Sensitivity assessment method for wind-solar complementation of water yield of silt dam control basin

Abstract

The invention discloses a method for evaluating sensitivity of water yield of a silt dam control drainage basin to wind-solar complementation, and the method specifically comprises the following steps: determining a research time period, and collecting related data of meteorological stations and silt dams inside and around a research region in the time period; respectively calculating to obtain the daily solar radiation amount and wind energy of each weather station; the year-by-year wind and light resource complementation rate of the corresponding silt dam is extracted; respectively selecting an 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 occurrence probabilities of different levels of water yields under the condition of different wind and light resource complementation rates of each silt dam; and finally, quantifying the sensitivity of different levels of water yields to wind-solar complementation by using a linear regression model. According to the method, the sensitive degree of the water yield of the control basin of the silt dam with different spatial distributions to the complementarity of wind and light resources can be accurately quantified, and a clean energy utilization strategy and technical support can be provided for sustainable use of water resources of the local silt dam.

Description

Technical Field

[0001] The present invention belongs to the field of water resource evaluation and wind-solar resource complementary new energy technology, and relates to a method for evaluating the sensitivity of water production in a silt dam-controlled basin to wind-solar complementarity. Background Art

[0002] As an important water and soil conservation project, the silt dam has a wide distribution and a large number of constructions. It intercepts sediment to form nutrient-rich dam land, conserves soil moisture, and plays a key role in effectively controlling soil erosion, improving the ecological environment, and promoting regional economic development. Against the background of intensified climate change and enhanced human activities, extreme events such as droughts occur frequently, water demand continues to increase, and the problem of water shortage is becoming more serious. At the same time, there are a large number of clean energy such as wind and solar energy that have not been fully utilized in the areas where the silt dams are distributed. Driven by the national "dual carbon" strategy, in order to make full use of these clean energy and overcome the shortcomings of randomness, volatility and intermittency of wind and solar energy, the reliable supply of energy can be guaranteed through the complementarity of wind and solar resources. In addition, the basins controlled by large-scale silt dams have high water production and water storage potential, which can provide certain water resources to alleviate regional water shortages. However, due to the complex topography and landforms in these areas, there are great challenges in the utilization of water resources. In summary, it is particularly important to enhance the availability of water resources in silt dams through wind and solar complementary technologies. Therefore, it is of great value to propose a sensitivity assessment method for controlling watershed water yield through silt dams to wind-solar complementarity. Summary of the invention

[0003] The purpose of the present invention is to provide a method for evaluating the sensitivity of water production in a watershed controlled by a silt dam to the complementarity of wind and solar resources. This method can accurately quantify the sensitivity of water production in a watershed controlled by a silt dam with different spatial distributions to the complementarity of wind and solar resources.

[0004] The technical solution adopted in the present invention is a method for evaluating the sensitivity of water production in a watershed controlled by a silt dam to wind-solar complementarity, which specifically includes the following processes: clarifying the research period and collecting data inside and around the study area during the period; calculating and obtaining the annual wind-solar resource complementarity rate raster data, and extracting the annual wind-solar resource complementarity rate of the corresponding silt dam; then calculating and obtaining the annual water production data of each silt dam since its construction; selecting the optimal theoretical marginal distribution function respectively; constructing a joint distribution function between the two, and calculating the conditional probability of occurrence of different levels of water production; and using a linear regression model to quantify the sensitivity of different levels of water production to wind-solar complementarity.

[0005] The present invention is also characterized in that:

[0006] Follow the steps below to implement it:

[0007] Step 1: Define the research period and collect the altitude, daily sunshine hours and wind speed meteorological data of the meteorological stations inside and around the study area during the period, as well as the latitude and longitude of the silt dam, the time of dam construction, the controlled watershed area and the annual runoff depth data;

[0008] Step 2: Use the altitude, daily sunshine hours and wind speed meteorological data of the meteorological stations inside and around the study area during the study period collected in step 1 as inputs of the solar radiation model and wind energy calculation model, respectively, to calculate the daily solar radiation and wind energy of each meteorological station;

[0009] Step 3: Based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual wind-solar resource complementarity rate of each meteorological station is calculated, and the annual wind-solar resource complementarity rate grid data is obtained through spatial interpolation; and the longitude and latitude data of each silt dam collected in step 1 are used to extract the annual wind-solar resource complementarity rate of the corresponding silt dam;

[0010] Step 4, based on the construction time of the silt dam, the controlled basin area and the annual runoff depth data collected in step 1, calculate and obtain the annual water yield data of each silt dam since its construction;

[0011] Step 5, according to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, select the part of each silt dam where the two data time periods are consistent, and select the optimal theoretical marginal distribution function respectively;

[0012] Step 6: Based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, a joint distribution function between the two is constructed using the Copula function, and a conditional probability model is constructed to calculate the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam;

[0013] Step 7, based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam calculated in step 6, a linear regression model is used to quantify the sensitivity of different levels of water production to wind-solar complementarity.

