Agricultural irrigation physical process simulation method and system based on land-gas coupling model

Abstract

The invention provides an agricultural irrigation physical process simulation method and system based on a land-gas coupling model. The method comprises the following steps: acquiring basic data of a research area; determining an irrigation cultivated land range based on the land utilization data and the irrigation area distribution data; based on the crop distribution data and the irrigation cultivated land range, generating a crop type mask to determine a crop type and a crop growth stage in the irrigation cultivated land; respectively setting irrigation triggering conditions of the dry land crops and the paddy field crops based on the determined crop types and soil attribute data; when the irrigation triggering condition is met, calculating the irrigation water amount of the subgrid scale irrigation cultivated land to obtain an irrigation rate, and inputting the irrigation rate into the WRF-Noah model in a rainfall form; aiming at the paddy field crops, simulating a hydrothermal process of a field surface water layer; and the WRF-Noah model is operated. According to the invention, the irrigation process and the influence on regional water circulation and climate can be accurately simulated at the sub-grid scale, and a basis is provided for sustainable water resource management and formulation of future climate adaptation strategies.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydro-meteorological simulation, and particularly relates to an agricultural irrigation physical process simulation method and system based on a land-atmosphere coupling model. BACKGROUND

[0002] As an important land management method, agricultural irrigation significantly changes the regional natural water cycle and surface energy distribution by supplementing water to the soil to maintain crop growth, which has an important impact on regional and even global climate. In-depth study of the impact of irrigation activities on regional climate not only helps to reveal the biophysical mechanism of climate change, but also has important significance for formulating sustainable climate adaptation strategies and ensuring food and water resource security.

[0003] Currently, the main method to study the climate effect of irrigation is to parameterize the irrigation process through land surface models (LSMs) and couple it with global climate models (GCMs) or regional climate models (RCMs) to build land-atmosphere coupling models (such as CESM, WRF-Noah, WRF-CLM, etc.). However, the existing irrigation parameterization scheme has the following defects: (1) Insufficient spatial resolution: Existing models usually run at a spatial resolution of 1 km-100 km, assuming only a single land use type within a single grid cell, ignoring sub-grid scale land use heterogeneity, leading to distorted irrigation cultivated land range and affecting the simulation accuracy of irrigation climate effect.

[0004] (2) Single crop type: Existing irrigation modeling assumes a uniform crop in the simulation area (such as dryland crops represented by wheat or paddy crops represented by rice), without considering crop rotation patterns (such as rice-wheat rotation), which cannot reflect the diversity of actual agricultural production.

[0005] (3) Ignoring the water layer on the field: Existing models usually ignore the flooded state of paddy crops during the growing season, only considering the contribution of soil evaporation and plant transpiration to latent heat flux, ignoring the water surface evaporation of the water layer on the field, leading to underestimation of the change in energy distribution caused by irrigation.

[0006] Existing technologies (such as the irrigation module of the WRF-Noah model) fail to fully consider sub-grid scale land use distribution and crop type differences when simulating irrigation processes, and lack modeling of the flooded state of paddy fields, limiting the model's accurate simulation of irrigation water and heat fluxes and regional climate effects.

[0007] While existing technologies have proposed simulations of irrigation in dry / paddy fields and water balance in paddy fields based on soil moisture differences, these schemes are all improvements at the level of isolated field experiments and have never been modeled in the Noah land surface model, thus failing to address the following coupled technical challenges: (1) Spatial scale mismatch: The subgrid flux output by Mosaic needs to be aggregated into the Noah grid energy balance equation. If it is directly weighted and averaged, the water surface evaporation of the paddy field subgrid will be diluted by the soil evaporation term, thus underestimating the latent heat flux. (2) Conflict in physical processes: During the flooding period of paddy fields, the Noah model assumes that the latent heat flux is the sum of soil evaporation and vegetation transpiration, which is fundamentally contradictory to the surface evaporation of the water layer and the advection heat transfer mechanism in the field. If they are forcibly superimposed, it will cause an error in energy conservation (net surface radiation ≠ sensible heat + latent heat + soil heat flux). (3) Feedback loop breakage: Irrigation triggering depends on soil moisture, while the evaporation of the field surface water layer has a reaction effect on the surface temperature, which in turn affects the calculation of soil moisture. Existing technology has never established this nonlinear closed loop, resulting in the accumulation of simulation bias over time.

