CN111461909A - Short-term prediction method for farmland evapotranspiration - Google Patents
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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; obtaining reference crop evapotranspiration ET of predicted reference day by PM method0(ii) a Obtaining actual crop evapotranspiration ET of target farmland prediction reference day by using vorticity correlation systemc(ii) a Calculating crop coefficient K for short-term prediction of target farmlandc(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 evapotranspiration amount of each short-term prediction day'cCalculating historical experience correction parameters α, and calculating predicted evapotranspiration ET of each short-term predicted day of the target farmlandcAnd 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 land hydrologic cycle, the Evapotranspiration volume (ET)c) 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 crop ET is urgently needed to be treatedcAnd (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 methodcWhen predicting, the method mainly depends on the amount of the reference crop transpiration (ET)0) And the crop coefficient KcAnd (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 source0Forecasting, commonly used methods are the Penman-Monteith (PM) method, Fourier analysis model andHargreaves-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 high forecasting precision and can be conveniently embedded into an irrigation system, but the model has great 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 conditionscAlso constantly changing, but the overall change law is stable during the crop growth period, KcK for crops of the same species, except in relation to the particular crop speciescAlso has obvious regional and time sequence differences which comprehensively reflect the types of crops, soil water and fertilizer conditions and field management level to ETcThe influence of (c). At present, ETcPrediction method KcMainly recommended by FAO-56, obtained by constant or linear difference 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 outcDuring short-term prediction, a predicted value has a slightly larger deviation from an actual value. Therefore, for KcThe acquisition of (2) needs to take into account the influence of the growth stage of the crop on its dynamic changes.
In summary, the short-term prediction of field evapotranspiration in the prior art has the following drawbacks:
(1) FAO-56 recommended fixed or simple difference KcIs suitable for long period ET0For short term ET0The 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)ETcthe 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 for performing prediction operation, and the short-term prediction day is 1-15 days from the next day of the prediction reference day;
s2, obtaining reference crop evapotranspiration ET of the predicted reference day by utilizing a PM method based on meteorological data of the target farmland0；
S3, obtaining actual crop evapotranspiration ET of the target farmland prediction reference day by using a vorticity correlation methodc；
S4, predicting reference crop evapotranspiration ET of reference day based on single crop coefficient method0And the actual measured evapotranspiration ET of the cropscAnd calculating to obtain a crop coefficient K used for short-term prediction of the target farmlandc；
S5, predicting reference crop water demand ET 'of each short-term prediction day by adopting Hargreaves-Samani model based on historical weather forecast data of target farmland'0；
S6 according to crop coefficient KcAnd ET 'of crop Water demand for each short-term forecast day'0Obtaining ET 'of evapotranspiration amount of each short-term predicted day'c；
S7, according to the relation function f (L AI) of the target farmland and the mean value of different growth stages of the actually measured evapotranspiration amount of the vorticity correlation system
Calculating a historical experience correction parameter α, and fitting a relation function f (L AI) according to the historical crops L AI of the target farmland and the evapotranspiration;
s8, correcting parameter α and short-term prediction day evapotranspiration amount ET 'according to historical experience'cCalculating the predicted evapotranspiration ET' of each short-term predicted day of the target farmlandcAnd completing short-term prediction of the evapotranspiration of the farmland.
Further, in the step S2, the crop evapotranspiration amount ET is referred to0The calculation formula of (a) 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, u is the2At a height of 2 m, esSaturated water vapor pressure, eaThe actual water vapor pressure.
Further, in the step S4, the crop coefficient
Further, in the step S5, reference crop water demand ET'0The calculation formula of (a) is as follows:
ET′0＝0.408K(Tmax-Tmin)n(Tmean+Toff)Ra
wherein K is a conversion coefficient; t ismax、TminRespectively the highest daily temperature and the lowest daily temperature; n is an exponential coefficient; t ismeanThe daily average air temperature; t isoffIs the temperature offset; raRadiating the top layer of the atmosphere.
Further, in the step S6, ET'c＝ET′0×Kc。
Furthermore, in step S7, the obtaining method of the relationship function f (L AI) includes taking the historical evapotranspiration of the target farmland as a dependent variable and the historical crop L AI as an independent variable, and performing regression analysis on different growth stages of the crop to obtain the relationship function f (L AI).
Further, in the step S7, the historical experience correction parameters
Further, in the step S8, the evapotranspiration ET ″c＝α×Kc×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 growthcThe 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.
