Climatic moisture conditions in the north-west of the Mid-Russian Upland during the Holocene
This study aimed to reconstruct the climatic moisture conditions of the Mid- Russian Upland through the Holocene. Surface moisture conditions in the study region were inferred from published pollen records from the Klukva peatland, in the north-west of the Mid-Russian Upland. Three climatic indices...
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Published in | Geography, environment, sustainability Vol. 12; no. 4; pp. 188 - 202 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Lomonosov Moscow State University
31.12.2019
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Subjects | |
Online Access | Get full text |
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Summary: | This study aimed to reconstruct the climatic moisture conditions of the Mid- Russian Upland through the Holocene. Surface moisture conditions in the study region were inferred from published pollen records from the Klukva peatland, in the north-west of the Mid-Russian Upland. Three climatic indices were derived from previously- published reconstructions of mean annual temperature and precipitation: the Climate Moisture Index, the Aridity Index and the Budyko Dryness Index. A simple modeling approach to reconstruct annual potential evapotranspiration and net radiation was developed and used to estimate the indices for different periods of the Holocene. The moisture indices were compared with independent proxies of climate moisture such as peatland surface wetness, reconstructed from testate amoebae and regional fire activity, reconstructed from charcoal. Results show that the surface moisture conditions in the study region were characterized by large variability. Periods of mild temperature and moderately wet conditions were followed by dry periods, which resulted in significant changes in palaeoenvironments. The method developed for calculation of potential evapotranspiration and indices of surface moisture conditions could be a useful tool for climate reconstructions. Our results demonstrate the detailed and nuanced palaeoclimate data which can be derived from pollen data. |
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ISSN: | 2071-9388 2542-1565 |
DOI: | 10.24057/2071-9388-2018-62 |