Method for evaluating sensitivity of water yield in a watershed controlled by check dam to wind-solar complementation

Abstract

The application discloses a method for evaluating the sensitivity of water yield of a dam-controlled watershed to wind-solar complementation, and specifically comprises the following steps: defining a research period, collecting relevant data of the dam and meteorological stations inside and around the research area in the period; calculating the daily solar radiation and wind energy of each meteorological station; extracting the annual wind-solar resource complementation rate of the dam; selecting the optimal theoretical marginal distribution function; constructing a joint distribution function between the two by using a Copula function, constructing a conditional probability model, and calculating the probability of different levels of water yield under different wind-solar resource complementation rates of each dam; and finally, quantifying the sensitivity of different levels of water yield to wind-solar complementation by using a linear regression model. The application can accurately quantify the sensitivity of water yield of a dam-controlled watershed with different spatial distributions to wind-solar resource complementation, and can provide clean energy utilization strategies and technical support for the sustainable use of local dam water resources.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water resource evaluation and wind-solar complementary new energy, and relates to a method for evaluating the sensitivity of water yield of silt dam controlled watershed to wind-solar complementation. BACKGROUND

[0002] As an important water and soil conservation project, silt dam plays a key role in effectively controlling water and soil loss, improving ecological environment and promoting regional economic development through intercepting sediment to form dam land rich in nutrients and conserving soil moisture. Under the background of intensified climate change and enhanced human activities, extreme events such as drought occur frequently, water resource demand increases continuously, and water resource shortage problem becomes increasingly serious. At the same time, there are a large amount of clean energy such as wind energy and light energy in the distribution area of silt dam that has not been fully utilized. Under the promotion of the national "double carbon" strategy, in order to make full use of these clean energy and overcome the randomness, volatility and intermittency of wind energy and light energy, wind-solar resource complementation can be used to ensure reliable energy supply. In addition, large-scale silt dam controlled watershed has high water yield and water storage potential, which can provide certain water resources for alleviating regional water shortage. However, due to the complex topography and geomorphology of these regions, the utilization of water resources is a big challenge. Therefore, it is particularly important to enhance the availability of silt dam water resources through wind-solar complementary technology. Therefore, it is of great value to propose a method for evaluating the sensitivity of water yield of silt dam controlled watershed to wind-solar complementation. SUMMARY

[0003] The purpose of the present application is to provide a method for evaluating the sensitivity of water yield of silt dam controlled watershed to wind-solar complementation, which can accurately quantify the sensitivity of water yield of silt dam controlled watershed with different spatial distribution to wind-solar complementation.

[0004] The technical solution adopted by the present application is a method for evaluating the sensitivity of water yield of silt dam controlled watershed to wind-solar complementation, which specifically includes the following processes: defining the research period, collecting data inside and around the research area in the period; calculating the wind-solar resource complementation rate grid data year by year, extracting the wind-solar resource complementation rate of each silt dam year by year; then calculating the water yield data of each silt dam since its construction every year; selecting the optimal theoretical marginal distribution function respectively; constructing the joint distribution function between the two, calculating the conditional probability of different levels of water yield; and quantifying the sensitivity of different levels of water yield to wind-solar complementation using a linear regression model.

[0005] The present application has the following characteristics:

[0006] The present application is implemented according to the following steps:

[0007] Step 1, determine the study period, collect the elevation, daily sunshine hours and wind speed meteorological data of the internal and peripheral weather stations in the study area within the period, and the latitude and longitude, dam building time, control watershed area and annual runoff depth data of the check dam;

[0008] Step 2, using the elevation, daily sunshine hours and wind speed meteorological data of the internal and peripheral weather stations in the study area within the period collected in step 1 as the input of the solar radiation model and the wind energy calculation model, the daily solar radiation and wind energy of each weather station are calculated;

[0009] Step 3, based on the daily solar radiation and wind energy of each weather station obtained in step 2, the annual wind-solar complementary rate of each weather station is calculated, and the annual wind-solar complementary rate grid data is obtained by spatial interpolation; then the latitude and longitude data of each check dam are used to extract the annual wind-solar complementary rate of the corresponding check dam;

[0010] Step 4, based on the dam building time, control watershed area and annual runoff depth data of the check dam collected in step 1, the annual water yield data of each check dam since the dam was built is calculated;

[0011] Step 5, according to the annual wind-solar complementary rate of each check dam extracted in step 3 and the annual water yield data of each check dam since the dam was built calculated in step 4, select the part of the two data time periods of each check dam, and select the optimal theoretical marginal distribution function respectively;

[0012] Step 6, based on the optimal theoretical marginal distribution function of the annual wind-solar complementary rate and water yield data of each check dam obtained in step 5, the joint distribution function between the two is constructed by using Copula function, the conditional probability model is constructed, and the probability of occurrence of different grades of water yield under different wind-solar complementary rates of each check dam is calculated;

[0013] Step 7, based on the probability of occurrence of different grades of water yield under different wind-solar complementary rates of each check dam calculated in step 6, the sensitivity of different grades of water yield to wind-solar complementation is quantified by using linear regression model.