[0014] Step 2 is implemented as follows:

[0015] Step 2.1: Use the daily sunshine hours data of the meteorological stations inside and around the study area during the study 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 amount of light energy. The specific formula is as follows:

[0016]

[0017] In the formula, R Sis the solar radiation, MJ·m -2 ; n is the daily sunshine hours, h; R a is the extraterrestrial radiation, MJ·m -2 ; N is the maximum possible sunshine hours, h; a s and b s is a parameter with values ​​of 0.25 and 0.5 respectively;

[0018] The calculation formulas for other variables in formula (1) are as follows:

[0019]

[0020]

[0021] In the formula, G sc is the solar constant, with a value of 0.082; ω s is the sunrise angle, rad; d r is the average distance between the reversed sun and the earth; is latitude, rad; δ is longitude, rad; J is date;

[0022] Step 2.2: Use the daily wind speed data of the meteorological stations inside and around the study area during the study period collected in step 1 as the input of the wind energy calculation model to calculate the annual wind energy of each meteorological station. The specific formula is as follows:

[0023]

[0024] ρ=1.225e -0.001Z (9);

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

[0026] The specific process of step 3 is:

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

[0028]

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

[0030]

[0031] In the formula, CR W,RS,i is the wind-solar resource complementarity rate in the i-th year, which represents the magnitude of wind-solar complementarity. The larger the value, the stronger the complementarity. SD RS,i , SD W,i and SD W,RS,i are the standard deviation of solar radiation, wind energy and the standard deviation of wind energy and light energy complemented by each other in the i-th year; E W,RS It represents the result of the daily complementation of wind energy and solar energy, MJ·m -2 ; R s,i,j 、E W,i,j and E W,RS,i,j are the solar radiation, wind energy, and the result of wind energy and light energy complementation on the jth day of the i-th year, MJ·m -2 ; and are the multi-year averages of daily solar radiation, wind energy, and the result of wind energy and light energy complementing each other, MJ·m -2 ;m i is the number of days in the i-th year;

[0032] Step 3.2, based on the wind-solar resource complementarity rate of each meteorological station calculated in step 3.1, the Kriging spatial interpolation method is used to calculate the wind-solar resource complementarity rate grid data of the study area year by year;

[0033] Step 3.3, based on the annual wind-solar resource complementarity rate grid data of the study area obtained in step 3.2, use the longitude and latitude data of the silt dam collected in step 1 to extract the annual wind-solar resource complementarity rate corresponding to the longitude and latitude on the wind-solar resource complementarity rate grid data, and obtain the annual wind-solar resource complementarity rate of each silt dam location point.

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

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

[0036]

[0037] In the formula, R W The water yield of the basin controlled by the silt dam, m 3 ; A is the catchment area controlled by the silt dam, km 2 ; H is the flow depth of the basin controlled by the silt dam, mm.

[0038] The specific process of step 5 is:

[0039] According to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, the sequence values ​​of the two data time periods of each silt dam are selected to select the optimal theoretical marginal distribution function respectively. The specific formula is as follows:

[0040] F(x)=p(X≤x) (16);

[0041]

[0042] Where F(x) represents the probability distribution function, which is the empirical probability distribution function and various 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 kth value of X; F mar (x k ) represents the theoretical marginal distribution cumulative probability of the kth value of X; l represents the sequence length of X; RMSE represents the root mean square error; by calculating the RMSE between the cumulative probabilities of multiple 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 value is selected as the optimal theoretical marginal distribution function of variable X.

[0043] The specific process of step 6 is:

[0044] Step 6.1, based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, the joint distribution function between the two is constructed using the Copula function. The specific formula is as follows:

[0045]

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

[0047] Where X1 represents the wind-solar complementarity ratio; X2 represents the water production; 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,X2is the optimal theoretical marginal distribution function of X2; C represents the Copula function; C(u1,u2) represents the cumulative probability of the joint distribution function between the two variables; u1 and u2 represent the cumulative probabilities of the optimal theoretical marginal distribution functions of the two variables calculated at the values ​​of x1 and x2, respectively; K represents the number of parameters of the Copula function, and L is the 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 selected, and the joint distribution function between the two variables is constructed.

