Basin moisture evapotranspiration simulation method based on BEPS model

Abstract

The invention discloses a basin moisture evapotranspiration simulation method based on a BEPS model, and the method comprises the steps: obtaining research parameters of a target river basin, and carrying out the extraction, resampling and cutting amount preprocessing of the research parameters; the preprocessed research parameters are quantified and sent to a BEPS model for simulation inversion to estimate the total primary productivity GPP and the evapotranspiration ET of the river basin, and the water utilization efficiency of the target river basin is calculated according to the total primary productivity GPP and the evapotranspiration ET; and utilizing the evapotranspiration and the water utilization efficiency to evaluate evapotranspiration change conditions of the target river basin on different time scales and space scales. According to the evapotranspiration simulation method, the uncertainty of time scale expansion caused by remote sensing inversion of the evapotranspiration is reduced, and the evapotranspiration estimation accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of river evapotranspiration prediction, and in particular relates to a basin water evapotranspiration simulation method based on a BEPS model. Background Art

[0002] The tropical region, located between Earth's Tropic of Cancer and the Tropic of Capricorn, is a prime area for cultivating cash crops. my country, in particular, is located within this tropical cash crop production zone. The yield and value of these crops are crucial for agricultural development and farmers' income growth, impacting their income and living standards. Therefore, under limited water conditions, achieving efficient and sustainable regional water resource utilization requires clarifying the water requirements and irrigation water usage of different vegetation types at different growth stages. This approach can effectively utilize water resources, increase crop yields, and ensure the rational allocation of water resources. Estimating the evapotranspiration and water use efficiency of different vegetation types within tropical regions is crucial for optimizing vegetation planting and regional water resource allocation. The Boreal Ecosystem Productivity Simulator (BEPS) model, an ecological process model derived from the Forest-BGC model, has been widely used to estimate carbon and water cycles in terrestrial ecosystems. While the model possesses strong mechanistic properties, it also has a complex structure due to its numerous input parameters. Through continuous algorithmic refinement and regional validation, the model has been widely applied to simulate the spatial distribution of regional productivity and evapotranspiration across a variety of ecosystems. Therefore, studying and analyzing the evapotranspiration and water use efficiency in tropical river basins and their influencing factors provides technical support for guiding the formulation of effective water use plans for crops, which has important practical significance for achieving efficient utilization of agricultural water resources, optimizing irrigation water strategies, and scientifically planning water resource utilization facilities. In China, it usually refers to special economic crops planted in tropical regions. Summary of the Invention

[0003] The present invention aims to address the above-mentioned issues by providing a method for simulating watershed evapotranspiration based on the BEPS model. This method not only reduces the uncertainty of time-scale expansion caused by remote sensing evapotranspiration inversion, but also improves the accuracy of evapotranspiration estimation. To achieve this objective, the present invention employs the following technical solutions:

[0004] The present invention provides a watershed evapotranspiration simulation method based on the BEPS model, characterized in that the evapotranspiration simulation method comprises the following steps:

[0005] Step 1: Obtain the research parameters of the target river basin, extract, resample and clip the research parameters;

[0006] Step 2: Quantify the pre-processed research parameters and send them to the BEPS model for simulation and inversion to estimate the gross primary productivity (GPP) and evapotranspiration (ET) of the river basin. Calculate the water use efficiency of the target river basin based on the gross primary productivity (GPP) and evapotranspiration (ET).

[0007] Step 3: Use evapotranspiration and water use efficiency to assess the changes in evapotranspiration of the target river basin at different temporal and spatial scales.

[0008] The above scheme is further preferred, and the evapotranspiration simulation method also includes obtaining measured evapotranspiration data of the target river basin through the ecological remote sensing comprehensive experimental station, and performing quantitative comparative analysis on the simulated evapotranspiration and the measured evapotranspiration data to determine the degree of correlation between the regional scale spatial distribution of evapotranspiration of the river basin and the measured evapotranspiration distribution.

