Seasonal temperatures for the past ∼400 years reconstructed from diatom and chironomid assemblages in a high-altitude lake (Lej da la Tscheppa, Switzerland)
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- von Gunten, L., Heiri, O., Bigler, C. et al. J Paleolimnol (2008) 39: 283. doi:10.1007/s10933-007-9103-4
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We analysed a 42 cm long sediment record from Lej da la Tscheppa, a high-altitude lake (2,616 m a.s.l.) in the Upper Engadine valley (Switzerland) for subfossil diatoms, chironomids and pollen. The chronology of the top 21 cm of the record was established using 210Pb analysis using a constant-rate-of-supply model, and validated with 137Cs measurements and the content of spheroidal carbonaceous particles. A tentative chronology for the lower part of the core was obtained through extrapolation of the sedimentation rates in the uppermost part of the record. Pollen assemblages in the record reflect regional changes in forestation and land-use patterns in the Upper Engadine valley and show no evidence of significant local human activity in the lake’s catchment. Diatom assemblages record a distinct increase in planktonic taxa since the early 19th century, suggesting a decrease in the duration of ice-cover. In contrast, chironomid assemblages remained stable during a large part of the record. We applied an established chironomid-based July air temperature transfer function and a newly developed diatom-based spring air temperature transfer function to reconstruct past seasonal air temperature changes at Lej da la Tscheppa. The reconstructions indicate a diatom-inferred warming trend in spring temperatures during the past ca. 400 years, whereas chironomid-inferred summer temperatures suggest a slight cooling trend. These biota-based reconstructions are in good agreement with the centennial-scale temperature trend in an independent reconstruction of regional temperatures in the Upper Engadine region based on instrumental records and documentary proxy evidence from the Alps. Our results suggest that, in high-altitude lakes, independent chironomid- and diatom-based seasonal temperature reconstruction is possible and can be successfully used to track seasonal temperature trends.