A method for evaluating the influence degree of surface evapotranspiration on dry-wet changes

Abstract

The present invention provides a method for evaluating the influence degree of surface evapotranspiration on dry-wet changes, including: constructing an input data set; calculating a dry-wet change monitoring index according to the moisture surplus and deficit between precipitation and potential evapotranspiration; conducting a trend analysis on the dry-wet change monitoring index; establishing a multiple linear regression model between the trend change of the dry-wet change monitoring index and each parameter to quantify the contribution of climate change to the dry-wet change monitoring index, and selecting variables with a relatively high ranking in the contribution to the dry-wet change monitoring index; selecting an appropriate three-dimensional Copula function by calculating the Akaike information criterion to obtain the relationship between evapotranspiration and variables with a relatively high ranking in the contribution to the dry-wet change monitoring index; combining the variables with a relatively high ranking in the contribution and the relationship between evapotranspiration and variables with a relatively high ranking in the contribution to the dry-wet change monitoring index to obtain the actual influence degree of surface evapotranspiration on the dry-wet change monitoring index. The present invention can effectively evaluate the actual influence degree of surface evapotranspiration on dry-wet changes.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy engineering, and particularly relates to a method for evaluating the influence degree of surface evapotranspiration on dry-wet changes. Background Art

[0002] With the intensification of global climate change, in the future scenario, the global dry-wet changes and their driving factors are becoming increasingly complex and difficult to predict, posing severe challenges to water resource management, agricultural production, and ecological systems. The study of dry-wet changes in the future scenario needs to comprehensively consider the influence of multiple meteorological elements and surface processes.

[0003] Evapotranspiration plays a crucial role in the water and energy transfer between the land and the atmosphere, and is the core link of the land water cycle second only to precipitation. The change of evapotranspiration not only directly affects precipitation and temperature, but also regulates the specific humidity by releasing water vapor into the atmosphere. In addition, since the transpiration of plants is closely linked to photosynthesis, the change of evapotranspiration also indirectly regulates the carbon cycle of the land, which is crucial for understanding and predicting future dry-wet changes.

[0004] The existing global dry-wet change analysis mostly focuses on the independent influence of a single meteorological factor on dry-wet changes while considering multiple meteorological variables, and ignores the possible interaction and synergistic effect between meteorological factors. There is a complex feedback relationship between evapotranspiration change and other meteorological factors, and these factors have complex and interrelated effects on the water cycle, thus affecting the development trend of dry-wet changes.

[0005] Therefore, comprehensively considering the feedback relationship between evapotranspiration change and other meteorological factors and developing a method for evaluating the influence degree of surface evapotranspiration on dry-wet changes is of great significance for comprehensively and deeply understanding the influence degree of surface evapotranspiration on dry-wet changes and the true influencing factors of dry-wet changes in the future scenario. Summary of the Invention

[0006] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method for evaluating the influence degree of surface evapotranspiration on dry-wet changes.

[0007] To achieve the above purpose, the present invention provides the following solution:

[0008] A method for evaluating the influence degree of surface evapotranspiration on dry-wet changes, comprising:

[0009] Constructing an input data set;

[0010] Based on the input data set, calculating a dry-wet change monitoring index according to the difference in water balance between precipitation and potential evapotranspiration;

[0011] Use the Theil-Sen slope method to conduct trend analysis on the dry-wet change monitoring index;

[0012] Establish a multiple linear regression model between the trend change of the dry-wet change monitoring index and temperature, precipitation, downward shortwave radiation, wind speed, specific humidity, and evapotranspiration to quantify the contribution of climate change to the dry-wet change monitoring index, and select the variables with the top-ranked contributions to the dry-wet change monitoring index;

[0013] Select a suitable three-dimensional Copula function by calculating the Akaike information criterion to obtain the relationship between evapotranspiration and the variables with the top-ranked contributions to the dry-wet change monitoring index;

[0014] Combine the variables with the top-ranked contributions and the relationship between evapotranspiration and the variables with the top-ranked contributions to the dry-wet change monitoring index to obtain the actual impact degree of surface evapotranspiration on the dry-wet change monitoring index.

