CN102176072A - Method for determining evapotranspiration - Google Patents

Method for determining evapotranspiration Download PDF

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CN102176072A
CN102176072A CN2011100215090A CN201110021509A CN102176072A CN 102176072 A CN102176072 A CN 102176072A CN 2011100215090 A CN2011100215090 A CN 2011100215090A CN 201110021509 A CN201110021509 A CN 201110021509A CN 102176072 A CN102176072 A CN 102176072A
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evapotranspiration
empirical
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empirical model
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王桥
赵少华
张峰
杨海军
聂忆黄
刘思含
李营
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SATELLITE ENVIRONMENT APPLICATION CENTER OF ENVIRONMENTAL PROTECTION DEPARTMENT
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Abstract

The invention discloses a method for determining evapotranspiration. The method comprises the following steps: S1, collecting meteorological data of an area to be detected; S2, calculating the evapotranspiration by use of two or three types of models comprising one Penman model and one or two empirical models, respectively; S3, providing a group of regression-based calibration coefficients to calibrate the empirical models by using the Penman model; and S4, comparing and checking the calibrated empirical models. The method can be used for improving the evapotranspiration estimation accuracy of the empirical models by estimating the evapotranspiration according to areas and calibrating the empirical models by using the Penman models, and is beneficial to the quick evapotranspiration estimation in areas lack of meteorological data, thereby providing an important technical basis for the agricultural water consumption estimation, drought monitoring, irrigation guidance and the like.

