sustainability
Article δ2H and δ18O in Precipitation and Water Vapor Disentangle Seasonal Wind Directions on the Loess Plateau
Fu-Qiang Huang 1, Jian-Zhou Wei 2, Xin Song 1, Yong-Hong Zhang 1, Qi-Feng Yang 1,3, Yakov Kuzyakov 4,5,6 and Feng-Min Li 1,*
1 State Key Laboratory of Grassland Agroecosystems, Institute of Arid Agroecology, School of Life Sciences, Lanzhou University, No. 222, South Tianshui Road, Lanzhou 730000, China; [email protected] (F.-Q.H.); [email protected] (X.S.); [email protected] (Y.-H.Z.); [email protected] (Q.-F.Y.) 2 Centre for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China; [email protected] 3 Department of Agriculture and Rural Affairs of Gansu Province, Gansu Agricultural University, Lanzhou 730000, China 4 Department of Soil Science of Temperate Ecosystems, Department of Agricultural Soil Science, University of Göttingen, 37077 Göttingen, Germany; [email protected] 5 Agro-Technological Institute, RUDN University, 117198 Moscow, Russia 6 Institute of Environmental Sciences, Kazan Federal University, 420049 Kazan, Russia * Correspondence: [email protected]; Tel.: +86-931-8912848
Abstract: In many areas of the Loess Plateau, groundwater is too deep to extract, making meteoric water (snow and rain) the only viable water resource. Here we traced the rainwater and water 2 18 vapor sources using the δ H and δ O signature of precipitation in the northern mountainous region of Yuzhong on the Loess Plateau. The local meteoric water line in 2016 and 2017 was Citation: Huang, F.-Q.; Wei, J.-Z.; defined as δ2H = 6.8 (±0.3)·δ18O + 4.4 (±2.0) and δ2H = 7.1 (±0.2)·δ18O + 1.5 (±1.6), respectively. Song, X.; Zhang, Y.-H.; Yang, Q.-F.; The temperature and precipitation amount are considered to be the main factor controlling the δ2H Kuzyakov, Y.; Li, F.-M. δ2H and δ18O and δ18O variation of precipitation, and consequently, relationships were first explored between in Precipitation and Water Vapor 18 Disentangle Seasonal Wind δ O and local surface air temperature and precipitation amount by linear regression analysis. The Directions on the Loess Plateau. temperature effect was significant in the wet seasons but was irrelevant in the dry seasons on daily Sustainability 2021, 13, 6938. and seasonal scales. The amount effect was significant in the wet seasons on a daily scale but https://doi.org/10.3390/su13126938 irrelevant in the dry seasons. However, based on the data of the Global Network of Isotopes in Precipitation (GNIP) (1985–1987, 1996–1999) of Lanzhou weather station, the amount effects were Academic Editor: absent at seasonal scales and were not useful to discriminate either wetter or drier seasons or even Georgios Koubouris wetter or drier decades. Over the whole year, the resulting air mass trajectories were consistent with the main sources of water vapor were from the Atlantic Ocean via westerlies and from the Received: 28 May 2021 Arctic region, with 46%, 64%, and 40% of water vapor coming from the westerlies, and 54%, 36%, Accepted: 17 June 2021 and 60% water vapor from the north in spring, autumn and winter, respectively. In the summer, Published: 20 June 2021 however, the southeast monsoon (21%) was also an important water vapor source in the Loess Plateau. Concluding, using the δ2H and δ18O signatures of precipitation water, we disentangled and Publisher’s Note: MDPI stays neutral quantified the seasonal wind directions that are important for the prediction of water resources for with regard to jurisdictional claims in published maps and institutional affil- local and regional land use. iations. Keywords: isotopic approaches; δ2H and δ18O of precipitation; water vapor sources; Loess Plateau
Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. This article is an open access article Understanding the precipitation sources is important for meteorological, hydrological, distributed under the terms and and ecological studies that underpin well-informed water resource management [1]. The 2 18 conditions of the Creative Commons combined measures of stable hydrogen ( H) and oxygen ( O) isotopes in precipitation Attribution (CC BY) license (https:// enable analyzing local and global hydrologic cycles accurately. The composition of stable creativecommons.org/licenses/by/ isotopes in precipitation is closely correlated with water vapor transport processes and the 4.0/).
Sustainability 2021, 13, 6938. https://doi.org/10.3390/su13126938 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 6938 2 of 15
origin of the water vapor source region and, therefore, provides useful information about hydrogeological processes and atmospheric circulation [2–4]. The δ18O and δ2H signatures of precipitation can change due to isotope fractionation during evaporation and condensation [5]. The isotopically lighter molecules have a higher vapor pressure, while the heavier isotopic molecules generally have higher binding en- ergy [6]. The water containing lighter isotopes thus evaporates at a slightly faster rate than water containing heavier isotopes. In turn, the heavier isotopes preferentially precipitate out in a series of condensations [7]. Therefore, during the evaporation, the remaining liquid phase is enriched with a heavier isotope. As the oceanic water vapor moves through the cycle, it becomes progressively lighter as the heavier isotopes preferentially precipitate out in a series of condensations [7]. In general, the δ18O and δ2H signatures of precipitation follow the Rayleigh-type fractionation model during the water cycle, influenced by meteo- rological factors, such as temperature, atmospheric humidity, precipitation amount, and residence time [8,9]. These effects of environmental factors on stable isotope signatures vary with geographical location, atmospheric circulation, weather, and terrain [10]. The δ18O and δ2H values are, therefore, region-specific and require local analyses [11,12]. To study the extent of isotope fractionation in atmospheric precipitation, the deuterium excess, also known as “d-excess” or “d”, was first proposed by Dansgaard in 1964 and is expressed by the equation: d-excess = δ2H − 8·δ18O[8]. The d-excess is mainly influenced by the wind speed, sea surface temperature, and relative humidity over the sea [13,14]. Earlier studies [15] found a direct connection between d-excess and evaporative processes at the moisture source location, and therefore, d-excess can be used to trace the mixing of moisture of various vapor sources. Generally, the average value is about 10 on the global scale. High d-excess (>10) in precipitation are typical for intensive moisture recycling along