[0015] Preferably, the input data set includes meteorological data, actual evapotranspiration data, potential evapotranspiration data, precipitation data, and surface runoff data; the meteorological data includes air temperature data, wind speed data, specific humidity data, and downward shortwave radiation data.

[0016] Preferably, based on the input data set, calculate the dry-wet change monitoring index according to the water surplus and deficit between precipitation and potential evapotranspiration, including:

[0017] Calculate the difference between monthly precipitation and potential evapotranspiration, that is, the water surplus and deficit D i , to establish cumulative sequences of different time scales; the formula for the cumulative sequence is: Among them, is the accumulation of water surplus and deficit at the k-month time scale in the j-th month of the i-th year, D i,m is the cumulative sequence value in the m-th month of the i-th year;

[0018] Use the log-logistic probability distribution to fit and standardize the cumulative sequence to obtain the dry-wet change monitoring index of different time scales; the formula for the dry-wet change monitoring index is: Among them, P = 1 - F(D); Among them, F(D) is the sequence after fitting; D is the cumulative sequence; P and W are the standardized distribution function values and probability weighted moments respectively; SPEI is the dry-wet change monitoring index; α, β, and γ are the scale, shape, and origin parameters of the log-logistic distribution; c 0 , c 1 , c 2 , d 1 , d 2 and d 3All are calculation parameters, c 0 = 2.515517, c 1 = 0.802853, c 2 = 0.010328, d 1 = 1.432788, d 2 = 0.189269, d 3 = 0.00130.

[0019] Preferably, the formula for trend analysis of the dry-wet change monitoring index using the Theil-Sen slope method is: where x i and x j are the index or climate factor values corresponding to time t i and t j respectively, β is the Sen trend value, β > 0 indicates an upward trend in the time series, and β < 0 indicates a downward trend in the time series.

[0020] Preferably, the calculation formula of the multiple linear regression model is: Index = aP + bET + cT + dSRD + eWS + fshum + ζ; the formula for the trend of the dry-wet change monitoring index is: where Index represents the value of the dry-wet change monitoring index; represents the trend of the dry-wet change monitoring index; represent the Sen trends of precipitation, actual evapotranspiration, air temperature, downward shortwave radiation, wind speed, and specific humidity respectively; a, b, c, d, e, and f are regression coefficients; ξ is the residual term, obtained by the least squares method; T is the air temperature, WS is the wind speed, shum is the specific humidity, SRD is the downward shortwave radiation, ET is the actual evapotranspiration data, PET is the potential evapotranspiration data, P is the precipitation data, and R is the surface runoff data.

[0021] Preferably, the formula for calculating the Akaike information criterion is AIC = 2p - 2In(L); where p is the number of variables selected with a relatively high contribution ranking to the dry-wet change monitoring index, L is the likelihood function, and AIC is the Akaike information criterion.

[0022] Preferably, the formula for the relationship between evapotranspiration and the variables with a relatively high contribution ranking to the dry-wet change monitoring index is: where H(x 1 , x 2 , x 3 , x 4 ) is the joint distribution function of the variables with a relatively high contribution ranking to the dry-wet change monitoring index and evapotranspiration represented by the Copula function, and are their respective marginal distribution functions, C1 , C 2 , C 3 is the correlation between evapotranspiration and the variables ranked higher, x 1 , x 2 , x 3 are the variables ranked higher, and x 4 is the evapotranspiration.

[0023] Preferably, the calculation formula for the actual influence degree is:

[0024]

[0025]

[0026] where C 1 ', C' 2 and C 3 ' are the influence degrees of evapotranspiration on the variables ranked higher x 1 , x 2 and x 3 , and respectively represent the Sen trends of the variables ranked higher, and f ET is the actual influence degree of surface evapotranspiration on the wet-dry change monitoring index.