Description

Determine the method for evapotranspiration
Technical field
The present invention relates to hydrometeorological technical field, particularly relate to a kind of method of definite evapotranspiration.
Background technology
As one of key character parameter in the arid generating process, the accurate estimation of evapotranspiration has important effect for the agriculture damage caused by a drought of timely understanding.The estimation of evapotranspiration mainly contains two kinds of methods at present, and first kind of physical model that is based on weather data is as the Peng Man model; Second kind is based on energy or water balance equation.Its remote sensing appraising also is on the basis of these two kinds of methods, some parameter employing remote sensing means is wherein obtained, and then estimated evapotranspiration indirectly.
Utilizing the model of weather data estimation evapotranspiration at present mainly is the Peng Man model, it is FAO56-Penman Monteith (being called for short FAO56-PM), this model has been considered the important meteorologic parameter in the evapotranspiration processes such as temperature, saturation vapour pressure reduction and wind speed, therefore has higher precision, adopted as standard by many researchers, FAO (Food and Agriculture Organization of the United Nation) also adopts this model.Yet though this model accuracy is higher, required meteorological data is various, is difficult to obtain, and in the deficient area of meteorological data, inconvenience was promoted during practical study was used.So, need the less empirical model of parameter to obtain bigger concern.Empirical model is meant the model that obtains according to the relation of the empirical statistics between variable to be measured and the measured data, and in such model, the relation between the variable is to obtain by data are carried out statistical fit.
ET 0(expression with reference to evapotranspiration) is the function of weather parameters, a series of comprise physics, experience with semiempirical method be used for assessing evapotranspiration (can be referring to Penman, 1948; Thornthwaite, 1948; Monteith, 1965; Priestly and Taylor, 1972; Hargreaves, 1974; Linacre, 1977; Hargreaves etc., 1985; Qiu, 1996 documents such as grade).Accurately and simply being suitable for is two crucial requirements (Dinpashoh, 2006 of estimation evapotranspiration; Gao etc., 2007; Gavil á n etc., 2006; Pereira and Pruitt, 2004 documents such as grade).For empirical method, Thornthwaite (can be referring to Thornthwaite, 1948 documents) and Hargreaves (can referring to Hargreaves etc., 1985 documents) method only needs the temperature parameter.This is easily, because often lack the required correct meteorological datas such as sun sunshine time, wind speed and relative humidity of physical model in many areas.Thornthwaite and Hargreaves equation carry out the locality at certain regional application need and proofread and correct.With equation proofread and correct or verify another equation method extensively utilization in the world (can be referring to Allen etc., 1998; Itenfisu etc., 2003; Irmak etc., 2003 documents such as grade).
Compare with the lysimeter measured value of standard, a large amount of studies show that the Penman-Monteith equation is better than other method (can be referring to Jensen etc., 1990; Allen etc., 1994a, b; Lecina etc., 2003; L ó pez-Urrea etc., 2006a, documents such as b).This situation makes FAO estimate ET to the Penman-Monteith equation as standard 0, and recommend FAO Penman-Monteith equation to demarcate or verify other ET 0Method (can be referring to Smith etc., 1991; Allen etc., 1998; Gavil á n etc., 2006 documents such as grade).This equation is proved to be able to be applicable to preferably many weather conditions (Allen etc., 1989; Smith, 1991; L ó pez-Urrea etc., 2006 documents such as grade).
According to the condition of locality, improve or the importance of assessment empirical model self-evident (can referring to Gavil á n etc., 2006 documents).Therefore, nearest 40 or five ten years, improved model worldwide extensively propose (can be referring to Hargreaves etc., 1985; Hargreaves, 1994; Willmott etc., 1985; Xu and Singh, 2001; Hargreaves and Allen, 2003; Pereira and Pruitt, 2004 documents such as grade).(can be although China has also carried out similar research in recent years referring to Li etc., 2002; Zhang etc., 2008 documents such as grade), but also seldom for the research of the Chinese northern area of water resources shortage.Liu etc. (2006) adopt the FAO-56PM equation to assess several empirical models, but survey region are also limited to very much based on the limited meteorological site in North China.
And the evapotranspiration model of experience such as Hargreaves model and Thornthwaite model, then because model is simple, the higher characteristics of precision are widely used.But the precision of these empirical models is compared with the FAO56-PM master pattern and is also had certain error.
Summary of the invention
(1) technical matters that will solve
The technical problem to be solved in the present invention is: the estimation precision that how to improve evapotranspiration fast.
(2) technical scheme
The invention provides a kind of method of definite evapotranspiration, may further comprise the steps:
The weather data in S1, collection district to be detected;
