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Journal of Arid Land  2022, Vol. 14 Issue (7): 753-770    DOI: 10.1007/s40333-022-0099-4
Research article     
Dew amount and its long-term variation in the Kunes River Valley, Northwest China
FENG Ting1,2,3,4, HUANG Farong1,2,4,5, ZHU Shuzhen1,2,3,4, BU Lingjie1,2,3,4, QI Zhiming6, LI Lanhai1,2,3,4,5,*()
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Tianshan Station for Snowcover and Avalanche Research, Chinese Academy of Sciences, Xinyuan 835800, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone, Urumqi 830011, China
5Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
6Department of Bioresource Engineering, McGill University, Montreal H3A0G4, Canada
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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.

Key wordsdew amount      long-term variation      meteorological variables      random forest model      multiple linear regression model      Kunes River Valley     
Received: 27 April 2022      Published: 31 July 2022
Corresponding Authors: LI Lanhai     E-mail:
Cite this article:

FENG Ting, HUANG Farong, ZHU Shuzhen, BU Lingjie, QI Zhiming, LI Lanhai. Dew amount and its long-term variation in the Kunes River Valley, Northwest China. Journal of Arid Land, 2022, 14(7): 753-770.

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Fig. 1 Location of the study area (a); national weather station in Xinyuan County (Xinyuan Station, b); observation field in Ili Station for Watershed Ecosystem Research, Chinese Academy of Sciences (Ili Station, c); automatic weather station used in this study (d); and measurement of the daily dew amount based on the cloth-plate method in observation filed (e).
Fig. 2 Nocturnal mean dew point temperature and nocturnal mean soil surface temperature (a) and soil moisture at 10 cm depth on 4 July (b), 9 August (c), 26 September (d), and 5 October (e) in 2021
Fig. 3 Measured dew amount based on the cloth-plate method in summer and autumn of 2021
Fig. 4 Correlation coefficient (r) between the dew amount and meteorological variables. Tmin, daily minimum air temperature; Tmean, daily mean air temperature; Tmax, daily maximum air temperature; RH, daily mean relative humidity; WS, daily mean wind speed; SM, soil moisture at 10 cm depth; ST, soil temperature at 10 cm depth; Rn, net solar radiation.
Meteorological variables Nights with the dew amount more than 0.10 mm Nights with the dew amount less than 0.10 mm Significance level
Air temperature (°C) Minimum 1.50 9.80 <0.001
Maximum 30.40 33.90
Mean 9.27 17.23
Relative humidity (%) Minimum 36.60 25.10 <0.001
Maximum 80.10 65.40
Mean 64.24 45.45
Wind speed (m/s) Minimum 0.00 0.00 <0.001
Maximum 4.57 5.58
Mean 1.22 1.52
Soil moisture at 10 cm
depth (%)
Minimum 8.26 8.29 0.034
Maximum 17.85 25.84
Mean 13.32 13.69
Soil temperature at 10 cm
depth (°C)
Minimum 9.10 13.56 <0.001
Maximum 21.76 23.66
Mean 14.25 19.40
Net solar radiation (W/m2) Minimum -105.05 -107.48 0.366
Maximum 220.70 207.20
Mean -55.98 -55.26
Table 1 Meteorological variables of nights with the dew amount more than 0.10 mm and those with the dew amount less than 0.03 mm in the study site
Fig. 5 Performance of multiple linear regression (MLR) and random forest (RF) models on the dew amount simulation. (a) and (c), the measured and simulated dew amount based on MLR and RF models, respectively; (b) and (d), the relationship of the measured dew amount with the dew amount simulated by MLR model and the dew amount simulated by RF model, respectively; RMSE, root mean square error; MAE, mean absolute error.
Fig. 6 Frequency distribution histograms of the dew amount for the testing dataset
Fig. 7 Simulated monthly dew amount and the daily mean, maximum, and minimum dew amount during June-October from 1980 to 2021
Fig. 8 Boxplot for the monthly dew amount during June-October from 1980 to 2021. The boxes represent the range from the lower quantile (Q25) to the upper quantile (Q75) of the total monthly dew amount during June-October from 1980 to 2021. The dots and horizontal lines inside the boxes represent the means and medians, respectively. The dots outside the boxes represent outliers. The upper and lower whiskers indicate the maximum and minimum values, respectively.
Fig. 9 Variation in the dew amount during June-October (a) and in June (b), July (c), August (d), September (e), and October (f) from 1980 to 2021
Month Temporal stage β |Z| Trend
June-October 1980-2002 0.01 0.32 Increase
2002-2013 -0.31 3.50 Significantly decrease
2013-2021 0.20 0.73 Increase
1980-2021 -0.02 2.08 Significantly decrease
June 1980-2002 0.01 0.85 Increase
2002-2013 -0.08 2.13 Significantly decrease
2013-2021 0.02 1.15 Increase
1980-2021 0.00 0.98 No obvious variation
July 1980-2002 0.00 0.26 No obvious variation
2002-2013 -0.05 2.95 Significantly decrease
2013-2021 0.05 1.15 Increase
1980-2021 -0.01 2.10 Significantly decrease
August 1980-2002 0.01 1.43 Increase
2002-2013 -0.06 2.95 Significantly decrease
2013-2021 0.03 0.94 Increase
1980-2021 0.00 0.80 No obvious variation
September 1980-2002 0.01 0.69 Increase
2002-2013 -0.05 2.81 Significantly decrease
2013-2021 0.02 1.15 Increase
1980-2021 -0.01 2.36 Significantly decrease
October 1980-2002 0.00 0.00 No obvious variation
2002-2013 -0.05 2.95 Significantly decrease
2013-2021 0.04 1.56 Increase
1980-2021 0.00 1.26 No obvious variation
Table 2 Long-term variation in the dew amount in different temporal stages
Fig. 10 Variation in daily minimum air temperature (a), daily mean relative humidity (b), total precipitation (c), and daily mean wind speed (d) during June-October from 1980 to 2021
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