Abstract

AbstractMeltwater from glaciers is not only a stable source of water but also affects downstream streamflow dynamics. One of these dynamics is the interannual variability of streamflow. Glaciers can moderate streamflow variability because the runoff in the glacierized part, driven by temperature, correlates negatively with the runoff in the non‐glacierized part of a catchment, driven by precipitation, thereby counterbalancing each other. This is also called the glacier compensation effect (GCE), and the effect is assumed to depend on relative glacier cover. Previous studies found a convex relationship between streamflow variability and glacier cover of different glacierized catchments, with lowest streamflow variability at a certain optimum glacier cover. In this study, we aim to revisit these previously found curves to find out if a universal relationship between interannual streamflow variability and glacier cover exists, which could potentially be used in a space‐for‐time substitution analysis. Moreover, we test the hypothesis that the dominant climate drivers (here precipitation and temperature) switch around the suggested optimum of the curve. First, a set of virtual nested catchments, with the same absolute glacier area but varying non‐glacierized area, were modelled to isolate the effect of glacier cover on streamflow variability. The modelled relationship was then compared with a multicatchment data set of gauged glacierized catchments in the European Alps. In the third step, changes of the GCE curve over time were analysed. Model results showed a convex relationship and the optimum in the simulated curve aligned with a switch in the dominant climate driver. However, the multicatchment data and the time change analyses did not suggest the existence of a universal convex relationship. Overall, we conclude that GCE is complex due to entangled controls and changes over time in glacierized catchments. Therefore, care should be taken to use a GCE curve for estimating and/or predicting interannual streamflow variability in glacierized catchments.

Highlights

  • Streamflow variability is related to climate variability, for instance heat waves, seasonality of precipitation and temperature, and climate modes (e.g., Barlow, Nigam, & Berbery, 2001; Dettinger & Diaz, 2000), and moderated by catchment storages, such as lakes, groundwater and soil characteristics (e.g., Andrés Doménech, García Bartual, Montanari, Segura, & Bautista, 2015; Milly & Wetherald, 2002)

  • Interannual variability of streamflow is important because it characterizes the reliability of water supply for diverse water uses, for example, hydropower (Schaefli, Manso, Fischer, Huss, & Farinotti, 2019)

  • The interannual streamflow variability of glacierized catchments will be the focus of this study

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Summary

| INTRODUCTION

Streamflow variability is related to climate variability, for instance heat waves, seasonality of precipitation and temperature, and climate modes (e.g., Barlow, Nigam, & Berbery, 2001; Dettinger & Diaz, 2000), and moderated by catchment storages, such as lakes, groundwater and soil characteristics (e.g., Andrés Doménech, García Bartual, Montanari, Segura, & Bautista, 2015; Milly & Wetherald, 2002). We test the different interpretations of the GCE curve and its climate controls with the aim of challenging the existence of a universal (convex) relationship between streamflow variability and glacier cover This will give a better insight in whether the relationship can be used for planning and predictions of future water reliability in glacierized catchments under climate change. We tested how stable the relationship is in time by calculating CVQ for two 20-year periods (1965–1984 and 1996–2015 for the multi-catchment data set, 1976–1995 and 1996–2015 for the model experiment) and relating them to the g values from the available glacier inventories in 1969 and 2006 for Austria and 1973 and 2003 for Switzerland. For each catchment, the CVQ of the subperiods against the g of the subperiods shows whether changes in g over time lead to expected changes in CVQ (following the curve, decreasing, or increasing CVQ)

| RESULTS
| DISCUSSION
| Limitations and other controls on GCE
| CONCLUSION
Findings
DATA AVAILABILITY STATEMENT

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