[0014] Step 2 is implemented according to the following steps:

[0015] Step 2.1, using the daily sunshine hours data of the internal and peripheral weather stations in the study area within the period collected in step 1 as the input of the solar radiation model, the daily solar radiation of each weather station is calculated, which represents the light energy, the specific formula is as follows:

[0016]

[0017] In the formula, R SSolar radiation, MJ·m -2 ; n is daily sunshine hours, h; R a is extraterrestrial radiation, MJ·m -2 ; N is maximum possible sunshine hours, h; a s and b s are parameters, taking values of 0.25 and 0.5 respectively;

[0018] The calculation formula of other variables in formula (1) is as follows:

[0019]

[0020]

[0021] In the formula, G sc is solar constant, taking a value of 0.082; ω s is sunrise hour angle, rad; d r is average distance from the earth to the sun; is latitude, rad; δ is longitude, rad; J is day sequence;

[0022] Step 2.2, using the daily wind speed data of the internal and peripheral meteorological stations of the study area collected in step 1 as the input of the wind energy calculation model, the annual wind energy of each meteorological station is calculated, and the specific formula is as follows:

[0023]

[0024] ρ=1.225e -0.001Z (9);

[0025] In the formula, E W is wind energy, MJ·m -2 ; W is wind energy density, W·m -2 ; ρ is air density, kg·m -3 ; v is daily wind speed, m·s -1 , h; e is absolute humidity, taking corresponding constant value according to water vapor parameter table at different temperatures, mm; z is elevation of meteorological station, m;

[0026] The specific process of step 3 is as follows:

[0027] Step 3.1, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual solar radiation standard deviation and annual wind energy standard deviation of each meteorological station are calculated, and then the annual wind-solar resource complementary rate of each meteorological station is calculated, and the specific formula is as follows:

[0028]

[0029] E W,RS= E W + R S (12);

[0030]

[0031] where CR W,RS,i is the wind-solar complementary ratio of the ith year, representing the size of wind-solar complementarity, and the larger the value, the stronger the complementarity; SD RS,i , SD W,i and SD W,RS,i are the standard deviations of solar radiation, wind energy and wind-solar complementarity in the ith year, respectively; E W,RS represents the daily wind-solar complementary results, MJ·m -2 ; R s,i,j , E W,i,j and E W,RS,i,j are the solar radiation, wind energy and wind-solar complementary results of the ith day of the ith year, MJ·m -2 ; and are the multi-year average values of daily solar radiation, wind energy and wind-solar complementary results, MJ·m -2 ; m i is the number of days in the ith year;

[0032] Step 3.2, on the basis of the annual wind-solar complementary ratio of each meteorological station calculated in step 3.1, the annual wind-solar complementary ratio grid data of the study area is calculated using the Kriging spatial interpolation method;

[0033] Step 3.3, on the basis of the annual wind-solar complementary ratio grid data of the study area obtained in step 3.2, the annual wind-solar complementary ratio corresponding to the latitude and longitude on the wind-solar complementary ratio grid data is extracted using the latitude and longitude data of the silt dam collected in step 1, and the annual wind-solar complementary ratio of each silt dam location point is obtained.

[0034] The specific process of step 4 is as follows:

[0035] Based on the silt dam construction time, control watershed area and annual runoff depth data collected in step 1, the annual water yield data of each silt dam since its construction is calculated, and the calculation formula is as follows:

[0036]

[0037] where R W is the water yield of the silt dam control watershed, m 3 ; A is the area of the silt dam control watershed, km 2 ; and H is the runoff depth of the silt dam control watershed, mm.

[0038] The specific process of step 5 is:

[0039] According to the wind and light resource complementary rate of each check dam obtained by step 3 and the water yield data of each check dam since the dam was built obtained by step 4, the sequence values of the two kinds of data of each check dam are selected, and the optimal theoretical marginal distribution function of each check dam is selected, and the specific formula is as follows:

[0040] F(x) = p(X≤x) (16);

[0041]

[0042] In the formula, F(x) represents the probability distribution function, specifically the empirical probability distribution function and various theoretical marginal probability distribution functions; X represents the wind and light resource complementary rate or water yield; F emp (x k ) represents the empirical distribution function cumulative probability of the kth X value; F mar (x k ) represents the theoretical marginal distribution cumulative probability of the kth X value; l represents the sequence length of X; RMSE represents the root mean square error; by calculating the RMSE between the cumulative probability of the various theoretical marginal probability distribution functions and the cumulative probability of the empirical distribution function, the theoretical marginal probability distribution function corresponding to the minimum RMSE is selected as the optimal theoretical marginal distribution function of the variable X.

[0043] The specific process of step 6 is:

[0044] Step 6.1, on the basis of the optimal theoretical marginal distribution function of the wind and light resource complementary rate and water yield data of each check dam obtained by step 5, the joint distribution function between the two is constructed by using the Copula function, and the specific formula is as follows:

[0045]

[0046] AIC = 2K-2lnL (19);

[0047] In the formula, X1 represents the wind and light complementary rate; X2 represents the water yield; P(X1≤x1,X1≤x2) represents the joint distribution of X1 and X2; F mar,X1 is the optimal theoretical marginal distribution function of X1; F mar,X2The optimal theoretical marginal distribution function of X2; C represents a Copula function; C (u1, u2) represents the joint distribution function cumulative probability between two variables; u1 and u2 represent the cumulative probability calculated at the x1 and x2 values of the optimal theoretical marginal distribution function of two variables, respectively; K represents the number of parameters of the Copula function, and L is a likelihood function; AIC represents the Akaike information criterion value, and the smaller the value, the higher the accuracy of the Copula function. By calculating the AIC values of different Copula functions, the optimal Copula function between the two variables is optimized, and the joint distribution function between the two variables is constructed.

