A rice field evapotranspiration forecasting method and device based on public weather forecast and crop coefficient method

Abstract

The application provides a rice field evapotranspiration prediction method and device based on public weather forecast and crop coefficient method, and relates to the technical field of agricultural information processing. The method comprises the following steps: obtaining the public weather forecast information of a target area, calculating a reference crop evapotranspiration dynamic prediction value; constructing an average crop coefficient model coupled with a density coefficient and a plant growth temperature constraint, and calculating an average crop coefficient, wherein the density coefficient is calculated by a canopy coverage and a crop height based on a nonlinear regression model of cumulative heat time; and calculating an actual evapotranspiration based on the reference crop evapotranspiration dynamic prediction value and the average crop coefficient. The method can break through the technical bottleneck of precise evapotranspiration prediction under the condition of limited meteorological information, realize daily dynamic evapotranspiration estimation based on easily obtained data such as air temperature and weather type, provide a scientific basis for efficient use and intelligent decision of farmland water resources, and promote the intelligent transformation of agriculture and water conservancy.

Description

Technical Field

[0001] This invention relates to the field of agricultural information processing technology, specifically to a method and apparatus for forecasting paddy field evapotranspiration based on public weather forecasts and crop coefficients. Background Technology

[0002] Evapotranspiration is a crucial component of the water cycle and energy balance in farmland. Accurate forecasting of crop evapotranspiration has significant theoretical and practical value for agricultural irrigation decisions, optimal water resource allocation, and drought monitoring and early warning. Rice, as a major food crop, has a high water requirement during its growth period. Therefore, accurate dynamic forecasting of paddy field evapotranspiration is of great importance for improving water use efficiency and ensuring food security. Currently, crop evapotranspiration simulation mainly employs a method that calculates reference crop evapotranspiration based on the Penman-Monteith formula recommended by the Food and Agriculture Organization of the United Nations (FAO), and then estimates it using crop coefficients. This method faces the following challenges in practical applications:

[0003] Calculating reference crop evapotranspiration requires various meteorological factors, including solar radiation, temperature, humidity, and wind speed. Solar radiation is a key input parameter, but conventional weather stations generally lack measured solar radiation data, and public weather forecasts typically do not include solar radiation forecast information. Existing studies mostly use empirical models based on sunshine duration or temperature for estimation, which limits the accuracy of reference crop evapotranspiration forecasts.

[0004] Traditional crop coefficient methods typically employ fixed segmented values ​​recommended by FAO-56, which fail to reflect the dynamic changes in crop canopy throughout its growth process and the impact of seasonal environmental variations (such as temperature). Studies have shown that crop coefficients are closely related to factors such as canopy coverage, leaf area index, and accumulated temperature, exhibiting significant spatiotemporal variability. Existing crop coefficient correction methods either rely on field measurement data, making dynamic forecasting difficult, or employ empirical statistical models that cannot accurately capture dynamic changes in the canopy.

[0005] Research on evapotranspiration forecasting based on public weather forecast data has attracted widespread attention. However, existing studies mostly focus on the separate forecasting of reference crop evapotranspiration or use simplified crop coefficient methods, and have not yet effectively integrated improved reference crop evapotranspiration forecasting methods with crop coefficient correction methods that consider canopy dynamics. How to achieve dynamic evapotranspiration forecasting that considers canopy dynamics and temperature constraints under the drive of public weather forecast data remains a weak link in current research.

[0006] Therefore, there is an urgent need to develop a method for forecasting paddy field evapotranspiration based on public weather forecast data: on the one hand, by introducing the daily temperature range and weather type to improve the estimation of solar radiation and enhance the accuracy of the evapotranspiration forecast for reference crops; on the other hand, by coupling dynamic crop coefficients to achieve dynamic forecasting of the actual daily evapotranspiration of paddy fields, thereby providing support for precise water management and intelligent irrigation decisions in paddy fields. Summary of the Invention

[0007] To address the problems of existing farmland evapotranspiration forecasting methods that rely on complete meteorological observation data and struggle to achieve high-precision dynamic forecasting using public weather forecasts, this invention proposes a method and device for paddy field evapotranspiration forecasting based on public weather forecasts and the crop coefficient method. This method overcomes the technical bottleneck of accurate evapotranspiration forecasting under limited meteorological information conditions, enabling daily dynamic evapotranspiration estimation based on easily obtainable data such as temperature and weather type. It provides a scientific basis for efficient utilization of farmland water resources and intelligent decision-making, promoting the intelligent transformation of agricultural water conservancy.

[0008] To achieve the above objectives, the specific technical solution of the present invention is as follows:

[0009] In a first aspect, the present invention provides a method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients, comprising:

[0010] Based on the obtained public weather forecast information for the target area, the reference crop evapotranspiration dynamic forecast value is calculated;

[0011] An average crop coefficient model coupling density coefficient and plant growth temperature constraint is constructed, and the average crop coefficient is calculated. The density coefficient is calculated by canopy coverage and crop height determined by a nonlinear regression model based on cumulative heat time.

