Modelling Catchment Rainfall Using Sum of Correlated Gamma Variables

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Zakaria, R. ◽  
Howlett, P. G. ◽  
Piantadosi, J. ◽  
Boland, J. W. ◽  
Moslim, N. H.

One of the major difficulties in simulating rainfall is the need to accurately represent rainfall accumulations. An accurate simulation of monthly rainfall should also provide an accurate simulation of yearly rainfall by summing the monthly totals. A major problem in this regard is that rainfall distributions for successive months may not be independent. Thus the rainfall accumulation problem must be represented as the summation of dependent random variables. This study is aimed to show if the statistical parameters for several stations within a particular catchment is known, then a weighted sum is used to determine a rainfall model for the entire local catchment. A spatial analysis for the sum of rainfall volumes from four selected meteorological stations within the same region using the monthly rainfall data is conducted. The sum of n correlated gamma variables is used to model the sum of monthly rainfall totals from four stations when there is significant correlation between the stations.

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Xiaochen Ma ◽  
Qunying Wu

In this article, we research some conditions for strong law of large numbers (SLLNs) for weighted sums of extended negatively dependent (END) random variables under sublinear expectation space. Our consequences contain the Kolmogorov strong law of large numbers and the Marcinkiewicz strong law of large numbers for weighted sums of extended negatively dependent random variables. Furthermore, our results extend strong law of large numbers for some sequences of random variables from the traditional probability space to the sublinear expectation space context.


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