[0048] Step 6.2, based on the joint distribution function between the wind-solar complementarity rate and water production constructed in step 6.1, a conditional probability model is further constructed to calculate the probability of different levels of water production under different wind-solar complementarity rates for each silt dam. The specific formula is as follows:

[0049]

[0050] Where P(X2≤x2|X1≤x1) represents the probability of occurrence of x2 value in X2 variable under the condition of occurrence of x1 value in X1 variable, that is, the probability of occurrence of different levels of water production under different wind and solar resource complementarity rates of the silt dam.

[0051] The specific process of step 7 is:

[0052] Based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates of the silt dam calculated in step 6.2, the sensitivity of different levels of water production to wind-solar complementarity is calculated using a linear regression model. The specific formula is as follows:

[0053]

[0054] In the formula, represents the probability of the fth value of X2 occurring under the condition that the gth value of X1 occurs; 1,g It 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 here to characterize the sensitivity of the fth level of water production to wind-solar complementarity. The larger the value, the more sensitive the water production is to wind-solar complementarity.

[0055] The beneficial effect of the present invention is that the present invention can not only calculate the water production of the watershed controlled by the silt dam and the wind energy and light energy at its corresponding position, but also calculate the complementarity of wind and light resources at that position, quantify the sensitivity of the water production of the silt dam to the wind and light complementarity, and provide clean energy utilization strategies and scientific support for the sustainable use of local silt dam water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1It is a flow chart of the sensitivity assessment method of the water yield of the watershed controlled by the silt dam to the wind-solar complementarity of the present invention;

[0057] Figure 2 It is a schematic diagram of the spatial distribution of meteorological stations and backbone silt dams in the Yellow River Jiziwan area in Example 3 of the method for evaluating the sensitivity of water production in a watershed controlled by a silt dam to wind-solar complementarity of the present invention;

[0058] Figure 3 It is a schematic diagram of the calculation results of the sensitivity of the water production of the backbone silt dam in the Ji-shaped bend area of ​​the Yellow River to the wind-solar complementarity in Example 3 of the method for evaluating the sensitivity of the water production of the watershed controlled by the silt dam to the wind-solar complementarity of the present invention. DETAILED DESCRIPTION

[0059] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Example 1

[0061] The sensitivity assessment method of the water yield of the silt dam-controlled watershed to wind-solar complementarity of the present invention has the following process: Figure 1 As shown, the specific steps include:

[0062] Step 1: Define the research period and collect the altitude, daily sunshine hours and wind speed meteorological data of the meteorological stations inside and around the study area during the period, as well as the latitude and longitude of the silt dam, the time of dam construction, the controlled watershed area and the annual runoff depth data;

[0063] Step 2: Use the altitude, daily sunshine hours and wind speed meteorological data of the meteorological stations inside and around the study area during the study period collected in step 1 as inputs of the solar radiation model and 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 hours data of the meteorological stations inside and around the study area during the study 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 amount of light energy. The specific formula is as follows:

[0065]

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

[0067] The calculation formulas for other variables in formula (1) are as follows:

[0068]

[0069] In the formula, G sc is the solar constant, with a value of 0.082; ω s is the sunrise angle, rad; d r is the average distance between the reversed sun and the earth; is latitude, rad; δ is longitude, rad; J is date;

[0070] Step 2.2: Use the daily wind speed data of the meteorological stations inside and around the study area during the study period collected in step 1 as the input of the wind energy calculation model to calculate the annual wind energy of each meteorological station. The specific formula is as follows:

[0071]

[0072] ρ=1.225e -0.001Z (9);

[0073] In the formula, E W For wind energy, MJ·m -2 ; W is wind energy density, W·m -2 ; ρ is air density, kg·m -3 ; v is the daily wind speed, m·s -1 , h; e is absolute humidity, which is the constant value corresponding to the water vapor parameter table at different temperatures, mm; z is the altitude of the weather 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 resource complementarity rate of each meteorological station is calculated, and the annual wind-solar resource complementarity rate grid data is further obtained through spatial interpolation; and the longitude and latitude data of each silt dam collected in step 1 are used to extract the annual wind-solar resource complementarity rate of the corresponding silt dam;

