CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20

Development of Process-based NICE Model and Simulation of Ecosystem Dynamics in
the Catchment of East Asia (Part IV)
Tadanobu Nakayama
Edited by:Center for Global Environmental Research (CGER)
National Institute for Environmental Studies (NIES)

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Shoji Kusunoki (Meteorological Research Institute, Japan Meteorological Agency)
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Akira Noda (Japan Agency for Marine-Earth Science and Technology)
Chiashi Muroi (Forecast Department, Japan Meteorological Agency)
Takashi Imamura (Center for Environmental Measurement and Analysis/NIES)
Hiroyuki Ishitobi (Planning Department /NIES)
Toshimasa Ohara (Center for Regional Environmental Research/NIES)
Nobuko Saigusa (Center for Global Environmental Research/NIES)

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ISSN 1341- 4356, CGER-I114-2014

Foreword
The Center for Global Environmental Research (CGER) at the National Institute forEnvironmental Studies (NIES) was established in October 1990, with the main objectives ofcontributing to the scientific understanding of global change and identifying solutions tocritical environmental problems. CGER conducts environmental research from aninterdisciplinary, multi-agency, and international perspective, and provides an intellectual
infrastructure for research activities in the form of databases and a supercomputer system.
CGER also ensures that data from its long-term monitoring of the global environment is madeavailable to the public.
CGER installed its first supercomputer system (NEC SX-3, Model 14) in March 1992,
and this was subsequently upgraded to an NEC Model SX-4/32 in 1997, an NEC Model SX-6
in 2002, and an NEC Model SX-8R/128M16 in 2007. In June 2013, the system was further
upgraded with the inclusion of an NEC Model SX-9/A(ECO), 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
consists of leading Japanese scientists in climate modeling, atmospheric chemistry, ocean
environment, computer science, and other areas concerned with global environmental
research, and one of its functions is to evaluate proposals of any research requiring the use of
the Supercomputer system. In the 2013 fiscal year (April 2013 to March 2014), seventeen
proposals were approved.
To promote the dissemination of results, we publish both an Annual Report andoccasional Monograph Reports. Annual Reports deliver results for all research projects thathave made use of the supercomputer system in a given year, while Monograph Reports
present the integrated results of a particular research program.
This Monograph Report presents results following the previously published 11th
publication (Part I), the 14th publication (Part II), and the 18th publication (Part III). The newprocess-based catchment model, named 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 interactions occurring between
human activities and water–heat–mass cycles, and their subsequent impacts on environmental
degradation. This will improve our ability to plan for sustainable development in catchments.
This integrated system also contributes to the development of process-based tipping-point
early warning systems and the improvement in boundless biogeochemical cycles along theterrestrial-aquatic continuum.
In the years to come we intend to continue our support of environmental research byenabling the use of our supercomputer resources and continue to disseminate practicalinformation based on our results.
January 2014
Hitoshi Mukai
Director
Center for Global Environmental Research
National Institute for Environmental Studies-i-Preface
This volume of the CGER’S SUPERCOMPUTER MONOGRAPH REPORT series is the20th publication of research outcomes achieved by users of the supercomputer facilities at theCenter for Global Environmental Research (CGER) at the National Institute forEnvironmental Studies (NIES).
The author has developed an advanced process-based model, known as the National
Integrated Catchment-based Eco-hydrology (NICE) model, for use in the evaluation of
ecosystem dynamics in catchments. NICE is a 3-D, grid-based eco-hydrology model thatsimulates the complex interactions between forest canopy, surface water, unsaturated zones,
aquifers, lakes, and rivers, and also simulates iteratively nonlinear interactions betweenhydro-geomorphic and vegetation dynamics. The model is also able to couple with complex
subsystems to simulate systems such as irrigation, urban water usage, stream junctions, anddams/canals, to develop integrated human and natural systems and to analyze the impact ofanthropogenic activity on eco-hydrologic change. Water resources are vital for human activity
and their overuse has caused serious environmental degradation, economic stagnation, and animmense burden on the surrounding environment. This is particularly evident in Asia, where itis crucial to find a solution for such integrated environmental pollution from many aspects.
This monograph (Part IV) succeeds the previously published 11th publication (Part I), the14th publication (Part II), and the 18th publication (Part III). This volume reviews the NICE
processes that have been newly developed since Part III was published, and describes how themodel has been applied for the scaling-up of both the Changjiang and Yellow River Basins in
China on a continental scale. Results of the application indicate that the effective managementof water resources is necessary in decision-making and adaptation strategies. The results alsosuggest the strong need for trans-boundary and trans-authority solutions for sustainable
development, with sound socio-economic conditions that will contribute to national andglobal securities.
NICE is now being expanded to include an up-scaling process through a transformationfrom a rectangular to a longitude-latitude coordinate system, which will be applicable on a
global scale by the application of a map factor and non-uniform grid. Because recent researchhas begun to reconsider how inland water might play a role in continental carbon cycling, afurther improvement of the simulation system will play an important role in the evaluation of
spatio-temporal hot spots on a regional scale, and boundless biogeochemical cycles on a
global scale. This subsequent research will be reported in the next monograph (Part V) in thenear future. The author hopes that the publication presented here will facilitate further
research on the sustainable development in catchments. In particular, the author hopes that the
publication will help highlight ways of solving the complex nature of water-related problems
and support decision-making on sustainable development.
January 2014

Tadanobu Nakayama
Senior Researcher
Center for Global Environmental Research
National Institute for Environmental Studies- ii -

Contents
Foreword .....................................................................................................................................i
Preface ....................................................................................................................................... ii
List of Figures ...........................................................................................................................vi
List of Tables .............................................................................................................................. x
Chapter 1
General Introduction ................................................................................................................ 1
1.1 Background ........................................................................................................................... 3
1.2 Model Description of Process-based Model Applicable to Continental Scale ..................... 6
1.3 Objective and Method ........................................................................................................... 7
References ................................................................................................................................... 9
Chapter 2
Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin ............ 13
Abstract ..................................................................................................................................... 14
2.1 Introduction ......................................................................................................................... 15
2.2 Regional Overview.............................................................................................................. 16
2.3 Methods ............................................................................................................................... 18
2.3.1 NICE Model Series in Natural and Agricultural Regions .......................................... 18
2.3.2 Coupling of NICE with Various Irrigation Procedures (NICE-DRY) ....................... 19
2.3.3 Satellite Analysis using NOAA/AVHRR Data .......................................................... 20
2.4 Input Data and Boundary Conditions for Simulation ......................................................... 21
2.4.1 Meteorological Forcing Data ..................................................................................... 21
2.4.2 Vegetation and Geological Properties ........................................................................ 21
2.4.3 Irrigation and Urban Water Use ................................................................................. 22
2.4.4 Boundary Conditions and Running the Simulation ................................................... 24
2.5 Results and Discussion....................................................................................................... 25
2.5.1 Verification of Hydrologic Cycle............................................................................... 25
2.5.2 Impact of Irrigation on River Discharge and Groundwater Level ............................. 29
2.5.3 Relationship between Hydrologic Change and Crop Productivity ............................ 31
2.6 Conclusion........................................................................................................................... 33
References ................................................................................................................................. 34
Chapter 3
Role of Lake Flood Storage Capability in the Humid Changjiang River Basin ............... 39
Abstract ..................................................................................................................................... 40
3.1 Introduction ......................................................................................................................... 41
3.2 Regional Overview.............................................................................................................. 43
3.3 Methods ............................................................................................................................... 45- iii -

3.3.1 Coupling of NICE with Rice Model .......................................................................... 45
3.3.2 Modeling of Flood Storage Ability of Dongting and Poyang Lakes (NICE-FLD) ... 45
3.4 Input Data and Boundary Conditions for Simulation......................................................... 47
3.4.1 Meteorological Forcing Data ..................................................................................... 47
3.4.2 Vegetation and Geological Properties ........................................................................ 47
3.4.3 Boundary Conditions and Running the Simulation ................................................... 51
3.5 Results and Discussion ...................................................................................................... 52
3.5.1 Hydrologic Changes under Different Climate Conditions .......................................... 52
3.5.2 Surface Water – Groundwater Interactions ................................................................. 55
3.5.3 Evaluation of Flood Storage Ability of Lakes ............................................................ 57
3.6 Conclusion........................................................................................................................... 58
References ................................................................................................................................. 59
Chapter 4
Relation between Uneven Water Resources, Withdrawal, and Ecosystem Degradationin the Changjiang and Yellow River Basins ........................................................................ 63
Abstract .................................................................................................................................... 64
4.1 Introduction ........................................................................................................................ 65
4.2 Materials and Methods ....................................................................................................... 67
4.2.1 Development of the Coupled Human and Natural System ........................................ 67
4.2.2 Model Input Data and Satellite Analysis ................................................................... 68
4.2.3 Model Modification for TGD and SNWTP ............................................................... 69
4.2.4 Boundary Conditions and Running the Simulation ................................................... 69
4.3 Results and Discussion ...................................................................................................... 70
4.3.1 Drought Caused by Over-Irrigation in the Downstream Area of the Yellow River ... 70
4.3.2 Impact of TGD and SNWTP on the Downstream Area of the Changjiang River ...... 71
4.3.3 Effect of Channel Morphology and Sediment Deposition on Hydrologic Change..... 73
4.3.4 Effect of Irrigation and Reclamation on Ecosystem Change in Continental Scale ..... 76
4.4 Conclusion .......................................................................................................................... 79
References ................................................................................................................................ 79
Chapter 5
Final Conclusions and Future Work ..................................................................................... 85
5.1 Final Conclusions ................................................................................................................ 87
5.2 Future Work ........................................................................................................................ 88
References ................................................................................................................................. 91
Appendix
Publications and Presentations .............................................................................................. 95
Original Papers and Reviews .................................................................................................... 96
Books......................................................................................................................................... 97- iv -

Conference Reports ................................................................................................................... 98Contact Person ........................................................................................................................ 100
CGER’S SUPERCOMPUTER MONOGRAPH REPORT .................................................... 101-v-

List of Figures
Chapter 1
1.1
1.2
1.3

Droughts in northern China (Yellow River) and floods in southern China (ChangjiangRiver) ...................................................................................................................................... 4
Process-based National Integrated Catchment-based Eco-hydrology (NICE) model ............ 6
Ecosystems highlighted with various regions in East Asia. Orange circles show
representative megalopolis...................................................................................................... 8(Some of the figures in Chapter 1 are reprinted from Nakayama T., Watanabe M. [2008b] Role offlood storage ability of lakes in the Changjiang River catchment. Global and Planetary Change, 63,
9-22, doi: 10.1016/j.gloplacha.2008.04.002.)

Chapter 2
2.1

2.2

2.3
2.4
2.5
2.6
2.7

2.8

2.9

Land cover in the study area of the Yellow River Basin in China: the bold black line
shows the boundary of the basin; the black dotted line is the border of the North
China Plain (NCP), which includes the downstream area of the Yellow River Basin.
Verification data are also plotted in this figure: river discharge (open red circle); soil
moisture (open brown triangle); and groundwater level (green dot). Details of theobservation stations are shown in Table 2.1. ........................................................................ 16
Comparison of dry biomass simulated by NICE-DRY (open circle) with: (a) NDVI;
and (b) Time-Integrated NDVI (TINDVI), for winter wheat in the 1986-1987 growing
season (top) and summer maize in the 1987 growing season (bottom) (filled triangle),in the downstream area of the Yellow River (R–6 in Table 2.1)... ....................................... 21
Crop types in the agricultural areas. Irrigation areas are also superimposed on the
figure. Irrigated fields cover most of the NCP and are used for double cropping of
winter wheat and summer maize, in addition to three large irrigation zones........................ 23
Verification of annual-averaged spatial distribution of evapotranspiration in 1987 and1988: (a) previous research estimated by NDVI; (b) simulated result.................................. 26
Simulated result of: (a) irrigation water use in 1988; (b) ratios of river to total
irrigation (= river + aquifer).................................................................................................. 27
Simulated annual average groundwater level in the basin: (a) calibration of simulated
value relative to the surface; (b) simulated groundwater level in 1988. In Fig. 2.6a, r2
shows the correlation between observed and simulated values.. .......................................... 28
Scenario analysis run of the conversion from unirrigated to irrigated land; (a-c)
prediction of river discharge in Yellow River mainstream during 1987-1988; (d)groundwater level change. In Fig. 2.7a-c, circles show observation data; solid line is
the simulated result without irrigation effect; bold line is the simulated result withirrigation. r2 shows the correlation between the observed and simulated values; NS
shows the Nash-Sutcliffe Criterion. MV, mean value; SD, standard deviation; CV,coefficient of variation (= SD/MV).. .................................................................................... 30
Prediction of hydrologic changes on the scenario of a conversion from dryland toirrigated fields: (a) river discharge in the upper-middle area (R–3; Qingtongxia) andthe lower area (R–6; Huayuankou) in Table 2.1; (b) change in groundwater level. In
Fig. 2.8a, the dotted line indicates the simulated result in Fig. 2.3, and the solid line
indicates the prediction that all the agricultural areas will be changed into irrigatedfields...................................................................................................................................... 31
TINDVI over the agricultural fields by NOAA/AVHRR satellite images ofagricultural fields; (a) trend of mean TINDVI in the grain filling season at
Huayuankou (R–6) and Juancheng (R–8) in Table 2.1; (b) spatial patterns in mean
TINDVI gradient (per year) during 1982-1999. The solid line in Fig. 2.9a is aleast-square regression line estimated from mean TINDVI (circle). The TINDVI- vi -

gradient shows a generally increasing tendency, particularly in the downstream area
and the NCP.. .............................................................................................................. 33(Some of the figures in Chapter 2 are reprinted from Nakayama T. [2011b] Simulation of the effectof irrigation on the hydrologic cycle in the highly cultivated Yellow River Basin. Agricultural andForest Meteorology, 151, 314-327, doi: 10.1016/j.agrformet.2010.11.006.)

Chapter 3
3.1

3.2
3.3
3.4
3.5
3.6

3.7
3.8

Location of study area in the Changjiang River Basin. The bold black line shows theborder of the basin. Plotted in this figure are the validation data for: river discharge(open circle); soil moisture (open triangle); and groundwater level (green dot). The
black dotted-line is the border of the groundwater change study area around both
Dongting and Poyang Lakes in Fig. 3.8.. .............................................................................. 42
Flow diagram of NICE-FLD. Bold frames indicate the new developed processes in
the NICE-FLD model. .......................................................................................................... 44
Soil texture from: (a) ISLSCP data with a resolution of 1° x 1°; and (b) categorized
and digitized data with 1-km mesh using the Vegetation and Soil Maps of China(1:4,000,000) in this study.. .................................................................................................. 48
Land cover around the Dongting lake in: (a) 1990s; and (b) 2000s. Land coverchanges (shrinking of the lake) are predominant around the dotted-circle area.. ................. 49
Simulation result of 1998 big-flood in the Changjiang mainstream. The circles
represent the observation data and the lines represent the simulated results by
NICE-FLD. r2 and NS are also plotted.. ............................................................................... 52
Simulation result of river discharge around the junction during both 1987-1988 and1998-1999: (a) around Dongting Lake during 1987-1988; (b) around Dongting Lake
during 1998-1999; (c) around Poyang Lake during 1987; and (d) around Poyang Lake
during 1998-1999. Simulated discharges were compared with observed values both at
the junctions (R–7; Chenglingji, and R–9; Hukou) and at downstream mainchannels(R–8; Luoshan, and R–10; Datong) in Fig. 3.1. Positive discharge at junction shows
the flow from the lakes to the mainstream Changjiang River. .............................................. 54
Surface water to groundwater interactions in the Changjiang River: (a) Simulated
seepage from groundwater to river and lake; (b)-(c) Power spectra of river discharge
at Yichang and Datong during normal and severe floods.. ................................................... 56
Simulation result of hydrologic change by recovering the areas of Dongting andPoyang Lakes to that of the 1950s (4,000 km2 and 5,000 km2): (a) river discharge
downstream of Dongting Lake (R–8; Luoshan); (b) river discharge downstream of
Poyang Lake (R–10; Datong); and (c) change of groundwater level around both lakes.
The circles in Fig. 3.8 a-b represent observation data and the lines represent thesimulated results from 1998-1999 (the same as in Fig. 3.6b and 3.6d). The bold line
represents results using a simulation of the lakes when recovered to an area of the
1950s. The plotted area in Fig. 3.8c is described in Fig. 3.1, and the black and pinklines represent the lakeshores in 1990s and 1950s. ............................................................... 58(Some of the figures in Chapter 3 are reprinted from Nakayama T., Watanabe M. [2008b] Role offlood storage ability of lakes in the Changjiang River catchment. Global and Planetary Change, 63,
9-22, doi: 10.1016/j.gloplacha.2008.04.002. and Nakayama T., Shankman D. [2013a] Impact of theThree-Gorges Dam and water transfer project on Changjiang floods. Global and Planetary Change,
100, 38-50, doi: 10.1016/j.gloplacha.2012.10.004.)- vii -

Chapter 4
4.1

4.2
4.3

4.4

4.5

4.6

4.7

4.8

Crop types in agricultural areas of the Changjiang and Yellow River Basins in China.
Irrigation areas are overshaded in the figure. The black dotted square represents theborder of the simulation area containing a grid of 300  200 blocks of Albers (WGS
1984) co-ordinates. Black dotted line is the border of the North China Plain (NCP),
which includes the downstream area of the Yellow River.................................................... 65
National Integrated Catchment-based Eco-hydrology (NICE) model. NICE is coupledwith DSSAT for agricultural fields, to improve the hydrologic cycle and evaluate therelation between uneven crop yield and water availability... ................................................ 68
Decrease in discharge of the Yellow River caused by over-irrigation: (a) simulated
result of river irrigation in 1987; (b) river discharge from middle to downstream areasof the river. In Fig. 4.3a, the area colored in white represents the region without
irrigation. In Fig. 4.3b, the solid line represents the simulated result with irrigation,and the circle represents the observed value. The right axis shows the temporalchange in average precipitation. r2 shows the correlation between observed andsimulated values, and NS shows the Nash-Sutcliffe Criterion.... ......................................... 71
Impact of the TGD and SNWTP on hydrologic changes around Poyang Lake: (a) lakewater level during severe flood event; and (b) difference between the averagegroundwater level before and after construction. In Fig. 4.4a, the spill discharge of the
TGD was changed in consideration of navigational benefits and flood risk
downstream. In Fig. 4.4b, the SNWTP scenario uses a water transfer scheme of 13.0
 109 m3 per year................................................................................................................... 72
Sensitivity of predicted hydrologic change for the effects of channel morphology andlake sediment deposition in the long-term simulation: (a) lake water level; and (b)river discharge. In Fig. 4.5b, the bold line represents the simulated discharge atjunction R–9; Hukou, and the solid line represents the downstream main-channel(R–10; Datong)..... ................................................................................................................ 73
Impact of sand mining on Poyang lake region during severe flood at 22 years after
completion of the TGD, using multiple scenarios: intensive dredging at the lake’s
mouth (bed digging); width extension at the lake’s mouth; and a diversion towards thelake that is currently disconnected from the mainstream.... .................................................. 74
Impact of irrigation on hydrologic change on a continental scale: (a) simulated resultsof annual-averaged unconfined groundwater level (a.s.l.) in the Changjiang andYellow River Basins; (b) topographical contour calculated using GTOPO30. In Fig.
4.7a and 4.7b, the black square represents the border of the simulation area in Albers
co-ordinates, in the same way as that in Fig. 4.1.. ................................................................ 77
TINDVI over the agricultural fields by NOAA/AVHRR satellite images in the
agricultural fields: (a) Comparison of TINDVI with trend in observed yields at 4
stations (Pt.I; Changsha, Pt.II; Hefei, Pt.III; Zhengzhou, Pt.IV; Tianshui); (b) Spatial
patterns in mean TINDVI gradient (per year) during 1982-1999. In Fig. 4.8b, theblack square line represents the border of the simulation area in Albers co-ordinates inthe same way as in Fig. 4.1.... ............................................................................................... 78(Some of the figures in Chapter 4 are reprinted from Nakayama T., Shankman D. [2013a] Impact of
the Three-Gorges Dam and water transfer project on Changjiang floods. Global and Planetary Change,
100, 38-50, doi: 10.1016/j.gloplacha.2012.10.004. and Nakayama T., Shankman D. [2013b]
Evaluation of uneven water resource and relation between anthropogenic water withdrawal andecosystem degradation in Changjiang and Yellow River basins. Hydrological Processes, 27(23),3350-3362, doi: 10.1002/hyp.9835.)- viii -Chapter 5
5.1
5.2
5.3
5.4

Down-scaling and feedback process in the extension of NICE ............................................ 88
Up-scaling and down-scaling in terrestrial/aquatic ecosystems............................................ 89
Coordinate transformation for up-scaling of terrestrial/aquatic ecosystems......................... 90
NICE coupled with a biogeochemical model for evaluation of the boundlessbiogeochemical cycle ............................................................................................................ 91(Some of the figures in Chapter 5 are reprinted from Nakayama T. [2013a] For improvement inunderstanding eco-hydrological processes in mire. Ecohydrology and Hydrobiology, 13, 62-72, doi:
10.1016/j.ecohyd.2013.03.004.)- ix -

List of Tables
Chapter 2
2.1
2.2
2.3

Lists of observation stations for calibration and validation shown in Fig. 2.1. .................... 17
Comparison of irrigation areas: Simulated conditions and conditions from previous
research.. ............................................................................................................................... 23
Validation of amount of irrigation water use simulated by the NICE-DRY with values
from the previous research.. .................................................................................................. 26(The tables in Chapter 2 are reprinted from Nakayama T. [2011b] Simulation of the effect of irrigation on
the hydrologic cycle in the highly cultivated Yellow River Basin. Agricultural and Forest Meteorology, 151,
314-327, doi: 10.1016/j.agrformet.2010.11.006.)

Chapter 3
3.1

Land cover change in the 1990s and 2000s in the Changjiang River Basin. ........................ 50(The table in Chapter 3 is reprinted from Nakayama T., Watanabe M. [2008b] Role of flood storage abilityof lakes in the Changjiang River catchment. Global and Planetary Change, 63, 9-22, doi:
10.1016/j.gloplacha.2008.04.002.)

Chapter 4
4.1

Channel morphology and lake sediment deposition around Poyang Lake. .......................... 75(The table in Chapter 4 is reprinted from Nakayama T., Shankman D. [2013a] Impact of the Three-Gorges
Dam and water transfer project on Changjiang floods. Global and Planetary Change, 100, 38-50, doi:
10.1016/j.gloplacha.2012.10.004.)-x-

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20
CGER-I114-2014, CGER/NIES

Chapter 1
General Introduction-1-1-

Chapter 1

General Introduction-2-2-

CGER-I114-2014, CGER/NIES

1.1 Background
Ecosystem dynamics in East Asia have been drastically changed by the human alterationof land use from areas of wilderness to areas of urban or agricultural lands. Furthermore,
anthropogenic activities resulting in environmental pollution have degraded water
environments, causing contamination and an imbalance in both the hydrologic cycle and thethermal environment. In addition to surface water shortages, the depletion of groundwater
resources is also becoming a serious problem in many areas, including the High Plains of thecentral United States, the North China Plain (NCP), northern India, the Sahel, South Africa,and Australia, although most people are unfamiliar with the dynamic nature of groundwater,
and its use as an important storage and reservoir of water resources (Alley et al., 2002; Millyet al., 2008; Wada et al., 2010). Lack of water now threatens potentially 80% of the world’s
population, due to the rising demands from population growth, urbanization, andindustrialization (Gleick and Palaniappan, 2010). It is clear that a cumulative threat diagnosis
framework is needed as a tool for the prioritization of policy and for management responses
to assure global water security for human existence and ecosystem biodiversity (Jackson et al.,
2001; Baron et al., 2002; Vorosmarty et al., 2010). For the facilitation of sustainable
development, it is necessary to quantify the mechanisms of ecosystem change through an
integrated approach, and to carry out an adaptation strategy to ensure sustainable development
and to actively recover local natural environments.
There is a need to solve territorial disagreements over water resources peacefully, throughtrade and international agreements (Barnably, 2009). It is, therefore, possible that watermarket based policy instruments could help to attain targets in the most cost-effective manner.
Numerical models are powerful tools for use in a river–basin wide initiative approach.
Although the assumption of stationarity (the idea that natural systems fluctuate within an
unchanging envelope of variability) has long been applied to human disturbance of river
basins, research has indicated that stationarity should no longer serve as a central, default
assumption in water-resource risk assessment and planning, and that non-stationarity
approaches are needed to predict global pattern of streamflow and to ensure sustainable water
availability under global climate changes (Milly et al., 2005, 2008). Recently, interestingstudies have been made using virtual and real water transfers (water saved in one region by
the importation of goods produced from water used in another region), and their relationshipto actual water demand, water scarcity, and water conflicts (Yang and Zehnder, 2001;
Hoekstra and Hung, 2005; Ma et al., 2006). It is of considerable interest that, in the long term,
the globalization of water resources might reduce societal resilience with respect to water
limitations; that is, it leaves fewer options available to cope with exceptional droughts andcrop failure (D’Odorico et al., 2010), while it is generally recognized that in the short term,
the globalization of water resources may prevent malnourishment, famine, and conflict.

