Asian Journal of atmospheric environment
Asian Journal of atmospheric environment Asian Journal of atmospheric environment
Asian Journal of atmospheric environment
  Aims and Scope Type of Manuscripts Best Practices Contact Information  
  Editor-in-Cheif Associate Editors Editorial Advisory Board  

Current Issue

Asian Journal of Atmospheric Environment - Vol. 15 , No. 2

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 15, No. 2
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Jun 2021
Received 17 Jan 2021 Revised 11 Apr 2021 Accepted 11 May 2021

Spatial Mapping of Atmospheric Precipitation Isotopes in Syria
Zuhair Kattan*
Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus, Syria

Correspondence to : *Tel: +963 11 6111926/7 E-mail:

Copyright © 2021 by Asian Association for Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Stable isotope ratios (δ2H and δ18O), tritium (3H). and deuterium excess (d-excess) values of atmospheric precipitation (P) at 16 stations were determined for mapping the spatial variation of oxygen and hydrogen P isotopes in Syria. The major geographical parameters (longitude east, LE; latitude north, LN; altitude, H; and inland distance from the coast, DFC) were used to find out the best fitting models for the spatial mapping of atmospheric P isotopes in Syria. The highest correlation coefficients (r>0.73) were found for the relationships δ18O-H and δ2H-H. The impacts of LE and LN factors were rather moderate (0.3<r<0.6). However, a high correlation coefficient (r>0.7) was found for the relationship d-excess-LN. The increase of d-excess value from 23‰ to 24‰ in southern Syria is due to the Mediterranean Sea (MS) moisture, likely as a high percentage (>65%) of prevailed winds usually comes over the MS. The concentrations of 3H in P samples (4-14 TU) during the period 1989-1993 were higher than those (3-10 TU) for the period 2004-2006, indicating thus a return back toward the levels of typical 3H production in nature. The high correlation coefficients (r>0.59) that were found for the relationships 3H-DFC and 3H-LE, suggest a continuous exchange between the low tritium moisture from the MS and the higher tritium moisture from the inland areas. Produced gridded isotopic values are quite satisfactory for covering certain areas in Syria and the neighboring Arab countries.

Keywords: Precipitation, Environmental isotopes, Modeling, Spatial variation, Syria


Determination of the environmental isotopes (2H, 3H and 18O) in atmospheric precipitation (P) has been proven to be a useful tool in many hydrological, hydrogeological, meteorological and climate change studies (Criss, 1999; Kendall and McDonnell, 1998; Clark and Fritz, 1997; Fritz and Fontes, 1980). The importance of such isotopes in the above mentioned domains is mainly due to the fact that all these atoms enter in the formulation of the water molecule, and thus they can remarkably respond to all modifications occurring within the water cycle, but most frequently unaffected by geological processes (Mook, 2001; Gat, 1980). The stable isotopes (2H and 18O) are both conservative constituents, thus they are classified as the most ideal natural tracers, especially for assessing the hydrological processes in relation to condensation, evaporation and mixing between the different water systems (Liu et al., 2014; Aggarwal et al., 2005; Cappa et al., 2003; Mook, 2001; Gat, 1996).

Knowledge on the distribution patterns of these isotopes in rainwater (RW) constitutes an important approach for comprehensive understanding of many hydrological processes, such as tracing the origin of vapor in the atmosphere (Bowen et al., 2012; Aizen et al., 2005; Aggarwal et al., 2004; Araguás-Araguás et al., 2000; Zahn et al., 1998), investigating the water flow dynamics (Sturm et al., 2005), identifying the intensity of evaporation and evapo-transpiration fluxes from rivers and lakes (Froehlich et al., 2005; Lambs, 2004), and estimating the altitude of groundwater recharge zones (Liu et al., 2014; Windhorst et al., 2013; Kumar et al., 2010; Harrington et al., 2002; Gonfiantini et al., 2001; Kattan, 1997a).

Tritium (3H) as the unique radioactive isotope of hydrogen, incorporated into the natural water cycle either naturally or artificially (Michel et al., 2015; Mook, 2001; Eriksson, 1983), is also a powerful radiotracer. This isotope, which has a half-life of 12.32 years (Lucas and Unterweger, 2000), was largely and successively applied in many hydrological studies, mostly as a dating tool for solving problems related to determination of the transit time of young surface and groundwaters (Fontes, 1983; Yurtsever, 1983), or tracing the water flow paths in small catchments (Herrmann et al., 1990; Maloszewski et al., 1983).

Based on the importance of such environmental isotopes in the diverse hydrological domains, a distinct international monitoring network, the so-called: “Global Network for Isotopes in Precipitation (GNIP)” was initiated in the early 1960s through a bilateral cooperation between the International Atomic Energy Agency (IAEA) and the World Meteorological Organization (IAEA-WMO, 2006). Since then a large number of studies all over the world were started, mostly for analyzing the temporal and spatial variation trends of P isotopes either on Global scales (Terzer et al., 2013; Bowen et al., 2005; Dutton et al., 2005; Schmidt et al., 2005; Bowen and Revenaugh, 2003; Bowen and Wilkinson, 2002; Rozanski et al., 1993; Dansgaard, 1964), or on smaller regional scales (Lykoudis et al., 2010; Lykoudis and Argiriou, 2007; Dutton et al., 2005; Sturm et al., 2005; Longinelli and Selmo, 2003).

Analysis of the spatial variability patterns of the stable isotopes in meteoric water globally has shown that the changes in the isotopic composition are usually because of fractionation of oxygen and hydrogen isotopes between liquid and vapor phases, likely as humid air masses move from one place to another (Bowen et al., 2012; Aggarwal et al., 2004; Kendall and Coplen, 2001; Araguás-Araguás et al., 2000; Rozanski et al., 1993).

Information on the variation patterns of P isotopes in the eastern Mediterranean areas is mostly referred to the datasets of the Bet-Dagan station, disposing the longest isotopic records in this region (IAEA-WMO, 2006).

Although a number of previous isotopic studies were devoted to characterizing the environmental P isotopes in Syria (Al-Charideh and Abou Zakhem, 2010; Abou Zakhem and Hafez, 2007; Kattan, 1997b) and in some of the neighboring Arab countries (Bajjali, 2012; Saad et al., 2005), the available data on P isotopes in this region is regrettably still limited, likely because of the short monitoring periods of these investigations. However, Kattan (2019) recently characterized the factors that control the stable isotope compositions of P in Syria.

In this paper the objectives are: (1) to explore the impact of the key geographical parameters, such as the longitude east (LE, degrees), latitude north (LN, degrees), altitude (H, m above sea level (a.s.l)), and inland distance from the coast (DFC, km), on the induced spatial changes in the isotopic composition of P in Syria; and (2) to provide further insights on the spatial simulation of P isotopes in Syria.

Constituently, this work will promote the better understanding of the role of the geographical factors that can affect the spatial variations of P isotopes in Syria and certain areas in the neighboring eastern Mediterranean countries, such as Lebanon and northern Jordan. This study, which may help to produce new theoretical isotope knowledge that could be considered as a valid basis for covering certain areas in this region that lack similar determinations, is definitely important to apply properly these natural isotopes in the diverse hydrological, hydrogeological and climate change studies in Syria.


