CN111461909B - Short-term prediction method for farmland evapotranspiration - Google Patents
Short-term prediction method for farmland evapotranspiration Download PDFInfo
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Abstract
The invention discloses a short-term prediction method for farmland evapotranspiration, which relates to the technical field of crop monitoring and comprises the steps of determining a prediction reference day and a short-term prediction day; reference crop evapotranspiration ET for predicting reference day by PM method 0 (ii) a Obtaining actual crop measurement evapotranspiration ET of target farmland prediction reference day by using vorticity correlation system c (ii) a Calculating crop coefficient K for short-term prediction of target farmland c (ii) a Prediction of reference crop Water demand ET 'for each short-term forecast day using the Hargreaves-Samani model' 0 (ii) a Obtaining ET 'of each short-term prediction day' c (ii) a Calculating a historical experience correction parameter alpha; calculating the predicted evapotranspiration ET of each short-term predicted day of the target farmland c And completing short-term prediction of the evapotranspiration of the farmland. The method has higher application value for short-term prediction of the evapotranspiration of the farmland, high prediction precision and good stability, and provides scientific basis for farmland water management in future plain areas.
Description
Technical Field
The invention relates to the technical field of crop monitoring, in particular to a farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and historical experience.
Background
The Evapotranspiration process is an important component of the terrestrial hydrologic cycle, the Evapotranspiration volume (ET) 0 ) Has important guiding significance for irrigation planning and regional water resource allocation. The accurate prediction of the evapotranspiration amount can save the irrigation water consumption to a certain extent, and particularly in arid and semiarid regions with larger agricultural water consumption and higher dependence of crop growth on irrigation. Therefore, in order to better manage the irrigation water consumption of crops and improve the water utilization efficiency of crops, the urgent need exists for crops ET 0 And (6) carrying out accurate prediction.
The prediction of evapotranspiration is generally carried out by methods classified into 4 types: time series methods, grey model methods, intelligent algorithms, and empirical formula methods. The time-series method has uncertainty in prediction accuracy because the data used is single (only historical data of the amount of evapotranspiration is needed), and the latest data contains extremely important prediction information. The gray prediction method is essentially an exponential model, and when the target function increases by zero, the systematic error is serious, and the error is serious when the prediction period is more. The intelligent algorithm needs a dynamic data training system, needs a large amount of data in the early stage and is not suitable for short-term prediction. Compared with other more complex mathematical models, the single crop coefficient method only needs less meteorological data and basic parameters of crops and soil, can carry out parameter real-time optimization according to the types of the crops, can reflect the actual situation better and has higher calculation precision.
ET based on single crop coefficient method c When predicting, the method mainly depends on the amount of the reference crop transpiration (ET) 0 ) And coefficient of crop K c And (4) predicting the precision. With the improvement of the precision of weather forecast, public weather forecast data is gradually applied to ET as an important data source 0 Forecasting is carried out by a Penman-Monteith (PM) method, a Fourier analysis model, a Hargreaves-Samani (H-S) model and the like. Wherein, although the PM method has higher calculation precision, the PM method needs more meteorological data; the Fourier analysis model has higher forecasting precision and can be conveniently embedded into an irrigation system, but the model has larger instability; the H-S model is widely applied due to the advantages of low complexity, few input factors (the highest temperature and the lowest temperature), high prediction precision and the like. Furthermore, K varies with the crop itself and the external conditions c Also constantly changing, but the overall change law is stable during the crop growth period, K c In addition to relating to specific crop species, K for crops of the same species c Also has obvious regional and time sequence differences which comprehensively reflect the ET of the crop species, soil water and fertilizer conditions and field management level c The influence of (c). At present, ET c Prediction method K c The method is mainly recommended by FAO-56 and obtained by a constant or a linear difference value between two constants, although the recommended value of FAO-56 can be adjusted according to local environmental climate conditions, the method can be used for calculating the terrestrial evapotranspiration process including vegetation transpiration and soil evaporation, is suitable for calculating the process with large time step, but cannot reflect the daily dynamic change condition of crops, and ET is carried out when ET is carried out c During short-term prediction, a predicted value has a slightly larger deviation from an actual value. Therefore, for K c The acquisition of the plant growth stage needs to consider the influence of the plant growth stage on the dynamic change of the plant growth stage.
