3.1. DDF Curves Parameters
According to Liuzzo et al. [23
] the annual maxima rainfall of 1-hour duration recorded in the Piazza Armerina is affected by a statistically significant increasing trend. The application of the Bayesian procedure described in Section 2.2
confirmed this result. Indeed, the linear regression of the aT
percentiles showed an increasing trend for each of the considered return periods (Figure 3
). The linear regression of the 95th and 5th percentile series delineated the upper and lower limits of the uncertainty bands related to the aT
shows that, for all the analyzed return periods, the aT
50th percentiles were affected by a quick increase in the last 30 years; nevertheless, the uncertainty bands, delimited by the 95th and the 5th percentiles, were divergent, meaning that the bandwidth was wider for the last years of the examined period compared to the earlier years of the series. This behavior is more emphasized for T
equal to 10 and 20 years (Figure 3
c,d, respectively) and can be attributed to the higher variability of the 95th percentiles. Specifically, an abrupt change of the 95th percentile can be observed in 1990. The divergence of the 95th and 5th linear regression lines indicates that the uncertainty related to climate change in the assessment of the aT
parameter increases over time. Therefore, medium- and long-term future projections will be affected by an increasing level of uncertainty.
The equations of the linear regressions were used to calculate the aT
values for the current climate conditions, the 2010 scenario, and for two future short-term projections to 2025 and 2050. Table 1
summarizes the values of the aT
parameters for each scenario and return period. In the examined scenarios, variations due to climate change of the scaling exponent nT
in Equation (1) were neglected [23
Focusing on the 50th percentiles, an increase ranging between 8% and 12% can be observed comparing the aT values in the 2010 and the 2025 scenarios. Specifically, the highest increase of aT is assessed in the 2025 scenario for T = 5 years. As regards the 2050 scenario, the aT increase varies between 21% and 28%, with the highest increase for T = 2 years. These increases are comparable to those assessed for the 5th percentiles, whereas the 95th percentiles show slightly higher increases (up to 30% for the 2050 scenario).
parameters in Table 1
were used to calculate the design storm provided as input of the FLO-2D hydraulic simulations. These simulations were performed considering all the aT
percentiles for each return period and scenario (36 simulations).
3.2. Maps of Maximum Flow Depths
The effects of climate change on the maximum flow depth and the extension of the flooded areas in the urban catchment of Piazza Armerina were investigated. The output of the FLOD-2D simulations was used to obtain some maps in which the maximum flow depth for rainfall events of different return periods was shown. Figure 4
shows the spatial distribution of maximum flood depth for the current climate conditions (2010 scenario). These results were obtained from the hydraulic simulations based on the 50th percentile of aT
. For T
equal to 2, 5, and 10 years (Figure 4
a–c, respectively), flooded areas have a small extension, and the flow depth does not exceed 0.3 m. For T
equal to 20 years (Figure 4
d), flooded areas are distributed over the whole urban catchment area, and in some locations, maximum flow depths are up to 0.8 m. The percentage of the flooded areas in the urban catchment ranges from 0.17% (for T
= 2 years) to 5.48% (for T
= 20 years).
shows the maps of maximum flood depth for the 2025 scenario. For T
equal to 2 and 5 (Figure 5
a,b, respectively), flooded areas are comparable with those detected for the 2010 scenario. An increase of flooded areas is observed for T
equal to 10 years. Further, the spatial distribution of these areas is affected by the increase of rainfall predicted by the Bayesian procedure for 2025. For T
equal to 20 years (Figure 5
d), maximum flow depths are up to 3 m. The percentage of the flooded areas in the urban catchment ranges from 0.18% (for T
= 2 years) to 6.94% (for T
= 20 years). In summary, for the 2025 scenario, the effects of climate change are negligible for small return periods, whereas they are more evident for the highest analyzed return periods. Specifically, the comparison of the maps for the 2010 and 2025 scenarios highlights that the increase of annual maximum rainfall has evident implications on flood risk related to events with a 10-year return period.