air mass trajectories [16]. After a raindrop is condensed, the stronger the re-evaporative fractionation under the cloud, the smaller the d value (<10) is [17]. Due to the complexity of water cycling, the d-excess in precipitation varies temporally and spatially. Since the 1950s, the stable isotope composition of precipitation has been extensively used in studies of hydrologic cycle processes [18–20], including the moisture transport trajectory [21], climate models with isotope capability [22–24], river or lake water sources, [25,26], groundwater recharge [27], amount effects and paleoclimate reconstruction in certain regions [28–31], and the use of isotopic time series derived from speleothems to study the past monsoon intensity [32–34]. The northern mountainous region of Yuzhong County is located in the northwest of the Loess Plateau (Figure1). Situated in north-central China, the Loess Plateau has a complex terrain and is a water-limited area. Many areas on the Loess Plateau have a deep groundwater level and no irrigation water; thus, as a limited and unique water resource in such areas, meteoric water is particularly important. Our study site also has very limited water resources, and therefore, low primary productivity. Intensive management results in serious water and soil loss, strongly decreasing the resistance and resilience of ecosystems to environmental perturbations. Therefore, limited water resources are one of the main factors restricting agricultural production. There is an urgent need to clarify the characteristics and sources of local precipitation to ensure the optimal utilization and management of scarce water resources. We used the δ2H and δ18O signatures of precipitation from April 2016 to December 2017 to identify meteorological factors affecting precipitation and its sources. Finally, we used a hydro-meteorological model to determine the moisture sources of precipitation on the Loess Plateau. Sustainability 2021, 13, 6938 3 of 15
Sustainability 2021, 13, 6938 3 3of of 15
Figure 1. The location of the study area in the northwest part of the Loess Plateau, with a cold semi- FigurearidFigure climate. 1. 1. TheThe location location ofof the the study study area area in the in thenorthwes northwestt partpart of the of Loess the Loess Plateau, Plateau, with a with colda semi- cold aridsemi-arid climate. climate. 2. Materials and Methods 2. Materials and Methods 2. Materials and Methods 2.1. The Study Site 2.1. The Study Site The study site is in the DrylandDryland Agroecology Research Station (36(36°02◦02′0 N, 104°25104◦25′0 E,E, altitudeThe 2400 study m) site of is Lanzhou in the Dryland University Agroecology at Zhonglianchuan, Research Station Yuzhong (36°02 County,′ N, 104°25 Gansu′ E, altitudeProvince, 2400 China China m) (Figure (Figureof Lanzhou 1).1). The The University site site has has a a coldat cold Zhonglianchuan, semi-arid semi-arid climate, climate, Yuzhong with with an an annualCounty, annual mean Gansu mean air Province,temperatureair temperature China of 4.0 (Figure of °C, 4.0 mean ◦1).C, The meanmaximum site maximum has temperat a cold temperaturesemi-aridure of 15.7 climate, °C of in 15.7 July, with◦C and an in annualmean July, andminimum mean mean air temperaturetemperatureminimum temperature ofof 4.0−12.6 °C, °C mean ofin −January. maximum12.6 ◦C The in temperat January. mean annualure The of mean 15.7precipitation °C annual in July, precipitation over and tenmean years minimum over (2007– ten temperature2017)years was (2007–2017 318 of mm,−12.6) was with °C 318in 285 January. mm, mm with in The 2016 285 mean and mm annual 350 in 2016mm precipitation andin 2017 350 (Figure mm over in ten2). 2017 Precipitationyears (Figure (2007–2). 2017)occursPrecipitation was from 318 May occurs mm, to with fromOctober, 285 May mmaccounting to October, in 2016 for accountingand 90% 350 of mm the for inannual 90% 2017 of prec(Figure the annualipitation. 2). precipitation.Precipitation The losses occursonThe the losses Chinesefrom on May the Loess Chineseto October, Plateau Loess are accounting Plateau formed are from for formed 90% materials of from the derived materialsannual from prec derived ipitation.the Qilian from The and the QilianlossesGobi- onAltayand the Gobi-Altay MountainsChinese Loess Mountains and Plateau transported and are transportedformed via rivers from via materialsand rivers winds and derived [35,36]. winds from Subsequently, [35 ,the36]. Qilian Subsequently, and a further Gobi- a Altaystudyfurther Mountainsconfirmed study confirmed andthat transportedthe that loess the loess deposits via depositsrivers fo andrmed formed winds via via the[35,36]. the Mountain Mountain Subsequently, provenance-river provenance-river a further studytransport-desert confirmed transition that the (MRD) loess deposits mode, and fo rmedth thee loess via depositsthe Mountain are relatively provenance-river continuous transport-desertand thick [37]. [37]. Due transition to the lack (MRD) of clay mode, particles and th to e loesshold waterdeposits within are relatively the deposits continuous [[38],38], the andgroundwater thick [37]. tabletable Due in into this thisthe site lacksite is isof more more clay than particlesthan 60 60 m m belowto below hold the water the soil soil surface,within surface, the which whichdeposits is unavailable is unavail-[38], the groundwaterablefor plants for plants and table difficultand indifficult this to getsite to for isget more irrigation. for irrigation. than 60 m below the soil surface, which is unavail- able for plants and difficult to get for irrigation.
Figure 2.2. TheThemonthly monthly precipitation precipitation and and the the average average temperature temperature in 2016 in and2016 2017 and of 2017 Yuzhong of Yuzhong County. ◦ FigureCounty.MAT means 2. MAT The Mean meansmonthly Annual Mean precipitation TemperatureAnnual Temperature and ( C),the andaverage (° MAPC), andtemperature means MAP Mean means in Annual 2016Mean and PrecipitationAnnual 2017 Precipitationof Yuzhong (mm). County.(mm). MAT means Mean Annual Temperature (°C), and MAP means Mean Annual Precipitation (mm). Sustainability 2021, 13, 6938 4 of 15
2.2. Sampling Methods Precipitation was collected after each rainfall event from April 2016 to December 2017. To prevent evaporation, the samples were gathered into sealed glass vials immediately after a rain event. Automated weather stations were used to record the precipitation amount, temperature, and relative humidity at the respective date. All precipitation samples were transported to the laboratory and preserved at 4 ◦C until analysis.
