173

Improvement of Runoff Simulation of the Amur River
Takeo ONISHI1*, Muneoki YOH2, Seiya NAGAO3 and Hideaki SHIBATA4
1

Faculty of Applied Biological Sciences, Gifu University, Japan
1-1 Yanagido, Gifu 501-1193, Japan
2
Tokyo University of Agriculture and Technology, Japan
3
Low Level Radioactivity Laboratory, Kanazawa University, Japan
4
Field Science Center for Northern Biosphere, Hokkaido University, Japan*e-mail: takeon@gifu-u.ac.jp

Abstract
Recent studies show that dissolved iron is a factor essential to the biological productivity of the Sea of
Okhotsk. It is also highly probable that wetlands in the Amur River basin play an important role in providing
dissolved iron to the Sea of Okhotsk. During the last century, large areas of wetlands within the Amur River
catchment have been cultivated, which, in turn, may have had a great impact on the production of dissolved
iron. To assess the impact of this conversion of land cover on the production of dissolved iron – and thereby
primary production in the Sea of Okhotsk – we have been constructed a numerical model to simulatedissolved iron production and transport in the Amur River.
Though the accuracy of hydrological models heavily depends upon the precipitation input, there has
been no observation-based gridded precipitation dataset covering a continental-scale region with highspatio-temporal resolution. Recently, however, datasets such as APHRODITE have been released. Thus, inthis article we evaluate how hydrological simulation results may be improved by these datasets. By comparing the results from the APHRODITE precipitation dataset with those from the NCEP2 precipitation dataset,
we show that the precipitation amount from NCEP2 around the Amur River basin is overestimated. Moreover,the closeness of the fit evaluated by the Nash-Sutcliffe criteria indicates that the results from APHRODITE
are significantly better than those from NCEP2. The results indicate that the precipitation of the APHRODITE
dataset for the Amur River basin has good accuracy.
Key words: Amur River, APHRODITE, dissolved iron, Sea of Okhotsk, wetland

1. Introduction
The Amur River, which rises in Mongolia, is one of
the world’s largest rivers, comprising a long stretch of theboundary between China and Russia. The catchment areaof the river is about 2,100,000 km2, the ninth largest rivercatchment in the world, and the total length of the river isabout 4,400 km. Therefore, a huge quantity of fresh water
is supplied by the Amur River to the Sea of Okhotsk (Ogiet al., 2001).
The Sea of Okhotsk is one of the most biologically
productive regions of the world; and it supports a highly
productive commercial fishery. Martin and Fitzwater(1988) found that phytoplankton growth depends on ironabundance as a limiting factor in the Northeast Pacific
Ocean. Recent studies show that dissolved iron plays animportant role in maintaining the biological productivityof the Sea of Okhotsk (Boyd et al., 2004), and it is highly
possible that one of the most important sources of dissolved iron is fresh water from the Amur River (Ducklow
et al., 2003).
Global Environmental Research
15/2011: 173-182
printed in Japan

Iron is an essential nutrient not only for the biologicalproductivity of the Sea of Okhotsk but also for most biota.
However, the production and transportation of dissolved
iron through the terrestrial part of this ecosystem is notwell understood. A complicating factor, especially in thelast few decades, has been that extensive regions of agricultural land have been developed through drainage
improvements to wetlands (Wang et al., 2004).
To investigate the mechanism of dissolved ironproduction and to evaluate the effect of land coverchange, we have developed a simple hydrological model
incorporating dissolved iron production to simulate discharge and the distribution of dissolved iron in the Amur
River basin. Though the accuracy of hydrological models
heavily depends upon the precipitation input, there has
been no global-scale daily gridded precipitation data set
based on observational data. Thus, global reanalysis data
sets such as National Centers for Environmental Predictions (NCEP) / Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II
Reanalysis (Kanamitsu et al., 2002) and European Centre©2011 AIRIEST. ONISHI et al.