Syria is located at the eastern edges of the MS. From a topographical point of view, this country is dominated by the following structures: the coastal plain (width <10 km) and the coastal mountains (H up to 1575 m a.s.l), expanding in parallel to the MS; the Mt Hermon (2814 m a.s.l) and Mt Anti-Lebanon (2466 m a.s.l), both located on the border with Lebanon; the interior plains and interior mountain series of the southern and northern Palmyrides (1308 m a.s.l), situated in central Syria; the Mt Al-Arab (1790 m a.s.l), located in southern Syria; and the Syrian desert region, extending near the borders with Iraq and Jordan (Fig. 1a).

Fig. 1. 
Location maps of Syria showing major topographical structures (a) and distribution of average annual P-amounts and locations of P sampling (b).

Climatically, Syria is particularly influenced by a Mediterranean type of climate, with mild wet winters, and long, hot and mostly dry summers. The air temperature (T) in the coastal areas usually ranges in winter between -3°C and 18°C, while in summer it is frequently higher than 22°C. The variation in air T value is generally small, likely because of the small size of the country. The annual average of air T value in the farther inland areas is usually >18°C, with a quite hot desert climate in south-eastern Syria (

The highest annual P-amounts (>1000 mm) mostly occur over the coastal mountains. The rainfalls over the interior mountains are rather high (400-800 mm), while comparatively lower than 200 mm around the Damascus region. In the farther inland desert areas of south-eastern Syria, the annual P-amounts are usually in the range of 25-125 mm (Fig. 1b). The rainy season in Syria is rather short (from October to May), with an average number of 35-92 for the frequent rainy days in this country (

The relative air humidity (RH) value in the coastal areas in winter is mostly around 60-70%, while it is frequently higher than 70-80% in summer. In the inland areas, the RH is also high in winter (60-80%), but very much lower (20-50%) in summer (

Recent analysis of the backward trajectory tracks of air masses producing rains in Syria revealed that the genesis of most rainy events were from the following sources: the Atlantic Ocean, via western Europe and the MS; the Northern Pole, via eastern Europe; the Siberian plateau, via Turkey and eastern Europe; North Africa, via the MS; and the Arabian Sea, via the Arabian Peninsula and the Red Sea (Kattan, 2019).

Determination of wind directions in the Damascus region during the period (1989-2006) showed that ≈65% of the prevailed winds were from the south (S), the south-west (SW), and the west (W) directions. Prevailed winds from each of the remaining other directions were lower than 10% (

3. 1 Rainfall Sampling

A total of 290 rainfall samples were collected from a network covering 16 stations, mainly distributed in western Syria (Fig. 1b). Table 1 summarizes the number (n) of RW samples collected from each site, the geographical coordinates of the sampling stations (LE, LN, H and DFC), and the average air T, P-amount, and RH values measured at each station during the period 1989-2006. Rainfall sampling was made on a monthly basis (monthly composite), with systematic collection of RW samples twice a day (8 AM and 8 PM), by using suitable plastic containers with catch-diameters of 30-80 cm, according to the P-amount falling over the considered station. The daily collected water from each station was then moved into a tightly-sealed container, large enough to store the cumulated amounts of monthly P. After returning back from the field to the laboratory, all collected samples were instantly stored in a refrigerated room (T below 5°C) until the time of analysis. More details on the sampling procedures were given by Kattan (2019).

Table 1. 
Summary data on the geographical coordinates (LE and LN), altitude (H), distance from the coast (DFC), average values of the meteorological parameter (P, T and RH), number (n) of RW samples, together with the average and amount-weighted mean (bold values)* δ18O, δ2H, d-excess and 3H values in the RW samples collected from the different stations during the period 1989-2006.
Station n LEDegr. LN Degr. H(m) DFC(km) P(mm) T(ºC) RH(%) δ18O(‰) δ2H(‰) d-excess(‰) 3H(TU)
Aleppo 10 37.0 36.1 410 110 35.3±24.3 7.9±5.6 70.9±11.6 -5.8±3.4 (-7.4±0.3) -32±20 (-40±2.4) 14±9 (18.9±0.8) 8.1±5.2 (7.4±0.3)
Al-Kounietra 20 35.9 33.2 930 60 89.0±72.2 9.0±3.3 73.9±12.4 -5.9±2.8 (-7.0±0.1) -28±17 (-33±1.1) 19±8 (22.7±1.2) 4.5±1.9 (4.2±0.2)
Al-Nabk 16 36.8 34.1 1290 100 13.6±8.6 10.2±6.2 63.8±9.1 -5.5±4.9 (-6.5±0.5) -31±31 (-36±2.5) 13±13 (16.7±0.3) 5.5±4.9 (4.4±0.3)
Al-Raqqa 5 39.1 35.9 250 290 27.2±18.5 9.3±7.2 66.6±13.0 -5.1±3.3 (-6.4±0.5) -26±16 (-33±4.2) 15±11 (19.0±1.5) 7.3±5.4 (6.3±0.5)
Al-Suwieda 14 36.7 32.8 1020 140 68.9±51.0 8.0±3.3 70.3±10.5 -6.3±2.6 (-7.3±0.2) -29±16 (-34±2.1) 21±7 (23.7±1.1) 7.0±2.3 (6.8±0.4)
Bab-Janeh 5 36.3 33.5 1050 30 92.8±77.8 10.9±4.4 66.2±6.7 -7.3±2.0 (-7.8±0.1) -38±13 (-40±0.9) 20±4 (22.9±1.3) 5.7±1.1 (5.5±0.2)
Bloudan 31 36.1 33.8 1540 55 95.8±81.9 7.8±6.1 68.4±14.3 -7.8±2.8 (-8.8±0.1) -42±21 (-48±0.9) 20±5 (22.3±0.9) 5.3±1.3 (4.7±0.2)
Damascus 30 36.1 33.4 675 80 31.1±21.9 10.9±4.6 59.9±15.5 -6.3±3.7 (-7.3±0.3) -35±25 (-39±2.6) 15±9 (19.2±0.8) 5.9±3.5 (5.5±0.3)
Arneh 12 35.9 33.4 1430 50 147.5±127.6 5.0±5.5 78.8±10.1 -6.9±3.3 (-8.6±0.2) -39±19 (-47±1.2) 16±9 (21.9±1.0) 6.8±3.8 (5.8±0.3)
Homs 29 36.7 34.8 490 70 66.9±45.9 9.7±4.1 70.5±11.7 -5.6±2.3 (-6.3±0.1) -28±14 (-31±0.7) 17±7 (19.5±1.3) 5.8±2.4 (4.9±0.3)
Idleb 20 36.9 36.3 450 65 68.7±47.7 11.1±4.6 71.2±9.6 -5.5±2.6 (-6.6±0.2) -30±16 (-36±3.6) 14±6 (16.8±1.2) 6.0±2.6 (5.6±0.4)
Izraa, 14 36.2 32.9 580 105 54.6±53.1 8.5±3.5 73.8±8.9 -6.6±2.0 (-7.7±0.2) -32±13 (-38±0.3) 21±6 (23.4±1.3) 6.8±1.9 (6.2±0.3)
Jarablous 24 38.0 36.8 350 160 42.9±26.4 9.6±4.8 68.8±10.0 -5.7±2.7 (-6.4±0.5) -31±17 (-35±1.6) 15±8 (17.0±1.0) 6.7±3.6 (6.0±0.4)
Palmyra 14 38.3 34.6 400 210 14.7±9.4 9.2±4.6 60.6±12.0 -5.3±4.0 (-5.8±0.6) -28±27 (-29±4.8) 14±5 (17.8±1.9) 9.6±3.4 (9.1±0.9)
Qatana 15 36.2 33.4 890 75 39.3±35.4 10.8±4.3 65.8±12.2 -6.5±2.6 (-7.6±0.1) -35±17 (-41±1.1) 16±7 (19.8±1.2) 4.9±2.1 (4.6±0.3)
Tartous 31 35.9 34.9 5 0 122.2±85.8 14.3±2.8 65.4±5.0 -4.9±2.1 (-5.8±0.2) -23±13 (-28±1.2) 16±5 (18.6±1.1) 4.8±1.5 (4.3±0.3)
All stations 290 -6.±3 (-7.2±0.2) -32±20 (-37±1.2) 16±5 (20.6±1.3) 6.3±1.5 (5.7±0.4)
*Errors of the amount-weighted mean values were calculated according to Gatzand Smith (1995).