-3-3-

Chapter 1

General Introduction

Fig. 1.1

Droughts in northern China (Yellow River) and floods in southern China (ChangjiangRiver).
River).

This report considers the areas surrounding the Changjiang (Yangtze), Yellow (Huanghe),and Hai Rivers (Fig. 1.1), which are among the “seven big rivers” of China. In China, thehydroclimate differs between the northern and southern regions (Nakayama, 2011a-b, 2012c;
Nakayama and Shankman, 2013a-b; Nakayama and Watanabe, 2008b). The semi-arid north isheavily irrigated, and the use of water combined with an increase in food demands, along witha diminishing availability of water, creates substantial pressure in the region of the Yellow
River (Brown and Halweil, 1998; Yang et al., 2004; Nakayama et al., 2006, 2010; Nakayama,
2011a-b). The North China Plain (NCP), located downstream of the Yellow River, is one of
the country’s most important agricultural regions, and 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 and downstream
areas, and similar conflicts have occurred between agricultural and municipal–industrial
sectors. Several researchers have stated that China’s demand for natural resources alreadyexceeds the environment’s capacity to sustain this densely populated land (Varis andVakkilainen, 2001).
However, in the humid southern region the hydroclimate is very different and severe
flooding causes considerable economic loss and serious damage to towns and farms, inparticular, across the middle and lower regions of the Changjiang River. Although somesevere floods have been attributed to abnormally high rainfall events, (such as those of 1954
and 1998), the changes in the landscape related to human activity have been the main cause of-4-4-

CGER-I114-2014, CGER/NIES

the increasing severity of major floods (Cheng, 1999; Yin and Li, 2001; Piao et al., 2003;
Zhao and Fang, 2004; Zhao et al., 2005; Nakayama and Watanabe, 2008b; Nakayama andShankman, 2013a). To compensate for this imbalance in water resources, China isaccelerating preparatory work on its ambitious project for the transference of 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 Three Gorges Dam project (TGD) and the SNWTP
would greatly reduce these imbalances (Yang and Zehnder, 2001; Li et al., 2007), there is anurgent need to clarify the relationship between these two extremes affected by human activity.
Therefore, it is essential to make an accurate evaluation of the optimum amount of transferredwater, and the socioeconomic and environmental consequences affecting both basins. The
development of a process-based model presents an integrated approach for appropriate water
resource management, for the achievement of sustainable development on a regional scale(Nakayama, 2012c; Nakayama and Shankman, 2013a-b), and makes it possible to: evaluate
water-heat-mass dynamics; predict future changes caused by human activity; and aid anaccurate estimation of the amount of water required for transference between the catchments.
The current monograph (Part IV) succeeds our 11th publication (Part I) of 2006
(Nakayama and Watanabe, 2006b), 14th publication (Part II) of 2008 (Nakayama, 2008c), and18th publication (Part III) of 2012 (Nakayama, 2012d). The catchments, or basins, describedin this monograph are the Changjiang and Yellow River Basins in China, where two hugenational projects, the TGD and the SNWTP are under construction (or are almost completed),in order to diminish flood risk and to compensate for the imbalance of environmental
resources as much as possible. Although water resources are vital for human activity, their
overuse causes significant hydrologic changes, ecosystem degradation, and sometimes asevere environmental burden in the surrounding areas. Therefore, the effective managementof water resources is important for planning and sustainable development strategies and fordeveloping an urgent solution to the intertwined environmental pollution problems. The
eco-hydrological model is a powerful tool for the evaluation of an optimum amount oftransferred water, and the socio-economic and environmental consequences from such atransfer. From this perspective, this monograph is further expanded to a continental scalefrom the 18th publication (Part III) of 2012 (Nakayama, 2012d) which showed the possibility
for a sound recovery of the hydrologic cycle with the simultaneous creation of thermallystable environments on a local/regional scale. An evaluation of the water-heat-mass cycle in
China, as described in the above, is also necessary relative to the national security of Japanand international contributions (Nakayama, 2011a-b; Nakayama and Watanabe, 2008b;
Nakayama et al., 2006, 2010). Such an integrated approach will not only aid an evaluation of
the complex processes involved from the view point of hydrologic and biogeochemical cycles,
but will also clarify how the substantial pressures caused by such intricately-woven problems
can be overcome by effective trans-boundary solutions.

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General Introduction

1.2 Model Description of Process-based Model Applicable to Continental Scale
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-c, 2009a, 2010a, 2011a-d, 2012a-d, 2013a; Nakayama andFujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Shankman, 2013a-b;
Nakayama and Watanabe, 2004, 2006a-b, 2008a-c; Nakayama et al., 2006, 2007, 2010, 2012)(Fig. 1.2). NICE is a 3-D, grid-based eco-hydrology model, and simulates iteratively
nonlinear interactions between hydro-geomorphic and vegetation dynamics.
The NICE 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 twolayers 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–heat fluxes, including the gradient ofhydraulic potential between the deepest unsaturated layer and the groundwater, effective
precipitation, and seepage between the river and groundwater.

Fig. 1.2

Process-based National Integrated Catchment-based Eco-hydrology (NICE) model.
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CGER-I114-2014, CGER/NIES

For agricultural applications, NICE is coupled with the Decision Support Systems forAgro-technology Transfer (DSSAT) (Ritchie et al., 1998), in which an automatic irrigation
mode supplies the crop water requirements for field cultivation (Nakayama et al., 2006;
Nakayama, 2011a-b) and for the cultivation of paddy fields (Nakayama and Shankman,
2013a; Nakayama and Watanabe, 2008b). This model includes the various functions ofrepresentative crops such as wheat, maize, soybean, and rice and automatically simulates theirdynamic growth processes. Plant growth is based on biomass formulation, which is limited byvarious reduction factors such as light, temperature, water, and nutrients (Nakayama, 2011a-b,
2012c; Nakayama and Shankman, 2013a-b; Nakayama and Watanabe, 2008b; Nakayama etal., 2006). Most of the water supplied to major cities and cultivated fields in the NCP and
Dalian is transported through a network of reservoirs and canals (Nakayama, 2011a;
Nakayama et al., 2006, 2010). Because limited data are available on the discharge control atmost of the dams adjacent to these reservoirs, a constant ratio of inflow to outflow—insteadof the more complicated storage–runoff function model—is used for the major dams forwhich no data were available (Nakayama, 2011a). NICE was also coupled with the urban
canopy model (UCM) (Nakayama and Fujita, 2010) to include the effects of the hydrothermal
cycle on various pavements, and with the Regional Atmospheric Modeling System (RAMS)(Pielke et al., 1992) to include hydrothermal interaction in urban areas (Nakayama andHashimoto, 2011; Nakayama et al., 2012). Downward short- and long-wave radiation,
precipitation, atmospheric pressure, air temperature, air humidity, and wind speed simulatedby the atmospheric model were input into the UCM, whereas momentum, sensible, latent, andlong-wave flux simulated by the UCM were input into the atmospheric model at each time
step. This procedure ensures that the feedback process of water and heat transfers between theatmospheric region and the land surface are implicitly included in the simulation process.
The NICE series has further expanded to include additional processes such as geomorphic
change and vegetation succession, to evaluate various ecosystems and their relation tosurrounding regions. These complex compartments are necessary for the application of NICE
to evaluate ecosystem dynamics on a continental scale, in addition to local or regional scales.
The details of the existing NICE series are presented in the following references.
1.3 Objective and Method
To study the ecosystem dynamics in catchments of East Asia, the author previously
developed a method to combine a grid-based numerical model, ground-truth observation
network, satellite data, and statistical analysis (Fig. 1.2). In this monograph (Part IV), theauthor reviews the new processes of NICE developed after Parts I (Nakayama and Watanabe,
2006b), Part II (Nakayama, 2008c), and Part III (Nakayama, 2012d), and describes how thesemodels are applied to the broader catchments or basins wherein the relationship between theenvironment and water resources, economic growth, and ecosystem degradation require an
urgent evaluation. The main study areas are the Changjiang and Yellow River Basins in China(Nakayama, 2011b, 2012c; Nakayama and Shankman, 2013a-b), and this is an extension of
the author’s previous research in China (Nakayama, 2011a; Nakayama and Watanabe, 2008b;
Nakayama et al., 2006, 2010). Some methodologies from the previous papers relating toJapan are considered valuable for use in this study (Fig. 1.3) (Nakayama, 2008a-b, 2010a,
2012a-b, 2013a; Nakayama and Watanabe, 2004, 2006a, 2008a; Nakayama and Fujita, 2010;
Nakayama and Hashimoto, 2011; Nakayama et al., 2007, 2012).

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Chapter 1

General Introduction

The final objective of the current monograph is to evaluate the eco-hydrological processes
in both the Changjiang and the Yellow River Basins, and predict the impact of anthropogenic
activities such as the TGD and SNWTP on eco-hydrologic change on a continental scale, withthe view of working towards trans-boundary and -authority solutions for sustainable
development under sound socio-economic conditions, contributing to national and global
security. During this evaluation and prediction, the monograph also presents an advanced
method by which a combination of numerical models, satellite imagery, observation data, andstatistical analysis clarify the relationship between water resources and economic growth,which adversely affects the ecological balance on a continental scale. Furthermore, thisprocedure offers an integrated assessment system for effective strategies which could reveal
methods for solving the complex nature of water-related challenges and support plans forsustainable development. This new simulation system would also describe the development ofprocess-based tipping-point early warning systems, and the improvement of an understandingof boundless biogeochemical cycles, related to recent research that has begun to reconsider
how inland water may play a role in continental/global carbon cycling.

Fig. 1.3

Ecosystems highlighted within various regions in East Asia. Orange circles show
representative megalopolis.

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General Introduction- 12 - 12 -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20
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Chapter 2
Effect of Irrigation on the Hydrologic Cycle
in a Semi-Arid Yellow River Basin- 13 - 13 -Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

Abstract
To gain an understanding of the complex and diverse water systems involving river
dry-up, groundwater degradation, agricultural/urban water use, and dam/canal effects in theheavily irrigated Yellow River Basin, the study related in this chapter coupled the National
Integrated Catchment-based Eco-hydrology (NICE) model series with more complex
sub-models (NICE-DRY).
The NICE-DRY made a reasonable reproduction of the hydrologic cycle with theinclusion of phenomena such as evapotranspiration, irrigation water use, groundwater level,
and river discharge during spring/winter wheat, summer maize, and summer rice cultivations.
Two scenario analyses predicted the impact of irrigation on both surface water andgroundwater, which had previously been difficult to evaluate. The simulated discharge withirrigation was improved in terms of the mean value, standard deviation, and coefficient ofvariation.
The scenario analysis of a conversion from dryland to irrigated fields predicted that theeffect on groundwater from irrigation was most severe in the middle and downstream areas of
the Yellow River, and the quantity of the groundwater diminished in areas where the surface
water was considerably limited. The simulated dry biomass of both wheat and maize werelinearly related to the Time-Integrated Normalized Difference Vegetation Index (TINDVI)
estimated from satellite images. The temporal gradient of the TINDVI between 1982 and1999 showed a spatially heterogeneous distribution with increasing trends in the wheat andmaize fields, indicating that the increases in production were related to the application ofirrigation water and the resultant hydrologic changes.
The Yellow River Basin experienced substantial river dry-up and groundwater
degradation at the end of the 20th century. By use of this integrated approach, it is potentiallypossible to estimate the close relationship between crop production, the hydrologic cycle, andwater availability, and the heterogeneity of vulnerable water resources could be predicted.
The application of this approach could therefore help to overcome substantial pressure tomeet increasing demands for food with a declining availability of water, and to decide on
appropriate measures to be taken for the management of whole water resources, in order toachieve sustainable development under sound socio-economic conditions.
Keywords: irrigation, hydrologic cycle, TINDVI, integrated catchment-based eco-hydrology
model, water availability, sustainable development- 14 - 14 -

CGER-I114-2014, CGER/NIES

2.1 Introduction
Water use in the Yellow River Basin has altered considerably in line with thedevelopment of regional economies (Fig. 2.1). The region is now heavily irrigated, and a
combination of increased food demands and declining water availability are creating
substantial pressures on the region. Tang et al. (2008a) indicated that climate change is thedominant contributor to changes in annual streamflow in the upper area of the basin, whereas
human activities (such as the withdrawal of water for irrigation) dominate the downstreamchanges in annual streamflow. The North China Plain (NCP), located in the downstream areaof the Yellow River, is one of the most important grain-cropping areas in China. As water
resources are essential for agricultural development, the demand for groundwater has been
increasing. Levels of groundwater have declined dramatically over the previous half century
due to over-pumping and drought, and an area of saline-alkaline land has now expanded(Brown and Halweil, 1998; Shimada, 2000; Chen et al., 2003b; Nakayama et al., 2006).
Since the completion of a large-scale irrigation project in 1969, a noticeable cessation offlow has been observed in the Yellow River (Yang et al., 1998b; Fu et al., 2004), resulting
from the increasing, and intense, competition between water supply and demand. The ratio of
irrigation water use (defined as the ratio of the annual gross use for irrigation relative to theannual natural runoff) has increased steadily from 21% to 68% over the last 50 years,
indicating that the current water shortage is closely related to irrigation development (Yang etal., 2004). This shortage also reduces the water renewal time (Liu et al., 2003) and therenewability of water resources (Xia et al., 2004), and such a reduction has been accompanied
by a precipitation decrease in most parts of the basin (Tang et al., 2008b). It is evident that toensure sustainable water resource use it is important to understand the contribution of humanintervention to climate change within this basin (Xu et al., 2002), in addition to clarifying therather complex and diverse water system in this highly cultivated region.
The research described in this chapter focuses on the impact of irrigation on thehydrologic cycle in the Yellow River Basin, which is an arid to semi-arid environment withintensive cultivation. A combination of the National Integrated Catchment-based
Eco-hydrology (NICE) model (Nakayama, 2008a-c, 2009a, 2010a, 2011a-d, 2012a-d, 2013a;
Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Shankman,
2013a-b; Nakayama and Watanabe, 2004, 2006a-b, 2008a-c; Nakayama et al., 2006, 2007,2010, 2012) and more complex components (sub-models) simulating processes such as
irrigation, urban water use, and dam/canal systems, has led to the development of the
NICE-DRY model, which simulates the balance of both the water budget and the energy inthe entire basin with a resolution of 10 km.
The objective of this study was to evaluate the complex hydrological processes of river
dry-up, agricultural/urban water use, groundwater pumping, and dam/canal effects, and toreveal the impact of irrigation on both surface water and groundwater in the basin. Based on
the relationship between satellite images of the Normalized Difference Vegetation Index(NDVI) and Time-Integrated NDVI (TINDVI), along with the simulated dry biomasses ofwheat and maize, a temporal gradient of TINDVI was estimated from 1982 - 1999. This wasthen linearly correlated with changes in crop production and indirectly correlated withchanges in irrigation water use and the resulting hydrologic changes. It is considered that thisintegrated approach will help to clarify how the substantial pressures that the country faces
from a combination of increased food demand and declining water availability, can beovercome, and how effective decisions can be made regarding sustainable development undersound socio-economic conditions in the basin.
- 15 - 15 -Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

Fig. 2.1 Land
Land cover in the study area of the Yellow River Basin in China: the bold black line
shows the boundary of the basin; the black dotted line is the border of the North China Plain(NCP), which includes the downstream area of the Yellow River Basin. Verification data arealso plotted in this figure: river discharge (open red circle); soil moisture (open brown
triangle); and groundwater level (green dot). Details of the observation stations are shown in
Table 2.1.

2.2 Regional Overview
The Yellow River is 5,464 km long and has a basin area of 794,712 km2 (including theErdos inner flow area) (Fig. 2.1). The basin is divided into: the upper region (3,472 km,
428,235 km2) from the headwater to Toudaoguai in Inner Mongolia (R–4 in Table 2.1); themiddle region (1,206 km, 343,751 km2) from Toudaoguai to Huayuankou in Henan province(R–6 in Table 2.1); and the lower region (786 km, 22,726 km2) from Huayuankou to theestuary. The upper region can be further divided into two sub-regions: the source area
(upstream of Tangnaihai) and the downstream area (between Tangnaihai and Toudaoguai).
The river is well known for its high sediment content, frequent floods, the unique channel
characteristics of the downstream area where the river bed lies above the surrounding land,
and its limited water resources. It is confined to a levee-lined course in the downstream area
as it flows across the NCP, a giant alluvial plain formed by deposition from the Yellow, Hai,
and Luan Rivers and their tributaries, with many palaeochannels of different stages forming
various geomorphological features (Chen, 1996), and is one of the most important grain
cropping areas in China because of its large area (about 13,600 km2) and huge population(about 112 million).
- 16 - 16 -

CGER-I114-2014, CGER/NIES

Table 2.1 Lists of observation stations for calibration and validation shown in Fig. 2.1.

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

Lanzhou
Lanzhou
Qingtongxia
Toudaoguai
HequHuayuankou
LankaoJuancheng
LijinRiver Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge
River Discharge

35°55.8'
36°4.2'38°21.0'40°16.2'
39°22.2'
34°55.2'
34°55.2'
35°55.8'
37°31.2'

103°19.8'
103°49.2'
106°24.0'
111°4.2'
111°9.0'113°39.0'
114°42.0'
115°54.0'
118°18.0'

1794.01622.01096.0
861.0
861.0
104.0
73.0
1.0
1.0

S–1

BamengSoil Moisture

40°46.2'

107°24.0'

1059.0

S–2
S–3
S–4
S–5
S–6
S–7

Xilingaole
Yongning
Xifengzhen
Tianshui
Lushi
Zhengzhou

Soil Moisture
Soil Moisture
Soil Moisture
Soil Moisture
Soil Moisture
Soil Moisture

39°4.8'38°15.0'35°43.8'
34°34.8'
34°0.0'34°49.2'

105°22.8'
106°13.8'
107°37.8'
105°45.0'
111°1.2'
113°40.2'

1238.01130.01435.01196.0
675.0
99.0

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
G–14
G–15
G–16
G–17
G–18

Shanxi-1
Shanxi-2
Shanxi-3
Shanxi-4
Shanxi-5
Shanxi-6
Shanxi-7
Shanxi-8
Shanxi-9
Henan-1
Henan-2
Henan-3
Henan-4
Henan-5
Henan-6
Henan-7
Henan-8
Henan-9

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
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level
Groundwater Level

37°44.0'
38°0.7'37°58.0'37°47.3'
37°43.2'
40°3.4'34°56.9'
38°47.1'
37°7.6'
34°48.2'
35°42.0'35°31.0'35°31.0'
34°1.5'33°49.0'
34°4.1'33°55.9'34°48.2'

112°34.2'
112°25.8'112°29.6'
112°31.3'
111°57.5'113°17.4'
110°45.1'
112°44.0'
111°54.2'
114°18.2'
115°1.3'
115°1.0'
115°12.5'
113°50.8'
113°56.3'
115°18.2'
116°22.3'
114°18.2'

772.51
831.10788.96779.03780.511059.73346.73823.18733.24
73.40
52.20
53.30
54.00
66.80
63.91
47.20
31.90
52.60

- 17 - 17 -

Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

Table 2.1

(Continued).

No.

Point Name

Type

Lat.

Lon.

Elev.(m)

G–19
G–20
G–21
G–22
G–23
G–24
G–25
G–26

Qinghai-1
Qinghai-2
Qinghai-3
Qinghai-4
Qinghai-5
Qinghai-6
Qinghai-7
Qinghai-8

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

36°35.5'
37°0.0'36°58.5'
36°34.3'
36°32.2'
36°43.1'
36°41.9'
36°14.8'

101°44.7'
101°37.9'
101°38.9'101°43.9'
101°40.5'
101°30.3'
101°30.9'
94°46.6'

2321.172506.942474.632346.37
2451.32
2425.32
2383.983007.81

2.3 Methods
2.3.1 NICE Model Series in Natural and Agricultural Regions
The author developed the NICE series of process-based catchment models forapplicability to natural, agricultural, and urban regions in various catchments and basins(Nakayama, 2008a-c, 2009a, 2010a, 2011a-d, 2012a-d, 2013a; Nakayama and Fujita, 2010;
Nakayama and Hashimoto, 2011; Nakayama and Shankman, 2013a-b; Nakayama and
Watanabe, 2004, 2006a-b, 2008a-c; Nakayama et al., 2006, 2007, 2010, 2012). The NICE
models link surface-unsaturated–saturated water and land-surface processes, and describe
variations in phenology on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) satellite data. The surface hydrology model of NICE consists of a hillslope
hydrology model based on kinematic wave theory, and a distributed stream network model
based on both kinematic and dynamic wave theories. NICE also solves a partial differential
equation describing three-dimensional groundwater flow for both unconfined and confined
aquifers. The submodels consider water and heat fluxes from the ground to the surface,
including: the gradient of hydraulic 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 crop water requirements for the cultivation of fields(Nakayama et al., 2006; Nakayama, 2011a-b) and paddy fields (Nakayama and Watanabe,
2008b). The model includes various functions for representative crops such as wheat, maize,soybean, and rice, and automatically simulates their dynamic growth processes. The details
are described in Chapter 1 and in previous related papers (Nakayama, 2008a-c, 2009a, 2010a,
2011a-d, 2012a-d, 2013a; Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011;
Nakayama and Shankman, 2013a-b; Nakayama and Watanabe, 2004, 2006a-b, 2008a-c;
Nakayama et al., 2006, 2007, 2010, 2012).

