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A new single-site monthly streamflow simulation method that combines the maximum entropy and mixed copula methods is proposed. In this method, the marginal distribution of monthly streamflow is estimated using the maximum entropy theory; this is achieved by the conjugate gradient method of superlinear convergence and low computational expense. Then, a mixed copula is used for the construction of joint distributions of the adjacent monthly streamflow. The developed method is applied to four stations of the Weihe River, China; the results show that the important statistical characteristics of monthly streamflow can be maintained by the entropy-based marginal distribution and that the complex linear and nonlinear dependence between adjacent monthly streamflow can be reproduced by the mixed copula method. Compared with the traditional marginal distribution and the individual copula joint distribution, the entropy-mixed copula method has obvious advantages in goodness-of-fit statistical tests on the marginal and joint distributions. Accurately understanding the monthly runoff distribution is beneficial for flood control during the flood season and solving water shortage problems during the dry season. First, the marginal probability distribution function of each month is established through the principle of maximum entropy. Then, a mixed copula model is used to construct a joint distribution of adjacent monthly runoff. Finally, Gibbs sampling is used to obtain the randomly simulated runoff for each month. The advantage of this method is that it does not require assuming the probability distribution of the runoff series but rather uses important statistical features of the historical series as constraints for deriving the marginal distribution. Accurately describing the complex relationship between water flow sequences, maintaining the statistical characteristics of observation sequences, and improving simulation accuracy are of great significance for water resource planning and management. [ABSTRACT FROM AUTHOR]

Mountain torrent disaster prevention is the focus of flood control and disaster reduction in China. Critical rainfall is an important indicator to determine the success or failure of mountain torrent disaster early warning. In this paper, the M-Copula function is introduced, the multi-dimensional joint distribution of critical rainfall is constructed, and the joint distribution of rainfall and peak rainfall intensity is analyzed. Taking A village in Xinxian County as an example. The critical rainfall of the combined probability is calculated, and the critical rainfall of the flash flood disaster water level, the pre-shift warning and the sharp-shift warning is warned and analyzed. The results show that the flood peak modulus calculated by Yishangfan group is 8.89, which has certain rules for the flood peak modulus of rivers in hilly areas. The larger the basin area is, the smaller the flood peak modulus is, the smaller the area is, and the larger the flood peak modulus is. The calculation result of the design flow of 533 m3/s is reasonable. It is reasonable and reliable to select the M-Copula function as the connection function to fit the joint distribution of rainfall and peak rainfall intensity, which can provide theoretical support for flash flood disaster warning in other regions.

With the rapid development of economy and society, the contradiction between supply and demand of water resources is increasing. Efficient utilization and allocation of limited water resources are one of the main means to solve the above contradictions. In this paper, the multidimensional joint distribution of natural streamflow series in reservoirs is constructed by introducing the mixed Copula function, and the probability of wet and dry encounters between natural streamflow is analyzed. Luan River is located in the northeastern part of Hebei Province, China, taking the group of Panjiakou Reservoir, Douhe Reservoir and Yuqiao Reservoir in the downstream of Luan River Basin as an example, the probabilities of synchronous and asynchronous abundance and depletion of inflow from the reservoirs are calculated. The results show that the probability of natural streamflow series between reservoirs is 61.14% for asynchronous wetness and dryness, which has certain mutual compensation ability. Therefore, it is necessary to minimize the risk of water supply security in Tianjin, Tangshan and other cities, and strengthen the optimal joint water supply scheduling of reservoirs. The research results are reasonable and reliable, which can provide reference for water supply operation of other basins. HIGHLIGHTS The mixed Copula function can more flexibly describe the correlation between variables of a hydrological system.; The M-Copula function is selected as the connection function to fit the joint distribution of runoff between reservoirs.; The results of the study can be used as a reference for the optimal scheduling of water resources in other basins.;

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Fluctuations are a key characteristic of the wind resource. It is important to quantitatively analyze wind direction fluctuation due to its influence on the optimization of wind turbine yaw control. Based on wind resource data available from SCADA systems, a method is proposed to describe wind direction fluctuations in terms of fluctuation amplitude A and fluctuation duration T. A Weibull distribution is employed to fit the marginal probability density of both these two measures of wind direction fluctuations, and a mixed Copula used to connect the marginal distributions, establishing the joint probability density function. This representation has been verified through comparison with the real operating SCADA data. A set of indicators are extracted from the probability distribution which can accurately quantify the local wind direction fluctuation characteristics of a wind turbine. These indicators can be helpful in the optimization of the yaw control system parameters, facilitating an improvement in the power generating performance of the wind turbine. • Wind direction fluctuations are studied with statistic method and SCADA data. • Weibull distribution and copula are used to model the wind direction fluctuations. • A set of indicators are extracted to quantify the wind direction fluctuations. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Annual rainfall is -sufficient in the Lower Huaihe River basin, which easily leails lo floods and Incomes ihe ihreat to the people's lives and properties. Thus, il has put forward higher requirements about the estimation of flood peak anil the whole process of flood volume and the construction of flood control engineering works. In this paper, the mixed Copula function was used lo build the relationship between the flood peak and volume, using the genetic algorithm lo calculate the coefficient combination of Frank Copula, Clayton Copula and Gumbel Hough Copula corresponding the minimum deviation squared sum. On this basis, il calculated the flood volume of time interval according to the relationship between the edge distribution of flood peak and the joint distribution for the references of flood control engineering design. The analysis results of measured data at Zhongdu Hydrological Station show thai using the mixed Copula is more accurate than using ihe single Copula function, the mixed Copula has the characteristics of flexibility and can fit the correlation of two variables well. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)