[0009] The above scheme is further preferred, and the quantitative preprocessing of the research parameters, including extraction, resampling and cropping, includes the following steps: first, connect to the application program interface of the data storage through a Python script, use the application program interface to access the data center, and obtain the research parameter data set; request and download data in the research parameter data set list, and then link the data type of each research parameter data set with the pre-simulated structural parameters through a lookup table, and then reproject, eliminate outliers, resample, and crop in sequence to make the range of the research parameter data set consistent with the range of the pre-simulated structural parameters.

[0010] The above scheme is further optimized. The sensitivity of the research parameters after preprocessing is mainly used to quantify them and then send them into the BEPS model for simulation inversion to estimate the evapotranspiration ET of the river basin and obtain the sensitivity SC of the research parameters. i And rank the sensitivity.

[0011] The above solution is further preferred, wherein the sensitivity SC i Satisfy the following calculation formula:

[0012]

[0013] SC i represents the sensitivity coefficient of the i-th variable, v i is the i-th independent variable, ET is evapotranspiration;

[0014] Evapotranspiration ET satisfies:

[0015] ET=T plant +E soil +E plant +S groud ;

[0016] T plant =Tsun *LAI sun +T shade *LAI shade ;

[0017] Among them, T plant is the transpiration of vegetation, E soil is the soil evaporation when the soil is uncovered, E plant When there is no vegetation cover, the evaporation of precipitation intercepted by vegetation, S plant is the amount of sublimation intercepted by vegetation when the vegetation is covered; S groud is the amount of sublimation of rainwater or dew on the surface;

[0018] T sun and T shade are the transpiration of sun leaves and shade leaves respectively; LAI is the total leaf area index of vegetation canopy, LAI sun and LAI shade are the leaf area index of sun leaves and shade leaves, respectively.

[0019] The above scheme is further preferred, wherein the research parameters include site information of the river basin, meteorological data, physiological and ecological parameters of various types of vegetation, and leaf area index; the site information includes longitude and latitude, land use type, soil texture, soil temperature, and soil moisture content; the meteorological data includes air temperature, precipitation, water vapor pressure difference, wind speed, and shortwave radiation recorded in hourly steps.

[0020] In the above scheme, the simulation inversion estimation of the gross primary productivity (GPP) of the river basin further preferably includes: setting the leaf area index (LAI) of the target river basin in a step size of days, wherein the leaf area index includes a leaf area index of sunny leaves and a leaf area index of shady leaves;

[0021] The leaf area index of the sun leaf and the leaf area index of the shade leaf are allocated to the aggregation index, and then the leaf area index LAI of the sun leaf is calculated separately. su and shade leaf area index A shade , get the gross primary productivity GPP of vegetation;

[0022] in:

[0023] LAI sun =2cosθ(1-e -0.5ΩL / cosθ );

[0024] LAI shade =L-LAI sun ;

[0025] GPP=(A sun ×LAI sun +A shade ×LAI shade)×daylenth;

[0026] LAI sun and A shade They are the leaf area index of sun leaves and the leaf area index of shade leaves, and the unit is: m 2 / m 2 , θ is the solar zenith angle, Ω is the concentration index, L is the leaf area index, A sun 、A shade are the photosynthesis rates of sun leaves and shade leaves respectively, and daylenth is the length of day.

[0027] In summary, since the present invention adopts the above technical solution, the present invention has the following beneficial technical effects:

[0028] (1) The present invention estimates evapotranspiration through simulation of the BEPS ecological process model, which reduces the uncertainty of the time scale expansion brought by remote sensing inversion evapotranspiration and improves the accuracy of evapotranspiration estimation. By analyzing the spatiotemporal variation characteristics and influencing factors of evapotranspiration of different land use types in the target river basin at different time scales through evapotranspiration and water use efficiency, the variation patterns of evapotranspiration of different land cover types at different time scales can be quickly understood.