- 18 - 18 -

CGER-I114-2014, CGER/NIES

2.3.2 Coupling of NICE with Various Irrigation Procedures (NICE-DRY)
Irrigation withdrawals in the basin account for about 90% of total surface abstraction and
60% of groundwater withdrawal (Chen et al., 2003a). The higher use of irrigation water fromthe river is attributed to the geographical ease of access and water availability, rather than thecrop water requirements (Yang et al., 2004).
NICE-DRY was further developed from previous NICE series for its application toirrigated fields where water is withdrawn from both groundwater and river, thereby
satisfactorily reproducing river drying-up processes. The initial conditions for the ratios ofriver to aquifer irrigation were set at constant values (basin average = 74% and 26%,
respectively; upper = 80% and 20%; downstream = 65% and 35%) (Yellow River
Conservancy Commission, 2002). These initial condition values were then changed in thecalibration procedure, to reproduce the observation data as closely as possible after repeatedtrial and error (Oreskes et al., 1994). A validation procedure was then conducted to confirm
the simulation under the same set of parameters, (including the ratios of river to aquifer
irrigation), and to closely reproduce the observed values.
Spring/winter wheat, summer maize, and summer rice were automatically simulated insuccession in the sequence analysis mode by inputting both previous point data for each crop
type (Wang et al., 2001b; Liu et al., 2002; Tao et al., 2006) and spatial distribution data
(Chinese Academy of Sciences, 1988; Fang et al., 2006). The automatic irrigation mode
supplied the crop water requirement at all times, assuming that the average available water inthe top layer of soil fell below the soil moisture at field capacity for wheat and maize, and thatthe water level in the rice fields was maintained in accordance with the irrigation schedule ofirrigated water depth (submerged depth) in previous research (Huang et al., 1998), as arepresentative of single rice. The water volume deficit in the irrigated fields was automaticallywithdrawn and supplied from the river or the aquifer, in order to satisfy the observed soil
moisture, river discharge, groundwater level, LAI (Leaf Area Index), evapotranspiration, andcrop coefficient. In doing so, NICE simulates the impact of drought and implicitly includes
the effect of water stress. Details are given in Nakayama et al. (2006) and Nakayama and
Watanabe (2008b).
There are a large number of dams and canals along the mainstream of the Yellow Riverand across the NCP, to meet the huge demand for agricultural, industrial, and domestic wateruse (Ren et al., 2002) (Fig. 2.1). The main river has six large dams: Longyangxia, Liujiaxia,
Qingtongxia, Wanjiazei, Sanmenxia, and Xiaolangdi (Yang et al., 2004). Because there arefew available data on the discharge control at most of these dams, the model uses a constantratio of dam inflow to outflow, which is a more simple approach than that of thestorage-runoff function model (Sato et al., 2008). There are also many complex canals in thethree large irrigation zones (Qingtongxia in Ningxia Hui, Hetao in Inner Mongolia, andWeisan in Shandong Province), in addition to on the NCP, making it very difficult to evaluate
the flow dynamics in these areas, and it is impossible to obtain the observed discharge anddata related to the control of the weirs and gates on every canal. In reality, it is not necessary
to evaluate the flow dynamics of all canals, but it is effective to estimate the flow dynamics inthe main canals for the first approximation, when attempting to evaluate the hydrologic cyclein the entire basin. Therefore, NICE simulates the discharge only in the main canals,
assuming that this is defined as the difference in the hydraulic potential at both junctions,(similar to the stream junction model) (Nakayama and Watanabe, 2008b).
Heavy sedimentation in the downstream area of the Yellow River causes the river bed toaggrade by 5 to 10 cm a year, thus elevating it relative to the surrounding landscape, although- 19 - 19 -

Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

this sedimentation has decreased with the increase in dam numbers and the consequent
decrease of river discharge, (which promotes river drying), and by the considerable leakage
through the levee banks (Zhang et al., 1990; Yang et al., 1998b; Ren et al., 2002). The
dynamic wave effect is also important when simulating meandering rivers and smaller slopes,
because the backwater effect is predominant (Nakayama and Watanabe, 2004). When a river
has a very low flow (almost zero at some points in the simulation), the dynamic wave theory
requires a longer computational time, and in the process can become unstable. NICE therefore
applies a threshold water level of 1 mm to ensure simulation stability and to include thedrying-up process, and also includes a seepage process, which is decided by parameters suchas the hydraulic conductivity of the river bed, cross-sectional area of the groundwater section,and river bed thickness.
2.3.3 Satellite Analysis using NOAA/AVHRR Data
NDVI was calculated from NOAA/AVHRR satellite data with a resolution of 1.1 km
collected over East Asia between 1982 and 1999. The original data were downloaded fromWebPaNDA (2008) and re-sampled to alter the resolution to 6.0 km, using thenearest-neighbor method. Theoretical research by Sellers et al. (1997) provided the following
relationship between NDVI and primary production, P (g/m2), defined as dry biomass by unitof surface, by using Light Use Efficiency (LUE) model (Gower et al., 1999):

Pt (a  NDVI  b)  PAR    Wdt
0

(2.1)

where  is the efficiency for converting radiant energy into dry biomass, and W is the water
stress index. Assuming that parameters , PAR, and W are constant for each developmental
stage, the above (Eq. 2.1) makes it possible to introduce the concept of Time-Integrated
NDVI (TINDVI), which is defined as the area under the curve described by NDVI with time(Yang et al., 1998a), as follows:
P = m  TINDVI + n(2.2)

The above (Eq. 2.2) indicates that dry biomass is linearly related to TINDVI, and that crop
growth rate CGR (g/m2/day) can be related to NDVI, as discussed on the basis ofexperimental field data (Calera et al., 2004). The dry biomass production P (g/m2) simulated
by NICE-DRY was compared with NDVI and TINDVI for winter wheat in the 1986-1987
growing season, and for summer maize in the 1987 growing season in the downstream area of
the Yellow River (R–6 in Table 2.1) (Fig. 2.2). It can be seen that the simulated dry biomass,
which agrees well with the observed value from previous field experiments (Nakayama et al.,2006), has a linear relationship to the TINDVI, with a high correlation (r2 is more than 0.950)(Fig. 2.2b) and with similar coefficients to those reported by Calera et al. (2004). The figure
also implies that the maximum growth rate generally occurs when the NDVI reaches a plateau
stage (Fig. 2.2a). These results mean that Eq. (2.2) is useful for estimating the potential rate ofprimary production in the basin, and also indicates that the simulated evapotranspiration isindirectly affected by the hydrologic change in NICE.

- 20 - 20 -

CGER-I114-2014, CGER/NIES

Fig. 2.2
2.2 Comparison
Comparison of dry biomass simulated by NICE-DRY (open circle) with: (a) NDVI;
Fig.
and (b) Time-Integrated NDVI (TINDVI), for winter wheat in the 1986-1987 growing
season (top) and summer maize in the 1987 growing season (bottom) (filled triangle), in
the downstream area of the Yellow River (R–6 in Table 2.1).

2.4 Input Data and Boundary Conditions for Simulation
2.4.1 Meteorological Forcing Data
The simulation period used in this study was between 1987 and 1988, and at that time,
MODIS data were too new to be applicable. Therefore, six-hour reanalyzed data for
downward short- and long-wave radiation, precipitation, atmospheric pressure, airtemperature, air humidity, wind speed at a reference level, fraction of photosynthetically
active radiation (FPAR) and LAI, were input into the model after interpolation of theInternational Satellite Land Surface Climatology Project (ISLSCP) data with a resolution of
1° × 1° (Sellers et al., 1996) in inverse proportion to the distance back-calculated in each gridcell. Because the ISLSCP precipitation data was the least reliable, and it underestimated theobserved values at peak times, rain gauge daily precipitation data collected at 3,352
meteorological stations throughout the study area were used to correct the this data. The
ISLSCP maximum and minimum temperatures and radiation were previously compared withobserved data and were shown to be relatively reliable (Nakayama et al., 2006).
2.4.2 Vegetation and Geological Properties
Mean elevation of each 10-km grid cell was calculated from the spatial average of a
global digital elevation model (DEM; GTOPO30), with a horizontal grid spacing of 30
arc–seconds (~1 km) (U.S. Geological Survey, 1996). Digital land cover data produced by the- 21 - 21 -

Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

Chinese Academy of Sciences (CAS), based on Landsat TM data from the early 1990s (Liu,
1996), were categorized for the simulation (Fig. 2.1). Forests, grasslands, bushes, and shrubs
cover the mountainous and hilly areas in the upper regions of the basin. Vegetation class andsoil texture were categorized and digitized into 1-km mesh data by using 1:4,000,000 and
1:1,000,000 vegetation and soil maps of China (Chinese Academy of Sciences, 1988, 2003),
and these finer-resolution products are highly correlated with the ISLSCP (Sellers et al.,1996). Approximately 50 vegetation and soil parameters were calculated in each cell. The
major parameters included: vegetation cover, green fraction, albedo, surface roughness lengthand zero displacement height, soil conductivity, and soil water potential at saturation. In
addition, several parameters were also included for stomatal resistance, as these are associatedwith environmental factors. From the soil texture and soil organic matter input, the model
calculated the hydrological parameters of the soil layers, such as the water saturation content,
the lowest water content required for crop growth, and the upper water content after freedrainage and saturated water conductivity.
The geology of the basin encompasses a broad range of tectonic series, ranging fromancient metamorphic rocks to modern fluvial–lacustrine sediments (Huang et al., 1992).
Schist, gneiss, marble, magmatite, and metamorphosed volcanic and detrital rocks of thebasement are exposed sporadically in the north-west and south of the basin. Carbonate(Cambrian-Ordovician), coal series (Carboniferous-Permian and Jurassic), and detrital rocks(Triassic-Cretaceous) from the Palaeozoic to the Mesozoic eras occur in the highlands and indeeply cut valleys along the river’s course. Quaternary loess deposits cover an area of
300,000 km2 in the middle section (approximately 40% of the total basin area), with athickness of up to 200 m (Huang et al., 1992). In the downstream area, the river drains the
Quaternary alluvial plains (principally the NCP), which are composed of thick lacustrine anddelta deposits at depths of 100 m to 500 m. These geological structures were determined fromthe Geological Atlas of China (2002), which includes core–sampling data at some points. The
geological structures can be divided into four types, based on parameters about their hydraulicconductivity, specific storage of porous materials, and specific yield, as described by
Nakayama et al. (2006).
2.4.3 Irrigation and Urban Water Use

The irrigation area was calculated from GIS data based on Landsat TM data from theearly 1990s (Liu, 1996), and the calculated value agrees well with the previous results fromthat period (Yang et al., 2004) (r2 = 0.996, BIAS = 1.4%, RMSE = 8.2  104 ha, RRMSE =0.053, MSSS = 0.995) (Table 2.2), where BIAS is the mean bias, RMSE is the
root-mean-square error, RRMSE is the relative root-mean-square error, and MSSS is themean square skill score (Tang et al., 2007). Most of the irrigated fields are distributed in the
middle and lower regions of the Yellow River mainstream and in the NCP (Fig. 2.3). The
agricultural areas in the upper regions and on the Erdos Plateau are dominated by dryland
fields, and the large Qingtongxia and Hetao irrigation zones have been described above.
Spring/winter wheat was predominant in the upper and middle areas of the arid and semi-arid
regions, and double cropping of winter wheat and summer maize was usually practiced in the
middle and downstream areas, and in the relatively warm and humid environment of the NCP(Wang et al., 2001b; Liu et al., 2002; Fang et al., 2006; Nakayama et al., 2006; Tao et al.,2006).

- 22 - 22 -

CGER-I114-2014, CGER/NIES

Table 2.2 Comparison of irrigation areas: Simulated conditions and conditions from previous
research.
research.

ReachesaaIrrigation area ( 104 ha)
GIS database(Liu, 1996)

Previous research(Yang et al., 2004)

Above LZ
LZ – TDG
TDG – LMLM – SMX
SMX – HYK
Below HYK

46.8
342.1
43.0
295.6
59.2
160.7

39.5
344.1
53.4
281.3
60.6
155.0

Sum

947.3

933.9

Abbreviations are as follows: LZ, Lanzhou; TDG, Toudaoguai; LM, Longmen; SMX, Sanmenxia;

HYK, Huayuankou.

Fig. 2.3
2.3 Crop
Crop types in the agricultural areas. Irrigation areas are also superimposed on the
Fig.
figure. Irrigated fields cover most of the NCP and are used for double cropping of winterwheat and summer maize, in addition to three large irrigation zones.

- 23 - 23 -Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

More than 90% of total water extracted was used for irrigation in the Yellow River Basin,
but a low proportion of this was used in the NCP (Liu and Xia, 2004; Cai, 2006). The
irrigation water use was automatically simulated by NICE-DRY, and the average water use in
1987 in main cities in the Yellow River Basin and the NCP (Hebei Department of Water
Conservancy, 1987; Yellow River Conservancy Commission, 2002) was input directly into
the model. It is evident that the water demand of municipal and industrial sectors has been
growing dramatically yearly, in line with the social economic development of the NCP (Guoand Cai, 2000), and this has greatly affected the hydrologic cycle and cone depressions ofgroundwater occurring around the bigger cities (Nakayama, 2011b; Nakayama et al., 2006).
However, the reuse of irrigation return flow is common in the basin. In the 1990s, theamount of return flow was as much as 35% of the withdrawal in the upper area, 25% in the
middle area, but close to 0% in the downstream area (Chen et al., 2003a; Cai and Rosegrant,
2004). There are two large irrigation zones in the upper area: Qingtongxia in Ningxia Hui,and Hetao in Inner Mongolia, (covering areas of 5,228 km2 and 11,900 km2), and a large zone
in the downstream area: Weisan in Shandong Province, (covering an area of 3,600 km2),
where there is a severe impact on the water quality due to salt leaching from the soil, due tothe semi-arid climate. The Hetao and Weisan irrigation zones are dominated by cultivatedfields, but the Qingtongxia irrigation zone is partially covered by paddy fields (Fig. 2.1). The
return flows at Qingtongxia and Hetao are 59% and 25% of withdrawal, whereas that of
Weisan is close to 0%, because the river bed is above the level of the plain (Chen et al.,2003a; Cai and Rosegrant, 2004). This information was input into NICE-DRY.
2.4.4 Boundary Conditions and Running the Simulation
A reflecting condition on the hydraulic head was used in the upstream boundaries, under
the assumption that there is no inflow from the mountains in the opposite direction(Nakayama and Watanabe, 2004). At the eastern sea boundary (Bohai Sea), a constant head
was set at 0 m. The hydraulic head values parallel to the observed ground level were input asinitial conditions for the groundwater sub-model. As initial conditions, the ratios of river andaquifer irrigation were set at the same constant values as in section 2.3.2. Digital river
networks produced by CAS (1982) from 1:50,000 and 1:100,000 topographic maps covering
the basin were used. In the 1,747 river cells (including the Yellow River, the Tao, Datong,Wei, Jing, Beiluo, Wuding, Fen, and Qin tributaries, and the Hai River (Fig. 2.1)), inflows
into, or outflows from, the mean elevation of river beds (Yellow River Conservancy
Commission, 1987-1988) were dependent on the difference between the hydraulic heads forthe groundwater and the river.
The simulation area covered 3,000 km by 1,000 km in Albers (WGS 1984) coordinates,
covering almost the entire Yellow River Basin and the NCP. The area was discretized into agrid of 300  100 blocks with a grid spacing of 10 km in the horizontal direction, and into 20
layers with a weighting factor of 1.1 (finer in the upper layers and coarser in the lower layers)
in the vertical direction (Nakayama and Watanabe, 2008b; Nakayama et al., 2006). The upper
layer was set at a depth of 2 m, and the 20th layer was defined as an elevation of -500 m fromthe sea surface. Simulations were performed with a time step of 6 hours for 2 years, from 1
January 1987 to 31 December 1988, after 6 months of warm-up period until equilibrium. The
author first calibrated the simulated values including irrigation water use against previous
results in 1987, and then validated the values in 1988. Previously observed data of riverdischarge (9 points; Yellow River Conservancy Commission, 1987-1988), soil moisture (7points of the Global Soil Moisture Data Bank; Entin et al., 2000; Robock et al., 2000), and- 24 - 24 -

CGER-I114-2014, CGER/NIES

groundwater level (26 points; China Institute for Geo-Environmental Monitoring, 2003) werealso used for verification of the model (Fig. 2.1, Table 2.1), in addition to values published in
the literature (Clapp and Hornberger, 1978; Rawls et al., 1982). Furthermore, two irrigation
supply scenarios were conducted using the same set of parameters, in order to evaluate how
irrigation affects the hydrologic cycle in the basin. The first is the scenario analysis of aconversion from irrigated to an unirrigated run, and the second scenario is from dryland toirrigated fields.
2.5 Results and Discussion
2.5.1 Verification of Hydrologic Cycle
The irrigation water use simulated in 1987 was firstly calibrated against previous results(Cai and Rosegrant, 2004; Liu and Xia, 2004; Yang et al., 2004; Cai, 2006), showing a close
agreement with the results of Cai and Rosegrant (2004) (r2 = 0.951, BIAS = -3.0%, RMSE =2.7  109 m3, RRMSE = 0.45, MSSS = 0.598). The simulated value in 1988 was thenvalidated with previous research (Table 2.3), and this indicated that there was reasonableagreement with each other, and that irrigation water use is higher in: the large irrigation zones(LZ-TDG); along the Wei and Fen rivers (LM-SMX) in the middle area; and in thedownstream area (below HYK). The results also show a high correlation between irrigation
area and water use: r2 = 0.986 (Chen et al., 2003a) and r2 = 0.826 (Liu and Xia, 2004). The
actual ET simulated by NICE-DRY reproduces the general trend of two years reasonably well.
In particular, it reproduced the drier condition in 1987, as estimated by integrated AVHRRNDVI data (Sun et al., 2004), and this achievement could support the predictive skill of
NICE-DRY (Fig. 2.4). Although there are some discrepancies, particularly for the lowest ET
area (EP < 200 mm/year), (mainly because of the banded color figures), the simulated result
adequately reproduces the characteristics of a lowest value downstream of the middle area andon the Erdos Plateau (less than 200-300 mm per year except in the irrigated area, where there
is desert vegetation and the soil consists mostly of sand), and reproduces the value gradually
increasing towards the south-east. The simulated result also indicates that this spatial
heterogeneity is related to human intervention and the resultant water stress by spring/wintercultivation in the upper/middle areas (Chen et al., 2003a; Tao et al., 2006), and winter wheat
and summer maize cultivations in the middle/downstream (including the Wei and Fen
tributaries) and the NCP (Wang et al., 2001b; Liu et al., 2002; Nakayama et al., 2006).
Although the satellite-derived data are effective for understanding the spatial distributionof the actual ET, there are some inefficiencies in terms of the underestimation in sparsely
vegetated regions (Inner Mongolia and Shaanxi Province) and the overestimation in densely
vegetated or irrigated regions (source area and Henan Province), as suggested by previous
research (Sun et al., 2004; Zhou et al., 2007). The simulation overcomes and improves these
inefficiencies, mainly due to the inclusion of drought impact within the model.

- 25 - 25 -Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

Table 2.3 Validation of amount of irrigation water use simulated by NICE-DRY with values
fromprevious
previousresearch.
research.
fromReachesa

Irrigation water use ( 109 m3)
Yang et al.
2004(1990s)b

Simulatedvalue(1988)b

Cai andRosegrant
2004 (2000)b

Above LZ
LZ – TDG
TDG – LMLM – SMX
SMX – HYK
Below HYK

1.5
6.8
1.1
10.4
2.0
8.4

2.9
12.2
1.0
7.3
2.4
10.6

10.8

9.5

11.2

Sum

30.2

36.4

30.0

28.5

28.4

a

Liu and Xia
2004(1990s)b
13.2

Cai 2006(1988–1992)b
12.4

18.9
6.0

4.8

Abbreviations are as follows: LZ, Lanzhou; TDG, Toudaoguai; LM, Longmen; SMX, Sanmenxia;

HYK, Huayuankou.
bValue in parenthesis shows the target year in the simulation and in literature.

Fig. 2.4

Verification of annual-averaged spatial distribution of evapotranspiration in 1987 and1988:
1988: (a)(a) previous research estimated by NDVI; (b) simulated result.
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CGER-I114-2014, CGER/NIES

A comparison with a previous study based on the Penman-Monteith method and the crop
coefficient (Fang et al., 2006), shows that the model is able to reasonably simulate the spatial
distribution of irrigation water use (Fig. 2.5a), not only in reach level (Table 2.3) but also in
spatial distribution. Although the model was in general agreement with previous research, thesimulated result shows an overestimation around the three large irrigation zones and anunderestimation around unirrigated fields, which could be attributed to the improvements
facilitated by the model due to the inclusion of the effect of drought impact and water stress.
In a previous simulation, the NICE-AGR was also able to adequately reproduce temporalvariations in irrigation water use and the spatial distribution of crop water use in the NCP(Nakayama et al., 2006). This indicates that the water requirement estimated by NICE-DRY
is valuable in explaining the general hydrologic cycle, and implies that there may be a greateruse of irrigation water in the three large irrigation zones and in the NCP than in other areas(Chen et al., 2003a; Cai and Rosegrant, 2004). Simulated ratios of river to total irrigation (=river + aquifer) show great variation within the basin (Fig. 2.5b); more water is drawn fromthe river for irrigation in the upper area, and most of the irrigation in the downstream area
depends on groundwater, particularly in the NCP (Nakayama et al., 2006). This difference inusage has caused large hydrologic changes in the basin.

Fig. 2.5

Simulated result of: (a) irrigation water use in 1988; (b) ratios of river to total
irrigation (= river + aquifer).

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Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

The simulated groundwater levels and soil moisture contents were calibrated andvalidated against observed data (Entin et al., 2000; Robock et al., 2000; China Institute for
Geo-Environmental Monitoring, 2003), as shown in Fig. 1 and Table 1. The simulation
reproduced the general distribution well (BIAS = -21.2%, RMSE = 5.6 m, RRMSE = -0.468,
MSSS = 0.356), but the correlation of groundwater level relative to the surface (r2 = 0.401)
was not as good as that of the absolute groundwater level (r2 = 0.983), and the simulated
value showed a tendency to overestimate the observed value in the calibration procedure for1987 (Fig. 2.6a). This disagreement was due to the difference in surface elevation on the
point-scale and mesh-scale (scale dependence), and the resolution of the groundwater flow
model (changes in elevation from 0 m to 3000 m - 4000 m in the basin). Because thesimulated level is the hydraulic head in an aquifer, it can have a larger value than that of theland surface, particularly for a grid cell near, or on, the river. Another reason for thedisagreement could be that the irrigation use simulated by the model is underestimatedbecause automatic irrigation which supplied the water requirement for crops in order tosatisfy the observed soil moisture, river discharge, groundwater level, LAI, evapotranspiration,and crop coefficient, was theoretically pumped up from the river or the aquifer in the model.
In reality, it is possible that farmers use a greater amount of irrigation water, although there is
not enough statistical or observed data to support this assumption. The simulated water levelin the validation for 1988 decreases rapidly around the source area, indicating that there aremany springs in this region (Fig. 2.6b), and is very low in the downstream area (below sea
level in some regions) because of the low elevation and overexploitation, as is the case in the
NCP (Nakayama, 2011b; Nakayama et al., 2006). The soil moisture is higher in the sourcearea and in the paddy-dominated Qingtongxia Irrigation Zone (data not shown),corresponding closely with the distribution of the groundwater level.

Fig. 2.6

Simulated annual average groundwater level in the basin: (a) calibration of simulated
value relative to the surface; (b) simulated groundwater level in 1988. In Fig. 2.6a, r2
shows the correlation between observed and simulated values.
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CGER-I114-2014, CGER/NIES

2.5.2 Impact of Irrigation on River Discharge and Groundwater Level

The scenario analysis run of a conversion from unirrigated to irrigated land predicted thehydrologic changes (Fig. 2.7). However, the prediction for land without irrigation (solid line)
overestimates the observed river discharge (Fig. 2.7 a-c); this effect is more prominent in the
middle and downstream areas, as supported by reports that the difference between natural andobserved runoff is larger downstream (Ren et al., 2002; Fu et al., 2004; Liu and Zheng, 2004).
The difference between simulations with and without irrigation strongly supports previous
studies, indicating that the influence of human interventions on river runoff has increased
downstream over the last five decades (Chen et al., 2003a; Liu and Xia, 2004; Yang et al.,
2004; Cai, 2006; Tang et al., 2007) (Table 2.3), and is also represented by the decline of water
renewal times (Liu et al., 2003) and water resource renewability (Xia et al., 2004). Althoughboth r2 and the Nash-Sutcliffe criterion (NS; Nash and Sutcliffe, 1970) have relatively low
values across the basin (max: r2 = 0.447, NS = 0.452), the simulated results with irrigation(bold line) reproduce these characteristics well, and the statistics for MV (mean value), SD(standard deviation), and CV (coefficient of variation; CV = SD/MV) generally agree wellwith the observed values, as also supported by the better reproduction of other components of
the hydrologic cycle, such as annual ET (Fig. 2.4) (data not shown in the case without
irrigation) and irrigation water use (Fig. 2.5a, Table 2.3). The simulated result with irrigation
also reproduces the observed data for a low flow, and sometimes drying-up (Zhang et al.,
1990; Yang et al., 1998b; Ren et al., 2002), being attributable to the inclusion of the dynamic
wave effect in NICE-DRY, which was not reproducible by other NICE series prior to
NICE-DRY. Furthermore, the model improves the reproduction of river discharge in the basin
in comparison with previous research (Yang and Musiake, 2003), where the ratio of absolute
error to the mean was more than 60% at Huayuankou hydrological station, one of the worst
such cases on a major river in Asia. The major reason for this disagreement is that
NICE-DRY general includes an artificial water regulation, such as reservoirs, water intake,and diversions, in addition to the extreme annual variation in flood seasons (Nakayama and
Watanabe, 2008b). The scenario analysis also predicted the groundwater level change andindicates that the effect of groundwater irrigation is predominant in the middle anddownstream areas (Fig. 2.7d) where surface water is seriously limited, as shown in Fig. 2.5b
and described in section 2.3.2 (Yellow River Conservancy Commission, 2002), mainly
because of the intensified water-use conflicts between the upstream and downstream areas,
and between agriculture, municipal, and industrial sectors (Brown and Halweil, 1998;
Shimada, 2000; Chen et al., 2003b; Nakayama et al., 2006).

