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composition of precipitation reflects seasonal and altitude

effects associated with the condensation of water vapor.

Hence, water isotopic composition has been widely used

as a tracer to identify precipitation contributing to

groundwater recharge and river water runoff (Scholl et

al., 1996; DeWalle et al., 1997; Asano et al., 2002;

O’Driscoll et al., 2005; Yamanaka, et al., 2007). In order to

study the evaporation process through its effect on δ18O

and δD values of water, a Rayleigh distillation model has

been used in hydrological studies particularly on arid and

semi-arid regions (Simpson and Herczeg 1991; Kattan,

2008; Wassenaar et al., 2011; Dogramici et al., 2012; Biggs

et al., 2015; Dogramaci et al., 2015) However, it is not easy

to quantify the ef fect of evaporation on a given water

body, particularly in a vast field such as a continent,

because the isotopic composition of the precipitation as

an initial water must be known and would vary greatly,

depending on location and season. Huang and Pang

(2014) noted that deuterium excess (d-excess), calculated

stable isotope data as d＝δD－8δ18O (Dansgaard, 1964),

1. Introduction

As two-thirds of Australia has a climate that is either

arid or semi-arid (Johnson, 2004), Australia is one of the

driest continent. In most of Australia, annual precipitation

is less than 400 mm and potential evaporation exceeds

precipitation, resulting in salt accumulation and scarcity

of water resources in extensive areas (Hart et al., 1990;

Jolly et al., 1993). Thus, the influence of evaporation on

water resources is a critical issue in arid Australia,

particularly on surface water flowing in rivers, which is

subjected to evaporation during runoff. Evaporation is

also related to climatic factors such as solar exposure and

maximum temperatures over various periods of time.

Better understanding of these factors may be helpful in

analyzing and managing utilization of surface water in

arid and semi-arid regions.

The stable isotope ratios of water (δ18O and δD) is

modified by exposure to physical processes such as

evaporation and condensation. For instance, the isotopic

Masaru YAMANAKA＊ and Shiho YABUSAKI＊＊

A negative correlation between d-excess (d＝δD－8δ18O) and δ18O values observed in the water samples was attrib-uted to the effect of evaporation on the water after precipitation, suggesting that a d-excess decrease in a given water body is a useful index of evaporation. Furthermore, d-excess in river water was negatively correlated with average daily solar exposure during the three (to four) month period before sample collection, and its value was higher at sampling sites with total precipitation exceeding 200 mm during the previous three months. We conclude that river water runoff is closely related to precipitation during the most recent three months, and that the influence of evaporation on this water is closely related to solar exposure during that period. In contrast, the absence of a relationship between d-excess and electrical conductivity of the water suggests that salt accumulation in water is not only a function of the evaporation process in short term, but also reflects other factors such as proximity to sources of sea salt.

Keywords : δD and δ18O, d-excess, daily solar exposure, salinity

Influence of Evaporation on River Water in Arid and Semi-arid Australia:Relationships between Stable Isotope Ratios and Climatic Factors

（Accepted November 11, 2016）

日本大学文理学部自然科学研究所研究紀要

No.52 （2017） pp.225－236

195

＊ Department of Geosystem Sciences, College of Humanities and Sciences, Nihon University: 3-25-40, Sakurajosui, Setagaya-ku, Tokyo, 156-8550, Japan

＊＊ Research Institute for Humanity and Nature:457-4, Motoyama, Kamigamo, Kita, Kyoto, 603-8047, Japan

Masaru YAMANAKA and Shiho YABUSAKI

─ ─226（ ）196

can be a useful index in evaluating evaporation based on

model calculations and field observations rather than

water isotope itself. Indeed, several studies have

successfully made use of this index in arid and semi-arid

environments (Tsujimura et al., 2007; Meredith et al.,

2009).