[0012] The actual evapotranspiration is calculated based on the reference crop evapotranspiration dynamic forecast value and the average crop coefficient.

[0013] Furthermore, the canopy coverage and crop height are determined in the following manner:

[0014] Based on the minimum, maximum, and optimal temperatures for crop growth, and combined with temperatures from public weather forecasts, the daily hot time is calculated.

[0015] The cumulative heat time is obtained by summing the daily heat times.

[0016] Based on measured data from the target area, nonlinear regression equations were established between cumulative heat time and canopy coverage and crop height, respectively, resulting in the nonlinear regression model shown in the following equation, and the canopy coverage and crop height were determined:

[0017] ;

[0018] Among them, f c Indicates canopy coverage; h c Indicates crop height; ATT t t represents the cumulative thermal time at day t; a1, b1, c1, a2, b2, and c2 represent empirical coefficients.

[0019] Furthermore, the density coefficient is calculated as follows:

[0020] Based on the solar altitude angle at the horizon around noon and combined with the aforementioned canopy coverage, the effective canopy coverage rate is calculated using the following formula:

[0021] ;

[0022] Among them, f c eff Indicates effective canopy coverage; α represents the solar altitude angle at the horizon around noon.

[0023] Based on the effective canopy coverage, the crop height, and the determined multiplier factor, the density coefficient is calculated according to the following formula:

[0024] ;

[0025] Among them, K d M represents the density coefficient; c Indicates a multiplier factor.

[0026] Furthermore, the average crop coefficient model is calculated as follows:

[0027] ;

[0028] Among them, K cm K represents the average crop coefficient. soil K represents the average crop coefficient on the bare soil surface. c full f represents the basic crop coefficient when the canopy is fully covered; t Indicates the temperature constraint for plant growth; K d This represents the density coefficient.

[0029] Furthermore, the plant growth temperature constraint is calculated using the following formula:

[0030] ;

[0031] Among them, T a This indicates air temperature, sourced from public weather forecasts; T g opt This indicates the optimal temperature for plant growth.

[0032] Furthermore, the calculation of the reference crop evapotranspiration dynamic forecast value based on the acquired public weather forecast information of the target area includes:

[0033] Based on the obtained public weather forecast information for the target area, the forecast solar radiation is calculated using a solar radiation estimation model.

[0034] The actual water vapor pressure is calculated using a dynamic correction formula;

[0035] Based on the predicted solar radiation and the actual water vapor pressure, the dynamic forecast value of reference crop evapotranspiration is calculated using the Penman-Monteith formula.

[0036] Furthermore, the calculation of the predicted solar radiation based on the acquired public weather forecast information of the target area using a solar radiation estimation model includes:

[0037] Based on the obtained public weather forecast information for the target area, the sunshine duration is calculated according to the following formula:

[0038] ;

[0039] Where, n new Indicates sunshine duration; α1 indicates weather type code; c n1 c n2 and c n3 T represents the empirical coefficient; d N represents the daily temperature range; N represents the theoretical sunshine duration.

[0040] Construct a solar radiation estimation model using the following formula, and calculate the predicted solar radiation based on the sunshine duration:

[0041] ;

[0042] Among them, R s_n new This indicates a forecast of solar radiation; b R1 b R2 and b R3 R represents the empirical coefficient; a It refers to astronomical radiation.

[0043] Furthermore, the calculation form of the dynamic correction formula is as follows:

[0044] ;

[0045] Among them, e a T represents the actual water vapor pressure; min Indicates the lowest temperature; AI represents the drought index; a T Indicates the correction factor;

[0046] The correction coefficient is determined according to the following formula:

[0047] ;

[0048] Among them, c a1 and c a2 This represents the empirical coefficient.

[0049] Secondly, the present invention provides a paddy field evapotranspiration forecasting device based on public weather forecasts and crop coefficient methods, comprising:

[0050] The first calculation module is used to calculate the reference crop evapotranspiration dynamic forecast value based on the obtained public weather forecast information of the target area.

[0051] The second calculation module is used to calculate the average crop coefficient based on the average crop coefficient model constrained by the coupling density coefficient and the plant growth temperature.

[0052] The results output module is used to calculate the actual evapotranspiration based on the reference crop evapotranspiration dynamic forecast value and the average crop coefficient.

[0053] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the paddy field evapotranspiration forecasting method based on public weather forecasting and crop coefficient method described in the first aspect.

