Seasonal hydrological and meteorological time series

Time series models are often used in hydrology and meteorology to model streamflows series in order to make forecasting and generate synthetic series which are inputs for the analysis of complex water resources systems. In this paper we introduce a new modeling approach for hydrologic and meteorolog...

Full description

Autores:
Cepeda Cuervo, Edilberto
Achcar, Jorge Alberto
Andrade, Marinho G.
Tipo de recurso:
Work document
Fecha de publicación:
2019
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/11821
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/11821
http://bdigital.unal.edu.co/9364/
Palabra clave:
33 Economía / Economics
55 Ciencias de la tierra / Earth sciences and geology
Hydrology time series data
Meteorological time series
Seasonal time series
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Time series models are often used in hydrology and meteorology to model streamflows series in order to make forecasting and generate synthetic series which are inputs for the analysis of complex water resources systems. In this paper we introduce a new modeling approach for hydrologic and meteorological time series assuming a continuous distribution for the data, where both the conditional mean and conditional variance parameters are modeled. Bayesian methods using standard MCMC (Markov Chain Monte Carlo Methods) are used to simulate samples for the joint posterior distribution of interest. Two applications to real data set illustrate the proposed methodology, assuming that the observations come from a normal, a gamma or a beta distribution. A first example is given by a time series of monthly averages of natural streamflows, measured in the year period ranging from 1931 to 2010 in Furnas hydroelectric dam, Brazil. A second example is given with a time series of 313 air humidity data measured in a weather station of Rio Claro, a Brazilian city located in southeastern of Brazil. These applications motivate us to introduce new classes of models to analyze hydrological and meteorological time series.