2.3. Data and Laboratory Measurements The δ2H and δ18O values of the water samples were measured using an isotopic water analyzer (Picarro, L2130-i) at the Key Laboratory of Western China’s Environmental Systems, Ministry of Education, China. This instrument uses a laser-based absorption tech- nique involving cavity ring-down spectroscopy. The working standard water samples were 18 added between every seven samples. They are listed as follows, S1: δ OV-SMOW = −13.1‰, 2 18 2 18 δ HV-SMOW = −96.4‰; S2: δ OV-SMOW = −7.69‰, δ HV-SMOW = −51‰; S3: δ OV-SMOW = −2.8‰, 2 and δ HV-SMOW = −9.5‰. All water sample was loaded in the isotope analyzer for isotope measurement by an injector with a 0.22 um filter membrane. Each sample was parallelly measured six times, and the last three results were used for data processing. Results were expressed as parts per thousand deviations from the Vienna Standard Mean Ocean Water (V-SMOW) in the following form:
18 2 δ O (or δ H, ‰) = (Rs/Rstd − 1) (1)
18 16 Rs and Rstd indicate the stable isotopic ratios of heavy to light isotopes ( O/ O, 2H/H) in the water sample and the standard sample. The analytical precision was ±0.025‰ and ±0.1‰, for δ18O for δ2H (delta notation relative to V-SMOW), respectively. Deuterium excess (d-excess) for each water sample was calculated as d-excess = δ2H − 8·δ18O. The calculation of amount-weighted average isotopic composition for a given period was calculated by the following equation:
n ∑i=1(PiRi) RW = n (2) ∑i Pi
18 2 where Pi is the precipitation amount. Ri is the values of δ O, δ H and d-excess for sample i, and n is the total number of samples.
3. Results Table S1 (in Supplementary Materials) and Figure3 show the δ18O, δ2H, d-excess values and the meteorological data of each precipitation event in the study area, and the amount-weighted, minimal, maximal values and the seasonal average values in 4 seasons are listed in Tables1 and2. The four seasons were defined as follows: winter (December, January, and February), spring (March, April, and May), summer (June, July, and August), and autumn (September, October, and November) according to the “Local records of Yuzhong County”. In total, 38 precipitation samples were collected in 2016, and 51 samples were collected in 2017. The δ2H and δ18O composition differed for individual precipitation events due to the complexity of vapor sources and the extreme natural climate in the cold semi-arid region. The δ18O values of precipitation in 2016 varied from −15.5‰ to 5.4‰, with the amount-weighted mean value of −8.2‰ and standard deviation of 4.7‰, while the δ2H values ranged from −108.4‰ to 35‰, with the amount-weighted mean value of −51‰ and standard deviation of 33‰. In 2017, the δ18O values varied from −17.8‰ to 5.8‰, with the amount-weighted mean value of −8.3‰ and standard deviation of 5.6‰, while the δ2H values ranged from −124.6‰ to 38.5‰, with the amount- weighted mean value of −58‰ and standard deviation of 40‰. Seasonal variations of the δ2H and δ18O values were substantial, with more enriched values in summer months and more depleted values in winter months. Generally, the seasonality in the isotope values of precipitation depends mostly on the seasonal variability of other controls such as Sustainability 2021, 13, 6938 5 of 15
cipitation depends mostly on the seasonal variability of other controls such as tempera- ture, precipitation, and moisture source [39]. Therefore, it is necessary to study further the relationship between isotopic composition and these meteorological parameters.
Table 1. Basic statistics of the isotopic composition of precipitation in the study area.
δ18O (‰) δ2H (‰) Year Amount- Amount- n Min Max SD n Min Max SD Weighted Mean Weighted Mean 2016.4–12 38 −15.5 5.4 −8.2 4.7 38 −108.4 35 −51 33 2017.1–12 51 −17.8 5.8 −8.3 5.6 51 −124.6 38.6 −58 40
Sustainability 2021, 13, 6938 5 of 15 Table 2. The seasonal amount-weighted average values of isotopic compositions in 2016 and 2017.
δ18O (‰) δ2H (‰) Year Spring Summer Autumn Winter Spring Summer Autumn Winter temperature,2016.4–12 −9.7 precipitation, −7.2 and−9.2 moisture source [−3958.6]. Therefore,−47.3 it is−53.8 necessary to study further2017.1–12 the relationship−9.2 −7.4 between −10.1 isotopic composition−14.2 −64.1 and these−51.8 meteorological −69.1 parameters.−98.3
Figure 3. Seasonal variations variations of of δδ1818O,O, δ2δH,2H, and and d-excess d-excess (= (=δ2Hδ2 −H 8−∙δ188O)·δ18 inO) the in precipitation the precipitation in the in the Yuzhong County. Colors represent the the dry dry and and wet wet seasons. seasons. The The black black dashed dashed lines lines reflect reflect the the Y = Y = 0. 0. Table 1. Basic statistics of the isotopic composition of precipitation in the study area. 4. Discussion 18 2 4.1. Local Meteoric Water Lineδ O (‰) δ H (‰) YearAs marine air moves over the coastAmount- and into continental areas, differentAmount- marine air particles mix andn homogenize,Min Max resultingWeighted in closelySD nalignedMin precipitation Max alongWeighted the so-SD called meteoric water lines [40]. CraigMean initially proposed the global meteoricMean water line (GMWL)2016.4–12 equation: 38 δ2−H15.5 = 8∙δ185.4O + 10 −[11].8.2 Friedman4.7 38 indicated−108.4 that35 the evaporation−51 of 33 raindrops2017.1–12 would 51 cause− 17.8the slope5.8 of the− LMWL8.3 5.6to be51 <8 [41].− 124.6Figure 38.64 shows the−58 relation- 40 ship between δ18O and δ2H in precipitation, defined as the local meteoric water line Table(LMWL). 2. The Several seasonal regression amount-weighted methods determin average valuese the LMWL of isotopic [42,43], compositions and different in 2016 methods and 2017. will lead to different slopes and intercepts [44]. To compare the MWL between Yuzhong county and Lanzhou city, theδ MWL18O (‰) in the study area was calculatedδ using2H (‰) the ordinary Year least squares regressionSpring (OLSR) Summer method. Autumn The Winter LMWL equation Spring in Summer 2016 and Autumn 2017 was Winterδ2H = 6.8 (±0.3)∙δ18O + 4.4 (±2.0) (R2 = 0.95, p < 0.001) and δ2H = 7.1 (±0.2)∙δ18O + 1.5 (±1.6) (R2 = 2016.4–12 −9.7 −7.2 −9.2 −58.6 −47.3 −53.8 0.97,2017.1–12 p < 0.001), −respectively.9.2 −7.4 The slope−10.1 and inte−14.2rcept slightly−64.1 differed−51.8 between−69.1 the two−98.3 years, but both were lower than the GMWL, indicating a significant imbalance of isotopic 4. Discussion 4.1. Local Meteoric Water Line As marine air moves over the coast and into continental areas, different marine air particles mix and homogenize, resulting in closely aligned precipitation along the so-called meteoric water lines [40]. Craig initially proposed the global meteoric water line (GMWL) equation: δ2H = 8·δ18O + 10 [11]. Friedman indicated that the evaporation of raindrops would cause the slope of the LMWL to be <8 [41]. Figure4 shows the relationship between δ18O and δ2H in precipitation, defined as the local meteoric water line (LMWL). Several regression methods determine the LMWL [42,43], and different methods will lead to different slopes and inter- cepts [44]. To compare the MWL between Yuzhong county and Lanzhou city, the MWL in the study area was calculated using the ordinary least squares regression (OLSR) method. The LMWL equation in 2016 and 2017 was δ2H = 6.8 (±0.3)·δ18O + 4.4 (±2.0) (R2 = 0.95, p < 0.001) and δ2H = 7.1 (±0.2)·δ18O + 1.5 (±1.6) (R2 = 0.97, p < 0.001), respectively. The slope and in- tercept slightly differed between the two years, but both were lower than the GMWL, indi- cating a significant imbalance of isotopic dynamic fractionation during precipitation. When compared to the MWL equation of Yuzhong County (δ2H = 7.70·δ18O + 9.41) and Lanzhou City (δ2H = 7.57·δ18O + 4.54) from April 2011 to February 2013, which were defined by Chen Sustainability 2021, 13, 6938 6 of 15
