CGER-REPORTISSN 1341-4356
CGER-I169-2024

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30

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

Tadanobu Nakayama

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

CGER-REPORTISSN 1341-4356
CGER-I169-2024

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30

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

Tadanobu Nakayama

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30

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

Coordination for Resource Allocation of the Supercomputer:

Center for Global Environmental Research (CGER) , Earth System Division (ESD)
National Institute for Environmental Studies (NIES)

Supercomputer Steering Committee (FY2023):

Masayoshi Ishii (Meteorological Research Institute, Japan Meteorological Agency)
Masaki Satoh (Atmosphere and Ocean Research Institute, The University of Tokyo)
Seiya Nishizawa (RIKEN Center for Computational Science)
Akinori Takami (Regional Environment Conservation Division/NIES)
Toshihiro Azuma (Planning Division/NIES)
Hideharu Akiyoshi (Earth System Division/NIES)
Tomoo Ogura (Earth System Division/NIES)

Maintenance of the Supercomputer System:

Environmental Information Division
National Institute for Environmental Studies (NIES)

Operation of the Supercomputer System:
NEC Corporation

Copies of this report can be obtained from:Center for Global Environmental Research (CGER) , Earth System Division (ESD)
National Institute for Environmental Studies (NIES)
16-2 Onogawa, Tsukuba, Ibaraki , 305-8506 Japan
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E-mail: www-cger@nies.go.jp
The report is also available as a PDF file.
See: https://www.cger.nies.go.jp/ja/activities/supporting/publications/report/

Copyright 2024:

NIES: National Institute for Environmental Studies

ISSN 1341-4356 (printed version), ISSN 2434-5679 (online version), CGER-I169-2024

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 environmental change and identifying
solutions to critical 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) in March 1992, and the system
was upgraded to an NEC SX-4 in 1997, an NEC SX-6 in 2002, an NEC SX-8R in 2007, an
NEC SX-9 in 2013, and an NEC SX-ACE in June 2015. The current system consists of an NEC
SX-Aurora TSUBASA/type A511-64 and 22PB file storage to provide more computational
resources and data capacity. The NIES supercomputer system is available for researchers at
research institutions and universities in Japan, including NIES. The Supercomputer Steering
Committee is comprised of Japan's leading scientists in climate modeling, carbon cycle,
atmospheric chemistry, marine environment, and other fields related to global environmental
research, and one of its functions is to evaluate research proposals that require the use of the
NIES supercomputer system. To promote the dissemination of results, we publish Annual
Reports and occasional Monograph Reports. Annual Reports deliver the results of all research
projects that used the NIES supercomputer system each year, whereas Monograph Reports
present the integrated outcomes of a particular research project.
This Monograph Report presents the results of the development process of the National
Integrated Catchment-based Ecohydrology (NICE) model after the 11th (Part I), 14th (Part II),
18th (Part III), 20th (Part IV), 26th publications (Part V), and 29th publications (Part VI). This
volume reviews the NICE processes developed since Part VI was published and describes how
the model has been applied to evaluate plastic dynamics of various basins from regional to
global scales. In order to solve this problem, NICE was extended to link it with a plastic debris(engineered materials) model for freshwater systems in this volume. This methodology is usefulfor quantifying the impacts of plastic waste on terrestrial and aquatic ecosystems, anddeveloping solutions and efficient measures to reduce the plastic load on a global scale.
In future, we intend to continue our support for environmental research by using
supercomputer resources and disseminating practical information based on our results.
March 2024

Nobuko Saigusa
Director
Center for Global Environmental Research
Earth System Division
National Institute for Environmental Studies-i-Preface
This volume of the CGER’s SUPERCOMPUTER MONOGRAPH REPORT series is the
30th publication on research outcomes achieved by the users of supercomputer facilities at theCenter for Global Environmental Research (CGER) at the National Institute for Environmental
Studies (NIES).
An advanced process-based model, known as the National Integrated Catchment-based
Eco-hydrology (NICE) model, has been developed to evaluate ecosystem dynamics incatchments. NICE is a 3-D, grid-based eco-hydrology model that simulates the complex
interactions between forest canopy, surface water, unsaturated zones, aquifers, lakes, and rivers.
It also iteratively simulates nonlinear interactions between hydrogeomorphic and vegetation
dynamics. The model can also be coupled with complex subsystems to simulate systems suchas irrigation, urban water usage, stream junctions, and dams/canals; develop integrated humanand natural systems; and analyze the impact of anthropogenic activity on eco-hydrologicchange. Water resources are vital for human activity, but their overuse has caused serious
environmental degradation, economic stagnation, and immense burdens on the surrounding
environment. This is particularly evident in Asia, where finding a solution to environmental
pollution is crucial.
This monograph (Part VII) succeeds the 11th (Part I), 14th (Part II), 18th (Part III), 20th
(Part IV), 26th publications (Part V), and 29th publications (Part VI). This volume reviews the
NICE processes developed since Part VI was published and describes how the model has been
applied to evaluate plastic dynamics of various basins from regional to global scales. In orderto solve this problem, NICE was extended to link it with a plastic debris model for freshwater
systems, including advection, dispersion, diffusion, settling, dissolution and deterioration due
to light and temperature. Establishment of complete model for the whole picture of plastic
dynamics on basin scale becomes a big step forward. The results are also important forpredicting and estimating the change of plastic dynamics under climate change. These results
help to quantify the impacts of plastic waste on terrestrial and aquatic ecosystems, and may be
useful for devising solutions and measures for reduction of plastic input to the ocean.
NICE is now being expanded to incorporate heteroaggregation, sorption, and biofouling,etc. interacting plastic with suspended matter, carbon cycle, and other media. Plastic pollution
is considered to be one of today’s main environmental problems, and such pollutants in streams,
rivers, and oceans pose potential risks to human health and the environment. From thisviewpoint, the results help develop solutions and efficient measures to reduce the plastic load
on a global scale. This will be reported in the next monograph (Part VIII). I hope that thispublication will help integrate knowledge and provide the understanding needed to reachsustainability by 2030 in the UN Sustainable Development Goals, which supports climate
action, clean water, sustainable cities, life below water, and life on land.
March 2024

Tadanobu Nakayama
Chief Senior Researcher
Regional Environment Conservation Division
National Institute for Environmental Studies- ii -

Contents
Foreword .....................................................................................................................................i
Preface ....................................................................................................................................... ii
Contents .................................................................................................................................... iii
List of Figures ...........................................................................................................................vi
List of Tables .............................................................................................................................ix
Chapter 1
General Introduction ............................................................................................................... 1
1.1 Background .......................................................................................................................... 3
1.2 Description of Process-Based Model NICE Applicable to Quantify Plastic Dynamics ...... 4
1.3 Objective and Methods toward Efficient Measures to Reduce Plastic Input to Ocean........ 6
References .................................................................................................................................. 8
Chapter 2
Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal
Dynamics of Macro and Microplastics for Whole of Japan .......................................... 13
Abstract .................................................................................................................................... 15
2.1 Introduction ........................................................................................................................ 16
2.2 Methods .............................................................................................................................. 18
2.2.1 Model Description of Mass Transport Process in NICE ................................................. 18
2.2.2 Modelling Fate and Transport of Macro and Microplastics in Land and Inland Water
by Extension of NICE-BGC ................................................................................................ 20
2.3 Input Data and Boundary Conditions for Simulation......................................................... 22
2.3.1 Model Input Data ............................................................................................................ 22
2.3.2 Plastic Generation at Point and Non-Point Sources and Removal at WWTP ................. 23
2.3.3 Running the Simulation and Verification........................................................................ 24
2.4 Results and Discussion ...................................................................................................... 25
2.4.1 Evaluation of Hydrologic Cycle in First-Class (Class A) River Basins in Entire Japan 25
2.4.2 Analysis of Sensitivity of Microplastic Transport to Various Factors ............................ 27
2.4.3 Impact of Plastic Density and WWTP on Riverine Export of Plastics into Ocean ......... 29
2.5 Conclusion .......................................................................................................................... 30
References ................................................................................................................................ 31- iii -

Chapter 3
Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal
Variability........................................................................................................................... 37
Abstract .................................................................................................................................... 39
3.1 Introduction ........................................................................................................................ 40
3.2 Methods .............................................................................................................................. 42
3.2.1 Coupling of NICE-BGC with Plastic Debris Model and its Application to Global
Scale..................................................................................................................................... 42
3.2.2 Extension of Settling Velocity Applicable to Uncertainty Analysis ............................... 44
3.3 Input Data and Boundary Conditions for Simulation......................................................... 45
3.3.1 Model Input Data including Plastic Generation and Removal ........................................ 45
3.3.2 Boundary Conditions and Running the Simulation......................................................... 47
3.3.3 Analysis of the Influence of Various Factors on Plastic Transport................................. 47
3.4 Results and Discussion ...................................................................................................... 48
3.4.1 Evaluation of Plastic Flux from World’s Major Rivers into Ocean ................................ 48
3.4.2 Seasonal Variation of Plastic Flux on a Global Scale ..................................................... 49
3.4.3 Uncertainty and Sensitivity Analyses of Riverine Plastic Transport on Continental andGlobal Scales ....................................................................................................................... 51
3.5 Conclusion .......................................................................................................................... 52
References ................................................................................................................................ 53
Chapter 4
Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a
Process-Based Eco-Hydrology Model .............................................................................. 57
Abstract .................................................................................................................................... 59
4.1 Introduction ........................................................................................................................ 60
4.2 Methods .............................................................................................................................. 61
4.2.1 General Model Framework of NICE-BGC ..................................................................... 61
4.2.2 Extension of Plastic Debris Model including Global Reservoirs and Lakes .................. 63
4.3 Input Data and Boundary Conditions for Simulation......................................................... 63
4.3.1 Model Input Data ............................................................................................................ 63
4.3.2 Boundary Conditions and Running the Simulation......................................................... 64
4.4 Results and Discussion ...................................................................................................... 65
4.4.1 Effect of Reservoirs on Differences in Horizontal Plastic Transport.............................. 65
4.4.2 Effect of Reservoirs on Differences in Plastic Burial ..................................................... 66
4.4.3 Change in Global Plastic Cycle by Considering Effects of Lentic Waters ..................... 67
4.5 Conclusion .......................................................................................................................... 69
References ................................................................................................................................ 69- iv -

Chapter 5
Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics
in Asian Region .................................................................................................................. 73
Abstract .................................................................................................................................... 75
5.1 Introduction ........................................................................................................................ 76
5.2 Methods .............................................................................................................................. 78
5.2.1 General Model Framework of NICE-BGC ..................................................................... 78
5.2.2 Extension of Plastic Debris Model including Effect of Waste Plastic Trade.................. 79
5.3 Input Data and Boundary Conditions for Simulation......................................................... 80
5.3.1 Model Input Data including Effect of Waste Plastic Trade ............................................ 80
5.3.2 Running the Simulation and Verification........................................................................ 83
5.4 Results and Discussion ...................................................................................................... 84
5.4.1 Seasonal Variations of Riverine Plastic Transport .......................................................... 84
5.4.2 Distribution of Microplastic in Asian major rivers ......................................................... 85
5.4.3 Impact of Waste Plastic Trade on Change in Riverine Plastic Transport in AsianRegion.................................................................................................................................. 86
5.5 Conclusion .......................................................................................................................... 87
References ................................................................................................................................ 88
Chapter 6
Final Conclusions and Future Work .................................................................................... 93
6.1 Final Conclusions ............................................................................................................... 95
6.2 Future Work ....................................................................................................................... 96
References .............................................................................................................................. 100
Appendix
Publications and Presentations ........................................................................................... 105
Original Papers and Reviews Related to This Monograph .................................................... 107
Conference Reports Related to This Monograph ................................................................... 107
NICE Series in CGER's Supercomputer Monograph Report (Part I - VI) ............................. 108
Contact Person........................................................................................................................ 108
CGER’S SUPERCOMPUTER REPORT (previously published) ......................................... 109-v-

List of Figures
Chapter 1
1.1
Process-based National Integrated Catchment-based Eco-hydrology (NICE)
model ........................................................................................................................... 6
1.2
NICE simulation for quantifying hydrologic cycle and its ecosystem assessment
in various basins/catchments from regional to global scales. Previous model
applications to regional, continental, and global scales (Part I–VI) and plastic
dynamics in the current monograph (Part VII) ........................................................... 7
1.3
Flow diagram of macro and microplastic transport in land and inland water byextending NICE ........................................................................................................... 8(Some of the figures in Chapter 1 are reprinted from Nakayama, T. (under review) Impact of settling
and resuspension on plastic dynamics during extreme flow and their seasonality in global major rivers.
Hydrological Processes)

Chapter 2
2.1
Location of the study area in entire Japan; (a) all the first-class of 109 river
basins, (b) elevation, (c) land cover, (d) population density, (e) pumping andwastewater treatment plant (WWTP), and (f) mismanaged plastic waste (MPW)................................................................................................................................... 18
2.2
Flow diagram of macro and microplastic transport in land and inland water byextending NICE-BGC ............................................................................................... 22
2.3
Simulated results of surface runoff in major rivers with large basin areas andlong river channels among the first-class rivers during 2014–2015; (a) total
surface runoff, (b) surface runoff, and (c) intermediate flow ................................... 26
2.4
Comparison of annual-averaged values in (a) discharge, (b) nitrogen transport,and (c) phosphorus transport in the first-class of river basins; red circle shows
the nutrient transports simulated by the time series for the entire calculation
period during 2014–2015, and blue triangle shows the values simulated only by
the base flow of rivers ............................................................................................... 27
2.5
Sensitivity analysis of various factors on micro-plastic concentration
downstream of Tone River from June 1 to July 20 in 2014; (a) effect of particle
diameter (diameter = 1, 10, 20 m with constant density = 1150 kg/m3), (b)effect of particle density (density = 900, 1010, 1350 kg/m3 with constantdiameter = 10 m), (c) effect of particle degradation (with the effect of watertemperature, UV irradiation, and both with diameter = 10 m and density =1150 kg/m3) ............................................................................................................... 28
2.6
Simulated plastic flux to the ocean; (a)-(b) annual fluxes in each first-class of
109 rivers, and (c) total flux in entire Japan with various scenarios; Blue and redbars show the flux of macro and microplastics ......................................................... 30(The figures in Chapter 2 are reprinted from Nakayama, T., Osako, M. [2023a] Development of aprocess-based eco-hydrology model for evaluating the spatio-temporal dynamics of macro- and microplastics
for
the
whole
of
Japan.
EcologicalModelling,476,
110243,
doi:10.1016/j.ecolmodel.2022.110243)- vi -

Chapter 3
3.1
Location of study area on the global scale; (a) elevation, (b) land cover, (c)fertilizer and manure, (d) population density, (e) untreated wastewater
calculated from wastewater treatment and wastewater production, and (f)mismanaged plastic waste (MPW) ............................................................................ 42
3.2
Flow diagram of macro- and micro-plastics transport in land and inland waterby extending NICE-BGC .......................................................................................... 44
3.3
Comparison between simulated annual plastic flux from the major global rivers
into the ocean and the previous material: (a) macro-plastic, (b) micro-plastic,and (c) total plastic fluxes ......................................................................................... 49
3.4
Relationships between population density and (a) macro-plastic concentration,(b) micro-plastic concentration, (c) macro-plastic flux per unit area, and (d)micro-plastic flux per unit area in global major rivers simulated by NICE .............. 50
3.5
Seasonal variations of (a) river discharge and plastic fluxes of (b) total, (c)macro, and (d) micro plastics in each continent in the same cases as that in Fig.
3.3; ‘AS’, ‘EU’, ‘OC’, ‘AF’, ‘NA’, and ‘SA’ mean Asia, Europe, Oceania,
Africa, North America, and South America.............................................................. 51
3.6
Uncertainty and sensitivity analyses using the Monte Carlo model simulations
to evaluate the influence of various factors on annual-averaged riverine plastictransport in global major rivers: (a) macro-plastic, (b) micro-plastic, and (c) total
plastic fluxes.............................................................................................................. 52(The figures in Chapter 3 are reprinted from Nakayama, T., Osako, M. [2023b] The flux and fate ofplastic in the world's major rivers: Modeling spatial and temporal variability. Global and Planetary
Change, 221, 104037, doi:10.1016/j.gloplacha.2023.104037)

Chapter 4
4.1
Study area of the world’s major rivers (153 basins, 325 river channels) with 82
major reservoirs (red circle) and 19 lakes (blue mesh) included in these basins
to simulate the plastic dynamic; Green circle line (25 reservoirs of the total 82)
and purple circle line (19 lakes) means the application of NICE-BGC and
LAKE2K coupled model in this study ...................................................................... 61
4.2
Flow diagram of macro- and micro-plastics transport in land and inland waterby extending NICE-BGC coupled with LAKE2K to lotic and lentic waters ........... 62
4.3
Difference of simulated horizontal plastic transport in inland waters with andwithout the presence of reservoirs; (a)-(c) macro-plastic and (d)-(f) microplastic transports without effect of settling and resuspension, with almost thesame as the density of water (density = 1000.1 kg/m3), and on averaged plastic
condition of all iterations of Monte Carlo model simulations (density = 1008.8
kg/m3) ........................................................................................................................ 66
4.4
Comparison of (a)-(c) macro-plastic and (d)-(f) micro-plastic burials on floating
condition (settling velocity = 0), with almost the same as the density of water(density = 1000.1 kg/m3), and on averaged plastic condition of all iterations ofMonte Carlo model simulations in global major reservoirs without the lake
model (horizontal axis) and with it (vertical axis) .................................................... 67
4.5
Simulated plastic deposition in the global (a) lakes and (b) reservoirs; Blue andred bars show the flux of macro and microplastics ................................................... 68(The figures in Chapter 4 are reprinted from Nakayama, T. (under review) Impact of global major- vii -

reservoirs and lakes on plastic dynamics by using a process-based eco-hydrology model. Lakes andReservoirs: Research and Management)

Chapter 5
5.1
Location of the study area in Japan and Asian regions; (a) land cover, (b)population density, (c) untreated wastewater calculated from wastewater
treatment and wastewater production, and (d) mismanaged plastic waste (MPW)................................................................................................................................... 78
5.2
Flow diagram of macro and microplastics transport in land and inland water byextending NICE-BGC to include the effect of waste plastic trade ........................... 79
5.3
Effect of waste plastic trade on MPW in Asian regions; (a)-(b) around Shanghai
District in importing country (China) without and with this effect; (c)-(d) aroundTokyo Metropolitan Area in exporting country (Japan) without and with thiseffect .......................................................................................................................... 83
5.4
Simulated results of seasonal variations of riverine plastic transport of Yangtze,
Yellow, and Mekong Ganges Rivers; (a) plastic concentration, and (b) plastictransport in these rivers ............................................................................................. 85
5.5
Simulated results of the size distribution of microplastic in the water andriverbed sediment of Yangtze, Mekong, and Ganges Rivers; (a) normal
frequency distribution in the water, (b) that in the sediment, and (c) relative
abundance across different size categories in the water and sediment ..................... 86
5.6
Impact of waste plastic trade on variations of riverine plastic transport in Asian
regions; (a) amount of plastic flux for each first-class of 109 rivers in Japan, (b)total flux in entire Japan, and (c) amount of plastic flux for Asian major rivers ...... 87(The figures in Chapter 5 are reprinted from Nakayama, T., Osako, M. [2024] Plastic trade-off: Impact
of export and import of waste plastic on plastic dynamics in Asian region. Ecological Modelling, 489,
110624, doi:10.1016/j.ecolmodel.2024.110624)

Chapter 6
6.1
Flow diagram of the original NICE regarding water resources, and recent
development of the eco-hydrological and biogeochemical coupling model(NICE-BGC) ............................................................................................................. 98
6.2
Percentages of (a) exporting micro-plastic load stored on the riverbed and (b)plastic load transported to the ocean during flood periods in some major global
rivers .......................................................................................................................... 99
6.3
Improvement of the carbon cycle by including global estuaries; (a) CO2 flux inglobal estuaries, and (b) CO2 evasion and sediment storage in each continent ...... 100(Some of the figures in Chapter 6 are reprinted from Nakayama, T. [2022] Impact of anthropogenic
disturbances on carbon cycle changes in terrestrial-aquatic-estuarine continuum by using an advanced
process-based model. Hydrological Processes, 36(2), e14471, doi:10.1002/hyp.14471, and Nakayama,
T. (under review) Impact of settling and resuspension on plastic dynamics during extreme flow and theirseasonality in global major rivers. Hydrological Processes)- viii -List of Tables
Chapter 2
2.1
List of input data sets for the NICE and NICE-BGC simulations ............................ 23
2.2
Per capita emission of macroplastics and microplastics into rivers in previous
materials .................................................................................................................... 24(The tables in Chapter 2 are reprinted from Nakayama, T., Osako, M. [2023a] Development of a processbased eco-hydrology model for evaluating the spatio-temporal dynamics of macro- and micro-plastics
for the whole of Japan. Ecological Modelling, 476, 110243, doi:10.1016/j.ecolmodel.2022.110243)

Chapter 3
3.1
List of input data sets for the NICE and NICE-BGC simulations ............................ 46
3.2
Per capita emission of macro-plastics and micro-plastics into rivers in previous
materials .................................................................................................................... 46(The tables in Chapter 3 are reprinted from Nakayama, T., Osako, M. [2023b] The flux and fate of plastic
in the world's major rivers: Modeling spatial and temporal variability. Global and Planetary Change,
221, 104037, doi:10.1016/j.gloplacha.2023.104037)

Chapter 4
4.1
List of input data sets for the NICE and NICE-BGC simulations ............................ 64
4.2
Weighted average of plastic budget in the global major rivers (153basins/watersheds, 325 river channels) ..................................................................... 69(The tables in Chapter 4 are reprinted from Nakayama, T. (under review) Impact of global major
reservoirs and lakes on plastic dynamics by using a process-based eco-hydrology model. Lakes andReservoirs: Research and Management)

Chapter 5
5.1
List of input data for NICE and NICE-BGC simulations in Japan and Asian
regions ....................................................................................................................... 81
5.2
Effect of waste plastic trade on MPW in Asia Pacific and major economies in2015 ........................................................................................................................... 82(The tables in Chapter 5 are reprinted from Nakayama, T., Osako, M. [2024] Plastic trade-off: Impact
of export and import of waste plastic on plastic dynamics in Asian region. Ecological Modelling, 489,
110624, doi:10.1016/j.ecolmodel.2024.110624)- ix -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30
CGER-I169-2024, CGER/NIES

Chapter 1
General Introduction-1-1-

Chapter 1 General Introduction-2-2-

CGER-I169-2024, CGER/NIES

1.1 Background
Plastic pollution is one of today’s main environmental problems, and such pollutants instreams, rivers, and oceans pose potential risks to human health and the environment (Siegfried
et al., 2017). Plastic accumulation on riverbanks, deltas, coastlines, and the ocean surface israpidly increasing, and it is estimated that 60% of the plastics ever manufactured have been
discarded in landfills or the natural environment (Meijer et al., 2021). Plastic waste can beroughly categorized as macro-plastic (≥5 mm) or micro-plastic (<5 mm), the latter being further
divided into primary and secondary micro-plastics according to origin (Siegfried et al., 2017).
Once plastic is released into the environment, it is gradually degraded by physical, chemical,
and biological processes, leading to further subdivision into many forms, all of which are
impossible to remove and remain indefinitely in the environment (Zhang et al., 2021).
Previous studies on the origin and fate of plastic waste in freshwater systems havesuggested that land-derived plastics are one of the main sources of marine plastic pollution(Thompson et al., 2004) because of either direct emission from coastal areas (Jambeck et al.,2015) or by transport via rivers (Lebreton et al., 2017; Schmidt et al., 2017). Over the last
decade, there has been great progress in modeling the fate and transport of plastics. Kooi et al.
(2018) classified plastic debris models for freshwater systems into four categories: emissionbased mass balance, global, multimedia, and spatiotemporally explicit models. Because mostof the above plastic debris models are based on an analogy with sediment models for inland
waters, it is questionable whether these models are reasonably adaptable for modeling the
dynamics of plastics with various sizes, densities, shapes, polymer types, and contact angles(Nizzetto et al., 2016; Waldschlager and Schüttrumpf, 2019).
As for the waste plastic trade, Japan was one of the top exporting countries after Europe
and America, and some countries, such as China, imported this waste plastic (Nakayama andOsako, 2024). China has imported approximately 45% of all cumulative imports of plastic
waste between 1988 and 2016 (Brooks et al., 2018; Dominish et al., 2020; EIA, 2021). Thissituation changed greatly after establishing the “Basel Convention” (United Nations, 2019)because some importing countries introduced import regulations mainly for environmental
protection. Developing a framework for reversing global water resource degradation, especiallyplastic pollution, is also powerful for achieving Sustainable Development Goals (SDG)(UNESCO, 2022). The recent G20 Osaka Summit in 2019 shared the “Osaka Blue Ocean
Vision” (Ministry of Foreign Affairs of Japan, 2019). A quantitative assessment of the fate andtransport of plastics will be necessary to realize this vision because countries worldwide havedecided on concrete future measures for dealing with plastic based on scientific results.
The author has developed a process-based model coupling eco-hydrology with thebiogeochemical cycle (National Integrated Catchment-based Eco-hydrology (NICE)-BGC)(Nakayama, 2017a,b), incorporating complex terrestrial-aquatic linkages in the hydrologicbiogeochemical cycle. Recently, the authors extended to couple this process-based NICE-BGC
with a plastic debris (engineered materials) model and evaluated spatiotemporal variations ofplastic debris in the entire Japan of regional scale (Nakayama and Osako, 2023a) and theworld’s major rivers of global scale (Nakayama and Osako, 2023b). The successful applicationof the NICE model in existing research has enabled the detection of the spatiotemporal hotspotsof plastic fluxes by quantifying the impacts of plastic waste on terrestrial and aquatic
ecosystems. The results also help improve the plastic cycle and develop better solutions andefficient measures to reduce plastic load globally as much as possible.