3. 2 Isotopic Analyses

At the end of each month in the rainy season (Nov-May) two rinsed bottles (polyethylene or glass) were filled for conducting the isotopic determinations. A first small bottle of 50 mL was filled for the determination of δ18O and δ2H, while the second bottle (one liter size) was filled for tritium (3H) determination. The subsequent analyses of δ18O and δ2H in the RW samples were performed by using a Finnigan Mat (Delta E or Delta Plus) mass spectrometer. Measurement accuracy for δ2H and δ18O are better than ±0.1‰ and ±1.0‰, respectively. The subsequent analyses of tritium in the RW samples were performed, after electrolytic enrichment of the water samples, either by using a Packard-3253 liquid scintillation counter or by an ultra low level liquid scintillation counter (Quantulus 1220). Detection limit for 3H measurements in both instruments is lower than 1 TU (1 TU=1 3H atom per 10181H atoms). More details on the analytical procedures of these isotopes in water samples were given by Kattan (2006 and 2019).

Analytical results of the stable isotope values are commonly expressed in δ notation in terms per mil (‰), defined as:


where R is the isotopic ratio (2H/1H or 18O/16O), and the indices sam and std denote the sample and the standard, respectively.

The international standard largely used for the calibration of 2H and 18O isotopes in RW samples is called “Vienna Standard Mean Ocean Water (VSMOW)”, precisely prepared and adopted by the IAEA for this purpose (Coplen, 1996; Gonfiantini, 1978). Specific requirements and quality assurance procedures according to the ISO/IEC 17025 standard are strictly applied in all the labs of the Atomic Energy Commission of Syria (AECS) in order to achieve the production of competent analysis results. Verification of the isotopic analysis results are periodically insured through the participation in the different analysis comparison programs organized either by the IAEA or by other regional laboratories.

3. 3 Statistical Analysis and Calculations

Descriptive statistics (average and standard deviation) for the entire dataset, together with those for the RW samples collected from each station were calculated by using the softwares Excel 2007 and Statview 5. The amount-weighted mean value (Cw), that takes into consideration the effect of rainfall intensity during any monitoring period, was calculated by using the following formula (Yurtsever and Gat, 1981):


where Pi is the monthly P-amount and Ci is the isotopic composition of RW sample for the month i.

The deuterium excess value (d-excess or d) in each RW sample was calculated by using the formula, defined by Dansgaard (1964) as:

d-excess=δ2H-8 δ18O(3) 

As the accurate modeling of the spatial distribution of P isotopes requires at least a gridded network of about 10′×10′ unit scale (Lykoudis and Argiriou, 2007), the map sheet of Syria and some parts of the neighboring countries was divided into 4590 grid-units. The software Arc-GIS-9 was first used to generate the values of the geographical coordinates (LE, LN and H) for each of the grid-units, and then to produce the simulated maps for the spatial distributions of P stable isotopes (δ18O and δ2H), tritium and d-excess values across Syria.

4. 1 Stable Isotopes in Precipitation

Table 1 illustrates the average stable isotope (δ18O and δ2H) ratios and d-excess values, compared to the amount-weighted mean values calculated for the RW samples collected at all the stations. The stable isotope ratios in the RW samples were in the range from -14.6‰ to +7.4‰ and from -101‰ to +35‰, with average values of -6 ±3‰ and -32±20‰ for δ18O and δ2H, respectively (Kattan, 2019). The d-excess value for these dataset varied from ≤0‰ in the highly evaporated samples to +30‰ in the RW samples collected from southern Syria. The amount-weighted mean δ18O and δ2H values were always lower than the average values at all the stations, likely as heavy stable isotopes are almost more concentrated in the evaporated RW samples (P-amounts<5 mm). The stable isotope ratios in the RW samples at the Arneh and Bloudan stations (H>1500 m a.s.l) were markedly the most depleted (δ18O<-6.5‰ and δ2H<-35‰), while those at the Tartous, Homs and Palmyra stations (H<400 m a.s.l) were distinctly higher (δ18O>-5.7‰ and δ2H>-32‰), mostly because of the evaporation of raindrops during their falling on the earth surface (Managave et al., 2016).

The relationship δ2H-δ18O for the entire RW samples (Fig. 2a) clearly shows that most of the sample points are scattered between the Global Meteoric Water Line (GMWL), defined by Craig (1961) as: δ2H=8 δ18O+10, and the Mediterranean Meteoric Water Line (MM WL), given by Nir (1967), as: δ2H=8 δ18O+22. However, a few sample points, with depleted stable isotope ratios, are regrouped close to the MMWL. The second part of the samples, with enriched isotopic ratios (δ18O>-4‰ and δ2H>-20‰), are located very close to the GMWL. The equation of the least squares regression line fitting all the sample points is given as:

δ2H=6.5±0.1δ18O+5.7±0.7with R2=0.92&n=290(4) 

Fig. 2. 
Relationships between monthly δ2H and δ18O values for all the RW samples collected in Syria (a) and between amount-weighted mean δ2H and δ18O values for all the stations (b). Black dark circles denote RW samples or station points with their error-bars. Black lines are the linear fittings for the RW samples or the station points. Red, olive and blue lines are arbitrary lines, with a slope of 8 and different Y-intercept values (17‰, 20‰ and 23‰, respectively), schematically plotted for approximate matching with the different station points.