Sustainability 2021, 13, 6938 dynamic fractionation during precipitation. When compared to the MWL equation of Yu-6 of 15 zhong County (δ2H = 7.70∙δ18O + 9.41) and Lanzhou City (δ2H = 7.57∙δ18O + 4.54) from April 2011 to February 2013, which were defined by Chen et al. [45], the slope and intercept of the LMWL were lower in Yuzhong. The Yuzhong station is near the Xinglong Mountain forest,et al. [45 which], the slopehas a andhigh intercept vegetation of the coverage LMWL and were relative lower in humidity, Yuzhong. and The therefore, Yuzhongstation the is slopenear the was Xinglong very close Mountain to the GWML forest,which [45]. The has Lanzhou a high vegetation station is coverage located andin an relative urban humidity,area (Northwestand therefore, Normal the slope University), was very and close due to to the the GWML heat island [45]. Theeffect Lanzhou and moderate station relative is located in humidityan urban area[45], (Northwestthe slope was Normal lower University),than at the Yuzhong and due tosite the but heat higher island than effect in our and study moderate area.relative Because humidity our sampling [45], the slopesites were was lowerlocated than in the at thenorthern Yuzhong arid site mountain but higher area thanof Yu- in our zhongstudy area.County, Because which ourhas samplinga cold semi-arid sites were temperate located climate, in the with northern low rainfall arid mountain and strong area of evaporation,Yuzhong County, the rainwater which has was a cold easily semi-arid affected temperate by secondary climate, evaporation with low during rainfall precipi- and strong tation.evaporation, the rainwater was easily affected by secondary evaporation during precipitation.
Figure 4. 4. TheThe local local meteoric meteoric water water line line (LMWL) (LMWL) of 2016 of 2016 and and 2017 2017 in Yuzhong in Yuzhong County. County. The global The global meteoric water water line line (GMWL) (GMWL) equations equations were were defined defined by Craig by Craig(1961). (1961). The MWL The equations MWL equations of Yu- of zhong and Lanzhou were defined by Chen (2015). The symbols represent the precipitation in two Yuzhong and Lanzhou were defined by Chen (2015). The symbols represent the precipitation in years. The blue dotted horizontal and vertical 0 lines reflect the δ18O and δ2H of the standards (X = δ18 δ2 0;two Y = years. 0). *** Therepresent blue dotted correlations horizontal significant and vertical at p < 0.001. 0 lines The reflect results the from Othe and p-testsH are of shown the standards in Table(X = 0; S3 Y (in = 0)Supplementary. *** represent Materials). correlations significant at p < 0.001. The results from the p-tests are shown in Table S3 (in Supplementary Materials). 4.2. Meteorological Effects on Precipitation Isotopes 4.2. Meteorological Effects on Precipitation Isotopes 4.2.1. Temperature Effect 4.2.1. Temperature Effect Generally, the δ2H and δ18O values in precipitation tend to have a positive linear cor- Generally, the δ2H and δ18O values in precipitation tend to have a positive linear relation with the air temperature, also known as the “temperature effect.” The rise in at- correlation with the air temperature, also known as the “temperature effect.” The rise in mospheric temperature causes 18O enrichment compared to 2H in precipitation due to sub- atmospheric temperature causes 18O enrichment compared to 2H in precipitation due to cloud evaporation. When water molecules gain external energy during evaporation, the hydrogensub-cloud bonds evaporation. between Whenthe relatively water light molecules isotopes gain of water external molecules energy are during preferentially evaporation, broken.the hydrogen The temperature bonds between effect theof precipitation relatively light isotopes isotopes in northwest of water moleculesChina is widely are preferen- ac- ceptedtially broken. due to the The many temperature observations effect that of precipitationhave been reported isotopes [46–48]. in northwest The linear China relation- is widely shipaccepted is plotted due toin theFigure many 5 according observations to a thatregression have been analysis reported between [46– the48]. amount-weight The linear relation- δship18O values is plotted and in the Figure monthly5 according average to temperat a regressionure based analysis on betweenthe data theof GNIP amount-weight (1985– 18 1987,1996–1999)δ O values and of the Lanzhou monthly station average and temperatureour measured based values. on Their the data correlation of GNIP between (1985–1987, 18 δ1996–1999)18O and temperature of Lanzhou at seasonal station andand ourlonger measured time scales values. is absent Their (p correlation= 0.27) in dry between seasons,δ O whileand temperature the correlation at seasonal is significant and longerin wet timeseasons. scales is absent (p = 0.27) in dry seasons, while the correlation is significant in wet seasons. Sustainability 2021, 13, 6938 7 of 15
Further study about the effect of temperature on the isotopic signature on daily scales (Figure 6) shows the linear relationship between δ18O values and the temperature for each precipitation period. The regression equations can be expressed as δ18O = 0.42T − 9.67 (R2 = 0.21; p < 0.01) and δ18O = 0.62T–11.83 (R2 = 0.20; p < 0.01) in rainy seasons of 2016 and 2017 (the date of April 26, 2016 was taken as the rainy season data since it is the only data in April, the same way in Section 4.2.2), respectively. The respective equation in dry sea- sons (δ18O = 0.73T−11) has very low significance level (R2 = 0.18; p = 0.12). Thus, the tem- perature effect is significant only in the wet seasons, which is probably connected with the different moisture sources in wet and dry seasons. Previous studies have also shown Sustainability 2021, 13, 6938 7 7of of 15 that different moisture sources also have specific effects on the temperature on a large scale [49].
