CN110501761B - Forecasting method for predicting and forecasting crops ETc in different forecast periods - Google Patents

Forecasting method for predicting and forecasting crops ETc in different forecast periods Download PDF

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CN110501761B
CN110501761B CN201910783921.2A CN201910783921A CN110501761B CN 110501761 B CN110501761 B CN 110501761B CN 201910783921 A CN201910783921 A CN 201910783921A CN 110501761 B CN110501761 B CN 110501761B
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陈鹤
魏征
韩信
李益农
张宝忠
蔡甲冰
彭致功
潘岩
谢天慧
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
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Abstract

The embodiment of the invention discloses a prediction method for crops ETc in different forecast periods, which is used for calculating ET (ET) by using a standard method Penman formula based on meteorological data0And obtaining the crop ETc according to the vorticity correlator, and further obtaining the crop coefficient Kc on the basis of the FAO-56 single crop coefficient method. Thus the 1d forecast period of ETc is based on the ET of day n0And the crop coefficient Kc on day n-1 to obtain ETc on day n-1 and ET on day n +10And Kc at day n, ETc at day n; ETc the 2d forecast period is based on the ET of day n0And the crop coefficient Kc on day n-2 ETc on day n-2 and ET on day n +20And Kc at day n, obtaining ETc at day n, and so on to obtain predicted values of different forecast crops ETc. Subsequently, the remote sensing data is used for carrying out regional scale lifting, and the method reduces the uncertainty of weather forecast data to ET0The influence of the prediction can be more accurately obtained, and the heterogeneity of ETc of regional crops can be more accurately obtained.

Description

Forecasting method for predicting and forecasting crops ETc in different forecast periods
Technical Field
The embodiment of the invention relates to the field of agricultural irrigation forecasting, in particular to a forecasting method for region crops ETc in different forecasting periods.
Background
The crop water demand information is essential basic data in irrigation management and agricultural high-efficiency water use evaluation. In different time-space scale ranges, main factors influencing crop narrative such as climate, terrain, crop growth, soil moisture and the like have randomness and uncertainty, so that the crop water requirement shows stronger spatial heterogeneity. Therefore, how to utilize the existing crop water demand information monitoring data, research the spatial distribution of the crop water demand by adopting a new theory or method, reveal different forms of the crop water demand space, seek turning points with different spatial scales, carry out representative analysis of a crop water demand information monitoring station, explore the transmission rule of the crop water demand information among different scales and a point-to-surface scale conversion technology, provide the estimation precision of the regional crop water demand and optimize a regional irrigation system, so that the reasonable irrigation system is the key for ensuring the effective and timely water supply of crops and saving water resources, and has important theoretical and practical significance for realizing the sustainable development of regional agriculture.
The water demand (ETc) of crops is a main factor influencing the water-heat balance of an area and is also an important link in water circulation, and the research on the ETc problem is always a problem which is commonly interested in relevant subjects such as hydrology, meteorology, agriculture, forestry, soil and the like. With the improvement of the accuracy and timeliness of weather forecast data, the water demand of crops is estimated in advance by means of the weather forecast data and an existing ETo calculation method, so that the method has important significance for implementing irrigation on demand and reasonably utilizing future rainfall to improve the agricultural irrigation management level and the agricultural water efficiency, and can also be used for reference for improving the rain storage of farmlands, reducing drainage and non-point source pollution output in rainy areas. Heretofore, there have been various methods for directly calculating the point scale ETc, in which a predicted value of the point scale ETc, that is, ETc ═ ETo × Kc, is indirectly obtained through ETo and the crop coefficient Kc recommended in FAO, the method has strict requirements on completeness and quality of meteorological data, and it is difficult to meet actual forecast meteorological data, and the indirect prediction method has a certain measurement gap between precision and theoretical basis, so that the method is difficult to popularize in a large range. The current methods for directly obtaining the area scale ETc are a spatial difference method and application, a research on the spatial difference of an area ET, a distributed hydrological model method for estimating the area ET and a remote sensing estimation area ET.
However, the existing regional method values are limited to estimation methods, the ETc prediction method for regional crops is not available, and ETc prediction based on weather forecast data has uncertainty and low reliability.
