Artikel,

Dune movement under climatic changes on the north‐eastern Tibetan Plateau as recorded by long‐term satellite observation versus ERA‐5 reanalysis

, , , , , , , , und .
Earth Surface Processes and Landforms, 48 (13): 2613–2629 (2023)
DOI: 10.1002/esp.5651

Zusammenfassung

The movement of active dunes is tightly linked to climatic conditions (e.g., wind regime, temperature and precipitation) as well as human influence (e.g., grazing, dune fixation and greening). Dune migration rates can be studied to draw conclusions of changing wind conditions over time. The Gonghe Basin (GB), located on the north-eastern Tibetan Plateau (TP), offers a good testing ground for these assumptions. The intramontane basin is highly influenced by two major wind regimes: the mid-latitude Westerlies and the East Asian summer monsoon. To investigate environmental changes, this study combines optical remote sensing techniques with climatic datasets. High-resolution satellite images of the last five decades, such as CORONA KH-4B, are used to map dunes and calculate their respective migration rates. Further, height information was extracted as well. Climatic changes from the ERA-5 reanalysis dataset and normalized difference vegetation index (NDVI) values were processed alongside. Relating the dunes' surface processes to climate model data shows an accordance between slowing migration, expanding vegetation and a decrease in sand drift potential. From 1968 to present time, an average dune migration rate of 7.3 m a−1 was extracted from the satellite images, with an overall reduction of −1.81 m a−1. The resultant drift potential (RDP) values for the GB are calculated to be below 10 m3 s−3 with a spatial decrease, following a direction from the NW to the SE, fitting well with a corresponding decrease in the migration rates. Our results indicate a good agreement between the development of aeolian landforms and the ERA-5 climate reanalysis model data, even in a high-altitude setting with complex topography, which is known to influence such datasets.

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  • @jwalk
  • @gstauch

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