[0029] (2) According to the variation patterns of evapotranspiration at different time scales, a scientific basis can be provided for agricultural irrigation and water resources management, a reference for the rational use of water resources in river basins, and the influence mechanism of different land cover types on evapotranspiration and water use efficiency can be extended to study more refined BEPS models to improve estimation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of a flow chart of a watershed evapotranspiration simulation method based on the BEPS model of the present invention;

[0031] Figure 2 It is a schematic diagram of the change of the evapotranspiration ET simulation result of the present invention;

[0032] Figure 3 Schematic diagram of the comparative distribution of the BEPS model simulation results of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described below with reference to the accompanying drawings and by way of preferred embodiments. However, it should be noted that many of the details listed in this specification are merely provided to help the reader gain a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be practiced even without these specific details.

[0034] like Figure 1 As shown, according to a watershed evapotranspiration simulation method based on the BEPS model of the present invention, the evapotranspiration simulation method includes the following steps:

[0035] Step 1: Obtain the research parameters of the target river basin, and perform extraction, resampling and clipping preprocessing on the research parameters; the research parameters include site information of the river basin, meteorological data, physiological and ecological parameters of various types of vegetation, and leaf area index; the site information includes latitude and longitude, land use type, soil texture, soil temperature, and soil moisture content; the meteorological data includes air temperature, precipitation, water vapor pressure difference, wind speed, and shortwave radiation recorded in hourly steps; the extraction, resampling, and clipping preprocessing of the research parameters include the following steps: first, connect to the data storage application program interface (CDS API) through a Python script, use the application program interface to access the data center, and obtain the research parameter data set; request and download data in the research parameter data set list, and then link the data type of each research parameter data set with the pre-simulated structural parameters through a lookup table, and then perform reprojection, outlier removal, resampling, and clipping in sequence to make the range of the research parameter data set consistent with the range of the pre-simulated structural parameters;

[0036] Step 2: Quantify the pre-processed research parameters and send them to the BEPS model for simulation and inversion to estimate the total primary productivity (GPP) and evapotranspiration (ET) of the river basin; the BEPS model includes a data acquisition module, a data processing module, a sensitive data analysis module, a evapotranspiration inversion simulation module, a data estimation module, and a data output module; the data acquisition module is used to access the data center through a Python script to connect to the application program interface of the data storage and obtain site information, meteorological data, physiological and ecological parameters, and leaf area index; the data processing module is used to perform quantitative pre-processing on the acquired site information, meteorological data, physiological and ecological parameters, and leaf area index; the sensitive data analysis module is used to perform quantitative pre-processing on the acquired site information, meteorological data, physiological and ecological parameters, and leaf area index. The processed data is used to calculate and analyze the sensitivity of the evapotranspiration (ET) output parameter and sort the sensitivities. The evapotranspiration inversion simulation module is used to perform inversion simulation on the sensitivity quantitative output data to obtain the evapotranspiration change situation. The data estimation module is used to perform quantitative comparative analysis on the evapotranspiration obtained by inversion and the measured evapotranspiration data to obtain the degree of correlation between the regional scale evapotranspiration spatial distribution and the measured evapotranspiration distribution of the target river basin. The water use efficiency of the target river basin is calculated based on the gross primary productivity (GPP) and the evapotranspiration (ET). In the present invention, the ratio of the gross primary productivity (GPP) to the evapotranspiration (ET) is used to characterize the water use efficiency (WUE), that is, the water use efficiency satisfies the following conditions: WUE=GPP / ET.

[0037] Step 3: Use evapotranspiration and water use efficiency to assess the changes in evapotranspiration of the target river basin at different temporal and spatial scales.

