ISSN 1341-4356

CGER-REPORT

CGER-I103-2012

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol. 18

Development of Process-based NICE Model and Simulation of
Ecosystem Dynamics in the Catchment of East Asia
(Part III)

Tadanobu Nakayama

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

ISSN 1341-4356

CGER-REPORT

CGER-I103-2012

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol. 18

Development of Process-based NICE Model and Simulation of
Ecosystem Dynamics in the Catchment of East Asia
(Part III)

Tadanobu Nakayama

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.18

Development of Process-based NICE Model and Simulation of Ecosystem Dynamics in
the Catchment of East Asia (Part III)
Tadanobu Nakayama

Edited by:Center for Global Environmental Research (CGER)
National Institute for Environmental Studies (NIES)

Coordination for Resource Allocation of the Supercomputer:
Center for Global Environmental Research (CGER)
National Institute for Environmental Studies (NIES)

Supercomputer Steering Committee (FY2011):
Yasumasa Kanada (University of Tokyo)
Shoji Kusunoki (Meteorological Research Institute)
Koki Maruyama (Central Research Institute of Electric Power Industry)
Akira Noda (Japan Agency for Marine-Earth Science and Technology)
Takashi Imamura (Center for Environmental Measurement and Analysis/NIES)
Kazumi Kishibe (Environmental Information Department/NIES)
Toshimasa Ohara (Center for Regional Environmental Research/NIES)
Hitoshi Mukai (Center for Global Environmental Research/NIES)

Maintenance of the Supercomputer System:
Environmental Information Department (EID)
National Institute for Environmental Studies (NIES)

Operation of the Supercomputer System:
NEC Corporation

Copies of this report can be obtained by contacting:
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16-2 Onogawa, Tsukuba, Ibaraki , 305-8506 Japan
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The report is also available as a PDF file.
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Copyright 2012:
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This publication is printed on paper manufactured entirely from recycled material (Rank A), in accordancewith the Law Concerning the Promotion of Procurement of Eco-Friendly Goods and Services by the Stateand Other Entities.
ISSN 1341- 4356, CGER-I103-2012

Foreword
The Center for Global Environmental Research (CGER) at the National Institute forEnvironmental Studies (NIES) was established in October 1990. CGER’s main objectives areto contribute to the scientific understanding of global change and to identify solutions forpressing environmental problems. CGER conducts environmental research frominterdisciplinary, multi-agency, and international perspectives, provides an intellectual
infrastructure for research activities in the form of databases and a supercomputer system, andmakes the data from its long-term monitoring of the global environment available to the
public.
CGER installed its first supercomputer system (NEC SX-3, Model 14) in March 1992.
That system was subsequently upgraded to an NEC Model SX-4/32 in 1997, and an NEC
Model SX-6 in 2002. In March 2007, we replaced that system with an NEC Model
SX-8R/128M16 in order to provide an increased capacity for speed and storage. We expect
our research to benefit directly from this upgrade.
The supercomputer system is available for use by researchers from NIES and otherresearch organizations and universities in Japan. The Supercomputer Steering Committee
evaluates proposals of research requiring the use of the system. The committee consists ofleading Japanese scientists in climate modeling, atmospheric chemistry, ocean environment,
computer science, and other areas of concern in global environmental research. In the 2007
fiscal year (April 2007 to March 2008), sixteen proposals were approved.
To promote the dissemination of the results, we publish both an Annual Report andoccasional Monograph Reports. Annual Reports give the results for all research projects thathave used the supercomputer system in a given year, while Monograph Report presents theintegrated results of a particular research program.
This Monograph Report presents the results following the 11th publication (Part I) andthe 14th publication (Part II) which were published previously. The new process-basedcatchment model, called the National Integrated Catchment-based Eco-hydrology (NICE)
model, was applied to various catchments in East Asia. The results obtained in this research
will allow us to clarify the various kinds of interactions among human activities andwater–heat–mass cycles and their impacts on environmental degradation for sustainable
development of human society in catchments.
In the years to come, we will continue to support environmental research with our
supercomputer resources and disseminate practical information on our results.

January 2012

Yasuhiro Sasano
Director
Center for Global Environmental Research
National Institute for Environmental StudiesiPreface
This volume of CGER’S SUPERCOMPUTER MONOGRAPH REPORT series is the
18th publication of research outcomes achieved by users of the supercomputer facilities at theCenter for Global Environmental Research (CGER), National Institute for Environmental
Studies (NIES). The author has so far developed the new process-based catchment model,
called the National Integrated Catchment-based Eco-hydrology (NICE) model, to evaluate
ecosystem dynamics in the catchments of East Asia.
Though water resources are vital for human activity, its overuse causes the serioushydrologic change such as drying out river, groundwater fall, and seawater intrusion, et al. It
also causes various ecosystem degradations and serious burden on the environment. However,effective management of water resource is also powerful for decision-making and adaptation
strategy for sustainable development.
This monograph (Part III) succeeds the 11th publication (Part I) and the 14th publication(Part II) published previously. This volume reviews the NICE processes newly developedsince Part I was published and describes how these models have been applied to various
catchments in East Asia. The result indicates effective management of water resource ispowerful for achieving win-win solution about hydrothermal pollutions in eco-conscious
society. The NICE model is now being expanded to include another process including
regional atmospheric and ocean models, et al. for evaluating various ecosystems and theirrelation to the surrounding regions. This subsequent research will be reported in the next
monograph (Part IV) in the near future. The author hopes that the publication we present here
will facilitate further research on the sustainable development of human society in catchments.
In particular, the author hopes that it will help to clarify the various kinds of interactions
among human activities and water–heat–mass cycles and their effects on environmental
degradation.
January 2012

Tadanobu Nakayama
Senior Researcher
Center for Global Environmental Research
National Institute for Environmental Studiesii

Contents
Foreword ..................................................................................................................................ϸ
Preface ......................................................................................................................................Ϲ
List of Figures ..........................................................................................................................ϼ
List of Tables ............................................................................................................................Ͽ

Chapter 1
General Introduction ................................................................................................................ 1
1.1 Background ........................................................................................................................... 3
1.2 Model Description of Process-based Model Applicable to Urban Areas ............................. 5
1.3 Objective and Method ........................................................................................................... 6
References ................................................................................................................................... 7

Chapter 2
Diverse Water System Accompanied by Human Intervention in the North China Plain.................................................................................................................................................... 9
Abstract ..................................................................................................................................... 10
2.1 Introduction ......................................................................................................................... 11
2.2 Methods ............................................................................................................................... 13
2.2.1 NICE Model Series in Natural and Agricultural Regions .......................................... 13
2.2.2 Water Transfer in Dam and Canal Systems ............................................................... 13
2.2.3 Satellite Analysis using NOAA/AVHRR Data .......................................................... 13
2.3 Input Data and Boundary Conditions for Simulation ......................................................... 14
2.3.1 Meteorological Forcing Data ..................................................................................... 14
2.3.2 Vegetation and Geological Properties ........................................................................ 15
2.3.3 Water Demand and Transfer Data in Urban Areas .................................................... 17
2.3.4 Boundary Conditions and Running the Simulation ................................................... 18
2.4Results and Discussion....................................................................................................... 21
2.4.1 River Discharge Affected by Dam and Canal Systems ............................................. 21
2.4.2 Groundwater Level..................................................................................................... 23
2.4.3 Hydrologic Changes and Ecosystem Degradation Caused by Human Intervention .. 25
2.5 Conclusion........................................................................................................................... 28
References ................................................................................................................................. 28

Chapter 3
Relation Between Water Resource and Urban Activity in Dalian..................................... 33
Abstract ..................................................................................................................................... 34
3.1 Introduction ......................................................................................................................... 35
3.2 Methods ............................................................................................................................... 39
3.2.1 Hydrologic Budget ...................................................................................................... 39iii

3.2.2 Flood Resourcification Ability .................................................................................... 40
3.2.3 Decoupling Indicator based on Water Consumption and Economic Growth ............. 40
3.3 Input Data and Boundary Conditions for Simulation ......................................................... 41
3.3.1 Meteorological Forcing Data ...................................................................................... 41
3.3.2 Vegetation and Geological Properties ......................................................................... 43
3.3.3 Statistical Data on Water Resources and GDP............................................................ 44
3.3.4 Boundary Conditions and Running the Simulation..................................................... 44
3.4Results and Discussion....................................................................................................... 45
3.4.1 Hydrologic Change Affected by the Reservoir Construction ..................................... 45
3.4.2 Vegetation Change Affected by the Change in Water Resources ............................... 49
3.4.3 Relationship Between Water Resources and Urban Activity...................................... 50
3.5 Conclusion........................................................................................................................... 52
References ................................................................................................................................. 52

Chapter 4
Ability of Water Resources to Reduce the Urban Heat Island in the Tokyo
Megalopolis ............................................................................................................................. 57
Abstract ..................................................................................................................................... 58
4.1 Introduction ......................................................................................................................... 59
4.2 Study Area ........................................................................................................................... 60
4.3 Materials and Methods ........................................................................................................ 62
4.3.1 Developing a Process-based Model Applicable to Urban Areas (NICE-URBAN) ..... 62
4.3.2 Input Data and Boundary Conditions........................................................................... 64
4.3.3 Building Structure and Anthropogenic Heat Source in Urban Areas .......................... 64
4.3.4 Simulation and Prediction for Political Scenarios ....................................................... 65
4.4Results and Discussion....................................................................................................... 69
4.4.1 Multi-scaled Characteristic in Hydrothermal Cycle .................................................... 69
4.4.2 Prediction of Hydrothermal Changes in the Symbiotic Urban Scenario ..................... 72
4.4.3 Heterogeneity of Hydrothermal Cycles in Urban Areas .............................................. 74
4.5 Conclusion........................................................................................................................... 76
References ................................................................................................................................. 76

Chapter 5
Final Conclusions and Future Work .................................................................................... 81
5.1 Final Conclusions ................................................................................................................ 83
5.2 Future Work ........................................................................................................................ 83
References ................................................................................................................................. 88

Appendix
Publications and Presentations ............................................................................................. 91
Publications and Presentations ................................................................................................. 92
Contact Person.......................................................................................................................... 95iv

List of Figures
Chapter 1
1.1
1.2
1.3

Evaluation of ecosystem degradation by cyclic changes in water, heat, and massdescribed in this study............................................................................................................. 3
Process-based National Integrated Catchment-based Eco-hydrology model ......................... 5
Ecosystem highlighting various regions in East Asia ............................................................. 7

Chapter 2
2.1

2.2

2.3

2.4

2.5

2.6

Study area in the NCP showing (a) mean elevation and (b) land cover. In (a), the redline indicates the border of the NCP. The number shows the groundwater observation
stations listed in Table 2.3. In (b), circles indicate the observation of river discharge,
triangles indicate that of soil moisture recorded in Table 2.3, and blue trapezoids
indicate the main reservoir. The yellow line represents the Grand Canal (South andNorth canals) where water is transfered from the southern to northern area in theSouth-to-North Water Transfer Project. The red square represents the simulation areaof seawater intrusion, as shown in Fig. 2.5. .......................................................................... 12
Vertical geological structure at latitude of 38°N in the NCP, obtained by (a) in thesimulation and (b) previous research. In (a), the soil type shows the same value as thatspecified in Table 2.1. Soil type=0 indicates the interface between sea and land. In (b),the cross-sectional profile of the NCP between Shijiazhuang (38.07°N, 114.55°E) andDezhou (37.50°N, 116.30°E) is illustrated: 1: stratum age; 2: stratum thickness; 3:
gravel and boulders; 4: clay and sandy clay; 5: aquifer boundary; 6: water table.. .............. 16
Simulated and observed river discharges in the Hai and Yellow River catchmentsfrom January 1, 1987, to December 31, 1988, at (a) Juancheng and Lijin rivers
downstream of the Yellow River (R-14 and 15 in Table 2.3), (b) the Luobaotan River(R-7 in Table 2.3), (c) the Yanchi River (R-3 in Table 2.3), and (d) the Haihezha
River (R-1 in Table 2.3) at the Hai River catchment. Open circles and triangles
represent observed values, and lines and dotted lines represent simulated values,. Cor
indicates the correlation between observed and simulated values, and NS indicates theNash–Sutcliffe criterion. ....................................................................................................... 22
Annually averaged groundwater levels in the NCP from 1959 to 1992 illusrating (a)observed value, (b) simulated result with only irrigation water usage, and (c)simulated result with irrigation and urban water usage. Bold lines represent the NCP
borders, and dotted lines indicate the groundwater levels at 0 m, 20 m, and 50 m
above sea level. In (c), circles represent the main cities where water usage in urban
areas is predominant, as described in Table 2.2. ................................................................... 24
Annually averaged contour of seawater intrusion in the NCP illustrating (a) observed
value in the shallower groundwater. In (b), the annually averaged simulation result of
NICE-CNL corresponds to the red squares in (a).. ............................................................... 26
NDVI in the NCP illustrating (a) the trend of NDVI in the grain filling season for
winter wheat in May, both in the coastal (I) and plain (II) areas, and (b) spatial
patterns in NDVI gradient in the NCP in 1982-1999. The bold line in (a) indicates aleast-square regression line estimated from monthly NDVI in May (circle). In (b), theblack square represents the border of the simulation area in a similar manner at thatshown in Fig. 2.1; the red square represents the simulation area of seawater
intrusion, as shown in Fig. 2.5. ............................................................................................. 27v

Chapter 3
3.1
3.2

3.3

3.4

3.5

3.6

3.7

3.8

Location of study area and elevation of the Biliu River catchment and Dalian. The
bold black line represents catchment border. ........................................................................ 36
Annual time series of GDP and water consumption in Dalian after the completion of
Biliu Reservoir illustrating (a) GDP and water consumption in the entire city og
Dalian, (b) GDP and water consumption in the urban area of the city, and (c) water
intake from Biliu Reservoir. The values for GDP and water consumption were plottedon the basis of values reported in 1985 (value at 1985 = 100) in (a) and (b). Rainfall
anomalies at the reservoir are plotted along the right-hand y-axis in (c). ............................. 37
Comparison between forcing precipitation data created by 13 points of rain gauge
data and previous research data: (a) rainfall anomalies (mm) in the forcing data and(b) rainfall anomalies (mm) in previous research data.......................................................... 43
Calibration in Biliu River catchment (D-1 – D-4; Table 3.1). Circles represent
observation data, and lines represent results simulated by NICE. Date = 0 on thehorizontal axis corresponds to January 1, 2005. Cor shows the correlation between
observed and simulated values. In (d), dotted and solid lines represent simulated
discharges without and with the water transfer at Biliu Reservoir, respectively. ................. 46
Simulated results of annual-averaged groundwater levels in the Biliu River catchment
illustrating (a) the comparison between simulated and observed values and (b) thesimulated contour in the catchment. Cor shows the correlation between the observed
value and the simulated value. MV, SD, CV represent mean value, standard deviation,and coefficient of variation (= SD/MV), respectively. .......................................................... 47
Simulation result of water resources affected by the construction of Biliu Reservoir
illustrating (a) river discharge downstream of the reservoir and (b) flood
resourcification ability downstream of the reservoir. ........................................................... 48
NDVI with a resolution of 1.1 km mesh in the Biliu River catchment illustrating (a)the annual trend of NDVI downstream of Biliu Reservoir (circles). Rainfall anomalies
at the reservoir are also plotted on the right-hand y-axis (bars). Dashed lines represent
least-square regression lines estimated from NDVI data. (b) Spatial patterns in NDVI
gradient in the entire catchment during 1984-2007 (image areas: 39.35°N–40.50°N,
122.05°E–123.00°E). The dashed square represents the border of the simulation area. ...... 50
Annual trends of decoupling indicators among (a) water consumption and GDP in theentire city of Dalian, (b) water consumption and GDP in the urban area of Dalian, and(c) NDVI and water consumption. Dashed lines represent least-square regression lines
estimated from annual decoupling indicators (bars). ............................................................ 51

Chapter 4
4.1

4.2
4.3

Study area highlighting land cover in the city of Kawasaki and the Kanto Plain,including the Tokyo metropolitan area, Tokyo Bay, and the Tama River catchment(area: 1,240 km2) . Sampled data of 206 points of geological structures were used for
input in the NICE-URBAN model. The observed data were as follows: 29 points ofweather data (W; ‫) ۑ‬, 6 points of river discharge data (R; ‫) ڹ‬, 9 points ofgroundwater level data(G; ‫ )ۃ‬during 2004-2006, 1 point of flux tower data at the
Todoroki car parking garage (F; ‫)ڧ‬, and 1 point of various pavement blocks at therooftop of the Keihin building (P; ‫)ۼ‬. The data points were used for validation of themodel (Table 4.1). ................................................................................................................. 61
Flow diagram of the NICE-URBAN model. Dotted frames indicate the newly
developed processes used in this study. ................................................................................ 63
Schematic representation of multi-scaled model illustrating (a) multi-layers in thevertical direction, (b) an example of simulated values, and (c) the diurnal cycle ofanthropogenic heat emission estimated by the GIS database................................................ 65

vi

4.4

4.5

4.6

4.7

Verification of heat flux in the interfacial area; (a) comparison of simulated airtemperature with observed value 2.0 m above the ground surface at points in W-1 to
W-29 in Table 4.1; (b) validation of heat-flux budget in the symbiotic water-holding
pavement after water irrigation at point F-1 in Table 4.1. In (a), the solid line
represens the observation data, and bold line represents the results simulated by
NICE-URBAN. Date=14 on the horizontal axis corresponds to August 15, 2006. r2
and RMSE represent correlation between observed value and simulated value, androot-mean-square error, respectively. In (b), the dotted line represents net radiation(NR); solid line, sensible heat flux (SH); bold line, latent heat flux (LH); anddash-dotted line, ground transfer heat flux (GH). Date=0 on the horizontal axis
corresponds to August 1, 2007.............................................................................................. 70
Verification of hydrologic cycle from regional to urban scales: (a) annual-averaged
groundwater level in the regional scale (Kanto Plain) interpolated by using theobserved value; (b) simulated value in the regional scale; (c) simulated value in theurban area downscaled from the regional scale in (b); and (d) comparison ofsimulated groundwater level with observed value at points G-1 to G-9 in Table 4.1. In(a), the dotted circle represents the observed location of 247 points. In (b), the dotted
purple frame represents the urban area simulated in (c). In (d), circles represent
observation data, and lines represent results simulated by NICE-URBAN. Date=0 on
the horizontal axis corresponds to January 1, 2004.. ............................................................ 71
Prediction of hydrothermal changes at 1500 JST on August 5, 2006, in the symbiotic
urban scenario: (a) present surface temperature; (b–c) prediction of surface
temperature after water-holding pavement and green areas, respectively; and (d–e)
prediction of monthly-averaged groundwater level change considering water-holding
pavement and green areas, respectively.. .............................................................................. 73
Environmental pollution in urban and surrounding areas: (a) simulated groundwaterlevel relative to the ground surface; (b) annual-averaged groundwater nitrogenconcentration (mg/L) interpolated by observed data. Urban and cultivated fields areplotted together in the figures. Dotted frames represent urban areas similar to thosedescribed in Fig. 4.5. The northeastern region of the urban area represents the Tokyo
metropolitan area. ................................................................................................................. 75

Chapter 5
5.1
5.2
5.3

Framework for visualization of hydrothermal interactions for favorable solutions.............. 84
Dynamics of water–heat–mass cycles in the Changjiang and Yellow River basinsobtained by the combined land–atmosphere model .............................................................. 85
Integrated procedures through meta-ecosystem analysis ...................................................... 87vii

List of Tables
Chapter 2
2.1
2.2
2.3

Geological parameters used in numerical simulation. .......................................................... 16
Average water usage in 1987-1988 by major cities in the NCP (u 10000 m3). .................... 17
Observation stations cited for validation. ............................................................................. 19

Chapter 3
3.1

List of observation stations for validation............................................................................. 42

Chapter 4
4.1
4.2

Lists of observation stations used for validation. .................................................................. 66
Present land use and available vacant space considered in the scenario simulation. ............ 68

viii

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.18
CGER-I103-2012, CGER/NIES

Chapter 1
General Introduction-1Chapter 1 General Introduction-2

CGER-I103-2012, CGER/NIES

1.1 Background
Human activity has dramatically changed ecosystem dynamics in East Asia. In particular,
adaptation from rural settings or the wilderness to urban or agricultural settings has greatly
affected the surrounding ecosystems in catchments. Environmental pollution has recently
come to be synonymous with degradation of water and thermal environments and thecontamination of urban areas (Fig. 1.1). To facilitate sustainable development, it is necessary
to quantify the mechanisms of ecosystem change and execute adaptation strategies to develop
sustainability for the active recovery of local natural environments that have been damaged.
In this context, numerical models are powerful tools used in river- and basin-wide initiatives.

Fig. 1.1

Evaluation of ecosystem degradation by cyclic changes in water, heat, and massdescribed in this study.

The hydroclimate between the northern and southern regions of China is diverse (Fig. 1.1)
(Nakayama, 2011a, 2011b; Nakayama and Watanabe, 2008b). The North China Plain (NCP)
is located downstream of the Yellow River and has a semi-arid climate. Because it is one of
the country’s most important agricultural regions, this area experiences critical water
shortages (Brown and Halweil, 1998; Shimada, 2000; Chen et al., 2003; Nakayama et al.,2006). In addition, this region includes vast metropolitan areas such as Beijing and Tianjin.
The demand for water here has intensified usage conflicts between the upstream anddownstream areas in addition to similar conflicts between agricultural andmunicipal–industrial sectors. This region, once water-rich in the 1950s, has become water-3

Chapter 1 General Introduction

deficient because of various ecosystem degradations including the drying of the Yellow River,the near closure of the Hai River, and groundwater degradation in the NCP (Liu and Xia,
2004; Nakayama, 2011a, 2011b; Nakayama et al., 2006). These conditions have created large
depression cones and land subsidence in urban areas. In addition, saline–alkaline areas have
expanded on land due to seawater intrusion in the coastal zone (Brown and Halweil, 1998;
Shimada, 2000; Chen et al., 2003; Nakayama, 2011a). Several researchers have stated that
China’s demand for natural resources already exceeds the environment’s capacity to sustain
this densely populated land (Varis and Vakkilainen, 2001). Because Dalian is located at thesouthern tip of the Liaodong Peninsula, the region has limited access to fresh water. Most of
the city’s water, necessary for its rapid economic growth, is drawn from the remote Biliu
Reservoir located at the middle of the river. Since the completion of this reservoir in 1984,
most of the water has been piped to Dalian, resulting in a shift from water-rich to water-poor
catchment areas, as evidenced by the drying of the downstream river, reduction in
groundwater levels, and seawater intrusion, all of which have been observed in other regions
in northern China (Ren et al., 2002; Nakayama et al., 2006, 2010).
Moreover, urban heat islands, created by the expansion of cities and industrial areas, havebecome a significant environmental problem, worldwide (Oke, 1987). The Japanese
government has taken various measures to mitigate this problem in the Tokyo metropolitan
area (Fig. 1.1) by rearranging surface layers, reconstructing older buildings and urban
geometry, applying green infrastructure systems, increasing energy efficiency, and managing
traffic–transportation systems. Another effective strategy for heat island mitigation is thepromotion of vaporization through the use of water-holding pavements in conjunction withlight-colored and vegetated surfaces (Chang et al., 2007; Nakayama and Fujita, 2010).
Because the heat relaxation properties in the newly symbiotic pavements are closely related tothe heat capacity of water and the conductivity of the pavement, it is estimated thatgroundwater use in almost constant temperature as a heat sink would be an effective method
for heat island compensation (Ministry of Environment, 2004). However, the metropolitan
area has also undergone severe damage through hydrologic cycle imbalances; for example,
flooding and buoyant forces have compromised underground infrastructures that were
installed to regulate groundwater usage (Endo, 1992; Nakayama et al., 2007). The severity ofwater and thermal pollution in marine areas has increased over time. Water resources such as
rivers, lakes, seas, and groundwater are important for the mitigation of urban heat islands.
Watersides, open parks, and green areas have become valuable urban cool islands (Ministryof Environment, 2004). A policy to promote infiltration into the aquifer is also important toprevent ground degradation (Nakayama and Hashimoto, 2011; Nakayama et al., 2007, inpress).
The current monograph (Part III) succeeds our 11th publication (Part I) in 2006
(Nakayama and Watanabe, 2006b) and our 14th publication (Part II) in 2008 (Nakayama,
2008c). The catchments described in this monograph are representative of areas in East Asia,
in which human activity has greatly affected the environment. Although water resources arevital for human activity, overuse causes significant hydrologic changes including drying
rivers, receding groundwater levels, and seawater intrusion in addition to ecosystemdegradation and severe environmental burden. However, the effective management of water
resources is also important in the planning and execution of sustainable development
strategies. This monograph illustrates the possibility for sound recovery of the hydrologic
cycle with simultaneous creation of thermally stable environments. The results indicate that
effective management of water resources is critical for achieving favorable solutions tohydrothermal pollution in an eco-conscious society.
-4CGER-I103-2012, CGER/NIES

1.2 Model Description of Process-based Model Applicable to Urban Areas
In a previous study, the author developed the National Integrated Catchment-based
Eco-hydrology (NICE) model, which includes surface-unsaturated–saturated water processes
and assimilates land-surface processes, describing phenology variations on the basis ofsatellite data (Nakayama, 2008a, 2008b, 2008c, 2009, 2010, 2011a, 2011b; Nakayama andFujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Watanabe, 2004, 2006a, 2006b,
2008a, 2008b, 2008c; Nakayama et al., 2006, 2007, 2010, in press) (Fig. 1.2). NICE is a 3-D,
grid-based eco-hydrology model. This model has been coupled with complex subsystems in
irrigation, urban water usage, stream junctions, and dams and canals to develop integrated
human and natural systems and to analyze the impact of anthropogenic activity on
eco-hydrologic change. In the Simple Biosphere model 2 (SiB2), the unsaturated layer
vertically divides the canopy into two layers and the soil into three layers (Sellers et al., 1996).
In the saturated layer, NICE solves 3-D groundwater flow for both unconfined and confined
aquifers. The hillslope hydrology including freezing–thawing processes can be expressed by
the two-layer surface runoff model. NICE links each submodel by considering water–heatfluxes, including the gradient of hydraulic potential between the deepest unsaturated layer andthe groundwater, effective precipitation, and seepage between the river and groundwater.

Fig. 1.2

Process-based National Integrated Catchment-based Eco-hydrology model.

