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 soilmoisture 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.
Bibliographical noteSpecial Issue Extreme Weather Events: Predictions, Management, Vulnerabilities of Economic Sectors, and Remote Impacts
- Auto correlation
- Compound events