[0054] Compared with the prior art, the advantages of the present invention are:

[0055] (1) By introducing plant growth temperature constraints to modify the existing average crop coefficient model, a dynamic response relationship between crop coefficient and plant physiological state is established. Unlike the traditional method of using a fixed crop coefficient table according to growth stage, this invention couples the plant growth temperature constraint factor and density coefficient into the average crop coefficient model, so that the crop coefficient can dynamically respond to the physiological state of the plant according to the predicted temperature. Under extreme high or low temperature conditions, the crop coefficient is automatically corrected, which effectively avoids the overestimation or underestimation of evapotranspiration, and the prediction results are more consistent with the actual water consumption law of crops.

[0056] (2) This invention uses only the temperature and weather type that are easily obtained from public weather forecast information as input, without relying on ground-based measured solar radiation, humidity, wind speed and other meteorological observation data, to complete the forecast from basic meteorological elements to actual evapotranspiration. This greatly reduces the dependence of farmland evapotranspiration forecast on high-cost observation equipment and dense station networks, and significantly improves the applicability of this method in areas with sparse meteorological observation stations. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart of a paddy field evapotranspiration forecasting method based on public weather forecasting and crop coefficient method provided in an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram illustrating the relationship between canopy coverage and cumulative heat time, provided in an embodiment of the present invention.

[0060] Figure 3 This is a schematic diagram illustrating the relationship between crop height and cumulative heat time provided in an embodiment of the present invention;

[0061] Figure 4 A block diagram illustrating the structure of a paddy field evapotranspiration forecasting device based on public weather forecasts and crop coefficients, provided for embodiments of the present invention. Detailed Implementation

[0062] To enable those skilled in the art to clearly and completely understand the technical solution of the present invention, the present invention will be further described in detail below with reference to embodiments. Obviously, the embodiments described herein are only for explaining the present invention and are not intended to limit the scope of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0063] In existing technologies, evapotranspiration forecasting methods based on public weather forecast information have limitations. On the one hand, the lack of solar radiation forecast information in public weather forecasts limits the accuracy of reference crop evapotranspiration calculations. On the other hand, the traditional crop coefficient method is difficult to reflect the dynamic changes in the canopy and the physiological constraints of temperature on crop growth. Furthermore, the reference evapotranspiration forecast and dynamic crop coefficient correction have not been effectively integrated, making it impossible to achieve accurate dynamic forecasting of the actual daily evapotranspiration of paddy fields.

[0064] Based on this, this invention proposes a method and device for predicting paddy field evapotranspiration based on public weather forecasts and the crop coefficient method. Using data from public weather forecasts as input, it improves the sunshine duration conversion method by introducing diurnal temperature range, establishing a solar radiation estimation model driven by both diurnal temperature range and weather type. The estimated solar radiation is then coupled to the Penman-Monteith formula, achieving a dynamic quantitative conversion from basic meteorological elements to reference crop evapotranspiration. Simultaneously, by combining the crop growth response mechanism to cumulative heat time, it dynamically estimates canopy coverage and crop height, thereby constructing a daily crop coefficient calculation method coupled with density coefficient and plant growth temperature constraints. Ultimately, this forms a complete technical path of "meteorological element-driven - reference evapotranspiration calculation - dynamic crop coefficient inversion - actual evapotranspiration prediction," solving the technical problem of achieving daily dynamic forecasting of paddy field evapotranspiration considering canopy dynamics and temperature constraints solely based on public weather forecast information, thus supporting efficient farmland water resource management.

[0065] Example 1

[0066] This invention proposes a method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients. (See reference...) Figure 1 Specifically, this may include the following steps:

[0067] S1. Based on the obtained public weather forecast information of the target area, the reference crop evapotranspiration dynamic forecast value is calculated.

[0068] In step S1 above, the public weather forecast information includes temperature (including the maximum temperature T). max Minimum temperature T min Average temperature value T mean (And air humidity) and weather type (including sunny, sunny turning cloudy, cloudy, overcast, and rainy). Using temperature and weather type data as the core, a dynamic transformation from basic meteorological elements to reference crop evapotranspiration is established by coupling a solar radiation forecasting method based on sunshine duration conversion with the Penman-Monteith formula. Specifically, this may include the following steps:

[0069] S11. Based on the obtained public weather forecast information for the target area, the forecasted solar radiation is calculated using a solar radiation estimation model. Here, the idea for improving the sunshine duration conversion model is to introduce a correction for sunshine duration based on the diurnal temperature range. Since weather forecasts can accurately predict the maximum and minimum temperatures, and sunshine duration and diurnal temperature range (T...) are related... d =T max –T min There is a good relationship between the two. Therefore, this embodiment proposes a method for integrating the diurnal temperature range T. dA novel method for converting sunshine duration from weather type code α1 and theoretical sunshine duration N was developed, and a solar radiation estimation model was constructed.