-5Chapter 1 General Introduction

For agricultural applications, NICE is coupled with Decision Support Systems forAgro-technology Transfer (DSSAT) (Ritchie et al., 1998), in which an automatic irrigation
mode supplies crop water requirements for field cultivation (Nakayama et al., 2006;
Nakayama, 2011a, 2011b) and cultivation of paddy fields (Nakayama and Watanabe, 2008b).
This model includes the various functions of representative crops such as wheat, maize,soybean, and rice and automatically simulates their dynamic growth processes. Plant growthis based on biomass formulation, which is limited by various reduction factors such as light,
temperature, water, and nutrients (Nakayama, 2011a; 2011b; Nakayama and Watanabe,
2008b; Nakayama et al., 2006). Most of the water supplied to major cities and cultivatedfields in the NCP and Dalian is transported through a network of reservoirs and canals(Nakayama, 2011a; Nakayama et al., 2006, 2010) (Fig. 1.1). Because limited data are
available on discharge control at most of the dams adjacent to these reservoirs, a constantratio of inflow to outflow—instead of the more complicated storage–runoff function
model—was used for the major dams for which no data were available (Nakayama, 2011a).
NICE was also coupled with the urban canopy model (UCM) to include the effects of thehydrothermal cycle on various pavements, and with the Regional Atmospheric Modeling
System (RAMS) (Pielke et al., 1992) to include hydrothermal interaction in urban areas(Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama et al., in press).
Downward short- and long-wave radiation, precipitation, atmospheric pressure, airtemperature, air humidity, and wind speed simulated by the atmospheric model were input
into the UCM, whereas momentum, sensible, latent, and long-wave flux simulated by the
UCM were input into the atmospheric model at each time step. This procedure ensures thatthe feedback process of water and heat transfers between the atmospheric region and the land
surface are implicitly included in the simulation process.
The NICE series has expanded to include additional processes, including regional
atmospheric and ocean models, for evaluating various ecosystems and their relation tosurrounding regions. The details of the existing NICE series are presented in the following
references.
1.3 Objective and Method
To study the ecosystem dynamics in the catchments of East Asia, the author has
previously developed a method to combine a grid-based numerical model, ground-truth
observation data, and satellite data (Fig. 1.3). In this monograph (Part III), the author reviews
the new processes of NICE developed after Parts I (Nakayama and Watanabe, 2006b) and II(Nakayama, 2008c) and describes how these models were applied to the catchments whereinwater resources, economic growth, and ecosystem degradation relations with the environment
require urgent evaluation. The main study areas are northern China, including the NCP(2011a, 2011b; Nakayama et al., 2006, 2010), and the Tokyo metropolitan area (Nakayama
and Hashimoto, 2011; Nakayama et al., 2007, in press) (Fig. 1.1). The final objective of thecurrent monograph is to present a method by which to combine numerical models, satellite
imagery, and statistical analysis to clarify the relationship between water resources andeconomic growth, which adversely affects the ecological balance. The process-based model
uses complex subsystems to simulate hydrological changes such as the drying of rivers andgroundwater degradation resulting from human intervention in order to evaluate therelationship between urban development and sustainable water resource management in thecatchments. In addition, the model simulates the effects of urban geometry and anthropogenic
exhaustion on changes in water and heat cycles in the megalopolis. Moreover, the author-6CGER-I103-2012, CGER/NIES

presents a procedure to visualize related environmental pollution such as the missing role ofhydrothermal interactions in atmospheric, land, and marine areas. This procedure offers an
integrated assessment system for effective strategies that could reveal methods of solving thecomplex nature of water challenges and support plans for sustainable development.

Fig. 1.3

Ecosystem highlighting various regions in East Asia.

References
Brown L.R., Halweil B. (1998) China’s water shortage could shake world food security. World Watch, 11(4),10-18.
Chang C.R., Li M.H., Chang S.D. (2007) A preliminary study on the local cool-island intensity of Taipei cityparks. Landscape and Urban Planning, 80, 386-395.
Chen J.Y., Tang C.Y., Shen Y.J., Sakura Y., Kondoh A., Shimada J. (2003) Use of water balance calculation andtritium to examine the dropdown of groundwater table in the piedmont of the North China Plain (NCP).
Environmental Geology, 44, 564-571.
Endo T. (1992) Confined groundwater system in Tokyo. Environmental Geology and Water Sciences, 20, 21-34.
Liu C., Xia J. (2004) Water problems and hydrological research in the Yellow River and the Huai and Hai Riverbasins of China. Hydrological Processes, 18, 2197-2210, doi:10.1002/hyp.5524.
Ministry of Environment (2004) Report on heat-island measures by controlling anthropogenic exhaustion heat in
the urban area. http://www.env.go.jp/air/report/h16-05/ (in Japanese).
Nakayama T. (2008a) Factors controlling vegetation succession in Kushiro Mire. Ecological Modelling, 215,
225-236, doi:10.1016/j.ecolmodel.2008.02.017.
Nakayama T. (2008b) Shrinkage of shrub forest and recovery of mire ecosystem by river restoration in northern
Japan. Forest Ecology and Management, 256, 1927-1938, doi:10.1016/j.foreco.2008.07.017.

-7

Chapter 1 General Introduction

Nakayama T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part II), CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
http://www-cger.nies.go.jp/publication/I083/i083.html.
Nakayama T. (2009) Simulation of ecosystem degradation and its application for effective policy-making inregional scale. In River pollution research progress, Mattia N. Gallo, Marco H. Ferrari (eds), 1-89 (Chapter1), Nova Science Pub., Inc., New York.
Nakayama T. (2010) Simulation of hydrologic and geomorphic changes affecting a shrinking mire. River
Research and Applications, 26(3), 305-321, doi:10.1002/rra.1253.
Nakayama T. (2011a) Simulation of complicated and diverse water system accompanied by human intervention
in the North China Plain. Hydrological Processes, 25, 2679-2693, doi:10.1002/hyp.8009.
Nakayama T. (2011b) Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivatedYellow
River
Basin.
Agricultural
andForestMeteorology,151,
314-327,
doi:10.1016/j.agrformet.2010.11.006.
Nakayama T., Fujita T. (2010) Cooling effect of water-holding pavements made of new materials on water andheatbudgetsinurban
areas.
Landscapeand
Urban
Planning,
96,
57-67,
doi:10.1016/j.landurbplan.2010.02.003.
Nakayama T., Hashimoto S. (2011) Analysis of the ability of water resources to reduce the urban heat island in
the Tokyo megalopolis. Environmental Pollution, 159, 2164-2173, doi:10.1016/j.envpol.2010.11.016.
Nakayama T., Watanabe M. (2004) Simulation of drying phenomena associated with vegetation change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402,
doi:10.1029/2004WR003174.
Nakayama T., Watanabe M. (2006a) Simulation of spring snowmelt runoff by considering micro-topography andphase changes in soil layer. Hydrology and Earth System Sciences Discussions, 3, 2101-2144.
Nakayama T., Watanabe M. (2006b) Development of process-based NICE model and simulation of ecosystem
dynamics in the catchment of East Asia (Part I), CGER's Supercomputer Monograph Report, 11, NIES,
100p., http://www-cger.nies.go.jp/publication/I063/I063.html.
Nakayama T., Watanabe M. (2008a) Missing role of groundwater in water and nutrient cycles in the shallow
eutrophic Lake Kasumigaura, Japan. Hydrological Processes, 22, 1150-1172, doi:10.1002/hyp.6684.
Nakayama T., Watanabe M. (2008b) Role of flood storage ability of lakes in the Changjiang River catchment.
Global and Planetary Change, 63, 9-22, doi:10.1016/j.gloplacha.2008.04.002.
Nakayama T., Watanabe M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verh
International Verein Limnology, 30.
Nakayama T., Yang Y., Watanabe M., Zhang X. (2006) Simulation of groundwater dynamics in North China
Plain by coupled hydrology and agricultural models. Hydrological Processes, 20(16), 3441-3466,
doi:10.1002/hyp.6142.
Nakayama T., Watanabe M., Tanji K., Morioka T. (2007) Effect of underground urban structures on eutrophic
coastalenvironment.
Science
of
the
Total
Environment,
373(1),
270-288,
doi:10.1016/j.scitotenv.2006.11.033.
Nakayama T., Sun Y., Geng Y. (2010) Simulation of water resource and its relation to urban activity in DalianCity, Northern China. Global and Planetary Change, 73, 172-185, doi:10.1016/j.gloplacha.2010.06.001.
Nakayama T., Hashimoto S., Hamano H. (in press) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes.
Oke T.R. (1987) Boundary layer climates, Methuen Press, London.
Pielke R.A., Cotton W.R., Walko R.L., Tremback C.J., Lyons W.A., Grasso L.D., Nicholls M.E., Moran M.D.,
Wesley D.A., Lee T.J., Copeland J.H. (1992) A comprehensive meteorological modeling system-RAMS.
Meteorology and Atmospheric Physics, 49, 69-91.
Ren L., Wang M., Li C., Zhang W. (2002) Impacts of human activity on river runoff in the northern area ofChina. Journal of Hydrology, 261, 204-217.
Ritchie J.T., Singh U., Godwin D.C., Bowen W.T. (1998) Cereal growth, development and yield. Understanding
Options for Agricultural Production, eds. Tsuji G.Y., Hoogenboom G. & Thornton P.K., Kluwer: GreatBritain, 79–98.
Sellers P.J., Randall D.A., Collatz G.J., Berry J.A., Field C.B., Dazlich D.A., Zhang C., Collelo G.D., Bounoua
L. (1996) A revised land surface prameterization (SiB2) for atomospheric GCMs. Part I : Modelformulation. Journal of Climate, 9, 676-705.
Shimada J. (2000) Proposals for the groundwater preservation toward 21st century through the view point ofhydrological cycle. Journal of Japan Association of Hydrological Sciences, 30, 63-72 (in Japanese).
Varis O., Vakkilainen P. (2001) China’s 8 challenges to water resources management in the first quarter of the
21st Century. Geomorphology, 41, 93-104.

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CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.18
CGER-I103-2012, CGER/NIES

Chapter 2
Diverse Water System Accompanied by Human Intervention
in the North China Plain-9Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

Abstract
The author has coupled the National Integrated Catchment-based Eco-hydrology (NICE)
model series with dam–canal systems (NICE-CNL) in agricultural and urban areas. This
combined model was applied to the North China Plain (NCP) and the lower reaches of the
Yellow River in China, where water shortages and the resultant environmental degradation
have become increasingly critical over time. This model provided a good reproduction of thehydrologic cycle, including river discharge, soil moisture, evapotranspiration, groundwaterlevel, and crop production of summer maize and winter wheat, through accurate estimationsof crop water usage in the absence of detailed statistical data. The simulation showed thatoverexploitation of groundwater for irrigation and industrial usage has a marked effect on thewater dynamics in the NCP because, in addition to industrial and domestic water usage, theriver water volume is smaller than that necessary for irrigation. The model reasonably
reproduced the trend in groundwater degradation observed during the previous half century
and included cone depression formations near major cities, which the author was unable toreproduce in previous research. Moreover, the simulation clarified the relationship of thiswater shortage and the low discharge rate of the Hai River into the Bohai Sea. Further, thesimulated result of the density current and solute transport processes clarified that these water
cycle changes have caused a considerable amount of seawater intrusion and a decrease in crop
productivity in coastal areas, as shown by the Normalized Difference Vegetation Index(NDVI) gradient estimated from satellite imagery. In this chapter, we show a significant
improvement in the process-based model, which enables clarification of the diverse water
system and contains important implications for the evaluation of degraded ecosystems andenvironments located on the fluctuating boundary between freshwater and marine areas.
Keywords: diverse water system, integrated catchment-based eco-hydrology model, water
shortage, seawater intrusion, NDVI, ecosystem degradation- 10 

CGER-I103-2012, CGER/NIES

2.1 Introduction
The North China Plain (NCP) is located in the eastern part of China, between 35°00cN
and 40°30cN and between 113°00cE and 119°30cE. It includes all the plains of the Hebei
Province, metropolitan Beijing and Tianjin, and the northern parts of the plains in the
Shandong and Henan provinces (Fig. 2.1). The region surrounded by the Taihang Mountains
to the West, the Yanshan Mountains to the North, the Bohai Sea to the East, and the Yellow
River to the south is a giant alluvial plain formed by the deposition of the Yellow, Hai, andLuan rivers and their tributaries (Chen, 1996). Located in a semi-arid climatic zone, this large
region—encompassing an approximate area of 1.36 u 105 km2 and about 112 million people(Brown and Halweil, 1998; Shimada, 2000; Chen et al., 2003; Nakayama et al., 2006)—has
suffered severe water shortages attributed to its function as one of the most important grain
producing areas in China. This water demand has intensified usage conflicts between the
upstream and downstream areas in addition to that between the agriculture andmunicipal–industrial sectors. As a result, this region, water-rich in the 1950s, has undergone
various ecosystem changes resulting in the drying of the Yellow River, the near closure of theHai River, groundwater degradation, and seawater intrusion in the NCP.
In addition, increasing development and use of water resources has led to the complete
drainage of the Yellow River along the main course of its lower reaches and along its major
tributaries. The most serious situation occurred in 1997, when the main river close to the sea
dried up for 226 days and the no-flow region extended upstream to Liuyuankou in Kaifeng
City, Henan Province, 704 km from the river mouth, which constituted 90% of the river
length in the lower reaches (Chen et al., 2003; Liu et al., 2003; Xia et al., 2004; Yang et al.,2004). This event caused considerable damage to communities and the surrounding
eco-environment. The Hai River catchment is located in the center of the NCP, occupying an
area of approximately 3.20 u 105 km2 and water resources of 372 u 108 m3. Increased
population and the developing economy in this region have resulted in severe water shortages
in the 21st century. Baiyangdian Lake in Hebei Province, the largest natural lake in northern
China, is also facing major water shortages due to severe drought and diversion for irrigationand drinking water (Yin and Lan, 1995; Liu et al., 2006). In this situation, the concept offlood resources development and utilization, known as flood resourcification, is important forincreasing the water supply; through this process, most of the water on land is retained as far
away as possible, (Wang et al., 2004). A previous survey of surface runoff and discharge into
the sea during the last five decades has shown that an increasing number of dams andreservoirs have been constructed to increase the flood resourcification ability in three obvious
phases (Guo and Cai, 2000). The stages characteristic of flood resourcification in thiscatchment are closely related to the history of water resource development. Because the rapid
development of industry and urbanization and an increase in farmland irrigation have doubled
water demand, the groundwater table has rapidly receded in the floodplains of the river’s
downstream areas over the previous half-century due to excessive irrigation water withdrawal(Liu and Xia, 2004; Liu and Zheng, 2004). This result has led to the creation of large
depression cones and land subsidence in urban areas in addition to the expansion ofsaline–alkaline regions on land due to seawater intrusion in coastal zones (Brown and Halweil,
1998; Shimada, 2000; Liu et al., 2001; Chen et al., 2003; Nakayama et al., 2006). Because
few studies have used numerical models to report the impact of human intervention onhydrologic changes in the NCP (Nakayama et al., 2006), it is important to evaluate thesecomplex phenomena resulting from agricultural–industrial water usage and water transfer via
dams, reservoirs, and canal systems.
- 11 

Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China PlainThe objective of this chapter is to evaluate these water–heat dynamics through aprocess-based model that combines the National Integrated Catchment-based Eco-hydrology(NICE) model series (Nakayama, 2008a, 2008b, 2008c, 2009, 2010, 2011a, 2011b; Nakayama
and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Watanabe, 2004, 2006a,
2006b, 2008a, 2008b, 2008c; Nakayama et al., 2006, 2007, 2010, in press) (Fig. 1.2)withdam–canal systems (NICE-CNL) and includes the density current and solute transport
processes to simulate the relationship between hydrological change resulting from humanintervention and seawater intrusion. In addition, the model clarifies the relationship between
the drying of rivers and groundwater degradation. This model is expected to reveal methodsof solving the complex nature of the water problems in the NCP, leading to more rational
water usage and sustainability of the catchment ecosystem(Nakayama, 2011a, 2011b;
Nakayama et al., 2006).

Fig. 2.1 Study area in the NCP showing (a) mean elevation and (b) land cover. In (a), the redline indicates the border of the NCP. The number shows the groundwater observation
stations listed in Table 2.3. In (b), circles indicate the observation of river discharge,
triangles indicate that of soil moisture recorded in Table 2.3, and blue trapezoids indicate
the main reservoir. The yellow line represents the Grand Canal (South and North canals)
where water is transferred from the southern to northern area in the South-to-North Water
Transfer Project. The red square represents the simulation area of seawater intrusion, as
shown in Fig. 2.5.

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CGER-I103-2012, CGER/NIES

2.2 Methods
2.2.1 NICE Model Series in Natural and Agricultural Regions
The author has developed the NICE series of process-based catchment models applicableto natural, agricultural, and urban regions in catchment areas (Nakayama, 2008a, 2008b,
2008c, 2009, 2010, 2011a, 2011b; Nakayama and Fujita, 2010; Nakayama and Hashimoto,2011; Nakayama and Watanabe, 2004, 2006a, 2006b, 2008a, 2008b, 2008c; Nakayama et al.,2006, 2007, 2010, in press). These NICE models link surface-unsaturated–saturated water andland-surface processes and describe variations in phenology on the basis of Moderate
Resolution Imaging Spectroradiometer (MODIS) satellite data (Fig. 1.2). The submodels
consider water and heat fluxes from the ground to the surface, including the gradient ofhydraulic potential between the deepest layer of unsaturated flow and the groundwater level;
the effective precipitation calculated from actual precipitation, infiltration into the upper soil
moisture store, and evapotranspiration; and the seepage between river water and groundwater.
In the agricultural field, NICE is coupled with Decision Support Systems for Agro-technology
Transfer (DSSAT) (Ritchie et al., 1998), in which an automatic irrigation mode supplies cropwater requirements for the cultivation of fields (Nakayama et al., 2006; Nakayama, 2011a,
2011b) and paddy fields (Nakayama and Watanabe, 2008b). The model includes various
functions of representative crops such as wheat, maize, soybean, and rice and automaticallysimulates their dynamic growth processes. The details are described in Chapter 1 and previous
related papers (Nakayama, 2008a, 2008b, 2008c, 2009, 2010, 2011a, 2011b; Nakayama andFujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Watanabe, 2004, 2006a, 2006b,
2008a, 2008b, 2008c; Nakayama et al., 2006, 2007, 2010, in press).
2.2.2 Water Transfer in Dam and Canal Systems
Numerous dams and canals populate the NCP because of the high agricultural, industrial,and domestic demand for water (Fig. 2.1b). Thousands of dams constructed beside the
Taihang Mountains have prevented the water supply from reaching the plain area. Most of
this stored water is transferred to major cities and to cultivated fields (Fig. 2.1b). Because
limited data are available on discharge control at most of these dams, a constant ratio of
inflow to outflow was used as the first approximation at the major dams, rather than the more
complex storage–runoff function model used in a previous research (Sato et al., 2008). The
water generally flows from the higher southwestern to the lower northeastern region in theGrand Canal (South and North canals) (Fig. 2.1b). When higher precipitation occurs at theHai River catchment on the northern side of the NCP, water occasionally flows backward
against the elevation. NICE-CNL simulates the discharge in the canal, assuming that thisphenomenon is decided as the difference in hydraulic potential between both natural rivers;
this indicates that the discharge that becomes backwater flow at the junction is dependent on
the hydraulic potential difference in the same manner as that observed in the stream junction
model (Nakayama and Watanabe, 2008b).
2.2.3 Satellite Analysis using NOAA/AVHRR Data
The NDVI is the difference in surface reflectivity between two wavelengths in the red(RED) and near-infrared (NIR) bands, normalized by their sum. The differential reflectance inthese bands correlates strongly with the photosynthetic activity of the vegetation canopy- 13 

Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

because of large spectral shifts in leaf optical properties. In the visible band, green leaves
strongly absorb solar radiation in proportion to the chlorophyll content, absorbing more than
80% of the incoming red energy (Bacour et al., 2006), whereas the reflectance of bare soil isonly slightly greater in the NIR than in the visible band.
The NDVI was calculated from NOAA/AVHRR satellite data with a resolution of 1.1
km; these data were collected across East Asia during 1982–1999. The original data were
downloaded from WebPaNDA (2008) and re-sampled to change the resolution to 6.0 km by
the nearest neighboring method. The data area was selected to match the simulation area (Fig.
2.1). To minimize the effects of clouds and atmospheric contaminants, we used composite
data within a 10-day or monthly period that had the fewest clouds, as identified by the highest
NDVI value. The NDVI was calculated as
NDVI

NIR  RED

NIR  RED ,(2.1)

where RED is band 1 (visible, 0.58–0.68 m), and NIR is band 2 (near-infrared, 0.725–1.10
m). Theoretically, the NDVI varies between 1.0 and 1.0. The NDVI values range between
0.2 and 0.05 for snow, inland water bodies, deserts, and exposed soils, and increase fromabout 0.05 to above 0.7 with an increase in density and greenness of vegetation (Myneni et al.,1997). The NDVI can be used as an indicator of vegetation stress, particularly that caused bywater shortage (Kogan, 1990; Singh et al., 2003). To reduce the impact of bare soil, clouds,
and water bodies on the NDVI trends, we excluded from the analysis grid cells with NDVI
<0.1 (Zhou et al., 2001, 2003; Piao et al., 2006). Crop production, defined as dry biomass byunit of surface, is also linearly related to time-integrated NDVI (TINDVI) (Yang et al., 1998)
by using the Light Use Efficiency (LUE) model (Gower et al., 1999). A linear function
existing between NDVI and absorbed photosynthetically active radiation (APAR) (Sellers etal., 1997) indicates that NDVI can be related to crop growth rate (CGR) as discussed on thebasis of experimental field data (Calera et al. 2004).
2.3 Input Data and Boundary Conditions for Simulation
2.3.1 Meteorological Forcing Data
Six-hour reanalysis data of downward short- and long-wave radiation, precipitation,
atmospheric pressure, air temperature, air humidity, wind speed at a reference level, fractionof photosynthetically active radiation (FPAR) and leaf area index (LAI), interpolated fromInternational Satellite Land Surface Climatology Project (ISLSCP) data with a resolution of
1° u1°, were input into each grid cell (Sellers et al., 1996). The precipitation, maximum andminimum temperature, and radiation of ISLSCP were compared with the observation data andwere shown to be correct in a previous study (Nakayama et al., 2006). These corrected sites
cover the entire study area in which groundwater fluctuation is influenced by agricultural
water use. Because of the scarcity of recharge rate data in natural regions, except for researchusing the chloride mass balance method (Lee, 1996), these values were simulated by the SiB2
submodel of NICE-CNL at each time step and were inputted into the groundwater submodel.
Details are given in Nakayama and Watanabe (2004).

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CGER-I103-2012, CGER/NIES



2.3.2 Vegetation and Geological Properties
The mean elevation of each 5-km grid cell was calculated by using the spatial average ofa global digital elevation model (DEM; GTOPO30) with a horizontal grid spacing of 30
arc-seconds (approximately 1-km mesh) (U.S. Geological Survey, 1996) throughout the NCP,
including the Hai River and the lower reaches of the Yellow River (Fig. 2.1). Approximately
50 vegetation and soil parameters were calculated in each cell on the basis of vegetation classand soil texture obtained from the Vegetation and Soil Maps of China (1:4,000,000) (Chinese
Academy of Sciences, 1988). The major parameters included vegetation cover, green fraction,
albedo, surface roughness length and zero displacement height, soil conductivity, and soil
water potential at saturation; in addition, several parameters of stomatal resistance were also
included as these are associated with environmental factors. From the input soil texture andsoil organic matter, the model calculated the hydrological parameters of the soil layers, suchas water saturation content, the lowest water content required for crop growth, and the upper
water content after free drainage and saturated water conductivity.
Around these catchments, the geology consists mainly of diluvium in the mountain areas(Taihang and Yanshan mountains and piedmont plains) and alluvium in the lowland andcoastal areas (flood and coastal plains and fan). After calibrating the fit for the simulated
hydraulic heads to the observed heads by scanning and digitizing the geological material(Geological Atlas of China, 2002), the author was able to divide the geological structure into
four types, with a resolution of 5 km in the horizontal direction and 20 layers in the verticaldirection, Kh and Kv, respectively, on the basis of hydraulic conductivity of specific stored
porous material (Ss) and specific yield (Sy). These values were kept within the initial estimatedknown range previously reported (Shimada, 2000; Chen et al., 2002; Chen et al., 2003)(Table 2.1). A deep quaternary formation observed in the NCP generally reaching 400–600 m
in depth was divided into four aquifers with unconfined groundwater in the first layer andconfined groundwater in the second, third, and fourth layers at 38°N (Fig. 2.2). The thicknessof the respective aquifers was 20–40 m, 60–130 m, 80–220 m, and 50–350 m; all aquifers
consist of sand, gravel, fine sand, and silt. These geological structures are very similar to
those reported previously (Zhu, 1992; Shimada, 2000; Chen et al., 2002), which indicates thatthe geological structures categorized by the author are sufficient for this simulation(Nakayama et al., 2006).

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Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

Table 2.1. Geological parameters used in numerical simulation.

Type

Soil Type

Horizontal
Hydraulic
Conductivity
Kh (m/h)1

Gravel

5.0E+01

1.0E+01

1.0E-05

1.5E-012

Fine-to-medium
sand

5.0E+00

1.0E+00

1.0E-04

1.5E-01

3

Finer than silt

5.0E-01

1.0E-01

1.0E-038.0E-02

4

Basement rock5.0E-01

1.0E-01

1.0E-03

4.0E-02

Vertical Hydraulic
Conductivity
Kv (m/h)Specific
StorageSs (1/m)Specific
Yield
Sy

Fig. 2.2 Vertical geological structure at latitude 38°N in the NCP, obtained by (a) simulation and(b) previous research. In (a), the soil type shows the same value as that specified in Table
2.1. Soil type = 0 indicates the interface between sea and land. In (b), the cross-sectional
profile of the NCP between Shijiazhuang (38.07°N, 114.55°E) and Dezhou (37.50°N,
116.30°E) is illustrated: 1: stratum age; 2: stratum thickness; 3: gravel and boulders; 4: clay
and sandy clay; 5: aquifer boundary; and 6: water table.

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CGER-I103-2012, CGER/NIES



2.3.3 Water Demand and Transfer Data in Urban Areas
Although previous research was able to reproduce the general trend of groundwater
degradation in the NCP during the previous half-century, NICE-AGR was unable toreproduce cone depressions that occurred near major cities (Nakayama et al., 2006).
Moreover, the water demands of the municipal and industrial sectors have dramaticallyincreased, year-on-year, with the development of the social economy in the Hai River
catchment area (Guo et al., 2000). These findings show that water usage in urban areas
greatly affects the hydrological cycle in these regions. Table 2.2 shows the average water
usage in 1987–1988 for the main cities in the NCP (Hebei Department of Water Conservancy,
1987, 1988). The amount of water used in the larger cities such as Beijing, Tianjin, Tangshan,
and Qinhuangdao increased yearly. In particular, Beijing and Tianjin used substantial
amounts of water for industrial and domestic applications. Therefore, because water usage inthe major cities in the NCP was not negligible relative to their hydrological cycle, it isnecessary to input them in the simulation (Nakayama, 2011a).

Table 2.2. Average water usage in 1987–1988 by major cities in the NCP (u 10000 m3).

No.

