Quantifying the Uncertainty Related to Climate Change in the Assessment of Urban Flooding—A Case Study

Abstract

Recent studies have pointed out that climate change is likely to have important implications on the extent and frequency of flooding events. Indeed, the intensification of the water cycle occurring in different areas of the world can dramatically affect the incidence of extreme events and, consequently, the flow in rivers or artificial channels, increasing the probability of disastrous floods. In this context, the criteria for the assessment of flood risk need to be improved to take into account the variability of rainfall due to climate change. In this study, a Bayesian procedure was used to update the parameters of the depth–duration–frequency (DDF) curves and quantify the uncertainty related to their assessment in some climate change scenarios. The critical storm obtained from these updated DDF curves was used as input for the FLO-2D hydraulic model, in order to investigate the effects of climate change on flood risk. The area of study was an urban catchment in Piazza Armerina, a small town located in Southern Italy. Results showed that rainfall variations remarkably affect not only the magnitude of flood events, but also the flood susceptibility of the study area.

An Urban Flooding Index for Unsupervised Inundated Urban Area Detection Using Sentinel-1 Polarimetric SAR Images

“…According to a report by the United Nations Office for Disaster Risk Reduction (UNISDR), floods accounted for nearly half of all weather-related disasters, affecting approximately 2.30 billion people between 1995 and 2015 [4]. Due to global climate change, extreme rainfall and flooding events are becoming increasingly frequent in urban areas [5][6][7][8]. At nearly two-thirds of the world's rainfall stations, measurements are on the increase [5].…”

“…At nearly two-thirds of the world's rainfall stations, measurements are on the increase [5]. The extreme precipitation caused by climate change intensifies the magnitude of and susceptibility to flood events [8]. Moreover, growing populations and rising urbanization exacerbate the potential losses and damages caused by floods [9].…”

Sea Level and Socioeconomic Uncertainty Drives High‐End Coastal Adaptation Costs

“…In the SSP5-8.5 pathway in particular, the median of the distribution of adaptation costs when both sea-level and socioeconomic uncertainties are sampled is substantially higher than the estimated median costs when only one or the other set of uncertainties is sampled (Figure 4d). Importantly, when considering the prevalence of using several cases (often quantiles) to represent uncertainty (e.g., Hinkel et al, 2014;Lincke & Hinkel, 2018;Liuzzo & Freni, 2019;Tiggeloven et al, 2020), it is prudent to compare the distribution of damages from the full sampling of both sea-level and socioeconomic uncertainties (Figure 4, gray distributions) and the three-percentile approach (Figure 4, black dots and whiskers). In absolute terms, using the three-percentile approach underestimates the 95th percentile of the distribution of NPV of adaptation costs by US$371 billion (SSP1-2.6), $906 billion (SSP2-4.5), $611 billion (SSP4-6.0), and $3 trillion (SSP5-8.5).…”

“…This method of using a few cases to characterize low‐, moderate‐, and high‐risk scenarios has the advantage of being computationally inexpensive and conceptually simple. Previous studies have leveraged this computational efficiency in assessing flood risks (e.g., Hinkel et al., 2014 ; Lincke & Hinkel, 2018 ; Liuzzo & Freni, 2019 ; Tiggeloven et al., 2020 ). However, previous work has also found that using only a point estimate of an exogenous model input can miss substantial variation (Sieg et al., 2019 ) and multimodality (Brown et al., 2015 ) in the distributions of outputs of interest, which can also lead to underestimating the probability of damaging floods (Zarekarizi et al., 2020 ).…”