Figure 3. a) Slope of the
regional trends for the number of seasonal storms; significant trends
(α-level=0.05) are marked. b) Median (across stations) seasonal
number of convective-like (decorrelation time ≤ 2 hr) and c)other (decorrelation time > 2 hr) storms in the 25% tail;
the Sen’s slope (S) and the p-value (pv) of the Regional Mann-Kendall
test are reported in case of significant trends (α-level=0.05).
5 Conclusions
We examine changes in extreme sub-daily precipitation intensities for
the relevant case of the eastern Italian Alps, where consistent
significant changes in annual maximum (AM) intensities were reported
(Libertino et al., 2019). Specifically, we aim at detecting and
quantifying trends in sub-daily AM and extreme return levels, and
linking the observed trends in extremes to specific changes in the local
precipitation regime. To do so, we adopt a novel unified framework for
extreme value analyses based on ordinary events, and we quantify trends
by means of the regional Mann-Kendall test. With respect to traditional
change-permitting extreme value models, the here presented method
provides a statistical tool for better quantifying changes in extremes
in spite of the large stochastic uncertainties, and for better
understanding the observed changes by separately considering
multi-duration storm intensity distributions and storm occurrence
frequency.
Results confirm the presence of significant positive trends in the AM.
Trends in the 2 yr return levels estimated yearly using our model are
consistent with the observed trends in AM. These trends are more marked
for 15 min to 1 hr durations and less marked for 3 hr to 24 hr
durations. The model parametrization allows to conclude that these
trends are likely due to a combination of (i) increasing number of storm
events per year and increasing intensity of the storms, and (ii) changes
in the tail properties of the storms. In particular, an increasing,
albeit not-significant, trend in tail heaviness at short durations seems
to mostly explain the changes in AM and return levels. A significant
increase in the proportion of convective-like storms is detected during
the summer (JJA). This could explain the observed trends in AM and
return levels emerged at the short durations in this study. This agrees
with results reported by Fowler et al. (2021a), who highlight that the
stronger increases in short-duration extremes are related to feedbacks
in convective clouds dynamics at the local scale. The approach can be
expanded to directly consider different types of storm events (Marra et
al., 2019), following previous works regarding mixed distributions like
the Two-component Extreme value distribution (Rossi et al., 1984) or the
mixed Gumbel (Kjeldsen et al., 2018).
The trends in this study are derived from a relatively short data series
and should be considered as representative of the examined period only
(1991-2020). Due to decadal climate variability, they should not be
considered as representative of climate change in general, nor
extrapolated to predict future conditions (Iliopoulou and Koutsoyiannis,
2020). Nevertheless, our approach could provide insights for better
describing local climatologies under change, and for enhancing our
understanding of the linkages with changes in the underlying physical
processes. This information can be valuable for improving our ability to
create and use process-based change-permitting statistical models for
hydrometeorological extremes.
Data Availability Statement
Precipitation data was provided by the Provincia Autonoma di Trento and
can be retrieved from https://www.meteotrentino.it (Last accessed:
September 2021). The codes used for the statistical model are available
at https://doi.org/10.5281/zenodo.3971558. The Regional Mann-Kendall
trend test was performed based on the functions by J. Burkey, downloaded
from
https://it.mathworks.com/matlabcentral/fileexchange/22389-seasonal-kendall-test-with-slope-for-serial-dependent-data
(retrieved July 2021). The codes developed in the study and the
elaborated data for reproducing the results of the paper are available
at
https://www.dropbox.com/sh/f7cf93racbg5hqv/AADXBHHTKebd5OtG9syKrIJOa?dl=0
for the purpose of peer review and, upon acceptance, will be made
publicly available in their final version.
CRediT authors’ contribution
ED : Data curation, Methodology, Formal analysis, Investigation,
Visualization, Writing – original draft, Writing – review & editing.MB : Conceptualization, Investigation, Writing – review &
editing, Supervision. MZ : Visualization, Writing – review &
editing. FM : Conceptualization, Methodology, Software,
Investigation, Writing – original draft, Writing – review & editing,
Supervision.
Acknowledgements
The authors declare no conflict of interests. This study was funded by
Provincia Autonoma di Trento through Accordo di Progamma GPR. FM thanks
the Institute of Atmospheric Sciences and Climate (ISAC), National
Research Council of Italy for the support.
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