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Open AccessArticle

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

1
FutureWater, Costerweg 1V, 6702 AA Wageningen, the Netherlands
2
Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
3
KNMI—Royal Netherlands Meteorological Institute, 3731 GA De Bilt, The Netherlands
4
Department of Physical Geography, Utrecht University, 3508 TC Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(4), 171; https://doi.org/10.3390/atmos10040171
Received: 19 February 2019 / Revised: 15 March 2019 / Accepted: 23 March 2019 / Published: 31 March 2019
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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. View Full-Text
Keywords: memory; auto correlation; compound events memory; auto correlation; compound events
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Khanal, S.; Lutz, A.F.; Immerzeel, W.W.; Vries, H.; Wanders, N.; Hurk, B. The Impact of Meteorological and Hydrological Memory on Compound Peak Flows in the Rhine River Basin. Atmosphere 2019, 10, 171.

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