-3-3-Chapter 1 General Introduction

1.2 Description of Process-Based Model NICE Applicable to Quantify Plastic Dynamics
The National Integrated Catchment-based Eco-hydrology (NICE) model, which includes
surface-unsaturated–saturated water processes and assimilates land-surface processes, has beendeveloped to describe phenology variations based on satellite data (Nakayama, 2008a–c, 2009,2010, 2011a–d, 2012a–d, 2013, 2014a,b, 2015, 2016, 2017a–c, 2018, 2019, 2020, 2022,
2023a,b; Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama andMaksyutov, 2018; Nakayama and Osako, 2023a,b, 2024; Nakayama and Pelletier, 2018;
Nakayama and Shankman, 2013a,b; Nakayama and Watanabe, 2004, 2005, 2006a,b, 2008a–c;
Nakayama et al., 2006, 2007, 2010, 2012, 2021a,b, 2023) (Fig. 1.1). NICE has been applied tovarious basins/catchments from local and regional scales, such as the Tokyo Metropolitan area
in Japan, Kushiro Wetland (the largest wetland in Japan), and Lake Kasumigaura catchment (ahighly eutrophic lake in Japan), and to continental and global scales, such as the Changjiang
and Yellow Rivers in China, Ob River in West Siberia, Mekong River in Southeast Asia,
Mongolia. The results of these studies are described in the papers cited above and have beensummarized in previous CGER Supercomputer Monograph Report Series Vols. 11 (Part I)(Nakayama and Watanabe, 2006b), 14 (Part II) (Nakayama, 2008c), 18 (Part III) (Nakayama,
2012d), 20 (Part IV) (Nakayama, 2014b), 26 (Part V) (Nakayama, 2019), and 29 (Part VI)(Nakayama, 2023b) (Fig. 1.2).
NICE consists of complex sub-models: a surface hydrology model, a land-surface model
including urban and crop processes, a groundwater model, a regional atmospheric model, amass transport model of sediment, nutrients, carbon, and plastics, and a vegetation succession
model. NICE applies a rectangular coordinate system based on the Albers or Universal
Transverse Mercator (UTM) projection (Nakayama, 2014a,b, 2015). It also incorporates
surface–groundwater interactions assimilating land-surface processes to describe the satellite
data-derived leaf area index (LAI) variations and a fraction of photosynthetically active
radiation (FPAR). The surface hydrology model of the NICE consists of a kinematic wave
theory-based hillslope hydrology model and a distributed stream network model based on bothkinematic and dynamic wave theories. NICE also solves a partial differential equation
describing the three-dimensional groundwater flow for unconfined and confined aquifers. The
integration of sub-models considers water and heat fluxes from the ground to the surface,
including the hydraulic potential gradient between the deepest unsaturated flow layer andgroundwater level, effective precipitation calculated from actual precipitation, infiltration into
the upper soil moisture store, evapotranspiration, and seepage between the river water andgroundwater.
The model can iteratively simulate nonlinear interactions between hydrologic, geomorphic,
and ecological processes and include new feedback through downscaling from regional to local
simulations employing fine resolution. Since NICE includes vegetation succession andcompetition between invasive alder and native mire vegetation, in addition to the stochastic
simulation of seedling establishment, mortality, and regeneration, the model improves previous
modeling approaches to the dynamic interaction between hydrological and biotic processes(Pastor et al., 2002; Rietkerk et al., 2004; van de Koppel et al., 2005; Ridolfi et al., 2006; Sacoet al., 2007). Using NICE, previous studies have attempted to extract the impacts of discharge,
groundwater level change, sediment deposition, and nutrient availability on the complex patternof alder invasion and vice versa (Nakayama, 2008a,b, 2009, 2010, 2012b, 2013, 2014a,b;
Nakayama and Watanabe, 2004, 2006a,b).
To fill the current eco-hydrology gap, the author has further developed a new model inwhich the original NICE is coupled with various biogeochemical cycle models (NICE-BGC)-4-4-

CGER-I169-2024, CGER/NIES(Nakayama, 2016, 2017a, 2017b) (Fig. 1.1). Each sub-model offers iterative simulation mostefficiently by combining on-line and off-line modes (on-line: data input/output through I/Omemory, off-line: data input/output through file); this means that the newly developed model
incorporates the connectivity of the biogeochemical cycle accompanied by the hydrologic cycle
between surface water and groundwater, hillslopes and river networks, and other intermediate
regions (Nakayama, 2016). Recently, the author modified NICE-BGC to include the effect ofreservoirs (Nakayama and Pelletier, 2018) and two parameters (soil organic carbon and carbon
emission from the intermediate soil pool) to improve the accuracy of long-term simulation(Nakayama, 2020).
Thus, NICE has been applied to analyze water, heat, sediment, nutrients, and carbon cycles
in various basins at the regional, continental, and global scales. Incorporating the plastic debris
model and its relations to these water and material cycles is important. Regarding the scale
similarity and discontinuity of eco-hydrological processes, identifying the spatial coupling of
local ecosystems, including energy, materials, and organisms, across ecosystem boundaries isheuristically important. Recent research has raised serious concerns regarding the extrapolationof small-scale experimental results to entire landscapes, suggesting that it is imperative tobridge the gap between ecosystems at various scales (Deegan et al., 2012). Therefore, theecosystems should be re-evaluated by extrapolating the ‘metabolic theory of ecology’ (Brown
et al., 2004) from the perspective of a meta-ecosystem analysis by considering multi-scaled
aspects at the global–regional–micro level, like the ‘river continuum concept’ (Vannote et al.,1980); this is also related to a previous finding that geophysical and microbial capacities
enhance net heterotrophy in inland waters (Battin et al., 2008). Therefore, an accurate model toquantify the fate and transport of plastics is needed to evaluate the spatiotemporal dynamics ofmacro- and micro-plastics in each basin.

-5-5-Chapter 1 General Introduction

Figure 1.1 Process-based National Integrated Catchment-based Eco-hydrology (NICE) model.

1.3 Objective and Methods toward Efficient Measures to Reduce Plastic Input to Ocean
To study ecosystem dynamics in catchments in East Asia, a method that combines a gridbased numerical model, a ground–truth observation network, satellite data, and statisticalanalysis has been developed previously. The current monograph (Part VII) succeeds the CGER
Supercomputer Monograph Report Series regarding the NICE model (Fig. 1.2).
(i) 11th publication (Part I), 2006: Governing Equation of NICE (Nakayama and Watanabe,
2006b)(ii) 14th publication (Part II), 2008: For Nature Restoration and Urban Regeneration(Nakayama, 2008c)(iii) 18th publication (Part III), 2012: Application of NICE to Urban Areas in Japan and China(Nakayama, 2012d)(iv) 20th publication (Part IV), 2014: Continental-scale in Changjiang and Yellow River Basins(Nakayama, 2014b)(v) 26th publication (Part V), 2019: Evaluation of Missing Role of Inland Water in Global
Biogeochemical Cycle (Nakayama, 2019).
(vi) 29th publication (Part VI), 2023: Evaluation of Anthropogenic Activity on Hydrologic
Alteration in Arid and Semi-Arid Regions of Mongolia (Nakayama, 2023b).
The process-oriented model should be linked with the plastic debris model to quantify the
impacts of plastic waste on the biosphere and to aid the development of solutions and measures
to reduce plastic input to the ocean. In addition, the relationship between the amount of carbon
assimilated by plants and water availability is one of the fundamentals of eco-hydrological-6-6-

CGER-I169-2024, CGER/NIES

models. Furthermore, the intensity of carbon cycling and distribution of dead and living organic
matter are related to temperature (Zalewski, 2002). A grid-based approach to solving the onedimensional time-dependent equations for thermal energy balance in advective river systems is
also powerful for estimating temperature with high accuracy (Yearsley, 2012), in addition to
the empirical relationship between air and water temperatures widely applied to many models.
To fill the gap in current plastic debris models in different categories (Kooi et al., 2018), a
comprehensive investigation was first conducted by surveying existing research on plastic
cycles as much as possible. Based on that, the original NICE was extended to couple it with theplastic debris model for quantifying the impacts of plastic waste on terrestrial and aquatic
ecosystems and devising solutions and measures for the reduction of plastic input to the ocean(Fig. 1.3). The model simulated quantified flood events have a great impact on the mobilizationof plastic and lead to high interannual variability in fluxes. This methodology is powerful for
detecting and predicting plastic dynamics in large rivers and for developing efficient measures
to reduce plastic input to the ocean on regional, continental, and global scales. Details of thisprocess are described in the following chapters.

Figure 1.2 NICE simulation for quantifying hydrologic cycle and its ecosystem assessment invarious basins/catchments from regional to global scales. Previous model applications
to regional, continental, and global scales (Part I–VI) and plastic dynamics in thecurrent monograph (Part VII).

-7-7-

Chapter 1 General Introduction

Figure 1.3 Flow diagram of macro and microplastic transport in land and inland water byextending NICE.

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CGER-I169-2024, CGER/NIES

Chapter 2
Development of a Process-Based Eco-Hydrology Model for Evaluating
Spatiotemporal Dynamics of Macro and Microplastics for Whole of Japan- 13 - 13 -Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan- 14 - 14 -

CGER-I169-2024, CGER/NIES

Abstract
Plastic contamination has been receiving considerable attention during the last few decades.
Although some models could simulate the transport and fate of plastic debris in freshwater
systems, a complete model for plastic dynamics on a basin scale has yet to be established. In
the present study, the authors extended process-based eco-hydrology models, NICE (National
Integrated Catchment-based Eco-hydrology) and NICE-BGC (BioGeochemical Cycle), to linkthem with plastic debris models, and applied them to all the 109 first-class (class A) river basinsthroughout Japan. The new model included advection, dispersion, diffusion, settling,
dissolution, and deterioration due to light and temperature. These processes could help evaluate
the effect of mismanaged plastic waste (MPW) and point sources (tires, personal care products,
dust, and laundry) on the spatiotemporal dynamics of macro and microplastics there. The model
showed that the simulated hydrologic cycle generally agreed with the observed one. In contrast,
the simulated value of annual-averaged nutrient transports was overestimated compared to theobserved value averaged over non-flood periods. Sensitivity analysis of microplastic transport
to various factors also implied degradation effects would not be negligible under someconditions. Generally, large amounts of plastic flowed out of some limited rivers (Tone, Kiso,
Yodo, and Ara Rivers) with a large proportion of macroplastic flux compared to microplastic
flux. Further, scenario analysis quantified the total plastic flux varied according to the efficiencyof microplastic removal in wastewater treatment plants (WWTP) and density of plastic and wasestimated as within the range of 1,100–3,500 tons/yr, relatively like that of existing values. It
was also clarified that only a limited proportion of plastics discharged onto land flowed out into
the ocean intensively during rainfall seasons. These results help quantify the impacts of plastic
waste on the biosphere and may aid the development of solutions and measures to reduce plastic
input to the ocean.
Keywords: Eco-hydrology model; mismanaged plastic waste; plastic debris; river basin- 15 - 15 -Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

2.1 Introduction
Plastic pollution is considered one of today’s main environmental problems, and such pollutantsin streams, rivers, and oceans pose potential risks to human health and the environment(Siegfried et al., 2017). Plastic accumulation on riverbanks, deltas, coastlines, and the ocean
surface is rapidly increasing, and it is estimated that 60% of the plastics ever manufactured havebeen discarded in landfills, or the natural environment (Meijer et al., 2021). Based on size,
plastic waste can be roughly categorized as macroplastic (≥5 mm) or microplastic (<5 mm),and the latter is sometimes further categorized partially as nanoplastic (<100 nm). Microplastic
is also divided into primary (intentionally manufactured as small) and secondary (arisingthrough decomposition, fragmentation, and degradation of larger pieces) microplastics
according to origin (Siegfried et al., 2017). Once plastic is released into the environment, it is
impossible to remove and remains indefinitely.
Previous studies on the origin and fate of plastic waste in freshwater systems havesuggested that land-derived plastics are one of the main sources of marine plastic pollution(Thompson et al., 2004) because of either direct emission from coastal areas (Jambeck et al.,2015) or by transport via rivers (Lebreton et al., 2017; Schmidt et al., 2017). Although Jambeck
et al. (2015) were the first to estimate the mass of land-based plastic waste entering the global
ocean, they did not consider plastic waste from inland water or its size class. Subsequent studies(Lebreton et al., 2017; Schmidt et al., 2017) estimated the distribution of global riverine
emissions of plastic into the ocean by using empirical indicators representative of waste
generation (mismanaged plastic waste: MPW) inside a river basin. Using a probabilistic
approach, Meijer et al. (2021) proposed a more accurate analysis to account for the spatial
distribution of plastic waste generation (Lebreton and Andrady, 2019) andclimatological/geographical differences within river basins. They estimated that more than
1,000 rivers account for 80% of global riverine plastic emissions into the ocean, which range
between 0.8 and 2.7 million metric tons per year. On a regional scale, only a few studies haveevaluated the concentration and riverine flux of plastics in Japan (Kataoka et al., 2019; Nihei etal., 2020). Such assessment of the fate and transport of plastics would also be necessary torealize the “Osaka Blue Ocean Vision” shared at the G20 Osaka Summit 2019 (Ministry ofForeign Affairs of Japan, 2019).
Over the last decade, there has been great progress in modeling the fate and transport ofplastics. Kooi et al. (2018) classified plastic debris models for freshwater systems into four
categories: emission-based mass balance, global, multimedia, and spatiotemporally explicit
models. Boucher et al. (2020) have summarized the previous development of science-based
metrics to measure the leakage and circularity of plastics. In particular, the global and spatially
explicit model is powerful for estimating the riverine export of plastics to coastal seas
(Praetorius et al., 2012; Quik et al., 2015; Nizzetto et al., 2016; Besseling et al., 2017; Siegfried
et al., 2017; van Wijnen et al., 2019; Whitehead et al., 2021). Some of these studies (Siegfried
et al., 2017; van Wijnen et al., 2019; Strokal et al., 2021) adapted a process-oriented model tocontinental and global scales by extending an existing non-dynamic nutrient export model.
However, the fate of microplastics is also dependent on their size, density, shape, and polymer
type: factors that were not included in these previous global analyses of generic material(Jambeck et al., 2015; Lebreton et al., 2017; Schmidt et al., 2017; Meijer et al., 2021). Nizzetto
et al. (2016) incorporated the settling and resuspension of microplastics to estimate theirretention in rivers by extending the in-stream sediment model used in a previous study (Lazaret al., 2010). They conducted a sensitivity analysis of the size and density of microplastics influxes using this spatially explicit model of INCA-Contaminants. Whitehead et al. (2021) also
- 16 - 16 -

CGER-I169-2024, CGER/NIES

used the same model to simulate the microplastic mass loads of four particle size classes invarious reaches of the Thames River. It is expected that the accuracy of global or regionalemissions estimated using the above probabilistic approach (Meijer et al., 2021) would be
improved by using the spatially explicit model (Unice et al., 2019). These models will help toidentify the hotspots and sinks of plastic pollution and resolve the source-flux-sink nexus withinriver basins (Windsor et al., 2019).
Because most of the above plastic debris models are based on an analogy with sedimentmodels for inland waters, it is questionable whether these models are reasonably adaptable formodeling the dynamics of plastics with various sizes, densities, shapes, polymer types, andcontact angles (Nizzetto et al., 2016; Waldschlager and Schüttrumpf, 2019). The transport of
microplastics in river ecosystems is either gravity-driven (vertical transport) or occurs throughsettling (horizontal transport) (Kumar et al., 2021). Cowger et al. (2021) modified the Rouseprofile of vertical concentration to include all traditional domains of transport (bed load, settlingsuspended load, and wash load) as well as additional domains specific to low-density materials
with increasing velocities in water (rising suspended load and surface load); this revealed thatthe vertical profile of low-density plastics such as polyethylene (PE), polypropylene (PP), andpolystyrene (PS) under the predominance of wind shear differs considerably from the original
Rouse profile of natural sediment (Liu et al., 2021). Particle size generally appears to be a moresensitive parameter influencing microplastic transport and retention efficiency because of its
wide range across several orders of magnitude. However, there is a sharp change in parameter
sensitivity when the density approaches that of water (Nizzetto et al., 2016). It is also important
to model the transport of plastics in air, water, and soil (Koutnik et al., 2021). These factors
must be considered when assessing the fate and transport of plastics in various basins overscales ranging from local/regional to continental/global.
One of the authors has developed a process-based model coupling eco-hydrology with thebiogeochemical cycle (National Integrated Catchment-based Eco-hydrology (NICE)-BGC)(Nakayama, 2016, 2017a, 2017b), incorporating complex terrestrial-aquatic linkages in thehydrologic-biogeochemical cycle. NICE-BGC can simulate nonlinear interactions betweenhydrologic, geomorphic, and ecological processes (water, heat, sediment, nutrient, and carbon
cycles, etc.). In this chapter, the process-based NICE-BGC was extended to link it with a plastic
debris (engineered materials) model for freshwater systems and applied to all 109 first-class(class A) river basins in the whole of Japan (Nakayama and Osako, 2023a) (Fig. 2.1a). The newmodel included advection, dispersion, diffusion, settling, dissolution, and deterioration due tolight and temperature but assumed no interaction with suspended matter (hetero-aggregation),
resuspension, biofouling, or wind effects. For model simplification, the authors also assumed
microplastics to be pure, inert polymers with spherical particles of constant size and density.
Based on this background, two basic issues were addressed: (i) How does emission change
relative to differences in the size and density of mismanaged plastic waste (MPW)? (ii) How
do hydraulic structures and water treatment plants affect riverine emissions of plastic into theocean? To clarify these issues, NICE-BGC simulated how about 36,000 tons/yr of MPW(Meijer et al., 2021) and point sources such as tires, personal care products (PCPs), dust, andlaundry in the entire country are transported from land to river, and finally to the ocean.
Establishing a complete model for plastic dynamics on a basin scale becomes a big step forward.
The results are also important for predicting and estimating the change of plastic dynamics
under climate change in the future. These results help quantify the impacts of plastic waste on
terrestrial and aquatic ecosystems. They may be useful for devising solutions and measures toreduce plastic input to the ocean.

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Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

Figure 2.1 Location of the study area in entire Japan; (a) all the first-class of 109 river basins, (b)elevation, (c) land cover, (d) population density, (e) pumping and wastewater
treatment plant (WWTP), and (f) mismanaged plastic waste (MPW)

2.2 Methods
2.2.1 Model Description of Mass Transport Process in NICE-BGC
The NICE series of process-based catchment models have been developed for application
to natural, agricultural, and urban regions in various catchments and basins (Nakayama, 2008a–c, 2009, 2010, 2011a–d, 2012a–d, 2013, 2014a,b, 2015, 2016, 2017a–c, 2018, 2019, 2020,
2022, 2023a,b; Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama andMaksyutov, 2018; Nakayama and Osako, 2023a,b, 2024; Nakayama and Pelletier, 2018;
Nakayama and Shankman, 2013a,b; Nakayama and Watanabe, 2004, 2005, 2006a,b, 2008a–c;
Nakayama et al., 2006, 2007, 2010, 2012, 2021a,b, 2023) (Fig. 1.1). The NICE model consistsof complex sub-models: a surface hydrology model; a land-surface model including urban andcrop processes; a groundwater model; a regional atmospheric model; a mass transport model ofsediment, nutrients, carbon, and plastics; and a vegetation succession model.
The two-dimensional diffusion model, including sedimentation (here corresponding toplastic in this study) in NICE (Fig. 1.1) simulates the mass transport in a hillslope after
simulation of the hillslope runoff model including snowmelt runoff (Nakayama and Watanabe,
2004; Nakayama, 2010).

