In the computation of the EC-Earth results, we accidentally included all grid boxes and not only the land points as we intended. We redid the calculations using the land points only. This implies an update to figure 6 (given below), but it makes only a slight differences to the EC-Earth results. The comparison with the observed fit for the model evaluation is somewhat better in the dispersion parameter / and now good in the shape parameter, although the model now requires a bias correction of 18%. The increase in intensity for land points only is ΔI = 17% (11% ... 23%), compared to the ΔI = 17% (10% ... 23%) for all points. The risk ratio is a bit higher, 2.5 (1.8 ... 6.7) instead of the 2.2 (1.5 ... 4.1) reported in the article. Updated synthesis and conclusionsThis changes figure 7 slightly as well, but does not affect the conclusions. The change in increase remains 15% with an uncertainty range 8%-19%. The change in risk ratio stays the same, a factor of three, but with a slightly higher uncertainty range, 1.6-6 rather than 1.5-5. This strengthens our conclusions by a negligible factor.

Adverse consequences of floods change in time and are influenced by both natural and socio-economic trends and interactions. In Europe, previous studies of historical flood losses corrected for demographic and economic growth (‘normalized’) have been limited in temporal and spatial extent, leading to an incomplete representation of trends in losses over time. Here we utilize a gridded reconstruction of flood exposure in 37 European countries and a new database of damaging floods since 1870. Our results indicate that, after correcting for changes in flood exposure, there has been an increase in annually inundated area and number of persons affected since 1870, contrasted by a substantial decrease in flood fatalities. For more recent decades we also found a considerable decline in financial losses per year. We estimate, however, that there is large underreporting of smaller floods beyond most recent years, and show that underreporting has a substantial impact on observed trends.

No abstract

No abstract

No abstract

Flooding is a function of hydrologic, climatologic, and land use characteristics. However, the relative contribution of these factors to flood risk over the long-term is uncertain. In response to this knowledge gap, this study quantifies how urbanization and climatological trends influenced flooding in the greater Houston region during Hurricane Harvey. The region-characterized by extreme precipitation events, low topographic relief, and clay-dominated soils-is naturally flood prone, but it is also one of the fastest growing urban areas in the United States. This rapid growth has contributed to increased runoff volumes and rates in areas where anthropogenic climate changes has also been shown to be contributing to extreme precipitation. To disentangle the relative contributions of urban development and climatic changes on flooding during Hurricane Harvey, we simulate catchment response using a spatially-distributed hydrologic model under 1900 and 2017 conditions. This approach provides insight into how timing, volume, and peak discharge in response to Harvey-like events have evolved over more than a century. Results suggest that over the past century, urban development and climate change have had a large impact on peak discharge at stream gauges in the Houston region, where development alone has increased peak discharges by 54% (±28%) and climate change has increased peak discharge by about 20% (±3%). When combined, urban development and climate change nearly doubled peak discharge (84% ±35%) in the Houston area during Harvey compared to a similar event in 1900, suggesting that land use change has magnified the effects of climate change on catchment response. The findings support a precautionary approach to flood risk management that explicitly considers how current land use decisions may impact future conditions under varying climate trends, particularly in low-lying coastal cities.

No abstract

Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea levels. When these drivers interact in space and time, they can exacerbate flood impacts; this phenomenon is known as compound flooding. In this paper, we build a Bayesian Network based on Gaussian copulas to generate the equivalent of 500 years of daily stochastic boundary conditions for a coastal watershed in Southeast Texas. In doing so, we overcome many of the limitations of conventional univariate approaches and are able to probabilistically represent compound floods caused by riverine and coastal interactions. We calculate the resulting water levels using a 1D steady-state hydraulic model and find that flood stages in the catchment are strongly affected by backwater effects from tributary inflows and downstream water levels. By comparing with a bathtub modeling approach, we show that simplifying the multivariate dependence between flood drivers can lead to an underestimation of flood impacts, highlighting that accounting for multivariate dependence is critical for the accurate representation of flood risk in coastal catchments prone to compound events.