Projection of the amount-weighted mean δ2H and δ18O values of all the stations in the diagram δ2H-δ18O permits to classify the monitoring stations in three different groups (Fig. 2b): (1) a group of stations (Bloudan, Arneh, Bab-Janeh, Al-Suwieda, Izraa and Al-Kounietra) fitting a line, with δ2H=8 δ18O+23; (2) a group of stations (Qatana, Aleppo, Damascus, Homs, Al-Raqqa and Tartous) matching a line, with δ2H=8 δ18O+20; and (3) a group of stations (Al-Nabk, Idleb, Jarblous and Palmyra) fitting a line, with δ2H=8 δ18O+17.

This distribution reflects not only the impacts of the T effect (Kattan, 2019; Rozanski et al., 1993) and secondary evaporation during the rain falling on the land surface (Managave et al., 2016; Froehlich et al., 2005; Araguás-Araguás et al., 2000), but also the exchange with the enriched moisture (d>22‰) produced by evaporation from the MS (Gat et al., 2003).

4. 2 Tritium in Precipitation

The concentrations of 3H in the RW samples during the period 1989-1993 were in the range of 4-14 TU, while during the period 2004-2006 they were in the range of 3-10 TU. This change, which means that the concentration of 3H is annually decreasing by ≈5-6%, clearly suggests a return back toward the natural cosmogenic production levels in the upper atmosphere before starting the nuclear weapons testing (Michel et al., 2015). Also, it can be observed that the amount-weighted mean 3H values in the RW samples at all the stations were lower than the average 3H values, with a difference of 0.2-1 TU (Table 1). The concentrations of 3H in the RW samples at the coastal station (Tartous) and Al-Kounietra and Qatana stations, located nearly close to the MS, were remarkably <5 TU, while at the Palmyra station, located at a farther distance from the MS coast, were higher than 9 TU. The amount-weighted mean 3H values in the RW samples at the Al-Suwieda and Izraa stations were relatively higher than 5 TU, most probably because of the short monitoring period (1989-1993). The measured 3H concentrations in the RW samples at both stations during that period (8-9 TU) were clearly very close to those reported for the Damascus and Aleppo stations that have nearly a similar DFC value (Kattan, 1997b).

The temporal variation of average monthly 3H concentrations in the RW samples during the rainy season (Fig. 3), clearly shows a progressive increase from ≈3 TU in November up to ≈12 TU during April to May. This trend, which indicates that more than 2/3 of the total 3H load was occurred during the spring season, clearly reflects the tropospheric injection, or the so-called “Spring Leak” phenomenon (Harms et al., 2016; Rozanski et al., 1991).

Fig. 3. 
Temporal variation of average monthly 3H value in the RW samples during the rainy season extended between November and May.

4. 3 Meteorological Controls on Stable Isotopes

Variations of the stable isotope ratios in meteoric water are known to be highly influenced by the ambient local weather conditions, specifically the common meteorological (T, P-amount and RH) parameters (Araguás-Araguás et al., 1998; Rozanski et al., 1993; Dansgaard, 1964). Pressure of the water vapor in the atmosphere, air depressions, weather cold fronts and backward trajectory tracks of humid air masses are also decisive factors that play certain roles in the formulation of the isotopic signature of meteoric water (Li et al., 2015; Muller et al., 2015; Baldini et al., 2010; IAEA, 2005).

Recently it was shown that the T effect was the major factor that strongly controls the temporal variation of P stable isotopes in Syria (Kattan, 2019). This effect was remarkably reflected by enrichments of ≈0.34‰/°C and ≈1.9‰/°C for δ18O and δ2H, respectively, accompanied by a decrease of ≈-0.8‰/°C for the d-excess value with T increasing. Fig. 4a shows the relationship between the monthly δ18O values and the average monthly T values for all the RW samples collected in Syria.

Fig. 4. 
Relationships between monthly δ18O and T values (a), monthly δ2H and P-amount values (b) and monthly d-excess and RH values (c) for all the RW samples in Syria. Black lines denote the linear fittings and their equations.

Table 2. 
Regression parameters of the linear straight lines and the second-order polynomial curves calculated for the different relationships between the amount weighted-mean isotopic values and the geographical site parameters (LE, LN, DFC and H).
Relationship Linear function Y=bX+c Second-order polynomial function Y=aX2+bX+c
Slope (b) Y–intercept (c) r P-value a b c r P-value
18O-LE 0.5±0.2 -25.4±7.9 0.53 0.037 -0.12±0.3 9.67±18.4 -196.36±342.8 0.54 0.108
18O-LN 0.36±0.2 -19.3±5.3 0.53 0.036 -0.19±0.2 13.43±10.5 245.93±182 0.60 0.058
18O-DFC 0.004±0.003 -7.5±0.4 0.36 0.174 2.36E-5±4E-5 -0.002±0.01 -7.20±0.6 0.40 0.328
18O-H -0.002±4E-4 -5.96±0.3 0.76 0.001 -5.67E-9±8E-7 -0.002±0.001 -5.97±0.5 0.76 0.004
2H-LE 2.43±1.5 -125.9±54.3 0.40 0.123 -0.25±1.7 21.14±127 -474.72±236.9 0.40 0.315
2H-LN 1.31±1.1 -81.6±37.6 0.30 0.253 -0.89±1.1 63.25±77.4 -1155.72±1341.5 0.37 0.390
2H-DFC 0.025±0.02 -39.2±2.5 0.31 0.241 1.64E-4±2E-4 -0.022±0.07 -36.95±4 0.36 0.398
2H-H -0.009±0.002 -29.9±2 0.73 <0.002 -2.7E-6±6E-6 -0.005±0.01 -31.18±3.3 0.73 0.007
d-LE -1.30±0.6 67.7±22.1 0.50 0.048 0.82±0.7 -62.26±48.8 1204.43±909.9 0.58 0.073
d–LN -1.39±0.3 67.6±11.2 0.75 <0.001 0.53±0.3 -38.10±21.30 704.17±369.4 0.80 0.001
d-DFC -0.01±0.01 20.9±1.1 0.26 0.337 -6.32E-7±1E-4 -0.01±0.03 20.88±1.8 0.26 0.641
d-H 0.003±0.001 18.0±1.1 0.50 0.051 -2.02E-6±3E-6 0.006±0.005 17.09±1.8 0.52 0.129
3H-LE 0.82±0.3 -24.3±10.9 0.59 0.015 -0.36±0.3 27.99±24.3 -530.85±452.4 0.64 0.032
3H–LN 0.22±0.3 -1.93±8.8 0.23 0.397 0.11±0.3 -7.72±18.3 135.85±317.3 0.26 0.645
3H-DFC 0.011±0.004 4.57±0.5 0.63 0.009 -5.02E-5±4E-5 0.026±0.012 3.88±0.7 0.68 0.018
3H-H -0.001±0.001 6.34±0.6 0.30 0.262 -1.31E-6±1.8E-6 0.001±0.003 5.747±1 0.36 0.417
Boldface r and P-values indicate significant relationships.

