Effect of Land Surface Processes on Precipitation Isotopes∗

Kei YOSHIMURA* Institute of Industrial Science, the University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japane-mail: kei@iis.u-tokyo.ac.jp

Abstract
Precipitation isotopes variability is dominantly controlled by large-scale atmospheric moisture transport
processes. However, we cannot neglect some possible effect of land surface processes on the variability of
precipitation isotopes; in particular, the effect on diurnal variations on precipitation seems quite large. To
take a deeper insight on short-term variability of precipitation isotopes, including diurnal variability, theauthors developed an isotope-incorporated land surface model (LSM) coupled with the existed atmosphericisotope circulation model. The original land surface model was MATSIRO (Minimal Advanced Treatmentsof Surface Interaction and Runoff) and the atmospheric model was the Rayleigh-type isotope circulation
model (ICM).
Keyword: Precipitation Isotopes, Kinetic Fractionation, LSM, GAME reanalysis

1. Introduction
Stable isotopes of water (HDO and H2 18 O) are good
tracers of hydrologic cycle, because their concentrations in water portion indicate integrated records ofphysical phase-changes. In particular, large heterogeneity of precipitation isotopes in time and space isregarded as proxies of complex atmospheric behavior.
In the past, many observational studies on precipitation isotopes have been curried out, and lately thesimple two-dimensional isotope circulation model by
Yoshimura et al. (2003) showed that the heterogeneityof observed precipitation isotopes is dominantly controlled by large-scale atmospheric moisture transport
processes. Their model reproduced daily H2 18 O variability over the sub-tropics, particularly Thailand, andmonthly averages at global scales with GNIP (Global
Network of Isotopes in Precipitation: WMO/IAEA).
However, there remain discrepancies between the observation and the simulations. Effect of land surface
processes, which was neglected in the model, is one of
the causes. This paper, to take a deeper insight on
short-term variability of precipitation isotopes, incorporates behavior of the isotopes into land surface modeland analyzes isotopic land-atmosphere interaction andeffect of land on the atmosphere by coupling it withthe atmospheric isotope circulation model.

2. Design of Models
2.1. Isotope Land Surface Model
The study used MATSIRO (Minimal Advanced Treatments of Surface Interaction and Runoff) in Takata
et al. (2003) as the original LSM. The main reasonsare; MATSIRO is designed to be coupled with atmospheric general circulation models (AGCMs) and optimized for large scale global simulations; it has three
distinct treatments of evapotranspiration that possibly make differences in isotopic fractionation, such as
evaporation from soil, transpiration from vegetation,and evaporation from intercepted water by canopy; andvertical soil water transport and advective water runoff
are explicitly considered based on physical equations.
In Iso-MATSIRO, all variables that contains wateramount or flux have respective isotopic concentrations.

In every timestep, these isotopic concentrations arecomputed with satisfaction of isotopic mass balances.
When phase-changes (e.g., liquid to gas) take place, isotopic fractionation are taken into account. Particularly
in case of release from the system (e.g., evapotranspiration), kinetic fractionation are conducted as follows(transpiration is shown):¡

Et = ρCEt |Vc | qs(Tc ) − qa¡¢∗Et∗ = ρCEt
|Vc | qs(Tc ) Rleaf α(Tc ) − qa Ra

REt =

∗

∗
CEt

Et
=
Et
CEt

·

qs(Tc ) α(Tc ) Rleaf − qa Ra
qs(Tc ) − qa

(1)

¢

¸

(2)
(3)

∗
where ρ is concentration of water; CEt and CEt
denotebulk coefficients of water and isotopes against water
vapor, respectively, where the roughness that considers surface resistances is taken into account; the ratio∗of the bulk coefficients, CEt/CEt is known as the kinetic fractionation coefficient, and this study regards
it as a function of wind speed (Merlivat and Jouzel,1979); qs and qa indicate saturated specific humidity
and that of air vapor, respectively; Rleaf and Ra denote isotopic composition of water in leaves and air vapor, respectively; Tc is temperature at a canopy height;
and α is equilibrium fractionation factor obtained from
Majoube (1979).
Any type of phase-changes (e.g., evaporation or sublimation) is similarly described as above formulae, by
substituting respective isotopic composition (e.g., soil
water, snow, etc.). Note that this study used the samethe kinetic fractionation factor regardless of type of
evaporation, but it may be affected by turbulence intensity (Riley et al., 2002).
For running the model, surface boundary conditions
are required; specific humidity, wind speed, temperature, pressure, downward radiation, precipitation (convective and large scale), and isotopic compositions ofprecipitation (convective and large scale) and vapor.
Land use types and soil types, and their physical parameters, are taken from the default setting of the
global soil wetness project 2 (Dirmeyer et al., 2002).
The model resolution is, horizontally 1◦ ×1◦ , and vertically five soil layers (5, 20, 25, 25, and 100 cm).

