CGER-REPORTISSN 1341-4356
CGER-I167-2023

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29

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

Tadanobu Nakayama

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

CGER-REPORTISSN 1341-4356
CGER-I167-2023

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29

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

Tadanobu Nakayama

Center for Global Environmental Research

National Institute for Environmental Studies, Japan

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29

Development of Process-based NICE Model and Simulation of Ecosystem Dynamics in
the Catchment of East Asia (Part VI)
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
Fax: +81-29-858-2645
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 2023:

NIES: National Institute for Environmental Studies

ISSN 1341-4356 (printed version), ISSN 2434-5679 (online version), CGER-I167-2023

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), and 26th publications (Part V). This volume reviews the NICE
processes developed since Part V was published and describes how the model has been applied
to scale down the Mongolian river basin on a regional scale. NICE was further extended tocouple with the inverse method for sensitivity analysis and parameter estimation ofanthropogenic water use (NICE-INVERSE). This methodology is useful for resolving futurecompetition for water resources in areas with limited inventory data, which could potentiallytrigger conflicts among urban, mining, industry, herders, and local communities.
In future, we intend to continue our support for environmental research by using
supercomputer resources and disseminating practical information based on our results.
November 2023

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
29th 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 VI) succeeds the 11th (Part I), 14th (Part II), 18th (Part III), 20th
(Part IV), and 26th publications (Part V). This volume reviews the NICE processes developedsince Part V was published and describes how the model has been applied to scale down the
Mongolian river basin on a regional scale. To improve the accuracy of the model results, a highresolution data grid representing water use by livestock, urban populations, and mining in theentire basin was built by combining a global dataset, statistical data, GIS data, observationaldata, and field survey data. NICE was further extended to couple with the inverse method forsensitivity analysis and parameter estimation of anthropogenic water use (NICE-INVERSE).
This methodology is useful for resolving future competition for water resources in areas withlimited inventory data, which could potentially trigger conflicts among urban, mining, industry,herders, and local communities.
NICE is now being expanded to couple with a plastic debris (engineered materials) model
to evaluate the spatiotemporal variations in plastic debris at regional and global scales. Plastic
pollution is presently considered one of the main environmental problems, and pollutants instreams, rivers, and oceans pose potential risks to human health and the environment. From thisviewpoint, the results help improve the plastic cycle and develop solutions and efficientmeasures to reduce the plastic load on a global scale. This will be reported in the next
monograph (Part VII). I hope that this publication will help integrate knowledge and provide
the understanding needed to reach sustainability by 2030 in the UN Sustainable Development
Goals, which supports climate action, clean water, sustainable cities, life below water, and life
on land.
November 2023

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
List of Pictures ........................................................................................................................... x
Chapter 1
General Introduction ............................................................................................................... 1
1.1 Background .......................................................................................................................... 3
1.2 Regional Overview of the Mongolian River Basins ............................................................ 5
1.3 Description of Process-Based Models Applicable to Arid and Semi-Arid Regions ............ 6
1.4 Objective and Methods toward Effective Utilization of Water Resources in Arid andSemi-Arid Regions ................................................................................................................ 8
References ................................................................................................................................ 11
Chapter 2
Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining
Hubs of Mongolia Using Process-Based Eco-Hydrology Model ................................... 15
Abstract .................................................................................................................................... 17
2.1 Introduction ........................................................................................................................ 18
2.2 Methods .............................................................................................................................. 20
2.2.1 Description of Process-Based Eco-Hydrology Model (NICE) ....................................... 20
2.2.2 NICE Application to Various Land Uses ........................................................................ 23
2.3 Input Data and Boundary Conditions for Simulation......................................................... 24
2.3.1 Model Input Data ............................................................................................................ 24
2.3.2 Estimation of Mining, Livestock, and Human Water Uses............................................. 25
2.3.3 Boundary Conditions and Running the Simulation......................................................... 27
2.4 Results and Discussion ...................................................................................................... 30
2.4.1 Evaluation of the Hydrologic Budget in Two Contrasting River Basins in Mongolia ... 30
2.4.2 Impact of Urbanization and Mining on Groundwater Level in the Two River Basins ... 33
2.4.3 Separating the Effects of Climatic Change and Anthropogenic Activity on
Spatiotemporal Change in the Groundwater Level ............................................................. 34
2.5 Conclusion .......................................................................................................................... 36
References ................................................................................................................................ 37- iii -

Chapter 3
Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use forQuantifying the Relation between Groundwater Overuse and Water Stress.............. 41
Abstract .................................................................................................................................... 43
3.1 Introduction ........................................................................................................................ 44
3.2 Description of NICE-INVERSE Model ............................................................................. 45
3.2.1 General Model Framework ............................................................................................. 45
3.2.2 Coupling Process-based NICE with Inverse Parameter Estimation................................ 45
3.3 Input Data and Boundary Conditions for Simulation......................................................... 46
3.3.1 Model Input Data ............................................................................................................ 46
3.3.2 Estimation of Unit Water Use in Five Typical Livestock Types .................................... 48
3.3.3 Boundary Conditions and Simulation Run...................................................................... 50
3.4 Results and Discussion ...................................................................................................... 52
3.4.1 Impact of Anthropogenic Activity on Groundwater Level in Urban and Mining Hubsof Mongolia ......................................................................................................................... 52
3.4.2 Spatio-temporal Variations of Livestock Water Use in Two Contrasting River Basins . 53
3.4.3 Sensitivity Analysis and Reliability of Parameter Estimation of Water Uses ................ 55
3.5 Conclusion .......................................................................................................................... 56
References ................................................................................................................................ 57
Chapter 4
Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire
Mongolian Basin ................................................................................................................ 59
Abstract .................................................................................................................................... 61
4.1 Introduction ........................................................................................................................ 62
4.2 Input Data and Boundary Conditions for Simulation......................................................... 63
4.2.1 Model Input Data ............................................................................................................ 63
4.2.2 Estimation of Water Use in Various Sectors ................................................................... 64
4.2.3 Boundary Conditions and Simulation Run...................................................................... 66
4.3 Results and Discussion ...................................................................................................... 69
4.3.1 Evaluation of River Discharge in Entire Mongolia during the Last Two Decades......... 69
4.3.2 Detecting Degradation Hotspots by Anthropogenic Activity in Extension to Entire
Mongolia .............................................................................................................................. 70
4.3.3 Relative Contribution of Various Sources to Groundwater Degradation in National
Scale..................................................................................................................................... 71
4.4 Conclusion .......................................................................................................................... 73
References ................................................................................................................................ 74
Chapter 5
Final Conclusions and Future Work .................................................................................... 77
5.1 Final Conclusions ............................................................................................................... 79
5.2 Future Work ....................................................................................................................... 80- iv -

References ................................................................................................................................ 84
Appendix
Publications and Presentations ............................................................................................. 89
Original Papers and Reviews Related to This Monograph ...................................................... 91
Books Related to This Monograph........................................................................................... 91
Conference Reports Related to This Monograph ..................................................................... 91
NICE Series in CGER's Supercomputer Monograph Report (Part I - V) ................................ 91
Contact Person.......................................................................................................................... 92
CGER’S SUPERCOMPUTER REPORT (previously published) ........................................... 93-v-

List of Figures
Chapter 1
1.1

Location of the 29 river basins in Mongolia ............................................................... 4

1.2

Annual meteorological changes in the Tuul and Galba River Basins during
1980–2018 ................................................................................................................... 4

1.3

Process-based National Integrated Catchment-based Eco-hydrology (NICE)
model ........................................................................................................................... 8

1.4

Hydrologic simulation for understanding of regional water cycle. Previous
model applications to various basins/catchments (Part I–IV), continental andglobal scales (Part V), and Mongolian River Basins in the current monograph(Part VI) .................................................................................................................... 10

1.5

Evaluation of water availability for improvement in pasture carrying capacity in
Mongolia by coupling National Integrated Catchment-based Eco-hydrology(NICE) with inverse model ..................................................................................... 11(Some of the figures in Chapter 1 are reprinted from 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(3),490-500, doi:10.1016/j.ecohyd.2021.07.006, and 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 and Hydrobiology, 23(4), doi:10.1016/
j.ecohyd.2023.04.006)

Chapter 2
2.1

Location of the Tuul and Galba River Basins in Mongolia ...................................... 19

2.2

Estimated water use at 0.05°×0.05° resolution in (a) the Tuul and (b) Galba
River Basins during 1980–2018 ................................................................................ 26

2.3

Estimated water use trends in the two basins during 1980–2018 ............................. 27

2.4

Comparison of (a) daily and (b) monthly mean discharge in the Tuul River Basin
during 1998-2018 at locations in Table 2.2. ............................................................. 31

2.5

Simulated results of annual-averaged hydrologic budget in the (a) Tuul and (b)Galba River Basins during 1998–2018 ..................................................................... 32

2.6

Comparison of simulated and observed changes in groundwater level during
2000-2018 in (a)–(c) the Tuul and (d)–(f) Galba River Basins ................................ 34

2.7

Simulated results for changes in groundwater level from the (a) 1980s to (b)1990s, (c) 2000s, and (d) 2010s in the Tuul and Galba River Basins in the same
area as in the enlarged view to the lower right in Fig. 2.2 ........................................ 35

2.8

Relative contribution of environmental factors to changes in groundwater level
during 1980-2018, compared to the reference simulation, in the (a) urban and(b) non-urban areas in the Tuul River Basin, and (c) mining and (d) non-mining
areas in the Galba River Basin in Table 2.2 .............................................................. 36- vi -

(The figures in Chapter 2 are reprinted from Nakayama, T., Wang, Q., Okadera, T. [2021a] Evaluationof spatio-temporal variations in water availability using a process-based eco-hydrology model in arid
andsemi-arid
regionsofMongolia.
EcologicalModelling,440,
109404,
doi:10.1016/j.ecolmodel.2020.109404)

Chapter 3
3.1

Spatial datasets for National Integrated Catchment-based Eco-hydrology(NICE) simulation in Tuul and Galba River Basins ................................................. 48

3.2

Relative contribution of environmental factors to changes in groundwater level
during 1980–2018, compared to the reference simulation, in (a) urban area and(b) non-urban area areas in the Tuul River Basin, and in (c) mining area and (d)non-mining area areas in the Galba River Basin ....................................................... 53

3.3

Simulated result of temporal change of unit water use in five typical livestock
types in (a) Tuul and (b) Galba River Basins during 2015–2018 ............................. 54

3.4

Simulated result of composite scaled sensitivity in five typical livestock typesin (a) Tuul and (b) Galba River Basins during 2015–2018 ....................................... 55(The figures in Chapter 3 are reprinted from 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(3), 490-500,
doi:10.1016/j.ecohyd.2021.07.006)

Chapter 4
4.1

Estimated water use at 0.1°×0.1° resolution in entire Mongolia at 1980, 2000,
and 2018 .................................................................................................................... 66

4.2

Comparison of (a) annual-averaged discharge in 29 river basins (Fig. 1.1 andTable 4.3) and (b)–(f) time-series of monthly-mean discharge of representative
rivers .......................................................................................................................... 69

4.3

Simulated results of annual-averaged hydrologic budget in Mongolia during
1998–2018: (a) precipitation, (b) evaporation, (c) effective precipitation, and (d)water yield ................................................................................................................. 70

4.4

Relative contribution of various water uses to changes in groundwater level
during 1980–2018 compared to the reference simulation in the Mongolian river
basins ......................................................................................................................... 72

4.5

Spatial distribution of relative contribution averaged during (left) 1997–2000
and (right) 2015–2018 compared to that during 1980 in the Mongolian river
basins ......................................................................................................................... 73(The figures in Chapter 4 are reprinted from Nakayama, T., Okadera, T., Wang, Q. [2023] Impact ofvarious anthropogenic disturbances on water availability in the entire Mongolian basins towards
effective utilization of water resources. Ecohydrology and Hydrobiology, 23(4), doi:10.1016/
j.ecohyd.2023.04.006)- vii -

Chapter 5
5.1

Flow diagram of macro and micro plastic transport in land and inland water byextending NICE ......................................................................................................... 82

5.2

Seasonal variations of (a) river discharge and (b) total, (c) macro, and (d) micro
plastic flux in each continent (Asia, Europe, Oceania, Africa, North America,and South America)................................................................................................... 83

5.3

Flow diagram of the original NICE regarding water resources, and recent
development of the eco-hydrological and biogeochemical coupling model(NICE-BGC) ............................................................................................................. 84(Some of the figures in Chapter 5 are reprinted from Nakayama, T., Osako, M. [2023a] Development ofa process-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, and Nakayama, T., Osako, M. [2023b] The flux and fate ofplastic in the world's major rivers: Modeling spatial and temporal variability. Global Planet. Change,
221, 104037, doi:10.1016/j.gloplacha.2023.104037)- viii -List of Tables
Chapter 2
2.1
List of input data sets for the NICE simulation......................................................... 25
2.2
List of observation stations for validation. NIES groundwater level observation
stations (Okadera et al., 2019) are shown in Fig. 2.1 ................................................ 29(The tables in Chapter 2 are reprinted from Nakayama, T., Wang, Q., Okadera, T. [2021a] Evaluation of
spatio-temporal variations in water availability using a process-based eco-hydrology model in arid andsemi-arid
regionsofMongolia.
EcologicalModelling,440,
109404,
doi:10.1016/j.ecolmodel.2020.109404)

Chapter 3
3.1
List of input data sets for the National Integrated Catchment-based Ecohydrology (NICE) and NICE-INVERSE simulations............................................... 47
3.2
List of NIES observation stations for validation and parameter estimation shownin Fig. 2.1 .................................................................................................................. 49
3.3
List of observation stations managed by other organizations for validation andparameter estimation ................................................................................................. 51
3.4
Simulated result of correlation between each parameter in the Tuul and Galba
River Basins. Q_horse, Q_cattle, Q_sheep, Q_goat, and Q_camel represent unit
water use for horses, cattle, sheep, goats, and camels, respectively ......................... 56(The tables in Chapter 3 are reprinted from 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(3), 490-500,
doi:10. 1016/j.ecohyd.2021.07.006)

Chapter 4
4.1
List of input data sets for the NICE simulation of entire Mongolia.......................... 64
4.2
List of NIES groundwater level observation stations for model validation in
Tuul and Galba River Basins shown in Fig. 1.1........................................................ 67
4.3
List of river discharge observation stations for model validation in Mongolianriver basins (NUM, 2018). ........................................................................................ 68(The tables in Chapter 4 are reprinted from 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 and Hydrobiology, 23(4), doi:10.1016/
j.ecohyd.2023.04.006)- ix -

List of Pictures
Chapter 1
1.1
A view of two severely degraded basins including an economic and a mining
hub (left: Tuul River Basin, right: Galba River Basin) ............................................... 5

Chapter 2
2.1
Dry and salty rivers in the Galba River Basin........................................................... 31

Chapter 3
3.1
Groundwater observation and equipment setting-up ................................................ 50
3.2
Estimation of unit water use in five typical livestock types (horse, cattle, sheep,goat, and camel) ........................................................................................................ 52

Chapter 4
4.1
Environmental degradation in Mongolia .................................................................. 63

Chapter 55.1
Effect of extreme flood; water tank before (May 2018) and after (September2018) the flood .......................................................................................................... 80-x-CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29
CGER-I167-2023, CGER/NIES

Chapter 1
General Introduction-1-1-

Chapter 1

General Introduction-2-2-

CGER-I167-2023, CGER/NIES

1.1 Background
Water resources are unevenly distributed in Mongolia, being abundant in the predominantly
rural north, but scarce or non-existent in the south. Particularly, >80% water resource was
groundwater-dependent at the beginning of the 21st century (Fig. 1.1). Rapid economic growthand political and socioeconomic shifts from socialism to capitalism around 1990 also caused
serious environmental degradation, such as groundwater resource overuse owing tourbanization, mineral resource exploitation and quarrying, a rapid increase in livestock such as
sheep and goats owing to economic liberalization (Tumur et al., 2018), decreased grassland
productivity owing to livestock overgrazing and over-pumping, and agricultural activity
expansion. Currently, national water resources are under increasing pressure, causing serious
environmental degradation due to climate change, urbanization, and rapid economic growth(Banerjee et al., 2014). The Ministry of Environment and Green Development (MEGD) directly
manages 29 river basins in the country (MNET, 2013) (Fig. 1.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
Hub (Banerjee et al., 2014).
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weakrural economy to a fast-developing mixed economy characterized by rapid growth inurbanization, industrialization, and mining (ADB, 2020). The river basin organizations for these
29 river basins are required to play a role in this transition by implementing effective water
management based on integrated water resource management (IWRM). The first organization
was established on the Tuul River Basin in 2012 (MEGD, 2012), where the river flows throughUlaanbaatar and groundwater is heavily drawn for its municipal and industrial water supply(Jamsran et al., 2018). In contrast, the Galba River Basin (Galba−Uush−Doloodiing Gobi River
Basin) is in the southern Gobi Desert and has experienced large mining activities, such as Oyu
Tolgoi (OT) and Tavan Tolgoi (Oyu Tolgoi LLC, 2013). The meteorology of these two regions
are substantially different, although the annual mean temperature has gradually increased,
except in some cold winters during the past four decades (Fig. 1.2). The river basinorganizations in these two regions (Picture 1.1) are particularly challenged to becomesustainable after meeting the projected water demand by the rapidly growing urban populationand expanding mining activity (ADB, 2014; Ulaanbaatar, 2018). Other river basinorganizations are relatively young and whether they have been sufficient against rapid andbroad socioeconomic development is difficult to say. This is because the hydrological cycle
mechanism in all basins and its changes due to past human activities and climate change havenot been quantitatively evaluated and should be improved beyond previous studies. The
successful application of the process-based National Integrated Catchment-based Ecohydrology (NICE) model in existing research (Nakayama et al., 2021a,b, 2023) has enabled
expansion from the two hubs to the entire country. Thus, a model should be urgently developed
to evaluate the impact of various anthropogenic disturbances on water availability in the entire
Mongolian Basin (29 river basins) with effective water resource utilization as the research scope.

-3-3-Chapter 1

General Introduction

Figure 1.1 Location of the 29 river basins in Mongolia

Figure 1.2 Annual meteorological changes in the Tuul and Galba River Basins during 1980–2018-4-4-

CGER-I167-2023, CGER/NIES

Picture 1.1 A view of two severely degraded basins including an economic and a mining hub(left: Tuul River Basin, right: Galba River Basin)

1.2 Regional Overview of the Mongolian River Basins
Mongolia, a landlocked country in Central Asia, has approximately 1.5 million km² areaand 1,580 m average elevation. Its climate is semi-arid to arid, with mean annual precipitation
ranging from 400 mm in the north to less than 50 mm in the southern Gobi region. Meanmonthly air temperatures in January range between −20 °C and −25 °C, with minimum airtemperatures dropping to −40 °C. The maximum air temperature in summer can increase to
40 °C during the hottest parts of the day. Due to global warming, the temperature in Mongolia
has increased by at least 2.14 °C since 1940 and is projected to increase by up to 5 °C by theend of the 21st century (FAO, 2012). According to the 2007 Surface Water Inventory, thecountry has 5,128 rivers, 3,747 lakes, and 9,306 springs (UNEP, 2009). Most Mongolian rivers
are distributed across the northern part of the country. The Selenge River is the largest riverwith 459,000 km2 catchment area and a major transboundary river flowing to Lake Baikal in
Russia. Approximately 60% surface runoff leaves the country, and the remaining 40% either
flows into lakes in the southern Gobi region or runs underground to recharge the aquifer.
Administratively, the country is divided into 21 aimags (provinces) and one municipality(capital city). The aimags are further subdivided into 329 soums (districts) and 1,568 bags (local
bodies). The MEGD directly manages 29 river basins in Mongolia (MNET, 2013; IFC, 2017)(Fig. 1.1). Water risk is already a reality that threatens the country’s current prosperous
economic growth. The areas at risk are predominantly the urban and economic hubs of
Ulaanbaatar and mining hubs in the southern Gobi region (Banerjee et al., 2014). To evaluate
the degradation of water availability due to anthropogenic activity and climate change in thesetwo contrasting hubs in Mongolia, the groundwater levels in these two basins are being
monitored since the summer of 2018 (Nakayama et al., 2021a).
The Tuul River Basin is located within the Selenge River Basin and is the major source ofinflow into Lake Baikal. The river originates in the Khan-Khentein-Nuruu Nature Reserve in
the Khentii Mountains and generally flows from northeast to southwest in a variably
meandering channel. It is 704 km long, with 49,840 km2 catchment area. The basin is mostly
covered by grassland and steppe, except forests in the upstream areas (Jamsran et al., 2018).
The central part of the river flows through Ulaanbaatar, the capital city of Mongolia (Fig. 1.1).
-5-5-

Chapter 1

General Introduction

Ulaanbaatar depends on pumped groundwater for its municipal and industrial water supply,
which is recharged by the Tuul River and surrounding mountainous areas. The population ofMongolia was approximately 3.24 million in 2018, with approximately 50% population (1.49
million) residing in or near the capital (National Statistics Office of Mongolia, 2019).
Groundwater was drawn from a network of shallow wells in an alluvial aquifer along the TuulRiver. Therefore, the water level in the source zone, the alluvial aquifer, has declined from 1.6
m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018).
In contrast, the Galba River Basin is in the southern Gobi Desert and named after three
Gobi regions (Galbiin Gobi, Uushiin Gobi, and Doloodiin Gobi). The basin mostly consists ofbare land and deserts, except some grasslands in the northeastern region (Jamsran et al., 2018).
Large mines, such as OT and Tavan Tolgoi, have recently started operating in this region (Fig.
1.1). Particularly, OT is one of the largest copper–gold deposits worldwide, and has started
operating in 2008. Once fully operational, the mine should increase Mongolian GDP by 35%
within 2020. The semi-arid area of southern Gobi has no perennial rivers, although dry
riverbeds may become seasonal after heavy rain events. To maximize productivity in this waterstressed environment, mines have installed extensive water reuse systems (approximately 88%
water used in production is recycled), highly efficient tailings, and zero-/low-water use
equipment. However, the average yearly water demand is approximately 60,000 m3/day, andwater is extensively pumped from 28 wells in the 150 m deep Gunii Hooloi aquifer (Oyu Tolgoi
LLC., 2013; Torquoise Hill Resources Ltd., 2019).
1.3 Description of Process-Based Models Applicable to Arid and Semi-Arid Regions
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, 2023;
Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Maksyutov,
2018; Nakayama and Osako, 2023a,b; Nakayama and Pelletier, 2018; Nakayama andShankman, 2013a,b; Nakayama and Watanabe, 2004, 2005, 2006a,b, 2008a–c; Nakayama etal., 2006, 2007, 2010, 2012, 2021a,b, 2023) (Fig. 1.3). NICE has been applied to various local
and regional basins/catchments, such as the Changjiang and Yellow Rivers in China, Ob Riverin West Siberia, Mekong River in Southeast Asia, Tokyo Metropolitan area in Japan, Kushiro
Wetland (the largest wetland in Japan), and Lake Kasumigaura catchment (a highly eutrophic
lake in Japan). 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), and 26th publications (Part V).
NICE is a three-dimensional (3-D) grid-based eco-hydrology model that iteratively
simulates nonlinear interactions between hydrogeomorphic and vegetation dynamics. Since it
incorporates a 3-D groundwater sub-model and has been expanded from previous one-/twodimensional and steady-state or so-called equilibrium versions into a global-scale model (Niuet al., 2007; Maxwell and Kollet, 2008; Fan et al., 2013), the advanced model can simulate
lateral groundwater transport in addition to surface runoff, which is extremely pronounced inregions with steep slopes or riparian/floodplain areas with frequent surface water–groundwater
connectivity. This model also extends the traditional concept of 'dynamic equilibrium' withatmospheric forcing on a global scale (Maxwell and Kollet, 2008), and would help improve theaccuracy of estimations of methane emissions from wetlands, where groundwater plays adominant role. Furthermore, the model can iteratively simulate nonlinear interactions between-6-6-

CGER-I167-2023, CGER/NIES

hydrologic, geomorphic, and ecological processes and include new feedback through a processof downscaling from regional to local simulations employing fine resolution. Since NICE
includes vegetation succession and competition between invasive alder and native mirevegetation, in addition to the stochastic simulation of seedling establishment, mortality, andregeneration, the model improves previous modeling approaches to the dynamic interaction
between hydrological and biotic processes (Pastor et al., 2002; Rietkerk et al., 2004; van deKoppel et al., 2005; Ridolfi et al., 2006; Saco et al., 2007). Using NICE, previous studies haveattempted to extract the impacts of discharge, groundwater level change, sediment deposition,and nutrient availability on the complex pattern of alder invasion, and vice versa (Nakayama,
2008a,b, 2009, 2010, 2012b, 2013, 2014a,b; Nakayama and Watanabe, 2004, 2006a,b).
Thus, NICE has been applied to analyze water and material cycles in various basins at theregional, continental, and global scales. However, in arid and semi-arid regions, where water
resources are a limiting factor, the accuracy of model calculations remains low. Regarding thescale similarity and discontinuity of eco-hydrological processes, identifying the spatial couplingof local ecosystems, including energy, materials, and organisms, across ecosystem boundaries
is heuristically important. Recent research has raised serious concerns regarding theextrapolation of small-scale experimental results to entire landscapes, suggesting that it isimperative to bridge the gap between ecosystems at various scales (Deegan et al., 2012).
Therefore, the ecosystems 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, similar to 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 applicable to arid and semi-arid regions is needed to evaluate spatiotemporal variations
in water availability in regions with limited inventory data on urban, mining, and livestock.

-7-7-

Chapter 1

General Introduction

Figure 1.3 Process-based National Integrated Catchment-based Eco-hydrology (NICE) model

1.4 Objective and Methods toward Effective Utilization of Water Resources in Arid andSemi-Arid Regions
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 VI) succeeds the CGER
Supercomputer Monograph Report Series in terms of the NICE model (Fig. 1.4).
(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).
The process-oriented model should be improved to resolve the competition for water
resources in areas with limited inventory data, which could potentially trigger conflicts between
urban, mining, industry, herders, and local communities. The case study in this monograph, theMongolian River Basin, is the most suitable area for solving this problem. In addition, the-8-8-CGER-I167-2023, CGER/NIES

relationship between the amount of carbon assimilated by plants and water availability is oneof the fundamentals of eco-hydrological 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 one-dimensional time-dependent equations for thermalenergy balance in advective river systems is also powerful for estimating temperature with highaccuracy (Yearsley, 2012) in addition to the empirical relationship between air and watertemperatures widely applied to many models.
To fill the gap in current eco-hydrology models in arid and semi-arid regions, where water
resources are a major limiting factor, a high-resolution data grid representing water use forlivestock, urban populations, and mining in the entire basin has been developed by combining
a global dataset, statistical data, GIS data, observation data, and field survey data to improve
the accuracy of the model results. The original NICE was further extended to couple it with theinverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). The model simulated the effects of past climatic changes andhuman activity on water resources in these regions during 1980–2018. In addition, groundwater
degradation hotspots due to anthropogenic activity has been detected on a national scale.
Analysis of the relative contribution of environmental factors further clarified thecharacteristics of these areas and quantified the spatiotemporal trends in groundwater levels
because of the changes in precipitation and various water uses. This methodology is powerfulfor resolving future competition for water resources in areas with limited inventory data, which
could potentially trigger conflicts between urban, mining, industry, herders, and local
communities. Details of this process are described in the following chapters.