We studied the isotopic characteristics of river water in

comparison with those of other types of water as

groundwater in three arid regions of the Australian

continent—central, southeastern, and western—to

investigate the influence of evaporation on river water and

to gain insight into river runoff processes with respect to

climatic factors.

2. Climate

Patterns of average temperature, annual precipitation,

and potential evaporation in Australia are illustrated in

Fig. 1. Temperatures are highest in the northwest and

decrease gradually southward, whereas precipitation

decreases with distance inland. Conversely, average

potential evaporation increases with distance inland,

reaching a maximum of 3300 mm in the central region

and greatly exceeding the average precipitation of 470

mm (Budkyo, 1974) at all locations. As illustrated in Fig.

1, central, southeastern, and western Australia are

characterized by low precipitation and high potential

evaporation irrespective of annual temperature. Of these,

central Australia experiences the most severe conditions.

3. Sampling and analytical methods

Water samples were collected at seven sites in central

Australia, two sites in southeastern Australia, and ten

sites in western Australia during March and April 2009

(Fig. 2). The samples were classified as either river water

or other types of water (ponds or groundwater). The river

water samples have the letter R appended to their sample

number. These sampling sites were previously studied by

Yamanaka (2010).

In central Australia, samples include surface water

remaining on impermeable bedrock and groundwater

(Samples 1 and 2) in or near Uluru–Kata Tjuta National

Park, river water and groundwater emerging from a

spring (Samples 4R and 5) at Watarrka National Park, and

a river with intermittent flow (Samples 6R and 7R) near

West MacDonnell National Park. In southeastern

Australia, samples were collected from the Murray River

(Sample 8R), the longest river in Australia, and from a

crater lake (Sample 9) in an area of limestone. In western

Australia, water samples were collected mostly near the

shoreline from an artificial reservoir (Sample 10), several

rivers (Samples 11R–17R and 19R), and a spring (Sample

18). Of these, the river water samples came from an

estuary river (Sample 11R), a small stream (Sample 12R),

three rivers in deep gorges (Samples 13R, 16R, and 17R),

and three large rivers with very large drainage basins

(Samples 14R, 15R and 19R). River discharges were low

Fig. 1　Maps of Australia showing a) average temperature, b) annual precipitation, and c) potential evaporation patterns, modified after Johnson (2004) and Linacre and Hobbs (1977).

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Influence of Evaporation on River Water in Arid and Semi-arid Australia

197

results are expressed as permil values with respect to

Vienna Standard Mean Ocean Water (VSMOW). The

overall measurement accuracy was ±0.25‰ for δ18O and

±1.0‰ for δD.

4. Results and discussion

4.1. Stable isotope ratios and d-excess

Stable isotope ratios (δ18O and δD) and d-excess values

of the water samples are listed in Table 1. The δ18O and

δD values ranged widely from –8.3 ‰ to 6.4 ‰ and from

–63‰ to 32‰, respectively. Of these, approximately half

had positive δ18O values and occurred in all par ts of

Australia. Table 2 lists monthly averages for δ18O, δD, and

d-excess in precipitation from central, southeastern, and

western Australia, measured at Alice Springs, Adelaide,

and Perth, respectively. These data are weighted means

of monthly precipitation, mainly from the 1960’s to the

throughout western Australia; even among large rivers,

flow was quite low at the sites of Samples 15R and 19R,

and was intermittent at the site of Samples 14R.

At the sampling sites, water temperature, pH, and

electrical conductivity (EC) were determined and 250 mL

water samples were collected. In the laboratory, HCO3－

concentrations of the water samples were determined by

pH 4.8 alkalinity titration using N/50 H2SO4. After dilution

to an EC of <200 μS/cm and filtration through a cellulose

nitrate sheet with 0.20 μm pore size, the samples were

analyzed for cation and anion concentrations with an ion

chromatograph (Shimadzu Co. Class LC10) at the

facilities of Nihon University. Errors in the charge balance

were less than ±5% in most of the samples. Stable isotope

ratios (δ18O and δD) of the water samples were measured

with a Thermo Fisher Delta Plus mass spectrometer at

the facilities of Rissho University, Kumagaya, Japan. The

Fig. 2　Map of Australia showing water sampling sites and the cities in which precipitation data were collected (IAEA/WMO, 2006).