[0070] The specific process of constructing a solar radiation estimation model and calculating the predicted solar radiation may include the following steps:

[0071] S111. Based on the obtained public weather forecast information of the target area, the sunshine duration is calculated according to the following formula (1):

[0072] (1)

[0073] In equation (1) above, n new This represents the sunshine duration; α1 represents the weather type code, which is quantified according to the weather type. For example, sunny, sunny turning cloudy, cloudy, overcast, and rainy days are set to 0.9, 0.7, 0.5, 0.3, and 0.1 respectively; c n1 c n2 and c n3 The values ​​represent empirical coefficients, nationally applicable empirical coefficients derived from daily weather forecast data from 86 radiation stations across the country from 2015 to 2019. For example, they are set to 0.4914, 0.0283, and -0.0272, respectively; T d This represents the daily temperature range, which is the difference between the highest and lowest temperatures; N represents the theoretical sunshine duration (in hours).

[0074] The theoretical sunshine duration N is calculated using the following formula (2):

[0075] (2)

[0076] In the above formula (2), Represents the angle at sunset (in radians); Indicates geographical latitude (unit: radians); Indicates solar declination (in radians); It refers to the Julian Days (1-365 or 366).

[0077] S112. Construct a solar radiation estimation model based on the combined driving force of daily temperature range and weather type. The calculation form is shown in the following formula (3), and the predicted solar radiation is calculated based on the sunshine duration:

[0078] (3)

[0079] In equation (3) above, R s_n new This indicates a forecast of solar radiation; b R1 b R2 and b R3R represents empirical coefficients, modeled based on daily radiation and sunshine duration data observed at 96 radiation stations nationwide from 1967 to 2016. For example, these coefficients are set to 0.2171, 0.5183, and 0.0022, respectively. a It refers to astronomical radiation.

[0080] Among them, astronomical radiation R a The calculation is performed according to the following formula (4):

[0081] (4)

[0082] In the above formula (4), It represents the reciprocal of the relative distance between the Earth and the Sun.

[0083] S12. The actual water vapor pressure is calculated using the dynamic correction formula; the calculation form of the dynamic correction formula is as follows:

[0084] (5)

[0085] In the above equation (5), e a T represents the actual water vapor pressure; min Indicates the lowest temperature ( o C); a T This represents the correction factor, without considering climate zones (aridity index AI ranges from 0.02 to 4.30), a T Since it exhibits a highly significant logarithmic relationship with AI, the correction coefficient is determined according to the following equation (6):

[0086] (6)

[0087] In equation (6) above, AI represents the drought index, defined as the ratio of multi-year average rainfall to multi-year average potential evapotranspiration calculated based on multi-year average monthly temperature data; c a1 and c a2 The coefficients represent empirical coefficients, obtained by regression analysis based on meteorological data from China from 1967 to 1996 and meteorological data from other countries. For example, they are set to -2.81 and 1.28, respectively.

[0088] S13. Based on the predicted solar radiation and actual water vapor pressure, the dynamic forecast value of reference crop evapotranspiration is calculated using the Penman-Monteith formula. It should be noted that the concept of "reference crop" here is different from that of "crop" in the following text. "Reference crop" refers to a hypothetical reference turf, which is the standard reference surface for the Penman-Monteith formula; "crop" in the following text refers to the target crop studied in this embodiment, which is the object of the actual evapotranspiration calculation, such as rice.

[0089] Specifically, the Penman-Monteith formula is the FAO-recommended standard method for calculating crop evapotranspiration, and its basic form is as follows:

[0090] (7)

[0091] In the above equation (7), ET o R represents the reference crop evapotranspiration dynamic forecast value (mm / d); n γ represents net radiation (MJ / m² / d); G represents soil heat flux (MJ / m² / d); γ represents wet / dry surface constant (kPa / ℃); T represents average air temperature (℃); u² represents wind speed at 2m height (m / s), using the multi-year average wind speed of the target area to reduce the impact of short-term forecast fluctuations on model stability; e s Δ represents the saturated vapor pressure (kPa); Δ represents the slope of the saturated vapor pressure curve (kPa / ℃).

[0092] In this embodiment, the predicted solar radiation and the actual water vapor pressure are coupled into the above equation (7), that is, the predicted solar radiation determined by the above equation (3) is used as the net radiation R. n The input is taken, and the actual water vapor pressure determined by the above equation (5) is substituted into the above equation (7) for calculation, thereby generating the reference crop evapotranspiration dynamic forecast value based on public weather forecast. By introducing the dual factors of daily temperature range and weather type coding to jointly drive the estimation of sunshine hours, the forecast bias caused by the traditional method relying on only a single factor is overcome, making the solar radiation estimation result based on public weather forecast closer to the actual physical process.

[0093] S2. Construct an average crop coefficient model that couples the density coefficient with plant growth temperature constraints, and calculate the average crop coefficient. The calculation method for the average crop coefficient model is as follows:

[0094] (8)

[0095] In the above equation (8), K cm K represents the average crop coefficient, a parameter that comprehensively considers the impact of soil evaporation. soil This represents the average crop coefficient on the bare soil surface, reflecting the influence of surface moisture and soil type. For example, it is calibrated based on the soil characteristics of paddy fields in Nanjing. K soil The value is 0.8; f t This represents the temperature constraint for plant growth, used to quantify the effect of temperature on crop growth; K d K represents the density coefficient. c full This represents the basic crop coefficient when the canopy is fully covered.

