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

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.

Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation

“…Nevertheless, before merging the databases we found that they need to be harmonized and quality assured, which has already been noted in previous studies (e.g. Kauffeldt et al, 2013). For meteorological data, global precipitation from re-analysis products are well known to contribute a lot to the output uncertainty in traditional global modelling (e.g.…”

“…There are a number of algorithms available to calculate potential evapotranspiration (PET) in HYPE. For WWH we used the algorithms that had been judged most appropriate in previous HYPE applications, giving Jensen-Haise (Jensen and Haise, 1963) in temperate areas, modified Hargreaves (Hargreaves and Samani, 1982) in arid and equatorial areas, and Priestly-Taylor (Priestly and Taylor, 1972) in polar and snow-/ice-dominated areas. River flow is routed from upstream catchments to downstream along the river network, where lakes and reservoirs may dampen the flow according to a rating curve.…”

Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation

“…Dynamic catchment models need to be initialised to account for adequate storage volumes, which may, for instance, dampen or supply the river flow based on catchment memory (e.g. Iliopoulou et al, 2019). The WWH was initialized by running for a 15-year warm-up period 1965-1980, which was judged to be enough for more than 90% of the catchments.…”

Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis

“…The seasonal timing of flood events is a useful indicator of how atmospheric processes interact with the local catchment, with recent papers showing the relevance of intense precipitation, snowmelt, and rain-on-snow events as mechanisms driving the timing of floods (Blöschl et al, 2017;Hall & Blöschl, 2018;Iliopoulou et al, 2019;Parajka et al, 2010;Villarini, 2016). An understanding of flood timing provides useful insights at many scales: (i) globally-because of the considerable attention devoted to the development of global hydrological models (Bierkens, 2015;Bierkens et al, 2015;Wood et al, 2011) and the need to reconcile patterns of nonstationarity in climatic drivers such as rainfall (Sharma et al, 2018;Westra et al, 2013;Westra et al, 2014) with those observed in streamflow (Do et al, 2017;Gudmundsson et al, 2017;Gudmundsson et al, 2019;Hodgkins et al, 2017); (ii) regionally-for analyses of flood frequency within homogeneous regions and for detection/attribution of historical changes in flooding (Cunderlik et al, 2004;Villarini, 2016); and (iii) locally-to assist understanding of flood mechanisms, as required by decision makers in designing strategies for flood prevention, mitigation, and response (Dhakal et al, 2015;Ward et al, 2015).…”