[0075] Step 3.1, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, calculate the annual standard deviation of solar radiation and annual standard deviation of wind energy of each meteorological station, and then calculate the annual wind-solar resource complementarity rate of each meteorological station. The specific formula is as follows:

[0076]

[0077] In the formula, CR W,RS,i is the wind-solar resource complementarity rate in the i-th year, which represents the magnitude of wind-solar complementarity. The larger the value, the stronger the complementarity. SD RS,i , SD W,i and SD W,RS,iare the standard deviation of solar radiation, wind energy and the standard deviation of wind energy and light energy complemented by each other in the i-th year; E W,RS It represents the result of the daily complementation of wind energy and solar energy, MJ·m -2 ; R s,i,j 、E W,i,j and E W,RS,i,j are the solar radiation, wind energy, and the result of wind energy and light energy complementation on the jth day of the i-th year, MJ·m -2 ; and are the multi-year averages of daily solar radiation, wind energy, and the result of wind energy and light energy complementing each other, MJ·m -2 ;m i is the number of days in the i-th year;

[0078] Step 3.2, based on the wind-solar resource complementarity rate of each meteorological station calculated in step 3.1, the Kriging spatial interpolation method is used to calculate the wind-solar resource complementarity rate grid data of the study area year by year;

[0079] Step 3.3, based on the annual wind-solar resource complementarity rate grid data of the study area obtained in step 3.2, use the longitude and latitude data of the silt dam collected in step 1 to extract the annual wind-solar resource complementarity rate corresponding to the longitude and latitude on the wind-solar resource complementarity rate grid data, and obtain the annual wind-solar resource complementarity rate of each silt dam location point.

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

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

[0082]

[0083] In the formula, R W The water yield of the basin controlled by the silt dam, m 3 ; A is the catchment area controlled by the silt dam, km 2 ; H is the flow depth of the basin controlled by the silt dam, mm;

[0084] Step 5, according to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, select the part of each silt dam where the two data time periods are consistent, and select the optimal theoretical marginal distribution function respectively;

[0085] According to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, the sequence values ​​of the two data time periods of each silt dam are selected to select the optimal theoretical marginal distribution function respectively. The specific formula is as follows:

[0086] F(x)=p(X≤x) (16);

[0087]

[0088] Where F(x) represents the probability distribution function, which is the empirical probability distribution function and various 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 kth value of X; F mar (x k ) represents the theoretical marginal distribution cumulative probability of the kth value of X; l represents the sequence length of X; RMSE represents the root mean square error. By calculating the RMSE between the cumulative probabilities of multiple theoretical marginal probability distribution functions and the cumulative probabilities 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 variable X.

[0089] Step 6: Based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, a joint distribution function between the two is constructed using the Copula function, and then a conditional probability model is further constructed to calculate the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam;

[0090] Step 6.1, based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, the joint distribution function between the two is constructed using the Copula function. The specific formula is as follows:

[0091]

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

[0093] Where X1 represents the wind-solar complementarity ratio; X2 represents the water production; 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,X2is the optimal theoretical marginal distribution function of X2; C represents the Copula function; C(u1,u2) represents the cumulative probability of the joint distribution function between the two variables; u1 and u2 represent the cumulative probabilities of the optimal theoretical marginal distribution functions of the two variables calculated at the values ​​of x1 and x2, respectively; K represents the number of parameters of the Copula function, and L is the 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 selected, and the joint distribution function between the two variables is constructed.

[0094] Step 6.2, based on the joint distribution function between the wind-solar complementarity rate and water production constructed in step 6.1, a conditional probability model is further constructed to calculate the probability of different levels of water production under different wind-solar complementarity rates for each silt dam. The specific formula is as follows:

[0095]

[0096] Where P(X2≤x2|X1≤x1) represents the probability of occurrence of x2 value in X2 variable under the condition of occurrence of x1 value in X1 variable, that is, the probability of occurrence of different levels of water production under different wind and solar resource complementarity rates of the silt dam.

[0097] Step 7, based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam calculated in step 6, a linear regression model is used to quantify the sensitivity of different levels of water production to wind-solar complementarity.

