scholarly journals The joint probability distribution of runoff and sediment and its change characteristics with multi-time scales

2014 ◽  
Vol 62 (3) ◽  
pp. 218-225 ◽  
Author(s):  
Jinping Zhang ◽  
Zhihong Ding ◽  
Jinjun You

Abstract River runoff and sediment transport are two related random hydrologic variables. The traditional statistical analysis method usually requires those two variables to be linearly correlated, and also have an identical marginal distribution. Therefore, it is difficult to know exactly the characteristics of the runoff and sediment in reality. For this reason, copulas are applied to construct the joint probability distribution of runoff and sediment in this article. The risk of synchronous-asynchronous encounter probability of annual rich-poor runoff and sediment is also studied. At last, the characteristics of annual runoff and sediment with multi-time scales in its joint probability distribution space are simulated by empirical mode decomposition method. The results show that the copula function can simulate the joint probability distribution of runoff and sediment of Huaxia hydrological station in Weihe River well, and that such joint probability distribution has very complex change characteristics at time scales.

2021 ◽  
Author(s):  
Manqiu Hao ◽  
Cheng Gao ◽  
Jian Chen

Abstract Taking the Taihu Lake Basin as an example, in this study, the characteristics of the rainfall factors in the study area were analyzed using daily rainfall data from 1955 to 2018. Three factors, i.e., the contribution rate of the rainfall in the flood season, the rainfall frequency, and the maximum daily rainfall, were selected to determine the optimal probability distribution function for each single factor. Furthermore, the root mean square error (RMSE) goodness of fit test was used to determine the optimal copula function for the three-dimensional joint probability distribution characteristics of the rainfall factors. The research results show that the three-dimensional copula joint probability method contains much more information than the results of the single variable probability calculation. The copula function can be used to analyze the multi-dimensional joint distribution of rainfall factors, which can fill the gap in research on multiple rainfall factors.


Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hao Li ◽  
Yichuan Tang ◽  
Shibo Li ◽  
Jianquan Ma ◽  
Xiaojie Zhao

The pore ratio is an important parameter affecting the stability and safety of tailings reservoirs; however, the relationship between the pore ratio and physical properties of tailings sand has not been researched in-depth. In this paper, using the tailings from a tungsten mine in southern Shaanxi as a case study, the correlation between the minimum void ratio and related parameters is analyzed, based on laboratory test data, and the optimal marginal distribution function of the parameters is determined. The Gumbel-Hougard copula function that best describes the correlation between parameters is identified, and it is used to establish the joint probability distribution model of the three parameters, and the guarantee rate α is introduced to estimate and analyze the minimum void ratio. The results show that the optimal edge distribution of the fine particle content and specific gravity follows a truncated normal distribution, and the optimal edge distribution of the minimum void ratio follows a logarithmic normal distribution. According to AIC criterion, the Gumbel-Hougard copula is the best three-dimensional copula function to fit the minimum void ratio and related parameters. When the guarantee rate α is 0.485, the joint probability distribution model achieves optimal performance in terms of estimating the minimum void ratio. The maximum error of the estimation is 1.99%, which is verified through data, and the estimation meets the requirements for practical engineering. The method proposed in this paper uses the existing measured data to establish a joint probability distribution model and combines the collected fine particle content and specific gravity data with the guarantee rate to estimate the minimum void ratio, providing a novel basis for the study of the physical properties of tailings.


2018 ◽  
Vol 49 (6) ◽  
pp. 1915-1928 ◽  
Author(s):  
Yang Liu ◽  
Shengle Cao ◽  
Xi Zhang ◽  
Fuzhen Li ◽  
Xitong Li

Abstract Based on the multivariate joint probability distribution of the discharge and water quality indicators, this paper analysed the occurrence probabilities and improvement probabilities of combinations of water quality indicators under different discharge conditions and then presented a method for calculating the optimal discharge to seek a balance between the discharge dispatch and water quality improvement. The method was used to construct the relationship curve between the discharge and joint improvement probability used by a copula function and then calculate the critical point on the curve. The proposed method was applied to the Yi River Basin above Gegou Station with data composed of the discharge and main pollution indicators (NH3-N and CODMn) from 1982 to 2015. The results showed that the trivariate joint probability distribution can more reasonably reveal the statistical characteristics of different combinations of discharge and water quality indicators. Furthermore, the optimal discharges and the corresponding improvement probabilities that improved NH3-N and CODMn to different grades were calculated. The calculation method took the interdependence of multiple water quality indicators into account, thereby providing a more reasonable method for using discharge dispatch data to improve the river water quality.


2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Huilin Huang

We consider an inhomogeneous growing network with two types of vertices. The degree sequences of two different types of vertices are investigated, respectively. We not only prove that the asymptotical degree distribution of typesfor this process is power law with exponent2+1+δqs+β1-qs/αqs, but also give the strong law of large numbers for degree sequences of two different types of vertices by using a different method instead of Azuma’s inequality. Then we determine asymptotically the joint probability distribution of degree for pairs of adjacent vertices with the same type and with different types, respectively.


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