The effect of P-amount is also an important factor. This effect, which highly depends on the local weather conditions during the time of rain falling, was reflected by a significant decline in the stable isotope ratios (Fig. 4b), accompanied by a remarkable increase in the d-excess value with the increasing of monthly P-amount. However, it can be observed that the gradient in d-excess value in the case of the mountainous stations located close to the MS was generally small, while it was remarkably much higher in the remaining stations.

The RH effect was reflected by a decrease in the stable isotope ratios, accompanied by a comparative enrichment in the d-excess value with RH increasing. Fig. 4c shows that the low d-excess values were usually associated with low RH values (≈40%), whereas the higher d-excess values were typically coupled with high RH values (>80%).

4. 4 Geographical Controls on Precipitation Isotopes

The common geographical factors that have strong controls on the spatial variability of stable isotopes in meteoric water are the altitude, latitude and longitude coordinates (Wang et al., 2016; Terzer et al., 2013; Lykoudis et al., 2010; Bowen et al., 2005). The DFC factor may also affect the spatial variation of P isotopes, likely the concentration of 3H, that tends to increase with the increasing of the inland distance (Harms et al., 2016; Michel et al., 2015; Kattan, 1997b; Michel, 1989; Gat and Carmi, 1970). Table 2 summarizes the regression parameters of the fitted straight lines and second-order polynomial curves for the different relationships between the amount weighted-mean δ18O, δ2H, d-excess and 3H values in the RW samples at all the stations in function with the corresponding values of LE, LN, H and DFC parameters. Accordingly, it can be observed that the correlation coefficients for the fitted second-order polynomial curves are almost similar to those of the fitted straight lines. The high correlation coefficients (r>0.73), that were found for the relationships δ18O-H and δ2H-H, strongly indicate that the so-called “altitude effect” was the most important factor controlling the spatial variability of both stable isotopes. The negative slope values for both relationships also show that this effect was shown up by progressive depletion gradients of -0.002±4E-4‰ and -0.009±0.002‰ for δ18O and δ2H, respectively (Fig. 5a).

Fig. 5. 
Relationships between amount-weighted mean δ18O ratios and LN values (a), amount-weighted mean δ2H and H values (b), amount-weighted mean d-excess and LN values (c), and amount-weighted mean 3H and DFC values (d) for all the stations in Syria. Black lines and red curves denote the linear and the second order polynomial fittings and their equations, respectively.

Although the altitude factor was with strong effect on the spatial variation of P stable isotopes, its impact on the spatial variation of the d-excess parameter was rather moderate (r≈0.5-0.52). The correlation coefficients for the relationships between the stable isotope ratios (δ18O and δ2H) and the geographical coordinates (LE and LN) were found also to be rather moderate (0.3<r<0.6), meaning that these factors have minor controls on the spatial variation of P stable isotopes, most likely because of the small geographical extent of the country (Fig. 5b). Similarly, it was observed that the correlation coefficients for the relationships (δ18O-DFC and δ2H-DFC) were also low (0.3<r<0.4). The reason is because the MS coast is typically extending in parallel to the global longitudinal lines (LE), and thus the impact of the DFC factor must be rather similar to that of the LE factor. However, it can be observed that the correlation coefficients for the relationship d-excess-LN for the linear and the second-order polynomial functions (Fig. 5c) were remarkably high (0.7<r<0.8 and P-value<0.001). The reason that the correlation coefficients for this relationship were remarkably high, and with a negative slope value (-1.39±0.3), is clearly due to the extensive exchange with the enriched MS moisture (d≈22‰), likely as a high percentage of prevalent winds (>50%) usually coming from the directions of SW and S.

A low correlation coefficient (r≈0.26) was found for the relationship d-excess-DFC, suggesting also a weak impact of the DFC factor on the spatial variability of d-excess value.

In the case of 3H spatial variation, the best correlation coefficient (r>0.63) was found for the relationship 3H-DFC (Fig. 5c). This later coefficient, which was rather close to the coefficient (r>0.59) calculated for the relationship 3H-LE, clearly indicates that both factors have somewhat similar controls on the spatial variability of 3H in Syria. The correlation coefficients for the relationships 3H-LN and 3H-H were both low (r<0.3), indicating thus weak influences of the H and LN factors on the spatial variation of this radio isotope.

4. 5 Modeling the Spatial Isotope Variability

Modeling the spatial variation of P isotopes is a smart alternative tool for mapping the approximate isotopic composition of P at any location on the Earth Globe (Bowen et al., 2005). Hence, extensive efforts were made to fit between the P isotopic data with the key geographical parameters (LE, LN and H), either on global scales (Bowen et al., 2005; Bowen and Revenaugh, 2003; Bowen and Wilkinson, 2002), or on regional scales (Lykoudis et al., 2010; Liu et al., 2010; Lykoudis and Argiriou, 2007; Longinelli and Selmo, 2003). However, some other works were focused on the development of certain algorithms and models that can determine, not only the average annual isotopic composition of P at a certain site, but also the seasonal, or even the daily isotopic values (Sturm et al., 2005).

In this study, attempts were made to simulate the spatial variation of P isotopes in Syria by using the amount weighted-mean δ18O, δ2H, 3H, and d-excess values in the RW samples collected at the different stations in this country, in conjunction with the corresponding key geographical parameter (LE, LN and H) values. The purpose behind such essays was to extract mathematically the appropriate empirical relationships that fit between the measured isotopic values with the corresponding geographical parameters. The final aim was to produce a high-resolution gridded isotopic data that can be used later for covering certain areas lacking similar isotopic data. Such simulated data can also be used to map the spatial distributions of P isotopes in this country, by using the traditional geographical information system (GIS), largely applied in this domain (Johnston et al., 2004).

By excluding the isotopic data of the Al-Nabk station, that has a special weather character strongly different from the other stations (Kattan, 2019), the best models (multi polynomial expressions) that were found to produce the finest gridded isotopic data for the above isotopic parameters in function with the allied geographical factors (LE, LN and H) are the following:

δ18O=0.168×LE-0.092×LN-0.002H-8.82with r=0.89&P-value=0.0005(5) 
δ2H=1.29×LE-2.0×LN-0.013×H-6.27with r=0.84&P-value=0.003(6) 
d-excess=0.084×LE-1.16×LN+0.001×H+56.05with r=0.85&P-value=0.002(7) 
H3=1.10×LE-0.33×LN-1.05E-4H-23.14with r=0.67&P-value=0.081(8) 

The correlation coefficients for the fitted polynomial models vary between r=0.67 in the case of 3H and r=0.89 in the case of δ18O. These coefficients are generally good, and thus they can safely be used to generate the isotopic data for the different gridded-units or cells for the selected sheet map of Syria.

Fig. 6 illustrates the relationships that were found between the calculated (simulated) and the observed (measured) amount weighted-mean δ18O, δ2H, 3H and d-excess values in P at the different Syrian stations. Accordingly, the above mentioned models were all used to generate the appropriate isotopic data for the gridded cells of the Syrian map, by assuming that the cell centre is representative for the simulated isotopic values.