174

for Medium-range Weather Forecasts (ECMWF) reanalysis (ERA40) (Kållberg et al., 2004) <http://www.
ecmwf. int/research/era/> have been commonly usedespecially for continental scale hydrological simulations.
Since the main objective of constructing reanalysis data
sets is producing a consistent global analysis of the stateof the atmosphere over an extended period of time,
precipitation figures in reanalysis data sets are entirelycomputationally reproduced fields through 4-dimensional data assimilating processes utilizing historic archivesof observations.
In contrast to this, several gridded highly-resolved
climate data sets such as the Asian Precipitation - HighlyResolved Observational Data Integration Towards
Evaluation of Water Resources (APHRODITE's Water
Resources) (Yatagai et al., 2009) or H08 (Hirabayashi
et al., 2008) have recently been released. Since these data
sets are basically based on observational data, and havehigh spatio-temporal resolution, these data sets shouldhave a potential to improve the accuracy of hydrologicalsimulation results. Thus, in this paper, we have attempted
to compare the result of our hydrological model withdifferent precipitation data sets. In the rest of paper, we
will refer to those datasets as NCEP2, ERA40,
APHRODITE, and H08 for brevity.

2. Site Description
The study site is shown in Fig. 1. The amount ofannual fresh water supplied to the Sea of Okhotsk by theriver is about 300 km3. The average annual precipitation
ranges from 300 mm in the west to more than 700 mm in

the east. The mean annual temperature also varies from
–7ºC in the north to 6ºC in the south.
Figure 2 shows the spatial pattern of land use and land
cover (LULC) types of the basin. LULC types of thebasin are categorized into 19 types. The most dominant
LULC type is forest consisting of mixed forest, deciduous forest, coniferous forest, shrubs, and sparse growth.
Next, agricultural lands (dryland and paddy fields)
occupy a major part of the land cover. Most of the drylands are located on the China side and occupy about 40%of the area of the Songhua River basin. The main crops
are maize, soybeans, wheat and rice (Park et al., 2001).
The rest of the basin mainly consists of grasslands andwetlands. Grasslands and wetlands occupy 12.2% and6.9% of the basin, respectively. While most of the grasslands cover the upper reaches of the basin, wetlands aremainly located along the main course of the lower reach(from the river mouth to Khabarovsk) of the Amur River.
These four different land cover types, i.e., forests,
agricultural lands, grasslands, and wetlands cover morethan 96% of the basin.

3. Materials and Methods
3.1 Model structure
We first divided the whole river basin into
0.5°×0.5°grids. We considered each grid as one
watershed as is usual in LSM. We then routed the discharge from each basin along the river network. A schematic diagram of the model for each divided basin isshown in Fig. 3. Each grid was again subdivided into
1,000 m × 1,000 m grids and the discharge from eachFig. 1 Outline and annual precipitation pattern of the Amur River Basin. Names of main tributaries areshown. Black circles indicate locations of observation stations the data of which are used in thispaper. For precipitation, a figure in Simonov and Dahmer (2008) was modified.
Original data sources: Atlas of the People's Republic of China, Foreign Language Press,
Beijing, 1989; Carmen Revenga, Siobhan Murray, Janet Abramovitz, and Allen Hammond,
Watersheds of the World: Ecological values and vulnerability, World Resources Institute,
Washington, 1998; Gerhard Heilig, Chinafood: Can China Feed Itself? (CD-ROM), IIASA,
Laxenburg, 1999.

Improvement of Runoff Simulation of the Amur River

Fig. 2 Land use / land cover type of the Amur River Basin in 2000 (Yermoshin et al., 2007). The map wasconstructed by combined utilization of vegetation maps of China, Mongolia and Russia and satellite
imageries.

Fig. 3 Schematic diagram of the hydrological model.

