The impact of meteorological and hydrological memory on compound peak flows in the Rhine river basin

Sonu Khanal; Arthur F. Lutz; Walter W. Immerzeel; Hylke de Vries; Niko Wanders; Bart van den Hurk

doi:10.3390/atmos10040171

The impact of meteorological and hydrological memory on compound peak flows in the Rhine river basin

Sonu Khanal^*
, Arthur F. Lutz
, Walter W. Immerzeel
, Hylke de Vries
, Niko Wanders
, Bart van den Hurk

^*Corresponding author for this work

FutureWater
Vrije Universiteit Amsterdam
Royal Netherlands Meteorological Institute

Research output: Contribution to journal › Article › Academic › peer-review

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 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.

Original language	English
Article number	171
Journal	ATMOSPHERE
Volume	10
Issue number	4
DOIs	https://doi.org/10.3390/atmos10040171
Publication status	Published - 1 Apr 2019

Funding

This research was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 676027. The project has also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement number 676819) and the Netherlands Organization for Scientific Research under the Innovational Research Incentives Scheme VIDI (grant agreement 016.181.308). The authors would like to thank the IMPREX project, in particular Erik van Meijgaard, for providing the downscaled RACMO simulations. The authors would like to acknowledge Nina Ridder for her contribution to discussions during this study.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

Auto correlation
Compound events
Memory

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Cite this

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title = "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 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.",

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author = "Sonu Khanal and Lutz, \{Arthur F.\} and Immerzeel, \{Walter W.\} and \{de Vries\}, Hylke and Niko Wanders and \{van den Hurk\}, Bart",

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AU - Khanal, Sonu

AU - Lutz, Arthur F.

AU - Immerzeel, Walter W.

AU - de Vries, Hylke

AU - Wanders, Niko

AU - van den Hurk, Bart

PY - 2019/4/1

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N2 - 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.

AB - 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.

KW - Auto correlation

KW - Compound events

KW - Memory

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The impact of meteorological and hydrological memory on compound peak flows in the Rhine river basin

Abstract

Funding

UN SDGs

Keywords

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