In summary, the short-term prediction of the field evapotranspiration in the prior art has the following disadvantages:
(1) FAO-56 recommended fixed or simple difference K c Is suitable for long period ET 0 For short term ET 0 The prediction application value of the method is low, and the basic crop coefficient curve in the crop coefficient method is only formed by the determined 3 nodes and then linear difference values, so that the crop growth process is simplified, and large deviation can be caused;
(2)ET c the historical experience rule of the method is not considered enough, and extreme deviation is easy to cause if the model estimation is adopted, so that the stability is poor.
Disclosure of Invention
The invention aims to provide a farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and historical experience so as to alleviate the problems.
In order to alleviate the problems, the technical scheme adopted by the invention is as follows:
a short-term prediction method for farmland evapotranspiration comprises the following steps:
s1, determining a prediction reference day and a short-term prediction day, wherein the prediction reference day is the current day of prediction operation, and the short-term prediction day is 1-15 days from the next day of the prediction reference day to the future;
s2, obtaining reference crop evapotranspiration ET of the predicted reference day by utilizing a PM method based on meteorological data of a target farmland 0 ；
S3, obtaining crop reality of the target farmland prediction reference day by using a vorticity correlation methodMeasuring evapotranspiration ET c ；
S4, based on a single crop coefficient method, predicting reference crop evapotranspiration ET of a benchmark day 0 And measured evapotranspiration ET of the crop c Calculating to obtain the crop coefficient K used for short-term prediction of the target farmland c ；
S5, predicting reference crop water demand ET 'of each short-term prediction day by adopting a Hargreaves-Samani model based on historical weather forecast data of a target farmland' 0 ；
S6, according to the crop coefficient K c And ET 'of crop Water demand for each short-term forecast day' 0 Obtaining ET 'of each short-term predicted day' c ；
S7, according to the relation function f (LAI) of the target farmland and the mean value of different growth stages of actually measured evapotranspiration of the vorticity correlation system
Calculating a historical experience correction parameter alpha, and fitting a relation function f (LAI) according to the LAI of the historical crops of the target farmland and the evapotranspiration amount to obtain the LAI;
s8, correcting parameter alpha and short-term prediction day evapotranspiration amount ET 'according to historical experience' c Calculating the predicted evapotranspiration ET' of each short-term predicted day of the target farmland c And completing short-term prediction of the evapotranspiration of the farmland.
Further, in the step S2, the crop evapotranspiration ET is referred to 0 The calculation formula of (c) is as follows:
wherein, delta is the slope of the saturated water-vapor pressure curve, rn is the surface net radiation, G is the soil heat flux, gamma is the dry-wet constant, T is the daily average temperature at a height of 2 meters, u 2 At a height of 2 m, e s Saturated water vapor pressure, e a The actual water vapor pressure.
Further, in the step S4, the crop coefficient
Further, in the step S5, reference crop water demand ET' 0 The calculation formula of (c) is as follows:
ET′ 0 ＝0.408K(T max -T min ) n (T mean +T off )R a
wherein K is a conversion coefficient; t is a unit of max 、T min Respectively the highest daily temperature and the lowest daily temperature; n is an exponential coefficient; t is a unit of mean The daily average temperature; t is a unit of off Is the temperature offset; r a Radiating the top layer of the atmosphere.
Further, in the step S6, ET' c ＝ET′ 0 ×K c 。
Further, in step S7, the method for obtaining the relationship function f (LAI) includes: and (3) taking the historical evapotranspiration of the target farmland as a dependent variable and the historical crop LAI as an independent variable, and performing regression analysis on different growth stages of the crops to obtain the relation function f (LAI).