[0098] Example 2

[0099] Based on Example 1, the specific process of step 7 is as follows:

[0100] Based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates of the silt dam calculated in step 6.2, the sensitivity of different levels of water production to wind-solar complementarity is calculated using a linear regression model. The specific formula is as follows:

[0101]

[0102] In the formula, represents the probability of the fth value of X2 occurring under the condition that the gth value of X1 occurs; 1,g It 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 here to characterize the sensitivity of the fth level of water production to wind-solar complementarity. The larger the value, the more sensitive the water production is to wind-solar complementarity.

[0103] Example 3

[0104] Taking the Yellow River Bend area as an example, the specific implementation steps are as follows:

[0105] Step 1: Determine the research period from 1965 to 2018, collect the altitude, daily sunshine hours and wind speed meteorological data of 135 meteorological stations in and around the Yellow River Bend area during this period, as well as the longitude and latitude, dam construction time, controlled basin 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 Yellow River Bend area is as follows: Figure 2 As shown;

[0106] Step 2: Use the altitude, daily sunshine hours and wind speed meteorological data of 135 meteorological stations in and around the Yellow River Bend area from 1965 to 2018 collected in step 1 as inputs of the solar radiation model and wind energy calculation model, respectively, to calculate the daily solar radiation and wind energy of each meteorological station;

[0107] Step 2.1, using the daily sunshine hours data of 135 meteorological stations in and around the Yellow River Bend area from 1965 to 2018 collected in step 1 as the input of the solar radiation model, the daily solar radiation of each meteorological station is calculated to represent the amount of light energy. The specific formula is 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 extraterrestrial radiation, MJ·m -2 ; N is the maximum possible sunshine hours, h; a s and b s is a parameter with values ​​of 0.25 and 0.5 respectively; the calculation formulas of other variables in formula (1) are as follows:

[0110]

[0111]

[0112] In the formula, G sc is the solar constant, with a value of 0.082; ω s is the sunrise angle, rad; d r is the average distance between the reversed sun and the earth; is latitude, rad; δ is longitude, rad; J is date;

[0113] Step 2.2, using the altitude and daily wind speed data of 135 meteorological stations in and around the Yellow River Bend area from 1965 to 2018 collected in step 1 as the input of the wind energy calculation model, the annual wind energy size of each meteorological station is calculated. The specific formula is as follows:

[0114]

[0115] ρ=1.225e -0.001Z

[0116] In the formula, E W For wind energy, MJ·m -2 ; W is wind energy density, W·m -2 ; ρ is air density, kg·m -3 ; v is the daily wind speed, m·s -1 , h; e is absolute humidity, which is the constant value corresponding to the water vapor parameter table at different temperatures, mm; z is the altitude of the weather station, m;

[0117] Step 3: Based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the wind-solar resource complementarity rate of each meteorological station from 1965 to 2018 is calculated, and the annual wind-solar resource complementarity rate grid data is further obtained through spatial interpolation; the longitude and latitude data of each silt dam collected in step 1 are then used to extract the annual wind-solar resource complementarity rate of the corresponding silt dam;

[0118] Step 3.1, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, calculate the annual standard deviation of solar radiation and annual standard deviation of wind energy of each meteorological station from 1965 to 2018, and then calculate the annual wind-solar resource complementarity rate of each meteorological station. 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 complementarity rate in the i-th year, which represents the magnitude of wind-solar complementarity. The larger the value, the stronger the complementarity. SD RS,i , SD W,i and SD W,RS,i are the standard deviation of solar radiation, wind energy and the standard deviation of wind energy and light energy complemented by each other in the i-th year; E W,RSIt represents the result of the daily complementation of wind energy and solar energy, MJ·m -2 ; R s,i,j 、E W,i,j and E W,RS,i,j are the solar radiation, wind energy, and the result of wind energy and light energy complementation on the jth day of the i-th year, MJ·m -2 ; and are the multi-year averages of daily solar radiation, wind energy, and the result of wind energy and light energy complementing each other, MJ·m -2 ;m i is the number of days in the i-th year;

[0124] Step 3.2, based on the wind-solar resource complementarity rate of each meteorological station calculated in step 3.1, the Kriging spatial interpolation method is used to calculate the grid data of the wind-solar resource complementarity rate of the Yellow River Bend area from 1965 to 2018;

[0125] Step 3.3, based on the annual wind-solar resource complementarity rate grid data of the Yellow River Ji-ziwan area from 1965 to 2018 obtained in step 3.2, use the longitude and latitude data of 4040 backbone silt dams collected in step 1 to extract the annual wind-solar resource complementarity rate corresponding to the longitude and latitude on the wind-solar resource complementarity rate grid data, and obtain the annual wind-solar resource complementarity rate of each silt dam location point.

