A method for simulating crop transpiration water consumption based on dynamic canopy extinction coefficient

Abstract

The present invention discloses a method for simulating crop transpiration water consumption based on a dynamic canopy extinction coefficient, including: determining the main controlling factor of the extinction coefficient through crop growth characteristics and environmental stress analysis; constructing a dynamic function of the canopy extinction coefficient using the main controlling factor; and introducing the dynamic function into the Penman-Monteith model to replace the fixed value scheme of the extinction coefficient to estimate the transpiration water consumption of farmland. The present invention realizes the quantitative characterization of the dynamic change law of the extinction coefficient, and compared with the traditional fixed value scheme, effectively improves the accuracy of the simulation of crop transpiration water consumption and reduces the uncertainty of the simulation results. It has been verified in many places that the simulation effect of transpiration water consumption has been significantly improved.

Description

Technical Field

[0001] The invention relates to the field of farmland crop irrigation, and in particular to a crop transpiration water consumption simulation method based on a dynamic canopy extinction coefficient. Background Art

[0002] As a key process linking water, energy and carbon cycles, crop transpiration plays a key role in coordinating the balance between agricultural water supply and demand. Accurate simulation of transpiration is an important basis for establishing an efficient water-saving irrigation technology system. Transpiration depends largely on available energy, that is, the net radiation intercepted by the canopy. This index is often calculated based on Beer's law using leaf area index LAI and extinction coefficient C. The extinction coefficient C represents the efficiency of canopy light interception. When the C value is small, it means that a large amount of radiation will penetrate the canopy and reach below the tree canopy. When the value is large, it means that a large amount of radiation is intercepted by the canopy, and only a small amount penetrates the canopy and reaches below the canopy. Therefore, accurately evaluating the size of the extinction coefficient is the basic prerequisite for the reasonable division of canopy and soil energy items. In many important hydrological models, the extinction coefficient is often used as an important basis for dividing evapotranspiration ET into transpiration T and soil evaporation E.

[0003] Field studies have shown that the extinction coefficient values ​​of different crops vary greatly, and the same crop fluctuates greatly under different climatic conditions and different growth periods. For example, the extinction coefficient values ​​of corn reported in different regions range from 0.40 to 0.72, while the values ​​of wheat range from 0.31 to 0.90. The change in extinction coefficient is affected by many factors such as growth status and environmental conditions. For example, environmental stress can change the canopy structure characteristics by changing leaf inclination and leaf distribution, thereby causing changes in light energy interception. However, due to the lack of measured data on dynamic changes, the C value is often set as a constant in current evapotranspiration simulation studies. This method of ignoring the dynamic changes in the extinction coefficient results in large errors in energy decomposition, which in turn brings about greater uncertainty in the simulation of evapotranspiration and its components. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a method for simulating crop transpiration water consumption based on the dynamic canopy extinction coefficient.

[0005] According to the technical solution provided in the embodiment of the present application, a method for simulating crop transpiration water consumption based on a dynamic canopy extinction coefficient comprises the following steps:

[0006] Step A: Use a linear photon sensor to measure the incident radiation intensity above the canopy and the penetrating radiation intensity below the canopy multiple times as the growth period progresses, and calculate the canopy extinction coefficient under different environmental conditions at different growth stages according to the following formula;

[0007]

[0008] Where C is the extinction coefficient, PAR in is the incident radiation intensity above the canopy, with the unit of MJ m -2 d -1 , PAR tr is the penetrating radiation intensity below the canopy, and its unit is MJ m -2 d -1 , LAI is the leaf area index, and its unit is m 2 m -2 .