175

T. ONISHI et al.

176

Table1 List of external parameters of the model. Parameters are classified into four categories. Phenological parameters and soil
physical parameters are prescribed according to land use/land cover type and soil type prescribed with horizontal distribution.
The number of calibration parameters is also kept to a minimum as much as possible.
SymbolDescription

Unit

Resolution

–
–
m
m

1000m
1°
1000m
1000m

Value

Sourceprescribed with horizontal distribution

a/tanβland use / land cover typesoil typeelevation
topographic index

Yermoshin et al. (2007)
ISLSCP IIb
SRTM-3
SRTM-3prescribed with land use / land cover typeLi et al. (2003), Liua (2005),
Scurlock (2001)
Kondo (1994)
Kondo (1994)
Yermoshin et al. (2007)

LAI

leaf area index

m2/m2

1000m

gs
ga
h
SB

surface conductanceaerodynamic conductancecanopy height
upper limit of canopy storage of snow
fitting parameter for canopy storage curve ofsnow
fitting parameter for canopy storage curve of
snow

m/s
m/s
m
mm

1000m
1000m
1000m
1000m

1/mm

1000m

mm

1000m

m/s
m

1°
1°

–

ISLSCP IIb
ISLSCP II

K
K
cm/K/day
K
m
–

–
–
–
–
–
–

0.0
4.5
0.5
0.0
0.1
1.4

DeWalle and Rango (2008)
DeWalle and Rango (2008)
Hock (2003)
DeWalle and Rango (2008)–
Oki and Sud (1998)

m
m/h
m/s
m/s
day

–
–
–
–
–

–
–
–
–
–

k
P0

Lundberg and Halldin (2001)

prescirbed with soil type
T0
SRmax

saturated hydraulic conductivity
maximum root zone deficitbprescirbed as constant
Ts
Tr
F
TF
PDc
rm

threshold temperature for 100％snow
threshold temperature for 100％rain
degree-day factor for snow melt
threshold temperature for snow melt
upper limit of ponding depth of paddy fields
river meandering ratio

calibration parameter (prescribed as constant)
szm
td
chv
rv
SDc

scaling parameter for runoff
time constant for recharge to the saturated zone
river routing velocity in one gridriver routing velocity in river runoff routing
dissolved iron production threshold

–
–
–
–
–

a: Product of Amur-Okhotsk projectb: Hall, Forrest G., G. Collatz, S. Los, E. Brown de Colstoun, D. Landis, eds. ISLSCP Initiative II. NASA. DVD/CD-ROM. NASA, 2005.

watershed was calculated using a runoff module. The
model consists of two parts, in one of which parameters
are determined and runoff is calculated (TOP-RUNOFF),and in the other, the dissolved iron production process iscalculated (TOP-FE). The calculation time step can be set
at any length, so we executed all calculations at a time
step of one day in the analysis. Detailed explanations of
the calculation algorithm for each process will be given
below in sections 3.1.1 to 3.1.5. All external and input
parameters needed to run the model are summarized inTable 1.
3.1.1 Precipitation / Interception / Snow melt
Depending on the daily mean air temperature, theprecipitation amount is divided into rainfall or snowfallin accordance with the following equations.

Pr = P, Ta ≥ Tr
T − Ts, Ps = P − Pr , Ts < Ta < Tr
Pr = P a
Tr − TsPr = 0, Ps = P, Ta ≤ Ts

Here, Pr is rainfall amount [mm day-1], Ps is snowfall
amount in equivalent water [mm day-1], Tr and Ts areupper and lower temperature threshold values, respectively, of the rainfall/snowfall division [K], and Ta is thedaily mean air temperature [K]. Tr and Ts are prescribed
constant parameters fixed as 4.5 and 0.0 respectively(Hock, 2003; DeWalle & Rango, 2008).
The interception ratio of land vegetation is prescribed
as a constant value according to the LULC type, except
for foresst. For forests (coniferous forests, deciduous
forests, and mixed forests), the rainfall interception rateand snowfall interception rate are separately estimated by
the following equations (Lundberg & Halldin, 2001;
Toba & Ohta, 2005).
I r = Prg exp( − 0.04 Prg ), Prg ≤ 40

(2)

I r = 0.2 Prg , Prg > 40

(1)
Is =

SB
1 + exp − k ( Psg − P0 )

(

)

(3)

