Seasonal Hydrological and Meteorological Time Series

Earth sciences research journal

Cepeda Cuervo, Edilberto¹  Achcar, Joige Alberto²  Andrade, Marinho G.³ 

[1] Universidad Nacional de Colombia, Bogotá, Colombia;Universidade de São Paulo, Ribeirão Preto, Brazil;Universidade de São Paulo, Sao Carlos, Brazil

关键词: Hydrology time series;    Meteorological time series;    Conditional regression models;    Bayesian analysis;    MCMC methods.;   

DOI  :  10.15446/esrj.v22n2.65577

学科分类：天文学(综合)

来源: Universidad Nacional de Colombia * Departamento de Geociencias

【 摘 要 】

Time series models are often used in hydrology and meteorology studies 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 sets 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.

【 授权许可】

CC BY