Masaru YAMANAKA and Shiho YABUSAKI

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composition of precipitation, with high δ18O values near

0‰ in September to November and low values near –10‰

for December to May.

In the water samples, d-excess values varied widely

from –32.4‰ to 7.7‰ whereas d-excess values of

precipitation varied much less and were entirely positive,

ranging from 5.9‰ to 17.0‰. As illustrated in Fig. 4, only

seven water samples had positive d-excess values

(Samples 1, 3, 5, 12R, 16R, 17R, and 18), and of these only

two (Samples 12R and 18) had values high as those in

2000’s from IAEA/WMO (2006).

The relationships between δ18O and δD of the water

samples and precipitation are compared graphically in

Fig. 3. All of the water samples plot below the Global

Meteoric Water Line (GMWL, δD＝8δ18O＋10) in δ18O－

δD space, with δD＝5.84δ18O＋12.3 as their regression

line, whereas the values of precipitation in all three

regions mostly plot on the GMWL with a regression line

of δD＝ 7.45δ18O＋ 9.83. The central region showed

par ticularly wide seasonal variation in the isotopic

Table 1　Chemical and isotopic compositions of the water samples.

Sample No. Sample nameDrainage area*（km2）

DateEC**

（μS/cm）pH**

Temp.**

（℃）

Na＋**

（mg/L）K＋**

（mg/L）Ca2＋**

（mg/L）Mg2＋**

（mg/L）Cl－**

（mg/L）SO42－**

（mg/L）HCO3－**

（mg/L）NO3

－**

（mg/L）δ18O（‰）

δD（‰）

d-excess（‰）

1 Maggie Springs － 15/Mar/2009 23 6.81 23.3 2.0 0.7 1.5 0.5 2.3 2.2 2.4 3.1 －5.71 －44.8 0.9

2 A small water in The Olgas － 16/Mar/2009 108 6.06 23.6 10.3 1.2 3.3 3.3 12.6 4.7 19.5 10.7 －2.75 －37.5 －15.5

3 Groundwater in a village － 17/Mar/2009 439 6.62 31.2 69.9 9.6 5.1 2.5 100 10.4 22.7 28.5 －5.60 －43.1 1.8

4R The Garden of Eden － 18/Mar/2009 47 5.66 19.3 3.2 1.8 2.5 1.0 5.7 1.5 7.3 1.8 4.67 7.9 －29.5

5 Kathleen Springs － 18/Mar/2009 392 6.56 27.0 35.7 11.3 13.7 10.5 69.0 13.1 82.7 2.3 －3.46 －26.8 0.9

6R Glen Helen Waterhole n.d. 19/Mar/2009 3450 7.64 27.0 504 20.9 117 57.8 855 250 303 0.7 0.92 －0.1 －7.5

7R Ormiston Gorge Waterhole n.d. 19/Mar/2009 234 6.78 26.3 19.8 6.2 15.2 5.3 14.4 1.4 112 1.4 3.74 11.8 －18.1

8R Murray River 1,060,000 22/Mar/2009 732 6.66 21.2 101 4.4 17.6 13.6 169 31.6 75.9 0.3 4.00 12.4 －19.6

9 Valley Lake － 29/Apr/2009 1946 7.97 16.8 271 34.9 29.3 64.4 437 25.3 464 0.8 8.07 32.1 －32.4

10 Wave Rock Dam － 11/Apr/2009 180 6.25 21.5 22.0 2.2 6.7 2.7 43.5 4.1 25.6 N.D. 2.90 2.9 －20.2

11R Swan River 121,000 12/Apr/2009 36500 6.75 22.6 7,286 235 481 881 13,904 1,620 165 N.D. 1.59 3.4 －9.4