[0009] Step B: Use mathematical methods to analyze the driving mechanism of leaf area index LAI and main environmental factors on extinction coefficient and identify the main controlling factors;

[0010] Step C: Use the main control factors to construct a dynamic extinction coefficient function. First, divide the main control factors into positive correlation factor group and negative correlation factor group. Divide the factorial term of the negative correlation factor by the factorial term of the positive correlation factor to obtain the comprehensive factor f. For example, for crops with large changes in leaf area index LAI, when the radiation R n When the temperature Ta and soil moisture SW are the main influencing factors of the extinction coefficient, the comprehensive factor expression is f = LAI × R n ×Ta / SW; then the extinction coefficient C is characterized by the following formula d Dynamic change rules:

[0011] C d =a×f b

[0012] In the formula, C d is the extinction coefficient, f is the comprehensive factor affecting the extinction coefficient, a and b are unknown parameters;

[0013] Step D: Replace Formula C d The following Penman-Monteith model is introduced to replace the extinction coefficient constant parameter C to obtain the transpiration water consumption T e .

[0014]

[0015] Where: T e is the transpiration rate, and its unit is MJ m -2 d -1 ; △ is the slope of the saturated water vapor pressure and temperature curve, its unit is kPa℃ -1 ; R nc is the net radiation intercepted by the canopy, with the unit of MJ m -2 d -1 ; G is soil heat flux, its unit is MJ m -2 d -1; ρ is the air density, its unit is g mm -3 ;c p is the specific heat at constant pressure, and its unit is MJ kg -1 ℃ -1 ; γ is the hygrometer constant, its unit is kPa℃ -1 ; VPD is the saturated vapor pressure difference, its unit is kPa; r c is the canopy impedance, its unit is sm -1 ; r a is the aerodynamic impedance, its unit is sm -1 ; Net radiation intercepted by the canopy R nc Calculate according to the following formula:

[0016] R nc =R n ×(1-exp(-C d ×LAI))

[0017] Where: R n is the net radiation at the top of the canopy, with the unit of MJ m -2 d -1 ; C d is the extinction coefficient, and its unit is dimensionless. nc Calculation; LAI is the canopy leaf area index, and its unit is m 2 m -2 .

[0018] In the present invention, further, the mathematical analysis method for determining the main controlling factors of the canopy extinction coefficient in step B adopts one of regression analysis, principal component analysis, path analysis, redundancy analysis, and structural equation model.

[0019] In the present invention, further, the aerodynamic impedance r in step D is a In general, the following formula can be used for calculation in the field, and various appropriate correction formulas can also be used according to actual conditions. For greenhouse conditions, the calculation formula applicable to greenhouse conditions should be used:

[0020]

[0021] Where: h c is the canopy height in meters; z is the reference height in meters; u is the wind speed at the reference height in milliseconds -1 ; k is the Karman constant, which is 0.40; z0 is the momentum transfer roughness length, which is in m; d is the zero plane displacement, which is in m;

[0022] Canopy impedance r c The following formula can be used for calculation, or other suitable canopy impedance calculation methods can be used:

[0023]

[0024] Where: rst min It is the minimum stomatal resistance value that may occur in leaves in the canopy under ideal conditions, and its unit is sm -1 ;LAI e is the effective leaf area index; X i Represents the environmental factors that affect canopy impedance; F i (Xi) is the environmental factor X i Limit function for canopy impedance, the value range is 0≤F i (X i )≤1.

[0025] In summary, the beneficial effects of this application are:

[0026] 1. The present invention establishes a dynamic function of the extinction coefficient based on the main control factor of the extinction coefficient identified by the mathematical analysis method; introduces it into the Penman-Monteith equation, and achieves the purpose of accurately estimating transpiration water consumption by correcting the canopy energy interception term;

[0027] 2. The method effectively considers the objective influence of environmental factors and vegetation growth status on the extinction coefficient, proposes a dynamic characterization function of the extinction coefficient, breaks through the defect of setting the extinction coefficient as a constant in the traditional transpiration estimation method, has a more sufficient theoretical basis, significantly improves the estimation accuracy of transpiration water consumption and reduces the uncertainty of the output results, achieves an improvement in the simulation effect of water consumption, is conducive to improving agricultural irrigation efficiency, and provides a reliable basis for the efficient management of water resources.

