Crop canopy interception-growth synergistic simulation method and system based on dynamic feedback

Abstract

The application discloses a crop canopy interception-growth synergistic simulation method and system based on dynamic feedback, obtains a leaf area index and canopy coverage of a crop module in an agricultural production system simulator APSIM, combines measured rainfall data and meteorological data to drive a revised Gash canopy interception model, calculates rainfall interception loss and effective rainfall, and feeds back the effective rainfall to a soil moisture module of the agricultural production system simulator in real time, so that dynamic influence simulation of water input on crop growth is realized. The method comprises a canopy interception calculation unit and a crop growth simulation unit, and bidirectional transmission and dynamic coupling are realized through a data interface. The method effectively improves the simulation accuracy of the crop growth process, especially the water process, effectively optimizes soil water balance prediction, significantly enhances the reliability of crop growth process and yield simulation, and is suitable for application scenarios such as optimization of agricultural water resources, water-saving irrigation management and precise production decision support of crops.

Description

Technical Field

[0001] This invention relates to the field of agricultural ecosystem modeling and crop growth simulation, and in particular to a method and system for simulating crop canopy interception-growth synergy based on dynamic feedback. Background Technology

[0002] In the field of agricultural ecosystem modeling, rainfall has a significant impact on crop growth and yield. The interception of rainfall by crop canopy is a key factor determining effective rainfall and its distribution in the soil. Currently, the Agricultural Production System Simulator (APSIM), as a widely used crop growth modeling framework, encompasses multiple modules including meteorological, biological, and soil processes, capable of simulating crop growth and development, water stress, and soil moisture dynamics. In APSIM, the meteorological module provides meteorological inputs such as daily rainfall, while the soil moisture module (e.g., SoilWat) simulates processes such as rainfall infiltration, runoff, and evaporation, interacting with the crop growth module to reflect plant water absorption and utilization. Furthermore, APSIM empirically addresses the impact of vegetation cover on rainfall interception through its microclimate module (Microclimate or Micromet). For example, adjustable parameters reflect the relationship between interception and rainfall amount and leaf area index, thus transferring the effective rainfall after deducting interception to the soil moisture module.

[0003] In the field of hydrology, several relatively mature rainfall interception models have been developed for forests or densely planted vegetation, such as the Rutter model and the Gash model. The Gash model, based on physical processes, divides a rainfall event into three stages: the initial wetting stage, the canopy saturation stage, and the drying stage after rainfall ceases. This model estimates the daily interception amount of the forest canopy using parameters such as the maximum canopy water storage capacity (S), the free penetration coefficient (p), and the evaporation rate (E) of the wet canopy. The revised Gash model, while retaining its physical mechanisms, has been appropriately simplified to make it applicable to agricultural crop simulations.

[0004] However, existing APSIM models and their related modules still have several shortcomings in handling crop canopy interception processes. First, the interception formula used by the microclimate module is a linear empirical relationship with fixed parameters, making it difficult to reflect the dynamic changes in canopy structure at different growth stages, thus limiting the model's detailed characterization of canopy interception processes. Second, when dealing with light rain events, APSIM models typically treat very small amounts of rainfall directly as evaporation dissipation, lacking a systematic consideration of canopy water storage dynamics, leading to biased estimates of canopy interception losses. Furthermore, traditional models lack a two-way feedback mechanism between the interception model and the crop growth model; that is, the calculation results of the interception process do not affect crop growth simulation in real time, nor do they dynamically correct the interception model parameters through feedback from crop growth status, thus weakening the ability to simulate the complex interactions between rainfall, canopy, soil, and crop. Under frequent light rain scenarios or high vegetation cover conditions, the above treatments may overestimate the effective rainfall entering the soil, leading to errors in soil moisture balance and crop growth predictions.

[0005] In summary, existing technologies for simulating crop canopy interception processes suffer from problems such as simplified model processing methods, fixed parameters, and a lack of dynamic feedback mechanisms, making it difficult to accurately reflect the real-time impact of canopy interception on effective rainfall and crop growth. Therefore, there is an urgent need for an interception model that can integrate physical mechanisms with crop growth dynamics and establish a two-way data coupling relationship with the crop module in the Agricultural Production System Simulator (APSIM) to improve the model's simulation accuracy of farmland hydrological processes and crop responses. Summary of the Invention

[0006] The present invention aims to at least partially solve one of the technical problems in the related art.

[0007] This invention proposes a crop canopy interception-growth synergistic simulation method based on dynamic feedback to overcome the inaccuracy caused by insufficient rainfall interception treatment in existing agricultural production system simulators (APSIM). This method enables the model to more accurately reproduce the interaction between rainfall, canopy, soil, and crop growth, thereby improving the simulation accuracy of crop growth processes, especially water processes.

[0008] Another objective of this invention is to propose a crop canopy interception-growth synergistic simulation system based on dynamic feedback.