Further, in the step S7, the historical experience correction parameter
Furthermore, in step S8, the evapotranspiration ET ″, is predicted c ＝α×K c ×ET′ c 。
Compared with the prior art, the invention has the beneficial effects that: on the basis of considering the dynamic change of the crop coefficient and the historical empirical rule of the crop growth, a crop coefficient dynamic prediction method is constructed based on a step-by-step deduction algorithm, and ET is corrected by combining the historical empirical threshold of the crop growth c The prediction model can obtain the crop evapotranspiration which is more accordant with the actual growth condition of crops in the future by improvement, has higher application value for short-term prediction of the farmland evapotranspiration, high prediction precision and good stability, and provides scientific basis for farmland water management in the future plain area.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a short term method of predicting field evapotranspiration in accordance with the invention;
FIG. 2 is a comparison graph of single crop coefficient method models and predicted results considering dynamic changes of crop coefficients during the growth period of winter wheat and summer corn;
FIG. 3 is a graph comparing predicted values for different forecast evapotranspiration values based on consideration of dynamic changes in crop coefficients.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1, the present embodiment provides a short-term farmland evapotranspiration prediction method considering dynamic changes of crop coefficients and historical experiences, which takes winter wheat and summer corn of a certain target farmland as research objects, and specifically includes the following steps:
s1, determining a prediction reference day and a short-term prediction day, wherein the prediction reference day is the current day of prediction operation, and the short-term prediction day is 1-15 days in the future from the next day of the prediction reference day.
In the present embodiment, the short-term prediction day is set according to the time specification of the weather forecast, and in the weather forecast, the short-term prediction day is set within 15 days.
S2, meteorological data (air pressure P) based on target farmland a Wind speed U, maximum air temperature T max Minimum air temperature T min Average relative humidity H R Sunshine duration n, rainfall P, etc.), and the reference crop evapotranspiration amount ET of the predicted reference day is obtained by the PM method 0 The calculation formula is as follows:
wherein, delta is the slope of the saturated water-vapor pressure curve, rn is the net surface radiation, G is the soil heat flux, gamma is the dry-wet constant, T is the daily average air temperature at 2 m height, u 2 At a height of 2 m, e s Saturated water vapor pressure, e a The actual water vapor pressure.
In the present embodiment, the meteorological data is measured data for predicting a reference day for a target farmland.
S3, obtaining actual crop evapotranspiration ET of the target farmland prediction reference day by using a vorticity correlation method o 。
In this example, the measured evapotranspiration ET of the crop is o Measured using the vorticity correlation system (Campbell Scientific inc., USA) and calculated by the formula:
in the formula: w' is the vertical wind speed pulsation quantity, m/s; q' is the water vapor density pulsation value, g/m 3 . The vorticity correlation system was developed by a CSAT3 model three-dimensional ultrasonic anemometer (Campbell Scientific inc,USA)、LI7500 CO 2 /H 2 an open-circuit O gas analyzer (LI-COR, USA), an HMP45C air temperature and humidity sensor (Campbell Scientific Inc., USA), and a CR5000 type data acquisition unit (Campbell Scientific Inc., USA). According to the climate characteristics of northeast wind and northwest wind prevailing in the test area, the system is installed in the central south part of the test area, and the length of a large wave area is ensured. The mounting height of each probe is 3.1m above the ground surface, and the net radiation R n By CNR4 net radiation sensor (Kipp)&Zonen, netherlands) at a mounting height of 4.0m above the surface of the earth, soil heat flux G was measured from two HFP01 soil heat flux plates (HuksefluxUSA, inc., USA) located 2cm below the surface of the earth, all measurements being taken as an average value of 30min for each recording, day ET 0 Accumulated from 24h data. In the process of processing the vorticity-related actual data, eliminating abnormal data according to the following principles: (1) data of precipitation time periods and 1h before and after precipitation; (2) data that clearly exceed physical meaning; (3) and (4) data of abnormal sensor states. In addition, errors caused by energy non-closure are eliminated by calculating the intraday Bowegian ratio to correct latent heat flux.
S4, based on a single crop coefficient method, predicting reference crop evapotranspiration ET of a benchmark day 0 And measured evapotranspiration ET of the crop c And calculating to obtain a crop coefficient K used for short-term prediction of the target farmland c The calculation formula is as follows:
in this embodiment, the single crop coefficient method (directly recommended by FAO-56) is a method that requires less meteorological data and basic parameters of crops and soil, and can perform real-time parameter optimization according to the types of crops, compared to other more complex mathematical models, and this method can reflect the actual situation better and has higher calculation accuracy.