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Chapter 2

Fig. 2.7

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

Scenario analysis run of the conversion from unirrigated to irrigated land; (a-c)
prediction of river discharge in Yellow River mainstream during 1987-1988; (d)groundwater level change. In Fig. 2.7 a-c, circles show observation data; solid line is thesimulated result without irrigation effect; bold line is the simulated result with irrigation. r2
shows the correlation between the observed and simulated values; NS shows the
Nash-Sutcliffe Criterion. MV, mean value; SD, standard deviation; CV, coefficient ofvariation (= SD/MV).
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CGER-I114-2014, CGER/NIES

2.5.3 Relationship between Hydrologic Change and Crop Productivity

The second scenario analysis of a conversion from dryland to irrigated land, assumingthat the ratios of river and aquifer irrigation are the same as those described in the above
sections, predicted a large decrease in the river discharge and groundwater level, particularlyin the middle and downstream areas, where most of the dryland fields are mainly distributed
because of the increase in ET (Fig. 2.8). The smaller change in groundwater level in the upperarea was largely attributed to its unsuitability for crop production and the higher dependencyof irrigation on surface water (Fig. 2.5b), as described previously (Yellow River Conservancy
Commission, 2002). The river discharge decreased mainly in the winter through wheat
cultivation, but increased slightly at times during the flood season, indicating that the
precipitation in irrigated fields can respond quickly to flood drainage. Some of these areaslack fertility, and it is therefore impossible to expect a substantial increase in crop
productivity. However, it is important to predict the relationship between the hydrologic cycle
and crop productivity because farmers are limited by water availability more than by thefertility of the land and there is a possibility that an increased quantity of water could beavailable through water transfer in the future, which would greatly affect the change in water
renewal times (Liu et al., 2003) and water resource renewability (Xia et al., 2004) in thebasin.

Fig. 2.8

Prediction of hydrologic changes on the scenario of a conversion from dryland to
Prediction
irrigated fields: (a) river discharge in the upper-middle area (R–3; Qingtongxia) andthe lower area (R–6; Huayuankou) in Table 2.1; (b) change in groundwater level. In
Fig. 2.8a, the dotted line indicates the simulated result in Fig. 2.3, and the solid line
indicates the prediction that all the agricultural areas will be changed into irrigated fields.

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Chapter 2

Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

The NDVI in both the irrigated and dryland fields was higher than in natural areas during
the grain filling and harvest seasons. Because the TINDVI is linearly related to the dry
biomass, the spatial distribution of this value is a good indicator of the rate of crop production.
Fig. 2.9 shows the temporal trend of the mean TINDVI during 1982 and 1999 in agricultural
fields, calculated by ordinary least-squares regression. Because the original NDVI data in
1994 contained a great deal of noise and low accuracy, this data was omitted from the
TINDVI analysis. The spatially heterogeneous TINDVI gradient corresponds well to theregional distribution of water resources. The increasing trend in the TINDVI in irrigated fields
is larger at Juancheng (R–8) than at Huayuankou (R–6), because of the higher use of
irrigation water (Fig. 2.5a) and a greater amount of evapotranspiration (Fig. 2.4) from thelarge irrigation zone (Weisan) in the downstream area (Fig. 2.9a). The spatial pattern of themean TINDVI gradient in agricultural fields shows a generally increasing tendency,
particularly in the downstream area and the NCP (Fig. 2.9b). This is caused mainly by anincreasing tendency for winter wheat production in the downstream area (USDA, 1994). The
decrease in the TINDVI gradient near the Bohai Sea and the Taihang Mountains was due toseveral effects, including that of groundwater degradation (Shimada, 2000; Chen et al.,2003b; Nakayama et al., 2006), seawater intrusion (Brown and Halweil, 1998; Shimada,
2000; Nakayama, 2011b), and rapid urbanization in the areas surrounding bigger cities(Kondoh and Oyamada, 2000). The slightly decreasing trend, particularly in the source areaand middle area, also implies environmental deterioration which is mainly due to humanactivities, and to damage attributed to rodents and insects, as reported previously (Wang et al.,2001a; Sato et al., 2008; Tang et al., 2008b), in spite of some improvements in land
management and conservation measures in the middle area (Yang et al., 1998b; Sun et al.,2004). Generally, these results suggest that the increased use of irrigation water is one of thereasons for the increase in crop production (Chen et al., 2003a; Cai and Rosegrant, 2004; Liuand Xia, 2004; Yang et al., 2004; Cai, 2006). The increased use of irrigation water is also
related to the construction of large dams, as reflected clearly in the long-term decrease indischarge (Yang et al., 2004) and the short-term changes in discharge (Fig. 2.7) andgroundwater level (Fig. 2.6).

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CGER-I114-2014, CGER/NIES

Fig. 2.9 TINDVITINDVI over the agricultural fields by NOAA/AVHRR satellite images of agricultural
fields; (a) trend of mean TINDVI in the grain filling season at Huayuankou (R–6) andJuancheng (R–8) in Table 2.1; (b) spatial patterns in mean TINDVI gradient (per year)
during 1982-1999. The solid line in Fig. 2.9a is a least-square regression line estimatedfrom mean TINDVI (circle). The TINDVI gradient shows a generally increasing tendency,
particularly in the downstream area and the NCP.

2.6 Conclusion
This study has addressed the important issue of simulating the rather complex and diverse
water system in the highly cultivated Yellow River Basin, including hydrological processes
such as river dry-up, groundwater deterioration, agricultural/urban water use, dam/canal
effects, and so on. This region is heavily irrigated, and the combination of an increased
demand for food with a declining availability of water is creating substantial pressures. The
author coupled the NICE series with more complex components such as irrigation, urban
water use, and dam/canal systems, in order to simulate the effects of irrigation on thehydrologic cycle in the basin (NICE-DRY). This simulation clarified the relationship between
the drying of rivers, groundwater degradation, seawater intrusion, and ecosystem degradation.
Scenario analyses of the impact of human intervention on hydrologic changes imply the needfor further correct evaluations and appropriate measures against such irrigation.

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Effect of Irrigation on the Hydrologic Cycle in a Semi-Arid Yellow River Basin

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CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20
CGER-I114-2014, CGER/NIES

Chapter 3
Role of Lake Flood Storage Capability
in the Humid Changjiang River Basin- 39 - 39 -

Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

Abstract
China is affected by severe flooding on a near annual basis. This causes considerable
economic loss and serious damage to towns and farms, and affects the middle and lower
regions of the Changjiang (Yangtze) River in particular. In addition to abnormal weather
patterns, previous research concludes that a significant cause of major flood disasters in thebasin are caused by human-induced, artificial effects, such as: (i) extreme deforestation andsoil erosion in the upper reaches; (ii) shrinking lake water volumes and a restriction of theconnection of lakes with the Changjiang River due to lake reclamation which caused a
retardation of water in the middle reaches; and (iii) restriction of the channel capacity
following construction of a levee.
The research in this chapter focuses on the flood storage capability of the Dongting andPoyang Lakes in the middle region of the river, and simulates the water/heat dynamics for the1998 large-scale flood, one of the largest floods in the 20th century, over the entire
Changjiang River Basin. The previous process-based model was expanded for its application
to broader basins (NICE-FLD) in order to evaluate large-scale flooding in the ChangjiangRiver, and used to compare the severe flood-period of 1998 with the usual flood-period of
1987–1988. The model excellently reproduces factors such as the river discharge, soil
moisture, evapotranspiration, and groundwater level. In order to evaluate the role of the flood
storage capability of the lakes, a back-casting simulation was also conducted by recreating theareas of both lakes in the 1950s, and by then inputting meteorological data from the 1998
severe flood. The simulation shows that the peak flow and groundwater level decrease botharound and downstream from the junction, as described qualitatively in previous research.
This indicates that by increasing the lake’s storage capacity the peak value of the lake waterlevel is moderated during flood periods and the groundwater level surrounding the lakes isdecreased. This result is very important for use in minimizing flood damage, and for making
informed decisions relating to sustainable development in the basin.
Keywords: Changjiang River, flood-storage capability, climate change, integrated
catchment-based eco-hydrology model, human activity- 40 - 40 -

CGER-I114-2014, CGER/NIES

3.1 Introduction
China is affected by severe flooding on a near-annual basis, causing considerable
economic loss and serious damage to towns and farms. Large-scale flood events areconcentrated mainly in the lower reaches of the “seven big rivers,” including the Changjiang(Yangtze) River, the Yellow (Huanghe) River, and the Huaihe River (Fig. 3.1). These areas
are home to 50% of China’s population and produce 70% of its industrial and agriculturalvalue (Nakayama and Watanabe, 2008b).
The Changjiang River and the Yellow River originate on the Qinghai Tibet Plateau andflow east for about 6,000 km, to the East China Sea. The Changjiang River is located in thehumid southern regions of China and is the fourth largest source of water supplying theoceans, after the Amazon, Zaire, and Orinoco Rivers. The Changjiang basin is divided into:
the upper region (length: 4300 km, area: 1,000,000 km2) from the headwater to Yichang in
Hubei province; the middle region (length: 950 km, area: 680,000 km2) from Yichang to
Hukou in Jiangxi province; and the lower region (length: 930 km, area: 120,000 km2) from
Hukou to the estuary (Fig. 3.1). The topography of the upper region is hilly with a highriverbed slope. In contrast, the vast plain in the middle and lower regions accounts for morethan half the area of flat land in the whole basin, and there is a difference in altitude of only
about 40 m from Yichang to the estuary, with a bed slope of 0.002%. Consequently, theChangjiang mainstream section is tidal downstream of Datong, which is 624 km upstream ofthe estuary.
Recently, large frequent flooding in the lowland areas of the middle and lower regions of
the Changjiang River Basin has hugely affected human lives, the environment, and theagricultural economy. In addition to abnormal weather events, artificial effects, such as
extreme deforestation and the reclamation of lakes, which has retarded water in the middle
reaches, are considered to be the main cause of such changes (Cheng, 1999; Yin and Li, 2001;
Zhao and Fang, 2004; Zhao et al., 2005). From June to September 1998, the area-averaged
precipitation total was significantly above the usual in the Changjiang River Basin. The mostexcessive rainfall occurred during June and July, when area-averaged totals exceeded 300 mm
and 220 mm, respectively. Overall, the largest anomalies were observed over the eastern
portion of the basin, in the region which includes Nanchang (Fig. 3.1). In this area, a record
amount of rainfall totaling 1000 mm fell during June and July 1998, surpassing the previous
record of approximately 950 mm set in 1954 (Bell et al., 1999; Zhu et al., 2003), although theflood discharge was less than that of 1954. The entire Changjiang River Basin suffered atremendous amount of flooding, but the river stage at peak was higher in the middle reaches.
A lower discharge in 1998 created a higher river stage, and this suggests a channel
aggradation along the lower section of the river in relation to environmental degradation by
artificial effects.
The types of degradation related to flood disasters in the Changjiang River Basin due tohuman intervention are (Yin and Li, 2001): (i) the destruction of vegetation and soil erosion in
the upper reaches, along with rapid urbanization (Piao et al., 2003); (ii) the shrinking of lakewater volumes and the reduced connection of lakes with the Changjiang River due toreclamation; and (iii) a restriction of the channel capacity following the construction of alevee. With reference to point (ii), the Dongting and Poyang Lakes shrank from respective
areas of 4,000 km2 and 5,000 km2 in the 1950s, to 2,600 km2 and 3,200 km2 in the 1990s.
Their capability of storing water decreased from 27 to 17 billion m3 and from 32 to 21 m3,
respectively (Du et al., 2001; Yin and Li, 2001; Shankman and Liang, 2003; Zhao and Fang,
2004; Peng et al., 2005; Zhao et al., 2005). It is therefore considered that there is an urgent- 41 - 41 -

Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basinneed to evaluate the water/heat/nutrient transport processes of the whole basin on a spatial
scale. These processes will be changed by the operation of the Three Gorges Dam (TGD),
which was already built and completed in 2009 (Fig. 3.1). Although the dam scheme will
provide flood control, irrigation water, and the generation of hydroelectric power, etc., it ispossible that changes in the aquatic environment of the Changjiang estuary and the East China
Sea might be brought about by changes in pollutant loads caused by the deposition of large
amounts of sediment carried from the dam in the upper region of the Changjiang River Basinand the artificial control of its discharge volume (Kwai-cheong, 1995; Zhang et al., 1999;
Chen, 2000; Chen et al., 2001). Although this huge construction project runs in opposition to
the current worldwide trend of removing huge dams, it is very important to predict the
response of the whole basin to completion of the artificial environmental modifications, as
described in the following Chapter 4. It will be further effective to quantify the natural forcesthat could reduce flood damage in the basin, by using a process-based model that can simulate
the dynamics of water, heat, and nutrients over various temporal scales. This would then be
related to the effective management of the six flood diversion works along the ChangjiangRiver Basin (Jingjiang, Dongting Lake, Honghu, Wuhan, Poyang Lake, and Huayang). The
appropriate development and use of this model would make it possible to establish amanagement tool that allows sustainable development in the basin.

Fig. 3.1

Location of study area in the Changjiang River Basin. The bold black line shows the
Locationborder of the basin. Plotted in this figure are the validation data for: river discharge (opencircle); soil moisture (open triangle); and groundwater level (green dot). The black
dotted-line is the border of the groundwater change study area around both Dongting andPoyang Lakes in Fig. 3.8.
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CGER-I114-2014, CGER/NIES

Because there have been few studies using numerical models related to changes inwater/heat dynamics resulting from human activity in the entire Changjiang River Basin, andthe role of flood storage capability of lakes in addition to irrigations, it is important toquantify the complex phenomena related to water/heat dynamics. The aim of the researchpresented in this chapter is to simulate the water/heat dynamics for the 1998 large-scale floodin the Changjiang River basin, (one of the largest floods in the 20th century, and the largest
flood since 1954). Using the National Integrated Catchment-based Eco-hydrology (NICE)
model, the author compares the severe flood-period of 1998 with the usual flood-period of
1987-1988. Furthermore, the author evaluates the role of the flood storage capability of lakes
in addition to irrigations in relation to the water/heat budget, and simulates changes inwater/heat dynamics resulting from human activity in order to aid decision-making onsustainable development in the basin.
3.2 Regional Overview
The headwaters of the Changjiang River are in the mountains of Tibet, and the river flows
in a general eastward direction towards the coast (Fig. 3.1). The upper section of the river andits major tributaries flow through narrow and incised river valleys (Geological Atlas of China,2002). The downstream section, below the Three-Gorges, is surrounded by broad alluvial
valleys that support intensive agriculture and are at the center of China’s rice growing region(Chinese Academy of Sciences, 1988; Liu, 1996; Nakayama and Watanabe, 2008b). The
fertile alluvial plains and humid sub-tropical climate of southern China make this region ideal
for cultivation, and the Changjiang River drains much of the humid subtropical region ofsouthern China. This region has short, mild winters, hot summers, and a long growing-season.
The rainy season typically begins in late spring or early summer, when southerly monsoon
winds move warm, humid air masses inland. These air masses encounter cold air from thearid part of central Asia and are lifted along a stationary front causing abundant rainfall. The
discharge and water level of the Changjiang River and its major tributaries increase during thelate spring and summer months. The most severe floods almost always occur during or
following El Nino events which cause more widespread and persistent rains in the region thanduring non-El Nino years (Shankman et al., 2006).

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Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

Fig. 3.2

Flow diagram of NICE-FLD. Bold frames indicate the new developed processes in the
NICE-FLD model.

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CGER-I114-2014, CGER/NIES

3.3 Methods
3.3.1 Coupling of NICE with Rice Model
The author developed the NICE series of process-based catchment models applicable tonatural, agricultural, and urban regions in various catchments and basins (Nakayama, 2008a-c,
2009a, 2010a, 2011a-d, 2012a-d, 2013a; Nakayama and Fujita, 2010; Nakayama andHashimoto, 2011; Nakayama and Shankman, 2013a-b; Nakayama and Watanabe, 2004,
2006a-b, 2008a-c; Nakayama et al., 2006, 2007, 2010, 2012). In the agricultural field, NICE
is coupled with the Decision Support Systems for Agro-technology Transfer (DSSAT)(Ritchie et al., 1998), in which an automatic irrigation mode supplies the crop waterrequirements for cultivated fields (Nakayama et al., 2006; Nakayama, 2011a-b) and paddy
fields (Nakayama and Watanabe, 2008b). The model includes various functions forrepresentative crops such as wheat, maize, soybean, and rice, and automatically simulates the
dynamic growth processes. (Details are described in Chapter 1 and previous related papers.)
Paddy fields are widely distributed in areas along the Changjiang mainstream, and coverabout 50% of the area downstream of Wuhan. According to the Vegetation Map of China(1:4,000,000) developed by the Institute of Geographical Science and Natural Resources
Research (IGSRR), Chinese Academy of Sciences (CAS) (1982), single cropping of rice waspracticed in the following areas: the Sichuan Basin, the upper region of the Hanjiang
catchment, and the area along the lower reaches of the Changjiang mainstream; and double
cropping was usually practiced in all other regions, including the catchment areas of Dongting
and Poyang lakes. Each crop was automatically simulated using the agricultural submodel ofthe NICE, and details are given in Nakayama et al. (2006).
3.3.2 Modeling of Flood Storage Ability of Dongting and Poyang Lakes (NICE-FLD)
Dongting and Poyang lakes drain into the Changjiang through narrow outlets (Fig. 3.1).
In order to evaluate the role of the flood storage capability of lakes it is useful tosimultaneously simulate the lake level with the river discharge from channels that connect themainstream with the two lakes (Fig. 3.2), and to execute this the original NICE was combinedwith a lake model (Nakayama and Watanabe, 2008a) and a stream junction model, in thesame way as the HEC-RAS model (U.S. Army Corps of Engineers, 2006) in NICE-FLD(Nakayama and Watanabe, 2008b). Seepage rates computed for each interface between lake
cells and horizontally or vertically adjacent aquifer cells are added to the appropriate elementsin the RHS (right-hand side residual) and HCOF (coefficient) matrices in the equation forgroundwater flow as follows (McDonald and Harbaugh, 1988; Nakayama and Watanabe,
2005, 2008a):
RHSijk  RHS ijk  cm hln1for ha  hbot in vertical column

 RHSijk  cm hln1  hbot
 RHSijk  0

 for h  h at horizontal interface
a

bot

(3.1)

for ha  hbot at vertical interfaceHCOFijk HCOFijk  c m

for ha  hbot

 HCOFijk  0

for ha  hbot
- 45 - 45 -

(3.2)

Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

where ijk designates the particular matrix term; m denotes a particular cell interface; cm (m2/s)
is the conductance across that interface; hbot (m) is the lakebed elevation; and n–1 denotes theprevious time step. For a transient simulation of the lake, the lake grid is set surrounded by
the aquifer, and the lake level is simulated and updated at each time step, t (s), by using thewater balance Eq. (3.3) (Merritt and Konikow, 2000; Nakayama and Watanabe, 2005, 2008a):

hlnew  hlold  tp  e  rnf  w  ql  Qsi  Qso
As

(3.3)

where ql (m3/s) is the seepage between the lake and the aquifer expressed by the application ofDarcy’s Law; hlnew and hlold (m) are the lake levels at the new time step and the previous
time step; p (m3/s) is the rate of precipitation on the lake during the time step; e (m3/s) is therate of evaporation from the lake surface during the time step; rnf (m3/s) is the rate of surface
runoff to the lake during the time step; w (m3/s) is the rate of water withdrawal from the lake
during the time step (a negative value indicates augmentation); Qsi (m3/s) is the rate of inflow
from streams during the time step; Qso (m3/s) is the rate of outflow to streams during the time
step; and As (m2) is the surface area of the lake at the beginning of the time step. Aftersufficient iterations of the groundwater submodel have been performed for the aquifer heads
to converge to a solution, the lake level ( hlnew ) is recomputed one more time for each lakeusing Eq. (3.3).
The above-mentioned Qso (m3/s) is simultaneously solved with the stream junction modelin the same way as within the HEC-RAS model (U.S. Army Corps of Engineers, 2006) (Fig.
3.2). This energy-based method solves river discharge across the junction by performing
standard step backwater and forewater calculations with the continuity equation and with
one-dimensional energy equations. The continuity equation of stream flow considers the flow
direction of the junction connecting the mainstream with two lakes (Fig. 3.1). Because moststreams have a highly subcritical flow, the influence of the tributary flow angle is often
insignificant, and the author therefore did not account for the angle of the junction flows andalso assumed that the expansion or contraction loss coefficient was constant (0.3 or 0.1) in
previous research (U.S. Army Corps of Engineers, 2006). The conversion from lake storage tolake level is based on the relationship between the average water level and the volume ofstorage in each lake.
For Dongting Lake, this relationship is given by simple static planar analysis of theincrease in the volume and surface area of water according to the increase in the water level,by using a digital elevation model (DEM) of the lake bed with a spatial resolution of 50 m
developed by IGSRR based on a topographic map with a scale of 1:25,000 developed by the
Nanjing Geology Institute, CAS (1988). This relationship is very similar to the previous result
for Dongting Lake (Peng et al., 2005). For Poyang Lake, the relationship is also given by asimilar DEM, based on a topographic map developed by the local government of Jiangxi
province (1983), and from the same static analysis as that used for Dongting Lake. The
simultaneous equations incorporating the continuity equation and energy equations weresolved using the Newton method. The author obtained the simulated values of riverdischarges including junctions, soil moistures, groundwater levels, lake water levels, lake
seepages, and other factors by using the NICE-FLD model.

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CGER-I114-2014, CGER/NIES

3.4 Input Data and Boundary Conditions for Simulation
3.4.1 Meteorological Forcing Data
Six-hour reanalyzed data for downward short- and long-wave radiation, precipitation,
atmospheric pressure, air temperature, air humidity, wind speed at a reference level, FPAR
(Fraction of Photosynthetically Active Radiation), and LAI (Leaf Area Index) were input into
the model after interpolation of ISLSCP (International Satellite Land Surface Climatology
Project) for 1987-1988 and ECMWF (European Centre for Medium-Range Weather
Forecasts) for 1998-1999 simulations in inverse proportion to the distance back-calculated ineach grid cell. Because the ECMWF precipitation data had the least reliability andunderestimated the observed values at the peak time, rain gauge daily precipitation datacollected in the Changjian River basin at 4,783 stations for 1987-1988, and 919 stations for
1998-1999, (operated by the China Meteorological Bureau and the Changjiang Water
Resources Commission (CWRC)), were also assimilated to create grid data. Because theforcing precipitation data created in this study agrees closely with the GPCC (Bell et al.,1999) during June and July 1998, these data were used in the simulation. Details are describedin Nakayama and Watanabe (2008b) and Nakayama and Shankman (2013a).
3.4.2 Vegetation and Geological Properties
The mean elevation of each 10-km grid cell was calculated using the spatial average of a
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
Changjiang River basin (Fig. 3.1). Vegetation class and soil texture were categorized anddigitized into 1-km mesh data using the Vegetation and Soil Maps of China (1:4,000,000)(Chinese Academy of Sciences, 1988). These finer-resolution data were more accurate thanthe ISLSCP data with a resolution of 1°  1° (Sellers et al., 1996) (Fig. 3.3). Using theserefined data, approximately 50 vegetation and soil parameters were calculated in each cell.
The major parameters included vegetation cover, green fraction, albedo, surface roughness
length and zero displacement height, soil conductivity and soil water potential at saturation,and some parameters of stomatal resistance that relate to environmental factors. Hydrologic
parameters of the soil layers, such as saturation water content, lowest water content for crop
growth, upper water content after free drainage and saturated water conductivity, were
calculated from the input soil texture and soil organic matter by the model itself.