[0028] 3. Compared with the only existing method for dynamic characterization of extinction coefficient in water consumption calculation (which only considers leaf area index), the method not only considers the influence of leaf area index on extinction coefficient, but also considers the influence of environmental factors on canopy light interception. Therefore, the dynamic change law of extinction coefficient is revealed more comprehensively.

[0029] Fourth, the method quantifies the extent to which environmental factors affect transpiration by affecting the extinction coefficient and then the canopy energy interception, while the canopy impedance sub-part quantifies the extent to which environmental factors affect transpiration by affecting the stomatal regulation behavior. When the two are used together, a more comprehensive quantitative comparison of the different ways in which environmental conditions affect transpiration can be made, which helps to deepen the understanding of the mechanism of transpiration. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0031] Figure 1 It is a schematic diagram of the calculation process of the present invention. DETAILED DESCRIPTION

[0032] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0033] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0034] See also Figure 1 The embodiment of the present invention provides a method for simulating crop transpiration water consumption based on a dynamic canopy extinction coefficient, comprising the following steps:

[0035] Step 1: Obtain the dynamic variation law of the extinction coefficient.

[0036] In this step, the photon sensor can be used to intermittently measure multiple times or to continuously monitor the incident radiation intensity above the canopy and the penetrating radiation intensity below the canopy at different positions in the farmland. The extinction coefficient can be calculated based on Beer's law using the following formula:

[0037]

[0038] Where C is the extinction coefficient, PAR in is the incident radiation intensity above the canopy (MJ m -2 d -1 ), PAR tr is the penetrating radiation intensity below the canopy (MJ m -2 d -1 ), LAI is the leaf area index (m 2 m -2 ).

[0039] Step 2: Identify the main controlling factor of the extinction coefficient and determine the comprehensive influencing factor f.

[0040] In this step, the main control factor identification methods include but are not limited to regression analysis, principal component analysis, path analysis, redundancy analysis, structural equation model, etc. The main control factors are identified by weight coefficients in mathematical analysis. Potential main control factors can be selected from the following common indicators: leaf area index LAI, net radiation R n, air temperature Ta, relative humidity RH, soil moisture SW, saturated water vapor pressure difference VPD, etc.). The method for determining the comprehensive factor f is: first, the main control factors are divided into positive correlation factor group and negative correlation factor group, and then the comprehensive factor f is obtained by dividing the negative correlation factor factorial term by the positive correlation factor factorial term. For example, when the leaf area index LAI, radiation R n , temperature Ta and soil moisture content SW are the main influencing factors of the extinction coefficient, and soil moisture content is positively correlated with the extinction coefficient, while other factors are negatively correlated with the extinction coefficient. The comprehensive factor expression is f = LAI × R n ×Ta / SW.

[0041] Step 3: Construct the extinction coefficient dynamic function C d .

[0042] In this step, the extinction coefficient dynamic function C d Calculate according to the following formula:

[0043] C d =a×f b

[0044] In the formula, C d is the extinction coefficient, f is the comprehensive factor affecting the extinction coefficient, and a and b are unknown parameters.

[0045] Step 4: Simulation of transpiration water consumption.

[0046] In this step, the dynamic extinction coefficient formula C d The Penman-Monteith model is introduced to replace the extinction coefficient constant parameter C to obtain the transpiration water consumption T e .

[0047]

[0048] Where: T e is the transpiration rate (MJ m -2 d -1 );△ is the slope of the saturated water vapor pressure and temperature curve (kPa℃ -1 );R nc is the net radiation intercepted by the canopy (MJ m -2 d -1 ); G is soil heat flux (MJ m -2 d -1 ); ρ is the air density (g mm -3 );c p is the specific heat at constant pressure (MJ kg -1 ℃ -1 ); γ is the hygrometer constant (kPa℃ -1 ) ; VPD is the saturated vapor pressure difference (kPa); r cis the canopy impedance (sm -1 );r a is the aerodynamic impedance (sm -1 ).