12R Irwin River 5,264 13/Apr/2009 3910 7.06 21.3 600 16.4 29.3 58.6 1,021 141 104 N.D. －2.02 －8.4 7.7

13R Murchison River 86,777 13/Apr/2009 9900 7.41 27.4 1,697 51.4 143 229 3,058 746 166 N.D. 1.65 －9.6 －22.8

14R Gascoyne River 73,400 15/Apr/2009 987 6.77 22.9 123 9.0 27.0 21.7 216 81.9 79.8 N.D. 0.49 －9.4 －13.3

15R Ashburton River 67,000 15/Apr/2009 1650 7.42 25.9 200 8.3 52.2 49.7 323 133 265 2.2 －6.76 －62.6 －8.5

16R Fortesucue River 50,000 16/Apr/2009 798 7.58 24.0 39.0 9.5 43.1 45.6 72.4 53.5 303 1.1 －8.32 －61.6 4.9

17R Joffre Creek n.d. 16/Apr/2009 331 6.92 25.0 32.6 4.2 5.8 11.3 73.7 1.3 45.4 N.D. －6.70 －53.1 0.5

18 Circular Pool － 17/Apr/2009 513 7.15 23.0 35.0 6.4 17.9 24.2 90.3 20.0 102 3.3 －8.14 －58.3 6.8

19R DeGrey River 57,000 17/Apr/2009 1360 7.40 29.7 185 5.4 31.7 30.7 255 64.2 255 3.2 －3.16 －32.8 －7.6

*  Data from Dodson (2009) and Mayer et al. (2005), n.d.: no data ** Data from Yamanaka (2010), N.D.: not determined

Table 2　δ18O and δD values of monthly weighted mean precipitation in Alice Springs (central), Adelaide (southeastern), and Perth (western region) (IAEA/WMO, 2006).

MonthAlice Springs （ASP） Adelaide （ADL） Perth （PER）

δ18O（‰）

[n]δD（‰）

[n]d-excess（‰）

[n]δ18O（‰）

[n]δD（‰）

[n]d-excess（‰）

[n]δ18O（‰）

[n]δD（‰）

[n]d-excess（‰）

[n]

Jan. －8.30 [16] －60.4 [13] 12.5 [13] －5.98 [11] －40.3 [11] 7.6 [11] －4.57 [6] －24.6 [8] 11.8 [6]Feb. －9.45 [13] －63.0 [12] 12.6 [12] －7.64 [8] －51.1 [8] 10.0 [8] －5.30 [7] －33.7 [7] 8.8 [6]Mar. －10.34 [12] －57.2 [10] 12.4 [10] －4.74 [7] －24.6 [7] 13.3 [7] －4.35 [14] －22.1 [15] 10.7 [14]Apr. －6.61 [11] －44.1 [9] 10.6 [9] －5.11 [12] －28.7 [13] 10.8 [12] －3.60 [15] －16.5 [16] 13.6 [15]May －5.76 [13] －34.3 [11] 12.7 [11] －4.64 [9] －26.5 [9] 12.2 [8] －4.64 [16] －20.7 [17] 15.6 [16]Jun. －5.18 [12] －28.8 [9] 16.8 [9] －5.38 [9] －32.1 [11] 11.3 [9] －4.27 [16] －17.9 [17] 16.3 [16]Jul. －6.10 [9] －36.8 [8] 12.9 [7] －5.07 [8] －24.3 [10] 13.9 [8] －4.45 [16] －18.5 [17] 17.0 [16]