-9-9-Chapter 1

General Introduction

Figure 1.4 Hydrologic simulation for understanding of regional water cycle. Previous model
applications to various basins/catchments (Part I–IV), continental and global scales(Part V), and Mongolian River Basins in the current monograph (Part VI)- 10 - 10 -

CGER-I167-2023, CGER/NIES

Figure 1.5 Evaluation of water availability for improvement in pasture carrying capacity in
Mongolia by coupling National Integrated Catchment-based Eco-hydrology (NICE)
with inverse model

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CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29
CGER-I167-2023, CGER/NIES

Chapter 2
Evaluation of Spatiotemporal Variations of Water Availability in Urban
and Mining Hubs of Mongolia Using Process-Based Eco-Hydrology Model- 15 - 15 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia Using Process-Based EcoHydrology Model

Abstract
Groundwater overuse and degradation is a serious issue in Mongolia, mainly the urban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
quantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
the National Integrated Catchment-based Eco-hydrology (NICE), was applied to twocontrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,statistical data, geographic information system (GIS) data, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past
climatic changes and human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
showed a clear difference in the hydrological cycle between them, suggesting the impact ofurbanization and mining on local–regional eco-hydrological degradation. Although drinking byherders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
severely reduced the groundwater level in the Tuul River over the last few decades, whereas
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
clarify the substantial impact of water use due to urbanization and mining on groundwater
degradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
inventory data on urbanization and mining.
Keywords: climate change; eco-hydrology model; groundwater degradation; human activity;
urban and mining hubs

- 16 - 16 -

Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction

Abstract

Although
Mongolia
groundwater
they are mainly
unevenly
Groundwater
overusehas
andsubstantial
degradation
is a serious resources,
issue in Mongolia,
thedistributed
urban andandabundant
predominantly
ruralnorth, butvery
scarceor non-existentin thesouth. At
economic
hubin
ofthe
Ulaanbaatar
and theSouthern
Gobi
MiningHub.
In this chapter,
to explicitly
thebeginning
of
the
21st
century,
more
than
80%
of
water
resourcesweregroundwaterquantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
dependent(Fig.Integrated
1.1). Currently,
Mongolia’s Eco-hydrology
water resources(NICE),
are under
increasing
the National
Catchment-based
wasapplied pressure
to twoowing
toclimatechange,urbanization,
and
rapid
economic
growth(Banerjee
et al.,and2014).
In
contrasting river basins: the Tuul River, which is characteristic of northern grassland
steppe,particular,
the River,numberwhichof deep
groundwater ofareas
increased
since
(FAO,
and the Galba
is characteristic
the hassouthern
Gobisubstantially
Desert. Before
the2000
simulation,2012),
andpermafrost
degradationdue
toclimaticchange,dryingofsurfacewater,
anda high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
increasedgroundwater
morehasserious.
Rapid economic
growth andpolitical
andpopulations,and mininguse
in hasthe become
entire basin
been developedby combining
a globaldataset,socioeconomic
shiftsfromsocialism
tocapitalism
around1990
also
causedseriousstatistical data, geographic information system (GIS) data, observational data, and field survey
environmental
such
as groundwater
overuseowingthetoeffects
urbanization,data
to improvedegradation
the accuracyof themodel results.resource
The model
simulatedof pastmineralresource
exploitation
andquarrying,arapidincreaseinlivestock
such
as
and
climatic changes and human-induced disturbances, such as urbanization and mining,sheepon water
goats
owing
to
economicliberalization(Tumuretal.,2018),
decreased
grassland
productivity
resources during 1980–2018 in these representative river basins. Particularly, the model results
due to livestock
overgrazinginand
and agriculturalactivity
expansion.
showeda clear difference
the over-pumping,hydrological cycle
between them,suggesting
the impact ofThe
MinistryofEnvironment
and
Green
Development(MEGDT)
directly
manages
29 riverurbanization and mining on local–regional eco-hydrological degradation. Althoughdrinkingbybasinsin
Mongolia(MNET,
2013;
IFC,
2017)
(Fig.
2.1).
Areas
at
serious
water
risk
are
herders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
predominantly
urban
and economiclevelhubsinsuch
as Ulaanbaatar
Gobiwhereas
Miningseverely reduced
the groundwater
the TuulRiver overand
the thelastSouthern
few decades,
Hub(Banerjee
et hasal., markedly2014). Todeclined
evaluatedegradationof since
water2010.
availabilitytoOyu Tolgoi
miningthatthein theGalba River
Analysisdueof theanthropogenic
activity
andclimatechangeinthese
twocontrasting
hubs,
the
groundwaterlevel
relative contribution of environmental factors also helped us separate the effects of climaticinthe twobasinsare being
monitoredsince thechanges
summerinofgroundwater2018 (Nakayamaal., 2021a).
changeandriverhuman
activities
on thespatiotemporallevel. et
Theseresults
The
Tuul
River
Basin
is
in
northerngrassland
andsteppe,exceptforforests
intheupstreamclarify the substantial impact of water use due to urbanization and mining on groundwaterarea(Jamsran
et al., water2018).sources,
In the middle
stretch,the river change.
flows throughUlaanbaatar,isthe
capitaldegradation
aroundin addition
to climaticThis methodology
powerfulcity(Fig. 2.1). Groundwater
is heavily
drawn froma networkof shallow
for its
municipal
for evaluating
the spatiotemporalvariations
in wateravailability
in wells
regionswithlimitedandindustrial
water
supply.
Therefore,
the
water
level
in
the
sourcezone,
thealluvial
aquifer,
inventory data on urbanization and mining.
declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
dataindicateclimatethat thechange;
current eco-hydrology
water use may not
be sustainable,
particularly
to meetthe projected
Keywords:
model;
groundwater
degradation;
human
activity;water
demandoftherapidly
growingurban
populationin
Ulaanbaatar(Banerjee
et
al.,
2016;
urban and mining hubs
Ulaanbaatar, 2018). In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
resource system must support this mining activity, while satisfying competing water demands
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in bothbasins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inwater availability in the entire basin have been insufficient till date. The impact of climatechange and human-induced disturbances, such as urbanization and mining development, onwater resources, grassland carrying capacity, and vulnerability should be clarified using aprocess-based model.
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
16 - 17 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction

Abstract

Although
Mongolia
groundwater
they are mainly
unevenly
Groundwater
overusehas
andsubstantial
degradation
is a serious resources,
issue in Mongolia,
thedistributed
urban andandabundant
predominantly
ruralnorth, butvery
scarceor non-existentin thesouth. At
economic
hubin
ofthe
Ulaanbaatar
and theSouthern
Gobi
MiningHub.
In this chapter,
to explicitly
thebeginning
of
the
21st
century,
more
than
80%
of
water
resourcesweregroundwaterquantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
dependent(Fig.Integrated
1.1). Currently,
Mongolia’s Eco-hydrology
water resources(NICE),
are under
increasing
the National
Catchment-based
wasapplied pressure
to twoowing
toclimatechange,urbanization,
and
rapid
economic
growth(Banerjee
et al.,and2014).
In
contrasting river basins: the Tuul River, which is characteristic of northern grassland
steppe,particular,
the River,numberwhichof deep
groundwater ofareas
increased
since
(FAO,
and the Galba
is characteristic
the hassouthern
Gobisubstantially
Desert. Before
the2000
simulation,2012),
andpermafrost
degradationdue
toclimaticchange,dryingofsurfacewater,
anda high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
increasedgroundwater
morehasserious.
Rapid economic
growth andpolitical
andpopulations,and mininguse
in hasthe become
entire basin
been developedby combining
a globaldataset,socioeconomic
shiftsfromsocialism
tocapitalism
around1990
also
causedseriousstatistical data, geographic information system (GIS) data, observational data, and field survey
environmental
such
as groundwater
overuseowingthetoeffects
urbanization,data
to improvedegradation
the accuracyof themodel results.resource
The model
simulatedof pastmineralresource
exploitation
andquarrying,arapidincreaseinlivestock
such
as
and
climatic changes and human-induced disturbances, such as urbanization and mining,sheepon water
goats
owing
to
economicliberalization(Tumuretal.,2018),
decreased
grassland
productivity
resources during 1980–2018 in these representative river basins. Particularly, the model results
due to livestock
overgrazinginand
and agriculturalactivity
expansion.
showeda clear difference
the over-pumping,hydrological cycle
between them,suggesting
the impact ofThe
MinistryofEnvironment
and
Green
Development(MEGDT)
directly
manages
29 riverurbanization and mining on local–regional eco-hydrological degradation. Althoughdrinkingbybasinsin
Mongolia(MNET,
2013;
IFC,
2017)
(Fig.
2.1).
Areas
at
serious
water
risk
are
herders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
predominantly
urban
and economiclevelhubsinsuch
as Ulaanbaatar
Gobiwhereas
Miningseverely reduced
the groundwater
the TuulRiver overand
the thelastSouthern
few decades,
Hub(Banerjee
et hasal., markedly2014). Todeclined
evaluatedegradationof since
water2010.
availabilitytoOyu Tolgoi
miningthatthein theGalba River
Analysisdueof theanthropogenic
activity
andclimatechangeinthese
twocontrasting
hubs,
the
groundwaterlevel
relative contribution of environmental factors also helped us separate the effects of climaticinthe twobasinsare being
monitoredsince thechanges
summerinofgroundwater2018 (Nakayamaal., 2021a).
changeandriverhuman
activities
on thespatiotemporallevel. et
Theseresults
The
Tuul
River
Basin
is
in
northerngrassland
andsteppe,exceptforforests
intheupstreamclarify the substantial impact of water use due to urbanization and mining on groundwaterarea(Jamsran
et al., water2018).sources,
In the middle
stretch,the river change.
flows throughUlaanbaatar,isthe
capitaldegradation
aroundin addition
to climaticThis methodology
powerfulcity(Fig. 2.1). Groundwater
is heavily
drawn froma networkof shallow
for its
municipal
for evaluating
the spatiotemporalvariations
in wateravailability
in wells
regionswithlimitedandindustrial
water
supply.
Therefore,
the
water
level
in
the
sourcezone,
thealluvial
aquifer,
inventory data on urbanization and mining.
declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
dataindicateclimatethat thechange;
current eco-hydrology
water use may not
be sustainable,
particularly
to meetthe projected
Keywords:
model;
groundwater
degradation;
human
activity;water
demandoftherapidly
growingurban
populationin
Ulaanbaatar(Banerjee
et
al.,
2016;
urban and mining hubs
Ulaanbaatar, 2018). In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
resource system must support this mining activity, while satisfying competing water demands
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in bothbasins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inwater availability in the entire basin have been insufficient till date. The impact of climatechange and human-induced disturbances, such as urbanization and mining development, onwater resources, grassland carrying capacity, and vulnerability should be clarified using aprocess-based model.
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 16
17 - 18 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction
andextends the traditional concept of dynamic equilibrium with atmospheric forcing on a
global scale (Maxwell and Kollet, 2008). Based on the background described above, three basic
scientific
Althoughquestions
Mongolia
werehasaddressed:
substantial(i)groundwater
How has past
resources,
climatetheychanges
are unevenly
influenced
distributed
water
and
resources
abundantin thesein thetwo
predominantly
contrasting river
rural basins?
north, but(ii)very
Howscarce
do anthropogenic
or non-existentactivities,
in the south.
such Atasthe
livestock
beginning
farming,of theurban
21stactivities,
century,andmore
mining,than change
80% ofwater
wateravailability
resources there?
were groundwater(iii) How dodependent
these hydrological(Fig. 1.1).
changes
Currently,
affect Mongolia’s
the pasture-carrying
water resources
capacityare
andundervulnerability?
increasingTopressure
answerowing
these questions,
to climateachange,high-resolution
urbanization,
griddedanddataset
rapid economic
representing
growthwater(Banerjee
use by livestock,
et al., 2014).
urban
In
particular,
activity, andthemining
numberwasof deep
derivedgroundwaterby combining
areas hasa globalincreaseddataset,substantially
statisticalsincedata,2000
geographic(FAO,2012),
informationand permafrost
system (GIS)
degradation
data, observational
due to climaticdata, andchange,field survey
drying dataof surface
to inputwater,
the NICE
andincreased
simulation.groundwater
The model use
simulates
has become
the effects
more ofserious.
climateRapidchangeeconomic
and human
growthactivityand political(livestock,andurban,
socioeconomic
and mining)
shiftson waterfrom resources
socialismin tothecapitalism
two river basinsaroundduring19901980–2018.
also caused
Analysisseriousoftheenvironmental
relative contribution
degradationof suchenvironmental
as groundwater
factorsresource
also helped
overuseto quantify
owing totheurbanization,interannual
variationmineral resourceor temporal
exploitation
trends in andgroundwater
quarrying,levela rapidcausedincreaseby thesein livestock
factors. The
such
results
as sheepobtainedandcangoatsbeowing
used toforeconomic
extendingliberalization
the uncertainty(Tumuranalysis
et al.,of
2018),
the hydrologic
decreased grassland
cycle to improve
productivity
theevaluation
due to livestockof pasture-carrying
overgrazing andcapacity
over-pumping,and its vulnerability.
and agricultural activity expansion.
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
basins in Mongolia (MNET, 2013; IFC, 2017) (Fig. 2.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelin the two river basins are being monitored since the summer of 2018 (Nakayama et al., 2021a).
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
area (Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capital
city (Fig. 2.1). Groundwater is heavily drawn from a network of shallow wells for its municipal
and industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
data indicate that the current water use may not be sustainable, particularly to meet the projectedwater demand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Ulaanbaatar, 2018). In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
resource system must support this mining activity, while satisfying competing water demands
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in bothbasins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inwater availability in the entire basin have been insufficient till date. The impact of climatechange and human-induced disturbances, such as urbanization and mining development, onwater resources, grassland carrying capacity, and vulnerability should be clarified using aprocess-based model.
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology
FigureLocationof the Tuul
and
Galba
River
Basinsin Mongolia(NICE)2.1
model(Nakayama,
2014,
2015)
was
appliedto tworepresentative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
18 - 19 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Abstract

2.2 Methods

2.2.1Groundwater
Description overuseof Process-Based
Eco-Hydrology
and degradation
is a seriousModelissue (NICE)
in Mongolia, mainly the urban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
The NICE
series of process-basedcatchment
modelsa process-based
have been developed
for application
quantify
spatiotemporalvariations in water
availability,
eco-hydrology
model,
thenatural,
National
Integrated
Eco-hydrology
wasapplied 2008a–
to twotoagricultural,and Catchment-based
urban regions in various
catchments(NICE),
and basins(Nakayama,contrasting
river2011a–d,basins: theTuul River,which
is characteristicof northernand steppe,c,2009, 2010,2012a–d,
2013,
2014a,b,2015, 2016,2017a–c,grassland
2018, 2019,2020,
and the2023;
GalbaNakayama
River, whichcharacteristicof the southern
Desert. Before
the simulation,
2022,
andis Fujita,
2010; Nakayama
and Gobi
Hashimoto,2011; Nakayama
andaMaksyutov,
high-resolution
data
grid
(0.05°
×0.05°
resolution)
representing
water
use
for
livestock,urban
2018; Nakayama and Osako, 2023a,b; Nakayama and Pelletier, 2018; Nakayama
populations,and2013a,b;
mining in
the entireandbasin
has been2004,
developed
combininga globaldataset,and
Shankman,
Nakayama
Watanabe,
2005,by
2006a,b,2008a–c;Nakayama
statistical
data,
geographic
information
system(GIS)
data,
observational
data,
and
field
surveyet al., 2006, 2007, 2010, 2012, 2021a,b, 2023) (Fig. 1.3). The NICE model consists of complex
data to improve
the accuracyof themodela results.
The model
effectsof croppast
submodels:a surfacehydrology
model;
land-surface
modelsimulated
includingtheurban
andclimaticchangesandhuman-induced
disturbances,suchasurbanization
andmining,on
water
processes; a groundwater model; a regional atmospheric model; a mass transport model ofresources during
1980–2018
in these
representative
river basins.
Particularly,
model
resultsa
sediment,
nutrients,
carbon, andplastics;
and a vegetation
succession
model.theNICE
applies
showedaclear
differenceinthehydrologicalcycle
betweenthem,suggestingtheimpactofrectangular coordinate system based on the Albers or Universal Transverse Mercator (UTM)
urbanization
andminingon
local–regional
eco-hydrologicaldegradation.
Althoughdrinking
byprojection (Nakayama, 2014a,b, 2015). It also incorporates surface–groundwater interactions
herders’ livestock
had some
impactto onhydrologicalchange,water useleaf
in area
Ulaanbaatar
has
assimilatingland-surface
processes
describe
the satellite
data-derived
index (LAI)
severely
reducedthegroundwater
level
in
the
Tuul
River
over
the
last
few
decades,
whereasvariations and fraction of photosynthetically active radiation (FPAR). The surface hydrology
Oyu Tolgoi
has markedly
declined wavethat intheory-based
the Galba Riversincehydrology
2010. Analysis
thea
model
of themining
NICE consistsof a kinematic
hillslopemodel ofandrelative
contributionofenvironmental
factors
also
helpedus
separatetheeffectsofclimatic
distributed stream network model based on both kinematic and dynamic wave theories. NICE
changeand humanon thespatiotemporalchangesin groundwater level.
These results
also
solvesa partialactivities
differential
equation
describing
the three-dimensional
groundwaterflow
clarify
thesubstantial
impact
of
water
use
due
to
urbanization
andminingon
groundwater
for both unconfined and confined aquifers. The integration of submodels considers
water anddegradation
aroundsources,
additionincluding
to climaticThismethodology
is powerfulheatfluxes fromthewatergroundto theinsurface,thechange.
hydraulicpotential
gradient
betweenforevaluatingthespatiotemporalvariationsinwater
availability
inregionswithlimitedthe deepest unsaturated flow layer and groundwater level, effective precipitation calculated
inventory
on urbanizationand mining.
from
actualdata
precipitation,
infiltration
into the upper soil moisture store, evapotranspiration, andseepage between the river water and groundwater.
Keywords:
climateequations
change; eco-hydrology
degradation;
human
activity;
The governing
for the originalmodel;
NICE groundwater
prognostic variables
for water
resources
are
urban
and
mininghubs
as follows (Nakayama and Watanabe, 2004):
Biophysical and soil moisture models:(a) Canopy, ground surface and deep soil temperatures:Cc

Tc
 Rnc  H c   Ec  cs
t

(2.1)

Cg

Tg
2Cd
 Rng  Hg   Eg 
(Tg  Td )  gs
t
d

(2.2)

Cd

Td
1

( Rng  H g   Eg )t2 365

(2.3)

The subscripts c, g, and d refer to the canopy, soil surface, and deep soil, respectively. Tc, Tg,
and Td (K) represent canopy, ground surface, and deep soil temperatures, respectively; Rnc andRng (W/m2) represent absorbed net radiation of canopy and ground, respectively; Hc and Hg(W/m2) represent sensible heat flux; Ec and Eg (kg/m2/s) represent evapotranspiration rates; Cc,- 16
19 - 20 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1
Cg, Introduction
and Cd (J/m2/K) represent effective heat capacities;  (J/kg) represents the latent heat ofvaporization; d (s) represents the daylength; and cs and gs (W/m2) represent energy transfer
Mongolia
has
substantial
groundwater
resources, they are unevenly distributed
Mg (describednext), respectively.
due Althoughto phase changes
in M
c and
and abundant in the predominantly rural north, but very scarce or non-existent in the south. At
the Interception
beginning ofstore:
the 21st century, more than 80% of water resources were groundwater(b)dependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
owing to climate
M c change, urbanization, and rapid economic growth (Banerjee et al., 2014). In P  of
Dddeep Dcgroundwater Eci /  w areas has increased substantially since 2000 (FAO,(2.4)
particular, thetnumber2012), and permafrost degradation due to climatic change, drying of surface water, andincreased groundwater use has become more serious. Rapid economic growth and political andMsocioeconomicg shifts
to capitalism around 1990 also caused serious
 Dd  fromDc  Esocialism(2.5)
gi /  w
tenvironmental degradation such as groundwater resource overuse owing to urbanization,mineral resource exploitation and quarrying, a rapid increase in livestock such as sheep andM
Mg (m)
represent thewater or snow/ice
the canopy
andgrassland
ground, respectively;
c and
goats
owing
to economic
liberalization(Tumurstored
et al., on2018),
decreased
productivityP(m/s)
represents
theprecipitation
rate;
D
(m/s)
is
the
canopythroughfall
rate;
D
d
c
due to livestock overgrazing and over-pumping, and agricultural activity expansion.(m/s) is the2
canopyEci and Egi (kg/m/s) areDevelopment
the interception
losses ofthe canopy
and 29ground,Thedrainage
Ministryrate;of Environment
and Green(MEGDT)
directly
manages
river
3

(kg/m
)
is
water
density.
respectively;
andw
basins in Mongolia (MNET, 2013; IFC, 2017) (Fig. 2.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining(c)moisture etstorage:
HubSoil(Banerjeeal., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelW1 basins
1 are being monitored1
in the two river
since the summer of 2018 (Nakayama et al., 2021a).
[Pw1  Q1,2 
Egs ]
(2.6)
The Tuul River
Basin
is
in
northerngrasslandand steppe, except for forests in the upstreamt s D1w
area (Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capital
city (Fig. 2.1).
is heavily1drawn from a network of shallow wells for its municipal
W2Groundwater1
and industrial water supply.
[Q1,2 Therefore, Q2,3  theEct ]water level in the source zone, the alluvial aquifer,
(2.7)
t

D

s
2
w
declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
data indicate that the current water use may not be sustainable, particularly to meet the projectedW3of the1rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
water demand[Q2,3  Q3 ](2.8)
Ulaanbaatar,t2018).
In3 contrast,
the Galba River Basin (Galba–Uush–Doloodiing Gobi River
sD
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslandsin the northeastern region (Jamsran et al., 2018). Large
3
3 mines, such as Oyu TolgoiWi is the soil moisture fraction of the i-th layer (=i/s); i (m /m ) is volumetric soil moisture(OT) and Tavan Tolgoi, have been developing and recently started operations in this region
in the i-th layer; s (m3/m3) is the volumetric soil moisture at saturation; Di (m) is the soil layer(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
thickness; Qi,j (m/s) is the flow between layers i and j; Q3 (m/s) is gravitational drainage fromoperations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
recharge soil moisture store; Ect (kg/m2/s) is canopy transpiration; Egs (kg/m2/s) is groundresource system must support this mining activity, while satisfying competing water demands
evaporation; and Pw1 (m/s) is infiltration of precipitation into the upper soil moisture store.
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in both(d) Canopy conductance to water vapor:basins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations ing cwater availability
ing (gthec 
entirehave been insufficient till date. The impact of climate kg c, inf basin
)
(2.9)
t
change and human-induced disturbances, such as urbanization and mining development, onwater resources, grassland carrying capacity, and vulnerability should be clarified using a
gprocess-based
conductance, kg (1/s) is a time constant, and gc,inf (m/s) is the estimated valuec (m/s) is canopy
model.
t  chapter,∞.
of gcInat thisthe process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
20 - 21 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Groundwater model

Abstracthg  andhg  is hg issue in Mongolia,hg degradation  overuse
Groundwatera serious
mainly the urban and(2.10)
Kxx
 K yy
 Kzz
  W  Ss
economic xhub of Ulaanbaatar
Gobi
MiningHub.
In
thischapter, to explicitly
zx  y and the
y Southern
z
t



quantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
the National Integrated Catchment-based Eco-hydrology (NICE), was applied to two
Kxx, Kyy, and Kzz (m/s) represent the hydraulic conductivity along the x, y, and z coordinate axes,
contrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,
respectively; hg (m) is the potentiometric head; W (1/s) is the volumetric flux per unit volume
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,
representing water sources and/or sinks; and Ss (1/m) is the specific storage.
a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,Surface hydrology models
statistical data, geographic information system (GIS) data, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past(a) Hillslope hydrological model:
climatic changes and human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
ha1 
showed a clear
thehydrologicalof(2.11)
 differenceqinb(x) r( x,t) coscycle( x) between them, suggesting the impact
x on local–regional eco-hydrological degradation. Although drinking by
t and b(x)urbanization
miningherders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
k sinthe (xgroundwater)
severely reduced
level in the Tuul River over the last few decades, whereas(2.12a)qha , (0  ha  d )Oyu Tolgoi mining
 has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
k sin (x )sin  (x)mclarify theand mining on groundwater(ha  d )of water use due
h ato, urbanization(ha  d )q substantial impact(2.12b)ndegradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
2
where
q (m
/s)on
is thedischargeandpermining.
unit width, r(x,t) (m/s) is the effective rainfall intensity atinventorydata
urbanization
position x and time t, b(x) (m) is the flow width, (x) is the riverbed gradient, k (m/s) is thehydraulicin layer
A with a depthof Dgroundwater(m) near the degradation;
ground surface,n (m-s)is theKeywords:conductivity
climate change;
eco-hydrology
model;
human
activity;
Manning
and m = 5/3. When H (m) is defined as the depth of the rainwater flow inurban andcoefficient,mining hubs
layer A, ha (m) is the apparent flow depth (= H), and  as the porosity of layer A, and d = D,then the true flow depth is given by ha/ for ha < d and by d/ + ha – d for ha > d.
(b) Stream network:
A Q

 ql
t
x

(2.13)hr
Q Q 2 
 gAI f  i   0
   gAxt
x  A 

(2.14)

where A (m2) is the cross-sectional area, Q (m3/s) is the discharge, ql (m2/s) is the lateral inflow
q entering along the side of the river channel as simulated by the hillslope model, hr (m) is theflow depth in the river, g (m/s2) is the gravitational acceleration If is the friction slope, and i isthe bed slope.