2.2. Coupling with Atmospheric Isotope Model
For atmospheric circulation computation, this study
uses the Rayleigh-type isotope circulation model (ICM)
in Yoshimura et al. (2003, hereafter Y03). ICM isforced by external meteorological conditions including
precipitation, evaporation, precipitable water vapor,and vapor fluxes, and it calculates isotopic fractionation by employing Rayleigh equation between precipitable water vapor and precipitation. Resolution is,horizontally 1◦ ×1◦ , and vertically one layer.
Coupling structure of ICM and Iso-MATSIRO isshown in Fig.1. Isotopes in evaporative flux over land
surface are given from Iso-MATSIRO to ICM, andisotopes in precipitation flux and ambient vapor are
given from ICM to Iso-MATSIRO. Over oceans, δ 18 O
in evaporative flux are assumed to have a fixed value,−5 06 00, whereas it was −9.4 06 00 in Y03. Other
boundary conditions are taken from GAME reanalysis(Yamazaki et al, 2000) every 6 hours. Global simulation is carried out for 1 April to 31 October in 1998,
after twice April as spin-up. Both the model timestep
and coupling frequency are 10 minutes.

averages for the simulation period (7 months) of landatmosphere water fluxes are shown in Table 1. It should
be noted that the common assumption in many isotopic models, evapotranspirated isotopes are equal toprecipitation isotopes, should be reconsidered. Finally,
the comparison with the observations shows the precipitation isotopes are better reproduced in the coupledsimulation than simulations with simple evaporation
isotopes in Y03. The correlation coefficient increases
a little, 0.74 to 0.75, but bias and RMSE (root meansquare error) significantly decrease (improve), −3.2 06 00
to 0.7 06 00 and 4.2 06 00 to 2.7 06 00 .
0.5

1998, Chiangmai, Soil Water Storage (m3/m3)

150.4
10 0.3
0.2
5
0.1
0 0.0
1 Apr.

1st (5cm)
1 May

2nd (20cm)
31 May3rd (25cm)

30 Jun30 July

4th (25cm)
29 Aug.

5th (100cm)
28 Sep.

28 Oct.

Fig. 2: Temporal variations in soil water storages offive layers, at a grid including Chiangmai, Thailand.
60

1998, Chiangmai, Precipitation & Runoff (mm/day)

0

50

10

40

20

10
30

30

Precipitation
Runoff

20

40
50

10
0

0
1 Apr.

1 May

31 May30 Jun30 July

29 Aug.

28 Sep.

60
28 Oct.

Fig. 3: As Fig.2, but for Precipitation and (total)
runoff amount.
Fig. 1: Schematic representation of coupling of IsoMATSIRO and ICM.

5

1998, Chiangmai, Isotope ratio of Soil Water Storage, Precip. and Runoff (‰)

15
0
10 -5

3. Results and Validations
3.1. At Specific Site
Figure 2-6 show simulated results at Chiangmai (18.8N,
99.0E), Thailand, for 1 April to 31 October in 1998.
In this simulation, land type of a grid including Chiangmai was categorized as mixture of coniferous andbroadleaf deciduous forest and woodland, and soil type
was clay loam. Fig.2 shows states of water storages
in each of five soil layers, Fig.3 presents precipitation
and runoff amount, Fig.4 is isotopic compositions ofsoil water storages, precipitation, and runoff, Fig.5 displays isotopic compositions of evaporation from baresoil, transpiration from vegetation, and evaporationfrom canopy-interception, and Fig.6 shows comparisonof precipitation isotopes between observation, the control simulation in Y03, and the land-atmosphere coupled simulation.
The results indicate isotopes in surface soil layer arelargely influenced by precipitation isotopes, but it ishardly affected below 25cm. Moreover, isotopic values in evapotranspiration flux widely fluctuate witha little relation with that in precipitation. Weighted

-10
5
-15

Precip.

0 -20
01 Apr.

1st (5cm)
1 May

Runoff
2nd (20cm)
31 May3rd (25cm)

30 Jun30 July

4th (25m)
29 Aug.

5th (100cm)
28 Sep.

28 Oct.

Fig. 4: As Fig.2, but for Isotopic compositions(δ 18 O) of soil water in each layer, precipitation, andrunoff.
40

1998, Chiangmai, Isotope ratio of Evaporative Water from Land Surface (‰)

20

0

-20
Total ET
-40
0
1 Apr.

Soil Ev.
1 May

Transpir.
31 MayCanopy Ev.
30 Jun30 July

29 Aug.

28 Sep.

28 Oct.

Fig. 5: As Fig.2, but for Isotopic compositions(δ 18 O) of evaporation from bare soil, transpiration,
evaporation from intercepted water, and total evapotranspiration.

5

1998, Chiangmai, Obs. and Simulated Precipitation δ18O (‰)

Iso-MAT and ICM PRECIP Ave for Apr-Oct 1998

15
0

60

10 -5
-10
5
-15
0 -20
0
1 Apr.

