A rice field evapotranspiration estimation method and system considering water storage heat term and advection term

Abstract

The application provides a rice field evapotranspiration estimation method considering water storage heat and advection, comprising the following steps: step 1, obtaining time series of meteorological data, flux data, water depth data and water temperature data of a research area; step 2, calculating the water storage heat in the rice growth period based on the time series of the water depth data and the water temperature data; step 3, calculating the advection based on the time series of the meteorological data; step 4, adding the water storage heat and the advection into an energy balance equation to modify a P-M model, and calculating an evapotranspiration estimation value based on the modified P-M model. The application comprehensively considers the particularity that the rice field is in a flooded state during the growth period, improves the energy balance closure degree by introducing the two secondary energy items of the water storage heat and the advection, quantifies all forms of energy participating in the energy transmission process to the maximum extent, and realizes the improvement of the estimation accuracy of the traditional P-M model and the more accurate estimation of the rice water consumption of the rice field.

Description

Technical Field

[0001] This invention relates to the field of evapotranspiration estimation technology, and specifically to a method and system for estimating paddy field evapotranspiration that considers water storage heat terms and advection terms. Background Technology

[0002] Evapotranspiration is a crucial link between the Earth's surface and the atmosphere, facilitating the transfer of water and energy. It is also a primary pathway for water consumption in farmland, directly impacting surface temperature, water transfer, vegetation growth, and the overall productivity of ecosystems. As a key parameter controlling global terrestrial ecosystem water cycles and climate change, evapotranspiration has a decisive influence on energy and carbon cycles. Approximately 70% of surface precipitation re-enters the atmosphere through evaporation and transpiration, and this proportion can reach as high as 90% in arid and semi-arid regions. Rice is one of the most water-demanding food crops, requiring flooding for most of its growth period. This makes rice paddies different from dryland agriculture and other ecosystems in terms of gas exchange. Furthermore, rice's high water consumption necessitates in-depth research into rice paddy evapotranspiration to better understand its water requirements, optimize irrigation systems, and has significant practical implications for the rational development and utilization of water resources and the expansion of rice cultivation.

[0003] Numerous methods exist for observing evapotranspiration, which can be broadly categorized into several types: micrometeorological methods, hydrological methods, infrared remote sensing methods, and plant physiological methods. With technological advancements, models based on meteorological data have become increasingly widely used. Among these, the PM model, a semi-empirical method based on the principles of energy balance and water transport, is widely recognized as one of the most widely used and accurate formulas due to its comprehensive consideration of both thermal and aerodynamic factors. The energy balance equation, expressed as Rn-G = LE+H, is a crucial theoretical foundation for evapotranspiration models. However, extensive observations of eddy covariance systems have revealed that the sum of turbulent energies, LE+H, is systematically lower than the available energy, Rn-G, with the difference reaching 10%-30% of the available energy. This phenomenon is known as the energy imbalance problem. This issue introduces significant uncertainty into the establishment and validation of evapotranspiration models.

[0004] While existing forced energy closure methods have improved the energy closure rate to some extent, the assumptions and simplifications introduced in the models may overlook some key physical processes, leaving certain uncertainties in the accurate estimation of paddy field evapotranspiration. Therefore, there is an urgent need to develop a paddy field evapotranspiration model that considers energy imbalance, incorporating other forms of energy involved in the energy transfer process into the energy balance equation and evapotranspiration estimation process. This would improve the degree of energy balance closure and the accuracy of the evapotranspiration model, providing technical support for accurately estimating rice water consumption, optimizing irrigation systems, and improving water resource utilization efficiency. Summary of the Invention

