Investigating potential future changes in surface water flooding hazard and impact

Abstract

Surface water flooding (SWF) is a recurrent hazard that affects lives and livelihoods.Climate change is projected to change the frequency of extreme rainfall events that can lead to SWF. Increasingly, data from Regional Climate Models (RCMs) are being used to investigate the potential water-related impacts of climate change; such assessments often focus on broad-scale fluvial flooding and the use of coarse resolution (>12 km) RCMs. However, high-resolution (<4 km) convection-permitting RCMs are now becoming available that allow impact assessments of more localised SWF to be made.At the same time, there has been an increasing demand for more robust and timely real-time forecast and alert information on SWF. In the UK, a real-time SWF Hazard Impact Model framework has been developed. The system uses 1-km gridded surface runoff estimates from a hydrological model to simulate the SWF hazard. These are linked to detailed inundation model outputs through an Impact Library to assess impacts on property, people, transport, and infrastructure for four severity levels.Here, a set of high-resolution (1.5 km and 12 km) RCM data has been used as input to a grid-based hydrological model over southern Britain to simulate Current (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and Future (~2100s; RCP8.5) surface runoff. Counts of thresholdexceedance for surface runoff and precipitation (at 1-, 3-and 6-hr durations) are analysed. Results show that the percentage increases in surface runoff extremes, are less than those of precipitation extremes. The higher-resolution RCM simulates the largest percentage increases, which occur in winter, and the winter exceedance counts are greater than summer exceedance counts. For property impacts, the largest percentage increases are also in winter; however, it is the 12-km RCM output that leads to the largest percentage increase in impacts. The added-value of highresolution climate model data for hydrological modelling is from capturing the more intense convective storms in surface runoff estimates. K E Y W O R D Sclimate change, hazard, impact, modelling, pluvial flooding, surface water flooding

Soil Organic Matter Research and Climate Change: Merely Re-storing Carbon Versus Restoring Soil Functions

“…The higher runoff that one is likely to observe, either directly as a result of the higher rainfall intensity or indirectly, e.g., as a consequence of fires caused by drought (e.g., Candela et al, 2005;Papathanasiou et al, 2012), should also contribute to increase the frequency of severe floods and in particular "flashfloods, " at least in some parts of the world, causing significant damages to infrastructures, forcing 500 million people to flee their homes (UNCCD, 2014), and leading to the frequent loss of lives. The intensity and severity of the problem is heightened in many countries by the widespread sealing of soils, in particular because of urban sprawl (e.g., Miller and Hutchins, 2017;Skougaard Kaspersen et al, 2017;Rudd et al, 2020). In 2018, a team of researchers in Germany and the United States (Willner et al, 2018) concluded that in the next two decades, most of the United States, Central Europe, Northeast and West Africa, as well as large parts of India and Indonesia, will require intensive and onerous adaptation efforts, at least at double the current level, to keep high-end fluvial flood risk in check.…”

Convection‐permitting modeling with regional climate models: Latest developments and next steps

“…In this context, CPRCM forcing provides a major improvement for hydrological applications by providing further dynamical downscaling of several variables that require a fine spatio‐temporal resolution and in which the physical consistency between variables is preserved. Several studies have already used CPRCMs in hydrological applications to assess future flood risks in the United States (Dougherty & Rasmussen, 2020, 2021; Lackmann, 2013), Eastern Alps (Reszler et al, 2018), Texas (Wang & Wang, 2019; Zhang, Wang, & Wang, 2020), Colorado River basin (Mendoza et al, 2016), Ouagadougou (Senior et al, 2021), and in the UK (Kay et al, 2015; Rudd et al, 2020). CPRCMs have also been applied to estimate the water budget of East Africa (Finney et al, 2019), Himalayan basins (Li, Gochis, et al, 2017), California (Dougherty et al, 2020), but also for droughts and low flows analysis (Lee, Bae, & Im, 2019; Qing et al, 2020).…”

Tracking the methodological evolution of climate change projections for UK river flows

“…In practice, RCM model outputs are often subjected to the same types of statistical bias adjustments as discussed above to correct for different biases (e.g., Cloke et al, 2010; Lafon et al, 2013; Kay et al, 2015; Pastén-Zapata et al, 2020). Studies have made use of the RCM outputs from multiple generations of the UK climate change projections (e.g., Bell et al, 2007; Kay et al, 2015; Rudd et al, 2020) and large ensemble experiments (e.g. weather@home: Guillod et al 2018) to drive regional-scale hydrological models.…”

“…The use of downscaled data at different spatial resolutions adds to the GCM-related uncertainty and increases overall uncertainty (Orr et al, 2021). Kay et al (2015) and Rudd et al (2020) both found that the magnitude of change in increased peak flows can vary between the 1.5 km and 12 km RCM data in different regions of the UK (e.g. East England) with largest uncertainty for projected changes in winter and spring.…”