City

Lat.
(°)

Lon.
(°)

Total

Irrigation Processing

C-1
Handan36.60 114.47 69176.5 29604.0
C-2
Xingtai
37.06 114.49 26232.5 16310.0
C-3 Shijiazhuang 35.24 112.55 88011.0 77756.0
C-4
Baoding
38.85 115.49 64515.0 41414.0
C-5
Cangzhou
38.32 116.87 17310.5 14303.0
C-6
Tangshan
39.63 118.18 127163.0 110064.5
C-7 Qinhuangdao 39.93 119.59 104212.0 88939.5
C-8
Nangong
37.36 115.37 14770.0 11570.0
C-9
Shahe
36.85 114.50 17250.0 14750.0
C-10
Dingzhou
38.51 115.00 40359.5 38747.5
C-11
Zhouxian
39.48 115.96 22200.0 19370.0
C-12
Botou
38.07 116.57 14246.0 12713.0
C-13
Hengshui37.73 115.70 6139.4
4549.4
C-14
Beijing40.23 116.60 424300.0 219900.0
C-15
Tianjin39.44 117.66 237776.0 161091.1
- 17 

290.5
0.0
728.5
1025.0
25.0
793.0
632.5
0.0
0.0
207.0
0.0
103.0
0.0
0.0
0.0

Industry

Domestic

27360.06599.57484.09780.01589.513550.09074.52500.01300.01280.02123.0533.0
800.0
140400.071267.7

11922.03323.02042.52106.01393.02755.55565.5700.0
1200.0
125.0
707.0
897.0
790.0
64000.025777.8

Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain



2.3.4 Boundary Conditions and Running the Simulation
At the upstream boundaries where no data is observed, the reflecting condition on thehydraulic head was used for the groundwater flow submodel on the supposition that no inflow
occurs from the mountains in the opposite direction (Nakayama and Watanabe, 2004). At theeastern sea boundary (Bohai Sea), a constant head was set at 0 m. The hydraulic head values
parallel to the ground level were input as the initial conditions for the groundwater flow
submodel. In the 1747 river cells including the Yellow, Dugai (Tuhai), Majia, Fuyang, Hutuo,
Ziya, Daqing, Yongding, and Hai (Haihe) rivers (Fig. 2.1), inflows or outflows from theriverbeds were considered depending on the difference in the hydraulic heads of thegroundwater and the river. The time-series of observed river discharge at Huayuankou, Henan
Province (R-13 in Table 2.3; 34.92°N, 113.65°E, mean elevation of 104 m), beside the lower
reaches of the Yellow River, was also inputted into the model (China Institute for
Geo-Environmental Monitoring, 2003). For the simulation of the groundwater trend, it can beassumed that the industrial water usage in 1959 was almost zero, along the lines of Nakayama
et al. (2006). Although some water usage data beginning in 1992 were available, they were
not as detailed as the statistical data contained in Table II. Therefore, the second-order curveof the year was estimated from the two periods between 1959 and 1987–1988 to represent a
curve of economic conditions, such as GDP; the interpolated values were then applied to datafor 1975 and 1992 (Nakayama et al., 2006). A constant chloride concentration of 34 psu wasset at the first layer of the seabed and at the eastern boundary of the Bohai Sea for thesaltwater simulation, as reported in a previous research (Zhang et al., 1990) (Fig. 2.1).

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CGER-I103-2012, CGER/NIES

Table 2.3. Observation stations cited for validation.

River Discharge

Lat.
Lon.
(°)
(°)
39.02 117.72

Elev.
(m)
4.0

River Discharge

40.23 115.60

436.0River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
Soil Moisture
Soil Moisture
Soil Moisture
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level

40.03
39.97
39.08
39.57
38.23
38.52
39.13
34.92
35.93
37.52
36.88
35.17
34.82
37.90
36.60
38.30
36.30
37.20
36.90
38.20
39.00
39.50
39.10
38.10
38.30
37.90

314.0
195.0
16.0
93.0
15.0
12.0
5.0
104.0
1.0
1.0
40.0
85.0
99.0
55.0
85.0
7.0
50.0
38.0
40.0
45.0
7.0
18.0
6.0
8.0
3.0
16.0

No.

Point Name

Type

R-1

Haihezha (Hai River)
Yanqing Guanting Reservoir(Yongding River)
Yanchi (Yongding River)
Sanjiadian (Yongding River)
Xingaifang (Daqing River)
Luobaotan (Juma River)
Xianju (Ziya River)
Jiugaozhuang (Ziya River)
Yangliuqing (Ziya River)
Huayuankou (Yellow River)
Juancheng (Yellow River)Lijin (Yellow River)
Luancheng
XinxianZhengzhou
Luancheng
Handan City
CangzhouDamingNangong
GuangzongWuji
Rongcheng
LangfangBazhouXianxian
Huanghua
Shenzhou

R-2
R-3
R-4
R-5
R-7
R-10
R-11
R-12
R-13
R-14
R-15
S-1
S-19
S-33
G-1
G-2
G-3
G-4
G-5
G-6
G-7
G-8
G-9
G-10
G-11
G-12
G-13

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115.88116.10116.03115.72
116.10116.50117.00113.65115.90118.30114.67113.82113.67114.56114.41116.90115.30115.60115.20114.90116.00116.70116.60116.20117.60115.70Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China PlainThe simulation area was 530 km in width by 840 km in length, with a grid spacing of 5
km in the Albers co-ordinates; this area covered almost the entire Hai River catchment andthe lower reaches of the Yellow River. The vertical layer was discretized in thickness withdepth, with each layer increasing in thickness by a factor of 1.1. The upper layer was set at a
depth of 2 m, and the 20th layer was defined as an elevation of –400 m from the sea surface.
The NICE-CNL simulation was performed on an NEC SX-6 supercomputer. The simulations
were performed from January 1, 1987, through December 31, 1988. In agricultural fields, the
NICE-CNL automatically simulated the winter wheat and maize rotations, following thesequence of 1987 winter wheat, 1987 maize, 1988 winter wheat, and 1988 maize. Automatic
irrigation was chosen to simulate the theoretical water requirements of the two crops. Thismethod calculates the theoretical water requirements at various sites on the basis ofdifferences in soil conditions and local weather conditions, particularly precipitation. To
determine a realistic amount of crop water usage, the author considered that the ratios ofagricultural area to the total area and of the irrigated area to the total agricultural area obtained
through published statistics (Governmental Office of Hebei Province, 1988, 1989; Hebei
Department of Water Conservancy, 1987, 1988) would yield practical spatial water usage.
The irrigated ratio was included at each grid point to input a more accurate amount of water
usage into the NICE-CNL (Nakayama et al., 2006).
The first six months of the study were used as a warm-up period until equilibrium waterlevels were reached, and parameters were estimated by a comparison of simulated steady-statevalues in a steady-state condition with previously published observed values (Clapp andHornberger, 1978; Rawls et al., 1982). A time step of 't = 6 h was used. The simulations
were validated by previously observed data for river discharge (12 points; Hai River andYellow River Conservancy Committees, 1987, 1988), soil moisture (3 points of the Global
Soil Moisture Data Bank; Entin et al., 2000; Robock et al., 2000), and groundwater level (13points; China Institute for Geo-Environmental Monitoring, 2003) (Fig. 2.1 and Table 2.3).
Moreover, the author calibrated and validated data for winter wheat in 1991–1992 and formaize in 1990 to develop genetic parameters for obtaining an accurate simulation ofagricultural water usage in 1987–1988 (Nakayama et al., 2006).

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2.4 Results and Discussion
2.4.1 River Discharge Affected by Dam and Canal Systems
Through previous comparison of simulated results for LAI, soil moisture, and irrigationwater usage with the observed values for winter wheat and summer maize, the author showedthat the NICE-AGR excellently reproduced the observed values (Cor = 0.97 for winter wheat,
and Cor = 0.99 for summer maize) (Nakayama et al., 2006). In addition, to estimate spatial
crop water usage, the simulated crop coefficients (Kc) for both crops were compared with thevalues observed in a previous research (Chen et al., 1995). This author’s previous study
suggested a relative reliability of the water usage simulated by the NICE-CNL (Nakayama,
2011a). This method is also effective for providing better evaluation of hydrological trends inlonger periods including, the “evaporation paradox” (Roderick and Farquhar, 2002), together
with observation networks because, depending on growing stage and type of crop, the model
does not require a crop coefficient for the calculation of actual evaporation. In addition,
simulation is direct and does not require detailed site-specific information or empirical
relations to calculate effective precipitation (Nakayama, 2011a; Nakayama et al., 2006).
Fig. 2.3 compares the simulated river discharges with the observed values (Hai River andYellow River Conservancy Committees, 1987, 1988) in the Hai and Yellow River catchmentsfrom January 1, 1987, to December 31, 1988. The correlation between the observed value andthe simulated value Cor and the Nash–Sutcliffe Criterion NS (Nash and Sutcliffe, 1970) arealso plotted in this figure. The model yields an excellent reproduction of the observed values
in the lower reaches of the Yellow River, indicating that less water is discharged downstream
mainly because more than 90% of the total withdrawal is for agriculture (Cai, 2006) (Fig.
2.3a; R-14 and 15 in Table 2.3; Cor = 0.83 and 0.96, NS = 0.66 and 0.90). At the downstream
point at Lijin (R-15 in Table 2.3; 37.52°N, 118.30°E), the river discharge dries out during thespring mainly because most of the water is used for the irrigation of winter wheat. Thisfinding is also related to the fact that groundwater cannot be used for irrigation because it ishighly contaminated by total dissolved solids (TDS) and seawater intrusion (Brown andHalweil, 1998; Shimada, 2000; Liu et al., 2001; Chen et al., 2003; Nakayama et al., 2006), as
described in the following section.
In the Hai River catchment, the simulated result correlates well with the observed value inthe river where no dams are present upstream (Fig. 2.3b; R-7 in Table 2.3; Cor = 0.64, NS =0.39). The simulated result for case-1, which includes only natural rivers, does not agree wellwith the observed value in the river where dams are constructed upstream (Fig. 2.3c; R-3 inTable 2.3; Cor = -0.48, NS = -3.81). The NICE-CNL result for case-2, which includes dam
and canal systems, can excellently reproduce the observed value (Cor = 0.92, NS = 0.84).
Although the simulated value agrees reasonably with the seasonal trend of the observed value
in the main channel in the downstream area of the Hai River, the reproduction is less accurate(Fig. 2.3d; R-1 in Table 2.3; Cor =0.51, NS =0.20), indicating that the complicated processesof seasonal water withdrawal for irrigation and water transfer in the dam and canal systems, inaddition to the natural rivers, can be generally reproduced. This difference is also related tothe accuracy of the relatively scattered observation variety in base flow or mean flow data.
This inaccuracy may be attributed to research differences and the method or standard ofobservation. The simulated result generally agrees well with the previous finding (Wang et al.,2004); in other words, flood resourcification ability is very high in the Hai River and lesswater enters the Bohai Sea (Nakayama, 2011a).

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Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

Fig. 2.3 Simulated and observed river discharges in the Hai and Yellow River catchments fromJanuary 1, 1987, to December 31, 1988, at (a) Juancheng and Lijin rivers downstream of
the Yellow River (R-14 and 15 in Table 2.3), (b) the Luobaotan River (R-7 in Table 2.3),(c) the Yanchi River (R-3 in Table 2.3), and (d) the Haihezha River (R-1 in Table 2.3) atthe Hai River catchment. Open circles and triangles represent observed values, and linesand dotted lines represent simulated values. Cor indicates the correlation between observed
and simulated values, and NS indicates the Nash–Sutcliffe criterion.

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CGER-I103-2012, CGER/NIES



2.4.2 Groundwater Level
The NICE-CNL simulates the changes in the groundwater levels over the previous
half-century (Fig. 2.4). The NICE-AGR simulation used in a previous study (Fig. 2.4b)(Nakayama et al., 2006) excellently reproduced the observed groundwater levels (Fig. 2.4a)(Shimada, 2000) during the same period and showed a yearly decrease in water table levels;
this is because the simulation included the effects of irrigation in the NCP. Hydraulicgradients were steeper near the Taihang and Yanshan mountains and were relatively flat in theplain area. Because the groundwater level is higher in the mountainous areas, groundwater
constantly flows into the plain, and the mountainous area plays an important role as arecharge region. NICE-AGR was unable to reproduce cone depressions that occurred near
major cities including Cangzhou, Hengshui, and Baoding because groundwater usage wasexcessive in 1975 and 1992, and this model does not consider industrial water usage in thearea (Fig. 2.4b). The NICE-CNL more accurately reproduced these cone depressions because
the new model explicitly includes processes for industrial and domestic water usage (Fig.
2.4c). Moreover, the NICE-CNL excellently reproduced observed groundwater fluctuations in
a similar manner as that by NICE-AGR; these fluctuations are listed in Table 2.3 (China
Institute for Geo-Environmental Monitoring, 2003) (Nakayama et al., 2006). This is because
this improved model can simulate both infiltration in non-agricultural periods and pumping in
agricultural periods. Groundwater levels generally recede from March to June–July mainly asa result of winter wheat production. The levels begin to recover with the onset of themonsoons. The simulated groundwater levels accurately reproduce actual levels in both themountainous and plain areas because the author used a 3-D groundwater submodel. In
addition, SiB2 used for natural regions (Nakayama and Watanabe, 2004) and DSSAT for
agricultural regions can correctly simulate the recharge rates in the upper layer, as describedin Chapter 1 (Nakayama, 2011a; Nakayama et al., 2006).

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Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

Fig. 2.4 Annually averaged groundwater levels in the NCP from 1959 to 1992 illustrating (a)observed value, (b) simulated result with only irrigation water usage, and (c) simulated
result with irrigation and urban water usage. Bold lines represent the NCP borders, anddotted lines indicate the groundwater levels at 0 m, 20 m, and 50 m above sea level. In (c),circles represent the main cities where water usage in urban areas is predominant, as
described in Table 2.2.

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CGER-I103-2012, CGER/NIES



2.4.3 Hydrologic Changes and Ecosystem Degradation Caused by Human Intervention
Groundwater levels in the study area have receded dramatically over the previous
half-century owing to over-pumping of groundwater. Therefore, seawater intrusion into theaquifers has a greater impact on agriculture near the coastal regions because saltwater cannot
be used for agriculture or for domestic use (Brown and Halweil, 1998; Shimada, 2000; Liu etal., 2001; Chen et al., 2003; Nakayama et al., 2006). The tidal range in the Bohai Sea—where
the difference in spring tide M2 + S2 + K1 + O1 is about 1 m at the mouth of the Yellow Riverand 4 m outside the Bohai Sea (Ogura, 1933),—also greatly affects this intrusion, particularlyat spring tide. Therefore, if the area of seawater intrusion can be evaluated both qualitatively
and quantitatively, the population will be able to use shallow and deep aquifers more
efficiently to prevent groundwater degradation in this area. The NICE-CNL spatiallysimulates the intrusion of seawater in the NCP (Fig. 2.5). The simulated results are similar toprevious data for the NCP (1:4,500,000) (Institute of Hydrogeology and Engineering Geologyof the National Geology Bureau, 1980). The high concentration area in shallower
groundwater expands to surround the Bohai Sea, which indicates a degradation ofgroundwater quality due to seawater intrusion. In addition, the results indicate that awedge-shaped body of denser seawater develops beneath the lighter freshwater (Essaid, 1990).
The simulated result shows that in addition to groundwater degradation, excessive
groundwater use for agriculture, industry, and domestic purposes has a substantial effect on
the intrusion of seawater in the aquifer.
The spatial distributions of NDVI calculated by using NOAA/AVHRR satellite images
were similar to the results of previous research (NASA, 2006; data not shown). Because
NDVI is linearly related to CGR (Calera et al., 2004; Nakayama, 2011b), the spatial
distribution of this value is a good indicator of the rate of crop production. It is interesting that
the NDVI in the cultivated field has a smaller value beside the Bohai Sea compared to thevalue inland, which is closely correlated with the higher regions of TDS observed in previous
studies (Fei, 1997; Kondoh and Oyamada, 2000). To examine spatial patterns in NDVI trends
in 1982–1999 in the NCP, the author further estimated the linear trend of monthly NDVI in
the grain filling season for winter wheat in May over the study period using an ordinary
least-squares regression method for each pixel (Fig. 2.6). The NDVI gradient was spatiallyheterogeneous, corresponding well to the regional distribution of water resources. The NDVI
in irrigated and non-irrigated cultivated fields is smaller beside the Bohai Sea (Pt. I) than it isinland (Pt. II); this is because of several effects, including groundwater degradation (Fig. 2.4),
seawater intrusion (Fig. 2.5), and rapid urbanization surrounding major cities (Kondoh andOyamada, 2000), which has decreased the NDVI yearly owing to the decrease in riverdischarge. Mountainous and plain areas upstream of the Hai River have higher NDVI values
than those observed in downstream areas. This tendency was also seen in the grain fillingseason for summer maize (August) (data not shown). This finding is closely connected towater shortages and seawater intrusion (Nakayama, 2011a) (Figs. 2.4 and 2.5), which havegreatly reduced crop growth and production in coastal areas (Ayers and Westcot, 1985;
Warrence et al., 2002).

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Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

Fig. 2.5 Annually averaged contour of seawater intrusion in the NCP illustrating (a) observed
value in the shallower groundwater. In (b), the annually averaged simulation result of
NICE-CNL corresponds to the red square in (a).

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CGER-I103-2012, CGER/NIESFig. 2.6 NDVI in the NCP illustrating (a) the trend of NDVI in the grain filling season for winterwheat in May, both in the coastal (I) and plain (II) areas, and (b) spatial patterns in
NDVI gradient in the NCP in 1982-1999. The bold line in (a) indicates a least-square
regression line estimated from monthly NDVI in May (circle). In (b), the black square
represents the border of the simulation area in a similar manner at that shown in Fig. 2.1; thered square represents the simulation area of seawater intrusion, as shown in Fig. 2.5.

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Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China Plain

2.5 Conclusion
Through simulation, this study addressed an important topic in a rather complicated anddiverse water system in the NCP that included the hydrological processes of dried rivers,
agricultural–urban water usage, groundwater pumping, seawater intrusion, and dam–canal
effects. For the first time, the author combined the NICE model series with more complicatedcomponents such as urban water usage, saltwater simulation, and dam–canal systems(NICE-CNL) in order to simulate the effects of human intervention on hydrological changes
in the catchments. This simulation clarified the relationship between the drying of rivers,
groundwater degradation, seawater intrusion, and ecosystem degradation.

References
Ayers R.S., Westcot D.W. (1985) Water quality for agriculture. FAO Irrigation and Drainage Paper, 29, FoodandAgriculture
OrganizationoftheUnitedNations,
Rome,
Italy.
http://www.fao.org/DOCREP/003/T0234E/T0234E00.HTM#TOC.
Bacour C., Breon F.M., Maignan F. (2006) Normalization of the directional effects in NOAA–AVHRR
reflectance measurements for an improved monitoring of vegetation cycles. Remote Sensing ofEnvironment, 102, 402–413.
Brown L.R., Halweil B. (1998) China’s water shortage could shake world food security. World Watch, 11(4),10-18.
Cai X. (2006) Water stress, water transfer and social equity in Northern China: Implication for policy reforms.
Human Development Report 2006, UNEP. http://hdr.undp.org/en/reports/global/hdr2006/papers/cai
ximing.pdf.
Calera A., Gonzalez-Piqueras J., Melia J. (2004) Monitoring barley and corn growth from remote sensing data at
field scale. International Journal of Remote Sensing, 25, 97-109.
Chen W. (1996) Preface. Geomorphology, 18, 1-4.
Chen J.Y., Tang C.Y., Sakura Y., Kondoh A., Shen Y.J. (2002) Groundwater flow and geochemistry in thelower reaches of the Yellow River: a case study in Shandang Province, China. Hydrogeology Journal, 10,587-599.
Chen J.Y., Tang C.Y., Shen Y.J., Sakura Y., Kondoh A., Shimada J. (2003) Use of water balance calculation andtritium to examine the dropdown of groundwater table in the piedmont of the North China Plain (NCP).
Environmental Geology, 44, 564-571.
Chen Y.M., Guo G.S., Wang G.X., Kang S.Z., Luo H.B., Zhang D.Z. (1995) Main crop water requirement andirrigation of China. Hydrologic and Electronic Press, Beijing, 73-102.
China Institute for Geo-Environmental Monitoring (CIGEM) (2003) China Geological Envoronment Infonet,
Database of groundwater observation in the People’s Republic of China. http://www.cigem.gov.cn.
Chinese Academy of Sciences (1988) Administrative division coding system of the People’s Republic of China.
Chinese Academy of Sciences, Beijing.
Clapp R.B., Hornberger G.M. (1978) Empirical equations for some soil hydraulic properties. Water Resources
Research, 14, 601-604.
Entin J.K., Robock A., Vinnikov K.Y., Hollinger S.E., Liu S., Namkhai A. (2000) Temporal and spatial scales ofobserved soil moisture variations in the extratropics. Journal of Geophysical Research, 105(D9),11865-11877.
Essaid HI. (1990) A multilayered sharp interface model of coupled freshwater and saltwater flow in coastal
systems: model development and application. Water Resources Research, 26(7), 1431—1454.
Fei J. (1997) Groundwater of the North China Plain. Proceedings of International Symposium on
Hydro-Environment in Asia. CEReS, China University, 25-32.
Geological Atlas of China (2002) Geological Publisher, Beijing (in Chinese).
Governmental Office of Hebei Province (1988) Hebei Agriculture Year Book in 1987. Economy Science
Publishing House, Beijing (in Chinese).
Governmental Office of Hebei Province (1989) Hebei Agriculture Year Book in 1988. Economy Science
Publishing House, Beijing (in Chinese).

- 28 

CGER-I103-2012, CGER/NIES
Gower S.T., Kucharik C.J., Norman J.H. (1999) Direct and indirect estimation of Leaf Area Index, fAPAR, andnet primary production of terrestrial ecosystems. Remote Sensing of Environment, 70, 29-51.
Guo H., Cai Y. (2000) Improving accomplishment of Hai river catchment in 50 years and view in future. Designof Water Resources & Hydroelectric Engineering (in Chinese).
Hai River Conservancy Committees (1987) Annual report of river discharge at Hai River. Interior report of thecommittee (in Chinese).
Hai River Conservancy Committees (1988) Annual report of river discharge at Hai River. Interior report of thecommittee (in Chinese).
Hebei Department of Water Conservancy (1987) Hebei year book of water conservancy for 1987. Department ofHebei Water Conservancy: Hebei, China (in Chinese).
Hebei Department of Water Conservancy (1988) Hebei year book of water conservancy for 1988. Department ofHebei Water Conservancy: Hebei, China (in Chinese).
Institute of Hydrogeology and Engineering Geology of the National Geology Bureau (1980) Hydrogeologicalmap for the People's Republic of China. China Map Publishing House, Beijing (in Chinese).
Kogan F.N. (1990) Remote sensing of weather impacts on vegetation in non-homogeneous areas. International
Journal of Remote Sensing, 11, 1405– 1419.
Kondoh A., Oyamada Y. (2000) Monitoring surface moisture and vegetation status by NOAA and GMS overnorth china plain. Advance in Space Research, 26(7), 1055-1058.
Lee T.M. (1996) Hydrogeologic controls on the groundwater interactions with an acidic lake in karst terrain,
Lake Barco, Florida. Water Resources Research, 32, 831-844.
Liu C., Xia J. (2004) Water problems and hydrological research in the Yellow River and the Huai and Hai Riverbasins of China. Hydrological Processes, 18, 2197-2210, doi:10.1002/hyp.5524.
Liu C., Yu J., Kendy E. (2001) Groundwater exploitation and its impact on the environment in the North China
Plain. Water International, 26(2), 265-272.
Liu C., Zheng H. (2004) Changes in components of the hydrological cycle in the Yellow River basin during thesecond half of the 20th century. Hydrological Processes, 18, 2337-2345, doi:10.1002/hyp.5534.
Liu C.L., Xie G.D., Huang H.Q. (2006) Shrinking and Drying up of Baiyangdian Lake Wetland: A Natural or
Human Cause? Chinese Geographical Science, 16, 314-319.
Liu L., Yang Z., Shen Z. (2003) Estimation of water renewal times for the middle and lower sections of the
Yellow River. Hydrological Processes, 17, 1941-1950, doi:10.1002/hyp.1219.
Myneni R.B., Keeling C.D., Tucker C.J., Asrar G., Nemani R.R. (1997) Increased plant growth in the northernhigh latitudes from 1981 to 1991. Nature, 386, 698–702.
Nakayama T. (2008a) Factors controlling vegetation succession in Kushiro Mire. Ecological Modelling, 215,
225-236, doi:10.1016/j.ecolmodel.2008.02.017.
Nakayama T. (2008b) Shrinkage of shrub forest and recovery of mire ecosystem by river restoration in northern
Japan. Forest Ecology and Management, 256, 1927-1938, doi:10.1016/j.foreco.2008.07.017.
Nakayama T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part II), CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
http://www-cger.nies.go.jp/publication/I083/i083.html.
Nakayama T. (2009) Simulation of ecosystem degradation and its application for effective policy-making inregional scale. In River pollution research progress, Mattia N. Gallo, Marco H. Ferrari (eds), 1-89 (Chapter1), Nova Science Pub., Inc., New York.
Nakayama T. (2010) Simulation of hydrologic and geomorphic changes affecting a shrinking mire. River
Research and Applications, 26(3), 305-321, doi:10.1002/rra.1253.
Nakayama T. (2011a) Simulation of complicated and diverse water system accompanied by human intervention
in the North China Plain. Hydrological Processes, 25, 2679-2693, doi:10.1002/hyp.8009.
Nakayama T. (2011b) Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivatedYellow
River
Basin.
Agricultural
andForestMeteorology,151,
314-327,
doi:10.1016/j.agrformet.2010.11.006.
Nakayama T., Fujita T. (2010) Cooling effect of water-holding pavements made of new materials on water andheatbudgetsinurban
areas.
Landscapeand
Urban
Planning,
96,
57-67,
doi:10.1016/j.landurbplan.2010.02.003.
Nakayama T., Hashimoto S. (2011) Analysis of the ability of water resources to reduce the urban heat island in
the Tokyo megalopolis. Environmental Pollution, 159, 2164-2173, doi:10.1016/j.envpol.2010.11.016.
Nakayama T., Watanabe M. (2004) Simulation of drying phenomena associated with vegetation change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402,
doi:10.1029/2004WR003174.

