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

doi:10.1007/s40333-022-0099-4

Journal of Arid Land

2022, Vol. 14

Issue (7): 753-770 DOI: 10.1007/s40333-022-0099-4 CSTR: 32276.14.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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Abstract

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 21^st 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 words： dew 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: lilh@ms.xjb.ac.cn)

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

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.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0099-4 OR http://jal.xjegi.com/Y2022/V14/I7/753

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. T_min, daily minimum air temperature; T_mean, daily mean air temperature; T_max, 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; R_n, net solar radiation.

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

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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