Low-frequency variability of European runoff

Abstract

Abstract. This study investigates the low-frequency components of observed monthly river flow from a large number of small catchments in Europe. The low-frequency components, defined as fluctuations on time scales longer than one year, were analysed both with respect to their dominant space-time patterns as well as their contribution to the variance of monthly runoff.The analysis of observed streamflow and corresponding time series of precipitation and temperature, showed that the fraction of low-frequency variance of runoff is on average larger than, and not correlated to, the fraction of lowfrequency variance of precipitation and temperature. However, it is correlated with mean climatic conditions and is on average lowest in catchments with significant influence of snow. Furthermore, it increases (decreases) under drier (wetter) conditions -indicating that the average degree of catchment wetness may be a primary control of low-frequency runoff dynamics. The fraction of low-frequency variance of runoff is consistently lower in responsive catchments, with a high variability of daily runoff.The dominant space-time patterns of low-frequency runoff in Europe, identified using nonlinear dimension reduction, revealed that low-frequency runoff can be described with three modes, explaining together 80.6 % of the variance. The dominant mode has opposing centres of simultaneous variations in northern and southern Europe. The secondary mode features a west-east pattern and the third mode has its centre of influence in central Europe. All modes are closely related to the space-time patterns extracted from time series of precipitation and temperature.Correspondence to: L. Gudmundsson (lukas.gudmundsson@geo.uio.no) In summary, it is shown that the dynamics of lowfrequency runoff follows well known continental-scale atmospheric features, whereas the proportion of variance attributed to low-frequency fluctuations is controlled by catchment processes and varies with mean climatic conditions. The results may have implications for interpreting the impact of changes in temperature and precipitation on river-flow dynamics.

Streamflow sensitivity to water storage changes across Europe

“…The imperfect fit between ϵ S and slopes of the FDC indicates that other factors, such as regional differences in climatic variability [Gudmundsson et al, 2011;Berghuijs et al, 2014b], nonstorage-related runoff-generating mechanisms [Dunne, 1983], and human influences [Poff et al, 2007;Jaramillo and Destouni, 2015], are also important for the nature of the catchment's flow regime. However, independent of its uncertainties and unaccounted factors, ϵ S still explains a significant part of the flow variability between places indicating that the storage-discharge relationships of the catchments partly determine the nature of flow regimes.…”

Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes

“…This procedure was already used with GRACE data by Baur (2012) and Hassan and Jin (2014) as a method to derive the long-term component, in Bergmann et al (2012) to robustly deseasonalize GRACE time series, and in Frappart et al (2013) to compare terrestrial water storage with monthly rainfall time series in the Amazon basin. It has also been successfully applied, for instance, in a hydro-climatological setting (Gudmundsson et al 2011) or to extract temperature trends (Dufresne et al 2013). The STL procedure is based on locally weighted smoothing of the deseasonalized time series in which the smoothing parameters are analytically optimized to minimize spectral leakage between the high-and the low-frequency components.…”

Towards observation-based gridded runoff estimates for Europe

“…investigating the link between climate variability and terrestrial water dynamics, including feedbacks (e.g. Tootle and Piechota, 2006;Jung et al, 2010;Gudmundsson et al, 2011b;Mueller and Seneviratne, 2012;de Linage et al, 2014;Miralles et al, 2014); 5. analysing the effect of climate change on freshwater resources (e.g. Krakauer and Fung, 2008;Stahl et al, 2012;Famiglietti and Rodell, 2013;Greve et al, 2014).…”

“…1), the time series of the individual catchments were assigned to the corresponding grid cells. Following previous studies (Arnell, 1995;Gudmundsson et al, 2011bGudmundsson et al, , 2012b, streamflow observations from the individual catchments were first converted into runoff rates per unit area and the coordinates of the gauging stations were assigned to the 0.5 • grid cells defined by the atmospheric forcing data. If more than one gauging station occurred in one catchment, the catchment area weighted average runoff rate was used.…”