- 29 

Chapter 2 Diverse Water System Accompanied by Human Intervention in the North China PlainNakayama T., Watanabe M. (2006a) Simulation of spring snowmelt runoff by considering micro-topography andphase changes in soil layer. Hydrology and Earth System Sciences Discussions, 3, 2101-2144.
Nakayama T., Watanabe M. (2006b) Development of process-based NICE model and simulation of ecosystem
dynamics in the catchment of East Asia (Part I), CGER's Supercomputer Monograph Report, 11, NIES,
100p., http://www-cger.nies.go.jp/publication/I063/I063.html.
Nakayama T., Watanabe M. (2008a) Missing role of groundwater in water and nutrient cycles in the shallow
eutrophic Lake Kasumigaura, Japan. Hydrological Processes, 22, 1150-1172, doi:10.1002/hyp.6684.
Nakayama T., Watanabe M. (2008b) Role of flood storage ability of lakes in the Changjiang River catchment.
Global and Planetary Change, 63, 9-22, doi:10.1016/j.gloplacha.2008.04.002.
Nakayama T., Watanabe M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verh
International Verein Limnology, 30.
Nakayama T., Yang Y., Watanabe M., Zhang X. (2006) Simulation of groundwater dynamics in North China
Plain by coupled hydrology and agricultural models. Hydrological Processes, 20(16), 3441-3466,
doi:10.1002/hyp.6142.
Nakayama T., Watanabe M., Tanji K., Morioka T. (2007) Effect of underground urban structures on eutrophic
coastalenvironment.
Science
of
the
Total
Environment,
373(1),
270-288,
doi:10.1016/j.scitotenv.2006.11.033.
Nakayama T., Sun Y., Geng Y. (2010) Simulation of water resource and its relation to urban activity in DalianCity, Northern China. Global and Planetary Change, 73, 172-185, doi:10.1016/j.gloplacha.2010.06.001.
Nakayama T., Hashimoto S., Hamano H. (in press) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes.
NASA (2006) Visible earth. A catalog of NASA images and animations of our home planet. Drought inNorthern China. http://visibleearth.nasa.gov/view_detail.php?id=17556.
Nash J.E., Sutcliffe J.V. (1970) Riverflow forecasting through conceptual model. Journal of Hydrology, 10,282-290.
Ogura N. (1933) A tide inshore Japan. Japan Coast Guard’s Report 7. Japan Coast Guard, Japan (in Japanese).
Piao S., Fang J., Zhou L. (2006) Variations in satellite derived phenology in China’s temperate vegetation.
Global Change Biology, 12, 672–685.
Rawls W.J., Brakensiek D.L., Saxton K.E. (1982) Estimation of soil water properties. Transactions of the ASAE,
25, 1316-1320.
Ritchie J.T., Singh U., Godwin D.C., Bowen W.T. (1998) Cereal growth, development and yield. In
Understanding Options for Agricultural Production, Tsuji G.Y., Hoogenboom G., Thornton P.K. (eds).
Kluwer, Great Britain, 79–98.
Robock A., Konstantin Y.V., Govindarajalu S., Jared K.E., Steven E.H., Nina A.S., Suxia L., Namkhai A.
(2000) The global soil moisture data bank. Bulletin of the American Meteorological Society, 81, 1281-1299.
http://climate.envsci.rutgers.edu/soil_moisture/.
Roderick M.L., Farquhar G.D. (2002) The cause of decreased pan evaporation over the past 50 years. Science,
298(15), 1410-1411.
Sato Y., Ma X., Xu J., Matsuoka M., Zheng H., Liu C., Fukushima Y. (2008) Analysis of long-term waterbalance in the source area of the Yellow River basin. Hydrological Processes, 22, 1618-1929.
DOI:10.1002/hyp.6730.
Sellers P.J., Randall D.A., Collatz G.J., Berry J.A., Field C.B., Dazlich D.A., Zhang C., Collelo G.D., Bounoua
L. (1996) A revised land surface prameterization (SiB2) for atomospheric GCMs. Part I : Modelformulation. Journal of Climate, 9, 676-705.
Sellers P.J., Dickinson R.E., Randall D.A., Betts A.K., Hall F.G., Berry J.A., Collatz G.J., Denning A.S.,
Mooney H.A., Nobre C.A., Sato N., Field C.B., Henderson-Sellers A. (1997) Modeling the exchanges ofenergy, water, and carbon between continents and the atmosphere. Science, 275, 502-509.
Shimada J. (2000) Proposals for the groundwater preservation toward 21st century through the view point ofhydrological cycle. Journal of Japan Association of Hydrological Sciences, 30, 63-72 (in Japanese).
Singh R.P., Roy S., Kogan F. (2003) Vegetation and temperature condition indices from NOAA AVHRR datafor drought monitoring over India. International Journal of Remote Sensing, 24(22), 4393– 4402.
U.S. Geological Survey (1996) GTOPO30 Global 30 Arc Second Elevation Data Set. USGS.
http://www1.gsi.go.jp/geowww/globalmap-gsi/gtopo30/gtopo30.html.
Wang Z., Wang X., Zhao F., Guo S., Hu S. (2004) The method of evaluation flood resources sustainableutilization capacity. Proceeding of Disaster Prevention Research Institute, Kyoto University.
http://www.wrrc.dpri.kyoto-u.ac.jp/~aphw/APHW2004/proceedings/JSD/56-JSD-A616/56-JSD-A616(resubmit).pdf.

- 30 

CGER-I103-2012, CGER/NIES
Warrence N.J., Bauder J.W., Pearson K.E. (2002) Basics of salinity and sodicity effects on soil physicalproperties. Department of Land Resources and Environmental Sciences, Montana State University –Bozeman. http://waterquality.montana.edu/docs/methane/basics.shtml#Salinity.
WebPaNDA (2008) NOAA AVHRR 10-days composite database over East Asia. University of Tokyo.
http://webpanda.iis.u-tokyo.ac.jp/.
Xia J., Wang Z., Wang G., Tan G. (2004) The renewability of water resources and its quantification in the
Yellow River basin, China. Hydrological Processes, 18, 2327-2336, doi:10.1002/hyp.5532.
Yang D., Li C., Hu H., Lei Z., Yang S., Kusuda T., Koike T., Musiake K. (2004) Analysis of water resources
variability in the Yellow River of China during the last half century using historical data. Water Resources
Research, 40, W06502, doi:10.1029/2003WR002763.
Yang L., Wylie B.K., Tieszen L.L., Reed B.C. (1998) An analysis of relationships among climate forcing andtime-integrated NDVI of grasslands over the U.S. Northern and Central Great Plains. Remote Sensing ofEnvironment, 65, 25-37.
Yellow River Conservancy Committees (1987) Annual report of river discharge at Yellow River. Interior reportof the committee (in Chinese).
Yellow River Conservancy Committees (1988) Annual report of river discharge at Yellow River. Interior reportof the committee (in Chinese).
Yin C.Q., Lan Z.W. (1995) The nutrient retention by ecotone wetlands and their modification for Baiyangdian
Lake restoration. Water Science and Technology, 32, 159-167.
Zhang J., Huang W.W., Shi M.C. (1990) Huanghe (Yellow River) and its estuary: sediment origin, transport anddeposition. Journal of Hydrology, 120, 203-223.
Zhou L.M., Tucker C.J., Kaufmann R.K., Slayback D., Shabanov N.V., Myneni R.B. (2001) Variations innorthern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal ofGeophysical Research, 106(D17), 20069-0083.
Zhou L., Kaufmann R.K., Tian Y., Myneni R.B., Tucker C.J. (2003) Relation between interannual variations insatellite measures of vegetation greenness and climate between 1982 and 1999. Journal of GeophysicalResearch, 108(D1), 4004, doi:10.1029/2002JD002510.
Zhu Y. (1992) Comprehensive hydro-geological evaluation of the Huang-Huai-Hai Plain. Geological Publishing
House of China, Beijing (in Chinese).

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CGER-I103-2012, CGER/NIES

Chapter 3
Relation Between Water Resource and Urban Activity in Dalian- 33 

Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

Abstract
The National Integrated Catchment-based Eco-hydrology (NICE) model was applied to
the Biliu River catchment in northern China to estimate the relationship between its water
resource and urban activity in the city of Dalian. NICE reasonably reproduced hydrologic
budgets following the construction of a reservoir at the middle reach of the river, which is an
important source of water for urban areas. Further, the model accurately backcasted thedegradation of water resources in the form of river discharge and groundwater after thecompletion of the reservoir. The normalized difference vegetation index (NDVI) estimatedfrom satellite data clearly showed vegetation degradation downstream of the reservoir.
Statistical analysis of a decoupling indicator based on the simulated water-carrying capacity
and satellite data of the vegetation index indicated that water-related stress in Dalian has
increased in accordance with the environmental degradation below the reservoir, although
Dalian’s economy has grown rapidly since the completion of the reservoir. These results
indicate a close relationship between water resources and economic growth, which has
adversely affected the ecosystem to create a significant burden on the environment in thecatchment and surrounding area. The simulated results highlight a close linkage between
development in the urban area and sustainable water resource management. Moreover, thisprocess-based model is important for the planning and execution of strategies to sustain socialand economic development while ensuring ecosystem integrity.
Keywords: water stress, carrying capacity, ecosystem degradation, decoupling indicator,
sustainable development, integrated catchment-based eco-hydrology model- 34 

CGER-I103-2012, CGER/NIES

3.1 Introduction
The demand for water in northern China has resulted in intensified water usage conflicts
between upstream and downstream areas and between agricultural and municipal–industrial
sectors (Nakayama et al., 2006; Nakayama, 2011a). Several researchers have reported that
China’s demand for natural resources already exceeds the environment’s capacity to sustain
this densely populated land (Niu and Harris, 1996; Varis and Vakkilainen, 2001). In particular,
more than 20,000 small catchments in China play important roles in supporting local
development and maintaining local ecosystem functions (Shi et al., 2002; Geng et al., 2010).
However, few studies have examined changes in water and heat dynamics, mainly because ofa lack of data for quantifying complex phenomena related to human activities(Nakayama etal., 2010).
The Biliu River catchment (2814 km2), located in the southern region of Liaoning
Province, is a heavy industrial base in northeast China (Fig. 3.1). Biliu, one of the largest
rivers in southern Liaoning with a total length of 156 km, is the primary water source for
Dalian, which has a population of more than 6 six million people and thousands of industries.
Because Dalian is located at the southern tip of the Liaodong Peninsula, flanked on the Eastand West with the Yellow and Bohai seas, respectively, the region has limited access to fresh
water. Therefore, its rapid economic growth has been supported by increased consumption ofwater transferred from remote fields (Fig. 3.2). The GDP increased relative to water
consumption until 1991 (Fig. 3.2a), at which time the Dalian Municipal Government
announced its new “Building an International City” policy (Dalian Planning Committee,
2001). Thereafter, Dalian entered a high-growth period, and the GDP increased more thantenfold in 1992–2007, which is attributed to overseas funding (Dalian Planning Committee,
2001). However, water consumption increased at a substantially slower rate. Although Dalian
was the first city in China to receive an award from the United Nations Environment
Programme’s (UNEP) Global 500 Forum (UNEP, 2001) and was honored as a “Water-saving
City” in 2002, the conflict between water supply and demand has exacerbated in recent years(Zhang and Xiong, 2006). The shortage of urban water has greatly influenced thedevelopment of the city and affected the lives of its residents, becoming the main obstruction
to sustainable development of economy and society (Han et al., 2008). Most of the city’swater (Fig. 3.2b) is drawn from Biliu Reservoir, in the middle of the river. Since thecompletion of the dam andreservoir in 1984, most of the water has been piped to Dalian, andthe catchment has changed from water-rich to water-poor (Fig. 3.2c), as evidenced by thedrying of the river downstream, receding of groundwater levels, and seawater intrusion, all ofwhich have also been observed in other regions of northern China (Qian and Zhu, 2001; Renet al., 2002; Nakayama et al., 2006; Nakayama, 2011a). To address the severe shortage ofvarious natural resources, including water, a national policy known as Circular Economy was
introduced in China around 2000 and was enacted into a law in 2008 (Ministry ofEnvironmental Protection, 2008). Through this legislation, effective and efficient use andrecycling of resources such as water and industrial waste were strengthened. In the city’s 11th
five-year plan for economic and social development in 2006, the Dalian government
implemented a local circular economy plan that aimed to cut water consumption about 20%
per million GDP (RMB) (Dalian City Government, 2006).

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Chapter 3 Relation Between Water Resource and Urban Activity in DalianFig. 3.1 Location of study area and elevation of the Biliu River catchment and Dalian. The boldblack line represents the catchment border.

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CGER-I103-2012, CGER/NIES

Fig. 3.2 Annual time series of GDP and water consumption in Dalian after the completion of
Biliu Reservoir illustrating (a) GDP and water consumption in the entire city of Dalian,(b) GDP and water consumption in the urban area of the city, and (c) water intake from
Biliu Reservoir. The values for GDP and water consumption were plotted on the basis ofvalues reported in 1985 (value at 1985 = 100) in (a) and (b). Rainfall anomalies at thereservoir are plotted along the right-hand y-axis in (c).

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Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

Although remote sensing derived from satellite images has been used for decades toassess and monitor vegetation health in many regions of the world, few such studies havebeen conducted on northeast China, except for those that map cropping patterns in east China(Nagatani et al., 2002), infer forest phenological patterns from moderate resolution imaging
spectroradiometer (MODIS) data in northeast China (Yu et al., 2005), and relate variations in
the NDVI according to climate (Piao et al., 2004, 2006). However, remote sensing has notbeen used to assess vegetation changes relative to water resources in the Biliu River
catchment. The ability to meet the water demands of its swelling city and industries without
undermining its own agriculture and the local ecosystem is the biggest challenge for the
Dalian government. The integrated management of water quantity and quality, along withinstitutional arrangements and land-use planning, is essential for sustaining social andeconomic development while ensuring ecosystem integrity (He and Chen, 2001; Mitchell,2005).
Decoupling analysis has become a popular approach that has been used widely amongscholars and policy makers during the past decade (OECD, 2001). To identify the relationshipbetween economic growth and environmental pressure, OECD (2001, 2002) explored aconceptual framework of decoupling indicators for the global economy related to climatechange, air pollution, water quality, waste management, material use, and natural resources
such as water, in addition to specific sectors in energy, transportation, agriculture, andmanufacturing. Decoupling indicators are primarily intended to track various environmental
problems for a single country or region such as Europe, the Pacific, and certain provinces andprefectures. OECD (2002) analyzed temporal changes in the relationship between
environmental pressures and economic growth in those countries belonging to theorganization. Moreover, the decoupling analyses of these parameters, including energy usage(Brown-Santirso and Thornley, 2006), transportation demands (Tight et al., 2004; OECD,
2004), carbon dioxide emissions (Lua et al., 2007), and metal consumption (Halada et al.,2007), were conducted in many developed countries. For example, Tachibana et al. (2008)
performed a case study of material flow on a regional scale. For water resource environment
analysis, Gleick (2000, 2002) decoupled economic development from increased water
consumption in the United States. However, few decoupling analyses for economic growth,water consumption, and environmental degradation on a regional scale or for a catchment
coupling with natural sciences are available.
The objective of this chapter is to evaluate the link between urban development in Dalianand sustainable water resource management in the Biliu River catchment (Nakayama et al.,2010). The author first applied the process-based NICE model (Nakayama, 2008a, 2008b,
2008c, 2009, 2010, 2011a, 2011b; Nakayama and Fujita, 2010; Nakayama and Hashimoto,2011; Nakayama and Watanabe, 2004, 2006a, 2006b, 2008a, 2008b, 2008c; Nakayama et al.,2006, 2007, 2010, in press) (Fig. 1.2)to the Biliu River catchment to estimate decadalchanges in water resources since the completion of the Biliu Reservoir. The author then used
NOAA/AVHRR satellite imagery analysis to evaluate the simulated result relative to theenvironmental degradation downstream of the reservoir in a similar manner as that describedin Chapter 1. Finally, the author statistically analyzed a decoupling indicator based on theestimated water-carrying capacity to evaluate water-related stress against economic
development in Dalian. This integrated assessment of the interactions between the water
source and demand supports effective strategies and planning for sustainable development in
the catchment (Nakayama et al., 2010).

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CGER-I103-2012, CGER/NIES

3.2 Methods
3.2.1 Hydrologic Budget
An accurate evaluation of the amount of water that can be drawn from Biliu Reservoir for
use in Dalian is critical, as is an evaluation of the amount that can be retained for preserving
the downstream ecosystem of the Biliu River. The hydrologic budget in the reservoir can be
described by the following equation in a similar manner as that reported in the author’s
previous research (Pollman et al., 1991; Lee, 1996; Nakayama and Watanabe, 2008a):'V 't

P  E  Qsf  Qri  Qro  Q gi  Q go  I  D ,(3.1)

where 'V (m3) is the change in reservoir volume for the period of interest, 't (s); P (m3/s) isthe precipitation rate; E (m3/s) is the evaporation rate; Qsf (m3/s) is the surface runoff flowing
into the reservoir; Qri (m3/s) is the inflow river discharge; Qro (m3/s) is the outflow riverdischarge; Qgi (m3/s) is the volume of groundwater inflow; Qgo (m3/s) is the volume ofgroundwater outflow; I (m3/s) is the total volume of water piped to Dalian; and D (m3/s) is thetotal drainage volume handled by wastewater treatment plants and public sewerage systems.
Because Biliu Reservoir is located in a mountainous region, its river discharge, Qro (m3/s), isevaluated as a residual term of the hydrologic budget, and the associated cumulative error is
calculated from the variance of the total error in the measured components, assuming thatsome of the terms in Eq. (3.1) can be neglected (Qsf  0, Qgi  0, Qgo  0, D  0):Qro

P  E  Qri  I  'V 't

(3.2)

E ro

e P2  e E2  eQ2 ri  e I2  e'2V ,(3.3)

where ei represents the error inherent in measuring term i. Because the errors of each term are
estimated by multiplying the budget value by the percentage error ascribed in the literature to
the measurement technique (Winter, 1981), the error of Qro is the aggregate of the errors
inherent in estimating each term in Eq. (3.2). This approach assumes that the measurement ofeach term is independent of the measurement of the other terms, and that no intercorrelations
or covariances exist between measurement errors (Nakayama and Watanabe, 2008a). The
budget-derived river discharge was used to validate the simulation results because data were
unavailable downstream of the reservoir.
The author first estimated the water budget of the reservoir by the lumped method. We
calculated the total river outflow from the observed river inflow data and the total
precipitation at the reservoir collected by the Dalian Water Resource Bureau (2005–2006).
Next, the author estimated the lake evaporation rate from the meteorological and watertemperature data (Dalian Water Resource Bureau, 2005–2006) by the Priestley–Taylor
method (Stewart and Rouse, 1976). The transfer of water to Dalian was calculated fromstatistical data on domestic and industrial water usage (Dalian Water Resource Bureau,
2007a). The change in reservoir volume was calculated from observed water level and bed
elevation data (Dalian Water Resource Bureau, 2005–2006). The river discharge from thereservoir was finally estimated by the residuals of the total reservoir budget in Eq. (3.2)(Nakayama et al., 2010).

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Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

3.2.2 Flood Resourcification Ability
Although floodwater usage for damming and irrigation is not a new concept, limited
studies have been performed to evaluate the potential of flood resourcification, which
describes flood resources development and utilization. From the perspective of flood
resourcification, Wang et al. (2004) evaluated the capacity for floodwater capture as thedifference between surface runoff and regional outflow downstream and out to sea:wf

ws  wo ,(3.4)

where wf (m3) is the flood resourcification ability; ws (m3) is the surface runoff; wo (m3) is theamount of outflow from the evaluation region, which includes water flowing downstream andinto the sea. To secure the city’s water supply, the Dalian government stated its intention touse floodwater capture to retain as much water as possible on land. Flood resourcification is
expected to be a significant tool for increasing water supply. The quantity of water entering
the sea, particularly during flood season, may show the flood resourcification potential in theentire catchment. A smaller amount of water entering the sea indicates greater potential forflood resourcification. The quantity of water entering the sea, particularly during flood season,
is a good indicator for estimating the amount of water capture. The retained water can be usedeither directly or indirectly by transporting it to Dalian, storing it in reservoirs, or forrecharging groundwater levels (Nakayama et al., 2010).
3.2.3 Decoupling Indicator based on Water Consumption and Economic Growth
In Dalian, the author used a decoupling indicator procedure to analyze the effects ofsocial activities, or driving force, on the environment and factors such as water consumption
and economic growth. The author additionally decoupled water consumption fromenvironmental degradation obtained from NDVI downstream of the catchment. The OECD
has developed procedures to decouple environmental challenges from economic growth(OECD, 2002). Decoupling can be either absolute or relative. Absolute decoupling occurs
when the environmental variable is stable, and it decreases when the economic driving force
rises. Relative decoupling occurs when the economic variable rises more rapidly compared tothe environmental variable (Brown-Santirso and Thornley, 2006).
The author first plotted two indexed time series on the same graph to express overall
trends of the decoupling of pressure from the driving force (value at 1985 = 100). A timeseries of the ratio of the two was then analyzed for a numerical examination of annual
decoupling trends by a decoupling indicator procedure (OECD, 2002). A decoupling indicator
was calculated from the ratio of pressure to driving force at the end against the value at thestart of a given time period:

Decoupling indicator = 1 – (EP/DF)end of period/(EP/DF)start of period.

(3.5)

Here, EP is environmental pressure, and DF is driving force. The decoupling indicator is zeroor negative in the absence of decoupling, positive in relative decoupling, and has a maximum
value of 1 when environmental pressure reaches zero, which describes absolute decoupling.
EP is the annual Biliu-derived water consumption in the entire city of Dalian or in its
urban area in a time series from 1985 to 2007; this includes the industrial consumption ofwater derived from the Biliu River from 2001 to 2007. DF is the GDP in Dalian or in the- 40 

CGER-I103-2012, CGER/NIES

urban area during the same period, including industrial GDP. Various indicators of driving
force exist in addition to GDP, such as population, standard of living, and urbanization. The
main objective of this chapter is to evaluate the link among urban development, water
resource management, and environmental degradation. Because the study area, Dalian, is ahighly industrialized city in China, it is preferable, as an initial step, to use the GDP as a
typical driving force. In our analysis of the impact of water abstraction from the Biliu Riveron the environment in the catchment, EP represents the NDVI as an index of environmental
degradation, and DF is water supply from the Biliu River (Nakayama et al., 2010).
3.3 Input Data and Boundary Conditions for Simulation
3.3.1 Meteorological Forcing Data
To create grid data with resolutions of 1 km, six-hour reanalysis data obtained by theEuropean Centre for Medium-Range Weather Forecasts (ECMWF) with a resolution of 1° u
1° were assimilated with daily observation data of solar radiation, precipitation, temperature,humidity, and wind speed at a weather station operated by Dalian Water Resource Bureau(2005–2006) and Yingkou City Government (2005–2006) (Table 3.1). These data were
inputted into each grid cell for a 2005–2006 simulation by interpolating the parameters ininverse proportion to the distance back-calculated in each cell. In particular, the daily
precipitation data collected at 13 weather stations in the study area (R-1–R-13 in Table 3.1)
were effective for the simulation because the ECMWF precipitation data had the least
reliability, both spatially and temporally, and underestimated the observed values at the peaktime (Nakayama and Watanabe, 2008b). This correction of ECMWF precipitation data byusing observed data is critical because slight changes in precipitation will result in large
variations in flood peaks and therefore are directly linked to the fluctuation of the water
budget in the catchment. The calculated precipitation distribution for the simulation agreed
well with previous research (Dalian Water Resource Bureau, 2005–2006; Yingkou City
Government, 2005–2006), which shows higher rainfall in the eastern side of the study area
(Fig. 3.3). Because the observed recharge rate data for natural regions are scarce, except forthat using the chloride mass balance method (Lee, 1996), these values were simulated by
NICE at each time step and were inputted into the groundwater submodel. Details are given in
Nakayama and Watanabe (2004, 2008a).

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Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

Table 3.1. List of observation stations for validation.

No.

Point Name

Type

Lat.

Lon.

Elev.
(m)

R-1
R-2
R-3
R-4
R-5
R-6
R-7
R-8
R-9
R-10
R-11
R-12
R-13

Xiao Shipeng
Meng JiadianKuang Donggou
Taiping Zhuang
Xi Pashan
Biliu Reservoir
Guiyunhua Village
Daiiang Village
Shifogou Village
TianyiShuangta Village
Xiaosongjia Village
Jian Chang

Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge
Rainfall Gauge

40°17.1'
40°14.6'
40°10.3'
40°8.7'40°7.3'39°49.2'
39°55.7'
40°1.7'39°47.6'
39°54.6'
39°46.2'
39°34.3'
40°1.7'122°27.0'
122°31.5'
122°43.0'
122°44.0'
122°32.3'
122°29.5'
122°35.9'122°42.6'
122°38.4'
122°26.4'
122°28.7'122°32.8'122°32.3'

199.0
191.0
233.0
227.0
115.0
53.0
80.0
219.0
84.0
133.0
27.0
9.0
91.0

W-1

Gaizhou

Weather Data

40°23.7' 122°22.1'

8.0

W-2
W-3
W-4
W-5

Biliu Reservoir
ZhuanghePulandianDalianWeather Data
Weather Data
Weather Data
Weather Data

39°49.2'
39°42.0'39°23.7'
38°54.7'122°29.5'
122°59.5'
121°58.0'
121°36.1'

53.0
3.0
2.0
14.0

G-1

Jian Chang

Groundwater Level

40°1.7'122°32.3'

91.0

G-2
G-3

Biliu Reservoir
Xiaosongjia Village

Groundwater Level
Groundwater Level

39°49.2' 122°29.5'
39°34.0' 122°33.0'

53.0
9.0

D-1

Jian Chang

River Discharge

40°1.7'122°32.3'

91.0

D-2
D-3

River Discharge
River Discharge
River Discharge(analyzed)
River Discharge

39°55.7' 122°35.9'
39°49.2' 122°29.5'

80.0
53.0

39°48.8' 122°29.6'

33.0

D-5

LingxiBiliu Reservoir
Downstream of
Biliu Reservoir
Xiaosongjia Village

39°34.3' 122°32.8'

9.0

S-1

Jian Chang

Soil Temp. & Moisture

40°1.7'122°32.3'

91.0

T-1

Chengzitan

Tidal Level

39°29.2' 122°33.9'

5.0

D-4

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CGER-I103-2012, CGER/NIES

Fig. 3.3 Comparison between the forcing precipitation data created by 13 points of rain gauge
data and previous research data: (a) rainfall anomalies (mm) in the forcing data and (b)rainfall anomalies (mm) in previous research data.

3.3.2 Vegetation and Geological Properties
The mean elevation of each 1-km grid cell was calculated by using the spatial average ofa global digital elevation model (DEM; GTOPO30) with a horizontal grid spacing of 30
arc-seconds (approximately 1-km mesh) (US Geological Survey, 1996) throughout the Biliu
River catchment (Fig. 3.1). Vegetation class and soil texture were categorized and digitized
into 1-km mesh data from the Vegetation and Soil Maps of China (1:4,000,000) (Chinese
Academy of Sciences, 1988) and the Soil Map of Dalian (1:100,000) (Liaoning Geology
Investigation Agency, 2007b). These finer-resolution data were more accurate than dataobtained from the International Satellite Land Surface Climatology Project (ISLSCP) with aresolution of 1°u 1° (Sellers et al., 1996). The geological structures were decided fromscanned and digitized geological material (Liaoning Geology Investigation Agency, 2007a;
Geological Atlas of China, 2002), which included core-sampling data at some points.

