) or a program loaded from storage portion 408 into RAM 403 (Random Access Memory). The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0120] RAM 403 stores various programs and data required for the operation of electronic device 400. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 402 and / or RAM 403. It should be noted that the programs may also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0121] According to embodiments of this application, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to a bus 404. The electronic device 400 may also include one or more of the following components connected to the input / output (I / O) interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output (I / O) interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0122] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0123] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 402 and / or RAM 403 and / or one or more memories other than ROM 402 and RAM 403 described above.

[0124] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0125] When the computer program is executed by the processor 401, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0126] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 409, and / or installed from removable medium 411. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0127] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by processor 401, it performs the functions defined in the system of this application embodiment. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0128] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0130] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

[0131] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A method for assessing the impact of vegetation on precipitation, characterized in that, The method includes: The remote sensing and meteorological raster monitoring data of the target area are preprocessed to obtain a multi-source dataset of the target area, wherein the multi-source dataset includes a meteorological dataset and a vegetation mask dataset. Based on the types of meteorological data in the meteorological dataset, construct multi-level driving variables to characterize the meteorological change features of the target area; Vegetation regulation variables are constructed using the vegetation mask dataset, and multiple interaction variables are constructed to characterize the interaction information between the vegetation regulation variables and the multi-level driving variables. A multi-level driving path model characterizing precipitation response variables is constructed using multiple interaction variables, the baseline path effect coefficient of the non-vegetated area in the target region, and the moderating effect coefficient of the vegetated area relative to the non-vegetated area in the target region. The precipitation response variables are used to represent precipitation information of the target region. The multi-source dataset is used to perform parameter fitting on the multi-level driving path model, and the parameter fitting results are used to determine the evaluation results of the vegetation's influence on the precipitation response variable in the target area.

2. The method according to claim 1, characterized in that, Preprocessing of remote sensing and meteorological raster monitoring data of the target area yields a multi-source dataset of the target area, including: The remote sensing and meteorological grid monitoring data of the target area are processed to unify the grid resolution according to the preset resolution grid to obtain remote sensing and meteorological grid monitoring data with standard grid. Spatial interpolation is performed on the resampled data in the remote sensing and meteorological grid monitoring data with standard grid to obtain remote sensing and meteorological grid monitoring data with unified spatial units; The remote sensing and meteorological grid monitoring data with unified spatial units are subjected to time aggregation processing to obtain remote sensing and meteorological grid monitoring data with the same spatiotemporal scale. The multi-source dataset is obtained by standardizing continuously changing data from remote sensing and meteorological raster monitoring data with the same spatiotemporal scale based on the same physical dimensions.

3. The method according to claim 1, characterized in that, Constructing vegetation moderating variables using the vegetation mask dataset includes: A binary vegetation region variable is constructed based on the vegetation mask threshold of the vegetation mask dataset; The data spatial units of the vegetation mask dataset are assigned values ​​using the vegetation binary region variable to obtain a vegetation adjustment variable that characterizes whether the data spatial unit belongs to the vegetation area.

4. The method according to claim 1, characterized in that, The construction of multiple interaction variables characterizing the interaction information between the vegetation regulation variable and the multi-level driving variable includes: Construct a surface water-vegetation interaction variable between the surface water variable and the vegetation regulation variable in the multi-level driving variables; Construct an evapotranspiration-vegetation interaction variable between the evapotranspiration variable and the vegetation regulation variable in the multi-level driving variables; Construct a layer-wide precipitable water vapor-vegetation interaction variable between the layer-wide precipitable water vapor variable and the vegetation regulation variable in the multi-level driving variables; Construct an atmospheric bottom or lower-level specific humidity-vegetation interaction variable between the atmospheric bottom or lower-level specific humidity variable and the vegetation regulation variable in the multi-level driving variables; Construct a horizontal pressure gradient-vegetation interaction variable between the horizontal pressure gradient variable and the vegetation regulation variable in the multi-level driving variables; Construct the wind field-vegetation interaction variable between the wind field variable and the vegetation regulation variable in the multi-level driving variables.

5. The method according to claim 4, characterized in that, A multi-level driving path model characterizing precipitation response variables is constructed using multiple interaction variables, the baseline path effect coefficient of the non-vegetated area in the target region, and the moderating effect coefficient of the vegetated area relative to the non-vegetated area in the target region. This includes: A first path equation characterizing the evapotranspiration variable is constructed using the surface water variable, the surface water-vegetation interaction variable, and the vegetation moderating variable. A second path equation characterizing the specific humidity variable of the lower atmosphere or lower layer is constructed using the evapotranspiration variable, the evapotranspiration-vegetation interaction variable, and the vegetation regulation variable. A third path equation characterizing the wind field variable is constructed using the horizontal pressure gradient variable, the horizontal pressure gradient-vegetation interaction variable, and the vegetation moderating variable. A fourth path equation characterizing the whole-layer precipitable water vapor variable is constructed using the specific humidity variable of the lower atmospheric layer, the specific humidity-vegetation interaction variable of the lower atmospheric layer, and the vegetation regulation variable. The first path equation, the second path equation, the third path equation, and the fourth path equation are combined and transformed to obtain a multi-level driving path equation that characterizes the vegetation regulation variable driving the precipitation response variable. The baseline path effect coefficient, the moderating effect coefficient, the random error term, the main effect coefficient of the vegetation zone, and the intercept term are added to the multi-level driving path equation to obtain the multi-level driving path model.

6. The method according to claim 5, characterized in that, In the multi-level driving path model, when the vegetation regulation variable is equal to the first preset value, the multi-level driving path model represents the benchmark interaction relationship between the non-vegetated area and the multi-level driving variable. Wherein, in the multi-level driving path model, when the vegetation regulation variable is equal to the second preset value, the multi-level driving path model represents the actual interaction between the vegetation area and the multi-level driving variable.

7. The method according to claim 5, characterized in that, The parameter fitting process for the multi-level driving path model using the multi-source dataset includes: The multi-source dataset is used to perform parameter fitting on the multi-level driving path model to obtain parameter fitting results on different driving paths. When the independent variable on the current driving path is the multi-level driving variable, the result of the parameter fitting is determined to be the fitting result of the baseline path effect coefficient of the non-vegetated area. When the independent variable on the current driving path is the interaction variable, the result of the parameter fitting is determined to be a weighted fitting result of the regulation effect coefficient of the vegetated area and the baseline path effect coefficient of the non-vegetated area. When the independent variable on the current driving path is the vegetation regulation variable, the result of the parameter fitting is the fitting result of the main effect coefficient of the vegetation area.

8. The method according to claim 5, characterized in that, The assessment results of determining the vegetation's impact on the precipitation response variable in the target area using parameter fitting results include: If the result of the parameter fitting indicates that the interaction variable exists in the multi-level driving path model, then the target path equation where the interaction variable is located is determined. If the parameter fitting result of the moderating effect coefficient is greater than the threshold, it is determined that the vegetation zone has an enhancing effect on the precipitation response variable on the driving path represented by the target path equation. If the parameter fitting result of the moderating effect coefficient is less than or equal to the threshold, it is determined that the vegetation zone has a weakening effect on the precipitation response variable on the driving path represented by the target path equation.

9. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 8.

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