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Environmental controls on stable isotopes of precipitation in Lanzhou, China: An enhanced network at city scale
Science of the Total Environment 609 (2017) 1013–1022
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Environmental controls on stable isotopes of precipitation in Lanzhou, China: An enhanced network at city scale Fenli Chen, Mingjun Zhang ⁎, Shengjie Wang ⁎, Xue Qiu, Mingxia Du College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• A precipitation isotope network at a city scale including 10 stations • Isoscape at city scale in an arid and semi-arid climate • A combined influence on precipitation isotopes of meteorological parameters
a r t i c l e
i n f o
Article history: Received 17 January 2017 Received in revised form 23 July 2017 Accepted 24 July 2017 Available online xxxx Editor: D. Barcelo Keywords: Precipitation isotope Observation network Lanzhou city Temperature effect
a b s t r a c t Stable hydrogen and oxygen isotopes in precipitation are very sensitive to environmental changes, and can record evolution of water cycle. The Lanzhou city in northwestern China is jointly influenced by the monsoon and westerlies, which is considered as a vital platform to investigate the moisture regime for this region. Since 2011, an observation network of stable isotopes in precipitation was established across the city, and four stations were included in the network. In 2013, six more sampling stations were added, and the enhanced network might provide more meaningful information on spatial incoherence and synoptic process. This study focused on the variations of stable isotopes (δ18O and δD) in precipitation and the environmental controls based on the 1432 samples in this enhanced network from April 2011 to October 2014. The results showed that the precipitation isotopes had great spatial diversity, and the neighboring stations may present large difference in δD and δ18O. Based on the observation at ten sampling sites, an isoscape in precipitation was calculated, and the method is useful to produce isoscape for small domains. The temperature effect and amount effect was reconsidered based on the dataset. Taking meteorological parameters (temperature, precipitation amount, relative humidity, water vapor pressure and dew point temperature) as variables in a multi-linear regression, the result of coefficients for these meteorological parameters were calculated. Some cases were also involved in this study, and the isotopic characteristics during one event or continuous days were used to understand the environmental controls on precipitation isotopes. © 2017 Published by Elsevier B.V.
⁎ Corresponding authors. E-mail addresses: cfl[email protected] (F. Chen), [email protected] (M. Zhang), [email protected] (S. Wang), [email protected] (X. Qiu), [email protected] (M. Du).
http://dx.doi.org/10.1016/j.scitotenv.2017.07.216 0048-9697/© 2017 Published by Elsevier B.V.
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1. Introduction The stable isotope composition (δD and δ18O) in natural water is widely used to investigate the Earth's hydrologic cycle and climatic change (e.g., Treblea et al., 2005; Argiriou and Lykoudis, 2006; Thompson et al., 2013; Jasechko et al., 2014; Li et al., 2016b; Wang et al., 2016a). Precipitation is a vital step in the natural water cycle, and spatial dependence and temporal variations in precipitation isotopes are useful in understanding hydrological, meteorological and ecological processes (Dansgaard, 1964; Craig, 1961). In Dansgaard (1964), temperature effect (positive correlation between surface air temperature and isotope ratio) and amount effect (negative correlation between precipitation amount and isotopic ratio) in precipitation isotopes were examined in detail. After that work, meteorological and geographical controls on stable hydrogen and oxygen isotopes in precipitation were discussed worldwide based on observation and modeling (e.g., Risi et al., 2008; Scholl et al., 2009; Moore et al., 2014; Sánchez-Murillo et al., 2016). The environmental controls of precipitation isotopes for a large domain can be logically applied to a small domain, that is, known meteorological and geographical controls (e.g., temperature effect, amount effect, altitude effect, and continental effect) should always be effective. To acquire data on the spatial distribution of stable isotopes in precipitation (also called the isoscape) at a larger or global scale, the latitude and elevation are usually selected as the two main factors in many studies (e.g., Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003; Liu et al., 2008). Because surface air temperature logically correlates with elevation, in some studies (e.g., van der Veer et al., 2009; Zhao et al., 2012) the temperature parameter is considered in lieu of elevation. However, further observation of the isotope compositions in precipitation has indicated that some local factors may also be considered (e.g., Smith and Evans, 2007; Guan et al., 2009). To investigate these local controls, an enhanced network is required. Limited measurements immensely impede the understanding of isotopic fractionation processes in this region. The monthly isotopic precipitation records derived from the Global Network of Isotopes in Precipitation (GNIP; IAEA/ WMO, 2017) are not suitable to study δD and δ18O in precipitation on a synoptic scale (Treblea et al., 2005; Barras and Simmonds, 2008; Crawford et al., 2013, 2017; Wang et al., 2017). In recent years, several intensive networks of precipitation isotopes have been established in small regions at even a city scale, such as in the Beijing Network of Isotopes in Precipitation (Li et al., 2017). Located in the western part of the Chinese Loess Plateau, the city of Lanzhou in the Gansu province is usually considered to be jointly influenced by westerlies and monsoon moisture (Araguás-Araguás et al., 1998; Tian et al., 2007; Yao et al., 2013). The systemic measurements of environmental isotopes in Lanzhou's precipitation date back to the monthly observation conducted during 1985–1999, and the isotopic values were submitted to the GNIP database (Station ID 5288900; including 41 monthly records for δ18O and 39 for δD). Based on this GNIP dataset, the seasonal characteristics of stable isotopes in precipitation in Lanzhou were examined in many publications (e.g., Zhang et al., 2002; Yao et al., 2013). However, the existing monthly GNIP database is not enough to investigate the synoptic and micro-climate details that the stable isotopes undergo during precipitation, and much of the interesting information may be masked by the lower frequency sampling (Crawford et al., 2013). To improve knowledge of stable isotopes in Lanzhou's precipitation, an observation network at the city scale was established by the Northwest Normal University in 2011. During the first years, four stations covered the urban area (Anning district) and three counties (Yuzhong, Gaolan and Yongdeng counties). The first year data (243 event-based samples collected from April 2011 to March 2012) at the four stations were presented by Ma et al. (2014), indicating that even in such a small domain the precipitation isotopes still have complex spatial diversity. As the observation continued, more samples of importance in
understanding the inter-annual variation of stable isotopes became available from this network. Based on the N 400 samples collected from 2011 to 2013, Chen et al. (2015a, 2015b) reconsidered the local meteoric water line (LMWL) for each sampling station as well as the influence of below-cloud evaporation in an arid and semi-arid climate. Considering the complex topography as a vital factor influencing the isoscape, a more enhanced observation network was established across the city in 2013. The enhanced network contains ten sampling stations covering a larger elevation range, which may provide more meaningful information for the region. In this study, the enhanced network (including the sampling stations added during 2013–2014) was introduced, and the years 2011– 2013 at the original four stations were also presented. The objective of this study was (1) to present a new detailed isoscape of precipitation in Lanzhou, (2) to clarify the environmental controls on the isotope composition in precipitation in Lanzhou based on the enhanced network, and (3) to present several cases demonstrating isotopic evolution in continuous rain events. The enhanced network provides a platform to further assess hydrological processes in this region. 2. Data and method 2.1. Area of study Lanzhou, an arid and semi-arid city in the western part of the Chinese Loess Plateau, is the provincial capital of Northwest China's Gansu province (Fig. 1). Within the boundary of this city, the altitude generally declines from southwest to northeast, and the Yellow (Huanghe) River also flows from southwest to northeast. Lanzhou contains five districts (Chengguan, Qilihe, Anning, Xigu and Honggu) and three counties (Yuzhong, Yongdeng and Gaolan). The densely populated part of the city is located within a narrow valley across the five districts. According to the long-term climatology from 1981 to 2010, the mean annual air temperature is 10.4 °C, ranging from −4.5 °C in January to 23.1 °C in July. In the continental climate, the precipitation events are largely concentrated in the summer months. 2.2. Sampling In the observation network of stable isotopes in precipitation, ten sampling stations were selected across Lanzhou (Fig. 1 and Table S1 in the supplementary material). From April 2011 to October 2014, precipitation at four sites were sampled, i.e., Anning (Northwest Normal University, Anning district), Yuzhong (Yuzhong County Meteorological Bureau, Yuzhong county), Gaolan (Gaolan County Meteorological Bureau, Gaolan county) and Yongdeng (Yongdeng County Meteorological Bureau, Yongdeng county). For the three counties, the county meteorological bureaus were selected; for the districts, we selected one sampling site at the Northwest Normal University. To improve spatial coverage from October 2013 to October 2014, six more stations were added including Renshoushan (Renshou Hill, Anning district), LREVC (Lanzhou Resources & Environment Voc-Tech College, Chengguan district), Wushengyi (Wushengyi town, Yongdeng county), Daheng (Daheng village, Heishi town, Gaolan county), Gongjing (Gongjing town, Yuzhong county) and Hezui (Hezui village, Huazhuang town, Honggu district). For the three counties, one more site was selected for each county; for the districts, three more sampling sites were selected along the narrow river valley from west to east. The specific locations were recommended by local meteorological bureaus. The sampling was undertaken by the full-time observers of local meteorological bureau for each site with an exception of Anning where the researchers of Northwest Normal University did the sampling. Liquid samples were collected immediately after the end of each event in order to prevent water evaporation. For some continuous and heavy precipitation events, samples were collected according to fixed hourly intervals. Liquid samples were stored in 50 mL or 60 mL HDPE bottles
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Fig. 1. Spatial distribution of sampling stations in Lanzhou. The administrative boundary of Lanzhou is marked by dashed gray polygons.