S5, predicting reference crop water demand ET 'of each short-term prediction day by adopting a Hargreaves-Samani model based on historical weather forecast data of a target farmland' 0 The calculation formula is as follows:
ET′ 0 ＝0.408K(T max -T min ) n (T mean +T off )R a
ET 'of' 0 The unit is mm/d; k is a conversion coefficient; t is max 、T min Respectively the highest daily temperature and the lowest daily temperature; n is an exponential coefficient; t is mean The daily average air temperature; t is off Is the temperature offset; r a Radiating the top layer of the atmosphere.
In the embodiment, the maximum temperature, the minimum temperature and the atmospheric top radiation of the meteorological station of the target farmland in 2000-2017 days are used as independent variables, the daily reference crop water demand calculated by the PM formula is used as a dependent variable, and the original parameter K =0.0023, n =0.5, T of the HS model is calculated as off Taking =17.8 as an initial value, carrying out nonlinear regression analysis on the HS model, carrying out parameter fitting optimization solution by using EXCEL software planning solution and search method alternate iteration, and obtaining model parameters of a target farmland through optimization, wherein the model parameters are respectively K =0.001138, n =0.4925 and T = off And =43.33, by taking the calculation result of the PM model as a comparison, the MAE and RMSE of the optimized HS model in the verification period (2016-2019) of the research area are maintained at a lower level through further analysis, so that the HS model can be applied to the subsequent reference crop water demand forecast.
S6, according to the crop coefficient K c And ET 'of crop Water demand for each short-term forecast day' 0 Obtaining ET 'of evapotranspiration amount of each short-term predicted day' c The calculation formula is as follows:
ET′ c ＝ET′ 0 ×K c
i.e. the crop coefficient K c Multiplying by the crop Water demand ET 'of the first short-term forecast day' 0 Then ET 'is obtained as the evapotranspiration amount of the first short-term predicted day' c The coefficient of crop K c Multiplying by the crop Water demand ET 'of the second short term forecast day' 0 Obtaining a second short-term predicted day evapotranspiration amount ET' c 8230and 8230. The crop coefficient used on each short-term prediction day is the same and is the crop coefficient K c 。
S7, according to the relation function f (LAI) of the target farmland and the actually measured evapotranspiration amount of the vorticity correlation systemMean value of same growth stage
And calculating a historical empirical correction parameter alpha according to the following calculation formula:
the relation function f (LAI) is obtained according to the historical crop LAI (leaf area index) and the evapotranspiration of the target farmland, regression analysis is carried out on different growth stages of crops by taking the historical evapotranspiration of the target farmland as a dependent variable and the historical crop LAI as an independent variable, and the relation function f (LAI) is obtained.
S8, correcting parameter alpha and short-term prediction day evapotranspiration amount ET 'according to historical experience' c Calculating the predicted evapotranspiration ET' of each short-term predicted day of the target farmland c The calculation formula is as follows:
ET″ c ＝α×K c ×ET′ 0 。
FIG. 2 is a comparison graph of the prediction results of the single crop coefficient method model and the prediction results considering the dynamic changes of the crop coefficients in the growing period of winter wheat and summer corn, and it can be seen from the graph that when the evapotranspiration amount is small, the goodness of fit of the prediction values of the single crop coefficient method and the prediction method considering the dynamic changes of the crop coefficients and the measured vorticity value is high, and with the increase of the evapotranspiration value, the goodness of fit is gradually reduced, but the single crop coefficient method has a larger deviation from the measured EC value than the prediction method considering the dynamic changes of the crop coefficients, and it can be found that the prediction method considering the dynamic changes of the crop coefficients has a higher accuracy and smaller errors, and when the short-term prediction of the evapotranspiration amount is performed, the prediction method considering the dynamic changes of the crop coefficients is superior to the single crop coefficient method.
Fig. 3 is a comparison graph of predicted evapotranspiration values for different prediction periods based on consideration of dynamic changes of crop coefficients, and it can be seen from the graph that errors between the evapotranspiration values and measured values for different prediction periods (1 d, 3d, 6d, 9d, 14 d) of winter wheat and summer corn predicted by the crop coefficient dynamic prediction model are small, and the prediction accuracy for the prediction periods 1 to 7d is more than 87%.
