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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (3): 355-372    DOI: 10.1007/s40333-024-0095-y     CSTR: 32276.14.s40333-024-0095-y
Research article     
Linkage between precipitation isotopes and water vapor sources in the monsoon margin: Evidence from arid areas of Northwest China
CHEN Fenli1,2, ZHANG Qiuyan1,2, WANG Shengjie1,2,*(), CHEN Jufan1,2, GAO Minyan1,2, Mohd Aadil BHAT3
1College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730000, China
2Key Laboratory of Resource Environment and Sustainable Development of Oasis of Gansu Province, Northwest Normal University, Lanzhou 730000, China
3School of Ocean and Earth Science, Tongji University, Shanghai 200092, China
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Abstract  

The isotope composition in precipitation has been widely considered as a tracer of monsoon activity. Compared with the coastal region, the monsoon margin usually has limited precipitation with large fluctuation and is usually sensitive to climate change. The water resource management in the monsoon margin should be better planned by understanding the composition of precipitation isotope and its influencing factors. In this study, the precipitation samples were collected at five sampling sites (Baiyin City, Kongtong District, Maqu County, Wudu District, and Yinchuan City) of the monsoon margin in the northwest of China in 2022 to analyze the characteristics of stable hydrogen (δD) and oxygen (δ18O) isotopes. We analyzed the impact of meteorological factors (temperature, precipitation, and relative humidity) on the composition of precipitation isotope at daily level by regression analysis, utilized the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)-based backward trajectory model to simulate the air mass trajectory of precipitation events, and adopted the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) to analyze the water vapor sources. The results showed that compared with the global meteoric water line (GMWL), the slope of the local meteoric water line (LMWL; δD=7.34δ18O-1.16) was lower, indicating the existence of strong regional evaporation in the study area. Temperature significantly contributed to δ18O value, while relative humidity had a significant negative effect on δ18O value. Through the backward trajectory analysis, we found eight primary locations that were responsible for the water vapor sources of precipitation in the study area, of which moisture from the Indian Ocean to South China Sea (ITSC) and the western continental (CW) had the greatest influence on precipitation in the study area. The hydrogen and oxygen isotopes in precipitation are significantly influenced by the sources and transportation paths of air mass. In addition, the results of PSCF and CWT analysis showed that the water vapor source areas were primarily distributed in the south and northwest direction of the study area.

Key words： water vapor      monsoon margin      stable water isotope      transport trajectory      air mass      d-excess      δ18O      δD     
Received: 20 September 2023      Published: 31 March 2024
Corresponding Authors: *WANG Shengjie (E-mail: wangshengjie@nwnu.edu.cn)
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CHEN Fenli
ZHANG Qiuyan
WANG Shengjie
CHEN Jufan
GAO Minyan
Mohd Aadil BHAT
Cite this article:

CHEN Fenli, ZHANG Qiuyan, WANG Shengjie, CHEN Jufan, GAO Minyan, Mohd Aadil BHAT. Linkage between precipitation isotopes and water vapor sources in the monsoon margin: Evidence from arid areas of Northwest China. Journal of Arid Land, 2024, 16(3): 355-372.

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http://jal.xjegi.com/10.1007/s40333-024-0095-y     OR     http://jal.xjegi.com/Y2024/V16/I3/355

