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DDC: 500 × Subject: chronology ×

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    Ice-volume-forced erosion of the Chinese Loess Plateau global Quaternary stratotype site
    ([London] : Nature Publishing Group UK, 2018) Stevens, T.; Buylaert, J.-P.; Thiel, C.; Újvári, G.; Yi, S.; Murray, A.S.; Frechen, M.; Lu, H.
    The International Commission on Stratigraphy (ICS) utilises benchmark chronostratigraphies to divide geologic time. The reliability of these records is fundamental to understand past global change. Here we use the most detailed luminescence dating age model yet published to show that the ICS chronology for the Quaternary terrestrial type section at Jingbian, desert marginal Chinese Loess Plateau, is inaccurate. There are large hiatuses and depositional changes expressed across a dynamic gully landform at the site, which demonstrates rapid environmental shifts at the East Asian desert margin. We propose a new independent age model and reconstruct monsoon climate and desert expansion/contraction for the last ~250 ka. Our record demonstrates the dominant influence of ice volume on desert expansion, dust dynamics and sediment preservation, and further shows that East Asian Summer Monsoon (EASM) variation closely matches that of ice volume, but lags insolation by ~5 ka. These observations show that the EASM at the monsoon margin does not respond directly to precessional forcing.
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    Spatiotemporal data analysis with chronological networks
    ([London] : Nature Publishing Group UK, 2020) Ferreira, Leonardo N.; Vega-Oliveros, Didier A.; Cotacallapa, Moshé; Cardoso, Manoel F.; Quiles, Marcos G.; Zhao, Liang; Macau, Elbert E. N.
    The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.