[0126] Step 4: Based on the construction time, controlled watershed area and annual runoff depth data of the 4040 key check dams collected in Step 1, calculate the annual water yield data of each check dam since its construction;

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

[0128]

[0129] In the formula, R W The water yield of the basin controlled by the silt dam, m 3 ; A is the catchment area controlled by the silt dam, km 2 ; H is the flow depth of the basin controlled by the silt dam, mm;

[0130] Step 5, according to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, select the part of each silt dam where the two data time periods are consistent, and select the optimal theoretical marginal distribution function respectively;

[0131] According to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction obtained in step 4, the sequence values ​​of the two data time periods of each silt dam are selected (the dam construction time is earlier than 1965, and the water production and wind-solar resource complementarity time series are selected from 1965 and later time periods; the dam construction time is later than 1965, and the water production and wind-solar resource complementarity time series are selected from the time of the dam construction and later time periods), and their optimal theoretical marginal distribution functions are selected respectively. The specific formula is as follows:

[0132] F(x)=p(X≤x)

[0133]

[0134] Where F(x) represents the probability distribution function, which is the empirical probability distribution function and various 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 kth value of X; F mar (x k ) represents the theoretical marginal distribution cumulative probability of the kth value of X; l represents the sequence length of X; RMSE represents the root mean square error. By calculating the RMSE between the cumulative probabilities of multiple theoretical marginal probability distribution functions and the cumulative probabilities 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 variable X.

[0135] Step 6: Based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, a joint distribution function between the two is constructed using the Copula function, and then a conditional probability model is further constructed to calculate the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam;

[0136] Step 6.1, based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, the Copula function is used to construct the joint distribution function between the two. The specific formula is as follows:

[0137]

[0138] AIC=2K-2lnL

[0139] Where X1 represents the wind-solar complementarity ratio; X2 represents the water production; 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,X2is the optimal theoretical marginal distribution function of X2; C represents the Copula function; C(u1,u2) represents the cumulative probability of the joint distribution function between the two variables; u1 and u2 represent the cumulative probabilities of the optimal theoretical marginal distribution functions of the two variables calculated at the values ​​of x1 and x2, respectively; K represents the number of parameters of the Copula function, and L is the 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 selected, and the joint distribution function between the two variables is constructed.

[0140] Step 6.2, based on the joint distribution function between the wind-solar complementarity rate and water production constructed in step 6.1, a conditional probability model is further constructed to calculate the probability of different levels of water production under different wind-solar complementarity rates for each silt dam. The specific formula is as follows:

[0141]

[0142] Where P(X2≤x2|X1≤x1) represents the probability of occurrence of x2 value in X2 variable under the condition of occurrence of x1 value in X1 variable, that is, the probability of occurrence of different levels of water production under different wind and solar resource complementarity rates of the silt dam.

[0143] Step 7, based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam calculated in step 6, a linear regression model is used to quantify the sensitivity of different levels of water production to wind-solar complementarity.

[0144] Based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates of the silt dam calculated in step 6.2, the sensitivity of different levels of water production to wind-solar complementarity is calculated using a linear regression model. The specific formula is as follows:

[0145]

[0146] In the formula, represents the probability of the fth value of X2 occurring under the condition that the gth value of X1 occurs; 1,g represents the g-th 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 here to characterize the sensitivity of the f-th level of water production to wind-solar complementarity. The larger the value, the more sensitive the water production is to wind-solar complementarity. In this example, f takes the values ​​of 0.05, 0.20, 0.35, 0.50, 0.65, 0.80 and other quantiles, X 2,fThe quantile values ​​corresponding to each quantile point are the quantile values ​​of the water yield of each backbone check dam in the controlled basin; g is taken as 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, 1 and other 20 quantile points, X 1,g The quantile values ​​corresponding to each quantile point, that is, the quantile values ​​of the wind-solar resource complementarity rate at each backbone silt dam location point, are calculated to determine the sensitivity of the six levels of water production of each silt dam to the wind-solar resource complementarity rate, such as Figure 3 As shown. Figure 3 It can be seen that the average sensitivity of the six levels of water production of 4040 backbone check dams to the wind-solar resource complementarity rate are 0.003, 0.006, 0.006, 0.006, 0.005 and 0.004, respectively, indicating that medium to small water production is more sensitive to wind-solar complementarity.

