Detecting precipitation trend using a multiscale approach based on quantile regression over a Mediterranean area

Abstract

One of the most relevant and debated topics related to the effects of the climate change is whether intense rainfall events have become more frequent over the last decades. It is a crucial aspect, since an increase in the magnitude and frequency of occurrence of heavy rainfall events could result in a dramatic growth of floods and, in turn, human lives losses and economic damages.Because of its central position in the Mediterranean area, Sicily has been often screened with the aim to capture some trends in precipitation, potentially related to climate change. While Mann-Kendall test has been largely used for the rainfall trend detection, in this work a different procedure is considered. Precipitation trends are here investigated by processing the whole rainfall time-series, provided by the regional agency SIAS at a 10-min resolution, through the quantile regression method by aggregating precipitation across a wide spectrum of durations and considering different quantiles. Results show that many rain gauges are characterized by an increasing trend in sub-hourly precipitation intensity, especially at the highest quantiles, thus suggesting that, from 2002 to 2019, sub-hourly events have become more intense in most of the island. Moreover, by analysing some spatial patterns, it has been revealed that the south and the east of Sicily are more interested in significant increasing rainfall trends, especially at the 10-min duration. Finally, the comparison between the two procedures revealed a stronger reliability of the quantile regression in the trend analysis detection, mainly due to the possibility of investigating the temporal variation of the tails of precipitation distribution.

Multilayer blue-green roofs as nature-based solutions for water and thermal insulation management

“…With the aim to better understand the differences among the case studies, Figure 2(a) shows the empirical complementary cumulative distribution function of the daily rainfall of historical time series in the four locations (circles) and the corresponding fit with a generalized Pareto distribution (thick lines), by the Multiple Threshold Method (Deidda 2010). Historical rainfall time series present different lengths for each case study: Cagliari (1947Cagliari ( -1997, Palermo (2002Palermo ( -2020, Perugia (1990-2021), and Viterbo (1916. The shape parameter k of the generalized Pareto distribution and the annual average rainfall m are reported in the legend of Figure 2(a).…”

“…Population is growing fast and moving from rural to urban areas: projections show that the global population will reach 9.7 billion by 2050 and twothirds of it will be living in cities (UN 2018), and a consequent increase in impermeabilization of natural surfaces is expected. At the same time, climate changes are expected to increase the global average temperature, with longer dry periods, alternated by short and highly intense rainfall events (IPCC 2007;Masson-Delmotte et al 2018) also in the Mediterranean region (Treppiedi et al 2021). The interplay of climate and urbanization changes is projected to facilitate increased pluvial flood generation and intensify urban heat islands (Arnone et al 2018).…”

Análise geoestatística das variáveis hidrometeorológicas na bacia hidrográfica do rio Xingu

“…), usam modelos de econometria espacial. Em que indicam uma possível influência da variável (ou de uma variável em relação à outra) nas áreas vizinhas, como o Índice Global de Moran (IGM) (Treppiedi et al, 2021) indica se há correlação espacial e o Índice Local de Moran (ILM) (Javari, 2017) mostra os locais onde as correlações são significativas.…”

Seasonal variability of future extreme precipitation and associated trends across the Contiguous U.S.

“…Robust statistical tools are essential in assessing precipitation characteristics (Treppiedi et al, 2021). Quantile regression (QR) has recently become a useful tool in quantifying trend magnitudes of the upper and lower tails of the distribution (Villarini et al, 2011;Bartolini et al, 2014;Lausier and Jain, 2018).…”