[0048] Step 6.2, on the basis of constructing the joint distribution function between the wind-light complementary rate and the water yield of step 6.1, a conditional probability model is further constructed to calculate the probability of different levels of water yield under the condition of different wind-light resource complementary rates of each silt dam, and the specific formula is as follows:

[0049]

[0050] In the formula, P (X2<=x2|X1<=x1) represents the probability of the occurrence of x2 value in X2 variable under the condition of x1 value in X1 variable, that is, the probability of the occurrence of different levels of water yield under the condition of different wind-light resource complementary rates of silt dam.

[0051] The specific process of step 7 is as follows:

[0052] On the basis of calculating the probability of different levels of water yield under the condition of different wind-light resource complementary rates of silt dam in step 6.2, the sensitivity of different levels of water yield to wind-light complementarity is calculated by using a linear regression model, and the specific formula is as follows:

[0053]

[0054] In the formula, represents the probability of the occurrence of the fth value in X2 variable under the condition of the occurrence of the gth value in X1 variable; X 1,g represents the size of the gth value in X1 variable; b is the intercept of the regression model; a is the slope of the linear regression model, which is used to represent the sensitivity of the fth level of water yield to wind-light complementarity, and the greater the value, the more sensitive the water yield to wind-light complementarity.

[0055] The beneficial effects of the present application are that the present application can not only calculate the water yield of the silt dam controlled watershed and the size of wind energy and light energy at the corresponding position, but also calculate the complementarity of wind-light resources at the position, quantify the sensitivity of silt dam water yield to wind-light complementarity, and provide clean energy utilization strategies and scientific support for local silt dam water resource sustainable use. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1is a flow chart of a method for evaluating the sensitivity of water yield of a silt dam controlled watershed to wind-solar complementation according to the present application;

[0057] Figure 2 is a schematic diagram of the spatial distribution of meteorological stations and backbone silt dams in the L-shaped bend area of the Yellow River in Embodiment 3 of the method for evaluating the sensitivity of water yield of a silt dam controlled watershed to wind-solar complementation according to the present application;

[0058] Figure 3 is a schematic diagram of the sensitivity calculation results of the water yield of the backbone silt dams in the L-shaped bend area of the Yellow River to wind-solar complementation in Embodiment 3 of the method for evaluating the sensitivity of water yield of a silt dam controlled watershed to wind-solar complementation according to the present application. DETAILED DESCRIPTION

[0059] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Embodiment 1

[0061] The method for evaluating the sensitivity of water yield of a silt dam controlled watershed to wind-solar complementation according to the present application has a flow as shown in Figure 1 , and specifically includes the following steps:

[0062] Step 1: Clearly define the research period, and collect the elevation, daily sunshine duration and wind speed meteorological data of the internal and peripheral meteorological stations in the research area in the research period, as well as the latitude and longitude, dam construction time, controlled watershed area and annual runoff depth data of the silt dam;

[0063] Step 2: Use the elevation, daily sunshine duration and wind speed meteorological data of the internal and peripheral meteorological stations in the research area in the research period collected in Step 1 as the input of the solar radiation model and the wind energy calculation model, respectively, to calculate the daily solar radiation and wind energy of each meteorological station;

[0064] Step 2.1: Use the daily sunshine duration data of the internal and peripheral meteorological stations in the research area in the research period collected in Step 1 as the input of the solar radiation model, to calculate the daily solar radiation of each meteorological station, which represents the light energy, and the specific formula is as shown below:

[0065]

[0066] In the formula, R S is the solar radiation, MJ·m -2 ; n is the daily sunshine duration, h; R a is the extraterrestrial radiation, MJ·m -2 ; N is the maximum possible sunshine duration, h; a s and b s are parameters, and the values are 0.25 and 0.5, respectively;

[0067] The calculation formula of other variables in formula (1) is as follows:

[0068]

[0069] In the formula, G sc is a solar constant, and the value is 0.082; ω s is a sunrise hour angle, rad; d r is an average distance from the earth to the sun; is a latitude, rad; δ is a longitude, rad; and J is a day sequence;

[0070] Step 2.2, using the daily wind speed data of the internal and peripheral meteorological stations in the study area collected in step 1 as the input of the wind energy calculation model, the annual wind energy of each meteorological station is calculated, and the specific formula is as follows:

[0071]

[0072] ρ=1.225e -0.001Z (9);

[0073] In the formula, E W is wind energy, MJ·m -2 ; W is wind energy density, W·m -2 ; ρ is air density, kg·m -3 ; v is daily wind speed, m·s -1 , h; e is absolute humidity, and the corresponding constant value is taken according to the water vapor parameter table at different temperatures, mm; and z is the elevation of the meteorological station, m;

[0074] Step 3, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual wind-solar complementary ratio of each meteorological station is calculated, and further spatial interpolation is performed to obtain the annual wind-solar complementary ratio grid data; and the latitude and longitude data of each dike collected in step 1 are used to extract the annual wind-solar complementary ratio of the corresponding dike;

