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A likelihood framework for deterministic hydrological models and the importance of non stationary autocorrelation

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Figure 1. Example of skewed Student’s0 t distributions withE[DQ] = Qdet(t) = 2.5 mm h−1 and standard deviation σDQ(t) =.6 mm h−1 for different values of skewness, γ , and degrees of free-dom, df.
Table 2. Properties of the two case study catchments.streamflow and precipitation). P is the precipitation and RC the runoff coefficient (calculated from cumulative Qobs,max, Qobs,min and Qobs are the minimum, the maximum and the average streamflow, respectively.IF,obs is the flashiness index.
Table 3. Prior distributions of the hydrological and error model parameters applied in all the cases where the respective parameter was used.N = Gaussian normal; LN = log-normal
Figure 4. Performance of the error models in terms of the relativecumulative error in streamflow, �Q, and the Nash–Sutcliffe effi-ciency, E�N,det, for both catchments and all temporal resolutions.Perr was smoothed (∗) exclusively for hourly data in the Maimaicatchment.
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