[0008] The aforementioned challenges cannot be solved by a single technology; it requires reconstructing the water-energy coupling path of the WRF-Noah land-atmosphere coupling model at a subgrid scale. Summary of the Invention

[0009] The purpose of this invention is to address the shortcomings of existing technologies by providing a method and system for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model. This method improves the irrigation parameter scheme of the WRF-Noah model to achieve sub-grid-scale land use heterogeneity modeling, comprehensively considers the irrigation needs of dryland crops, paddy crops, and rice-wheat rotation patterns, and introduces surface water layer simulation to accurately describe the impact of irrigation processes on regional water cycles and land-atmosphere feedback. This provides a scientific basis for studying the regional climate effects of irrigation and formulating sustainable water resource management and climate adaptation strategies.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A simulation method for agricultural irrigation physical processes based on a land-atmosphere coupling model includes: Obtain basic data for the study area, including meteorological, soil properties, land use, crop distribution, and irrigation area distribution data; Based on the land use data and irrigation area distribution data, the scope of irrigated farmland is determined using a subgrid scale analytical method. Based on the crop distribution data and the irrigated farmland area, a crop type mask is generated to determine the crop types and crop growth stages within the irrigated farmland; the crop types include at least dryland crops and paddy field crops. Based on the determined crop type and soil property data, irrigation trigger conditions for dryland crops and paddy crops are set respectively; When the irrigation trigger condition is met, the irrigation water amount of the sub-grid scale irrigated farmland is calculated to obtain the irrigation rate, and the irrigation rate is input into the WRF-Noah model in the form of rainfall, while the corresponding irrigation rate is deducted in the surface runoff parameter; For the paddy crops, the water and heat processes of the surface water layer are simulated, the water surface evaporation is calculated, and the ground energy distribution is corrected; Run the WRF-Noah model to simulate the agricultural irrigation process and its influence on regional water cycle and land-atmosphere feedback.

[0011] Further, the process of determining the irrigation farmland range specifically includes: Based on the land use data, the Mosaic method is used to analyze the N dominant land use types in the grid; Identify the farmland type from the dominant land use type; Combine the irrigation area distribution data to determine the final irrigation farmland range from the farmland type.

[0012] Further, the rules for determining the crop type in the irrigation farmland include: If the area proportion of wheat in the sub-grid is greater than 50% and the area proportion of rice is less than 50%, it is marked as dryland crops; If the area proportion of wheat in the sub-grid is less than 50% and the area proportion of rice is greater than 50%, it is marked as paddy crops; If the area proportions of wheat and rice in the sub-grid are both less than 50%, it is marked as a rice-wheat rotation mode; The rest is marked as non-irrigated farmland.

[0013] Further, the rules for determining the crop growth stage in the irrigation farmland include: The crop growth stage is determined by the day sequence number, where the day sequence number is less than 151 for dryland crop growth period, and greater than 151 for paddy crop growth period; the rest is marked as non-irrigated farmland.

[0014] Further, the irrigation trigger condition is set by the following method: for paddy crops, the top two layers of soil moisture content are calculated, and irrigation is triggered when the soil moisture content is less than the field water holding capacity; for dryland crops, the relative soil humidity is calculated, and irrigation is triggered when the relative soil humidity is lower than the preset threshold.

[0015] Further, the formula for calculating the soil moisture content is:

[0016] The formula for calculating the relative soil humidity is:

[0017] In the formula: i represents the i-th soil layer; SM represents the soil moisture content; SM represents the soil moisture of the i-th soil layer; D(i) represents the thickness of the i-th soil layer; FC Indicates field water holding capacity parameter; SM WP Represents the wilting coefficient; RSM represents relative soil moisture.

[0018] Furthermore, the formula for calculating the irrigation rate is as follows:

[0019] In the formula: IRR(t) represents the irrigation rate at the time step that triggers the irrigation; PRE(t) represents the precipitation at the current time step, in mm / s; SurRun(t) represents the surface runoff at the current time step, in mm / s.

[0020] Furthermore, the hydrothermal process of the simulated field surface water layer includes the following steps: Determining the existence of a field water layer: When the crop type is paddy field crop or is in the growth stage of paddy field crop, the existence of a field water layer is marked; Calculating water balance: Based on precipitation input, irrigation input, water surface evaporation, soil infiltration, and field drainage, the depth change of the field water layer is calculated; Calculating water surface evaporation: The water surface evaporation per unit time is calculated using the Priestley-Taylor equation; Simulating energy balance: The water surface evaporation is introduced into the surface energy balance equation to correct the latent heat flux distribution of the Noah land surface model; Updating water layer status: The field water layer depth parameters are dynamically updated based on the calculated changes in field water volume.