[0075] Step 3.1, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual solar radiation standard deviation and the annual wind energy standard deviation of each meteorological station are calculated, and then the annual wind-solar complementary ratio of each meteorological station is calculated, and the specific formula is as follows:

[0076]

[0077] In the formula, CR W,RS,i is the wind-solar complementary ratio of the i-th year, which represents the wind-solar complementary ratio, and the larger the value is, the stronger the complementary ratio is; SD RS,i , SD W,i , and SD W,RS,irespectively, are the standard deviation of solar radiation, wind energy and the result of wind energy and light energy complementation, respectively, in the i th year; E W,RS represents the result of daily wind energy and light energy complementation, MJ·m -2 ; R s,i,j , E W,i,j and E W,RS,i,j respectively, are the solar radiation, wind energy, the result of wind energy and light energy complementation, respectively, on the j th day of the i th year, MJ·m -2 ; and respectively, are the multi-year average of daily solar radiation, wind energy, the result of wind energy and light energy complementation, respectively, MJ·m -2 ; m i is the number of days in the i th year;

[0078] Step 3.2, on the basis of the annual wind-light resource complementation rate of each meteorological station calculated in step 3.1, the annual wind-light resource complementation rate grid data of the study area is calculated using the Kriging spatial interpolation method;

[0079] Step 3.3, on the basis of the annual wind-light resource complementation rate grid data of the study area obtained in step 3.2, the annual wind-light resource complementation rate corresponding to the latitude and longitude on the wind-light resource complementation rate grid data is extracted using the latitude and longitude data of the silt dam collected in step 1, to obtain the annual wind-light resource complementation rate of each silt dam location point.

[0080] Step 4, based on the silt dam construction time, control watershed area and annual runoff depth data of each year collected in step 1, the annual water yield data of each silt dam since its construction is calculated;

[0081] Based on the silt dam construction time, control watershed area and annual runoff depth data of each year collected in step 1, the annual water yield data of each silt dam since its construction is calculated, and the calculation formula is as follows:

[0082]

[0083] In the formula, R W is the water yield of the silt dam control watershed, m 3 ; A is the area of the silt dam control watershed, km 2 ; H is the runoff depth of the silt dam control watershed, mm;

[0084] Step 5, according to the annual wind-light resource complementation rate of each silt dam extracted in step 3 and the annual water yield data of each silt dam since its construction calculated in step 4, the part of the two kinds of data time period of each silt dam is selected, and the optimal theoretical marginal distribution function is respectively optimized;

[0085] According to the wind and light resource complementary rate of each check dam obtained in step 3 and the water yield data of each check dam obtained in step 4, the optimal theoretical marginal distribution function of each check dam is selected, and the specific formula is as follows:

[0086] F(x)=p(X≤x) (16);

[0087]

[0088] In the formula, F(x) represents a probability distribution function, specifically an empirical probability distribution function and a plurality of theoretical marginal probability distribution functions; X represents the wind and light resource complementary rate or water yield; F emp (x k ) represents the cumulative probability of the empirical distribution function of the kth X value; F mar (x k ) represents the cumulative probability of the theoretical marginal distribution of the kth X value; l represents the sequence length of X; and RMSE represents the root mean square error. By calculating the RMSE between the cumulative probability of the plurality of theoretical marginal probability distribution functions and the cumulative probability of the empirical distribution function, the theoretical marginal probability distribution function corresponding to the minimum RMSE is selected as the optimal theoretical marginal distribution function of the variable X.

[0089] Step 6, on the basis of the optimal theoretical marginal distribution function of the wind and light resource complementary rate and the water yield data of each check dam obtained in step 5, a joint distribution function between the two is constructed by using a Copula function, and a conditional probability model is further constructed to calculate the probability of occurrence of different grades of water yield under different wind and light resource complementary rates of each check dam.

[0090] Step 6.1, on the basis of the optimal theoretical marginal distribution function of the wind and light resource complementary rate and the water yield data of each check dam obtained in step 5, a joint distribution function between the two is constructed by using a Copula function, and the specific formula is as follows:

[0091]

[0092] AIC=2K-2lnL (19);

[0093] In the formula, X1 represents the wind and light complementary rate; X2 represents the water yield; P(X1≤x1,X1≤x2) represents the joint distribution of X1 and X2; F mar,X1 is the optimal theoretical marginal distribution function of X1; and F mar,X2The optimal theoretical marginal distribution function of X2; C represents a Copula function; C(u1, u2) represents a joint distribution function cumulative probability between two variables; u1 and u2 represent cumulative probabilities of the optimal theoretical marginal distribution function of two variables at x1 and x2 values, respectively; K represents a number of parameters of the Copula function, and L is a likelihood function; AIC represents an AIC value, and the smaller the AIC value, the higher the accuracy of the Copula function. By calculating the AIC values of different Copula functions, the optimal Copula function between the two variables is optimized, and a joint distribution function between the two variables is constructed.