[0038] In the embodiment of the present invention, the pre-processed research parameters are quantified mainly by sensitivity quantification and then sent to the BEPS model for simulation inversion to estimate the evapotranspiration ET of the river basin and obtain the sensitivity SC of the research parameters. i And sort the sensitivity to estimate the evapotranspiration ET of the river basin and obtain the sensitivity SC of the research parameters i And sort the sensitivity; the sensitivity of the input parameters in the BEPS model to ET estimation is arranged from large to small as follows: air temperature (Temp), leaf aggregation index (CI), leaf area index (LAI), relative humidity (Rh), precipitation (Pre), soil temperature (soiltemp), soil water content (soilwater) and solar radiation (Rad). The sensitivity SC i Satisfy the following calculation formula:

[0039]

[0040] SC i represents the sensitivity coefficient of the i-th variable, v i is the i-th independent variable, ET is evapotranspiration;

[0041] The evapotranspiration ET satisfies:

[0042] ET=T plant +E soil +E plant +S groud ;

[0043] When calculating vegetation transpiration separately based on shade leaves and sun leaves, T plant satisfy:

[0044] T plant =T sun *LAI sun +T shade *LAI shade ;

[0045] Among them, T plant is the transpiration of vegetation, E soil is the soil evaporation when the soil is uncovered, E plant When there is no vegetation cover, the evaporation of precipitation intercepted by vegetation, S plant is the amount of sublimation intercepted by vegetation when the vegetation is covered; S groud is the amount of sublimation of rainwater or dew on the surface;

[0046] T sun and Tshade are the transpiration of sun leaves and shade leaves respectively; LAI is the total leaf area index of vegetation canopy, LAI sun and LAI shade are the leaf area index of sun leaves and shade leaves, respectively;

[0047] Among them, E soil According to the Penman equation, the soil surface impedance varies with the saturation degree of the first layer of soil, E soil The calculation formula is as follows:

[0048]

[0049] Where, E soil is the soil evaporation (mmd -1 ), R n is the net radiation of the soil canopy surface (Wm -2 ),

[0050] Δ is °C -1 The slope of the curve of saturated water vapor pressure under -1 ); ρ is the air density (1.225 kgm at 15°C) -3 );C p is the specific heat of air at room temperature (1010jkg -1 ℃ -1 ); γ is the psychrometric constant (kPa℃ -1 ); VPD is the saturated vapor pressure difference (kPa); r a is the aerodynamic impedance (ms -1 );r s is the surface impedance (ms -1 ); is the latent heat of vaporization of water (Jkg -1 ).

[0051]

[0052]

[0053] S groud =min(snow,(R ad -R ad_int )C snow / λ s );

[0054] Where, P int is the vegetation interception precipitation (mm), b abs_water is the absorption rate of water to the total solar radiation; b abs_snow_water is the absorption rate of total solar radiation; is the latent heat of water evaporation (2.5*10 6J / kg); λ s is the latent heat of sublimation (2.8*10 6 J / kg); R ad is the total solar radiation (Jm -2 d -1 );

[0055] R ad_int is the total solar radiation intercepted by vegetation (Jm -2 d -1 ); snow is water equivalent (mm); C snow is the proportional coefficient of total solar radiation converted into latent heat during sublimation.

[0056] Due to SC i The positive or negative value reflects the correlation between the independent variable and ET (dependent variable). i A positive value indicates that ET is positively correlated with the independent variable parameter, and increases with the increase of the input parameter, and vice versa. i The larger the |, the greater the influence of the independent variable parameter vi on the dependent variable. Therefore, we set one of the parameters to control the change range between ±30% and ±30% with a step size of 5% and 10%, keep other variables unchanged, and calculate the sensitivity of the ET output parameter.

[0057] In the present invention, the simulation inversion estimation of the gross primary productivity (GPP) of the river basin includes: setting the leaf area index (LAI) of the target river basin in a day-unit step, wherein the leaf area index includes the leaf area index of the sun leaf and the leaf area index of the shade leaf; assigning the leaf area index of the sun leaf and the leaf area index of the shade leaf to the aggregation index, and then calculating the leaf area index LAI of the sun leaf respectively. su and shade leaf area index A shade , get the gross primary productivity GPP of vegetation;

[0058] in:

[0059] LAI sun =2cosθ(1-e -0.5ΩL / cosθ );LAI shade =L-LAI sun :

[0060] GPP=(A sun ×LAI sun +A shade ×LAI shade )×daylenth;

[0061] LAI sun and A shade They are the leaf area index of sun leaves and the leaf area index of shade leaves, and the unit is: m 2 / m 2, θ is the solar zenith angle, n is the concentration index, L is the leaf area index, A sun 、A shade are the photosynthesis rates of sun leaves and shade leaves respectively, and daylenth is the length of day.