Disclosure of Invention
Therefore, the embodiment of the invention provides a prediction method for regional crops ETc in different prediction periods, which aims to solve the problems of uncertainty and low reliability of prediction caused by the fact that a method for directly obtaining a regional scale ETc in the prior art is a spatial difference method and application, a regional ET spatial difference research, a distributed hydrological model method for estimating a region ET and a remote sensing estimation region ET.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
according to a first aspect of an embodiment of the invention: a prediction forecasting method for crops ETc in different forecast periods comprises the following specific steps:
the method comprises the following steps: calculating ETo by using a PM method based on historical meteorological data, and taking ETo obtained by calculation as a standard value;
step two: obtaining an actually measured research area point scale ETct by using a vorticity correlation system;
step three: based on an FAO-56 single crop coefficient method, obtaining a crop coefficient Kc according to ETo obtained in the step one and ETct obtained in the step two, wherein a calculation formula of the crop coefficient Kc is as follows:
Figure GDA0002661502000000021
step four: ETo on the nth day of the forecast day is multiplied by Kc on the nth-1 day to obtain a crop ETc with 1d of forecast period, ETo on the nth day of the forecast day is multiplied by Kc on the nth-2 day to obtain a crop ETc with 2d of forecast period, and values are gradually taken, so that a plurality of crops ETc with different forecast periods can be obtained;
step five: the predicted crop ETc with different forecast periods and the point scale ETct directly obtained by the vorticity correlation system are corrected in real time;
step six: obtaining an area ETc't of the whole area of the research area based on an SEBS model by using MODIS data;
step seven: ETo is obtained by combining the existing meteorological stations in the research area, and a space interpolation method is adopted to obtain the whole area ETo';
step eight: calculating to obtain a regional crop coefficient Kc ' according to the regions ETc't and ETo ' obtained in the sixth step and the seventh step;
step nine: the region ETc' can be predicted by repeating the loop step four.
Further, the PM method is a penman formula calculation method, and the specific formula is as follows:
Figure GDA0002661502000000022
wherein PE is possible evaporation capacity, delta is slope of saturated water pressure curve, Rn is net surface radiation, G is soil heat flux, gamma is psychrometric constant, and T ismeanIs the average daily temperature u2At a height of 2 m, esSaturated water pressure, eaFor actual water pressure, PE matched ETo values.
Further, the model checking index in the fifth step is a determination coefficient R2And the root mean square error RMSE, which is specifically formulated as follows:
Figure GDA0002661502000000023
Figure GDA0002661502000000024
wherein n is the number of days after the start of the predicted day, yiIs the ETc value for the nth day,
Figure GDA0002661502000000025
is the ETc median value for n days,
Figure GDA0002661502000000026
average ETc values for n days.
Further, the calculation formula of the area crop coefficient Kc' in the step eight is specifically as follows:
Figure GDA0002661502000000031
the reverse calculation gave ETc ' ═ Kc '. ETo '.
According to a second aspect of an embodiment of the invention: the method comprises the steps of adopting a model construction system and a model application system, wherein the model construction system is used for collecting MODIS data and constructing the SEBS model, and the model application system is used for obtaining the region ETc't according to the SEBS model.
Further, the SEBS model is constructed based on the ground reflectivity, the surface temperature, the vegetation coverage index and the leaf area index.
Furthermore, the vorticity correlation system is set as a vorticity correlation instrument, a large-aperture scintillation instrument is arranged at the connecting end of the vorticity correlation instrument, satellite remote sensing is further arranged at the connecting end of the vorticity correlation instrument, and the vorticity correlation instrument, the large-aperture scintillation instrument and the satellite remote sensing are matched to obtain regional meteorological information and verify point dimensions.
The embodiment of the invention has the following advantages:
1. according to the crop ETc prediction method, based on historical meteorological data, ETo predicted by a PM method is utilized, crop coefficients Kc are combined to obtain crops ETc, the obtained ETc predicted values of different prediction periods and ETct actually measured by a vorticity correlation system are verified in real time, the theoretical basis of the method is utilized, remote sensing data are utilized to carry out regional scale lifting, the method reduces the influence of uncertainty of weather forecast data on ETo prediction, the heterogeneity of ETc regional crops can be accurately obtained, and compared with the prior art, the method is higher in prediction accuracy;
2. by setting a model construction system and a model application system, MODIS data are collected, an SEBS model is constructed, an area ETc't is obtained, accurate meteorological data are used for obtaining ETo, crop coefficients Kc are combined to obtain accurate crops ETc, a formula is used for calculating and verifying processes and accurate numerical values, and crops ETc in a short period in the future are obtained according to accurate existing ETct.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
The structures, ratios, sizes, and the like shown in the present specification are only used for matching with the contents disclosed in the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical significance, and any structural modifications, changes in the ratio relationship, or adjustments of the sizes, without affecting the effects and the achievable by the present invention, should still fall within the range that the technical contents disclosed in the present invention can cover.