- 16
21 - 22 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Integration
2.1
Introductionof models: distributed
Although Mongolia has substantial groundwater resources,
they are unevenly







g
3
and abundantpredominantly
but3 very scarcein the south.
At(2.15)K  K ruralK north,K  or non-existent 1
q f  in theD3 thanDz
the beginning of the 21st century,
more
80%
of
water
resourcesweregroundwater 3  (Dg  h g )  (D g h g ) 2are under increasing2
dependent (Fig. 1.1). Currently, Mongolia’s
water resources
pressure
owing to climate change, urbanization, and rapid economic growth (Banerjee et al., 2014). In
particular, the number of1deep groundwater areas has increased substantially since 2000 (FAO,r P  Pw1 ( Ec  E g )(2.16)
2012), and permafrost
due to climatic change, drying of surface water, anddegradationw
increased groundwater use has become more serious. Rapid economic growth and political andsocioeconomic shifts from socialism to capitalism around 1990 also caused serious
Qs  k b Ab , ( h g  H b ),(2.17a)environmental
degradation such as groundwater resource overuse owing to urbanization,mineral resource exploitation and quarrying, a rapid increase in livestock such as sheep andhg  H b liberalization (Tumur et al., 2018), decreased grassland productivity
goats owing to economic(2.17b)Qs  k b
Ab , (hg  H b )due to livestock overgrazing
and over-pumping, and agricultural activity expansion.
bb
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
basinsin̄ (m/s)
Mongolia
2013;
IFC, 2017)(Fig.conductivity2.1). Areas between
at seriousunsaturated
water risk andarewhere Kis the(MNET,
estimated
effective
hydraulicpredominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
saturated layers; g (= hg)and 3 (= 3 + Dg + D3/2) (m) are hydraulic potentials at the
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due togroundwater surface and lower unsaturated flow layer; Dg (m) is the distance between the top
anthropogenic activity and climate change in these two contrasting hubs, the groundwater levelof the second layer and the bottom of the 20th layer in the groundwater model; hg (m) is thein the two river basins are being monitored since the summer3of 2018 (Nakayama et al., 2021a).
hydraulic head simulated by the groundwater model; Qs (m /s) is the volumetric seepage flux
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
between river and groundwater (= ql l; l is the channel length of each component); kb (m/s) is
area (Jamsran et al., 2018). In the middle stretch, 2the river flows through Ulaanbaatar, the capitalthe hydraulic conductivity of the riverbed; Ab (m ) is the cross-sectional area of the groundwater
city (Fig. 2.1). Groundwater is heavily drawn from a network of shallow wells for its municipal
section; bb (m) is riverbed thickness; hg (m) is groundwater head; and Hb (m) is the hydraulicand industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,potential of the river. NICE also includes surface–groundwater interactions assimilating landdeclined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statisticalsurface processes to describe the satellite data-derived LAI variations and FPAR, which aredata indicate that the current water use may not be sustainable, particularly to meet the projected
important parameters for evaluating vegetation growth (Nakayama and Watanabe, 2004;
water demand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Nakayama, 2013).
Ulaanbaatar, 2018). In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except some
2.2.2 NICE Application to Various Land Uses
grasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region
To study the various aspects of agricultural water use, NICE was coupled with Decision(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
Support Systems for Agro-technology Transfer (DSSAT) (Ritchie et al., 1998), in which an
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
automatic irrigation mode supplies the water required for fields cultivating crops (Nakayama et
resource system must support this mining activity, while satisfying competing water demandsal., 2006; Nakayama, 2011a,b) and paddy (Nakayama and Watanabe, 2008b) (Fig. 1.3). DSSAT
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
includes various functions for representative crops such as wheat, maize, soybean, and rice, andindicating the impact of climate change and anthropogenic factors on water resources in bothautomatically simulates their dynamic growth processes. The water is mostly transferred via
basins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hilldams (reservoirs) and canals to main cities and cultivated fields. Since the data available on
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations indischarge control at most dams is less, a constant inflow to outflow ratio was used at the majorwater availability in the entire basin have been insufficient till date. The impact of climate
dams for which no data were available as a simpler alternative to the storage–runoff functionchange and human-induced disturbances, such as urbanization and mining development, on(Nakayama, 2011a). This reservoir model was further modified to couple NICE with a simple
water resources, grassland carrying capacity, and vulnerability should be clarified using a
one-dimensional water budget (Nakayama and Watanabe, 2008a) and a reservoir operation
process-based model.
similar to the Soil and Water Assessment Tool (SWAT) (Neitsch et al., 2011) by considering
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology
the relationship between water level and storage volume for estimating water release from the(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the Tuul
Three Gorges Dam (TGD) in the Changjiang River during very severe floods, such as those inRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,
1998 (Nakayama and Shankman, 2013a,b) (Fig. 1.3). In urban areas, NICE is coupled with thecharacterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition tourban canopy model (UCM) to include the effect of the hydrothermal cycle for various
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel
pavement types and the Regional Atmospheric Modeling System (RAMS) (Pielke et al., 1992)- 17
22 - 23 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Modelto include hydrothermal interactions (Nakayama
and Fujita, 2010; Nakayama and Hashimoto,
Abstract2011; Nakayama et al., 2012) (Fig. 1.3). This procedure implies that the feedback processes ofwater
and heat transfer
betweenthe atmosphereand land-surface
were implicitlyin
Groundwater
overuseand degradation
is a serious
issue in Mongolia,
mainly theincluded
urban andthesimulation
process.
economic
hub ofUlaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitlyThe
detailsofthe model
are described
the papers cited
above and CGER
Supercomputer
quantify spatiotemporalvariations
in waterinavailability,
a process-based
eco-hydrology
model,
Monograph
Series Catchment-based
Vols. 11 (Part I) (Nakayama
and Watanabe,
2006b),
14 (PartII)
the NationalReport
Integrated
Eco-hydrology(NICE), wasappliedto two(Nakayama,
2008c),
18
(Part
III)
(Nakayama,
2012d),
20
(Part
IV)
(Nakayama,
2014b),
and26
contrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,(PartV)Galba(Nakayama,2019) is(Figure1.4). of the southern Gobi Desert. Before the simulation,and theRiver, whichcharacteristic

a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,
2.3 Input data,
Datageographic
and Boundary
Conditions
for(GIS)
Simulation
statisticalinformation
systemdata, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past
2.3.1 Model
InputandData
climaticchanges
human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
For aforcing
data, six-hour
re-analyzed cycle
data fordownward
radiation, theprecipitation,
showedclear differencein the hydrologicalbetweenthem, suggesting
impact of
atmospheric pressure,airon
temperature,
air humidity,
wind speed,
cloud cover,
FPAR,
and LAIurbanizationand mining
local–regional
eco-hydrologicaldegradation.
Althoughdrinking
by
were
input
to the original
NICE
afteroninterpolating
thechange,
ECMWF-Reanalysis
Interim Productherders’
livestock
had some
impacthydrologicalwater use in Ulaanbaatar
has(ERA-interim)(ECMWF,2019) inlevel
inverseproportion
to theback-calculated
distancewhereas
in eachseverely
reducedthe groundwaterin theTuul Riverover
the last few decades,
grid.
Annualmining
rainfallhas
decreased
before2010that
andingradually
increased
after2010.
2010Analysis
in both basins.
Oyu Tolgoi
markedly
declinedthe Galba
River sinceof theIncontrast,
the annualofmean
temperature
graduallyin somecold winters,
sorelative
contribution
environmental
factors
also increased
helped usexcept
separatethe effects
of climaticcalleddzud(massive
livestockmortality),
during
2001–2002and
2009–2011.
(Gomboluudev
change and human activities on the spatiotemporal changes in groundwater level. These results
etal., 2018)(Fig. 1.2). The
input
at various
resolutionsand
were
preparedarrangedclarify
the substantial
impactofdatawater
use duespatial
to urbanization
miningon andgroundwatertocalculateaspatially
averaged
0.05°×0.05°
grid(approximately
4–6
km
resolution)
using
the
degradation around water sources, in addition to climatic change. This methodology is powerful
SpatialAnalyst theTools
in ArcGIS v10.2
softwareinfor
the simulation
2.1) Elevation,
land
for evaluating
spatiotemporalvariations
water
availability(Tablein regionswith limited
cover,
soil
texture,vegetation
type,
river
networks,
lakes
andwetlands,
reservoir
types
andinventory data on urbanization and mining.
storage, and geological structures were categorized as inputs to NICE (U.S. Geological Survey,
1996a,b;Döll, eco-hydrology
2004; Hartmann
and groundwater
Moosdorf, 2012;
NASA, 2013;
Keywords:Lehnerclimateandchange;model;
degradation;
humanEuropean
activity;
Commission,
2012,hubs2015). The land cover was constant during 1980–2018 in the simulation.
urban and mining
Sub-basins in both rivers were calculated using topography and river channel data in ArcGIS
software (Fig. 2.1), and the calculated data were compared with basin boundary data for
Mongolia from a previous study (MNET, 2013). In the Tuul River Basin, a large amount ofgroundwater has been used in Ulaanbaatar over the last few decades, and the share of waterused by the mining sector (84%) in the Galba River Basin has significantly increased since 2010
(Nakayama et al., 2021a).

- 16
23 - 24 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1
IntroductionTable
2.1 List of input datasets for the NICE simulation
Mongolia
has substantial
groundwater resources,
are unevenly distributed
DataAlthoughset
Original
resolution Year
Source andtheyreferenceandabundant in the predominantly
rural1980-2018
north, but very ERA-interim;
scarce or non-existent
Climatology
1.0°
ECMWF (2019)in the south. At
Elevationaround1996 80% ofGTOPO30;
U.S. Geological
Survey(1996a)thebeginning of the 1.0km
21st century, morethanwater resourcesweregroundwaterLand
cover
1.0km
around2000
GLC2000;
EuropeanCommission(2015)
dependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
Soil texture
1.0kmurbanization,around1970-2000
HWSD;
EuropeanCommission
owing
to climate change,
and rapid
economic
growth(Banerjeeet(2012)
al., 2014). In
Vegetation
type
0.25°
around2000
GLDAS
Vegetation
Class;
NASA(2013)(FAO,
particular, the number of deep groundwater areas has increased substantially since 2000
River networks
1.0km
around 1996
HYDRO1K; U.S. Geological Survey (1996b)
2012), and permafrost degradation due to climatic change, drying of surface water, andLakes and wetlands
0.5 minaround 1990-2000 GLWD; Lehner and Döll (2004)
increased groundwater use has become more serious. Rapid economic growth and political andGeological structures
0.5°
around 1970-2000 GLiM; Hartmann and Moosdorf (2012)
socioeconomic
shifts5 minfrom socialismaroundto 2000
capitalism MIRCA2000;
around 1990
also caused serious
Crop typePortmann et al. (2010)environmental
degradation
resource
owing to urbanization,
Irrigation type
5 min such as groundwater
2000-2008
GMIA;overuse
FAO (2016)mineralresource
exploitation
andquarrying,arapidincreaseinlivestock
Irrigation water use
5 min
1998-2002
GCWM; Siebert and Döll such
(2010) as sheep and
goats
owing
to
economicliberalization(Tumuretal.,2018),
decreased
grassland
productivity
Basin boundary data
polygonaround 2010
MNET (2013)
due to livestock overgrazing and over-pumping, and agriculturalactivity expansion.
Ministry of Environment(2015), Batsaikhan et al.
Core samplingpointaround 2010
The Ministry of Environment and Green Development(MEGDT) directly manages 29 river(2018)

basinsMongolia (MNET,
2017) (Fig. National
2.1). Areas
atOfficeseriouswater (2019)risk areNumbersinof livestock
each soum 2013; IFC,
1980-2018
Statistics
of Mongolia
predominantly
urbanSouthern
Gobi Mining
Source of water supply
in and economic hubs such as Ulaanbaatar
JICA andand
CTI theEngineering
InternationalCo.,
polygonaround 2010
Ulaanbaatar
Ltd. (2010)of water availability due to
Hub(Banerjee et al., 2014). To evaluate the degradation
Population
1980-2018
National Statistics
OfficeMongolia (2019)level
anthropogenic
activityeach
andsoum
climate change
in these two contrasting
hubs,
theofgroundwater
Population
1.0km
2000-2015
NASA(2018)
in the two river basins are being monitored since the summer of 2018 (Nakayama et al., 2021a).
WaterTuuluse inRiver
Ulaanbaatar
2002-2015 and steppe,
MNET(2010),for
MEGD(2012),inSato
The
Basinpoint
is in northern grassland
exceptforests
the(2015)
upstream
Oyu
Tolgoi
LLC.
(2013),Torquoise
Hill
area(Jamsranetal.,2018).
In
themiddlestretch,
the
river
flows
throughUlaanbaatar,thecapital
Water supply in OT
point
2008-2018
Resources Ltd. (2019)city (Fig. 2.1). Groundwater is heavily drawn from a network of shallow wells for its municipal
and industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
2.3.2indicate
Estimationof Mining,
Livestock,andnotHuman
Water Uses
data
that thecurrent water
use may
be sustainable,
particularly to meet the projectedwater demand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Gridded data
regardingwatertheuseGalba
for livestock,urban
activity, and mining wereGobi
estimated
Ulaanbaatar,
2018).
In contrast,
River Basin(Galba–Uush–Doloodiing
River
for
the
entire
Tuul
and
Galba
River
Basinsduring
1980–2018bycombiningaglobaldataset,Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somestatistical data,data, literature,observation
data forLarge
inputmines,
into theNICE
simulation
grasslandsin theGIS
northeasternregionand(Jamsran
et al., 2018).
suchas OyuTolgoi(Table
2.1).
The
total
numberoflivestock(sheep,horses,cattle,goats,
andcamels)ineach(OT) and Tavan Tolgoi, have been developing and recently started operations in this region
soum(district)of
bothbasinswasestimatedusing
statisticaldata(National
Statistics
Officeof(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
Mongolia, in2019)
calculateannuallivestockwater use.
Urban
water
in
operations
2008to(Oyu
Tolgoithe
LLC,
2013;average
Torquoise
Hill ResourcesLtd.,2019).
Theusewater
Ulaanbaatar
wasestimatedbycombiningstatistical
population
data
for
each
soum
every
year
resource system must support this mining activity, while satisfying competing water demands(National
Statistics Office of Mongolia,
2019)
and adjusted
accordingto gridded
populationfromthe mining–urbanization–herdernexus(Banerjee
et al., 2014).
Despiteprevious
findings
data
with
a
1-km
spatialresolution
every
five
years
(NASA,
2018).
There
are
six
groundwater
indicating the impact of climate change and anthropogenic factors on water resources in both
sourcesunder(Upper,Central,Industrial,
Meat-Complex,
Gachuurt,
Yarmag),and two morebasinsdifferent
scenarios(Banerjee
et al., 2014,2016; and
ADB,
2014; TorquoiseHillsources(Bio-industrial
Complex,and
EmertoWater
Sources)areplannedtosupplythecapitalResources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inin the availability
future (JICA
CTI EngineeringCo.,tillLtd.,2010).
the absenceofwater
in andthe entire
basin have International
been insufficientdate.
The Inimpactof climate
perennial
riversin the Southern
Gobi region,
the asgroundwateruseandin OTminingwas calculatedchangeandhuman-induced
disturbances,suchurbanization
miningdevelopment,onusing
previous
data(Oyu
Tolgoi
LLC.,
2013;
ADB,
2014;
Torquoise
Hill
Resources
Ltd.,2019).
water resources, grassland carrying capacity, and vulnerability should be clarified
usingaThese data were
finally gridded to 0.05°×0.05° resolution during 1980–2018 using the ArcGIS
process-basedmodel.
software(Figs.
2.2 and
In the TuulNational
River Basin,a large Catchment-based
amount of groundwater
has been
In thischapter,
the2.3).
process-based
Integrated
Eco-hydrology
used in Ulaanbaatar
over the2014,
last few
decades,and theof water usedriverby themining(NICE)
model (Nakayama,
2015)
was applied
to sharetwo representativebasins:thesector
Tuul
(84%)
has
considerablyincreased
since
2010
(ADB
2014)
(Fig.
2.3).
River Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
24 - 25 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Abstract
Groundwater overuse and degradation is a serious issue in Mongolia, mainly the urban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
quantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
the National Integrated Catchment-based Eco-hydrology (NICE), was applied to twocontrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,statistical data, geographic information system (GIS) data, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past
climatic changes and human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
showed a clear difference in the hydrological cycle between them, suggesting the impact ofurbanization and mining on local–regional eco-hydrological degradation. Although drinking byherders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
severely reduced the groundwater level in the Tuul River over the last few decades, whereas
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
clarify the substantial impact of water use due to urbanization and mining on groundwater
degradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
inventory data on urbanization and mining.
Keywords: climate change; eco-hydrology model; groundwater degradation; human activity;
urban and mining hubs

Figure 2.2 Estimated water use at 0.05°×0.05° resolution in (a) the Tuul and (b) Galba River
Basins during 1980–2018- 16
25 - 26 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction
Although Mongolia has substantial groundwater resources, they are unevenly distributed
and abundant in the predominantly rural north, but very scarce or non-existent in the south. At
the beginning of the 21st century, more than 80% of water resources were groundwaterdependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
owing to climate change, urbanization, and rapid economic growth (Banerjee et al., 2014). In
particular, the number of deep groundwater areas has increased substantially since 2000 (FAO,
2012), and permafrost degradation due to climatic change, drying of surface water, andincreased groundwater use has become more serious. Rapid economic growth and political andsocioeconomic shifts from socialism to capitalism around 1990 also caused serious
environmental degradation such as groundwater resource overuse owing to urbanization,mineral resource exploitation and quarrying, a rapid increase in livestock such as sheep andgoats owing to economic liberalization (Tumur et al., 2018), decreased grassland productivity
due to livestock overgrazing and over-pumping, and agricultural activity expansion.
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
basins in Mongolia (MNET, 2013; IFC, 2017) (Fig. 2.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelin the two river basins are being monitored since the summer of 2018 (Nakayama et al., 2021a).
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
area (Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capital
city (Fig. 2.1). Groundwater is heavily drawn from a network of shallow wells for its municipal
and industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
data indicate that the current water use may not be sustainable, particularly to meet the projectedwater demand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Figure2.3 Estimatedwater
use trends
in the Rivertwo basins
1980–2018
Ulaanbaatar,
2018). In
contrast,
the Galba
Basinduring(Galba–Uush–Doloodiing
Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi
2.3.3
andbeen
Running
the Simulation(OT) Boundary
and TavanConditions
Tolgoi, havedeveloping
and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
At the upstream
boundaries,
the conditions
at the hydraulichead assumed
no inflowfromoperationsin 2008 (Oyu
Tolgoi LLC,
2013; Torquoise
Hill Resources
Ltd., 2019).
The waterthemountains
opposite
andsatisfying
Watanabe,
2004; Nakayama
andresource
system inmust
supportdirections
this mining(Nakayama
activity, while
competingwater demands
Shankman,2013a,b).
For
thehillslopehydrologicalsubmodel,theflow
depth,
discharge,
andfrom the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
other
water
concentrations
attheuppermostbasinboundaries
were
set
to
zero
throughouttheindicating the impact of climate change and anthropogenic factors on water resources in both
simulation.
calculatescenarios
these boundaries,
was2016;
first ADB,
used to2014;
calculateeach basin
basins underTodifferent(Banerjee GTOPO30
et al., 2014,
TorquoiseHillboundaryat
1×1
km²
resolution,andthencalculated
the
0.05°×0.05°
grid
using
ArcGIS
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations insoftwarefor modelinsimulation.
or outflows
from the riverbeds
simulated
at eachwater availability
the entire Inflows
basin havebeen insufficienttill date. were
The impact
of climate
time
step
dependingonthedifferenceinthegroundwater
and
river
hydraulicheads.
Detailschange and human-induced disturbances, such as urbanization and mining development, on
have
described
in previous
studies(Nakayama
and Watanabe,should
2004; beNakayama
waterbeen
resources,
grassland
carrying
capacity,and vulnerability
clarified2015).
using aThe
simulation
areas
in
the
Tuul
and
Galba
River
Basins
were
7.0°
wide
× 3.0° long andprocess-based model.
11.0°In wide
× 6.0° long
longitude-latitude
coordinates,
The Tuuland Galba
this chapter,the inprocess-based
National
Integratedrespectively.
Catchment-based
Eco-hydrology
River
Basins
have
29
and
106
river
channels,
respectively
(Fig.
2.1).
NICE
simulationof Tuuleco(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: thehydrological
was performed
at 0.05°×0.05°(approximately
resolution
in theRiver Basin, processes
characterized
by northerngrassland andsteppe, and 4–6
the km)Galba
River Basin,
horizontal
directionandin20
layerswith
1.1
weightingfactor(layerthicknesses
beingcharacterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition tograduatedthinnerin the upper
to thickerainthree-dimensional
the deeper layer) in
the vertical submodel
direction.
the surfacefrom
runoffsubmodel,NICElayer
incorporates
groundwater
The upper land layer was set at 0–2 m from the ground surface, and the 20th land layer was set- 17
26 - 27 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Modelat 200 m below sea level. The model simulates
the river network among the elements andAbstract
terrestrial ecosystem in grid cells. The contribution of the river to the grid cell was estimatedby calculating
theoveruse
areal ratio
the river (riverwidth multipliedby river mainly
length)thein eachGroundwater
andof
degradation
is a serious
issue in Mongolia,
urbangrid.
andDetails of hub
the ofmodel
setup andanalysis
showHub.
quantitative
measuresof modeleconomic
Ulaanbaatar
andsensitivity
the Southern
Gobi toMiningIn this chapter,
to explicitly
performancefor various variations
parametersin(suchthe correlation
coefficient, root
mean squaremodel,
error,
quantify
spatiotemporalwaterasavailability,
a process-based
eco-hydrology
and Nash-Sutcliffe
efficiency)
are provided in previous
papers (Nakayama,
2014,
2017a,to2020).
theNational Integrated
Catchment-based
Eco-hydrology(NICE), wasappliedtwo
Simulations
wereperformed
with
a
time
step
of

t
=
6
h
for
1980–2018aftera1-yearcontrasting river basins: the Tuul River, which is characteristic of northern grassland
and warmsteppe,up
to allow
time-varying
equilibrium.
The simulated
resultsBefore
were thecalibrated
andandperiod
the Galba
River, awhich
is characteristicof the southern
Gobi Desert.
simulation,
validatedusing
groundwaterlevel
observations(Byambasuren
andOdonsetseg,
2015;
IGG,
a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
2018;
Nakayama
et al., 2021a),and riverdischarge
for the TuulRiver (NUM,
2018)
(Fig.
populations,and mining
in the entirebasin
has beendata
developedby combining
a globaldataset,
2.1;
Table
2.2).
statistical data, geographic information system (GIS) data, observational data, and field surveyof climaticchangeresults.
and humanactivities
on spatiotemporal
dataToto separate
improve thetheeffects
accuracyof the model
The model
simulatedthe effectschangesof pastinthegroundwaterlevel,
therelative
contributionofenvironmental
factorswasfurther analyzed.
climatic changes and human-induced disturbances, such as urbanization and mining,on waterThe
modelduring
simulatedchangesinintheseprecipitation,
livestockuse,
and urban andminingresultswaterresources
1980–2018
representative
riverwaterbasins.
Particularly,
the modeluse
separatelybysimplykeeping
one
of
these
effects
constant.
The
reference
simulation
showed a clear difference in the hydrological cycle between them, suggesting the impactwas
ofrepeated to use
data 39 times
during 1980–2018.
Finally, thegroundwaterlevel
urbanization
and1980’s
miningforcing
on local–regional
eco-hydrologicaldegradation.
Althoughdrinkingby
inthe reference
simulation
wassubtracted
from that inchange,
the alternative
simulations
to quantify
herders’
livestock
had some
impacton hydrologicalwater usein Ulaanbaatar
hasthetemporaltrendsofthegroundwaterlevel
causedbyvariousfactors.
severely reduced the groundwater level in the Tuul River over the last few decades, whereas
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
clarify the substantial impact of water use due to urbanization and mining on groundwater
degradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
inventory data on urbanization and mining.
Keywords: climate change; eco-hydrology model; groundwater degradation; human activity;
urban and mining hubs

- 16
27 - 28 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Table
2.2 List of observation stations for validation. NIES groundwater level observation stations
2.1 Introduction
are shown in Fig. 2.1

Although Mongolia has substantial groundwater resources, they are unevenly distributed
Point NameType
Lat. (°) Lon. (°) Elev. (m) Areal Class Data Source and Reference
and abundantin the predominantly
rural north, but very scarce or non-existent in the south. At
1Galba Khanbogd-1
Groundwater level 43.25
Nakayama et al. (2021a)
the beginning of the 21st century, more107.07
than 1071.0
80% ofMining
water resourceswere groundwater2
Galba Khanbogd-2
Groundwater level 43.29
107.24 1016.0
MiningNakayama et al. (2021a)
dependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
3
Galba Khanbogd-3
Groundwater level 43.29
107.24 1017.0
Mininget al. (2021a)
owing
to climate change,
urbanization, andrapid economic
growthNakayama(Banerjee
et al., 2014). In
4
Galba Khanbogd-4
Groundwater level 43.04
106.97 1171.0
MiningNakayama et al. (2021a)
particular, the number of deep groundwater areas has increased substantially since 2000 (FAO,
5
Galba Khanbogd-5
Groundwater level 43.18
1192.0MiningNakayama et al. (2021a)
2012),and permafrost
degradation due 106.79
to climaticchange,dryingof surface water, and6
Galba Khanbogd-6
Groundwater level 43.08
106.77 1211.0
MiningNakayama et al. (2021a)
increased groundwater use has become more serious. Rapid economic growth and political and7
Galba Khanbogd-7
43.10
107.02 1186.0
Nakayama et al. (2021a)
socioeconomic
shiftsGroundwaterfrom levelsocialism
tocapitalism Mining
around 1990
also caused serious
8
Galba Manlai-1
Groundwater level 43.74
106.80 1268.0
Non-mining
Nakayama et al. (2021a)
environmental degradation such as groundwater resource overuse owing to urbanization,
9
Galba Manlai-2
Groundwater level 43.92
106.87 1300.0
Non-mining
Nakayama et al. (2021a)
mineral resource exploitation and quarrying, a rapid increase in livestock such as sheep and
10 TuulUlaanbaatar-1
Groundwater level 47.81
106.64 1234.0
Urban
Nakayama et al. (2021a)
goats owing to economic liberalization (Tumur et al., 2018), decreased grassland productivity
11 TuulUlaanbaatar-2
Groundwater level 47.81
106.64 1234.0
Urban
Nakayama et al. (2021a)
due to livestock overgrazing and over-pumping, and agricultural activity expansion.
12 TuulUlaanbaatar-3
Groundwater level 47.99
106.96 1399.0
Urban
Nakayama et al. (2021a)
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
13 TuulUlaanbaatar-4
Groundwater level 47.94
106.93 1315.0
Urban
Nakayama et al. (2021a)
basins in Mongolia (MNET, 2013; IFC, 2017) (Fig. 2.1). Areas at serious water risk are
14 TuulNalaikh-1
Groundwater level 47.66
107.43 1644.0
Non-urban
Nakayama et al. (2021a)
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
15 TuulLun-1
Groundwater level 47.88
105.32 1022.0
Non-urban
Nakayama et al. (2021a)
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due to
16 TuulLun-2
Groundwater level 47.87
105.26 1001.0
Non-urban
Nakayama et al. (2021a)
anthropogenic activity and climate change in these two contrasting hubs, the groundwater level
17 Tuul
Hustai-1
Groundwater level 47.59
105.85 1216.0
Non-urban
Nakayama et al. (2021a)
in the two river basins are being monitored since the summer of 2018 (Nakayama et al., 2021a).
18 Tuul
Hustai-2
Groundwater level 47.59
105.82 1254.0
Non-urban
Nakayama et al. (2021a)
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
19 TuulNalaikh-2
Groundwater level 47.69
107.44 1534.0
Non-urban
Nakayama et al. (2021a)
area(Jamsran
et al., 2018).
In the middle stretch,the river flows throughUlaanbaatar, the capital
20 TuulUlaanbaatar-5
Groundwater level 47.93
106.68 1322.0 * Urban
Nakayama et al. (2021a)city(Fig. Ulaanbaatar-62.1). Groundwater
is heavily drawn
from a network of shallow
wells for its municipal
21 TuulGroundwater level 47.83
107.46 1445.0 * Urban
Byambasuren & Odonsetseg (2015)
andindustrial
water
supply.
Therefore,
the
water
level
in
the
sourcezone,
the alluvial aquifer,
22 TuulUlaanbaatar-7
Groundwater level 47.90
106.99 1299.0 * Urban
Byambasuren & Odonsetseg (2015)
declinedfrom
1.6 m in1948 to 3.1 m in 1998
(Batsaikhan et al., 2018).
Furthermore, statistical
23 TuulUlaanbaatar-8
Groundwater level 47.79
106.61 1223.0 * Non-urban
Byambasuren & Odonsetseg (2015)
data
indicatethat
thecurrent
water
use
may
not
be
sustainable,
particularly
to meet the projected
24 TuulUlaanbaatar-9
Groundwater level 47.91
106.97 1298.1
Urban
Byambasuren & Odonsetseg (2015)
water
demandof the rapidly growing urban
population Urban
in Ulaanbaatar(Banerjee et al., 2016;25 TuulUlaanbaatar-10 Groundwater level 47.91
107.11 1326.3
Byambasuren & Odonsetseg (2015)
Ulaanbaatar,
2018).
In
contrast,
the
Galba
River
Basin
(Galba–Uush–Doloodiing
Gobi River
26 TuulUlaanbaatar-11 Groundwater level 47.89
106.84 1273.6
Urban
Byambasuren & Odonsetseg (2015)
Basin)is Ulaanbaatar-12
in the southern
Gobi Desert, which
is mostly bare landIGGanddeserts, except some
27 TuulGroundwater level 47.83
107.48 1408.0 * Urban(2018)
grasslands
inthenortheasternregion(Jamsranetal.,2018).
Large
mines,
such as Oyu Tolgoi
28 TuulUlaanbaatar-13 Groundwater level 47.83
107.31 1377.0 * Urban
IGG (2018)(OT)
andUlaanbaatar-14
Tavan Tolgoi,
have been developing
and recently started
operations in this region
29 TuulGroundwater level 47.90
106.98 1297.0 * Urban
IGG (2018)
(Fig.
2.1).
OT,
particularly,
is
one
of
the
largestcopper-gold
deposits
globally and began
30 TuulUlaanbaatar-15 Groundwater level 47.89
106.91 1298.0 * Urban
IGG (2018)
operations
in
2008
(Oyu
Tolgoi
LLC,
2013;
Torquoise
Hill
Resources
Ltd.,2019). The water
31 TuulUlaanbaatar-16 Groundwater level 47.90
106.85 1289.0 * Urban
IGG (2018)
resource
systemmustGroundwatersupport level
this mining
activity,
whilesatisfyingIGGcompeting
water demands
32 TuulUlaanbaatar-17
47.88
106.771265.0* Urban(2018)
fromthemining–urbanization–herdernexus(Banerjeeetal.,2014).
Despiteprevious
findings
33 TuulUlaanbaatar-18 Groundwater level 47.88
106.77 1265.0 * Urban
IGG (2018)indicating
the impactGroundwaterof climate
anthropogenic
factorsIGG
on(2018)
water resources in both
34 TuulUlaanbaatar-19
levelchange
47.80 and
106.581222.0 * Non-urban
basinsunder
different
scenarios(Banerjee
et
al.,
2014,
2016;
ADB,
2014; Torquoise Hill
35 TuulBosgo-1
River discharge
48.03
107.73 1530.0 * Non-urban
NUM (2018)
Resources
Ltd., 2019),
and explicit
evaluationsof spatiotemporalvariations in
36 TuulUlaanbaatar-20
Riverquantitative
discharge
47.89
106.95 1290.0* Urban
NUM (2018)
water
availability
intheentire
basin
have
been
insufficient
till
date.
The
impactof climate
37 TuulAltanbulag-1
River discharge
47.68
106.28 1187.0 * Non-urban
NUM (2018)changehuman-induced
disturbances,such asurbanization
miningdevelopment, on
38 Tuul and
Lun-3
River discharge
47.85
105.181012.0* Non-urban andNUM(2018)
water
resources,
grassland
carrying
capacity,
andvulnerability
shouldbeclarifiedusing a* Elevation was determined by the global digital elevation model (DEM; GTOPO30) (U.S. Geological Survey, 1996a)
process-based model.
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel
No. Basin- 17
28 - 29 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.4 Results and Discussion

Abstract

2.4.1Groundwater
Evaluation of
the Hydrologic
Budgetisin
Two Contrasting
River Basinsin theMongolia
overuseand degradationa serious
issue in Mongolia,
mainlyurban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
The observed(NUM,2018) andriver discharge
in the TuulRiver Basin model,
duringquantify
spatiotemporalvariations
in simulated
water availability,
a process-based
eco-hydrology
1998–2018
betweensite Nos.
35-38 (Bosgo, Ulaanbaatar,
Altanbulag,Lun)applied
were compared,
the National
Integrated
Catchment-based
Eco-hydrology(NICE),andwas
to two
as
shown
in
Fig.
2.1
and
Table
2.2
(Fig.
2.4).
The
discharge
maximized
duringcontrasting river basins: the Tuul River, which is characteristic of northern grasslandthe
andsummer
steppe,rainfallfloods,increasing
inadownstream
direction
from
Bosgo
to
Ulaanbaatar
where
the
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,
forestcoveredtheupstreamarea,changing
littleasfar
asAltanbulag,
where
the
net
amountofa high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
waterthat leaves
the sub-basin
low,basin
and thenincreasing
downstream
to Lun,dataset,
where
populations,and mining
in the isentirehas gradually
been developedby combining
a globalsometributaries
merge
(Fig.
2.1).
The
model
reasonably
reproduced
thevaluesobserved
overstatistical data, geographic information system (GIS) data, observational data, and field survey
the last
two decades.
In particular,
the daily(Fig. 2.4a)and monthly(Fig.of2.4b)
data
to improve
the accuracyof thecomparing
model results.
The model
simulatedthe effects
past
aggregatedvalidations
showed
that
the
root
mean
squareerror(RMSE)
ofthemonthlymean
climatic changes and human-induced disturbances, such as urbanization and mining, on water
discharge during
was smaller
than thatof therepresentative
daily mean discharge.
However,such comparisons
wereresources
1980–2018
in theseriver basins.
Particularly,
the model results
not madea inthe difference
Galba RiverBasinbecause nocycle
observed
valueswereavailable theandimpact
the rivershowedclear
in thehydrologicalbetweenthem,suggestingof
remainsdryandintermittent
throughout
the
year,
exceptduringsomeheavyrainfalleventsurbanization and mining on local–regional eco-hydrological degradation. Although drinking
by(Nakayama
et al., 2021a)(Picture2.1).on hydrological change, water use in Ulaanbaatar has
herders’ livestock
had some
impact
The
model
also
simulatedtheannualthedecades,
Tuul andGalba
severely reduced the groundwater levelin average
the Tuulhydrologic
River over budget
the lastinfew
whereasRiver
Basinsmining
duringhas
1998–20182.5). that
The inspatialdistributionof the2010.
hydrological
Oyu Tolgoi
markedly(Fig.
declinedthe Galba
River since
Analysisbudgetof thefor
each
sub-catchment
inthesebasinsweredifferent.
The
model
showedthat
precipitation
wasrelative contribution of environmental factors also helped us separate the effects of climatic
higherthehumannorthern
Tuul River(upstreamchanges
and downstreamof thelevel.
river)These(Fig.results2.5a).
change inandactivities
on the Basinspatiotemporalin groundwaterInterestingly,thedifference
betweenprecipitation,
evaporation,
and
water
yield
(the
net
amountclarify the substantial impact of water use due to urbanization and mining on groundwaterofwater thataroundleaves water
the sub-basin
contributes
to stream
flowThisin thereach: surface
runoff +degradation
sources, andin addition
to climaticchange.
methodology
is powerfullateralflow + groundwatertransmissionloss) inwaswaterminimalin the middleof thewith
riverlimited
basin,
for evaluating
the spatiotemporalvariations
availability
in regions
where
most
of
the
large
cities,such
as
Ulaanbaatar,arelocated.
Thus,
water
resourcesarevery
inventory data on urbanization and mining.
limited and are greatly affected by urbanization. In contrast, the water yield was higher in thesoutheastern
Galba change;
River Basin,
although itmodel;
was muchlower thandegradation;that in the Tuul
River
Basin
Keywords: climate
eco-hydrology
groundwater
human
activity;(Fig.
The severe
urban2.5b).
and mining
hubswater limitation in the OT region is serious because the need to pumpmore groundwater might cause considerable hydrological changes (Nakayama et al., 2021a).

