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Browsing by Author "Cancelliere, Antonino, author"

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    Effect of trends on the estimation of extreme precipitation quantiles
    (Colorado State University. Libraries, 2010) Cancelliere, Antonino, author; Bonaccorso, Brunella, author; Rossi, Giuseppe, author; Colorado State University, publisher
    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.
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    Stochastic characterization of droughts in stationary and periodic series
    (Colorado State University. Libraries, 2008) Cancelliere, Antonino, author; Salas, Jose D., advisor; Boes, Duane C., advisor
    Stochastic modelling of droughts is a topic of great interest in water resources management. For instance, estimating drought probabilities and return periods helps in implementing risk based management decisions of water supply systems and provide useful information for drafting drought management plans. Due to the limited number of droughts that can be generally observed in historical series, the inferential approach, e.g. fitting a probability distribution to drought characteristics from an observed hydrological sample, leads to unreliable results. Furthermore, the multiyear spanning of droughts, as well as their multivariate framework requires the development of concepts and tools that differ significantly from those generally adopted to analyze other hydrological extremes, such as floods.
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    Uncertainty analysis of the Standardized Precipitation Index in the presence of trend
    (Colorado State University. Libraries, 2009) Cancelliere, Antonino, author; Bonaccorso, Brunella, author; Colorado State University, publisher
    The Standardized Precipitation Index (SPI) is an index widely used for drought monitoring purposes. Since its computation requires the preliminary fitting of a probability distribution to monthly precipitation aggregated at different time scales, the SPI value for a given year and a given month will depend on the particular sample of observed precipitation adopted for its estimation and in particular on the sample size. Furthermore, the presence of trend in the underlying precipitation will affect adversely the estimation of parameters, and therefore the computation of SPI. Objective of the present paper is to investigate the variability of the SPI with respect to the size of the sample used for estimating its parameters, either in the case of stationary or non stationary precipitation series. In particular, sampling properties of SPI, such as bias and root mean squared error (RMSE), are analytically derived assuming the underlying precipitation series without trend and normally distributed. Results related to the normal case can find application also in the case of other distributions, namely when sample data can be transformed into normal values (i.e. lognormal or cube root normal distributed data). Moreover, sampling properties when precipitation is affected by trend are investigated by means of Monte Carlo simulation. Results indicate that SPI values are significantly affected by the size of the sample adopted for its estimation. In particular, while for the case of underlying stationary series, RMSE tends asymptotically to zero as sample size increases as expected, in the presence of a linear trend a minimum RMSE value can be determined corresponding to a specific sample size. This suggests that an optimal sample size (in RMSE sense) can be determined, when the underlying series is affected by trend.