with waterproof seals, and then refrigerated. Solid samples were not sealed in bottles until they were melted at room temperature in ziplock bags. The sampling procedure for this network and its basic characteristics during the first years were also described in our previous studies (Ma et al., 2014; Chen et al., 2015a, 2015b). Precipitation amounts were measured by observers using China Meteorological Administration standard pluviometers (CMA, 2007). Other meteorological parameters such as air temperature and relative humidity during precipitation events were recorded by the automatic weather stations interrogating the sensors every hour. The total number of precipitation samples is 1432 from April 2011 to October 2014, including 1291 liquid and 141 solid samples (Table S2 in the supplementary material). During this period, 959 precipitation samples were collected at the four stations with longer periods (Anning, Yuzhong, Gaolan and Yongdeng). 2.3. Lab analysis The stable hydrogen and oxygen isotope compositions of all the precipitation samples were analyzed using a liquid water isotope analyzer DLT-100 (Los Gatos Research, Inc.) in the Stable Isotope Laboratory, College of Geography and Environmental Science, Northwest Normal University, with a precision of ± 0.6‰ and ± 0.2‰, for δD and δ18 O, respectively. The frozen samples in the HDPE bottles were melted at room temperature before being analyzed. A group with six samples and three isotopic standards was tested, and each sample or standard was injected six times using a microliter syringe. Considering the memory effect of isotopic composition, the last four injection results were accepted while the first two injections were discarded in the calculation (Lis et al., 2008). Ratios of hydrogen and oxygen isotopes per mil (‰) in the samples relative to those in the Vienna Standard Mean Ocean Water (V-SMOW) are expressed using a δ-notation:
δsample ¼
Rsample −Rstandard 1000‰ Rstandard
ð1Þ
where Rsample and Rstandard are isotope ratios (D/H or 18O/16O) in the samples and references relative to V-SMOW, respectively.
2.4. Isoscape calculation Many methods have been applied in previous studies to calculate the spatial distribution of the isotopes in precipitation, also known as the isoscape (Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003; Liu et al., 2008; van der Veer et al., 2009; Bowen, 2010; Terzer et al., 2013). In this study, in order to investigate the isoscape of Lanzhou using the enhanced observation network, three main procedures were applied: (i) A model-based determination of spatial variation of δ18O in precipitation across China and the globe was acquired from Liu et al. (2008) (Liu Model, for short) and Bowen and Wilkinson (2002) (BW Model, for short), respectively, and the stable oxygen isotope ratio in ‰ can be expressed as:
δ18 O ¼ −0:0073Lat 2 þ 0:3261Lat−0:0015Alt−9:7776
ð2Þ
δ18 O ¼ −0:0051Lat 2 þ 0:1805Lat−0:002Alt−5:247
ð3Þ
where Lat and Alt are the latitude in degrees and altitude in m, respectively. The altitude is based on Digital Elevation Model acquired from NASA Shuttle Radar Topographic Mission (SRTM) (available at http:// srtm.csi.cgiar.org). (ii) A correction is considered based on the residual error between the modeled and measured values in the isotopic network. A similar method has been applied in other regions (e.g., Bowen and Wilkinson, 2002; Liu et al., 2008; Wang et al., 2016c). The residual error is calculated for each sampling station, and then spatially interpolated using a Kriging method in ArcGIS 10.2. (iii) A combined map of the stable isotopes in precipitation is based on the modeled isoscape and interpolated residual. 3. Results and discussion 3.1. Observation network: Main pattern Table 1 shows the amount-weighted values for stable isotopes in precipitation of each sampling station in Lanzhou. During 2013–2014,
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Table 1 Amount-weighted δD, δ18O and d in precipitation for each sampling station in Lanzhou during the sampling period. Station
Annual
Summer months (Jun to Sep)
Winter months (Oct to May)
δD/‰
δ18O/‰
d/‰
δD/‰
δ18O/‰
d/‰
δD/‰
δ18O/‰
d/‰
Apr 2011 to Oct 2014 Anning Yuzhong Gaolan Yongdeng
−37.98 −52.09 −40.05 −41.04
−5.92 −8.01 −6.31 −6.53
9.38 11.96 10.39 11.19
−40.75 −50.53 −43.65 −39.18
−6.18 −7.75 −6.76 −6.13
8.73 11.46 10.40 9.87
−31.14 −56.93 −31.92 −47.10
−5.26 −8.80 −6.76 −7.83
10.98 13.49 10.36 15.51
Oct 2013 to Oct 2014 Anning Yuzhong Gaolan Yongdeng LREVC Wushengyi Daheng Gongjing Hezui Renshoushan
−39.68 −40.78 −29.93 −36.92 −42.60 −30.13 −48.57 −53.68 −27.91 −27.11
−6.25 −6.40 −4.87 −6.09 −6.97 −5.12 −7.39 −8.19 −4.80 −4.46
10.28 10.46 9.01 11.78 13.16 10.81 10.55 11.85 10.52 8.56
−36.17 −40.88 −15.02 −32.02 −42.99 −24.00 −48.32 −52.31 −24.51 −24.44
−5.71 −6.42 −3.34 −5.26 −6.82 −4.20 −7.35 −7.86 −4.35 −3.95
9.51 10.45 11.71 10.03 11.53 9.60 10.52 10.58 10.33 7.14
−48.15 −39.65 −38.05 −46.00 −41.62 −45.87 −49.14 −56.63 −37.92 −32.58
−7.54 −6.28 −5.66 −7.63 −7.36 −7.48 −7.47 −8.90 −6.13 −5.51
12.15 10.60 7.25 15.02 17.26 13.93 10.63 14.56 11.09 11.46
6 more sampling stations were added into the observation network for a year (Table 1). Generally, the annual weighted mean δD and δ18O values were higher in the west and lower in the east. The highest weighted mean δD and δ18O values observed at Renshoushan were − 27.11‰ and − 4.46‰, respectively. The lowest values of δD and δ18O were − 53.68‰ and − 8.19‰, respectively, which were both observed at Gongjing. Overall, the weighted mean d values in precipitation were low in the west and high in the east. The eastern station of LREVC reached the highest value (13.16‰) and the western station of Renshoushan reached the lowest value (8.56‰). According to the correlation coefficient matrix for isotopic composition in precipitation for each sampling station on a monthly basis (Tables 2 and S3 in the supplementary material), a strong spatial coherence can be examined for this observation network. Considering the precipitation frequency for these stations, the correlation coefficients are based on the monthly amount-weighted values. Generally, on a seasonal basis, the seasonal variation is consistent across the sampling stations. It should be mentioned that detailed information on an eventbased or synoptic scale is not considered here. 3.2. Isoscape calculation While the isoscapes have been interpolated on a global scale in previous studies (Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003; van der Veer et al., 2009; Terzer et al., 2013), here the formulas of Liu et al. (2008) and Bowen and Wilkinson (2002) are used for a basic estimation, respectively. The method used in Liu et al. (2008) is considered as a case study of Bowen and Wilkinson (2002) and is more suitable in China due to the involvement of more stations. The accuracy of the methods was described in Liu et al. (2008) and Bowen and Wilkinson (2002), respectively. The isoscape of the oxygen isotopes was
calculated based on latitude and elevation within the study domain (Fig. 2). According to the residual errors for each sampling station, the estimations of both two models are better for eastern portion. The residual errors for the western stations are greater than those for most areas. Compared with the map of elevation in Fig. 1, the high error is related to the relatively high elevation. It is clear for the highest station Wushengyi with an elevation of 2297 m, and the residual error at this station is larger than other surrounding stations. In the calculation of isoscape, elevation diversity should be carefully considered in network arrangement and station choosing. When compared with the observed values at these stations, the isoscape provides integrated information with continuous space. It is clear that the isoscape in Lanzhou is related to the complex topography. The lower river valleys usually have depleted levels of heavy isotopes, while the higher mountain mountains have enriched values. Even in such a small domain, the precipitation isotopes present a complex spatial incoherence. However, the two models present a very similar isoscape of oxygen isotope composition in the study region. The procedure to create isoscape at a small domain in this study is practical and referential for other regions. We also use the Online Isotopes in Precipitation Calculator (OIPC) version 3.0 (Bowen, 2017; Bowen and Revenaugh, 2003) to calculate the isotopic composition at the sampling locations (Figs. 3 and 4). This calculator is based on the global database GNIP, and the isotopes in precipitation can be estimated using the latitude, longitude and elevation. Because the OIPC calculated isotope compositions are expressed on a monthly basis, the monthly amount-weighted isotopes for each sampling station are used. Shown in Fig. 3, there are positive correlations for both hydrogen and oxygen isotopes, indicating that the OIPCbased estimations are generally consistent with the observations. The correlation coefficients are not very high, but both the two correlations
Table 2 Correlation coefficient matrix of δ18O in precipitation for each sampling station in Lanzhou from October 2013 to October 2014.