[0005] This invention aims to address the limitations of existing technologies in improving energy imbalance and thus enhancing the accuracy of evapotranspiration model estimation. It proposes a method for estimating paddy field evapotranspiration considering water-heat storage and advection terms. This method does not rely on forcibly closing the energy balance equation; instead, it improves energy closure by adding secondary energy terms—water-heat storage and advection terms—to the energy balance equation, thereby increasing the accuracy of the evapotranspiration model. This overcomes the estimation bias caused by the omission of some key physical processes due to assumptions and simplifications introduced in existing studies. By adding secondary energy terms—water-heat storage and advection terms—to the energy balance equation, this invention delves into the contributions of water-heat storage and advection terms in paddy fields to energy transfer processes. Incorporating these secondary energy terms into the energy balance equation yields an energy balance equation that differs from the traditional one that only considers net radiation, surface heat flux, latent heat flux, and sensible heat flux, resulting in a higher degree of energy closure. Based on this, the water-heat storage and advection terms are added to the classic PM formula, resulting in a paddy field evapotranspiration model considering both terms. The model estimation results are verified using actual observations from an eddy covariance system, demonstrating higher evapotranspiration estimation accuracy. This invention innovatively solves the structural error caused by energy imbalance in traditional evapotranspiration models. It fully considers the special characteristics of paddy fields being flooded during their growth period and improves the energy balance closure by introducing two secondary energy terms: water storage and heat storage terms and advection terms. This maximizes the quantification of all forms of energy involved in the energy transfer process, thereby improving the estimation accuracy of traditional PM models and providing a more accurate estimate of paddy field water consumption.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for estimating evapotranspiration in paddy fields that considers water storage and heat storage terms and advection terms includes the following steps:

[0008] Step 1: Obtain time series data of meteorological data, flux data, water depth data, and water temperature data for the study area;

[0009] Step 2: Calculate the water heat storage term during the paddy field growth period based on the time series of the water depth and water temperature data;

[0010] Step 3: Calculate the advection term based on the time series of the meteorological data;

[0011] Step 4: Add the water storage term and the advection term to the energy balance equation to modify the PM model, and calculate the evapotranspiration estimate based on the modified PM model.

[0012] Furthermore, step 1 includes the following sub-steps:

[0013] Step 1-1: Using the center of the paddy field in the study area as the origin of the coordinate system, near-end and far-end meteorological observation stations are set up in four directions to form a nine-point observation network. The Kriging spatial interpolation method is used to generate the global temperature and humidity field and to obtain the wind speed data of the study area.

[0014] Steps 1-2: Obtain the net radiation Rn, soil heat flux G, latent heat flux LE, and sensible heat flux H of the study area through the eddy covariance system;

[0015] Steps 1-3: Install water level gauges in paddy fields in the study area to monitor the water depth and temperature data of paddy fields during the rice growth period, and calculate the average water depth and temperature at different scales.

[0016] Furthermore, step 2 includes the following sub-steps:

[0017] Step 2-1: Based on the average water temperature at different scales, the temperature gradient of the paddy field water temperature is calculated using the five-point differentiation method, including central difference, forward difference, and backward difference.

[0018] Step 2-2: Based on the principle of heat balance of water bodies, establish the equation for the change of water body heat storage under the general scenario, and calculate the water heat storage term at the corresponding scale.

[0019] Furthermore, the water thermal storage term is expressed as:

[0020]

[0021] Among them, S w C represents the thermal energy storage term. w ρ is the specific heat capacity of water. w Where is the density of water; D is the depth of the water layer in the paddy field, in meters. The temperature gradient of paddy field water is given in K / s, where Δt represents the time step and Tw represents the paddy field water temperature.

[0022] Furthermore, step 3 includes the following sub-steps:

[0023] Step 3-1: Calculate the specific humidity based on the actual water vapor pressure and atmospheric pressure;

[0024] Step 3-2: Calculate the air thermal storage term, heat advection term, and water vapor advection term using the finite difference method, wherein the water vapor advection term is calculated based on the specific humidity in Step 3-1.