- 47 - 47 -Chapter
Chapter33 Role
RoleofofLake
LakeFlood
FloodStorage
StorageCapability
Capabilityininthe
theHumid
HumidChangjiang
ChangjiangRiver
RiverBasin
Basin

Fig.
Fig. 3.3
3.3 Soil
Soil texture
texture from:from: (a)(a) ISLSCPISLSCP data
data with
with aa resolution
resolution ofof 1°
1°  1°;1°; andand (b)(b) categorizedcategorizedandand digitizeddigitized data
data with
with 1-km
1-km meshmesh using
using thethe Vegetation
Vegetation andand Soil
Soil Maps
Maps ofof China
China(1:4,000,000)ininthisthisstudy.
study.
(1:4,000,000)

DigitalDigital land
land cover
cover data
data produced
produced byby thethe Institute
Institute ofof Remote
Remote Sensing
Sensing Applications
Applications (IRSA)(IRSA)
atat thethe Chinese
Chinese Academy
Academy ofof Sciences
Sciences (CAS),(CAS), based
based onon Landsat
Landsat TM
TM datadata fromfrom thethe early
early 1990s
1990s
(Liu,
(Liu, 1996),
1996), were
were categorizedcategorized into
into 10-km
10-km meshmesh data
data forfor thethe simulation.
simulation. The
The vegetation
vegetation ofof thethemountainous
mountainous andand hilly
hilly areas
areas inin thethe upper
upper region
region ofof thethe basin
basin consists
consists ofof forests,
forests, grasses,
grasses,bushes,bushes, andand shrubs.
shrubs. Paddy
Paddy andand cultivatedcultivated fields
fields areare widely
widely distributed
distributed inin thethe Sichuan
Sichuan Basin,
Basin,
andand inin thethe middle
middle andand lower
lower reaches
reaches ofof thethe Changjiang
Changjiang mainstream
mainstream area.
area. From
From thethe 1990s
1990s totothethe 2000s,
2000s, human
human activity
activity inin thethe basin
basin led
led toto various
various types
types ofof degradation
degradation (Fig.
(Fig. 3.4
3.4 andand Table
Table
3.1).
3.1). Deforestation
Deforestation (Yin
(Yin andand Li,Li, 2001),
2001), reclamation
reclamation ofof lakes
lakes (Du(Du etet alal.,., 2001;
2001; Zhao
Zhao andand Fang,
Fang,al.,., 2005),
2005), andand rapid
rapid urbanization
urbanization (Piao(Piao etet alal.,., 2003)
2003) have
have also
also occurredoccurred inin thethe2004;
2004; Zhao
Zhao etet al
last
last decade,decade, particularly
particularly aroundaround thethe lakes
lakes inin thethe middle
middle andand lower
lower reaches
reaches ofof thethe basin.
basin.
ExtremeExtreme deforestation
deforestation andand rapid
rapid urbanization
urbanization predominate
predominate inin thethe upper
upper reaches
reaches (Yin
(Yin andand Li,Li,
2001;
2001;Piao
Piaoetetalal.,.,2003),
2003),and
andreclamation
reclamationof
oflakeslakesandandrapid
rapidurbanization
urbanizationhave
havealso
alsooccurredoccurredininthethe middle
middle andand lower
lower reaches
reaches (Du(Du etet alal.,., 2001;
2001; Piao
Piao etet alal.,., 2003;
2003; Zhao
Zhao andand Fang,Fang, 2004;
2004; Zhao
Zhao
etet alal.,., 2005),
2005), leading
leading toto depletion
depletion ofof lakelake water
water volumes
volumes andand aa reduction
reduction inin theirtheir connection
connection
withwiththetheChangjiang
ChangjiangRiver,River,due
dueto
toaaretardation
retardationof
ofwaterwaterininthe
thebasin.
basin.

--48
48-- 48 -CGER-I114-2014,
CGER-I114-2014,CGER/NIES
CGER/NIES

Fig.
Fig. 3.4
3.4 LandLand cover
cover aroundaround thethe Dongting
Dongting lake
lake in:in: (a)(a) 1990s;
1990s; andand (b)(b) 2000s.
2000s. Land
Land cover
cover changes
changes(shrinking(shrinking ofof thethe lake)lake) areare predominantpredominant aroundaround thethe dotted-circle
dotted-circle area.
area.

-- 49
49 -- 49 -Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

Table 3.1 Land cover change in the 1990s and 2000s in the Changjiang River Basin.
Upper region
1990

2000

Middle region
2000/
1990

1990

2000

Lower region
2000/
1990

2000

2000/
1990

Paddy

5.5E+10 5.5E+10 100.5% 1.6E+11 1.6E+11

6.9E+10 6.9E+10

99.6%

Farm

1.4E+11 1.5E+11 100.9% 6.1E+10 6.1E+10 100.2% 8.0E+09 8.0E+09

99.4%

Forest1.5E+11 1.5E+11

99.4%

2.6E+11 2.6E+11

99.9%

1990

99.9%

2.0E+10 2.0E+10 100.0%

Grassland 6.1E+11 6.1E+11 100.1% 1.8E+11 1.8E+11 100.1% 8.9E+09 8.9E+09 100.0%
River &
Lake
Glacier
area
Urban
area
Sandy
land
Others1.1E+10 1.1E+10 102.7% 2.3E+10 2.3E+10 100.2% 9.4E+09 9.4E+09
1.9E+09 1.9E+09 100.0%

-

-

-

-

-

99.9%
-

1.9E+09 2.2E+09 115.7% 3.6E+09 3.7E+09 104.0% 3.2E+09 3.5E+09 108.7%
1.4E+10 1.4E+10 100.9%

-

-

-

-

-

2.9E+10 2.9E+10 100.2% 1.8E+09 1.8E+09 101.9% 5.6E+07 5.5E+07

98.1%

In this study, using the NICE-FLD the author evaluated the effect of the shrinking
volumes of lake water on the hydrologic budget. In the back-casting simulation, both
Dongting and Poyang Lakes were recovered to their respective areas during the 1950s (areas;
4,000 km2 and 5,000 km2), in accordance with previous research (Du et al., 2001; Yin and Li,
2001; Shankman and Liang, 2003; Zhao and Fang, 2004; Peng et al., 2005; Zhao et al., 2005)
and meteorological data of the 1998 severe flood was input.The geological structures of theplateau were ascertained by scanning and digitizing geological material (Geological Atlas ofChina, 2002), which also included core-sampling data at some points. The geology of theupper region is composed of metamorphic rock, alpine meadows, and sub-alpine soils (Qu etal., 1993; Geological Atlas of China, 2002). The eastern border of the plateau is covered witha brown soil. The plateau has an elevation of 4,000-5,000 m at its highest point, and on theeastern side the elevation falls to 1,500-2,500 m, except in the Sichuan Basin. The dominantrock type in the north of the plateau is of detrital sedimentary material, basic and ultra-basic
rocks in the southwest, limestone in the mid-south, and outcrops of granite and detrital
sedimentary rocks in the southeast. The soils of the plateau range from brown to yellow to red.
The middle and lower reaches of the basin are dominated by paddy soils (Hydrgric
Anthrosols) and loose sediments such as yellow earth (Ferralic Cambisol), red earth (Haplic
Acrisol), and yellow-brown earth (Haplic Luvisol). The author was able to divide thegeological structure into four types with a resolution of 10 km in the horizontal direction and
20 layers in the vertical direction, on the basis of hydraulic conductivity (Kh and Kv), thespecific storage of porous material (Ss) and specific yield (Sy), in the same way as Nakayama
et al. (2006).
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3.4.3 Boundary Conditions and Running the Simulation
The reflecting condition on the hydraulic head in the upper sections of the drainage basin
was used with the assumption that there was no inflow from the mountains in the opposite
direction (Nakayama and Watanabe, 2004), and at the eastern sea boundary (East China Sea
and South China Sea) a constant head was set at 0 m. The hydraulic head values parallel toground level were input as the initial conditions for the groundwater submodel. The digitalriver network, produced by the IGSRR (1982) from 1:50,000 and 1:100,000 topographic
maps covering the basin was used in this study. In the late 1980s previous research fixed thediversion ratio of water at 0.15 from three openings in the Dongting Lake region, theso-called Jingjiang reach (Songzi, Taiping, and Ouchi), and from the Changjiang River (Fig.
3.1) (Li and Ni, 2001; Dai et al., 2005). In 5,124 river cells, including the Changjiang River,
the inflow and outflow from the riverbed was simulated at each time step depending on the
difference between the hydraulic heads for groundwater and the river.
The simulation area is 3,000 km wide by 1,000 km long by Albers (WGS 1984)
co-ordinates, and covers most of the Changjiang basin. The area was discretized into a grid of
300  100 blocks with a grid spacing of 10 km in the horizontal direction and into 20 layers,
with a weighting factor of 1.1 (finer at the upper layers) in the vertical direction. The upper
layer was set at a depth of 2 m, and the 20th layer was defined as an elevation of -500 m fromthe sea surface. Simulations were performed with a time step of 6 hours for 2 years from 1
January 1987 to 31 December 1988 (normal summer flood) and from 1 January 1998 to 31
December 1999 (severe summer flood), after a 1 year warm-up period until equilibrium.
Previous observation data for river discharge (10 points; Changjiang Water Conservancy
Committee, 1998–1999), soil moisture (3 points from the Global Soil Moisture Data Bank;
Entin et al., 2000; Robock et al., 2000), and groundwater level (25 points; China Institute for
Geo-Environmental Monitoring, 2003), were used for calibration and validation of thesimulations (Fig. 3.1). Details are described in Nakayama and Watanabe (2008b) andNakayama and Shankman (2013a).

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Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

3.5 Results and Discussion
3.5.1 Hydrologic Changes under different Climate Conditions
The observed discharge during both normal and severe floods in the upstream tributaries
and the mainstream of the Changjiang River Basin has been reproduced well in research(Nakayama and Watanabe, 2008b; Nakayama and Shankman, 2013a). The heavy rainfall
during the 1998 flood, which surpassed the flood storage ability of the lake resulting inwide-spread floods around the lake and along the lower Changjiang, has also been verified(Shankman and Liang, 2003; Shankman et al., 2006). Fig. 3.5 shows the simulated riverdischarge of the Changjiang mainstream during 1998-1999, as verified by observed data
(Changjiang Water Conservancy Committee, 1998-1999). It is clear that although the flood
diversion and inundated area in the 1998 flood were smaller than that of the biggest flood of
1954, the discharge was much greater than that in 1987-1988. The large-scale flood dischargethat occurred from June-September in 1998 was 33% greater than the usual annual discharge.
The result of this simulation shows that the effect of land cover changes on riverdischarge in the Changjian mainstream made between 1990s and 2000s (Fig. 3.4 and Table3.1), such as deforestation (Yin and Li, 2001), reclamation of lakes and sedimentation (Du etal., 2001; Zhao and Fang, 2004; Zhao et al., 2005), and rapid urbanization (Piao et al., 2003),
was less marked than that of the effect of extreme weather and climate events, such as theheavy rainfall in 1998. The forcing precipitation data created from the rain gauge data and
ECMWF in this study closely agreed with the GPCC (Bell et al., 1999), and thus thesimulation adequately reproduces the peak value of the observed discharge.

Fig. 3.5

Simulation result of 1998 big-flood in the Changjiang mainstream. The circles represent
the observation data and the lines represent the simulated results by NICE-FLD. r2 and NS
are also plotted.

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CGER-I114-2014, CGER/NIES

The river discharge around the junction during the usual flood-period from 1987 to 1988
were compared with those from the severe flood-period in 1998 (Fig. 3.6). Poyang Lake plays
an important role as a flood storage reserve (Yin and Li, 2001; Shankman and Liang, 2003;
Shankman et al., 2006), and as such, a portion of the water resulting from heavy rain in thesummer of 1987 flowed from the mainstream into Poyang Lake (negative value) (Fig. 3.6c).
This usually occurs during the middle and late summer months. The discharge at the junction
and in the downstream section of the mainstream is excellently reproduced by the simulation,in the same way as in previous research (Shankman and Liang, 2003; Shankman et al., 2006).
Because an even greater amount of rainfall occurred around the Poyang Lake catchment from
June to July in 1998, a greater amount of lake water flowed into the mainstream (Fig. 3.6d).
This means that the amount of rain that fell during the 1998 large-scale flood surpassed theflood storage ability of Poyang Lake, resulting in a broader, inundated, area around the lakes
and along the mainstream, and also in the serious damage that occurred in the downstream
regions.
The discharge at the junction around Dongting Lake flowed into the mainstream in both
1987-1988 and in 1998 (Fig. 3.6 a-b). Because a higher rainfall occurred around Dongting
Lake than that in a usual year, there was no backwater flow at this junction, even during theflood period. The observed discharge and water budget around Dongting Lake was
excellently reproduced by the NICE-FLD, in the range of previous research (Li and Ni, 2001;
Dai et al., 2005), and the model reproduced the observed values reasonably well, in
accordance with the change of the river-lake relationship (Hongfu et al., 2007; Wang et al.,2008), and also the surface water-groundwater interaction (Eltahir and Yeh, 1999; Dai et al.,2010; Gribovszki et al., 2010). In particular, the discharge from Poyang Lake into the
Changjiang River and the backflow into the lake depend on the relationship between the flood
storage ability of the lake, groundwater seepage, and the hydro-climatic conditions around thelake’s mouth.

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Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

Fig. 3.6

Simulation result of river discharge around the junction during both 1987-1988 and1998-1999: (a) around Dongting Lake during 1987-1988; (b) around Dongting Lake
during 1998-1999; (c) around Poyang Lake during 1987; and (d) around Poyang Lake
during 1998-1999. Simulated discharges were compared with observed values both at thejunctions (R–7; Chenglingji, and R–9; Hukou) and at downstream mainchannels (R–8;
Luoshan, and R–10; Datong) in Fig. 3.1. Positive discharge at junction shows the flowfrom the lakes to the mainstream Changjiang River.

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CGER-I114-2014, CGER/NIES

3.5.2 Surface Water – Groundwater Interactions
Using Radium 226 (226Ra) specific activity, Dai et al. (2010) evaluated that the
groundwater discharge from Dongting Lake to the main channel during the extreme drought
event (from September to December 2006) accounted for 22% of the discharge difference
between Jianli and Luoshan (or the outlet discharge into the main channel at Chenglingji);indicating a noticeable contribution of groundwater discharge from the lake basin into themain channel. This contribution became even greater when the TGD empounded water from20 September to 27 October 2006 during the extreme drought, preventing drying out of theriver.
Fig. 3.7a shows the distribution of simulated seepage from groundwater to the river andlake along the mid-lower reaches. Positive values indicate groundwater seepage into the riveror lake. The ratio of seepage to outlet discharge becomes greater during normal periods thanduring periods of extreme flood because the groundwater level along the river or lake
becomes higher than the surface water level. This result indicates that groundwater seepage
into the river and lake could play a more important role along the mid-lower reaches in anormal year than in years with extreme floods (Nakayama and Shankman, 2013b). Such a
possibility is also supported by a previous study using a 226Ra method, revealing its
importance in maintaining stream flow in the dry season (Dai et al., 2010). In particular, lakestages change significantly near the mouths of the two lakes and their major tributaries, andthis indicates complex river-lake-groundwater interactions.
Fig. 3.7 b-c shows the inter-annual power spectra of river discharge at Yichang and
Datong for 1987-1988 (normal and drought) and 1998–1999 (extreme flood). Generally, thespectrum at Yichang (upstream of lake region) follows values of S(f)~f -0.3 within time scales
from one week to one year, and that at Datong (downstream of lake region) is steeper, withvalues of f -0.5 up to two months; in accordance with previous studies (Pelletier and Turcotte,
1997; Blender and Fraedrich, 2006). Such variability is due to the lake’s capability of storingwater and the delayed release of such water from the extended lakes connected to the main
channel (Wang et al., 2008). In particular, this characteristic changes between normal periods
and flood periods. Because the backflow from the main channel into Poyang Lake occurs attimes during normal periods (Nakayama and Watanabe, 2008b), the water retarded from thedischarge outlet into the main channel affects the steeper damping of spectrum in the higher
frequency at the junction at Hukou. This mechanism also causes a steeper damping ofspectrum at the downstream main channel at Datong, due to the flood storage capability of thelakes, and therefore the behavior of the regime differs in the upper and lower reaches(Nakayama and Watanabe, 2008b). The change is also related to an increase in groundwater
seepage as a baseflow during normal periods, as described above.

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Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

Fig. 3.7

Surface water to groundwater interactions in the Changjiang River: (a) Simulated
seepage from groundwater to river and lake; (b)-(c) Power spectra of river discharge
at Yichang and Datong during normal and severe floods.

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CGER-I114-2014, CGER/NIES

3.5.3 Evaluation of Flood Storage Ability of Lakes
Observed discharge in the lower Changjiang reaches during normal and severe floods has
been excellent reproduced by previous research (Nakayama and Watanabe, 2008b). It has also
been verified that the heavy rainfall occurring during the 1998 flood surpassed the flood
storage ability of the lake, resulting in wide-spread floods around the lake and along the lower
Changjiang (Shankman and Liang, 2003; Shankman et al., 2009). The observed values werereproduced reasonably well by the model, in accordance with both a change in the river-lakerelationship (Hongfu et al., 2007; Wang et al., 2008), and the surface water-groundwaterinteraction (Eltahir and Yeh, 1999; Dai et al., 2010).
We conducted the test simulation by recreating the historical areas of Dongting andPoyang Lakes from the 1950s (4,000 km2 and 5,000 km2, respectively), and by inputting themeteorological data for the large-scale flood of 1998 (Fig. 3.8). The simulation showed thatthe river discharges at the junction (and downstream from the junction) decreased due to thewater storage effect of Dongting and Poyang Lakes, as qualitatively described by previous
research (Du et al., 2001; Yin and Li, 2001; Shankman and Liang, 2003; Zhao and Fang,
2004; Peng et al., 2005; Zhao et al., 2005) (Fig. 3.8 a-b). The peak flow, in particular, wasreduced by about 5-15% at the junction, indicating that both Dongting and Poyang Lakes
have an important flood storage function. It is of interest that the groundwater level decreases
greatly around the inlet of major tributaries into both lakes and around the junction ofmainstream (Fig. 3.8c). This indicates that the increase in the lakes’ storage capacity
moderates the peak value of the lake water level during the flood periods and decreases thegroundwater level surrounding the lakes. The NICE-FLD suggests that it would be very
effective if the past areas of the lakes were recovered as much as possible, to prevent
large-scale floods such as that of 1998, from the view point of the return period in thehydrologic cycle.

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Chapter 3 Role of Lake Flood Storage Capability in the Humid Changjiang River Basin

Fig. 3.8

Simulation result of hydrologic change by recovering the areas of Dongting andPoyang Lakes to that of the 1950s (4,000 km2 and 5,000 km2): (a) river discharge
downstream of Dongting Lake (R–8; Luoshan); (b) river discharge downstream of
Poyang Lake (R–10; Datong); and (c) change of groundwater level around both lakes.
The circles in Fig. 3.8 a-b represent observation data and the lines represent the simulated
results from 1998-1999 (the same as in Fig. 3.6b and 3.6d). The bold line represents results
using a simulation of the lakes when recovered to an area of the 1950s. The plotted area in
Fig. 3.8c is described in Fig. 3.1, and the black and pink lines represent the lakeshores in
1990s and 1950s.

3.6 Conclusion
The NICE model was coupled with the lake and junction models (NICE-FLD) in order toevaluate the river-lake-groundwater relationship and the role of the flood storage capability of
the lake area in the Changjiang River Basin. Using this simulation we compared the severe
flood-period of 1998 with the usual flood-period of 1987-1988. NICE showed thepreviously-unrevealed role of surface water-groundwater interactions and lateral subsurface
flows, which have not been considered important in previous research on continental scales,
when assuming stationarity. Furthermore, the author conducted a back-casting simulation byrecovering the lake water volume of the 1950s and by inputting meteorological data of the1998 severe flood, in order to evaluate the role of the flood storage capability of the lakes inrelation to the water/heat budgets.

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CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20
CGER-I114-2014, CGER/NIES

Chapter 4
Relation between Uneven Water Resources, Withdrawal, andEcosystem Degradation in the Changjiang and Yellow River Basins- 63 - 63 -

Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
BasinsAbstract
The hydro-climate differs vastly between northern and southern China, and this led to an
imbalance in environmental resources related to an increased demand for food and declining
water availability. The Three Gorges Dam (TGD) was designed to diminish flooding and theSouth-to-North Water Transfer Project (SNWTP) has been planned to enable the transfer ofwater from the Changjiang River basin to the Yellow and Hai Rivers. However, these projects
will not necessarily solve all problems associated with water availability within the basins,
and there is an urgent need for the development of a sophisticated eco-hydrological model toevaluate the optimum amount of transferred water, and the socio-economic and environmental
consequences from such endeavors/projects.
In this chapter, a process-based National Integrated Catchment-based Eco-hydrology(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
operation, and water transfer subsystems, for the development of a coupled human and natural
systems model, and to evaluate the causes and effects of uneven water resources. The modelclarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologicchange after operation of the TGD and SNWTP, to estimate whether there would be a
reduction in problems associated with water stress, crop productivity, and ecosystemdegradation. The result also revealed the missing roles of surface water-groundwaterinteractions and lateral subsurface flow, previously considered to be not as important inprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the Time-Integrated Normalized Difference Vegetation Index (TINDVI) gradient inagricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
from the view point of the hydrologic and biogeochemical cycles, but also to clarify how
substantial pressures from complex issues can be overcome by effective trans-boundary
solutions.
Keywords: crop yield, eco-hydrology model, extremes, TINDVI, water withdrawal- 64 - 64 -

CGER-I114-2014, CGER/NIES

4.1 Introduction
The Changjiang and Yellow Rivers originate on the Qinghai Tibet Plateau and flow
eastwards for about 6,000 km to the East China Sea (Fig. 4.1). Both rivers represent
50%-60% of all the water discharge from Chinese rivers to the western Pacific Ocean, and up
to 80% of the sediment load. In China, the hydro-climate differs between the north and south.
The upper section of the Changjiang River Basin in the humid south flows through narrowand incised river valleys, whereas the downstream section is surrounded by broad alluvial
valleys supporting intensive rice growth. Deforestation and land reclamation have induced
serious soil erosion and an increase of floods in this river basin, and in particular there is ashrinkage of the flood storage capability around lakes (Zhao et al., 2005; Nakayama and
Watanabe, 2008b; Nakayama and Shankman, 2013).
Although the Three Gorges Dam (TGD) provides flood control, changes in the aquatic
environment of the estuary and the East China Sea will eventually occur from changes inpollutant loads caused by the deposition of large amounts of sediment carried from the upper
region in the dam and the artificial control of discharge volume (Yang et al., 2006). From thetime of its operation there have been increasing problems with flood occurrence in thedownstream area of the Changjiang river associated with the TGD, and such issues were notconsidered when originally justifying the dam’s construction (Shankman and Liang, 2003;
Zhao et al., 2005; Nakayama and Watanabe, 2008b; Nakayama and Shankman, 2013).