[0009] To achieve the above objectives, this invention proposes a crop canopy interception-growth synergistic simulation method based on dynamic feedback, comprising:

[0010] Obtain crop canopy parameters at the current time step in the crop module of the APSIM agricultural production system simulator, including at least leaf area index and canopy coverage;

[0011] The crop canopy parameters and the rainfall data at the current time step are input into the revised Gash canopy cut-off model to calculate the rainfall cut-off loss and effective rainfall at the time step.

[0012] The soil moisture input in the soil moisture module of the agricultural production system simulator APSIM is updated based on the effective rainfall, and the crop growth process is simulated based on the updated soil moisture status.

[0013] The simulated crop growth status is fed back to the Gash model to dynamically update the canopy parameters at the next time step, forming a two-way data coupling and dynamic feedback mechanism between models.

[0014] The crop growth simulation method of this invention may also have the following additional technical features:

[0015] In one embodiment of the invention, the revised Gash canopy interception model calculates intercepted evaporation loss by relating the evaporation rate per unit area of ​​the wet canopy to the canopy coverage, wherein the evaporation rate E of the wet canopy is calculated according to the Penman-Monteith formula and multiplied by the canopy coverage percentage to reflect the impact of the unvegetated portion on evaporation.

[0016] In one embodiment of the present invention, the Gash model considers the calculation of the interception threshold, using the canopy storage capacity S, the free penetration coefficient p, and the wet canopy evaporation rate E to calculate the rainfall threshold required for canopy saturation. By comparing the actual rainfall P G and Determine the stage of the interception process, when P G When the value exceeds the threshold, calculate the amount of evaporation retained after the canopy is saturated.

[0017] In one embodiment of the present invention, a functional relationship between the crop leaf area index (LAI) and the canopy interception parameter is introduced to dynamically adjust the maximum canopy interception capacity (S) for different growth stages, wherein S has a linear or other monotonic relationship with LAI; at the same time, the free penetration coefficient (p) is updated according to LAI to adapt to the change from sparse to dense crop canopy.

[0018] In one embodiment of the present invention, the bidirectional coupling process is executed cyclically on a daily time step, and the effective rainfall calculated each day is fed back to the crop model in real time, so that the impact of canopy interception on soil moisture and crop growth is reflected in the model in real time.

[0019] In one embodiment of the present invention, within a time step, rainfall information provided by the environmental meteorological module is acquired, including the rainfall P in the current step. GIn addition, rainfall intensity distribution and meteorological parameters, if necessary; crop canopy parameters and rainfall information are input into the revised Gash canopy cutoff model, which uses the following core formula:

[0020]

[0021] The storage capacity of the tree trunk and the proportion of precipitation flowing into the trunk stem flow were estimated using the negative intercept and slope obtained from linear regression analysis between precipitation and stem flow to estimate the fully saturated canopy P′. G and tree trunk P′ t The required rainfall amount is determined according to the following formula:

[0022]

[0023] in, Indicates average rainfall intensity. The average evaporation rate of a saturated tree canopy during rain is defined as follows: Sc represents the canopy storage capacity per unit area, defined as Sc = S / c; the evaporation rate E of the rainforest canopy is calculated using the Penman-Monteith equation, which applies to saturated canopies and assumes zero canopy resistance.

[0024]

[0025] Where Δ represents the slope of the curve relating saturated vapor pressure and temperature, and R n ρ represents the net radiation at the surface of the canopy. a C represents the density of dry air. p The value of g represents the specific heat capacity of air, D represents the vapor pressure difference, and g represents the vapor pressure difference. a λ represents the aerodynamic flux between the leaf surface and the reference point, λ represents the latent heat evaporation of water, and γ is the wet / dry constant.

[0026] In one embodiment of the invention, canopy interception is calculated as follows: each rainfall event is divided into three stages: a wetting stage, where the rainfall is below the threshold required to saturate the canopy; a saturation stage, where the average rainfall intensity exceeds the average evaporation rate of a fully wetted canopy; and a drying stage, i.e., after the rainfall stops.

[0027]

[0028] The effective rainfall P1 output by the intercepted model is fed back to APSIM's soil moisture processing module in real time, replacing the original rainfall input in the daily soil moisture balance calculation.

[0029] In one embodiment of the present invention, the FAO56-PM equation is determined as the sole criterion for calculating ET0. In the revised Gash model coupled with the APSIM model, ET0 is calculated using the following formula:

[0030]

[0031] Where ET0 represents the reference evapotranspiration; Δ represents the slope of the slope vapor pressure curve; R n The net radiation at the crop surface is given by G; soil heat flux density is given by γ; specific humidity constant is given by T; daily average air temperature at a height of 2 meters is given by U2; wind speed at a height of 2 meters is given by e. s Indicates saturated vapor pressure; e a Indicates the actual water vapor pressure;

[0032] Es is estimated based on Ritchie’s evaporation model, which assumes that the Es process is divided into two stages, represented by two parameters U and CONA; U represents the cumulative evaporation when the surface soil moisture supply exceeds atmospheric demand; CONA is an empirical coefficient.