Further study about the effect of temperature on the isotopic signature on daily scales (Figure 6) shows the linear relationship between δ18O values and the temperature for each precipitation period. The regression equations can be expressed as δ18O = 0.42T − 9.67 (R2 = 0.21; p < 0.01) and δ18O = 0.62T–11.83 (R2 = 0.20; p < 0.01) in rainy seasons of 2016 and 2017 (the date of April 26, 2016 was taken as the rainy season data since it is the only data in April, the same way in Section 4.2.2), respectively. The respective equation in dry sea- sons (δ18O = 0.73T−11) has very low significance level (R2 = 0.18; p = 0.12). Thus, the tem- perature effect is significant only in the wet seasons, which is probably connected with the different moisture sources in wet and dry seasons. Previous studies have also shown that different moisture sources also have specific effects on the temperature on a large scale [49].
Figure 5. 5. Amount-weightedAmount-weighted means means δ18O δfor18O wet for seasons wet seasons (May–October) (May–October) and dry andseasons dry (Novem- seasons ber–April)(November–April vs. monthly) vs. monthlyaverage temperature. average temperature. The data Theare from data the are Wiser-GNIP from the Wiser-GNIP dataset of dataset IAEA. 18 Theof IAEA. blue line The is blue the line linear is the relationship linear relationship between betweenthe δ O values the δ18 Oand values temperature and temperature in wet seasons, in wet while the red line is the linear relationship between δ18O values and temperature in dry seasons. seasons, while the red line is the linear relationship between δ18O values and temperature in dry The continuous regression line is significant at p < 0.05; the dashed line has p > 0.05. The results from seasons. The continuous regression line is significant at p < 0.05; the dashed line has p > 0.05. The the p-tests are shown in Table S2 (in Supplementary Materials). results from the p-tests are shown in Table S2 (in Supplementary Materials). Further study about the effect of temperature on the isotopic signature on daily scales (Figure6) shows the linear relationship between δ18O values and the temperature for each precipitation period. The regression equations can be expressed as δ18O = 0.42T − 9.67 (R2 = 0.21; p < 0.01) and δ18O = 0.62T–11.83 (R2 = 0.20; p < 0.01) in rainy seasons of 2016 and 2017 (the date of April 26, 2016 was taken as the rainy season data since it is the only data in April, the same way in Section 4.2.2), respectively. The respective equation in dry Figure 5. Amount-weighted means δ18O for wet seasons (May–October) and dry seasons (Novem- δ18 − 2 ber–April)seasons ( vs.O monthly = 0.73T average11) has temperature. very low significanceThe data are levelfrom the (R Wiser-GNIP= 0.18; p = 0.12).dataset Thus, of IAEA. the Thetemperature blue line is effect the linear is significant relationship only between in the wet the seasons,δ18O values which and istemperature probably connected in wet seasons, with whilethe different the red line moisture is the linear sources relationship in wet and between dry seasons. δ18O values Previous and temperature studies have in alsodry seasons. shown Thethat continuous different regression moisture line sources is significant also have at p specific< 0.05; the effects dashed on line the has temperature p > 0.05. The results on a large from thescale p-tests [49]. are shown in Table S2 (in Supplementary Materials).
Figure 6. Correlations between δ18O and temperature for all individual samples in 2016 and 2017. The blue line is the linear relationship between the δ18O values and temperature from May to Octo- ber in 2016 and 2017, while the red dashed line in the right-hand is the linear relationship between δ18O values and temperature from February 2017 to April 2017. The continuous regression lines are significant at p < 0.01; the dashed line has p > 0.05. The results from the p-tests are shown in Table S2 (in Supplementary Materials).
FigureFigure 6. Correlations between δδ1818OO and and temperature temperature for for all all indivi individualdual samples samples in in 2016 2016 and and 2017. 2017. The blue line is thethe linearlinear relationshiprelationship between between the theδ 18δ18OO values values and and temperature temperature from from May May to to October Octo- berin 2016in 2016 and and 2017, 2017, while while the the red red dashed dashed line line in in the the right-hand right-hand is is thethe linearlinear relationshiprelationship between δ18O values and temperature from February 2017 to April 2017. The continuous regression lines are δ18O values and temperature from February 2017 to April 2017. The continuous regression lines are significant at p < 0.01; the dashed line has p > 0.05. The results from the p-tests are shown in Table S2 significant at p < 0.01; the dashed line has p > 0.05. The results from the p-tests are shown in Table S2 (in Supplementary Materials). (in Supplementary Materials). Sustainability 2021, 13, 6938 8 of 15
4.2.2. Amount Effect Working with the IAEA Database of Isotopes in Precipitation [8], a negative correla- tion between the isotopic composition and amount of rain based on monthly means was first proposed, which is also known as the amount effect. The higher the precipitation is, the lower the δ2H and δ18O values are precipitation. This is explained by the fact that lower temperatures lead to more rainfall, while the fractionation during condensation at lower temperatures always results in higher isotopic fractionation in liquid water relative to the vapor it comes from. The precipitation amount is crucial for the hydrogen and oxygen isotope composition in the southeast and southwest monsoon regions but decreases from the coast to inland regions [50]. The precipitation effect is strong in subtropical regions where precipitation is dominated by deep convection [51]. Based on the data of GNIP(1985–1987, 1996–1999) [52] of Lanzhou station and our measured values, the rela- tionship between seasonal amount-weighted mean δ18O values and amount was calcu- lated (Figure 7). A significant correlation between δ18O and amount at seasonal and longer time scales is absent (p > 0.05), both in dry seasons and wet seasons. This indicates that the Sustainability 2021, 13, 6938 8 of 15 local precipitation amount is not the major factor controlling the δ18O variability at the monthly scale, and the amount effects are not useful to discriminate either wetter or drier rainy seasons or wetter or drier decades in the study site. 4.2.2.To Amount better Effectunderstand the relationship between precipitation and isotope composi- tion, Workingthe correlation with the and IAEA regression Database analyses of Isotopes between in Precipitation the daily isotope [8], a data negative and precipi- correla- 18 tiontation between amount the were isotopic calculated composition (Figure 8). and A amount relationship of rain between based onδ O monthly values and means precip- was firstitation proposed, amount is which absent is in also theknown dry seasons as the (pamount = 0.77) but effect. is significant The higher (p