Fig. 4.1

Crop types in agricultural areas of the Changjiang and Yellow River Basins in China.
Irrigation areas are overshaded in the figure. The black dotted square represents the borderof the simulation area containing a grid of 300  200 blocks of Albers (WGS 1984)
co-ordinates. Black dotted line is the border of the North China Plain (NCP), which
includes the downstream area of the Yellow River.
- 65 - 65 -

Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
BasinsIn contrast, the Yellow River is locatedin the semi-arid north and is well known for its
Abstracthigh suspended sediments concentration, frequent floods, unique channel characteristics in the
lower(the river differs
bed isvastly
raisedbetween
above theland andoutsideits banks),
Thereachhydro-climate
northernsouthern
China,andand limited
this led water
to an
resources (Nakayama,
2011b).
The North
China(NCP) (Fig.
4.1) foris a food
giantandalluvial
plain
imbalancein environmental
resources
relatedto Plainan increased
demanddecliningformedbydeposition
fromthe
Yellow,
Hai,
and
Luan
Riversandtheirtributaries,
andiswater availability. The Three Gorges Dam (TGD) was designed to diminish flooding and one
theof
China’smostimportantgrain
cropping
areas(because
of
its
large
area
of
approximately
South-to-North
Water Transfer Project (SNWTP) has been planned to enable the transfer of
km2Changjiang
and population
around112Yellow
million).
palaeochannels
1.36 from
105 thewater
Riverofbasin
to theandMany
Hai Rivers.
However, fromthese different
projects
stagesform
thevariousgeomorphologic
features(Chen,
1996).
Over
the
past
half
century,
thewill not necessarily solve all problems associated with water availability within the basins,
groundwaterhas
lowereddramatically
owing
to
both
drought
andover-pumping,
and
in
areas
and there is an urgent need for the development of a sophisticated eco-hydrological model tothis has thebeenaccompanied
expansionof andsaline-alkaline
land (Brown
and Halweil,
evaluate
optimum
amountbyof thetransferredwater,
the socio-economicand environmental
1998;
Liu
and
Xia,
2004;
Nakayama,
2011a;
Nakayamaetal.,2006).
Sincethecompletionofconsequences from such endeavors/projects.
a large-scale
irrigation
project in 1969,
over-withdrawal
water in the Yellow
River has
In this chapter,a process-based
National
Integrated ofCatchment-based
Eco-hydrology
resulted
innoticeableriver
dryingoccurringonanincreasingly
frequent
timescale(Fu
et al.,(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
2004). Theandcombinationof ansubsystems,
increased demand
for food and aofdeclining
water
operation,water transfer
for the developmenta coupledavailability
human andofnaturalarecreating
substantial
pressuresfor
thisheavily
irrigated
area(BrownandHalweil,
1998;
Fu
systems model, and to evaluate the causes and effects of uneven water resources. The model
et
al.,
2004;
Yang
et
al.,
2004a;
Nakayama,
2011b).
clarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologic
In afterprevious
researchauthorevaluated tothe
generalcharacteristics
changeoperationof thetheTGD
and SNWTP,
estimate
whether
there wouldof betheaeco-hydrologicalprocess
intheentireChangjiang
andYellow
River
Basinsextensions
reduction in problems associated with water stress, crop productivity,and
andin ecosystemof separate basins,
to evaluate
optimumof potentially-transferable
water, and thedegradation.
The result
also therevealed
theamount
missingroles of surface water-groundwaterassociatedsocio-economic
andenvironmental
consequences,byconstructing
the basic
interactions and lateral subsurface flow, previously considered to be not as importantinstructure
of
the
model
with
different
functionsofrepresentative
crops(wheat,maize,
soybean,previous studies that assumed stationarity on continental scales. The heterogeneous pattern ofand rice)(Nakayama, 2012c).
At present,
China isacceleratingIndex
its huge
project togradientdrive watertheTime-Integrated
Normalized
Difference
Vegetation(TINDVI)
in
from
the
Changjiang
totheYellow
andHai
Rivers(South-to-North
Water
Transfer
Project;
agricultural fields helped to estimate uneven crop yield and its relation to water availability.
SNWTP)(Yang andapproach
Zehnder,not
2001;
et al.,
2007).
Although it hasthese
beencomplex
estimatedthat theThis integrated
onlyLihas
a rolein re-considering
processes
TGD theand view
the SNWTP
the geographicaland socio-economic
as muchfrompoint ofwill
theremovehydrologic
and biogeochemical
cycles, but imbalances
also to clarify
howaspossible(Yang
andZehnder,
2001;
Li
et
al.,
2007),
thereisanurgentneedtoclarify
thesubstantial pressures from complex issues can be overcome by effective trans-boundary
relations
of
the
two
extremesofdrought
and
flood
which
are
affectedbyhuman
activity.
The
solutions.
development of a process-based model is therefore necessary to present an integrated
approach fortheyield,appropriate
managementof water resources
and towithdrawal
achieve sustainable
Keywords:
crop
eco-hydrology
model, extremes,
TINDVI, water
development on a regional scale.
In this study the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2008a-c, 2009a, 2010a, 2011a-d, 2012a-d, 2013a; Nakayama andFujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Shankman, 2013a-b;
Nakayama and Watanabe, 2004, 2006a-b, 2008a-c; Nakayama et al., 2006, 2007, 2010, 2012)
was coupled with complex sub-systems in irrigation, stream junction, reservoir operation, andwater transfer, to evaluate the cause and effect of uneven water resources in the entire
Changjiang and Yellow River Basins. The model includes the different growth processes ofrepresentative crops and predicts the hydrologic changes after operation of the TGD and
SNWTP, to estimate whether critical problems related to water stress, crop productivity, andecosystem degradation would diminish in the basins. NICE also evaluates the missing role ofsurface water-groundwater interactions and lateral subsurface flow, which has previously
been considered to be unimportant when assuming stationarity in previous research on acontinental scale (Milly et al., 2008). Furthermore, Time-Integrated NDVI (TINDVI) was
analyzed using satellite NDVI images in NOAA/AVHRR (Advanced Very High Resolution
Radiometer), and by ascertaining heterogeneous patterns of the TINDVI gradient inagricultural fields, an uneven crop yield, and its relation to water availability was estimatedfor both surface and groundwater. The integrated approach explored in this chapter not only
assists in the re-consideration of complex processes from the view point of the hydrologic and- 64
66 - 66 -CGER-I114-2014, CGER/NIES

biogeochemical cycles, but also in clarifying how substantial pressures from complicatedissues can be overcome by effective trans-boundary solutions.
4.2 Materials and Methods
4.2.1 Development of the Coupled Human and Natural System
The process-based National Integrated Catchment-based Eco-hydrology (NICE) model
includes surface-unsaturated-saturated water processes, and assimilates land-surfacehydrothermal processes describing phenology with satellite data (Fig. 4.2) (Nakayama,
2008a-c, 2009a, 2010a, 2011a-d, 2012a-d, 2013a; Nakayama and Fujita, 2010; Nakayama andHashimoto, 2011; Nakayama and Shankman, 2013a-b; Nakayama and Watanabe, 2004,
2006a-b, 2008a-c; Nakayama et al., 2006, 2007, 2010, 2012). The unsaturated layer divides
the canopy into two layers in the vertical dimension using the SiB2 (Simple Biosphere model2), and the soil into three layers (Sellers et al., 1996). For the saturated layer, NICE solves
three-dimensional groundwater flow for both unconfined and confined aquifers. Hillslope
hydrology, including freezing/thawing processes, can be expressed using the two-layer
surface runoff model. NICE connects each sub-model by considering: the water/heat fluxes;
the gradient of hydraulic potentials between the deepest unsaturated layer and thegroundwater; effective precipitation; and seepage between river and groundwater (Nakayama
and Watanabe, 2004). NICE is coupled with DSSAT (Decision Support Systems forAgro-technology Transfer) (Ritchie et al., 1998) in an agricultural field to include thedifferent functions of representative crops (wheat, maize, soybean, and rice) (Nakayama et al.,2006). The model automatically simulates dynamic growth processes and biomass
formulation by inputting: previous point data for each crop type (Wang et al., 2001; Liu et al.,
2002; Tao et al., 2006), the spatial distribution of crop types (Chinese Academy of Sciences,
1988; Fang et al., 2006), and the irrigation area (Liu, 1996), (in which the automatic irrigation
mode supplies the crop water requirement, assuming that average available water in the top
layer falls below soil moisture at field capacity for cultivated fields (Nakayama, 2011a,
2011b; Nakayama et al., 2006) and that water level is maintained in the irrigation schedule ofthe irrigated water depth for paddy fields) (Nakayama and Watanabe, 2008b). NICE is also
combined with lake and stream junction models (Nakayama and Shankman, 2013; Nakayama
and Watanabe, 2008b) to evaluate the role of the flood storage capability of Dongting andPoyang lakes, which drain into the Changjiang through narrow outlets (Fig. 4.1). This
energy-based method addresses river discharge across the junction by performing standard
step backwater and forewater calculations with continuity and energy equations.

- 67 - 67 -

Chapter
Chapter
Chapter444 Relation
Relation
Relationbetween
betweenbetweenUneven
UnevenUnevenWater
Water
WaterResources,
Resources,
Resources,Withdrawal,
Withdrawal,
Withdrawal,andandand
Ecosystem
Ecosystem
EcosystemDegradation
Degradation
Degradationin
in
inthe
the
theChangjiang
Changjiang
Changjiangand
andandYellow
Yellow
YellowRiver
River
RiverBasins
BasinsBasinsAbstract
The hydro-climate differs vastly between northern and southern China, and this led to an
imbalance in environmental resources related to an increased demand for food and declining
water availability. The Three Gorges Dam (TGD) was designed to diminish flooding and theSouth-to-North Water Transfer Project (SNWTP) has been planned to enable the transfer ofwater from the Changjiang River basin to the Yellow and Hai Rivers. However, these projects
will not necessarily solve all problems associated with water availability within the basins,
and there is an urgent need for the development of a sophisticated eco-hydrological model toevaluate the optimum amount of transferred water, and the socio-economic and environmental
consequences from such endeavors/projects.
In this chapter, a process-based National Integrated Catchment-based Eco-hydrology(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
operation, and water transfer subsystems, for the development of a coupled human and natural
systems model, and to evaluate the causes and effects of uneven water resources. The modelclarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologicchange after operation of the TGD and SNWTP, to estimate whether there would be a
reduction in problems associated with water stress, crop productivity, and ecosystemdegradation. The result also revealed the missing roles of surface water-groundwaterinteractions and lateral subsurface flow, previously considered to be not as important inprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the Time-Integrated Normalized Difference Vegetation Index (TINDVI) gradient inagricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
Fig.
Fig. 4.2
4.2 National
National Integrated
Integrated Catchment-based
Catchment-based Eco-hydrology
Eco-hydrology (NICE)(NICE) model.
model. NICE
isis coupledcoupledfromthe view
pointof the hydrologic
and biogeochemical
cycles,but
alsoNICE
to clarify
howwithwith DSSATDSSAT forfor agricultural
agricultural fields,
fields, toto improve
improve thethe hydrologic
hydrologic cycle
cycle andand evaluate
evaluate thethesubstantial
pressures
fromunevencomplexissuescanwaterbe overcome
by effective trans-boundary
relationcropavailability.
relation between
between unevencrop yield
yield andand water
availability.
solutions.
Keywords:
yield,
eco-hydrology
extremes, TINDVI, water withdrawal
4.2.2
4.2.2 Model
Modelcrop
Input
Input
Data
Data
and
and Satellite
Satellitemodel,
AnalysisAnalysisSix-hourSix-hour reanalyzed
reanalyzed data
data ofof downward
downward radiation,
radiation, precipitation,
precipitation, atmosphericatmospheric pressure,pressure, airairtemperature,temperature, air
air humidity,
humidity, wind
wind speed,
speed, FPARFPAR (Fraction(Fraction ofof Photosynthetically
Photosynthetically Active
ActiveRadiation),
Radiation), andand LAILAI (Leaf(Leaf Area
Area Index)
Index) were
were input
input into
into thethe model
model after
after interpolationinterpolation ofof
ECMWF
ECMWF (European(European Centre
Centre forfor Medium-Range
Medium-Range Weather
Weather Forecasts),
Forecasts), in
in an
an inverse
inverse proportion
proportiontoto thethe distancedistance back-calculated
back-calculated inin eacheach grid.
grid. Because
Because thethe ECMWF
ECMWF precipitation
precipitation had
had thethe least
least
reliability
reliability andand underestimatedunderestimated observed
observed peak
peak values,
values, rain
rain gauge
gauge daily
daily precipitation
precipitation collectedcollected
at
at 4,271
4,271 meteorologicalmeteorological stations
stations were
were used
used toto correct
correct thethe ECMWF
ECMWF value.
value. Mean
Mean elevation
elevation was
was
calculated
calculated using
using aa global
global digital
digital elevation
elevation model
model (DEM;(DEM; GTOPO30)
GTOPO30) (U.S.
(U.S. GeologicalGeological Survey,
Survey,1996).
1996). Digital
Digital land
land cover
cover data
data were
were categorizedcategorized into
into forests,
forests, grasses,
grasses, bushes,
bushes, andand shrubs
shrubs cover
cover
mountainous
mountainous andand hilly
hilly areas
areas inin thethe upper
upper regions;regions; paddy
paddy fields
fields widely
widely distributed
distributed along
along thethe
Changjiang
Changjiang mainstream
mainstream andand cultivatedcultivated fields
fields mainly
mainly along
along thethe Yellow
Yellow River
River (Fig.
(Fig. 4.1).
4.1).
Vegetation
Vegetation classclass andand soil
soil texture
texture were
were categorizedcategorized andand digitizeddigitized using
using thethe Vegetation
Vegetation andand Soil
Soil
Maps
Maps ofof China
China (1:4,000,000)(1:4,000,000) (Chinese(Chinese Academy
Academy ofof Sciences,
Sciences, 1988).
1988). GeologicalGeological structures
structures
were
were divided
divided into
into four
four types
types onon thethe basis
basis ofof hydraulichydraulic conductivity,
conductivity, thethe specificspecific storage
storage ofofporousporous material,material, andand specificspecific yield,
yield, by
by scanningscanning andand digitizing
digitizing thethe geologicalgeological materialmaterial(Geological(Geological Atlas
Atlas ofof China,
China, 2002)
2002) andand core-samplingcore-sampling data.
data.

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CGER-I114-2014, CGER/NIES

After downloading original data from WebPaNDA (2008), the NDVI was calculated from
NOAA/AVHRR satellite data collected over East Asia between 1982 and 1999. TINDVI was
calculated from satellite NDVI images with a resolution of 6 km, using the nearest-neighbormethod. This value is defined as the area under a curve described by NDVI with time,
because dry biomass is linearly related to TINDVI and crop growth rate related to NDVI(Yang et al., 1998). These values agree well with observed values from previous field
experiments (Nakayama et al., 2006), showing that simulated dry biomass has a linear
relationship to TINDVI with a high correlation (r2 is greater than 0.950) (Nakayama, 2011b).
The result also implies that maximum growth rate generally occurs when the NDVI reaches aplateau stage. This relation is useful for estimating the potential rate of primary production in
the basin. These input data are adequate for estimating eco-hydrological process on a scale ofhundreds to thousands of kilometers in this study. Details are described in Nakayama (2012c)
and Nakayama and Shankman (2013b).
4.2.3 Model Modification for TGD and SNWTP
Water level of the TGD is maintained between 145 m (flood season) and 175 m (normal
season) above sea level (a.s.l.), depending on optimal flood-control water-level schemes, on
the condition that spill discharge is limited to less than 55,000 m3/s to minimize flood risk inthe downstream area, and is larger than 35,000 m3/s for navigational benefits (Liu et al.,2008). The author modified the NICE using a one-dimensional water budget with theoperation of the reservoir, by applying the relation between water level and storage volume toestimate the released discharge (Fig. 4.2). The model showed that the estimated discharge ofthe reservoir during a flood event in late summer did not greatly change after the dam’s
construction, which implies the dam’s inability to make a substantial effect on dischargeduring severe floods (Shankman and Liang, 2003). For the prediction of hydrologic changes
in normal floods, the author input monthly discharge changes (Yang et al., 2006; Li et al.,2007; Dai et al., 2010; Yi et al., 2010) directly in the model. The model shows that after
filling the TGD due to increased evaporation and water usage, the flood water level would
become less extreme, and the range at which the water level increases would be dampened
downstream (Yi et al., 2010).
The middle route of the SNWTP diverts water from the Danjiangkou Dam upstream fromthe Hanjiang River to northern China (Fig. 4.1). Therefore, the author also modified the NICE
to directly input the water transfer schemes of 9.5  109 m3 and 13.0  109 m3 per year into
the model, by considering monthly runoff (Li et al., 2007).
4.2.4 Boundary Conditions and Running the Simulation
In the upstream boundaries a reflecting condition on the hydraulic head was used,assuming that there is no inflow from the mountains in the opposite direction (Chen et al.,2005; Nakayama, 2011b; Nakayama and Watanabe, 2008b). At the eastern sea boundary, aconstant head was set at 0 m. In river grids (decided by the digital river network from
1:50,000 and 1:100,000 topographic maps), inflows and outflows from riverbeds were
simulated at each time step, depending on the difference in the hydraulic heads for ground andriver water.
The simulation area is 3,000 km wide by 2,000 km long in the Albers co-ordinates (WGS
1984), and covers most of the Changjiang and Yellow River basins as referred to in Chapters
2 and 3 (Fig. 4.1). This area is discretized into a grid of 300  200 blocks with a grid spacing- 69 - 69 -

Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
Basinsof 10 km in the horizontal direction and into
20 layers with a weighting factor of 1.1 (finer at
Abstract
the upper layers) in the vertical direction. The upper layer is set at a depth of 2 m, and the
20thThelayer
is defined differs
as an elevationof -500
m fromsea surface.
werehydro-climate
vastly between
northernandthesouthern
China, Simulations
and this led toanperformed inwitha time step resourcesof t = 6 related
hours htofor
and 1998-1999
a 1 year
imbalance
environmental
an 1987-1988
increased demand
for food afterand declining
warm-upperiod, until
equilibrium.
observeddata of toriverdischarge(Changjiang
wateravailability.
The Three
Gorges Previous
Dam (TGD)
was designed
diminishflooding
and the
Water
Conservancy
Committee,
1987,
1988,
1998,
1999;
Yellow
River
Conservancy
South-to-North Water Transfer Project (SNWTP) has been planned to enable the transfer ofCommittee,
1987,1988), Riversoil moisture(Entinet andal., Hai
2000;
Robocket al.,these
2000),
andwater
from theChangjiangbasin to the
Yellow
Rivers.
However,
projects
groundwaterlevel (China
for Geo-Environmental
Monitoring,
2003)
were
for
will
not necessarily
solve Instituteall problems
associated with water
availability
withintheusedbasins,calibration
andvalidation,inadditiontovaluespublishedinliterature(ClappandHornberger,
and there is an urgent need for the development of a sophisticated eco-hydrological model to1978; Rawls
et al., 1982)
as inofthe
author’s water,
previouson the Changjiang(Nakayama
evaluate
the optimum
amounttransferred
andresearch
the socio-economicand environmental
andWatanabe,
2008b;
Nakayama
andShankman,2013a-b)andYellow
Rivers(Nakayama,consequences from such endeavors/projects.
2011a-b).
In this chapter, a process-based National Integrated Catchment-based Eco-hydrology(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
operation, and water transfer subsystems, for the development of a coupled human and natural
4.3 Results
andandDiscussion
systemsmodel,
to evaluate the causes and effects of uneven water resources. The modelclarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologic
4.3.1 Drought
Caused byofOver-Irrigation
the DownstreamAreaof the Yellow
River be achangeafter operation
the TGD and in
SNWTP,
to estimate
whether
there would
reduction in problems associated with water stress, crop productivity, and ecosystem
After verificationof hydrologic
variables
2011a-b;
Nakayama
and Watanabe,degradation.
The result
also revealedthe (Nakayama,
missing rolesof surface
water-groundwater
2008b;
Nakayamaetal.,2006),
theeffectofover-irrigationonthedecreaseinriverdischarge
interactions and lateral subsurface flow, previously considered to be not as important
in
in
the
downstream
area
of
the
Yellow
River
is
shown
(Fig.
4.3).
Becausethemodel
includesprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the irrigation
procedureand dynamic
wave effect(Nakayama
Watanabe,2004), inittheTime-Integrated
Normalized
Difference
Vegetation
Index and
(TINDVI)
gradient
excellently reproduces
discharge
foryielda lowwith a highcorrelation,
and atagricultural
fields helpedtheto observed
estimate unevencrop
andflow
its relation
to wateravailability.
times
for
river
dry-up(Yellow
River
Conservancy
Committee,
1987,
1988)
(Fig.
4.3b).
It
This integrated approach not only has a role in re-considering these complex processes
reproduces
particularly
the withdrawalof water for agricultural
being morefromthe view
point of wellthe hydrologic
and biogeochemical
cycles, butpurposes;
also to clarify
how
than
90%
of
the
total
water
withdrawn
downstream(Cai,2006).
The
result
shows
that
there
is
substantial pressures from complex issues can be overcome by effective trans-boundary
noclearlyobserved
decreaseintheamountofdischarge
between
upstream
anddownstream
solutions.
areas, except for the decrease between Pt. C and Pt. D, which is attributed to predominantevaporationcrop
occurringon the area of model,
Loess Plateau
expansion
Pt. A and Pt. B, andKeywords:
yield, eco-hydrology
extremes,
TINDVI,between
water withdrawal
also to over-withdrawal and the unique channel characteristics where the river bed lies above
the surrounding land in the area downstream. The model also indicates that the peak discharge
is higher downstream than upstream because of the amount of drainage water produced
during the rainy season, which is a characteristic of semi-arid regions. Furthermore, thespatial distribution of river irrigation (Fig. 4.3a) is reproduced reasonably well by the model,
not only in reach level but in spatial distribution, as validated by a comparison with a previous
study based on the Penman-Monteith method and the crop coefficient (Nakayama, 2011b;
Nakayama et al., 2006). At a downstream point at Lijin (Pt. D; 37.52 °N, 118.30 °E), the riverdischarge dries out during spring, mainly because a large amount of the water is used for theirrigation of winter wheat. The result also supports the result of the author’s previous research,which showed that the effect of groundwater irrigation is predominant downstream(Nakayama, 2011b), mainly because of water-use conflicts that are intensifying between
upstream and downstream areas, and between various sectors such as agricultural, municipal,
and industrial (Brown and Halweil, 1998; Nakayama, 2011a-b; Nakayama et al., 2006). These
results show the smaller change in the hydrologic cycle is largely attributable to its
unsuitability for crop production and the higher dependence of water irrigation.

- 64
70 - 70 -CGER-I114-2014, CGER/NIES

Fig. 4.3 Decrease in discharge of the Yellow River caused by over-irrigation: (a) simulated
result of river irrigation in 1987; (b) river discharge from middle to downstream areasof the river. In Fig. 4.3a, the area colored in white represents the region without irrigation.
In Fig. 4.3b, the solid line represents the simulated result with irrigation, and the circle
represents the observed value. The right axis shows the temporal change in average
precipitation. r2 shows the correlation between observed and simulated values, and NS
shows the Nash-Sutcliffe Criterion.