Aug. －4.23 [15] －19.7 [13] 16.3 [13] －4.23 [10] －21.4 [12] 12.0 [10] －3.76 [16] －13.0 [18] 16.9 [16]Sep. －0.88 [10] －3.6 [8] 5.9 [7] －3.15 [10] －14.5 [11] 10.4 [10] －4.00 [16] －16.5 [17] 16.5 [15]Oct. －0.70 [13] 4.6 [15] 8.3 [13] －3.52 [9] －18.6 [10] 10.2 [9] －3.39 [15] －12.0 [17] 14.3 [15]Nov. 1.07 [17] 18.4 [16] 7.9 [15] －3.02 [9] －12 [10] 12.2 [9] －4.38 [14] －19.4 [16] 14.6 [14]Dec. －6.64 [14] －38.0 [14] 15.6 [13] －2.53 [11] －8.7 [12] 11.4 [11] －2.17 [12] －9.6 [14] 7.9 [12]

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199

Fig. 3　�Plot of δD against δ18O for water samples and precipitation data (IAEA/WMO, 2006): ASP, Alice Springs in central; ADL, Adelaide in southeastern; PRT, Perth in western region. The regression line for the water samples is defined by δD＝5.84δ18O＋12.3.

Fig. 4　�Plot of δ18O against d-excess for water samples and precipitation data (IAEA/WMO, 2006): ASP, Alice Springs in central; ADL, Adelaide in southeastern; PRT, Perth in western region. The water samples showed a negative linear relationship, with samples highly influenced by evaporation having lower d-excess values and higher δ18O values.

Masaru YAMANAKA and Shiho YABUSAKI

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point were most ly less than 30 km (Austra l ian

Government Bureau of Meteorology, 2015). Correlation

coefficients between these climatic factors and d-excess

values (Table 3) show that d-excess values were most

strongly correlated with average daily solar exposure

during the previous 90 days while they were not

correlated with average daily solar exposure during the

other days. As shown graphically in Fig. 5b, the river

water samples fall along a well-defined line, except for two

samples. One of these outliers, Sample 12R, is from a

river with a fairly small drainage area (5264 km2; Mayer et

al., 2005; Table 1) in which water would be less likely to

be influenced by evaporative concentration during runoff.

Exclusion of Sample 12R apparently increased the

magnitude of the correlation coefficients of the remaining

samples during around 90 days from –0.61 (90 days) to

–0.80 (110 days) as illustrated in Fig. 6. On the other

hand, when d-excess values were plotted against average

daily solar exposure during the last 30 days (Fig. 5a) or

150 days (Fig. 5c), their scatter was greater similarly as

the samples include non-river water such as groundwater

(Fig. 5). These findings imply that lower d-excess values

in river water being highly evaporated are caused by

higher solar exposure during the preceding three (to

four) months and not during the other days.

In contrast to Sample 12R, Sample 9 from a crater lake

has a low d-excess value despite extremely low solar

exposure (Fig. 5). This would be attributed to the

characteristics that lake water is more easily exposed by

solar radiation than river water.

We found no clear relationship between d-excess values

and either average daily maximum temperature or total

precipitation. In the water samples, δ18O and d-excess

values were negatively correlated: d-excess values

decreased and δ18O values increased relative to their

ranges in precipitation.

4.2. Relationships between d-excess and climatic

factors

River water is easily influenced by evaporative

concentration, because it flows on the ground surface.

Moreover, most Australian rivers have immense drainage

areas in arid and semi-arid regions. Hence, Australian

rivers are expected to be strongly influenced by

evaporative concentration. For evaluating the influence of

evaporation, d-excess is a more useful index than isotopic

composition (δ18O or δD), because isotopic composition of

precipitation varies widely with location and season

whereas d-excess values in precipitation are relatively

constant as shown in Fig. 4. Thus, the decrease in

d-excess in a given water body should more clearly

express the influence of evaporation on precipitated water.

Indeed, several studies have used d-excess values to

evaluate the evaporation process in arid and semi-arid

regions (Tsujimura et al., 2007; Meredith et al., 2009;

Huang and Pang, 2014). Therefore, we investigated the

relationship between climatic factors and d-excess values

of water samples in order to characterize climatic factors

that control evaporative concentration.