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Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

Geological structures were divided into four types with a resolution of 1 km in the horizontal
direction and 20 layers in the vertical direction, Kh and Kv, respectively, on the basis ofhydraulic conductivity, the specific storage of porous material (Ss), and specific yield (Sy), as
described by Nakayama et al. (2006).
The NDVI was calculated from NOAA/AVHRR satellite data with a resolution of 1.1 km
over East Asia during 1984–2007. The data area was selected from the original data to matchthe simulation area (39.35°N–40.50°N, 122.05°E–123.00°E). The important point of thisstudy is that economic growth in the urban area, evaluated through water consumption, isclosely related with environmental degradation in the water source area downstream of the
Biliu River catchment, which is shown in the NDVI trend in the following section. Althougha certain relationship exists between economic growth in Dalian and the city’s NDVI
value—such as the shrinkage of green areas inside the city accompanied by the expansion ofurban areas and pollution—this factor is not the primary objective of this study because the
NDVI value was evaluated for the Biliu River catchment rather than Dalian. Moreover,evaluation of the NDVI distribution inside the city requires the use of additional satellite datawith higher resolution, such as data obtained by an advanced spaceborne thermal emission
and reflection radiometer (ASTER).
3.3.3 Statistical Data on Water Resources and GDP
The consumption of water derived from the Biliu River during 1985–2007 was compiled
from statistical data (Dalian Water Resource Bureau, 2007a), as was the total water
consumption in Dalian during 2000–2007 (Dalian Water Resource Bureau, 2000–2007).
Because official data on water consumption in Dalian from 1985 to 1999 was not available,
we estimated the consumption for this period. The author first calculated the percentage ofraw water consumption that was acquired from the environment and which was subsequently
treated or purified in a water purification plant to produce potable water (Dalian Water
Resource Bureau, 2000–2007) relative to the total water consumption in Dalian, and thepercentage of Biliu-derived water consumption relative to total water consumption in a timeseries for the period 2000–2007. Because both annual-averaged percentages were nearly
constant throughout the period, the author multiplied severally the two averages by raw water
consumption in Dalian and water consumption from the Biliu River and estimated the twotime series of water consumption in Dalian from 1985 to 1999. Finally, the author consideredthe 1985–1999 average to be representative of Dalian’s total water consumption.
Annual GDP data during 1985–2007 was obtained from the Dalian Planning Committee(2001), the Dalian City Government (1990–2007), and the Dalian Bureau of Statistics(2002–2007). The annual GDP of the urban area (1985–2007) was estimated by multiplying
the per-capita GDP of Dalian by the population of the urban area (Dalian City Government,
1990–2007). Because the rural–urban income disparity in eastern China was 54.4% in 1980
and 43.2% in 2000 (Wang and Ron, 2008), the author conducted a statistical analysis of
decoupling indicators on the assumption that rural and urban incomes followed the same
trend.
3.3.4 Boundary Conditions and Running the Simulation
Digital land cover data produced by the Institute of Remote Sensing Applications (IRSA)
at the Chinese Academy of Sciences (CAS) (1988) based on Landsat TM data from the early
1990s (Liu, 1996) were categorized and divided into 1-km mesh data for the simulation.
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CGER-I103-2012, CGER/NIES

Details are given in Nakayama and Watanabe (2008b). Land cover in the catchment (Liaoning
Hydrology Bureau, 2007) was scanned and digitized to increase the accuracy of the IRSA
data. Forest, grass, and scrub cover the mountainous and hilly areas in the upper catchment.
Cultivated fields are widely distributed in the valley and lower regions. At the upstream
boundaries, for which no observation data are available, the reflecting condition on thehydraulic head was used in the groundwater flow submodel on the supposition that there is no
inflow from the mountains in the opposite direction (Nakayama and Watanabe, 2004). At thesouthern sea boundary (Yellow Sea), a variable head was set by using observed monthly data
(T-1 in Table 3.1; Dalian Water Resource Bureau, 2005–2006). The hydraulic head values
parallel to the ground level were input as the initial conditions for the groundwater flow
submodel. The author used the digital river network produced by the IGSRR (1982) from
1:100,000 topographic maps covering the catchment. In 660 river cells, including the Biliu
River, inflows into or outflows from the riverbed were simulated at each time step from thedifference in the groundwater and river hydraulic heads.
The simulation area is 60 km wide by 110 km long on the basis of the Albers (WGS
1984) co-ordinates, covering almost the entire Biliu River catchment. The area was divided
into a grid of 60 u 110 blocks with a grid spacing of 1 km in the horizontal direction and into
20 vertical layers with a weighting factor of 1.1; sediment was finer in the upper layers. The
upper layer was set at a depth of 2 m, and the 20th layer was defined as an elevation of 200 m
below sea level. The NICE simulation was conducted on a NEC SX-6 supercomputer.
Simulations covering January 1, 2005, to December 31, 2006, were performed for calibration.
The first six months of the study were used as a warm-up period until equilibrium water levels
were reached, and parameters were estimated by comparison of simulated steady-state values
with observed values that were previously published (Clapp and Hornberger, 1978; Rawls etal., 1982). A time step of 't = 1 h was used. Simulations were validated against observed
river discharge at five points, groundwater level at three points, and soil temperature andmoisture at one point (Dalian Water Resource Bureau, 2005–2006; Yingkou City Government,
2005–2006) (Table 3.1).
3.4 Results and Discussion
3.4.1 Hydrologic Change Affected by the Reservoir Construction
The simulated results were calibrated for the Biliu River catchment during 2005–2006(Fig. 3.4). The upper catchment is mountainous, and most of the cover is natural. The lower
catchment comprises a vast, flat area covered mainly by paddy and other fields. The riverdischarge is greater from spring to fall because rainfall is higher in this period. The model
showed a good reproductions of the observed river discharge from the upper to the lower
regions, particularly around the reservoir (D-3 in Table 3.1) (Cor = 0.60). The Cor in the main
upstream area (D-1 in Table 3.1) was as small as that observed downstream of the reservoir(D-4 in Table 3.1) mainly because the grid spacing in this simulation was slightly rough forsimilar reproduction of the river discharge as that reported in previous research (Nakayama
and Watanabe, 2008b). In addition, inaccuracy of meteorological and other inputted data waspossible, and the discharge downstream of the reservoir was greatly affected by the transfer ofwater to Dalian. Because there were no available observation data downstream of thereservoir (D-4 in Table 3.1), the author evaluated the outflow discharge from the reservoir byusing a simple hydrologic budget (shown in Eq. (3.2)) (Nakayama and Watanabe, 2008a).
This budget-derived value underestimates the simulated value, mainly by ignoring surface- 45 

Chapter 3 Relation Between Water Resource and Urban Activity in Dalianrunoff and groundwater seepage into the reservoir. The simulated values of groundwaterlevels were validated against actual groundwater level data (Fig. 3.5a). Since the author used a
3-D groundwater submodel, the simulated groundwater fluctuations agree strongly with themeasured values, as shown in the values of Cor, MV, SD, and CV, which depend on
precipitation, local topography, and water withdrawal. The groundwater level decreased
rapidly with distance at the reservoir due to the rapid decrease of recharge; this is because
surface runoff decreased rapidly below the reservoir (Fig. 3.5b). The groundwater level issignificantly low in the middle and lower regions because of low elevation; therefore, it issensitive to and greatly affected by tides and decreased discharge following the completion of
the Biliu Reservoir. These results indicate that the simulation is highly effective for evaluating
the link between urban development in Dalian and sustainable water resource management(Nakayama et al., 2010).

Fig. 3.4 Calibration in Biliu River catchment (D-1–D-4; Table 3.1). Circles represent observation
data, and lines represent results simulated by NICE. Date = 0 on the horizontal axis
corresponds to January 1, 2005. Cor shows the correlation between observed and simulated
values. In (d), dotted and solid lines represent simulated discharges without and with the watertransfer at Biliu Reservoir, respectively.

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CGER-I103-2012, CGER/NIES

Fig. 3.5 Simulated results of annual-averaged groundwater levels in the Biliu River
catchment illustrating (a) the comparison between simulated and observed valuesand (b) the simulated contour in the catchment. Cor shows the correlation between
the observed value and the simulated value. MV, SD, CV represent mean value,
standard deviation, and coefficient of variation (= SD/MV), respectively.

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Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

NICE backcasted the river discharge and flood resourcification ability downstream of thereservoir following its completion (Fig. 3.6). The simulated result clarified that the riverdischarge decreased yearly since the completion of the reservoir because water demand
increased in the urban area (Fig. 3.6a). In particular, very little water was discharged into thewaterway, and the river downstream became seasonal. In the dry autumn and winter seasons,the main waterway nearly dried out, while in the rainy season, it became a flood spillway.
This trend is characteristic of catchments in northern China, where the downstream flow
depends greatly on precipitation over the catchment and withdrawal from upstream (Qian andZhu, 2001; Ren et al., 2002). It is evident that most of the water in the river was already being
used at the beginning of this century, with the flood resourcification ability reaching a
constant value (Fig. 3.6b). The degree of floodwater capture in this catchment is closely
related to the history of water resource development, as is seen mainly in the construction of
Biliu Reservoir. Because rapid development of industry and urbanization, in addition to theincrease in farmland irrigation, has also pushed water demand to the limits of its carrying
capacity (Varis and Vakkilainen, 2001), the groundwater table has receded in the floodplainsof the river’s downstream areas (Nakayama et al., 2010) (Fig. 3.5).

Fig. 3.6 Simulation result of water resources affected by the construction of Biliu Reservoir
illustrating (a) river discharge downstream of the reservoir and (b) flood
resourcification ability downstream of the reservoir.

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CGER-I103-2012, CGER/NIES

3.4.2 Vegetation Change Affected by the Change in Water Resources
Trend analysis was used to study long-term vegetation trends (Fig. 3.7). The variation in
annual NDVI corresponds to the variation in annual rainfall in the catchment (Fig. 3.7a).
Along with a general decrease during the period of study, several fluctuations were observed.
A closer examination revealed that since the construction of the reservoir in 1984, negative
trends have predominated (1984–1994 and 2003–2007), whereas rainfall slightly increased
during 1995–2002. The earlier decrease during 1984–1994 was associated with theconversion to farmland and a reduction in river flow. After the reservoir construction, large
areas of native vegetation downstream of the floodplains were converted into farmland (Liu etal., 2005). Paddy fields were converted into dryland fields in direct response to water scarcity
attributed to the cultivation of more farmland through the construction of reservoirs, which
resulted in reduced river flow downstream. Low NDVI values during 1992–1994 wereassociated with the coldest and driest year in the 1990s (Piao et al., 2004). Increased values of
NDVI can result from both a lengthened growing season and accelerated vegetation activity;
Piao et al. (2004) suggested that a longer growing season is a primary contributor to increased
NDVI.
The spatial pattern in the NDVI trend during 1984–2007 shows heterogeneity (Fig. 3.7b)that corresponds well to the regional distribution of water resources. A significant decrease in
the NDVI occurred primarily downstream of the Biliu reservoir and downstream of theZhujiaweizi and Liuda reservoirs, which were built in 1968 and 1971, respectively (DalianCity Government, 2003). Downstream of the Zhujiaweizi Reservoir and in its surrounding
areas, the NDVI decreased as a result of several factors, including a reduction in water
resources and rapid urbanization surrounding the city of Zhuanghe (Wang, 1997). In the
coastal zone near Chengzitan, topographic depressions, most likely formed by fluvial
processes, have shallow water tables and are susceptible to salinization. The low NDVI in thisarea is the result of high salt concentrations in the subsurface water (Kondoh and Oyamada,
2000). NDVI in cultivated fields is smaller beside the Yellow Sea than it is inland, which has
increased yearly because of the decrease in river discharge. In addition, NDVI values showed
the most rapid rate of decline near the Yellow Sea. Mountainous areas upstream of the Biliu
Reservoir exhibit higher NDVI values than those observed in the downstream areas. Thisdifference is closely related to the water shortage and seawater intrusion occurring since theconstruction of Biliu Reservoir (Nakayama et al., 2010) (Fig. 3.5 and 3.6), which has greatly
reduced crop growth and production in coastal areas.

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Chapter 3 Relation Between Water Resource and Urban Activity in DalianFig. 3.7 NDVI with a resolution of 1.1 km mesh in the Biliu River catchment illustrating (a) the
annual trend of NDVI downstream of Biliu Reservoir (circles). Rainfall anomalies at
the reservoir are also plotted on the right-hand y-axis (bars). Dashed lines represent
least-square regression lines estimated from NDVI data. (b) Spatial patterns in NDVI
gradient in the entire catchment during 1984-2007 (image areas: 39.35°N–40.50°N,
122.05°E–123.00°E). The dashed square represents the border of the simulation area.

3.4.3 Relationship Between Water Resources and Urban Activity
Fig. 3.8a shows annual decoupling trends between water consumption and GDP in theentire city of Dalian. One noteworthy outcome is the relatively small decoupling indicators,
although they are mostly positive. This result shows that environmental demand for water
consumption in Dalian is higher than that in developed regions (OECD, 2002). An additional
noteworthy outcome is the slight tendency of decline in the decoupling indicator from 1985 to
2007. The relative decoupling in this period was due to remarkable economic growth with acomparatively low increase in water consumption, although environmental demand for water
consumption continues to increase with economic growth in Dalian.
The decoupling indicator value for the urban area was higher than that for the entire cityof Dalian during some periods when the environmental demand from local areas wasconsiderably less than that from Dalian; however, the values in 1985–1988 were irregularowing to the beginning of dam operation (Fig. 3.8b). The decoupling indicator values
declined, were unstable, and were negative twice in eighty years (1998–1999 and 2004–2005),- 50 

CGER-I103-2012, CGER/NIES

although the Dalian government increased the water price several times to control
consumption (Dalian Water Resource Bureau, 2007b). The value dropped from 0.43 in
1993–1994 to –0.37 in 1998–1999, became positive at 0.33 again in 2003–2004, and thendeclined. These trends show that the environmental demand for water has increased witheconomic growth in the urban area, although relative decoupling predominated, particularly
when the decoupling indicator value was negative.
The impact of water withdrawal from Biliu Reservoir on environmental degradation of
NDVI downstream is shown in Fig. 3.8c. The decoupling indicator value gradually decreased
from 0.75 (relative decoupling) in 1988–1989 to –0.25 (no decoupling) in 2006–2007,
although values were irregular in 1985–1988 due to the start of dam operation and inaccurate
data. This result shows increasing environmental degradation with increasing abstraction ofwater from the Biliu River, which indicates that the environmental degradation in the Biliu
River catchment will worsen with economic growth in the future (Nakayama et al., 2010).

Fig. 3.8 Annual trends of decoupling indicators among (a) water consumption and GDP in theentire city of Dalian, (b) water consumption and GDP in the urban area of Dalian, and(c) NDVI and water consumption. Dashed lines represent least-square regression lines
estimated from annual decoupling indicators (bars).

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Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

3.5 Conclusion
The author evaluated the hydrologic budget in Biliu Reservoir to estimate the relationshipbetween water withdrawal and outflow from the dam. The NICE model appropriately
reproduced river discharge, groundwater level, evapotranspiration, and crop productivity inthe Biliu River catchment. Moreover, the model correctly backcasted the gradual decrease inwater resources since the completion of the reservoir (in the middle of the catchment),
showing reduced discharge into the Yellow Sea, the groundwater drop in the floodplain, andthe reduced NDVI downstream of the reservoir. The statistical analysis of the decoupling
indicators suggests that the limited water resources in the Biliu River catchment has degraded
the environment and poses a threat to economic growth in the Dalian municipality,
particularly in the central urban area. The results highlight the connection between urban
development in Dalian and sustainable water resource management.

References
Brown-Santirso M., Thornley A. (2006) Decoupling economic growth and energy use in New Zealand. Statistics
New
Zealand.
http://www.stats.govt.nz/NR/rdonlyres/CB3DE050-3163-4C6E-8871-AEF8014FD9E7/0/decouplingecono
micgrowthnzaepaperjun06.pdf.
Chinese Academy of Sciences (1988) Administrative division coding system of the People’s Republic of China,
Beijing.
Clapp R.B., Hornberger G.M. (1978) Empirical equations for some soil hydraulic properties. Water Resources
Research, 14, 601-604.
Dalian Bureau of Statistics (2002-2007) Dalian statistical yearbook. China Statistics Press (in Chinese).
Dalian City Government (1990-2007) Dalian yearbook. Dalian Publisher (in Chinese).
DalianCity
Government(2003)
Reservoir
projects
inDalianCity.
http://szb.dl.gov.cn/dlsz/003/004/005/12818_12883.htm (in Chinese).
Dalian City Government (2006) Outline of 11th five-year plan for Dalian City's economic and social
development, 39 (in Chinese).
Dalian Planning Committee (2001) Strategic research on Dalian's tenth five-year plan. Dalian, China, Dongbei
University of Finance & Economics Press. (in Chinese).
Dalian Water Resource Bureau (2000-2007) The water resources bulletin of Dalian City.
http://www.swj.dl.gov.cn/info/info/fangxun.aspx?id=78 (in Chinese).
Dalian Water Resource Bureau (2005-2006) Annual report of weather, river discharge, groundwater level, tidal
level, and soil characteristics at the Biliu River catchment. Interior report of the committee (in Chinese).
Dalian Water Resource Bureau (2007a) Five years water demand prediction. Official document, Dalian City
Government (in Chinese).
Dalian Water Resource Bureau (2007b) The classification of the water rent and the collection.
http://www.swj.dl.gov.cn/info/info/fangxun.aspx?id=81 (in Chinese).
Geng Y., Mitchell B., Fujita T., Nakayama T. (2010) Perspectives on small watershed management in China: thecase of Biliu. International Journal of Sustainable Development and World Ecology, 17(2), 172-179,
doi:10.1080/13504500903549528, 2010.
Geological Atlas of China (2002) Geological Publisher, Beijing, China (in Chinese).
Gleick P. (2000) The changing water paradigm: a look at twenty first century water resources development.
Water International, 25, 127-138.
Gleick P. (2002) Water management: Soft water paths. Nature, 418, 373.
Halada K. ,Shimada M., Ijima K. (2007) Decoupling status of metal consumption from economic growth.
Journal of the Japan Institute of Metals, 71(10), 823-830 (in Japanese, with Abstr. English).
Han Y., Xu S., Xu X. (2008) Modeling multisource multiuse water resources allocation. Water Resources
Management, 22, 911-923.

- 52 

CGER-I103-2012, CGER/NIESHe D., Chen J. (2001) Issues, perspectives and need for integrated watershed management in China.
Environmental Conservation, 28(4), 368-377.
Kondoh A., Oyamada Y. (2000) Monitoring surface moisture and vegetation status by NOAA and GMS overnorth china plain. Advance in Space Research, 26(7), 1055-1058.
Lee T.M. (1996) Hydrogeologic controls on the groundwater interactions with an acidic lake in karst terrain,
Lake Barco, Florida. Water Resources Research, 32, 831-844.
Liaoning Geology Investigation Agency (2007a) Geology Map of Dalian.
Liaoning Geology Investigation Agency (2007b) Soil Map of Dalian.
Liaoning Hydrology Bureau (2007) Land cover at the upstream area of Biliu River.
Liu J.Y. (1996) Macro-scale survey and dynamic study of natural resources and environment of China by remotesensing. Chinese Science and Technology Publisher, Beijing, ChiElsevier B.V.na. (in Chinese).
Liu Y., Wang D., Gao J., Deng W. (2005) Land use/cover changes, the environment and water resources inNortheast China. Environmental Management, 36(5), 691–701.
Lua I.J., Lin S.J., Lewisc C. (2007) Decomposition and decoupling effects of carbon dioxide emission fromhighway transportation in Taiwan, Germany, Japan and South Korea. Energy Policy, 35, 3226–3235.
Minstry of environmental protection of the people’s republic of china (2008) Promotion law for circular
economy, 4. http://www.sepa.gov.cn/law/law/200809/t20080901_128001.htm (in Chinese).
Mitchell B. (2005) Integrated water resource management, institutional arrangements, and land-use planning.
Environmental Planning A, 37, 1335-1352.
Nagatani I., Saito G., Toritani H., Sawada H. (2002) Agricultural map of Asian region using time series AVHRRNDVI data. http://www.gisdevelopment.net/aars/acrs/2002/pos2/184.pdf
Nakayama T. (2008a) Factors controlling vegetation succession in Kushiro Mire. Ecological Modelling, 215,
225-236, doi:10.1016/j.ecolmodel.2008.02.017.
Nakayama T. (2008b) Shrinkage of shrub forest and recovery of mire ecosystem by river restoration in northern
Japan. Forest Ecology and Management, 256, 1927-1938, doi:10.1016/j.foreco.2008.07.017.
Nakayama T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part II), CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
http://www-cger.nies.go.jp/publication/I083/i083.html.
Nakayama T. (2009) Simulation of ecosystem degradation and its application for effective policy-making inregional scale. In River pollution research progress, Mattia N. Gallo, Marco H. Ferrari (eds), 1-89 (Chapter1), Nova Science Pub., Inc., New York.
Nakayama T. (2010) Simulation of hydrologic and geomorphic changes affecting a shrinking mire. River
Research and Applications, 26(3), 305-321, doi:10.1002/rra.1253.
Nakayama T. (2011a) Simulation of complicated and diverse water system accompanied by human intervention
in the North China Plain. Hydrological Processes, 25, 2679-2693, doi:10.1002/hyp.8009.
Nakayama T. (2011b) Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivatedYellow
River
Basin.
Agricultural
andForestMeteorology,151,
314-327,
doi:10.1016/j.agrformet.2010.11.006.
Nakayama T., Fujita T. (2010) Cooling effect of water-holding pavements made of new materials on water andheatbudgetsinurban
areas.
Landscapeand
Urban
Planning,
96,
57-67,
doi:10.1016/j.landurbplan.2010.02.003.
Nakayama T., Hashimoto S. (2011) Analysis of the ability of water resources to reduce the urban heat island in
the Tokyo megalopolis. Environmental Pollution, 159, 2164-2173, doi:10.1016/j.envpol.2010.11.016.
Nakayama T., Watanabe M. (2004) Simulation of drying phenomena associated with vegetation change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402,
doi:10.1029/2004WR003174.
Nakayama T., Watanabe M. (2006a) Simulation of spring snowmelt runoff by considering micro-topography andphase changes in soil layer. Hydrology and Earth System Sciences Discussions, 3, 2101-2144.
Nakayama T., Watanabe M. (2006b) Development of process-based NICE model and simulation of ecosystem
dynamics in the catchment of East Asia (Part I), CGER's Supercomputer Monograph Report, 11, NIES,
100p., http://www-cger.nies.go.jp/publication/I063/I063.html.
Nakayama T., Watanabe M. (2008a) Missing role of groundwater in water and nutrient cycles in the shallow
eutrophic Lake Kasumigaura, Japan. Hydrological Processes, 22, 1150-1172, doi:10.1002/hyp.6684.
Nakayama T., Watanabe M. (2008b) Role of flood storage ability of lakes in the Changjiang River catchment.
Global and Planetary Change, 63, 9-22, doi:10.1016/j.gloplacha.2008.04.002.
Nakayama T., Watanabe M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verh
International Verein Limnology, 30.

- 53 

Chapter 3 Relation Between Water Resource and Urban Activity in Dalian

Nakayama T., Yang Y., Watanabe M., Zhang X. (2006) Simulation of groundwater dynamics in North China
Plain by coupled hydrology and agricultural models. Hydrological Processes, 20(16), 3441-3466,
doi:10.1002/hyp.6142.
Nakayama T., Watanabe M., Tanji K., Morioka T. (2007) Effect of underground urban structures on eutrophic
coastalenvironment.
Science
of
the
Total
Environment,
373(1),
270-288,
doi:10.1016/j.scitotenv.2006.11.033.
Nakayama T., Sun Y., Geng Y. (2010) Simulation of water resource and its relation to urban activity in DalianCity, Northern China. Global and Planetary Change, 73, 172-185, doi:10.1016/j.gloplacha.2010.06.001.
Nakayama T., Hashimoto S., Hamano H. (in press) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes.
Niu W.Y., Harris W.M. (1996) China: the forecast of its environmental situation in the 21st Century. J.
Environmental Management, 47, 101-114.
OECD (2001) Decoupling: a conceptual overview. OECD paper No.10, OECD, Paris.
OECD (2002) Indicators to measure decoupling of environmental pressure from economic growth.
http://www.olis.oecd.org/olis/2002doc.nsf/LinkTo/sg-sd(2002)1-final.
OECD (2004) Analysis of the links between transport and economic grow. Environment Directorate.
http://www.oecd.org/dataoecd/29/37/31661238.pdf
Qian W., Zhu Y. (2001) Climate change in China from 1880 to 1998 and its impact on the environmental
condition. Climatic Change, 50, 419-444.
Piao S., Fang J., Ji W., Guo Q., Ke J., Tao S. (2004) Variation in a satellite-based vegetation index in relation toclimate in China. Journal of Vegetation Science, 15, 219-226.
Piao S., Fang J., Zhou L. (2006) Variations in satellite derived phenology in China’s temperate vegetation.
Global Change Biology, 12, 672–685.
Pollman C.D., Lee T.M., Andrews J.W., Sacks L.A., Gherini S.A., Munson R.K. (1991) Preliminary analysis of
the hydrologic and geochemical controls on acid-neutralizing capacity in two acidic seepage lakes in
Florida. Water Resources Research, 27, 2321-2335.
Rawls W.J., Brakensiek D.L., Saxton K.E. (1982) Estimation of soil water properties. Transactions of the ASAE,
25, 1316-1320.
Ren L., Wang M., Li C., Zhang W. (2002) Impacts of human activity on river runoff in the northern area ofChina. Journal of Hydrology, 261, 204-217.
Sellers P.J., Randall D.A., Collatz G.J., Berry J.A., Field C.B., Dazlich D.A., Zhang C., Collelo G.D., Bounoua
L. (1996) A revised land surface prameterization (SiB2) for atomospheric GCMs. Part I : Modelformulation. Journal of Climate, 9, 676-705.
Shi S.X., Zhang Z.Y., Li W.Z. (2002) Perspectives on small watershed’s sustainable development, Beijing,
Science Press. (in Chinese).
Stewart R.B., Rouse W.R. (1976) A simple equation for determining the evaporation from shallow lakes andponds. Water Resources Research, 12, 623–628.
Tachibana J., Hirota K., Goto N., Fujie K. (2008) A method for regional-scale material flow and decoupling
analysis: A demonstration case study of Aichi prefecture, Japan. Resources Conservation and Recycling, 52,
1382-1390.
Tight M.R., Delle-Site P., Meyer-Ruhile O. (2004) Decoupling transport from economic Growth: towards
transport sustainability in Europe. European Journal of Transport and Infrastructure Research, 4(4),381-404.
UNEP (2001) Global 500 forum. http://www.global500.org/default.asp.
U.S. Geological Survey (1996) GTOPO30 Global 30 Arc Second Elevation Data Set. USGS,
http://www1.gsi.go.jp/geowww/globalmap-gsi/gtopo30/gtopo30.html.
Varis O., Vakkilainen P. (2001) China’s 8 challenges to water resources management in the first quarter of the
21st Century. Geomorphology, 41, 93-104.
Wang M.Y.L. (1997) Urban growth and the transformation of rural China: the case of Southern Manchuria. Asia
Pacific Viewpoint, 38(1), 1-18.
Wang X.L., Ron D.C. (2008) Rural-urban income disparity and WTO impact on China’s agricultural sector:
policy considerations. In: Chen, C., and Ron, D.C. (Eds.), Agriculture and food security in China. Asia
Pacific Press, Australia, 85-103, http://epress.anu.edu.au/afsc/pdf/whole_book.pdf.
Wang Z., Wang X., Zhao F., Guo S., Hu S. (2004) The method of evaluation flood resources sustainableutilization capacity. Proceeding of Disaster Prevention Research Institute, Kyoto University,
http://www.wrrc.dpri.kyoto-u.ac.jp/~aphw/APHW2004/proceedings/JSD/56-JSD-A616/56-JSD-A616(resubmit).pdf.