Fig. 6. 
Relationships between modeled and measured δ18O (a), δ2H (b), d-excess (c), and 3H (d) values for15 stations in Syria.

The modeled spatial distributions for the stable isotopes (δ18O and δ2H) imply that their values change remarkably in accordance with the topographical features in the country (Figs. 7 and 8). This distribution highly reflects the significant role of the altitude factor on the spatial variability of both isotopes, with clearly depleted isotopic ratios (δ18O<-7.5‰ and δ2H<-45‰) in the high altitude areas (Mts Hermon, Anti-Lebanon and Al-Arab), and enriched isotopic ratios (δ18O>-5.5‰ and δ2H>-33‰) in the interior inland areas, most specifically in the low altitude areas in north-eastern Syria. However, it is noteworthy to observe that the produced map for the distribution of d-excess values in P in this country is rather of a particular spatial variation trend (Fig. 9), with a remarkable high d-excess value (d>20‰) in southern and south-western Syria (Mts Al-Arab, Hermon and Anti-Lebanon), and gradually lower d-excess value (d<20‰) in the eastern and north-eastern parts of Syria.

Fig. 7. 
Spatial distribution of simulated P δ18O values (‰, VSMOW) in Syria.

Fig. 8. 
Spatial distribution of simulated P δ2H values (‰, VSMOW) in Syria.

Fig. 9. 
Spatial distribution of simulated P d-excess values (‰, VSMOW) in Syria.

Although the correlation coefficient for the relationship between predicted and measured amount weighted-mean 3H values in P at the different stations (r=0.67) was lower than those found for the stable isotopes and d-excess parameters (r>0.84), the simulated map for the spatial variability of this radioisotope was also remarkable (Fig. 10). This map permits to observe a progressive build up or 3H enrichment with the increasing distance from the Syrian coast, from a value of about 5 TU along the MS coast to values higher than 9 TU in the farther inland areas in the eastern and north-eastern parts of the country. This distribution clearly explains the continuous mixing between the low tritium of the MS moisture and the higher tritium of the continental moisture (Michel et al., 2015; Mook, 2001).

Fig. 10 
Spatial distribution of simulated P 3H values (TU) in Syria.


This paper explores the impacts of the key geographical parameters (LE, LN, H and DFC) that supposed to have certain controls on the spatial variations of atmospheric P isotopes in Syria. The major conclusions that can be drawn from this particular study are the following:

• The stable isotope ratios in the RW samples at the high mountainous regions (H>1500 m a.s.l), such as the Mts Hermon, Anti-Lebanon and Al-Arab, were generally the most depleted, while those at the low elevation areas, such as the coastal station (Tartous) and the farther inland stations (Jarablous and Palmyra) were the most enriched, likely because of the secondary evaporation during rain falling. The isotopic composition of rainfalls at the stations of in-between elevations (Aleppo, Damascus, Qatana, Homs and Al-Raqqa), were clearly of intermediate values between the above mentioned two groups.

• The high correlation coefficients (r>0.73), that were found for the relationships (δ18O-H and δ2H-H), clearly indicate that the T effect, or indirectly the H factor, was the primary cause for the vertical spatial variation of both stable isotopes.

• The rather moderate correlation coefficient (r<0.52), that was found for the relationship d-excess-H, clearly suggests that the d-excess parameter was more dependent on the initial isotopic composition of the water vapor and kinetic isotope fractionation during secondary evaporation rather than on the T effect.

• The rainfalls over the mountainous stations (Bloudan, Arneh and Bab-Janeh) and southern Syria (Izraa, Al-Suwieda, and Al-Kounietra stations), where the altitude is rather high and the DFC parameter is generally short (<50 km), tend to have a high d-excess value very close to the value (22‰), mostly because of the strong exchange with the enriched moisture produced from the MS.

• The high percentage of prevalent winds (>65%), frequently comes from the W, SW and S directions, strongly support the interactions between the humid air masses passing over the MS and the enriched MS moisture. This also means that the impact of the MS moisture is progressively decreasing towards the inland plains and the northern areas, where the exchange with the continental moisture of a lower d-excess value (≈10‰) becomes more frequent. The RW at the Idleb and Al-Nabk stations, located in specific interior inland areas, tend to be more influenced by predominance exchange with the continental moisture.

• The impacts of LE and LN factors on the spatial variation of P stable isotopes were almost weak to moderate (0.3<r<0.6), revealing that both factors have minor controls on the spatial variability of these isotopes, likely because of the small geographical extent of this country. However, it was noteworthy to observe that the stable isotope ratios clearly increase with the increasing of LN values in an opposite trend to the usual spatial distribution on a Global scale. This trend strongly reflects the effect of mixing with the enriched moisture originated from the MS, likely as most of the rainy clouds that pass over the MS proceed farther northward towards the north-eastern plains.

• The measured concentration of 3H in the RW samples collected during the period 1989-1993 (4-14 TU) were higher than those (3-10 TU) observed during the period 2004-2006. This change means that the activity of this radioisotope is returning back toward the natural production levels before the starting of the nuclear weapon testing.

• The correlation coefficients for the relationships 3H-DFC and 3H-LE were rather similar (r>0.63 and r>0.59, respectively), but distinctly higher than those found for the relationships 3H-LN and 3H-H (r<0.3). Physically, this reflects the impact of the boundary of the eastern MS coast, which stands in parallel to the LE lines. This permits to conclude that the continuous mixing between the low tritium moisture from the MS with the higher tritium moisture produced in the inland areas was the primary reason for the spatial decline of 3H concentration toward the MS coast.

• Incorporation of other meteorological factors, such as RH, water vapor pressure and evaporation may help to improve the spatial mapping of atmospheric P isotopes in such a semi-arid to arid country.


The author would like to greatly thank Prof. I. Othman, Director General of the Atomic Energy Commission of Syria (AECS) for his encouragement and continuous support. Special thanks are also due to Prof. W. Rasoul-Agha for his constructive comments and kind suggestions to improve this paper. The International Atomic Energy Agency (IAEA) Organization, particularly Mr. L. Araguás-Araguás, are deeply acknowledged for the rendered assistance during the implementation of the coordinated research project (contract No. 14053). The author is also indebted to the technical staff of the Geology Department at AECS for their assistance in collection and analysis of precipitation samples and rendered services in drawing related GIS maps.