[0025] Furthermore, the air heat storage term, heat advection term, and water vapor advection term are respectively:

[0026]

[0027] Among them, F s This is the air heat storage term, representing the amount of heat stored per unit volume of atmosphere over a certain period of time; FTHA For heat advection, F represents the heat transport caused by horizontal wind speed; qHA This is the water vapor advection term, representing water vapor transport under the influence of the wind field. Similar to the heat advection term, this term measures the coupling effect between wind speed and the water vapor gradient; T a q represents the air temperature; q represents the specific humidity, indicating the mass of water vapor in one unit of moist air. These are the average components of wind speed in the x and y directions, respectively; V m The control volume element represents the study region; Δz is the vertical thickness of the computational region; z r The reference height is the height of the measurement point; N is the total number of grid points; t is the calculation time interval. The rate of change of temperature over time; and Let be the horizontal temperature gradient, representing the rate of change of temperature in the x and y directions; and The horizontal gradient of water vapor concentration represents the rate of change of water vapor concentration in the x and y directions; Let x be the air temperature at time i; j ,y k ) represents the coordinates of the grid point.

[0028] Furthermore, step 5 includes the following sub-steps:

[0029] Step 5-1: Use Jarvis's surface impedance calibration model to estimate the surface impedance r. s ,

[0030] Step 5-2: Add the water thermal storage term and the advection term to the energy balance equation to modify the PM model, and calculate the evapotranspiration estimate based on the modified PM model.

[0031] Furthermore, the estimated evapotranspiration calculated based on the modified PM model is expressed as follows:

[0032]

[0033] Where ET represents evapotranspiration; M represents the thermal energy storage term or advection term; ρ a Indicates air density; C p Indicates the specific heat capacity of air; e s and e a Represents the saturated vapor pressure and the actual vapor pressure, respectively; r s and r a These represent surface impedance and aerodynamic impedance, respectively; Δ is the slope of the saturated vapor pressure curve, indicating the sensitivity of saturated water vapor pressure to temperature changes at the current temperature; γ is the hygrometer constant.

[0034] Furthermore, it also includes: calculating the energy balance ratio before and after the introduction of the water storage and advection terms, conducting year-by-year analysis of the energy balance ratio and evapotranspiration estimates after the introduction of the water storage and advection terms, revealing the dynamic change patterns of the secondary energy terms, namely the water storage and advection terms and their impact on the evapotranspiration estimates; and verifying the evapotranspiration estimates using evapotranspiration data observed by the eddy covariance system.

[0035] On the other hand, the present invention provides a paddy field evapotranspiration estimation system that considers water storage heat terms and advection terms, comprising:

[0036] The data acquisition module is used to acquire time series of meteorological data, flux data, water depth data, and water temperature data for the study area.

[0037] A water storage heat term calculation module is used to calculate the water storage heat term of the paddy field during the growing season based on the time series of the water depth data and water temperature data.

[0038] The advection term calculation module is used to calculate the advection term based on the time series of the meteorological data.

[0039] The correction module is used to add the water thermal storage term and the lateral term to the energy balance equation to correct the PM model, and calculate the evapotranspiration estimate based on the corrected PM model.

[0040] The verification module is used to verify the evapotranspiration estimates using evapotranspiration data observed through the eddy covariance system.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) This invention focuses on the structural error caused by energy imbalance in the evapotranspiration model, fully considers the advection term and the water storage term, and breaks through the modeling assumptions of steady state and one-dimensional vertical water and heat transfer in the traditional evapotranspiration model.

[0043] (2) This invention designs an experiment that combines eddy covariance system observation with meteorological element matrix observation. It makes full use of the spatiotemporal gradient information of meteorological elements to explore the mechanism of energy imbalance in paddy field system and the change law of secondary energy terms. This law is then used to construct an evapotranspiration model, which is a further deepening of the traditional energy imbalance problem research. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a technical roadmap for estimating paddy field evapotranspiration considering water storage and advection terms in embodiments of the present invention.

[0046] Figure 2 This is a schematic diagram of the eddy covariance system and wind, humidity, and temperature profile stations according to an embodiment of the present invention.