- 16
29 - 30 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction
Although Mongolia has substantial groundwater resources, they are unevenly distributed
and abundant in the predominantly rural north, but very scarce or non-existent in the south. At
the beginning of the 21st century, more than 80% of water resources were groundwaterdependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
owing to climate change, urbanization, and rapid economic growth (Banerjee et al., 2014). In
particular, the number of deep groundwater areas has increased substantially since 2000 (FAO,
2012), and permafrost degradation due to climatic change, drying of surface water, andincreased groundwater use has become more serious. Rapid economic growth and political andsocioeconomic shifts from socialism to capitalism around 1990 also caused serious
environmental degradation such as groundwater resource overuse owing to urbanization,mineral resource exploitation and quarrying, a rapid increase in livestock such as sheep andgoats owing to economic liberalization (Tumur et al., 2018), decreased grassland productivity
due to livestock overgrazing and over-pumping, and agricultural activity expansion.
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
basins in Mongolia (MNET, 2013; IFC, 2017) (Fig. 2.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelin the two river basins are being monitored since the summer of 2018 (Nakayama et al., 2021a).
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
area (Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capital
city (Fig. 2.1). Groundwater is heavily drawn from a network of shallow wells for its municipal
and industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,
Figure 2.4 Comparison of (a) daily and (b) monthly mean discharge in the Tuul River Basin during
declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
1998–2018 at locations in Table 2.2
data indicate that the current water use may not be sustainable, particularly to meet the projectedwater demand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Ulaanbaatar, 2018). In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
resource system must support this mining activity, while satisfying competing water demands
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in bothbasins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inwater availability in the entire basin have been insufficient till date. The impact of climatechange and human-induced disturbances, such as urbanization and mining development, onwater resources, grassland carrying capacity, and vulnerability should be clarified using aprocess-based model.
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel
Picture 2.1 Dry and salty rivers in the Galba River Basin- 17
30 - 31 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Abstract
Groundwater overuse and degradation is a serious issue in Mongolia, mainly the urban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
quantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
the National Integrated Catchment-based Eco-hydrology (NICE), was applied to twocontrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,statistical data, geographic information system (GIS) data, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past
climatic changes and human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
showed a clear difference in the hydrological cycle between them, suggesting the impact ofurbanization and mining on local–regional eco-hydrological degradation. Although drinking byherders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
severely reduced the groundwater level in the Tuul River over the last few decades, whereas
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
clarify the substantial impact of water use due to urbanization and mining on groundwater
degradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
inventory data on urbanization and mining.
Keywords: climate change; eco-hydrology model; groundwater degradation; human activity;
urban and mining hubs

Figure 2.5 Simulated results of annual-averaged hydrologic budget in the (a) Tuul and (b) Galba
River Basins during 1998–2018- 16
31 - 32 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction
2.4.2Although
Impact ofMongolia
Urbanization
has substantial
and Mining
groundwateron Groundwater
resources,
Level
they
in theare Two
unevenlyRiverdistributed
Basinsand abundant in the predominantly rural north, but very scarce or non-existent in the south. At
the Inbeginning
additionofto thethe 21sthydrologic
century,
budgets,
more than
the simulated
80% of water
data forresources
groundwater
were levelgroundwaterchanges
were compareddependent(Fig.with1.1).theCurrently,
observed values
Mongolia’sin the water
Tuul andresources
Galba Riverare under
Basinsincreasing
during 2000–2018pressure(Fig. 2.6).
owing
to climate
The simulation
change, urbanization,
showed thatandthe rapidhydrologicaleconomic
cycle
growthin the(Banerjee
Tuul Riveret al.,Basin
2014).wasInsignificantly
particular,
thealterednumberbyofoveruse
deep groundwaterin Ulaanbaatar
areas(Figs.
has increased
2.6a–c). substantially
The trends insincevarious
2000regions:(FAO,
upstream2012),
andof thepermafrost
capital (sitedegradation
Nos.27 anddue28toin climatic
Table 2.2),change,which drying
is affectedof bysurfacethe Upper
water,Water
andSource (Fig.
increasedgroundwater2.6a); the central
use hasregion
become(site
moreNos.30serious.
and 32
Rapid
in Table
economic
2.2), which
growthhas
andbeen
political
seriously
anddegraded by the Central,socioeconomic
shifts from
Industrial,socialism
Meat-Complex,
to capitalism
Gachuurt,
aroundand Yarmag
1990 also
Water
causedSources
serious(Fig.
2.6b), the downstream
environmental
degradation
area (sitesuchNo.34
as groundwaterin Table S1),resource
and the natural
overusesteppeowingregion
to urbanization,(site No.18
in Table resourcemineral
S1) wereexploitation
substantiallyand
different
quarrying,(Fig.a2.6c)
rapid(JICA
increase
andinCTI
livestock
Engineeringsuch asInternational
sheep andCo., Ltd.,
goats
owing
2010).
to economic
The groundwater
liberalizationlevel(Tumur
generally
et al.,
declined2018),bydecreased
approximately
grassland
0.7productivitym (averagevaluetoduring
due
livestocktheovergrazing
study period)
andbecauseover-pumping,of livestockand drinking
agricultural
andactivity
further expansion.
by approximately 3–
15 mThe(averaged
Ministryvalue)of Environment
in Ulaanbaatar
and Green
becauseDevelopmentof total water(MEGDT)use (sites
directly
30 andmanages
32 in Table
29 river2.2
basinsand Fig.in2.6b).
Mongolia
This decline(MNET,became
2013; severe
IFC, 2017)
after 2000
(Fig. in2.1).
the Areas
centralatregion
serious(Nakayamawater risk
et al.,
arepredominantly
2021a).
urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
HubIn(Banerjee
contrast, inettheal.,Galba2014).
River
To(Figs.
evaluate
2.6d–f),thethedegradation
intake near of(sitewaterNos.2availability
and 3 in Table
due2.2)toanthropogenic(Fig. 2.6d) andactivity
outsideand(siteclimate
Nos.4 change
and 7 ininTable
these 2.2)
two contrasting
the OT mining
hubs,area
the weregroundwaterconsiderablylevelindifferent
the two(Fig.
river 2.6e),basinsandare beingsites without
monitored
thissince
effectthe(sites
summer
8 andof 2018
9 in (Nakayama
Table 2.2 andet al.,
Fig.
2021a).
2.6f).
The
Although
Tuul RiverrainfallBasindecreased
is in northernslightlygrassland
after 2000
and insteppe,the Galba
except Riverfor forests
Basin,
in theconsiderable
upstreamarea
fluctuations(Jamsran
were
et al.,
observed.
2018). InThetheannual
middlemean
stretch,temperature
the river flows
increasedthrough
gradually,
Ulaanbaatar,
whichthepromoted
capitalcity
evaporation(Fig. 2.1).after
Groundwater
2000, graduallyis heavily
decreasing
drawn fromthea groundwater
network of shallow
level wells
duringfor2005–2015
its municipal
inandaddition
industrial
to lowwater
precipitation.
supply. Therefore,
The groundwater
the water
level
level
declinedin the source
slightlyzone,
by approximately
the alluvial aquifer,
0.1 m(average fromdeclinedvalue1.6
duringm in 1948
the study
to 3.1period)m in 1998
because(Batsaikhan
of drinking
et al.,by2018).
livestock.
Furthermore,
Furthermore,statistical
the
model
data
indicate
resultsthat
show
thethatcurrent
the levelwaterhasusedeclined
may not bemarkedlysustainable,
by maximum
particularly
1.3tommeet
afterthe2012
projected
due towater demanduse for ofminingthe rapidly
in thegrowing
area nearurban
OT population(site Nos.2inand
Ulaanbaatar
3 in Table(Banerjee
2.2 andetFig.
al., 2016;
2.6d)(Nakayama
Ulaanbaatar,et2018).
al., 2021a).
In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
resource system must support this mining activity, while satisfying competing water demands
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in bothbasins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inwater availability in the entire basin have been insufficient till date. The impact of climatechange and human-induced disturbances, such as urbanization and mining development, onwater resources, grassland carrying capacity, and vulnerability should be clarified using aprocess-based model.
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
32 - 33 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Abstract
Groundwater overuse and degradation is a serious issue in Mongolia, mainly the urban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
quantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
the National Integrated Catchment-based Eco-hydrology (NICE), was applied to twocontrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,statistical data, geographic information system (GIS) data, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past
climatic changes and human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
showed a clear difference in the hydrological cycle between them, suggesting the impact ofurbanization and mining on local–regional eco-hydrological degradation. Although drinking byherders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
severely reduced the groundwater level in the Tuul River over the last few decades, whereas
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
clarify the substantial impact of water use due to urbanization and mining on groundwater
degradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
inventory data on urbanization and mining.
Figure 2.6 Comparison of simulated and observed changes in groundwater level during 2000–
Keywords: climate
eco-hydrology
model;
groundwater2018 in change;(a)–(c) the
Tuul and (d)–(f)GalbaRiver Basinsdegradation; human activity;

urban and mining hubs

2.4.3 Separating the Effects of Climatic Change and Anthropogenic Activity on
Spatiotemporal Change in the Groundwater Level
Categorizing the effects of climate change and human activity on spatiotemporal changes
in the hydrological cycle is valuable. NICE was used to simulate changes in groundwater levels
around the groundwater sources in the Tuul and Galba River Basins from the 1980s to the 1990s,
2000s, and 2010s. In this analysis, the differences in mean values among these periods were
calculated (Fig. 2.7). Generally, the simulated results showed that the groundwater level
decreased decade-by-decade in both basins because of the decreasing annual rainfall before
2010. More importantly, the model showed that the groundwater level around Ulaanbaatar has
gradually decreased from the 1990s to the present owing to over-pumping (Fig. 2.7b). In
contrast, the groundwater level around the water source used for OT mining in the Galba River
Basin suddenly decreased after the 2010s, beginning from 2008 (Fig. 2.7d) (Nakayama et al.,2021a).
Furthermore, the simulation facilitated the analysis of temporal trends and control ofgroundwater level changes to quantify the interannual variations attributable to the changes inprecipitation and water use for livestock, Ulaanbaatar, and OT mining (Fig. 2.8). In the TuulRiver Basin, the groundwater level tended to rise before 2000, mainly owing to changes inprecipitation, and then gradually decreased (Figs. 2.8a–b). The model results also showed that- 16
33 - 34 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introductionurbanwater use caused more than 50% groundwater degradation in urban areas after 2000 (Fig.
2.8a) compared to that in non-urban areas (Fig. 2.8b). In contrast, this shift occurred around2010Although
in the Galba
Mongolia
River Basin,
has substantial
which wasgroundwaterslightly laterresources,
than that inthey
theareTuulunevenlyRiver Basin
distributed(Figs.
and2.8c-d).
abundant
The model
in thequantified
predominantly
a morerural
seriousnorth,
situation
but veryinscarce
whichorwater
non-existentuse in OTincaused
the south.
almostAt
the
all groundwater
beginning ofdegradation
the 21st century,in miningmoreareasthanafter80%
2010of(Fig.
water
2.8c)
resources
in comparison
were groundwaterwith that independent
non-mining(Fig.
areas1.1).
(Fig. Currently,
2.8d). In both
Mongolia’s
basins, thewater
effectresourcesof water use
areon
under
livestock
increasing
was lowerpressure
than
owingthat attributable
to climatetochange,
urbanization,
urbanization,mining,and
andrapid
precipitation
economicchanges.
growthThus,(Banerjeetheseetanalysesal., 2014).
of theInparticular,
relative contributions
the number ofof deep
environmental
groundwater
factors
areas hashelpedincreased
to directly
substantiallyquantifysincethe 2000interannual(FAO,2012),
variations
andorpermafrost
temporal trends
degradationin groundwater
due to levelclimaticchangeschange,causeddryingby thoseof surface
factors. water,
Our results
andincreasedalso revealed
groundwater
differences
use inhasthebecomecharacteristics
more serious.
of theRapidhydrologicaleconomic growth
cycle between
and political
the twoandsocioeconomic
contrasting rivershifts
basins,fromwhichsocialism
are predominantly
to capitalism
grassland/steppearound 1990 andalso barecausedland/desert,
seriousenvironmental
affected or unaffecteddegradation
by urbanizationsuch as groundwaterand mining (Nakayama
resource overuse
et al., 2021a).
owing to urbanization,mineral resource exploitation and quarrying, a rapid increase in livestock such as sheep andgoats owing to economic liberalization (Tumur et al., 2018), decreased grassland productivity
due to livestock overgrazing and over-pumping, and agricultural activity expansion.
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
basins in Mongolia (MNET, 2013; IFC, 2017) (Fig. 2.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelin the two river basins are being monitored since the summer of 2018 (Nakayama et al., 2021a).
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
area (Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capital
city (Fig. 2.1). Groundwater is heavily drawn from a network of shallow wells for its municipal
and industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
data indicate that the current water use may not be sustainable, particularly to meet the projectedwater demand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Ulaanbaatar, 2018). In contrast, the Galba River Basin (Galba–Uush–Doloodiing Gobi River
Basin) is in the southern Gobi Desert, which is mostly bare land and deserts, except somegrasslands in the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi(OT) and Tavan Tolgoi, have been developing and recently started operations in this region(Fig. 2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations in 2008 (Oyu Tolgoi LLC, 2013; Torquoise Hill Resources Ltd., 2019). The water
resource system must support this mining activity, while satisfying competing water demands
from the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
indicating the impact of climate change and anthropogenic factors on water resources in bothbasins under different scenarios (Banerjee et al., 2014, 2016; ADB, 2014; Torquoise Hill
Resources Ltd., 2019), quantitative and explicit evaluations of spatiotemporal variations inwater availability in the entire basin have been insufficient till date. The impact of climatechange and human-induced disturbances, such as urbanization and mining development, on
Figure2.7 Simulated
results forchangesin groundwaterlevel from should
the (a) 1980s
to (b) 1990s,
water resources,
grassland
carrying
capacity,and vulnerability
be clarified
using(c)a2000s,
and
(d)
2010s
in
the
Tuul
and
Galba
River
Basins
in
the
same
area
as
in theprocess-based model.
enlarged view to the lower right in Fig. 2.2
In this chapter, the process-based National Integrated Catchment-based Eco-hydrology(NICE) model (Nakayama, 2014, 2015) was applied to two representative river basins: the TuulRiver Basin, characterized by northern grassland and steppe, and the Galba River Basin,characterized by the southern Gobi Desert (Nakayama et al., 2021a) (Fig. 2.1). In addition to
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
34 - 35 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Abstract
Groundwater overuse and degradation is a serious issue in Mongolia, mainly the urban andeconomic hub of Ulaanbaatar and the Southern Gobi Mining Hub. In this chapter, to explicitly
quantify spatiotemporal variations in water availability, a process-based eco-hydrology model,
the National Integrated Catchment-based Eco-hydrology (NICE), was applied to twocontrasting river basins: the Tuul River, which is characteristic of northern grassland and steppe,
and the Galba River, which is characteristic of the southern Gobi Desert. Before the simulation,a high-resolution data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
populations, and mining in the entire basin has been developed by combining a global dataset,statistical data, geographic information system (GIS) data, observational data, and field survey
data to improve the accuracy of the model results. The model simulated the effects of past
climatic changes and human-induced disturbances, such as urbanization and mining, on water
resources during 1980–2018 in these representative river basins. Particularly, the model results
showed a clear difference in the hydrological cycle between them, suggesting the impact ofurbanization and mining on local–regional eco-hydrological degradation. Although drinking byherders’ livestock had some impact on hydrological change, water use in Ulaanbaatar has
severely reduced the groundwater level in the Tuul River over the last few decades, whereas
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of therelative contribution of environmental factors also helped us separate the effects of climaticchange and human activities on the spatiotemporal changes in groundwater level. These results
clarify the substantial impact of water use due to urbanization and mining on groundwater
degradation around water sources, in addition to climatic change. This methodology is powerfulfor evaluating the spatiotemporal variations in water availability in regions with limited
Figure2.8 data
Relative
contributionandof environmental
factors to changes in groundwater level during
inventory
on urbanization
mining.
1980–2018, compared to the reference simulation, in the (a) urban and (b) non-urban
areas inchange;
the Tuuleco-hydrology
River Basin, and(c) mining
and (d) non-miningareas
in theGalba
Keywords: climate
model;
groundwater
degradation;
human
activity;River Basin in Table 2.2

urban and mining hubs
2.5 Conclusion

This chapter describes the application of process-based eco-hydrology model NICE to twocontrasting river basins: the Tuul River Basin in the northern grassland and steppe, and the
Galba River Basin in the southern Gobi Desert. The model simulated the effects of past climatechange and human activity (livestock, urban usage, and mining) on water resources in the tworiver basins during 1980–2018 at 0.05°×0.05° resolution. Simulation results revealed thedifferences in the spatial distribution of hydrological budget and its response to climatic andanthropogenic disturbances. The Tuul River has limited water resources in the middle area ofthe basin and seriously affected by urbanization in Ulaanbaatar. In contrast, the Galba River
has a complicated hydrological cycle and is substantially affected by mining at the OT.
Although drinking by the herders’ livestock has some hydrologic effect (less than 1 m), thegroundwater level in the Tuul River Basin has been markedly degraded by Ulaanbaatar overthe last few decades, and that in the Galba River Basin has declined markedly since 2010
because of OT mining. In addition, analysis of the relative contribution of environmental factors
helped us quantify the interannual variation or temporal trends in groundwater levels due to thechanges in precipitation, as well as the above water uses. This methodology is powerful forevaluating the spatiotemporal variations in water availability in regions for which inventory- 16
35 - 36 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

