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  • Copernicus  (2)
  • Sage  (1)
  • Springer Science + Business Media
  • 2015-2019  (3)
  • 1
    Publication Date: 2016-06-28
    Description: Sea-level rise (SLR) is one of the most conspicuous examples of the environmental impact of recent climate change. Since SLR rates are not uniform around the planet, local and regional data are needed for proper adaptation plans. 210 Pb-dated sediment cores were analyzed to determine the trends of sediment accretion rates (SARs) at three tropical saltmarshes in the Estero de Urias lagoon (Gulf of California, Mexico), in order to estimate the SLR trends during the past ~100 years, under the assumption that these ecosystems accrete at a similar rate to SLR. A chemometric approach, including multivariate statistical analysis (factor analysis) of geochemical data (including 13 C; 15 N; C/N ratios; and Br, Na, and Cl as proxies for marine transgression) was used to identify the marine transgression in the sediment records. Based on core geochemistry, only one of the three cores provided a long-term record attributable to marine transgression. SLR trends, estimated from SARs, showed increasing values, from a minimum of 0.73 ± 0.03 mm yr –1 at the beginning of the 20th century and up to 3.87 ± 0.12 mm yr –1 during the period 1990–2012. The estimated SLR trend between 1950 and 1970 was comparable to the tide gauge records in Mazatlan City for the same period. Results showed the caveats and strengths of this methodology to reconstruct decadal SLR trends from the sedimentary record, which can be used to estimate long-term SLR trends worldwide in regions where monitoring data are scarce or absent.
    Print ISSN: 0959-6836
    Electronic ISSN: 1477-0911
    Topics: Geography , Geosciences
    Published by Sage
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  • 2
    Publication Date: 2015-10-29
    Description: Seasonal information of temperature, salinity, dissolved oxygen (DO) and chlorophyll, combined with meteorological and river discharge time series were used to describe the oceanographic conditions in the Reloncavi fjord (41°35' S; 72°20' W). The winds in the fjord valley blow mainly down-fjord during winter, reinforcing the upper layer outflow, while in spring–summer winds have a predominant up-fjord direction contrary to upper layer outflow. The fjord, with a deep sill at the mouth, was well stratified year-round and showed a thin surface layer of brackish water with mean salinities between 10.4 ± 1.4 (spring) and 13.2 ± 2.5 (autumn). The depth of the upper layer changed slightly along the different studied seasons remaining at about 4.5 m near the mouth. This upper layer presented a mean outflow (Q1) of 3185 ± 223 m3 s−1, which imply a flushing time of about 3 days of this layer. The vertical salt flux was ∼ 37 tons of salt per second, similar to the horizontal salt flux observed in the upper layer. These estimations will contribute to a better management of the aquaculture on this region.
    Print ISSN: 1812-0806
    Electronic ISSN: 1812-0822
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2015-07-30
    Description: This research reveals new insights into the climatic drivers of anomalies in land surface phenology (LSP) across the entire European forest, while at the same time establishes a new conceptual framework for predictive modelling of LSP. Specifically, the Random Forest method, a multivariate, spatially non-stationary and non-linear machine learning approach, was introduced for phenological modelling across very large areas and across multiple years simultaneously: the typical case for satellite-observed LSP. The RF model was fitted to the relation between LSP anomalies and numerous climate predictor variables computed at biologically-relevant rather than human-imposed temporal scales. In addition, the legacy effect of an advanced or delayed spring on autumn phenology was explored. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP anomalies, with relative errors of 10 and 20 %, respectively: a level of precision that has until now been unobtainable at the continental scale. Multivariate linear regression models explained only 36 and 25 %, respectively. It also allowed identification of the main drivers of the anomalies in LSP through its estimation of variable importance. This research, thus, shows clearly the inadequacy of the hitherto applied linear regression approaches for modelling LSP and paves the way for a new set of scientific investigations based on machine learning methods.
    Print ISSN: 1810-6277
    Electronic ISSN: 1810-6285
    Topics: Biology , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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