[0033] The relationship between Es and CONA can be expressed as follows:

[0034] E s =CONA×t 1 / 2

[0035] In the second stage, the evapotranspiration rate Es is specified by the parameter CONA as a function of the square root of time; Tc is estimated based on evapotranspiration efficiency and vapor pressure difference; at the same time, the sum of Es and Tc represents evapotranspiration, excluding surface runoff and deep drainage below 200 cm.

[0036] Crop water efficiency calculation formula:

[0037] Water use efficiency (WUE) = Crop yield / (ET)

[0038] ET, as the crop's evapotranspiration, is calculated through water balance:

[0039] P1+I r =S init -S fin +R+D+ET

[0040] In the formula: P1 is the effective precipitation, I r S represents the amount of irrigation water used in the field. init S represents the soil moisture content before sowing. fin R represents the soil water storage after harvest, D represents the runoff, and D represents the infiltration.

[0041] In one embodiment of the invention, canopy interception is considered in the revised Gash and APSIM models, where the effective rainfall input to the system is P1 = P G -I:

[0042]

[0043] The water retention parameter S is calculated based on the anterior soil moisture conditions predicted by the water balance sub-model; the CN value is used to represent S on a scale of 0 to 100; in the model, the CN value of the average anterior soil moisture content is used to adjust the relationship between S and soil moisture according to soil type and treatment.

[0044] The rate of residue decomposition is controlled by first-order kinetics:

[0045]

[0046] R, t, and k represent the residual mass per unit area, time, and rate coefficient, respectively; the rate coefficient k is a dimensionless scalar, calculated as follows:

[0047] k = D max ×F C:N ×F temp ×F moist ×F contact

[0048] D max F C:N F temp F moist and F contact These are the four key factors controlling the decomposition rate; D max The maximum decomposition rate coefficient is represented by the coefficient, while the other four factors reflect the limitations of the decomposition process under different residual carbon-nitrogen ratios, soil temperature, humidity, and residue-soil contact conditions.

[0049] Crop yield: The formula for calculating crop loss rate due to drought is expressed as follows:

[0050]

[0051] R loss Y represents the crop loss rate due to drought. m Y represents the potential yield of the crop. s Simulated crop yield;

[0052] The cumulative water stress value of biomass is calculated as follows:

[0053]

[0054] f represents the crop water stress value, W u W represents the soil's water supply.d Water requirements for crops;

[0055] Structural drought intensity index:

[0056]

[0057] Wherein, HI is the drought intensity index during the crop growth period. yj Let f be the drought intensity at point j in year y. i Let represent the crop water stress value on day i, n be the number of days in the growing season, and maxHI and minHI be the maximum and minimum values ​​for all stations in all years, respectively.

[0058] In one embodiment of the present invention, the root mean square error (RMSE), the consistency index, and the coefficient of determination (R²) are calculated. 2 And the Nash-Sutcliffe efficiency coefficient E ns To evaluate the model's performance:

[0059]

[0060] Q sim,i These are simulated values. Q is the simulated average value. obs,i For the observed values, This represents the average observed value.

[0061] To achieve the above objectives, another aspect of the present invention provides a crop growth simulation system, comprising:

[0062] The canopy interception calculation unit is used to receive the crop canopy parameters of the current time step provided by the crop module in the agricultural production system simulator APSIM, and perform the revised Gash model calculation in combination with rainfall data to output rainfall interception loss and effective rainfall.

[0063] The crop growth simulation unit includes the soil moisture module and crop growth module in the agricultural production system simulator APSIM, which are used to receive effective rainfall and simulate soil moisture balance and crop growth process.

[0064] The data interface is used for real-time bidirectional data transmission between the canopy interception calculation unit and the crop growth simulation unit, so as to realize the dynamic coupling of rainfall interception calculation based on crop canopy status and crop growth simulation.

[0065] The crop canopy interception-growth synergistic simulation method and system based on dynamic feedback of the present invention can adjust the rainwater interception calculation in real time according to the dynamic changes of the crop canopy, which significantly improves the model's accuracy in depicting the rainfall-canopy-soil process.

[0066] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0067] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0068] Figure 1 This is a flowchart of a crop canopy interception-growth synergistic simulation method based on dynamic feedback according to an embodiment of the present invention;

[0069] Figure 2 This is a schematic diagram of the simulation process of bidirectional coupling between the improved canopy cutoff model (Gash) and the APSIM model according to an embodiment of the present invention;

[0070] Figure 3 This is a map of effective precipitation simulated using the revised Gash model according to an embodiment of the present invention;

[0071] Figure 4 This is a simulation flowchart of the APSIM model according to an embodiment of the present invention;

[0072] Figure 5 This is a structural diagram of a crop canopy interception-growth synergistic simulation system based on dynamic feedback, according to an embodiment of the present invention. Detailed Implementation

[0073] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0074] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0075] The following describes, with reference to the accompanying drawings, a method and system for simulating crop canopy interception-growth synergy based on dynamic feedback, according to embodiments of the present invention.