Claims

1. A method for assessing the sensitivity of water yield in a watershed controlled by a silt dam to wind-solar complementarity, characterized by: The specific process includes the following: clarify the research period, collect data inside and around the study area during this period; calculate and obtain the annual wind-solar resource complementarity rate raster data, and extract the annual wind-solar resource complementarity rate of the corresponding silt dam; then calculate and obtain the annual water production data of each silt dam since its construction; select the optimal theoretical marginal distribution function respectively; construct the joint distribution function between the two, and calculate the conditional probability of occurrence of different levels of water production; use the linear regression model to quantify the sensitivity of different levels of water production to wind-solar complementarity.

2. The sensitivity assessment method of water yield in a silt dam-controlled watershed to wind-solar complementarity according to claim 1 is characterized by: The specific steps include: Step 1: Define the research period and collect the altitude, daily sunshine hours and wind speed meteorological data of the meteorological stations inside and around the study area during the period, as well as the latitude and longitude of the silt dam, the time of dam construction, the controlled watershed area and the annual runoff depth data; Step 2: Use the altitude, daily sunshine hours and wind speed meteorological data of the meteorological stations inside and around the study area during the study period collected in step 1 as inputs of the solar radiation model and wind energy calculation model, respectively, to calculate the daily solar radiation and wind energy of each meteorological station; Step 3: Based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, the annual wind-solar resource complementarity rate of each meteorological station is calculated, and the annual wind-solar resource complementarity rate grid data is obtained through spatial interpolation; and the longitude and latitude data of each silt dam collected in step 1 are used to extract the annual wind-solar resource complementarity rate of the corresponding silt dam; Step 4, based on the construction time of the silt dam, the controlled watershed area and the annual runoff depth data collected in step 1, calculate and obtain the annual water yield data of each silt dam since its construction; Step 5, according to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, select the part of each silt dam where the two data time periods are consistent, and select the optimal theoretical marginal distribution function; Step 6: Based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, a joint distribution function between the two is constructed using the Copula function, and a conditional probability model is constructed to calculate the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam; Step 7, based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates for each silt dam calculated in step 6, a linear regression model is used to quantify the sensitivity of different levels of water production to wind-solar complementarity.

3. The sensitivity assessment method of water yield in a silt dam-controlled watershed to wind-solar complementarity according to claim 2 is characterized in that: The specific process of step 2 is: Step 2.1: Use the daily sunshine hours data of the meteorological stations inside and around the study area during the study 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 amount of light energy. The specific formula is as follows: In the formula, R S is the solar radiation, MJ·m -2 ; n is the daily sunshine hours, h; R a is the extraterrestrial radiation, MJ·m -2 ; N is the maximum possible sunshine hours, h; a s and b s is the parameter; The calculation formulas for other variables in formula (1) are as follows: In the formula, G sc is the solar constant; ω s is the sunrise angle, rad; d r is the average distance between the reversed sun and the earth; is latitude, rad; δ is longitude, rad; J is date; Step 2.2: Use the daily wind speed data of the meteorological stations inside and around the study area during the study period collected in step 1 as the input of the wind energy calculation model to calculate the annual wind energy of each meteorological station. The specific formula is as follows: p=1.225e -0.001Z (9); In the formula, E W For wind energy, MJ·m -2 ; W is wind energy density, W·m -2 ; ρ is air density, kg·m -3 ; v is the daily wind speed, m·s -1 , h; e is absolute humidity, which takes the corresponding constant value according to the water vapor parameter table at different temperatures, mm; z is the altitude of the meteorological station, m.