[0094] Step 6.2, on the basis of constructing the joint distribution function between the wind-solar complementary rate and the water yield in step 6.1, a conditional probability model is further constructed to calculate the probability of different levels of water yield under the condition of different wind-solar resource complementary rates of each silt dam, and the specific formula is as follows:

[0095]

[0096] In the formula, P(X2≤x2|X1≤x1) represents the probability of the occurrence of the x2 value in the X2 variable under the condition of the occurrence of the x1 value in the X1 variable, that is, the probability of the occurrence of different levels of water yield under the condition of different wind-solar resource complementary rates of the silt dam.

[0097] Step 7, on the basis of the probability of different levels of water yield under the condition of different wind-solar resource complementary rates of each silt dam calculated in step 6, the sensitivity of different levels of water yield to wind-solar complementarity is quantified by using a linear regression model.

[0098] Example 2

[0099] On the basis of example 1, the specific process of step 7 is as follows:

[0100] On the basis of the probability of different levels of water yield under the condition of different wind-solar resource complementary rates of the silt dam calculated in step 6.2, the sensitivity of different levels of water yield to wind-solar complementarity is calculated by using a linear regression model, and the specific formula is as follows:

[0101]

[0102] In the formula, P(X2≤x2|X1≤x1) represents the probability of the occurrence of the x2 value in the X2 variable under the condition of the occurrence of the x1 value in the X1 variable, that is, the probability of the occurrence of different levels of water yield under the condition of different wind-solar resource complementary rates of the silt dam. represents the probability of the occurrence of the fth value in the X2 variable under the condition of the occurrence of the gth value in the X1 variable; X 1,g represents the size of the gth value in the X1 variable; b is the intercept of the regression model; a is the slope of the linear regression model, which is used to represent the sensitivity of the fth level of water yield to wind-solar complementarity, and the larger the value, the more sensitive the water yield to wind-solar complementarity.

[0103] Example 3

[0104] Take the Jizi bend area of the Yellow River as an example, and implement the following steps:

[0105] Step 1, determine the research period as 1965-2018, collect the elevation, daily sunshine hours and wind speed meteorological data of 135 meteorological stations in the Jizi bend area of the Yellow River and its surrounding area, and the longitude and latitude, dam construction time, control watershed area and annual runoff depth data of 4040 backbone silt dams, the spatial distribution of 135 meteorological stations and 4040 backbone silt dams in the Jizi bend area of the Yellow River is shown in Figure 2 ;

[0106] Step 2, use the elevation, daily sunshine hours and wind speed meteorological data of 135 meteorological stations in the Jizi bend area of the Yellow River and its surrounding area collected in step 1 as the input of the solar radiation model and the wind energy calculation model, respectively, to calculate the daily solar radiation and wind energy of each meteorological station;

[0107] Step 2.1, use the daily sunshine hours data of 135 meteorological stations in the Jizi bend area of the Yellow River and its surrounding area collected in step 1 as the input of the solar radiation model, to calculate the daily solar radiation of each meteorological station, representing the light energy, the specific formula is shown as follows:

[0108]

[0109] In the formula, R S is the solar radiation, MJ·m -2 ; n is the daily sunshine hours, h; R a is the earth's external radiation, MJ·m -2 ; N is the maximum possible sunshine hours, h; a s and b s are parameters, taking values of 0.25 and 0.5 respectively; the calculation formula of other variables in formula (1) is shown as follows:

[0110]

[0111]

[0112] In the formula, G sc is the solar constant, taking a value of 0.082; ω s is the sunrise hour angle, rad; d r is the average distance from the earth to the sun; is the latitude, rad; δ is the longitude, rad; J is the day sequence;

[0113] Step 2.2, using the elevation and daily wind speed data collected in step 1 from 1965 to 2018 for 135 weather stations in the several-character bend area of the Yellow River and its surrounding area as input for the wind energy calculation model, the annual wind energy of each weather station was calculated, and the specific formula is as follows:

[0114]

[0115] ρ=1.225e -0.001Z

[0116] E W = E -2 + R -2 ; W is the wind energy density, W·m -3 ; ρ is the air density, kg·m -1 ; v is the daily wind speed, m·s W,RS , h; e is the absolute humidity, and the corresponding constant value is taken from the water vapor parameter table at different temperatures, mm; z is the elevation of the weather station, m;

[0117] Step 3, based on the daily solar radiation and wind energy of each weather station obtained in step 2, the wind-solar resource complementary rate of each weather station in each year from 1965 to 2018 was calculated, and further spatial interpolation was performed to obtain the annual wind-solar resource complementary rate grid data; then the latitude and longitude data of each silt dam collected in step 1 were used to extract the annual wind-solar resource complementary rate of the corresponding silt dam;

[0118] Step 3.1, based on the daily solar radiation and wind energy of each weather station obtained in step 2, the standard deviation of annual solar radiation and the standard deviation of annual wind energy of each weather station from 1965 to 2018 were calculated, and then the annual wind-solar resource complementary rate of each weather station was calculated, and the specific formula is as follows:

[0119]

[0120]

[0121] E W,RS = E W + R S

[0122]