[0076] Figure 1 This is a flowchart of a crop canopy interception-growth synergistic simulation method based on dynamic feedback according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes:

[0077] S1, Obtain the crop canopy parameters at the current time step in the crop module of the agricultural production system simulator APSIM, including at least leaf area index and canopy coverage;

[0078] S2, input the crop canopy parameters and the rainfall data at the current time step into the revised Gash canopy interception model to calculate the rainfall interception loss and effective rainfall at the time step;

[0079] S3 updates the soil moisture input in APSIM's soil moisture module based on the effective rainfall, and simulates the crop growth process based on the updated soil moisture status.

[0080] S4 feeds back the simulated crop growth status to the Gash model to dynamically update the canopy parameters at the next time step, forming a two-way data coupling and dynamic feedback mechanism between models.

[0081] Specifically, this embodiment of the invention acquires crop canopy parameters at the current time step in the crop module of the Agricultural Production System Simulator (APSIM), including at least leaf area index (LAI) and canopy coverage; acquires rainfall and related meteorological data at the current time step, and inputs the canopy parameters and meteorological data into an improved Gash canopy interception model for calculation; calculates the rainfall interception loss I and the effective rainfall P1 after deducting interception at the current time step based on the canopy interception model, wherein the calculation includes: dynamically updating the maximum canopy water storage capacity S and free penetration coefficient p according to the crop LAI, determining whether rainfall saturates the canopy, and if saturated... The calculation of I, based on the physical model, includes two parts: canopy storage capacity and evaporation loss during rainfall. The calculated effective rainfall P1 is input in real time into the soil water molecule module of the agricultural production system simulator to replace the original rainfall for soil moisture balance calculation, including processes such as soil infiltration, evaporation, and crop water absorption. The updated soil moisture status drives crop growth simulation, calculating the crop growth and development process and canopy parameter update values ​​at the current time step. The updated crop canopy parameters are fed back to the canopy interception model to enter the next time step cycle, thereby realizing bidirectional data coupling and dynamic feedback between the canopy interception model and crop simulation.

[0082] Furthermore, the revised Gash canopy interception model calculates intercepted evaporation loss by correlating the evaporation rate per unit area of ​​the wet canopy with the canopy coverage rate, where the evaporation rate E of the wet canopy is calculated according to the Penman-Monteith formula and multiplied by the canopy coverage rate to reflect the impact of the unvegetated portion on evaporation.

[0083] Furthermore, the Gash model considers the calculation of the interception threshold, using the canopy storage capacity S, the free penetration coefficient p, and the wet canopy evaporation rate E to calculate the rainfall threshold required for canopy saturation. By comparing the actual rainfall PG and Determine the stage of the interception process, when P G When the value exceeds the threshold, calculate the amount of evaporation retained after the canopy is saturated.

[0084] Furthermore, a functional relationship between the crop leaf area index (LAI) and the canopy interception parameter is introduced to dynamically adjust the maximum canopy interception capacity (S) at different growth stages, where S has a linear or other monotonic relationship with LAI; at the same time, the free penetration coefficient (p) is updated according to LAI to adapt to the change from sparse to dense crop canopy.

[0085] Furthermore, the bidirectional coupling process is executed cyclically on a daily time step, feeding back the effective rainfall calculated each day to the crop model in real time, so that the impact of canopy interception on soil moisture and crop growth is reflected in the model in real time.

[0086] like Figure 2 , 3 As shown in Figures 4 and 5, the present invention further elaborates on the crop canopy interception-growth synergistic simulation method based on dynamic feedback in conjunction with the accompanying drawings.

[0087] By embedding a revised Gash model with clearly defined physical mechanisms into the crop growth simulation process, and through two-way data exchange, the real-time impact of canopy interception on crop growth simulation is realized. The technical solution is as follows:

[0088] In one embodiment of the invention, at the beginning of each time step (e.g., daily simulation) of the APSIM crop model, relevant parameters of the current crop canopy are obtained from the crop model, including but not limited to the crop's leaf area index (LAI), canopy coverage, and vegetation height. These parameters reflect the dynamic state of the crop canopy.

[0089] In one embodiment of the present invention, within this time step, rainfall information provided by the environmental meteorological module is acquired, including the rainfall P in the current step. G Including, if necessary, rainfall intensity distribution and meteorological parameters. Input the above crop canopy parameters and rainfall information into the revised Gash canopy cutoff model. The revised Gash model uses the following core formula:

[0090]

[0091] The storage capacity of the tree trunk (St) and the proportion of precipitation flowing into the trunk stem flow (P) t The negative intercept and slope obtained from linear regression analysis between precipitation and stem flow were used to estimate the fully saturated canopy (P′). G ) and trunk (P′ t The required rainfall amount is determined according to the following formula:

[0092]

[0093] in, The average rainfall intensity (mm h) -1 ), The average evaporation rate of a saturated tree canopy during rain (mm h) -1 ), defined as S c The canopy storage capacity (mm) per unit area is defined as S. c =S / c. The evaporation rate E of a forest canopy in rain is calculated using the Penman-Monteith equation, which applies to saturated canopies and assumes zero canopy drag:

[0094]

[0095] Where Δ represents the slope of the curve relating saturated vapor pressure and temperature (kPa℃). -1 ), R n Represents the net radiation (Wm) at the canopy surface -2 ), ρ a Density of dry air (kg m³) -3 ), C p The specific heat capacity of air (J kg℃) -1 ), D represents the vapor pressure difference (kPa), g a The aerodynamic flux (ms) between the blade surface and the reference point -1 ), where λ represents the latent heat of water evaporation (kPa℃). -1 ), γ is the wet / dry constant (J / kg) -1 ).