[0096] In equation (8) above, the plant growth temperature constraint f tThe basic crop coefficient K when the canopy is fully covered c full and density coefficient K d It shall be determined in the following manner:

[0097] (1) Temperature constraints for plant growth f t The calculation is performed using the following formula (9):

[0098] (9)

[0099] In the above equation (9), T a This indicates air temperature, sourced from public weather forecasts; T g opt The optimal temperature for plant growth, expressed in °C, can be obtained through meta-analysis. For example, the average T for rice... g opt It is 27.6℃.

[0100] (2) Basic crop coefficient K when the canopy is fully covered c full Based on the FAO-recommended single-crop coefficient method, and combined with measured meteorological data from the target area for parameter calibration, the calculation formula is as follows:

[0101] (10)

[0102] In the above equation (10), h c-mid Indicates the height of mid-term crops; u 2-mid This indicates the wind speed at a height of 2m during the medium term; RH min-mid This indicates the lowest relative humidity during the medium term.

[0103] Taking Nanjing as an example, based on the rice planting situation in Nanjing, the mid-term crop height was calibrated to 1.05m; the wind speed at a height of 2m in the mid-term was taken as the multi-year average of 1.94 m / s in Nanjing during August and September over the past 20 years. -1 The minimum relative humidity in the medium term is based on a 3-year average of 55% observed in Nanjing. After calibration using the aforementioned localized parameters, the K value for the Nanjing area was calculated. c full The value is 1.124.

[0104] (3) Density coefficient K d Characterizing the impact of vegetation cover on the crop coefficient. To achieve the density coefficient K based on public weather forecast information. d The daily forecast needs to simultaneously forecast the canopy cover f c With crop height h c The estimation method for canopy coverage and crop height employs a nonlinear regression model based on cumulative heat time, using air temperature from public weather forecasts as a driving factor to achieve dynamic prediction of crop growth parameters. Specifically, the density coefficient K is determined in the following way. d :

[0105] Step 1: Based on the minimum, maximum, and optimum temperatures for crop growth, and combined with temperatures from public weather forecasts, the daily thermal time is calculated. Specifically, the daily thermal time is calculated using an improved temperature response function based on the three cardinal temperatures of minimum, optimum, and maximum temperatures for crop growth, to accurately quantify the nonlinear driving effect of temperature on crop development rate. Its calculation form is as follows:

[0106] (11)

[0107] In equation (11) above, DTT represents the daily thermal time (°C d); K(T) represents the temperature response curve describing the rate of phenological development, which is calculated by equation (12) below:

[0108] (12)

[0109] In the above equation (12), T g max and T g min These represent the highest and lowest temperature values ​​(°C) for crop growth, respectively. For example, the values ​​for rice are 41°C and 12°C. mean This represents the average temperature value (°C). This indicates the coefficients to be calculated.

[0110] Step 2: The cumulative heating time is obtained by summing up the daily heating times. The calculation method is as follows:

[0111] (13)

[0112] In the above equation (13), ATT t The effective accumulated temperature at day t, i.e., the accumulated heat time, in °C / d; DTT x The daily thermal time for day t, in °C / d.

[0113] Step 3: Based on the measured data of the target area, establish nonlinear regression equations between cumulative heat time and canopy coverage and crop height, respectively, to obtain the nonlinear regression model shown in equation (14), and determine the canopy coverage and crop height:

[0114] (14)

[0115] In the above equation (14), f c Indicates canopy coverage; h c The value represents the crop height; a1, b1, and c1 represent empirical coefficients, determined based on measured data from the target area; a2, b2, and c2 represent empirical coefficients, determined based on measured data from the target area.

[0116] For example, based on measured data from Nanjing area from 2018 to 2020, a nonlinear regression analysis was performed, yielding the following results: Figure 2 The diagram showing the relationship between cumulative heat time and canopy coverage illustrates this relationship. The empirical coefficients a1, b1, and c1 are 0.76, -4.05, and 3.55, respectively. The nonlinear regression model of cumulative heat time and canopy coverage also shows the coefficient of determination (R²)... 2 The coefficient of performance (COP) reached 0.95, indicating that the model can well describe the response relationship between cumulative heat time and canopy coverage, and the fitting accuracy is high. Similarly, based on the measured data of Nanjing area from 2018 to 2020, a nonlinear regression analysis was performed, yielding the following results: Figure 3 The diagram showing the relationship between cumulative heat time and crop height yields empirical coefficients a2, b2, and c2 with values ​​of 0.302, -2.48, and 2.84, respectively. The nonlinear regression model of cumulative heat time and crop height also shows a coefficient of determination (R²). 2 The accuracy reached 0.97, indicating that the model can well describe the response relationship between cumulative heat time and crop height, and the fitting accuracy is high.

