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| Titel |
Towards a quantitative climate reconstruction linking meteorological, limnological and sedimentological datasets: the Lake Sanabria (NW Spain) case. |
| VerfasserIn |
María Teresa Rico-Herrero, Santiago Giralt, Blas L. Valero-Garcés, José Carlos Vega |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250036662
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| Zusammenfassung |
| It is well-known that lacustrine sediment records constitute one of the best environmental
sensors to reconstruct climate variability. Nevertheless, our knowledge of how the climate
signal (precipitation, temperature, wind stress) is transferred from the atmosphere
to the lake water masses (through the limnological variables such as pH, nutrient
inputs or water chemistry) and to the sediments is very poor. Besides there are few
reliable and temporal long limnological and/or meteorological datasets. This lack also
prevents the conversion of these qualitative climate reconstructions into quantitative
ones.
Lake Sanabria (Zamora) is located in the northwestern of the Iberian Peninsula (42°07’30” N,
06°43’00” W), at 1.000 m a.s.l. It is the largest glacial lake (368 ha, 51 m of water depth at
the deepest point and 96 Hm3 of water volume) in the Iberian Peninsula. The main water and
sediments input and output is the Tera River.
Monthly limnological (secchi disk, water temperature profiles, conductivity, pH, dissolved
oxygen), nutrients (nitrates, silicon, total phosphorous, reactive phosphorous, total
chlorophyll and a-chlorophyll), hydrological (Tera river discharge) and meteorological
(precipitation and air temperature from the Ribadelago meteorological station) datasets
covering the period 1992 - 2005 were employed to explore the relationships between
the atmosphere and the Lake Sanabria hydrological balance, and the limnological
variables.
X-Ray Fluorescence (XRF) core scanner data of two gravity cores (SAN04-3A
and SAN07-1M) allowed us to characterize with high resolution the evolution of
the chemical composition of the uppermost sedimentary infill. SAN07-1M was
dated using gamma-spectrometry (210Pb) and a key bed corresponding to the dam
failure of the Vega de Tera Reservoir located upstream occurred in 1959 AD. The
relationships between the sedimentological and limnological datasets allowed us to
characterize the transference of the climate signal from the limnological towards the
sediments.
These relationships were studied using a statistical approach, such as ordination analyses
(Principal Component and Redundancy Analyses), time series (auto- and cross-correlation
funtions) and generalised linear models (glm).
The precipitation and temperature oscillations account for more than 75% of the total
variance of the Tera River discharge, and only precipitation explained more than the 55%.
The lake reacts inmediately to changes in the precipitation as shown by best correlation
between the three variables occurring at 0 lag-time. When exploring the possible
relationships between meteorological and the limnological and nutrient datasets, it was
evidenced that total phosphorous showed the best fit with 28% of the total explained variance.
The best correlation was also observed at 0 lag, indicating that the main nutrient input occurs
by the Tera River.
Principal Component Analysis (PCA) on the XRF dataset showed that the first eigenvector
explained more than 44% of the total variance and it was related mainly to the organic matter
changes. Oscillations of this first eigenvector have been interpreted in terms of fluctuations of
the primary productivity of Lake Sanabria.
The comparison between the reconstructed primary productivity with the total phosphorous
highlighted that lakes generally act as a low-pass filters, smoothing the climate signal when
transfers it to the sediments. The explained variance between the smoothed reconstructed
primary productivity and the total phosphorous is 24%, similar to that between the total
phosphorous and the Tera River discharge.
This study opens the possibility to a quantitative reconstruction of past climate data
(temperature and precipitation) from high-resolution sedimentological datasets. |
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