Spatial cross‐correlation patterns of European low, mean and high flows

Abstract

Low and high flows are associated with different hydrological processes. High flows correspond to the direct response of catchments to water input, whereas low flows occur in pronged dry periods and are governed by depleting storages. Therefore, the inter‐annual dynamics of high and low flows are often considered to be independent. To shed light on this assumption, we analysed a pan‐European dataset of 615 streamflow records, summarized as time series of annual streamflow percentiles (5th, 10th, …, 95th). The analysis was based on comparing the spatial cross‐correlation patterns derived from the different percentile series. Their interrelation was visualized by projecting them into a low‐dimensional space. We found that large parts of the cross‐correlations of the percentile series can be summarized by one dominating component. This component represents geographical continuous regions in Europe of correlated streamflow. Departures from this mean pattern occurred for low and high flows and were characterized by the corresponding spatial correlation functions. Generally, spatial correlations appear to be stronger for high flows than for mean flows, particularly for short distances (<400 km). Low flows, on the other hand, have the lowest spatial correlations for short distances. For longer distances (>800 km), this pattern reverses and the spatial correlation of low flows become largest. This discrepancy between low and high flows suggests that hydrological systems are more homogeneously linked to climatic fluctuations under wet conditions. Under dry conditions, local catchment properties appear to play a larger role in translating climatic fluctuations into hydrological response. Copyright © 2010 John Wiley & Sons, Ltd.

Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

“…The fast reaction of runoff to precipitation corresponds to the findings of, for example, Haddeland et al (2011);Stahl et al (2012); Gudmundsson et al (2012). Based on their analysis of spatial cross-correlation patterns and runoff percentiles, Gudmundsson et al (2011Gudmundsson et al ( , 2012 conclude that discharge during dry conditions is largely influenced by terrestrial hydrological processes (catchment storage and release), in contrast to floods, which are mostly determined by forcing data. Stahl et al (2012) and Gudmundsson et al (2012) found that these terrestrial hydrological processes are poorly replicated in the simplified storage schemes of large-scale models.…”

Comparing Large-Scale Hydrological Model Simulations to Observed Runoff Percentiles in Europe

“…A good model performance with respect to interannual variability of all runoff percentiles (as reflected by relatively high R 2 ) is most likely related to the fact that the dynamics of annual runoff closely follow those of the atmospheric drivers. Shorthouse andArnell (1997, 1999), for example, have demonstrated the coupling between atmospheric oscillation indices and river flow in Europe, and recently Gudmundsson et al (2011b) showed that the dominant space-time patterns of European lowfrequency runoff variability (variability on time scales longer than 1 yr) were closely related to the corresponding patterns of precipitation and temperature. This dependence of runoff on atmospheric variability suggests that simulated runoff on interannual time scales may be more sensitive to the data product used to force the models than to the parameterization of terrestrial hydrological processes.…”

“…To provide insights into the entire flow range, two additional percentile series were introduced to characterize moderately low (Q 25 ) and high (Q 75 ) values. It can be argued that this set of five percentile series is sufficient to characterize the overall flow range, as previous results have demonstrated that the information gain by introducing additional percentile levels is limited for continental-scale analysis (Gudmundsson et al 2011a). This procedure resulted in a set of five time series of annual runoff percentiles for both observed and modeled runoff in each grid cell.…”

Low-frequency variability of European runoff

“…On these time scales runoff variability is known to have systematic continental scale space-time patterns (e.g. Lins, 1997;Gudmundsson et al, 2011) that are related to large-scale atmospheric drivers (e.g. Shorthouse and Arnell, 1997;Barlow et al, 2001;Tootle and Piechota, 2006).…”