[0117] Step 4: Based on the solar altitude angle of the horizon around noon determined by the following formula (15), and combined with the canopy coverage, calculate the effective canopy coverage rate according to the following formula (16):

[0118] (15)

[0119] In the above formula (15), α represents the solar altitude angle of the horizon around noon (i.e., around 12:00); δ represents the solar declination angle (in radians), which is calculated by the above formula (2).

[0120] (16)

[0121] In the above equation (16), f c eff This indicates the effective canopy coverage rate.

[0122] Step 5: Based on the effective canopy coverage, crop height, and the determined multiplier factor, the density coefficient is calculated according to the following formula (17):

[0123] (17)

[0124] In the above equation (17), K d M represents the density coefficient; c This represents the multiplier factor, specifically the effective canopy coverage rate f. c eff The multiple factor, ranging from 1.5 to 2.0, is based on the analysis of crop physiological characteristics in the target region. For example, based on the analysis of rice physiological characteristics in Nanjing, M... c Take 1.5.

[0125] In this embodiment, unlike traditional methods that use a fixed crop coefficient table based on growth stages, this invention introduces plant growth temperature constraints into the average crop coefficient model. This allows the crop coefficient to dynamically respond to changes in plant physiological state based on the predicted temperature. Under extreme high or low temperature conditions, the plant growth temperature constraint automatically reduces the crop coefficient, effectively avoiding overestimation or underestimation of evapotranspiration and significantly improving the accuracy and physiological rationality of the forecast results.

[0126] Using temperature from public weather forecasts as the driving factor, and employing a cumulative heat time model and nonlinear regression methods, dynamic forecasts of daily canopy coverage and crop height were achieved, thereby driving daily updates of the density coefficient. This method enables reasonable predictions of crop growth status even when real-time remote sensing imagery or field measurement data is unavailable, filling the technological gap where dynamic crop parameters cannot be obtained solely from weather forecast data.

[0127] S3. The actual evapotranspiration is calculated based on the reference crop evapotranspiration dynamic forecast value and the average crop coefficient. Specifically, to achieve daily dynamic evapotranspiration estimation, the actual evapotranspiration ET is calculated. c The estimation formula is:

[0128] (18)

[0129] In the above equation (18), ET c ET represents actual evapotranspiration (mm / d). o K represents the reference crop evapotranspiration dynamic forecast value calculated in step S1 above; cm This represents the average crop coefficient calculated in step S2 above.

[0130] To verify the prediction accuracy of the method in this embodiment, based on data from Nanjing from 2018 to 2020, the actual evapotranspiration (predicted value) was obtained using the methods described in steps S1-S3 above, and compared with the historical data (measured value) from 2018 to 2020 in the local area. The prediction accuracy was judged based on statistical indicators, including the regression coefficient b (ideally 1, the closer to 1, the better the consistency between the predicted and measured values), the correlation coefficient R (used to measure the linear correlation strength between the predicted and measured values, the closer to 1, the better the linear consistency between the predicted and measured values), the root mean square error RMSE (the smaller the value, the lower the prediction error), and the consistency index d. IA (The closer to 1, the better the trend consistency between the predicted and measured values). Table 1 shows the changes of each indicator by month (January to July).

[0131] Table 1. Calculation Results of Statistical Indicators for Actual Evapotranspiration and Historical Data

[0132]

[0133]

[0134] As shown in Table 1 above, the regression coefficient b for each year ranges from 0.894 to 1.011, generally close to 1, indicating good consistency between the predicted and measured values. The predicted values ​​obtained by the method in this embodiment do not systematically overestimate or underestimate the values. The correlation coefficient R ranges from 0.539 to 0.827, showing good overall consistency, indicating that the method in this embodiment can accurately capture the temporal fluctuation characteristics of evapotranspiration. The root mean square error (RMSE) ranges from 1.02 to 1.435 mmd. -1 The range indicates that there is an average daily deviation of approximately 1–1.4 mm between the predicted and measured values, which is relatively small. Consistency index d IA The overall range is 0.666–0.855, generally greater than 0.7, indicating good consistency between the predicted and measured values. The method in this embodiment can highly match the actual measured evapotranspiration process. In summary, the method in this embodiment can maintain high prediction consistency and high prediction accuracy in different years.