4.3.2 Impact of TGD and SNWTP on the Downstream area of the Changjiang River
The river discharge around lake regions along the Changjiang River are very sensitive,
and fluctuate depending on the hydro-climatic conditions around the junction, which areclosely related to the flood storage capability of the lakes (Nakayama and Watanabe, 2008b).
The flood risk around Poyang Lake decreases moderately with a decrease in the controlled
discharge from the TGD from July to September. However, if the controlled discharge isequal to 40,000 m3/s, the model predicted that water released by the TGD could increase thelake water level and flood occurrence during May to June, regardless of the amount ofcontrolled discharge, in exchange for the benefit of a flood decrease during the summer flood
season (in the same way as in previous research (Shankman and Liang, 2003) (Fig. 4.4a).
Results show that the discharge and water level that occurred during the severe 1998 flood
would not alter greatly unless there was a limitation of the spill discharge by the dam, assuggested by Shankman and Liang (2003). The simulated results also show that after 22 yearsof the TGD being operational, the flood risk around the lake could be further accelerated
through a constant increase in the water level of about 1-2 m, if hydrologic changes cause- 71 - 71 -

Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
Basinsdownstream channel aggradation by sedimentdeposition (Yang et al., 2006; Chen et al.,Abstract2008; Xu and Milliman, 2009), although sediment deposition in the lake may not greatly
affecthydrologicchanges
overvastly
a long-term(Daiandet southernal., 2005;China,
de Leeuw
et al.,The
hydro-climate
differs
betweenperiod
northern
and this
led2009).
to an
This resultinindicatesthat it isresources
importantto manage
not only theflood fordischarge
butdeclining
also theimbalance
environmental
relatedto an increased
demandfood andsedimentload in The
the wholebasin during
the operationof thetoTGD
and flooding
the SNWTP,
inwater
availability.
Three Gorges
Dam (TGD)
was designed
diminishand theaddition to permanently
removingsome(SNWTP)
of the leveesof thetheothersduringSouth-to-North
Water Transfer
Project
has andbeenopening
plannedsome
to enabletransferofseverefloods.
Thoughtheeffect
of
the
SNWTP
will
not
be
so
remarkable
as
that
of
the
TGD,
water from the Changjiang River basin to the Yellow and Hai Rivers. However, these projects
the hydrologicchangeswouldoccur in associatedspring and withearlywatersummeraround thelaketheregion,inwill
not necessarily
solve
all problems
availability
withinbasins,accordancewiththewater-supply
periodofrice
planting(U.S.
Department
ofAgriculture,and there is an urgent need for the development of a sophisticated eco-hydrological model to1994), andthe probability
water shortage
wouldhigh, in particulararound theevaluate
the optimum
amountofof atransferredwater, andthe besocio-economicand environmental
HanjiangRivercatchment,
due
to
its
sensitivity
toclimatechange(Lietal.,2007).
The
consequences from such endeavors/projects.
groundwater
seepage
to
lakes
may
play
an
important
role
along
the
mid-lower
reachesin
In this chapter, a process-based National Integrated Catchment-based Eco-hydrologya
normal modelyear (Fig.
4.4b), andsuppositionis supported
by stream
previous
research
which(NICE)
was modified
andthiscoupledwith complex
irrigation,
junction,reservoir
indicatedthatgroundwater
seepage
to
lakes
in
this
area
is
involved
in
about
30%
of
the
operation, and water transfer subsystems, for the development of a coupled human and naturalhydrologic
budget,maintaining
stream andfloweffects
in theofdrought(Dai et al.,The
2010).
In
systemsmodel,
and byto evaluate
the causes
unevenseason
water resources.
model
particular,theaveragelevelaround
the
inlet
of
major
tributaries
into
the
lake
would
changeclarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologic
greatly, after
and aroundthe ofjunctionof theafter whether
the TGDthere
and SNWTP
changeoperation
the TGD
andmainstream
SNWTP, toarea,
estimatewould beareaoperational,
whichhighlights
thecomplexriver-lake-groundwaterinteractions
in
this
area.
reduction in problems associated with water stress, crop productivity, and ecosystemdegradation. The result also revealed the missing roles of surface water-groundwaterinteractions and lateral subsurface flow, previously considered to be not as important inprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the Time-Integrated Normalized Difference Vegetation Index (TINDVI) gradient inagricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
from the view point of the hydrologic and biogeochemical cycles, but also to clarify how
substantial pressures from complex issues can be overcome by effective trans-boundary
solutions.
Keywords: crop yield, eco-hydrology model, extremes, TINDVI, water withdrawal

Fig. 4.4

Impact of the TGD and SNWTP on hydrologic changes around Poyang Lake: (a) lakewater level during severe flood event; and (b) difference between the averagegroundwater level before and after construction. In Fig. 4.4a, the spill discharge of the
TGD was changed in consideration of navigational benefits and flood risk downstream. In
Fig. 4.4b, the SNWTP scenario uses a water transfer scheme of 13.0  109 m3 per year.
- 64
72 - 72 -CGER-I114-2014, CGER/NIES

4.3.3 Effect of Channel Morphology and Sediment Deposition on Hydrologic Change
Hydrologic changes in the lower Changjiang River and in Poyang Lake significantly
affect channel morphology and lake sediment deposition (Table 4.1). The model predicted thehydrologic changes for a period 22 years after completion of the TGP, attributed to channel
aggradation (Li et al., 2004; Yang et al., 2006; Chen et al., 2008; Shankman et al., 2009; Xu
and Milliman, 2009) (Fig. 4.5). Simulated results show a stage increase of 1 m - 2 m and ahigher flood risk around the lake, assuming channel aggradation after the completion of the
TGD. Sediment deposition in the lake is offset by substantial sand mining, and is therefore
not likely to promote a risk of floods (Xiang et al., 2002; Dai et al., 2005; de Leeuw et al.,2009) (Fig. 4.5a). It is anticipated that the flood risk associated with hydrologic changes
would increase during May and June in addition to the summer floods, in the same way assuggested in other studies (Shankman and Liang, 2003; Shankman et al., 2009). This change
is closely related to the complicated hydrologic cycle at the mouth of the lake (Fig. 4.5b).
This result indicates that it is important not only to manage the flood discharge, but also thesediment loads throughout the entire basin during operation of the TGD. The back-casting
simulation also implies that by recovering the lake water volume to that of the 1950s, thefloodwater storage capability is further increased, if the long-term effects of channel
morphology and lake sediment deposition are included in the simulation (Nakayama and
Watanabe, 2008b).

Fig. 4.5

Sensitivity of predicted hydrologic change for the effects of channel morphology andlake sediment deposition in the long-term simulation: (a) lake water level; and (b)river discharge. In Fig. 4.5b, the bold line represents the simulated discharge at junction
R–9; Hukou, and the solid line represents the downstream main-channel (R–10; Datong).
- 73 - 73 -Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
BasinsExtensive sand mining in Poyang Lakebegan in 2001 after dredging on the lower
Abstract
Changjiang River was prohibited (Wu et al., 2007; de Leeuw et al., 2009). Fig. 4.6 shows theimpactsand miningdiffers
on thevastly
PoyangLakenorthern
region during
a severeflood,
after
The ofhydro-climate
betweenand southern
China,
and22
thisyears
led toancompletioninofenvironmental
the TGD. The
simulated
resultof increased
dredging at
the mouthof andPoyangLake
imbalance
resources
relatedto an
demandfor food
declining(deepeningof the The
channel
59 cmDamper year;
4.1) showsthat the flood
riskandwould
water
availability.
ThreebyGorges(TGD)Table
was designed
to diminish
flooding
theincreasegreatlyduringspringandsummerbecauseofthecloserinteraction
betweenSouth-to-North Water Transfer Project (SNWTP) has been planned to enable the transfertheofChangjiangRiverand theRiverlake.basin
Using
thisYellow
scenario,
thattheprojects
annualwater
from theChangjiang
to theandtheHaimodelRivers.predicts
However,these
averagedlake stage solve
wouldallincreaseby aassociatedfew meters
or watermore, availability
and that thewithinriver stage
at thewill
not necessarily
problems
withthe basins,
lake
mouth
would
also
increase
by
about
10
cm
assumingawidth
extensionatthelake’sand there is an urgent need for the development of a sophisticated eco-hydrological model
tomouth,
although
the
level
would
decreasesomewhatduringSeptember
andNovemberevaluate the optimum amount of transferred water, and the socio-economic and environmental
because of thefrom
suddenin discharge and the lake stage during operation of the TGD
consequences
suchdecrease
endeavors/projects.
(Shankman
Liang,(Fig. 4.4a).
In an alternative
the model Eco-hydrology
predicts that if
In this andchapter,
a 2003)process-based
National
Integratedscenario,
Catchment-basedadiversion
towards
the
lake
which
is
currently
disconnectedfromthemainstream
was(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
increased by
few centimetersat theit would be ofmore
effectiveat reducing
theoperation,andonly
watera transfer
subsystems,
for lake,the developmenta coupledhumanand natural
flood
risk
than
the
intensive
dredging
along
thejunctionchannel,which
provesqualitatively
systems model, and to evaluate the causes and effects of uneven water resources. The model
the validity
the previous
research
Leeuw et al., 2009).
clarifiedtheof
impactof irrigationon (deeco-hydrologicalprocesses, and predicted the hydrologicchange after operation of the TGD and SNWTP, to estimate whether there would be a
reduction in problems associated with water stress, crop productivity, and ecosystemdegradation. The result also revealed the missing roles of surface water-groundwaterinteractions and lateral subsurface flow, previously considered to be not as important inprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the Time-Integrated Normalized Difference Vegetation Index (TINDVI) gradient inagricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
from the view point of the hydrologic and biogeochemical cycles, but also to clarify how
substantial pressures from complex issues can be overcome by effective trans-boundary
solutions.
Keywords: crop yield, eco-hydrology model, extremes, TINDVI, water withdrawal

Fig. 4.6 Impact of sand mining on Poyang lake region during severe flood at 22 years after
completion of the TGD, using multiple scenarios: intensive dredging at the lake’s
mouth (bed digging); width extension at the lake’s mouth; and a diversion towards thelake that is currently disconnected from the mainstream.

- 64
74 - 74 -CGER-I114-2014, CGER/NIES

Table 4.1 Channel morphology and lake sediment deposition around Poyang Lake.
SourceXiang et al.,2002
Dai et al.,2005
de Leeuw et al.,2009
de Leeuw et al.,
2009

Place

Period/Condition

Sediment depositiona
Average deposition

Volumerates (mm/year)(106 m3/year)

2.8

12.5

Poyang Lake

1960–1990

Poyang Lake

1956–2003

Poyang Lake

After TGD
(2003–)

2.5

10.3

Junction channel

Intensive dredging(2003–)-590.0-236.04.7

de Leeuw et al.,Diversion towards
Poyang Lake
-50.2
2009
lakes (2003–)
Dai et al.,Chenglingji 1956–2003
2005
Hukou
Chen et al.,Yichang - Datong
1953–2000
2008
Xu andMilliman,
Yichang - Datong
1950–2000
2009
Chen et al.,Yichang - Datong
2003–2005
2008
Xu andMilliman,
Yichang - Datong
2001–20062009
Li et al.,Wuhan - Jiujiang
22 years after TGD
5.2
2004
Li et al.,Jiujiang - Hukou
22 years after TGD
70.4
2004
Li et al.,Hukou - Datong
22 years after TGD
47.7
2004
Yang et al.,Below Yichang
2010–2030
2006
Yang et al.,Below Yichang
2030–2060
2006
Yang et al.,Below Yichang
2060–2110
2006
a
Positive values represent accumulation, and negative values represent erosion.

- 75 - 75 --200.8
36.1
115.6
48.0
-99.1
-50.0

-83.0
-44.0
14.0

Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
Basins

4.3.4 Effect of Irrigation and Reclamation
on Ecosystem Change in Continental Scale
Abstract
Afterhydro-climate
the verification
procedure(Nakayama,
2011b;and Watanabe,
2008b),
The
differs
vastly between
northern
andNakayama
southern China,
and this led
to an
the model insimulated
the effectof irrigation
for food
eachandcropin theimbalance
environmental
resources
related toonan evapotranspiration
increased demand for
declining
Changjiang
and Yellow
River Gorges
Basins, Dam
as in (TGD)
the author’s
previous research(Nakayama,
water
availability.
The Three
was designed
to diminish
flooding 2012c).
and theIrrigation
duringthewinter-wheat
growing
periodcausesagreaterincreaseinSouth-to-North Water Transfer Project (SNWTP) has been planned to enable the transfer of
evapotranspiration
than
that
duringthesummer-maize
growing
periodintherotation
betweenwater from the Changjiang River basin to the Yellow and Hai Rivers. However, these projects
winterand summer
maize
in the associateddownstreamwith
of Yellow
River because
more
is
will
notwheatnecessarily
solve allproblems
water availability
withinthewaterbasins,withdrawn
duringthewinter-wheat
period,
due
to
the
small
amountofrainfallinthenorth.
In
and there is an urgent need for the development of a sophisticated eco-hydrological model toparticular,of theamountirrigationwater is withdrawn
fromin the NCP
because of theevaluate
themostoptimumof transferredwater, andthe aquifers
socio-economicand environmental
extremely
limitedamountofsurfacewater(Nakayama,
2011b;
Nakayamaetal.,2006). In theconsequences from such endeavors/projects.
south,
for irrigation
is usually extracted
from the riverto fill the paddy
fields to a
In waterthis chapter,a process-based
National Integrated
Catchment-based
Eco-hydrology
pondingwater
depth(Nakayama
and
Watanabe,
2008b;
Nakayama
andShankman,2013),(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
which causes
greaterincreasein evapotranspiration
during ofthea coupleddrier season.
Basedon theoperation,
and awatertransfer
subsystems,
for the developmenthumanand naturalprevious
researchmentioned
above,
the
model
also
simulatedboththeconfinedandsystems model, and to evaluate the causes and effects of uneven water resources. The model
unconfinedgroundwaterlevelsinboththeChangjiang
andYellowRiverbasins.
The
clarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologic
unconfineddecreases
rapidly
the sourcetoarea
and onwhether
the Qinghaichangeafterlevel
operationof theTGDaroundand SNWTP,
estimate
there Tibet
wouldPlateau
be a
(Fig.
4.7a),
which
feeds
many
sourcesofspring
water
in
these
regions.
The
model
shows
reduction in problems associated with water stress, crop productivity, and also
ecosystema very low value
the downstream
area, attributed
not only
but to the low
degradation.
Thein result
also revealed
the missingrolestoofoverexploitation
surface water-groundwater
elevation(Fig.
4.7b),
particularly
on
the
NCP
(Nakayama,
2011a-b;
Nakayama
al., 2006).
interactions and lateral subsurface flow, previously considered to be not as et
importantinThis
resultindicatesthatthehydrologic
cycle,
includingthegroundwaterlevel,
isnot
only
previous studies that assumed stationarity on continental scales. The heterogeneous pattern ofhighlyrelated to the topography
but also
to the useof irrigationIndex
water.(TINDVI) gradient intheTime-Integrated
Normalized
Difference
Vegetation
agricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
from the view point of the hydrologic and biogeochemical cycles, but also to clarify how
substantial pressures from complex issues can be overcome by effective trans-boundary
solutions.
Keywords: crop yield, eco-hydrology model, extremes, TINDVI, water withdrawal- 64
76 - 76 -CGER-I114-2014, CGER/NIES

Fig. 4.7 Impact of irrigation on hydrologic change on a continental scale: (a) simulated resultsof annual-averaged unconfined groundwater level (a.s.l.) in the Changjiang and Yellow
River Basins; (b) topographical contour calculated using GTOPO30. In Fig. 4.7a and
4.7b, the black square represents the border of the simulation area in Albers co-ordinates, inthe same way as that in Fig. 4.1.

- 77 - 77 -

Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
BasinsThe mean TINDVI gradients from 1982
to 1999 in various field crops (wheat, maize, andAbstractrice), at four stations, were compared with the trends of crop yields in the previous research of
TaoTheet al.hydro-climate(2006) (reprint
fromvastly
Nakayama,
2012c)(Fig.and
4.8a).
The correlationbothdiffers
betweennorthernsouthern
China, andofthis
ledvalues
to an2is relativelyingood(r = 0.986)resources
and the TINDVI
has a linear
relationto thetrend.
imbalance
environmental
related togradientan increased
demandfor food
andyielddeclining
The spatial
patternTheof Threethe mean
TINDVI
gradientwas
in designed
agricultural
fields showsa generally
water
availability.
GorgesDam (TGD)
to diminish
flooding
and theincreasing
tendency,
particularlyinthedownstream
area
of
the
Yellow
River
and
on
the
NCP
South-to-North Water Transfer Project (SNWTP) has been planned to enable the transferof(Fig.
4.8b),
whichisclosely
relatedtofactors
such
as:
the
increasing
tendencyofwater from the Changjiang River basin to the Yellow and Hai Rivers. However, these projects
winter-wheat
production
1994);
increasein irrigation
water
use
will
not necessarily
solve downstream
all problems(USDA,
associatedwith thewater
availability
within thebasins,
(Yang
et
al.,
2004a)
andchemicalfertilizer;
changesincropvarieties;
improvements
inand there is an urgent need for the development of a sophisticated eco-hydrological model totechnology
as agricultural
otherandagronomicchanges. However,it shows aevaluate
thesuchoptimum
amount ofmachines;
transferredandwater,
the socio-economicand environmental
generally
decreasing
tendency,
particularly
inthe
mid-lower
reachesandaroundthelakes inconsequences from such endeavors/projects.
the InChangjiangRiver.
Thisismainlyattributedtoanincreaseinlakereclamation,
levee
this chapter, a process-based National Integrated Catchment-based Eco-hydrology
construction,
resultantin riceirrigation,
productivity
in junction,
the lowerreservoir
reaches(NICE)
modelandwasthemodified
andrelative
coupleddecrease
with complex
stream(USDA,
1994;
ShankmanandLiang,
2003;
Zhao
et
al.,
2005;
Nakayama
and
Watanabe,operation, and water transfer subsystems, for the development of a coupled human and natural
2008b). The
decrease
the TINDVI
gradient
the ofBohai
Sea,water
the East
China Sea,thesystemsmodel,and toinevaluate
the causes
andneareffects
unevenresources.
Theandmodel
Taihang
Mountainsisduetoseveraleffects,
includingthatofgroundwater
degradation,clarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologic
seawaterafter
intrusion,and rapid
in the areas
bigger there
cities would(Brownbeandchangeoperationof theurbanization
TGD and SNWTP,
to surrounding
estimate whetheraHalweil,1998).
Generally,
these
resultssuggestthat
anincreasein
irrigationwater
use is one
reduction in problems associated with water stress, crop productivity, and ecosystemof the reasonsThefor theincreasecrop production(Yang roles
et al., 2004a).
degradation.
resultalsoinrevealed
the missingof surface water-groundwaterinteractions and lateral subsurface flow, previously considered to be not as important inprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the Time-Integrated Normalized Difference Vegetation Index (TINDVI) gradient inagricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
from the view point of the hydrologic and biogeochemical cycles, but also to clarify how
substantial pressures from complex issues can be overcome by effective trans-boundary
solutions.
Keywords: crop yield, eco-hydrology model, extremes, TINDVI, water withdrawal

Fig. 4.8 TINDVI over the agricultural fields by NOAA/AVHRR satellite images in the
agricultural fields: (a) Comparison of TINDVI with trend in observed yields at 4
stations (Pt.I; Changsha, Pt.II; Hefei, Pt.III; Zhengzhou, Pt.IV; Tianshui); (b) Spatial
patterns in mean TINDVI gradient (per year) during 1982-1999. In Fig. 4.8b, the blacksquare line represents the border of the simulation area in Albers co-ordinates in the same
way as in Fig. 4.1.

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78 - 78 -CGER-I114-2014, CGER/NIES

4.4 Conclusion
In this chapter the process-based NICE model was coupled with complex sub-systems in
irrigation, stream junction, reservoir operation, and water transfer, to evaluate the cause andeffect of uneven water resources in the entire Changjiang and Yellow River Basins. The
model included different growth processes of representative crops (wheat, maize, soybean,
and rice), and predicted the hydrologic change after operation of the TGD and the SNWTP, toestimate whether critical issues related to water stress, crop productivity, and ecosystemdegradation would diminish in the basins. Of considerable importance is that NICE
highlighted the missing roles of surface water-groundwater interactions and lateral subsurface
flow, which have usually not been considered as important when assuming stationarity inprevious research on a continental scale. Furthermore, the heterogeneous pattern of the
TINDVI gradient in agricultural fields helped to estimate an uneven crop yield and its relation
to water availability. This integrated approach will assist in any re-consideration of thecomplex environmental processes within the region from the view point of hydrologic andbiogeochemical cycles, and will also help to clarify how the substantial pressures ofcomplicated problems can be overcome by effective trans-boundary solutions.
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Water Resources,
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Global30 Arc Second
Elevation
Set. USGS.
fromthe view point
the hydrologic
biogeochemical
cycles,but alsoData
to clarify
how
http://www1.gsi.go.jp/geowww/globalmap-gsi/gtopo30/gtopo30.html.
substantial pressures from complex issues can be overcome by effective trans-boundary
Wang H., Zhang L., Dawes W.R., Liu C. (2001) Improving water use efficiency of irrigated crops in the North
solutions.
China Plain – measurements and modeling. Agricultural and Forest Meteorology, 48, 151–167.
Wang G., Jiang T., Blender R., Fraedrich K. (2008) Yangtze 1/f discharge variability and the interacting
Keywords:
crop
yield,
eco-hydrology
extremes,
TINDVI, water withdrawal
river–lakesystem.
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351, 230-237,
doi: 10.1016/j.jhydrol.2007.12.016.
WebPaNDA (2008) NOAA AVHRR 10-days composite database over East Asia. University of Tokyo.
http://webpanda.iis.u-tokyo.ac.jp/.
Wu G., de Leeuw J., Skidmore A.K., Prins H.H.T., Liu Y. (2007) Concurrent monitoring of vessels and water
turbidity enhances the strength of evidence in remotely sensed dredging impact assessment. Water Research,
41, 3271-3280, doi: 10.1016/j.watres.2007.05.018.
Xu K., Milliman J.D. (2009) Seasonal variations of sediment discharge from the Yangtze River before and after
impoundment
of
the
Three
Gorgesdam.
Geomorphology,
104,276-283,
doi:
10.1016/j.geomorph.2008.09.004.
Xiang L., Lu X.X., Higgitt D.L., Wang S.M. (2002) Recent lake sedimentation in the middle and lower Yangtze
basin inferred from 137Cs and 210Pb measurements. Journal of Asian Earth Sciences, 21, 77-86.
Yang H., Zehnder A. (2001) China’s regional water scarcity and implications for grain supply and trade.
Environmental Planning A, 33, 79-95.
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.
Yang D., Li C., Hu H., Lei Z., Yang S., Kusuda T., Koike T., Musiake K. (2004a) 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 H.S., Dobermann A., Lindquist J.L., Walters D.T., Arkebauer T.J., Cassman K.G. (2004b)
Hybrid-maize–a maize simulation model that combines two crop modeling approaches. Field Crops
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Yang Z., Wang H., Saito Y., Milliman J.D., Xu K., Qiao S., Shi G. (2006) Dam impacts on the Changjiang(Yangtze) River sediment discharge to the sea: The past 55 years and after the Three Gorges Dam. Water
Resources Research, 42, W04407, doi: 10.1029/2005WR003970.
Yellow River Conservancy Committee (1987-1988) Annual report of river discharge at Yellow River. Interior
report of the committee (in Chinese).
Yi Y., Wang Z., Yang Z. (2010) Impact of the Gezhouba and Three Gorges Dams on habitat suitability of carps
in the Yangtze River. Journal of Hydrology, 387, 283-291, doi: 10.1016/j.jhydrol.2010.04.018.
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.