S2, calculate evapotranspiration with two or three model respectively, in described two or three model, wherein a kind of is the Peng Man model, another kind of or two kinds be empirical model;
S3, utilize described Peng Man model that empirical model is demarcated, propose one group of calibration coefficient, utilize described calibration coefficient to demarcate described empirical model based on regression Calculation;
S4, calibrated empirical model is compared checking.
Wherein, among the step S2, empirical model is Thornthwaite model and Hargreaves model, or wherein any.
Wherein, among the step S1, weather data is gathered in the subregion.
Wherein, among the step S2, calculate the evapotranspiration of each regional every month respectively with two or three model.
Wherein, among the step S3, utilize described Peng Man model that empirical model is demarcated, be specially: the evapotranspiration that utilizes the Peng Man Model Calculation to go out returns with the evapotranspiration that empirical model estimates respectively, and the subregion proposes calibration coefficient.
Wherein, described weather data comprises the highest temperature, the lowest temperature, wind speed, relative humidity, quantity of precipitation, sunshine time, longitude and latitude and elevation information.
(3) beneficial effect
The evapotranspiration estimation is carried out in subregion of the present invention, and adopt the Peng Man model that empirical model is proofreaied and correct, not only improved the precision of empirical model estimation evapotranspiration, and help the quick estimation of the evapotranspiration in the rare area of weather data, and then provide the important techniques foundation for estimating agriculture water requirement, monitoring arid and instructing to irrigate etc.
Description of drawings
Fig. 1 is a method flow diagram of the present invention;
Fig. 2 is the meteorological site distribution plan in district to be detected;
Fig. 3 is the Chinese northern FAO56-PM model that the embodiment of the invention adopted and the relation of two ET (expression evapotranspiration) appraising model;
Fig. 4 is the checking comparative result of two ET model assessment evapotranspirations after the improvement;
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples are used to illustrate the present invention, but are not used for limiting the scope of the invention.
In order to reduce this error, the present invention is based on the weather data in the China north, utilize the FAO56-PM of standard that two wider Hargreaves and Thornthwaite models of use are demarcated, for arid or semiarid Chinese northern area, subregion (North China, northwest and northeast) proposes the empirical model correction coefficient of one group of zones of different, and then improved two empirical models, and utilize some meteorological site to verify application, thus improve the estimation precision of evapotranspiration.
Fig. 1 shows the process flow diagram of the embodiment of the invention, and as shown in Figure 1, a kind of method of accurately quick estimation evapotranspiration may further comprise the steps:
The collection of S1, district to be detected data:
Adopt the weather data of 37 years (1996-2002) of northern 296 meteorological site (as shown in Figure 2) of China altogether, comprise the highest, the lowest temperature, data such as wind speed, relative humidity, precipitation, sunshine time, longitude and latitude and elevation information.276 websites wherein are used for regression Calculation, promptly demarcate two empirical models (being " the recurrence website " among Fig. 2) according to the FAO-56PM equation of standard, other 20 websites (as shown in table 1) are used for verifying calibrated empirical model (being " the checking website " among Fig. 2).
Count in the weather station of three climatic region each province/municipalities directly under the Central Government of table 1
Figure BDA0000044424620000041
Figure BDA0000044424620000051
S2, estimate evapotranspiration with three kinds of models respectively.
Three models of the present invention are respectively classical Penman-Monteith physical model (being FAO-56PM equation above-mentioned), empirical model Thornthwaite and Hargreaves models, and these three models all are prior aries.
Be introduced respectively below.
The equation of FAO-56Penman-Monteith model (FAO-56PM) is:
ET 0 = 0.408 Δ ( R n - G ) + γ ( 900 / ( T + 273 ) ) U 2 ( e s - e a ) Δ + γ ( 1 + 0.34 U 2 ) - - - ( 1 )
ET 0Be with reference to evapotranspiration (mm/d); Δ is the slope (kPa/ ℃) of saturation vapour pressure to ambient temperature curve; R nBe net radiation, G is a soil heat flux, U 2It is the per day wind speed of 2m eminence.γ is wet and dry bulb constant (kPa/ a ℃); T be the 2m eminence temperature on average (℃); e sBe saturation vapour pressure (kPa); e aBe that actual vapor is pressed (kPa).The method that all CALCULATION OF PARAMETERS all adopt (1998) such as Allen to provide.
The Thornthwaite model is:
Figure BDA0000044424620000061
TC i＝0.5k(3T max，i-T min，i) (3)
Figure BDA0000044424620000062
a＝6.75×10 -7I 3-7.71×10 -5I 2+1.7612×10 -2I+0.49239 (5)
TC iBe permanently effective temperature, Pereira and Pruitt (2004) are seen in its definition.T Max, i, T Min, iRepresent i days the highest temperature and the lowest temperature, i represents the sequence number in every month sky.
Hargreaves model (HG-1985) is:
ET 0＝0.0023R a(T+17.8)(T max-T min) 0.5 (6)
R a = M + C 1 cos ( 2 πJ 12 + C 2 ) + C 3 cos ( 4 πJ 12 + C 4 ) - - - ( 7 )
