Abstract
Abstract. Many winter deep low-pressure systems passing over Western Europe have the potential to induce significant storm surge levels along the coast of the North Sea. The accompanying frontal systems lead to large rainfall amounts, which can result in river discharges exceeding critical thresholds. The risk of disruptive societal impact increases strongly if river runoff and storm-surge peak occur near-simultaneously. For the Rhine catchment and the Dutch coastal area, existing studies suggest that no such relation is present at time lags shorter than six days. Here we re-investigate the possibility of finding near-simultaneous storm surge and extreme river discharge using an extended data set derived from a storm surge model (WAQUA/DCSMv5) and two hydrological river-discharge models (SPHY and HBV96) forced with conditions from a high-resolution (0.11°/12 km) regional climate model (RACMO2) in ensemble mode (16 × 50 years). We find that the probability for finding a co-occurrence of extreme river discharge at Lobith and storm surge conditions at Hoek van Holland are up to four times higher (than random chance) for a broad range of time lags (−2 to 10 days, depending on exact threshold). This highlights that the hazard of a co-occurrence of high river discharge and coastal water levels cannot be neglected in a robust risk assessment.
Highlights
Floods are a major cause of casualties due to natural disasters, with over 6.8 million deaths globally during the 20th century (Doocy et al, 2013), and an annual average loss of 104 billion US$ (Blöschl et al, 2017)
By applying two hydrological models, we demonstrate the importance of proper physical model configurations to correctly assess the lag time correlation for compounding high coastal water level and high discharge events
Having determined the correlation for a range of time lags in discharge and TWL (Figure 7), we examine the dependence in the tail of the distribution for a lag of 3 days, i.e., with a water level event at Hoek van Holland (HvH) occurring 3 days before the discharge peak at Lobith
Summary
Floods are a major cause of casualties due to natural disasters, with over 6.8 million deaths globally during the 20th century (Doocy et al, 2013), and an annual average loss of 104 billion US$ (Blöschl et al, 2017). In an attempt to use more realistic data to assess the statistical relationship between surge and river discharge, Klerk et al (2015) subsequently used variables diagnosed from hydrological, hydraulic and storm surge models In their coarse-resolution dataset covering the relatively short historical period from 1981 to 2010 they found a clear correlation between the two variables, but only when a substantial time lag of 6 days was taken into account. To assess the performance of the two hydrological models, both HBV and SPHY (together with routing model) were forced with bias-corrected E-OBS daily precipitation and temperature data for the period 1951–2000 The output of both models produced (hereafter referred to as EOBS runs) is compared to the observed discharge at Lobith. In SPHY fewer occasions of overestimated flood durations affect the mean of the flood wave
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