Fig. 1 is a flowchart of a forecasting method for regional crop ETc provided in embodiment 1 of the present invention;
fig. 2 is a comparison of predicted values of ETc different forecast periods of the 2014-year winter wheat growth period provided by the embodiment 2 of the invention;
fig. 3 is a comparison of the predicted values of ETc for different forecasted periods of the growing period of summer corn in 2014 provided in example 2 of the present invention;
FIG. 4 is a comparison of the predicted values of ETc for different foreseeable periods of the growing period of 2015 winter wheat provided by example 2 of the invention;
FIG. 5 is a comparison of the predicted values of ETc for different foreseen periods of the growing period of 2015 summer maize provided by example 2 of the invention;
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. 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.
Example 1:
as shown in fig. 1, a prediction forecasting method for crops ETc in different forecast periods includes a model construction system and a model application system, the model construction system is configured to collect MODIS data and construct an SEBS model, the model application system is configured to obtain a forecast area ETc't according to the SEBS model, the SEBS model is constructed based on ground reflectivity, ground surface temperature, vegetation coverage index and leaf area index, the vorticity correlation system is set as a vorticity correlation instrument, a large-aperture scintillator is arranged at a connection end of the vorticity correlation instrument, a satellite remote sensing is further arranged at a connection end of the vorticity correlation instrument, the large-aperture scintillator and the satellite remote sensing are matched to obtain area meteorological information and verify point dimensions, and the prediction forecasting method includes the following specific steps:
the method comprises the following steps: ETo is calculated by utilizing a PM method based on historical meteorological data, wherein the PM method is a Peneman formula calculation method, and the specific formula is as follows:
Figure GDA0002661502000000041
wherein PE is possible evaporation capacity, delta is slope of saturated water pressure curve, Rn is net surface radiation, G is soil heat flux, gamma is psychrometric constant, and T ismeanIs the average daily temperature u2At a height of 2 m, esSaturated water pressure, eaThe actual water pressure is obtained;
PE is matched with ETo numerical value, and ETo obtained by calculation is used as a standard value;
step two: obtaining an actually measured research area point scale ETct by using a vorticity correlation system;
step three: based on an FAO-56 single crop coefficient method, according to ETo obtained in the step one and ETct obtained in the step two, a calculation formula for obtaining a crop coefficient Kc is concretely as follows:
Figure GDA0002661502000000042
step four: ETo on the nth day of the forecast day is multiplied by Kc on the nth-1 day to obtain a crop ETc with a 1d forecast period, and the nth day of the forecast day, ETo on the nth day of the forecast day and Kc on the nth-2 day are multiplied to obtain a crop ETc with a 2d forecast period, and values are gradually taken, so that a plurality of crops ETc with different forecast periods can be obtained, specifically as follows:
TABLE 1 prediction of crops at different forecast periods ETc
Figure GDA0002661502000000051
Step five: the predicted crop ETc with different forecast periods and the point scale ETct directly obtained by the vorticity correlation system are corrected in real time;
the model checking index is the coefficient of determination R2And the root mean square error RMSE, which is specifically formulated as follows:
Figure GDA0002661502000000052
Figure GDA0002661502000000053
wherein n is the number of days after the start of the predicted day, yiIs the ETc value for the nth day,
Figure GDA0002661502000000055
is the ETc median value for n days,
Figure GDA0002661502000000056
ETc mean value for n days;
step six: obtaining an area ETc't of the whole area of the research area based on an SEBS model by using MODIS data;
step seven: ETo is obtained by combining the existing meteorological stations in the research area, and a space interpolation method is adopted to obtain the whole area ETo';
step eight: the calculation formula for calculating the regional crop coefficients Kc 'and Kc' according to the regions ETc't and ETo' obtained in the sixth step and the seventh step is specifically as follows:
Figure GDA0002661502000000054
ETc ' ═ Kc ' ETo ' was obtained by reverse calculation;
step nine: repeating the loop step four to predict the area ETc';
the method is based on historical meteorological data, ETo predicted by a PM method is utilized, crop coefficients Kc are combined to obtain crops ETc, the obtained ETc predicted values of different prediction periods and ETct actually measured by a vorticity correlation system are verified in real time, the theoretical basis of the method is utilized, remote sensing data are utilized to carry out regional scale improvement, the influence of uncertainty of weather forecast data on ETo prediction is reduced, formula calculation and verification processes are utilized, accurate numerical values are obtained, and the crops ETc in the short term in the future can be obtained according to the accurate existing ETct.