Obs δ18O
1 May

L-A Coupled
31 May0

Ctrl in Y03

30 Jun30 July

29 Aug.

28 Sep.

28 Oct.

Fig. 6: Comparison of simulated δ 18 O in precipitation with observations (bar) and control simulationin Y03 (dashed line), at Chiangmai, Thailand.
Table 1: Weighted averages of isotopic compositionsin fluxes for Apr.1 to Oct.31, 1998, at Chiangmai,
Thailand.
flux (mm/day) δ 18 O (06 00)
Soil evap.
0.78
–9.65
Transp.
0.69–5.35
Canopy evap.
1.30
–7.21
Total ET
2.77
–7.43
Precip.
4.67–7.00

3.2. Global Perspective

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(‰)

18

Fig. 7: Global distribution of δ O in precipitation
as weighted average for Apr. to Oct., 1998.
Iso-MAT and ICM QFLUXL Ave for Apr-Oct 1998

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18

Figure 7-11 show global δ O distributions in principal
surface water variables, precipitation (Fig.7), evapotranspiration (Fig.8), runoff (Fig.9), soil storage in thesurface layer (Fig.10), and canopy intercepted water(Fig.11), for 7 months. In Fig.7, we can find large scale“continental effect” north-eastward in Eurasian continent, northward in the north America, and westward
in the south America. They reflect more or less similar
isotopic distributions in other variables. However, because of influences of complex land surface processes,
intensity of direct impact of precipitation varies. For
example, canopy water (Fig.11) and surface soil storage(Fig.10) are relatively more influenced by precipitation,but evaporation (Fig.8) and runoff (Fig.9) are less.
These results indicate that previous treatments ofisotopes in land surface, such as “no fractionation anddirect return of precipitation isotopes (Jouzel et al.,
1987; Hoffmann et al., 1998; Mathieu et al., 2002)” or“uniform isotopic value in evaporation (Yoshimura etal.,2003)”, are quite rough estimates. Fig.12 displays
the impact of this “physically reasonable, but computationally expensive” treatment on precipitation isotopes. The coupled model simulation monthly results
are compared with control simulation in Y03 and the
GNIP observations. Similarly as the point validation in
Fig.6, there is no change in correlation, but the systematic bias decreases, from −3.4 06 00 to 0.9 06 00. RMSE
decreases, too, from 4.6 06 00 to 3.3 06 00. Therefore, theland surface treatments for isotope circulation are necessary for not only for runoff and river water isotope
estimations, but also for more reasonable precipitation
isotope estimation.

4. Summary and Conclusion
In this study, physical behavior of stable water isotopes
is incorporated to one of LSMs, MATSIRO, which isdesigned and optimized to be coupled with global atmospheric model. In this Iso-MATSIRO, three distinct
treatments of evapotranspiration that possibly make

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-9

60
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120
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3

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(‰)

Fig. 8: As Fig.7, but for total evapotranspiration.
Iso-MAT and ICM runoff Ave for Apr-Oct 1998

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0

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0
-9

60
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120
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3

180
(‰)

Fig. 9: As Fig.7, but for total runoff.
differences in isotopic fractionation are taken into account, such as evaporation from soil, transpiration fromvegetation, and evaporation from intercepted water bycanopy. In case phase-changes (e.g., liquid to gas) takeplace, kinetic isotopic fractionation, in addition to equilibrium fractionation, are explicitly conducted.
The results show there are very large fluctuationsof isotopic compositions in evaporative fluxes and very
near surface water, such as canopy water and the firstlayer (5cm) of soil. By taking those into account,the daily variations of observed precipitation δ 18 O
was slightly better reproduced in Chiangmai, Thailand, than the atmospheric isotope circulation simulation without land surface processes in Yoshimura etal. (2003). Moreover, global comparison with GNIP

of diagnose of the LSM. Even though reasonable energy
and water budgets are somehow computed in LSMs,
these results may be systematically wrong answer, thus
isotopes can additionally tell that it is “truly” reasonable or not.
Nevertheless, this study indicated land surface processes should be reasonably taken into account for more
precise estimation of precipitation isotope distribution.
In these regards, reproduction of d-excess parameter(δD−8 × δ 18 O) in precipitation would be the next step.

Iso-MAT and ICM GLW Ave for Apr-Oct 1998

60

0

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-120
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0

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-9

60
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(‰)

Fig. 10: As Fig.7, but for soil water storage of thefirst soil layer.
Iso-MAT and ICM GLWC Ave for Apr-Oct 1998

References

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Fig. 11: As Fig.7, but for canopy intercepted water.
Ctl in Y03
This study(Y=0.55*X-6.4:(Y=0.63*X-1.6:

R=0.69)R=0.69)Simulated (‰)

0

-10

-20

-30
0

Acknowledgments. Parts of this study were supported by Core Research for Evolutional Science andTechnology (CREST), the Japan Science and Technology Corporation (JST), Special Coordination Funds for
Promoting Science and Technology, and Research Institute for Humanity and Nature (RIHN).

-30

-20

-10
Observed (‰)

0

Fig. 12: Simulated monthly precipitation δ 18 O0are compared with GNIP observations for Apr. toOct.,1998. Land-atmosphere coupled simulation results are shown as blue circles, and control simulation results in Y03 (without land surface processes)
are shown as red crosses.

observations show better agreement in this study, too.
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examine variables except isotopes, such as water andenergy variables. These processes are significantly important and more direct way for improvement of theLSM, but what the current study tries is another way

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