[0027] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0028] The present invention provides a method for evaluating the influence degree of surface evapotranspiration on wet-dry changes, including: constructing an input data set; based on the input data set, calculating a wet-dry change monitoring index according to the difference in water balance between precipitation and potential evapotranspiration; using the Theil-Sen slope method to perform trend analysis on the wet-dry change monitoring index; establishing a multiple linear regression model between the trend change of the wet-dry change monitoring index and temperature, precipitation, downward shortwave radiation, wind speed, specific humidity, and evapotranspiration to quantify the contribution of climate change to the wet-dry change monitoring index, and selecting the variables ranked higher in the contribution to the wet-dry change monitoring index; selecting an appropriate three-dimensional Copula function by calculating the Akaike information criterion to obtain the relationship between evapotranspiration and the variables ranked higher in the contribution to the wet-dry change monitoring index; combining the variables ranked higher in the contribution and the relationship between evapotranspiration and the variables ranked higher in the contribution to the wet-dry change monitoring index to obtain the actual influence degree of surface evapotranspiration on the wet-dry change monitoring index. The present invention can effectively evaluate the actual influence degree of surface evapotranspiration on wet-dry changes. Description of the Drawings

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention. Detailed implementation manners

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0032] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0033] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention. As Figure 1 shown, the present invention provides a method for evaluating the influence degree of surface evapotranspiration on dry-wet changes, including:

[0034] Step 100: Construct an input data set;

[0035] Step 200: Based on the input data set, calculate the dry-wet change monitoring index according to the difference in water surplus and deficit between precipitation and potential evapotranspiration;

[0036] Step 300: Use the Theil-Sen slope method to perform trend analysis on the dry-wet change monitoring index;

[0037] Step 400: Establish a multiple linear regression model between the trend change of the dry-wet change monitoring index and temperature, precipitation, downward short-wave radiation, wind speed, specific humidity, and evapotranspiration, quantify the contribution of climate change to the dry-wet change monitoring index, and select the variables with the top-ranked contributions to the dry-wet change monitoring index;

[0038] Step 500: Select an appropriate three-dimensional Copula function by calculating the Akaike information criterion to obtain the relationship between evapotranspiration and the variables with the top-ranked contributions to the dry-wet change monitoring index;

[0039] Step 600: Combine the variables with the top-ranked contributions in the contribution ranking and the relationship between the evapotranspiration and the variables with the top-ranked contributions in the contribution ranking of the dry-wet change monitoring index to obtain the actual impact degree of the surface evapotranspiration on the dry-wet change monitoring index.

[0040] The steps of this embodiment include the following steps:

[0041] Step 1: Initially construct an input data set, including meteorological data (air temperature T, wind speed WS, specific humidity shum, and downward shortwave radiation SRD), actual evapotranspiration ET data, potential evapotranspiration PET data, precipitation P data, and surface runoff R data;

[0042] Step 2: Calculate the dry-wet change monitoring index SPEI based on the moisture balance deficit between precipitation and potential evapotranspiration;

[0043] Step 3: Use the Theil-Sen slope method to perform a trend analysis on the SPEI obtained in Step 2;

[0044] Step 4: Establish a multiple linear regression model between the trend change of the dry-wet change monitoring index SPEI and temperature, precipitation, downward shortwave radiation, wind speed, specific humidity, and evapotranspiration to quantify the contribution of climate change to the drought index SPEI, and select the variables with the top-ranked contributions to SPEI;

[0045] Step 5: Select an appropriate three-dimensional Copula function by calculating the Akaike information criterion AIC to obtain the relationship between evapotranspiration and the variables with the top-ranked contributions to SPEI;

[0046] Step 6: Combine Step 4 and Step 5 to obtain the actual impact degree of surface evapotranspiration on SPEI.

[0047] Specifically, in Step 2, the calculation of the dry-wet change monitoring index SPEI based on the moisture balance deficit between monthly precipitation and potential evapotranspiration is as follows:

[0048] (1) Calculate the difference between monthly precipitation and potential evapotranspiration, that is, the moisture balance deficit D i , and establish cumulative sequences on different time scales, Formula 1:

[0049]

[0050] where is the accumulation of the moisture balance deficit at the k-month time scale for the j-th month of the i-th year, and D i,m is the D value for the m-th month of the i-th year.