[0049] Net radiation intercepted by the canopy R nc Calculate according to the following formula:

[0050] R nc =R n ×(1-exp(-C d ×LAI))

[0051] Where: R n is the net radiation at the top of the canopy (MJ m -2 d -1 );C d is the extinction coefficient (dimensionless), which is calculated using the above formula for the dynamic function of the extinction coefficient; LAI is the canopy leaf area index (m 2 m -2 ).

[0052] Aerodynamic impedance r a Field conditions are calculated using the following formula:

[0053]

[0054] Where: h c is the canopy height (m); z is the reference height (m); u is the wind speed at the reference height (ms -1 ), k is the Karman constant (valued at 0.40), z0 is the momentum transfer roughness length (m), and d is the zero plane displacement (m).

[0055] Greenhouse conditions a The following formula is used for greenhouse conditions:

[0056]

[0057] Where: ρ is the air density (g mm -3 );C p is the specific heat at constant pressure (MJ kg -1 ℃ -1 ); LAI is the canopy leaf area index (m 2 m -2 );h s For the heat transfer coefficient, targeted calculation methods are selected for the three convection types: forced convection, mixed convection and free convection.

[0058] Canopy impedance r c The Jarvis method is used and calculated according to the following formula:

[0059]

[0060] Where: rst min is the minimum stomatal resistance value (sm) that may occur in leaves within the canopy under ideal conditions. -1 ); LAI e is the effective leaf area index. When LAI≤2, its value is equal to the actual leaf area index LAI. When LAI≥4, its value is equal to LAI / 2. In other cases, its value is 2. i Represents the environmental factors that affect canopy impedance; F i (Xi) is the environmental factor X i The limiting function of canopy impedance, for photosynthetically active radiation PAR, air temperature Ta, saturated water vapor pressure difference VPD and soil water content SW, the limiting function expression is as follows:

[0061]

[0062] Where: B is the asymptotic value of stomatal conductance when the radiation is infinite (W m -2 );T opt is the optimum temperature for transpiration (℃); VPD opt is the optimum VPD for transpiration [kPa]; k1 and k2 are empirical coefficients; s is the soil water deficit corresponding to the beginning of stomatal conductance decrease; f0 is the stomatal opening degree corresponding to the soil water deficit reaching 1; SMD is the soil water deficit calculation formula (SW max -SW) / (SW max -SW min ), where SW max and SW min are the maximum and minimum water contents in the root zone, respectively.

[0063] Embodiment 1:

[0064] The experiment was conducted from June 2020 to October 2021 at the "Yuanjiang Dry and Hot Valley Soil and Water Conservation Ecological Station" (24°03′N-101°35′E) in Gasa Town, Xinping County, Yuxi City, Yunnan Province. It is located in the Yuanjiang Dry and Hot Valley area, with a subtropical dry and hot climate and typical dry red soil. The typical economic forest orange orchard in the area (row spacing 2×3m) was used as the research object for data monitoring. The variety was sweet orange (Citrus sinensis (L.) Osbeck). During the experiment, drip irrigation was used to supplement soil moisture from January to March in the dry season, and rain-fed soil was used for other periods. Under semi-irrigation and semi-rain-fed conditions, the soil moisture content of the orange orchard can be replenished to the field water holding capacity (about 30%) after heavy rainfall, but the minimum value in the dry season is only about 12%. Orange trees are evergreen fruit trees that grow vigorously and have dense branches and leaves throughout the year, and the leaf area index fluctuates relatively little.