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CGER-I169-2024, CGER/NIES

=

�

��

{𝐾𝐾𝐾𝐾 𝐾𝐾𝐾𝐾

��
��

�

��

{𝐷𝐷 𝐷 𝐷𝐷} +}+�

�(�𝐷�)����{𝐾𝐾𝐾𝐾 𝐾𝐾𝐾𝐾

��

��

+

�(�𝐷�)
��} + 𝐷𝐷 𝐷 𝐷𝐷(𝐶𝐶) + 𝑞𝑞�� − 𝑤𝑤� ⋅ 𝑐𝑐�

(2.1)

where M and N are discharges per unit width along the x and y coordinate axes; H (m) is theelevation of the hillslope bottom; S (m) is the hydraulic head; D (m) is the flow depth (=S-H);
C (mg/l) is the mass concentration; Kx and Ky (m/s) are the kinematic eddy diffusivities along
the x and y coordinate axes; L(C) is the concentration of MPW in this study; qsu (m/s) is the rateof resuspension from the hillslope bed; wf (m/s) is the settling velocity of macro andmicroplastics; and cb (mg/l) is the standard concentration.
The suspended load per unit width in two-dimensional uniform flow is expressed by thefollowing equation (Itakura, 1984):
�

(2.2)

𝑞𝑞𝑞𝑞 = � � 𝑐𝑐(𝑧𝑧)𝑢𝑢(𝑧𝑧)𝑑𝑑𝑑𝑑

where h (m) is flow depth; c(z) is the concentration at z above the riverbed; u(z) is the flow
velocity at z above the riverbed; and b (m) is the riverbed elevation. Although the concentration
would greatly depend on the value of b, the author used the semi-empirical relation (b=0.05h)
in the following sections. When the riverbed is at equilibrium conditions, and the particle size
distribution is uniform, the resuspension rate from the riverbed is equal to the sedimentation
volume. cb and qsu are expressed by using a theory based on an energy equation of solid-liquid
two-phase flow and the Monin-Obukhov length in the following equations (2.3)-(2.7) (Itakura,1984):� ��

𝑐𝑐� = 𝐾𝐾[𝛼𝛼∗ �

��

���

�� � ��𝛺𝛺 =��= 𝐾𝐾[𝛼𝛼∗

�∗

�∗

�

⋅

�

��

�

�

⋅

��

�∗ ��

⋅ 𝛺𝛺 𝛺 1]√�∗�� � ��

�

−

��

�
���[�� � ] ��√�����[�� � ] ��
�� √�

(2.5)

�

exp(−𝑎𝑎𝑎� )

(2.6)

�

exp(−𝑎𝑎� ) ⋅ (𝛼𝛼� 𝑇𝑇 + 𝛼𝛼� 𝑇𝑇 � + 𝛼𝛼� 𝑇𝑇 � )

�

(2.7)

�

exp[−𝜉𝜉 � ] 𝑑𝑑𝑑𝑑 ≅

√��

��

𝜉𝜉

√�

�∗

�∗ ��

√��

��

(2.4)

−1

+

exp[−𝜉𝜉 � ] 𝑑𝑑𝑑𝑑 =

√�

(2.3)

]

�

𝜉𝜉

�

�

�

�

��

⋅

where a’ (=B*/* - 1/0), T (= 1/(1+0.33267 √2a’)), 0 =0.5, B* =0.143, * =0.14, K =0.008,
1 =0.4361836, 2 = -0.1201676, and 3 =0.937298 are constants;  (kg/m3) and s (kg/m3)
are the density of water and fine sand, respectively; g (m/s2) is the gravitational acceleration; s’(=s/-1) is the relative gravity of fine sand in water; and d (m) is particle size. The- 19 - 19 -

Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

concentration c is expressed in equation (2.8) (Shimizu and Arai, 1988), and the settling
velocity wf uses Rubey’s formula (Rubey, 1933) in equation (2.9):
�

𝑐𝑐 = � (1 − 𝑒𝑒 �� ) , 𝛽𝛽 =
�

�� �

(2.8)

�

�

𝑤𝑤� = � (𝑠𝑠 𝑠 1)𝑔𝑔𝑔𝑔 + 𝛼𝛼 � − 𝛼𝛼 , 𝛼𝛼 =����, 𝜀𝜀 =

��

(2.9)

�

A recent study has shown that the velocity of microplastic deposition follows patterns fornaturally occurring allochthonous particles (Hoellein et al., 2019). A one-dimensional diffusion
model including sedimentation (here corresponding to plastic in this study) in the following
equation (2.10) simulates the mass transport in a river channel after simulation of the stream
network model (Nakayama and Watanabe, 2004; Nakayama, 2010):
���

(𝐴𝐴 𝐴 𝐴𝐴) +�(�𝐴�𝐴�)
��

=

�

��

{𝐴𝐴 𝐴 𝐴𝐴𝐴𝐴 𝐴

����(2.10)} + 𝑆𝑆 + 𝑞𝑞�� − 𝑤𝑤� ⋅ 𝑐𝑐�

where A (m2) is the cross-sectional area of the river channel, and S is the sources and sinks ofthe constituent due to reactions and mass transfer mechanisms. The above equation (2.10)
equals equation (2.1) when the cross-section is rectangular. The simulated lateral inflow of mass
was input to equation (2.10) if the lateral inflow simulated by a hillslope model enters along theside of the river channel.
The bed material load is expressed in Einstein’s formula (Einstein, 1950) in equation (2.11):
𝑞𝑞�∗ =

���(�� ���)��

, 𝑞𝑞� = (8𝜏𝜏∗ − 0.047)�� , 𝜏𝜏∗ =��∗�(�� ���)��

(2.11)

where d (m) is the particle diameter of the bed material load, * is the non-dimensional tractive
force, and u* (m/s) is friction velocity, respectively. The continuity equation of riverbed
sediment is expressed by using the suspended load qsu and the bed material load qB in thefollowing:
���
��

=−

�

���

(𝑞𝑞�� − 𝑤𝑤� 𝑐𝑐� +

���
��

)

(2.12)

where zB (m) is the bed elevation; and  is the porosity of the bed material (void ratio). It was
assumed that a little water would remain in the grid to keep dissolving the mass flux, even incases where the flow depth is almost zero, which is necessary to simulate flush-out of the massprocess at higher precipitation. Furthermore, it was assumed that all the water and mass would
mix completely in a vertical direction when the flow depth rose to the upper grid in the nexttime step. This model applies to the surface runoff comprising surface and intermediate flows.
2.2.2 Modelling Fate and Transport of Macro and Microplastics in Land and Inland Water
by Extension of NICE
In this study, NICE-BGC (Nakayama, 2016, 2017a, 2017b) was extended to model the fate
and transport of macro and microplastics in land and water (Nakayama and Osako, 2023a) (Fig.
2.2). The new model included advection, dispersion, diffusion, settling, dissolution and- 20 - 20 -

CGER-I169-2024, CGER/NIES

deterioration due to light and temperature, but assumed no interaction with suspended matter(heteroaggregation) (Praetorius et al., 2012), resuspension (Waldschlager and Schüttrumpf,
2019), biofouling (Kooi et al., 2017), or wind effects. Thus, the model can simulate thehydrograph for plastic transport with various shear stresses during storm events, as proposedby Kumar et al. (2021). The authors also assumed that microplastics are pure, inert polymers
with spherical particles of constant size and density for model simplification.
The transport of plastic on a hillslope was simulated in the above equations (2.1)-(2.9),
except (2.4). Because equation (2.4) assumes the resuspension rate when a riverbed is underequilibrium conditions, and the particle size distribution is uniform, applying this formula
directly to plastic is difficult. It is difficult to grasp the heterogeneous distribution of settled anddeposited plastic on hillslopes, although some studies have evaluated the erosion behavior of
microplastic particles compared to sediments (Waldschlager and Schüttrumpf, 2019). For
model simplification, we also assume that this plastic is negligible compared to discharged
plastic and that resuspension is zero on hillslopes. After the simulation of plastic transport on ahillslope, the authors used the estimated rate of conversion of macroplastics to microplastics(3%/yr) in the same way as van Wijnen et al. (2019), with the extension of the conversion rate
employed by Jambeck et al. (2015) (Fig. 2.2).
NICE-BGC simulates plastic transport in a river channel by extending QUAL2Kw(Pelletier et al., 2006). Macro and microplastics become buried through settling, dissolution,and deterioration due to light, temperature, advection, dispersion, and diffusion. The terms ofsources and sinks for the constituent S due to reactions and mass transfer mechanisms can be
added to the above equation (2.10):
�𝑆𝑆���� = − ���� 𝑃𝑃��� −𝑘𝑘���� (𝑇𝑇)𝑃𝑃��� − 𝛼𝛼������𝑆𝑆���� = − ���� 𝑃𝑃��� −𝑘𝑘���� (𝑇𝑇)𝑃𝑃��� − 𝛼𝛼������(�)/���� �

�(�)/���� �(1 − 𝑒𝑒 ���� )𝑃𝑃���(2.13)

�(�)/��

(2.14)(1 − 𝑒𝑒 ���� )𝑃𝑃���+𝑘𝑘���� (𝑇𝑇)𝑃𝑃��� + 𝛼𝛼������ �(1 − 𝑒𝑒 ���� )𝑃𝑃���

where Pmac (g/l) and Pmic (g/l) are the macro and microplastic concentrations; T is the watertemperature; vPmac and vPmic are the settling velocities of macro and microplastics (m/d); kPmac(T)and kPmic(T) are the temperature-dependent plastic dissolution rates (/d); and Pmac and Pmic are
the light efficiency factors (-), respectively. We assume that the deterioration due to light canbe modeled as a first-order temperature-dependent decay, and the deterioration rate ke due tolight is based on the Beer-Lambert law. We also assume that there is no interaction withsuspended matter (hetero-aggregation), resuspension, biofouling, or the effects of wind.
Furthermore, we assume that the deterioration of macroplastics due to light and temperatureshows the transition to microplastics, and this disappearance term in equation (2.13) was added
to equation (2.14).

- 21 - 21 -

Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

Figure 2.2 Flow diagram of macro and microplastic transport in land and inland water byextending NICE-BGC

2.3 Input Data and Boundary Conditions for Simulation
2.3.1 Model Input Data
The input data at various spatial resolutions were prepared and arranged to calculate aspatially averaged 0.1° x 0.1° grid (about 9-11 km resolution) using the Spatial Analyst Tools
in ArcGIS v10.3 software for a simulation of Japan as a whole (Fig. 2.1 and Table 2.1);
elevation, land cover, soil texture, vegetation type, river networks, lakes and wetlands,
reservoirs and dams, and geological structures were categorized based on the global andJapanese datasets. To create the grid data from point, polyline, and polygon data for different
sources with different spatial resolutions in a reasonable way, overlay analysis was conductedusing GIS tools (intersect, union, and identity) (ESRI, 2019).
The authors also subdivided the agricultural regions of GLC2000 into major crops in
MIRCA2000 (Portmann et al., 2010) and the FAO crop definition (Leff et al., 2004) (Table 1).
The irrigation type (surface water, groundwater, and other) and irrigation water use derived
using the global datasets were also defined in the same way as in the author’s previous study(Nakayama, 2020). The total nutrient nitrogen and phosphorus consumptions for each crop
were calculated to obtain the totals for N and P fertilizer, N and P manure, and N deposition- 22 - 22 -

CGER-I169-2024, CGER/NIES

using ArcGIS software to merge the data with MIRCA2000 in each 5 arc-minute grid (≈10 km);this confirmed that the calculated total consumption of each nutrient was within the range ofthe previous data (Bouwman et al., 2009). Therefore, for the NICE-BGC simulation, the authors
used the spatially distributed data for fertilizer and manure application for each crop after that.
Although data for plastics are scarce, the authors used some data to simulate the plastic
debris for Japan as a whole; population data at 250 m resolution was obtained from the Japanese
Census in 2015 (Japanese Government Statistics, 2021), wastewater treatment plants (WWTP)
at 2,185 locations (Ministry of Land, Infrastructure, Transport and Tourism, 2013) as point
sources, and MPW at 1 km resolution (Lebreton and Andrady, 2019) as a diffuse source (Table
2.1).
Table 2.1 List of input data sets for the NICE and NICE-BGC simulations
Data set
Climatology
Elevation
Land cover
Soil texture
Vegetation typeRiver networks
Lakes and wetlands
Geological structures
Crop typeIrrigation typeIrrigation water use
Basin boundary
Reservoirs and dams
Fertilizer use
Manure use
Population
Wastewater treatment plants
Mismanaged plastic waste

Original resolution
1.0 °1.0 km
1.0 km
1.0 km0.25 °
line
0.5 min
0.5 °
5 min
5 min
5 min
50 m
point
5 min
5 min
250 m
point
1.0 kmYear
1998-2015
around 1996
around 2000
around 1970-2000
around 2000
around 2000
around 1990-2000
around 1970-2000
around 2000
2000-2008
1998-2002
around 20102014
around 2000
1998-2014
2015
2010
2015

Source and reference
ERA-interim; ECMWF (2019)
GTOPO30; U.S. Geological Survey (1996)
GLC2000; European Commission (2015)
HWSD; European Commission (2012)
GLDAS Vegetation Class; NASA (2013)
MLIT (2012)
GLWD; Lehner and Döll (2004)
GLiM; Hartmann and Moosdorf (2012)
MIRCA2000; Portmann et al. (2010)
GMIA; FAO (2016)
GCWM; Siebert and Döll (2010)
MLIT (2011)
MLIT (2016)
Earth Stat; Mueller et al. (2012)
Zhang et al. (2017)
Japanese Government Statistics (2021)
MLIT (2013)
Lebreton and Andrady (2019)

2.3.2 Plastic Generation at Point and Non-Point Sources and Removal at WWTP
Some previous studies of plastic dynamics on a global scale (Lebreton and Andrady, 2019;
Strokal et al., 2021). Lebreton and Andrady (2019) have predicted future plastic waste
generation by considering population and GDP growth rates per country. These proposed three
scenarios: (A) business as usual, (B) improvement of waste management, and (C) reduction ofplastic use and improvement of waste management. This approach effectively evaluates future
MPW production on a regional scale. Strokal et al. (2021) developed five scenarios withdifferent levels of urbanization and wastewater treatment rates by considering different levelsof socioeconomic challenge for mitigation and adaptation as five Shared Socioeconomic
Pathways (SSPs). This approach is also useful for assessing the impact of urbanization on future
river pollution by adopting a multi-pollutant approach. In the UK, Whitehead et al. (2021) also
considered three scenarios for wastewater effluent, sewage sludge application, and mitigationin the basin of the Thames River.
Table 2.2 summarizes the per capita emission of macroplastics and microplastics into rivers
determined in previous studies (Siegfried et al., 2017; van Wijnen et al., 2019; Boucher et al.,2020; Strokal et al., 2021). Generally, microplastics at point sources originate from four
- 23 - 23 -Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

sources: personal care products (PCPs), laundry fibers, car tire wear, and fragmentation ofmacroplastics. We estimated microplastic fluxes by multiplying these per capita emissions andtheir population distribution for each catchment flow into WWTP in the NICE-BGC simulation(Table 2.1). For estimating the variations of total flux in Japan as a whole, we used the removal
efficiency of microplastic in WWTP (99, 97, and 95%) (higher removal efficiency for almost
all secondary and tertiary treatments in Japan) and the density of the plastic (1010.0, 1001.0,
and 1000.5 kg/m3) (the lower-density plastics, PE, PP, and PS, are overwhelmingly present inJapanese rivers) in the model based on previous studies for Japan (Kataoka et al., 2019; Nihei
et al., 2020) and other developed countries (Siegfried et al., 2017; van Wijnen et al., 2019;
Strokal et al., 2021).
Table 2.2 Per capita emission of macroplastics and microplastics into rivers in previous materials
Plastic sources
Personal care products (PCPs)
Household dust (HD)Laundry inputs or fibres (LD)Tyre wear (TRWP)
Personal care products (PCPs)
Household dust (HD)Laundry inputs or fibres (LD)Tyre wear (TRWP); HDI<0.785
Tyre wear (TRWP); HDI>0.785
Macroplastics (non-point sources)
Personal care products (PCPs)
Laundry inputs or fibres (LD)Tyre wear (TRWP)
Personal care products (PCPs)
Laundry inputs or fibres (LD)Tyres (car)Tyres (truck)
Tyres (plane)
Plastic production (pellet loss)

QuantitativeOECD
Africaestimate
Countries(kg/capita/year)
0.0071
0.08
0.12
0.18
0.0071
0.08
0.12
0.018
0.18
17.3
8.8
0.00280.00550.04510.11
0.0662
0.18
0.005
0.015
0.43
0.43
0.01
0.005

Middle East
& NorthAfricaEast Asia &Pacific (incl.
Japan)

Eastern &Central Asia

Latin America South Asia

Reference

Siegfried et al.(2017)

Strokal et al.(2021)

27
0.00070.007
0.0072160.0007
0.047
0.068

23
0.0049
0.041
0.018

15
0.0049
0.047
0.077

23
0.0025
0.028
0.041

8.5
0.0007
van Wijnen et al.(2019)
0.036
0.072

Boucher et al.(2020)

2.3.3 Running the Simulation and Verification
The simulation area in all the 109 first-class river basins throughout Japan was 26.0◦ wideby 22.0◦ long in longitude-latitude coordinates (Nakayama and Osako, 2023a) (Fig. 2.1). The
NICE simulation of eco-hydrological processes was performed at 0.1° x 0.1° resolution in thehorizontal direction and 20 layers in the vertical direction. Simulations were performed with atime step of t = 1 h for two years during 2014-2015 after a 6-month warm-up period until atime-varying equilibrium. The periods of some input data, such as vegetation type and land
cover, do not cover the simulation period (Table 2.1).
Subsequently, NICE-BGC simulation for terrestrial ecosystems was conducted at the same
spatial resolution with a time step of t = 1 day for the same period by inputting some of theresults simulated by NICE iteratively after calculating the daily-averaged data from 1-hourly
data. The authors used previous data (Ministry of Land, Infrastructure, Transport and Tourism,2013) for per capita emission of microplastics into rivers (PCPs, dust, laundries, and tires) andremoval efficiency in WWTP (Siegfried et al., 2017) as a point source (Fig. 2.2 and Table 2.2).
The model also simulated the outflow of macroplastics originating from MPW (Lebreton andAndrady, 2019) as a diffuse source (Fig. 2.1f).
Then, NICE-BGC simulation for aquatic ecosystems was conducted by inputting thesimulated results for land to a stream network model with a time step of t = 0.70 min to ensure
the stability of the model. The simulation was conducted for the whole of Japan in the same
way as the author’s previous study on the global scale (Nakayama, 2017a), where thehydrologic and nutrient cycles in the world’s major rivers were simulated and verified within- 24 - 24 -

CGER-I169-2024, CGER/NIES

the range of previous datasets and materials as far as possible. The results yielded quantitative
measures of model performance in reproducing the seasonality and inter-annual variability innutrient and carbon fluxes, as described previously by Nakayama (2020, 2022). These mean
the extension of NICE-BGC coupled with the plastic debris model in the present study would
be a better choice for simulating the fate and transport of plastic waste on a basin scale, as
described below (Nakayama and Osako, 2023a).

2.4 Results and Discussion
2.4.1 Evaluation of Hydrologic Cycle in First-Class (Class A) River Basins in Entire JapanThe authors evaluated the hydrologic cycle in first-class river basins in Japan. Fig. 2.3
shows the simulated results for surface runoff in major rivers with large basin areas and long
river channels among the first-class rivers (No. 28; Tone River, No. 36; Shinano River, No. 54;
Kiso River, and No. 62; Yodo River in Fig. 2.1a) during 2014–2015. The results show that theintermediate flow was almost constant (Fig. 2.3c) compared to the surface runoff (Fig. 2.3b),
implying that surface runoff is intensive during rainfall events and greatly contributes to theoutflow of plastic. It is also apparent that surface runoff is limited in small river basins but
cannot be ignored. Because it is difficult to validate the hillslope runoff with almost no
observation data, the authors compared the simulated annual-averaged values for discharge,
nitrogen, and phosphorus transport with the previous observation data (Kataoka et al., 2019;
Ministry of Land, Infrastructure, Transport, and Tourism, 2021a) for first-class river basins (Fig.
2.4). Although the simulated discharges are slightly underestimated relative to the observed
values, particularly in rivers with snow areas (No. 3; Ishikari River, and No. 17; Kitakami River,No. 35; Agano River, No. 36; Shinano River in Fig. 2.1a), the simulated results are generally
in good agreement with observed ones (Fig. 2.4a). Transports of nutrients simulated by the timeseries for the entire calculation period during 2014–2015 (red circle in the figures) areoverestimated in comparison with the observed values (Figs 2.4b-c) because observation ofconcentrations was conducted only in the non-flood periods (Kataoka et al., 2019). The
simulated values averaged only for the base flow of rivers are within the range of those in theobserved values (blue triangle in the figures). The hydrologic regimes of baseflow andstormflow events are important for remobilizing plastic particles in inland waters (Drummondet al., 2020).

- 25 - 25 -Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

Figure 2.3 Simulated results of surface runoff in major rivers with large basin areas and long river
channels among the first-class rivers during 2014–2015; (a) total surface runoff, (b)surface runoff, and (c) intermediate flow

- 26 - 26 -

CGER-I169-2024, CGER/NIES

Figure 2.4 Comparison of annual-averaged values in (a) discharge, (b) nitrogen transport, and (c)phosphorus transport in the first-class of river basins; red circle shows the nutrienttransports simulated by the time series for the entire calculation period during 2014–2015, and blue triangle shows the values simulated only by the base flow of rivers.

2.4.2 Analysis of Sensitivity of Microplastic Transport to Various Factors
The authors analyzed the sensitivity of microplastic concentration to various factors in thedownstream stretch of the Tone River in summer (June 1 to July 20, 2014) (Fig. 2.5). It was
found that the concentration of microplastics was greatly affected by their particle size anddensity, which determine the settling velocity (Nizzetto et al., 2016). Almost all plastic particles
with a density lower than water (polypropylene; PP) are more buoyant than those with a higher
density (polystyrene; PS and polyethylene terephthalate; PET) (Fig. 2.5b). The simulated value
was generally within the range of previous observations for limited, non-flood periods in thesame river (microplastic concentration between 0.03 and 2.36 g/L) (Kataoka et al., 2019; Nihei
et al., 2020). The simulated result also agreed with previous work showing that microplastics
smaller than 0.2 mm would be poorly retained in the Thames River, UK, and would eventually
be conveyed to the marine environment (Nizzetto et al., 2016; Whitehead et al., 2021). The
simulation also showed that the effects of degradation (i.e., a decrease in concentration)
resulting from both water temperature and UV irradiation (in Fig. 2.5c) would not be negligible
because there is a great variation with these two values. This effect is particularly predominant- 27 - 27 -

Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

under some conditions, for example, in hot and cold water and with stronger UV irradiation insummer.

Figure 2.5 Sensitivity analysis of various factors on microplastic concentration downstream ofTone River from June 1 to July 20 in 2014; (a) effect of particle diameter (diameter =1, 10, 20 m with constant density = 1150 kg/m3), (b) effect of particle density (density= 900, 1010, 1350 kg/m3 with constant diameter = 10 m), (c) effect of particle
degradation (with the effect of water temperature, UV irradiation, and both withdiameter = 10 m and density = 1150 kg/m3).

- 28 - 28 -

CGER-I169-2024, CGER/NIES

2.4.3 Impact of Plastic Density and WWTP on Riverine Export of Plastics into Ocean
The model simulated the total plastic flux to the ocean for Japan as a whole (Fig. 2.6).
Generally, large amounts of plastic flow out of some limited rivers such as the Tone (No. 28),
Kiso (No. 54), Yodo (No. 62), and Ara Rivers (No. 29), where there is a large proportion ofmacroplastic flux in comparison with microplastic flux (Fig. 2.6b). However, the modeled
results for microplastic flux are a little overestimated compared to previous observations(Kataoka et al., 2019) in some rivers (Fig. 2.6a). The analysis of this scenario also shows thatthe total flux for Japan varies according to the removal efficiency of microplastics in WWTP
(99, 97, and 95%) and the density of the plastic (1010.0, 1001.0, and 1000.5 kg/m3) (Fig. 2.6c).
The simulated flux is within the range of 1,100–3,500 tons/yr, with 1,395 tons/yr in case-Ab(removal efficiency = 99% and density of plastic = 1001.0 kg/m3). This result is relatively
within the range of previously determined values, i.e., 1,835 tons/yr (Meijer et al., 2021) and
210–4,976 tons/yr (Nihei et al., 2020); this shows that only limited plastics are discharged
intensively to land out into the ocean during rainfall seasons, in agreement with a previous study
indicating that nearly 99% of microplastics are deposited during their transport to coastal
regions (Koutnik et al., 2021). It will be necessary to improve the accuracy of the modeled
results further by validating observations.

- 29 - 29 -

Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan

Figure 2.6 Simulated plastic flux to the ocean; (a)-(b) annual fluxes in each first-class of 109 rivers,and (c) total flux in entire Japan with various scenarios; Blue and red bars show theflux of macro and microplastics.

