The Impact of Meteorological and Hydrological Memory on Compound Peak Flows in the Rhine River Basin

Abstract

Spatio-temporal variation of hydrological processes that have a strong lagged autocorrelation (memory), such as soil moisture, snow accumulation and the antecedent hydro-climatic conditions, significantly impact the peaks of flood waves. Ignoring these memory processes leads to biased estimates of floods and high river levels that are sensitive to the occurrence of these compounding hydro-meteorological processes. Here, we investigate the role of memory in hydrological and meteorological systems at different temporal scales for the Rhine basin. We simulate the hydrological regime of the Rhine river basin using a distributed hydrological model (SPHY) forced with 1950–2000 atmospheric conditions from an ensemble simulation with a high resolution (0.11°/12 km) regional climate model (RACMO2). The findings show that meltwater from antecedent anomalous snowfall results in a time shift of the discharge peak. Soil moisture modulates the rainfall-runoff relationship and generates a strong runoff response at high soil moisture levels and buffers the generation of runoff peaks at low levels. Additionally, our results show that meteorological autocorrelation (manifesting itself by the occurrence of clustered precipitation events) has a strong impact on the magnitude of peak discharge. Removing meteorological autocorrelation at time scales longer than five days reduces peak discharge by 80% relative to the reference climate. At time scales longer than 30 days this meteorological autocorrelation loses its significant role in generating high discharge levels.

Improving hydrological climate impact assessments using multirealizations from a global climate model

“…The Rhine is one of the largest river basins in Western Europe and is intensively used for agriculture, industry, and navigation (Kwadijk & Rotmans, 1995; Van Alphen, 2016). The basin area is 185,000 km 2 with 58 million inhabitants of which more than 10 million live in flood‐prone areas (ICPR, 2001; Khanal et al, 2019). The river originates in the Swiss Alps and flows along the Southern boundary between France and Germany and continues through Germany before it enters the Netherlands at Lobith (Te Linde et al, 2011; van Osnabrugge et al, 2017; Figure 1).…”

Storm Surge and Extreme River Discharge: A Compound Event Analysis Using Ensemble Impact Modeling

“…Every year on average two strong storm events (winds higher than 19-20 m/s) are recorded at Hoek van Holland (Smits et al, 2005). Further, these depression systems are also characterized by humid Atlantic moisture conditions and primarily transport the moisture toward the Netherlands (Khanal et al, 2019).…”

Statistical modelling and climate variability of compound surge and precipitation events in a managed water system: a case study in the Netherlands

“…The associated impacts strongly depend on the catchment features and the characteristics of the storms (Wahl et al, 2015). For discharge peaks originating from remote precipitation or snowmelt inputs (for instance in larger river systems), delays between the surge and discharge peaks are usually due to the finite travel speed of the discharge wave (Khanal et al, 2019b;Klerk et al, 2015).…”

“…Using SMILEs is a physically based approach to increase the size of the database and therefore increase the number of simulated extreme compound events. Apart from van den Hurk et al (2015), SMILEs have been applied as a tool to investigate compound events by, for example, Zhou and Liu (2018), Khanal et al (2019a), andPoschlod et al (2020). This methodology allowed van den Hurk et al (2015) to demonstrate a positive dependence between storm surge and heavy precipitation and showed that the probability of occurrence of these extreme water levels can be greatly underestimated if such dependence is omitted.…”