[0021] Furthermore, the process of running the WRF-Noah model includes: generating an executable file by modifying the module_irrigation module and the module_sf_noahdrv.F land surface model driver file of the WRF-Noah model; configuring model parameters through the namelist file, including setting the sf_irrigation_crop_demand parameter to control the opening and closing of the irrigation scheme, and setting the irrigation_tagger parameter to adjust the irrigation triggering timing; and checking based on water balance and energy balance during model operation, and outputting error message when an error is detected.

[0022] On the other hand, the present invention provides a simulation system for agricultural irrigation physical processes based on a land-atmosphere coupling model, comprising: Basic data acquisition module: It is used to acquire basic data of the study area, including meteorological, soil properties, land use, crop distribution and irrigation area distribution data; irrigation range determining module: for determining the irrigation farmland range based on the land use data and the irrigation area distribution data by using a sub-grid scale analysis method; crop type determining module: for generating a crop type mask to determine the crop type and the crop growth stage in the irrigation farmland based on the crop distribution data and the irrigation farmland range; the crop type at least includes dryland crops and paddy crops; irrigation trigger mechanism module: for setting irrigation trigger conditions of the dryland crops and the paddy crops respectively based on the determined crop type and the soil property data; irrigation rate calculating module: for calculating the irrigation water volume of the sub-grid scale irrigation farmland to obtain an irrigation rate when the irrigation trigger conditions are met, and inputting the irrigation rate in the form of rainfall into the WRF-Noah model while deducting the corresponding irrigation rate in the surface runoff parameter; surface water layer module: for simulating the water and heat processes of the surface water layer for the paddy crops, calculating the water surface evaporation and correcting the surface energy distribution; running module: for running the WRF-Noah model to simulate the agricultural irrigation process and its influence on the regional water cycle and the land-air feedback.

[0023] Compared with the prior art, the present application has the following beneficial effects: (1) Sub-grid scale modeling improves spatial accuracy: By introducing the Mosaic method, sub-grid scale land use heterogeneity modeling is realized, overcoming the limitations of single land use type in existing models, improving the identification accuracy of irrigation farmland range and the accuracy of climate effect simulation. The Mosaic method combined with the crop mask reduces the irrigation farmland identification error from ±30% at the grid scale to ±8% at the sub-grid scale.

[0024] (2) Differentiated irrigation water volume simulation for different crop types: Considering dryland crops, paddy crops and rice-wheat rotation mode, differentiated irrigation trigger conditions and water volume calculation are set according to crop type and growth stage, truly reflecting the diversity of agricultural production. Different trigger conditions for different crop irrigation combined with the rice field surface water layer module improve the determination coefficient R² of simulated irrigation water volume and measured value from 0.32 (only Mosaic) to 0.58.

[0025] (3) Surface water layer simulation corrects water and energy budget: For paddy crops, a surface water layer module is added to model the water surface evaporation process through water balance and energy balance, correcting the energy distribution scheme of the Noah land surface model and making up for the defects of existing models that ignore the flooded state of paddy fields. After introducing the water surface evaporation process and advection heat transfer process, the Nash coefficient of latent heat flux simulation is improved from 0.51 (without surface water layer) to 0.76, and the RMSE of surface temperature is reduced by 0.7°C; (4) Dynamic scenario analysis to capture the climate effect: support the simulation of multiple irrigation management modes and flooding depth scenarios, reveal the dynamic response law of regional climate effect of irrigation activities, and provide scientific basis for water resources management and climate adaptation strategy. Sensitivity experiments show that the regional average temperature decreases by 0.8°C (compared with the no irrigation scenario) in the irrigation opening scenario, which is better than the 0.3°C temperature reduction of the single module improvement, verifying the amplification effect of the closed loop feedback.

[0026] (5) High integration: based on the WRF-Noah model framework, developed using Fortran 90 language, easy to integrate with other land-atmosphere coupled models, high running efficiency, and convenient maintenance and update.

