1,721,234 research outputs found
Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters
Full text linkThe use of goodness of fit tests based on Cramer-von Mises and Anderson-Darling statistics is discussed, with reference to the composite hypothesis that a sample of observations comes from a distribution, FH, whose parameters are unspecified. When this is the case, the critical region of the test has to be redetermined for each hypothetical distribution FH. To avoid this difficulty, a transformation is proposed that produces a new test statistic which is independent of FH. This transformation involves three coefficients that are determined using the asymptotic theory of tests based on the empirical distribution function. A single table of coefficients is thus sufficient for carrying out the test with different hypothetical distributions; a set of probability models of common use in extreme value analysis is considered here, including the following: extreme value 1 and 2, normal and lognormal, generalized extreme value, three-parameter gamma, and log-Pearson type 3, in all cases with parameters estimated using maximum likelihood. Monte Carlo simulations are used to determine small sample corrections and to assess the power of the tests compared to alternative approaches
Uso di dati storici e sistematici per l'analisi probabilistica delle portate di piena della Dora Baltea
No full textHydrological Applications of the Burr Distribution: Practical Method for Parameter Estimation
No full textThe three-parameter Burr XII distribution has been seldom used in hydrological applications, although it is particularly appealing
because its range covers positive values only, which is convenient when modeling streamflows or rainfall data. Moreover, it has two shape
parameters, allowing it to be quite adaptable to different samples because it covers a wide range of skewness and kurtosis values. Parameter
estimation methods currently available in the literature require the numerical solution of two joint nonlinear equations to estimate the shape
parameters of the distribution. This work proposes a simplified, although accurate, method to analytically compute the two shape parameters
starting from the dimensionless L-moments ratios representing the distribution’s variability (L-CV) and skewness (L-skewness). The obtained
parameters can be directly used in practical applications or otherwise may be useful to properly initialize the algorithms to obtain a numerical
solution for the shape parameters. A detailed analysis of the accuracy of the approximated solution is performed, showing that the errors in the
estimation of the distribution quantiles are negligible compared with the sample variability typically affecting hydrological samples. An
extensive data set of empirical flow duration curves from stations located in northwestern Italy is considered to demonstrate the suitability
of the extended Burr XII distribution to represent flow duration curves in a wide range of situations
Aggiornamento delle procedure di valutazione delle piene in Piemonte, con particolare riferimento ai bacini sottesi da invasi artificiali. Vol II: Costruzione e applicazione delle procedure di stima delle portate al colmo di piena.
Full text linkCan continuous streamflow data support flood frequency analysis? An alternative to the Partial Duration Series approach
No full textHuman-impacted waters: New perspectives from global high-resolution monitoring
Full text linkThe human presence close to streams and rivers is known to have consistently increased worldwide, therefore introducing dramatic anthropogenic and environmental changes. However, a spatiotemporal detailed analysis is missing to date. In this paper, we propose a novel method to quantify the temporal evolution and the spatial distribution of the anthropogenic presence along streams and rivers and in their immediate proximity at the global scale and at a high-spatial resolution (i.e., nearly 1 km at the equator). We use satellite images of nocturnal lights, available as yearly snapshots from 1992 to 2013, and identify five distinct distance classes from the river network position. Our results show a temporal enhancement of human presence across the considered distance classes. In particular, we observed a higher human concentration in the vicinity of the river network, even though the frequency distribution of human beings in space has not significantly changed in the last two decades. Our results prove that fine-scale remotely sensed data, as nightlights, may provide new perspectives in water science, improving our understanding of the human impact on water resources and water-related environment
Significant drivers of the virtual water trade evaluated with a multivariate regression analysis
Full text linkInternational trade of food is vital for the food security of many countries, which rely on trade to compensate for an agricultural production insufficient to feed the population. At the same time, food trade has implications on the distribution and use of water resources, because through the international trade of food commodities, countries virtually displace the water used for food production, known as "virtual water". Trade thus implies a network of virtual water fluxes from exporting to importing countries, which has been estimated to displace more than 2 billions of m3 of water per year, or about the 2% of the annual global precipitation above land. It is thus important to adequately identify the dynamics and the controlling factors of the virtual water trade in that it supports and enables the world food security. Using the FAOSTAT database of international trade and the virtual water content available from the Water Footprint Network, we reconstructed 25 years (1986-2010) of virtual water fluxes. We then analyzed the dependence of exchanged fluxes on a set of major relevant factors, that includes: population, gross domestic product, arable land, virtual water embedded in agricultural production and dietary consumption, and geographical distance between countries. Significant drivers have been identified by means of a multivariate regression analysis, applied separately to the export and import fluxes of each country; temporal trends are outlined and the relative importance of drivers is assessed by a commonality analysis. Results indicate that population, gross domestic product and geographical distance are the major drivers of virtual water fluxes, with a minor (but non-negligible) contribution given by the agricultural production of exporting countries. Such drivers have become relevant for an increasing number of countries throughout the years, with an increasing variance explained by the distance between countries and a decreasing role of the gross domestic product. The worldwide adjusted coefficient of determination of fitted gravity-law model is 0.57 (in 2010), and it has increased in time, confirming the good descriptive capability of selected drivers for the virtual water trad
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