[0062] In the present invention, the selection and determination of research parameters are important steps in realizing the simulation of river basins in the BEPS model. The BEPS model includes parameter variables such as vegetation physiological parameters, meteorological data, initialization and intermediate variables. The physiological characteristics of vegetation vary between different vegetation types, directly or indirectly affecting physiological processes such as photosynthesis, respiration and stomatal behavior of vegetation. This embodiment selects 8 input parameters under different vegetation types, adopts the control variable method, keeps the values ​​of other input parameters unchanged, and analyzes the changes in the evapotranspiration ET simulation results of each variable within the range of -30% to 30%. The range of parameter changes is shown in Table 1.

[0063] Table 1 BEPS model parameter value range (value range is ±30%)

[0064]

[0065] When doing parameter sensitivity analysis, different parameters will affect the results of the model, so the control variable method is used to analyze the changes in ET simulation results of different parameter variables in the range of -30% to 30%. Figure 2 As shown. Figure 2The influence of various parameter changes on the simulated ET results is evident. As shown in the figure, Temp, CI, LAI, Rh, and Pre have more pronounced effects, while soiltemp, soilwater, and Rad have less significant impacts. Simple sensitivity parameter analysis using the control variable method only considers the impact of a single parameter on the model output. However, the BEPS model is a complex, multi-parameter coupled model. Changes in individual parameters not only directly affect the output but also influence changes in other parameters within the model, indirectly impacting the final results. To quantify the input parameters, parameter ranges, and parameter selection for different land use types while keeping other input parameters constant, and to provide a basis for rapid regionalized model parameter adjustments, the BEPS model can identify coefficients that are most sensitive to ET estimation. The BEPS model's input parameters, ranked from most sensitive to ET estimation, are Temp, CI, LAI, Rh, Pre, soiltemp, soilwater, and Rad. The input parameter range is between -15% and 15%, and the parameter sensitivity coefficient is relatively large. Among them, soiltemp, Rad, Rh, CI, LAI and Temp have a positive correlation with the simulation results, while Temp has a negative correlation. In addition, when using the BEPS model for ET estimation, special attention should be paid to the sensitive parameters mentioned above, and appropriate parameter adjustments should be made according to the regional climate characteristics to improve the regional application accuracy of the model.

[0066] In the present invention, during the evapotranspiration simulation process, measured evapotranspiration data of the target river basin is obtained through the ecological remote sensing comprehensive experimental station, and the simulated evapotranspiration is quantitatively compared and analyzed with the measured evapotranspiration data to determine the degree of correlation between the regional scale spatial distribution of evapotranspiration of the river basin and the measured evapotranspiration distribution; the present invention simulates the continuous time series evapotranspiration (ET) of the target river basin through the BEPS model, and selects the measured data of lysimeter monitoring set up at the observation station in the ecological remote sensing comprehensive experimental station and the historical evapotranspiration data set to verify and evaluate the accuracy of the BEPS model model at the regional scale of the station with daily, monthly, growing season and annual time scales, thereby obtaining the spatiotemporal variation characteristics of the target river basin and water use efficiency simulated by the BEPS model at different spatiotemporal scales, and at the same time selects the measured evapotranspiration data and the simulated evapotranspiration for comparative analysis and verification, and analyzes the spatiotemporal variation characteristics and influencing factors of evapotranspiration and water use efficiency at different time scales. The input and output of water in the target river basin over a period of time are in a balanced state, and its water balance evapotranspiration satisfies: P = ET + L R +ΔS;