[0027] In summary, the present application improves the irrigation parameter scheme, realizes more realistic and accurate simulation of agricultural irrigation process, can effectively reveal the biophysical mechanism of irrigation on regional water cycle and land-atmosphere feedback, and provides an important tool for studying the regional climate effect of irrigation and formulating sustainable water resources management strategies. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0029] Figure 1 The figure is a whole structure framework of the land-atmosphere coupling simulation method related to the embodiment of the present application. Figure 2 The figure is a flowchart of the irrigation process simulation method related to the embodiment of the present application. Figure 3 The figure is an internal flowchart of the irrigation range determination related to the embodiment of the present application. Figure 4 The figure is a flowchart of crop type determination, irrigation triggering and irrigation water quantity calculation related to the embodiment of the present application. Figure 5 The figure is a flowchart of the simulation of the field surface water layer related to the embodiment of the present application. Figure 6Figures for comparing irrigation water simulated by the improved WRF-Noah model with measured values according to embodiments of the present application; (a) is a figure for comparing irrigation water in the HN region with measured values, (b) is a figure for comparing irrigation water in the SD region with measured values, (c) is a figure for comparing irrigation water in the HUN region with measured values, (d) is a figure for comparing irrigation water in the HB region with measured values, (e) is a figure for comparing irrigation water in the AH region with measured values, (f) is a figure for comparing irrigation water in the JS region with measured values, and (g) is a figure for comparing irrigation water in the JS region with measured values. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described below in detail with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0031] In specific implementation, the method proposed in the technical solutions of the present application can be automatically run by computer software technology, and the system device of the method, such as a computer readable storage medium storing the corresponding computer program of the technical solutions of the present application and a computer device including running the corresponding computer program, should also be within the scope of protection of the present application.

[0032] <Embodiment One> As shown in Figure 1 and 2 The agricultural irrigation physical process simulation method based on a land-atmosphere coupled model provided in the present embodiment takes a certain typical agricultural irrigation area as the research object, simulates the regional agricultural irrigation process and its influence on the regional climate, and specifically includes the following steps: Step 1: Obtain the basic data of the research region, including meteorological data, soil property data, land use data, crop distribution data, and irrigation area distribution data; Through field investigation and data collection, meteorological data (rainfall, temperature, humidity, etc.), soil property data (soil type, water holding capacity, wilting coefficient, etc.), land use data (farmland, forest land, water area, etc.), and crop distribution data (wheat, rice, etc.) of the research region are obtained. The WPS module of the WRF model is used to process these data to generate standardized input files, including terrain, land use, and meteorological element fields. The total area of a certain typical agricultural irrigation area selected in the present embodiment is about 320,000 square kilometers, and the main crops planted in the domain are rice and wheat. The land use types include farmland (accounting for 52%), forest land (accounting for 20%), water area (accounting for 16%), and other types (accounting for 12%).

[0033] Step 2: Based on the land use data and irrigation area distribution data, determine the irrigation farmland range using a sub-grid scale analysis method; Based on the Mosaic method, analyze the land use type at the sub-grid scale within the grid Figure 3 ), set N = 4 dominant types, and the calculation formula is as follows:

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] where: i is the number of sub-grid; A G represents the area in the grid; N represents the number of dominant land use types in the grid; A i represents the area of a sub-grid scale dominant land use type in the grid, is the normalized area fraction; per represents the percentage of the area of all dominant types in the grid when the grid contains N dominant land use types; represents the crop farmland area; IRF represents the irrigation area fraction; represents the surface flux or surface state variable of the i-th sub-grid, is the average value of the variable in the sub-grid unit; H is the flux of the grid unit, is the irrigation fraction, is the flux of the irrigation part of the grid unit, is the flux of the non-irrigation part of the grid unit.

[0041] Step 3: Based on the crop distribution data and the irrigation farmland range, generate a crop type mask to determine the crop type and crop growth stage within the irrigation farmland; the crop type at least includes dryland crops and paddy crops; According to the crop distribution data, make a sub-grid scale crop type mask Figure 4 ), the classification rules are as follows: (1) If the wheat area ratio Wheatfrac> 50% and the rice area ratio Ricefrac< 50%, mark as dryland crops (crop_mask = 1); (2) If the wheat area proportion Wheatfrac < 50% and the rice area proportion Ricefrac > 50%, mark as paddy crop (crop_mask = 2); (3) If the wheat area proportion Wheatfrac < 50% and the rice area proportion Ricefrac < 50%, mark as rice-wheat rotation (crop_mask = 3); (4) The rest is non-irrigated farmland (crop_mask = 0).

[0042] The crop growth stage is determined by the Julian day: (1) If the Julian day < 151, mark as dryland crop growth period (crop_season_mask = 1); (2) If the Julian day > 151, mark as paddy crop growth period (crop_season_mask = 2).

[0043] In this embodiment, it is assumed that from February 1 to May 1 (JULIAN < 151) is the wheat growth period, and from May 2 (JULIAN > 151) is the rice growth period. Figure 4 ).

[0044] Step 4: Based on the determined crop type and soil property data, set the irrigation trigger conditions for dryland crops and paddy crops respectively; Based on the soil moisture content parameter, set the irrigation trigger conditions for paddy and dryland crops respectively: (1) For paddy crops represented by rice, calculate the soil moisture content of the top two layers (0-10 cm and 10 cm-40 cm) in the paddy crop subgrid. When the moisture content of the two layers is less than the field water holding capacity, trigger paddy irrigation.

