Abstract
In the context of climate and environmental change assessment, the use of probabilistic models in which the parameters of a given distribution may vary in accordance with time has reinforced the need for appropriate procedures to recognize the “statistical significance” of trends in data series arising from stochastic processes. This paper introduces a parametric methodology, which exploits a measure based on the Akaike Information Criterion (AICΔ), and a Rescaled version of the Generalized Extreme Value distribution, in which a linear deterministic trend in the position parameter is accounted for. A Monte Carlo experiment was set up with the generation of nonstationary synthetic series characterized by different sample lengths and covering a wide range of the shape and scale parameters. The performances of statistical tests based on the parametric AICΔ and the non-parametric Mann-Kendall measures were evaluated and compared with reference to observed ranges of annual maxima of precipitation, peak flow, and wind speed. Results allow for sensitivity analysis of the test power and show a strong dependence on the trend coefficient and the L-Coefficient of Variation of the parent distribution from the upper-bounded to the heavy-tailed special cases. An analysis of the sample variability of the position parameter is also presented, based on the same generation sets.
Highlights
The environment and climate change are recognized worldwide as an interdisciplinary area of investigation which reaches into different fields of research (e.g., [1,2])
The goals of this paper are (i) to further develop the use of parametric tests, based on model selection criteria (Akaike Information Criterion, AIC∆) described in Section 2, (ii) to provide a generalized methodology which exploits a Rescaled Generalized Extreme Value (GEV) distributed variable, and (iii) to extend the evaluations performed by Totaro et al [14] to parameter ranges covering the variability of different physical variables suitable for climate change assessment
We introduced a GEV distributed variable rescaled by the position parameter
Summary
The environment and climate change are recognized worldwide as an interdisciplinary area of investigation which reaches into different fields of research (e.g., [1,2]). It is an area of investigation in life sciences, including ecology and the biology of any living organism on earth, and has strong implications for social, economic, and industrial sciences, considering new branches of emerging risks and cascading effects Due to such interactions, multi-disciplinary approaches should be more developed, as proposed by Metzger et al [3] and Cagle et al [4], considering that, even in contiguous fields of environmental analyses and applications, the perspective and scale of investigations can be very different, ranging from global to regional, catchment-size and at-site assessment of physical quantities and their observations [5,6]. The same approach could be adapted to describe other phenomena, such as heatwaves and droughts
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