Anning Yuzhong Gaolan Yongdeng LREVC Wushengyi Daheng Gongjing Hezui Renshoushan a b
Anning
Yuzhong
Gaolan
Yongdeng
LREVC
Wushengyi
Daheng
Gongjing
Hezui
0.70 0.59 0.56 0.80b 0.68b 0.87a 0.74b 0.68b 0.94a
0.12 0.91a 0.83b 0.87b 0.78 0.86b 0.83b 0.76
0.32 0.83b 0.93a 0.62 0.78a 0.73b 0.59
0.52 0.45 0.67b 0.58 0.60 0.50
0.88a 0.75b 0.98a 0.84a 0.82b
0.68b 0.85a 0.68b 0.57
0.79a 0.77b 0.75b
0.89a 0.65b
0.74b
Statistically significant at the 0.01 level. Statistically significant at the 0.05 level.
Renshoushan
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Fig. 2. Spatial distribution of residual error and weighted δ18O in precipitation in Lanzhou from October 2013 to October 2014. Liu Model and BW Model are derived from Liu et al. (2008) and Bowen and Wilkinson (2002), respectively, and the measurements in this study are used in calculation of residual error.
are statistically significant at the 0.0001 level. Many factors may cause the difference between OIPC-based and observed isotopes in precipitation, including the sampling periods and frequency. The differences for each sampling station are shown in Fig. 4. Generally, the fluctuation ranges for the OIPC-based and observed data are similar, although the medians and arithmetic average are not always very close to each other. 3.3. Seasonal variation In the study region, the δD and δ18O in precipitation show seasonal variation, as stated in previous studies (Ma et al., 2014; Chen et al., 2015a, 2015b) and in the current enhanced network. To describe the seasonality, a trigonometric function fitting equation is applied in Fig.
5. All ten sampling stations are involved in Fig. 5, and the isotopes in the summer precipitation are usually more enriched than those in winter months. The variations of precipitation isotopes for the six newly added stations are also presented in Fig. S1 in the supplementary material. The δD and δ18O values in precipitation at the six stations generally have similar characteristics as previously described in Chen et al. (2015a). The four-year observations at other stations are provided in Figs. S2 to S5 in the supplementary material. The phenomenon in the study region is consistent with temperature effect in mid-latitude and high-latitude regions (Dansgaard, 1964; Zhang and Wang, 2016). To examine the variations in δ18O, we focused on Anning, Yuzhong, Gaolan and Yongdeng from April 2011 to October 2014. The maxima, minima, arithmetic averages, weighted averages, standard deviation
Fig. 3. Correlation between OIPC-based and measured values of δ18O and δD in precipitation at each sampling stations in Lanzhou from October 2013 to October 2014.
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Fig. 4. Monthly variation of OIPC-based and measured values of δ18O and δD in precipitation at each sampling station in Lanzhou from October 2013 to October 2014. The bottom of the box indicates the 25th percentile, and the top indicates the 75th percentile; a line within the box marks the 50th percentile (median), and a point within the box indicates the arithmetic average; whiskers indicate the 90th and 10th percentiles, and points above and below the whiskers show the 95th and 5th percentiles.
and coefficient of variation were shown in Fig. S6 in the supplementary material. At Anning, the minimum and maximum were − 19.8‰ and 10.9‰, respectively, and the average value was − 5.0‰. At Yuzhong, the δ18O value ranged between −19.7‰ and 3.9‰, and the mean was −7.6‰. At Gaolan, the variation range was from −17.7‰ to 3.2‰ and the mean was − 5.6‰. At Yongdeng, the δ18O was between − 18.3‰ and 9.0‰ and the mean was − 5.0‰. The variation ranges and the means at the four stations show similarities on a larger scale, indicating the precipitates originated from the same water vapor source. Furthermore, the event-based values of δD and δ18O in precipitation also show an obvious fluctuation each month. Concerning the coefficient of variation, the fluctuation ranges were similar at the four stations. In addition, the fluctuations of δD and δ18O during the summer were more obvious than those of winter. The fluctuation of temperature in summer was more frequent than that in winter, leading to a complex fluctuation of isotopes in summer. The differences in arithmetic and weighted averages can also have implications for the sub-cloud evaporation in Lanzhou.
The effect of sub-cloud evaporation usually leads to an enrichment of δ18O and decline of d (Peng et al., 2007; Wu et al., 2015). In our previous studies (Ma et al., 2014; Chen et al., 2015a, 2015b), the variation of d as well as meteoric water line slope and intercept have been used to reflect the sub-cloud evaporation. In this study, the updated result is generally consistent with previous finding (Fig. S7, Tables S4 and S5 in the supplementary material). The sub-cloud secondary evaporation has a significant effect on isotopes in the small rainfall events, but not in snow and heavy rain events. 3.4. Meteorological controls: Temperature and amount effects 3.4.1. Individual consideration The annual and seasonal correlation coefficients between δ18O and major meteorological parameters (air temperature, precipitation, relative humidity, vapor pressure, dew point temperature and depression of the dew point) are shown in Table S6 in the supplementary material. The δ18O values demonstrate strong dependence on air temperature,
Fig. 5. Variation of daily δ18O and δD in precipitation at each sampling stations in Lanzhou from November 2013 to October 2014.
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and the positive correlations between δ18O and air temperature are statistically significant at the 0.01 level for the four stations. This coincides with the temperature effect widely detected in mid- and high-latitude regions. However, seasonal differences exist for the four stations, and non-significant negative correlations between δ18O and air temperature can be seen at Anning in winter and at Yuzhong in autumn. Due to its location in the transitional zone among the monsoon area of East China, the arid area of Northwest China and the cold area of Tibetan Plateau, Lanzhou has complex water vapor sources. All 959 samples in the four long-term stations are applied to calculate the best-fit δ18O-T equation, which is generally consistent with our previous result in Chen et al. (2015a) and GNIP database (Zhang et al., 2002); this can be represented as: δ18 O ¼ 0:23T−8:85 r 2 ¼ 0:16; pb0:01; σ a ¼ 0:02; σ b ¼ 0:30; n ¼ 959
ð4Þ
where T is air temperature in °C for each precipitation sample. Concerning amount effect, the relationship between precipitation amount and δ18O is negative at the four stations on an annual scale, and the correlations for Anning and Yongdeng are significant at the 0.05 level (Table S6 in the supplementary material). In Table S6, δ18OP correlations are significantly negative for Anning and Yongdeng in summer. The correlations between δ18O and precipitation amount are significantly positive for Anning in spring and for Gaolan in autumn, and the remaining seasons are not significantly positive or negative. This reflects that the amount effect cannot be detected for all the stations and seasons. Amount effect is closely related to strong convection, especially in areas with heavy rainfall, and the isotopic ratios can reflect the monsoon intensity (Yapp, 1982). The best-fit equation using all 959 samples is: δ18 O ¼ −0:13P−5:34 r ¼ 0:02; pb0:01; σ a ¼ 0:03; σ b ¼ 0:19; n ¼ 959 2
ð5Þ
where P is precipitation amount in mm for each sample. Significant negative correlations (p ≤ 0.01) are apparent between δ18O and relative humidity for each station (Table S6 in the supplementary material). The value of r2 for Yongdeng is much larger than that for the other stations. The negative seasonal relationship is seen at each station for most periods. For δ18O and vapor pressure, the correlations were positive for Anning and Yongdeng and negative for Yuzhong and Gaolan. In addition, δ18O-Td correlations were largely positive for most stations during different periods. The change in humidity primarily depends on the precipitation process; when precipitation occurs, atmospheric humidity usually increases and δ18O in precipitation decreases (Yu et al., 2006). Therefore, the correlation between δ18O and dew point temperature (Td) can also reflect the relationship of atmospheric humidity and δ18O. Depression of the dew point (ΔTd), i.e., difference of air temperature (T) and dew point temperature (Td), is an important parameter reflecting atmospheric humidity (Xue et al., 2008). The higher the depression of the dew point, the lower the level of atmospheric humidity; dry conditions enrich the isotope composition in precipitation. When the depression of the dew point equals zero, the atmosphere is saturated. The positive correlations were examined between δ18O and ΔTd for each station. Due to the linear relationship between δD and δ18O, the correlations between δD and major meteorological parameters (air temperature, precipitation amount, relative humidity, water vapor pressure, dew point temperature and depression of dew point) are generally similar to those between δ18O and meteorological elements (Table S7 in the supplementary material). As far as d is concerned (Table S8 in the supplementary material), the annual and seasonal correlations are not the same for δD and δ18O, which is generally consistent with different seasonality between δ18O and d.