2.5 Conclusion
This chapter describes the extension of the process-based eco-hydrology model NICE tocouple with the plastic debris (engineered materials) model for freshwater systems and apply it
to all the first-class (class A) river basins in the entire Japan (109 river basins). The new model
was constructed to include as many processes related to plastic dynamics in the biosphere,
applicable to regional and basin scales, advection, dispersion, diffusion, settling, dissolution,and deterioration due to light and temperature. NICE-BGC simulated how mismanaged plastic
waste (MPW) of about 36,000 tons/yr and point sources such as tires, personal care products(PCPs), dust, and laundry in the entire country are transported from land to river and finally to- 30 - 30 -

CGER-I169-2024, CGER/NIES

the ocean. The simulated result quantified large amounts of plastic flowing out of some limited
rivers intensively during rainfall seasons and that macroplastic flux accounts for a larger
proportion than there. Although scenario analysis quantified the total flux varied according tosome factors and was estimated as within the range of existing studies, it is further necessary tominimize errors and uncertainties by using more accurate, more long-term, and more
observations. These results help quantify the impacts of plastic waste on terrestrial and aquatic
ecosystems and find solutions and measures to reduce plastic input to the ocean.
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in Asian region. Ecological Modelling, 489, 110624, doi:10.1016/j.ecolmodel.2024.110624
Nihei, Y., Yoshida, T., Kataoka, T., Ogata, R. (2020) High-resolution mapping of Japanese microplastic andmicroplastic emissions from the land into the sea. Water, 12, 951, doi:10.3390/w12040951
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Pelletier, G.J., Chapra, S.C., Tao, H. (2006) QUAL2Kw – A framework for modeling water quality in streams andrivers using a genetic algorithm for calibration. Environmental Modelling and Software, 21, 419-425,
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Shimizu, Y, Arai, N. (1988) Numerical simulation of flow and bed variations in the river mouth region. Research
Report of Civil Engineering Research Institute of Hokkaido, 419, 5-36
Siegfried, M., Koelmans, A.A., Besseling, E., Kroeze, C. (2017) Export of microplastics from land to sea. A
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Strokal, M., Bai, Z., Franssen, W., et al. (2021) Urbanization: an increasing source of multiple pollutants to rivers
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doi:10.5066/F7DF6PQS
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environment: Sources and future trends. Science of the Total Environment, 673, 392-401,
doi:10.1016/j.scitotenv.2019.04.078
Waldschlager, K., Schüttrumpf, H. (2019) Erosion behavior of different microplastic particles in comparison tonatural sediments. Environmental Science & Technology, 53, 13219-13227, doi:10.1021/acs.est.9b05394
Whitehead, P.G., Bussi, G., Hughes, J.M.R., et al. (2021) Modelling microplastics in the River Thames: sources,
sinks and policy implications. Water, 13, 861, doi:10.3390/w13060861
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This article was published in Ecological Modelling, 476, Nakayama, T., Osako, M., Development of a processbased eco-hydrology model for evaluating the spatio-temporal dynamics of macro- and micro-plastics for thewhole of Japan, 110243, Copyright Elsevier (2023a).

- 35 - 35 -

Chapter 2 Development of a Process-Based Eco-Hydrology Model for Evaluating Spatiotemporal Dynamics of Macro and Microplastics for
Whole of Japan- 36 - 36 -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30
CGER-I169-2024, CGER/NIES

Chapter 3
Flux and Fate of Plastic in World’’s Major Rivers: Modeling Spatial andTemporal Variability- 37 - 37 -

Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability- 38 - 38 -

CGER-I169-2024, CGER/NIES

Abstract
Over the past few decades, environmental contamination from plastics has received
considerable attention from scientists, policymakers, and the public. Although some models
successfully simulated the transport and fate of plastic debris in freshwater systems, a complete
model of the dynamics of plastics in rivers on a global scale has yet to be elucidated. Recently,
two process-based eco-hydrology models, NICE and NICE-BGC, were applied to evaluate
biogeochemical cycling in various river basins on local/regional to continental/global scales.
This effort provided insights into quantifying the role of inland waters on global
biogeochemical cycles but was limited in its treatment of plastic dynamics. Here, we linked
NICE-BGC to a plastic debris model that accounts for the transport and fate of plastic debris(advection, dispersion, diffusion, settling, dissolution, and biochemical degradation by light andtemperature). We evaluated spatiotemporal variations of plastic debris in the world’s majorrivers (325 rivers) by simulating the amount of plastic that flows from land into rivers andfinally into the ocean. Our NICE-BGC simulations show how diffuse sources of mismanaged
plastic waste (MPW) and point sources of tires, personal care products (PCPs), dust, andlaundry in these 325 rivers are transported from land to rivers and finally to the ocean. We
compare continental-scale simulated plastic transport from agricultural and urban land uses toprevious studies. Our model results confirm previous assessments that flood events greatly
impact the mobilization of plastic and lead to high interannual variability in fluxes. Using“untreated wastewater” and a two-dimensional diffusion model simulation for macro-plastictransport helped decrease the model’s uncertainty beyond previous studies that included only
sewerage connectivity, removal efficiency, and transport probability. Uncertainty andsensitivity analyses using Monte Carlo model simulations also clarified that most plastic fluxes
originate in 20 global rivers, largely in Asia, and are greatly influenced by the effects of thesummer monsoon. The new model presented here may be valuable for detecting and predicting
plastic dynamics in large rivers and developing efficient measures to reduce plastic input to theocean on a global scale.
Keywords: Eco-hydrology model; global river basins; plastic debris; untreated wastewater- 39 - 39 -

Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

3.1 Introduction
Plastic pollution is considered one of today’s main environmental problems, and suchpollutants in streams, rivers, and oceans pose potential risks to human health and theenvironment (Siegfried et al., 2017). Plastic waste can be roughly categorized as macroplastic(≥5 mm) or microplastic (<5 mm), the latter being further divided into primary and secondary
microplastics according to origin (Siegfried et al., 2017). Once plastic is released into theenvironment, it is gradually degraded by physical, chemical, and biological processes, leading
to further subdivision into many forms, all of which are impossible to remove and remainindefinitely in the environment (Zhang et al., 2021; Nakayama and Osako, 2023a).
Previous studies on the origin and fate of plastic waste in freshwater systems havesuggested that land-derived plastics are one of the main sources of marine plastic pollution(Thompson et al., 2004). Although Jambeck et al. (2015) were the first to estimate the mass ofland-based plastic entering the global ocean, they did not consider plastic waste from inland
water. They did not classify the plastic according to particle size. Subsequent studies (Lebreton
et al., 2017; Schmidt et al., 2017) estimated the distribution of global riverine emissions ofplastic into the ocean using empirical indicators representative of waste generation(mismanaged plastic waste: MPW) inside a river basin. Using a probabilistic approach, Meijer
et al. (2021) proposed a more accurate analysis to account for the spatial distribution of plastic
waste generation (Lebreton and Andrady, 2019) and climatological/geographical differences
within river basins. Other studies evaluated the impact of flood events on plastic mobilizationand suggested its high inter-annual variability (van Emmerik et al., 2019; Roebroek et al., 2021),
which had been previously ignored in global plastic transport models. Larger plastic debris fromlow-density polymers such as polyethylene (PE), polypropylene (PP), and polystyrene (PS) are
predominantly transported in normal flat water.
In contrast, the relatively larger fraction of plastic litter retained in the bottom sediment, forexample, high-density polymer such as polyvinyl chloride (PVC), is transported as bedload andefficiently flushed from riverbeds during flooding (Hurley et al., 2018; Schwarz et al., 2019).
It is also expected that extreme events such as floods and droughts will increase further withclimate change. The recent G20 Osaka Summit in 2019 shared the “Osaka Blue Ocean Vision”(Ministry of Foreign Affairs of Japan, 2019), which is positioned alongside COP (Conferenceof the Parties) as part of global warming countermeasures. A quantitative assessment of the fate
and transport of plastics will be necessary to realize this vision because countries worldwide
have decided on concrete future measures for dealing with plastic based on scientific results.
Over the last decade, there has been great progress in modeling the fate and transport ofplastics. Kooi et al. (2018) classified plastic debris models for freshwater systems into four
categories: an emission-based mass balance model, a global model, a multimedia model, and a
spatiotemporally explicit model. Some studies (Siegfried et al., 2017; van Wijnen et al., 2019;
Strokal et al., 2021) adapted a process-oriented model to continental and global scales byextending an existing non-dynamic nutrient export model. However, the fate of microplastics
is also dependent on their size, density, shape, and polymer type: factors that were not includedin the previous global analyses (Jambeck et al., 2015; Lebreton et al., 2017; Schmidt et al.,2017; Meijer et al., 2021). Some studies also quantified plastic pathways in a hypotheticalcatchment (Mellink et al., 2022) and incorporated the settling and resuspension of microplastics
to estimate their retention in rivers and conducted a sensitivity analysis of the size and densityof microplastics in fluxes (Nizzetto et al., 2016; Whitehead et al., 2021). Further, Cowger et al.
(2021) modified the Rouse profile of vertical concentration (depth profile of suspended loadconcentration assuming a steady and uniform flow and equilibrium between upward turbulence- 40 - 40 -

CGER-I169-2024, CGER/NIES

and settling velocity) to include all traditional domains of transport (bed load, settlingsuspended load, and wash load) as well as additional domains specific to low-density materials
such as PE, PP, and PS (rising suspended load and surface load). These factors must be
considered when assessing the fate and transport of plastics in various basins over scales
ranging from local/regional to continental/global.
Recently, a process-based model coupling eco-hydrology with the biogeochemical cycle(National Integrated Catchment-based Eco-hydrology (NICE)-BGC) was developed toincorporate complex terrestrial-aquatic linkages in the hydrologic-biogeochemical cycle(Nakayama, 2017a, 2017b). This model was applied to evaluate the biogeochemical cycle invarious basins on scales ranging from local/regional to continental/global. This effort gave
insights into the quantification of the role of inland water on the biogeochemical cycle but has
been limited in evaluating plastic dynamics there. In the present study, the process-based NICEBGC was extended to link it with a plastic debris (engineered materials) model and evaluatedspatiotemporal variations of plastic debris in the world’s major rivers (325 rivers) by simulating
the amount of plastic that flows from land into rivers and finally into the ocean (Nakayama andOsako, 2023b) (Fig. 3.1). The new model included both transport and fate of plastic debris(advection, dispersion, diffusion, settling, dissolution and biochemical degradation by light andtemperature), unlike the case in previous studies. For model simplification, it was also assumed
that plastics are pure and inert polymers with a continuous probability distribution of size, shape,and density (Kooi and Koelmans, 2019). In this chapter, we developed a new model to addressthese gaps. Specifically, we quantified: (i) How does plastic flux change spatiotemporally inmajor global rivers? (ii) how do floods alter this? (iii) How much uncertainty and sensitivity doplastic flux simulated by the model have? To clarify these issues, NICE-BGC simulated how –among 81.8 Tg/yr of global MPW across the globe (Meijer et al., 2021) – about 39.2 Tg/yr, andpoint sources such as tires, personal care products (PCPs), dust, and laundry in these 325 rivers
are transported from land to rivers, and finally to the ocean. The model showed that hydrometeorological change, particularly during flood periods, affected the seasonal variations ofplastic transport. Using “untreated wastewater” and the simulation using a two-dimensional
diffusion model for macroplastic transport helped decrease the model’s uncertainty beyond
previous studies only with sewerage connectivity, removal efficiency, and transport probability.
The new model developed in this study might detect the spatiotemporal hotspots of plastic
fluxes by quantifying the impacts of plastic waste on terrestrial and aquatic ecosystems. The
results also help improve the plastic cycle and develop better solutions and efficient measures
to reduce plastic load on a global scale as much as possible.

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Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

Figure 3.1 Location of study area on the global scale; (a) elevation, (b) land cover, (c) fertilizer
and manure, (d) population density, (e) untreated wastewater calculated fromwastewater treatment and wastewater production, and (f) mismanaged plastic waste(MPW)

3.2 Methods
3.2.1 Coupling of NICE-BGC with Plastic Debris Model and its Application to Global Scale
The original NICE (National Integrated Catchment-based Eco-hydrology) comprises
complex sub-compartments such as the surface hydrology of hillslope and stream flows, a landsurface model including urban and crop processes, a groundwater model, a regional
atmospheric model, a sediment and nutrient mass transport model, and a vegetation succession
model (Nakayama and Watanabe, 2004). To fill the current eco-hydrology gap, one of theauthors has further developed a new model in which the original NICE is coupled with various
biogeochemical cycle models (NICE-BGC) (Nakayama, 2017a, 2017b, 2022) (Fig. 1.1). Each
sub-model offers iterative simulation most efficiently by combining on-line and off-line modes(on-line: data input/output through I/O memory, off-line: data input/output through file); thismeans that the newly developed model incorporates the connectivity of the biogeochemical
cycle accompanied by the hydrologic cycle between surface water and groundwater, hillslopes
and river networks, and other intermediate regions (Nakayama, 2016). Recently, one of theauthors modified NICE-BGC to include the effect of reservoirs (Nakayama and Pelletier, 2018),- 42 - 42 -

CGER-I169-2024, CGER/NIES

improve the accuracy of long-term simulation (Nakayama, 2020), and extend it to theterrestrial-aquatic-estuarine continuum (Nakayama, 2022).
In this study, NICE-BGC (Nakayama, 2017a, 2017b) was extended to model the fate andtransport of macro and microplastic in land and water (Nakayama and Osako, 2023b) (Fig. 3.2).
The authors also conducted uncertainty and sensitivity analyses using Monte Carlo model
simulations by extending the concept from spherical particles to a continuous probability
distribution of size, shape, and density (Kooi and Koelmans, 2019) to evaluate plastic dynamics
on the continental and global scales. The new model also included advection, dispersion,diffusion, settling, dissolution, and biochemical degradation by light and temperature but
assumed no interaction with suspended matter (hetero-aggregation) (Praetorius et al., 2012),
resuspension (Waldschläger and Schüttrumpf, 2019), biofouling (Kooi et al., 2017), physicalabrasion, or wind effects. The previous model of Meijer et al. (2021) used the probabilistic
approach of plastic transport and did not explicitly include overland transport and accumulation
processes. The model of Mellink et al. (2022) quantified plastic pathways within a rivercatchment but in a hypothetical local area under simpler conditions. In contrast, the present newmodel, by extending NICE-BGC, explicitly simulated overland transport and accumulation
processes globally and simulated the fate and transport of both macro and microplastics there.
Thus, the model could simulate the hydrograph for plastic transport with various shear stresses
during storm events, as proposed by Kumar et al. (2021). For model simplification, it was also
assumed that microplastics are pure and inert polymers with a continuous probability
distribution of size, shape, and density (Kooi and Koelmans, 2019).
The transport of plastic on a hillslope was simulated in equations (2.1)-(2.9), except (2.4),
in Chapter 2. Because equation (2.4) assumes the resuspension rate when a riverbed is underequilibrium conditions, and the particle size distribution is uniform, applying this formula
directly to plastic is difficult. It is difficult to grasp the heterogeneous distribution of settled anddeposited plastic on hillslopes, although some studies have evaluated the erosion behavior of
microplastic particles compared to sediments (Waldschläger and Schüttrumpf, 2019). For
model simplification, we also assumed that this plastic is negligible compared to discharged
plastic and that resuspension is zero on hillslopes. After the simulation of plastic transport on ahillslope, the authors used the estimated rate of conversion of macroplastics to microplastics(3%/yr) in the same way as van Wijnen et al. (2019), with the extension of the conversion rate
employed by Jambeck et al. (2015) (Fig. 3.2).
NICE-BGC simulates plastic transport in a river channel by extending QUAL2Kw (Pelletier
et al., 2006). Macro and microplastics become buried through settling, dissolution, anddeterioration due to light, temperature, advection, dispersion, and diffusion. The terms ofsources and sinks for the constituent S due to reactions and mass transfer mechanisms can be
added to equation (2.10) in Chapter 2:�(�)/��
�(1 − 𝑒𝑒 ���� )𝑃𝑃���(3.1)
𝑆𝑆���� = − ���� 𝑃𝑃��� −𝑘𝑘���� �𝑇𝑇�𝑃𝑃��� − 𝛼𝛼������𝑆𝑆���� = − ���� 𝑃𝑃��� −𝑘𝑘���� (𝑇𝑇)𝑃𝑃��� − 𝛼𝛼������� �

�(�)/��

+𝑘𝑘���� (𝑇𝑇)𝑃𝑃��� + 𝛼𝛼������ �(1 − 𝑒𝑒 ���� )𝑃𝑃����(�)/���� �(1 − 𝑒𝑒 ���� )𝑃𝑃���(3.2)

where Pmac (g/l) and Pmic (g/l) are the macro and microplastic concentrations; T is the watertemperature; vPmac and vPmic are the settling velocities of macro and microplastics (m/d); kPmac(T)and kPmic(T) are the temperature-dependent plastic dissolution rates (/d); and Pmac and Pmic are- 43 - 43 -

Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

the light efficiency factors (-), respectively. We assume that the deterioration due to light canbe modeled as a first-order temperature-dependent decay, and the deterioration rate ke due tolight is based on the Beer-Lambert law. We also assume that there is no interaction withsuspended matter (hetero-aggregation), resuspension, biofouling, or the effects of wind.
Furthermore, if the deterioration of macroplastics due to light and temperature shows thetransition to microplastics, this disappearance term in equation (3.1) was added to equation(3.2).

Figure 3.2 Flow diagram of macro and microplastics transport in land and inland water byextending NICE-BGC

3.2.2 Extension of Settling Velocity Applicable to Uncertainty Analysis
For the calculation of settling velocity, the authors extended Rubey’s formula (Rubey,1933) for spherical particles in equation (2.9) in Chapter 2 to Stokes’ formula for different
shapes by applying the equivalent sphere diameter dequi (Waldschläger et al., 2020):
� ����� ��𝑤𝑤� = �

� ��

CSF =

�

|

�

− 1|𝑔𝑔√��- 44 - 44 -

(3.3)
(3.4)

CGER-I169-2024, CGER/NIES

𝑑𝑑���� = (𝐿𝐿𝐿𝐿𝐿𝐿)�/� ≈𝐿𝐿(CSF)�/�

where L≈W

(3.5)

where CSF is the dimensionless Corey Shape factor determined by length:width:height (L:W:H)
ratios for different shapes of the plastic particle (shortest H, middle W, and longest L side
lengths); CD is the drag coefficient at a constant value (= 0.44) for turbulent flow at a highReynolds number in actual rivers (Re >1000).
3.3 Input Data and Boundary Conditions for Simulation
3.3.1 Model Input Data including Plastic Generation and Removal
The input data at various spatial resolutions were prepared and arranged to calculate aspatially averaged 1°x1° grid using the Spatial Analyst Tools in ArcGIS v10.3 software for the
global simulation (Fig. 3.1 and Table 3.1); elevation, land cover, soil texture, vegetation type,river networks, lakes and wetlands, reservoirs and dams, geological structures, crop type,irrigation type, irrigation water use, fertilizer use, manure use, and atmospheric N deposition
were categorized based on global datasets. Although data for plastics are scarce, the authors
used some data to simulate global plastic debris, gridded population data at 1 km resolution in2015 (NASA, 2018) (Fig. 3.1d), wastewater production, collection, treatment and reuse data at
5 arcmin (about 10 km) resolution (Jones et al., 2021) (Fig. 3.1e), and MPW at 1 km resolution(Lebreton and Andrady, 2019) (Fig. 3.1f) as a diffuse source (Table 3.1). To create the grid datafrom point, polyline, and polygon data for different sources with different spatial resolutions ina reasonable way, overlay analysis was conducted using GIS tools (intersect, union, andidentity) (ESRI, 2019).
Table 3.2 summarizes the per capita emission of macroplastics and microplastics into rivers
determined in previous studies (Siegfried et al., 2017; van Wijnen et al., 2019; Boucher et al.,2020; Strokal et al., 2021). Generally, microplastics at point sources originate from four
sources: personal care products (PCPs), laundry fibers, car tire wear, and fragmentation ofmacro-plastics. We estimated the potential microplastic fluxes by multiplying these per capita
emissions and their population distribution for each catchment flow into WWTPs (Waste Water
Treatment Plants) in the NICE-BGC simulation (Fig. 3.1d and Table 3.1). We also applied“untreated wastewater” and converted it to the removal efficiency of microplastic, the formerof which was calculated by subtracting the ratio of wastewater treatment and wastewaterproduction from 1 at 5 arcmin resolution (Jones et al., 2021). This method differs from thoseused in most previous studies (Siegfried et al., 2017; van Wijnen et al., 2019; Meijer et al.,2021; Strokal et al., 2021), which considered only sewerage connectivity (0-1), microplastic
removal efficiency, and transport probability of plastics (did not include overland transport andaccumulation processes) with relatively large uncertainty. It was also assumed that microplastic
concentration on hillslopes was constant (Wagner et al., 2019), and the time series proportional
to hillslope runoff was calculated at each time step.

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Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

Table 3.1 List of input data sets for the NICE and NICE-BGC simulations
Data set

Original resolution

Year

Source and reference

Climatology

1.0°
1.0°

1998-2015
1998-2015

ERA-interim; ECMWF (2013)
CRU TS3.24; CRU (2017)

Elevation

1.0km

Land cover
Soil texture
Vegetation typeRiver networks
Lakes and wetlands
Reservoirs and Dams
Geological structures
Crop typeIrrigation typeIrrigation water use
Fertilizer use
Manure use
Atmospheric N deposition
Population
Wastewater production andtreatment
Mismanaged plastic waste

1.0km
1.0km
0.25°
1.0km
0.5 min
point
0.5°
5 min
5 min
5 min
5 min
5 min
0.5°
1.0km

around 1996
around 2018
around 2000
around 1970-2000
around 2000
around 1996
around 1990-2000
around 2010
around 1970-2000
around 2000
2000-2008
1998-2002
around 2000
1998-2014
1998-2015
2015

GTOPO30; U.S. Geological Survey (1996a)
SRTM; U.S. Geological Survey (2018)
GLC2000; European Commission (2015)
HWSD; European Commission (2012)
GLDAS Vegetation Class; NASA (2013)
HYDRO1K; U.S. Geological Survey (1996b)
GLWD; Lehner and Döll (2004)
GRanD; Lehner et al. (2011)
GLiM; Hartmann and Moosdorf (2012)
MIRCA2000; Portmann et al. (2010)
GMIA; FAO (2016)
GCWM; Siebert and Döll (2010)
Earth Stat; Mueller et al. (2012)
Zhang et al. (2017)
ISIMIP2a; Tian et al. (2018)
NASA (2018)

5 min

2015

Jones et al. (2021)

1.0 km2015

Lebreton and Andrady (2019)

Table 3.2 Per capita emission of macroplastics and microplastics into rivers in previous materials

Plastic sources
Personal care products (PCPs)
Household dust (HD)Laundry inputs or fibres (LD)Tyre wear (TRWP)
Personal care products (PCPs)
Household dust (HD)Laundry inputs or fibres (LD)Tyre wear (TRWP); HDI<0.785
Tyre wear (TRWP); HDI>0.785
Macroplastics (non-point sources)
Personal care products (PCPs)
Laundry inputs or fibres (LD)Tyre wear (TRWP)
Personal care products (PCPs)
Laundry inputs or fibres (LD)Tyres (car)Tyres (truck)
Tyres (plane)
Plastic production (pellet loss)

Mid. East East Asia Eastern &
Latin
OECD
South Asia
& Pacific Central& NorthAfricaWorld ave.
America
Reference
Countries(kg/cap./yr)incl. Japan Asia
(kg/cap./yr) Africa(kg/cap./yr)(kg/cap./yr)(kg/cap./yr)(kg/cap./yr) (kg/cap./yr) (kg/cap./yr)
0.0071
0.08
0.12
0.18
0.0071
0.08
0.12
0.018
0.18
17.3
0.00280.0451
0.0662
0.005
0.015
0.43
0.43
0.01
0.005

Siegfried et al.
(2017)