[0067] Where P represents the total precipitation (mm), L RIndicates runoff (mm), including the sum of surface runoff and underground runoff, and ΔS indicates the change of water storage in the system. R It can be observed through observation stations, and ΔS represents the change over a long time scale; the water balance evapotranspiration formula calculates the evapotranspiration every 30 minutes by counting the water inflow and outflow of the lysimeter, and then removes outliers and performs statistical accumulation processing on the statistical data, and then accumulates the evapotranspiration every 30 minutes to obtain hourly and daily evapotranspiration data; linear interpolation is performed on data with a short missing time (<2h), and the daily average method is used to interpolate data for data with a long missing time; in order to further compare the spatial distribution characteristics between the evapotranspiration results obtained by the simulation inversion of the BEPS model and the measured evapotranspiration, ArcGIS is used to create a 0.1°×0.1° fishing net to compare the values ​​within the same grid points in the same longitude and latitude directions. In the present invention, SC i The positive or negative value reflects the correlation between the independent variable and ET (dependent variable). The present invention uses the Pearson Person correlation analysis to analyze the response relationship (correlation coefficient R) between the measured data and the ET obtained by BEPS model translation simulation. The correlation coefficient R satisfies:

[0068]

[0069] Where R is the Person correlation coefficient, x i is the ith simulated evaporation independent variable, y i is the i-th measured evapotranspiration dependent variable, n represents the number of samples;

[0070] The correlation coefficient R ranges from -1 to 1, reflecting the degree of linear relationship. R < 0 indicates negative correlation, and R > 0 indicates positive correlation. Therefore, the correlation coefficient R is used to judge the degree of correlation between the spatial distribution of regional-scale evapotranspiration in the river basin and the distribution of measured evapotranspiration. From this, we can roughly see the changes in the spatial distribution of regional-scale evapotranspiration in the target river basin over a continuous time. At the regional scale of the site, the BEPS model can objectively characterize the temporal changes in evapotranspiration.

[0071] In order to illustrate the accuracy and relevance of the evapotranspiration of the present invention, the present invention compares the spatial distribution of the BEPS model simulation results with those of the MODIS model and the PML model. The fishnet rasterized data results are used to select evenly distributed points in the same longitude and latitude directions to draw a scatter plot, as shown in the following figure: Figure 3 As shown in the figure, the BEPS model simulated values ​​are similar to those of MODIS evapotranspiration, and the consistency is good. The correlation coefficients between MODIS evapotranspiration ET values ​​and model estimates on the annual, monthly, and daily scales are 0.723 (R 2=0.523, p<0.01), 0.703 (R 2 =0.495, p<0.01), 0.724 (R 2 =0.525, p<0.01), as Figure 3 As shown in (a), (b), and (c), the correlations all passed the two-tailed significance test and were extremely significant (p<0.01).

[0072] Comparing the evapotranspiration of the BEPS model with that of the PML model, the estimated value of the BEPS model is slightly larger than that of the PML model. The correlation coefficients on the annual, monthly, and daily scales are 0.637 (R 2 =0.407, p<0.01), 0.742 (R 2 =0.551, p<0.01), 0.661 (R 2 =0.437, p<0.01), as Figure 3 As shown in Figures (d), (e), and (f), the relationships all passed the two-tailed significance test and were extremely significant (p < 0.01). Linear correlation analysis shows that R < 0 indicates a negative correlation, R > 0 indicates a positive correlation, and the closer |R| is to 1, the stronger the correlation between the variables. The BEPS model can objectively represent the temporal variation of evapotranspiration. The regional evapotranspiration values ​​estimated by the BEPS model can well represent the variation of evapotranspiration at different time scales at the regional spatial scale, indicating that the simulation results of the BEPS model are comparable.

[0073] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for simulating water evapotranspiration in a watershed based on the BEPS model, characterized by: The evapotranspiration simulation method comprises the following steps: Step 1: Obtain the research parameters of the target river basin, extract, resample and pre-process the research parameters; Step 2: Quantify the preprocessed research parameters and send them to the BEPS model for simulation and inversion to estimate the gross primary productivity (GPP) and evapotranspiration (ET) of the river basin, and calculate the water use efficiency of the target river basin based on the gross primary productivity (GPP) and evapotranspiration (ET); Step 3: Use evapotranspiration and water use efficiency to assess changes in evapotranspiration in the target river basin at different temporal and spatial scales.

