An Amplification Model for the Regional Estimation of Extreme Rainfall within Orographic Areas in Campania Region (Italy)

Orography strongly interacts with the atmospheric circulation, especially during frontal events, generating an enhanced spatial variability of the rainfall field. Regional models of extreme rainfall have to deal with these circumstances in order to provide good spatial estimation of the regionalized variable. We present a model for the regional estimation of the mean of the probability distribution of the annual daily rainfall maxima in a region (Campania, Southern Italy) with complex orography. In a recent work, we found that areas with enhanced variability of extreme rainfall, in the same region, correspond to a particular set of orographic objects, which had been classified through an automatic, GIS-based geomorphological procedure. Here, we propose an approach that considers the same orographic objects as building blocks for a regional model that is able to capture the amplification of extreme rainfall caused by orography. The regional model is then the product of a basic stationary random spatial process and an amplification factor, whose values are related to the topographic features of the orographic objects. This approach represents a step towards the improvement of the predictive ability of regional models of extreme rainfall within orographically complex areas

Regional Assessment of Sub-Hourly Annual Rainfall Maxima

The assessment of rainfall extremes at sub-hourly scales is generally hindered by a lack of rainfall data at small timescale resolutions. This study proposes a methodology for assessing mean annual maximum rainfall at the sub-hourly scale by blending historical time series of annual maxima recorded by mechanical stations (operating at hourly scales) up to the end of the past century with newer time series of annual maxima at higher time resolutions recorded by automatic stations installed over the past twenty years. A linear correlation was found at the regional scale between the shape parameter controlling the dependency of rainfall maxima with a duration longer than one hour and the shape parameter of the dependency of rainfall maxima with the durations shorter than one hour. Thanks to this correlation, data recorded at the mechanical stations could be exploited to assess sub-hourly mean annual maxima. The proposed hybrid procedure was verified and was found to provide estimates with an accuracy close to those obtained with the high-resolution data, i.e., our best estimates. Moreover, the proposed procedure outperforms what could be achieved by spatially interpolating the best estimates at those locations where only hourly data are availabl

Sistemi di preannuncio in tempo reale delle piene: un approccio gerarchico e parametrico per la previsione delle portate di piena nei corsi d'acqua italiani

Dottorato di ricerca in ingegneria idraulica. 12. ciclo. Coordinatore Goffredo La Loggia. Tutore Giuseppe RossiConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7 , Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Improved moment scaling estimation for multifractal signals

A fundamental problem in the analysis of multifractal processes is to estimate the scaling exponent K(q) of moments of different order q from data. Conventional estimators use the empirical moments μ^[subscript r][superscript q]=⟨ | ε[subscript r](τ)|[superscript q]⟩ of wavelet coefficients ε[subscript r](τ), where τ is location and r is resolution. For stationary measures one usually considers "wavelets of order 0" (averages), whereas for functions with multifractal increments one must use wavelets of order at least 1. One obtains K^(q) as the slope of log(μ^[subscript r][superscript q]) against log(r) over a range of r. Negative moments are sensitive to measurement noise and quantization. For them, one typically uses only the local maxima of |ε[subscript r](τ)| (modulus maxima methods). For the positive moments, we modify the standard estimator K^(q) to significantly reduce its variance at the expense of a modest increase in the bias. This is done by separately estimating K(q) from sub-records and averaging the results. For the negative moments, we show that the standard modulus maxima estimator is biased and, in the case of additive noise or quantization, is not applicable with wavelets of order 1 or higher. For these cases we propose alternative estimators. We also consider the fitting of parametric models of K(q) and show how, by splitting the record into sub-records as indicated above, the accuracy of standard methods can be significantly improved.MIT-Portugal ProgramUniversity of Salern