[0096] Canopy interception calculation: Each rainfall event can be divided into three stages, (1) wetting stage, at which time the rainfall (P) G (1) The average rainfall intensity is below the threshold required to saturate the canopy; (2) The saturation stage, at which point the average rainfall intensity exceeds the average evaporation rate of the canopy to be fully moistened; (3) The drying stage, after the rainfall stops.

[0097]

[0098] The effective rainfall P1 output by the interception model is fed back to APSIM's soil moisture processing module in real time, replacing the original rainfall input in the daily soil moisture balance calculation. Specifically, the APSIM soil moisture module calculates processes such as rainfall infiltration, surface runoff, and soil evaporation based on this. Light rain below the interception threshold is completely intercepted, and the APSIM soil module receives zero effective rainfall, thus not increasing soil moisture content; in the case of heavy rain, only the portion after deducting the interception amount enters the soil.

[0099] In one embodiment of the invention, the APSIM crop growth module uses updated soil moisture conditions to simulate crop growth, development, and physiological processes (such as transpiration and dry matter accumulation). Because the effective rainfall input is corrected, the soil moisture simulation is more accurate, and therefore the crop water stress condition is more realistic. Changes in crop growth conditions (e.g., LAI increase or decrease) will then affect the interception model's calculations in the next time step through canopy parameters, thus forming a closed-loop dynamic feedback mechanism.

[0100] A key feature of APSIM is its focus on soil rather than a single crop. The system continuously simulates changes in soil state variables based on weather variations and management practices. Crops sequentially enter and leave specific states, resulting in different soil conditions. For analytical purposes, APSIM modules controlling soil moisture and nitrogen are particularly important. The Soil Wat block, operating as a cascaded water balance model, is primarily used to estimate the movement of water and solutes between soil layers, between soil layers and the soil surface (i.e., runoff and evaporation), and between the soil system and the external environment (i.e., drainage).

[0101] The bidirectional coupled simulation system of this invention mainly consists of six functional modules: a revised Gash model module, an APSIM crop module, a soil module, a management module, a climate module, and a system control module. These modules communicate via a data interface. Under normal circumstances, the APSIM soil moisture module receives daily rainfall from meteorological input and uses it to calculate the soil water budget for the day. Without considering interception, all rainfall (after deducting runoff) enters the soil or is evaporated. However, in this invention, due to the introduction of an interception model module, the rainfall received by the soil moisture module is adjusted based on the interception calculation results.

[0102] In one embodiment of the invention, the SoilWat module: The Food and Agriculture Organization of the United Nations (FAO) has determined the FAO56-PM equation as the sole standard for calculating ET0. In the revised Gash model coupled with the APSIM model, ET0 is calculated using the following formula:

[0103]

[0104] Wherein, ET0 represents the reference evapotranspiration (mm·d) -1 ); Δ represents the slope of the slope vapor pressure curve (kPa℃). -1 Rn is the net radiation on the crop surface (MJ·m). -2 ·day -1 G represents soil heat flux density, with units of MJ·m³. -2 ·day -1 γ represents the specific humidity constant, with units of kPa℃. -1 T represents the average daily temperature at a height of 2 meters, in °C; U2 represents the wind speed at a height of 2 meters, in m / s. -1 ;e s This represents the saturated vapor pressure, expressed in kPa; e a This represents the actual water vapor pressure, in kPa.

[0105] Es estimates are based on Ritchie's (1972) evaporation model, which assumes the Es process occurs in two phases, represented by two parameters, U and CONA. U represents the cumulative evaporation when the surface soil moisture supply exceeds atmospheric demand; CONA is an empirical coefficient that depends heavily on the soil's hydraulic properties and potential evapotranspiration.

[0106] The relationship between Es and CONA can be expressed as:

[0107] E s =CONA×t 1 / 2

[0108] In the second stage, the transpiration rate Es is specified by parameter CONA as a function of the square root of time (t). Tc is estimated based on transpiration efficiency (TE; i.e., the ratio of biomass production to transpiration) and vapor pressure difference. Meanwhile, the sum of Es and Tc represents evapotranspiration (ET), excluding surface runoff and deep drainage below 200 cm.

[0109] Crop water efficiency calculation formula:

[0110] Water use efficiency (WUE) = Crop yield / (ET)

[0111] ET, as the evapotranspiration rate of crops, can be calculated through water balance:

[0112] P1+I r =S init -S fin +R+D+ET

[0113] In the formula: P1 is the effective precipitation, I r S represents the amount of irrigation water used in the field. initS represents the soil moisture content before sowing (mm). fin R represents the soil water storage after harvest, R represents the runoff (mm), and D represents the infiltration (mm).