[0047] Figure 3 This is a schematic diagram showing the calculation results of water heat storage during the rice growing season in an embodiment of the present invention.

[0048] Figure 4 This is a schematic diagram of the calculation results of the advection term in paddy fields according to an embodiment of the present invention.

[0049] Figure 5 This is a schematic diagram illustrating the accuracy of paddy field evapotranspiration estimation considering water storage heat in an embodiment of the present invention.

[0050] Figure 6 This is a schematic diagram illustrating the accuracy of paddy field evapotranspiration estimation considering the advection term in an embodiment of the present invention. Detailed Implementation

[0051] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0052] Example 1

[0053] like Figure 1 As shown, this invention provides a method for estimating paddy field evapotranspiration considering water storage and advection terms. The process of this embodiment includes the following steps:

[0054] Step 1 involves acquiring time series data on flux, meteorological, water depth, and water temperature for the study area. This step includes the following sub-steps:

[0055] Step 1-1: Obtain meteorological data for the study area:

[0056] Using the eddy covariance system deployed at the center of the paddy field as the coordinate origin (0, 0), two meteorological observation stations were set up in the four directions of east, south, west, and north: the near-end station was 125m from the origin (e.g., North 1 (0, 125), East 1 (125, 0) etc.), and the far-end station was 250m from the origin (e.g., North 2 (0, 250), East 2 (250, 0) etc.), forming a nine-point observation network together with the central station. Based on the temperature and humidity data of these nine stations within a 500m × 500m study area (resolution 1m × 1m), a continuous temperature and humidity field of the entire space was generated using the Kriging space interpolation method. In the embodiment, the arrangement of the nine temperature and humidity profile observation points in the paddy field area is as follows: Figure 2 As shown, the spatial autocorrelation of known temperature and humidity data from these nine stations (i.e., neighboring samples are more similar than distant samples) is used to predict the temperature and humidity values ​​of all pixels in the entire paddy field area.

[0057] Steps 1-2: Obtain flux data for the study area:

[0058] Flux data were obtained from observations using the eddy covariance system and mainly include net radiation (Rn), soil heat flux (G), latent heat flux (LE), and sensible heat flux (H).

[0059] Steps 1-3: Obtain water depth and temperature data for the study area:

[0060] Several water level gauges were deployed within the study area to monitor and record water depth and temperature data during the rice growing season. During the study period, the water depth and temperature data recorded by all water level gauges were averaged to obtain representative values ​​for each time period. Subsequently, the average values ​​on 30-minute and daily timescales were calculated to analyze the temporal variation characteristics of water depth and temperature. In this embodiment, since the paddy field is only flooded during the growing season, all water depth and temperature data were obtained during the rice growing season.

[0061] Step 2, calculate the water-heat storage term during the paddy field growth period based on the time series of the water depth and water temperature data; specifically, it includes the following sub-steps:

[0062] Step 2-1, Calculation of temperature gradient:

[0063] Based on the meteorological data obtained in steps 1-2, the temperature gradient ΔT / Δt is calculated using the five-point differentiation method. The five-point differentiation method is a high-precision numerical differentiation method. It is based on Taylor expansion and the finite difference method, and by linearly combining data points, it eliminates low-order error terms, thereby improving the accuracy of derivative calculation. Its central difference formula (1), forward difference formula (2), and backward difference formula (3) are as follows:

[0064]

[0065] Among them, Tw i Represents t i Temperature at time t, K; Δt represents the time step; N is the total number of data points; Tw i+1 Tw i+2 Tw i-1 Tw i-2 These are the temperature data points of adjacent paddy field water temperatures. For data at the start time (e.g., i=0) and end time (e.g., i=N-1), the central difference formula cannot be used; the forward difference and backward difference formulas must be used respectively.