2.1 Introduction
data,such as those for urbanization and mining, are lacking. Further uncertainty analyses of thehydrologic cycle should be conducted to improve the evaluation of pasture-carrying capacity
and Althoughvulnerability,
Mongolia
as describedhas substantial
in the following
groundwater
chapters.
resources,
These approaches
they are unevenly
may help
distributed
improvethe accuracy
andabundantofinhydrologic
the predominantly
budgetingrural
by north,
facilitating
but verystrictscarcebias analysis.
or non-existent in the south. At
the beginning of the 21st century, more than 80% of water resources were groundwaterdependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
owing to climate change, urbanization, andrapid economic growth (Banerjee et al., 2014). In
References
particular, the number of deep groundwater areas has increased substantially since 2000 (FAO,
2012),Development
and permafrost
degradation
due to inclimaticchange,dryingof surface water,Nexus.
and
Asian
Bank (ADB).
(2014) Demand
the Desert:
Mongolian’s
Water-Energy-Mining
Mandaluyong
City,
Philippines
increased groundwater use has become more serious. Rapid economic growth and political and
Banerjee,R., Möller-Gulland,J., vander Linden,
et al. (2014)
Mongolia:
Targeted
Watersocioeconomic
shifts fromsocialism
to W.,
capitalism
around1990
also Analysis
caused onserious
Resources
Management
Issues.
Technical
Report.
2030
Water
Resources
Group.
environmental
degradation such as groundwater resource overuse owing to urbanization,
https://www.2030wrg.org/mongolia-targeted-analysis-wrm-issues/mineralresource
exploitationand quarrying,a rapidincrease inlivestocksuch as sheep
andBanerjee, R., Möller-Gulland,J., Fawcett,C., et al. (2016)
Hydro-economic
Analysison Cost-Effective
Solutionsgoatsto owing
toeconomicliberalization(Tumuretal.,2018),
decreased
grassland
productivity
Close Ulaanbaatar’s Future Water Gap. Technical Report. 2030 Water Resources Group.
due https://www.2030wrg.org/ulaanbaatar-mongolia-hydro-economic-analysis-cost-effective-solutions-closeto livestock overgrazing and over-pumping, and agricultural activity expansion.
future-water-gap/
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
Batsaikhan,N., Lee, J.M.,
Nemer, B.,
Woo,IFC,
N.C. 2017)(2018) (Fig.
Water 2.1).
resources
sustainabilityof Ulaanbaatar
basins in Mongolia(MNET,
2013;
Areas
at serious
water riskCity,
are
Mongolia. Water 10, 750, doi:10.3390/w10060750
predominantly
urban
and
economic
hubs
such
as
Ulaanbaatar
andtheSouthern
Gobi
MiningByambasuren, B.O., Odonsetseg, D. (2015) Survey on Groundwater Monitoring in Ulaanbaatar City Water Supply
HubSources.
(Banerjee
et ofal.,Geography2014). To
degradationwaterGeoecology
availability
due toInstituteand evaluate
Geoecologythe
Knowledge
portal, of
11-119,of Mongolia.
Ulaanbaatar activity and climate change in these two contrasting hubs, the groundwater level
anthropogenic
Europeanfor Medium-Range
Weather since
Forecasts(ECMWF).
(2019)(Nakayama
ERA-Interium.
inthe twoCentre
river basinsare being monitored
the summerof 2018
et al.,http://data2021a).
portal.ecmwf.int/data/d/interim_daily/
The Tuul River Basin is in northern grassland and steppe, except for forests in the upstream
EuropeanCommission.
(2012)
Harmonized
World
Soil
Database.
areahttp://eusoils.jrc.ec.europa.eu/ESDB_Archive/soil_data/global.htm(Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capitalcity(Fig. 2.1). Groundwater
drawn fromGlobal
a network of Land
shallow wellsCoverfor its municipal
EuropeanCommission. is heavily(2015)
2000.
and http://bioval.jrc.ec.europa.eu/products/glc2000/products.php
industrial water supply. Therefore, the water level in the source zone, the alluvial aquifer,
Food and Agriculture
Organization
the m
UnitedNations(FAO). (2012)
Irrigation
Southern and EasternAsia
declinedfrom 1.6 m
in 1948 toof3.1
in 1998(Batsaikhan
et al.,
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AQUASTAT
Survey
2011.
FAO
Water
Reports
37,
Rome.
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http://www.fao.org/3/i2809e/i2809e.pdf
waterdemandP.,ofKurosaki,
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U.,
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In
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the
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Gobi
River
Tamura, K., Luvsan, N., Yamanaka, N. [Eds] Rangeland Ecosystems of Mongolia. Munkhiin Useg, Co.,Ltd.,
Ulaanbaatar,
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is in the75-107
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Hartmann, J.,inMoosdorf,
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The new(Jamsran
global lithologicalmap database
representationof rock
grasslands
the northeastern
et al., 2018).
Large GLiM:
mines,Asuchas Oyu Tolgoi
properties
at
the
Earth
surface.
Geochem.
Geophys.
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doi:10.1029/2012GC004370(OT) and Tavan Tolgoi, have been developing and recently started operations in this region
Institute of Geography and Geoecology (IGG). (2018) Ulaanbaatar city water supply sources; Land Water(Fig.Monitoring
2.1). OT,
(in particularly,
Mongolian) is one of the largest copper-gold deposits globally and began
operations
in2008(Oyu Tolgoi
Torquoise
Hill
Resources
Thetraining.
water
International Finance Corporation(IFC).LLC,(2017)2013;
Mining,groundwater
management
andLtd.,water2019).
monitoring
Jamsran, U.,
Yamanaka,
Zorigbaatar,(2018) activity,
Vegetationwhileof Mongolia.
In: Jamsran,U., Tamura,Luvsan,resource
systemmustN.,support
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competingwaterK.,
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Despiteprevious
findings
139
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basinsunder
different
scenarioswater(Banerjee
et al., 2014,
2016;
ADB, 2014;
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supply development
project
in Gachuurt
in Mongolia.
Ministryof
Resources
Ltd., 2019),
quantitative
andDevelopment
explicit evaluations
spatiotemporal
in
Roads, Transport,
Construction
and Urban
Water Supplyof& Sewerage
Authorityvariationsof Ulaanbaatar
City,
Mongolia in the entire basin have been insufficient till date. The impact of climate
water
availability
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climate change. Nature Geoscience 1, 665-669, doi:10.1038/ngeo315
In thischapter,the process-based
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Ministryof Nature,
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Basin boundary
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Mongolian
Economic2014,
Development
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Ulaanbaatar
Nakayama,
T., Watanabe,
M. (2004)
drying phenomena
associatedchangeBasin,causedRiver
Basin,
characterized
by Simulation
northern ofgrasslandand steppe,
andwiththevegetation
Galba Riverbyinvasionofalder(Alnusjaponica)inKushiro
Mire.
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Resources
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40,
W08402,
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to10.1029/2004WR003174
the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
36 - 37 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Nakayama, T., Watanabe, M. (2005) Re-evaluationAbstractof groundwater dynamics about water and nutrient budgets in
Lake Kasumigaura, Annual Journal of Hydroscience and Hydraulic Engineering, 49, 1231-1236 (Abst. in
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overuseand degradationa serious
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mainlythe urban andNakayama,
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M. (2006a)
Simulation ofisspringsnowmeltrunoffby considering
micro-topography
andphase changes
soil layer. Hydrology
Earth System
3, 2101-2144
economic
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simulation of ecosystem
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a process-basedmodel,
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the http://www.cger.nies.go.jp/publications/report/i063/I063e
National Integrated Catchment-based Eco-hydrology (NICE), was applied to twocontrastingT.,river
basins:the Tuul
is characteristicof northerndynamics
grassland
Nakayama,
Yang,
Y., Watanabe,
M.,River,Zhang,whichX. (2006)
Simulation of groundwaterin andNorthsteppe,
China
and Plain
the Galba
River,hydrology
which is andcharacteristic
the southern
GobiProcesses,
Desert. Before
simulation,by coupled
agricultural ofmodels.
Hydrological20(16),the3441-3466,doi:
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a high-resolution
data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
Nakayama,
T., Watanabe,M.,inTanji,
Morioka,(2007)of undergroundurban structureson eutrophic
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the K.,
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beenEffectdevelopedby combining
a globaldataset,coastalenvironment.
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data
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Shrinkage ofand recoveryriverof mireecosystemby river restorationin northern
resources T.
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showed a clear difference in the hydrological cycle between them, suggesting the impact ofNakayama, T. (2008c) Development of process-based NICE model and simulation of ecosystem dynamics in theurbanizationand mining on local–regional eco-hydrological degradation. Although drinking by
catchment of East Asia (Part II). CGER's Supercomputer Monograph Report, 14, NIES, 91p.,
herders’
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http://www.cger.nies.go.jp/publications/report/i083/i083_e
severely reduced
the groundwaterlevel rolein theTuul Riverinoverfew cycles
decades,Nakayama,
T., Watanabe,
M. (2008a) Missingof groundwater
watertheandlastnutrient
in thewhereas
shalloweutrophic
Lake
Kasumigaura,
Japan.
Hydrological
Processes,
22,1150-1172,
doi:
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Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of the
Nakayama,
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storageof lakesin the ChangjiangRiverrelative contribution
factors
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climatic
Global and Planetary Change, 63, 9-22, doi: 10.1016/j.gloplacha.2008.04.002
change and human activities on the spatiotemporal changes in groundwater level. These results
Nakayama, T., Watanabe, M. (2008c) Modelling the hydrologic cycle in a shallow eutrophic lake. Verhclarify
the substantial
impact of(International
water useAssociation
due to urbanization
on groundwaterInternational
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application forinfor evaluating
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spatiotemporalvariations
in waterineffective
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in the North China Plain. Hydrological Processes, 25, 2679-2693, doi: 10.1002/hyp.8009
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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(4), 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 missing role of hydrothermal interactions in Japanese megalopolis for winwin solution. Water Science and Technology, 66(2), 409-414, doi: 10.2166/wst.2012.205
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37 - 38 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Nakayama,
T., Hashimoto, S., Hamano, H. (2012) Multi-scaled analysis of hydrothermal dynamics in Japanese
2.1 Introduction
megalopolis by using integrated approach. Hydrological Processes, 26(16), 2431-2444, doi:
10.1002/hyp.9290
Although Mongolia has substantial groundwater resources, they are unevenly distributed
Nakayama, T. (2013) For improvement in understanding eco-hydrological processes in mire. Ecohydrology andand Hydrobiology,
abundant in the
predominantly
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13, 62-72,
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the beginningof the D.
21stcentury,
80% ofDam
water
resources
Nakayama,
T., Shankman,(2013a)Impactmore
of the thanThree-Gorges
and watertransferwere
projectgroundwateron Changjiang
floods.
GlobalandPlanetary
Change,100,38-50,
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dependent (Fig. 1.1). Currently, Mongolia’s water resources are under increasing pressure
Nakayama,
T., Shankman,
D. (2013b)
Evaluation
uneveneconomic
water resource
and relation
betweenanthropogenic
owing to climatechange,urbanization,
andof rapid
growth(Banerjee
et al.,
2014). In
water withdrawal and ecosystem degradation in Changjiang and Yellow River basins. Hydrological Processes,
particular,
the number of deep groundwater areas has increased substantially since 2000 (FAO,
27(23), 3350-3362, doi: 10.1002/hyp.9835
2012),
anddegradation due
to climaticchange, S.
dryingof surfaceof water,
andNakayama, T. permafrost(2014a) Hydrology–ecology
interactions.
In: Eslamian,[Ed] Handbook
Engineering
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and Applications(Chapter16), Taylor
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Nakayama,
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Developmentof process-based
NICE model andsimulationof ecosystem
dynamics
in thesocioeconomic
shiftsfrom socialism
to capitalism
around1990
also caused
seriouscatchment
of
East
Asia
(Part
IV).
CGER'sSupercomputer
Monograph
Report,20,
NIES,102p.,
environmental degradation such as groundwater resource overuse owing to urbanization,
http://www.cger.nies.go.jp/publications/report/i114/en/
mineralresource exploitation and quarrying, a rapid increase in livestock such as sheep andNakayama, T. (2015) Integrated assessment system using process-based eco-hydrology model for adaptation
goatsstrategy
owingandto effective
economicliberalization(Tumur et2018),
water
resources management.
In: al.,LakshmiV. decreased[Ed] Remotegrassland
Sensing of productivity
the Terrestrial
due Water
to livestockand over-pumping,
and agriculturalactivity 33),
expansion.
Cycleovergrazing(Geophysical
Monograph Series
206) (Chapter
AGU, 521-535
doi:10.1002/9781118872086.ch33
The Ministry of Environment and Green Development (MEGDT) directly manages 29 river
Nakayama,(2016) New
perspective
for eco-hydrology
constrainrole of water
inland watersonbasins in T.
Mongolia(MNET,
2013;
IFC, 2017) model(Fig. to2.1).
Areasmissing
at serious
risk areboundless biogeochemical cycle in terrestrial-aquatic continuum. Ecohydrology and Hydrobiology, 16, 138predominantly
urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
148, doi: 10.1016/j.ecohyd.2016.07.002
Hub (Banerjee
et Developmental., 2014).ofToevaluatethe degradationof water coupling
availability
due toNakayama,
T. (2017a)
an advanced
eco-hydrologic
and biogeochemical
model aimed
atanthropogenic
in thethese
twobiogeochemical
contrasting hubs,
groundwaterlevel
clarifying theactivity
missingand
roleclimate
of inlandchangewater inglobalcycle.theJournalof GeophysicalResearch
Biogeosciences,122, 966-988,
doi: 10.1002/2016JG003743
in thetwo riverbasins are being
monitoredsince the summer of 2018 (Nakayama et al., 2021a).
Nakayama,T.
(2017b)Scaled-dependence
andseasonal andvariationsof carbon
through development
of an
The Tuul River Basin is in northern grassland
steppe,exceptcycle
for forestsin the upstream
advanced eco-hydrologic and biogeochemical coupling model. Ecological Modelling, 356, 151-161, doi:area10.1016/j.ecolmodel.2017.04.014(Jamsran et al., 2018). In the middle stretch, the river flows through Ulaanbaatar, the capitalcity(Fig. 2.1).
Groundwater
is heavily
drawn
from
a networkof shallow
wellsoffor
its municipal
Nakayama,
T. (2017c)
Biogeochemical
contrast
betweendifferent
latitudes
and the effecthuman
activity on
and spatio-temporalindustrial watersupply.
Therefore,
the
water
level
in
the
sourcezone,
thealluvial
aquifer,carbon cycle change in Asian river systems. Biogeosciences Discussions, doi:10.5194/bg2017-447
declined from 1.6 m in 1948 to 3.1 m in 1998 (Batsaikhan et al., 2018). Furthermore, statistical
Nakayama,
T. that(2018)
betweenwater
and groundwaterand its effecton theecosystem
anddata
indicate
theInteraction
current water
usesurface
may not
be sustainable,
particularly
to meetprojected
biogeochemical cycle. Journal of Groundwater Hydrology, 60(2), 143-156, doi: 10.5917/jagh.60.143 (inwaterdemand of the rapidly growing urban population in Ulaanbaatar (Banerjee et al., 2016;
Japanese)
Ulaanbaatar,
2018). In contrast,
the GalbaofRiver
Basin (Galba–Uush–Doloodiing
GobinorthernRiver
Nakayama, T., Maksyutov,
S. (2018) Application
process-based
eco-hydrological model to broader
Basin)
is inwetlands
the southern
Desert,which is Ecohydrology
mostly bare andlandHydrobiology,
and deserts,18,except
some
Eurasia
through Gobi
coordinatetransformation.
269-277,doi:
10.1016/j.ecohyd.2017.11.002
grasslandsin the northeastern region (Jamsran et al., 2018). Large mines, such as Oyu Tolgoi
Nakayama,
Pelletier,
G.J. have(2018)been
Impactof global major
on carbon
cycle changes
by usingan(OT)
and T.,
Tavan
Tolgoi,developing
and reservoirs
recently started
operationsin thisregionadvanced eco-hydrologic and biogeochemical coupling model. Ecological Modelling, 387, 172-186, doi:(Fig.10.1016/j.ecolmodel.2018.09.007
2.1). OT, particularly, is one of the largest copper-gold deposits globally and began
operations
2008Development(Oyu Tolgoi
2013; NICE
Torquoise
Resources
2019).
The water
Nakayama, T.in(2019)of LLC,
process-basedmodel Hilland simulationof Ltd.,ecosystem
dynamics
in theresource
systemthisV).mining
activity,
while satisfying
competingwater
demands
catchmentof mustEast support
Asia (PartCGER'sSupercomputer
Monograph
Report, 26,
NIES,
122p.,
fromhttp://www.cger.nies.go.jp/publications/report/i148/en/
the mining–urbanization–herder nexus (Banerjee et al., 2014). Despite previous findings
Nakayama,(2020)
Inter-annual
simulationof global
carbon cycle variations
a terrestrial-aquatic
continuum.
indicatingT.theimpactof climatechangeand anthropogenic
factorsinon
water resourcesin bothHydrological Processes, 34(3), 662-678, doi: 10.1002/hyp.13616
basinsunder
different
scenarios(Banerjee
et
al.,
2014,
2016;
ADB,
2014;
Torquoise
Hill
Nakayama, T., Wang, Q., Okadera, T. (2021a) Evaluation of spatio-temporal variations in water availability using
Resources
Ltd., 2019),
quantitativeevaluationsof spatiotemporalvariations440,
ina process-based
eco-hydrology
model inandarid explicit
and semi-arid
regions of Mongolia.
Ecological Modelling,water
availability
in the entire basin have been insufficient till date. The impact of climate
109404,
doi:10.1016/j.ecolmodel.2020.109404
Nakayama,
T.,human-induced
Wang, Q., Okadera,T. (2021b) Sensitivity
and parameter
estimationof anthropogenic
changeanddisturbances,such asanalysis
urbanizationand mining
development,
on
water
uses
for
quantifying
relation
betweengroundwater
overuseandwater
stressin
Mongolia.
Ecohydrology
water resources, grassland carrying capacity, and vulnerability should be clarified
using aand Hydrobiology, 21(3), 490-500, doi:10.1016/j.ecohyd.2021.07.006
process-basedmodel.
Nakayama, T. (2022) Impact of anthropogenic disturbances on carbon cycle changes in terrestrial-aquaticIn
this chapter,
process-based
National Integrated
Catchment-based
Eco-hydrology
estuarine
continuumthe
by using
an advanced process-basedmodel. Hydrological
Processes, 36(2),e14471, doi:
(NICE)
model(Nakayama,
2014,
2015)
was
appliedtotworepresentativeriver
basins:the Tuul
10.1002/hyp.14471
Nakayama,
T. (2023)
Evaluation byof global
biogeochemical
lotic anddevelopinganRiver
Basin,
characterized
northerngrassland cycle
and insteppe,andlentic
thewaters
GalbabyRiver
Basin,
advanced eco-hydrologic
and biogeochemical
model.etEcohydrology,
doi: 10.1002/eco.2555
characterized
by the southern
Gobi Desertcoupling(Nakayamaal., 2021a) e2555,(Fig. 2.1).
In addition to

the surface runoff submodel, NICE incorporates a three-dimensional groundwater submodel- 17
38 - 39 -Chapter 2 Evaluation of Spatiotemporal Variations of Water Availability in Urban and Mining Hubs of Mongolia
CGER-I167-2023,
Using Process-Based
CGER/NIES
EcoHydrology Model

Nakayama, T., Okadera, T., Wang, Q. (2023) Impact
of various anthropogenic disturbances on water availability
Abstractin the entire Mongolian basins towards effective utilization of water resources. Ecohydrology andHydrobiology, 23(4), doi:10.1016/ j.ecohyd.2023.04.006
Groundwateranddegradationa serious issue
in Mongolia,
the urban
andNakayama,
T., Osako,overuse
M. (2023a)
Developmentof aisprocess-based
eco-hydrology
modelmainlyfor evaluating
the spatiotemporal
dynamics
of macro- andfor Gobi
the wholeof Japan.
EcologicalModelling,to476,
110243,
economichubof Ulaanbaatar
andmicro-plastics
the Southern
MiningHub.
In this chapter,
explicitly
doi:10.1016/j.ecolmodel.2022.110243
quantify
spatiotemporal variations in water availability, a process-based eco-hydrology model,
Nakayama, T., Osako, M. (2023b) The flux and fate of plastic in the world's major rivers: Modeling spatial andthe temporal
National
Integrated Catchment-based Eco-hydrology (NICE), was applied to twovariability. Global and Planetary Change, 221, 104037, doi:10.1016/j.gloplacha.2023.104037
contrasting
river
basins:the TuulRiver,
which is characteristic of northern grassland and steppe,
NASA. (2013) GLDASVegetation
Class.
http://ldas.gsfc.nasa.gov/gldas/GLDASvegetation.php
and the Galba
River, Gridded
which is characteristic
Gobi Desert.
Before theNASA.
(2018)
Population ofofthe southern
theWorld,
Version4 simulation,(GPWv4).
https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-countrya high-resolution
data grid (0.05° × 0.05° resolution) representing water use for livestock, urban
totals-rev11
populations,and mining in the entire basin has been developed by combining a global dataset,
National Statistics Office of Mongolia. (2019) Mongolian Statistical Information Service. Ulaanbaatar.
statistical
data,
geographic information system (GIS) data, observational data, and field survey
http://www.1212.mn/
data to improveaccuracy(NUM).
of the (2018)
modelRiver
results.
The model
effectsof pastNational
University theof Mongolia
discharge
data in simulated
Tuul River,theSpatialanalysisandclimaticchangesand human-induced
disturbances, such as urbanization and mining, on water
Biological
Resources
Mapping Lab, Ulaanbaatar
Neitsch,S.L.,
Arnold,J.G., Kiniry,
J.R., Williams,
J.R. (2011)
and water
assessment
Theoreticalresources
during
1980–2018
in these
representative
riverSoilbasins.
Particularly,
thetool:model
results
documentation
version
2009.
Texas
Water
Resources
Institute
Technical
Report
No.
406,
Texas
A&M
showed a clear difference in the hydrological cycle between them, suggesting the impact
ofUniversity, http://swat.tamu.edu/software/swat-model/
urbanization
andminingon
local–regional
eco-hydrologicaldegradation.
Althoughdrinking
byOyu Tolgoi LLC. (2013) Report on environmental protection plan and environmental monitoring program,
herders’
livestock had some impact on hydrological change, water use in Ulaanbaatar has
Mongolia
severely
reducedgroundwaterin theTuul River overthe last few
decades,
whereasPielke,R.A.,
Cotton, theW.R.,
Walko, R.L., etlevelal. (1992)
A comprehensive
meteorologicalmodeling
system-RAMS.
Meteorology
andAtmosphericPhysics,49,
69-91
Oyu Tolgoi mining has markedly declined that in the Galba River since 2010. Analysis of the
Ritchie,
Singh, U., Godwin,
D.C., Bowen, W.T.
(1998)
Cerealgrowth,andeffects
yield. In:ofTsuji,
G.Y.,
relativeJ.T.,
contributionof environmental
factors
also
helpedus development
separate theclimatic
Hoogenboom, G., Thornton, P.K. [Eds], Understanding Options for Agricultural Production, Kluwer, Greatchangeand human activities on the spatiotemporal changes in groundwater level. These results
Britain, 79-98
clarifytheimpactwater useOyu
dueTolgoi
to urbanizationand mining
Torquoise Hillsubstantial
Resources Ltd.
(2019)of
Extraordinary2018 Annual Report,
Canadaon groundwater
degradation
aroundsources,in addition
to climaticchange.
This methodology
powerful
Tumur,
B., Jamsran,U.,waterZorigbaatar,
N. (2018)
Livestockof Mongolia.
In: Jamsran,U., Tamura, K.,isLuvsan,N.,[Eds]spatiotemporal
Rangeland Ecosystemsof Mongolia.
Munkhiin
Useg, Co.,inLtd.,
Ulaanbaatar,for Yamanaka,evaluatingN.thevariations
in wateravailability
regionswith 207-355
limitedUlaanbaatar.
Green Development
inventory data(2018)
on urbanizationand mining. Strategic Action Plan for Ulaanbaatar 2020.
https://www.greengrowthknowledge.org/national-documents/mongolia-green-development-strategic-actionplan-ulaanbaatar-2020
Keywords:
climatechange;eco-hydrology
model;
groundwater
degradation;
activity;
U.S.
Geological
Survey(USGS).
(1996a) GTOPO30
Global30 Arc Second
Elevation human
Data Set.
USGS.
urban
and mining hubs
http://www1.gsi.go.jp/geowww/globalmap-gsi/gtopo30/gtopo30.html
U.S. Geological Survey (USGS). (1996b) HYDRO1K. USGS. https://lta.cr.usgs.gov/HYDRO1K

This article was published in Ecological Modelling, 440, Nakayama, T., Wang, Q., Okadera, T., Evaluation of
spatio-temporal variations in water availability using a process-based eco-hydrology model in arid and semi-arid
regions of Mongolia, 109404, Copyright Elsevier (2021).

- 16
39 - 40 -CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29
CGER-I167-2023, CGER/NIES

Chapter 3
Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use
for Quantifying the Relation between Groundwater Overuse and Water
Stress- 41 - 41 -

Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the Relation between Groundwater
Overuse and Water Stress

Abstract
In Mongolia, water resources are under increasing pressure, mainly in the urban andeconomic hubs of Ulaanbaatar and Southern Gobi Mining Hub. In the previous chapter,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, was used to explicitly quantify the spatiotemporal variations in water availability inthese two contrasting river basins. In this chapter, the model was further extended to couple itwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
water use (NICE-INVERSE). The new model quantified the spatiotemporal variations inlivestock water use in these river basins in comparison with the estimated values by aggregating
statistical data, GIS data, observational data, and field survey data from previous studies. The
amount of water used by animals was generally low for each soum (district) and could be
returned to the ecosystem. The model showed an interesting result as the unit water use in sometypical livestock (cattle, sheep, and goats) had a temporally decreasing trend, suggesting thatwater stress substantially increased due to local–regional eco-hydrological degradation by
urbanization and mining. These results clarify that the substantial increase in water stress fromgroundwater degradation was mainly caused by urbanization and local mining, in addition toclimate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
improve the accuracy of hydrologic budgets in the basins. This methodology is powerful for
quantifying the spatiotemporal variations in water availability and water stress in regions withlimited inventory data on urbanization, mining, and livestock.
Keywords: eco-hydrology model; human activity; inverse estimation; parameter estimation;
sensitivity analysis- 42 - 42 -

Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.1 Introduction

Abstract

Water
resources
are unevenly
Mongolia, being
abundantin thepredominantly
In Mongolia,
water
resourcesdistributed
are underin increasingpressure,mainlyin theurban andrural
north hubs
but scarceor non-existent
the south.
Particularly,>80%Inofthewater
resourceswere
economicof Ulaanbaatar
and inSouthern
Gobi
Mining Hub.
previous
chapter,groundwater-dependentatthebeginning
of
the
21st
century(Fig.
1.1).
Currently,
thenational
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
waterincreasingpressure,causing serious
environmental
model,resources
was usedare
to underexplicitly
quantify
the spatiotemporalvariations
in waterdegradation
availabilitydue
intoclimatechange,urbanization,
and
rapid
economic
growth(Banerjee
et
al.,
The
these two contrasting river basins. In this chapter, the model was further extended 2014).
to coupleit
MinistryofEnvironment
and
Green
Development(MEGD)
directly
manages29
river
basinsinwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
thecountry(MNET, 2013) (Fig.The
1.1).newUrban
and quantified
economic hubs
such as Ulaanbaatar
and thewater
use (NICE-INVERSE).
model
the spatiotemporalvariations
inSouthern
Gobi
MiningHub
arepredominantlyatseriouswater
risk(Banerjeeetal.,2014).
The
livestock water use in these river basins in comparison with the estimated values by aggregating
groundwaterlevel
the tworiver basinsdata,in thesummerof 2018
wasfrommonitored
to studies.
evaluateThethestatistical data,
GISindata,
observationalandfield survey
data
previousdegradationof wateravailability
duewasto anthropogenic
andsoum
climatechangeandin theseamount of water
usedby animals
generally lowactivity
for each(district)couldtwobecontrasting
hubs(Nakayamaetal.,2021a).
Then,theprocess-based
National
Integrated
returned to the ecosystem. The model showed an interesting result as the unit water use in someCatchment-based
Eco-hydrology(NICE)
2014, 2015)
was suggesting
applied to thattwo
typical livestock (cattle,
sheep, andgoats)model
had a (Nakayama,
temporally decreasing
trend,
contrastingbasins including
the dueabove
hubs, the Tuuland Galba River
Basins. The
water stressriver
substantially
increased
to twolocal–regional
eco-hydrologicaldegradationbymodel
successfully
simulatedtheeffectsofpast
climatechangesandhuman-induced
urbanization and mining. These results clarify that the substantial increase in water stress fromdisturbances,such as urbanization,mining,herding,
on waterresources
over thegroundwater degradation
was mainly
causedand
bylivestockurbanizationand local
mining,in addition
to
last
four
decades(Nakayamaetal.,2021a).
climate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
The TuulRiver Basinis in the northernand steppe,
and the riverisflows
through
improvethe accuracyof hydrologic
budgetsgrasslandin the basins.
This methodology
powerfulfor
Ulaanbaatar
inthemiddlestretch(Jamsran
et
al.,
2018)
(Fig.
2.1).
Groundwaterisheavilyquantifying the spatiotemporal variations in water availability and water stress in regions
with
drawn
municipal
water
supplies.
contrast, the Galba River Basin (Galba–
limitedfor
inventorydataandon industrial
urbanization,mining,andIn
livestock.
Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and includes large mines,such
as Oyueco-hydrology
Tolgoi (OT) andTavan
Tolgoi(Jamsran
et al.,estimation;2018) (Fig.parameter
2.1). Particularly,
OT
Keywords:
model;
human
activity;
inverseestimation;is
one of theanalysis
largest copper–gold mines globally, which started in 2008 (Oyu Tolgoi LLC, 2013;
sensitivity
Torquoise Hill Resources Ltd., 2019). These two regions are similar as the current water use
may not be sustainable for meeting the projected water demand owing to the rapidly growing
urban population and expanding mining activity (Banerjee et al., 2016; Ulaanbaatar, 2018). The
spatiotemporal variations in water availability in both basins has been evaluated quantitatively
and explicitly (Nakayama et al., 2021a). However, constant unit water use for five typical
livestock types (sheep, horses, cattle, goats, and camels) was assumed, with reference toprevious studies (Sekine, 1993; Yoshida, 2009). As drinking water requirements for livestock
vary according to animal type, species, age, diet, and temperature (Wada et al., 2016), there aresome ranges and equations of water requirements for major livestock from a global perspective(Alcamo et al., 1997; FAO, 2006; Schlink et al., 2010). Different methods are used for
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,2014).
In this chapter, the process-based NICE was applied to these representative river basins as
an extension of a recent study (Nakayama et al., 2021b). The original NICE was coupled with
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions were addressed: (i) How does anthropogenic water use influence the hydrologicalcycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestock types in each basin? and (iii) How does the relationship between groundwater overuseand stress change? To answer these questions, a NICE-INVERSE simulation (Fig. 1.5) was- 43
42 - 43 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.1 Introduction