Strokal et al.
(2021)
8.8
0.0055
0.11
0.18

27
0.00070.007
0.0072160.0007
0.047
0.068

23
0.0049
0.041
0.018

15
0.0049
0.047
0.077

23
0.0025
0.028
0.041

8.5
0.0007 van Wijnen et al.
0.036 (2019)
0.072

Boucher et al.
(2020)- 46 - 46 -

CGER-I169-2024, CGER/NIES

3.3.2 Boundary Conditions and Running the Simulation
After the NICE simulation of eco-hydrological processes at 1°x1° resolution in thehorizontal direction and in 20 layers in the vertical direction, NICE-BGC simulation for
terrestrial ecosystems was conducted at the same spatial resolution with a time step of t = 1
day for the same period by inputting some of the results (soil temperature, surface runoff, andgroundwater level) simulated by NICE iteratively after calculating the daily-averaged data from
1-hourly data. The authors used previous data for per capita emission of microplastics intorivers (PCPs, dusts, laundries, and tires) (Table 3.2) and the revised removal efficiency in
WWTPs (as described in section 3.3.1) as a point source (Fig. 3.2). The model also simulated
the outflow of macro-plastics originating from MPW (Lebreton and Andrady, 2019) as a diffuse
source. Then, NICE-BGC simulation for aquatic ecosystems was conducted by inputting thesimulated results for land to a stream network model with a time step of t = 0.70 min to ensuremodel stability. The hydrologic and carbon cycles were simulated and verified using theprevious datasets and materials as far as possible. Though the present study assumed nointeraction with suspended matter (hetero-aggregation), resuspension, biofouling, and windeffects in the model, these processes are also important to improve the accuracy of the plastic
cycle in the future. The simulated values were generally within the range of previous data for
27 different rivers, although the model simulated only the world’s major rivers (325 rivers), as
described by Nakayama (2017a). The continental values were estimated by compilation of theworld’s major river basins (153 basins), which occupy about 65.9% of the total continental area
(Nakayama and Osako, 2023b). A basin-scale comparison for representative rivers between themodel results and previous results in the GloRiCh database (Hartman et al., 2014) was also
conducted. The results yielded quantitative measures of model performance in terms ofreproducing the seasonality and inter-annual variability in carbon fluxes, as described by
Nakayama (2020).
3.3.3 Analysis of the Influence of Various Factors on Plastic Transport
Because there is insufficient data on the characteristics of plastics on the continental andglobal scales, it is important to evaluate the uncertainty of plastic transport and its sensitivity tovarious factors (Nakayama and Osako, 2023b). The authors also assumed plastics to be pureand inert polymers with a continuous probability distribution, a power law probability
distribution for plastic size (L: length of plastic particle), a universal bimodal shape probability
distribution for CSF in equation (3.4), and a normal-inverse Gaussian shape probability
distribution for density (s) (Kooi and Koelmans, 2019):
𝐏𝐏𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔 (𝒙𝒙) =𝟏𝟏��

𝑼𝑼𝑼𝑼𝟏𝟏�� �𝑼𝑼𝑼𝑼𝟏𝟏��

𝐏𝐏𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔 (𝒙𝒙) =𝝀𝝀𝟏𝟏

�𝟐𝟐𝟐𝟐𝟐𝟐𝟏𝟏 𝟐𝟐

𝐏𝐏𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 (𝒙𝒙) =

𝒙𝒙��

(3.6)
𝟐𝟐

𝟐𝟐

𝒆𝒆�(𝒙𝒙��𝟏𝟏 ) /𝟐𝟐𝟐𝟐𝟏𝟏 +𝜽𝜽𝜽𝜽𝜽𝜽𝟏𝟏 [𝜽𝜽�𝜹𝜹𝟐𝟐 �(𝒙𝒙��)𝟐𝟐 ]𝝅𝝅�𝜹𝜹𝟐𝟐 �(𝒙𝒙��)𝟐𝟐

𝟐𝟐

𝝀𝝀𝟐𝟐

�𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐 𝟐𝟐𝟐𝟐𝟐𝟐𝒆𝒆�(𝒙𝒙��𝟐𝟐 ) /𝟐𝟐𝟐𝟐𝟐𝟐

𝒆𝒆𝜹𝜹�𝜽𝜽 �� ��(𝒙𝒙��)

𝟐𝟐

(3.7)
(3.8)

where LL and UL are the lower and upper limits of plastic size;  is the exponent (= 1.5 ~ 2.5);
i, i, i are the relative contribution, mean, and standard deviation of a two normal distribution(1 = 0.059, 2 = 0.941, 1 = 0.076, 2 = 0.441, 1 = 0.030, 2 = 0.189); K1 is a modified Bessel- 47 - 47 -Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

function of the third kind with order 1; ,, ,  are the location, scale, trail heaviness, andasymmetry of the distribution ( = 0.839,  = 0.097,  = 75.13,  = 71.30), respectively.
For uncertainty and sensitivity analyses, the authors first calculated the cumulative
probability distribution of size, shape, and density and their inverse functions. It was assumed
that these probability distributions of size, shape, and density would also apply to macroplastics by extension from microplastics in the previous study (Kooi and Koelmans, 2019).
Secondly, Monte Carlo model simulations (10 iterations) were conducted to generate a uniform
random number between 0 and 1, and the size, shape, and density were determined under the
fixed random number. Thirdly, the equivalent sphere diameter (equation (3.5)) and settling
velocity (equation (3.3)) were calculated to determine plastic transport on the continental andglobal scales in each case. Finally, uncertainty and sensitivity analyses were conducted toevaluate the effect of various factors on plastic transport by ranking the model parameters
according to their contribution to prediction uncertainty (Nakayama and Osako, 2023b).
3.4 Results and Discussion
3.4.1 Evaluation of Plastic Flux from World’s Major Rivers into Ocean
The authors compared the simulated annual plastic flux into the ocean with data forprevious materials in global major rivers (Lebreton et al., 2017; Schmidt et al., 2017; Best,
2018; Mai et al., 2020; Meijer et al., 2021; Roebroek et al., 2021) (Fig. 3.3). Simulation forhillslope runoff was conducted in three cases for microplastics; case-A and case-B used thesame value of per capita emission of microplastics for the whole world (Siegfried et al., 2017)and a different value of per capita emission for the whole world (Boucher et al., 2020). In
contrast, case-C used different values for each region (van Wijnen et al., 2019) (Table 3.2). The
simulation of hillslope runoff was also conducted for macroplastics with three different
densities: case-a, case-b, and case-c (1001.0, 1000.001, and 1000.0001 kg/m3). The simulated
macroplastics with values exceeding 1001.0 kg/m3 were similar for case-a, and those withvalues of less than 1000.0001 kg/m3 were similar for case-c because almost all were depositedin shallower hillslope runoff in the former and flowed into inland water in the latter. The
simulated plastic flux generally agreed with the values estimated in previous studies. However,the former value underestimated the latter in the Yangtze River. It overestimated it in theGanges River due to the high uncertainty of the estimated flux, particularly in South andSoutheast Asia. In particular, the simulated microplastics in case-A and case-B were relatively
similar to the results reported by Schmidt et al. (2017). Though the value in case-C wasunderestimated, the result was reasonable considering another previous study in which the
MPW-based models generally overestimated the reported field measurements (Mai et al., 2020).
Interestingly, the simulated total fluxes were within the range of the results reported by
Roebroek et al. (2021), representing potential plastic mobilization between non-flood
conditions and the undefended 10-year return period. This result suggests that it is important toconsider the effect of inter-annual variability to improve the accuracy of plastic flux estimation.

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CGER-I169-2024, CGER/NIES

Figure 3.3 Comparison between simulated annual plastic flux from the major global rivers into
the ocean and the previous material: (a) macroplastic, (b) microplastic, and (c) total
plastic fluxes

3.4.2 Seasonal Variation of Plastic Flux on a Global Scale
The authors estimated the relationships between population density, plastic concentrations,and plastic fluxes per unit area in global major rivers (Fig. 3.4). This revealed a relatively linear
relationship between population density and plastic fluxes in global basins (Figs 3.4c-d). While
there was a positive relationship for macroplastic flux (R2 = 0.33 and PCC = 0.53) (Fig. 3.4c),
the correlations were relatively high for microplastic flux (R2 = 0.82 and PCC = 0.91) (Fig.
3.4d). In contrast, there was no clear relationship between population density and concentration
for macro and microplastics (Figs 3.4a-b). Because the concentration was often almost zerodespite the significant population density, this suggests a certain threshold at which theconcentration has a non-zero value. The result also implies that there is not necessarily atendency for a positive relationship between concentration and water discharge in river basins(such as the Ganges, Mississippi, St. Lawrence, and the Yangtze); this may be because of alarge shear stress causing resuspension during flood periods, or a relatively large urban area
ratio. It will be further necessary to prove that the positive relationship results from plastic
concentration increasing with river discharge due to remobilization from the riverbed and banks- 49 - 49 -Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

as well as activation of additional inputs from stormwater drains and combined sewer overflows
during flood periods (Wagner et al., 2019).
NICE-BGC also simulated seasonal variations of river discharge and plastic fluxes in eachcontinent in the same way as that shown in Fig. 3.3 (Fig. 3.5). The simulated result showed thatthe seasonal variations of plastic fluxes depend largely on per capita emission and density. In
particular, the simulated values for microplastics in case-A and case-B were larger than that incase-C (Fig. 3.5d), and the simulated value for macroplastics in case-b was intermediate
between the other two (Fig. 3.5c). The author’s previous study had shown that river discharge
had a seasonal pattern, with a peak flow typically occurring during the spring snowmelt season
and the summer/autumn high rainfall seasons and a low flow during winter (Nakayama, 2017b)(Fig. 3.5a). The simulation indicated that the total plastic flux ranged between 0.8 and 1.5 Tg/yr
in major global rivers (Fig. 3.5b), in reasonably good agreement with previous studies (Jambeck
et al., 2015; Lebreton et al., 2017; Schmidt et al., 2017; Meijer et al., 2021). The simulated
result showed that changes in plastic fluxes (Figs 3.5b-d) are greatly affected by those ofdischarge in major global rivers. However, the simulated value showed a dominant contributionof the summer monsoon in Asia and differed from the magnitude of river discharge, where
South America following Asia contributed the most. This characteristic is related to agriculturaland urban land ratio on a continental scale and agrees reasonably with previous studies
indicating that more than 74% of waste is delivered between May and October from limited
large rivers in Asia (Lebreton et al., 2017; Best, 2018).

Figure 3.4 Relationships between population density and (a) macroplastic concentration, (b)microplastic concentration, (c) macro-plastic flux per unit area, and (d) microplastic
flux per unit area in global major rivers simulated by NICE
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CGER-I169-2024, CGER/NIES

Figure 3.5 Seasonal variations of (a) river discharge and plastic fluxes of (b) total, (c) macro, and(d) microplastics in each continent in the same cases as that in Fig. 3.3; ‘AS’, ‘EU’,‘OC’, ‘AF’, ‘NA’, and ‘SA’ mean Asia, Europe, Oceania, Africa, North America, andSouth America

3.4.3 Uncertainty and Sensitivity Analyses of Riverine Plastic Transport on Continental
and Global Scales
The authors simulated annual-averaged riverine plastic transport by conducting uncertainty
and sensitivity analyses coupled with Monte Carlo model simulations to evaluate the influenceof various factors on plastic transport (Fig. 3.6). For the simulation, we also assumed that lowerdensity plastics such as PE, PP, and PS, which make up the majority of plastics in most rivers(Siegfried et al., 2017; van Wijnen et al., 2019; Strokal et al., 2021), are also affected by
biofouling and show a 4% increase in their densities, regarding previous studies (Kaiser et al.,2017; Kooi et al., 2017). The simulated result showed that some rivers, such as the Ganges,
Yangtze, Yellow, Nile, and Indus, dominate plastic transport on both the continental and global
scales. It was also interesting that the flux of microplastic (Fig. 3.6b) showed greater uncertainty
than macroplastic (Fig. 3.6a) in rivers, making larger contributions, mainly because of the wider
range of microplastic particle size. Meijer et al. (2021) did not include the effect of microplastictransport in their model (Fig. 3.6c). Although there was some scattering of plastic fluxes in each- 51 - 51 -

Chapter 3 Flux and Fate of Plastic in World's Major Rivers: Modeling Spatial and Temporal Variability

iteration, the averaged values for both macro and microplastics from all iterations were within
the previous data range (Schmidt et al., 2017; Meijer et al., 2021). Most plastic fluxes flowedout of approximately 20 global rivers, in general agreement with the plastic outflow from alimited river pointed out by previous studies (Lebreton et al., 2017; Schmidt et al., 2017; Meijer
et al., 2021). This result also indicates that the application of the microplastic probability
distribution in equations (3.6)-(3.8) (Kooi and Koelmans, 2019) to macroplastic is not a bad
assumption for major global rivers.

Figure 3.6 Uncertainty and sensitivity analyses using the Monte Carlo model simulations toevaluate the influence of various factors on annual-averaged riverine plastic transport
in global major rivers: (a) macroplastic, (b) microplastic, and (c) total plastic fluxes.

3.5 Conclusion
This chapter describes the extension of NICE to incorporate a plastic debris (engineeredmaterials) model for freshwater systems and applied to the world’s major rivers (325 rivers).
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CGER-I169-2024, CGER/NIES

Unlike previous research, the new model included a comprehensive process of plastic dynamics
to simulate the fate and transport of plastics on a global scale. NICE-BGC simulated how –among 81.8 Tg/yr of global mismanaged plastic waste (MPW) across the globe – approximately
39.2 Tg/yr and point sources such as tires, PCPs, dust, and laundry in these 325 rivers aretransported from land to rivers, and finally to the ocean. Using untreated wastewater, simulation
with a two-dimensional diffusion model for macroplastic transport, and the connection between
hillslope and stream network models helped decrease the model’s uncertainty beyond previous
studies. A relatively linear relationship between population density and plastic fluxes in the
global basins was demonstrated. The model also revealed a seasonal variation of plastic flux inAsia and its large contribution that was affected by the summer monsoon; this differed inmagnitude from river discharge and was related to the ratio of agricultural to urban land on acontinental scale. Uncertainty and sensitivity analyses using Monte Carlo model simulations
assuming a continuous probability distribution of size, shape, and density also clarified theeffects of various factors on plastic transport. Most of the plastic fluxes were shown to flow outof about 20 global rivers, reflecting that flood events greatly impact plastic mobilization andhigh inter-annual variability. The new model developed in this study might contributesignificantly to detecting these spatiotemporal hotspots of plastic fluxes and to develop better
solutions and efficient measures to reduce plastic load on a global scale as much as possible.
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Siebert, S., Döll, P. (2010) Quantifying blue and green virtual water contents in global crop production as well as
potential production losses without irrigation. Journal of Hydrology, 384, 198–217
Siegfried, M., Koelmans, A.A., Besseling, E., Kroeze, C. (2017) Export of microplastics from land to sea. A
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CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30
CGER-I169-2024, CGER/NIES

Chapter 4
Impact of Global Major Reservoirs and Lakes on Plastic Dynamics byUsing a Process-Based Eco-Hydrology Model- 57 - 57 -

Chapter 4 Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a Process-Based Eco-Hydrology Model- 58 - 58 -

CGER-I169-2024, CGER/NIES

Abstract
Environmental contamination by plastics has received considerable attention fromscientists, policymakers, and the public. In this study, the process-based model NICE-BGC was
extended to couple with LAKE2K in a stratified water quality model to evaluate the global
plastic dynamics in both lotic and lentic waters. The new model could simulate riverine plastictransport in inland waters with and without the presence of major global reservoirs and lakes.
The result showed the simulated plastic transport with the presence of reservoirs becomesslightly smaller than that without the presence of reservoirs. In particular, the plastic burial
simulated by the model became different with and without the lake model when the density ofplastic was higher than that of water. This result showed there are limits to applying the same
partial differential equations as inorganic carbon for the derivatives either with or without thereservoirs, as assumed by the author’s previous study, especially when the plastic density ishigher than that of water. The model also simulated plastic sedimentation in the global lakes
and reservoirs together and showed that there were more plastic deposits in the reservoirs thanthe lakes except for the Caspian Sea, and most lentic waters are found to deposit more
microplastics than macro-plastics as pointed out by the previous study. Finally, the author
quantified the weighted average of plastic budget in the major global rivers with the effect ofanthropogenic factors such as the construction of artificial dams and global lakes in lentic water.
The simulated result also showed that incorporating the lake model in NICE-BGC led to
improved estimates of plastic dynamics in inland waters and may aid the development ofsolutions and measures to reduce plastic input to the ocean.
Keywords: Eco-hydrology model; lake model; lotic and lentic waters; regulated rivers;
riverine plastic transport- 59 - 59 -

Chapter 4 Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a Process-Based Eco-Hydrology Model

4.1 Introduction
Plastic pollution is considered one of today’s main environmental problems, and suchpollutants in streams, rivers, and oceans pose potential risks to human health and theenvironment (Siegfried et al., 2017). Once plastic is released into the environment, it isgradually degraded by physical, chemical, and biological processes, leading to further
subdivision into an enormous number of forms, all of which are impossible to remove andremain indefinitely in the environment. Previous studies on the origin and fate of plastic waste
in freshwater systems have suggested that land-derived plastics are one of the main sources ofmarine plastic pollution (Thompson et al., 2004). Some studies (Jambeck et al., 2015; Lebreton
et al., 2017; Schmidt et al., 2017) estimated the distribution of global riverine emissions ofplastic into the ocean using empirical indicators representative of waste generation(mismanaged plastic waste: MPW) inside a river basin. Using a probabilistic approach, Meijer
et al. (2021) proposed a more accurate form of analysis to account for the spatial distributionof plastic waste generation (Lebreton and Andrady, 2019) and climatological/geographicaldifferences within river basins. Other studies (Siegfried et al., 2017; van Wijnen et al., 2019;
Strokal et al., 2021) adapted a process-oriented model to continental and global scales byextending an existing non-dynamic nutrient export model. There is no doubt that these studies
are valuable for quantifying the plastic dynamics, though they didn’t explicitly address theeffects of reservoirs and lakes in lentic waters.
In contrast, regulated rivers have increased year by year, and other previous studies haveevaluated the effect of damming on microplastic transport in rivers (Watkins et al., 2019; Leiser
et al., 2020; Dhivert et al., 2022; Wu et al., 2022; Shen et al., 2023). Within global freshwater,
hydraulic structures such as dams and weirs intercept 65% of plastic waste before it reaches theocean (Lebreton et al., 2017). It is important that the dams intercepted a large amount of
microplastics in sediment but did not trap floating microplastics in water (Shen et al., 2023);
this is also related to the fact that microplastics settled out of the water column and into thesediment in the slower moving waters of the impoundment where sediment microplastic
concentrations were an order of magnitude higher than those in water samples (Watkins et al.,2019) and where there is a reservoir-dominated transition from riverine zone to lacustrine-likezone towards the downstream by hydro-sedimentary processes (Dhivert et al., 2022). On a basin
scale, the previous study also showed that local gross domestic product (GDP) and basin area
were the key factors controlling microplastic distribution and that population density might not
be the decisive factor (Wu et al., 2022). Further, another study evaluated that sinking velocities
of microplastics are related to biofouling, metal sorption, and aggregation in the reservoirs(Leiser et al., 2020). It is necessary to incorporate these effects in modeling plastic dynamics inlotic and lentic waters. Quantitative assessment of the fate and transport of plastics in thesewaters is also important for achieving the Sustainable Development Goals (SDG) (UNESCO,2022) because countries worldwide have decided on concrete future measures for dealing withplastic based on scientific results.
The author has developed a process-based model coupling eco-hydrology with thebiogeochemical cycle (National Integrated Catchment-based Eco-hydrology (NICE)-BGC)(Nakayama, 2017a, 2017b), incorporating complex terrestrial-aquatic linkages in thehydrologic-biogeochemical cycle. Recently, the author extended to couple this process-based
NICE-BGC with a plastic debris (engineered materials) model and evaluated spatiotemporalvariations of plastic debris in the entire Japan of regional scale (Nakayama and Osako, 2023a)
and the world’s major rivers of global scale (Nakayama and Osako, 2023b) without the effectof major reservoirs and great lakes. Based on this background, there are two basic science- 60 - 60 -

CGER-I169-2024, CGER/NIES

questions: (i) How many plastics are trapped in major global reservoirs and lakes? (ii) How dothese global lentic waters influence macro and microplastic dynamics in inland waters? To
clarify these issues, in this chapter, the author extended NICE-BGC with a plastic debris modelfrom inland water to regulated rivers and lakes and included changes in the plastic dynamic of
the biosphere (terrestrial and aquatic ecosystems) resulting from anthropogenic activities suchas construction of artificial dams (Nakayama, under review) (Fig. 4.1). The present study is also
an important step not only for qualification of processes, but identification of hierarchy ofdrivers in various river catchments affected by anthropogenic activities.

Figure 4.1 Study area of the world’s major rivers (153 basins, 325 river channels) with 82 major
reservoirs (red circle) and 19 lakes (blue mesh) included in these basins to simulate
the plastic dynamic; Green circle line (25 reservoirs of the total 82) and purple circle
line (19 lakes) means the application of NICE-BGC and LAKE2K coupled model inthis study

4.2 Methods
4.2.1 General Model Framework of NICE-BGC
The author has so far developed an advanced process-based model derived by coupling aprocess-based National Integrated Catchment-based Eco-hydrology (NICE) model with various
biogeochemical cycle models (NICE-BGC) (Nakayama, 2017a, 2017b, 2022). This model
incorporates the connectivity of the biogeochemical cycle accompanied by the hydrologic cycle
between surface water and groundwater, hillslopes and river networks, and other intermediate
regions. Recently, the author extended to couple this process-based NICE-BGC with a plastic
debris (engineered materials) model and evaluated spatiotemporal variations of plastic debris
in the entire Japan of regional scale (Nakayama and Osako, 2023a) and the world’s major riversof global scale (Nakayama and Osako, 2023b) (Fig. 4.1). The new model also included
advection, dispersion, diffusion, settling, dissolution and deterioration due to light and- 61 - 61 -

Chapter 4 Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a Process-Based Eco-Hydrology Modeltemperature, but assumed no interaction with suspended matter (hetero-aggregation)(Praetorius et al., 2012), resuspension (Waldschläger and Schüttrumpf, 2019), biofouling (Kooiet al., 2017, 2018), or wind effects.
In the above previous study, the author incorporated resuspension and settling by extending
the author’s previous studies (Nakayama and Osako, 2023a, 2023b, 2024). The original model
assumed the resuspension rate when a riverbed is under equilibrium conditions, and the particle
size distribution is uniform; it was difficult to apply this formula directly to plastic. It is difficult
to grasp the heterogeneous distribution of settled and deposited plastic on hillslopes, althoughsome studies have evaluated the erosion behavior of microplastic particles compared tosediments (Waldschläger and Schüttrumpf, 2019). The new model included the plastic budget
in the water and the bed sediment of any given reach in the same way as the previous studies(Nizzetto et al., 2016; Koutnik et al., 2021). Thus, the model could simulate the concentrationof plastics in the water and the bed sediment and evaluate the hydrograph for plastics transportwith various shear stresses during storm events, as proposed by Kumar et al. (2021). For model
simplification, it was also assumed that microplastics are pure and inert polymers with acontinuous probability distribution of size, shape, and density (Kooi and Koelmans, 2019).
After the simulation of plastic transport on a hillslope, the author used the estimated rate ofconversion of macroplastics to microplastics (3%/yr) in the same way as van Wijnen et al.
(2019), with the extension of the conversion rate employed by Jambeck et al. (2015) (Fig. 4.2).