Improvement of Runoff Simulation of the Amur River

Here, Ir is the interception loss of rainfall [mm], Prg isthe cumulative rainfall in one event, Is is the interception
loss of snow in water equivalent [mm], Psg is the cumulative snow fall, SB is the upper limit of canopy storage[mm], k is a fitting parameter [mm-1], and P0 is anotherfitting parameter [mm]. According to Lundberg andHalldin (2001), SB, k, and P0 are given as 4.13, 0.72, and2.58 respectively.
The snow pack accumulation amount was calculated
by simply summing up daily snowfall amounts. In thesnow pack melting process, our model adopted the simplest form of the degree-day method as shown in the next
equation
M = F max(0, Ta − TF )(4)

Here, M is the melt rate as a water equivalent per unit
area [cm day-1], F is the degree-day factor [cm K-1 day-1],
Ta is the mean daily air temperature [K], and TF is thethreshold temperature [K]. F and TF are prescribed constant parameters fixed at 0.5 and 0.0.
3.1.2 Evapotranspiration
The actual evapotranspiration rate was estimated by
the Penman-Monteith (Monteith, 1965) equation asshown in the following equation:
λv E =Δ (Rn + L − G ) + ρ a c p (es − ea )g ag
Δ + γ ⎛⎜1 + a ⎞⎟
gs ⎠
⎝

(5)

Here, E is the evapotranspiration rate [mm d-1], Rn is
net shortwave radiation [W m-2], L is net longwave
radiation [W m-2], G is soil heat flux [W m-2], ρa is thedensity of dry air [kg m-3], cp is the specific heat capacityof air [J kg-1 K-1], es is the saturated vapor pressure at that
air temperature [Pa], ea is the vapor pressure of air [Pa],λv is the latent heat of water vaporization [J kg-1], Δ is therate of change of the saturation vapor pressure with airtemperature [Pa K-1], γ is the psychrometric constant [PaK-1], ga is aerodynamic conductance [m s-1], and gs issurface conductance [m s-1]; with λv set as 2.5 x 106[J kg-1], and γ set as 0.66.
To realize the simplest model and to reduce the computational burden as much as possible, heat conduction ofsoil is not considered in the present version of the model,i.e., G is assumed to be 0. Since no calculation algorithmof upward long wave radiation is incorporated in themodel, net long wave radiation is given as forcing datausing NCEP2 data. For the same reason, net short wave
radiation is also given as forcing data using NCEP2 data.
This means that the separation between latent heat flux
and sensible heat flux is calculated by equation (5) under
the assumption that the net heat flux to the ground surface
is appropriately given. Thus, aerodynamic conductanceand surface conductance are unknown parameters inequation (5). Aerodynamic conductance is determined by
the following equation:⎡ z − d ⎤ ⎡ zh − d ⎤ln ⎢ wln
z0 ⎥⎦ ⎢⎣ zT ⎥⎦
⎣
ra =
k 2u

177

(6)

Here, zw is the wind measurement height [m], zh is the
specific humidity measurement height [m], d is zeroplane displacement [m], z0 is surface roughness for wind[-], zT is surface roughness for specific humidity [-], k is
the von Karman constant = 0.4 [-], and u is the windvelocity [m s-1]. Since analytical solutions of zero plane
displacement, surface roughness for wind and surface
roughness for specific humidity are obtained under theassumption of vertically homogeneous vegetation(Watanabe & Kondoh, 1990; Watanabe, 1994), we
utilized these formulas to calculate the aerodynamic
conductance of each LULC type.
On the other hand, surface conductance is considered
to be a changing parameter with respect to soil watercontent, and determined by the next equation:g s = g s max

S rz
SRmax

(7)