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3.4.2. Combined regression Compared with single parameter regressions, multi-linear regressions may provide more information on the meteorological controls especially for air temperature and precipitation amount (Hughes and Crawford, 2013; Yu et al., 2016). For this study, the multi-linear regression equation using air temperature, precipitation amount, relative humidity, water vapor pressure and dew point temperature is: δ18 O ¼ aT þ bP þ ch þ de þ eT d þ f
ð6Þ
where T is air temperature in °C, P is precipitation amount in mm, h is relative humidity in %, e is water vapor pressure in hPa, Td is dew point temperature in °C, and a, b, c, d, e, and f are fitting parameters. A common problem in multiple linear regression models is the possible correlation among the predictors. Considering the issue of multicollinearity, stepwise regressions are also applied, and the fitting parameters are presented in Table 3 and Table S9 in the supplementary material. The correlation matrices among these meteorological parameters are provided in Table S10 in the supplementary material. When compared with air temperature only, the r2 value in the equation using more meteorological parameters shows a slight improvement at all the stations. The variance inflation factor of stepwise regression is generally smaller than that of ordinary regression. According to the previous research in Lanzhou (Ma et al., 2014), there are two main pathways for moisture: (1) the westerly path from Lanzhou and Yongdeng to Gaolan and Yuzhong, and (2) the easterly path from Gaolan to Lanzhou, Yuzhong and Yongdeng. In addition, the contributions of moisture evaporated from land surface (Froehlich et al., 2008; Peng et al., 2010; Xu et al., 2011; Wang et al., 2016a; Li et al., 2016a) and sub-cloud evaporation (Peng et al., 2007; Liu et al., 2014; Wang et al., 2016b) cannot be ignored, and the contribution rate of recycled moisture is estimated to be 3.6% on average (Ma et al., 2014). The sub-cloud evaporation has a significant effect on isotopes when the rainfall amount is small, but the impact is not significant for snowfall or heavy rainfall; as the temperature increases, the secondary evaporation is enhanced; water vapor content greatly impacts the sub-cloud evaporation of raindrop but has less influence on snow events (Chen et al., 2015b). 3.5. Cases of continuous precipitation days To discuss the hydrological and synoptic processes, the hourly δ18O, δD and d values at four typical sequential rainfalls in April, June and August, 2014 were examined at Yongdeng (Fig. 6). Overall, 18O becomes more depleted as the rainfall continues. On 15 April 2014, similar trends were presented between δ18O and relative humidity, while an inverse phase trend was exhibited between δ18O and air temperature. That is, the larger the relative humidity, the higher the δ18O value; the lower the temperature, the higher the δ18O value. On 28 June and 3 August 2014, the variation was similar to that of 15 April 2014. On 18 June 2014, δ18O and relative humidity presented inverse trends while δ18O and air temperature showed similar trends. The patterns of the δD were similar to those of the δ18O except on 15 April 2014. The traditional theory of stable isotope in precipitation considers that Rayleigh distillation occurs in the precipitation process; that is, the condensing temperature and the residual water vapor influence the stable isotopes in precipitation (Craig, 1961). Generally, stable isotopes will be gradually depleted in the rainout process, but the δ18O, δD and d values in the four rainfall processes did not always show downward trends. It is useful to notice the continuous precipitation days, i.e., two consecutive days with precipitation amount N 0.1 mm per day. For these continuous precipitation days, the δ18O values in precipitation did not always present the amount effect at each station (Table S11 in the supplementary material). When the precipitation amount on the first day was larger than that on the second day, the δ18O values on the first day were also higher than those of the second day. The positive
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Table 3 Fitting parameters (C—unstandardized coefficient, σ—standard deviation, Cstd—standardized coefficient, and VIF—variance inflation factor) for δ18O dependence on air temperature (T), precipitation amount (P), relative humidity (h), water vapor pressure (e) and dew point temperature (Td) in Lanzhou from April 2011 to October 2014. Method
Parameter
T
P
h
e
Td
Intercept
r2
Ordinary regression
C σ Cstd VIF C σ Cstd VIF
0.29a 0.06 0.46 10.12 0.30a 0.03 0.47 2.14
−0.15a 0.03 −0.16 1.08 −0.15a 0.03 −0.16 1.08
−0.04b 0.02 −0.13 2.72 −0.04a 0.01 −0.12 1.36
−0.12a 0.04 −0.13 2.00 −0.12a 0.04 −0.13 1.93
0.01 0.06 0.01 9.55 – – – –
−4.64a 1.51
0.24a
−4.78a 0.93
0.24a
Stepwise regression
a b
Statistically significant at the 0.01 level. Statistically significant at the 0.05 level.
correlation between amount and δ value shows an inverse amount effect. This phenomenon indicates that the first-day precipitation process affects the δ18O value in the second day, and the rainout process during the two consecutive days leads to a depleting trend for the heavy isotopes in the precipitation. According to Rayleigh distillation, the δ18O
value in the later precipitation is usually more depleted, which may cause an inverse amount effect at synoptic scale (Pang et al., 2006). Similar situations were also reported in other regions, including southeast China (e.g., Xue et al., 2008) and southwest China (e.g., Pang et al., 2006; Yu et al., 2016).
Fig. 6. Variation of hourly δ18O, δD and d in precipitation, air temperature, precipitation amount and relative humidity for four typical events at Yongdeng.
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4. Conclusions In this study, an enhanced observation network of stable hydrogen and oxygen isotopes in precipitation was established. Although the isotopic characteristics of a four-station network have been reported in our previous investigation, the enhanced network including ten sampling stations may provide a platform to examine the isotopic fractionation in this region. Generally, the variations of δD and δ18O in precipitation in Lanzhou are spatially coherent, indicating that the large scale environmental controls are similar. However, the detail differences are also examined for the enhanced measurement network. Based on the observation at ten sampling sites, a new isoscape in precipitation was calculated; this is the first isoscape in Lanzhou. This method is useful for producing an isoscape at the city scale, and can be applied to other small domains. The meteorological controls are examined for the study region, including simple and multiple regressions of meteorological variables. Taking many meteorological parameters into account, the correlation coefficients generally show a slight improvement. Several individual cases were also examined in this study, and the isotopic characteristics during one event or continuous days were used to understand the environmental controls on precipitation isotopes. Acknowledgments This work was supported by the National Natural Science Foundation of China [grant number 41461003]; the Scientific Research Program in University in Gansu Province [grant number 2016B-019]; and the Foundation for Young Teachers of Northwest Normal University [grant number NWNU-LKQN-15-8]. The authors greatly thank all the weather observers from local meteorological bureaus and colleagues in the Northwest Normal University for their help in sampling and lab analysis. References Araguás-Araguás, L., Froehlich, K., Rozanski, K., 1998. Stable isotope composition of precipitation over southeast Asia. J. Geophys. Res. 103:28721–28742. http://dx.doi.org/ 10.1029/98JD02582. Argiriou, A.A., Lykoudis, S., 2006. Isotopic composition of precipitation in Greece. J. Hydrol. 327:486–495. http://dx.doi.org/10.1016/j.jhydrol.2005.11.053. Barras, V., Simmonds, I., 2008. Synoptic controls upon δ18O in southern Tasmanian precipitation. Geophys. Res. Lett. 35, L02707. http://dx.doi.org/10.1029/2007GL031835. Bowen, G.J., 2010. Isoscapes: spatial pattern in isotopic biogeochemistry. Ann. Rev. Earth Planetary Sci. 38:161–187. http://dx.doi.org/10.1146/annurev-earth-040809152429. Bowen, G.J., 2017. The Online Isotopes in Precipitation Calculator, Version 3.0. http:// www.waterisotopes.org. Bowen, G.J., Revenaugh, J., 2003. Interpolating the isotopic composition of modern meteoric precipitation. Water Resour. Res. 39, 1299 (http://dx.doi.org/10.1029/ 2003WR002086). Bowen, G.J., Wilkinson, B., 2002. Spatial distribution of δ18O in meteoric precipitation. Geology 30:315–318. http://dx.doi.org/10.1130/0091-7613(2002)030b0315: SDOOIMN2.0.CO;2. Chen, F., Zhang, M., Ma, Q., Wang, S., Li, X., Zhu, X., 2015a. Stable isotopic characteristics of precipitation in Lanzhou city and its surrounding areas, northwestern China. Environ. Earth Sci. 73:4671–4680. http://dx.doi.org/10.1007/s12665-014-3776-6. Chen, F., Zhang, M., Wang, S., Ma, Q., Zhu, X., Dong, L., 2015b. Relationship between subcloud secondary evaporation and stable isotopes in precipitation of Lanzhou and surrounding area. Quat. Int. 380:68–74. http://dx.doi.org/10.1016/j.quaint.2014.12.051. CMA (China Meteorological Administration), 2007. Specifications for Surface Meteorological Observation. China Meteorological Press, Beijing (in Chinese). Craig, H., 1961. Isotopic variations in meteoric waters. Science 133:1702–1703. http:// dx.doi.org/10.1126/science.133.3465.1702. Crawford, J., Hughes, C.E., Stephen, D.P., 2013. Is the isotopic composition of event based precipitation driven by moisture source or synoptic scale weather in the Sydney Basin, Australia? J. Hydrol. 507:213–226. http://dx.doi.org/10.1016/ j.jhydrol.2013.10.031. Crawford, J., Hollins, S.E., Meredith, K.T., Hughes, C.E., 2017. Precipitation stable isotope variability and sub-cloud evaporation processes in a semi-arid region. Hydrol. Process. 31:20–34. http://dx.doi.org/10.1002/hyp.10885. Dansgaard, W., 1964. Stable isotopes in precipitation. Tellus 16:437–468. http:// dx.doi.org/10.3402/tellusa.v16i4.8993. Froehlich, K., Kralik, M., Papesch, W., Rank, D., Scheifinger, H., Stichler, W., 2008. Deuterium excess in precipitation of Alpine regions-moisture recycling. Isot. Environ. Health 44:61–70. http://dx.doi.org/10.1080/10256010801887208.