1. Abou Zakhem, B., Hafez, R. (2007) Chemical and isotopic composition of precipitations in Syria. AECS-G\RSS No. 729, Atomic Energy Commission of Syria, Damascus.
2. Aggarwal, P.K., Frohlich, K., Kulkarni, K.M., Gourcy, L.L. (2004) Stable isotope evidence for moisture sources in the Asian summer monsoon under present and past climate regimes. Geophysical Research Letter, 31, L08203. https://
3. Aggarwal, P.K., Gat, J.R., Froehlich, K.F.O. (2005) Isotopes in the water cycle. Past, Present and Future of a Developing Science. Springer, The Netherlands, Vienna.
4. Aizen, V.B., Aizen, E.M., Fujita, K., Nikitin, S., Kreutz, K., Takeuchi, N. (2005) Stable-isotope time series and precipitation origin from firn cores and snow samples, Altai glaciers, Siberia. Journal of Glaciology, 51(175), 637-654.
5. Al-Charideh, A.R., Abou Zakhem, B. (2010) Distribution of tritium and stable isotopes in precipitation in Syria. Hydrological Sciences Journal, 55(5), 1-13.
6. Araguás-Araguás, L., Froehlich, K., Rozanski, K. (1998) Stable isotope composition of precipitation over southeast Asia. Journal of Geophysical Research, 103(D22), 28721-28742.
7. Araguás-Araguás, L., Froehlich, K., Rozanski, K. (2000) Deuterium and oxygen-18 isotope composition of precipitation and atmospheric moisture. Hydrological Processes, 14(8), 1341-1355.
8. Bajjali, W. (2012) Spatial variability of environmental isotope and chemical content of precipitation in Jordan and evidence of slight change in climate. Applied Water Science, 2, 271-283.
9. Baldini, L.M., McDermott, F., Baldini, J.U., Fischer, M.J., Möllhoff, M. (2010) An investigation of the controls on Irish precipitation δ18O values on monthly and event time scales. Climate Dynamics, 35(6), 977-993.
10. Bowen, G.J., Kennedy, C.D., Henne, P.D., Zhang, T. (2012) Footprint of recycled water subsidies downwind of Lake Michigan. Ecosphere, 3(6), 53.
11. Bowen, G.J., Revenaugh, J. (2003) Interpolating the isotopic composition of modern meteoric precipitation. Water Resource Research, 39(10), 1299.
12. Bowen, G.J., Wassenaar, L.I., Hobson, K.A. (2005) Global application of stable hydrogen and oxygen isotopes to wildlife forensics. Oecologia, 143, 337-348.
13. Bowen, G.J., Wilkinson, B. (2002) Spatial distribution of delta O-18 in meteoric precipitation. Geology, 30(4), 315-318.
14. Cappa, C.D., Hendricks, M.B., De Paolo, D.J., Cohen, R.C. (2003) Isotopic fractionation of water during evaporation. Journal of Geophysical Research, 108(D16), 4522-4534.
15. Clark, I., Fritz, P. (1997) Environmental Isotopes in Hydrogeology. Lewis Publishers, Boca Raton, New York.
16. Coplen, T.B. (1996) New guidelines for reporting stable hydrogen, carbon, and oxygen isotope-ratio data. Geochimica et Cosmochimica Acta, 60, 3359-3360.
17. Craig, H. (1961) Isotopic variations in meteoric waters. Science, 133, 1702.
18. Criss, R.E. (1999) Principles of stable isotope distribution. Oxford University Press, New York.
19. Dansgaard, W. (1964) Stable isotopes in precipitation. Tellus, 16, 436-468.
20. Dutton, A., Wilkinson, B.H., Welker, J.M, Bowen, G.J., Lohmann, K.C. (2005) Spatial distribution and seasonal variation in 18O/16O of modern precipitation and river water across the conterminous United States. Hydrological Processes, 19, 4121-4146.
21. Eriksson, E. (1983) Stable isotopes and tritium in precipitation. In Guidebook on nuclear techniques in hydrology, Technical Report Series No. 91, IAEA, Vienna, pp. 19-33.
22. Fontes, J.Ch. (1983) Dating of groundwater. In Guidebook on nuclear techniques in hydrology, Technical Report Series No. 91, IAEA, Vienna, pp. 285-317.
23. Fritz, P., Fontes, J.Ch. (1980) Handbook of environmental isotope geochemistry 1, The terrestrial environment, Elsevier, Amsterdam.
24. Froehlich, K.F.O., Gonfiantini, R., Rozanski, K. (2005) Isotopes in lake studies: A historical perspective. In Isotopes in the water cycle, past, present and future of a developing science (Aggarwal, P.K., Gat, J.R, and Froehlich, K.F.O. Eds), Springer, The Netherlands, Vienna, pp. 139-150.
25. Gat, J.R. (1980) The isotopes of hydrogen and oxygen in precipitation. In Handbook of environmental isotope geochemistry 1, The terrestrial environment (Fritz, P. and Fontes, J.Ch. Eds), Elsevier, Amsterdam, pp. 21-47.
26. Gat, J.R. (1996) Oxygen and hydrogen isotopes in the hydrologic cycle. Annual Review of Earth and Planetary Sciences, 4, 225-262.
27. Gat, J.R., Carmi, I. (1970) Evolution of the isotopic composition of atmospheric waters in the Mediterranean Sea area. Journal of Geophysical Research, 75, 3039-3048.
28. Gat, J.R., Klein, B., Kushnir, Y., Roether, W., Wernli, H., Yam, R., Shemesh, A. (2003) Isotope composition of air moisture over the Mediterranean Sea: An index of the air-sea interaction pattern. Tellus, B55(5), 953-965.
29. Gatz. D.F., Smith, L. (1995) The standard error of a weighted mean concentration-II. Estimating confidence intervals. Atmospheric Environment, 29, 1195-1200.
30. Gonfiantini, R. (1978) Standard for stable isotope measurements in natural compounds. Nature (London), 271, 534-536.
31. Gonfiantini, R., Roche, M.A., Olivry, J.C., Fontes, J.Ch., Zuppi, G.M. (2001) The altitude effect on the isotopic composition of tropical rains. Chemical Geology, 181, 147-167.
32. Harms, P.A., Visser, A., Moran, J.E., Esser, B.K. (2016) Distribution of tritium in precipitation and surface water in California. Journal of Hydrology, 534, 63-72.
33. Harrington, G.A., Cook, P.G., Herczeg, A.L. (2002) Spatial and temporal variability of groundwater recharge in Central Australia: a tracer approach. Groundwater, 40(5), 518-528.
34. Herrmann, A., Finke, B., Schoniger, M., Maloszewski, P., Stichler, W. (1990) The environmental tracer approach as a tool for hydrological evaluation and regionalization of catchment systems. In Proceedings of the Ljublians Symposium, No.191, IAHS Publications, London, pp. 45-58,
35. IAEA (International Atomic Energy Agency) (2005) Isotopic composition of precipitation in the Mediterranean basin in relation to air circulation patterns and climate. TECDOC-1453, IAEA, Vienna.
36. IAEA-WMO (International Atomic Energy Agency-World Meteorological Organization) (2006) Global Network of Isotopes in Precipitation; The GNIP Database, IAEA, Vienna.
37. Johnston, K., Ver Hoef, J., Krivoruchko, K., Lucas, N. (2004) Using Arc GIS geostatistical analyst. USA, ESRI.