Abstract

Water
resources
are unevenly
Mongolia, being
abundantin thepredominantly
In Mongolia,
water
resourcesdistributed
are underin increasingpressure,mainlyin theurban andrural
north hubs
but scarceor non-existent
the south.
Particularly,>80%Inofthewater
resourceswere
economicof Ulaanbaatar
and inSouthern
Gobi
Mining Hub.
previous
chapter,groundwater-dependentatthebeginning
of
the
21st
century(Fig.
1.1).
Currently,
thenational
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
waterincreasingpressure,causing serious
environmental
model,resources
was usedare
to underexplicitly
quantify
the spatiotemporalvariations
in waterdegradation
availabilitydue
intoclimatechange,urbanization,
and
rapid
economic
growth(Banerjee
et
al.,
The
these two contrasting river basins. In this chapter, the model was further extended 2014).
to coupleit
MinistryofEnvironment
and
Green
Development(MEGD)
directly
manages29
river
basinsinwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
thecountry(MNET, 2013) (Fig.The
1.1).newUrban
and quantified
economic hubs
such as Ulaanbaatar
and thewater
use (NICE-INVERSE).
model
the spatiotemporalvariations
inSouthern
Gobi
MiningHub
arepredominantlyatseriouswater
risk(Banerjeeetal.,2014).
The
livestock water use in these river basins in comparison with the estimated values by aggregating
groundwaterlevel
the tworiver basinsdata,in thesummerof 2018
wasfrommonitored
to studies.
evaluateThethestatistical data,
GISindata,
observationalandfield survey
data
previousdegradationof wateravailability
duewasto anthropogenic
andsoum
climatechangeandin theseamount of water
usedby animals
generally lowactivity
for each(district)couldtwobecontrasting
hubs(Nakayamaetal.,2021a).
Then,theprocess-based
National
Integrated
returned to the ecosystem. The model showed an interesting result as the unit water use in someCatchment-based
Eco-hydrology(NICE)
2014, 2015)
was suggesting
applied to thattwo
typical livestock (cattle,
sheep, andgoats)model
had a (Nakayama,
temporally decreasing
trend,
contrastingbasins including
the dueabove
hubs, the Tuuland Galba River
Basins. The
water stressriver
substantially
increased
to twolocal–regional
eco-hydrologicaldegradationbymodel
successfully
simulatedtheeffectsofpast
climatechangesandhuman-induced
urbanization and mining. These results clarify that the substantial increase in water stress fromdisturbances,such as urbanization,mining,herding,
on waterresources
over thegroundwater degradation
was mainly
causedand
bylivestockurbanizationand local
mining,in addition
to
last
four
decades(Nakayamaetal.,2021a).
climate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
The TuulRiver Basinis in the northernand steppe,
and the riverisflows
through
improvethe accuracyof hydrologic
budgetsgrasslandin the basins.
This methodology
powerfulfor
Ulaanbaatar
inthemiddlestretch(Jamsran
et
al.,
2018)
(Fig.
2.1).
Groundwaterisheavilyquantifying the spatiotemporal variations in water availability and water stress in regions
with
drawn
municipal
water
supplies.
contrast, the Galba River Basin (Galba–
limitedfor
inventorydataandon industrial
urbanization,mining,andIn
livestock.
Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and includes large mines,such
as Oyueco-hydrology
Tolgoi (OT) andTavan
Tolgoi(Jamsran
et al.,estimation;2018) (Fig.parameter
2.1). Particularly,
OT
Keywords:
model;
human
activity;
inverseestimation;is
one of theanalysis
largest copper–gold mines globally, which started in 2008 (Oyu Tolgoi LLC, 2013;
sensitivity
Torquoise Hill Resources Ltd., 2019). These two regions are similar as the current water use
may not be sustainable for meeting the projected water demand owing to the rapidly growing
urban population and expanding mining activity (Banerjee et al., 2016; Ulaanbaatar, 2018). The
spatiotemporal variations in water availability in both basins has been evaluated quantitatively
and explicitly (Nakayama et al., 2021a). However, constant unit water use for five typical
livestock types (sheep, horses, cattle, goats, and camels) was assumed, with reference toprevious studies (Sekine, 1993; Yoshida, 2009). As drinking water requirements for livestock
vary according to animal type, species, age, diet, and temperature (Wada et al., 2016), there aresome ranges and equations of water requirements for major livestock from a global perspective(Alcamo et al., 1997; FAO, 2006; Schlink et al., 2010). Different methods are used for
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,2014).
In this chapter, the process-based NICE was applied to these representative river basins as
an extension of a recent study (Nakayama et al., 2021b). The original NICE was coupled with
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions were addressed: (i) How does anthropogenic water use influence the hydrologicalcycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestock types in each basin? and (iii) How does the relationship between groundwater overuseand stress change? To answer these questions, a NICE-INVERSE simulation (Fig. 1.5) was- 42
43 - 44 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.1 Introduction
performed
to estimate the spatiotemporal change in unit water use of five typical livestock types
by comparing it with the high-resolution gridded dataset of water use for livestock, urban
activity,
Water
andresources
mining developed
are unevenly
in adistributed
recent study
in Mongolia,(Nakayama
being
et al.,
abundant
2021b). inThe
themodeledpredominantly
resultsalso clarified
ruralnorth butthe
scarcerelationshipor non-existent
betweeningroundwater
the south. Particularly,
overuse and>80%
water ofstresswater
byresources
separatingwere
theeffects of climate changeatand
groundwater-dependent
thehuman
beginning
activityof the(livestock,
21st century
urban,(Fig.
and1.1).
mining)Currently,
on watertheresources
national
over thewater
resources
past decades.
are under
The increasing
results obtainedpressure,can causing
be used serious
as a basis
environmental
for extendingdegradation
the uncertainty
due
toanalysisclimateof thechange,hydrologic
urbanization,
cycle to andimprove
rapidtheeconomic
evaluation
growthof pasture(Banerjee
carrying
et al.,
capacity2014).
andTheits
Ministry
vulnerability.
of Environment and Green Development (MEGD) directly manages 29 river basins in
the country (MNET, 2013) (Fig. 1.1). Urban and economic hubs such as Ulaanbaatar and theSouthern Gobi Mining Hub are predominantly at serious water risk (Banerjee et al., 2014). The
groundwaterlevel
the two river basinsin the summer of 2018 was monitored to evaluate the3.2 Description
ofinNICE-INVERSE
Modeldegradation of water availability due to anthropogenic activity and climate change in these twocontrasting
hubs(Nakayama
et al., 2021a). Then, the process-based National Integrated
3.2.1 General
Model
Framework
Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014, 2015) was applied to twocontrasting
river basins
the above
two thehubs,following
the Tuul andGalba sub-compartments
River Basins. The
The original
NICEincluding
framework
includescomplexmodel
successfully
simulatedtheeffectsofpast
climatechangesand(Nakayama, 2014, 2015): a surface hydrology submodel (hillslope runoff), human-induceda land-surface
disturbances,such as urbanization,mining,and livestockon waterover thea
submodel including
urban and crop
processes(urban herding,metabolism
and resources
crop growth);
last
four
decades(Nakayamaetal.,2021a).
groundwater submodel (subsurface hydrology); a regional atmospheric submodel; a mass
The TuulRiver Basinin the northern
the riverdynamics
flows throughtransport
submodelfor isconstituentsin grassland
sediment andandsteppe,waterand(nutrientandUlaanbaatar
inthemiddlestretch(Jamsran
et
al.,
2018)
(Fig.
2.1).
Groundwaterisheavilygeomorphology); a vegetation succession submodel (vegetation growth and change); adrawn
for municipal
andsubmodelindustrial(Nakayama,water supplies.
In contrast,
Galba River
Basinsubmodel(Galba–biogeochemical
cycle
2017a,b);and thea plastic
dynamic
Uush–Doloodiing
Gobi
River
Basin)
isinthesouthern
Gobi
Desertandincludeslarge
mines,(Nakayama and Osako, 2023a,b). The model has been applied to various basins and catchments
such
as
Oyu
Tolgoi
(OT)
and
Tavan
Tolgoi(Jamsran
et
al.,
2018)
(Fig.
2.1).
Particularly,
from local–regional to continental–global scales (Nakayama, 2020, 2022, 2023). Recently, OT
the
is
one
of
the
largestcopper–goldmines
globally,
which
started
in
2008
(Oyu
TolgoiLLC,
2013;
model was applied to the Tuul and Galba River Basins (Fig. 2.1) to evaluate the effects of past
Torquoise
Hill Resources
Ltd., 2019).disturbances,
These two regions
similar as andthe current
useclimate changes
and human-induced
such asare
urbanization
mining,wateron watermay
not beduring
sustainablemeetingthe(Nakayama
projected water
resources
the lastfor
four
decades
et al., demand
2021a,b).owing to the rapidly growing
urban population and expanding mining activity (Banerjee et al., 2016; Ulaanbaatar, 2018). The
spatiotemporalin wateravailability
in bothbasins has
been evaluated quantitatively
3.2.2 Coupling variations
Process-based
NICEwith Inverse
Parameter
Estimationand explicitly (Nakayama et al., 2021a). However, constant unit water use for five typical
livestock(sheep,horses, NICE
cattle,ingoats,
andmodecamels)assumed,withreferenceto
In thistypes
chapter,the original
forwardwas wascoupledwith theinversemethodprevious
studies(Sekine,
1993;
Yoshida,2009).
As
drinking
water
requirements
forlivestock
for sensitivity analysis and parameter estimation of anthropogenic water use (NICE-INVERSE)
vary
animaltype, species,age, diet,withand temperature(Wada et al., 2016),
thereasarea(Fig. according
1.5). ThetoNICE
submodelwas coupled
an inverse computational
programsome
rangesandequationsofwater
requirements
for
major
livestockfromaglobalperspective
subroutine to include the sensitivity analysis and parameter estimation processes (Hill et al.,(Alcamoet inverse
al., 1997;
FAO,
2006; Schlink
et al., 2010).
Different
methodsthroughout
are used thefor2000). Thismethodcalculates
the sensitivities
of hydraulicparameters
estimating
water
use, such asequation
direct animal
drinking
and consumptive
theusedviewpoint
model using
the sensitivity
method.
The sensitivity
equationuse.
canFrom
only beif theofwaterbalance,
drinking
water
mainly
from
blue
water
sources
accounts
forapproximately
horizontal interblock transmissivities are calculated using harmonic averaging (Hill et al., 2000).
two-third
for a lactating
Holsteindairy cow (FAO,2019).
seems
toThis new watermodelintake
also solves
a modified
Gaussian–Newton
methodto Eachadjustlivestock
the valuesof thehaveadifferent
water
use
unit,
both
temporally
andspatially,in
Mongolia,
andthecompeting
input parameters in an iterative procedure to minimize the value of the weighted least-squares
waterdemands
fromthedifference
mining–urbanization–herdernexus
be hydraulicsatisfied (Banerjee
al.,objectivefunction(thebetween the simulated
and shouldobserved
heads andetriver2014).
flows):In this chapter, the process-based NICE was applied to these representative river basins as
𝑵𝑵an extension of a recent study (Nakayama
et al., 2021b).
𝟐𝟐 The original NICE was coupled with
𝑱𝑱 = �
�𝒉𝒉𝒐𝒐𝒐𝒐𝒐𝒐,𝒊𝒊− 𝒉𝒉𝒄𝒄𝒄𝒄𝒄𝒄,𝒊𝒊 � estimation of anthropogenic water(3.1)
the inverse method for sensitivity
analysisand parameteruse𝒊𝒊��
(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions
How doeswater
use influence
the thathydrologicalwhere hobs,iwere
and addressed:
hcal,i are the(i)observed
and anthropogenic
calculated values
at timet Please noteelements
cyclesinthese
twocontrasting
river
basins?
(ii)
How
does
unit
water
use
changeinfivetypicalin a series are usually separated by commas but are separated by semicolons if the elements
livestock
types
in
each
basin?
and
(iii)
How
does
the
relationshipbetweengroundwater
overusethemselves contain commas.i = 1, 2, 3, …, N are the individual observations, and N is the total
andstress
To answera NICE-INVERSE
simulation
wasnumberof change?
observations.
In this thesestudy,questions,the unit wateruse of five typical
livestock(Fig.
types1.5)(sheep,- 43
44 - 45 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stresshorses, cattle, goats, and camels) were applied
as input parameters to estimate the best values
Abstractin the model. The sensitivity equations for the hydraulic heads and river flows throughout thegrid In
withrespect towatereach resources
parameterare
weresolvedusing thepressure,preconditioned
gradient
Mongolia,
underincreasing
mainly conjugate
in the urban
and(PCG2)
a strongly
implicit
procedure
was applied
thetheoriginal
NICE
in theeconomicsolver,
hubs whereasof Ulaanbaatar
andSouthern
Gobi Mining
Hub.inIn
previous
chapter,
forward model.
TheCatchment-based
PCG2 solver has Eco-hydrology
some characteristics
thata process-based
are important forsolving the
National
Integrated(NICE),
eco-hydrology
sensitivity
is fasterthan thequantify
direct solver(Hill et al., 2000).
model,
wasandused
to explicitly
the spatiotemporalvariations in water availability inthese two contrasting river basins. In this chapter, the model was further extended to couple itwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
3.3 Input
and Boundary The
Conditionsfor Simulationwater
use Data(NICE-INVERSE).
new model
quantified the spatiotemporal variations inlivestock water use in these river basins in comparison with the estimated values by aggregating
3.3.1 ModelInput
statisticaldata,
GISDatadata, observational data, and field survey data from previous studies. The
amount of water used by animals was generally low for each soum (district) and could be
Six-hourdownward
precipitation,
atmosphericair
returnedto thereanalyzed
ecosystem.data
Theformodel
showedradiation,
an interesting
result as theunit waterpressure,use in sometemperature,airhumidity,wind
speed,
cloud
cover,
fractionofphotosynthetically
activetypical livestock (cattle, sheep, and goats) had a temporally decreasing trend, suggesting thatradiation(FPAR),and leaf
area index
were inputeco-hydrologicalinto the original
NICE after
water
stresssubstantially
increased
due to(LAI)
local–regional
degradationbyinterpolating andthe mining.
ECMWF-Reanalysis
Interimthat
Product(ERA-interim)
2019)
inurbanization
These results clarify
the substantial
increase(ECMWF,
in water stressfrominverse proportion
to the wasdistance
back-calculated
in each grid,
described
previousgroundwater
degradation
mainlycaused by urbanization
andaslocal
mining,ininthe
additiontosection. change.
The input
data sensitivity
at various spatial
were
preparedofandarrangedto calculate
climateThus,
analysisresolutions
and inverse
estimationmodel
parametershelpedaspatially theaveraged
0.05°×0.05°
grid (about
4–6
using
the SpatialisAnalyst
Tools
improveaccuracyof hydrologic
budgets
inkm
theresolution)basins. Thismethodology
powerfulforin ArcGIS v10.2
software for thesimulation(Table3.1) Elevation,
landstresscover,
soil texture,quantifying
the spatiotemporalvariations
in wateravailability
and waterin regions
withvegetation
type, riverlakes andwetlands,
limitedinventory
datanetworks,
on urbanization,mining,and reservoir
livestock.types and storage, and geologicalstructures were categorized as inputs to NICE (U.S. Geological Survey, 1996a,b; Lehner and
Döll,
2004; eco-hydrology
Hartmann and Moosdorf,
2012;activity;
IIASA, 2012;2013; parameter
European Commission,
Keywords:
model; humaninverseNASA,estimation;
estimation;2015).
Theanalysis
Tuul River Basin is mostly covered with grassland and steppe, except forests in thesensitivity
upstream area, whereas the Galba River Basin is mostly covered by bare land (desert), except
grassland in the northeastern region in the classification of GLC2000 (Nakayama et al., 2021a)(Fig. 3.1).
Gridded data regarding water use for livestock, urban activity, and mining were estimatedfor the entire Tuul and Galba River Basins during 1980–2018 by combining a global dataset,statistical data, GIS data, literature, and observational data as far as possible for input into the
NICE simulation (Table 3.1). The total number of livestock (sheep, horses, cattle, goats, andcamels) was estimated for each soum (district) in both basins using statistical (National
Statistics Office of Mongolia, 2019) and global gridded data (Gilbert et al., 2018) data. We
assumed that all water intake was from groundwater in both basins and used conversion factors(1 horse = 7 sheep, 1 cattle = 6 sheep, 1 goat = 0.9 sheep, and 1 camel = 5 sheep) (Yoshida,2009) and water consumption per sheep (2–8 L/day, average = 5 L/day) (Sekine, 1993) tocalculate the average annual livestock water use. In Ulaanbaatar, urban water use was estimatedby combining statistical population data for each soum every year (National Statistics Office ofMongolia, 2019). There are six groundwater sources (Upper, Central, Industrial, Meat-Complex,
Gachuurt, and Yarmag), and two more sources (Bio-industrial Complex, and Emerto Water
Sources) are planned to supply water to the capital in the future (JICA and CTI Engineering
International Co., Ltd., 2010). In the absence of perennial rivers in the southern Gobi region,
the groundwater use in OT mining was calculated using previous data (Oyu Tolgoi LLC., 2013;
ADB, 2014; Torquoise Hill Resources Ltd., 2019). These data were finally gridded to
0.05°×0.05° resolution during 1980–2018 using the ArcGIS software.

- 42
45 - 46 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water StressTable
3.1 List of input data sets for the National Integrated Catchment-based Eco-hydrology
3.1 Introduction(NICE) and NICE-INVERSE simulations

Water resources are unevenly distributed in Mongolia, being abundant in the predominantly

Data set
resolution Year
Source and reference
ruralnorth but scarce Original
or non-existentin the south. Particularly,>80% of water resources were
Climatology
1.0°
1980-2018
ERA-interim; ECMWF (2019)
groundwater-dependent at the beginning of the 21st century (Fig. 1.1). Currently, the national
Elevation
1.0km
around 1996
GTOPO30; U.S. Geological Survey (1996a)
waterresources are underincreasing pressure,causing serious
environmental degradation due
Land cover
1.0km
around 2000
GLC2000; European Commission (2015)
toclimatechange,urbanization,
and
rapid
economic
growth(Banerjee
Soil texture
1.0km
around 1970-2000 HWSD; IIASA(2012) et al., 2014). The
Ministry
directly
manages
29 riverbasins in
Vegetation of
typeEnvironment
0.25°and Green Development
around 2000 (MEGD)GLDASVegetation
Class; NASA
(2013)
thecountry(MNET,
2013)
(Fig.
1.1).
Urban
and
economic
hubs
such
as
Ulaanbaatar
and theRiver networks
1.0km
around 1996
HYDRO1K; U.S. Geological Survey (1996b)
Southern
Gobi Mining0.5Hubat seriousGLWD;
water Lehnerrisk (Banerjee
et al., 2014). The
Lakes and wetlands
min are predominantly
around 1990-2000
and Döll (2004)
Geological structures
0.5°two river basinsaround1970-2000
Hartmannand Moosdorfto(2012)
groundwaterlevel in thein thesummer GLiM;
of 2018
was monitored
evaluate the
Crop
type
5
min
around2000
MIRCA2000;
Portmannetal.
(2010)
degradation of water availability due to anthropogenic activity and climate change in these two
Irrigation type hubs (Nakayama
5 min2000-2008 Then, theGMIA;
FAO (2016) National Integrated
contrasting
et al., 2021a).
process-based
Irrigation
water
use
5
min
1998-2002
GCWM;
Siebert2015)
and Döllwas(2010)Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014,
applied to two
Basin boundary data
polygonaround 2010
MNET (2013)
contrasting river basins including the above two hubs, the Tuul and Galba River Basins. The
Ministry of Environment (2015), Batsaikhan et al.
model
successfully simulated
the effectsof past climatechanges and human-inducedCore samplingpointaround 2010(2018)

disturbances, such as urbanization, mining, and livestock herding, on water resources over the
Numbers of livestock
each soum
1980-2018
National Statistics Office of Mongolia (2019)last
four decades (Nakayama
et al., 2021a).
Numbers of livestock
5 minaround 2010
Gilbert et al. (2018)
The Tuul River Basin is in the northern grassland and steppe, and the river flows throughSource of water supply in
JICA and CTI Engineering International Co.,
polygon stretch (Jamsranaround 2010
Ulaanbaatar
inthemiddle
et
al.,
2018)
(Fig. 2.1). Groundwater is heavily
Ulaanbaatar
Ltd. (2010)
drawn
for municipal andindustrial watersupplies. In contrast,
the Galba River Basin (Galba–
Populationeach soum
1980-2018
National Statistics Office of Mongolia (2019)
Uush–Doloodiing
Gobi
River
Basin)
isinthesouthern
Gobi
DesertPopulation
1.0km
2000-2015
NASA (2018) and includes large mines,
such
as
Oyu
Tolgoi
(OT)
and
Tavan
Tolgoi(Jamsranetal., 2018)
Particularly,
Water use in Ulaanbaatar point
2002-2015
MNET(2010),(Fig.
MEGD2.1).
(2012),
Sato (2015) OT
is one of the largest copper–gold mines globally, which started
in 2008(Oyu Torquoise
Tolgoi LLC,
Oyu Tolgoi
LLC. (2013),
Hill 2013;
Water supply in OT
point
Torquoise
Hill Resources
Ltd., 2019). 2008-2018
These two regionsaresimilaras
thecurrent
water use
Resources Ltd. (2019)
may not be sustainable for meeting the projected water demand owing to the rapidly growing
urban population and expanding mining activity (Banerjee et al., 2016; Ulaanbaatar, 2018). The
spatiotemporal variations in water availability in both basins has been evaluated quantitatively
and explicitly (Nakayama et al., 2021a). However, constant unit water use for five typical
livestock types (sheep, horses, cattle, goats, and camels) was assumed, with reference toprevious studies (Sekine, 1993; Yoshida, 2009). As drinking water requirements for livestock
vary according to animal type, species, age, diet, and temperature (Wada et al., 2016), there aresome ranges and equations of water requirements for major livestock from a global perspective(Alcamo et al., 1997; FAO, 2006; Schlink et al., 2010). Different methods are used for
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,2014).
In this chapter, the process-based NICE was applied to these representative river basins as
an extension of a recent study (Nakayama et al., 2021b). The original NICE was coupled with
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions were addressed: (i) How does anthropogenic water use influence the hydrologicalcycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestock types in each basin? and (iii) How does the relationship between groundwater overuseand stress change? To answer these questions, a NICE-INVERSE simulation (Fig. 1.5) was- 43
46 - 47 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

Abstract
In Mongolia, water resources are under increasing pressure, mainly in the urban andeconomic hubs of Ulaanbaatar and Southern Gobi Mining Hub. In the previous chapter,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, was used to explicitly quantify the spatiotemporal variations in water availability inthese two contrasting river basins. In this chapter, the model was further extended to couple itwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
water use (NICE-INVERSE). The new model quantified the spatiotemporal variations inlivestock water use in these river basins in comparison with the estimated values by aggregating
statistical data, GIS data, observational data, and field survey data from previous studies. The
amount of water used by animals was generally low for each soum (district) and could be
returned to the ecosystem. The model showed an interesting result as the unit water use in sometypical livestock (cattle, sheep, and goats) had a temporally decreasing trend, suggesting thatwater stress substantially increased due to local–regional eco-hydrological degradation by
urbanization and mining. These results clarify that the substantial increase in water stress fromgroundwater degradation was mainly caused by urbanization and local mining, in addition toclimate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
improve the accuracy of hydrologic budgets in the basins. This methodology is powerful for
quantifying the spatiotemporal variations in water availability and water stress in regions withlimited inventory data on urbanization, mining, and livestock.
Keywords: eco-hydrology model; human activity; inverse estimation; parameter estimation;
sensitivity analysis

Figure 3.1 Spatial datasets for National Integrated Catchment-based Eco-hydrology (NICE)
simulation in Tuul and Galba River Basins

3.3.2 Estimation of Unit Water Use in Five Typical Livestock Types
In addition to the original NICE simulation, the unit water use of each livestock in the 20
groundwater level observation stations was calculated (Nakayama et al., 2021b) to comparewith the simulated water use using NICE-INVERSE and the above conversion factors inprevious studies (Sekine, 1993; Yoshida, 2009). First, the number of livestock in eachobservation station was compiled using global data (Gilbert et al., 2018), statistical data
(National Statistics Office of Mongolia, 2019), and field survey data during the summer of 2018.
Second, annual groundwater use was calculated by counting the timing and magnitude of thewater level decrease in the time series of groundwater level fluctuations with t = 30 min ineach observation station (Table 3.2) (Picture. 3.1). Third, n simultaneous equations (n = 11 inTuul and n = 9 in Galba) with r unknown parameters (r = 5, unit water use of horses, cattle,
sheep, goats, and camels) were solved. Since the number of equations was higher than that of
the unknown parameters (n > r), we used nCr types of solutions in probability theory.
(3.2)

𝒏𝒏𝒏𝒏𝒏𝒏 = 𝒏𝒏!/𝒓𝒓!/�𝒏𝒏 � 𝒏𝒏�!
- 42
47 - 48 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.1 Introduction
where
nCr denotes the number of ways in which r objects can be selected from a set of n objects.
Each set of r simultaneous equations was solved by calculating the inverse matrix of the lefthandWater
side, theresources
productare
ofunevenly
this matrix,
distributed
and the right-handin Mongolia,
sidebeing
usingabundant
MicrosoftinExcelthe predominantly
365. Finally,
the averageruralnorth butandscarce
medianorvalues,
non-existent
range, inandthestandard
south. Particularly,
deviations of>80%
unit waterof wateruse wereresources
calculated
weregroundwater-dependent
for each livestock, whichatwere
the beginningderived fromof thenCr21stsolutionscenturyin (Fig.
the Tuul
1.1).andCurrently,
Galba Riverthe national
Basins.
This averagewaterresources
orare
medianundervalue
increasingfor eachpressure,
livestock,causingin addition
serious environmental
to previous materials,
degradation
candueberegardedtoclimateas change,
the best approximate
urbanization,solution
and rapid
in n simultaneous
economic growthequations(Banerjee(Nakayama
et al.,et2014).
al., 2021b).
The
Ministry of Environment and Green Development (MEGD) directly manages 29 river basins in
the country (MNET, 2013) (Fig. 1.1). Urban and economic hubs such as Ulaanbaatar and theTable 3.2 List
NIES observation
stations for validation
parameter
estimation
shownin TheFig.
Southern
GobiofMining
Hub are predominantly
at seriousandwaterrisk (Banerjee
et al.,
2014).
2.1level in the two river basins in the summer of 2018 was monitored to evaluate thegroundwater
degradation of water availability due to anthropogenic activity and climate change in these two
Data Sourceandcontrasting
et al.,
2021a).
National
Integrated
No. Basin Pointhubs
Name (Nakayama
Type
Lat. (°)
Lon. (°) Then,
Elev. (m) the
Arealprocess-based
Class Time Interval
Reference
Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014, 2015) was applied to two
1
Galba Khanbogd-1
Groundwater level 43.25
107.07 1071.0
30 minNakayama et al. (2021a)
contrasting
river basins
including the above
two hubs, Mining
the Tuul andGalba River
Basins. The
2
Galba Khanbogd-2
Groundwater level 43.29
107.24 1016.0
Mining
30 minet al. (2021a)
model successfully simulated the effects of past climate changes andNakayama
human-induced
3
Galba Khanbogd-3
Groundwater level 43.29
107.24 1017.0
Mining
30 minNakayama et al. (2021a)
disturbances, such as urbanization, mining, and livestock herding, on water resources over the4
Galba Khanbogd-4
Groundwater level 43.04
106.97 1171.0
Mining
30 minNakayama et al. (2021a)
lastfour decades (Nakayama
et al., 2021a).
5
Galba Khanbogd-5
Groundwater level 43.18
106.79 1192.0
Mining
30 minNakayama et al. (2021a)
The Tuul River Basin is in the northern grassland and steppe, and the river flows through6
Galba Khanbogd-6
Groundwater level 43.08
106.77 1211.0
Mining
30 minNakayama et al. (2021a)
Ulaanbaatar in the middle stretch (Jamsran et al., 2018) (Fig. 2.1). Groundwater is heavily
7
Galba Khanbogd-7
Groundwater level 43.10
107.02 1186.0
Mining
30 minNakayama et al. (2021a)
drawn for municipal and industrial water supplies. In contrast, the Galba River Basin (Galba–
8
Galba Manlai-1
Groundwater level 43.74
106.80 1268.0
Non-mining
30 minNakayama et al. (2021a)
Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and includes large mines,
9
Galba Manlai-2
Groundwater level 43.92
106.87 1300.0
Non-mining
30 minNakayama et al. (2021a)
suchas OyuTolgoi (OT)and Tavan Tolgoi(Jamsran etUrbanal., 2018)30 (Fig.
2.1).Nakayama
Particularly,
OT
10 TuulUlaanbaatar-1
Groundwater level 47.81
106.64 1234.0
min
et al. (2021a)
is
one of thelargest copper–goldmines globally,
which started
in 2008(Oyu Tolgoi
LLC, 2013;11 TuulUlaanbaatar-2
Groundwater level 47.81
106.64 1234.0
Urban
30 minNakayama et al. (2021a)
Torquoise
Hill
Resources
Ltd.,2019).
Thesetworegionsaresimilaras
thecurrent
water use
12 TuulUlaanbaatar-3
Groundwater level 47.99
106.96 1399.0
Urban
30 minNakayama et al. (2021a)may
not besustainable
for meeting the projectedwaterUrban
demand owing
to theNakayama
rapidlyet al.growing
13 TuulUlaanbaatar-4
Groundwater level 47.94
106.93 1315.0
30 min(2021a)urban
population
andexpandingminingactivity(Banerjee
et
al.,
2016;
Ulaanbaatar,
2018). The
14 TuulNalaikh-1
Groundwater level 47.66
107.43 1644.0
Non-urban
30 minNakayama et al. (2021a)
spatiotemporalvariations
in water availability
in both basins
has been
evaluated
quantitatively
15 TuulLun-1
Groundwater level 47.88
105.32 1022.0
Non-urban
30 minNakayama et al. (2021a)
andexplicitly(Nakayamaetal.,2021a).
However,
constant
unit
water
use
for
five
16 TuulLun-2
Groundwater level 47.87
105.26 1001.0
Non-urban
30 minNakayama
et al. typical(2021a)livestock
types (sheep,
horses,was 30assumed,withreference
17 Tuul
Hustai-1
Groundwaterlevel cattle,
47.59 goats,
105.85 and1216.0camels)
Non-urbanmin
Nakayama
et al. (2021a)toprevious
studies
1993;2009).1254.0
As drinking
requirements
foretlivestock
18 Tuul
Hustai-2 (Sekine,
GroundwaterlevelYoshida,47.59
105.82
Non-urbanwater
30 minNakayamaal. (2021a)vary
accordingtype,levelspecies,
diet,1534.0
and temperature(Wada
et al.,
2016),et thereare
19 TuulNalaikh-2to animal
Groundwater
47.69 age,
107.44
Non-urban
30
minNakayamaal. (2021a)
somerangesandequationsofwater
requirements
for
major
livestockfromaglobalperspective
20 TuulUlaanbaatar-5
Groundwater level 47.93
106.68 1322.0 * Urban
30 minNakayama et al. (2021a)(Alcamoal., 1997;
FAO,
2006; model
Schlink
et al., (U.S.
2010).
Different
methods are used for* Elevation waset
determinedby the global
digital elevation(DEM; GTOPO30)
Geological
Survey, 1996a)
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,2014).
In this chapter, the process-based NICE was applied to these representative river basins as
an extension of a recent study (Nakayama et al., 2021b). The original NICE was coupled with
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions were addressed: (i) How does anthropogenic water use influence the hydrologicalcycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestock types in each basin? and (iii) How does the relationship between groundwater overuseand stress change? To answer these questions, a NICE-INVERSE simulation (Fig. 1.5) was- 43
48 - 49 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

Abstract
In Mongolia, water resources are under increasing pressure, mainly in the urban andeconomic hubs of Ulaanbaatar and Southern Gobi Mining Hub. In the previous chapter,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, was used to explicitly quantify the spatiotemporal variations in water availability inthese two contrasting river basins. In this chapter, the model was further extended to couple itwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
water use (NICE-INVERSE). The new model quantified the spatiotemporal variations inlivestock water use in these river basins in comparison with the estimated values by aggregating
statistical data, GIS data, observational data, and field survey data from previous studies. The
amount of water used by animals was generally low for each soum (district) and could be
returned to the ecosystem. The model showed an interesting result as the unit water use in some
Picture livestock
3.1 Groundwaterobservationand equipment
setting-updecreasing trend, suggesting thattypical(cattle, sheep,and goats)
had a temporallywater stress substantially increased due to local–regional eco-hydrological degradation by
urbanization and mining. These results clarify that the substantial increase in water stress fromgroundwater degradation was mainly caused by urbanization and local mining, in addition to
3.3.3
Boundaryand Simulation
Run
climatechange. Conditions
Thus, sensitivity
analysis andinverse estimation of model parameters helped
improve the accuracy of hydrologic budgets in the basins. This methodology is powerful for
At the upstream
boundaries,
the conditions
at the hydraulicheadstresswereinbased
onwiththequantifying
the spatiotemporalvariations
in water availability
and water
regions
assumptionofnoinflowfromthemountainsintheopposite
directions(Nakayama
andlimited inventory data on urbanization, mining, and livestock.
Watanabe, 2004). For the hillslope hydrology, the flow depth and discharge at the uppermost
basinboundaries
were setmodel;
to zerohumanthroughout
the inverse
simulation.
Inflowsparameteror outflows
from theKeywords:
eco-hydrology
activity;
estimation;
estimation;
riverbeds
weresimulated
at
each
time
step
dependingonthedifferenceinthegroundwaterandsensitivity analysis
river hydraulic heads. The details have been described previously (Nakayama et al., 2021a).
The simulation areas in the Tuul and Galba River Basins were 7.0° wide × 3.0° long and
11.0° wide × 6.0° long in longitude and latitude coordinates, respectively. The Tuul and Galba
River Basins have 29 and 106 river channels, respectively, as described in the previous section(Fig. 2.1). NICE simulation in the original forward mode was performed at 0.05°×0.05°(approximately 4–6 km) resolution horizontally and in 20 layers with 1.1 weighting factor (layer
thicknesses being graduated from thinner in the upper layer to thicker in the deeper layer)
vertically. The upper land layer was set at 0–2 m from the ground surface, and the 20th land
layer was defined as a depth of 200 m below sea level. Simulations were performed with a time
step of t = 6 h for 1980–2018 after a 1-year warm-up period to allow a time-varying
equilibrium. The simulated results were calibrated and validated using groundwater level andriver discharge observations in both basins (Tables 3.2 and 3.3).
The NICE-INVERSE simulation (Fig. 1.5) was conducted backward using sensitivity
analysis and parameter estimation modes at the same resolution as the forward simulation for
2015–2018 in both basins. This inverse method enabled the regression to measure the model fit
to the observations (groundwater level and river discharge) (Tables 3.2 and 3.3) as much as
possible. The maximum number of parameter estimation iterations was 10, the maximum
fractional change for parameter values in each iteration was 2.0, and the head-change closure
criterion was 0.001 in the simulation. After this procedure, the model backward calculated theresiduals, dimensionless and one-percent scaled sensitivities (Hill et al., 2000); parametervalues; statistics of unit water use in five typical livestock types, and a correlation matrix foreach parameter (Picture. 3.2) (Nakayama et al., 2021b).

