Abstract
River floods are one of the most devastating extreme hydrological events, with oftentimes remarkably negative effects for human society and the environment. Economic losses and social consequences, in terms of affected people and human fatalities, are increasing worldwide due to climate change and urbanization processes. Long-term dynamics of flood risk are intimately driven by the temporal evolution of hazard, exposure and vulnerability. Although needed for effective flood risk management, a comprehensive long-term analysis of all these components is not straightforward, mostly due to a lack of hydrological data, exposure information, and large computational resources required for 2-D flood model simulations at adequately high resolution over large spatial scales. This study tries to overcome these limitations and attempts to investigate the dynamics of different flood risk components in the Murray-Darling basin (MDB, Australia) in the period 1973–2014. To this aim, the LISFLOOD-FP model, i.e., a large-scale 2-D hydrodynamic model, and satellite-derived built-up data are employed. Results show that the maximum extension of flooded areas decreases in time, without revealing any significant geographical transfer of inundated areas across the study period. Despite this, a remarkable increment of built-up areas characterizes MDB, with larger annual increments across not-flooded locations compared to flooded areas. When combining flood hazard and exposure, we find that the overall extension of areas exposed to high flood risk more than doubled within the study period, thus highlighting the need for improving flood risk awareness and flood mitigation strategies in the near future.
Highlights
Economic losses and social consequences associated with riverine inundations appear to increase worldwide and the intensification of extreme hydrological events due to climate change is often pointed out as the main cause (de Moel et al, 2011; Barnes, 2017; IPCC, 2021)
Our study period includes one of the largest flood events ever occurred across the Murray-Darling Basin (MDB), which was the biggest event in the considered study period
Given the selected percentile-based classification, the maximum extension of flooded areas shows a remarkable reduction in H1 and H2 classes, which identify unfrequently inundated locations likely associated to major flood events (Figure 3B)
Summary
Economic losses and social consequences associated with riverine inundations appear to increase worldwide and the intensification of extreme hydrological events due to climate change is often pointed out as the main cause (de Moel et al, 2011; Barnes, 2017; IPCC, 2021). Future projections of increasing population and economic activities on river floodplains as derived from socioeconomic growth scenarios, as well as increasing heavy rainfall estimates associated to climate variability and change, will likely result in increasing flood risk (Hirabayashi et al, 2013; Winsemius et al, 2016; Kam et al, 2021). It is crucial to unravel long-term dynamics of flood risk and its components
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