- 54 

CGER-I103-2012, CGER/NIES

Yingkou City Government (2005-2006) Annual report of weather, river discharge, groundwater level, tidal level,and soil characteristics at the Biliu River catchment. Interior report of the committee (in Chinese).
Yu X., Zhuang D., Hou X., Chen H. (2005) Forest phenological patterns of Northeast China inferred from
MODIS data. Journal of Geographical Sciences, 15(2), 239-246.
Zhang J., Xiong B.Y. (2006) Towards a healthy water cycle in China. Water Science and Technology, 53, 9-15.
- 55 

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.18
CGER-I103-2012, CGER/NIES

Chapter 4
Ability of Water Resources to Reduce the Urban Heat Islandin the Tokyo Megalopolis- 57 

Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

Abstract
An innovative simulation procedure of the hydrothermal cycle integrated with multiple scalesinhorizontally
regional–urban–pointlevelsandinverticallyatmosphere–surface–unsaturated–saturated layers was developed to estimate and predict theeffects of urban geometry and anthropogenic exhaustion on the hydrothermal changes in theatmospheric–land regions and the interfacial areas of a Japanese megalopolis. The simulated
results suggests that the latent heat flux in a new water-holding pavement, which consists ofporous asphalt and a water-holding filler made of steel by-products based on silica
compounds, has a strong impact on the hydrologic cycle and cooling temperatures in addition
to a passive cooling effect of green areas in comparison with the observed heat budget. The
author evaluated the relationship between the effects of groundwater usage as a heat sink toaddress the heat island and the effects of infiltration on the water cycle in the urban area.
Moreover, the model predicted that hydrothermal change would affect the target andsurrounding areas, implying the necessity of precise arrangement of a symbiotic urban
scenario in the study area with neighboring administrations from a political perspective.
These results indicate that effective management of water resources is important forameliorating the heat island and recovering a sound hydrologic cycle. Further improvement in
the process-based model is important for achieving a favorable solution for hydrothermal
pollution in future eco-conscious societies.
Keywords: favorable solution, multi-scaled model, hydrothermal characteristics, heat island,
imbalance of hydrologic cycle, symbiotic urban scenario- 58 

CGER-I103-2012, CGER/NIES

4.1 Introduction
Recently, a link has been found between environmental pollution and degradations ofwater and thermal environments and contamination in Asian urban areas (Endo, 1992;
Taniguchi et al., 2009; Nakayama and Hashimoto, 2011; Nakayama et al., in press). In
particular, urban heat islands have presented significant environmental challenges with the
expansion of cities and industrial areas worldwide (Oke, 1987; Golden, 2003). Surfaces
covered by concrete or asphalt can absorb a large amount of heat during the day and release
this heat into the atmosphere at night. Many researchers have attempted to reproduce heatislands by using regional weather forecast models (Pielke et al., 1992; Dudhia, 1993;
Skamarock et al., 2005). Zhang et al. (2008) used the Regional Atmospheric Modeling
System (RAMS) coupled with a single-layer urban canopy model (UCM) (Kusaka et al.,
2001; Kusaka and Kimura, 2004) to improve the reproducibility of a heat island; the original
RAMS was unable to reproduce this environment accurately. Ichinose et al. (1999)
investigated the impact of anthropogenic heat on heat islands compared with shortwave
radiation. Ohashi et al. (2007) integrated building energy analysis models (BEM) into theUCM, which demonstrated the effect of anthropogenic heating on indoor workplaces toreproduce diurnal temperature variations during the workweek in Tokyo office settings.
The evaporation of water provides an important counterpoint to this effect; therefore,
open parks and water surfaces in addition to green areas in urban settings are vital for creating
cool islands (Spronken-Smith and Oke, 1999; Chang et al., 2007). The convective heattransfer of urban surfaces, defined as the sum of advective and diffusive transfer, is a crucial
parameter for estimating the turbulent heat flux in urban areas, and this convective heattransfer should be estimated quantitatively (Hagishima et al., 2005).Several researchers haveused process-based models to estimate the transport of water vapor and the regulation oftemperature distribution above a paved surface (Grimmond and Oke, 1991; Asaeda and Ca,
1993; Dupont et al., 2006). Numerical models show that road hydrological behavior
influences thermal exchange and evaporation creates significantly lower temperatures in baresoil than on covered surfaces (Asaeda and Ca, 1993). To aid the reduction of
evapotranspiration from natural surfaces, Objective Hysteresis Model(OHM) (Grimmond andOke, 1991) and Sub-Mesoscale Soil Model, Urbanized version(SM2-U) (Dupont et al., 2006)
were used along with a submesoscale urban scheme to show that urban hydrologicalparameters determine the amount of water available for evaporation from artificial surfaces
such as engineered pavements. However, in these models, a lack of accurate measured data on
water infiltration into largely impermeable urban surfaces has led to the assumption by
several urban hydrologists that infiltration is almost zero and runoff is 100% of rainfall, which
could lead to overestimation of runoff and underestimation of groundwater recharge. Hollis
and Ovenden (1988a, 1988b) and Ragab et al. (2003) showed that streets cannot be
considered fully impermeable and that runoff also represents a significant source of pollution.
In addition, it is important to assess the impacts of land use alteration scenarios on hydrology
on the basis of observations, which are closely related to water resource management and themitigation of heat islands (Alberti et al., 2007).
Permeable pavement and other stormwater control devices are increasingly being used tocollect stormwater, which evaporates in fine weather (Andersen et al., 1999; Sansalone andTeng, 2005). Ramier et al. (2004) used a lysimeter to assess losses due to infiltration,
evaporation, and runoff on engineered pavement. The promotion of vaporization by using
water-holding pavement consisting of porous asphalt and water-holding filler made of steel
by-products based on silica compounds would be an effective strategy for the mitigation of- 59 

Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

heat islands when used in combination with light-colored and vegetated surfaces (Saaroni etal., 2000; Chang et al., 2007; Nakayama and Fujita, 2010). Because this thermal relaxation in
newly symbiotic pavement is closely related to the heat capacity of water and the conductivityof the pavement, it is estimated that groundwater usage at nearly constant temperatures as aheat sink would be a highly effective measure for addressing the issue of heat islands(Ministry of Environment, 2003). A policy to promote infiltration into the aquifer is also
important to prevent ground degradation, which has occurred in the past (Endo, 1992;
Nakayama et al., 2007). The heat island related to the hydrologic cycle also differs between
surface and street levels (Saaroni et al., 2000) and on local versus regional scales (Nichol and
Wong, 2005), which makes evaluation difficult in actual urban regions. Therefore, it is criticalto develop the process-based model and to evaluate the relationship between the effects ofgroundwater usage to ameliorate the heat island and the effect of infiltration on the hydrologic
cycle in catchment areas and on a regional scale (Nakayama and Hashimoto, 2011; Nakayama
et al., in press). Moreover, model simulation in a political scenario for effective selection andusage of ecosystem service sites (Millennium Ecosystem Assessment, 2005) would be astrong approach in the creation of thermally-pleasant environments and the achievement ofsustainable development in megacities.
The objective of this chapter is to construct a simulation system of the hydrothermal cycle
integrated with multi-scaled levels in horizontal regions of the urban area and in verticalatmosphere–surface unsaturated–saturated layers. The author has previously developed the
National Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2008a,
2008b, 2008c, 2009, 2010, 2011a, 2011b; Nakayama and Fujita, 2010; Nakayama andHashimoto, 2011; Nakayama and Watanabe, 2004, 2006a, 2006b, 2008a, 2008b, 2008c;
Nakayama et al., 2006, 2007, 2010, in press)(Fig. 1.2), which was newly coupled with UCM(Nakayama and Fujita, 2010) and RAMS (Pielke et al., 1992) to simulate the effects of urban
geometry and anthropogenic exhaustion on the change in water and heat cycles in theatmospheric–land and interfacial areas of the Japanese megalopolis (NICE-URBAN).
Moreover, the author predicted hydrothermal changes for several political scenarios to reduce
air temperatures above heat islands and to create thermally-pleasant environments in themegalopolis (Nakayama and Hashimoto, 2011; Nakayama et al., in press).
4.2 Study Area
The study area of Kawasaki with approximately 1.3 million inhabitants is in the Keihin
industrial zone, the largest industrial area in Japan, located on the western shore of Tokyo Bay,
halfway between the Tokyo metropolitan area and Yokohama (Nakayama and Hashimoto,2011; Nakayama et al., in press) (Fig. 4.1). The bay is a eutrophic coastal area typical in
Japan. The environment has changed extensively because of a rapid rise in the number ofindustries and residents, seashore concrete dikes and embankments built for reclamation andshipping from the tidal areas, and the construction of wastewater treatment plants and publicsewerage systems. After heavy and chemical industry prosperity in the 1950s, theenvironmental amenity deteriorated, with air pollution reaching alarming levels in the early
1970s. The city government established a pollution prevention ordinance in 1972 andstipulated stricter standards than those mandated by Japan’s 1967 Basic Law forEnvironmental Protection (Kawasaki City Government, 2004; van Berkel et al., 2009). As aresult, pollution levels reduced gradually since the late 1970s. In addition, the city
government issued policies to revitalize coastal industries and improve environmental
amenities to compensate for the collapse of Japan’s bubble economy in 1993.
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CGER-I103-2012, CGER/NIES

Fig. 4.1 Study area highlighting land cover in the city of Kawasaki and the Kanto Plain,including the Tokyo metropolitan area, Tokyo Bay, and the Tama River catchment(area: 1,240 km2). Sampled data of 206 points of geological structures were used for input
in the NICE-URBAN model. The observed data were as follows: 29 points of weather data
(W; ‫)ۑ‬, 6 points of river discharge data (R; ‫)ڹ‬, 9 points of groundwater level data (G;‫ )ۃ‬during 2004–2006, 1 point of flux tower data at the Todoroki car parking garage (F; ‫)ڧ‬,and 1 point of various pavement blocks at the rooftop of the Keihin building (P; ‫)ۼ‬. The
data points were used for validation of the model (Table 4.1).

Various environmental degradations such as groundwater level depression, groundsubsidence, and seawater intrusion became serious issues resulting from the intensive
utilization of groundwater as an important water resource for industries during the period of
rapid development from the 1970s to the 1980s (Endo, 1992; Nakayama et al., 2007). The
government has since prohibited such groundwater usage, which has allowed groundwaterlevels to recover substantially. However, this measure has created further complications
marked by the “floating” of subways, stations, and buildings (Ministry of Environment,
2004a). The study area is currently one of the most severe heat island regions intermingledwith hydrologic cycle imbalance. Government officials have taken various measures tomitigate the effects of the heat island by changing and rearranging surface layers via albedo
and emissivity and installing elements such as sky views, ventilation or wind paths, androoftop gardens. Other undertakings are the use of light-colored surfaces and highly infrared
reflective coating, reconstruction of elderly buildings and urban geometry by spatiallyadequate arrangement, and green-infrastructure systems in parks and flood plains, in addition
to increasing energy efficiency and managing traffic–transportation systems (Ministry ofEnvironment, 2003; Kawasaki City Government, 2008).
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Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

The city is situated on an alluvial plain in the lower Tama River and has the
Shimosueyoshi upland in the middle. The city is comprised of residential (40%),
commercial–industrial (28%), and other (32%) areas. Lowland areas are covered by alluvial
delta clay with a thickness of 10–40 m, which is underlain by alternating layers of silt, sand,and gravel to constitute a semi-confined aquifer system. The northern region borders theMusashino upland, and the western region lies on the Tama hills, which are generally coveredby relatively permeable volcanic ash known as Kanto loam (thickness of 5–10 m) (Arai andSun, 1996). The river, recently considered an important source for flood control, drinking
water, and irrigation, is also an environmental amenity in the Japanese megalopolis. In
particular, the roles of water resources such as rivers, lakes, seas, and groundwater areimportant in the mitigation of urban heat islands.
4.3 Materials and Methods
4.3.1 Developing a Process-based Model Applicable to Urban Areas (NICE-URBAN)
In this chapter, the NICE model was newly combined with the UCM to include the effectsof the hydrothermal cycle on various engineered pavements and with the RAMS to include
the water–heat interaction between land and atmosphere regions (NICE-URBAN) (Nakayama
and Hashimoto, 2011; Nakayama et al., in press) (Fig. 4.2). The general equations for theatmospheric model (RAMS) are standard hydrostatic Reynolds-averaged primitive equations(Pielke et al., 1992) consisting of motion, thermodynamics, water species mixing ratio
continuity, mass continuity, and hydrostatic equations. Downward short- and long-wave
radiation, precipitation, atmospheric pressure, air temperature, air humidity, and wind speed
simulated by the atmospheric model were inputted into the UCM, whereas momentum,
sensible, latent, and long-wave flux simulated by the UCM were inputted into the atmosphericmodel at each time step. This procedure ensures that the feedback process on water and heattransfers between atmospheric regions and land surfaces are implicitly included in thesimulation process (Fig. 4.2). Moreover, one-dimensional equations of momentum, heat, andvapor (wind velocity, air temperature, and specific humidity) inside the boundary layer
between buildings were coupled with the UCM by considering the influence of buildings on
diffusing potentials. In this process, anthropogenic heat emissions were also simulated
automatically by coupling with a building energy analysis model on the assumption that allemissions originated from the buildings and depended on the difference between the outside
air temperature and the temperature inside the buildings. The heat source data evaluated by
the GIS data were also inputted directly as the forcing data in another simulation forverification of the simulated anthropogenic heat emissions. Although this scheme simplifies
the anthropogenic heat calculation by coupling the urban canopy and atmospheric models, themodel can automatically simulate the feedback processes of hydrothermal cycles between theoutdoor conditions and each urban building and is instrumental in predicting futurehydrothermal changes, as described in Nakayama et al.(in press). A horizontal momentum
equation was then solved, preserving the continuity equation at each time step. In addition,
the author expanded specific heat conductivity and heat conductivity for natural soil (Sellers
et al., 1996) into those for engineered pavement by including the effect of the amount ofwater on the heat characteristics in materials; heat capacity is an important factor for
reproducing the amplitude and phase of the diurnal cycle (Nakayama and Fujita, 2010;
Nakayama and Hashimoto, 2011; Nakayama et al., in press) (Fig. 4.2).

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CGER-I103-2012, CGER/NIES

Fig. 4.2 Flow diagram of the NICE-URBAN model. Dotted frames indicate the newly
developed processes used in this study.

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Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

4.3.2 Input Data and Boundary Conditions
Hourly observation data obtained from the Automated Meteorological Data Acquisition
System (AMeDAS) (Japan Meteorological Agency, 2005–2006a) at 126 points wereassimilated with NICE-URBAN. Artificial augmentation of waterworks and seweragesystems discharging into the sea was inputted to the model using statistical data for domestic
and industrial water usage (Digital National Land Information GIS Data of Japan, 2002).
Annual groundwater-use data covering the Kanto Plain (Digital National Land Information
GIS Data of Japan, 2002) were digitized and inputted into the model. The rate of water
withdrawal in the surrounding (western) area of the Kawasaki district was about 149,000
ton/day in 2004 (Kawasaki City Government, 2004–2006).
Vertical geological structures were divided into 10 layers with a weighting factor of 1.1
by using a 206-sample database and a digital elevation model (DEM) (Geographical Survey
Institute of Japan, 1999). The upper layer was set at a depth of 2 m, and the 10th layer was
defined by an elevation of –500 m. The geological structure was divided into four types on
the basis of hydraulic conductivity (Kh and Kv), specific storage (Ss), and specific yield (Sy)(Nakayama et al., 2007). Approximately 50 vegetation and soil parameters were calculated on
the basis of vegetation class and soil texture obtained from the Digital National Land
Information GIS data of Japan (2002).
At the upstream boundaries, conditions affecting the hydraulic head were used for themodel on the supposition that no inflow occurs from the opposite direction of the mountains.
A time series of tidal levels was inputted as a constant head at the sea boundary (Nakayama etal., 2007). At the lateral boundaries of the region, meteorological data such as temperature,humidity, wind speed, altitude, and sea surface temperatures obtained by the European Centre
for Medium-Range Weather Forecasts (ECMWF) were inputted into the model with aresolution of 1° u 1°; similar data obtained from the Meso Scale Model (MSM) with aresolution of 10 km u 10 km were also inputted (Japan Meteorological Agency, 2005–2006b).
4.3.3 Building Structure and Anthropogenic Heat Source in Urban Areas
The anthropogenic exhaustion heat generated by energy consumption considering
differences in sensible and latent heat was analyzed by using the GIS database forfundamental investigation of urban planning in the study area (Kawasaki City Government,
1994; Tokyo Metropolitan Government, 2001; Yokohama City Government, 2003) (Fig. 4.3).
Because several studies have reported that the main sources of anthropogenic heat emission
are buildings and factories in Kawasaki, both of which are located at the center of the Keihin
industrial area, the anthropogenic sensible and latent heats generated by buildings such as
offices and homes, and factory smokestacks were analyzed according to anthropogenic heat
per unit. The anthropogenic sensible, latent, and sewage heats at each mesh (200 m
resolution) were calculated every hour by multiplying the area of each building-use andland-use with unit energy consumption in eight types of land—detached houses, apartment
houses, offices, businesses, hotels, schools, factories, and other—reported previously(Ministry of Environment, 2004b). The calculated heat results were inputted into each layer ofthe model including the vertical direction of a vent and an exhaust fan within an averagebuilding height as often as possible because this type of location differs significantly frombuildings and factories. These calculated heat results were also used to calibrate and validate
the anthropogenic heat emissions that were automatically simulated by the feedback process
of NICE-URBAN (Nakayama et al., in press) (Fig. 4.2).
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CGER-I103-2012, CGER/NIES

Fig. 4.3 Schematic representation of multi-scaled model illustrating (a) multi-layers in thevertical direction, (b) an example of simulated values, and (c) the diurnal cycle ofanthropogenic heat emission estimated by the GIS database.

4.3.4 Simulation and Prediction for Political Scenarios
The simulation was conducted with multi-scale levels in a horizontal regional area (260km wide by 260 km long with a grid spacing of 2 km covering the Kanto region) nesting one
way to an urban area (36 km wide by 26 km long with 200 m grid covering Kawasaki)(Nakayama et al., 2007) and in vertical atmosphere–surface unsaturated–saturated layers (Fig.
4.3) for three years from January 1, 2004, to December 31, 2006. There was a six-monthwarm-up period until equilibrium was reached. A time step of the simulation was changed
from 't = 1.5 s to 1 h depending on spatial scale and submodel. Observed data ofgroundwater levels, river discharge, air temperature, and humidity (Kanto Regional
Development Bureau, 2005–2006; Kawasaki City Government, 2004–2006) were used tovalidate the simulations (Fig. 4.1 and Table 4.1).

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Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

Table 4.1. Lists of observation stations used for validation.

No.

Point Name

Type

Lat.

Lon.

Elev.
(m)

W-1

Daishi-1

Weather Data

35°32'1''

139°44'8''

1.0

W-2
W-3
W-4
W-5
W-6
W-7
W-8
W-9
W-10
W-11
W-12
W-13
W-14
W-15
W-16
W-17
W-18
W-19
W-20

Sakamoto
Oda
Minamigawara
Ogura
Kariyado
Nishimaruko
Hisamoto
Hisasue
Nishiarima
Sugao
Miyazakidai
Noborito
Minamisuge
Nagasawa
Chiyogaoka
Shinpukuji
Kurikidai
Ougimachi
Ikuta

Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data

35°31'14''
35°30'41''
35°32'14''
35°32'28''
35°33'45''
35°35'12''
35°35'43''
35°34'5''35°34'19''
35°35'50''
35°35'36''
35°37'22''
35°37'22''
35°35'55''
35°36'60''
35°35'23''
35°36'41''
35°30'14''
35°36'35''

139°43'19''
139°42'6''
139°41'27''
139°39'56''
139°39'49''
139°39'19''
139°37'0''
139°37'9''139°34'38''
139°33'27''
139°35'23''
139°33'34''
139°31'57''
139°31'40''
139°30'9''139°30'25''
139°28'35''
139°42'52''
139°33'30''

-1.0
1.0
2.0
3.0
6.0
8.0
14.0
45.0
51.0
44.0
37.0
21.0
62.0
83.0
108.0
47.0
83.0
2.0
78.0

W-21

Daishi-2

Weather Data

35°31'55''

139°44'2''

1.0

W-22
W-23
W-24
W-25
W-26
W-27
W-28
W-29

TajimaKawasaki
SaiwaiNakahara
TakatsuMiyamae
Tama
Asao

Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data
Weather Data

35°30'54''
35°31'53''
35°32'39''
35°34'34''
35°35'56''
35°35'22''
35°37'19''
35°36'8''

139°42'42''
139°42'11''
139°41'9''139°39'21''
139°36'50''
139°35'10''
139°33'33''
139°30'56''

1.0
2.0
3.1
8.0
13.0
45.9
23.0
104.0

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CGER-I103-2012, CGER/NIES

Table 4.1. Lists of observation stations used for validation (continued).

No.

Point Name

Type

Lat.

Lon.

Elev.
(m)

G-1
G-2
G-3
G-4
G-5
G-6
G-7
G-8
G-9

Chidoricyo
Kannongawa
TajimaWatadaRokugou
KomukiShinjyo
SakadoInada

Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level

35°31'21''
35°31'24''
35°31'02''
35°30'38''
35°32'9''35°32'46''
35°34'51''
35°35'45''
35°36'48''

139°45'11''
139°44'10''
139°42'52''
139°42'40''
139°42'15''
139°41'16''
139°37'53''
139°37'23''
139°34'44''

2.0
0.0
0.0
1.0
2.0
3.0
9.0
12.0
18.0

R-1

Tamagawakakou

River Level

35°32'12''

139°46'1''

2.0

R-2

Denencyofu

35°35'21''

139°39'57''

8.0

R-3

Ishihara

35°38'39''

139°31'40''

35.0

R-4

Takahatabashi

35°39'52''

139°24'37''

67.0

R-5

Hinobashi

35°41'1''

139°24'49''

83.0

R-6

Cyofubashi

River Level
River Discharge andLevel
River Discharge andLevel
River Discharge andLevel
River Discharge andLevel

35°46'52''

139°16'0''

182.0

F-1

Todoroki

Flux Tower

35°35'10''

139°39'0''

9.0

P-1

KeihinPavement Block

35°30'38''

139°43'1''

2.0- 67 

Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

Scenario-based analyses have been used extensively since the 1940s for better
understanding of the effectiveness of alterative policies and technology options under
uncertain situations (Kahyaoglu-Koracin et al., 2008). In this study, the author prepared two
types of land cover scenarios to reduce air temperature. The first is a technology-oriented
approach that introduces a water-retaining pavement to promote positive cooling throughevaporative latent heat effects (Nakayama and Fujita, 2010). In contrast, the second scenario
is a natural-restoration-oriented approach that introduces new green areas or expands existingones to promote passive cooling effects through plant evapotranspiration (Nakayama et al.,2007). These scenarios were designed to follow the techno-garden and adapting-mosaic
scenarios, both of which were developed for the Millennium Ecosystem Assessment project(Millennium Ecosystem Assessment, 2005). Using the GIS database originally developed forcity planning, these two scenarios were applied to all vacant spaces of building sites and toopen spaces like parks in Kawasaki (Table 4.2); these areas were identical to the target areasof existing land use legislation enacted to restore nature and to create amenities (Kawasaki
City Government, 2008). Identification of cooling effects would assist policymakers inprioritizing alternatives or deciding which approach to adopt as a countermeasure against heatislands (Nakayama and Hashimoto, 2011; Nakayama et al., in press).

Table 4.2. Present land use and available vacant space considered in the scenario simulation.

Area (km2)Land use
Industrial sites
Low-rise residential areas (general)
Low-rise residential areas (high-density)
Medium-to-high-rise residential areas
Commercial and business areas
Parks and green areas
Public institution areas
Roads
Forests
Rice paddy fields
Cultivated landRivers and lakes
Sea
Other
Total

18.4
40.0
0.5
17.0
4.8
2.2
16.6
10.8
9.9
0.1
2.2
6.3
8.8
6.7
144.4

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Available vacant
space (km2)
13.5
12.8
0.1
5.6
3.0
1.9
5.3
N/a
N/a
N/a
N/a
N/a
N/a
N/a
42.1

CGER-I103-2012, CGER/NIES

4.4 Results and Discussion
4.4.1 Multi-scaled Characteristic in Hydrothermal Cycle
The simulated air temperature was compared with values observed across the Kawasaki
region from August 15–31, 2006 (Kawasaki City Government, 2006) (Fig. 4.4a). Despite
better reproduction of cloud cover, the simulated air temperature underestimates conditions inAugust 16–19 and overestimates conditions in August 24–27 mainly because of an inaccurate
precipitation simulation by RAMS when nesting with one way to local scale (Pielke et al.,1992). The simulated air temperatures in NICE-URBAN are generally in good agreementwith the observed values at various land covers such as cultivated fields, residential areas,business districts, and industrial areas. This is evident by the high correlation (r2 = 0.70 –0.80) and relatively high accuracy (root-mean-square error; RMSE = 1.6–2.1 °C), similar to
those observed in previous cases (Nakayama et al., 2007; Nakayama and Fujita, 2010). The
simulated heat-flux budget was compared to the analyzed value at the water-holding
pavement in the Todoroki car parking area (F-1 in Table 4.1) from August 3 to August 7,
2007 (Fig. 4.4b). The water-holding pavement exhibited a surface temperature about 10.0 °C
lower than that of the infiltration pavement, which is closely associated with the promotion ofvaporization and the higher surface reflectance (0.48) of this material; this result continued forfive days after the saturation of the water-holding pavement (Nakayama and Fujita, 2010). In
the field experiment, these heat fluxes were estimated by using the bulk method and,following water irrigation of about 5.4 liter per 1 m2 on August 3, the errors in the cumulative
heat flux ranged from –0.2% to 1.2% in the daytime (Nakayama et al., in press). The
simulated maximum sensible and latent heat fluxes in the water-holding pavement recorded
just after water irrigation were 130 W/m2 and 345 W/m2, respectively, which had relatively
good approximations of 161 W/m2 and 337 W/m2, respectively, in observed values and inprevious research (Yamagata et al., 2008). The simulated result shows that the newly
symbiotic pavement is a highly effective in promoting cooler temperatures (Nakayama andHashimoto, 2011; Nakayama et al., in press).