Figure 7. 7. Amount-weightedAmount-weighted means means δ18Oδ for18O wet for seasons wet seasons (May–October) (May–October) and dry andseasons dry (Novem- seasons (ber–April)November–April vs. precipitation) vs. precipitation amounts. amounts. The data Theare from data arethe fromWiser-GNIP the Wiser-GNIP dataset of dataset IAEA. ofThe IAEA. blue 18 Thedashed blue line dashed is the line linear is the relationship linear relationship between betweenthe δ O thevaluesδ18 Oand values precipitation and precipitation amount in amount wet sea- in 18 wetsons, seasons, while the while red the dashed red dashed line lineis the is thelinear linear relationship relationship between between δδ18OO values values and and precipitation amount in dry seasons. The dashed lines have p > 0.05. The results from the p-tests are shown in amount in dry seasons. The dashed lines have p > 0.05. The results from the p-tests are shown in Table S2 (in Supplementary Materials). Table S2 (in Supplementary Materials). To better understand the relationship between precipitation and isotope composition, the correlation and regression analyses between the daily isotope data and precipitation amount were calculated (Figure8). A relationship between δ18O values and precipitation amount is absent in the dry seasons (p = 0.77) but is significant (p < 0.01) in the rainy seasons (May–October) in 2016 and 2017. The precipitation amount is not uniformly distributed in the area: continuous precipitation events and greater amounts of precipitation usually occur during the rainy seasons (90%), while the precipitation in the dry seasons is usually very low (10%) (Figure2). The smaller precipitation events are most likely exposed to evaporation so that the temperature effect may hide the amount of effects in some regions during the whole year. This result was consistent with Liu et al. (2008) [53], who also conducted studies in arid and cold semi-arid areas of northwest China. The precipitation effect is usually not significant in the inland areas of China. The δ18O values were characterized by a weak correlation with the precipitation amount over the whole year. However, this relationship was better visualized when only the values during the rainy season were considered. Additionally, it is also of great importance to future study the relationship between isotope and precipitation amount considering the annual precipitation in the study area shows an increasing tendency (285 mm in 2016, 350 mm in 2017, 461 mm in 2018, 401 mm in 2019). Sustainability 2021, 13, 6938 9 of 15 Sustainability 2021, 13, 6938 9 of 15
Figure 8. CorrelationsCorrelations between δ18OO and and precipitation precipitation amount for al alll individual samples in 2016 and 2017. The blueblue continuouscontinuous lines lines are are the the linear linear relationships relationships between between the theδ18 δO18 valuesO values and and precipitation precipita- tionamount amount from from May May to October to October in 2016 in 2016 and and 2017, 2017, while while the redthe dashedred dashed line line in the in right-handthe right-hand is the is 18 thelinear linear relationship relationship between betweenδ18O δ valuesO values and and precipitation precipitation amount amount from from February February 2017 to2017 April to April 2017. 2017. The continuous regression lines are significant at p < 0.01; the dashed line has p > 0.05. The The continuous regression lines are significant at p < 0.01; the dashed line has p > 0.05. The results results from the p-tests are shown in Table S2 (in Supplementary Materials). from the p-tests are shown in Table S2 (in Supplementary Materials).
4.2.3. Sub-Cloud Evaporation When the air temperature is low, water vapor can condense into water droplets. Raindrops falling from from the the cloud cloud base base to to the the ground ground are are affected affected by by evaporation, evaporation, which which is knownis known as asbelow-cloud below-cloud evaporation evaporation or or sub-cl sub-cloudoud evaporation evaporation [54]. [54]. The The evaporation of raindrops falling below the cloud base is an important physical process, especially under the low relative humidity conditions below the cloud base. In arid and and semi-arid semi-arid regions, regions, where the air air is is dry, dry, and and vegetation vegetation is is sparse, sparse, falling falling raindrops raindrops are are more more susceptible susceptible to evaporationto evaporation [5]. [Precipitation5]. Precipitation falling falling to the to su therface surface is therefore is therefore characterized characterized by a higher by a δhigher18O valueδ18O and value lower and d-excess lower d-excess value [55]. value Conv [55ersely,]. Conversely, the secondary the secondary evaporation evaporation becomes weakbecomes with weak an increase with an in increase relative in humidity relative humidity and results and in resultsa negative in a negativecorrelation correlation between relativebetween humidity relative humidityand δ18O andvaluesδ18 andO values a positive and acorrelation positive correlation between relative between humidity relative andhumidity d-excess. and Figure d-excess. 9 shows Figure the9 showscorrelations the correlations between relative between humidity relative and humidity δ18O and and d- excessδ18O and in 2016 d-excess and 2017. in 2016 The and correlati 2017.ons The between correlations relative between humidity relative and δ18 humidityO were δ18 andO = −δ0.2118Oh were + 11.1δ18 (RO2 = 0.42;−0.21 ph < +0.001), 11.1(R and2 = with 0.42; d-excessp < 0.001), = 0.34 andh − with 17.1 d-excess(R2 = 0.29; = 0.34p < 0.001)h − 17.1 in (R20162 = and 0.29; δ18Op <= − 0.001)0.24h + in 12.6 2016 (R2 and = 0.31;δ18 pO-value = −0.24 < 0.001),h + 12.6 d-excess (R2 = = 0.31;0.3h −p16.9-value (R2 <= 0.21; 0.001), p- valued-excess < 0.001) = 0.3 hin− 201716.9 respectively.(R2 = 0.21; p -valueSimilarly < 0.001 to [45],) in there 2017 is respectively. a significant Similarlynegative correla- to [45], 18 tionthere between is a significant the δ18O negative values correlationand relative between humidity the anδ dO a valuespositive and correlation relative humidity between relativeand a positive humidity correlation and d-excess between values relative in the humiditytwo years.and The d-excessrelative humidity values in is the an twoim- years.portant The factor relative controlling humidity evaporation, is an important and secondary factor controlling evaporation evaporation, below the and cloud secondary base canevaporation increase belowin low thehumidity cloud baseconditions. can increase The slope in low of humiditythe LMWL conditions. clearly shows The slopethat the of studythe LMWL area clearlyexperienced shows intensive that the study secondary area experienced evaporation, intensive which secondarycan provide evaporation, a basis to whichstudy the can relationship provide a basisbetween to study the evaporatio the relationshipn ratio of between raindrops the and evaporation the difference ratio ofin deuteriumraindrops andexcess the from difference cloud inbase deuterium to ground. excess from cloud base to ground. Sustainability 2021, 13, 6938 10 of 15 Sustainability 2021, 13, 6938 10 of 15
Figure 9. Correlations betweenbetweenrelative relative humidity humidity and andδ 18δ18OO and and d-excess d-excess for for all all individual individual samples samples in in2016 2016 and and 2017. 2017. All All regression regression lines lines are significantare significant at p < at 0.001. p < 0.001. The results The results from the fromp-tests the arep-tests shown are shownin Table in S3 Table (in Supplementary S3 (in Supplementary Materials). Materials).