- 42
49 - 50 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water StressTable
3.3 List of observation stations managed by other organizations for validation and
3.1 Introduction
parameter estimation

Water resources are unevenly distributed in Mongolia, being abundant in the predominantly
Dataresources
Source and were
rural
north
butNamescarceTypeor non-existentof waterNo. Basin
Point
Lat. in(°) theLon.south.
(°) Elev.Particularly,(m) Areal Class >80%
Time Interval
Referencegroundwater-dependent at the beginning of the 21st century (Fig. 1.1). Currently, the national
Byambasuren&
water
are under
increasingpressure,
serious environmental
degradationdue
21 Tuulresources
Ulaanbaatar-6
Groundwaterlevel 47.83
107.46 causing
1445.0 * Urban
monthly
Odonsetseg (2015)
to climate change, urbanization, and rapid economic growth (Banerjee et al., 2014). The&
MinistryofEnvironment
and Green
Development(MEGD)
managesByambasuren
29 river basinsin
22 TuulUlaanbaatar-7
Groundwaterlevel 47.90
106.99 1299.0* Urban directly
monthly
Odonsetseg (2015)
the country (MNET, 2013) (Fig. 1.1). Urban and economic hubs such as Ulaanbaatar and the&Southern
Gobi Mining
Hub arelevel
predominantly
seriouswater riskmonthly(BanerjeeByambasuren
et al., 2014).
The
23 TuulUlaanbaatar-8
Groundwater
47.79
106.61 at1223.0* Non-urban
Odonsetseg (2015)
groundwater level in the two river basins in the summer of 2018 was monitored to evaluate the
Byambasuren&
degradationof water Groundwater
availability
to anthropogenic
activity
andmonthly
climate change
in thesetwo
24 TuulUlaanbaatar-9
level due
47.91
106.97 1298.1
Urban
Odonsetseg (2015)
contrasting hubs (Nakayama et al., 2021a). Then, the process-based National Integrated
Byambasuren
Catchment-based
Eco-hydrology
model1326.3(Nakayama,
2014,2015) wasapplied& to two
25 TuulUlaanbaatar-10
Groundwater level (NICE)
47.91
107.11Urban
monthly
Odonsetseg (2015)
contrasting river basins including the above two hubs, the Tuul and Galba River Basins. The&
model
simulated
past climatechangesandByambasuren
human-induced
26 Tuul successfully
Ulaanbaatar-11 Groundwaterlevel the47.89effects
106.84 of1273.6
Urban
monthly
Odonsetseg (2015)
disturbances, such as urbanization, mining, and livestock herding, on water resources over the
27 TuulUlaanbaatar-12 Groundwater level 47.83
107.48 1408.0 * Urban
monthly
IGG (2018)lastfour decades(Nakayama et al., 2021a).
28 TuulUlaanbaatar-13 Groundwater level 47.83
107.31 1377.0 * Urban
monthly
IGG (2018)
The Tuul River Basin is in the northern grassland and steppe, and the riverflows through29 TuulUlaanbaatar-14 Groundwater level 47.90
106.98 1297.0 * Urban
monthly
IGG (2018)
Ulaanbaatar
in the middle stretch (Jamsran
et al., 2018) (Fig. 2.1).
Groundwater
is heavily
30 TuulUlaanbaatar-15 Groundwater level 47.89
106.91 1298.0 * Urban
monthly
IGG (2018)
drawn for municipal and industrial water supplies. In contrast, the Galba River Basin (Galba–
31 TuulUlaanbaatar-16 Groundwater level 47.90
106.85 1289.0 * Urban
monthly
IGG (2018)
Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and includes large mines,
32 TuulUlaanbaatar-17 Groundwater level 47.88
106.77 1265.0 * Urban
monthly
IGG (2018)such as Oyu Tolgoi (OT) and Tavan Tolgoi (Jamsran et al., 2018) (Fig. 2.1). Particularly, OT
33 TuulUlaanbaatar-18 Groundwater level 47.88
106.77 1265.0 * Urban
monthly
IGG (2018)
is one of the largest copper–gold mines globally, which started in 2008 (Oyu Tolgoi LLC, 2013;34 TuulUlaanbaatar-19 Groundwater level 47.80
106.58 1222.0 * Non-urban
monthly
IGG (2018)
Torquoise Hill Resources Ltd., 2019). These two regions are similar as the current water use
35 Galba OT-35
Groundwater level 43.51
107.24 1045.1
Miningdaily
Oyu Tolgoi LLC. (2018)
may not be sustainable for meeting the projected water demand owing to the rapidly growing
36 Galba OT-36
Groundwater level 43.33
107.52 949.0
Miningdaily
Oyu Tolgoi LLC. (2018)urban
population
andexpandingminingactivity(Banerjee
et
al.,
2016;
Ulaanbaatar,
2018). The
37 Galba OT-37
Groundwater level 43.58
107.72 937.1
Miningdaily
Oyu Tolgoi LLC. (2018)
spatiotemporalvariations
in water availability
in both basins
has been
evaluated
quantitatively
38 Galba OT-38
Groundwater level 43.02
106.75 1205.6
Miningdaily
Oyu Tolgoi LLC. (2018)
andexplicitly(Nakayamaetal.,2021a).
However,
constant
unit
water
use
for
five typical
39 Galba OT-39
Groundwater level 42.99
106.96 1138.3
Miningdaily
Oyu Tolgoi LLC. (2018)
livestock
types (sheep,
horses, cattle,
goats,and camels) was daily
assumed, withreference to
40 TuulBosgo-1
River discharge
48.03
107.73 1530.0 * Non-urban
NUM (2018)previous
studies(Sekine,
1993;
Yoshida,2009).
As
drinking
water
requirements
forlivestock
41 TuulUlaanbaatar-20 River discharge
47.89
106.95 1290.0 * Urban
daily
NUM (2018)vary
accordingto animal
type, species,age,
diet, and temperature daily(Wada et al.,2016), there are
42 TuulAltanbulag-1
River discharge
47.68
106.28 1187.0 * Non-urban
NUM (2018)
some
rangesandequationsofwater
requirements
for
major
livestockfromaglobalperspective
43 Tuul
Lun-3
River discharge
47.85
105.18 1012.0 * Non-urban
daily
NUM (2018)(Alcamo
et
al.,
1997;
FAO,
2006;
Schlinketal.,2010).
Different
methods
areused for* Elevation was determined by the global digital elevation model (DEM; GTOPO30) (U.S. Geological Survey, 1996a)
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,2014).
In this chapter, the process-based NICE was applied to these representative river basins as
an extension of a recent study (Nakayama et al., 2021b). The original NICE was coupled with
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions were addressed: (i) How does anthropogenic water use influence the hydrologicalcycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestock types in each basin? and (iii) How does the relationship between groundwater overuseand stress change? To answer these questions, a NICE-INVERSE simulation (Fig. 1.5) was- 43
50 - 51 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

Abstract
In Mongolia, water resources are under increasing pressure, mainly in the urban andeconomic hubs of Ulaanbaatar and Southern Gobi Mining Hub. In the previous chapter,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, was used to explicitly quantify the spatiotemporal variations in water availability inthese two contrasting river basins. In this chapter, the model was further extended to couple itwith the inverse method for sensitivity analysis and parameter estimation of anthropogenic
water use (NICE-INVERSE). The new model quantified the spatiotemporal variations inlivestock water use in these river basins in comparison with the estimated values by aggregating
statistical data, GIS data, observational data, and field survey data from previous studies. The
amount of water used by animals was generally low for each soum (district) and could be
returned to the ecosystem. The model showed an interesting result as the unit water use in sometypical(cattle,and usegoats)
hadtypicala temporally
decreasing
trend,
suggestingthat
Picture livestock
3.2 Estimationof sheep,
unit waterin five
livestock
types (horse,
cattle,sheep, goat,water stressand
substantially
increaseddue
tolocal–regional
eco-hydrologicaldegradationbycamel)
urbanization and mining. These results clarify that the substantial increase in water stress fromgroundwater degradation was mainly caused by urbanization and local mining, in addition toclimate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
improvetheandaccuracyof hydrologic budgets in the basins. This methodology is powerful for
3.4
Results
Discussion
quantifying the spatiotemporal variations in water availability and water stress in regions withlimitedinventory
data on urbanization,and livestock.
3.4.1
Impactof Anthropogenic
Activitymining,on Groundwater
Level in Urban and Mining Hubsof Mongolia
Keywords: eco-hydrology model; human activity; inverse estimation; parameter estimation;
sensitivity
analysis
The effectsof past climate changes and human-induced disturbances, such as urbanizationand mining, on water resources during 1980–2018 in these two river basins have beenpreviously quantified (Fig. 3.2). The model results showed that more than 50% groundwater
degradation in the Tuul River Basin urban areas was caused by urban water use after 2000 (Fig.
3.2a) compared to that in the non-urban areas (Fig. 3.2b). In contrast, the model showed a more
serious situation in the Galba River Basin, where almost all groundwater degradation in themining areas was caused by OT mining water use after 2010 (Fig. 3.2c) compared to that in
non-mining areas (Fig. 3.2d). The amount of water used by animals is generally low for eachsoum (the ratio of livestock water use to total usage in 2018 was 1.8% in Ulaanbaatar of theTuul River Basin and 4.4% in Khanbogd of the Galba River Basin) and can also be heavily
returned to the ecosystems. These results indicate the significant impact of water use due tourbanization and mining on local groundwater degradation around water sources, in addition toclimate change (Nakayama et al., 2021b).

- 42
51 - 52 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.1 Introduction
Water resources are unevenly distributed in Mongolia, being abundant in the predominantly
rural north but scarce or non-existent in the south. Particularly, >80% of water resources weregroundwater-dependent at the beginning of the 21st century (Fig. 1.1). Currently, the national
water resources are under increasing pressure, causing serious environmental degradation due
to climate change, urbanization, and rapid economic growth (Banerjee et al., 2014). The
Ministry of Environment and Green Development (MEGD) directly manages 29 river basins in
the country (MNET, 2013) (Fig. 1.1). Urban and economic hubs such as Ulaanbaatar and theSouthern Gobi Mining Hub are predominantly at serious water risk (Banerjee et al., 2014). The
groundwater level in the two river basins in the summer of 2018 was monitored to evaluate thedegradation of water availability due to anthropogenic activity and climate change in these twocontrasting hubs (Nakayama et al., 2021a). Then, the process-based National Integrated
Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014, 2015) was applied to twocontrasting river basins including the above two hubs, the Tuul and Galba River Basins. The
model successfully simulated the effects of past climate changes and human-induced
disturbances, such as urbanization, mining, and livestock herding, on water resources over thelast four decades (Nakayama et al., 2021a).
The Tuul River Basin is in the northern grassland and steppe, and the river flows throughUlaanbaatar in the middle stretch (Jamsran et al., 2018) (Fig. 2.1). Groundwater is heavily
drawn for municipal and industrial water supplies. In contrast, the Galba River Basin (Galba–
Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and includes large mines,
such as Oyu Tolgoi (OT) and Tavan Tolgoi (Jamsran et al., 2018) (Fig. 2.1). Particularly, OT
is one of the largest copper–gold mines globally, which started in 2008 (Oyu Tolgoi LLC, 2013;
Torquoise Hill Resources Ltd., 2019). These two regions are similar as the current water use
may not be sustainable for meeting the projected water demand owing to the rapidly growing
urban population and expanding mining activity (Banerjee et al., 2016; Ulaanbaatar, 2018). The
spatiotemporal variations in water availability in both basins has been evaluated quantitatively
and explicitly (Nakayama et al., 2021a). However, constant unit water use for five typical
livestock types (sheep, horses, cattle, goats, and camels) was assumed, with reference toprevious studies (Sekine, 1993; Yoshida, 2009). As drinking water requirements for livestock
vary according to animal type, species, age, diet, and temperature (Wada et al., 2016), there aresome ranges and equations of water requirements for major livestock from a global perspective(Alcamo et al., 1997; FAO, 2006; Schlink et al., 2010). Different methods are used for
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,Figure 3.2 Relative contribution of environmental factors to changes in groundwater level during2014).
1980–2018, compared to the reference simulation, in (a) urban and (b) non-urban
In this chapter,the process-based NICE was applied to these representative river basins as
areas in the Tuul River Basin, and (c) mining and (d) non-mining areas in the Galba
an extensionRiverof a recent
study (Nakayama et al., 2021b). The original NICE was coupled withBasin
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestionswere addressed:(i) How
does anthropogenic
useContrasting
influence thehydrological
3.4.2
Spatiotemporal
Variationsof Livestock
Water Usewaterin TwoRiver
Basinscycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestock
types in eachsimulated
basin? andthe(iii)
How does
the relationshipoveruseNICE-INVERSE
temporalchangein unit waterbetween
use for groundwater
five typical livestockand stresschange?
answerthese questions,
NICE-INVERSE
1.5) wastypes
in eachbasin Toduring
2015–2018(Fig. 3.3).a The
unit water usesimulation
was almost(Fig.
constantand- 43
52 - 53 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stresswithin the range used during this period reported
previously (Yoshida, 2009). However, water
Abstractuse, particularly in the Galba River Basin, temporally decreased in some typical livestock (cattle,
sheep,
goats). NICE-INVERSE
is beneficial
as it can calculateinterannualin
In andMongolia,
water resources areunder increasingpressure, themainlyin the changes
urban and
unit water hubsuse., Theannual rainfall
the semi-aridof Hub.
GalbaInRiveris lessthaneconomicof Ulaanbaatar
andinSouthern
Gobi region
Miningthe Basin
previous
chapter,100
mm/year,and
OT
mining
water
use
has
seriously
causedgroundwater
degradation
since
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
2010 (Nakayama
2021a).quantify
Further, thealmost
85% total livestockduringthe last
2010s weremodel,
was used etto al.,
explicitly
spatiotemporalvariations
in wateravailability
in
sheep
and
goats
(National
Statistics
OfficeofMongolia,
2019).
This
resultimpliesasubstantial
these two contrasting river basins. In this chapter, the model was further extended tocouple itincreasein watermethod
stress forduesensitivity
to local–regional
eco-hydrologicaldegradation
caused bywiththe inverse
analysis andparameter estimationof anthropogenic
urbanization
andmining(Nakayamaetal.,2021b).
water use (NICE-INVERSE). The new model quantified the spatiotemporal variations inlivestock water use in these river basins in comparison with the estimated values by aggregating
statistical data, GIS data, observational data, and field survey data from previous studies. The
amount of water used by animals was generally low for each soum (district) and could be
returned to the ecosystem. The model showed an interesting result as the unit water use in sometypical livestock (cattle, sheep, and goats) had a temporally decreasing trend, suggesting thatwater stress substantially increased due to local–regional eco-hydrological degradation by
urbanization and mining. These results clarify that the substantial increase in water stress fromgroundwater degradation was mainly caused by urbanization and local mining, in addition toclimate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
improve the accuracy of hydrologic budgets in the basins. This methodology is powerful for
quantifying the spatiotemporal variations in water availability and water stress in regions withlimited inventory data on urbanization, mining, and livestock.
Keywords: eco-hydrology model; human activity; inverse estimation; parameter estimation;
sensitivity analysis

Figure 3.3 Simulated result of temporal change of unit water use in five typical livestock types in(a) Tuul and (b) Galba River Basins during 2015–2018- 42
53 - 54 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.4.3
3.1
Introduction
Sensitivity Analysis and Reliability of Parameter Estimation of Water Uses
Water
The new
resources
model arealsounevenly
simulateddistributed
the composite-scaledin Mongolia, sensitivity
being abundant(Fig. in
3.4)
theandpredominantly
correlation
between
rural
north
each
but parameterscarce or non-existent(Table 3.4). inThe
thecomposite-scaled
south. Particularly,
sensitivity>80% of indicateswater resources
whetherwere
theavailable observations are
groundwater-dependent
at thelikelybeginning
to provideof theadequate
21st century
information(Fig. 1.1).toCurrently,
estimate the national
definedparameters.
waterresources
The areresults
undershow
increasingthat thepressure,sensitivity
causing
for unitserious
water environmental
use in each livestock
degradation
generallydue
increasedtoclimateforchange,all observations,urbanization,
which
and
indicates
rapid economic
a temporalgrowthdecreasing(Banerjee
trend inetwateral., use
2014).
in someThe
typical livestock
Ministryof Environment(cattle, andsheep,
Green
andDevelopment
goats) (Fig.(MEGD)
3.3), wasdirectlyaccompanied
manages 29
by riveran increased
basins inthesensitivity
countryof(MNET,
the parameters.
2013) (Fig.
The1.1).
correlation
Urban andbetweeneconomic
each parameterhubs such inasTableUlaanbaatar
3.4 showsand that
theSouthern
Q_sheep and
GobiQ_goat
MininginHub
the areTuul,
predominantly
as well as Q_horse
at serious
andwaterQ_sheep,
risk (Banerjee
and Q_cattle
et al.,
and2014).
Q_camelThegroundwaterin the Galba are
levelnegatively
in the twocorrelated
river basins(correlationin the summer
is −0.96).
of 2018
Q_horsewas monitored
and Q_camel
to evaluate(correlation
thedegradation= 0.79), Q_cattleof waterand availability
Q_sheep (correlation
due to anthropogenic= 0.77) in Tuul,
activity
andandQ_cattle
climateandchangeQ_sheep
in these
in Galba
two
contrasting(correlation =hubs
0.68)(Nakayama
are positively
et correlated.
al., 2021a).These
Then,
correlations
the process-based
are importantNational
in understanding
Integrated
Catchment-based
the relationship between
Eco-hydrology
water use(NICE)
and livestock
model (Nakayama(Nakayama,et2014,
al., 2021b).
2015) was applied to twocontrasting river basins including the above two hubs, the Tuul and Galba River Basins. The
model successfully simulated the effects of past climate changes and human-induced
disturbances, such as urbanization, mining, and livestock herding, on water resources over thelast four decades (Nakayama et al., 2021a).
The Tuul River Basin is in the northern grassland and steppe, and the river flows throughUlaanbaatar in the middle stretch (Jamsran et al., 2018) (Fig. 2.1). Groundwater is heavily
drawn for municipal and industrial water supplies. In contrast, the Galba River Basin (Galba–
Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and includes large mines,
such as Oyu Tolgoi (OT) and Tavan Tolgoi (Jamsran et al., 2018) (Fig. 2.1). Particularly, OT
is one of the largest copper–gold mines globally, which started in 2008 (Oyu Tolgoi LLC, 2013;
Torquoise Hill Resources Ltd., 2019). These two regions are similar as the current water use
may not be sustainable for meeting the projected water demand owing to the rapidly growing
urban population and expanding mining activity (Banerjee et al., 2016; Ulaanbaatar, 2018). The
spatiotemporal variations in water availability in both basins has been evaluated quantitatively
and explicitly (Nakayama et al., 2021a). However, constant unit water use for five typical
livestock types (sheep, horses, cattle, goats, and camels) was assumed, with reference toprevious studies (Sekine, 1993; Yoshida, 2009). As drinking water requirements for livestock
vary according to animal type, species, age, diet, and temperature (Wada et al., 2016), there aresome ranges and equations of water requirements for major livestock from a global perspective(Alcamo et al., 1997; FAO, 2006; Schlink et al., 2010). Different methods are used for
estimating water use, such as direct animal drinking and consumptive use. From the viewpointof water balance, drinking water mainly from blue water sources accounts for approximately
two-third water intake for a lactating Holstein dairy cow (FAO, 2019). Each livestock seems tohave a different water use unit, both temporally and spatially, in Mongolia, and the competing
water demands from the mining–urbanization–herder nexus should be satisfied (Banerjee et al.,2014).
In this chapter, the process-based NICE was applied to these representative river basins as
an extension of a recent study (Nakayama et al., 2021b). The original NICE was coupled with
the inverse method for sensitivity analysis and parameter estimation of anthropogenic water use(NICE-INVERSE) (Fig. 1.5). Based on the background described above, three basic scientificquestions were addressed: (i) How does anthropogenic water use influence the hydrologicalcycles in these two contrasting river basins? (ii) How does unit water use change in five typical
livestockin eachbasin?
and (iii) How does
the relationshipbetweengroundwater
overuseFigure 3.4types
Simulated
resultof composite-scaledsensitivity
in five typical
livestock
types in (a)Tuul
and stress change?
To answera NICE-INVERSE simulation (Fig. 1.5) wasand (b) Galba
River theseBasinsquestions,
during 2015–2018- 43
54 - 55 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

Abstract

Table 3.4 Simulated result of correlation between each parameter in the Tuul and Galba River
Basins. Q_horse, Q_cattle, Q_sheep, Q_goat, and Q_camel represent unit water use for
In Mongolia,
water resources are under increasing pressure, mainly in the urban andhorses, cattle, sheep, goats, and camels, respectively.

economic hubs of Ulaanbaatar and Southern Gobi Mining Hub. In the previous chapter,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
Tuul River Q_horse
Q_cattle
Q_sheep
Q_goatQ_camelmodel, was used to explicitly quantify the spatiotemporal variations in water availability in
1.000-0.692
0.791
these two Q_horse
contrasting river
basins. In -0.554
this chapter, themodel was0.466
further extended
to couple it
Q_cattle-0.5541.000
0.773-0.762-0.741
with the inverse method for sensitivity analysis and parameter estimation of anthropogenic-0.692 The new
0.773model quantified
1.000-0.708water useQ_sheep(NICE-INVERSE).
the-0.959
spatiotemporalvariations in
Q_goat0.466-0.762-0.9591.000
0.545
livestock water use in these river basins in comparison with the estimated values by aggregating-0.741-0.7080.545
1.000studies. The
statistical Q_camel
data, GIS data,0.791
observationaldata, and fieldsurvey datafrom previous
amount of water used by animals was generally low for each soum (district) and could be
returnedGalba
to theRiverecosystem.
The modelQ_cattle
showed an interesting
as the unitQ_camelwater use in some
Q_horseQ_sheep result
Q_goattypical livestock
sheep, and goats)
decreasing
suggesting that
Q_horse(cattle, 1.000-0.611had a temporally-0.964-0.165 trend,0.423
water stresssubstantially
increaseddue
tolocal–regional
eco-hydrologicaldegradation
byQ_cattle-0.6111.000
0.679-0.222-0.964
urbanization
andmining.
These
results
clarifythatthesubstantial
increaseinwater
stressfromQ_sheep-0.9640.679
1.000-0.095-0.480groundwater degradation was mainly caused by urbanization and local mining, in addition to
Q_goat-0.165-0.222-0.0951.000
0.167
climate change. Thus, sensitivity analysis and inverse estimation of model parameters helped
Q_camel
0.423-0.964-0.4800.167
1.000
improve the accuracy of hydrologic budgets in the basins. This methodology is powerful for
quantifying the spatiotemporal variations in water availability and water stress in regions withlimited inventory data on urbanization, mining, and livestock.
3.5 Conclusion
Keywords: eco-hydrology model; human activity; inverse estimation; parameter estimation;
In this analysis
chapter, the process-based eco-hydrology model NICE is applied to two contrasting
sensitivity
river basins with urban and mining hubs in Mongolia: the Tuul and Galba River Basins, as
described in the previous chapter. Particularly, NICE was extended to couple it with the inverse
method for sensitivity analysis and parameter estimation of anthropogenic water use (NICEINVERSE). This new model quantified the spatiotemporal variations in livestock water use inthese river basins by improving the estimated values by aggregating statistical data, GIS data,
observation data, and field survey data from previous studies. NICE-INVERSE is beneficial
because it can calculate the interannual changes in unit water use. The results also showed that
unit water use temporally decreased in some typical livestock (cattle, sheep, and goats),suggesting a substantial increase in water stress due to local–regional eco-hydrologicaldegradation caused by urbanization and mining. Overall, these results clarify the significant
impact of water use due to urbanization and mining on groundwater degradation around water
sources, in addition to climate change. Thus, sensitivity analysis and inverse estimation ofmodel parameters helped improve the accuracy of hydrologic budgets in the basins. Thismethodology is useful for evaluating the spatiotemporal variations in water availability inregions where inventory data, such as those for urbanization, mining, and livestock, are lacking.
These approaches may also help improve the accuracy of hydrologic budgeting by facilitating
strict bias analysis.