[0066] Step 2-2, Calculation of water storage thermal term:

[0067] Based on the temperature gradient ΔTw / Δt calculated in step 2-1, and according to the principle of heat balance in water, an equation for the change of water thermal storage under a general scenario is constructed to calculate the water thermal storage term. The formula can be expressed as:

[0068]

[0069] Among them, C w ρ is the specific heat capacity of water, usually taken as 4186 J / (kg·K); w The density of water is usually taken as 1000 kg / m³. 3 D represents the water depth in the paddy field, in meters; ΔT / Δt represents the time-varying rate of change of the paddy field water temperature, in kJ / s. The half-hour and daily temperature gradients obtained in step 2-1 are substituted into formula (4) to calculate the time series changes of the water storage term at the corresponding scales. In this embodiment, the daily values ​​of the water storage term for the three years from 2022 to 2024 were calculated, and their variation curves are shown below. Figure 3 As shown, the average value of the water-heat storage term during the three-year growing season in paddy fields was 5.47 W / m², with a maximum value of 70.57 W / m² and a minimum value of 68.41 W / m². This indicates that the magnitude of the water-heat storage term is sometimes comparable to that of the turbulent flux, and therefore its role in energy transfer cannot be ignored.

[0070] Step 3: Calculate the advection term based on the time series of the meteorological data; this specifically includes the following sub-steps:

[0071] Step 3-1, Calculation of specific humidity:

[0072] Specific humidity refers to the mass of water vapor contained in a unit mass of moist air, that is, the ratio of the mass of water vapor to the total mass of moist air. It is an important meteorological variable describing the water vapor content in the air. The calculation formula is as follows:

[0073]

[0074] Where q is the specific humidity; mv m is the mass of water vapor. d Let be the mass of dry air. Since the density ratio of water vapor to dry air can be expressed using actual water vapor pressure and atmospheric pressure, specific humidity can be further expressed as:

[0075]

[0076] Where e is the actual water vapor pressure, p is the atmospheric pressure, and 0.622 is the gas constant R of water vapor. v The gas constant R of dry air d The ratio, i.e., 0.622 = R v / R d .

[0077] Step 3-2: Calculation of air heat storage term, heat advection term, and water vapor advection term:

[0078] For continuous data, the following differential formula is used for calculation:

[0079]

[0080] For discrete data, the formula is discretized using the finite difference method, and the result is as follows:

[0081]

[0082] Among them, F s This is the air heat storage term, representing the amount of heat stored per unit volume of atmosphere over a certain period of time; F THA For heat advection, F represents the heat transport caused by horizontal wind speed; qHA This is the water vapor advection term, representing water vapor transport under the influence of the wind field. Similar to the heat advection term, this term measures the coupling effect between wind speed and the water vapor gradient; T a q represents the air temperature; q represents the specific humidity, indicating the mass of water vapor in one unit of moist air. These are the average components of wind speed in the x and y directions, respectively; V m The control volume element represents the study region; Δz is the vertical thickness of the computational region; z r The reference height is the height of the measurement point; N is the total number of grid points; t is the calculation time interval. The rate of change of temperature over time; and Let be the horizontal temperature gradient, representing the rate of change of temperature in the x and y directions; and The horizontal gradient of water vapor concentration represents the rate of change of water vapor concentration in the x and y directions; Let x be the air temperature at time i; j ,y k) represents the coordinates of the grid point.

[0083] The advection term for the study area can be calculated based on the regional temperature and wind speed data obtained in step 1-1 and the specific humidity data obtained in step 3-1. The advection term was calculated using 2022 data, and its half-hourly and diurnal variations were plotted as line graphs. The results are shown below. Figure 4 As shown in the figure, it can be seen that in the embodiment, water vapor advection is generally positive, heat advection is generally negative, and the positive and negative distribution of air heat storage is relatively uniform (its mean is close to 0).