4. According to the sensitivity assessment method of water yield in a silt dam-controlled watershed to wind-solar complementarity according to claim 3, the specific process of step 3 is as follows: Step 3.1, based on the daily solar radiation and wind energy of each meteorological station obtained in step 2, calculate the annual standard deviation of solar radiation and annual standard deviation of wind energy of each meteorological station, and then calculate the annual wind-solar resource complementarity rate of each meteorological station. The specific formula is as follows: AND W,RS =And W +R S (12); In the formula, CR W,RS,i is the wind-solar resource complementarity rate in the i-th year; SD RS,i , SD W,i and SD W,RS,i are the standard deviation of solar radiation, wind energy and the standard deviation of wind energy and light energy complementing each other in the i-th year; E W,RS It represents the result of the daily complementation of wind energy and solar energy, MJ·m -2 ; R s,i,j 、E W,i,j and E W,RS,i,j are the solar radiation, wind energy, and the result of wind energy and light energy complementation on the jth day of the i-th year, MJ·m -2 ; and are the multi-year averages of daily solar radiation, wind energy, and the result of wind energy and light energy complementing each other, MJ·m -2 ; m i is the number of days in the i-th year; Step 3.2, based on the wind-solar resource complementarity rate of each meteorological station calculated in step 3.1, the Kriging spatial interpolation method is used to calculate the wind-solar resource complementarity rate grid data of the study area year by year; Step 3.3, based on the annual wind-solar resource complementarity rate grid data of the study area obtained in step 3.2, use the longitude and latitude data of the silt dam collected in step 1 to extract the annual wind-solar resource complementarity rate corresponding to the longitude and latitude on the wind-solar resource complementarity rate grid data, and obtain the annual wind-solar resource complementarity rate of each silt dam location point.

5. The method for evaluating the sensitivity of water yield in a watershed controlled by a silt dam to wind-solar complementarity according to claim 4 is characterized in that: The specific process of step 4 is as follows: Based on the construction time of the silt dam, the controlled basin area and the annual runoff depth data collected in step 1, the annual water yield data of each silt dam since its construction is calculated. The calculation formula is as follows: In the formula, R W The water yield of the basin controlled by the silt dam, m 3 ; A is the catchment area controlled by the silt dam, km 2 ; H is the flow depth of the basin controlled by the silt dam, mm.

6. The sensitivity assessment method of water yield in a silt dam-controlled watershed to wind-solar complementarity according to claim 5 is characterized in that: The specific process of step 5 is as follows: According to the annual wind-solar resource complementarity rate of each silt dam extracted in step 3 and the annual water production data of each silt dam since its construction calculated in step 4, the sequence values ​​of the two data time periods of each silt dam are selected to determine the optimal theoretical marginal distribution function respectively. The specific formula is as follows: F(x)=p(X≤x) (16); Where F(x) represents the probability distribution function, which is the empirical probability distribution function and various 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 kth value of X; F mar (x k ) represents the theoretical marginal cumulative probability of the kth value of X; l represents the sequence length of X; RMSE represents the root mean square error.

7. The sensitivity assessment method of water yield in a watershed controlled by a silt dam to wind-solar complementarity according to claim 6 is characterized in that: The specific process of step 6 is as follows: Step 6.1, based on the optimal theoretical marginal distribution function of the wind-solar resource complementarity rate and water production data of each silt dam obtained in step 5, the joint distribution function between the two is constructed using the Copula function. The specific formula is as follows: AIC = 2K-2lnL (19); Where X1 represents the wind-solar complementarity ratio; X2 represents the water production; 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,X2 is the optimal theoretical marginal distribution function of X2; C represents the Copula function; C(u1,u2) represents the cumulative probability of the joint distribution function between two variables; u1 and u2 represent the cumulative probabilities of the optimal theoretical marginal distribution functions of the two variables calculated at the values ​​of x1 and x2, respectively; K represents the number of parameters of the Copula function, and L is the likelihood function; AIC represents the Akaike Information Criterion value; Step 6.2, based on the joint distribution function between the wind-solar complementarity rate and water production constructed in step 6.1, a conditional probability model is constructed to calculate the probability of occurrence of different levels of water production under different wind-solar complementarity rates for each silt dam. The specific formula is as follows: Where P(X2≤x2|X1≤x1) represents the probability of occurrence of x2 value in X2 variable under the condition of occurrence of x1 value in X1 variable, that is, the probability of occurrence of different levels of water production under different wind and solar resource complementarity rates of the silt dam.

8. The method for evaluating the sensitivity of water yield in a watershed controlled by a silt dam to wind-solar complementarity according to claim 7, characterized in that: The specific process of step 7 is as follows: Based on the probability of occurrence of different levels of water production under different wind-solar resource complementarity rates of the silt dam calculated in step 6.2, the sensitivity of different levels of water production to wind-solar complementarity is calculated using a linear regression model. The specific formula is as follows: In the formula, represents the probability of the fth value of X2 occurring under the condition that the gth value of X1 occurs; 1,g It 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 here to characterize the sensitivity of the fth level of water production to wind-solar complementarity. The larger the value, the more sensitive the water production is to wind-solar complementarity.

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