[0114] In one embodiment of the invention, the APSIM-RESIDUE model treats crop residues on the soil surface as a separate component from the soil organic matter pool. This model assesses the impact of crop residues on runoff based on the USDA's Curve Number (CN) method, where CN is a term describing a series of curves that, based on the soil moisture retention parameter (S), correlate total runoff (Q) with total rainfall (P). G Connect them.

[0115] In the revised Gash and APSIM models, canopy interception is considered, therefore the effective rainfall input to the system is P1 = P G -I.

[0116]

[0117] The water retention parameter S is calculated based on antecedent soil moisture conditions predicted by the water balance submodel. The CN value is used to represent S on a scale of 0 to 100. In the model, the CN value (CN2) of the average antecedent soil moisture content is used to adjust the relationship between S and soil moisture, depending on the soil type and treatment (e.g., CT or NTR).

[0118] The rate of residue decomposition is controlled by first-order kinetics:

[0119]

[0120] R, t, and k represent the residual mass per unit area, time, and rate coefficient, respectively. The rate coefficient k is a dimensionless scalar, calculated as follows:

[0121] k = D max ×F C:N ×F temp ×F moist ×F contact

[0122] D max F C:N F temp F moist and F contact These are four key factors controlling the decomposition rate (with values ​​ranging from 0 to 1). D maxThe maximum decomposition rate coefficient is represented by , while the other four factors reflect the limitations of the decomposition process under different residual carbon-to-nitrogen ratios, soil temperature, humidity, and residue-soil contact conditions. Default parameter values ​​from the RESIDUE database were used in this invention design.

[0123] Crop yield: The formula for calculating crop loss rate due to drought can be expressed as follows:

[0124]

[0125] R loss Y represents the crop loss rate due to drought. m Y represents the potential yield of the crop. s Simulated crop yield.

[0126] The cumulative water stress value of biomass is calculated as follows:

[0127]

[0128] f represents the crop water stress value, W u W represents the soil's water supply. d Crop water requirements

[0129] Structural drought intensity index:

[0130]

[0131] Wherein, HI is the drought intensity index during the crop growth period. yj Let f be the drought intensity at point j in year y. i Let HI be the crop water stress value on day i, n be the number of days in the growing season, and maxHI and minHI be the maximum and minimum values ​​for all stations across all years, respectively.

[0132] The root mean square error (RMSE), the consistency index (d; a standardized measure of model prediction error with values ​​between 0 and 1), and the coefficient of determination (R²) were calculated. 2 ) and Nash-Sutcliffe efficiency coefficient (E ns To evaluate the performance of the model.

[0133]

[0134] Q sim,i These are simulated values. Q is the simulated average value. obs,i For the observed values, This represents the average observed value.

[0135] Table 1. Components of precipitation redistribution in the revised Gash model

[0136]

[0137]

[0138] In one embodiment of the present invention, a data interface is used to transfer information between the intercepted model module and the APSIM crop module of the agricultural production system simulator, achieving bidirectional coupling. The interface includes:

[0139] At the end of each time step, the crop module sends the latest canopy-related parameters (LAI, vegetation cover, canopy height, crop growth stage, etc.) to the cutoff model module via an interface for parameter updates and cutoff calculations in the next time step.

[0140] After calculating the effective rainfall P1 at the current time step, the interception model module sends this value to the soil water molecule module in the agricultural production system simulator APSIM via an interface, replacing the original rainfall input. For the APSIM framework, this process is equivalent to passing the remaining rainfall after deducting the interception amount to the soil module through the microclimate module.

[0141] As needed, the interception model module can also send the calculated interception loss I and interception evaporation back to the crop model for subsequent energy balance calculations or environmental output. Simultaneously, the crop model can also provide crop physical characteristic parameters (such as leaf water-holding capacity and canopy layer number) to the interception model to refine the calculations.

[0142] The implementation process of the bidirectional coupling method of this invention will be explained in detail below with specific steps. It is assumed that the simulation uses a daily time step, and the initial crop growth parameters and soil moisture status are known.

[0143] Initialization: Reads or sets the initial conditions of the APSIM crop module in the agricultural production system simulator, including initial soil moisture content and initial crop growth status (such as LAI at sowing or seedling stage, vegetation cover, etc.). Sets the initial parameters of the interception model, which can be assigned empirically based on crop type (such as initial canopy interception capacity, initial free penetration coefficient, etc.). Links the revised Gash model module with the APSIM model, establishing communication through a data interface.

[0144] Obtain input: At the start of the i-th simulated day, obtain the daily rainfall P from the meteorological input. G Other meteorological factors (average rainfall intensity, daily average net radiation, saturation deficit, etc.). Simultaneously, the system retrieves updated canopy parameters from the previous day from the APSIM crop growth module, such as LAI, canopy coverage, canopy height, and indicators of the current crop growth stage.