[0084] Step 4: Add the water thermal storage term and the advection term to the energy balance equation to modify the PM model, and calculate the evapotranspiration estimate based on the modified PM model; this specifically includes the following sub-steps:

[0085] Step 4-1, Inversion of Surface Impedance:

[0086] The surface impedance r is estimated using Jarvis's surface impedance calibration model. s The calculation formula is as follows:

[0087]

[0088] Where, r smin For the minimum pore resistance, f1(R) n ),f2(VPD),f3(T a f4(θ) represents the weighting function of the effects of net solar radiation, humidity, air temperature, and soil moisture on plant stress, respectively; VPD is the water vapor deficit; θ is the actual soil moisture content, θ s and θ w α represents the saturated soil moisture content and wilting point, respectively; a1, a2, and a3 are the correlation coefficients obtained during the calibration of the model. Since the paddy field under study has been in a state of long-term flooding, the surface impedance response function f4(θ) to soil moisture content can be regarded as a constant 1.

[0089] Step 4-2, Estimation of evapotranspiration:

[0090]

[0091] Where ET represents evapotranspiration; M represents the thermal energy storage term or advection term; ρ a Indicates air density; C p Indicates the specific heat capacity of air; e s and e a Represents the saturated vapor pressure and the actual vapor pressure, respectively; r s and r aHere, denoted as surface impedance and aerodynamic impedance, respectively; Δ is the slope of the saturated vapor pressure curve, representing the sensitivity of saturated water vapor pressure to temperature changes at the current temperature; γ is the hygrometer constant, and the meanings of the other variables are the same as in formula (1). The aerodynamic drag calculation formula is as follows:

[0092]

[0093] Among them, z m Indicates the altitude at which wind speed is measured; z h The height at which humidity is measured is indicated; d represents the zero-plane displacement elevation, taken as 0.63hc; z om The roughness length controlling momentum transfer is 0.123hc; z oh This represents the roughness length that controls heat and water vapor transfer; here, it is set as the momentum roughness length z. om 10%; k is the von Kármán constant, taken as 0.4; u z This represents the wind speed at height z.

[0094] The slope of the saturated vapor pressure curve is an important parameter describing the vaporization process, and its calculation formula is as follows:

[0095]

[0096] Where Δ represents the slope of the saturated vapor pressure curve, kPa / ℃; T a Indicates air temperature, °C.

[0097] Substituting the net radiation, surface heat flux and meteorological data obtained in steps 1-2, the results of the water storage thermal term obtained in step 3-2, and the surface impedance obtained in step 5-1, along with the aerodynamic drag obtained by formula (19) and the slope of the saturated vapor pressure curve obtained by formula (20), into formula (18), we can calculate the evapotranspiration estimation result after considering the water storage thermal term in the energy balance calculation.

[0098] Step 5. Calculate the energy balance ratio before and after introducing the water storage and advection terms respectively. Analyze the energy balance ratio and evapotranspiration estimate after introducing the water storage and advection terms year by year to reveal the dynamic change law of the secondary energy terms, namely the water storage and advection terms and their impact on the evapotranspiration estimate. Verify the evapotranspiration estimate using evapotranspiration data observed by the eddy covariance system.

[0099] The energy balance equation (21) is an important theoretical basis for the evapotranspiration model. It is expressed as net radiation minus surface heat flux equals the sum of latent heat flux and sensible heat flux:

[0100] R n -G=LE+H (21)

[0101] Where Rn represents net radiation (W / m²) 2 ), which is the difference between total surface radiation and reflected radiation, characterizing the total energy input; G represents soil heat flux (W / m²). 2 ), which is the heat transferred from the Earth's surface to the soil; LE represents the latent heat flux (W / m³). 2 ), which is the energy exchange during the phase transition caused by water evaporation and plant transpiration; H represents the sensible heat flux (W / m). 2 Energy transfer between the Earth's surface and the atmosphere via conduction or convection due to temperature differences is non-phase-change energy. Generally, due to energy imbalances, the equality in the above equation does not hold. The degree of energy closure can be measured using the energy balance ratio (2):

[0102]

[0103] The closer the energy balance ratio (EBR) is to 1, the better the energy closure.