4.3. Moisture Sources 4.3.1. Variation of d-Excess As shown in Figure3 and Table3, the d-excess values varied from −13‰ to 24.6‰, As shown in Figure 3 and Table 3, the d-excess values varied from −13‰ to 24.6‰, with an amount-weighted mean value of 14.3‰ in 2016. In 2017, the d-excess values varied with an amount-weighted mean value of 14.3‰ in 2016. In 2017, the d-excess values var- from −18.4‰ to 21.2‰, with an amount-weighted mean value of 8.6‰. In addition to ied from −18.4‰ to 21.2‰, with an amount-weighted mean value of 8.6‰. In addition to such variables as elevation and sub-cloud evaporation of raindrops which are the major such variables as elevation and sub-cloud evaporation of raindrops which are the major factors that control the d-excess in continental precipitation [21], the changes in water factors that control the d-excess in continental precipitation [21], the changes in water va- vapor sources and vapor transport also may affect d in non-unique ways [56]. There were por sources and vapor transport also may affect d in non-unique ways [56]. There were seasonal differences and large fluctuations of the d-excess values, implying the influence seasonal differences and large fluctuations of the d-excess values, implying the influence of different moisture sources in different seasons, as will be discussed in detail in the of different moisture sources in different seasons, as will be discussed in detail in the fol- following section. lowing section. Table 3. The minimum, maximum, and seasonal amount-weighted average values of d-excess in Table2016 and 3. The 2017. minimum, maximum, and seasonal amount-weighted average values of d-excess in 2016 and 2017. Mean (‰) Date Min (‰) Max (‰) Mean (‰) Date Min (‰) Max (‰) Annual Spring Summer Autumn Winter Annual Spring Summer Autumn Winter 20162016 −13 −1324.6 24.614.3 14.3 19 19 10.6 10.619.4 19.4 2017 −18.4 21.2 8.6 9.7 7.3 11.4 15.5 2017 −18.4 21.2 8.6 9.7 7.3 11.4 15.5 2016–2017 14.2 8.6 15.0 15.5 2016–2017 14.2 8.6 15.0 15.5
4.3.2. Moisture Transport Geographically, thethe experimental experimental site site is is located located in in the the cold cold semi-arid semi-arid area area of northwest of north- westChina. China. Previous Previous studies studies have have shown shown that that local local water water evaporation, evaporation, westerly westerly and and polar polar air masses, the East Asian summer monsoon, and the Indian monsoon are the major potential air masses, the East Asian summer monsoon, and the Indian monsoon are the major po- sources for atmospheric moisture across northwestern China [1]. So the isotopic compo- tential sources for atmospheric moisture across northwestern China [1]. So the isotopic sition of precipitation will result in the continental effect, which is also referred to as the composition of precipitation will result in the continental effect, which is also referred to distance-from-coast effect, i.e., a progressive 18O depletion in precipitation with increasing as the distance-from-coast effect, i.e., a progressive 18O depletion in precipitation with in- distance from the ocean [55]. The lower amount-weighted mean δ18O value (−8.2‰) of the creasing distance from the ocean [55]. The lower amount-weighted mean δ18O value Sustainability 2021, 13, 6938 11 of 15
study site in comparison to δ18O of Zhangye (−6.0‰) in the Hexi Corridor [57] supports the continental effect. To track moisture sources, the Hybrid Single-Particle Lagrangian Integrated Trajectories (HYSPLIT) model (ver 4.9) of the Air Resources Laboratory of the National Oceanic and Atmospheric Administration [58] was used to calculate back tra- jectories based on the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) global reanalysis data from January 2016 to December 2017 generated by the Global Data Assimilation System (GDAS). Each backward trajectory was calculated for 240 h (10 days) back in time, which is considered to be the average residence time of water vapor in the atmosphere [59]. The backtracking trajectories were calculated four times a day over four seasons (00:00, 06:00, 12:00, and 18:00 UTC), with the final level being 500 m above the ground. The corresponding trajectories were clustered to obtain a mean. Finally, the clustered result (Figure S1 in Supplementary Material) was inputted to ArcGIS 10.2 software for further processing to obtain the backward trajectory in the study area at different seasons [45]. In summary, there were two main pathways in the vapor source trajectory over the year, with most water vapor originating from marine sources in the west and northwest directions (Figure 10). One source was from the Atlantic Ocean via westerlies, and the other was from the Arctic region. In spring (Figure 10a), there were two main sources of water vapor to the study area; 46% was transported by westerlies (T1), while 54% originated from the north (T2). One of the air masses was advected eastward through Xinjiang and the Hexi Corridor, while the other was advected southward through Inner Mongolia to the study site. In summer, the d-excess value was the lowest over the whole year. The high relative humidity over the Atlantic Ocean carried by westerly air masses gives rise to a comparatively lower d-excess in the subsequent precipitation events [60]. In addition, in summer, the re-evaporation of raindrops below the cloud base in the study area also dropped the d-excess values to extremely low. The water vapor could be disentangled for three sources (Figure 10b). The first source accounted for 30% of the total and was derived from westerlies (T1), the second source accounted for 49% and originated from northerlies, while the origin of the final 21% was derived from southeasterlies. This situation was due to the influence of the monsoon. Generally, the Asian summer monsoon begins in May and retreats at the end of September or early October [61]. The sources of water vapor in summer are complex in this area. In addition to transport by westerlies and northerlies, water vapor is also affected by the southeast monsoon and local recirculation of water vapor. In autumn (Figure 10c), the area is affected by westerly (T1, T2) and northerly (T3) moisture sources. As before, an air mass (T3, 39%) is advected eastward through Xinjiang and the Hexi Corridor to the study area, but then another air mass (T1, 25%) from the west takes a different route, passing through Xinjiang and eastern Qinghai. The final air mass (T2, 36%) from the north is advected southward through Inner Mongolia and Ningxia to the destination. In winter (Figure 10d), the T1 (40%) and T2 (60%) air masses containing moisture originate from the west and northwest, respectively. The T1 air mass passed through Xinjiang and Qinghai to reach the study area, while the T2 air mass passed through Xinjiang and then moved from the northwest of Gansu. However, due to the spatial resolution of the meteorological data, this backward trajectory model could only simulate the air mass source on a large scale [62]. Further analysis is needed to determine the details of the air masses and the characteristics of the precipitation of the study area. Sustainability 2021, 13, 6938 12 of 15 Sustainability 2021, 13, 6938 12 of 15