Here, gsmax is maximum surface conductance [m s-1],
Srz is root zone water content [mm], and SRmax is maximum root zone water content [mm]. gsmax is a prescribed
constant based on LULC type. Srz is a prognostic variablecalculated in the runoff component, and SRmax is aprescribed constant based on soil type.
3.1.3 Irrigation for paddy fields
Most of the agricultural land in the basin is dry land,and paddy fields occupy less than 10% of total agricultural lands, which means that less than 4% of the basin is
paddy fields. However, water needed for growing rice issignificantly higher than that needed for dry land fields.
Thus, since irrigation for paddy fields might have asignificant impact on the hydrological cycle of the basin,
an irrigation scheme for paddy fields was incorporated in
the model. Though actual water management by farmers
varies with each farmer, we adopted one presumably
standard water management scheme in the region basedon interviews with farmers. River water and groundwater
are two main sources of irrigation water in the agricultural area of the basin, and the farmer can select which touse. At the beginning of the irrigation period, 10 mm
water is first provided to the paddies. During the normal
period of the growing season, losses from evapotranspiration and infiltration are compensated by irrigation tomaintain the water level of each plot at 10 mm. The leveeheight of the plot is uniformly set at 30 cm. When the
ponding depth of the paddy lot exceeds the prescribed
levee height, excess water overflows. All of the overflowed water is considered to contribute to river flow
directly without any retarding process by ponding incanals.

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T. ONISHI et al.

3.1.4 Runoff
The basic concept of the TOP-Runoff module is basedon TOPMODEL (Beven & Kirkby, 1979). Though the
TOPMODEL concept was originally derived from smallscale catchments, the same concept is now used in
global-scale land surface models (LSM) such as
MATSIRO (Takata, 2003), which is coupled with anatmospheric general circulation model developed at theCenter for Climate System Research, the University ofTokyo, and the National Institute for Environmental
Studies, CCSR/NIES AGCM (Numaguti et al., 1997).
While many variations of the original version of
TOPMODEL have been developed, the basic
TOPMODEL concept is adopted in our model.
3.1.5 River runoff routing
Calculated runoff and dissolved iron flux are routed
along the river network. The TRIP data set for LSM as a
GCM component (Oki & Sud, 1998) was used as theriver network. Since some part of the TRIP data set did
not follow the actual river course, the data set was modified according to another detailed river network data set,
which was constructed based on topographical maps
(Yermoshin et al., 2007). In the schematic diagram of thehydrological model shown in Fig. 3, the river network of
the basin is also shown. River runoff velocity is one of
the calibration variables, and prescribed as a constantvalue. In addition, a river meandering coefficient wasprescribed with a constant value of 1.4.
We did not consider the freezing of river water. In
reality, the major part of the river water in the Amur
River basin is partly frozen during the period from theend of November to the middle of April. However,requiring a precise prediction of the discharge during theperiod of river water freezing is not important from theviewpoint of assessing the total amount of dissolved iron,
since the total amount of dissolved iron during the freezing season is much less than that during the non-freezing
season.
3.2 Data and parameter setting
An NCEP2 data set with a spatial resolution of about
2.0°×2.0° was utilized as climate forcing data. These data
included short wave radiation, long wave radiation,
specific humidity, wind speed, and air temperature.
The daily precipitation rate was obtained from the
APHRODITE dataset (Yatagai et al., 2009) at a spatial
resolution of 0.5°×0.5°.
As for the validation of the results of the hydrologicalmodel, river discharge was utilized. The observation
points for river discharge are shown in Fig. 1. We
obtained observed discharge data from the main course(stations 1, 2, 8, 9, 10) and at several large tributaries(stations 3-7). The discharge data were provided by theFederal Service for Hydrometeorology and Environmental Monitoring (ROSHYDROMET) and the Global
Runoff Data Center (GRDC) in Koblenz, Germany<http://grdc.bafg.de>. The time resolution was daily atstations 8 to 10, and monthly at other stations.

Spatially distributed data are LULC, soil properties
and DEM. Since the LULC map shown in Fig. 2 wasoriginally in a vector format, the data were converted to araster format with spatial resolution of 1,000 m. SRTM3
data derived from the Shuttle Radar Topography Missionof NASA were used for DEM. A coarser DEM data set
with a grid size of 1,000 m was produced by averaging
SRTM3 for the analysis. Soil properties such as hydraulicconductivity, and field capacity were obtained from the
ISLSCP2 data set (Hall et al., 2005) with a spatial
resolution of 0.5º. According to these spatially distributed data, parameters needed to run the model wereassigned.