Fig. 1 Location of sampling sites at the margin area of the East Asian monsoon
Sampling site Latitude Longitude Altitude (m) Temperature (°C) Precipitation (mm) Relative humidity (%)
Baiyin City 36°55′N 104°18′E 1719 9.03 210.40 56.00
Kongtong District 35°55′N 106°67′E 1364 9.70 486.40 51.00
Maqu County 34°00′N 102°08′E 3471 2.30 612.30 63.00
Wudu District 33°00′N 104°92′E 1116 15.30 470.40 61.00
Yinchuan City 38°48′N 106°22′E 1010 10.10 193.80 52.00
Table 1 Climate characteristics of the five sampling sites from 1991 to 2020
Sampling site δD (‰) δ18O (‰) d-excess (‰)
Max Min Mean SD Max Min Mean SD Max Min Mean SD
Baiyin City 34.53 -120.02 -17.65 32.84 4.69 -16.02 -2.22 4.49 16.01 -14.37 0.11 8.25
Kongtong District 28.81 -118.99 -40.87 31.95 3.12 -16.37 -6.40 4.18 21.17 -8.88 6.22 6.92
Maqu County -3.90 -120.68 -71.06 17.36 -2.21 -15.02 -9.38 3.14 18.45 -27.28 3.94 13.85
Wudu District 47.18 -84.22 -28.13 29.81 7.42 -11.37 -3.79 4.05 20.67 -25.25 2.16 9.81
Yinchuan City 35.22 -145.35 -31.25 38.75 5.96 -19.34 -4.76 5.23 22.07 -20.11 7.21 9.97
Total 47.18 -145.35 -37.79 34.94 7.42 -19.34 -5.31 4.79 19.67 -19.18 3.93 11.27
Table 2 Statistic values of stable hydrogen (δD), oxygen (δ18O), and deuterium excess (d-excess) for each sampling site during June-September 2022
Fig. 2 Relationship between hydrogen (δD) value and oxygen (δ18O) value of precipitation in the study area in 2022. (a), relationship between δD value and δ18O value of precipitation during summer half year and winter half year of 2022; (b), relationship between δD value and δ18O value of precipitation at each sampling site in 2022; (c), relationship between δD value and δ18O value of precipitation in the whole study area in 2022. LMWL, local meteoric water line; GMWL, global meteoric water line.
Fig. 3 Air mass trajectory (a, c, e, g, and i) and its clustering result (b, d, f, h, and j) at each sampling site in 2022. Different color lines in the right panel indicate that air mass comes from different directions; of which yellow line represents air mass comes from the northwest continent, grey line represents air mass comes from the northwest continent in the direction of Russia, red line represents air mass comes from the western continent, blue line represents air mass comes from the Black Sea, green line represents air mass comes from the Mediterranean, pink line represents air mass comes from the Atlantic Ocean, purple line represents air mass comes from the southwest continent, orange line represents air mass comes from the southeast continent, and white line represents air mass comes from the proximity continent. Note that the base map used for computerizing this map is from the Meteoinfo website (http://www.meteothink.org/index.html#).
Water vapor source n Length (km) ν (m/s) Tem
(°C)		 RH (%) Pre (mm) δD (‰) δ18O (‰) d-excess (‰)
Mean SD Mean SD Mean SD
CNW 33 7133 2.08 15.00 63.58 185.00 -23.24 31.65 -3.39 4.57 3.85 11.01
CW 76 6011 2.10 13.20 62.37 330.80 -39.83 37.70 -5.53 5.05 4.40 12.20
CP 13 3140 2.02 10.40 79.60 45.00 -46.30 28.14 -6.11 3.66 2.57 12.70
CSW 10 3152 2.19 9.70 76.50 31.80 -62.38 33.84 -8.22 5.62 3.38 13.86
CSE 9 3611 1.58 13.90 84.90 97.60 -36.88 22.38 -6.17 2.62 12.49 5.02
ITSC 79 5078 10.72 15.90 78.20 772.30 -58.28 24.81 -7.96 3.58 5.41 10.64
OA 10 16,815 2.30 8.60 70.79 37.90 -56.98 42.70 -7.54 5.62 3.32 12.14
PR 6 13,121 1.88 14.40 63.10 44.70 -9.55 23.41 -1.44 3.40 1.97 11.69
Table 3 Climatic, δD, and δ18O characteristics of each kind of water vapor source for the monsoon margin in 2022
Fig. 4 Water vapor sources of precipitation in the monsoon margin in 2022. (a), continental moisture from northwest (CNW); (b), continental moisture from west (CW); (c), continental moisture from proximity region (CP); (d), continental moisture from southwest (CSW); (e), continental moisture from southeast (CSE); (f), moisture from the Indian Ocean to South China Sea (ITSC); (g), moisture from the Atlantic Ocean (OA); (h), moisture from polar region (PR). The denser the line, the more water vapor from this direction. Note that the base map used for computerizing this map is from the Meteoinfo website (http://www.meteothink.org/index.html#).
Fig. 5 Results of potential source contribution function (PSCF; a, c, e, g, and i) and concentration weighted trajectory (CWT; b, d, f, h, and j) for the potential water vapor source area analysis of the selected five sampling sites in the monsoon margin in 2022. Note that the base map used for computerizing this map is from the Meteoinfo website (http://www.meteothink.org/index.html#).
Fig. 6 Rose diagrams showing the relationship of deuterium excess (d-excess) value with wind speed (ν) and direction at each sampling site. (a), Baiyin City; (b), Kongtong District; (c), Maqu County; (d), Wudu District; (e), Yinchuan City.
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