[0135] This invention addresses the technical bottlenecks of current farmland evapotranspiration prediction methods, which largely rely on complete meteorological observation data and are difficult to apply in areas with sparse meteorological observation stations or where measured data is lacking. It also addresses the problems of existing weather forecast-based evapotranspiration prediction methods, which often simply apply empirical formulas, have low accuracy in estimating solar radiation, and fail to fully consider the dynamic changes in crop growth, leading to insufficient reliability of prediction results. This invention provides a paddy field evapotranspiration prediction method based on public weather forecast information and an improved crop coefficient method. By analyzing public weather forecast information, key elements such as daily temperature and weather type are extracted; and the predicted solar radiation R is calculated based on an improved sunshine duration conversion method. s_n new The actual water vapor pressure e is calculated using a dynamic correction formula. a The forecast of solar radiation R s_n new and actual water vapor pressure e a The Penman-Monteith formula is used to calculate the dynamic forecast value of reference crop evapotranspiration. Simultaneously, based on temperature data from public weather forecasts, a nonlinear regression model of cumulative heat time is used to estimate the daily canopy cover f. c With crop height h c Then calculate the dynamic density coefficient K. d and plant growth temperature constraints f t Based on these parameters, daily evapotranspiration predictions for paddy fields are generated. Its beneficial effects are mainly reflected in the following aspects:

[0136] (1) This invention uses only the temperature (highest temperature and lowest temperature) and weather type that are easily obtained from public weather forecast information as input. It does not rely on meteorological observation data such as ground-measured solar radiation, humidity, and wind speed. It can complete the forecast from basic meteorological elements to actual evapotranspiration, which greatly reduces the dependence of farmland evapotranspiration forecast on high-cost observation equipment and dense station networks, and significantly improves the applicability of this method in areas with sparse meteorological observation stations.

[0137] (2) The method for converting sunshine duration has been improved, which significantly enhances the accuracy of solar radiation estimation based on weather forecasts. By introducing the dual factors of daily temperature range and weather type coding to jointly drive the estimation of sunshine duration, the forecast bias caused by relying on a single factor or simple empirical assignment in traditional methods has been overcome. This makes the solar radiation estimation results based on public weather forecast information closer to the actual physical process and provides a more reliable input for the calculation of reference crop evapotranspiration.

[0138] (3) Unlike the traditional crop coefficient method that uses a table to look up a fixed value based on the growth stage, this invention couples the plant growth temperature constraint factor into the average crop coefficient model, so that the crop coefficient can reflect the physiological constraints of cover change and temperature on crop transpiration capacity in a synchronous manner. Under extreme high or low temperature conditions, the crop coefficient is automatically corrected, which effectively avoids overestimation or underestimation of evapotranspiration, and the forecast results are more consistent with the actual water consumption pattern of crops.

[0139] (4) This invention uses temperature data from public weather forecast information as the driving factor, introduces a cumulative heat time model based on three base point temperatures and the Wang-Engel function, and combines it with nonlinear regression methods to realize the dynamic estimation of daily canopy coverage and crop height, thereby driving the daily update of density coefficient, significantly improving the accuracy and applicability of evapotranspiration forecast, promoting the intelligent transformation of agricultural water resource management, and developing new quality productivity of smart agriculture.

[0140] (5) This invention organically integrates the improved sunshine duration conversion method, dynamic water vapor pressure correction method, crop growth parameter estimation method based on cumulative heat time, and temperature-constrained crop coefficient method into a complete system, forming a full-chain technical path of "meteorological element driving - radiation estimation optimization - reference evapotranspiration calculation - dynamic crop coefficient inversion - actual evapotranspiration prediction", which is used to support the efficient management of farmland water resources.

[0141] (6) This invention introduces a drought index to reflect the constraint of climate background on water vapor pressure and dynamically estimates crop parameters through a cumulative heat time model, so that the model can maintain high forecast accuracy in different climate zones and different growth stages, and has strong universality and promotion value.

[0142] In summary, the method provided by this invention significantly improves the accuracy and timeliness of evapotranspiration forecasting, effectively promotes the transformation of agricultural water resource management from a data-driven model to an intelligent model based on publicly available meteorological information, and has important application value for developing new productivity in smart agriculture and supporting the efficient utilization and intelligent decision-making of farmland water resources.

[0143] Example 2

[0144] Based on the same inventive concept, this invention proposes a paddy field evapotranspiration forecasting device based on public weather forecasts and crop coefficient methods, see reference. Figure 4 ,include:

[0145] The first calculation module 101 is used to calculate the reference crop evapotranspiration dynamic forecast value based on the obtained public weather forecast information of the target area.

[0146] The second calculation module 102 is used to calculate the average crop coefficient based on the average crop coefficient model constrained by the coupling density coefficient and the plant growth temperature.

[0147] The result output module 103 is used to calculate the actual evapotranspiration based on the reference crop evapotranspiration dynamic forecast value and the average crop coefficient.

[0148] The paddy field evapotranspiration forecasting device based on public weather forecasting and crop coefficient method provided in this embodiment of the invention has a similar implementation principle and technical effect to that of Embodiment 1, and will not be repeated here.

[0149] Example 3

[0150] Based on the same inventive concept, this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the paddy field evapotranspiration forecasting method based on public weather forecasts and crop coefficient method in Embodiment 1.

