Changes in glacier equilibrium-line altitude in the western Alps from 1984 to 2010: evaluation by remote sensing and modeling of the morpho-topographic and climate controls

J.-P. Dedieu,N. Eckert,A. Letréguilly,A. Rabatel

doi:10.5194/tc-7-1455-2013

Abstract

Abstract. We present time series of equilibrium-line altitude (ELA) measured from the end-of-summer snow line altitude computed using satellite images, for 43 glaciers in the western Alps over the 1984–2010 period. More than 120 satellite images acquired by Landsat, SPOT and ASTER were used. In parallel, changes in climate variables, summer cumulative positive degree days (CPDD) and winter precipitation, were analyzed over the same time period using 22 weather stations located inside and around the study area. Assuming a continuous linear trend over the study period: (1) the average ELA of the 43 glaciers increased by about 170 m; (2) summer CPDD increased by about 150 PDD at 3000 m a.s.l.; and (3) winter precipitation remained rather stationary. Summer CPDD showed homogeneous spatial and temporal variability; winter precipitation showed homogeneous temporal variability, but some stations showed a slightly different spatial pattern. Regarding ELAs, temporal variability between the 43 glaciers was also homogeneous, but spatially, glaciers in the southern part of the study area differed from glaciers in the northern part, mainly due to a different precipitation pattern. A sensitivity analysis of the ELAs to climate and morpho-topographic variables (elevation, aspect, latitude) highlighted the following: (1) the average ELA over the study period of each glacier is strongly controlled by morpho-topographic variables; and (2) the interannual variability of the ELA is strongly controlled by climate variables, with the observed increasing trend mainly driven by increasing temperatures, even if significant nonlinear, low-frequency fluctuations appear to be driven by winter precipitation anomalies. Finally, we used an expansion of Lliboutry's approach to reconstruct fluctuations in the ELA of any glacier of the study area with respect to morpho-topographic and climate variables, by quantifying their respective weight and the related uncertainties in a consistent manner within a hierarchical Bayesian framework. This method was tested and validated using the ELA measured on the satellite images.

Highlights

The glacier’s annual surface mass balance and equilibrium-line altitude (ELA) have been computed from direct field measurements of snow accumulation and snow/ice ablation through point measurements using a network of ablation stakes and snow pits on individual glaciers
In Sect. 3.2., we present the variability of climate variables
We already mentioned the relationship between the ELA and the latitude (Fig. 6a) in Sect. 3.1.2, where we noted that the average ELA of the glaciers located in the southern part of the study area was about 150 m higher in altitude than the ELA of the glaciers located in the northern part of the study area

Summary

Introduction

The glacier’s annual surface mass balance and equilibrium-line altitude (ELA) have been computed from direct field measurements of snow accumulation and snow/ice ablation through point measurements using a network of ablation stakes and snow pits on individual glaciers. Satellite derived DEMs are not accurate enough to compute variations in the annual volume of mountain glaciers Another alternative emerged from recent studies based on ELA and distributed mass balance modeling using meteorological input fields derived from local weather stations, data reanalysis, or regional climate models For mid-latitude mountain glaciers, the end-of-summer snow line altitude (SLA) is a good indicator of the ELA and of the annual mass balance (Lliboutry, 1965; Braithwaite, 1984; Rabatel et al, 2005) This enables ELA changes to be reconstructed for long time periods from remote-sensing data (Demuth and Pietroniro, 1999; Rabatel et al, 2002, 2005, 2008; Barcaza et al, 2009; Mathieu et al, 2009), because the snow line is generally easy to identify using aerial photographs and satellite images (Meier, 1980; Rees, 2005). It is possible to study the climate–glacier relationship at a massif- or regional scale (Clare et al, 2002; Chinn et al, 2005), which is useful in remote areas where no direct measurements are available

Objectives

Results

Discussion

Conclusion