Understanding hydrologic variability across Europe through catchment classification

Anna Kuentz,Yeshewatesfa Hundecha,Berit Arheimer,Thorsten Wagener

doi:10.5194/hess-21-2863-2017

Anna Kuentz, Yeshewatesfa Hundecha + Show 2 more

Open Access

https://doi.org/10.5194/hess-21-2863-2017

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Abstract

Abstract. This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures – taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33 % (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisture-limited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables.

Highlights

Hydrological systems exhibit a tremendous variability in their physical properties and in the hydrological variables we observe, such as streamflow and soil moisture patterns (Bloeschl et al, 2013)
To gain better understanding of processes behind the hydrologic variability, we further examined similarities in both flow signatures and catchment descriptors for each of the classes based on the classification and regression tree (CART) classification
Class No 2, which contains only four gauges, was excluded from the CART analysis for consistency

Summary

Introduction

Hydrological systems exhibit a tremendous variability in their physical properties and in the hydrological variables we observe, such as streamflow and soil moisture patterns (Bloeschl et al, 2013). We assume (or at least hope) that the aggregated response behavior, e.g., the hydrograph, is related to average or dominating characteristics and that smaller-scale differences are less relevant. We generally make the same assumption in the search for a catchment classification framework where our aim is to group catchments that somehow exhibit similar hydrologic behavior (McDonnell and Woods, 2004). While the preferred classification system will depend to a degree on the specific objective of a study or the data availability, it is generally agreed upon that even the search for such an organizing principle is an important undertaking for hydrology (Wagener et al, 2007). Approaches include the use of physical and climatic characteristics

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