[0151] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to Embodiment 1 of the present invention.

[0152] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for forecasting paddy field evapotranspiration based on public weather forecasts and crop coefficients, characterized in that, include: Based on the obtained public weather forecast information for the target area, the reference crop evapotranspiration dynamic forecast value is calculated; An average crop coefficient model coupling density coefficient and plant growth temperature constraint is constructed, and the average crop coefficient is calculated. The density coefficient is calculated by canopy coverage and crop height determined by a nonlinear regression model based on cumulative heat time. The actual evapotranspiration is calculated based on the reference crop evapotranspiration dynamic forecast value and the average crop coefficient.

2. The method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients according to claim 1, characterized in that, The canopy coverage and crop height are determined in the following manner: Based on the minimum, maximum, and optimal temperatures for crop growth, and combined with temperatures from public weather forecasts, the daily hot time is calculated. The cumulative heat time is obtained by summing the daily heat times. Based on measured data from the target area, nonlinear regression equations were established between cumulative heat time and canopy coverage and crop height, respectively, resulting in the nonlinear regression model shown in the following equation, and the canopy coverage and crop height were determined: ； Among them, f c Indicates canopy coverage; h c Indicates crop height; ATT t t represents the cumulative thermal time at day t; a1, b1, c1, a2, b2, and c2 represent empirical coefficients.

3. The method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients according to claim 2, characterized in that, The density coefficient is calculated as follows: Based on the solar altitude angle at the horizon around noon and combined with the aforementioned canopy coverage, the effective canopy coverage rate is calculated using the following formula: ； Among them, f c eff Indicates effective canopy coverage; α represents the solar altitude angle at the horizon around noon. Based on the effective canopy coverage, the crop height, and the determined multiplier factor, the density coefficient is calculated according to the following formula: ； Among them, K d M represents the density coefficient; c Indicates a multiplier factor.

4. The method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients according to claim 1, characterized in that, The calculation method for the average crop coefficient model is as follows: ； Among them, K cm K represents the average crop coefficient. soil K represents the average crop coefficient on the bare soil surface. c full f represents the basic crop coefficient when the canopy is fully covered; t Indicates the temperature constraint for plant growth; K d This represents the density coefficient.

5. The method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients according to claim 4, characterized in that, The plant growth temperature constraint is calculated using the following formula: ； Among them, T a This indicates air temperature, sourced from public weather forecasts; T g opt This indicates the optimal temperature for plant growth.

6. The method for predicting paddy field evapotranspiration based on public weather forecasting and crop coefficient method according to claim 1, characterized in that, The calculation of the reference crop evapotranspiration dynamic forecast value based on the acquired public weather forecast information of the target area includes: Based on the obtained public weather forecast information for the target area, the forecast solar radiation is calculated using a solar radiation estimation model. The actual water vapor pressure is calculated using a dynamic correction formula; Based on the predicted solar radiation and the actual water vapor pressure, the dynamic forecast value of reference crop evapotranspiration is calculated using the Penman-Monteith formula.

7. The method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients according to claim 6, characterized in that, The forecast solar radiation, calculated using a solar radiation estimation model based on the acquired public weather forecast information for the target area, includes: Based on the obtained public weather forecast information for the target area, the sunshine duration is calculated according to the following formula: ； Where, n new Indicates sunshine duration; α1 indicates weather type code; c n1 c n2 and c n3 T represents the empirical coefficient; d N represents the daily temperature range; N represents the theoretical sunshine duration. Construct a solar radiation estimation model using the following formula, and calculate the predicted solar radiation based on the sunshine duration: ； Among them, R s_n new This indicates a forecast of solar radiation; b R1 b R2 and b R3 R represents the empirical coefficient; a It refers to astronomical radiation.

8. The method for predicting paddy field evapotranspiration based on public weather forecasts and crop coefficients according to claim 6, characterized in that, The calculation form of the dynamic correction formula is as follows: ； Among them, e a T represents the actual water vapor pressure; min Indicates the lowest temperature; AI represents the drought index; a T Indicates the correction factor; The correction coefficient is determined according to the following formula: ； Among them, c a1 and c a2 This represents the empirical coefficient.

9. A paddy field evapotranspiration forecasting device based on public weather forecasts and crop coefficient methods, characterized in that, include: The first calculation module is used to calculate the reference crop evapotranspiration dynamic forecast value based on the obtained public weather forecast information of the target area. The second calculation module is used to calculate the average crop coefficient based on the average crop coefficient model constrained by the coupling density coefficient and the plant growth temperature. The results output module is used to calculate the actual evapotranspiration based on the reference crop evapotranspiration dynamic forecast value and the average crop coefficient.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the paddy field evapotranspiration forecasting method based on public weather forecasting and crop coefficient method as described in any one of claims 1-8.

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