Content area

Abstract

In order to understand the behavior of the glaciers, their mass balance should be studied. The loss of water produced by melting, known as glacier discharge, is one of the components of this mass balance. In this paper, a vine copula structure is proposed to model the multivariate and nonlinear dependence among the glacier discharge and other related meteorological variables such as temperature, humidity, solar radiation and precipitation. The multivariate distribution of these variables is expressed as a mixture of four components according to the presence or not of positive discharge and/or positive precipitation. Then, each of the four subgroups is modelled with a vine copula. The conditional probability of zero discharge for given meteorological conditions is obtained from the proposed joint distribution. Moreover, the structure of the vine copula allows us to derive the conditional distribution of the glacier discharge for the given meteorological conditions. Three different prediction methods for the values of the discharge are used and compared. The proposed methodology is applied to a large database collected since 2002 by the GLACKMA association from a measurement station located in the King George Island in the Antarctica. Seasonal effects are included by using different parameters for each season. We have found that the proposed vine copula model outperforms a previous work where we only used the temperature to predict the glacier discharge using a time-varying bivariate copula.

Details

Title
Vine copula models for predicting water flow discharge at King George Island, Antarctica
Author
Gómez, Mario 1   VIAFID ORCID Logo  ; Ausín, M Concepción 2   VIAFID ORCID Logo  ; Domínguez, M Carmen 3 

 Department of Statistics, Universidad Carlos III de Madrid, Getafe, Spain 
 Department of Statistics, Universidad Carlos III de Madrid, Getafe, Spain; UC3M-BS Institute of Financial Big Data (IFiBiD), Getafe, Spain 
 Department of Applied Mathematics, University of Salamanca, Salamanca, Spain 
Pages
2787-2807
Publication year
2018
Publication date
Oct 2018
Publisher
Springer Nature B.V.
ISSN
14363240
e-ISSN
14363259
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2093910352
Copyright
Stochastic Environmental Research and Risk Assessment is a copyright of Springer, (2018). All Rights Reserved.