38. Kattan, Z. (1997a) Environmental isotope study of the major karst springs in Damascus limestone aquifer systems: Case of the Figeh and Barada springs. Journal of Hydrology, 193, 161-182.
39. Kattan, Z. (1997b) Chemical and environmental isotope study of precipitation in Syria. Journal of arid Environments, 35, 601-615.
40. Kattan, Z. (2006) Characterization of surface water and groundwater in the Damascus Ghotta Basin: hydrochemical and environmental isotopes approaches. Environmental Geology, 51(2), 173-201.
41. Kattan, Z. (2019) Factors controlling stable isotopes variability in precipitation in Syria: Statistical analysis approach. Journal of Earth System Science, 128, 151.
42. Kendall, C., Coplen, T.B. (2001) Distribution of oxygen-18 and deuterium in river waters across the United States. Hydrological Processes, 15, 1363-1393.
43. Kendall, C., McDonnell, J.J. (1998) Isotope tracers in catchment hydrology. Elsevier, Amsterdam.
44. Kumar, U.S., Kumar, B., Rai, S.P., Sharma, S. (2010) Stable isotope ratios in precipitation and their relationship with meteorological conditions in the Kumaon Himalayas, India. Journal of Hydrology, 391, 1-8.
45. Lambs, L. (2004) Interactions between groundwater and surface water at river banks and the confluence of rivers. Journal of Hydrology, 288(3-4), 312-326.
46. Li, J., Tao, T., Pang, Z.H., Tan, M., Kong, Y.L., Duan, W.H., Zhang, Y. W. (2015) Identification of different moisture sources through isotopic monitoring during a storm event. Journal of Hydrometeorology, 16(4), 1918-1927.
47. Liu, Y., Yamanaka, T., Zhou, X., Tian, F., Ma, W. (2014) Combined use of tracer approach and numerical simulation to estimate groundwater recharge in an alluvial aquifer system: A case study of Nasunogahara area, central Japan. Journal of Hydrology, 519, 833-847.
48. Liu, Z., Bowen, G.J., Welker, J.M. (2010) Atmospheric circulation is reflected in precipitation isotope gradients over the conterminous United States. Journal of Geophysical Research, 115, D22120.
49. Longinelli, A., Selmo, E. (2003) Isotopic composition of precipitation in Italy: A first overall map. Journal of Hydrology, 270(1-2), 75-88.
50. Lucas, I.I., Unterweger, M.P. (2000) Comprehensive review and critical evaluation of the half-life of tritium. Journal of Research of the National Institute of Standards and Technology, 105(4), 541-549.
51. Lykoudis, S.P., Argiriou, A.A. (2007) Gridded data set of the stable isotopic composition of precipitation over the eastern and central Mediterranean. Journal of Geophysical Research, 112, D18107.
52. Lykoudis, S.P., Argiriou, A.A., Dotsika, E. (2010) Spatially interpolated time series of δ18O in Eastern Mediterranean precipitation. Global Planetary Change, 71, 150-159.
53. Maloszewski, P., Rauert, W., Stichler, W., Herrmann, A. (1983) Application of flow models in an Alpine catchment area using tritium and deuterium data. Journal of Hydrology, 66, 319-330.
54. Managave, S.R., Jani, R.A., Narayana Rao, T., Sunilkumar, K., Satheeshkumar, S., Ramesh, R. (2016) Intra-event isotope and raindrop size data of tropical rain reveal effects concealed by event averaged data. Climate Dynamics, 47(3-4), 981-987.
55. Michel, R.L. (1989) Tritium deposition over the continental United States, 1953-1983. In Atmospheric Deposition (International Association of Hydrological Sciences Ed), Oxford, pp. 105-115.
56. Michel, R.L., Aggarwal, P., Araguas-Araguas, L., Kurttas, T., Newman, B.D., Vitvar, T. (2015) A simplified approach to analysing historical and recent tritium data in surface waters. Hydrological Processes, 29(4), 572-578.
57. Mook, W.M.E. (2001) Environmental isotopes in the hydrological cycle: Principles and Applications. UNESCO/IAEA Series.
58. Muller, C.L., Baker, A., Fairchild, I.J., Kidd, C., Boomer, I. (2015) Intra-event trends in stable isotopes: exploring midlatitude precipitation using a vertically pointing micro rain radar. Journal of Hydrometeorology, 16, 194-213.
59. Nir, A. (1967) Development of isotope methods applied to groundwater hydrology. In Proceeding of a symposium on isotope techniques in the hydrological cycle, Geophysical Monograph Series No. 11. American Geophysical Union.
60. Rozanski, K., Araguás-Araguás, L., Gonfiantini, R. (1993) Isotopic patterns in modern global precipitation. In Climate change in continental isotopic records (Swart, P.K. et al. Eds), Geophysical Monograph Series No. 78, DC, American Geophysical Union, Washington, pp. 1-37.
61. Rozanski, K., Gonfiantini, R., Araguás-Araguás, L. (1991) Tritium in the global atmosphere: Distribution patterns and recent trends. Physical Geography Journal, 17, S523-S536.
62. Saad, Z., Kazpard, V., El Samrani, A.G., Slim, K. (2005) Isotopic composition of rainwater, Lebanon. Journal Environmental Hydrology, 13(29), 1-11.
63. Schmidt, G.A., Hoffmann, G., Shindell, D.T., Hu, Y. (2005) Modeling atmospheric stable water isotopes and the potential for constraining cloud processes and stratosphere-troposphere water exchange. Journal of Geophysical Research, 110(D2), 1314.
64. Sturm, K., Hoffmann, G., Langmann, B., Stichler, W. (2005) Simulation of δ18O in precipitation by the regional circulation model REMOiso. Hydrological Processes, 19(17), 3425-3444.
65. Terzer, S., Wassenaar, L., Araguás-Araguás, L., Aggarwal, P.K. (2013) Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models. Hydrology and Earth System Sciences, 17, 4713-4728.
66. Wang, S., Zhang, M., Hughes, C.E., Zhu, X., Dong, L., Ren, Z., Chen, F. (2016) Factors controlling stable isotope composition of precipitation in arid conditions: an observation network in the Tianshan Mountains, central Asia. Tellus B: Chemical and Physical Meteorology, 68(1), 26206.
67. Windhorst, D., Waltz, T., Timbe, E., Frede, H.G., Breuer, L. (2013) Impact of elevation and weather patterns on the isotopic composition of precipitation in a tropical montane rainforest. Hydrology and Earth System Sciences, 17, 409-419.
68. Yurtsever, Y. (1983) Models for tracer data analysis. In Guidebook on nuclear techniques in hydrology, Technical Report Series No. 91, IAEA, Vienna, pp. 381-402.
69. Yurtsever, Y., Gat, J.R. (1981) Atmospheric waters. In Stable isotope hydrology, deuterium and oxygen-18 in the water cycle (Gat, J.R. and Gonfiantini, R. Eds), Technical Report Series No. 210, IAEA, Vienna, pp. 103-142.
70. Zahn, A., Barth, V., Pfeilsticker, K., Platt, U. (1998) Deuterium, oxygen-18, and tritium as tracers for water vapour transport in the lower stratosphere and tropopause region. Journal of Atmospheric Chemistry, 30, 25-47.