[0051] (2) Use the log-logistic probability distribution to fit and standardize the D sequence to obtain the dry-wet change monitoring index SPEI on different time scales, Formula 2:

[0052]

[0053]

[0054] Among them, F(D) is the sequence after fitting; D is the cumulative sequence; P and W are the standardized distribution function values and probability weighted moments respectively; SPEI is the dry-wet change monitoring index; α, β, and γ are the scale, shape, and origin parameters of the log-logistic distribution; c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, d3 = 0.001308.

[0055] Optionally, in step 3, the Theil-Sen slope estimation method is used to perform a trend analysis on SPEI of the dry-wet change monitoring index. The size of the change trend is represented by the Sen slope β. Formula three:

[0056]

[0057] where x i and x j are the index or climate factor values corresponding to time t i and t j (i > j) respectively. β is the Sen trend value. β > 0 indicates that the time series shows an upward trend, and β < 0 indicates that the time series shows a downward trend.

[0058] Specifically, in step 4, a multiple linear regression model between the trend change of the dry-wet change monitoring index SPEI and temperature, precipitation, downward shortwave radiation, wind speed, specific humidity, and evapotranspiration is established to quantify the contribution of climate change to the drought index SPEI. Formula four:

[0059] Index = aP + bET + cT + dSRD + eWS + fshum + ζ

[0060]

[0061] where Index represents SPEI; represents the trend of SPEI; represent the Sen trends of precipitation, actual evapotranspiration, air temperature, downward shortwave radiation, wind speed, and specific humidity respectively; a, b, c, d, e, f are regression coefficients; ξ is the residual term, which is obtained by the least squares method.

[0062] Further, in the step 4, the variables with the top-ranked contributions to SPEI are selected, that is, according to the regression coefficients a, b, c, d, e, and f in Formula 4, the corresponding variables with the top-ranked contributions (for example, the top three ranked from largest to smallest) are determined.

[0063] Furthermore, in the step 5, the appropriate three-dimensional Copula function is selected by calculating the Akaike information criterion AIC to obtain the relationship between evapotranspiration and the variables with the top-ranked contributions to SPEI. The calculation of the Akaike information criterion AIC is shown in Formula 5:

[0064] AIC = 2p - 2In(L)

[0065] where p is the number of variables with the top-ranked contributions to SPEI, and L is the likelihood function;

[0066] Specifically, when selecting the appropriate three-dimensional Copula function by calculating the Akaike information criterion AIC, generally, the smaller the AIC, the better the model. Therefore, the three-dimensional Copula function with the smallest AIC is selected.

[0067] Optionally, the relationship between evapotranspiration and the variables with the top-ranked contributions to SPEI is shown in Formula 6:

[0068]

[0069] where H(x1, x2, x3, x4) is the joint distribution function of the variables with the top-ranked contributions to SPEI (the top three) and evapotranspiration represented by the Copula function, and are their respective marginal distribution functions, and C1, C2, and C3 are the correlations between evapotranspiration and the top-ranked variables.

[0070] Further, in the step 6, by combining steps 4 and 5, the actual influence degree f of surface evapotranspiration on SPEI is obtained ET , as shown in Formula 7:

[0071]

[0072]

[0073] where C’1, C’2, and C’3 are the influence degrees of evapotranspiration on the top-ranked variables x1, x2, and x3, and respectively represent the Sen trends of the top-ranked variables, and fET is the actual influence degree of surface evapotranspiration on the dry-wet change monitoring index SPEI.