4. Results and Discussion
4.1 Comparison of spatial distribution of
precipitation
We compared the spatial distribution of averageannual precipitation during the period from 1981 to 1990
between NCEP2 data and APHRODITE data. The results
are shown in Fig. 4. NCEP2 data were interpolated tomatch the spatial resolution of the APHRODITE data
set.The results show clearly that the NCEP2 data set
gives a significantly larger amount of precipitation compared with APHRODITE. The aerial average value from
NCEP2 data and APHRODITE data is 701mm and
535mm, respectively. Figure 4 also shows the spatial
distribution of precipitation amount differences between
the two data sets. Within most of the eastern part of thebasin, the precipitation amount of the NCEP2 data islarger than that of the APHRODITE data. Though it isvery difficult to acquire a reliable actual precipitation
data set, we can refer to one figure from Simonov andDahmer (2008) as a reference of the actual precipitation
distribution in the basin, shown with a red line in Fig. 1.
Compared with Fig. 4, the spatial distribution pattern andabsolute value from the APHRODITE data seem moresimilar to Fig. 1. This suggests that the APHRODITE
data could have a potential to improve the results ofhydrological simulation.
4.2 Simulation results
Figure 5 shows the calculated discharges by using
two different precipitation data sets (NCEP2 andAPHRODITE) and observed discharges along the maincourse and several tributaries of the Amur River. Though
no parameter calibration was executed, we obtained afairly good result when we used the APHRODITE
precipitation dataset. In contrast, when we used theNCEP2 precipitation dataset, the obtained discharges
were significantly overestimated at most points. Especially at observation stations 8 to 10, calculated discharges using the NCEP2 dataset were almost double theobserved discharges in the annual base. On the other
hand, the differences between the observed and calculated discharge at observation points 1 and 2 werecomparably small. This overall trend of calculated discharge differences coincides with the spatial patternImprovement of Runoff Simulation of the Amur River

179

Fig. 4 Comparison of spatial distribution patterns of average annual precipitation between NCEP2 data and
APHRODITE data. The period averaged is from 1981 to 1990. Discharge observation stations are also
indicated by black-filled circles in each figure.

difference between NCEP2 and APHRODITE; i.e., thespatial pattern of discrepancy between the NCEP2 andAPHRODITE data sets is largest in the middle to lower
part of the basin and smallest in the upper part of thebasin as shown in the bottom figure of Fig. 5. This
indicates that the NCEP2 data set might give an overestimation of precipitation, especially in the middle tolower parts of the Amur River basin. Our results are also
consistent with the results of Betts et al. (2006), whose
study showed that the NCEP2 precipitation of this region
is much higher than the Global Precipitation Climatology
Project (GPCP) data set.
We evaluated the accuracy of each simulated result
using Nash and Sutcliffe (1970) criteria (Table 2). Since aNash and Sutcliffe criteria value larger than 0.5 means
moderately good or good agreement with observed

values, it can be said that the results simulated using
APHRODITE data give relatively good results comparedwith those using the NCEP2 data set. Of course, after
executing parameter calibrations, the results calculatedusing NCEP2 reanalysis can also give good agreementwith observed data. The calibrated parameter sets, however, were not within the physically valid ranges of eachparameter. Thus, considering that calculated discharge
data can be a gross hydrological index of a basin, we canconclude that the APHRODITE precipitation dataset ismore accurate than the NCEP2 dataset, especially in
the Amur River basin. It has also been proven that
APHRODITE can give better results in discharge
simulation of the Amur River basin.

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T. ONISHI et al.

Fig. 5 Comparison of discharge calculated by using NCEP2 data and that by using APHRODITE data.