[0123] In the formula, CR W,RS,i is the wind-solar resource complementary rate of the i-th year, which represents the wind-solar complementary rate, and the larger the value, the stronger the complementarity; SD RS,i , SD W,i , and SD W,RS,i are the standard deviations of solar radiation, wind energy, and wind-solar complementary energy in the i-th year, respectively; E W,RSDaily wind and light energy complementary results, MJ·m -2 ; R s,i,j , E W,i,j , and E W,RS,i,j are the solar radiation, wind energy, and wind and light energy complementary results on the jth day of the ith year, MJ·m -2 ; and are the multi-year average values of daily solar radiation, wind energy, and wind and light energy complementary results, MJ·m -2 ; m i is the number of days in the ith year;

[0124] Step 3.2, based on the annual wind and light energy complementary rate of each meteorological station calculated in step 3.1, the Kriging spatial interpolation method is used to calculate the annual wind and light energy complementary rate grid data of the Ji-Zi Bend area of the Yellow River from 1965 to 2018;

[0125] Step 3.3, based on the annual wind and light energy complementary rate grid data of the Ji-Zi Bend area of the Yellow River from 1965 to 2018 obtained in step 3.2, the longitude and latitude data of 4040 backbone silt dams collected in step 1 are used to extract the annual wind and light energy complementary rate corresponding to the longitude and latitude on the wind and light energy complementary rate grid data, and the annual wind and light energy complementary rate of each silt dam location point is obtained.

[0126] Step 4, based on the dam construction time, controlled watershed area, and annual runoff depth data of 4040 backbone silt dams collected in step 1, the annual water yield data of each silt dam since its construction is calculated;

[0127] Based on the dam construction time, controlled watershed area, and annual runoff depth data of 4040 backbone silt dams collected in step 1, the annual water yield data of each silt dam since its construction is calculated, and the calculation formula is as follows:

[0128]

[0129] In the formula, R W is the water yield of the silt dam controlled watershed, m 3 ; A is the area of the silt dam controlled watershed, km 2 ; H is the runoff depth of the silt dam controlled watershed, mm;

[0130] Step 5, according to the annual wind and light energy complementary rate of each silt dam extracted in step 3 and the annual water yield data of each silt dam since its construction calculated in step 4, the part of the two data time periods of each silt dam is selected, and the optimal theoretical marginal distribution function is respectively optimized;

[0131] According to the wind and light resource complementary rate of each check dam obtained in step 3 and the water yield data of each check dam obtained in step 4, the sequence values of the two kinds of data of each check dam are selected (if the dam is built before 1965, the sequence values of the water yield and the wind and light resource complementary rate are selected from 1965 and after; if the dam is built after 1965, the sequence values of the water yield and the wind and light resource complementary rate are selected from the time when the dam is built and after), and the optimal theoretical marginal distribution function is selected respectively, and the specific formula is as follows:

[0132] F(x) = p(X≤x)

[0133]

[0134] In the formula, F(x) represents a probability distribution function, specifically an empirical probability distribution function and a plurality of theoretical marginal probability distribution functions; X represents the wind and light resource complementary rate or the water yield; F emp (x k ) represents the cumulative probability of the empirical distribution function of the kth X value; F mar (x k ) represents the cumulative probability of the theoretical marginal distribution of the kth X value; l represents the sequence length of X; and RMSE represents the root mean square error. By calculating the RMSE between the cumulative probability of the plurality of theoretical marginal probability distribution functions and the cumulative probability of the empirical distribution function, the theoretical marginal probability distribution function corresponding to the minimum RMSE is selected as the optimal theoretical marginal distribution function of the variable X.

[0135] Step 6, on the basis of the optimal theoretical marginal distribution function of the wind and light resource complementary rate and the water yield data of each check dam obtained in step 5, a joint distribution function between the two is constructed by using a Copula function, and a conditional probability model is further constructed to calculate the probability of occurrence of water yield of different grades under different wind and light resource complementary rates of each check dam;

[0136] Step 6.1, on the basis of the optimal theoretical marginal distribution function of the wind and light resource complementary rate and the water yield data of each check dam obtained in step 5, a joint distribution function between the two is constructed by using a Copula function, and a conditional probability model is further constructed to calculate the probability of occurrence of water yield of different grades under different wind and light resource complementary rates of each check dam;

[0137]

[0138] AIC = 2K-2lnL

[0139] In the formula, X1 represents the wind and light complementary rate; X2 represents the water yield; P(X1≤x1,X1≤x2) represents the joint distribution of X1 and X2; F mar,X1 is the optimal theoretical marginal distribution function of X1; and F mar,X2The optimal theoretical marginal distribution function of X2; C represents a Copula function; C(u1, u2) represents a joint distribution function cumulative probability between two variables; u1 and u2 represent cumulative probabilities of the optimal theoretical marginal distribution function of two variables at x1 and x2 values, respectively; K represents a number of parameters of the Copula function, and L is a likelihood function; AIC represents an AIC value, and the smaller the AIC value, the higher the precision of the Copula function. By calculating the AIC values of different Copula functions, the optimal Copula function between two variables is optimized, and a joint distribution function between two variables is constructed.

[0140] Step 6.2, on the basis of constructing the joint distribution function between the wind-solar complementary rate and the water yield in step 6.1, a conditional probability model is further constructed to calculate the probability of different levels of water yield under the condition of different wind-solar complementary rates of each silt dam, and the specific formula is as follows:

[0141]

[0142] In the formula, P(X2≤x2|X1≤x1) represents the probability of the occurrence of the x2 value of the X2 variable under the condition of the occurrence of the x1 value of the X1 variable, that is, the probability of the occurrence of different levels of water yield under the condition of different wind-solar complementary rates of the silt dam.