[0145] Update the cutoff model parameters: The parameter update unit sets the cutoff model parameters based on the current crop canopy status (feedback data from the crop model).

[0146] Determining the rainfall interception stage: (1) Wet stage, at which time the rainfall (P) G (1) The average rainfall intensity is below the threshold required to saturate the canopy; (2) The saturation stage, at which point the average rainfall intensity exceeds the average evaporation rate of a fully moistened canopy; (3) The drying stage, i.e., after the rainfall stops.

[0147] Calculate effective rainfall and update soil moisture: Based on the above determination results, calculate the effective rainfall P1 for the day. Send P1 to the APSIM soil moisture module via the data interface as the rainfall input entering the soil on day i. The APSIM soil moisture module then performs a soil moisture balance simulation for the day, including: calculating surface infiltration, updating the soil reservoir, updating the water content of each soil layer based on the infiltration amount, and calculating deep seepage, etc.

[0148] Crop Growth Update: After the soil moisture status is updated, the APSIM crop growth module uses the new soil moisture content to calculate the plant's water supply and stress level. It then simulates the crop's physiological response and growth on day i. At the end of the simulation for that day, the crop module outputs the new growth status, including biomass increase, LAI(i+1), and changes in vegetation cover.

[0149] Feedback loop: The updated crop canopy parameters LAI are fed back to the cutoff model module to prepare for the cutoff calculation in the next time step (i+1). Then, the simulation enters day (i+1) and repeats the above steps until the simulation ends.

[0150] Through the above process, this invention achieves information interaction between the canopy interception model and the crop model at each time step: the output of the crop model (canopy state) affects the next interception calculation, while the output of the revised Gash model (effective rainfall) instantly affects the soil moisture input of the crop model. This two-way coupling ensures that the model can capture dynamic feedback processes such as "canopy densification leading to increased interception—reduced soil water supply—restricted crop growth—canopy thinning—reduced interception," making the simulation more closely resemble the behavior of real farmland systems.

[0151] In summary, the improved Gash canopy trapping model was successfully coupled with the crop module in the APSIM agricultural production system simulator. This method achieves real-time correction of effective rainfall utilization through bidirectional data interaction, demonstrating significant improvements over existing technologies in simulating crop growth and soil moisture dynamics. Those skilled in the art can make various equivalent modifications or variations to the implementation based on the above disclosure without departing from the principles of this invention; the scope of this invention is defined by the appended claims.

[0152] The crop canopy interception-growth co-simulation method based on dynamic feedback according to embodiments of the present invention dynamically corrects the effective rainfall input entering the APSIM crop model by calculating the rainfall canopy interception amount in real time, thereby more accurately simulating the crop growth process and soil moisture balance. The technical solution includes: extracting crop canopy parameters (such as leaf area index) from the crop module of the Agricultural Production System Simulator (APSIM), calculating the canopy interception loss and effective rainfall amount for the current time period using a revised Gash model, and feeding these back to the soil water molecule module of APSIM, thus realizing data interaction between the canopy interception model and the crop model at each time step. This coupled system consists of a canopy interception calculation unit and a crop growth simulation unit, which exchange information through a data interface. Compared with the prior art, the present invention can adjust the rainwater interception calculation in real time according to the dynamic changes of the crop canopy, significantly improving the model's accuracy in depicting the rainfall-canopy-soil process.

[0153] To achieve the above embodiments, such as Figure 5 As shown, this embodiment also provides a crop canopy interception-growth synergistic simulation system 10 based on dynamic feedback, including:

[0154] The canopy interception calculation unit 100 is used to receive the crop canopy parameters of the current time step provided by the crop module in the agricultural production system simulator, and perform the revised Gash model calculation in combination with the rainfall data to output the rainfall interception loss and effective rainfall.

[0155] The crop growth simulation unit 200 includes a soil moisture module and a crop growth module in the agricultural production system simulator APSIM, which are used to receive effective rainfall and simulate soil moisture balance and crop growth process.

[0156] Data interface 300 is used for real-time bidirectional data transmission between the canopy interception calculation unit and the crop growth simulation unit to achieve dynamic coupling between rainfall interception calculation based on crop canopy status and crop growth simulation.

[0157] Furthermore, the canopy interception calculation unit further includes an evaporation calculation submodule, which is used to calculate the evaporation rate of the wet canopy based on meteorological data and use it as an input parameter for interception loss calculation.

[0158] Furthermore, the evaporation calculation submodule uses the Penman-Monteith formula for calculation, and the canopy resistance parameter is set to zero to reflect the evaporation characteristics of the saturated canopy.

[0159] Furthermore, the soil moisture module further includes runoff and deep drainage calculation units for simulating rainfall infiltration, surface runoff, and deep drainage processes based on the updated effective rainfall P1.

