The use of partial probability weighted moments (PPWM) for estimating hydrological extremes is compared to that of probability weighted moments (PWM). Firstly, estimates from at-site data are considered. Two Montecarlo analyses, conducted using continuous and empirical parent distributions (of peak discharge and daily rainfall annual maxima) and applying four different distributions (Gumbel, Fréchet, GEV and generalized Pareto), show that the estimates obtained from PPWM's are better than those obtained from PWM's if the parent distribution is unknown, as happens in practice. Secondly, the use of partial L-moments (obtained from PPWM's) as diagnostic tools is considered. The theoretical partial L-diagrams are compared with the experimental data. Five different distributions (exponential, Pareto, Gumbel, GEV and generalized Pareto) and 297 samples of peak discharge annual maxima are considered. Finally, the use of PPWM's with regional data is investigated. Three different kinds of regional analyses are considered. The first kind is the regression of quantile estimates on basin area. The study is conducted applying the GEV distribution to peak discharge annual maxima. The regressions obtained with PPWM's are slightly better than those obtained with PWM's. The second kind of regional analysis is the parametric one, of which four different models are considered. The congruence between local and regional estimates is examined, using peak discharge annual maxima. The congruence degree is sometimes higher for PPWM's, sometimes for PWM's. The third kind of regional analysis uses the index flood method. The study, conducted applying the GEV distribution to synthetic data from a lognormal joint distribution, shows that better estimates are obtained sometimes from PPWM's, sometimes from PWM's. All the results seem to indicate that using PPWM's can constitute a valid tool, provided that the influence of ouliers, of course higher with censored samples, is kept under control.
On the use of partial probability weighted moments in the analysis of hydrological extremes
MOISELLO, UGO
2007-01-01
Abstract
The use of partial probability weighted moments (PPWM) for estimating hydrological extremes is compared to that of probability weighted moments (PWM). Firstly, estimates from at-site data are considered. Two Montecarlo analyses, conducted using continuous and empirical parent distributions (of peak discharge and daily rainfall annual maxima) and applying four different distributions (Gumbel, Fréchet, GEV and generalized Pareto), show that the estimates obtained from PPWM's are better than those obtained from PWM's if the parent distribution is unknown, as happens in practice. Secondly, the use of partial L-moments (obtained from PPWM's) as diagnostic tools is considered. The theoretical partial L-diagrams are compared with the experimental data. Five different distributions (exponential, Pareto, Gumbel, GEV and generalized Pareto) and 297 samples of peak discharge annual maxima are considered. Finally, the use of PPWM's with regional data is investigated. Three different kinds of regional analyses are considered. The first kind is the regression of quantile estimates on basin area. The study is conducted applying the GEV distribution to peak discharge annual maxima. The regressions obtained with PPWM's are slightly better than those obtained with PWM's. The second kind of regional analysis is the parametric one, of which four different models are considered. The congruence between local and regional estimates is examined, using peak discharge annual maxima. The congruence degree is sometimes higher for PPWM's, sometimes for PWM's. The third kind of regional analysis uses the index flood method. The study, conducted applying the GEV distribution to synthetic data from a lognormal joint distribution, shows that better estimates are obtained sometimes from PPWM's, sometimes from PWM's. All the results seem to indicate that using PPWM's can constitute a valid tool, provided that the influence of ouliers, of course higher with censored samples, is kept under control.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