[0143] Step 7, on the basis of the probability of different levels of water yield under the condition of different wind-solar complementary rates of each silt dam calculated in step 6, the sensitivity of different levels of water yield to wind-solar complementarity is quantified by using a linear regression model.

[0144] On the basis of the probability of different levels of water yield under the condition of different wind-solar complementary rates of each silt dam calculated in step 6.2, the sensitivity of different levels of water yield to wind-solar complementarity is calculated by using a linear regression model, and the specific formula is as follows:

[0145]

[0146] In the formula, represents the probability of the occurrence of the fth value of the X2 variable under the condition of the occurrence of the gth value of the X1 variable; X 1,g represents the size of the gth value of the X1 variable; b is the intercept of the regression model; a is the slope of the linear regression model, which is used to represent the sensitivity of the fth level of water yield to wind-solar complementarity, and the greater the value, the more sensitive the water yield is to wind-solar complementarity. In this example, f takes values of 0.05, 0.20, 0.35, 0.50, 0.65, 0.80, and the like, X 2,fThe quantile values ​​corresponding to each quantile point represent the quantile values ​​of the water yield controlled by each key silt-retaining dam in the watershed; g takes values ​​of 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, and 1, respectively, representing 20 quantile points. 1,g The quantile values ​​corresponding to each quantile point, i.e., the quantile values ​​of the wind-solar resource complementarity rate at each key silt-retaining dam location, are used to calculate the sensitivity of the six levels of water yield of each silt-retaining dam to the wind-solar resource complementarity rate. Figure 3 As shown. From Figure 3 The data shows that the average sensitivity of the six levels of water yield of 4040 backbone silt-retaining dams to the wind-solar resource complementarity rate is 0.003, 0.006, 0.006, 0.006, 0.005 and 0.004, respectively, indicating that medium to small water yields are more sensitive to wind-solar complementarity.

Claims

1. A method for assessing the sensitivity of watershed water yield controlled by silt-retention dams to wind-solar hybridization, characterized by: Specifically comprising the following steps: Step 1, determine the research period, collect the elevation, daily sunshine hours and wind speed meteorological data of the internal and peripheral meteorological stations in the research area in the period, and the latitude and longitude, dam building time, control watershed area and annual runoff depth data of the check dam; Step 2, using the elevation, daily sunshine hours and wind speed meteorological data of the internal and peripheral meteorological stations in the research area in the period collected in step 1 as the input of the solar radiation model and the wind energy calculation model, the daily solar radiation and wind energy of each meteorological station are calculated; Step 3, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual wind-solar complementary rate of each meteorological station is calculated, and the annual wind-solar complementary rate grid data is obtained by spatial interpolation; then the latitude and longitude data of each check dam are used to extract the annual wind-solar complementary rate of the corresponding check dam; Step 4, based on the dam building time, control watershed area and annual runoff depth data of the check dam collected in step 1, the annual water yield data of each check dam since the dam was built is calculated; Step 5, according to the annual wind-solar complementary rate of each check dam extracted in step 3 and the annual water yield data of each check dam since the dam was built calculated in step 4, select the part of the two data time periods of each check dam, and select the optimal theoretical marginal distribution function; Step 6, based on the optimal theoretical marginal distribution function of the annual wind-solar complementary rate and the water yield data of each check dam obtained in step 5, a joint distribution function between the two is constructed by using a Copula function, a conditional probability model is constructed, and the probability of occurrence of different grades of water yield under different wind-solar complementary rates of each check dam is calculated; Step 7, based on the probability of occurrence of different grades of water yield under different wind-solar complementary rates of each check dam calculated in step 6, the sensitivity of different grades of water yield to wind-solar complementation is quantified by using a linear regression model.

2. The method of claim 1, wherein the method is characterized by, The specific process of step 2 is: Step 2.1, using the daily sunshine hours data of the internal and peripheral meteorological stations in the research area in the period collected in step 1 as the input of the solar radiation model, the daily solar radiation of each meteorological station is calculated, which represents the light energy, and the specific formula is as follows: （1） where R S is solar radiation, MJ m -2 -2 ; n is daily sunshine hours, h; R a is extraterrestrial radiation, MJ m -2 -2 ; N is maximum possible sunshine hours, h; a s and b s are parameters; The calculation formula of other variables in formula (1) is as follows: （2） （3） （4） （5） （6） where G sc is the solar constant; ω s is the sunrise hour angle, rad; d r is the mean distance from the sun; φ is the latitude, rad; δ is the longitude, rad; and J is the day of the year. Step 2.2, using the daily wind speed data of the internal and peripheral meteorological stations in the research area in the period collected in step 1 as the input of the wind energy calculation model, the annual wind energy of each meteorological station is calculated, and the specific formula is as follows: （8） （9） where E W is the wind energy, MJ·m -2 ; W is the wind energy density, W·m -2 ; ; -3 ; v is the daily wind speed, m·s -1 ; e is the absolute humidity, and the corresponding constant value is taken from the water vapor parameter table at different temperatures, and z is the elevation of the weather station, m.

