A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling

Abstract

Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.

Assessing the Effects of Climate Change on Compound Flooding in Coastal River Areas

“…Based on the above, the model is considered to provide high fidelity hydraulic simulations of the inundation process in this area for the purposes of this work. For a detailed description of the model's configuration for this river reach, see Bermúdez et al (2020).…”

“…The method relies primarily on the simulated large-scale atmospheric circulation to obtain future precipitation series. The ability of the selected GCMs to reproduce the large-scale atmospheric circulation in this area was verified in Bermúdez et al (2020), by comparing the WT classification obtained from the control runs of the GCMs (1961-1990 period) and that obtained from the ERA-Interim data set.…”

Predicted the Cumulative Annual Rainfall in Iraq Using SDSM Modal

“…Two-step processes. The first is to develop the statistical relationship between local climate variables (predictand) and large-scale atmospheric variables (predictor variables) and next is to apply the established relationship to the output of GCM to simulate future climatic data [15]. Statistical downscaling is based on the following assumptions:(i) suitable relationships can be developed between the large-scale predictors and local predictands; (ii) the relationships are valid under future climate conditions; and (iii) the predictor variables and their changes are well simulated by GCMs.…”

Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review

“…The complexity of flooding is a consequence of the various sources of flood risk, including not only fluvial, tidal and coastal flooding but also exposure to urban runoff and local drainage failure, as well as the different management strategies that can be proposed. Climate change adds a further layer of complexity, with the impact of the processes of climate change likely to increase flood risk, both inland and coastal, in the future, due to rising sea levels and storm surges, as well as to the increased frequency of extreme precipitation events [2][3][4][5][6][7].…”