As climatic factors, we chose average daily solar

exposure, average daily maximum temperature and total

precipitation over periods of 10 to 150 days before the

sampling date, as extracted from observational data at the

closest station to the sampling point; the distances to the

Table 3　 Correlation coefficients of d-excess in all samples (upper column) and river water samples (lower column) against average daily solar exposure, average daily maximum temperature and total precipitation, during periods ranging from 10 to 150 days preceding the sampling date. Climatic data are from Australian Government Bureau of Meteorology (2015).

10 days 20 days 30 days 40 days 50 days 60 days 70 days 80 days 90 days 100 days 110 days 120 days 130 days 140 days 150 days

All samples

Average daily solar exposure (MJ/m2) 0.32 0.27 0.33 0.34 0.28 0.29 0.27 0.25 0.24 0.25 0.25 0.22 0.28 0.34 0.36

Average daily maximum temperature (ºC) 0.56 0.59 0.61 0.61 0.58 0.57 0.58 0.60 0.62 0.64 0.66 0.66 0.67 0.67 0.67

Total precipitation(mm) －0.50 －0.40 －0.42 －0.42 －0.06 0.27 0.31 0.33 0.36 0.40 0.44 0.47 0.44 0.36 0.36

River water samples

Average daily solar exposure (MJ/m2) －0.37 －0.35 －0.33 －0.34 －0.45 －0.52 －0.58 －0.58 －0.61 －0.59 －0.55 －0.51 －0.38 －0.16 －0.13

Average daily maximum temperature (ºC) 0.32 0.37 0.40 0.37 0.29 0.26 0.30 0.36 0.39 0.46 0.49 0.50 0.50 0.51 0.51

Total precipitation(mm) －0.38 －0.27 －0.27 －0.27 0.27 0.49 0.49 0.50 0.49 0.48 0.47 0.45 0.36 0.32 0.34

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201

Fig. 5　�Plots of d-excess against average daily solar exposure during a) 30 days, b) 90 days, and c) 120 days preceding the water sampling date. Solar exposure data are from Australian Government Bureau of Meteorology (2015).

Fig. 6　�Relationships between correlation coefficients and periods ranging from 10 to 150 days preceding the sampling date. The correlation coefficients of d-excess in river water samples were against three data; average daily solar exposure (circle plots), average daily maximum temperature (triangle plots) and total precipitation (square plots). Solid plots are for a case of all the river water samples and open plots are of the river water samples exclusive of Sample 12R in a fairly small drainage area. Climatic data are from Australian Government Bureau of Meteorology (2015).

Masaru YAMANAKA and Shiho YABUSAKI

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samples with EC exceeding 1000 μS/cm were mainly

from sites distributed near the shoreline. Detailed results

for these samples are listed in Table 1 and displayed in

Fig. 8 using Stiff diagrams. The chemical compositions of

the water samples were reported by Yamanaka (2010) and

are summarized below.

The water samples were dominantly of the Na-Cl type,

and Ca-HCO3 type water was absent (Fig. 8). Compositions

of Samples 11R, 12R, and 13R were similar to that of

seawater; Cl－ was in excess of 1000 mg/L, and Na/Cl

ratios were almost identical to that of seawater (Fig. 9).

Thus, we inferred that the Cl－ and Na＋ in these samples

originated mostly from seawater. Sample 11R may have

been directly affected by seawater intrusion due to the

sampling location in an estuary area, but Samples 12R and

13R probably were not. Johnson (2004) used δ34S analysis

to show that sea spray contributes 100% of the sulfur to

salt lakes within approximately 200 km of the shoreline in

southwestern Australia and its contribution decreases

toward the northeast. Herczeg and Edmunds (2000)

showed that groundwater in the Murray Basin of

southeastern Australia has 7000 to 13,000 mg/L Cl－ as a

result of sea salt accumulation. These findings suggest

precipitation, except for d-excess values of river water

samples against total precipitation over 90 days (Table 3

and Fig. 6). Although the correlation coefficient of this

case was only 0.49 (during 90 days, Table 3), exclusion of

Sample 12R in a small drainage area raised it to 0.77

(during 60 days, Fig. 6). The d-excess values of the

samples were higher when their sampling sites had over

200 mm of total precipitation in the period (Fig. 7b),

because the influence of evaporation at such sites would

be small, in accordance with the general relationship of

evaporation versus precipitation in Australia (Fig. 1).

