Cancelliere, Antonino, authorBonaccorso, Brunella, authorRossi, Giuseppe, authorColorado State University, publisher2020-02-112020-02-112010https://hdl.handle.net/10217/200827http://dx.doi.org/10.25675/10217/2008272010 annual AGU hydrology days was held at Colorado State University on March 22 - March 24, 2010.Includes bibliographical references.Estimation of quantiles of hydrological variables, i.e. values corresponding to fixed non-exceedence probabilities or return periods, is traditionally carried out by fitting a probability distribution function to an observed sample under the assumption of stationarity. Recent concerns about potential changes in present and future climate, however have led to challenge the hypothesis of stationary series. Despite several methods have been developed and applied to model non stationary series, very few studies have addressed the problem of how non stationarity affects the error of estimation of quantiles. In the paper, preliminary analyses regarding how the presence of trend in precipitation series affects the sampling properties of estimated quantiles are illustrated. To this end, sampling properties of precipitation quantiles, namely bias and Mean Squared Error (MSE) are investigated with respect to the size of the estimation sample, assuming a trend in the parameters of the underlying distribution. In particular, analytical results are derived for the cases of exponential distribution, while more complex cases (e.g. Gumbel distribution) are investigated numerically by simulation. Also the effect of preliminary trend removal is investigated and compared to the case when trend is neglected.born digitalproceedings (reports)engCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text