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Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

Fig. 4.4 Verification of heat flux in the interfacial area: (a) comparison of simulated airtemperature with observed value 2.0 m above the ground surface at points W-1 to
W-29 in Table 4.1; (b) validation of heat-flux budget in the symbiotic water-holding
pavement after water irrigation at point F-1 in Table 4.1. In (a), the solid line represents
observation data, and the bold line represents the results simulated by NICE-URBAN. Date
= 14 on the horizontal axis corresponds to August 15, 2006. r2 and RMSE represent
correlation between observed value and simulated value, and root-mean-square error,
respectively. In (b), the dotted line represents net radiation (NR); solid line, sensible heat
flux (SH); bold line, latent heat flux (LH); and dash-dotted line, ground transfer heat flux(GH). Date = 0 on the horizontal axis corresponds to August 1, 2007.

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Fig. 4.5 Verification of hydrologic cycle from regional to urban scales: (a) annual-averaged
groundwater level in the regional scale (Kanto Plain) interpolated by using the observed
value; (b) simulated value in the regional scale; (c) simulated value in the urban area
downscaled from the regional scale in (b); and (d) comparison of simulated
groundwater level with observed value at points G-1 to G-9 in Table 4.1. In (a), the dotted
circle represents the observed locations of 247 points. In (b), the dotted purple frame
represents the urban area simulated in (c). In (d), circles represent observation data, and lines
represent results simulated by NICE-URBAN. Date = 0 on the horizontal axis corresponds toJanuary 1, 2004.

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4.4.2 Prediction of Hydrothermal Changes in the Symbiotic Urban Scenario
As described in Section 4.3.4, the author predicted hydrothermal changes in the symbiotic
urban scenario (Fig. 4.6). The previous research showed that in addition to air temperature
above the surface, the water content of the block also affects the surface temperature(Nakayama and Fujita, 2010). Because the model estimates an air temperature at thewater-holding pavement 1–2 °C lower than that at the lawn and 3–5 °C lower than that at thebuilding rooftop as the surface level (Saaroni et al., 2000), this material is instrumental for
proving the positive cooling effects when combined with lawns for passive cooling at thesurface level in addition to the street level and at the regional scale (Nakayama and Fujita,
2010; Nakayama et al., 2007). Because the air temperature was relatively high with norainfall, the temperature on the heat island was severe at 1500 JST on August 5, 2006; these
conditions are effective in demonstrating the cooling effects of the water-holding pavement inaddition to green areas. The simulated surface temperature in the scenario of thewater-holding pavement showed a dramatic decrease in the entire city of Kawasaki (Figure10b). In particular, the business district area beside the sea (Fig. 4.6a), where the urban heatisland is predominant—because of the paved surfaces (Fig. 4.1) and a larger amount ofanthropogenic heat sources (Fig. 4.3)—underwent effective cooling in this scenario. The
simulation shows that the scenario of a natural zone and green area was also effective forcooling temperatures in the study area (Fig. 4.6c).
The predicted groundwater level change in August is interesting in contrast to thepreviously simulated air temperature (Fig. 4.6d-e). In the water-holding-pavement scenario,
all the water required to saturate the water-holding pavement was automatically simulated byconsidering the difference between precipitation and evaporation, and withdrawn fromsubsurface groundwater in the NICE-URBAN model, which indicates that the pavement
remained saturated. The increase in recharge rate and the consequent groundwater level
increase in the scenario of natural zones and green areas are also very effective in promoting
groundwater resources in urban areas covered by impermeable pavements (Nakayama et al.,2007). In the scenario of the water-holding pavement, the groundwater level decreased
dramatically, particularly in the commercial and industrial areas beside the sea; the level also
decreased in the inland residential areas as a consequence of dramatic cooling in thecorresponding and surrounding areas. Because these phenomena are closely related to the heat
capacity of water and the conductivity of the pavement, the subsurface water temperature isan additional important factor for effective cooling. Because the water temperature in an
aquifer remains nearly constant throughout the year, the use of groundwater as a heat sink is ahighly effective tool for addressing the urban heat island phenomenon, particularly during thesummer (Ministry of Environment, 2003; Nakayama and Fujita, 2010). Generally, predicted
temperatures and groundwater levels in the business districts of Kawasaki and the Tokyo
metropolitan area are greatly affected. This factor is critical from a political perspective andhighlights the importance of greater precision when estimating the arrangement of symbiotic
urban scenarios in the study area together with neighboring administrations (Nakayama andHashimoto, 2011; Nakayama et al., in press).

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Fig. 4.6 Prediction of hydrothermal changes at 1500 JST on August 5, 2006, in the symbiotic
urban scenario: (a) present surface temperature; (b–c) prediction of surface
temperature considering water-holding pavement and green areas, respectively; and(d–e) prediction of monthly-averaged groundwater level change considering
water-holding pavement and green areas, respectively.

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Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

4.4.3 Heterogeneity of Hydrothermal Cycles in Urban Areas
Simulated groundwater levels relative to the surface clearly indicate the heterogeneity ofwater resources as a slight imbalance of the hydrologic cycle in the urban and surrounding
areas (Fig. 4.7a). The levels become deeper in the northern part of Kawasaki and Kanto,
where extensive groundwater extraction continues through the cultivation of spring wateraround the foot of the mountain. This cultivation of spring water takes place because
regulated areas are few and regulatory standards are relatively less severe (Hayashi et al.,2009). For these reasons, land subsidence remains a serious environmental issue. In addition
to the thermal environment, water contamination also shows some relation to theheterogeneity of water resources (Fig. 4.7b). For example, higher nitrogen concentrations ingroundwater exceed environmental standards (10 mg/L) in some areas (Ministry ofEnvironment, 2000; Nakayama et al., 2007; Nakayama and Watanabe, 2008a), creating
substandard drinking water that could threaten human health and the ecosystem (Ministry ofEnvironment, 2004a). These contaminants could leach into the water supply and also play animportant role in eutrophication, which indicates that groundwater is a relatively large sourceof nitrogen loading to aquatic regions, even though the seepage is not as large as the overland
flow (Nakayama et al., 2007; Nakayama and Watanabe, 2008a; Taniguchi et al., 2009). In
addition, nitrogen concentration varies according to the hydrologic cycle and watertemperature through complex chemical reactions. Therefore, it is necessary to further evaluate
water resources, heat islands, and contamination issues relative to environmental pollution(Nakayama et al., 2007; Nakayama and Watanabe, 2008a; Nakayama and Hashimoto, 2011).

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Fig. 4.7

Environmental pollution in urban and surrounding areas: (a) simulated groundwaterlevel relative to the ground surface; (b) annual-averaged groundwater nitrogenconcentration (mg/L) interpolated by observed data. Urban and cultivated fields areplotted together in the figures. Dotted frames represent urban areas similar to thosedescribed in Fig. 4.5. The northeastern region of the urban area represents the Tokyo
metropolitan area.

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Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

4.5 Conclusion
The process-based NICE model was coupled with the UCM and RAMS models tosimulate the effects of urban structures and anthropogenic emission on hydrothermal change
in the Japanese megalopolis (NICE-URBAN). The simulation, which integrated multi-scale
horizontal regional–urban–point levels and vertical atmosphere–surface unsaturated–saturated
layers, was created to predict the effect of urban geometry and anthropogenic exhaustion onhydrothermal changes in the atmospheric–land and the interfacial areas of the Japanese
megalopolis. The results suggest that the latent heat flux in the novel water-holding
pavement—consisting of porous asphalt and water-holding filler made of steel by-products
based on silica compounds—strongly affects the hydrologic cycle and cooling temperatures incomparison with the observed heat budget. The author further evaluated the relationshipbetween the effects of groundwater used as a heat sink to address the temperatures on the heatisland and the effects of infiltration on the water cycle in urban areas. The results indicate that
effective water resource management is instrumental for cooling heat islands and recovering asound hydrologic cycle.


References
Alberti M., Booth D., Hill K., Coburn B., Avolio C., Coe S., Spirandelli D. (2007) The impact of urban patterns
on aquatic ecosystems: an empirical analysis in Puget lowland sub-basin. Landscape and Urban Planning,
80, 345-361.
Andersen C.T., Foster I.D.L., Pratt C.J. (1999) The role of urban surfaces (permeable pavements) in regulating
drainage and evaporation: development of a laboratory simulation experiment. Hydrological Processes, 13,597-609.
Arai T., Sun S. (1996) The Kanto plain and the Yangtze delta. Kokon-Shoin Co. Ltd., Tokyo, 163 pp.
Asaeda T., Ca V.T. (1993) The subsurface transport of heat and moisture and its effect on the environment: anumerical model. Boundary-Layer Meteorology, 65, 159-179.
Chang C.R., Li M.H., Chang S.D. (2007) A preliminary study on the local cool-island intensity of Taipei cityparks. Landscape and Urban Planning, 80, 386-395.
Deardorff J.W. (1978) Efficient prediction of ground surface temperature and moisture with inclusion of a layerof vegetation. Journal of Geophysical Research, 83, 1889-1903.
Digital National Land Information GIS Data of Japan (2002) Database of groundwater in Japan. Ministry ofLand Infrastructure and Transport of Japan. http://www.nla.go.jp/ksj/.
Dudhia J. (1993) A nonhydrostatic version of the Penn State-Ncar mesoscale model: Validation tests andsimulation of an atlantic cyclone and cold front. Monthly Weather Review, 121, 1493-1513.
Dupont S., Mestayer P.G., Guilloteau E., Berthier E., Andrieu H. (2006) Parameterization of the urban water
budget with the submesoscale soil model. Journal of Applied Meteorology and Climatology, 45, 624-648.
Endo T. (1992) Confined groundwater system in Tokyo. Environmental Geology and Water Sciences, 20, 21-34.
Ferguson G., Woodbury A.D. (2004) Subsurface heat flow in an urban environment. Journal of GeophysicalResearch, 109, B02402, doi:10.1029/2003JB002715.
Geographical Survey Institute of Japan (1999) Digital map 50m grid (elevation), Nippon-II. CD-ROM.
Golden J.S. (2003) The built environment induced urban heat island effect in rapidly urbanizing arid regions – A
sustainable urban engineering complexity. Environmental Sciences, 4, 321-349.
Grimmond C.S.B., Oke T.R. (1991) An evapotranspiration-interception model for urban areas. Water Resources
Research, 27(7), 1739-1755.
Hagishima A, Tanimoto J, Narita K. (2005) Intercomparisons of experimental convective heat transfer
coefficients and mass transfer coefficients of urban surfaces. Boundary-Layer Meteorology, 117, 551-576,
doi:10.1007/s10546-005-2078-7.
Hayashi T., Tokunaga T., Aichi M., Shimada J., Taniguchi M. (2009) Effects of human activities andurbanization on groundwater environments: An example from the aquifer system of Tokyo and thesurrounding area. Science of the Total Environent, 407, 3165-3172, doi:10.1016/j.scitotenv.2008.07.012.

- 76 

CGER-I103-2012, CGER/NIES

Hollis G.E., Ovenden J.C. (1988a) One year irrigation experiment to assess losses and runoff volume
relationships for a residential road in Hertfordshire, England. Hydrological Processes, 2, 61-74.
Hollis G.E., Ovenden J.C. (1988b) The quantity of stormwater runoff from ten stretches of road, a car park andeight roofs in Hertfordshire, England during 1983. Hydrological Processes, 2, 227-243.
Ichinose T., Shimodozono K., Hanaki K. (1999) Impact of anthropogenic heat on urban climate in Tokyo.
Atmospheric Environment, 33, 3897-3909.
Japan Meteorological Agency (JMA) (2005-2006a) AMeDAS (Automated Meteorological Data Acquisition
System) Annual Reports 2005-2006. Japan Meteorological Business Support Center (CD-ROM).
Japan Meteorological Agency (JMA) (2005-2006b) MSM (Meso Scale Model) Objective Analysis Data
2005-2006. Japan Meteorological Business Support Center (CD-ROM).
Kahyaoglu-Koracin J., Bassett S., Mouat D., Gertler A. (2008) Application of a scenario-based modelingsystem to evaluate the air quality impacts of future growth. Atmospheric Environment, 43(5), 1021-1028,
doi:10.1016/j.atmosenv.2008.04.004.
Kanto Regional Development Bureau (2000-2001) Observation data of river discharge, groundwater level, river
quality, and groundwater quality.
Kanto Regional Development Bureau (2005-2006) Observation data of river discharge and level at Tama River
2005–2006.
Kawasaki City Government (1994) GIS database about building-use and land-use. Fundamental investigation for
urban planning.
Kawasaki City Government (2004) Eco-industrial revolution underway in Kawasaki - Kawasaki Model (inJapanese).
Kawasaki City Government (2004-2006) Observation data of groundwater level, air temperature, and humidity
2004-2006.
Kawasaki City Government (2008) Implementation plan for open space planning (in Japanese).
Kusaka H., Kimura F. (2004) Coupling a single-layer urban canopy model with a simple atmospheric mode:
Impact on urban heat island simulation for an idealized case. Journal of Meteorological Society of Japan, 82,
67-80.
Kusaka H., Kondo H., Kikegawa Y., Kimura F. (2001) A simple single-layer urban canopy model foratmospheric models: Comparison with multi-layer and slab models. Boundary-Layer Meteorology, 101,
329-358.
Millennium Ecosystem Assessment (2005) Strengthening capacity to manage ecosystems sustainability forhuman well-being. http://www.millenniumassessment.org/en/index.aspx.
Ministry of Environment (2000) Observation data of groundwater nitrogen concentration in the Kanto district.
Ministry of Environment (2003) Research report about the effect of heat island phenomena on surrounding
environments. http://www.env.go.jp/air/report/h15-02/ (in Japanese).
Ministry of Environment (2004a) Present condition and provision against groundwater pollution by nitrate
nitrogen. In: Japan Environment Association (Ed) Environment 29: 14-20 (in Japanese).
Ministry of Environment (2004b) Report on heat-island measures by controlling anthropogenic exhaustion heatin the urban area. http://www.env.go.jp/air/report/h16-05/ (in Japanese).
Nakayama T. (2008a) Factors controlling vegetation succession in Kushiro Mire. Ecological Modelling, 215,
225-236, doi:10.1016/j.ecolmodel.2008.02.017.
Nakayama T. (2008b) Shrinkage of shrub forest and recovery of mire ecosystem by river restoration in northern
Japan. Forest Ecology and Management, 256, 1927-1938, doi:10.1016/j.foreco.2008.07.017.
Nakayama T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part II), CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
http://www-cger.nies.go.jp/publication/I083/i083.html.
Nakayama T. (2009) Simulation of ecosystem degradation and its application for effective policy-making inregional scale. In River pollution research progress, Mattia N. Gallo, Marco H. Ferrari (eds), 1-89 (Chapter1), Nova Science Pub., Inc., New York.
Nakayama T. (2010) Simulation of hydrologic and geomorphic changes affecting a shrinking mire. River
Research and Applications, 26(3), 305-321, doi:10.1002/rra.1253.
Nakayama T. (2011a) Simulation of complicated and diverse water system accompanied by human intervention
in the North China Plain. Hydrological Processes, 25, 2679-2693, doi:10.1002/hyp.8009.
Nakayama T. (2011b) Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivatedYellow
River
Basin.
Agricultural
andForestMeteorology,151,
314-327,
doi:10.1016/j.agrformet.2010.11.006.

- 77 

Chapter 4 Ability of Water Resources to Reduce Urban Heat Island in the Tokyo Megalopolis

Nakayama T., Fujita T. (2010) Cooling effect of water-holding pavements made of new materials on water andheatbudgetsinurban
areas.
Landscapeand
Urban
Planning,
96,
57-67,
doi:10.1016/j.landurbplan.2010.02.003.
Nakayama T., Hashimoto S. (2011) Analysis of the ability of water resources to reduce the urban heat island in
the Tokyo megalopolis. Environmental Pollution, 159, 2164-2173, doi:10.1016/j.envpol.2010.11.016.
Nakayama T., Watanabe M. (2004) Simulation of drying phenomena associated with vegetation change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402,
doi:10.1029/2004WR003174.
Nakayama T., Watanabe M. (2006a) Simulation of spring snowmelt runoff by considering micro-topography andphase changes in soil layer. Hydrology and Earth System Sciences Discussions, 3, 2101-2144.
Nakayama T., Watanabe M. (2006b) Development of process-based NICE model and simulation of ecosystem
dynamics in the catchment of East Asia (Part I), CGER's Supercomputer Monograph Report, 11, NIES,
100p., http://www-cger.nies.go.jp/publication/I063/I063.html.
Nakayama T., Watanabe M. (2008a) Missing role of groundwater in water and nutrient cycles in the shallow
eutrophic Lake Kasumigaura, Japan. Hydrological Processes, 22, 1150-1172, doi:10.1002/hyp.6684.
Nakayama T., Watanabe M. (2008b) Role of flood storage ability of lakes in the Changjiang River catchment.
Global and Planetary Change, 63, 9-22, doi:10.1016/j.gloplacha.2008.04.002.
Nakayama T., Watanabe M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verh
International Verein Limnology, 30.
Nakayama T., Yang Y., Watanabe M., Zhang X. (2006) Simulation of groundwater dynamics in North China
Plain by coupled hydrology and agricultural models. Hydrological Processes, 20(16), 3441-3466,
doi:10.1002/hyp.6142.
Nakayama T., Watanabe M., Tanji K., Morioka T. (2007) Effect of underground urban structures on eutrophic
coastalenvironment.
Science
of
the
Total
Environment,
373(1),
270-288,
doi:10.1016/j.scitotenv.2006.11.033.
Nakayama T., Sun Y., Geng Y. (2010) Simulation of water resource and its relation to urban activity in DalianCity, Northern China. Global and Planetary Change, 73, 172-185, doi:10.1016/j.gloplacha.2010.06.001.
Nakayama T., Hashimoto S., Hamano H. (in press) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes.
Nichol J., Wong M.S. (2005) Modeling urban environmental quality in a tropical city. Landscape and Urban
Planning, 73, 49-58.
Ohashi Y., Genchi Y., Kondo H., Kikegawa Y., Yoshikado H., Hirano Y. (2007) Influence of air-conditioning
waste heat on air temperature in Tokyo during summer: Numerical experiments using an urban canopy
model coupled with a building energy model. Journal of Applied Meteorology and Climatology, 46, 66-81.
Oke T.R. (1987) Boundary layer climates. Methuen Press, London.
Pielke R.A., Cotton W.R., Walko R.L., Tremback C.J., Lyons W.A., Grasso L.D., Nicholls M.E., Moran M.D.,
Wesley D.A., Lee T.J., Copeland J.H. (1992) A comprehensive meteorological modeling system-RAMS.
Meteorology and Atmospheric Physics, 49, 69-91.
Ragab R., Rosier P., Dixon A., Bromley J., Cooper J.D. (2003) Experimental study of water fluxes in a
residential area: 2. Road infiltration, runoff and evaporation. Hydrological Processes, 17, 2423-243,
doi:10.1002/hyp.1251.
Ramier D., Berthier E., Andrieu H. (2004) An urban lysimeter to assess runoff losses on asphalt concrete plates.
Physics and Chemistry of the Earth, 29, 839-847, doi:10.1016/j.pce.2004.05.011.
Saaroni H., Ben-Dor E., Bitan A., Potchter O. (2000) Spatial distribution and microscale characteristics of theurban heat island in Tel-Aviv, Israel. Landscape and Urban Planning, 48, 1-18.
Sansalone J.J., Teng Z. (2005) Transient rainfall-runoff loadings to a partial exfiltration system: implications forurban water quantity and quality. Journal of Environmental Engineering-ASCE, 131(8), 1155-1167.
Sellers P.J., Randall D.A., Collatz G.J., Berry J.A., Field C.B., Dazlich D.A., Zhang C., Collelo G.D., Bounoua
L. (1996) A revised land surface prameterization (SiB2) for atomospheric GCMs. Part I : Modelformulation. Journal of Climate, 9, 676-705.
Skamarock W., Klemp J.B., Dudhia J., Gill D., Barker D., Wang W., Powers J. (2005) A description of theadvanced research wrf version 2. NCAR Tech. Note NCAR/TN-468 STR, 88pp.
Spronken-Smith R.A., Oke T.R. (1999) Scale modeling of nocturnal cooling in urban parks. Boundary-Layer
Meteorology, 93, 287-312.
Taniguchi M., Burnett W.C., Ness G.D. (2009) Integrated research on subsurface environments in Asian urban
areas. Science of the Total Environent, 407, 3076-3088, doi:10.1016/j.scitotenv.2009.02.001.
Tokyo Metropolitan Government (2001) GIS database about building-use and land-use. Fundamental
investigation for urban planning.

- 78 

CGER-I103-2012, CGER/NIES

van Berkel R., Fujita T., Hashimoto S., Fujii M. (2009) Quantitative assessment of urban and industrial
symbiosis in Kawasaki, Japan. Environmental Science and Technology, 43, 1271-1281.
Yamagata H., Nasu M., Yoshizawa M., Miyamoto A., Minamiyama M. (2008) Heat island mitigation using
water retentive pavement sprinkled with reclaimed wastewater. Water Science and Technology, 57,
763-771, doi:10.2166/wst.2008.187.
Yokohama City Government (2003) GIS database about building-use and land-use. Fundamental investigation
for urban planning.
Zhang H., Sato N., Izumi T., Hanaki K., Aramaki T. (2008) Modified rams-urban canopy model for heat island
simulation in chongqing, china. Journal of Applied Meteorology and Climatology, 47, 509-524.
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Chapter 5
Final Conclusions and Future Work- 81 

Chapter 5 Final Conclusions and Future Work- 82 

CGER-I103-2012, CGER/NIES

5.1 Final Conclusions
In a previous work, the author developed the National Integrated Catchment-based
Eco-hydrology (NICE) model, which is a 3-D, grid-based eco-hydrology model that includes
interactions among surface water, canopies, unsaturated water, aquifers, lakes, and rivers(Nakayama, 2008a, 2008b, 2008c, 2009, 2010, 2011a, 2011b; Nakayama and Fujita, 2010;
Nakayama and Hashimoto, 2011; Nakayama and Watanabe, 2004, 2006a, 2006b, 2008a,
2008b, 2008c; Nakayama et al., 2006, 2007, 2010, in press) (Fig. 1.2). In this monograph(Part III), NICE was enhanced to couple human and natural systems and to assess the impact
of water degradation on ecosystem changes in urban areas.
In Chapter 2, an integrated approach including the process-based model showed a close
relationship among water resources, economic growth, and ecosystem degradation in theenvironment. Although water resources are vital for human activity, overexploitation causes
severe hydrologic changes such as drying up of rivers, receding groundwater levels, andseawater intrusion (Nakayama, 2011a, 2011b; Nakayama et al., 2006).
In Chapter 3, the results indicated a close relationship between water resources andeconomic growth, which strongly affects ecosystem degradation and places a significant
burden on the environment in catchment areas. In addition, the simulated results highlighted
the association between urban development and sustainable water resource management(Nakayama et al., 2006, 2010).
The results of Chapter 4 indicate that effective water resource management is instrumental
to the planning and execution of sustainable development strategies; the author also suggests
the potential for recovering a sound hydrologic cycle in addition to creating thermallypleasant environments (Nakayama and Hashimoto, 2011; Nakayama et al., 2007, in press).
These above results are crucial for clarifying the effect of human activity on ecosystemdegradation caused by water–heat–mass cycle changes. The improved model and theprediction of ecosystem dynamics in these catchments are effective for achieving favorable
solutions to hydrothermal pollution challenges in future eco-conscious societies.
5.2 Future Work
This monograph suggests that effective water resource management is instrumental to themitigation of heat islands. This indicates the potential for achieving favorable solutions tohydrothermal pollution challenges in an eco-conscious society (Fig. 5.1). An estimation of thecooling effects of water resources and areas through image analysis including remote sensing,satellite data, and aerial images would be based on quantification through high-resolution
imaging of spatio-temporally cooling effects. In addition, the identification of hot spots inhydrothermal cycles through down-scaling is necessary through assimilation withground-truth networking systems. An initiative to assess alterative policies and technologicaloptions can then lead to a more powerful integrated assessment system. The evaluation ofhydrothermal interactions by flux observation systems (Nakayama et al., in press), isotope
analysis (Nakayama and Watanabe, 2008a), and tracer techniques, in addition to theemployment of existing databases, can become a more viable solution. Appropriate
combinations among the establishment of a network system and the up-scaling method,development of cross-referencing databases of heat sinks and sources, and estimation ofpotential thermal relaxation of water resources are necessary. Visualization and assessment
through an integrated multi-scaled model is the final goal in creating thermally pleasantenvironments and achieving sustainable development in urban regions. This ideal necessitates- 83 

Chapter 5 Final Conclusions and Future Workthe construction of a highly accurate integrated management model, the reproduction ofhydrothermal cycles on the basis of flow and stock mechanisms, and the identification of
ecosystem vulnerability and sustainability. These procedures to construct an integrated
assessment system for favorable solutions would be beneficial for adaptation to climatechanges and urbanization on a global scale, proposal of sustainability index, and the creationof an eco-conscious society (Ministry of Environment, 2004; Nakayama and Hashimoto,2011; Nakayama et al., 2007, in press). Moreover, the construction of an integrated
assessment system would provide visualization of hydrothermal feedback about thermal
pollution in air, on land, and in marine areas (Nakayama, 2011c).

Fig. 5.1 Framework for visualization of hydrothermal interactions for favorable solutions.