Figure 4.2 Flow diagram of macro and microplastics transport in land and inland water byextending NICE-BGC coupled with LAKE2K to lotic and lentic waters- 62 - 62 -

CGER-I169-2024, CGER/NIES

4.2.2 Extension of Plastic Debris Model including Global Reservoirs and Lakes
The author’s previous study (Nakayama and Pelletier, 2018) applied the deficit ratio
depending on the water level difference above and below the dam to calculate the inorganiccarbon mass balance. About the rationale for the influence of reservoirs on organic carbon(DOC, POC), nutrients, etc., it is difficult to generalize whether reservoirs would either increaseor decrease POC or DOC because each reservoir would be different in the balance of factorsthat would change the DOC and POC concentrations. So, this previous study assumed the same
partial differential equations for the derivatives either with or without the reservoirs except for
the extra reaeration effect of the dams after solving DIC concentration (as mass balance). To
solve this problem and to evaluate the effect of lotic and lentic waters on carbon cycle changes,
the author recently coupled NICE-BGC with LAKE2K in stratified water quality model(Chapra and Martin, 2012) in his study (Nakayama, 2023). LAKE2K simulates the lake as a
one-dimensional system consisting of three vertical layers (epilimnion, metalimnion, andhypolimnion). So, this coupled model enables the simulation of the hydrologic andbiogeochemical cycles in global reservoirs and lakes of lentic water. It is possible to simulate
implicitly the effect of reservoirs and lakes on organic carbon (DOC, POC) and nutrient fluxes
without assuming the deficit ratio (Nakayama and Pelletier, 2018). In the present study, theauthor further extended this coupled model to simulate the plastic dynamic in both lotic andlentic waters (Nakayama, under review), extending the author’s previous studies (Nakayama
and Osako, 2023a, 2023b, 2024). This model was applied to the world’s major rivers (153basins, 325 river channels), with 25 global reservoirs and 19 lakes included in these basins (Fig.
4.1).
4.3 Input Data and Boundary Conditions for Simulation
4.3.1 Model Input Data
The input data at hybrid spatial resolutions were prepared and arranged to calculatespatially averaged 1°x1° grid in global scale using the Spatial Analyst Tools in ArcGIS v10.3
software for the simulation (Fig. 4.1 and Table 4.1); elevation, land cover, soil texture,
vegetation type, river networks, lakes and wetlands, reservoirs and dams, geological structures,
crop type, irrigation type, irrigation water use, fertilizer use, and manure use were categorizedbased on the global and Japanese datasets. Although data for plastics are scarce, the author usedsome data to simulate the plastic debris in the major global rivers, gridded population data at 1km resolution from global data in 2015 (NASA, 2018), wastewater production, collection,
treatment and reuse data at 5 arcmin (about 10 km) resolution (Jones et al., 2021), and MPW at
1 km resolution (Lebreton and Andrady, 2019) as a diffuse source (Table 4.1). To create thegrid data from point, polyline, and polygon data for different sources with different spatial
resolutions in a reasonable way, overlay analysis was conducted using GIS tools (intersect,union, and identity) (ESRI, 2019). MPW was input to the model as a diffuse source of plastics
in the two-dimensional hillslope runoff model of NICE-BGC (Fig. 4.2). In particular, the author
used the estimated rate of conversion of macro-plastics to microplastics (3%) in the same way
as van Wijnen et al. (2019).
The author also used the per capita emission of microplastics in each economic region
determined in a previous study (van Wijnen et al., 2019). Generally, microplastics at point
sources originate from four sources: personal care products (PCPs), laundry fibers, car tire wear,- 63 - 63 -

Chapter 4 Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a Process-Based Eco-Hydrology Modeland fragmentation of macroplastics. We estimated the potential fluxes of microplastics bymultiplying these per capita emissions and their population distribution for each catchment flow
into WWTPs (Waste Water Treatment Plants) in the NICE-BGC simulation (Fig. 4.2). We also
applied “untreated wastewater” and converted to removal efficiency of microplastic, the formerof which was calculated by subtracting the ratio of wastewater treatment and wastewaterproduction at 5 arcmin resolution (Jones et al., 2021). The result shows that the removal
efficiency of microplastic in the developed countries was much higher than in the otherdeveloping countries. It was also assumed that microplastic concentration on hillslopes wasconstant (Wagner et al., 2019), and the time series proportional to hillslope runoff wascalculated at each time step. Details are described in the author’s previous study (Nakayama
and Osako, 2023a, 2023b).
Table 4.1 List of input data sets for the NICE and NICE-BGC simulations
Data set

Original resolution

Year

Source and reference

Climatology
Elevation
Land cover
Soil texture
Vegetation typeRiver networks
Lakes and wetlands
Reservoirs and Dams
Geological structures
Crop typeIrrigation typeIrrigation water use
Fertilizer use
Manure use
Atmospheric N deposition
Estuary
Estuary type
Coastal line
Water level
Population
Wastewater production andtreatment
Mismanaged plastic waste

1.0°
1.0km
1.0km
1.0km
0.25°
1.0km
0.5 min
point
0.5°
5 min
5 min
5 min
5 min
5 min
0.5°
polygon
0.5°
0.5°
point
1.0km

2014-2015
around 1996
around 2000
around 1970-2000
around 2000
around 1996
around 1990-2000
around 2010
around 1970-2000
around 2000
2000-2008
1998-2002
around 2000
1998-2014
1998-2015
around 2000
around 2000
around 2000
1992-2021
2015

ERA-interim; ECMWF (2019)
GTOPO30; U.S. Geological Survey (1996a)
GLC2000; European Commission (2015)
HWSD; European Commission (2012)
GLDAS Vegetation Class; NASA (2013)
HYDRO1K; U.S. Geological Survey (1996b)
GLWD; Lehner and Döll (2004)
GRanD; Lehner et al. (2011)
GLiM; Hartmann and Moosdorf (2012)
MIRCA2000; Portmann et al. (2010)
GMIA; FAO (2016)
GCWM; Siebert and Döll (2010)
Earth Stat; Mueller et al. (2012)
Zhang et al. (2017)
ISIMIP2a; Tian et al. (2018)
Global Estuary Database; Alder (2003)
Global Coastal Typology; Dürr et al. (2011)
MARCATS; Laruelle et al. (2013)
G-REALM; USDA (2022)
NASA (2018)

5 min

2015

Jones et al. (2021)

1.0 km2015

Lebreton and Andrady (2019)

4.3.2 Boundary Conditions and Running the Simulation
After the NICE simulation of eco-hydrological processes at 1°x1° resolution in thehorizontal direction and in 20 layers in the vertical direction, NICE-BGC simulation for
terrestrial ecosystems was conducted at the same spatial resolution with a time step of t = 1
day for two years during 2014–2015 by inputting some values simulated by NICE iteratively
after calculating the daily-averaged data from 1-hourly data (Fig. 4.2). The author used previous
data for per capita emission of microplastics into rivers (PCPs, dusts, laundries, and tires) andremoval efficiency in WWTPs as a point source. The model also simulated the outflow ofmacroplastics originating from MPW (Lebreton and Andrady, 2019) as a diffuse source. Then,
NICE-BGC simulation for aquatic ecosystems was conducted by inputting the simulated results
for land to a stream network model with a time step of t = 0.70 min and to a lake model with- 64 - 64 -

CGER-I169-2024, CGER/NIES

a time step of t = 6 hours to ensure model stability. The hydrologic cycle and plastic transport
in the world’s major river basins (153 basins) were simulated and verified using the previous
datasets and materials as far as possible in the continental scale in the author’s previous study(Nakayama and Osako, 2023b).
For the simulation of plastic flux, the author calculated the weighted average value by
multiplying each probability with each flux and their summation; this assumes that both micro
and macroplastics are pure and inert polymers with a continuous probability distribution of size,
shape, and density (Kooi and Koelmans, 2019) in the same way as the author’s previous study(Nakayama and Osako, 2023b). Uncertainty and sensitivity analyses were conducted by using
Monte Carlo model simulations to rank the model parameters according to their contribution toprediction uncertainty. The results yielded quantitative measures of model performance inreproducing the seasonality and intra-annual variability in the hydrologic cycle and plastictransport there.

4.4 Results and Discussion
4.4.1 Effect of Reservoirs on Differences in Horizontal Plastic Transport
The author simulated riverine plastic transport in inland waters with and without reservoirs(Fig. 4.3). The simulated results are shown with and without the effect of settling andresuspension. It can be seen that the simulated plastic transport with the presence of reservoirs
becomes slightly smaller than that without the presence of reservoirs on floating condition, i.e.,
settling velocity = 0 (Figs 4.3a and 4.3d), with almost the same as the density of water (density= 1000.1 kg/m3) (Figs 4.3b and 4.3e), and on averaged plastic condition of all iterations ofMonte Carlo model simulations (density = 1008.8 kg/m3) (Nakayama and Osako, 2023b) (Figs
4.3c and 4.3f). In particular, the simulated microplastic transport (Figs 4.3d-f) becomes smaller
than the simulated macro-plastic transport with reservoirs (Figs 4.3a-c). This result also showsthat the dams trap some amounts of floating microplastics in water (22%), contrary to the more
trapped (65%) in the previous study (Lebreton et al., 2017).

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Chapter 4 Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a Process-Based Eco-Hydrology Model

Figure 4.3 Difference of simulated horizontal plastic transport in inland waters with and without
the presence of reservoirs; (a)-(c) macroplastic and (d)-(f) microplastic transports
without effect of settling and resuspension, with almost the same as the density ofwater (density = 1000.1 kg/m3), and on averaged plastic condition of all iterations ofMonte Carlo model simulations (density = 1008.8 kg/m3)

4.4.2 Effect of Reservoirs on Differences in Plastic Burial
Fig. 4.4 compares plastic burial in major global reservoirs with and without the lake model.
The simulated burial with the lake model is similar to that without the lake model on floating
conditions, i.e., settling velocity = 0 (Figs 4.4 and 4.4d). In contrast, the results become different
with and without the lake model with almost the same as the density of water (density = 1000.1
kg/m3) (Figs 4.4b and 4.4e) and on averaged plastic condition of all iterations of Monte Carlomodel simulations (Nakayama and Osako, 2023b) (Figs 4.4c and 4.4f). This result showed there
are limits to the application of the same partial differential equations as in inorganic carbon forthe derivatives either with or without the reservoirs as assumed by the author’s previous study(Nakayama and Pelletier, 2018) especially when the plastic density is slightly higher than thatof water; this is also related to the improvement of retention time and the accuracy of thereservoir’s hypsographic information in the form of an elevation-area curve inputted into thelake model (Chapra and Martin, 2012) (Fig. 4.2) and the associated plastic dynamics ofhorizontal and vertical transports there.

- 66 - 66 -

CGER-I169-2024, CGER/NIES

Figure 4.4 Comparison of (a)-(c) macro-plastic and (d)-(f) microplastic burials on floating
condition (settling velocity = 0), with almost the same as the density of water (density= 1000.1 kg/m3), and on averaged plastic condition of all iterations of Monte Carlomodel simulations in global major reservoirs without the lake model (horizontal axis)
and with it (vertical axis)

4.4.3 Change in Global Plastic Cycle by Considering Effects of Lentic Waters
Fig. 4.5 compares plastic sedimentation in the global lakes and reservoirs (Hoffman and
Hittinger, 2017; Ghayebzadeh et al., 2020; Gao et al., 2023). Most plastics are deposited in the
global river basins, such as the Caspian Sea, the Three Gorges Dam (Yangtze River), and theTarbela Dam (Indus River). Though the observed data are limited, the simulated results
reproduce reasonably the observed values. The result also shows that there are more plastic
deposits in the reservoirs than the lakes, except for a significant amount of plastic deposits in
the Caspian Sea, which agrees reasonably that lotic systems have higher densities of plastic
accumulation than lentic systems (D’Avignon et al., 2022). In addition, except for some rivers,
such as the Yangtze and Yellow Rivers, most lentic waters deposit more microplastics thanmacroplastics, as pointed out by the previous study (Chen et al., 2022).
Table 4.2 shows the weighted average of plastic budget in major global rivers. The
simulated result shows that the plastic deposition in the global major lakes (0.110±0.079 Tg/yr)
is smaller than that in the global major reservoirs (0.386±0.103 Tg/yr), which is the opposite of
the carbon cycle estimated by the previous author’s study (Nakayama, 2023). The result also
shows that it is difficult to ignore the plastics sinking into lakes and reservoirs beds. However,
the amount of deposited plastics simulated by the model there (0.496 Tg/yr) is smaller than that- 67 - 67 -

Chapter 4 Impact of Global Major Reservoirs and Lakes on Plastic Dynamics by Using a Process-Based Eco-Hydrology Modelin the previous material (3.1 Tg/yr) (OECD, 2022). In addition, the simulated result showedthat artificial structures such as dams and lakes intercept some amounts of plastics before theyreach the ocean (22%; reservoir + 6%; lakes = 28%; total), though the amount trapped is not as
great as 65% in the previous study (Lebreton et al., 2017). The result also showed thatmicroplastics are more retained (38%) by the lakes and reservoirs than macroplastics (22%),
which agreed with the previous study (Chen et al., 2022) that the hydraulic sorting preferentiallyretains higher proportions of small-sized plastics and that the fluvial flooding more easily
evacuates large-sized plastics. The riverine plastic transport to the ocean is 1.253±0.393 Tg/yr,
with a macroplastic flux of 0.815±0.305 Tg/yr and a microplastic flux of 0.439±0.248 Tg/yr,
which is in the range of previous values 0.41–4.0 Tg/yr (Meijer et al., 2021).

Figure 4.5 Simulated plastic deposition in the global (a) lakes and (b) reservoirs; Blue and redbars show the flux of macro and microplastics- 68 - 68 -

CGER-I169-2024, CGER/NIES

Table 4.2 Weighted average of plastic budget in the global major rivers (153 basins/watersheds,
325 river channels)

River

Macro P flux (Tg/yr)

Micro P flux (Tg/yr)

Total P flux (Tg/yr)

Riverine Transport to Ocean
Reservoir Storage
Lake Storage
Net Transport to Ocean

1.056±0.294
0.206±0.065
0.035±0.049
0.815±0.305

0.693±0.227
0.180±0.080
0.075±0.061
0.439±0.248

1.749±0.371
0.386±0.103
0.110±0.079
1.253±0.393

4.5 Conclusion
The process-based model NICE-BGC was extended to a couple with LAKE2K in a
stratified water quality model to evaluate the global plastic dynamics in both lotic and lentic
waters. The new model could simulate riverine plastic transport and burial in inland waters withand without the presence of major global reservoirs and lakes. In particular, the plastic burial
simulated by the model became different with and without the lake model when the density ofplastic was higher than that of water. This result showed there are limits to applying the same
partial differential equations as inorganic carbon for the derivatives either with or without thereservoirs, as assumed by the author’s previous study. The model also simulated plastic
sedimentation in both lakes and reservoirs and showed that there were more plastic deposits in
the reservoirs than the lakes except for the Caspian Sea, and most lentic waters are found todeposit more microplastics than macroplastics as pointed out by the previous study. Finally, theauthor evaluated global changes in plastic dynamics due to anthropogenic factors such as theconstruction of artificial dams and natural lakes in lentic water. The simulated result also
showed that incorporating the lake model in NICE-BGC led to improved estimates of plastic
dynamics in inland waters and may aid the development of solutions and measures to reduce
plastic input to the ocean.
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Chapter 5
Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic
Dynamics in Asian Region- 73 - 73 -Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region- 74 - 74 -

CGER-I169-2024, CGER/NIES

Abstract
Environmental contamination by plastics has received considerable attention fromscientists, policymakers, and the public. In this study, the authors applied the eco-hydrology
model coupled with the plastic debris model in Asian regions by including the effect of waste
plastic trade. Results showed that settling and resuspension of plastic particles greatly impact
the mobilization of plastic and its high intra-annual variability and that plastic transport
becomes drastically higher during the flood periods, accounting for most of the annual flux. In
particular, the model simulated the size distribution of plastics in the water and riverbed
sediment of the major global rivers and showed that the coarser particles are more distributedin the riverbed than those in the water. Further, the authors evaluated the impact of waste plastic
trade on changes in riverine plastic transport by including the effect of plastic trade on
Mismanaged Plastic Waste (MPW) in the extension of previous studies. They modified thecalculation of municipal plastic waste by including these effects of plastic trade and disposal.
They revised MPW, assuming that the import of plastics is treated the same as the export ofplastics as the first approximation. The modified MPW became smaller than the original one in
the exporting countries and vice versa in the importing countries. Further, this analysis clarified
waste plastic trade has promoted a decrease in riverine plastic transport in Japanese rivers, itsslight increase in the Yangtze, Yellow, and Ganges Rivers, and almost constant in the Mekong,Indus, Irrawaddy, and Brahmaputra Rivers. The present results help quantify plastic trade-off
about the impact of the export and import of waste plastic on plastic dynamics at a regional
scale.
Keywords: Eco-hydrology model; flux; plastics; regional scale; riverine plastic transport;
waste disposal; waste plastic trade- 75 - 75 -Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region

5.1 Introduction
Plastic pollution is a serious environmental problem, and such pollutants from lands torivers and finally to oceans severely affect human health and the environment (Siegfried et al.,2017). The difference between plastic and natural particles such as sand and sediment is thatthe plastic is gradually degraded by physical, chemical, and biological processes and leads tofurther subdivision into an enormous number of forms once released into the environment.
Previous studies on the origin and fate of plastic waste in freshwater systems have suggested
that land-derived plastics are the dominant sources of marine plastic pollution (Thompson etal., 2004). Some studies (Jambeck et al., 2015; Lebreton et al., 2017; Schmidt et al., 2017)
estimated the distribution of global riverine emissions of plastic into the ocean using empiricalindicators representative of waste generation (mismanaged plastic waste: MPW) inside a river
basin. In contrast to these studies, Meijer et al. (2021) recently proposed a more accurate formof analysis to account for the spatial distribution of plastic waste generation (Lebreton andAndrady, 2019) and climatological/geographical differences within river basins by using aprobabilistic approach. Other studies (Siegfried et al., 2017; van Wijnen et al., 2019; Strokal etal., 2021) adapted a process-oriented model to continental and global scales by extending an
existing non-dynamic nutrient export model. However, these global plastic transport models
ignored the impact of flood events on plastic mobilization and its high inter-annual variability,
where the plastic transport increases up to a factor of five to ten at the floods (van Emmerik etal., 2019; Roebroek et al., 2021). Larger plastic debris from low-density polymers such aspolyethylene (PE), polypropylene (PP), and polystyrene (PS) are predominantly transported innormal flat water.
In contrast, the relatively larger fraction of plastic litter retained in the bottom sediment isefficiently flushed from riverbeds during flooding (Nizzetto et al., 2016; Hurley et al., 2018;
Schwarz et al., 2019), which should be modeled and clarified from the viewpoint of seasonalvariations of plastic riverine plastic transport; this is also related to the fact that the larger plastic
particles are efficiently distributed in the bed sediment than those in the water column because
the aggregation of micro-plastics mixed with organic minerals is one of the mechanisms for the
micro-plastic sedimentation (Kooi et al., 2018; Koutnik et al., 2021; Liu et al., 2021). Because
extreme events such as floods and droughts are expected to increase further with climate change,
it is important to evaluate the impact of flood events on plastic mobilization and its highvariability using the process-oriented model.
In contrast, recent study shows that previous studies about riverine plastic outflows basedon the concept of MPW were substantially greater than reported field data and advocates theneed for robust model using the Human Development Index (HDI) as the main predictor (Maiet al., 2020; Strokal et al., 2021). From another point of view, MPW (Lebreton and Andrady,2019) used in the authors’ previous study (Nakayama and Osako, 2023a, 2023b) did not include
the effect of waste plastic trade flow between Japan and abroad (Fig. 5.1). Japan was some of
top exporting countries of waste plastic after Europe and America, and some countries such as
China imported this waste plastic. China has imported approximately 45% of all cumulative
imports of plastic waste between 1988 and 2016 (Brooks et al., 2018; Dominish et al., 2020;
EIA, 2021). This situation changed greatly after establishing the “Basel Convention” (United
Nations, 2019) because some importing countries introduced import regulations mainly forenvironmental protection. China also attempted to reduce the import of low-value plastic via
the “Green Fence” policy in 2015 (Dominish et al., 2020). Combining the fate and transportmodel with social system methods such as material flow analysis is important to assess
environmental emissions under climate change. Developing a framework for reversing global- 76 - 76 -

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water resource degradation, especially plastic pollution, is also powerful for achieving
Sustainable Development Goals (SDG) (UNESCO, 2022). The recent G20 Osaka Summit in2019 shared the “Osaka Blue Ocean Vision” (Ministry of Foreign Affairs of Japan, 2019),
which is positioned alongside COP (Conference of the Parties) as part of global warming
countermeasures. A quantitative assessment of the fate and transport of plastics will benecessary to realize this vision because countries worldwide have decided on concrete future
measures for dealing with plastic based on scientific results. In particular, the previous studies
clarified a dominant contribution of the summer monsoon in Asia and that more than 74% of
waste is delivered between May and October from limited large rivers in Asia (Lebreton et al.,2017; Best, 2018; Nakayama and Osako, 2023b).
One of the authors has developed a process-based model coupling eco-hydrology with thebiogeochemical cycle (National Integrated Catchment-based Eco-hydrology (NICE)-BGC)(Nakayama, 2017a, 2017b), incorporating complex terrestrial-aquatic linkages in thehydrologic-biogeochemical cycle. Recently, the authors extended to couple this process-based
NICE-BGC with a plastic debris (engineered materials) model and evaluated spatiotemporalvariations of plastic debris in the entire Japan of regional scale (Nakayama and Osako, 2023a)
and the world’s major rivers of global scale (Nakayama and Osako, 2023b). Based on thisbackground, three basic issues were addressed (Nakayama and Osako, 2024): (i) How does the
movement of plastic particles between water and riverbed sediment affect the mobilization ofplastic and its high intra-annual variability? (ii) How does the waste plastic trade affectmismanaged plastic waste? (iii) How does the waste plastic trade affect riverine plastic transportin Asia? To clarify these issues, NICE-BGC simulated the seasonal variations of riverine plastictransport during 2014-2015. In particular, the model could include the effect of waste plastic
trade flow between Japan and abroad. The authors evaluated the impact of waste plastic tradeon change in riverine plastic transport in Asian regions by including the effect of waste plastic
trade on Mismanaged Plastic Waste (MPW) in the extension of the previous studies (Lebretonand Andrady, 2019; Meijer et al., 2021). This analysis also clarified whether the waste plastic
trade has promoted a decrease or an increase in riverine plastic transport in Asian rivers. The
present study is also an important step not only for the qualification of processes but also for
identifying the hierarchy of drivers in various river catchments.

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Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region

Figure 5.1 Location of the study area in Japan and Asian regions; (a) land cover, (b) population
density, (c) untreated wastewater calculated from wastewater treatment andwastewater production, and (d) mismanaged plastic waste (MPW)

5.2 Methods
5.2.1 General Model Framework of NICE-BGC
One of the authors has so far developed an advanced process-based model derived bycoupling a process-based National Integrated Catchment-based Eco-hydrology (NICE) modelwith various biogeochemical cycle models (NICE-BGC) (Nakayama, 2017a, 2017b, 2022).
This model incorporates the connectivity of the biogeochemical cycle accompanied by thehydrologic cycle between surface water and groundwater, hillslopes and river networks, andother intermediate regions. Recently, the authors extended to couple this process-based NICEBGC with a plastic debris (engineered materials) model and evaluated spatiotemporal variationsof plastic debris in the entire Japan of regional scale (Nakayama and Osako, 2023a) and theworld’s major rivers of global scale (Nakayama and Osako, 2023b) (Fig. 5.1). The new model
also included advection, dispersion, diffusion, settling, dissolution and deterioration due to lightand temperature, but assumed no interaction with suspended matter (hetero-aggregation)(Praetorius et al., 2012), resuspension (Waldschläger and Schüttrumpf, 2019), biofouling (Kooiet al., 2017, 2018), or wind effects.

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CGER-I169-2024, CGER/NIES

5.2.2 Extension of Plastic Debris Model including Effect of Waste Plastic Trade
In this chapter, the authors newly incorporated resuspension in addition to settling byextending the authors’ previous studies (Nakayama and Osako, 2023a, 2023b, 2024). The
original model assumed the resuspension rate when a riverbed is under equilibrium conditions
and the particle size distribution is uniform; applying this formula directly to plastic wasdifficult. It is difficult to grasp the heterogeneous distribution of settled and deposited plastic
on hillslopes. However, some studies have evaluated the erosion behavior of microplastic
particles compared to sediments (Waldschläger and Schüttrumpf, 2019). The new model
included the mass budget in the water and the bed sediment of any given reach in the same way
as the previous studies (Nizzetto et al., 2016; Koutnik et al., 2021). Thus, the model could
simulate the concentration of plastics in the water and the bed sediment and evaluate thehydrograph for plastics transport with various shear stresses during storm events, as proposedby Kumar et al. (2021). For model simplification, it was also assumed that microplastics are
pure and inert polymers with a continuous probability distribution of size, shape, and density(Kooi and Koelmans, 2019). After simulation of plastic transport on a hillslope, the authors
used the estimated rate of conversion of macroplastics to microplastics (3%/yr) in the same way
as van Wijnen et al. (2019), with extension of the conversion rate employed by Jambeck et al.
(2015) (Fig. 5.2). MPW is the most important variable for the diffuse input of macroplastics
and secondary microplastics into rivers.