[0104] Similarly, by substituting the advection term, the estimated EBR and evapotranspiration results after incorporating the advection term into the energy balance calculation are obtained. By analyzing the annual EBR and evapotranspiration estimates after introducing the hydrothermal storage and advection terms, the dynamic variation patterns of the secondary energy terms (i.e., the hydrothermal storage and advection terms) and their impact on the evapotranspiration model estimation results are revealed. After obtaining the evapotranspiration estimates, the estimation results are verified using evapotranspiration observed by the eddy covariance system, and the coefficient of determination (R²) is used. 2 The accuracy of the model is evaluated using metrics such as root mean square error (RMSE). The daily evapotranspiration estimation results after considering the water storage term are as follows: Figure 5 As shown, the estimated results of half-hour evapotranspiration and daily evapotranspiration after considering the advection term are as follows: Figure 6 As shown, the model effectively estimates paddy field evapotranspiration after considering the water storage and heat terms and the advection terms, thus achieving dynamic inversion of paddy field evapotranspiration.

[0105] Example 2

[0106] A specific embodiment of the present invention provides a paddy field evapotranspiration estimation system that considers water storage and heat storage terms and advection terms, comprising:

[0107] The data acquisition module is used to acquire time series of meteorological data, flux data, water depth data, and water temperature data for the study area.

[0108] A water storage heat term calculation module is used to calculate the water storage heat term of the paddy field during the growing season based on the time series of the water depth data and water temperature data.

[0109] The advection term calculation module is used to calculate the advection term based on the time series of the meteorological data.

[0110] The correction module is used to add the water thermal storage term and the lateral term to the energy balance equation to correct the PM model, and calculate the evapotranspiration estimate based on the corrected PM model.

[0111] The verification module is used to verify the estimated evapotranspiration using evapotranspiration data observed through the eddy covariance system.

[0112] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0113] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0114] It should be understood that the above description of the preferred embodiments is quite detailed, but it should not be considered as a limitation on the scope of protection of this invention. Those skilled in the art, under the guidance of this invention, can make substitutions or modifications without departing from the scope of protection of the claims of this invention, and all such substitutions or modifications fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for estimating evapotranspiration in a rice field considering water storage and advection, characterized by, The method comprises the following steps: Step 1, obtaining time series of meteorological data, flux data, water depth data and water temperature data of the study area; Step 2, calculating water storage heat term in rice field growth period based on the time series of water depth data and water temperature data; Step 3, calculating advection term based on the time series of meteorological data; Step 4, adding the water storage heat term and the advection term into the energy balance equation to modify the P-M model, and calculating the evapotranspiration estimation value based on the modified P-M model; comprising the following sub-steps: Step 4-1 : Estimating the surface impedance using Jarvis' surface impedance calibration model , Step 4-2: adding the water storage heat term and the advection term into the energy balance equation to modify the P-M model, and calculating the evapotranspiration estimation value based on the modified P-M model; The calculation of the evapotranspiration estimation value based on the modified P-M model is represented as: where ET represents the evapotranspiration amount; M represents the water storage term or the advection term; represents the air density; represents the air specific heat capacity; and respectively represent the saturated water vapor pressure and the actual water vapor pressure; and respectively are the surface resistance and the aerodynamic resistance; is the saturated vapor pressure curve slope, representing the sensitivity of the saturated water vapor pressure to the temperature change at the current temperature; is the psychrometer constant; Rn is the net radiation of the study area; and G is the soil heat flux.

2. The method for estimating evapotranspiration in rice field considering water storage and advection term according to claim 1, wherein, The step 1 comprises the following sub-steps: Step 1-1: taking the center of the rice field in the study area as the coordinate origin, arranging proximal and distal meteorological observation stations in four directions to form a nine-point observation network, generating a global temperature and humidity field by using the Kriging spatial interpolation method, and obtaining wind speed data of the study area; Step 1-2: obtaining net radiation Rn, soil heat flux G, latent heat flux LE and sensible heat flux H of the study area through the eddy covariance system; Step 1-3: arranging water level gauges in the rice field in the study area to monitor the water depth and water temperature data of the rice field during the rice growth period, and calculating the average water depth and water temperature of different scales, respectively.