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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 of single crop coefficient method models with predicted results considering dynamic changes in 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, 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:
and S1, determining a prediction reference day and a short-term prediction day, wherein the prediction reference day is the current day for performing the 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 this 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 (pressure P) based on target farmlandaWind speed U and maximum air temperature TmaxMinimum air temperature TminAverage relative humidity HRSunshine duration n, rainfall P, etc.), and the reference crop evapotranspiration amount ET of the predicted reference day is obtained by the PM method0The 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, u is the2At a height of 2 m, esSaturated water vapor pressure, eaThe actual water vapor pressure.
In the present embodiment, the meteorological data is measured data for predicting a reference day for a target farmland.
S3, utilizing vorticity phaseClosing the method, obtaining the actual crop evapotranspiration ET of the target farmland prediction reference dayc。
In this example, the measured evapotranspiration ET of the cropcMeasured 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/m3Vorticity correlation system was manufactured by CSAT3 model three-dimensional ultrasonic anemometer (Campbell Scientific inc., USA), L I7500 CO2/H2O open-circuit gas analyzer (L I-COR, USA), HMP45C air temperature and humidity sensor (Campbell Scientific Inc., USA) and CR5000 type data collector (Campbell Scientific Inc., USA). The system is installed in the central and south of the test area according to the climatic features of the test area for northeast and northwest wind, ensuring large wind wave zone length.the installation height of each probe is 3.1m above the ground surface, and the net radiation R is 3.1mnNet radiation sensor (Kipp) by CNR4&Zonen, Netherlands) at an installation height of 4.0m above the surface, soil heat flux G was measured from two HFP01 soil heat flux panels (HuksefluxUSA, inc., USA) located 2cm below the surface, and the average of all measurements taken over 30min was taken as the recorded value for each time, day ETcAnd accumulating the 24h data, eliminating abnormal data according to the following principles that ① precipitation time periods and data of 1h before and after precipitation time periods, ② data obviously exceeding physical meanings, ③ data with abnormal sensor states and eliminating errors caused by energy non-closure by calculating a daily wave-text ratio and correcting latent heat flux in the process of processing the actual data related to the vorticity.
S4, predicting reference crop evapotranspiration ET of reference day based on single crop coefficient method0And the actual measured evapotranspiration ET of the cropscAnd calculating to obtain a crop coefficient K used for short-term prediction of the target farmlandcThe 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 Hargreaves-Samani model based on historical weather forecast data of target farmland'0The calculation formula is as follows:
ET′0＝0.408K(Tmax-Tmin)n(Tmean+Toff)Ra
wherein, ET'0The unit is mm/d; k is a conversion coefficient; t ismax、TminRespectively the highest daily temperature and the lowest daily temperature; n is an exponential coefficient; t ismeanThe daily average air temperature; t isoffIs the temperature offset; raRadiating the top layer of the atmosphere.
In this embodiment, the maximum temperature of the meteorological station 2000-year old 2017 belonging to the target farmland day by day, the minimum temperature and the atmospheric top radiation are used as independent variables, the day-by-day reference crop water demand calculated by the PM formula is used as a dependent variable, and the HS model original parameter K is 0.0023, n is 0.5, T is 0.0023offCarrying out nonlinear regression analysis on the HS model by taking 17.8 as an initial value, carrying out parameter fitting optimization solution by using EXCE L software programming solution and search method alternate iteration, and obtaining model parameters of a target farmland through optimization, wherein the model parameters are respectively K0.001138, n 0.4925 and T0.001138off43.33, using the PM model calculation result as a control, the MAE and RMSE of the optimized HS model in the validation period (2016-.
S6 according to crop coefficient KcAnd ET 'of crop Water demand for each short-term forecast day'0Obtaining ET 'of evapotranspiration amount of each short-term predicted day'cThe calculation formula is as follows:
ET′c＝ET′0×Kc
i.e. the crop coefficient KcMultiplying by the crop Water demand ET 'of the first short-term forecast day'0Obtaining an amount of evapotranspiration ET 'of the first short-term predicted day'cThe coefficient of crop KcMultiplying by the crop Water demand ET 'of the second short term forecast day'0Obtaining a second short-term predicted day evapotranspiration amount ET'c… … are provided. The crop coefficient used on each short-term prediction day is the same and is the crop coefficient Kc。
S7, according to the relation function f (L AI) of the target farmland and the mean value of different growth stages of the actually measured evapotranspiration amount of the vorticity correlation system
Calculating the historical empirical correction parameter α according to the following formula:
the relation function f (L AI) is obtained according to the historical crop L AI (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 L AI as an independent variable, and the relation function f (L AI) is obtained.
S8, correcting parameter α and short-term prediction day evapotranspiration amount ET 'according to historical experience'cCalculating the predicted evapotranspiration ET' of each short-term predicted day of the target farmlandcThe calculation formula is as follows:
ET″c＝α×Kc×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.