- 83 - 83 -Chapter
Chapter 44 Relation
Relation between
between Uneven
Uneven Water
Water Resources,
Resources, Withdrawal,
Withdrawal, andand
Ecosystem
Ecosystem Degradation
Degradation inin thethe Changjiang
Changjiang andand Yellow
Yellow River
River Basins
BasinsAbstract
The hydro-climate differs vastly between northern and southern China, and this led to an
imbalance in environmental resources related to an increased demand for food and declining
water availability. The Three Gorges Dam (TGD) was designed to diminish flooding and theSouth-to-North Water Transfer Project (SNWTP) has been planned to enable the transfer ofwater from the Changjiang River basin to the Yellow and Hai Rivers. However, these projects
will not necessarily solve all problems associated with water availability within the basins,
and there is an urgent need for the development of a sophisticated eco-hydrological model toevaluate the optimum amount of transferred water, and the socio-economic and environmental
consequences from such endeavors/projects.
In this chapter, a process-based National Integrated Catchment-based Eco-hydrology(NICE) model was modified and coupled with complex irrigation, stream junction, reservoir
operation, and water transfer subsystems, for the development of a coupled human and natural
systems model, and to evaluate the causes and effects of uneven water resources. The modelclarified the impact of irrigation on eco-hydrological processes, and predicted the hydrologicchange after operation of the TGD and SNWTP, to estimate whether there would be a
reduction in problems associated with water stress, crop productivity, and ecosystemdegradation. The result also revealed the missing roles of surface water-groundwaterinteractions and lateral subsurface flow, previously considered to be not as important inprevious studies that assumed stationarity on continental scales. The heterogeneous pattern of
the Time-Integrated Normalized Difference Vegetation Index (TINDVI) gradient inagricultural fields helped to estimate uneven crop yield and its relation to water availability.
This integrated approach not only has a role in re-considering these complex processes
from the view point of the hydrologic and biogeochemical cycles, but also to clarify how
substantial pressures from complex issues can be overcome by effective trans-boundary
solutions.
Keywords: crop yield, eco-hydrology model, extremes, TINDVI, water withdrawal- 64
84 - 84 -CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20
CGER-I114-2014, CGER/NIES

Chapter 5
Final Conclusions and Future Work- 85 - 85 -

Chapter 5

Final Conclusions and Future Work- 86 - 86 -

CGER-I114-2014, 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 between surface water, canopies, unsaturated water, aquifers, lakes, and rivers(Nakayama, 2008a-c, 2009a, 2010a, 2011a-d, 2012a-d, 2013a; Nakayama and Fujita, 2010;
Nakayama and Hashimoto, 2011; Nakayama and Shankman, 2013a-b; Nakayama and
Watanabe, 2004, 2006a-b, 2008a-c; Nakayama et al., 2006, 2007, 2010, 2012) (Fig. 1.2). In
this monograph (Part IV), NICE was enhanced to couple human and natural systems and toevaluate the impact of anthropogenic activity on eco-hydrologic change and the response ofriver-lake-groundwater interactions in the extremes of the Changjiang and Yellow Riverbasins, in order to assist with decision-making processes for the Three-Gorges Dam (TGD)
and the South-to-North Water Transfer Project (SNWTP).
In Chapter 2, NICE was coupled with more complex components such as irrigation, urban
water use, and dam/canal systems, in order to simulate the effects of irrigation on thehydrologic cycle in the highly cultivated Yellow River basin (NICE-DRY). The modelclarified the relationship between the drying of rivers, groundwater degradation, seawater
intrusion, and ecosystem degradation. Scenario analyses of the impact of human intervention
on hydrologic changes imply the need for further correct estimation and appropriate measuresagainst such irrigation (Nakayama, 2011a-b; Nakayama et al., 2006, 2010).
In Chapter 3, the NICE model was coupled to the lake and junction models (NICE-FLD)
in order to evaluate the river-lake-groundwater relationship and the role of the flood storage
capability around lakes in the Changjiang River basin. The model highlighted the missing roleof surface water-groundwater interactions and lateral subsurface flow, which have not usually
been considered to be so important when assuming stationarity in previous research on
continental scales. Furthermore, the model predicted hydrologic change by recovering thevolume of lake water to that of the 1950s, and by inputting meteorological data of the 1998
severe flood, in order to evaluate the role of flood storage capability in lakes in relation towater/heat budgets (Nakayama and Shankman, 2013a; Nakayama and Watanabe, 2008b).
In Chapter 4, the NICE model was coupled with complex sub-systems in irrigation,
stream junction, reservoir operation, and water transfer, to evaluate the cause and effect of theuneven water resources in both the Changjiang and the Yellow River basins. The model
predicted hydrologic change after operation of the TGD and SNWTP, to estimate whether
critical issues involving water stress, crop productivity, and ecosystem degradation would
diminish in the basins. Furthermore, the heterogeneous pattern of the TINDVI gradient inagricultural fields helped to estimate an uneven crop yield and its relation to water availability(Nakayama, 2012c; Nakayama and Shankman, 2013b).
These above results are crucial for clarifying the effect of human activity on ecosystemdegradation caused by water-heat-mass cycle changes on a continental scale. This integrated
approach will have assist in any re-consideration that is made of the complex environmental
processes from the view point of hydrologic and biogeochemical cycles, and also to clarify
how the substantial pressures from complex problems can be overcome by effective
trans-boundary solutions.

- 87 - 87 -

Chapter 5

Final Conclusions and Future Work

5.2 Future Work
This monograph indicates that an effective management of water resources is powerful inenvironmental decision-making and adaptation strategies. The result also strongly suggests
the need for trans-boundary and -authority solutions to ensure sustainable development undersound socio-economic conditions in order to contribute to national and global securities.
The author has also so far researched the mutual interaction and feedback ofhydro-geomorphology and vegetation dynamics in an ecosystem to further explain thepositive feedback between geomorphology and eco-hydrology on irregular slopes and inheterogeneous vegetation (Nakayama, 2008a-b, 2012b, 2013a). After improving thisprocess-based NICE model to include new feedback and down-scaling processes from a
regional to a local simulation with finer resolution, the author then attempted to extract the
impact of hydrologic change, sedimentation, and nutrient availability, on the complex patternof alder invasion, and vice versa (Fig. 5.1). The model also predicted the effectiveness of riverrestoration in conservation plans for the recovery of a native ecosystem, which showed theimportance of the process-based model in assessing these ecosystem dynamics. The author
further attempted to re-evaluate the ecosystem by extending it with the metabolic theory of
ecology (Brown et al., 2004) from the perspective of a meta-ecosystem, by considering themulti-scaled aspects between global-regional-micro (genetic) levels. In addition, the author
assessed the impact of water-heat-material cycle changes attributed to human activities on theecosystem function, and the limiting factors which are accompanied by changes in ecosystem
function (Fig. 5.2).

Fig. 5.1

Down-scaling and feedback process in the extension of NICE.
- 88 - 88 -

CGER-I114-2014, CGER/NIES

Fig. 5.2

Up-scaling and down-scaling in terrestrial/aquatic ecosystems.

- 89 - 89 -

Chapter 5

Final Conclusions and Future Work

Previous research has suggested that variations and uncertainties in the biogeochemical
cycle in terrestrial ecosystems are relatively larger than those in the atmosphere and ocean,and that the terrestrial biosphere sequesters most of the available carbon (Cole et al., 2007).
Recently, however, studies have started to reconsider the importance of inland waters
including rivers, lakes, and groundwater (Cole et al., 2007; Battin et al., 2009). In particular,
inland waters may play a role in the transport, mineralization, and sequestration of carbon, theprocess of which is complicated by surface-groundwater interactions around wetland andriparian areas; water movement in these areas drives carbon storage and flux.
NICE has so far been based on the rectangular coordinate system of the Albers projection(WGS 1984) or UTM projection, on scales of 1 km-1,000 km. When the model is applied tobroader scales, an error increases at the corner of the projection and at the polar zone, and it isnecessary to include the curvature of the earth and any distortion through map projection (Fig.
5.3). NICE is therefore now expanding to include an up-scaling process throughtransformation of the coordinate system from rectangular to longitude-latitude, which isapplicable on global scale by the application of a map factor and non-uniform grid. The
further improved simulation system will play an important role in the evaluation of
spatio-temporal hot spots on a regional scale (Frei et al., 2012), and of boundlessbiogeochemical cycles on a global scale (Cole et al., 2007; Battin et al., 2009) (Fig. 5.4).
Assimilation of NICE with image analysis including remote sensing, satellite data, and aerial
images, will be based on quantification through spatio-temporally high-resolution imaging. In
addition, the identification of hot spots within hydrological and biogeochemical cycles
through a down-scaling process needs to be achieved through assimilation with ground-truth
networking systems. This system will also help to contribute to an improvement of thetipping-point early warning system for sustainable development under the constraint of global
security and environmental degradation.
The author intends to report this research in a succeeding monograph (Part V) in the near
future.

Fig. 5.3

Coordinate transformation for up-scaling of terrestrial/aquatic ecosystems.
- 90 - 90 -

CGER-I114-2014, CGER/NIES

Fig. 5.4

NICE coupled with a biogeochemical model for evaluation of the boundlessbiogeochemical cycle.

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Brown J.H., Gillooly J.F., Allen A.P., Savage V.M., West G.B. (2004) Toward a metabolic theory of ecology.
Ecology, 85, 1771–1789.
Cole J.J., Prairie Y.T., Caraco N.F., McDowell W.H., Tranvik L.J., Striegl R.G., Duarte C.M., Kortelainen P.,
Downing J.A., Middelburg J.J., Melack J. (2007) Plumbing the global carbon cycle: integrating inland
waters into the terrestrial carbon budget. Ecosystems, 10, 171-184, doi: 10.1007/s10021-006-9013-8.
Frei S., Knorr K.H., Peiffer S., Fleckenstein J.H. (2012) Surface micro-topography causes hot spots ofbiogeochemical activity in wetland system: A virtual modeling experiment. Journal of GeophysicalResearch, 117, G00N12, doi: 10.1029/2012JG002012.
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/publications/report/i083/i083_e.html.
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.

- 91 - 91 -Chapter 5

Final Conclusions and Future Work

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) Feedback mechanism and complexity in ecosystem － development of integrated
assessment system towards eco-conscious society－, Chemical Engineering of Japan, 75, 789-791 (inJapanese).
Nakayama T. (2011d) Construction of integrated assessment system for win-win solution of hydrothermal
degradations in urban area towards eco-conscious society, Chemical Information and Computer Sciences,29(4), 63-65 (in Japanese).
Nakayama T. (2012a) Visualization of missing role of hydrothermal interactions in Japanese megalopolis forwin-win solution. Water Science and Technology, 66(2), 409-414, doi: 10.2166/wst.2012.205.
Nakayama T. (2012b) Feedback and regime shift of mire ecosystem in northern Japan. Hydrological Processes,
26(16), 2455-2469, doi: 10.1002/hyp.9347.
Nakayama T. (2012c) Impact of anthropogenic activity on eco-hydrological process in continental scales.
Procedia Environmental Sciences, 13, 87-94, doi: 10.1016/j.proenv.2012.01.008.
Nakayama T. (2012d) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part III), CGER's Supercomputer Monograph Report, 18, NIES, 98p.,
http://www.cger.nies.go.jp/publications/report/i103/en/.
Nakayama T. (2013a) For improvement in understanding eco-hydrological processes in mire. Ecohydrology andHydrobiology, 13, 62-72, doi: 10.1016/j.ecohyd.2013.03.004.
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 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. (2013a) Impact of the Three-Gorges Dam and water transfer project on Changjiang
floods. Global and Planetary Change, 100, 38-50, doi: 10.1016/j.gloplacha.2012.10.004.
Nakayama T., Shankman D. (2013b) Evaluation of uneven water resource and relation between anthropogenic
water withdrawal and ecosystem degradation in Changjiang and Yellow River basins. Hydrological
Processes, 27(23), 3350-3362, doi: 10.1002/hyp.9835.
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. (2005) Re-evaluation of groundwater dynamics about water and nutrient budgets in
Lake Kasumigaura, Annual Journal of Hydroscience and Hydraulic Engineering, 49, 1231-1236 (Abst. in
English).
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/publications/report/i063/I063e.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 (International Association of Theoretical and Applied Limnology), 30(3),345-348.
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.

- 92 - 92 -

CGER-I114-2014, CGER/NIES

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. (2012) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes, 26(16), 2431-2444, doi:
10.1002/hyp.9290.

- 93 - 93 -

Chapter 5

Final Conclusions and Future Work- 94 - 94 -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.20
CGER-I114-2014, CGER/NIES

Appendix
Publications and Presentations- 95 - 95 -Appendix Publications and Presentations

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) Feedback mechanism and complexity in ecosystem － development of integrated
assessment system towards eco-conscious society－, Chemical Engineering of Japan, 75, 789-791 (inJapanese).
Nakayama T. (2011d) Construction of integrated assessment system for win-win solution of hydrothermal
degradations in urban area towards eco-conscious society, Chemical Information and Computer Sciences,29(4), 63-65 (in Japanese).
Nakayama T. (2012a) Visualization of missing role of hydrothermal interactions in Japanese megalopolis forwin-win solution. Water Science and Technology, 66(2), 409-414, doi: 10.2166/wst.2012.205.
Nakayama T. (2012b) Feedback and regime shift of mire ecosystem in northern Japan. Hydrological Processes,
26(16), 2455-2469, doi: 10.1002/hyp.9347.
Nakayama T. (2012c) Impact of anthropogenic activity on eco-hydrological process in continental scales.
Procedia Environmental Sciences, 13, 87-94, doi: 10.1016/j.proenv.2012.01.008.
Nakayama T. (2013a) For improvement in understanding eco-hydrological processes in mire. Ecohydrology andHydrobiology, 13, 62-72, doi: 10.1016/j.ecohyd.2013.03.004.
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 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. (2013a) Impact of the Three-Gorges Dam and water transfer project on Changjiang
floods. Global and Planetary Change, 100, 38-50, doi: 10.1016/j.gloplacha.2012.10.004.
Nakayama T., Shankman D. (2013b) Evaluation of uneven water resource and relation between anthropogenic
water withdrawal and ecosystem degradation in Changjiang and Yellow River basins. Hydrological
Processes, 27(23), 3350-3362, doi: 10.1002/hyp.9835.
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. (2005) Re-evaluation of groundwater dynamics about water and nutrient budgets in
Lake Kasumigaura, Annual Journal of Hydroscience and Hydraulic Engineering, 49, 1231-1236 (Abst. in
English).
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 (International Association of Theoretical and Applied Limnology), 30(3),345-348.
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.
- 96 -- 96 -CGER-I114-2014, CGER/NIES

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. (2012) Multi-scaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes, 26(16), 2431-2444, doi:
10.1002/hyp.9290.

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/publications/report/i083/i083_e.html.
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. (2011e) 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. (2011f) Impact of water degradation on ecosystem change and adaptation strategy for sustainable
development, In: Water and Society, D. W. Pepper, C. A. Brebbia (eds), pp.139-150, WIT Press, doi:
10.2495/WS110131.
Nakayama T. (2012d) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part III), CGER's Supercomputer Monograph Report, 18, NIES, 98p.,
http://www.cger.nies.go.jp/publications/report/i103/en/.
Nakayama T. (2012e) Impact of irrigation on hydrologic change in highly cultivated basin, In:
Evapotranspiration - Remote Sensing and Modeling, A. Irmak (ed), pp.125-146 (Chapter 7), InTech,
Croatia.
Nakayama T. (2012f) Future prospects of water crisis and adaptation strategy for effective water resourcesmanagement, In: 2012 International Water Market Symposium: Experiences and Challenges, M. Kondo(ed), pp.1-13.
Nakayama T. (2013b) Effect of evapotranspiration on hydrothermal changes in regional scale, In:
Evapotranspiration - An Overview, Stavros G. Alexandris (ed), pp.127-142 (Chapter 7), InTech, doi:
10.5772/52808.
Nakayama T. (2013c) Hydrology–ecology interactions, In: Handbook of Engineering Hydrology - Vol. 1:
Fundamentals and Applications, Saeid Eslamian (ed), pp.329-344 (Chapter 16), Taylor and Francis, CRC
Press, USA.
Nakayama T., Shankman D. (2011) Response of river–lake–groundwater interaction to extremes in ChangjiangRiver, In: Meteorological, Hydrological and Tsunami Disasters: Social Adaptation and Future, G. Arduino,
K. Takara (eds), pp.117-124, UNESCO-IHP.
Nakayama T., Shankman D. (2013c) Prediction of effect of huge structures on eco-hydrological changes inChangjiang Basin, In: Floods: From Risk to Opportunity, A. Chavoshian, K. Takeuchi (eds), pp.127-134,
IAHS Publ. 357, IAHS Press.
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/publications/report/i063/I063e.html.
Nakayama T., Watanabe M. (2008d) Evaluation of ecosystem dynamics in the catchment of East Asia by using
process-based model, CGER’s Supercomputer Annual Report 2006, NIES, pp.103-108 (Abst. in English).
- 97 -- 97 -

Appendix Publications and Presentations

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
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).
- 98 -- 98 -CGER-I114-2014, CGER/NIES

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).
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, Water and Society 2011, Las Vegas, USA, 5 – 7 December.
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.
Nakayama T. (2012) Coupling with satellite data and eco-hydrology model for evaluation of two extremes incontinental scale, paper number MM-11, AGU Chapman Conference on Remote Sensing of the Terrestrial
Water Cycle, Hawaii, USA, 19 – 22 February.
Nakayama T. (2012) Toward construction of process-based tipping-point early warning system, session number
P1.055, Planet Under Pressure 2012, London, UK, 26 – 29 March.

- 99 -- 99 -

Appendix Publications and Presentations

Nakayama T. (2012) Achievement of win-win solution to intertwined environmental pollutions for
eco-conscious society, session number P3.157, Planet Under Pressure 2012, London, UK, 26 – 29 March.
Nakayama T., Shankman D. (2012) Integrated assessment toward trans-boundary solution and sustainable
development, session number P2.017, Planet Under Pressure 2012, London, UK, 26 – 29 March.
Nakayama T. (2012) Adaptation strategy for integrated water resources management toward eco-conscious
society, paper number NR-14, AGU Science Policy Conference 2012, Washington DC, USA, 30 April – 3
May.
Nakayama, T. (2012) Toward improvement in boundless biogeochemical cycles in terrestrial-aquatic ecosystems,
session number 13R, AWRA 2012 Summer Specialty Conference, Denver, Colorado, USA, 25 – 29 June
(Website).
Nakayama, T. (2012) Local heterogeneity and scaled dependence of eco-hydrological process in mire, session
number 6-11, BIOGEOMON 2012, North Port, Maine, USA, 15 – 20 July.
Nakayama, T. (2012) Toward better understanding of heterogeneous succession process in mire, session number
COS100-2, 97th ESA Annual Meeting, Portland, Oregon, USA, 5 – 10 August.
Nakayama, T. (2012) Toward early-warning integrated system by meta-ecosystem analysis in mire, session
number S49.10, Eco Summit 2012, Columbus, Ohio, USA, 30 September – 5 October.
Nakayama, T., Shankman, D. (2012) Interaction between surface water and groundwater and its impact onboundless biogeochemical cycle in continental scale, session number 12-4, AWRA 2012 Annual Water
Resources Conference, Jacksonville, Florida, USA, 12 – 15 November (Website).
Nakayama, T. (2012) Simulation of hydrogeomorphic feedbacks in terrestrial-aquatic continuum, session title
Hydrologic-Geomorphic Feedbacks, AGU Chapman Conference on Hydrogeomorphic Feedbacks and Sea
Level Rise in Tidal Freshwater River Ecosystems, Reston, Virginia, USA, 13 – 16 November.
Nakayama, T., Shankman, D. (2012) Impact of river–lake–groundwater interaction on boundless carbon cycle incontinental basin, paper number GC24A-2, AGU Fall Meeting 2012, San Francisco, USA, 3 – 7 December.
Nakayama, T. (2013) Up-scaling and down-scaling of process-based eco-hydrological model for improvement inboundless biogeochemical cycles in terrestrial-aquatic continuum, HydroEco2013, Rennes, France, 13 – 16
May (Proceeding).
Nakayama, T., Shankman, D. (2013) Integrated assessment system toward improvement in boundless carbon
cycle and trans-boundary sustainable development, GWSP Int. Conf. 2013, Bonn, Germany, 21 – 24 May(Proceeding).
Nakayama, T., Shamil, M. (2013) Application of process-based eco-hydrological model to northern Eurasia
wetlands toward improvement in boundless biogeochemical cycles, IAHS-IAPSO-IASPEI Joint Assembly,
Gothenburg, Sweden, 22 – 26 July.
Nakayama, T. (2013) Impact of spatio-temporal scales on eco-hydrological changes in advanced process-basedmodel, session number 5, ISRS2013, Beijing, China, 5 – 9 August (Proceeding).
Nakayama, T., Shamil, M. (2013) Application of advanced process-based model to West Siberian Lowland,
APHW2013, Seoul, Korea, 18 – 21 August.
Nakayama, T., Shamil, M. (2013) Improvement in process-based eco-hydrology model toward evaluation ofboundless biogeochemical cycles in global scale, session number MGC.14, ISEM2013, Toulouse, France,
28 – 31 October.
Nakayama, T., Shamil, M. (2013) Up-scaling of process-based eco-hydrology model to global scale for
identification of hot spots in boundless biogeochemical cycles, paper number GC21A-0804, AGU Fall
Meeting 2013, San Francisco, USA, 9 – 13 December.

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- 100 -

- 100 -CGER’S SUPERCOMPUTER MONOGRAPH REPORT
Vol. 1CGER-I021-’96 (Out of stock)
KOMORI S.: Turbulence Structure and CO2 Transfer at the Air-Sea Interface and Turbulent Diffusion in
Thermally-Stratified Flows

Vol. 2CGER-I022-’96
TOKIOKA T., NODA A., KITOH A., NIKAIDOU Y., NAKAGAWA S., MOTOI T., YUKIMOTO S., TAKATA K.:
A Transient CO2 Experiment with the MRI CGCM -Annual Mean Response-Vol. 3CGER-I025-’97
NUMAGUTI A., SUGATA S., TAKAHASHI M., NAKAJIMA T., SUMI A.: Study on the Climate System and
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AKIYOSHI H.: Development of a Global 1-D Chemically Radiatively Coupled Model and an
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HAYASHI Y.Y., TOYODA E., HOSAKA M., TAKEHIRO S., NAKAJIMA K., ISHIWATARI M.: Tropical
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NODA A., YUKIMOTO S., MAEDA S., UCHIYAMA T., SHIBATA K., YAMAKI S.: A New MeteorologicalResearch Institute Coupled GCM (MRI-CGCM2) -Transient Response to Greenhouse Gas andAerosol Scenarios-

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NOZAWA T., EMORI S., NUMAGUTI A., TSUSHIMA Y., TAKEMURA T., NAKAJIMA T., ABE-OUCHI A.,KIMOTO M.: Transient Climate Change Simulations in the 21st Century with the CCSR/NIES CGCM
under a New Set of IPCC Scenarios

Vol. 9CGER-I057-2004
MIYAZAKI T., FUJISHIMA S., YAMAMOTO M., WEI Q., HANAZAKI H.: Vortices, Waves and Turbulencein a Rotating Stratified Fluid

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HAYASHI S., MURAKAMI S., XU K., WATANABE M.: Modeling of Daily Runoff in the Changjiang(Yangtze) River Basin and Its Application to Evaluating the Flood Control Effect of the Three Gorges
Project

Vol. 11CGER-I063-2006
NAKAYAMA T., WATANABE M.: Development of Process-based NICE Model and Simulation of
Ecosystem Dynamics in the Catchment of East Asia (Part I)

Vol. 12CGER-I073-2007
NOZAWA T., NAGASHIMA T., OGURA T., YOKOHATA T., OKADA N., SHIOGAMA H.: Climate Change
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Vol. 13CGER-I080-2008
SHIBATA K., DEUSHI M.: Simulations of the Stratospheric Circulation and Ozone during the Recent
Past (1980-2004) with the MRI Chemistry-Climate Model

Vol. 14CGER-I083-2008
NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics
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CGER-I092-2010 (Out of stock)
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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NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics
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Vol. 19

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KOMORI S.: Numerical Simulations of Turbulence Structure and Scalar Transfer across the Air-Water
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