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Environmental controls on stable isotopes of precipitation in Lanzhou, China: An enhanced network at city scale Fenli Chen, Mingjun Zhang ⁎, Shengjie Wang ⁎, Xue Qiu, Mingxia Du College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• A precipitation isotope network at a city scale including 10 stations • Isoscape at city scale in an arid and semi-arid climate • A combined influence on precipitation isotopes of meteorological parameters
a r t i c l e
i n f o
Article history: Received 17 January 2017 Received in revised form 23 July 2017 Accepted 24 July 2017 Available online xxxx Editor: D. Barcelo Keywords: Precipitation isotope Observation network Lanzhou city Temperature effect
a b s t r a c t Stable hydrogen and oxygen isotopes in precipitation are very sensitive to environmental changes, and can record evolution of water cycle. The Lanzhou city in northwestern China is jointly influenced by the monsoon and westerlies, which is considered as a vital platform to investigate the moisture regime for this region. Since 2011, an observation network of stable isotopes in precipitation was established across the city, and four stations were included in the network. In 2013, six more sampling stations were added, and the enhanced network might provide more meaningful information on spatial incoherence and synoptic process. This study focused on the variations of stable isotopes (δ18O and δD) in precipitation and the environmental controls based on the 1432 samples in this enhanced network from April 2011 to October 2014. The results showed that the precipitation isotopes had great spatial diversity, and the neighboring stations may present large difference in δD and δ18O. Based on the observation at ten sampling sites, an isoscape in precipitation was calculated, and the method is useful to produce isoscape for small domains. The temperature effect and amount effect was reconsidered based on the dataset. Taking meteorological parameters (temperature, precipitation amount, relative humidity, water vapor pressure and dew point temperature) as variables in a multi-linear regression, the result of coefficients for these meteorological parameters were calculated. Some cases were also involved in this study, and the isotopic characteristics during one event or continuous days were used to understand the environmental controls on precipitation isotopes. © 2017 Published by Elsevier B.V.
⁎ Corresponding authors. E-mail addresses: cfl[email protected] (F. Chen), [email protected] (M. Zhang), [email protected] (S. Wang), [email protected] (X. Qiu), [email protected] (M. Du).
http://dx.doi.org/10.1016/j.scitotenv.2017.07.216 0048-9697/© 2017 Published by Elsevier B.V.
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1. Introduction The stable isotope composition (δD and δ18O) in natural water is widely used to investigate the Earth's hydrologic cycle and climatic change (e.g., Treblea et al., 2005; Argiriou and Lykoudis, 2006; Thompson et al., 2013; Jasechko et al., 2014; Li et al., 2016b; Wang et al., 2016a). Precipitation is a vital step in the natural water cycle, and spatial dependence and temporal variations in precipitation isotopes are useful in understanding hydrological, meteorological and ecological processes (Dansgaard, 1964; Craig, 1961). In Dansgaard (1964), temperature effect (positive correlation between surface air temperature and isotope ratio) and amount effect (negative correlation between precipitation amount and isotopic ratio) in precipitation isotopes were examined in detail. After that work, meteorological and geographical controls on stable hydrogen and oxygen isotopes in precipitation were discussed worldwide based on observation and modeling (e.g., Risi et al., 2008; Scholl et al., 2009; Moore et al., 2014; Sánchez-Murillo et al., 2016). The environmental controls of precipitation isotopes for a large domain can be logically applied to a small domain, that is, known meteorological and geographical controls (e.g., temperature effect, amount effect, altitude effect, and continental effect) should always be effective. To acquire data on the spatial distribution of stable isotopes in precipitation (also called the isoscape) at a larger or global scale, the latitude and elevation are usually selected as the two main factors in many studies (e.g., Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003; Liu et al., 2008). Because surface air temperature logically correlates with elevation, in some studies (e.g., van der Veer et al., 2009; Zhao et al., 2012) the temperature parameter is considered in lieu of elevation. However, further observation of the isotope compositions in precipitation has indicated that some local factors may also be considered (e.g., Smith and Evans, 2007; Guan et al., 2009). To investigate these local controls, an enhanced network is required. Limited measurements immensely impede the understanding of isotopic fractionation processes in this region. The monthly isotopic precipitation records derived from the Global Network of Isotopes in Precipitation (GNIP; IAEA/ WMO, 2017) are not suitable to study δD and δ18O in precipitation on a synoptic scale (Treblea et al., 2005; Barras and Simmonds, 2008; Crawford et al., 2013, 2017; Wang et al., 2017). In recent years, several intensive networks of precipitation isotopes have been established in small regions at even a city scale, such as in the Beijing Network of Isotopes in Precipitation (Li et al., 2017). Located in the western part of the Chinese Loess Plateau, the city of Lanzhou in the Gansu province is usually considered to be jointly influenced by westerlies and monsoon moisture (Araguás-Araguás et al., 1998; Tian et al., 2007; Yao et al., 2013). The systemic measurements of environmental isotopes in Lanzhou's precipitation date back to the monthly observation conducted during 1985–1999, and the isotopic values were submitted to the GNIP database (Station ID 5288900; including 41 monthly records for δ18O and 39 for δD). Based on this GNIP dataset, the seasonal characteristics of stable isotopes in precipitation in Lanzhou were examined in many publications (e.g., Zhang et al., 2002; Yao et al., 2013). However, the existing monthly GNIP database is not enough to investigate the synoptic and micro-climate details that the stable isotopes undergo during precipitation, and much of the interesting information may be masked by the lower frequency sampling (Crawford et al., 2013). To improve knowledge of stable isotopes in Lanzhou's precipitation, an observation network at the city scale was established by the Northwest Normal University in 2011. During the first years, four stations covered the urban area (Anning district) and three counties (Yuzhong, Gaolan and Yongdeng counties). The first year data (243 event-based samples collected from April 2011 to March 2012) at the four stations were presented by Ma et al. (2014), indicating that even in such a small domain the precipitation isotopes still have complex spatial diversity. As the observation continued, more samples of importance in
understanding the inter-annual variation of stable isotopes became available from this network. Based on the N 400 samples collected from 2011 to 2013, Chen et al. (2015a, 2015b) reconsidered the local meteoric water line (LMWL) for each sampling station as well as the influence of below-cloud evaporation in an arid and semi-arid climate. Considering the complex topography as a vital factor influencing the isoscape, a more enhanced observation network was established across the city in 2013. The enhanced network contains ten sampling stations covering a larger elevation range, which may provide more meaningful information for the region. In this study, the enhanced network (including the sampling stations added during 2013–2014) was introduced, and the years 2011– 2013 at the original four stations were also presented. The objective of this study was (1) to present a new detailed isoscape of precipitation in Lanzhou, (2) to clarify the environmental controls on the isotope composition in precipitation in Lanzhou based on the enhanced network, and (3) to present several cases demonstrating isotopic evolution in continuous rain events. The enhanced network provides a platform to further assess hydrological processes in this region. 2. Data and method 2.1. Area of study Lanzhou, an arid and semi-arid city in the western part of the Chinese Loess Plateau, is the provincial capital of Northwest China's Gansu province (Fig. 1). Within the boundary of this city, the altitude generally declines from southwest to northeast, and the Yellow (Huanghe) River also flows from southwest to northeast. Lanzhou contains five districts (Chengguan, Qilihe, Anning, Xigu and Honggu) and three counties (Yuzhong, Yongdeng and Gaolan). The densely populated part of the city is located within a narrow valley across the five districts. According to the long-term climatology from 1981 to 2010, the mean annual air temperature is 10.4 °C, ranging from −4.5 °C in January to 23.1 °C in July. In the continental climate, the precipitation events are largely concentrated in the summer months. 2.2. Sampling In the observation network of stable isotopes in precipitation, ten sampling stations were selected across Lanzhou (Fig. 1 and Table S1 in the supplementary material). From April 2011 to October 2014, precipitation at four sites were sampled, i.e., Anning (Northwest Normal University, Anning district), Yuzhong (Yuzhong County Meteorological Bureau, Yuzhong county), Gaolan (Gaolan County Meteorological Bureau, Gaolan county) and Yongdeng (Yongdeng County Meteorological Bureau, Yongdeng county). For the three counties, the county meteorological bureaus were selected; for the districts, we selected one sampling site at the Northwest Normal University. To improve spatial coverage from October 2013 to October 2014, six more stations were added including Renshoushan (Renshou Hill, Anning district), LREVC (Lanzhou Resources & Environment Voc-Tech College, Chengguan district), Wushengyi (Wushengyi town, Yongdeng county), Daheng (Daheng village, Heishi town, Gaolan county), Gongjing (Gongjing town, Yuzhong county) and Hezui (Hezui village, Huazhuang town, Honggu district). For the three counties, one more site was selected for each county; for the districts, three more sampling sites were selected along the narrow river valley from west to east. The specific locations were recommended by local meteorological bureaus. The sampling was undertaken by the full-time observers of local meteorological bureau for each site with an exception of Anning where the researchers of Northwest Normal University did the sampling. Liquid samples were collected immediately after the end of each event in order to prevent water evaporation. For some continuous and heavy precipitation events, samples were collected according to fixed hourly intervals. Liquid samples were stored in 50 mL or 60 mL HDPE bottles
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Fig. 1. Spatial distribution of sampling stations in Lanzhou. The administrative boundary of Lanzhou is marked by dashed gray polygons.
