Inferencia Bayesiana conjunta de modelos hidrológicos y modelos de error generalizados, para la evaluación de las incertidumbres predictiva y de los parámetros

Over the years, the method of least squares (SLS) has been the method of inference commonly applied in hydrological modeling, although its hypotheses are not respected by the modeling errors. Awareness of the fact that the hydrological modeling process is affected by more, and more important, sources of uncertainty that the purely observational, the only source of error considered by SLS, has contributed to the appearance of publications that suggest the need for applying more appropriate inference methods on hydrological models, and in general, on environmental models. The adequacy of inference methods involves considering all sources of error, or their effects, which influence the modeling process. Only in this way it is possible to obtain reliable parameters, a non-biased prediction and a correct estimation of the uncertainty of both, these being the main objectives of this Doctoral Thesis. To this end, this thesis proposes the joint inference, following the Bayesian inference paradigm, of hydrological parameters and the parameters of a generalized error model, which provides the necessary flexibility to relax all hypotheses (Gaussian errors with null mean, independent and identically distributed), which disable the SLS error model to infer hydrological models. The main contribution of the thesis is the proposition of the methodology to follow, for the correct application of the direct modeling (without previous transformation of the variables) of the variance of the errors. This methodology is based on the need to consider the coupling, during the joint inference, between the variations of the marginal distribution of the errors and the variations of their conditional distributions, which are modeled by the error model.
[Valencia: 2017]

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