Furthermore, the d-excess values of river water were

correlated most strongly with total precipitation over the

past 90 days (Table 3). Thus, we conclude that river water

runoff in arid and semi-arid Australia is closely related to

the previous three months of precipitation and that the

influence of evaporation on river water is closely

constrained by solar exposure during those months.

4.3. Chemical composition of water and its

controlling factors

Electrical conductivity (EC) of the water samples

ranged widely from 23 to 36,500 μS/cm. The water

Fig. 7　�Plots of d-excess against a) average daily maximum temperature and b) total precipitation during 90 days preceding the water sampling date. Daily maximum temperature and precipitation data are from Australian Government Bureau of Meteorology (2015).

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Influence of Evaporation on River Water in Arid and Semi-arid Australia

203

concentration of 437 mg/L and was a Na-Cl type water

rather than a Ca-HCO3 type (Fig. 9 and Table 1). Thus,

influences of the geologic setting of the sampling sites on

water chemistry is very limited as shown in a few cases of

Sample 7R (Na-HCO3 type), Sample 16R (Mg-HCO3 type),

and Sample 18R (Mg-Cl type).

Electrical conductivity values of the water samples were

not significantly correlated with any climatic data (Table

4) or with d-excess values as an index of evaporation of

water samples (Fig. 10) in either case of all samples or

river water samples alone. Deduced from the findings that

the very strong correlation between EC values and Cl－

concentrations (r＝0.998) and high Cl－ water samples

have similar Na/Cl ratio to that of seawater, we conclude

that the compositions of the high EC (high-salinity) water

samples reflected the influence of sea spray and related to

that the chemistry of Samples 12R and 13R reflects the

accumulation of sea salt carried from the western coast

by weather systems.

Sample 6R was also Na-Cl type water, with a Cl－

concentration of 855 mg/L and a similar Na/Cl ratio to

that of seawater. However, the sampling site is more than

1000 km from the sea, which rules out of the influence of

sea spray. However, a shallow marine sandstone is

exposed around the sampling site (Johnson, 2004); thus,

it is plausible that evaporite mineral in the sandstone

af fected the chemical compositions of the water. All

samples with more than 100 mg/L Cl－, except for Sample

6R, were from sites located near the shoreline. Hence, we

attribute the high Cl－ concentrations in these water

samples to sea spray.

Sample 9, which came from a limestone area, had a Cl－

Fig. 8　�Water chemistry of the water samples expressed by Stiff diagrams. Note that the dimensions of the Stiff diagrams have been reduced for some samples with high concentrations of ions (indicated by ×5, ×10, ×50, or ×100). Modified after Yamanaka (2010).

Masaru YAMANAKA and Shiho YABUSAKI

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river water in Taklimakan Desert of the northwestern

China. These findings are compatible with our conclusion

in this study that the chemical composition of river water

is controlled by factors other than short-term direct

evaporation. However, there is no doubt that prolonged

evaporation process causes degradation of water

resources and it remains essential to quantify the effect of

the evaporation concentration process on water

resources.

distance from the ocean, not to the geologic setting of the

sampling site or short-term evaporation concentration

alone. Mayer et al. (2005) attributed higher salinity in

river water in southwestern Australia partly to lower

rainfall during the previous 10 years, on the basis of

relationships between river water chemistry and climatic

data. Huang and Pang (2014) used d-excess values to

show that mineral dissolution and transpiration mainly

contributed to high salinity in groundwater recharged by

Fig. 9　�Plot of Na/Cl ratio against the Cl－ concentration (logarithmic scale) of water samples. The broken line shows the Na/Cl ratio of seawater. Modified after Yamanaka (2010).