with waterproof seals, and then refrigerated. Solid samples were not sealed in bottles until they were melted at room temperature in ziplock bags. The sampling procedure for this network and its basic characteristics during the first years were also described in our previous studies (Ma et al., 2014; Chen et al., 2015a, 2015b). Precipitation amounts were measured by observers using China Meteorological Administration standard pluviometers (CMA, 2007). Other meteorological parameters such as air temperature and relative humidity during precipitation events were recorded by the automatic weather stations interrogating the sensors every hour. The total number of precipitation samples is 1432 from April 2011 to October 2014, including 1291 liquid and 141 solid samples (Table S2 in the supplementary material). During this period, 959 precipitation samples were collected at the four stations with longer periods (Anning, Yuzhong, Gaolan and Yongdeng). 2.3. Lab analysis The stable hydrogen and oxygen isotope compositions of all the precipitation samples were analyzed using a liquid water isotope analyzer DLT-100 (Los Gatos Research, Inc.) in the Stable Isotope Laboratory, College of Geography and Environmental Science, Northwest Normal University, with a precision of ± 0.6‰ and ± 0.2‰, for δD and δ18 O, respectively. The frozen samples in the HDPE bottles were melted at room temperature before being analyzed. A group with six samples and three isotopic standards was tested, and each sample or standard was injected six times using a microliter syringe. Considering the memory effect of isotopic composition, the last four injection results were accepted while the first two injections were discarded in the calculation (Lis et al., 2008). Ratios of hydrogen and oxygen isotopes per mil (‰) in the samples relative to those in the Vienna Standard Mean Ocean Water (V-SMOW) are expressed using a δ-notation:
δsample ¼
Rsample −Rstandard 1000‰ Rstandard
ð1Þ
where Rsample and Rstandard are isotope ratios (D/H or 18O/16O) in the samples and references relative to V-SMOW, respectively.
2.4. Isoscape calculation Many methods have been applied in previous studies to calculate the spatial distribution of the isotopes in precipitation, also known as the isoscape (Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003; Liu et al., 2008; van der Veer et al., 2009; Bowen, 2010; Terzer et al., 2013). In this study, in order to investigate the isoscape of Lanzhou using the enhanced observation network, three main procedures were applied: (i) A model-based determination of spatial variation of δ18O in precipitation across China and the globe was acquired from Liu et al. (2008) (Liu Model, for short) and Bowen and Wilkinson (2002) (BW Model, for short), respectively, and the stable oxygen isotope ratio in ‰ can be expressed as:
δ18 O ¼ −0:0073Lat 2 þ 0:3261Lat−0:0015Alt−9:7776
ð2Þ
δ18 O ¼ −0:0051Lat 2 þ 0:1805Lat−0:002Alt−5:247
ð3Þ
where Lat and Alt are the latitude in degrees and altitude in m, respectively. The altitude is based on Digital Elevation Model acquired from NASA Shuttle Radar Topographic Mission (SRTM) (available at http:// srtm.csi.cgiar.org). (ii) A correction is considered based on the residual error between the modeled and measured values in the isotopic network. A similar method has been applied in other regions (e.g., Bowen and Wilkinson, 2002; Liu et al., 2008; Wang et al., 2016c). The residual error is calculated for each sampling station, and then spatially interpolated using a Kriging method in ArcGIS 10.2. (iii) A combined map of the stable isotopes in precipitation is based on the modeled isoscape and interpolated residual. 3. Results and discussion 3.1. Observation network: Main pattern Table 1 shows the amount-weighted values for stable isotopes in precipitation of each sampling station in Lanzhou. During 2013–2014,
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Table 1 Amount-weighted δD, δ18O and d in precipitation for each sampling station in Lanzhou during the sampling period. Station
Annual
Summer months (Jun to Sep)
Winter months (Oct to May)
δD/‰
δ18O/‰
d/‰
δD/‰
δ18O/‰
d/‰
δD/‰
δ18O/‰
d/‰
Apr 2011 to Oct 2014 Anning Yuzhong Gaolan Yongdeng
−37.98 −52.09 −40.05 −41.04
−5.92 −8.01 −6.31 −6.53
9.38 11.96 10.39 11.19
−40.75 −50.53 −43.65 −39.18
−6.18 −7.75 −6.76 −6.13
8.73 11.46 10.40 9.87
−31.14 −56.93 −31.92 −47.10
−5.26 −8.80 −6.76 −7.83
10.98 13.49 10.36 15.51
Oct 2013 to Oct 2014 Anning Yuzhong Gaolan Yongdeng LREVC Wushengyi Daheng Gongjing Hezui Renshoushan
−39.68 −40.78 −29.93 −36.92 −42.60 −30.13 −48.57 −53.68 −27.91 −27.11
−6.25 −6.40 −4.87 −6.09 −6.97 −5.12 −7.39 −8.19 −4.80 −4.46
10.28 10.46 9.01 11.78 13.16 10.81 10.55 11.85 10.52 8.56
−36.17 −40.88 −15.02 −32.02 −42.99 −24.00 −48.32 −52.31 −24.51 −24.44
−5.71 −6.42 −3.34 −5.26 −6.82 −4.20 −7.35 −7.86 −4.35 −3.95
9.51 10.45 11.71 10.03 11.53 9.60 10.52 10.58 10.33 7.14
−48.15 −39.65 −38.05 −46.00 −41.62 −45.87 −49.14 −56.63 −37.92 −32.58
−7.54 −6.28 −5.66 −7.63 −7.36 −7.48 −7.47 −8.90 −6.13 −5.51
12.15 10.60 7.25 15.02 17.26 13.93 10.63 14.56 11.09 11.46
6 more sampling stations were added into the observation network for a year (Table 1). Generally, the annual weighted mean δD and δ18O values were higher in the west and lower in the east. The highest weighted mean δD and δ18O values observed at Renshoushan were − 27.11‰ and − 4.46‰, respectively. The lowest values of δD and δ18O were − 53.68‰ and − 8.19‰, respectively, which were both observed at Gongjing. Overall, the weighted mean d values in precipitation were low in the west and high in the east. The eastern station of LREVC reached the highest value (13.16‰) and the western station of Renshoushan reached the lowest value (8.56‰). According to the correlation coefficient matrix for isotopic composition in precipitation for each sampling station on a monthly basis (Tables 2 and S3 in the supplementary material), a strong spatial coherence can be examined for this observation network. Considering the precipitation frequency for these stations, the correlation coefficients are based on the monthly amount-weighted values. Generally, on a seasonal basis, the seasonal variation is consistent across the sampling stations. It should be mentioned that detailed information on an eventbased or synoptic scale is not considered here. 3.2. Isoscape calculation While the isoscapes have been interpolated on a global scale in previous studies (Bowen and Wilkinson, 2002; Bowen and Revenaugh, 2003; van der Veer et al., 2009; Terzer et al., 2013), here the formulas of Liu et al. (2008) and Bowen and Wilkinson (2002) are used for a basic estimation, respectively. The method used in Liu et al. (2008) is considered as a case study of Bowen and Wilkinson (2002) and is more suitable in China due to the involvement of more stations. The accuracy of the methods was described in Liu et al. (2008) and Bowen and Wilkinson (2002), respectively. The isoscape of the oxygen isotopes was
calculated based on latitude and elevation within the study domain (Fig. 2). According to the residual errors for each sampling station, the estimations of both two models are better for eastern portion. The residual errors for the western stations are greater than those for most areas. Compared with the map of elevation in Fig. 1, the high error is related to the relatively high elevation. It is clear for the highest station Wushengyi with an elevation of 2297 m, and the residual error at this station is larger than other surrounding stations. In the calculation of isoscape, elevation diversity should be carefully considered in network arrangement and station choosing. When compared with the observed values at these stations, the isoscape provides integrated information with continuous space. It is clear that the isoscape in Lanzhou is related to the complex topography. The lower river valleys usually have depleted levels of heavy isotopes, while the higher mountain mountains have enriched values. Even in such a small domain, the precipitation isotopes present a complex spatial incoherence. However, the two models present a very similar isoscape of oxygen isotope composition in the study region. The procedure to create isoscape at a small domain in this study is practical and referential for other regions. We also use the Online Isotopes in Precipitation Calculator (OIPC) version 3.0 (Bowen, 2017; Bowen and Revenaugh, 2003) to calculate the isotopic composition at the sampling locations (Figs. 3 and 4). This calculator is based on the global database GNIP, and the isotopes in precipitation can be estimated using the latitude, longitude and elevation. Because the OIPC calculated isotope compositions are expressed on a monthly basis, the monthly amount-weighted isotopes for each sampling station are used. Shown in Fig. 3, there are positive correlations for both hydrogen and oxygen isotopes, indicating that the OIPCbased estimations are generally consistent with the observations. The correlation coefficients are not very high, but both the two correlations
Table 2 Correlation coefficient matrix of δ18O in precipitation for each sampling station in Lanzhou from October 2013 to October 2014.