[0160] The crop canopy interception-growth synergistic simulation system based on dynamic feedback according to embodiments of the present invention dynamically corrects the effective rainfall input entering the APSIM crop model by calculating the rainfall canopy interception amount in real time, thereby more accurately simulating the crop growth process and soil moisture balance. The technical solution includes: extracting crop canopy parameters (such as leaf area index) from the crop module of the Agricultural Production System Simulator (APSIM), calculating the canopy interception loss and effective rainfall amount for the current time period using a revised Gash model, and feeding these back to the soil water molecule module of the Agricultural Production System Simulator (APSIM), thereby achieving data interaction between the canopy interception model and the crop model at each time step. This coupled system consists of a canopy interception calculation unit and a crop growth simulation unit, which exchange information through a data interface. Compared with the prior art, the present invention can adjust the rainwater interception calculation in real time according to the dynamic changes of the crop canopy, significantly improving the model's accuracy in depicting the rainfall-canopy-soil process.

[0161] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0162] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for simulating crop canopy interception-growth synergy based on dynamic feedback, characterized in that, include: Obtain crop canopy parameters at the current time step in the crop module of the agricultural production system simulator APSIM, including at least leaf area index and canopy coverage; Based on the leaf area index at the current time step, the maximum canopy interception capacity S and the free penetration coefficient p are dynamically updated according to a preset functional relationship. Among them, the maximum canopy interception capacity S has a linear or other monotonic relationship with the leaf area index, and the free penetration coefficient p is updated according to the leaf area index to adapt to the change of crop canopy from sparse to dense. The updated maximum canopy retention capacity S, free penetration coefficient p, canopy parameters, and rainfall data at the current time step are input into the revised Gash canopy retention model to calculate the rainfall retention loss and effective rainfall at the current time step. The soil moisture input in the soil moisture module of the agricultural production system simulator is updated based on the effective rainfall, and the crop growth process is simulated based on the updated soil moisture status. The simulated crop growth status is fed back to the revised Gash canopy cutoff model to dynamically update the canopy parameters at the next time step, forming a two-way data coupling and dynamic feedback mechanism between models.

2. The method according to claim 1, characterized in that, The revised Gash canopy interception model calculates intercepted evaporation loss by correlating the evaporation rate per unit area of ​​the wet canopy with the canopy coverage. The evaporation rate E of the wet canopy is calculated according to the Penman-Monteith formula and multiplied by the canopy coverage percentage to reflect the impact of the unvegetated portion on evaporation.

3. The method according to claim 1, characterized in that, The Gash model considers the calculation of the interception threshold, using the canopy storage capacity S, the free penetration coefficient p, and the wet canopy evaporation rate E to calculate the rainfall threshold required for canopy saturation. And by comparing the actual rainfall P G and Determine the stage of the interception process, when P G When the value exceeds the threshold, calculate the amount of evaporation retained after the canopy is saturated.

4. The method according to claim 1, characterized in that, A functional relationship between crop leaf area index (LAI) and canopy interception parameters is introduced to dynamically adjust the maximum canopy interception capacity (S) at different growth stages, where S has a linear or other monotonic relationship with LAI; at the same time, the free penetration coefficient (p) is updated according to LAI to adapt to the change from sparse to dense crop canopy.

5. The method according to claim 1, characterized in that, The bidirectional coupling process is executed cyclically on the daily time step, feeding back the effective rainfall calculated each day to the crop model in real time, so that the impact of canopy interception on soil moisture and crop growth is reflected in the model in real time.

6. A crop canopy interception-growth synergistic simulation system based on dynamic feedback, characterized in that, include: The canopy interception calculation unit is used to receive crop canopy parameters from the crop module in the agricultural production system simulator APSIM at the current time step. The crop canopy parameters include at least leaf area index and canopy coverage. Based on the leaf area index at the current time step, the unit dynamically updates the maximum canopy interception capacity S and the free penetration coefficient p according to a preset functional relationship. The maximum canopy interception capacity S has a linear or other monotonic relationship with the leaf area index, and the free penetration coefficient p is updated according to the leaf area index to adapt to the change of crop canopy from sparse to dense. Combined with rainfall data, the unit performs a revised Gash model calculation based on the updated maximum canopy interception capacity S and free penetration coefficient p, and outputs the rainfall interception loss and effective rainfall. The crop growth simulation unit includes the soil moisture module and crop growth module in the agricultural production system simulator APSIM, which are used to receive effective rainfall and simulate soil moisture balance and crop growth process. The data interface is used for real-time bidirectional data transmission between the canopy interception calculation unit and the crop growth simulation unit, so as to realize the dynamic coupling of rainfall interception calculation based on crop canopy status and crop growth simulation.

7. The system according to claim 6, characterized in that, The canopy interception calculation unit further includes an evaporation calculation submodule, which is used to calculate the evaporation rate of the wet canopy based on meteorological data and use it as an input parameter for interception loss calculation.

8. The system according to claim 7, characterized in that, The evaporation calculation submodule uses the Penman-Monteith formula for calculation, and the canopy resistance parameter is set to zero to reflect the evaporation characteristics of the saturated canopy.

9. The system according to claim 8, characterized in that, The soil moisture module further includes runoff and deep drainage calculation units, which are used to simulate rainfall infiltration, surface runoff and deep drainage processes based on the updated effective rainfall P1.

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