Monthly limnological monitoring in Lake Sanabria (Spain) since 1986 provided a unique opportunity to test relationships among climate, hydrology and lake dynamics and how they are recorded in the lake sediments. Four datasets were employed: (1) meteorological (monthly maximum and minimum air temperature and total precipitation), (2) limnological (Secchi disk, water temperature, conductivity, pH, dissolved oxygen, nitrate, silicon, total and reactive phosphorus, and total chlorophylls and chlorophyll a), (3) hydrological (Tera River water input and output), and (4) XRF core scanner measurements carried out in short cores. Linear models between the different dataset variables allowed us to characterize the climate signal transmission from one to the other and cross-correlation analyses permitted us to identify the different response times (if any) between them. Principal Component Analyses (PCA) of the limnological and geochemical datasets allowed us to identify the main processes that link lake dynamics, primarily nutrient supply and organic productivity, with some sedimentological processes, e. g. organic matter and phosphorus accumulation. Sediment chronology was established by gamma spectrometry (210Pb). Water input to Lake Sanabria is controlled mostly by the Tera River input and is linked directly to precipitation. Response of the Lake Sanabria water budget to climate oscillations is immediate, as the strongest correlation between these two datasets occurs with no lag time. PCA of the limnological dataset indicated that most of the variance is related to nutrient input, and comparison with the Tera River water discharge shows that nutrient input was controlled mainly by oscillations in the hydrological balance. The lag time between the hydrological and limnological datasets is 1 month. The PCA of the XRF core scanner dataset showed that the principal process that controls the chemical composition of the Lake Sanabria sediments is related to sediment and nutrient delivery from the Tera River and organic productivity. Comparison of the nutrient input reconstructed using the limnological dataset and the XRF core scanner data indicated that the sediments act as a low-pass filter, smoothing the climate signal. It was, however, possible to establish the link between these datasets, and obtain a quantitative reconstruction of precipitation for the 1959-2005 AD period that captures the regional variability. This quantitative precipitation reconstruction suggests it is possible to obtain accurate climate reconstructions using non-laminated sediments.
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Acknowledgments The Spanish Ministry of Science and Innovation funded the research at Lake Sanabria through the projects LIMNOCLIBER (REN2003-09130-C02-02/CLI), CALIBRE (CGL2006-13327-C04/CLI), IBERLIMNO (CGL2004-20236-E) and CONSOLIDER-GRACCIE (CSD2007-00067). The Limnological Research Center and the Large Lakes Observatory (University of Minnesota, USA) are acknowledged for technical assistance with the ITRAX XRF-core scanner and the GEOTEK, and Daniel R. Engstrom from St. Croix Watershed Research Station (Science Museum of Minnesota, USA) for radiometric dating of the SAN07-2 M core. We thank Javier Sigró and Manola Brunet (Centre for Climate Change, University Rovira i Virgili, Tarragona, Spain) for providing the precipitation and temperature data. We are grateful to the ‘‘Parque Natural del Lago de Sanabria y Alrededores’’ of the Consejería de Medio Ambiente y Ordenación del Territorio de la Junta de Castilla y León (Environmental Council of Castilla and León Autonomous Spanish Region) and owner of the Laboratorio de Limnología del Parque Natural (Laboratory of Limnology of the Natural Park) for the field and administrative facilities. Dr. Mark Brenner, Dr. Pere Anadón and an anonymous referee are acknowledged for their exhaustive reviews and very constructive comments that greatly helped to improve this manuscript.
Copyright 2011 Elsevier B.V., All rights reserved.
- Iberian Peninsula
- Lacustrine sediments
- Multiproxy approach
- Quantitative precipitation reconstruction
- Statistical modeling