[0045]

[0046]

[0047] (2) For dryland crops (wheat), calculate the soil moisture content of the top two layers (0-10 cm and 10 cm-40 cm) in the dryland crop subgrid. At the same time, calculate the difference between the two layer moisture contents and the wilting coefficient, the difference between the field water holding capacity and the wilting coefficient, and the ratio of the two (defined as relative soil humidity). When the relative soil humidity is less than the threshold value, trigger dryland irrigation.

[0048]

[0049]

[0050] wherein: i represents the i-th layer of soil; SM represents soil moisture; D(i) represents the thickness of the i-th layer of soil; wherein: SM(i) represents the soil moisture of the i-th layer of soil; FC wherein: SM represents the field capacity parameter; WP wherein: RSM represents the relative soil moisture; RSM represents the relative soil moisture threshold value. T wherein: RSM represents the relative soil moisture threshold value.

[0051] In this embodiment, the thickness of the first layer of soil is set to 10 cm, the thickness of the second layer of soil is set to 30 cm, the field capacity and the wilting coefficient are determined by soil sampling, and the dryland irrigation triggering threshold value is set to 0.55 times the field capacity. After multiple field crop growth tests, the field capacity is finally set to 0.55 times the field capacity, and this parameter is an adjustable parameter, which can be adjusted according to the actual irrigation strategy in different regions.

[0052] Step 5: When the irrigation triggering condition is met, the irrigation water amount of the sub-grid scale irrigated land is calculated to obtain the irrigation rate, and the irrigation rate is input into the WRF-Noah model in the form of rainfall, and the corresponding irrigation rate is deducted from the surface runoff parameter; After triggering irrigation, the irrigation rate is calculated as follows:

[0053]

[0054]

[0055] wherein: IRR(t) represents the irrigation rate of the triggering irrigation time step; PRE(t) represents the precipitation (mm / s) containing the irrigation water amount in the current time step, wherein: PRE(t) represents the net precipitation in the current time step; SurRun(t) represents the surface runoff (mm / s) in the current time step excluding the irrigation water amount, wherein: SurRun(t) represents the net surface runoff in the current time step, and the other parameters are the same as above.

[0056] The irrigation rate is input into the WRF-Noah model in the form of rainfall, and the surface runoff is correspondingly deducted. In this embodiment, the irrigation water amount is verified by regional hydrological data, and the simulation result is compared with the measured irrigation water amount (Fig. 4) Figure 6 ), and the determination coefficient R² is not less than 0.55, indicating that the simulation accuracy of irrigation water is high.

[0057] Step 6: As shown in Fig. 5, for the paddy crop, the water and heat processes of the water surface layer are simulated, the water surface evaporation is calculated, and the surface energy distribution is corrected: Figure 5 ​For paddy field crops with rice as a typical example, the existence of a water layer on the field surface is first determined: when crop_mask = 2 or crop_season_mask = 2, it is marked as having a water surface (field_water_mask = 1); otherwise, it is marked as not having a water surface (field_water_mask = 0).

[0058] When the presence of a water layer is determined, a simulation of the water surface recession process is performed on the field water layer, based on the field water balance:

[0059]

[0060]

[0061]

[0062]

[0063] In the formula: IRR(t) represents the irrigation rate at the current time step t that triggered irrigation; PRE(t) represents the precipitation (mm / s) at the current time step t; ET The water surface evaporation rate (mm / s) represents the current time step t. dW(t) represents the soil permeability coefficient; DR(t) represents the drainage volume (mm) at the current time step t, currently assuming no drainage; dW(t) represents the change in field water volume at the current time step t of this subgrid. If dW(t)>0, it indicates an increase in water volume, maintaining the field surface water layer; if dW(t)<0, it indicates a decrease in the water layer. The initial value of dW is 0; H(t) and H(t-1) represent the water surface height at the current time step t and the previous time step t-1, respectively, in mm. It represents the saturated hydraulic conductivity. This represents the maximum depth of the field water layer.

[0064] Based on the consensus reached through field trials and research, this embodiment sets the maximum water depth parameter for the field. Take 100mm.

[0065] In water balance Soil permeability coefficient calculated using the Priestley-Taylor equation Determined based on soil type (in this embodiment, it is taken as...) =0.012 mm / h).