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 actual measurement values for different prediction periods (1d, 3d, 6d, 9d, 14d) of winter wheat and summer corn predicted by a dynamic crop coefficient prediction model are small, and the prediction accuracy for the prediction periods 1-7 d 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 α correction is reduced by 1.60%, the RMSE and the MAE are respectively reduced by 0.221mm/d, 0.134mm/d and R2And 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 (8)
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 for performing prediction operation, and the short-term prediction day is 1-15 days from the next day of the prediction reference day;
s2, obtaining reference crop evapotranspiration ET of the predicted reference day by utilizing a PM method based on meteorological data of the target farmland0；
S3, obtaining actual crop evapotranspiration ET of the target farmland prediction reference day by using a vorticity correlation methodc；
S4, predicting reference crop evapotranspiration ET of reference day based on single crop coefficient method0And the actual measured evapotranspiration ET of the cropscAnd calculating to obtain a crop coefficient K used for short-term prediction of the target farmlandc；
S5, predicting reference crop water demand ET 'of each short-term prediction day by adopting Hargreaves-Samani model based on historical weather forecast data of target farmland'0；
S6 according to crop coefficient KcAnd ET 'of crop Water demand for each short-term forecast day'0Obtaining ET 'of evapotranspiration amount of each short-term predicted day'c；
S7, according to the relation function f (L AI) of the target farmland and the mean value of different growth stages of the actually measured evapotranspiration amount of the vorticity correlation system
Calculating a historical experience correction parameter α, and fitting a relation function f (L AI) according to the historical crops L AI of the target farmland and the evapotranspiration;
s8, correcting parameter α and short-term prediction day evapotranspiration amount ET 'according to historical experience'cCalculating the predicted evapotranspiration ET' of each short-term predicted day of the target farmlandcAnd completing short-term prediction of the evapotranspiration of the farmland.
2. The method for short-term prediction of agricultural evapotranspiration as claimed in claim 1, wherein in step S2, the crop evapotranspiration ET is referred to0The calculation formula of (a) is as follows:
whereinDelta 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, u2At a height of 2 m, esSaturated water vapor pressure, eaThe actual water vapor pressure.
3. The method for short-term prediction of field evapotranspiration as claimed in claim 2, wherein in step S4, the crop coefficient is set to
4. The method for short-term prediction of agricultural evapotranspiration according to claim 3, wherein in step S5, reference is made to the crop water demand ET'0The calculation formula of (a) is as follows:
ET′0＝0.408K(Tmax-Tmin)n(Tmean+Toff)Ra
wherein K is a conversion coefficient; t ismax、TminRespectively the highest daily temperature and the lowest daily temperature; n is an exponential coefficient; t ismeanThe daily average air temperature; t isoffIs the temperature offset; raRadiating the top layer of the atmosphere.
5. The method for short-term prediction of farmland evapotranspiration as claimed in claim 4, wherein in step S6, ET'c＝ET′0×Kc。
6. The method for short-term prediction of farmland evapotranspiration as claimed in claim 5, wherein in step S7, the obtaining method of the relation function f (L AI) comprises the step of carrying out regression analysis on different growth stages of crops by taking the historical evapotranspiration of the target farmland as a dependent variable and the historical crop L AI as an independent variable to obtain the relation function f (L AI).
7. The method for short-term prediction of farmland evapotranspiration as claimed in claim 6, wherein in step S7, history is recordedEmpirical correction parameters
8. The method for short-term prediction of farmland evapotranspiration as claimed in claim 7, wherein in step S8, the evapotranspiration ET ″c＝α×Kc×ET′c。
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| CN112136667A (en) * | 2020-11-26 | 2020-12-29 | 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 |
| CN112507558A (en) * | 2020-12-15 | 2021-03-16 | 中国水利水电科学研究院 | Reference crop evapotranspiration amount prediction method based on main control factor correction |
| CN114021486A (en) * | 2022-01-05 | 2022-02-08 | 中化现代农业有限公司 | Crop evapotranspiration prediction method and device |
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| CN115125903B (en) * | 2022-07-08 | 2023-09-08 | 中水三立数据技术股份有限公司 | Automatic irrigation and drainage method for field integrated gate based on water demand prediction |
| CN117678506A (en) * | 2024-02-02 | 2024-03-12 | 水利部牧区水利科学研究所 | Visual irrigation decision-making auxiliary system based on weather forecast and control method |
| CN117678506B (en) * | 2024-02-02 | 2024-04-26 | 水利部牧区水利科学研究所 | Visual irrigation decision-making auxiliary system based on weather forecast and control method |
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