R aBe the day outer space radiation (mm/d) with value such as water evaporation, T be the 2m eminence temperature on average (℃), T MaxAnd T MinAdd is respectively the daily maximum temperature and the lowest temperature.
J is the sequence number of the moon, M=14.9425-0.0098L a-0.00175L a 2, C 1=-0.5801+0.1834L a-0.00066L a 2, C 2=3.1365-0.00489L a+ 0.000061L a 2, C 3=0.597-5.36 * 10-6L a 3, C 4=2.9588-0.00909L a+ 0.00024L a 2L aRefer to latitude.
The ET that formula (1), (2) and (6) calculate 0All be the mean value of every day in one month, unit is mm/d (" d " expression " my god "), respectively result of calculation is added up, and they can be converted to the moon evapotranspiration ET of accumulative total respectively 0Value, thus the moon evapotranspiration ET in 37 years of each meteorological site obtained 0Value.
S3, utilize FAO56-PM model (the following P-M model that also abbreviates as) that two empirical models are demarcated, propose one group of calibration coefficient (be also referred to as and return calibration coefficient), improve two empirical models based on regression Calculation.
Fig. 3 has shown the linear regression relation between the P-M model of two empirical models in Chinese northern three ecoclimate districts and standard respectively.The result of Fig. 3 shows, probability P＜0.001 o'clock, two empirical models all with P-M model utmost point significant correlation.Wherein, the correlativity of Hargreaves and P-M model is better than the correlativity of Thornthwaite and P-M model, the P-M model that this explanation Hargreaves model more is near the mark to the estimation of evapotranspiration.
The evapotranspiration that the FAO56-PM model assessment is gone out carries out regression Calculation with the evapotranspiration that two empirical models estimate respectively, and the subregion proposes to return calibration coefficient, obtains demarcating latter two empirical model, two empirical models (as shown in table 2) after just improving.
Improved ET model in the Chinese northern subregion of table 2 and error analysis
Figure BDA0000044424620000071
Figure BDA0000044424620000081
In the table 2, in the equation of calibrated model Thornthwaite, TC iExpression is permanently effective temperature, and I and a are TC iFunction.In the equation of calibrated model Hargreaves, R aIt is day outer space radiation with value such as water evaporation.MAE (Mean Absolute Error) is an absolute average error.RMSE (Root Mean Square Error) is a root-mean-square error.They are standards of weighing a model quality.
M, n among a～f that described recurrence calibration coefficient is Fig. 3 among each linear equation y=mx+n, " * * * " expression utmost point significant correlation among Fig. 3.Because two empirical models itself all have coefficient,, thus empirical model is improved so the equation coefficient in the table 2 is the result after the multiplication of described recurrence calibration coefficient and each empirical model itself.
S4, calibrated model is verified.
Verify above-mentioned improved two subregion evapotranspiration empirical models with 20 typical meteorological websites, a～f of its result such as Fig. 4 (" Calibrated " expression " correction " among Fig. 4).As can be seen, the model after the improvement can coincide with the P-M model of standard to the estimation of evapotranspiration.
With the model before the improvement, the average MAE and the RMSE of the Thornthwaite model after the improvement reduce about 50 and 45% respectively, this result of study with Garcia et al. (2004) is consistent, he finds under arid and the semiarid climate, for the evapotranspiration of meadow and crop, master mould has underestimated about 50% or more.This situation may be because model is based on humid region development, do not consider saturation of the air steam pressure reduction (can be with reference to Stanhill, 1961; Pruitt and Doorenbos, 1977; Garcia etc., 2004).Concerning the Hargreaves model, MAE after the improvement and RMSE reduce about 39 and 26% respectively, this research with (2006) such as L ó pez-Urrea is consistent, relative error in his research is 25%, also approaching with the research of (2006) such as Gavil á n, its MAE and RMSE reduce 60 and 15% respectively.
Northeastward and the North China, the error of two kinds of improved models approximately is two times of (2007) results of study such as Cai.This may be that its result is not strong in these regional space-time representativenesses because the data of (2007) such as Cai et al. only comes from 15 years data of two weather stations.
Yet two kinds of errors of improving one's methods are less on the whole, can accept.Above error analysis shows that the error of Hargreaves model is almost all less than the Thornthwaite model.This is to have the characteristics of two models self to determine.For example, the latter only needs temperature as input, and the former has then considered temperature, radiation and altitude effect.Therefore, these two models are compared, and before and after no matter improving, the precision of Hargreaves model all will be higher than the Thornthwaite model.Therefore, in step S2～S4, more recommend to use Hargreaves model assessment evapotranspiration.
Above embodiment only is used to illustrate the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; under the situation that does not break away from the spirit and scope of the present invention; can also make various variations and modification; therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.