Anning Yuzhong Gaolan Yongdeng LREVC Wushengyi Daheng Gongjing Hezui Renshoushan a b
Anning
Yuzhong
Gaolan
Yongdeng
LREVC
Wushengyi
Daheng
Gongjing
Hezui
0.70 0.59 0.56 0.80b 0.68b 0.87a 0.74b 0.68b 0.94a
0.12 0.91a 0.83b 0.87b 0.78 0.86b 0.83b 0.76
0.32 0.83b 0.93a 0.62 0.78a 0.73b 0.59
0.52 0.45 0.67b 0.58 0.60 0.50
0.88a 0.75b 0.98a 0.84a 0.82b
0.68b 0.85a 0.68b 0.57
0.79a 0.77b 0.75b
0.89a 0.65b
0.74b
Statistically significant at the 0.01 level. Statistically significant at the 0.05 level.
Renshoushan
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Fig. 2. Spatial distribution of residual error and weighted δ18O in precipitation in Lanzhou from October 2013 to October 2014. Liu Model and BW Model are derived from Liu et al. (2008) and Bowen and Wilkinson (2002), respectively, and the measurements in this study are used in calculation of residual error.
are statistically significant at the 0.0001 level. Many factors may cause the difference between OIPC-based and observed isotopes in precipitation, including the sampling periods and frequency. The differences for each sampling station are shown in Fig. 4. Generally, the fluctuation ranges for the OIPC-based and observed data are similar, although the medians and arithmetic average are not always very close to each other. 3.3. Seasonal variation In the study region, the δD and δ18O in precipitation show seasonal variation, as stated in previous studies (Ma et al., 2014; Chen et al., 2015a, 2015b) and in the current enhanced network. To describe the seasonality, a trigonometric function fitting equation is applied in Fig.
5. All ten sampling stations are involved in Fig. 5, and the isotopes in the summer precipitation are usually more enriched than those in winter months. The variations of precipitation isotopes for the six newly added stations are also presented in Fig. S1 in the supplementary material. The δD and δ18O values in precipitation at the six stations generally have similar characteristics as previously described in Chen et al. (2015a). The four-year observations at other stations are provided in Figs. S2 to S5 in the supplementary material. The phenomenon in the study region is consistent with temperature effect in mid-latitude and high-latitude regions (Dansgaard, 1964; Zhang and Wang, 2016). To examine the variations in δ18O, we focused on Anning, Yuzhong, Gaolan and Yongdeng from April 2011 to October 2014. The maxima, minima, arithmetic averages, weighted averages, standard deviation
Fig. 3. Correlation between OIPC-based and measured values of δ18O and δD in precipitation at each sampling stations in Lanzhou from October 2013 to October 2014.
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Fig. 4. Monthly variation of OIPC-based and measured values of δ18O and δD in precipitation at each sampling station in Lanzhou from October 2013 to October 2014. The bottom of the box indicates the 25th percentile, and the top indicates the 75th percentile; a line within the box marks the 50th percentile (median), and a point within the box indicates the arithmetic average; whiskers indicate the 90th and 10th percentiles, and points above and below the whiskers show the 95th and 5th percentiles.
and coefficient of variation were shown in Fig. S6 in the supplementary material. At Anning, the minimum and maximum were − 19.8‰ and 10.9‰, respectively, and the average value was − 5.0‰. At Yuzhong, the δ18O value ranged between −19.7‰ and 3.9‰, and the mean was −7.6‰. At Gaolan, the variation range was from −17.7‰ to 3.2‰ and the mean was − 5.6‰. At Yongdeng, the δ18O was between − 18.3‰ and 9.0‰ and the mean was − 5.0‰. The variation ranges and the means at the four stations show similarities on a larger scale, indicating the precipitates originated from the same water vapor source. Furthermore, the event-based values of δD and δ18O in precipitation also show an obvious fluctuation each month. Concerning the coefficient of variation, the fluctuation ranges were similar at the four stations. In addition, the fluctuations of δD and δ18O during the summer were more obvious than those of winter. The fluctuation of temperature in summer was more frequent than that in winter, leading to a complex fluctuation of isotopes in summer. The differences in arithmetic and weighted averages can also have implications for the sub-cloud evaporation in Lanzhou.
The effect of sub-cloud evaporation usually leads to an enrichment of δ18O and decline of d (Peng et al., 2007; Wu et al., 2015). In our previous studies (Ma et al., 2014; Chen et al., 2015a, 2015b), the variation of d as well as meteoric water line slope and intercept have been used to reflect the sub-cloud evaporation. In this study, the updated result is generally consistent with previous finding (Fig. S7, Tables S4 and S5 in the supplementary material). The sub-cloud secondary evaporation has a significant effect on isotopes in the small rainfall events, but not in snow and heavy rain events. 3.4. Meteorological controls: Temperature and amount effects 3.4.1. Individual consideration The annual and seasonal correlation coefficients between δ18O and major meteorological parameters (air temperature, precipitation, relative humidity, vapor pressure, dew point temperature and depression of the dew point) are shown in Table S6 in the supplementary material. The δ18O values demonstrate strong dependence on air temperature,
Fig. 5. Variation of daily δ18O and δD in precipitation at each sampling stations in Lanzhou from November 2013 to October 2014.
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and the positive correlations between δ18O and air temperature are statistically significant at the 0.01 level for the four stations. This coincides with the temperature effect widely detected in mid- and high-latitude regions. However, seasonal differences exist for the four stations, and non-significant negative correlations between δ18O and air temperature can be seen at Anning in winter and at Yuzhong in autumn. Due to its location in the transitional zone among the monsoon area of East China, the arid area of Northwest China and the cold area of Tibetan Plateau, Lanzhou has complex water vapor sources. All 959 samples in the four long-term stations are applied to calculate the best-fit δ18O-T equation, which is generally consistent with our previous result in Chen et al. (2015a) and GNIP database (Zhang et al., 2002); this can be represented as: δ18 O ¼ 0:23T−8:85 r 2 ¼ 0:16; pb0:01; σ a ¼ 0:02; σ b ¼ 0:30; n ¼ 959
ð4Þ
where T is air temperature in °C for each precipitation sample. Concerning amount effect, the relationship between precipitation amount and δ18O is negative at the four stations on an annual scale, and the correlations for Anning and Yongdeng are significant at the 0.05 level (Table S6 in the supplementary material). In Table S6, δ18OP correlations are significantly negative for Anning and Yongdeng in summer. The correlations between δ18O and precipitation amount are significantly positive for Anning in spring and for Gaolan in autumn, and the remaining seasons are not significantly positive or negative. This reflects that the amount effect cannot be detected for all the stations and seasons. Amount effect is closely related to strong convection, especially in areas with heavy rainfall, and the isotopic ratios can reflect the monsoon intensity (Yapp, 1982). The best-fit equation using all 959 samples is: δ18 O ¼ −0:13P−5:34 r ¼ 0:02; pb0:01; σ a ¼ 0:03; σ b ¼ 0:19; n ¼ 959 2
ð5Þ
where P is precipitation amount in mm for each sample. Significant negative correlations (p ≤ 0.01) are apparent between δ18O and relative humidity for each station (Table S6 in the supplementary material). The value of r2 for Yongdeng is much larger than that for the other stations. The negative seasonal relationship is seen at each station for most periods. For δ18O and vapor pressure, the correlations were positive for Anning and Yongdeng and negative for Yuzhong and Gaolan. In addition, δ18O-Td correlations were largely positive for most stations during different periods. The change in humidity primarily depends on the precipitation process; when precipitation occurs, atmospheric humidity usually increases and δ18O in precipitation decreases (Yu et al., 2006). Therefore, the correlation between δ18O and dew point temperature (Td) can also reflect the relationship of atmospheric humidity and δ18O. Depression of the dew point (ΔTd), i.e., difference of air temperature (T) and dew point temperature (Td), is an important parameter reflecting atmospheric humidity (Xue et al., 2008). The higher the depression of the dew point, the lower the level of atmospheric humidity; dry conditions enrich the isotope composition in precipitation. When the depression of the dew point equals zero, the atmosphere is saturated. The positive correlations were examined between δ18O and ΔTd for each station. Due to the linear relationship between δD and δ18O, the correlations between δD and major meteorological parameters (air temperature, precipitation amount, relative humidity, water vapor pressure, dew point temperature and depression of dew point) are generally similar to those between δ18O and meteorological elements (Table S7 in the supplementary material). As far as d is concerned (Table S8 in the supplementary material), the annual and seasonal correlations are not the same for δD and δ18O, which is generally consistent with different seasonality between δ18O and d.