[0074] In the present specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0075] Specific examples are used herein to illustrate the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A method for evaluating the impact of surface evapotranspiration on dry-wet changes, characterized in that: include: Constructing an input data set; the input data set includes meteorological data, actual evapotranspiration data, potential evapotranspiration data, precipitation data and surface runoff data; the meteorological data includes temperature data, wind speed data, specific humidity data and downlink shortwave radiation data; Based on the input data set, the dry-wet change monitoring index is calculated according to the moisture deficit of the difference between precipitation and potential evapotranspiration; The Theil-Sen slope method is used to perform trend analysis on the dry-wet change monitoring index; Establish a multivariate linear regression model between the trend change of the dry-wet change monitoring index and temperature data, precipitation, downward shortwave radiation, wind speed, specific humidity and actual evapotranspiration, quantify the contribution of climate change to the dry-wet change monitoring index, and select the variables with the highest contribution to the dry-wet change monitoring index; By calculating the Akaike information criterion and selecting an appropriate three-dimensional Copula function, the relationship between actual evapotranspiration and the variables with the highest contribution ranking of the dry-wet change monitoring index is obtained; Combining the variables ranked top in contribution and the relationship between the actual evapotranspiration and the variables ranked top in contribution to the wet-dry change monitoring index, the actual influence degree of the actual evapotranspiration on the wet-dry change monitoring index is obtained; The formula for the relationship between actual evapotranspiration and the variables with the highest contribution to the dry-wet change monitoring index is: Among them, H(x1,x2,x3,x4) is the joint distribution function of the variables with the highest contribution to the dry-wet change monitoring index expressed by the Copula function and the actual evapotranspiration. and are their respective marginal distribution functions, C1, C2, and C3 are the correlations between actual evapotranspiration and the top-ranked variables, x1, x2, and x3 are the top-ranked variables, and x4 is actual evapotranspiration; The calculation formula for the actual impact degree is: Among them, C1', C'2 and C3' are the influence of actual evapotranspiration on the top-ranked variables x1, x2 and x3. and They represent the Sen trends of the top ranked variables, is the trend of actual evapotranspiration, ET is the actual evapotranspiration data, f ET It is the actual impact of actual evapotranspiration on the dryness and wetness change monitoring index.

2. The method for evaluating the influence of surface evapotranspiration on dry-wet changes according to claim 1, characterized in that: Based on the input data set, the dry-wet change monitoring index is calculated according to the difference between precipitation and potential evapotranspiration, including: Calculate the difference between monthly precipitation and potential evapotranspiration, i.e., water surplus or deficit D i , in order to establish the accumulation sequence of different time scales; the formula of the accumulation sequence is: in, is the accumulated water deficit in the jth month of the i-th year on the k-month time scale, D i,m is the cumulative sequence value of the mth month in the i-th year; The log-logistic probability distribution is used to fit and standardize the cumulative sequence to obtain the dry-wet change monitoring index at different time scales; the formula of the dry-wet change monitoring index is: in, P = 1 - F (D); Among them, F(D) is the fitted sequence; D is the cumulative sequence; P and W are the standardized distribution function value and probability weighted moment, respectively; SPEI is the dry-wet change monitoring index; α, β and γ are the scale, shape and origin parameters of the logarithmic logistic distribution; c0, c1, c2, d1, d2 and d3 are all calculated parameters, c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, d3 = 0.00130.

3. The method for evaluating the influence of surface evapotranspiration on dry-wet changes according to claim 1, characterized in that: Using the Theil-Sen slope method, the formula for trend analysis of the dry-wet change monitoring index is: Among them, x i and x j The time t i and t j The corresponding index or climate factor value, β is the Sen trend value, β greater than 0 indicates that the time series shows an upward trend, and β less than 0 indicates that the time series shows a downward trend.

4. The method for evaluating the influence of surface evapotranspiration on dry-wet changes according to claim 1, characterized in that: The calculation formula of the multivariate linear regression model is: Index = aP + bET + cT + dSRD + eWS + fshum + ζ; the formula for the trend of the dry-wet change monitoring index is: Among them, Index represents the value of the dry-wet change monitoring index; Indicates the trend of the dry-wet change monitoring index; represent the Sen trends of precipitation, temperature data, downlink shortwave radiation, wind speed and specific humidity respectively; a, b, c, d, e and f are regression coefficients; ξ is the residual term, which is solved by the least squares method; T is the temperature data, WS is the wind speed, shum is the specific humidity, SRD is the downlink shortwave radiation, and P is the precipitation data.

5. The method for evaluating the influence of surface evapotranspiration on dry-wet changes according to claim 1, characterized in that: The formula for calculating Akaike's information criterion is AIC=2p-2In(L); where p is the number of variables selected that rank top in their contribution to the dry-wet change monitoring index, L is the likelihood function, and AIC is the Akaike's information criterion.

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