首页|Dew amount and its long-term variation in the Kunes River Valley, Northwest China

Dew amount and its long-term variation in the Kunes River Valley, Northwest China

扫码查看
原文链接
  • 万方数据
Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions. Yet estimating the dew amount and quantifying its long-term variation are challenging. In this study, we elucidate the dew amount and its long-term variation in the Kunes River Valley, Northwest China, based on the measured daily dew amount and reconstructed values (using meteorological data from 1980 to 2021), respectively. Four key results were found: (1) the daily mean dew amount was 0.05 mm during the observation period (4 July–12 August and 13 September–7 October of 2021). In 35 d of the observation period (i.e., 73％ of the observation period), the daily dew amount exceeded the threshold (>0.03 mm/d) for microorganisms; (2) air temperature, relative humidity, and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables; (3) for estimating the daily dew amount, random forest (RF) model outperformed multiple linear regression (MLR) model given its larger R2 and lower MAE and RMSE; and (4) the dew amount during June–October and in each month did not vary significantly from 1980 to the beginning of the 21st century. It then significantly decreased for about a decade, after it increased slightly from 2013 to 2021. For the whole meteorological period of 1980–2021, the dew amount decreased significantly during June–October and in July and September, and there was no significant variation in June, August, and October. Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity. This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount, which provides valuable information for us to better understand the dew amount and its relationship with climate change.

dew amountlong-term variationmeteorological variablesrandom forest modelmultiple linear regression modelKunes River Valley

FENG Ting、HUANG Farong、ZHU Shuzhen、BU Lingjie、QI Zhiming、LI Lanhai

展开 >

State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China

Tianshan Station for Snowcover and Avalanche Research,Chinese Academy of Sciences,Xinyuan 835800,China

University of Chinese Academy of Sciences,Beijing 100049,China

Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone,Urumqi 830011,China

Research Center for Ecology and Environment of Central Asia,Chinese Academy of Sciences,Urumqi 830011,China

Department of Bioresource Engineering,McGill University,Montreal H3A0G4,Canada

展开 >

国家自然科学基金Project of State Key Laboratory of Desert and Oasis Ecology,Xinjiang Institute of Ecology and Geography,Chinese Academy of SProject of National Cryosphere Desert Data Center of ChinaYouth Innovation Promotion Association of the Chinese Academy of Sciences

41901048E1510301012021kf022021438

2022

干旱区科学
中国科学院新疆生态与地理研究所,科学出版社

干旱区科学

CSTPCDCSCDSCI
影响因子：1.743
ISSN：1674-6767
年,卷(期)：2022.14(7)
  • 1
  • 2