3. The sensitivity evaluation method of the check dam control watershed water yield to wind-solar complementation according to claim 2, the specific process of step 3 is: Step 3.1, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual solar radiation standard deviation and the annual wind energy standard deviation of each meteorological station are calculated, and then the annual wind-solar complementary rate of each meteorological station is calculated, and the specific formula is as follows: （10） （11） （12） （13） （14） In the formula, CR W, RS, i For the first i Annual wind and solar resource complementarity rate; SD RS, i , SD W, i and SD W, RS, i The first i The standard deviation of annual solar radiation, the standard deviation of wind energy, and the standard deviation of wind and solar energy complementation; E W, RS This represents the result of daily wind and solar energy complementarity, in MJ·m -2 ; R s, i, j , E W, i, j and E W, RS, i, j The first i Year j The result of solar radiation, wind energy, and complementary wind and solar energy, in MJ·m -2 ; , and These represent the multi-year averages of daily solar radiation, wind energy, and the results of wind and solar energy complementarity, respectively, in MJ·m⁻². -2 ; m i For the first i The number of days in a year; Step 3.2, on the basis of the wind-solar resource complementary rate of each meteorological station calculated in step 3.1, the kriging spatial interpolation method is used to calculate the wind-solar resource complementary rate grid data of the study area year by year; Step 3.3, on the basis of the wind-solar resource complementary rate grid data of the study area year by year obtained in step 3.2, the longitude and latitude data of the silt dam collected in step 1 are used to extract the wind-solar resource complementary rate corresponding to the longitude and latitude on the wind-solar resource complementary rate grid data, and the wind-solar resource complementary rate of each silt dam location point year by year is obtained.

4. The method of claim 3, wherein the method is characterized by, The specific process of step 4 is as follows: Based on the silt dam building time, control basin area and annual runoff depth data collected in step 1, the annual water yield data of each silt dam since the dam was built is calculated, and the calculation formula is as follows: （15） In the formula, R W m is the water yield of the control basin of the check dam 3 A is the area of the control basin of the check dam, km 2 H is the runoff depth of the control basin of the check dam, mm.

5. The method for assessing the sensitivity of the water yield of a dammed watershed to wind-solar complementarity according to claim 4, characterized in that, The specific process of step 5 is as follows: According to the wind-solar resource complementary rate of each silt dam extracted in step 3 and the annual water yield data of each silt dam since the dam was built calculated in step 4, the sequence values of each silt dam with the same time period are selected, and the optimal theoretical marginal distribution function is determined, and the specific formula is as follows: （16） （17） where F( x ) represents a probability distribution function, specifically an empirical probability distribution function and a variety of theoretical marginal probability distribution functions; X represents the wind-solar resource complementarity rate or water production; F emp ( x k ) represents the cumulative probability of the empirical distribution function of the k X th value; F mar ( x k ) represents the cumulative probability of the theoretical marginal distribution of the k X th value; l represents the sequence length of X ; RMSE represents the root mean square error.​​ 6. The method for assessing the sensitivity of the water yield of a dammed watershed to wind-solar complementarity according to claim 5, characterized in that, The specific process of step 6 is as follows: Step 6.1, on the basis of the wind-solar resource complementary rate and water yield data of each silt dam obtained in step 5, the joint distribution function between the two is constructed by using the Copula function, and the specific formula is as follows: （18） （19） wherein, X 1 represents the wind-solar complementary ratio; X 2 represents the water production; P X 1≤ x 1, X 1≤ x 2) represents X 1 and X 2; F mar,X1 is the optimal theoretical marginal distribution function of X 1; F mar,X2 is the optimal theoretical marginal distribution function of X 2; C represents a Copula function; C( u 1, u 2) represents the joint distribution function cumulative probability between two variables; u 1 and u 2 respectively represent the cumulative probability calculated by the optimal theoretical marginal distribution function of two variables at x 1 and x 2 values; K represents the number of parameters of the Copula function, L is the likelihood function; AIC represents the Akaike information criterion value;​ Step 6.2, on the basis of the joint distribution function between the wind-solar complementary rate and water yield constructed in step 6.1, the conditional probability model is constructed, and the probability of different levels of water yield under different wind-solar resource complementary rates of each silt dam is calculated, and the specific formula is as follows: （20） In the formula, P ( X 2≤ x 2| X 1≤ x 1) represents X 1 variable x 1 value occurs under the condition X 2 variable x 2 value occurs under the condition of the probability of complementary rate of different wind and light resources of silt dam, probability of different grade water yield occurs.

7. The method for assessing the sensitivity of the water yield of a dammed watershed to wind-solar complementarity according to claim 6, characterized in that, The specific process of step 7 is as follows: On the basis of the probability of different levels of water yield under different wind-solar resource complementary rates of silt dam calculated in step 6.2, the sensitivity of different levels of water yield to wind-solar complementarity is calculated by using linear regression model, and the specific formula is as follows: In the formula, express X The first variable g Under the condition that a value occurs X The second variable f The probability of each value occurring; express X The first variable g Size of each value; b The intercept of the regression model; a Let be the slope of the linear regression model, which is used here to characterize the . f The sensitivity of water production to wind-solar hybridization is categorized into different levels, with higher values ​​indicating greater sensitivity.

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