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3.4.2. Combined regression Compared with single parameter regressions, multi-linear regressions may provide more information on the meteorological controls especially for air temperature and precipitation amount (Hughes and Crawford, 2013; Yu et al., 2016). For this study, the multi-linear regression equation using air temperature, precipitation amount, relative humidity, water vapor pressure and dew point temperature is: δ18 O ¼ aT þ bP þ ch þ de þ eT d þ f
ð6Þ
where T is air temperature in °C, P is precipitation amount in mm, h is relative humidity in %, e is water vapor pressure in hPa, Td is dew point temperature in °C, and a, b, c, d, e, and f are fitting parameters. A common problem in multiple linear regression models is the possible correlation among the predictors. Considering the issue of multicollinearity, stepwise regressions are also applied, and the fitting parameters are presented in Table 3 and Table S9 in the supplementary material. The correlation matrices among these meteorological parameters are provided in Table S10 in the supplementary material. When compared with air temperature only, the r2 value in the equation using more meteorological parameters shows a slight improvement at all the stations. The variance inflation factor of stepwise regression is generally smaller than that of ordinary regression. According to the previous research in Lanzhou (Ma et al., 2014), there are two main pathways for moisture: (1) the westerly path from Lanzhou and Yongdeng to Gaolan and Yuzhong, and (2) the easterly path from Gaolan to Lanzhou, Yuzhong and Yongdeng. In addition, the contributions of moisture evaporated from land surface (Froehlich et al., 2008; Peng et al., 2010; Xu et al., 2011; Wang et al., 2016a; Li et al., 2016a) and sub-cloud evaporation (Peng et al., 2007; Liu et al., 2014; Wang et al., 2016b) cannot be ignored, and the contribution rate of recycled moisture is estimated to be 3.6% on average (Ma et al., 2014). The sub-cloud evaporation has a significant effect on isotopes when the rainfall amount is small, but the impact is not significant for snowfall or heavy rainfall; as the temperature increases, the secondary evaporation is enhanced; water vapor content greatly impacts the sub-cloud evaporation of raindrop but has less influence on snow events (Chen et al., 2015b). 3.5. Cases of continuous precipitation days To discuss the hydrological and synoptic processes, the hourly δ18O, δD and d values at four typical sequential rainfalls in April, June and August, 2014 were examined at Yongdeng (Fig. 6). Overall, 18O becomes more depleted as the rainfall continues. On 15 April 2014, similar trends were presented between δ18O and relative humidity, while an inverse phase trend was exhibited between δ18O and air temperature. That is, the larger the relative humidity, the higher the δ18O value; the lower the temperature, the higher the δ18O value. On 28 June and 3 August 2014, the variation was similar to that of 15 April 2014. On 18 June 2014, δ18O and relative humidity presented inverse trends while δ18O and air temperature showed similar trends. The patterns of the δD were similar to those of the δ18O except on 15 April 2014. The traditional theory of stable isotope in precipitation considers that Rayleigh distillation occurs in the precipitation process; that is, the condensing temperature and the residual water vapor influence the stable isotopes in precipitation (Craig, 1961). Generally, stable isotopes will be gradually depleted in the rainout process, but the δ18O, δD and d values in the four rainfall processes did not always show downward trends. It is useful to notice the continuous precipitation days, i.e., two consecutive days with precipitation amount N 0.1 mm per day. For these continuous precipitation days, the δ18O values in precipitation did not always present the amount effect at each station (Table S11 in the supplementary material). When the precipitation amount on the first day was larger than that on the second day, the δ18O values on the first day were also higher than those of the second day. The positive
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Table 3 Fitting parameters (C—unstandardized coefficient, σ—standard deviation, Cstd—standardized coefficient, and VIF—variance inflation factor) for δ18O dependence on air temperature (T), precipitation amount (P), relative humidity (h), water vapor pressure (e) and dew point temperature (Td) in Lanzhou from April 2011 to October 2014. Method
Parameter
T
P
h
e
Td
Intercept
r2
Ordinary regression
C σ Cstd VIF C σ Cstd VIF
0.29a 0.06 0.46 10.12 0.30a 0.03 0.47 2.14
−0.15a 0.03 −0.16 1.08 −0.15a 0.03 −0.16 1.08
−0.04b 0.02 −0.13 2.72 −0.04a 0.01 −0.12 1.36
−0.12a 0.04 −0.13 2.00 −0.12a 0.04 −0.13 1.93
0.01 0.06 0.01 9.55 – – – –
−4.64a 1.51
0.24a
−4.78a 0.93
0.24a
Stepwise regression
a b
Statistically significant at the 0.01 level. Statistically significant at the 0.05 level.
correlation between amount and δ value shows an inverse amount effect. This phenomenon indicates that the first-day precipitation process affects the δ18O value in the second day, and the rainout process during the two consecutive days leads to a depleting trend for the heavy isotopes in the precipitation. According to Rayleigh distillation, the δ18O
value in the later precipitation is usually more depleted, which may cause an inverse amount effect at synoptic scale (Pang et al., 2006). Similar situations were also reported in other regions, including southeast China (e.g., Xue et al., 2008) and southwest China (e.g., Pang et al., 2006; Yu et al., 2016).
Fig. 6. Variation of hourly δ18O, δD and d in precipitation, air temperature, precipitation amount and relative humidity for four typical events at Yongdeng.
F. Chen et al. / Science of the Total Environment 609 (2017) 1013–1022
4. Conclusions In this study, an enhanced observation network of stable hydrogen and oxygen isotopes in precipitation was established. Although the isotopic characteristics of a four-station network have been reported in our previous investigation, the enhanced network including ten sampling stations may provide a platform to examine the isotopic fractionation in this region. Generally, the variations of δD and δ18O in precipitation in Lanzhou are spatially coherent, indicating that the large scale environmental controls are similar. However, the detail differences are also examined for the enhanced measurement network. Based on the observation at ten sampling sites, a new isoscape in precipitation was calculated; this is the first isoscape in Lanzhou. This method is useful for producing an isoscape at the city scale, and can be applied to other small domains. The meteorological controls are examined for the study region, including simple and multiple regressions of meteorological variables. Taking many meteorological parameters into account, the correlation coefficients generally show a slight improvement. Several individual cases were also examined in this study, and the isotopic characteristics during one event or continuous days were used to understand the environmental controls on precipitation isotopes. Acknowledgments This work was supported by the National Natural Science Foundation of China [grant number 41461003]; the Scientific Research Program in University in Gansu Province [grant number 2016B-019]; and the Foundation for Young Teachers of Northwest Normal University [grant number NWNU-LKQN-15-8]. The authors greatly thank all the weather observers from local meteorological bureaus and colleagues in the Northwest Normal University for their help in sampling and lab analysis. References Araguás-Araguás, L., Froehlich, K., Rozanski, K., 1998. Stable isotope composition of precipitation over southeast Asia. J. Geophys. Res. 103:28721–28742. http://dx.doi.org/ 10.1029/98JD02582. Argiriou, A.A., Lykoudis, S., 2006. Isotopic composition of precipitation in Greece. J. Hydrol. 327:486–495. http://dx.doi.org/10.1016/j.jhydrol.2005.11.053. Barras, V., Simmonds, I., 2008. Synoptic controls upon δ18O in southern Tasmanian precipitation. Geophys. Res. Lett. 35, L02707. http://dx.doi.org/10.1029/2007GL031835. Bowen, G.J., 2010. Isoscapes: spatial pattern in isotopic biogeochemistry. Ann. Rev. Earth Planetary Sci. 38:161–187. http://dx.doi.org/10.1146/annurev-earth-040809152429. Bowen, G.J., 2017. The Online Isotopes in Precipitation Calculator, Version 3.0. http:// www.waterisotopes.org. Bowen, G.J., Revenaugh, J., 2003. Interpolating the isotopic composition of modern meteoric precipitation. Water Resour. Res. 39, 1299 (http://dx.doi.org/10.1029/ 2003WR002086). Bowen, G.J., Wilkinson, B., 2002. Spatial distribution of δ18O in meteoric precipitation. Geology 30:315–318. http://dx.doi.org/10.1130/0091-7613(2002)030b0315: SDOOIMN2.0.CO;2. Chen, F., Zhang, M., Ma, Q., Wang, S., Li, X., Zhu, X., 2015a. Stable isotopic characteristics of precipitation in Lanzhou city and its surrounding areas, northwestern China. Environ. Earth Sci. 73:4671–4680. http://dx.doi.org/10.1007/s12665-014-3776-6. Chen, F., Zhang, M., Wang, S., Ma, Q., Zhu, X., Dong, L., 2015b. Relationship between subcloud secondary evaporation and stable isotopes in precipitation of Lanzhou and surrounding area. Quat. Int. 380:68–74. http://dx.doi.org/10.1016/j.quaint.2014.12.051. CMA (China Meteorological Administration), 2007. Specifications for Surface Meteorological Observation. China Meteorological Press, Beijing (in Chinese). Craig, H., 1961. Isotopic variations in meteoric waters. Science 133:1702–1703. http:// dx.doi.org/10.1126/science.133.3465.1702. Crawford, J., Hughes, C.E., Stephen, D.P., 2013. Is the isotopic composition of event based precipitation driven by moisture source or synoptic scale weather in the Sydney Basin, Australia? J. Hydrol. 507:213–226. http://dx.doi.org/10.1016/ j.jhydrol.2013.10.031. Crawford, J., Hollins, S.E., Meredith, K.T., Hughes, C.E., 2017. Precipitation stable isotope variability and sub-cloud evaporation processes in a semi-arid region. Hydrol. Process. 31:20–34. http://dx.doi.org/10.1002/hyp.10885. Dansgaard, W., 1964. Stable isotopes in precipitation. Tellus 16:437–468. http:// dx.doi.org/10.3402/tellusa.v16i4.8993. Froehlich, K., Kralik, M., Papesch, W., Rank, D., Scheifinger, H., Stichler, W., 2008. Deuterium excess in precipitation of Alpine regions-moisture recycling. Isot. Environ. Health 44:61–70. http://dx.doi.org/10.1080/10256010801887208.
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