[0065] Embodiment 2:

[0066] The experiment was conducted from May to October 2012 to 2014 at the Red Jujube Demonstration Base in Mizhi County, Yulin City, Shaanxi Province (38°11′N-109°28′E), which is located in the hinterland of the Loess Plateau. The climate is a mid-temperate arid and semi-arid climate, and the soil is loess soil. The typical rain-fed jujube forest (row spacing 2×3m) in the area was used as the research object, and the variety was Ziziphus jujuba Mill. Due to the lack of irrigation throughout the year, the minimum soil moisture content in the jujube forest was less than 10%, and the maximum value was less than 70% of the field water holding capacity (about 16%). Jujube trees are deciduous trees. During the growth period, the leaf area index fluctuates greatly with the early stage of the growth process, and tends to be stable in the middle and late stages.

[0067] Embodiment 3:

[0068] The experiment was conducted from April to September 2017 to 2020 at Shenyang Agricultural University (41°48′N-123°57′E) in Shenyang, Liaoning Province, which is located in the Northeast Plain, with a temperate semi-humid climate and sandy loam. The typical greenhouse vineyard in the area (row spacing 0.5×4m) was used as the research object, and the variety was Vitis vinifera L.cv.Muscat Hamburg. Regular drip irrigation management was implemented in the grape greenhouse to control the soil moisture content during the growth period to above 70% of the field water holding capacity (22.5%). Grapes are deciduous fruit trees. During the growth period, the leaf area index fluctuates greatly with the early stage of the growth process, and tends to be stable in the middle and late stages.

[0069] The above three examples use the Penman-Monteith model to estimate transpiration water consumption (the transpiration water consumption of jujube trees and orange trees is determined by the heat diffusion method, while the transpiration water consumption of grapes is determined by the heat balance method). The extinction coefficient constant mode is used as the comparison benchmark, the parameter calibration and optimization are carried out by Bayesian analysis, and the posterior distribution sampling is carried out by the Markov Chain Monte Carlo (MCMC) method based on Gibbs sampling. In the process of model comparison, the accuracy is measured by the determination coefficient (R 2 ) and relative error (MRE) are used as measurement indicators, and the average bandwidth (BA) of the simulation output is used as the evaluation indicator for uncertainty.

[0070] As shown in Table 1, the accuracy and uncertainty of transpiration water consumption estimated by the present invention are better than those of the Penman-Monteith model under the constant extinction coefficient mode. For orange forests, jujube forests and grape greenhouses, R 2The above three examples span across southwest, northwest and northeast my country, covering different drought-type areas and different vegetation types, which can fully demonstrate the effect of the present invention.

[0071] The present invention has significantly improved application effects in dry and hot areas where drought and high temperature coexist, and the accuracy and uncertainty of transpiration water consumption estimation are greatly improved. In the semi-arid Loess Plateau area with only drought stress and the semi-humid Northeast Plain area without drought stress under irrigation conditions, the present invention also has a significant effect on improving the accuracy of transpiration water consumption estimation, and also has a certain effect on reducing the uncertainty of estimation. In comparison, the present invention has a more obvious overall improvement in the estimation effect of transpiration water consumption in areas with complex environmental stresses. In areas with milder environmental conditions, the present invention can mainly significantly improve the estimation effect of transpiration water consumption in local time periods, avoid the large estimation deviation phenomenon that occurs in a few cases, and effectively ensure the stability of the transpiration water consumption simulation effect.

[0072] Table 1 Comparison of transpiration water consumption estimation effects between the present invention and the extinction coefficient fixed value scheme under the Penman-Monteith model framework

[0073]

[0074] The above description is only an explanation of the preferred embodiments of the present application and the technical principles and other schemes used. At the same time, the scope of the invention involved in the present application is not limited to the technical scheme formed by a specific combination of the above technical features, but also should cover other technical schemes formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) by each other to form a technical scheme.