Figure 10. BackwardBackward trajectories trajectories cluster cluster of of moisture moisture in inthe the study study area. area. (a), (Springa), Spring (March, (March, April, April, and andMay); May); (b), summer (b), summer (June, (June, July, and July, August); and August); (c), autumn (c), autumn (September, (September, October, October, and November); and Novem- (d), ber);winter(d (December,), winter (December, January, and January, February). and February).T1, T2, and T3 T1, represent T2, and the T3 representdirections theof vapor directions sources. of vapor sources. 5. Conclusions 5. Conclusions The δ2H and δ18O signatures of precipitation on the Loess Plateau were investigated 2 18 duringThe 2016–2017.δ H and δ TheO signatureslocal meteoric of precipitation water line (LMWL) on the Loess in 2016 Plateau and 2017 were were investigated defined duringas δ2H = 2016–2017. 6.8 (±0.3)∙δ The18O local+ 4.4 meteoric(±2.0) and water δ2H line= 7.1 (LMWL) (±0.2)∙δ18 inO 2016 + 1.5 and (±1.6), 2017 respectively. were defined The as δ2H = 6.8 (±0.3)·δ18O + 4.4 (±2.0) and δ2H = 7.1 (±0.2)·δ18O + 1.5 (±1.6), respectively. The LMWL slope was lower than the global meteoric water line (GMWL) due to the secondary LMWL slope was lower than the global meteoric water line (GMWL) due to the secondary evaporation during precipitation. Atmospheric temperature is the main environmental evaporation during precipitation. Atmospheric temperature is the main environmental factor controlling the isotopic variation in rainy seasons both on daily and monthly scales. factor controlling the isotopic variation in rainy seasons both on daily and monthly scales. The precipitation amount is the dominant factor controlling isotope values of precipita- The precipitation amount is the dominant factor controlling isotope values of precipitation tion in rainy seasons on a daily scale, but it is not the major factor at the monthly scale, in rainy seasons on a daily scale, but it is not the major factor at the monthly scale, and and the amount effects are not useful to discriminate either wetter or drier rainy seasons the amount effects are not useful to discriminate either wetter or drier rainy seasons or or wetter or drier decades in the study site. However, longer periods of precipitation data wetter or drier decades in the study site. However, longer periods of precipitation data accumulation are required for detailed characterization of the water vapor cycle in rainfall accumulation are required for detailed characterization of the water vapor cycle in rainfall processes, and also how does the precipitation converts into soil water for storage should processes, and also how does the precipitation converts into soil water for storage should be clarified clarified in in future future studies. studies. There There were were seasonal seasonal differences differences and and large large fluctuations fluctuations of the of thed-excess d-excess values, values, indicating indicating the theinfluences influences of various of various moisture moisture sources. sources. The The resulting resulting air airmass mass trajectories trajectories were were consistent consistent with with that that of the of themain main sources sources of water of water vapor vapor over over the thewhole whole year year from from the Atlantic the Atlantic Ocean Ocean via westerlies via westerlies and the and Arctic the Arctic region. region. There There are 46%, are 46%,64%, and 64%, 40% and vapor 40% vaporfrom the from westerlies the westerlies and 54%, and 36%, 54%, and 36%, 60% and vapor 60% from vapor the from north the in northspring, in autumn, spring, autumn,and winter, and respectively. winter, respectively. In summer, In however, summer, however,the southeast the southeastmonsoon monsoonwas also a was water also vapor a water source vapor (21%). source Concluding, (21%). Concluding, using δ2 usingH andδ 2δH18O and signaturesδ18O signatures of pre- ofcipitation precipitation water, water, we disentangled we disentangled and quantified and quantified the seasonal the seasonal wind wind directions directions that that are areimportant important for the for prediction the prediction of water of water resources resources for local for localand regional and regional land use land on use the on Loess the LoessPlateau. Plateau. These findings These findings advance advance our understa our understandingnding of the oftemporal the temporal variation variation of stable of stableisotopes isotopes (δ2H and (δ2 Hδ18 andO) inδ 18precipitationO) in precipitation on the Loess on the Plateau Loess Plateauand provide and providedeep insights deep insightsinto climatic into climaticand environmental and environmental controls controlson precipitation on precipitation in semi-arid in semi-arid regions. regions.
Supplementary Materials:Materials:The The following following are available are available online at https://www.mdpi.com/article/10online at www.mdpi.com/arti- .3390/su13126938/su13126938/s1cle/10.3390/su13126938/s1, Table S1:, Table Daily S1: isotope Daily isotopeand meteorological and meteorological information, information, Table S2: Table Corre- S2: 18 Correlationslations of daily of and daily monthly and monthly δ O withδ18O local with surface local surface air temperatur air temperaturee and precipitation and precipitation amount amount in the 2 18 18 indry the seasons dry seasons and wet and seasons, wet seasons, Table S3: Table Correlations S3: Correlations of δ H with of δ2HO withand δδ18O,O d-excess and δ18 O,values d-excess with valuesrelative with humidity relative in humiditystudy site infrom study 2016 site to from2017,2016 Figure to 2017,S1: The Figure cluster S1: result The clusterof the model result ofin thethe study site. model in the study site. Sustainability 2021, 13, 6938 13 of 15
Author Contributions: Data curation, F.-Q.H., J.-Z.W., X.S., and Y.-H.Z.; investigation, F.-Q.H.; methodology, J.-Z.W.; project administration, F.-M.L.; resources, Q.-F.Y. and F.-M.L.; software, Y.-H.Z.; supervision, F.-M.L.; validation, Y.K. and F.-Q.H.; visualization, X.S. and Y.K.; writing—original draft, F.-Q.H. and F.-M.L.; writing—review and editing, F.-Q.H., Q.-F.Y., Y.K., and F.-M.L. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Natural Science Foundation of China (31470496) and the “111” Programme (BP0719040). Y.K. thanks for the support by the Russian Government Program of Competitive Growth of Kazan Federal University and “RUDN University Strategic Academic Leadership Program”. Data Availability Statement: The original contributions generated for this study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflict of interest.
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