Claims (6)

1. the method for a definite evapotranspiration is characterized in that, may further comprise the steps:
The weather data in S1, collection district to be detected;
S2, calculate evapotranspiration with two or three model respectively, in described two or three model, wherein a kind of is the Peng Man model, another kind of or two kinds be empirical model;
S3, utilize described Peng Man model that empirical model is demarcated, propose one group of calibration coefficient, utilize described calibration coefficient to demarcate described empirical model based on regression Calculation;
S4, calibrated empirical model is compared checking.
2. the method for claim 1 is characterized in that, among the step S2, empirical model is Thornthwaite model and Hargreaves model, or wherein any.
3. the method for claim 1 is characterized in that, among the step S1, weather data is gathered in the subregion.
4. the method for claim 1 is characterized in that, among the step S2, calculates the evapotranspiration of each regional every month respectively with two or three model.
5. method as claimed in claim 3, it is characterized in that, among the step S3, utilize described Peng Man model that empirical model is demarcated, be specially: the evapotranspiration that utilizes the Peng Man Model Calculation to go out returns with the evapotranspiration that empirical model estimates respectively, and the subregion proposes calibration coefficient.
6. as each described method of claim 1～5, it is characterized in that described weather data comprises the highest temperature, the lowest temperature, wind speed, relative humidity, quantity of precipitation, sunshine time, longitude and latitude and elevation information.
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Cited By (5)

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Publication number Priority date Publication date Assignee Title
CN106702981A (en) * 2016-12-22 2017-05-24 中国水利水电科学研究院 Parameter generation method and device of irrigated area drainage system
CN107727061A (en) * 2017-09-27 2018-02-23 武汉霸云创新科技有限公司 A kind of electro-optical distance measurement system and method for autonomous atmospheric correction
CN111833202A (en) * 2020-07-14 2020-10-27 中国水利水电科学研究院 Farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall
CN114647820A (en) * 2022-03-17 2022-06-21 昆明理工大学 Estimation method of evapotranspiration of reference crops in pseudo-ginseng cultivation facility
CN114943361A (en) * 2022-03-15 2022-08-26 水利部交通运输部国家能源局南京水利科学研究院 Method for estimating evapotranspiration of reference crops in data-lacking areas

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CN106702981A (en) * 2016-12-22 2017-05-24 中国水利水电科学研究院 Parameter generation method and device of irrigated area drainage system
CN107727061A (en) * 2017-09-27 2018-02-23 武汉霸云创新科技有限公司 A kind of electro-optical distance measurement system and method for autonomous atmospheric correction
CN111833202A (en) * 2020-07-14 2020-10-27 中国水利水电科学研究院 Farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall
CN111833202B (en) * 2020-07-14 2022-06-03 中国水利水电科学研究院 Farmland evapotranspiration short-term prediction method considering crop coefficient dynamic change and rainfall
CN114943361A (en) * 2022-03-15 2022-08-26 水利部交通运输部国家能源局南京水利科学研究院 Method for estimating evapotranspiration of reference crops in data-lacking areas
CN114647820A (en) * 2022-03-17 2022-06-21 昆明理工大学 Estimation method of evapotranspiration of reference crops in pseudo-ginseng cultivation facility
CN114647820B (en) * 2022-03-17 2024-06-07 昆明理工大学 Estimation method for evapotranspiration of reference crops in pseudo-ginseng cultivation facility

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