Seasonal copula models for the analysis of glacier discharge at King George Island, Antarctica

• Published in: Stochastic Environmental Research and Risk Assessment
• Main Authors: Gómez Díaz, Mario, Ausín Olivera, María Concepción, Dominguez, M. Carmen
• Other Authors: Ministerio de Educación y Ciencia (España)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer 2017
• Subjects: Bayesian Inference; Copulas; Glacier discharge; Seasonality; MCMC; Melt; Modelling; Estadística; Antarctic; King George Island; Artigas; Carlini Station; Collins Glacier; Antarc*; Antarctica
• Online Access: http://hdl.handle.net/10016/35190; https://doi.org/10.1007/s00477-016-1217-7

Modelling glacier discharge is an important issue in hydrology and climate research. Glaciers represent a fundamental water resource when melting of ice and snow contributes to runoff. Glaciers are also studied as natural global warming sensors. GLACKMA association has implemented one of their Pilot Experimental Catchment areas at the King George Island in the Antarctica which records values of the liquid discharge from Collins glacier. In this paper, we propose the use of time-varying copula models for analyzing the relationship between air temperature and glacier discharge, which is clearly non constant and non linear through time. A seasonal copula model is defined where both the marginal and copula parameters vary periodically along time following a seasonal dynamic. Full Bayesian inference is performed such that the marginal and copula parameters are estimated in a one single step, in contrast with the usual two-step approach. Bayesian prediction and model selection is also carried out for the proposed model such that Bayesian credible intervals can be obtained for the conditional glacier discharge given a value of the temperature at any given time point. The proposed methodology is illustrated using the GLACKMA real data where there is, in addition, a hydrological year of missing discharge data which were not possible to measure accurately due to problems in the sounding. We are very grateful to the GLACKMA association. The second author acknowledges financial support by UC3M-BS Institute of Financial Big Data at Universidad Carlos III de Madrid. The third author would like to thank the Russian, Argentinean, German, Uruguayan and Chilean Antarctic Programs for their continuous logistic support over the years. The crews of Bellingshausen, Artigas, and Carlini station as well as the Dallmann Laboratory provided a warm and pleasant environment during fieldwork. GLACKMA’s contribution was also partially financed by the European Science Foundation, ESF project IMCOAST (EUI2009-04068) and the Ministerio de ...