Further evaluation of the water–heat–mass cycle in China is also necessary relative to thenational security of Japan and international contributions (Nakayama, 2011a, 2011b;
Nakayama and Watanabe, 2008b; Nakayama et al., 2006, 2010). The Changjiang (Yangtze),
Yellow (Huanghe), and Huaihe rivers (Fig. 5.2) are among the “seven big rivers” of China.
Their lower reaches are affected by severe flooding nearly every year, which causes
significant damage to surrounding towns and farms. In China, the hydroclimate between the
North and South is diverse. The semi-arid North is heavily irrigated, which, combined withincreased food demands and diminishing water availability, creates substantial pressure in theregion of the Yellow River (Brown and Halweil, 1998; Yang et al., 2004; Nakayama et al.,- 84 

CGER-I103-2012, CGER/NIES

2006, 2010; Nakayama, 2011a,b). However, the flood storage ability around lakes has
decreased, and the impact of the Three Gorges Dam (TGD) on flood occurrences inChangjiang downstream against the dam’s original purpose is an increasing problem in thehumid South region (Shankman and Liang, 2003; Zhao et al., 2005; Nakayama and Watanabe,
2008b). Moreover, China is accelerating preparatory work on its ambitious project fortransferring water from the Changjiang to the Yellow River (South-to-North Water Transfer
Project; SNWTP) (Yang and Zehnder, 2001; Li et al., 2007). Although it is estimated that the
TGD and SNWTP would remove these imbalances as far as possible (Yang and Zehnder,
2001; Li et al., 2007), there is an urgent need to clarify the relations of these two extremes
affected by human activity, develop a process-based model, and present an integrated
approach for appropriate water resource management to achieve sustainable development on aregional scale (Nakayama, 2011d; Nakayama and Shankman, 2011a, 2011b). Therefore,
accurate evaluation of the optimum amount of transferred water, and socioeconomic andenvironmental consequences are essential in both basins. Future installments of this study will
evaluate water–heat–mass dynamics using the NICE model to simulate the changes caused byhuman activity and to aid accurate estimation on the amount of water to be transferred
between catchments.

Fig. 5.2 Dynamics of water–heat–mass cycles in the Changjiang and Yellow River basinsobtained by the combined land–atmosphere model.

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Chapter 5 Final Conclusions and Future Work

The author has also researched the mutual interaction and feedback ofhydro-geomorphology and vegetation dynamics in the ecosystem to further explain thepositive feedback between geomorphology and eco-hydrology on irregular slopes and inheterogeneous vegetation, whereas most previous studies have considered regular slopes andtheir relation to string or maze patterns. The heterogeneous vegetation succession was
evaluated in relation to self-organization and regime shift. These results suggest theeffectiveness of river restoration in conservation plans for mire recovery and the importanceof the process-based model in assessing the connection between hydrologic change andvegetation succession (Nakayama, 2011e). On the basis of this dispatch, the author iscurrently attempting to re-evaluate the ecosystem in the catchment from the perspective of a“meta-ecosystem” by considering global–local interaction and hydrology–ecology
connections (Fig. 5.3). In addition, the author plans to assess the impact ofwater–heat–material cycle changes through human activities on ecosystem function, analysis
environmental impact and limiting factor accompanied by the change in ecosystem function.
Moreover, a quantitative estimation is planned for more effective measures and available
technologies as alternative scenarios for the final objective of sustainable development under
the constraint of global security in addition to environmental degradation. The author’s goal is
to develop integrated modeling-system flexibly available to various-scaled ecosystems
consisting of urban areas, forests, grasslands, cultivated fields, paddy fields, wetlands, rivers,
lakes, groundwater, oceans, and atmosphere on the basis of integrations between numericalsimulation, satellite data, and ground-truth observation on a global scale, as described in thefollowing objectives:(A) Construction of assessment models in multiple scales, including dynamism inregional scales, impact assessment of urban activities on regional water–heat–material cycles,
quantification of potential capacity in the ecosystem, numerical simulation in the scenario oftechnology for environmental improvement, and integration of modeling and politicalscenarios through an evaluation of sustainability.
(B) Impact assessment of various stress factors on a multi-scaled ecosystem, developmentof a modeling system including the multi-scaled aspect between global–regional–micro(genetic) levels and a feedback mechanism between hydrology and ecology, and evaluation ofvulnerability in ecosystem functions.
(C) Impact assessment to contribute to global security relative to (A) and (B), up-scaling
and down-scaling procedures focusing on urban, agricultural, and natural areas, re-evaluationof natural energy and environmental resources, and objective-oriented research combinedwith political scenarios and decision making on the basis of multiple criteria.
The author intends to report this research in a succeeding monograph (Part IV) in the near
future.

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CGER-I103-2012, CGER/NIES

Fig. 5.3 Integrated procedures through meta-ecosystem analysis.

- 87 

Chapter 5 Final Conclusions and Future Work

References
Brown L.R., Halweil B. (1998) China’s water shortage could shake world food security. World Watch, 11(4),10-18.
Li Q., Zou Z., Xia Z., Guo J., Liu Y. (2007) Impacts of human activities on the flow regime of the Yangtze River.
Changes in Water Resources Systems: Methodologies to Maintain Water Security and Ensure Integrated
Management, IAHS Publication 315, 266-275.
Ministry of Environment (2004) Report on heat-island measures by controlling anthropogenic exhaustion heat in
the urban area. http://www.env.go.jp/air/report/h16-05/ (in Japanese).
Nakayama T. (2008a) Factors controlling vegetation succession in Kushiro Mire. Ecological Modelling, 215,
225-236, doi:10.1016/j.ecolmodel.2008.02.017.
Nakayama T. (2008b) Shrinkage of shrub forest and recovery of mire ecosystem by river restoration in northern
Japan. Forest Ecology and Management, 256, 1927-1938, doi:10.1016/j.foreco.2008.07.017.
Nakayama T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part II), CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
http://www-cger.nies.go.jp/publication/I083/i083.html.
Nakayama T. (2009) Simulation of ecosystem degradation and its application for effective policy-making inregional scale. In River pollution research progress, Mattia N. Gallo, Marco H. Ferrari (eds), 1-89 (Chapter1), Nova Science Pub., Inc., New York.
Nakayama T. (2010) Simulation of hydrologic and geomorphic changes affecting a shrinking mire. River
Research and Applications, 26(3), 305-321, doi:10.1002/rra.1253.
Nakayama T. (2011a) Simulation of complicated and diverse water system accompanied by human intervention
in the North China Plain. Hydrological Processes, 25, 2679-2693, doi:10.1002/hyp.8009.
Nakayama T. (2011b) Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivatedYellow
River
Basin.
Agricultural
andForestMeteorology,151,
314-327,
doi:10.1016/j.agrformet.2010.11.006.
Nakayama T. (2011c) Visualization of missing role of hydrothermal interactions in Japanese megalopolis forwin-win solution. Water Science and Technology (submitted).
Nakayama T. (2011d) Impact of anthropogenic activity on eco-hydrological process in continental scales.
Procedia Environmental Sciences (accepted).
Nakayama T. (2011e) Feedback and regime shift of mire ecosystem in northern Japan. Hydrological Processes(revised).
Nakayama T., Fujita T. (2010) Cooling effect of water-holding pavements made of new materials on water andheatbudgetsinurban
areas.
Landscapeand
Urban
Planning,
96,
57-67,
doi:10.1016/j.landurbplan.2010.02.003.
Nakayama T., Hashimoto S. (2011) Analysis of the ability of water resources to reduce the urban heat island in
the Tokyo megalopolis. Environmental Pollution, 159, 2164-2173, doi:10.1016/j.envpol.2010.11.016.
Nakayama T., Shankman D. (2011a) Impact of the Three-Gorges Dam and water transfer project on Changjiang
floods. Global and Planetary Change (revised).
Nakayama T., Shankman D. (2011b) Effect of two extremes on ecosystem variation in continental scale.
Hydrological Processes (submitted).
Nakayama T., Watanabe M. (2004) Simulation of drying phenomena associated with vegetation change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402,
doi:10.1029/2004WR003174.
Nakayama T., Watanabe M. (2006a) Simulation of spring snowmelt runoff by considering micro-topography andphase changes in soil layer. Hydrology and Earth System Sciences Discussions, 3, 2101-2144.
Nakayama T., Watanabe M. (2006b) Development of process-based NICE model and simulation of ecosystem
dynamics in the catchment of East Asia (Part I), CGER's Supercomputer Monograph Report, 11, NIES,
100p., http://www-cger.nies.go.jp/publication/I063/I063.html.
Nakayama T., Watanabe M. (2008a) Missing role of groundwater in water and nutrient cycles in the shallow
eutrophic Lake Kasumigaura, Japan. Hydrological Processes, 22, 1150-1172, doi:10.1002/hyp.6684.
Nakayama T., Watanabe M. (2008b) Role of flood storage ability of lakes in the Changjiang River catchment.
Global and Planetary Change, 63, 9-22, doi:10.1016/j.gloplacha.2008.04.002.
Nakayama T., Watanabe M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verh
International Verein Limnology, 30.

- 88 

CGER-I103-2012, CGER/NIES

Nakayama T., Yang Y., Watanabe M., Zhang X. (2006) Simulation of groundwater dynamics in North China
Plain by coupled hydrology and agricultural models. Hydrological Processes, 20(16), 3441-3466,
doi:10.1002/hyp.6142.
Nakayama T., Watanabe M., Tanji K., Morioka T. (2007) Effect of underground urban structures on eutrophic
coastalenvironment.
Science
of
the
Total
Environment,
373(1),
270-288,
doi:10.1016/j.scitotenv.2006.11.033.
Nakayama T., Sun Y., Geng Y. (2010) Simulation of water resource and its relation to urban activity in DalianCity, Northern China. Global and Planetary Change, 73, 172-185, doi:10.1016/j.gloplacha.2010.06.001.
Nakayama T., Hashimoto S., Hamano H. (in press) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes.
Shankman D., Liang Q. (2003) Landscape changes and increasing flood frequency in China’s Poyang Lakeregion. Professional Geographer, 55(4), 434-445.
Yang H., Zehnder A. (2001) China’s regional water scarcity and implications for grain supply and trade.
Environmental Planning A, 33, 79-95.
Yang D., Li C., Hu H., Lei Z., Yang S., Kusuda T., Koike T., Musiake K. (2004) Analysis of water resources
variability in the Yellow River of China during the last half century using historical data. Water Resources
Research, 40, W06502, doi:10.1029/2003WR002763.
Zhao S., Fang J., Miao S., Gu B., Tao S., Peng C., Tang Z. (2005) The 7-decade degradation of a large
freshwater lake in central Yangtze River, China. Environmental Science and Technology, 39, 431-436.

- 89 

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.18
CGER-I103-2012, CGER/NIES

Appendix
Publications and Presentations- 91 

Appendix Publications and Presentations

Original Papers and Reviews:
Nakayama T. (2008a) Factors controlling vegetation succession in Kushiro Mire. Ecological Modelling, 215,
225-236., doi: 10.1016/j.ecolmodel.2008.02.017.
Nakayama T. (2008b) Shrinkage of shrub forest and recovery of mire ecosystem by river restoration in northern
Japan. Forest Ecology and Management, 256, 1927-1938, doi: 10.1016/j.foreco.2008.07.017.
Nakayama T. (2010a) Simulation of hydrologic and geomorphic changes affecting a shrinking mire. River
Research and Applications, 26(3), 305-321, doi: 10.1002/rra.1253.
Nakayama T. (2011a) Simulation of complicated and diverse water system accompanied by human intervention
in the North China Plain. Hydrological Processes, 25, 2679-2693, doi: 10.1002/hyp.8009.
Nakayama T. (2011b) Simulation of the effect of irrigation on the hydrologic cycle in the highly cultivatedYellow
River
Basin.
Agricultural
andForestMeteorology,151,
314-327,doi:
10.1016/j.agrformet.2010.11.006.
Nakayama T. (2011c) Visualization of missing role of hydrothermal interactions in Japanese megalopolis forwin-win solution. Water Science and Technology (submitted).
Nakayama T. (2011d) Impact of anthropogenic activity on eco-hydrological process in continental scales.
Procedia Environmental Sciences (accepted).
Nakayama T. (2011e) Feedback and regime shift of mire ecosystem in northern Japan. Hydrological Processes(revised).
Nakayama T., Fujita T. (2010) Cooling effect of water-holding pavements made of new materials on water andheat budgets in urban areas. Landscape and Urban Plannin, 96, 57-67, doi:
10.1016/j.landurbplan.2010.02.003.
Nakayama T., Hashimoto S. (2011) Analysis of the ability of water resources to reduce the urban heat island in
the Tokyo megalopolis. Environmental Pollution, 159, 2164-2173, doi: 10.1016/j.envpol.2010.11.016.
Nakayama T., Shankman D. (2011a) Impact of the Three-Gorges Dam and water transfer project on Changjiang
floods. Global and Planetary Change (revised).
Nakayama T., Shankman D. (2011b) Effect of two extremes on ecosystem variation in continental scale.
Hydrological Processes (submitted).
Nakayama T., Watanabe M. (2004) Simulation of drying phenomena associated with vegetation change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402, doi:
10.1029/2004WR003174.
Nakayama T., Watanabe M. (2006a) Simulation of spring snowmelt runoff by considering micro-topography andphase changes in soil layer. Hydrology and Earth System Sciences Discussions, 3, 2101-2144.
Nakayama T., Watanabe M. (2008a) Missing role of groundwater in water and nutrient cycles in the shallow
eutrophic Lake Kasumigaura, Japan. Hydrological Processes, 22, 1150-1172, doi: 10.1002/hyp.6684.
Nakayama T., Watanabe M. (2008b) Role of flood storage ability of lakes in the Changjiang River catchment.
Global and Planetary Change, 63, 9-22, doi: 10.1016/j.gloplacha.2008.04.002.
Nakayama T., Watanabe M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verh
International Verein Limnology, 30.
Nakayama T., Yang Y., Watanabe M., Zhang X. (2006) Simulation of groundwater dynamics in North China
Plain by coupled hydrology and agricultural models. Hydrological Processes, 20(16), 3441-3466, doi:
10.1002/hyp.6142.
Nakayama T., Watanabe M., Tanji K., Morioka T. (2007) Effect of underground urban structures on eutrophic
coastalenvironment.
Science
of
the
Total
Environment,
373(1),
270-288,doi:
10.1016/j.scitotenv.2006.11.033.
Nakayama T., Sun Y., Geng Y. (2010) Simulation of water resource and its relation to urban activity in DalianCity, Northern China. Global and Planetary Change, 73, 172-185, doi: 10.1016/j.gloplacha.2010.06.001.
Nakayama T., Hashimoto S., Hamano H. (in press) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes.

Books:
Nakayama T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part II), CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
http://www-cger.nies.go.jp/publication/I083/i083.html.

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CGER-I103-2012, CGER/NIES

Nakayama T. (2009a) Simulation of ecosystem degradation and its application for effective policy-making inregional scale. In River pollution research progress, Mattia N. Gallo, Marco H. Ferrari (eds), pp.1-89(Chapter 1), Nova Science Pub., Inc., New York.
Nakayama T. (2009b) Evaluation of intertwined relations between water stress and crop productivity ingrain-cropping plain area by using process-based model. In Agricultural runoff, coastal engineering andflooding, Christopher A. Hudspeth, Timothy E. Reeve (eds), pp.107-136 (Chapter 5), Nova Science Pub.,
Inc., New York.
Nakayama T. (2010b) Evaluation of flood storage ability of Dongting and Poyang Lakes in Yangtze River byusing process-based model, In River Deltas: Types, Structures & Ecology, Paul E. Schmidt (ed), pp.69-94(Chapter 4), Nova Science Pub., Inc., New York.
Nakayama T. (2011f) Multi-level evaluation of hydrologic cycle in the catchment including shallow eutrophic
lake, In Advances in Environmental Research, Volume 11, Justin A. Daniels (ed), pp.1-37 (Chapter 1),
Nova Science Pub., Inc., New York.
Nakayama T. (2011g) Impact of water degradation on ecosystem change and adaptation strategy for sustainable
development, In Water and Society, D. W. Pepper and C. A. Brebbia (eds), pp.139-150, WIT Press, doi:
10.2495/WS110131.
Nakayama T. (in press) Impact of irrigation on hydrologic change in highly cultivated basin, In
Evapotranspiration / Book 1, A. Irmak (ed), InTech, Croatia.
Nakayama T., Watanabe M. (2006b) Development of process-based NICE model and simulation of ecosystem
dynamics in the catchment of East Asia (Part I), CGER's Supercomputer Monograph Report, 11, NIES,
100p., http://www-cger.nies.go.jp/publication/I063/I063.html.
Nakayama T., Sun Y., Geng Y. (2011) Relation between river management and economic growth in urban
regions, In River Ecosystems: Dynamics, Management and Conservation, Hannah S. Elliot, Lucas E.
Martin (eds), pp.301-312 (Chapter 9), Nova Science Pub., Inc., New York.

Conference Reports:
Nakayama T., Watanabe M. (2006) Simulation of 1998-big flood in Changjiang River Catchment, China, paper
number H23D-24, AGU Joint Assembly 2006, Baltimore, USA, 23 – 26 May (CD-ROM).
Nakayama T., Watanabe M., Tanji K., Morioka T. (2006) Effect of underground urban structures on hydrologic
budget in Tokyo metropolitan area, Japan, session number TS-C04, ASLO 2006 Summer Meeting, Victoria,
Canada, 4 – 9 June (CD-ROM).
Nakayama T. (2006) Influence of river channelization and re-meandering on vegetation change in Kushiro Mire,
ICEM2006, Yamaguchi, Japan, 28 August – 1 September (CD-ROM).
Nakayama T. (2006) Development of process-based NICE model and simulation of ecosystem dynamics in the
East Asia, Wuhee-NIES Joint Meeting (Internal Workshop), Wuhan, China, 25 September.
Nakayama T., Watanabe M. (2006) Simulation of irrigation effect on water cycle in Yellow River catchment,
China, paper number GC21B-3, AGU Fall Meeting 2006, San Francisco, USA, 11 – 15 December(CD-ROM).
Nakayama T., Watanabe M. (2007) Effect of vegetation change on hydrologic cycle in Changjiang and Yellow
River catchments, paper number H52B-07, AGU Joint Assembly 2007, Acapulco, Mexico, 22 – 25 May(CD-ROM).
Nakayama T. (2007) Relationship between hydrogeological process and vegetation change in Kushiro Mire,session number #COS43-5, ESA-SER Joint Meeting, San Jose, USA, 5 – 10 August (CD-ROM).
Nakayama T. (2007) Innovative analysis model of integrative environmental fluxes, NICE model, its applicationin Asian regions and findings for policy proposal, Int. Workshop of Innovative Theory, Technologies andPractice for Industrial Symbiosis in Dalian (Internal Workshop), Dalian, China, 30 May – 2 June.
Nakayama T., Watanabe M. (2007) Estimation of hydrologic cycle in shallow eutrophic lake by integrated
approach, session number #SS13, SIL2007, Montreal, Canada, 12 – 18 August (CD-ROM).
Nakayama T., Watanabe M. (2007) Shrinking of Dongting and Poyang Lakes and its relation to large-scale floodin Changjiang River catchment, ICGRHWE'07/FM2S'07, Guangzhou, China, 9 – 13 September(CD-ROM).
Nakayama T. (2007) General structure of process-based NICE (NIES Integrated Catchment-based
Eco-hydrology) model and simulation of ecosystem dynamics in the catchment of East Asia, CEH-NIES
research meeting (Internal Workshop), Wallingford, UK, 21 September.
Nakayama T. (2007) Integrative disaggregated environmental assessment model, theory and application in basin- 93 

Appendix Publications and Presentations

region and urban district, Int. Workshop for Sustainable Urban and Industrial Management (InternalWorkshop), Wuhan, China, 3 – 6 December.
Nakayama T., Fujita T. (2007) Simulation of cooling effect of newly-innovated urban pavements on water andheat budgets, paper number H43D-1608, AGU Fall Meeting 2007, San Francisco, USA, 10 – 14 December(CD-ROM).
Nakayama T. (2008) Simulation of intertwined relations between water stress, crop productivity, and ecosystemdegradation in Northern China, ENVR176OR, Food Security and Environmental Change, Univ. of Oxford,
UK, 2 – 4 April.
Nakayama T. (2008) Development of NICE-URBAN model applicable to urban district, Int. Workshop on
Sustainable Regional Development through Circular Economy (Internal Workshop), Shenyang, China, 19
May.
Nakayama T., Fujita T., Hashimoto S., Hamano H. (2008) Simulation of hydrothermal recoveries by adopting
symbiotic urban scenario in the Japanese megalopolis, paper number GC43A-11, AGU Joint Assembly
2007, Florida, USA, 27 – 30 May (CD-ROM).
Nakayama T. (2008) Effect of freshwater over-exploitation on seawater intrusion in grain-cropping plain area,
session number GS21, ASLO 2008 Summer Meeting, Newfoundland, Canada, 8 – 13 June (CD-ROM).
Nakayama T. (2008) Simulation of hydrologic change related to ecological condition in the Japanese mire,
session number 15, 2008 AWRA Summer Specialty Conference, Virginia, USA, 30 June – 2 July
(CD-ROM).
Nakayama T. (2008) Estimation of relationship between hydrologic change, seawater intrusion, and crop
production by using integrated approach, 93rd ESA Annual Meeting, Milwaukee, USA, 3 – 8 August(CD-ROM).
Nakayama T., Fujita T., Geng Y., Hashimoto S. (2008) Simulation of water resource and its relation to urban
activity in Dalian City, Northern China, session number Y2, HydroPredict2008, Prague, Czech Rep., 15 –18 September (CD-ROM).
Nakayama T. (2008) Integrative model approach for sustainable watershed management in Liaohe River Basin,
Expert Workshop on Sustainable Watershed Management (Internal Workshop), NIES, Japan, 26
September.
Nakayama T. (2008) Simulation of anthropogenic effects on hydrologic cycle in Changjiang and Yellow River
Catchments, China, session number 54, AWRA 2008 Annual Water Resources Conference, New Orleans,
USA, 17 – 20 November (CD-ROM).
Nakayama T. (2008) Impact of anthropogenic activities on floods and droughts in Changjiang and Yellow River
Catchments, WATCH Project Workshop (Internal Workshop), Beijing, China, 24 – 26 November.
Nakayama T., Fujita T., Hashimoto S., Hamano H. (2008) Multi-scale analysis of hydrologic change in theJapanese megalopolis by using integrated approach, paper number GC34A-08, AGU Fall Meeting 2008,
San Francisco, USA, 15 – 19 December (CD-ROM).
Nakayama T., Fujita T., Hashimoto S., Hamano H. (2009) Impact assessment of hydrothermal dynamics inurban area by using multi-scaled integrated approach, session number 26, AWRA 2009 Annual Water
Resources Conference, Seattle, USA, 9 – 12 November (CD-ROM).
Nakayama T., Fujita T., Hashimoto S. (2010) Multi-scaled analysis of water resource ability to tackle heat island
in Japanese megalopolis, paper number O34, Urban Environmental Pollution 2010, Boston, USA, 20 – 23
June (CD-ROM).
Nakayama T., Sun Y., Don N.C. , Fujita T., Geng Y. (2010) Assessment of simulated water resource and its
relation to economic growth in urban city of northern China, paper number P2.37, Urban Environmental
Pollution 2010, Boston, USA, 20 – 23 June (CD-ROM).
Nakayama T. (2010) Nonlinear interaction and feedback of mire ecosystem in northern Japan, paper number
COS28-2, 95rd ESA Annual Meeting, Pittsburgh, Pennsylvania, USA, 1 – 6 August (Proceeding).
Nakayama T. (2010) Irrigation effect and feedback process of hydrologic cycle in highly cultivated region ofnorthern China, paper number O8.3, Hydrology Conference 2010, San Diego, USA, 11 – 13 October(CD-ROM).
Nakayama T. (2010) Impact of anthropogenic activities on droughts in Northern China and floods in Southern
China, session number 72, AWRA 2010 Annual Water Resources Conference, Philadelphia, Pennsylvania,
USA, 1 – 4 November (Proceeding).
Nakayama T. (2011) Impact of anthropogenic activity on eco-hydrological process in continental scales, paper
number 00027, ISEM2011, Beijing, China, 20 – 13 September (CD-ROM).
Nakayama T., Shankman D. (2011) Prediction of effect of huge structures on eco-hydrological changes inChangjiang basin, session number J-1-265, ICFM5, Tokyo, Japan, 27 – 29 September (CD-ROM).

- 94 

CGER-I103-2012, CGER/NIES

Nakayama T. (2011) Visualization of missing role of hydrothermal interactions in Japanese megalopolis forwin-win solution, session number 19-2-3, 4th IWA-ASPIRE Conference, Tokyo, Japan, 2 – 6 October(CD-ROM).
Nakayama T., Shankman D. (2011) Seesaw between droughts and floods affected by anthropogenic activities in
China, session 4-3, EXTREME2011, Kyoto, Japan, 24 – 28 October (Proceeding).
Nakayama T. (2011) Understanding linkages in boundary spheres through multi-level evaluation of hydrologic
cycle, 14th World Lake Conference, Austin, Texas, USA, 31 October – 4 November (Proceeding).
Nakayama T. (2011) Nonlinear interaction between hydrology, geomorphology, and ecology in mire, session
number 32, AWRA 2011 Annual Water Resources Conference, Albuquerque, New Mexico, USA, 1 – 4November (Website).
Nakayama T. (2011) Impact of water degradation on ecosystem change and adaptation strategy for sustainable
development, session4-3, Water and Society 2011, Las Vegas, USA, 5 – 7 December (CD-ROM).
Nakayama T. (2011) Impact of hydrologic cycle on ecosystem function and evaluation of complexity, WatershedSymposium endorsed by Japan Society of Civil Engineers, Tokyo, Japan, 15 December.
Nakayama T. (2012) Future prospects of water crisis in the globe (Keynote speech), 2012 International Water
Market Symposium: Experiences and Challenges, Shiga, Japan, 13 – 14 February (accepted).
Nakayama T. (2012) Coupling with satellite data and eco-hydrology model for evaluation of two extremes incontinental scale, AGU Chapman Conference on Remote Sensing of the Terrestrial Water Cycle, Hawaii,
USA, 19 – 22 February (accepted).
Nakayama T. (2012) Toward construction of process-based tipping-point early warning system, Planet Under
Pressure 2012, London, UK, 26 – 29 March (accepted).
Nakayama T. (2012) Achievement of win-win solution to intertwined environmental pollutions for
eco-conscious society, Planet Under Pressure 2012, London, UK, 26 – 29 March (accepted).
Nakayama T., Shankman D. (2012) Integrated assessment toward trans-boundary solution and sustainable
development, Planet Under Pressure 2012, London, UK, 26 – 29 March (accepted).

Contact Person
Tadanobu Nakayama
Center for Global Environmental Research (CGER),
National Institute for Environmental Studies (NIES)
16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
Phone: (+81)29-850-2564, Facsimile: (+81)29-850-2219, E-mail: nakat@nies.go.jp
http://www.nies.go.jp/rsdb/vdetail-e.php?id=100190- 95 

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MAKSYUTOV, S., NAKATSUKA Y., VALSALA V., SAITO M., KADYGROV N., AOKI T., EGUCHI N.,
HIRATA R., IKEDA M., INOUE G., NAKAZAWA T., ONISHI R., PATRA P.K., RICHARDSON A.D., SAEKI T.,
YOKOTA T.: Algorithms for Carbon Flux Estimation Using GOSAT Observational Data

Vol. 16

CGER-I097-2011
NAKAJIMA K.: Idealized Numerical Experiments on the Space-time Structure of Cumulus Convection
Using a Large-domain Two-dimensional Cumulus-Resolving Model

Vol. 17

CGER-I098-2011
UEDA H.: Atmospheric Motion and Air Quality in East Asia

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National Institute for Environmental Studies
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