Abstract
Nonlinear serial dependence and skewness of annual hydrologic time series {X t } have been challenging the classical theory of Gaussian stochastic processes, particularly if the study of extremes (dry or wet years) is required as it is often the case. In this paper, a new and general model is proposed assuming that the geophysical system which is responsible forX t can take different states and that this state process is modeled by a Markov chain. At each time,X t is generated from a statistical distribution which depends on the state that has occurred. This model can preserve non-linear structures of serial dependence and it can produce a skewed marginal distribution ofX t without any transformation. A successful application of this model to the study of annual rainfall at Fortaleza (Northeast of Brazil) is also presented.
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Tavares, L.V., Souza, R.C. A multiple distribution Markovian model for annual hydrologic time series. Stochastic Hydrol Hydraul 2, 295–302 (1988). https://doi.org/10.1007/BF01544042
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DOI: https://doi.org/10.1007/BF01544042