[0066]

[0067]

[0068]

[0069]

[0070] wherein, Evap is the evaporation rate from open water in each time step; is the aerodynamic component, which is the evaporation component of the turbulent motion of water vapor generated by eddy diffusion; is the net radiation; is the slope of the saturation vapor pressure curve; is the humidity constant; is the latent heat of vaporization (2.45 MJ kg 1); u2 is the wind speed at 2 m, is the vapor pressure difference, is the saturated water vapor pressure, is the actual water vapor pressure in the air. Transp is the transpiration rate from open water in each time step; is the crop coefficient, is the soil moisture stress coefficient, is the reference crop evapotranspiration; represents the soil heat flux; represents the average air temperature.

[0071] After determining the water surface change process step by step, the energy balance process of the water surface in the field is simulated based on the surface energy balance formula:

[0072] wherein, represents the net shortwave radiation of the surface; represents the net longwave radiation of the surface; represents the net adiabatic sensible heat; represents the net adiabatic latent heat; represents the vertical sensible heat flux; represents the vertical latent heat flux; represents the heat stored in the accumulated water.

[0073] The calculation formula of the net adiabatic sensible heat (DH) and the net adiabatic latent heat (DV) is as follows:

[0074]

[0075] wherein, represents the specific heat capacity of air; represents the density of air; represents the along-wind (horizontal) distance between the upwind and downwind temperature profiles Tu(z) and Td(z); u(z) represents the wind profile; the ageostrophic energy is the sum over the vertical cross section of the control volume from the ground (z = 0) to the measurement height Z.

[0076] The vertical sensible heat flux (H) and the vertical latent heat flux (E) are calculated as follows:

[0077]

[0078]

[0079] where, is the leaf temperature; is the air temperature at the measurement height Z above the canopy; is the aerodynamic resistance. VPD is the vapor pressure deficit measured above the crop. d is the zero-point displacement; is the momentum roughness length, and similarly, is the roughness length for vapor and heat transport; k is the von Karman constant (0.41); is the wind speed at the measurement height z above the same canopy.

[0080] The heat flux stored in the standing water (Qs) is calculated as follows: where,

[0081] is the specific heat capacity of water, is the density of water, is the water surface height at the current time step t. The water surface evaporation parameter and the heat flux stored in the standing water parameter are passed to the Noah land surface model to correct the latent heat flux. In this embodiment, since the distribution of the surface heat flux directly determines the surface temperature, the performance of the heat flux simulation is characterized by the more popular surface temperature variable. The simulated surface temperature is compared with the measured value, and the Nash coefficient is more than 0.7, which verifies the effectiveness of the simulation of the water layer on the field. At the same time, the simulation of regional precipitation is improved.

[0082]

[0083] Step 7: Run the WRF-Noah model to simulate the agricultural irrigation process and its impact on regional water cycle and land-atmosphere feedback: ​​The module_irrigation module of the WRF-Noah model is improved by Fortran 90 language, and the module_sf_noahdrv.F land surface model driving file is modified to generate an executable file. The input parameters are set through a namelist file, the sf_irrigation_crop_demand controls the irrigation scheme switch, and the irrigation_tagger adjusts the triggering time. Errors are checked through water balance and energy balance during running, and if an error occurs, the output is "Fatal error: irrigation process error". The simulation results of the embodiment show that the improved model can effectively capture the influence of irrigation on the surface latent heat flux and the regional air temperature.

[0084] Further, the embodiment also includes step 8: scenario simulation: Two sensitive experimental scenarios of irrigation opening and irrigation closing are set to analyze the response of irrigation to the regional climate effect. The simulation results show that in the irrigation opening mode, the simulation performance of the regional soil moisture content is improved, the regional latent heat flux increases by 10%-15%, the ground temperature decreases by 0.5-1°C, and the cooling effect and humidification effect of irrigation are detected.

[0085] In order to verify the necessity of the cooperation of each module, an ablation experiment is carried out: Scheme A: only Mosaic method is used. The irrigation water simulation Nash coefficient is Nash=0.52; Scheme B: Mosaic method is used in combination with different crop difference triggering irrigation. The irrigation water simulation Nash coefficient is Nash=0.61; Scheme C: Mosaic method is used in combination with different crop difference triggering irrigation and modeling of the paddy field water layer (without energy advection correction). The irrigation water simulation Nash coefficient is Nash=0.64; The complete scheme of the application. The irrigation water simulation Nash coefficient is Nash=0.76.

[0086] The results show that the water balance process of the water layer and the energy advection correction are the modules that make the greatest contribution to performance improvement, and only take effect on the basis of using the Mosaic method to process land use heterogeneity and different crops to trigger irrigation, which proves that the three are indispensable.

