A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

Theano Iliopoulou,Alberto Montanari,María José Polo,Guillaume Thirel,Juraj Parajka,Andrea Ficchì,Nejc Bezak,María Bermúdez,Cristina Aguilar,Demetris Koutsoyiannis,Berit Arheimer

doi:10.5194/hess-23-73-2019

Abstract

Abstract. The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes.

Highlights

Recent analyses for the Po River and the Danube River highlighted that catchments may exhibit significant correlation between peak river flows and average flows in the previous months (Aguilar et al, 2017)
We focus on persistence properties of both high and low flows by investigating the following research questions: (i) what are the physical conditions, in terms of catchment properties, i.e. geology and climate, which may induce seasonal persistence in river flow, and (ii) can floods and droughts be predicted, in probabilistic terms, by exploiting the information provided by average flows in the previous months? These questions are relevant for gaining a better comprehension of catchment dynamics and planning mitigation strategies for natural hazards
The investigation of the persistence properties of river flows focuses separately on both high and low discharges and is articulated in the following steps: (a) identification of the high- and low-flow seasons, (b) correlation assessment between the peak flow in the high-flow season and average flows in the previous months, (c) analysis of the physical drivers for streamflow persistence and its predictability through a principal component analysis (PCA), and (d) real-time updating of the frequency distribution of high and low flows for a selected case study with significant seasonal correlation by employing a meta-Gaussian approach

Summary

Introduction

Recent analyses for the Po River and the Danube River highlighted that catchments may exhibit significant correlation between peak river flows and average flows in the previous months (Aguilar et al, 2017). Such correlation is the result of the behaviours of the physical processes involved in the rainfall–runoff transformation that may induce memory in river flows at several different timescales. The above previous studies postulated that seasonal streamflow correlation may be due to the persistence of the catchments storage and/or the weather, but no attempt was made to identify the physical drivers

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Conclusion