Figure 5.2 Flow diagram of macro and microplastics transport in land and inland water byextending NICE-BGC to include the effect of waste plastic trade- 79 - 79 -

Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region

5.3 Input Data and Boundary Conditions for Simulation
5.3.1 Model Input Data including Effect of Waste Plastic Trade
The input data at hybrid spatial resolutions were prepared and arranged to calculatespatially averaged 0.1°x0.1° grid in Japan and 1°x1° grid in Asian regions using the Spatial
Analyst Tools in ArcGIS v10.3 software for the simulation (Fig. 5.1 and Table 5.1); elevation,
land cover, soil texture, vegetation type, river networks, lakes and wetlands, reservoirs anddams, geological structures, crop type, irrigation type, irrigation water use, fertilizer use, andmanure use were categorized based on the global and Japanese datasets. Although data forplastics are scarce, the authors used some data to simulate the plastic debris in the entire Japanand Asian regions: gridded population data at 250 m resolution from the Japanese Census in2015 (Japanese Government Statistics, 2021) and 1 km resolution from global data in 2015
(NASA, 2018), wastewater production, collection, treatment and reuse data at 5 arcmin (about
10 km) resolution (Jones et al., 2021), and MPW at 1 km resolution (Lebreton and Andrady,2019) as a diffuse source (Table 5.1). To create the grid data from point, polyline, and polygon
data for different sources with different spatial resolutions in a reasonable way, overlay analysis
was conducted using GIS tools (intersect, union, and identity) (ESRI, 2019).
In this chapter, the authors added the effect of waste plastic trade on MPW in the Asian
Pacific and major economies in 2015 (Nakayama and Osako, 2024) in the reference of theprevious studies (United Nations, 2015; Brooks et al., 2018; Lebreton and Andrady, 2019)(Table 5.2). In Asia, Japan was the top exporter of waste plastic in 2015 (1.60 million MT ofplastic), and most Southeast Asian countries exported more than they imported. China was the
top importer of plastic (7.35 million MT), but approximately 1.3 million to 3.5 million MT ofplastic was estimated to enter the ocean annually from the coastline (Brooks et al., 2018). The
authors modified the calculation of municipal plastic waste by including these effects of plastic
trade and disposal. They revised MPW, assuming that the ratio between MPW and municipal
plastic waste was constant in each country. While it is reasonable to assume that the export ofplastics is treated as a diffuse source of variations in MPW, the same treatment in importing
plastics is likely to have great uncertainty; this is because the geographical distribution of plastic
imports cannot be assumed equal to that of the population density grid used to estimate thegeographical distribution of MPW. In this study, the authors assumed that the import of plastics
is treated as the same as the export of plastics as the first approximation. The result shows thatthe modified MPW was smaller than the original one in the exporting countries and vice versa
in the importing countries (Fig. 5.3). These data were input to the model as a diffuse source ofplastics in two-dimensional hillslope runoff model of NICE-BGC including mobilization (Fig.
5.2).
The authors also used the per capita emission of micro-plastics in each economic region
determined in a previous study (van Wijnen et al., 2019). Generally, microplastics at point
sources are considered to originate from four sources: personal care products (PCPs) (0.0007–
0.0049 kg/cap./yr), laundry fibers (0.036–0.041 kg/cap./yr), and car tire wear (0.018–0.077
kg/cap./yr) in Asian regions (Nakayama and Osako, 2023a, 2023b). We estimated the potential
fluxes of microplastics by multiplying these per capita emissions and their population
distribution for each catchment flow into WWTPs (Waste Water Treatment Plants) in the
NICE-BGC simulation (Fig. 5.2). We also applied “untreated wastewater” and converted toremoval efficiency of microplastic, the former of which was calculated by subtracting the ratioof wastewater treatment and wastewater production at 5 arcmin resolution (Jones et al., 2021).
This method differs from those used in most previous studies (Siegfried et al., 2017; van Wijnen- 80 - 80 -

CGER-I169-2024, CGER/NIES

et al., 2019; Meijer et al., 2021; Strokal et al., 2021) because these studies considered only
sewerage connectivity (0–1) microplastic removal efficiency, and transport probability ofplastics with relatively large uncertainty. The removal efficiency of microplastic in the Japanese
WWTPs was much higher than in the other Asian regions (Fig. 5.1c), which was caused byalmost all secondary and tertiary treatments in Japan (Nakayama and Osako, 2023a). It was also
assumed that micro-plastic concentration on hillslopes was constant (Wagner et al., 2019), andthe time series proportional to hillslope runoff was calculated at each time step. Details aredescribed in the authors’ previous study (Nakayama and Osako, 2023a, 2023b).
Table 5.1 List of input data for NICE and NICE-BGC simulations in Japan and Asian regions
Data set

Original resolution

Year

Source and reference

Climatology
Elevation
Land cover
Soil texture
Vegetation type

1.0°
1.0km
1.0km
1.0km
0.25°

River networks

line

Lakes and wetlands

0.5 minReservoirs and Dams

point

Geological structures
Crop typeIrrigation typeIrrigation water use
Fertilizer use
Manure use

0.5°
5 min
5 min
5 min
5 min
5 min

Population

1.0km

1998-2015
around 1996
around 2000
around 1970-2000
around 2000
around 1996
around 2000
around 1990-2000
around 20102014
around 1970-2000
around 2000
2000-2008
1998-2002
around 2000
1998-2014
2015
2015

ERA-interim; ECMWF (2019)
GTOPO30; U.S. Geological Survey (1996a)
GLC2000; European Commission (2015)
HWSD; European Commission (2012)
GLDAS Vegetation Class; NASA (2013)
HYDRO1K; U.S. Geological Survey (1996b)
MLIT (2012)
GLWD; Lehner and Döll (2004)
GRanD; Lehner et al. (2011)
MLIT (2016)
GLiM; Hartmann and Moosdorf (2012)
MIRCA2000; Portmann et al. (2010)
GMIA; FAO (2016)
GCWM; Siebert and Döll (2010)
Earth Stat; Mueller et al. (2012)
Zhang et al. (2017)
NASA (2018)
Japanese Government Statistics (2021)

2015

Jones et al. (2021)

2010
2015

MLIT (2013)
Lebreton and Andrady (2019)

Wastewater production and
5 mintreatment
Wastewater treatment plants point
Mismanaged plastic waste
1.0 km- 81 - 81 -Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region

Table 5.2 Effect of waste plastic trade on MPW in Asia Pacific and major economies in 2015

CountryJapan
China
Hong Kong
Malaysia
ThailandRep. of Korea
Indonesia
VietnamPhilippines
Singapore
Bangladesh
Cambodia
Lao PDR
Myanmar
India
Pakistan
RussiaMongolia
USA
EU
UK
CanadaAustralia
New ZealandWorld
Reference

Municipal
platic waste(ton/yr)6

MPW(ton/yr)4

7.16×10
4.75×107
7
2.49×101.74×1054
5.88×10
1.18×106
6
1.12×10
1.32×106
6
2.98×10
1.79×106
4
1.21×10
2.42×106
6
1.64×10
6.57×106
6
1.47×10
1.23×106
6
5.37×10
4.56×105
3
4.90×102.45×106
6
1.08×10
1.08×1055
2.83×10
2.61×1055
1.48×10
1.48×106
6
1.07×10
1.07×107
7
1.71×101.45×106
6
1.42×10
1.35×106
5
8.84×10
3.97×10
4
4
6.22×10
4.10×107
5
2.68×10
5.36×107
6
3.55×10
3.14×106
4
3.02×10
6.04×106
4
2.38×10
4.76×1054
1.07×10
5.33×1053
3.50×101.75×108
7
1.81×10
8.18×10
Lebreton and Lebreton andAndrady
Andrady(2019)(2019)

Plastic
export(ton/yr)

Plastic
import(ton/yr)6

2.11×106
7.35×106
2.87×105
2.50×104
5.62×104
6.80×104
9.71×104
8.84×103
1.94×103
4.80×104
1.28×102
5.75×103
1.26×102
9.87×105
1.86×104
3.39×1042.01×101
8.30×105
3.93×105
4.12×104
8.73×105
2.49×104
1.40×103
6.42×107
1.53×10
UnitedNations(2015)

1.60×1043.04×106
2.82×105
1.82×105
2.66×105
1.88×105
1.49×105
1.19×105
1.17×104
6.23×104
3.90×104
1.43×102
8.62×102
3.74×103
5.61×1043.02×1041.01×103
7.01×106
2.05×106
3.08×105
7.92×105
2.11×1052.06×10
4
4.72×107
1.46×10
UnitedNations(2015)- 82 - 82 -Revisedmunicipal
platic waste(ton/yr)

Directdisposal to
sea

4

6

6

1.30 - 3.50×10

Brooks et al.
(2018)

3.17×107
2.98×1056.38×106
1.39×10
6
2.77×10
6
1.09×10
6
6.52×106
1.43×10
6
5.25×105
1.88×106
1.05×105
2.69×105
1.48×106
1.07×107
1.73×106
1.42×10
6
3.98×10
4
5.53×107
2.51×107
2.87×106
2.32×106
2.42×105
3.41×105
1.34×108
1.82×10

RevisedMPW(ton/yr)
4

4.78×107
2.08×105
1.28×106
1.17×106
1.66×104
2.18×106
1.62×106
1.20×10
6
4.46×103
3.75×106
1.05×105
2.48×105
1.48×106
1.07×107
1.47×106
1.35×105
8.86×104
3.64×1055.03×106
3.24×10
4
4.63×10
44.84×103
6.85×103
2.68×107
8.21×10

CGER-I169-2024, CGER/NIES

Figure 5.3 Effect of waste plastic trade on MPW in Asian regions; (a)-(b) around Shanghai Districtin importing country (China) without and with this effect; (c)-(d) around Tokyo
Metropolitan Area in exporting country (Japan) without and with this effect5.3.2 Running the Simulation and Verification
The authors used a hybrid model with different spatial resolutions between Japan and Asian
regions (Fig. 5.1). The simulation in all of the 109 first-class (class A) river basins throughout
Japan (MLIT, 2012) was performed at 0.1° x 0.1° resolution in the horizontal direction and 20
layers in the vertical direction (Nakayama and Osako, 2023a). The simulation in Asian regions
was conducted at 1°x1° resolution in the horizontal direction and in the same layers in thevertical direction (Nakayama and Osako, 2023b). NICE-BGC simulation for terrestrial
ecosystems was conducted at the same spatial resolution with a time step of t = 1 day for two
years during 2014-2015 by inputting some values simulated by NICE iteratively after
calculating the daily-averaged data from 1-hourly data (Fig. 5.2). The authors used previous
data for per capita emission of micro-plastics into rivers (PCPs, dust, laundries, and tires) andremoval efficiency in WWTPs as a point source. The model also simulated the outflow ofmacroplastics originating from MPW (Lebreton and Andrady, 2019), including the effect ofwaste plastic trade flow between Japan and abroad as a diffuse source (Nakayama and Osako,2024). Then, NICE-BGC simulation for aquatic ecosystems was conducted by inputting thesimulated results for land to a stream network model with a time step of t = 0.70 min to ensuremodel stability. The hydrologic cycle and river plastic transport were simulated and verified
using the previous datasets and materials as far as possible in Japan (Nakayama and Osako,
2023a) and continental scale (Nakayama and Osako, 2023b).
- 83 - 83 -Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region

For uncertainty and sensitivity analyses, the authors first calculated the cumulative
probability distribution of size, shape, and density and their inverse functions in the assumptionthat these probability distributions of size, shape, and density would also apply to macroplastics
by extension from micro-plastics in the previous study (Kooi and Koelmans, 2019). Shapecategories were considered the dimensionless Corey Shape factor determined bylength:width:height ratios for different shapes of plastic particles (Nakayama and Osako,
2023b). Secondly, Monte Carlo model simulations (10 iterations) were conducted to generatea uniform random number between 0 and 1, and the size, shape, and density were determined
under the fixed random number. Thirdly, the equivalent sphere diameter and settling velocity
were calculated to determine plastic transport in each case on the continental and global scales.
Finally, uncertainty and sensitivity analyses were conducted by ranking the model parameters
according to their contribution to prediction uncertainty. Details are described in the authors’previous study (Nakayama and Osako, 2023b). The results yielded quantitative measures ofmodel performance in reproducing the seasonality and intra-annual variability in the hydrologic
cycle and plastic transport there.

5.4 Results and Discussion
5.4.1 Seasonal Variations of Riverine Plastic Transport
The authors simulated riverine plastic transport with the effect of settling and resuspensionof plastic particles in the extension of the authors’ previous studies (Nakayama and Osako,
2023a, 2023b). The model simulated seasonal variations of riverine plastic transport in AsianRivers (Fig. 5.4) in comparison to the previous studies in the Yangtze (Zhao et al., 2019; Hanet al., 2023), Yellow (Han et al., 2020; Qian et al., 2023), and Mekong Rivers (Haberstroh etal., 2021; van Emmerik et al., 2023), respectively. The simulated plastic concentrations werehigher from summer to fall and linearly corresponded to the observed concentrations of plastics,
though the units are different. The simulated plastic transport in the Yangtze River was muchhigher than that in the previous data (Zhao et al., 2019) because this data accounted only for thesurface layer (around 30 cm depth) and might be underestimated (their estimated flux was
537.6–905.9 tons/yr, and much smaller than other previous studies). The simulated plastictransports in these rivers were generally in the range of previous materials (Jambeck et al.,2015; Lebreton et al., 2017; Schmidt et al., 2017) and showed that the plastic transport became
drastically higher during these periods, accounting for most of the annual flux.

- 84 - 84 -

CGER-I169-2024, CGER/NIES

Figure 5.4 Simulated results of seasonal variations of riverine plastic transport of Yangtze, Yellow,
and Mekong Ganges Rivers; (a) plastic concentration, and (b) plastic transport inthese rivers

5.4.2 Distribution of Microplastic in Asian major rivers
The model also simulated the size distribution of micro-plastic in the water and sedimentof the Yangtze, Mekong, and Ganges Rivers (Fig. 5.5). Generally, the coarser particles are more
distributed in the riverbed than those in the water in the same way as the previous studies (Kooiet al., 2018; Liu et al., 2021) (Fig. 5.5c); this is also related to the fact that the micro-plastic
sedimentation is promoted by the aggregation of micro-plastic mixed with organic minerals andsediments (Liu et al., 2021). However, the simulated result (Figs 5.5a-b) showed the opposite
trend to some field observations in the Yangtze (Li et al., 2020), Mekong (Haberstroh et al.,2021), and Ganges Rivers (Singh et al., 2021), respectively; this is related to the fact that PP
tends to be most prevalent in the water and PE is more prevalent in the sediment (Koutnik etal., 2021). In contrast, the model accounted for the different polymer types via the density ofthe particles. Other studies also showed that most plastics in most rivers are affected by
biofouling and an increase in their densities (Kaiser et al., 2017; Kooi et al., 2017). It is a great
step to model this process in the present study because few previous studies evaluated the modelof the fate and transport of plastics, including settling and resuspension in the global river basins.
Interestingly, the simulated total fluxes were within the range of the results reported by
Roebroek et al. (2021), representing potential plastic mobilization between non-flood
conditions and the undefended 10-year return period. This result suggests that it is important toconsider the effect of inter-annual variability to improve the accuracy of plastic flux estimation.

- 85 - 85 -

Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region

Figure 5.5 Simulated results of the size distribution of microplastic in the water and riverbed
sediment of Yangtze, Mekong, and Ganges Rivers; (a) normal frequency distribution
in the water, (b) that in the sediment, and (c) relative abundance across different size
categories in the water and sediment5.4.3 Impact of Waste Plastic Trade on Change in Riverine Plastic Transport in AsianRegionThe authors simulated to evaluate the impact of waste plastic trade on variations of riverine
plastic transport in Asian regions (Fig. 5.6). The result reveals that the waste plastic trade causes
large differences in plastic transport between exporting and importing countries. The riverine
plastic transport decreased greatly in the river basin of the exporting country (Japan) (Figs 5.6ab), where the plastic in case-c (its density is 1000.5 kg/m3) is most sensitive to the plastic trade.
In contrast, the transport increases slightly in the Yangtze, Yellow, and Ganges Rivers. It isalmost constant in the Mekong, Indus, Irrawaddy, and Brahmaputra Rivers (Fig. 5.6c) by theeffect of plastic trade. In particular, the macroplastic flux is mainly affected by the plastic trade
because MPW is the important variable for the diffuse input of macroplastics and secondary
micro-plastics into rivers in NICE-BGC in the same way as the previous study (van Wijnen etal., 2019) (Fig. 5.2). The result is also related to the dynamic of plastic movement between
water and riverbed sediment, which might affect the mobilization of plastic and its high intraannual variability in each river (Fig. 5.4). Anyway, this result shows that it is necessary to assess- 86 - 86 -

CGER-I169-2024, CGER/NIES

the trade-off of riverine plastic transports between exporting and importing countries in moredetail.

Figure 5.6 Impact of waste plastic trade on variations of riverine plastic transport in Asian
regions; (a) amount of plastic flux for each first-class of 109 rivers in Japan, (b) total
flux in entire Japan, and (c) amount of plastic flux for Asian major rivers

5.5 Conclusion
This chapter applied process-based NICE-BGC to Asian regions during the past two years
by including the effect of waste plastic trade flow between Japan and abroad. The model wasable to simulate the seasonal variations of riverine plastic transport. The result showed that thesettling and resuspension of plastic particles greatly impact plastic mobilization and its highintra-annual variability. Further, the authors evaluated the impact of the waste plastic trade on- 87 - 87 -

Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Regionchanges in riverine plastic transport in Asian regions by including the effect of waste plastic
trade on Mismanaged Plastic Waste (MPW) in the extension of the previous studies. This
analysis clarified the waste plastic trade has promoted the decrease of riverine plastic transportin Japanese rivers, its slight increase in the Yangtze, Yellow, and Ganges Rivers, and almost
constant in the Mekong, Indus, Irrawaddy, and Brahmaputra Rivers. The present results help toquantify the plastic trade-off about the impact of the export and import of waste plastic onplastic dynamics on a regional scale. They may aid the development of solutions and measures
to reduce plastic input to the ocean.
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This article was published in Ecological Modelling, 489, Nakayama, T., Osako, M., Plastic trade-off: Impact ofexport and import of waste plastic on plastic dynamics in Asian region, 110624, Copyright Elsevier (2024).

- 91 - 91 -Chapter 5 Plastic Trade-Off: Impact of Export and Import of Waste Plastic on Plastic Dynamics in Asian Region- 92 - 92 -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30
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Chapter 6
Final Conclusions and Future Work- 93 - 93 -

Chapter 6 Final Conclusions and Future Work- 94 - 94 -

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6.1 Final Conclusions
In a previous work, the National Integrated Catchment-based Eco-hydrology (NICE) model,
which includes surface–groundwater interactions and assimilates land-surface processes, wasdeveloped to describe phenology variations based on satellite data (Nakayama, 2008a-c, 2009,2010, 2011a–d, 2012a–d, 2013, 2014a,b, 2015, 2016, 2017a–c, 2018, 2019, 2020, 2022,
2023a,b; Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama andMaksyutov, 2018; Nakayama and Osako, 2023a,b, 2024; Nakayama and Pelletier, 2018;
Nakayama and Shankman, 2013a,b; Nakayama and Watanabe, 2004, 2005, 2006a,b, 2008a–c;
Nakayama et al., 2006, 2007, 2010, 2012, 2021a,b, 2023) (Fig. 1.1). NICE has been applied tovarious basins/catchments from local and regional scales, such as the Tokyo Metropolitan area
in Japan, Kushiro Wetland (the largest wetland in Japan), and Lake Kasumigaura catchment (ahighly eutrophic lake in Japan), and to continental and global scales, such as the Changjiang
and Yellow Rivers in China, Ob River in West Siberia, Mekong River in Southeast Asia, andMongolia. The results of these studies have been summarized in previous monograph series:
the 11th (Part I), 14th (Part II), 18th (Part III), 20th (Part IV), 26th publications (Part V), and29th publications (Part VI) (Fig. 1.2). In this monograph (Part VII), NICE was further extended
to couple it with the plastic debris model for quantifying the spatiotemporal dynamics of both
macro- and micro-plastics and the impacts of plastic waste on terrestrial and aquatic ecosystems
and devising solutions and measures for reduction of plastic input to the ocean (Fig. 1.3).
Chapter 2 reports the extension of NICE-BGC to couple with the plastic debris model for
freshwater systems and applied it to all the first-class (class A) river basins in Japan (109 river
basins). The result showed that large amounts of plastic flowed out of some limited rivers
intensively during rainfall seasons. Scenario analysis also quantified the total plastic flux varied
according to the efficiency of micro-plastic removal in WWTP and the density of plastic. It wasestimated to be within the range of 1,100–3,500 tons/yr (Nakayama and Osako, 2023a).
Chapter 3 reports the application of NICE-BGC coupled with the plastic debris model tothe world’s major rivers (325 rivers) to simulate the fate and transport of plastics on a global
scale. The model revealed a seasonal variation of plastic flux in Asia and its large contributionthat was affected by the summer monsoon. Monte Carlo model simulations assuming acontinuous probability distribution of size, shape, and density also clarified the effects ofvarious factors on plastic transport. Most plastic fluxes were shown to flow out of about 20
global rivers, reflecting that flood events greatly impact plastic mobilization and high interannual variability (Nakayama and Osako, 2023b).
Chapter 4 reports the extension of NICE-BGC to couple with LAKE2K in a stratified water
quality model to evaluate the global plastic dynamics in both lotic and lentic waters. The model
simulated plastic sedimentation in both lakes and reservoirs and showed more plastic deposits
than in the lakes except for the Caspian Sea. Most lentic waters deposit more micro-plastics
than macro-plastics (Nakayama, under review).
Chapter 5 reports the application of the above model to Asian regions during the past two
years by including the effect of waste plastic trade flow between Japan and abroad. The model
evaluated the impact of the waste plastic trade on changes in riverine plastic transport in Asian
regions by including the effect of the waste plastic trade on MPW in the extension of theprevious studies (Nakayama and Osako, 2024).
In this monograph, the new model simulated the spatiotemporal dynamics of the plastic
cycle in various basins, both on regional and global scales. In particular, they simulated theretention and degradation of plastics in inland water by including advection, dispersion,diffusion, settling, dissolution and deterioration due to light and temperature (Figs. 2.5 and 2.6);- 95 - 95 -Chapter 6 Final Conclusions and Future Work

this is a great improvement from previous studies that did not account for remobilization orburial of microplastic particles (Siegfried et al., 2017; Meijer et al., 2021), and assumed aconstant retention factor and transport of a constant fraction of MPW to rivers (van Wijnen etal., 2019). Using untreated wastewater with a reasonable spatial distribution (Jones et al., 2021)
and simulation of a two-dimensional diffusion model for macro-plastic transport (Fig. 3.3)
represented an extension of previous studies on a global scale. The result showed that theprocess-based model is powerful enough to quantify the detailed processes of the plastic cycle,
some of which were simulated in black boxes by most previous models. In contrast, more
spatiotemporal input data on MPW in various sectors is required. However, the new model isuseful for evaluating the spatiotemporal dynamics of plastic cycles in basin scales. The use of
more ground data, GPS tracking, image analysis of drones, and aerial photographs, and theapplication of a learning algorithm, such as random forest, to improve census data downscaling(Nicolas et al., 2016) are necessary to improve the accuracy of the heterogeneous distributionof plastic dynamics and their seasonal variations on various basins.
The model also clarified that the horizontal transport and vertical burial of plastic litter
might be greatly affected by reservoirs and lakes in the major global rivers (Fig. 4.5) and thatthe application of the same partial differential equations as in inorganic carbon for thederivatives as assumed by the author’s previous study (Nakayama and Pelletier, 2018) is limited
when the plastic density is higher than that of water (Figs 4.3 and 4.4). However, many small
and medium rivers and their reservoirs and lakes are also not yet included, though the model
includes the world’s major river basins (153 basins), which occupy about 65.9% of the total
continental area (Nakayama, 2017a, 2017b, 2022). Further, the model showed that the transportof plastic litter might be affected by urbanization and waste plastic trade on a regional scale(Brooks et al., 2018; Strokal et al., 2021) (Fig. 5.6). However, the authors assumed that the ratio
between MPW and municipal plastic waste was constant both with and without the effects ofplastic trade and disposal in each country (Fig. 5.3 and Table 5.2). From this viewpoint, it isnecessary to understand the relevance of the plastic waste trade and verify the value of thisrevised MPW, considering the plastic waste trade in more detail in the future. From anotherpoint of view, it is also valuable to combine NICE with social system methods such as material
flow analysis for assessing loss and emissions into the aquatic environment (OECD, 2022)
instead of inputting the given value of MPW (Fig. 5.2).
NICE is a useful tool for predicting and resolving future problems about plastic pollution
in various basins. This methodology is also useful for evaluating spatiotemporal variations inplastic dynamics with limited inventory data, particularly in developing countries. The future
effects of extensive plastic disposal have not been clarified yet. Therefore, it is also a useful
tool to quantify plastic trade-offs about the impact of the export and import of waste plastic onplastic dynamics on a regional scale and may aid the development of solutions and measures toreduce plastic input to the ocean.
6.2 Future Work
This monograph (Part VII) indicates the new development, coupled with eco-hydrology
and plastic debris model, and its application to various basins from regional to global scales forquantifying the fate and transport of plastic dynamics there. The result about regional scale
made clear that more detailed data about size, shape, and density is needed (Fig. 2.6). Futureadvances in the uncertainty model would greatly contribute to the elucidation of the transport
and the retention of macro- and micro-plastics in rivers together with further field surveys and- 96 - 96 -