3. The method for estimating evapotranspiration in rice field considering water storage and advection term according to claim 2, wherein, Step 2 comprises the following sub-steps: Step 2-1: calculating the temperature gradient of the water temperature of the rice field by using five-point derivation method based on the average water temperature of different scales; including central difference, forward difference and backward difference; Step 2-2: based on the heat balance principle of water body, establishing the water body heat storage change equation under general conditions, and calculating the water storage heat term of the corresponding scale.

4. The method for estimating evapotranspiration in rice field considering water storage and advection term according to claim 3, wherein, The water storage heat term is represented as: wherein, represents the water storage term, is the specific heat capacity of water; is the density of water; D is the water layer depth in the paddy field, m; is the temperature gradient of the water temperature in the paddy field, K / s, represents the time step, T w represents the water temperature in the paddy field.

5. The method for estimating evapotranspiration in rice field considering water storage and advection term according to claim 1, wherein, Step 3 comprises the following sub-steps: Step 3-1: calculating the specific humidity based on the actual vapor pressure and atmospheric pressure; Step 3-2: calculating the air storage heat term, heat advection term and water vapor advection term by using the finite difference method, wherein the water vapor advection term is calculated based on the specific humidity in step 3-1.

6. The method for estimating evapotranspiration in rice field considering water storage and advection term according to claim 5, wherein, The air storage heat term, heat advection term and water vapor advection term are respectively: where, is the heat storage term for air, representing the heat stored by unit volume of air in a certain time; is the heat advection term, representing the heat transport caused by horizontal wind speed; is the water vapor advection term, representing the water vapor transport under the action of wind field, similar to the heat advection term, this term measures the coupling effect of wind speed and water vapor gradient;T a is the air temperature; q is the specific humidity, representing the water vapor mass per unit of moist air; , are the average components of wind speed in x and y directions, respectively; represents the control volume unit of the study area; is the vertical thickness of the calculation area; is the reference height, i.e. the height of the measurement point; N is the total number of grid points; t is the time interval of calculation; is the time rate of change of temperature; and are the horizontal gradients of temperature, representing the rate of change of temperature in x and y directions; and are the horizontal gradients of water vapor concentration, representing the rate of change of water vapor concentration in x and y directions; is the air temperature at time i; is the coordinate of the grid point.

7. The method for estimating evapotranspiration in rice field considering water storage and advection term according to claim 1, wherein, Further comprising: calculating the energy balance ratio before and after introducing the water storage heat term and the advection term, respectively, and analyzing the energy balance ratio and the evapotranspiration estimation value after introducing the water storage heat term and the advection term year by year, revealing the dynamic change law of the secondary energy term, i.e. the water storage heat term and the advection term, and the influence on the evapotranspiration estimation value; and verifying the evapotranspiration estimation value by the evapotranspiration data observed by the eddy correlation system.

8. A system for estimating evapotranspiration in a rice field taking into account a water storage term and an advection term, characterized by Comprising: a data acquisition module for obtaining time series of meteorological data, flux data, water depth data and water temperature data of the study area; a water storage heat term calculation module for calculating water storage heat term in rice field growth period based on the time series of water depth data and water temperature data; an advection term calculation module for calculating advection term based on the time series of meteorological data; a modification module for adding the water storage heat term and the advection term into the energy balance equation to modify the P-M model, and calculating the evapotranspiration estimation value based on the modified P-M model; a verification module for verifying the evapotranspiration estimate by evapotranspiration data observed by the vorticity correlation system; The rice field evapotranspiration estimation system considering water storage heat term and advection term is used to perform the steps in the rice field evapotranspiration estimation method considering water storage heat term and advection term according to any one of claims 1-7.

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