Table 1 shows that the prediction accuracy comparison of the pre-and post-model evapotranspiration is improved, the corrected model can accurately track the dynamic change of the evapotranspiration, compared with the model before correction, the prediction error of the mean value of the model after alpha correction is considered to be reduced by 1.60%, the RMSE and the MAE are respectively reduced by 0.221mm/d, 0.134mm/d and R 2 And ACC are raised by 0.221 and 10.24% respectively compared to the model before correction. The correction model takes account of the historical empirical laws and regulations of crop growth, reduces the evapotranspiration prediction error possibly caused by the extreme state of crops in the crop growth process, and further improves the prediction precision and the applicability of the model.
TABLE 1
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A short-term prediction method for farmland evapotranspiration is characterized by comprising the following steps:
s1, determining a prediction reference day and a short-term prediction day, wherein the prediction reference day is the current day of prediction operation, and the short-term prediction day is 1-15 days from the next day of the prediction reference day to the future;
s2, acquiring reference crop evapotranspiration ET of a predicted reference day by utilizing a PM method based on meteorological data of a target farmland 0 ；
In the step S2, the crop evapotranspiration ET is referred to 0 The calculation formula of (c) is as follows:
wherein Δ isSlope of saturated water vapor pressure curve, R n For surface net radiation, G is soil heat flux, gamma is dry-wet constant, T is daily average temperature at 2 m height, u 2 At a height of 2 m, e s To saturated water vapour pressure, e a The actual vapor pressure;
s3, obtaining actual crop evapotranspiration ET of the target farmland prediction reference day by using a vorticity correlation method c ；
S4, based on a single crop coefficient method, predicting reference crop evapotranspiration ET of a benchmark day 0 And the actual measured evapotranspiration ET of the crops c And calculating to obtain a crop coefficient K used for short-term prediction of the target farmland c ；
S5, predicting reference crop water demand ET of each short-term prediction day by adopting a Hargreaves-Samani model based on historical weather forecast data of a target farmland 0 ′；
S6, according to the crop coefficient K c And the crop water demand ET for each short-term forecast day 0 ' obtaining the evapotranspiration ET for each short-term predicted day c ′；
S7, according to the relation function f (LAI) of the target farmland and the mean value of different growth stages of actually measured evapotranspiration of the vorticity correlation system
Calculating a historical experience correction parameter alpha, and fitting a relation function f (LAI) according to the LAI of the historical crops of the target farmland and the evapotranspiration amount to obtain the LAI;
in step S7, the method for obtaining the relationship function f (LAI) includes: taking the historical evapotranspiration of the target farmland as a dependent variable and the historical crop LAI as an independent variable, and performing regression analysis on different growth stages of crops to obtain the relation function f (LAI);
in the step S7, the historical experience correction parameter
S8, correcting the parameter alpha and the evapotranspiration ET of each short-term prediction day according to historical experience c ' calculating the predicted evapotranspiration of each short-term predicted day of the target farmlandQuantity ET c Completing short-term prediction of the evapotranspiration of the farmland;
in the step S8, the evapotranspiration ET is predicted c ”＝α×K c ×ET c '。
2. The method for short-term prediction of field evapotranspiration as claimed in claim 1, wherein the crop coefficient is used in step S4
3. The method for short-term prediction of farmland evapotranspiration as claimed in claim 2, wherein in the step S5, the reference crop water demand ET 0 ' is calculated as follows:
ET 0 ′＝0.408K(T max -T min ) n (T mean +T off )R a
wherein K is a conversion coefficient; t is max 、T min Respectively the highest daily temperature and the lowest daily temperature; n is an exponential coefficient; t is a unit of mean The daily average temperature; t is a unit of off Is the temperature offset; r is a Radiating the top layer of the atmosphere.
4. The method for short-term prediction of agricultural evapotranspiration according to claim 3, wherein in step S6, ET c ′＝ET 0 ′×K c 。
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| CN112136667B (en) * | 2020-11-26 | 2021-02-12 | 江苏久智环境科技服务有限公司 | Intelligent sprinkling irrigation method and system based on edge machine learning |
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