2. The method for simulating water evapotranspiration of a watershed based on the BEPS model according to claim 1, characterized in that: The evapotranspiration simulation method also includes obtaining measured evapotranspiration data of the target river basin through an ecological remote sensing comprehensive experimental station, performing quantitative comparative analysis on the simulated evapotranspiration and the measured evapotranspiration data, and determining the degree of correlation between the regional scale evapotranspiration spatial distribution of the river basin and the measured evapotranspiration distribution.

3. The method for simulating water evapotranspiration of a watershed based on the BEPS model according to claim 1, characterized in that: The quantitative preprocessing of extraction, resampling and cropping of research parameters includes the following steps: first, connect to the application program interface of the data storage through a Python script, use the application program interface to access the data center, and obtain the research parameter data set; request and download data in the research parameter data set list, and then link the data type of each research parameter data set with the pre-simulated structural parameters through a lookup table, and then reproject, remove outliers, resample, and crop in turn to make the range of the research parameter data set consistent with the range of the pre-simulated structural parameters.

4. The method for simulating water evapotranspiration of a watershed based on the BEPS model according to claim 1, characterized in that: The pre-processed research parameters are quantified mainly by sensitivity and then sent to the BEPS model for simulation inversion to estimate the evapotranspiration ET of the river basin and obtain the sensitivity SC of the research parameters. i And rank the sensitivity.

5. The method for simulating water evapotranspiration of a watershed based on the BEPS model according to claim 4 is characterized by: The sensitivity SC i Satisfies the following calculation formula: SC i represents the sensitivity coefficient of the i-th variable, v i is the ith independent variable, ET is the evapotranspiration; Evapotranspiration ET satisfies: ET=T plant +E soil +E plant +S groud ; T plant =T sun *LAI sun +T sbade* Hybrid shade : Among them, T plant is the transpiration of vegetation, E soil is the soil evaporation when the soil is not covered, E plant When there is no vegetation cover, the evaporation of precipitation intercepted by vegetation, S plant is the amount of sublimation intercepted by vegetation when the vegetation is covered; S groud It is the amount of sublimation of rain or dew on the surface; T sun and T shade are the transpiration of sun leaves and shade leaves respectively; LAI is the total leaf area index of the vegetation canopy, LAI sun and LAI shade are the leaf area index of sun leaves and shade leaves, respectively.

6. The method for simulating water evapotranspiration of a watershed based on the BEPS model according to claim 1, characterized in that: The research parameters include site information of the river basin, meteorological data, physiological and ecological parameters of various types of vegetation, and leaf area index. The site information includes longitude and latitude, land use type, soil texture, soil temperature, and soil moisture content. The meteorological data includes air temperature, precipitation, water vapor pressure difference, wind speed, and shortwave radiation recorded in hours.

7. The method for simulating water evapotranspiration of a watershed based on the BEPS model according to claim 1, characterized in that: The simulation inversion estimation of the gross primary productivity (GPP) of the river basin includes: setting the leaf area index (LAI) of the target river basin in days, wherein the leaf area index includes the leaf area index of sunny leaves and the leaf area index of shady leaves; The leaf area index of the sun leaf and the leaf area index of the shade leaf are allocated to the aggregation index, and then the leaf area index LAI of the sun leaf is calculated separately. su and shade leaf area index A shade , get the gross primary productivity GPP of vegetation; in: GOT IT sun =2cosθ(Le -0.5ΩL / cosθ ); LAI shade =L-LAI sun : GPP=(A sun ×LAI sun +A shade ×LAI shade )×daylenth; LAI sun and A shade They are the leaf area index of sun leaves and the leaf area index of shade leaves, and the unit is: m 2 / m 2 , θ is the solar zenith angle, Ω is the concentration index, and A is the leaf area index. sun , A shade are the photosynthesis rates of sun leaves and shade leaves respectively, and daylenth is the length of day.

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