Improvement of Runoff Simulation of the Amur River
Table 2 Values of Nash-Sutcliffe criteria at each observation
point.
Station
APHRODITENCEP2Catchment area
(×103km2)
1
0.5
0.5
175
2
0.7
0.4
200
3
0.2
–7.5
391
4
0.1
–2.4
44
5
0.7
–4.9
400
6
0.9
0.4
67
7
0.5
0.5
24
8
0.8
–1.8
1,600
9
0.7
–1.1
1,730
10
0.6
–1.2
1,790

5. Conclusion
We constructed a hydrological model of the Amur
River basin to evaluate dissolved iron production
amounts. Since the accuracy of hydrological models issignificantly related to precipitation input, results simulated using two different precipitation data sets were
examined. For precipitation data, NCEP2 andAPHRODITE were used. We confirmed that the NCEP2
dataset might give overestimated precipitation, especially in the middle to lower part of the Amur River basin.
It was also suggested that APHRODITE data have apotential to give better results compared with NCEP2
data for the Amur River basin.

Acknowledgments
This work was undertaken under the framework of aninternational project entitled ‘Human activities inNortheastern Asia and their impact on the biologicalproductivity of the North Pacific Ocean’ funded by the
Research Institute for Humanity and Nature (RIHN)
during the period from 2005 to 2009. The authors express
their thanks to Global Soil Data Task, the IGBP, ORNLDAAC and ISLSCP Initiative II for providing soil
characteristic data. We deeply thank all the staff in
ROSHYDROMET, and in particular G. Alexander for his
special collaboration in this work.
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Takeo ONISHI
Takeo ONISHI is an Assistant Professor of theFaculty of Applied Biological Sciences, Gifu
University. He received his Doctorate in Agriculture at Kyoto University (2004). His current
interests are hydrological modeling, interactions
between hydrological cycles and biogeochemical
cycles on terrestrial area and hydrological interactions between land andocean. His research works place especial emphasis on the evaluation ofanthropogenic impacts such as agriculture, forestry and fisheries onhydrological and biogeochemical cycles.

Muneoki YOH
Dr. Muneoki YOH works in the Division ofEnvironmental Science on Biosphere, Institute ofAgriculture, Tokyo University of Agriculture andTechnology (Fuchu, Tokyo, Japan). He got his
doctorate in science from Nagoya University in1991 with a dissertation study on the biogeochemical behavior of N2O in freshwater lakes. His major interest has been
biogeochemistry, mainly regarding nitrogen, but recently extending to thecycling of other elements such as carbon and iron. His present fieldwork
includes lake, stream, river, wetland, agricultural field, forest and alpine
ecosystems. His recent typical subjects include 1) nitrogen saturation andacidification in forest ecosystems in China and Japan and 2) dissolved ironin terrestrial environments.

Seiya NAGAO
Seiya NAGAO is a Professor at the Low Level
Radioactivity Laboratory, Institute of Nature andEnvironmental Technology, Kanazawa University. His current interests are 1) biogeochemistryof dissolved and particulate organic matter andiron in river watershed systems including coastal
marine environments, 2) migration behavior and its characteristics and thefunctionality of dissolved organic matter such as humic substances ingroundwater, 3) transport processes of radiocesium in river, lake andmarine environments. His research work uses radioisotopes and stable
isotopic ratios as tracers of organic matter and suspended solids. He alsouses spectroscopic and chromatographic techniques to characterizedissolved organic matter in natural waters.

Hideaki SHIBATA
Hideaki SHIBATA is a Professor at the Field
Science Center for Northern Biosphere, Hokkaido
University. His current interests are forest
biogeochemistry, ecosystem ecology, hydrology
and soil science with the impact of humanactivities (such as atmospheric deposition, climatechange, land-use and land-cover changes) inmainly forest ecosystems. At present, his principal projects focus on (1)nitrogen and carbon cycling and stream chemistry in forest watersheds, (2)effects of natural and anthropogenic disturbances on ecosystem processes
and services in forest landscapes and (3) the role of sasa dwarf bamboo oncarbon and nitrogen cycling in forest ecosystems.

(Received 27 September 2011, Accepted 26 December 2011)