shows the maps of maximum flood depth for the 2050 scenario. As in the previous future scenario, the increase of rainfall does not affect the maximum flow depth and the extension of the flooded areas for T
equal to 2 years (Figure 6
a). Nevertheless, in this case, the effects of climate change are already more evident for T
equal to 5 years. In Figure 6
b, it can be observed that a remarkable increase of flooded areas occurred, if compared with the same maps obtained for the 2010 and the 2025 scenarios. The percentage of the flooded areas in the urban catchment ranges from 0.20% (for T
= 2 years) to 8.82% (for T
= 20 years). For T
equal to 20 years (Figure 6
d), the comparison between the maximum flow depth maps for the 2010 and the 2050 scenario highlights significant changes in the extension of the flooded areas. Further, the maximum flow depth area is affected, with an increase of the values up to 1.5 m in many locations.
shows the detail of a flood-susceptible area of Piazza Armerina for T
= 20 years. Comparing the climate scenarios, an increase of the flooded areas occurs. Wider flooded areas are not the only effect of the rainfall positive trend. Further, an increase of the maximum flow depth can be observed in most of the flooded areas, with values up to 1.5 m. This result highlights that the implication of climate change on extreme rainfall can have important short-term consequences on urban flooding. Basically, rainfall events that in current climate conditions are not particularly severe are likely to become more dangerous in the immediate future.
shows the percentage of flooded area for each range of maximum flow depth reported in the previous maps and for each return period. For all the considered return periods and climate scenarios, in most of the flooded area (65–80%), the maximum flow depth ranges between 0 and 0.3 m, whereas a percentage ranging from 12% to 20% of the flooded area is interested by maximum flow depths between 0.3 and 0.8 m. Higher values of maximum flow depth do not exceed 1% of the flooded areas in most cases. Nevertheless, a small increase of the maximum flow depth in the ranges 0.3–0.8 m and 0.8–1.5 m occurs for the 2025 and 2050 scenarios, with a reduction of the percentage of flooded area with the lowest maximum flow depth (0–0.3 m). This effect is not affected by the return period of the rainfall event. These results point out that the positive trend of annual maximum rainfall has consequences not only on the extension of the flooded areas in the urban catchment, but also on the values of the maximum flow depth.
As previously mentioned, the maps of Figure 4
, Figure 5
and Figure 6
were obtained from FLO-2D simulations in which the design storm was assessed using the 50th percentile of the aT
parameters (Table 1
). However, for all the examined return periods, hydraulic simulations were also carried out using the design storm obtained using the DDF curves with 95th and the 5th percentiles of the aT
parameters. Results were reported in maps, and the flooded areas were calculated. Assessing the extension of the flooded areas also using the 95th and the 5th percentiles of the aT
parameter allowed quantifying the uncertainty related to the estimations illustrated in Figure 4
, Figure 5
and Figure 6
. Specifically, the maps obtained with the 95th and the 5th percentiles of aT
identified the upper and lower limit of the flooded areas, respectively. Basically, the application of the Bayesian procedure for the assessment of the design storms allowed defining a potential range of variation for the flooded areas in climate change scenarios.
summarizes the flooded areas for each scenario, return period, and aT
percentile. The analysis of these results shows that in the 2010 scenario, for T
equal to 2 and 5 years, there are no substantial differences between the flooded areas, and the range of variations identified with the percentiles of aT
is very small. This behavior is also observed for the 2025 and the 2050 scenarios, but only when T
is equal to 2 years. The uncertainty related to the assessment of flooded areas is increasing with the increase of the return period. Therefore, the uncertainty is minimum for every scenario with T
equal to 2 years, while it is maximum for the 2050 scenario when T
is equal to 20 years. In this case, there is a difference of 0.54 km2
in the flooded areas assessed with the 95th and the 5th percentiles of the aT