CGER-I169-2024, CGER/NIES

laboratory experiments about plastic degradation through photodegradation, temperature,physical abrasion, biodegradation, and weathering. The result on a global scale showed thatflood events have a great impact on plastic mobilization and its high inter-annual variability(Fig. 3.5). In addition, the concrete composition of artificial structures and their operations
might have different effects on the variations of the plastic cycle and should be evaluated moreaccurately (Fig. 4.5). Evaluating efficient spatiotemporal extrapolation methods from limited
data, particularly in developing countries, is becoming extremely important. Further, the result
showed that the transport of plastic litter might be affected by urbanization and waste plastic
trade on a regional scale (Fig. 5.6). It is further necessary to improve the accuracy to resolve
the impact of plastic trade-off on the plastic cycle in the basin scale, so-called, thetransboundary pollution problem of the plastic cycle, which has become increasingly serious inrecent years.
To solve the above plastic problems, now the model is being further extended to include
another process to incorporate various datasets and to improve the accuracy of simulated resultson both regional and global scales concerning the following:(i) The interaction with suspended matter (hetero-aggregation), resuspension, biofouling, andwind effects should be included in the model (Praetorius et al., 2012; Kooi et al., 2017;
Waldschläger and Schüttrumpf, 2019). NICE was further developed by coupling it withbiogeochemical cycle models (NICE-BGC), thus incorporating hydrological, nutrient, andcarbon cycles in terrestrial–aquatic linkages, including aquatic metabolism and terrestriallyderived carbon (Nakayama, 2017a,b, 2020, 2022, 2023) (Fig. 6.1). In the future, NICE-BGC
should be expanded to include hetero-aggregation, resuspension, biofouling, and wind effects
to improve the accuracy of the plastic cycle.
(ii) Most models ignored the impact of flood events on plastic mobilization and suggested its
high inter-annual variability where the plastic transport increases up to a factor of five to ten at
the floods (van Emmerik et al., 2019; Roebroek et al., 2021). Because it is expected that extreme
events such as floods and droughts will increase further with climate change, it is important toevaluate the impact of flood events on plastic mobilization and its high variability by using theprocess-oriented model (Nakayama, under review) (Fig. 6.2). This is also effective to clarify
the dominant contribution of the summer monsoon in Asia and that more than 74% waste isdelivered between May and October from few large rivers in Asia (Lebreton et al., 2017; Best,2019) (Fig. 3.5).
(iii) The author’s previous study showed that estuarine carbon cycle was sensitive to intense
anthropogenic disturbances reflected by nutrient load, seawater temperature, sea level rise, andocean acidification (Nakayama, 2022) (Fig. 6.3). Because the plastic dynamics are closely
related to the carbon cycles, such as flocculation, heteroaggregation, and biofouling, it isimportant to extended NICE-BGC to evaluate biogeochemical and plastic dynamics interrestrial-aquatic-estuarine continuum (Nakayama, in prep.).
(iv) In more detail, the model should include the effects of waste plastic trade and disposal
between exporting and importing countries (Brooks et al., 2018). Therefore, the fate andtransport models should be combined with social system methods such as material flow analysis
for assessing loss and emissions into the aquatic environment (OECD, 2022) instead of
inputting the given value of MPW (Fig. 5.2) under climate change conditions.
Plastic pollution has recently been considered a serious environmental problem, andpollutants from land to rivers and finally to oceans severely affect human health and theenvironment (Siegfried et al., 2017). Previous studies on the origin and fate of plastic waste in
freshwater systems suggested that land-derived plastics are the dominant source of marine
plastic pollution (Thompson et al., 2004). It is necessary to identify plastic pollution hotspots- 97 - 97 -Chapter 6 Final Conclusions and Future Workand sinks and resolve the source–flux–sink nexus within river basins (Windsor et al., 2019).
From another point of view, developing a framework to reverse the degradation of global water
resources, particularly plastic pollution, is crucial for achieving the Sustainable Development
Goals (SDG) (UNESCO, 2022). The recent G20 Osaka Summit in 2019 shared the “Osaka Blue
Ocean Vision” (Ministry of Foreign Affairs of Japan, 2019), which is positioned alongside theConference of the Parties (COP) as a part of global warming countermeasures. A quantitative
assessment of plastic fate and transport will be necessary to realize this vision because countries
worldwide have decided on concrete future measures for dealing with plastics based on
scientific results.
The ongoing studies (i)–(iv) are expected to be reported in the next monograph (Part VIII)in the future.

Figure 6.1 Flow diagram of the original NICE regarding water resources and recent developmentof the eco-hydrological and biogeochemical coupling model (NICE-BGC)- 98 - 98 -

CGER-I169-2024, CGER/NIES

Figure 6.2 Percentages of (a) exporting micro-plastic load stored on the riverbed and (b) plastic
load transported to the ocean during flood periods in some major global rivers.

- 99 - 99 -Chapter 6 Final Conclusions and Future Work

Figure 6.3 Improvement of the carbon cycle by including global estuaries; (a) CO2 flux in global
estuaries, and (b) CO2 evasion and sediment storage in each continent

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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 cultivated Yellow
River Basin. Agricultural and Forest Meteorology, 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 (in Japanese)
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,
63-65 (in Japanese)
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. (2012a) Visualization of the missing role of hydrothermal interactions in a Japanese megalopolis
for a win-win solution. Water Science and Technology, 66, 409-414, doi:10.2166/wst.2012.205
Nakayama, T. (2012b) Feedback and regime shift of mire ecosystem in northern Japan. Hydrological Processes,
26, 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- 101 - 101 -Chapter 6 Final Conclusions and Future Work

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., Hashimoto, S., Hamano, H. (2012) Multiscaled analysis of hydrothermal dynamics in Japanese
megalopolis by using integrated approach. Hydrological Processes, 26, 2431-2444, doi:10.1002/hyp.9290
Nakayama, T. (2013) For improvement in understanding eco-hydrological processes in mire. Ecohydrology andHydrobiology, 13, 62-72, doi:10.1016/j.ecohyd.2013.03.004
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. (2014a) Hydrology–ecology interactions. In: Eslamian, S. (Ed.) Handbook of Engineering
Hydrology, 1: Fundamentals and Applications (Chapter 16), Taylor & Francis, pp. 329-344
Nakayama, T. (2014b) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part IV). CGER’s supercomputer monograph report, 20. NIES, 102p.,
http://www.cger.nies.go.jp/publications/report/i114/en/
Nakayama, T. (2014) Integrated assessment system using process-based eco-hydrology model for adaptation
strategy and effective water resources management. In: Lakshmi, V. (Ed.) Remote Sensing of the Terrestrial
Water Cycle (Geophysical Monograph Series 206) (Chapter 33), AGU, pp. 521-535,
doi:10.1002/9781118872086.ch33
Nakayama, T. (2016) New perspective for eco-hydrology model to constrain missing role of inland waters onboundless biogeochemical cycle in terrestrial-aquatic continuum. Ecohydrology and Hydrobiology, 16, 138148, doi:10.1016/j.ecohyd.2016.07.002
Nakayama, T. (2017a) Development of an advanced eco-hydrologic and biogeochemical coupling model aimed atclarifying the missing role of inland water in the global biogeochemical cycle. Journal of GeophysicalResearch: Biogeosciences, 122, 966-988, doi:10.1002/2016JG003743
Nakayama, T. (2017b) Scaled-dependence and seasonal variations of carbon cycle through development of anadvanced eco-hydrologic and biogeochemical coupling model. Ecological Modelling, 356, 151-161,
doi:10.1016/j.ecolmodel.2017.04.014
Nakayama, T. (2017c) Biogeochemical contrast between different latitudes and the effect of human activity on
spatiotemporal carbon cycle change in Asian river systems. Biogeosciences Discussions, doi:10.5194/bg2017-447
Nakayama, T. (2018) Interaction between surface water and groundwater and its effect on ecosystem andbiogeochemical cycle. Journal of Groundwater Hydrology, 60, 143-156, doi:10.5917/jagh.60.143 (inJapanese)
Nakayama, T., Maksyutov, S. (2018) Application of process-based eco-hydrological model to broader northernEurasia wetlands through coordinate transformation. Ecohydrology and Hydrobiology, 18, 269-277,
doi:10.1016/j.ecohyd.2017.11.002
Nakayama, T., Pelletier, G.J. (2018) Impact of global major reservoirs on carbon cycle changes by using an
advanced eco-hydrologic and biogeochemical coupling model. Ecological Modelling, 387, 172-186,
doi:10.1016/j.ecolmodel.2018.09.007
Nakayama, T. (2019) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part V). CGER’s supercomputer monograph report, 26. NIES, 122p.,
http://www.cger.nies.go.jp/publications/report/i148/en/
Nakayama, T. (2020) Inter-annual simulation of global carbon cycle variations in a terrestrial-aquatic continuum.
Hydrological Processes, 34, 662-678, doi:10.1002/hyp.13616
Nakayama, T., Wang, Q., Okadera, T. (2021a) Evaluation of spatio-temporal variations in water availability using
a process-based eco-hydrology model in arid and semi-arid regions of Mongolia. Ecological Modelling, 440,
109404, doi:10.1016/j.ecolmodel.2020.109404
Nakayama, T., Wang, Q., Okadera, T. (2021b) Sensitivity analysis and parameter estimation of anthropogenic
water uses for quantifying relation between groundwater overuse and water stress in Mongolia. Ecohydrology
and Hydrobiology, 21, 490-500, doi:10.1016/j.ecohyd.2021.07.006
Nakayama, T. (2022) Impact of anthropogenic disturbances on carbon cycle changes in terrestrial-aquaticestuarine continuum by using an advanced process-based model. Hydrological Processes, 36, e14471,
doi:10.1002/hyp.14471
Nakayama, T. (2023a) Evaluation of global biogeochemical cycle in lotic and lentic waters by developing an
advanced eco-hydrologic and biogeochemical coupling model. Ecohydrology, e2555, doi:10.1002/eco.2555- 102 - 102 -CGER-I169-2024, CGER/NIES

Nakayama, T. (2023b) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part VI). CGER’s supercomputer monograph report, 29. NIES, 95p.,
http://www.cger.nies.go.jp/publications/report/i164/en/
Nakayama, T., Okadera, T., Wang, Q. (2023) Impact of various anthropogenic disturbances on water availability
in the entire Mongolian basins towards effective utilization of water resources. Ecohydrology andHydrobiology, 23(4), doi:10.1016/j.ecohyd.2023.04.006
Nakayama, T., Osako, M. (2023a) Development of a process-based eco-hydrology model for evaluating the spatiotemporal dynamics of macro- and micro-plastics for the whole of Japan. Ecological Modelling, 476, 110243,
doi:10.1016/j.ecolmodel.2022.110243
Nakayama, T., Osako, M. (2023b) The flux and fate of plastic in the world’s major rivers: Modelling spatial andtemporal variability. Global and Planetary Change, 221, 104037, doi:10.1016/j.gloplacha.2023.104037
Nakayama, T., Osako, M. (2024) Plastic trade-off: Impact of export and import of waste plastic on plastic dynamics
in Asian region. Ecological Modelling, 489, 110624, doi:10.1016/j.ecolmodel.2024.110624
Nakayama, T. (under review) Impact of global major reservoirs and lakes on plastic dynamics by using a processbased eco-hydrology model. Lakes and Reservoirs: Research and Management
Nakayama, T. (under review) Impact of settling and resuspension on plastic dynamics during extreme flow andtheir seasonality in global major rivers. Hydrological Processes
Nakayama, T. (under review) Evaluation of flux and fate of plastic in terrestrial-aquatic-estuarine continuum byusing an advanced process-based model. Ecohydrology
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livestock census data. PLOS ONE, 11, e0150424, doi:10.1371/journal.pone.0150424
OECD. (2022) Global Plastics Outlook: Economic Drivers, Environmental Impacts and Policy Options, OECD
Publishing, Paris. https://doi.org/10.1787/de747aef-en
Praetorius, A., Scheringer, M., Hungerbühler, K. (2012) Development of environmental fate models for engineered
nanoparticles - A case study of TiO2 nanoparticles in the Rhine River. Environmental Science andTechnology, 46, 6705-6713, doi:10.1021/es204530n
Roebroek, C.T.J., Harrigan, S., van Emmerik, T.H.M., et al. (2021) Plastic in global rivers: are floods making it
worse? Environmental Research Letters, 16, 025003, doi:10.1088/1748-9326/abd5df
Siegfried, M., Koelmans, A.A., Besseling, E., Kroeze, C. (2017) Export of microplastics from land to sea. A
modelling approach. Water Research, 127, 249-257, doi:10.1016/j.watres.2017.10.011
Strokal, M., Bai, Z., Franssen, W., et al. (2021) Urbanization: an increasing source of multiple pollutants to rivers
in the 21st century. Urban Sustainability, 1, 24, doi:10.1038/s42949-021-00026-w
Thompson, R.C., Olsen, Y., Mitchell, R.P., et al. (2004) Lost at sea: Where is all the Plastic? Science, 304, 838,
doi:10.1126/science.1094559
UNESCO (2022) IHP-IX: Strategic Plan of the intergovernmental hydrological programme: Science for a water
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van Wijnen, J., Ragas, A.M.J., Kroeze, C. (2019) Modelling global river export of microplastics to the marine
environment: Sources and future trends. Science of the Total Environment, 673, 392-401,
doi:10.1016/j.scitotenv.2019.04.078
Waldschläger, K., Schüttrumpf, H. (2019) Erosion behavior of different microplastic particles in comparison tonatural sediments. Environmental Science and Technology, 53, 13219-13227, doi:10.1021/acs.est.9b05394
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Change Biology, 25, 1207-1221, doi:10.1111/gcb.14572- 103 - 103 -Chapter 6 Final Conclusions and Future Work- 104 - 104 -CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.30
CGER-I169-2024, CGER/NIES

Appendix
Publications and Presentations- 105 - 105 -Appendix Publications and Presentations- 106 - 106 -CGER-I169-2024, CGER/NIES

Publications and Presentations
Original Papers and Reviews Related to This Monograph:
Nakayama, T., Osako, M. (2023a) Development of a process-based eco-hydrology model for evaluating the spatiotemporal dynamics of macro- and micro-plastics for the whole of Japan. Ecological Modelling, 476, 110243,
doi:10.1016/j.ecolmodel.2022.110243
Nakayama, T., Osako, M. (2023b) The flux and fate of plastic in the world's major rivers: Modeling spatial andtemporal variability. Global and Planetary Change, 221, 104037, doi:10.1016/j.gloplacha.2023.104037
Nakayama, T., Osako, M. (2024) Plastic trade-off: Impact of export and import of waste plastic on plastic dynamics
in Asian region. Ecological Modelling, 489, 110624, doi:10.1016/j.ecolmodel.2024.110624
Nakayama, T. (under review) Impact of global major reservoirs and lakes on plastic dynamics by using a processbased eco-hydrology model. Lakes and Reservoirs: Research and Management
Nakayama, T. (under review) Impact of settling and resuspension on plastic dynamics during extreme flow andtheir seasonality in global major rivers. Hydrological Processes
Nakayama, T. (under review) Evaluation of flux and fate of plastic in terrestrial-aquatic-estuarine continuum byusing an advanced process-based model. Ecohydrology

Conference Reports Related to This Monograph:
Nakayama, T., Imaizumi, Y., Ishigaki, T., Osako, M. (2021) Evaluation of spatio-temporal dynamics of
microplastics developing a process-based eco-hydrology model in Japan. paper number VPA-062, Waste
Management and Valorisation for a Sustainable Future, Seoul, South Korea (Virtual Conference), 26– 28
October (Website)
Nakayama, T., Imaizumi, Y., Ishigaki, T., Osako, M. (2022) Simulation of plastic pathways from land to oceanusing spatio-temporally explicit eco-hydrology model in entire Japan. paper number IN01A, Ocean Sciences
Meeting 2022, Honolulu, USA (Virtual Conference), 27 February – 4 March (Website)
Nakayama, T., Osako, M. (2022) Evaluation of fate and transport of macro- and micro-plastics in terrestrial-aquatic
continuum of entire Japan by developing a spatio-temporally explicit eco-hydrology model. paper number
EGU22-1566, EGU General Assembly 2022, Vienna, Austria (Virtual Conference), 23– 27 May (Website),doi: 10.5194/egusphere-egu22-1566
Nakayama, T., Osako, M. (2022) Seasonal variations of fate and transport of plastic debris in global scale. session
number TS-2.2, 7th International Marine Debris Conference, Busan, Korea (Virtual Conference), 18– 23
September (Website)
Nakayama, T., Osako, M. (2022) Spatio-temporal dynamics of riverine plastic transport in continental and global
scales by developing an advanced process-based model. paper number H54G-07, AGU Fall Meeting 2022,
Chicago, USA (Virtual Conference), 12– 16 December (Website)
Nakayama, T., Osako, M. (2023) Seasonal variations of plastic transport in global major rivers. session number
12dO1, Goldschmidt2023, Lyon, France (Virtual Conference), 9-14 July (Website)
Nakayama, T. (2023) Impact of extreme flow and seasonality on plastic dynamics in global river basins. paper
number H52F-06, AGU23, San Francisco, USA (Virtual Conference), 11– 15 December (Website)
Nakayama, T. (2024) Evaluation of biogeochemical cycles and their relation to plastic dynamics in terrestrialaquatic-estuarine continuum by using an advanced process-based model. paper number OP31F-07, Ocean
Sciences Meeting 2024, New Orleans, USA (Virtual Conference), 18-23 February (Website)- 107 - 107 -Appendix Publications and Presentations

NICE Series in CGER's Supercomputer Monograph Report (Part I - VI):
Nakayama, T., Watanabe, M. (2006) 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
Nakayama, T. (2008) 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
Nakayama, T. (2012) 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. (2014) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part IV). CGER's Supercomputer Monograph Report, 20, NIES, 102p.,
http://www.cger.nies.go.jp/publications/report/i114/en/
Nakayama, T. (2019) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part V). CGER's Supercomputer Monograph Report, 26, NIES, 122p.,
http://www.cger.nies.go.jp/publications/report/i148/en/
Nakayama, T. (2023) Development of process-based NICE model and simulation of ecosystem dynamics in thecatchment of East Asia (Part VI). CGER's Supercomputer Monograph Report, 29, NIES, 95p.,
http://www.cger.nies.go.jp/publications/report/i167/en/

Contact Person
Tadanobu Nakayama
Regional Environment Conservation Division
National Institute for Environmental Studies (NIES)
16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
Phone: +81-29-850-2564
E-mail: nakat@nies.go.jp
Profile: https://www.nies.go.jp/researchers-e/100190.html
ORCID: https://orcid.org/0000-0002-8233-034X
Web of Science: https://www.webofscience.com/wos/author/record/938567
ResearchGate: https://www.researchgate.net/profile/Tadanobu_Nakayama- 108 - 108 -CGER’S SUPERCOMPUTER ACTIVITY REPORT(Out of stock)Vol. 1-1992 (CGER-I010-1994)
Vol. 2-1993 (CGER-I016-1994)
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Vol.13-2004 (CGER-I064-2006)
Vol.14-2005 (CGER-I070-2007)……………………………………………
国立環境研究所スーパーコンピュータ利用研究年報
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令和 2 年度 2020 (CGER-I156-2021)
令和 3 年度 2021(CGER-I161-2022)
令和 4 年度 2022(CGER-I168-2024)……………………………………………
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

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TOKIOKA T., NODA A., KITOH A., NIKAIDOU Y., NAKAGAWA S., MOTOI T., YUKIMOTO S., TAKATA K.:
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NAKAYAMA T., WATANABE M.: Development of Process-based NICE Model and Simulation of
Ecosystem Dynamics in the Catchment of East Asia (Part I)

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SHIBATA K., DEUSHI M.: Simulations of the Stratospheric Circulation and Ozone during the Recent Past
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NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics in
the Catchment of East Asia (Part II)

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

Vol. 16

CGER-I097-2011 (Out of stock)
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 (Out of stock)
UEDA H.: Atmospheric Motion and Air Quality in East Asia

Vol. 18

CGER-I103-2012 (Out of stock)
NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics in
the Catchment of East Asia (Part III)

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KOMORI S.: Numerical Simulations of Turbulence Structure and Scalar Transfer across the Air-Water
Interfaces- 110 -

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NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics in
the Catchment of East Asia (Part IV)

Vol. 21CGER-I120-2015
SHIOGAMA H.: Influence of Anthropogenic Aerosol Emissions on Pattern Scaling Projections

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SATOH M., ROH, W., HASHINO, T.: Evaluations of Clouds and Precipitations in NICAM Using the Joint
Simulator for Satellite Sensors

Vol. 23

CGER-I132-2017
GOTO, D., SCHUTGENS, N.A.J., OIKAWA, E., TAKEMURA, T., NAKAJIMA, T.: Improvement of a global
aerosol transport model through validation and implementation of a data assimilation system

Vol. 24

CGER-I138-2018
TAKEMURA, T., AND SPRINTARS DEVELOPER TEAM : Development of a global aerosol climate model
SPRINTARS

Vol. 25

CGER-I143-2019
MAKSYUTOV, S., ODA, T., SAITO, M., TAKAGI, H., BELIKOV, D., VALSALA, V.: Transport modelingalgorithms for application of the GOSAT observations to the global carbon cycle modelingVol. 26

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NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics
in the Catchment of East Asia (Part V)

Vol. 27

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ISHIWATARI M., NAKAJIMA K., TAKEHIRO, S., KAWAI Y., TAKAHASHI Y. O., HASHIMOTO G. L., SASAKIY., and HAYASHI Y.-Y.: Numerical studies on the variety of climates of exoplanets using idealistic
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CGER-I158-2022
YOKOHATA T.: Development of an integrated land surface model with ecosystems, human water
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CGER-I167-2023
NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem
Dynamics in the Catchment of East Asia (Part VI)

Vol. 30

CGER-I169-2024
NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem
Dynamics in the Catchment of East Asia (Part VII)

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