[0087] <Embodiment Two> The embodiment provides an agricultural irrigation physical process simulation system based on the above method, which comprises the following modules: A basic data acquisition module is used to acquire basic data of a research region, including meteorological data, soil properties, land use, crop distribution and irrigation area distribution data; An irrigation range determining module is configured to determine an irrigation farmland range based on the land use data and irrigation area distribution data by using a sub-grid scale analysis method; A crop type determining module is configured to generate a crop type mask to determine crop types and crop growth stages in the irrigation farmland based on the crop distribution data and the irrigation farmland range; the crop types at least include dryland crops and paddy crops; An irrigation trigger mechanism module is configured to set irrigation trigger conditions for the dryland crops and the paddy crops respectively based on the determined crop types and soil property data; An irrigation rate calculating module is configured to calculate irrigation water amounts of the sub-grid scale irrigation farmland to obtain irrigation rates when the irrigation trigger conditions are met, and input the irrigation rates in the form of rainfall into the WRF-Noah model while deducting the corresponding irrigation rates in the surface runoff parameters; A surface water layer module is configured to simulate water and heat processes of a surface water layer, calculate water surface evaporation, and correct surface energy distribution for the paddy crops; A running module is configured to run the WRF-Noah model to simulate agricultural irrigation processes and their influences on regional water cycles and land-air feedbacks; In addition, the embodiment also includes a control module configured to coordinate operations of the modules; An input display module is configured to support input of parameters through a namelist file and display simulation results (such as irrigation water amounts and latent heat fluxes).

[0088] The input display module displays results in the form of data tables and two-dimensional images and supports dynamic adjustment of irrigation parameters. The embodiment is run on a Linux system (16-core CPU, 32 GB RAM) and has high calculation efficiency and convenient maintenance.

[0089] The above embodiment is only an example of the technical solutions of the present application. The agricultural irrigation physical process simulation method and system based on a land-air coupling model according to the present application are not limited to the content described in the above embodiment, but are subject to the scope defined in the claims. Any modification, supplement or equivalent replacement made by a person skilled in the art on the basis of the above embodiment is within the scope of the claims of the present application.

[0090] In the above embodiments, the technical solutions such as the sub-grid scale land use modeling based on the Mosaic method, the differentiated irrigation triggering mechanism for dryland crops and paddy crops, and the field surface water layer simulation based on water balance and energy balance are adopted, but the present application is not limited thereto. Other technical solutions, such as land use classification methods based on different spatial resolutions, irrigation triggering conditions for other crop types (such as corn, cotton, etc.), different irrigation methods and technologies (such as drip irrigation, flooding irrigation, etc.), or different water and heat flux calculation models (such as the Penman-Monteith equation instead of the Priestley-Taylor equation), etc., can be equivalent replacements or extensions of the present application, and are suitable for irrigation simulation needs in different regions and climate conditions.

[0091] It should be understood that parts not elaborated in the specification are all prior art.

[0092] It should be understood that the above description of the preferred embodiments is more detailed and should not be considered as a limitation on the scope of patent protection of the present application. It is not necessary or possible to enumerate all embodiments here. Those skilled in the art can make substitutions or modifications without departing from the scope of the claims of the present application, and all fall within the scope of protection of the present application. The scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model, characterized in that, include: Obtain basic data for the study area, including meteorological, soil properties, land use, crop distribution, and irrigation area distribution data; Based on the land use data and irrigation area distribution data, the scope of irrigated farmland is determined using a subgrid scale analytical method. Based on the crop distribution data and the irrigated farmland area, a crop type mask is generated to determine the crop types and crop growth stages within the irrigated farmland; the crop types include at least dryland crops and paddy field crops. Based on the determined crop type and soil attribute data, irrigation trigger conditions are set for dryland crops and paddy field crops respectively. When the irrigation triggering conditions are met, the irrigation water volume of irrigated farmland at the subgrid scale is calculated to obtain the irrigation rate, and the irrigation rate is input into the WRF-Noah model in the form of rainfall. At the same time, the corresponding irrigation rate is subtracted from the surface runoff parameters. For the aforementioned paddy field crops, the hydrothermal process of the water layer on the field surface is simulated, water surface evaporation is calculated, and the surface energy distribution is corrected. The WRF-Noah model was run to simulate agricultural irrigation processes and their impact on the regional water cycle and land-atmosphere feedback.

2. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 1, characterized in that, The process of determining the scope of irrigated farmland specifically includes: Based on the land use data, the Mosaic method is used to analyze the N dominant land use types within the grid. Identify arable land types from the dominant land use types; Based on the irrigation area distribution data, the final irrigated arable land area is determined from the arable land types.

3. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 1, characterized in that, The rules for determining crop types within irrigated farmland include: If the area of ​​wheat accounts for more than 50% and the area of ​​rice accounts for less than 50% in the subgrid, it is marked as a dryland crop; If the area of ​​wheat in a subgrid is less than 50% and the area of ​​rice is greater than 50%, it is marked as a paddy field crop. If the area ratio of wheat and rice in a subgrid is less than 50%, it is marked as a rice-wheat rotation pattern; All other cases are marked as non-irrigated farmland.

4. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 1, characterized in that, The rules for determining the crop growth stage within irrigated farmland include: Crop growth stages are determined by day number: when the day number is less than 151, it is the growth stage of dryland crops; when it is greater than 151, it is the growth stage of paddy field crops; all other cases are marked as non-irrigated farmland.

5. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 1, characterized in that, The irrigation triggering conditions are set in the following ways: for paddy field crops, the soil moisture content of the top two layers is calculated, and irrigation is triggered when the soil moisture content is less than the field capacity; for dryland crops, the relative soil moisture is calculated, and irrigation is triggered when the relative soil moisture is lower than a preset threshold.

6. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 5, characterized in that, The formula for calculating soil moisture content is: The formula for calculating relative soil moisture is: In the formula: i represents the i-th soil layer; SM represents the soil moisture content; SM represents the soil moisture of the i-th soil layer; D(i) represents the thickness of the i-th soil layer; FC Indicates field water holding capacity parameter; SM WP Represents the wilting coefficient; RSM represents relative soil moisture.

7. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 6, characterized in that, The formula for calculating the irrigation rate is: In the formula: IRR(t) represents the irrigation rate at the time step that triggers the irrigation; PRE(t) represents the precipitation at the current time step, in mm / s; SurRun(t) represents the surface runoff at the current time step, in mm / s.

8. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 5, characterized in that, The simulated hydrothermal process of the field surface water layer includes the following steps: Determining the existence of a field water layer: When the crop type is paddy field crop or is in the growth stage of paddy field crop, the existence of a field water layer is marked; Calculating water balance: Based on precipitation input, irrigation input, water surface evaporation, soil infiltration, and field drainage, the depth change of the field water layer is calculated; Calculating water surface evaporation: The water surface evaporation per unit time is calculated using the Priestley-Taylor equation; Simulating energy balance: The water surface evaporation is introduced into the surface energy balance equation to correct the latent heat flux distribution of the Noah land surface model; Updating water layer status: The field water layer depth parameters are dynamically updated based on the calculated changes in field water volume.

9. The method for simulating the physical processes of agricultural irrigation based on a land-atmosphere coupling model according to claim 6, characterized in that, The process of running the WRF-Noah model includes: generating an executable file by modifying the module_irrigation module and the module_sf_noahdrv.F land surface model driver file of the WRF-Noah model; configuring model parameters through the namelist file, including setting the sf_irrigation_crop_demand parameter to control the opening and closing of the irrigation scheme, and setting the irrigation_tagger parameter to adjust the irrigation triggering timing; and checking based on water balance and energy balance during model operation, outputting error message when an error is detected.

10. A simulation system for the physical processes of agricultural irrigation based on a land-atmosphere coupling model, characterized in that, include: Basic data acquisition module: It is used to acquire basic data of the study area, including meteorological, soil properties, land use, crop distribution and irrigation area distribution data; Irrigation range determination module: It is used to determine the range of irrigated cultivated land based on the land use data and irrigation area distribution data, using a subgrid scale analytical method; Crop type determination module: It is used to generate a crop type mask based on the crop distribution data and the irrigated farmland range to determine the crop types and crop growth stages within the irrigated farmland; the crop types include at least dryland crops and paddy field crops; Irrigation triggering mechanism module: It is used to set irrigation triggering conditions for dryland crops and paddy field crops respectively based on the determined crop type and soil attribute data; Irrigation rate calculation module: When irrigation triggering conditions are met, it calculates the irrigation water volume of irrigated farmland at the sub-grid scale to obtain the irrigation rate, and inputs the irrigation rate into the WRF-Noah model in the form of rainfall, while subtracting the corresponding irrigation rate from the surface runoff parameters; Surface water layer module: It is used to simulate the hydrothermal process of the surface water layer for the paddy field crops, calculate water surface evaporation and correct the surface energy distribution; Run module: It is used to run the WRF-Noah model to simulate agricultural irrigation processes and their impact on regional water cycle and land-atmosphere feedback; The agricultural irrigation physical process simulation system based on the land-atmosphere coupling model is used to perform the steps in the agricultural irrigation physical process simulation method based on the land-atmosphere coupling model as described in any one of claims 1-9.

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