Example 2:
as shown in fig. 2-5, in this embodiment, historical data of winter wheat and summer corn in the buxing region 2014-2015 of beijing city is taken to perform model modeling and prediction calculation, data in the data selection section is processed, actual measurement ETc of 1d, 7d and 15d and predicted value ETc under the method are respectively taken to record and draw a waveform diagram, and the result shows:
the actual value of ETc during the growing period of the winter wheat in 2014 basically coincides with the predicted value of ETc during different forecast periods, and the actual values of ETc in the time period all fall within the predicted values;
the actual value of the summer maize growing period ETc in 2014 substantially coincided with the predicted value of the different forecast period ETc, and the actual values of ETc in this time period all fell within the predicted values;
the actual value of ETc during the growing period of 2015 winter wheat substantially coincides with the predicted value of ETc during different forecast periods, and the actual values of ETc during the period all fall within the predicted values;
the actual value of the summer maize growing period ETc in 2015 substantially coincides with the predicted value of the different forecast period ETc, and the actual values of ETc during this period all fall within the predicted values;
compared with the traditional prediction method, the water demand prediction method can effectively perform prediction, and ETc obtained by the prediction method has high fitting degree with the actually measured ETct of the vorticity correlation system, so that the prediction method has better stability and reliability.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (7)

1. A prediction forecasting method for crops ETc in different forecast periods is characterized in that: the method comprises the following specific steps:
the method comprises the following steps: calculating ETo by using a PM method based on historical meteorological data, and taking ETo obtained by calculation as a standard value;
step two: obtaining an actually measured research area point scale ETct by using a vorticity correlation system;
step three: based on an FAO-56 single crop coefficient method, obtaining a crop coefficient Kc according to ETo obtained in the step one and ETct obtained in the step two, wherein a calculation formula of the crop coefficient Kc is as follows:
Figure FDA0002661501990000011
step four: ETo on the nth day of the forecast day is multiplied by Kc on the nth-1 day to obtain a crop ETc with 1d of forecast period, ETo on the nth day of the forecast day is multiplied by Kc on the nth-2 day to obtain a crop ETc with 2d of forecast period, and values are gradually taken, so that a plurality of crops ETc with different forecast periods can be obtained;
step five: the predicted crop ETc with different forecast periods and the point scale ETct directly obtained by the vorticity correlation system are corrected in real time;
step six: obtaining an area ETc't of the whole area of the research area based on an SEBS model by using MODIS data;
step seven: ETo is obtained by combining the existing meteorological stations in the research area, and a space interpolation method is adopted to obtain the whole area ETo';
step eight: calculating to obtain a regional crop coefficient Kc ' according to the regions ETc't and ETo ' obtained in the sixth step and the seventh step;
step nine: the region ETc' can be predicted by repeating the loop step four.
2. The forecasting method for the crop ETc in different forecast periods according to claim 1, wherein: the method comprises the steps of adopting a model construction system and a model application system, wherein the model construction system is used for collecting MODIS data and constructing the SEBS model, and the model application system is used for obtaining the region ETc't according to the SEBS model.
3. The forecasting method for the crop ETc in different forecast periods according to claim 1, wherein: and the SEBS model is constructed on the basis of the ground reflectivity, the surface temperature, the vegetation coverage index and the leaf area index.
4. The forecasting method for the crop ETc in different forecast periods according to claim 1, wherein: the vorticity correlation system is set as a vorticity correlation instrument, a large-aperture scintillation instrument is arranged at the connecting end of the vorticity correlation instrument, satellite remote sensing is further arranged at the connecting end of the vorticity correlation instrument, and the vorticity correlation instrument, the large-aperture scintillation instrument and the satellite remote sensing are matched to obtain regional meteorological information and verify the point scale.
5. The forecasting method for the crop ETc in different forecast periods according to claim 1, wherein: the PM method is a Peneman formula calculation method, and the specific formula is as follows:
Figure FDA0002661501990000012
wherein PE is possible evaporation capacity, delta is slope of saturated water pressure curve, Rn is net surface radiation, G is soil heat flux, gamma is psychrometric constant, and T ismeanIs the average daily temperature u2At a height of 2 m, esSaturated water pressure, eaFor actual water pressure, PE matched ETo values.
6. The forecasting method for the crop ETc in different forecast periods according to claim 1, wherein: the model inspection index in the fifth step is a determination coefficient R2And the root mean square error RMSE, which is specifically formulated as follows:
Figure FDA0002661501990000021
Figure FDA0002661501990000022
wherein n is the number of days after the start of the predicted day, yiIs the ETc value for the nth day,
Figure FDA0002661501990000023
is the ETc median value for n days,
Figure FDA0002661501990000024
average ETc values for n days.
7. The forecasting method for the crop ETc in different forecast periods according to claim 1, wherein: the calculation formula of the area crop coefficient Kc' in the step eight is specifically as follows:
Figure FDA0002661501990000025
the reverse calculation gave ETc ' ═ Kc '. ETo '.
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