Claims

1. A method for simulating crop transpiration water consumption based on dynamic canopy extinction coefficient. Its characteristics are: The method comprises the following steps: Step A: Use a linear photon sensor to measure the incident radiation intensity above the canopy and the penetrating radiation intensity below the canopy multiple times as the growth period progresses, and calculate the canopy extinction coefficient under different environmental conditions at different growth stages according to the following formula; Where C is the extinction coefficient, PAR in is the incident radiation intensity above the canopy, with the unit of MJ m -2 d -1 , PAR tr is the penetrating radiation intensity below the canopy, and its unit is MJ m -2 d -1 , LAI is the leaf area index, and its unit is m 2 m -2 ; Step B: Use mathematical methods to analyze the driving mechanism of leaf area index LAI and main environmental factors on extinction coefficient and identify the main controlling factors; Step C: Use the main control factors to construct a dynamic extinction coefficient function. First, divide the main control factors into positive correlation factor group and negative correlation factor group. Divide the factorial term of the negative correlation factor by the factorial term of the positive correlation factor to obtain the comprehensive factor f. For crops with large changes in leaf area index LAI, when the radiation R n When the temperature Ta and soil moisture SW are the main influencing factors of the extinction coefficient, the comprehensive factor expression is f = LAI × R n ×Ta / SW; then the extinction coefficient C is characterized by the following formula d Dynamic change rules: C d =a×f b In the formula, C d is the extinction coefficient, f is the comprehensive factor affecting the extinction coefficient, a and b are unknown parameters; Step D: Replace Formula C d The following Penman-Monteith model is introduced to replace the extinction coefficient constant parameter C to obtain the transpiration water consumption T e ; Where: T e is the transpiration rate, and its unit is MJ m -2 d -1 ; △ is the slope of the saturated water vapor pressure and temperature curve, its unit is kPa℃ -1 ; R nc is the net radiation intercepted by the canopy, with the unit of MJ m -2 d -1 ; G is soil heat flux, its unit is MJ m -2 d -1 ; ρ is the air density, its unit is g mm -3 ;c p is the specific heat at constant pressure, and its unit is MJ kg -1 ℃ -1 ; γ is the hygrometer constant, its unit is kPa℃ -1 ; VPD is the saturated vapor pressure difference, its unit is kPa; r c is the canopy impedance, its unit is sm -1 ; r a is the aerodynamic impedance, its unit is sm -1 ; Net radiation intercepted by the canopy R nc Calculate according to the following formula: R nc =R n ×(1-exp(-C d ×LAI)); Where: R n is the net radiation at the top of the canopy, with the unit of MJ m -2 d -1 ; C d is the extinction coefficient, and its unit is dimensionless. nc Calculation; LAI is the canopy leaf area index, and its unit is m 2 m -2 .

2. A method for simulating crop transpiration water consumption based on dynamic canopy extinction coefficient as claimed in claim 1, Its characteristics are: The mathematical analysis method for determining the main controlling factors of the canopy extinction coefficient in step B adopts one of regression analysis, principal component analysis, path analysis, redundancy analysis, and structural equation model.

3. A method for simulating crop transpiration water consumption based on dynamic canopy extinction coefficient as claimed in claim 1, Its characteristics are: The aerodynamic impedance r in step D a In general, the following formula can be used for calculation in the field, and various appropriate correction formulas can also be used according to actual conditions. For greenhouse conditions, the calculation formula applicable to greenhouse conditions should be used: Where: h c is the canopy height in meters; z is the reference height in meters; u is the wind speed at the reference height in milliseconds -1 ; k is the Karman constant, which is 0.40; z 0 is the momentum transfer roughness length, its unit is m; d is the zero plane displacement, its unit is m; Canopy impedance r c The following formula can be used for calculation, or other suitable canopy impedance calculation methods can be used: Where: rst min It is the minimum stomatal resistance value that may occur in leaves in the canopy under ideal conditions, and its unit is sm -1 ; LAI e is the effective leaf area index; X i Represents the environmental factors that affect canopy impedance; F i (Xi) is the environmental factor X i Limit function for canopy impedance, the value range is 0≤F i (X i )≤1.

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