Plain Language Summary
Quantifying past trends in extreme rainfall is important because it can help us understand future changes caused by global warming. Climate scientists and hydrologists use specific statistical models to to do so, but interpreting the results is complicated because extremes are rare and the structure of the models is not linked to the local meteorology. We use a new statistical model that allows to better understand the mechanisms behind the trends we detect. We find that extreme rainfall in the easter Italian Alps increased over the past decades and we associate this change to an increased proportion of summer thunderstorms.
1 Introduction
Understanding past and future changes in extreme subdaily precipitation intensities is of enormous interest because they are responsible for flash floods, urban floods, landslides and debris flows, and cause numerous casualties and huge damages every year (Borga et al., 2014; Cristiano et al., 2017; Paprotny et al., 2018). Physical laws translate increasing atmospheric temperature into increasing water vapor holding capacity. Together with changes in the atmospheric dynamics, this is expected to drive future precipitation changes (Trenberth et al., 2003; Pendergrass et al., 2020; Fowler et al., 2021b). In general, larger reponses are expected for precipitation extremes because mean precipitation, on a global scale, is limited by energy constraints (Allan and Soden, 2008; Pendergrass & Hartmann, 2014). However, detecting changes in extreme precipitation is highly affected by the stochastic uncertainty characterizing the sampling of extremes. This uncertainty may mask the influence of climate forcing on the processes which locally control the extremes (Fatichi et al., 2016; Marra et al., 2019).
Statistically significant changes in the frequency of extreme precipitation in the past decades were reported, often with stronger trends in subdaily extremes, as opposed to daily (Guerreiro et al., 2018; Markonis et al., 2019; Papalexiou & Montanari, 2019). In some cases, opposing trends between short and long durations emerged, with complex implications for flood risk (Zheng et al., 2015). Available observations show different temporal trends for precipitation intensities associated to different exceedance probabilities (Schär et al., 2016; Pendergrass, 2018). In general, increasing trends are reported for rarer events (Myhre et al., 2019), but the specific differences depend on duration, season, and local conditions, such as the dominating meteorological features contributing extremes (Blanchet et al., 2021; Moustakis et al., 2021). Extreme return levels characterized by different exceedance probabilities are thus changing at different rates (Myhre et al., 2019; Marra et al., 2021).
Nonstationary extreme value models could aid the detection and quantification of trends in extreme precipitation of different exceedance probability (e.g., Min et al. 2009). However, the information these models can provide is impacted by stochastic uncertainties (Serinaldi and Kilsby, 2015; Fatichi et al., 2016), and their flexibility is limited by the assumptions concerning high order statistical moments. In fact, due to intrinsic limitations in parameter estimation accuracy, the shape (and sometimes also the scale) parameter of the extreme value distribution is usually assumed to be constant (Prosdocimi and Kjeldsen, 2021). Additionally, due to the structure of these statistical models, a link between the properties of the underlying process, such as precipitation occurrence frequency and intensity distribution, and extremes is difficult to establish (e.g. Marra et al., 2019). As such, the possibility to attribute the observed changes to specific physical and meteorological processes is hampered.
This background suggests that there is a need to move beyond traditional trend detection techniques applied to extremes only and develop novel methodologies. These methods should be able to detect general changes in extreme precipitation at multiple durations, quantify changes at different exceedance probabilities, and attribute these to changes in the underlying physical processes.
Miniussi and Marani (2020) proposed the so-called Metastatical Extreme Value approach (Marani and Ignaccolo, 2015) as a viable way for addressing these issues. The idea relies on the concept ofordinary events , that is all the independent realizations of a process of interest, and proved highly effective in reducing stochasting uncertainties (Zorzetto et al., 2016; Marra et al., 2018). As opposed to traditional methods, the distribution describing the ordinary events is assumed to be known, and the extreme value distribution is derived by explicitly considering the occurrence frequency of the ordinary events. Miniussi and Marani (2020) provided an example application in which extreme return levels where computed over moving time windows, highlighting temporal changes that could not be appreciated using traditional methods. The adopted ordinary events (daily precipitation amounts), however, were not directly connected with meteorological systems, so that direct relations between changes in extremes and changes in the underlying storm properties is still missing.
Here, we combine a novel approach for ordinary-events-based precipitation frequency analyses across durations (Marra et al., 2020) with a regional trend detection technique to: (a) detect and quantify trends in sub-daily annual maxima and extreme return levels by independently considering the changes in properties and occurrence frequency of storms, and (b) attribute the observed trends in extremes to specific changes in the local precipitation regime. We examine the relevant case of the eastern Italian Alps, where consistent significant changes in annual maximum precipitation intensities at subdaily and daily duration were reported (Libertino et al., 2019).
2 Data and methodology
2.1 Study area and data
We focus on Trentino, a 6000 km2-wide mountainous area in the Eastern Italian Alps (Figure 1 a) which experienced significant increases in extreme short-duration rain intensities over the last decades (Libertino et al., 2019). Mean annual precipitation varies from ~1300 mm yr-1 in the south-eastern portion of the area to lower amounts (~900 mm yr-1) typical of the “inner alpine province” in the north (Borga et al., 2005). A dense network of more than one hundred rain gauges is present. From these, 30 stations (density ~1/200 km-2) with at least 27 complete years (<10% missing data) of 5-minute resolution data in the period 1991-2020 are selected (Figure 1 a; see Table S1 in the Supporting Information).