Table 4　 Correlation coefficients of EC in all samples (upper column) and river water samples (lower column) against average daily solar exposure, average daily maximum temperature and total precipitation, during periods ranging from 10 to 150 days preceding the sampling date. Climatic data are from Australian Government Bureau of Meteorology (2015).

10 days 20 days 30 days 40 days 50 days 60 days 70 days 80 days 90 days 100 days 110 days 120 days 130 days 140 days 150 days

All samples

Average daily solar exposure (MJ/m2) －0.15 －0.15 －0.20 －0.21 －0.23 －0.26 －0.21 －0.20 －0.18 －0.13 －0.07 －0.01 0.02 0.10 0.10

Average daily maximum temperature (ºC) －0.10 －0.24 －0.29 －0.27 －0.33 －0.30 －0.29 －0.28 －0.30 －0.27 －0.26 －0.27 －0.28 －0.29 －0.30

Total precipitation(mm) －0.06 －0.10 －0.05 －0.05 －0.09 －0.21 －0.21 －0.21 －0.22 －0.27 －0.29 －0.32 －0.37 －0.37 －0.36

River water samples

Average daily solar exposure (MJ/m2) －0.25 －0.27 －0.39 －0.43 －0.44 －0.55 －0.45 －0.40 －0.35 －0.27 －0.13 －0.04 －0.01 0.11 0.13

Average daily maximum temperature (ºC) －0.33 －0.47 －0.48 －0.48 －0.55 －0.59 －0.58 －0.54 －0.51 －0.44 －0.42 －0.42 －0.44 －0.45 －0.46

Total precipitation(mm) －0.25 －0.03 0.26 0.23 －0.11 －0.32 －0.31 －0.30 －0.32 －0.34 －0.37 －0.38 －0.42 －0.41 －0.41

─ ─235 （ ）

Influence of Evaporation on River Water in Arid and Semi-arid Australia

205

months. This interpretation is consistent with the

finding that d-excess values of the samples were

high, indicating a small influence of evaporation,

when total precipitation at the sampling sites

exceeded 200 mm during the preceding three

months.

 3. In arid and semi-arid Australia, d-excess values of

water samples were not correlated with EC values

(an index of salt accumulation), signifying that salt

accumulation in the water did not result from

evaporation alone. Although we found that short-

term evaporation was not a major cause of water

quality degradation, prolonged evaporation can

degrade regional water resources, in addition to

other factors such as proximity to sources of sea salt.

Acknowledgements We thank Dr. Andrew Herczeg (CSIRO Land and Water) for

his cooperation during the water sampling and Emeritus Prof. Norio Tase (University of Tsukuba) for his comments that help to improve this manuscript.

5. Conclusions

In this study, we used stable isotope data to investigate

the influence of the evaporation process on river water

and non-river water in arid and semi-arid Australia. The

results are summarized as follows:

 1. Oxygen isotopic compositions (δ18O) are strongly

negatively correlated with d-excess values of water

samples from arid and semi-arid Australia; d-excess

values decreased and δ18O values increased relative

to their ranges in precipitation through the effect of

evaporation on the water. This result indicates that

decreasing d-excess is a useful index of evaporation.

 2. In river water samples, d-excess values were

negatively correlated with average daily solar

exposure during the three (to four) months preceding

the sampling date. Because lower d-excess can be

attributed to higher evaporation, this finding

indicates that the influence of evaporation on river

waters is strongly associated with solar exposure

during runof f over the preceding three (to four)

Fig. 10　�Plot of d-excess against EC values (logarithmic scale), an index of salt accumulation, of the water samples. Definitions of fresh water (white), brackish water (stippled), and saline water (shaded) are from Suttar (1990).

Masaru YAMANAKA and Shiho YABUSAKI

─ ─236（ ）206

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