- 42
55 - 56 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

3.1 Introduction

References

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properties
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Geochemistry,
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Torquoise
Hill Resources Ltd., 2019). These two regions are similar as the current water use
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Geological
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Environment
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Basin boundary
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explicitlySocial(Nakayamaal., 2021a).
However,
constant unit water use for five typical
EconomicetDevelopment
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previous studies (Sekine, 1993; Yoshida, 2009). As drinking water requirements for livestock
Jamsran, U., Yamanaka, N., Zorigbaatar, N. (2018) Vegetation of Mongolia. In: Jamsran, U., Tamura, K., Luvsan,vary
according to animal type, species, age, diet, and temperature (Wada et al., 2016), there areN., Yamanaka, N. [Eds] Rangeland Ecosystems of Mongolia. Munkhiin Useg, Co., Ltd., Ulaanbaatar, 109some139
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water 296,
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T., Watanabe,
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vegetation
change caused
by invasion of alder (Alnus japonica) in Kushiro Mire. Water Resources Research, 40, W08402, doi:2014).
10.1029/2004WR003174
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this chapter, the process-based NICE was applied to these representative river basins as
Nakayama,
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interactions.
In: Eslamian[Ed]
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an extensionof a recent
study (Nakayama
et al.,
2021b). S.
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analysisandparameter
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use
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(Fig.
1.5).
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above,Sensing
three basic
strategy and effective
water
resources
In: Lakshmi
V. [Ed) Remoteof the scientific
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cycles in these two contrasting river basins? (ii) How does unit water use change in five typical
Nakayama, types
T. (2017a)
Developmentof an(iii)
advancedeco-hydrologic
and biogeochemical
coupling modeloveruse
aimed atlivestockin eachbasin? andHow does
the relationshipbetween groundwater
clarifying the missing role of inland water in the global biogeochemical cycle. Journal of Geophysicaland Researchstress change?
To answer
these questions,a NICE-INVERSE simulation (Fig. 1.5) was
Biogeosciences,122, 966-988,
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56 - 57 -Chapter 3 Sensitivity Analysis and Parameter Estimation of Anthropogenic Water Use for Quantifying the CGER-I167-2023,
Relation between Groundwater
CGER/NIES
Overuse and Water Stress

Nakayama, T. (2017b) Scaled-dependence and seasonalvariations of carbon cycle through development of an
Abstract
advanced eco-hydrologic and biogeochemical coupling model. Ecological Modelling, 356, 151-161, doi:
10.1016/j.ecolmodel.2017.04.014
In Mongolia,
water resources
areof underincreasingpressure,
in the urban
andNakayama,
T. (2020) Inter-annual
simulation
global carbon
cycle variationsin amainly
terrestrial-aquatic
continuum.
Hydrological34(3), 662-678,
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hubsProcesses,of Ulaanbaatar
and Southern
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Nakayama,IntegratedT., Wang, Q.,Catchment-based
Okadera, T. (2021a)Eco-hydrology
Evaluation of spatio-temporalin water eco-hydrology
availability using
National(NICE), avariations
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model,
was used to explicitly quantify the spatiotemporal variations in water availability in
109404, doi:10.1016/j.ecolmodel.2020.109404
these
two
contrastingbasins.
In thisSensitivity
chapter, analysis
the model
further
extended
to couple it
Nakayama, T.,
Wang, Q.,river
Okadera,T. (2021b)
and wasparameter
estimationof anthropogenic
withwater
the uses
inversemethod forsensitivity
analysis andparameterof anthropogenic
for quantifying
relation
between groundwater
overuseand waterestimationstress in Mongolia.
Ecohydrology
anduseHydrobiology,
21(3), 490-500,
doi:10.1016/j.ecohyd.2021.07.006
water(NICE-INVERSE).
The
new model quantified the spatiotemporal variations in
Nakayama, water
T. (2022)
Impactanthropogenic
disturbances oncarboncycle changes
in terrestrial-aquaticlivestock
use intheseofriver
basins in comparison
withthe estimatedvaluesby aggregating
estuarine continuum by using an advanced process-based model. Hydrological Processes, 36(2), e14471, doi:
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M. (2023a)
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for evaluating
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decreasing
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Nakayama, T., Osako, M. (2023b) The flux and fate of plastic in the world's major rivers: Modeling spatial andgroundwater
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sensitivity
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NASA. (2013)
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Statistical
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parameter
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Revue scientifiqueet techniqueinternationalépizooties 29(3),603-619estimation;
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analysis
sheep and goats given a conventional diet. Japanese Journal of Sheep Science 30, 22-24 (in Japanese)
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and Solutions (WFaS) initiative and its approaches. Geoscientific Model Development 9, 175-222,
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This article was published in Ecohydrology and Hydrobiology, 21(3), Nakayama, T., Wang, Q., Okadera, T.,
Sensitivity analysis and parameter estimation of anthropogenic water uses for quantifying relation between
groundwater overuse and water stress in Mongolia, 490-500, Copyright Elsevier (2021).

- 42
57 - 58 -CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29
CGER-I167-2023, CGER/NIES

Chapter 4
Impact of Anthropogenic Activity and Climate Change on Water Resources
in Entire Mongolian Basin- 59 - 59 -

Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin

Abstract
In Mongolia, groundwater overuse and degradation are serious issues. In previous chapters,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, and NICE-INVERSE has been used to urban and mining hubs to quantify
spatiotemporal variations in water availability and estimate the parameters of anthropogenic
water use. In this chapter, NICE was further scaled up to 29 river basins in the entire country at
0.1° × 0.1° resolution. The model simulated the effects of past climate changes and humanactivity on a broad scale of water resources during 1980–2018. The model reasonably
reproduced the observed river discharge with a maximum value during the summer rainy season.
The simulation also revealed the heterogeneous distributions of hydrological budget and its
responses to climatic and anthropogenic disturbances. In addition, hotspots of groundwater
degradation caused by anthropogenic activity were detected on a national scale. Analyzing therelative contribution of environmental factors further clarified the characteristics in these areas
and quantified the spatiotemporal trends in groundwater levels due to the effects of changes inprecipitation and various water uses. Generally, the results show that changes in precipitation
substantially affected changes in groundwater levels until 2000. In contrast, the model clarified
that recent human activities have significantly impacted the changes in groundwater level. This
trend was particularly conspicuous in river basins with urbanization and mining development,such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. Thismethodology is powerful for resolving future competition for water resources in areas with lessinventory data, which could potentially trigger conflicts among urban, mining, industry, herders,
and local communities.
Keywords: eco-hydrology model; groundwater degradation; national scale; relative
contribution of environmental factors; river basins- 60 - 60 -

Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introduction

AbstractAs
introducedgroundwaterin the previous
chapters,
Mongolia are
has serious
undergone
rapid
urban expansion,
In Mongolia,
overuseand degradation
issues.
In previous
chapters,
miningand agricultural landdevelopmentthat promoted
public settlement
due toNationaldevelopment,
Integrated Catchment-based
Eco-hydrology(NICE),
a process-based
eco-hydrology
the
rapid
socioeconomicchangesthataccompaniedtheintroductionofthemarketeconomyinmodel, and NICE-INVERSE has been used to urban and mining hubs to quantify
1990. The country
has achieved
a higheconomicand
growthrate theand parameterssocial development
in recentspatiotemporalvariations
in wateravailability
estimateof anthropogenic
years,
ashave
emerging
countries
worldwide.
However,itcausesvariousecosystem
water use. In this chapter, NICE was further scaled up to 29 river basins in the entire country at
degradations
such .asThewaterresource
depletion,waterpollution,
permafrost,
0.1° × 0.1° resolution
model
simulatedthe effectsof pastclimate thawing
changes andhumandesertification,andpasturedegradation,inadditiontotheimpactofclimatechange(Banerjee
activity on a broad scale of water resources during 1980–2018. The model reasonably
et al., 2014)the(Picture4.1).riverParticularly,
comprehensiveof waterresources
reproduced
observeddischargeawith
a maximum basinvalue management
during the summer
rainy
season.
shouldbe
immediately
implemented
because
>80%
of
water
resourcesaregroundwaterThe simulation also revealed the heterogeneous distributions of hydrological budget and its
dependent,toandthe excess
and depletionof groundwater
has becomea serious
problem in
responses
climatic
and useanthropogenic
disturbances.
In addition,
hotspotsof groundwater
recentyears.
The
MinistryofEnvironment
and
Green
Development(MEGD)
directly
managesdegradation caused by anthropogenic activity were detected on a national scale. Analyzing
the
29
river
basinsinthecountry(MNET,
2013)
(Fig.
1.1).
Areas
at
serious
water
risk
are
relative contribution of environmental factors further clarified the characteristics in these areas
predominantly
urban
and economic
hubsinsuch
as Ulaanbaatar
and tothetheSouthern
Gobi
Miningandquantified thespatiotemporal
trendsgroundwaterlevels due
effects ofchangesin
Hub(Banerjeeetal.,2014).
To
evaluatethedegradationofwater
availability
due toprecipitation and various water uses. Generally, the results show that changes in precipitation
anthropogenicaffected
activitychanges
and climatechange in thesecontrasting
hubs, thethe
groundwaterlevel
substantiallyin groundwaterlevelstwo
until2000. In contrast,
model clarified
has
been
monitoredfromthesummerof
2018(Nakayamaetal.,2021a).
Then,theprocessthat recent human activities have significantly impacted the changes in groundwater level. This
based wasNational
Integrated
Catchment-based
Eco-hydrology(NICE) andmodel(Nakayama,
2014,
trend
particularly
conspicuousin river basins
with urbanization
miningdevelopment,
2015)
was
appliedtotwocontrasting
river
basinsincluding
the
above
two
hubs,
the
Tuul
and
such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. This
Galba River Basins.
The formodel
successfully
simulated thepast climatechangeandmethodology
is powerfulresolving
future competition
foreffects
water ofresources
in areaswith lesshuman-induced
disturbances,suchasurbanization,mining,andlivestockherding,onwater
inventory data, which could potentially trigger conflicts among urban, mining, industry, herders,resources
during the last four decades (Nakayama et al., 2021a,b).
and
local communities.
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weakrural
economyto a fast-developing
mixed economy
characterized
by rapidin
Keywords:
eco-hydrology
model; groundwater
degradation;national
scale;growth
relative
urbanization, of
industrialization,miningsustainable development (ADB, 2020). Under
contribution
environmental andfactors;
riverforbasinssuch conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
(MEGD, 2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
for its municipal and industrial water supply (Jamsran et al., 2018). In contrast, the Galba River
Basin (Galba–Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and has
experienced extensive mining activities, such as Oyu Tolgoi (OT) and Tavan Tolgoi (Oyu
Tolgoi LLC, 2013). The river basin organizations in these two regions are particularly
challenged to meet the projected water demand of the rapidly growing urban population,
expanding mining activity, and becoming sustainable (ADB, 2014; Ulaanbaatar, 2018). Other
river basin organizations are relatively young and whether these have performed sufficient
functions against rapid and broad socioeconomic development is difficult to say. One reason
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilization of water resources as the research scope.
In this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 61
60 - 61 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introduction

AbstractAs
introducedgroundwaterin the previous
chapters,
Mongolia are
has serious
undergone
rapid
urban expansion,
In Mongolia,
overuseand degradation
issues.
In previous
chapters,
miningand agricultural landdevelopmentthat promoted
public settlement
due toNationaldevelopment,
Integrated Catchment-based
Eco-hydrology(NICE),
a process-based
eco-hydrology
the
rapid
socioeconomicchangesthataccompaniedtheintroductionofthemarketeconomyinmodel, and NICE-INVERSE has been used to urban and mining hubs to quantify
1990. The country
has achieved
a higheconomicand
growthrate theand parameterssocial development
in recentspatiotemporalvariations
in wateravailability
estimateof anthropogenic
years,
ashave
emerging
countries
worldwide.
However,itcausesvariousecosystem
water use. In this chapter, NICE was further scaled up to 29 river basins in the entire country at
degradations
such .asThewaterresource
depletion,waterpollution,
permafrost,
0.1° × 0.1° resolution
model
simulatedthe effectsof pastclimate thawing
changes andhumandesertification,andpasturedegradation,inadditiontotheimpactofclimatechange(Banerjee
activity on a broad scale of water resources during 1980–2018. The model reasonably
et al., 2014)the(Picture4.1).riverParticularly,
comprehensiveof waterresources
reproduced
observeddischargeawith
a maximum basinvalue management
during the summer
rainy
season.
shouldbe
immediately
implemented
because
>80%
of
water
resourcesaregroundwaterThe simulation also revealed the heterogeneous distributions of hydrological budget and its
dependent,toandthe excess
and depletionof groundwater
has becomea serious
problem in
responses
climatic
and useanthropogenic
disturbances.
In addition,
hotspotsof groundwater
recentyears.
The
MinistryofEnvironment
and
Green
Development(MEGD)
directly
managesdegradation caused by anthropogenic activity were detected on a national scale. Analyzing
the
29
river
basinsinthecountry(MNET,
2013)
(Fig.
1.1).
Areas
at
serious
water
risk
are
relative contribution of environmental factors further clarified the characteristics in these areas
predominantly
urban
and economic
hubsinsuch
as Ulaanbaatar
and tothetheSouthern
Gobi
Miningandquantified thespatiotemporal
trendsgroundwaterlevels due
effects ofchangesin
Hub(Banerjeeetal.,2014).
To
evaluatethedegradationofwater
availability
due toprecipitation and various water uses. Generally, the results show that changes in precipitation
anthropogenicaffected
activitychanges
and climatechange in thesecontrasting
hubs, thethe
groundwaterlevel
substantiallyin groundwaterlevelstwo
until2000. In contrast,
model clarified
has
been
monitoredfromthesummerof
2018(Nakayamaetal.,2021a).
Then,theprocessthat recent human activities have significantly impacted the changes in groundwater level. This
based wasNational
Integrated
Catchment-based
Eco-hydrology(NICE) andmodel(Nakayama,
2014,
trend
particularly
conspicuousin river basins
with urbanization
miningdevelopment,
2015)
was
appliedtotwocontrasting
river
basinsincluding
the
above
two
hubs,
the
Tuul
and
such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. This
Galba River Basins.
The formodel
successfully
simulated thepast climatechangeandmethodology
is powerfulresolving
future competition
foreffects
water ofresources
in areaswith lesshuman-induced
disturbances,suchasurbanization,mining,andlivestockherding,onwater
inventory data, which could potentially trigger conflicts among urban, mining, industry, herders,resources
during the last four decades (Nakayama et al., 2021a,b).
and
local communities.
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weakrural
economyto a fast-developing
mixed economy
characterized
by rapidin
Keywords:
eco-hydrology
model; groundwater
degradation;national
scale;growth
relative
urbanization, of
industrialization,miningsustainable development (ADB, 2020). Under
contribution
environmental andfactors;
riverforbasinssuch conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
(MEGD, 2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
for its municipal and industrial water supply (Jamsran et al., 2018). In contrast, the Galba River
Basin (Galba–Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and has
experienced extensive mining activities, such as Oyu Tolgoi (OT) and Tavan Tolgoi (Oyu
Tolgoi LLC, 2013). The river basin organizations in these two regions are particularly
challenged to meet the projected water demand of the rapidly growing urban population,
expanding mining activity, and becoming sustainable (ADB, 2014; Ulaanbaatar, 2018). Other
river basin organizations are relatively young and whether these have performed sufficient
functions against rapid and broad socioeconomic development is difficult to say. One reason
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilization of water resources as the research scope.
In this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 60
61 - 62 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introductionchangeinfluenced water resources in each basin of the entire country? (ii) How doanthropogenic activities, such as livestock, irrigation, mining, and industrial and domestic wateruse, Aschangeintroduced
water availability
in the previous
there?chapters,(iii) HowMongolia
do these hydrologicalhas undergonechanges
rapid affecturbantheexpansion,
pasturemining
carryingdevelopment,
capacity andandvulnerability?
agricultural land
To answer
development
these thatquestions,promoteda high-resolution
public settlementgridded
due tothedatasetrapidrepresenting
socioeconomic
waterchanges
use forthat
eachaccompanied
water use thetypeintroduction
was derived
of bythecombining
market economy
a globalin
1990.
dataset,The
statisticalcountry data,
has achieved
GIS data,
a highobservation
economicdata,
growthand ratefieldandsurveysocialdata
development
to input thein recent
NICE
years,
simulationas have
as an emerging
extension ofcountries
previousworldwide.
studies. TheHowever,model simulated
it causesthevarious
effects ofecosystem
climatedegradations
change and humansuch activity
as wateron waterresource
resources
depletion,
duringwater
1980–2018pollution,in all 29
thawingriver basins
permafrost,in thedesertification,
country. Analyzing
and pasture
the relative
degradation,
contributionin additionof environmental
to the impact
factorsof climate
also helped
changeto clarify(Banerjee
the
et
water
al., 2014)degradation(Picture
hotspots
4.1). Particularly,
and quantify
a comprehensive
the interannualbasin
variation
management
or temporalof watertrendsresourcesin theshouldgroundwater
be immediately
level caused
implemented
by these factors.
because
Thus,
>80%
a new
of watermodelresources
to evaluate
are thegroundwaterimpact of
dependent,
anthropogenic
anddisturbances
the excess use
onand
water
depletion
availabilityof groundwaterin the entirehas
country
becomewasa serious
developed
problemfor theinrecenteffective
years.
utilization
The Ministryof waterof Environment
resources, whichand provides
Green Development
new insights(MEGD)
into thedirectly
current manages
research
29
compared
river basins
with previous
in the country
research.(MNET,
The results
2013)
obtained(Fig. can1.1).beAreas
used asata serious
basis forwater
extendingrisk thearepredominantly
uncertainty analysis
urbanofand
theeconomichydrologicalhubs cycle
such asto Ulaanbaatar
improve the andevaluation
the Southernof pasture-carrying
Gobi Mining
Hubcapacity(Banerjee
and its vulnerability.
et al., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelhas been monitored from the summer of 2018 (Nakayama et al., 2021a). Then, the processbased National Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014,2015) was applied to two contrasting river basins including the above two hubs, the Tuul andGalba River Basins. The model successfully simulated the effects of past climate change andhuman-induced disturbances, such as urbanization, mining, and livestock herding, on water
resources during the last four decades (Nakayama et al., 2021a,b).
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weakrural economy to a fast-developing mixed economy characterized by rapid growth inurbanization, industrialization, and mining for sustainable development (ADB, 2020). Under
such conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
(MEGD, 2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
for its municipal and industrial water supply (Jamsran et al., 2018). In contrast, the Galba River
Basin (Galba–Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and has
Picture 4.1 Environmental degradation in Mongolia
experienced extensive mining activities, such as Oyu Tolgoi (OT) and Tavan Tolgoi (Oyu
Tolgoi LLC, 2013). The river basin organizations in these two regions are particularly
challenged to meet the projected water demand of the rapidly growing urban population,
4.2 Input Data and Boundary Conditions for Simulation
expanding mining activity, and becoming sustainable (ADB, 2014; Ulaanbaatar, 2018). Other
river basin organizations are relatively young and whether these have performed sufficient
4.2.1 Model
Inputrapid
Dataand broad socioeconomic development is difficult to say. One reason
functionsagainstfor this is that the mechanism of the hydrological cycle in all basins and its changes due to pastSix-hourreanalyzed
data fordownward
radiation,
precipitation, atmosphericpressure,human
activities
and climatechangehave notbeen quantitatively
evaluated andshould air
betemperature,airhumidity,wind
speed,
cloud
cover,
fractionofphotosynthetically
activeimproved beyond previous studies. The successful application of NICE in existing studies
radiation (FPAR),and leaf
area its
index(LAI) fromwerethese
inputtwo
into
theto originalafter(Nakayama
et al., 2021a,b)
enables
expansion
hubs
the entireNICE
country.
Ainterpolation
ofthe
ECMWF-Reanalysis
Interim
Product(ERA-interim)(ECMWF,2019)
inmodel to evaluate the impact of various anthropogenic disturbances on water availability in the
inverseMongolian
proportion to
the back-calculated
distancein eachgrid. Thedeveloped
input data forat various
spatialentireBasin(29 river basins)shouldbe urgently
the effective
resolutions
were
preparedandarrangedtocalculateaspatially
averaged
0.1°×0.1°
grid(aboututilization of water resources as the research scope.
8–12Inkm
the Spatial
AnalystTools toin 29
ArcGISv10.3 insoftware
for(Fig.
thethis resolution)
chapter, theusing
process-based
NICE
was appliedriver basins
Mongolia
simulation(Table4.1);
elevation,
landcover,
soil
texture,vegetation
type,
river
networks,
lakes1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgroundand wetlands,
reservoirstorage,and geologicalstructures werecategorized
input
described
above,
three types
basic andscientificquestionswere addressed:(i) How
has pastforclimate- 61
62 - 63 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

to NICE (U.S. Geological Survey, 1996a,b;
Lehner and Döll, 2004; Hartmann and Moosdorf,
Abstract2012; IIASA, 2012; NASA, 2013; European Commission, 2015). The land cover was assumed
to beconstant during
1980–2018
in andthe degradation
simulation.are
Sub-basinsin 29In river
basinswere
In Mongolia,
groundwater
overuseserious issues.
previous
chapters,
monitored
using topography
and river channel
data in ArcGIS
and the calculated
data
National Integrated
Catchment-based
Eco-hydrology(NICE),software,a process-based
eco-hydrology
werecompared
with
the
basin
boundary
dataforMongolia(MNET,
2013).
Mostof
thecountrymodel, and NICE-INVERSE has been used to urban and mining hubs to quantify
iscovered with variations
grassland in
andwatersteppe(which coverof theRiver Basin),
except for
spatiotemporalavailability
and most
estimate
theTuulparametersof anthropogenic
agriculturalland
andforestinthenorthern
region,
and
bare
land
and
desertinthesouthernwater use. In this chapter, NICE was further scaled up to 29 river basins in the entire countryatregion(whichcovers
most
of
the
Galba
River
Basin)
intheclassificationof
GLC2000
0.1° × 0.1° resolution. The model simulated the effects of past climate changes and human(Nakayamaal., 2021a)
3.1). Thegeologicalstructures
were The
dividedinto reasonably
four types
activity on eta broadscale(Fig.
of waterresources
during
1980–2018.
model
based
on thethehydraulicspecificof value
porousduring
material,
specificrainy
yieldseason.
using
reproduced
observedconductivity,
river discharge
with a storage
maximumthe summer
GLiM,
core
sampling
data(MinistryofEnvironment,2015),
andliteraturesources(Batsaikhan
The simulation also revealed the heterogeneous distributions of hydrological budget and its
et al., 2018).
responses
to climatic and anthropogenic disturbances. In addition, hotspots of groundwater

degradation caused by anthropogenic activity were detected on a national scale. Analyzing therelative contribution of environmental factors further clarified the characteristics in these areas
Table 4.1 List of input data sets for the NICE simulation of entire Mongolia
and quantified the spatiotemporal trends in groundwater levels due to the effects of changes inprecipitation and various water uses. Generally, the results show that changes in precipitation
Data set
Original resolution Year
Source and reference
substantiallyaffected 1.0°
changes in groundwaterlevels untilERA-interim;
2000. In contrast,
the model clarified
Climatology
1980-2018
ECMWF (2019)
that
recenthuman
activities
havesignificantly
impactedthechangesingroundwaterlevel. This
Elevation
1.0km
around 1996
GTOPO30; U.S. Geological Survey (1996a)
trend
was particularly1.0km
conspicuous in riverandCommission
mining development,
Land coveraroundbasins
2000 with urbanization
GLC2000; European(2015)
suchasthe
Orkhon,Kharaa,Tuul,
Galba,
Ongi,
Altain
Uvur
Govi,
and
Taats
Soil texture
1.0km
around 1970-2000 HWSD; IIASA (2012) River Basins. Thismethodology
competition
for water
resources
in areaswith less
Vegetation type is powerful
0.25°for resolving futurearound 2000
GLDAS
Vegetation
Class; NASA
(2013)
River networks
around 1996
HYDRO1K;
U.S.mining,
Geologicalindustry,
Survey (1996b)
inventorydata, which 1.0km
could potentially trigger
conflicts among
urban,
herders,Lakes
andwetlands
0.5
minaround1990-2000
GLWD;
Lehnerand
Döll(2004)and local communities.
Geological structures
0.5°
around 1970-2000 GLiM; Hartmann and Moosdorf (2012)
Crop type5 minaround 2000
MIRCA2000; Portmann
et al.scale;(2010) relative
Keywords:
eco-hydrology
model; groundwater
degradation;national
Irrigation type
5 min
2000-2008
GMIA; FAO (2016)
contribution of environmental factors; river basins
Irrigation water use
5 min
1998-2002
GCWM; Siebert and Döll (2010)
Basin boundary data
polygonaround 2010
MNET (2013)
Ministry of Environment (2015), Batsaikhan et al.
Core samplingpointaround 2010(2018)
Numbers of livestock

each soum

1980-2018

National Statistics Office of Mongolia (2019)

Mineral production

national

1980-2018

British Geological Survey (2022)

around 2010
around 2010
1980-2018
2002-2015

Gunson (2013)
Ulaanbaatar (2018)
National Statistics Office of Mongolia (2019)
MNET (2010), MEGD (2012), Sato (2015)

Water demand coefficient each mineralIndustrial water use
each aimag
Populationeach soum
Water use in Ulaanbaatar point

4.2.2 Estimation of Water Use in Various Sectors
Gridded data with 0.1°×0.1° resolution regarding various sectors of water use (livestock,
irrigation, mining, industrial, and domestic) for the entire country during 1980–2018 were
estimated by combining a global dataset, statistical data, GIS data, literature, and observational
data for input to the NICE simulation in an extension of the work reported in previous chapters(Fig. 4.1 and Table 4.1).
(A) Livestock water use (Fig. 4.1a): The total number of livestock (sheep, horses, cattle, goats,
and camels) was estimated for each soum (district) of both basins using statistical data
(National Statistics Office of Mongolia, 2019) and global gridded data (Gilbert et al., 2018).
- 60
63 - 64 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introduction
All water intake was assumed to be from groundwater in each basin and constant conversion
factors (1 horse = 7 sheep, 1 cattle = 6 sheep, 1 goat = 0.9 sheep, and 1 camel = 5 sheep)(Yoshida,
As introduced2009)inandthewaterprevious
consumption
chapters, per
Mongolia
sheep (2–8
has L/day,undergoneaveragerapid= 5urban
L/day)
expansion,(Sekine,mining1993)
development,
were used toandcalculateagricultural
the annual
land development
average livestockthat promoted
water use.
publicLivestock
settlement
water
dueusetothe rapid
per sheepsocioeconomic
was 1.2–2.3changes
m3/yr inthat
Mongolia.
accompanied the introduction of the market economy in
(B)
1990.
Irrigated
The country
water has
use achieved(Fig. 4.1b):
a highTheeconomic
agricultural
growthregions
rateaccording
and socialtodevelopment
GLC2000 werein recentalso
years,
subdivided
as haveinto
emerging
17 majorcountries
crops of 26
worldwide.
crop classes
However,for both itirrigatedcausesandvariousrain-fed
ecosystem
crops indegradations
MIRCA2000
such(Portmann
as wateret resourceal., 2010).
depletion,
This corresponds
water pollution,
to previously
thawingreported
permafrost,crop
desertification,
pasture degradation,in addition
toal.,
the2004).
impactTheof climate
commodity andclassifications
and definitions(Leff etGlobal change
Map of (Banerjee
Irrigation
et al.,Areas
2014)(GMIA)(Picture(FAO,
4.1). Particularly,2016) and Global
a comprehensive
Crop Waterbasin
Modelmanagement(GCWM) of(Siebert
water andresources
Döll,
shouldbewereimmediately
implemented
because>80%
waterwater,
resources
are groundwater2010)
also used todetermine theirrigation
type of(surfacegroundwater,or other),dependent,
and irrigationand the water
excess use,
use andrespectively.
depletion ofThegroundwater
crop model
hasdetails
becomehavea serious
beenproblem
described
inrecentpreviously
years. The(Nakayama,
Ministry of2020,
Environment2022). Irrigation
and Greenwater
Development
use per unit(MEGD)
area in Mongolia
directly manages
is 2.87–
Mongolia.
29 riverin inthecountry (MNET, 2013) (Fig. 1.1). Areas at serious water risk are
6.484 basins
m3/ha/yr(C)
Mining wateruse (Fig.
4.1c): Mining
was estimatedan extensionof theMining
results
predominantly
urban
and economic
hubswatersuch use
as Ulaanbaatar
andastheSouthern Gobia previousetstudy
on OT To
miningin thethe
Galba
River Basinet al., 2021a).
Hubof(Banerjeeal., 2014).
evaluatedegradationof (Nakayama
water availability
due toMining water
consumption
for each
aimag(province)
was estimatedmineralanthropogenic
activity
and climatechangein these
two contrasting
hubs, by
thecombining
groundwater
level
from fromthe WorldMineralData (British
Geological
Survey,1999),
has production
been monitored
the summerof Statistical2018 (Nakayama
et al., 2021a).
Then,
the processreported
valuesfor available
major minesEco-hydrology
in the country, (NICE)
and the model
water demandcoefficient
based
National
Integrated
Catchment-based(Nakayama,
2014,
forwaseachappliedmineraltotype
2013).
2015)
two (Gunson,
contrasting
river basins including the above two hubs, the Tuul and(D)
Industrial
water use
4.1d):
Industrialsimulated
water use thewaseffects
estimatedas thesum ofwater anduse
Galba
River Basins.
The(Fig.
model
successfullyof pastclimatechangein the manufacturing,
electricity/gas/heatsupply,mining,and constructionindustries.
Theonamounthuman-induced
disturbances,such as urbanization,and livestock
herding,waterof water
usedtheby lastotherindustrial
was obtainedby subtracting the amount of water
resources
duringfour
decadessectors(Nakayama
et al., 2021a,b).
used
for mining
the atotal
amountof waterby industries,
theMongolia
is nowfrom
seekingsmoothtransition
in itswithdrawn
socioeconomic
structureincludingfrom a weaksectorto(Ulaanbaatar,
2018). mixed economy characterized by rapid growth inruralmining
economya fast-developing(E)
Domestic industrialization,water use (Fig. 4.1e):
Water useto thedevelopment
sum of urban(ADB,
and rural
water
use)
urbanization,and miningfor (equal
sustainable2020).
Under
estimatedtheasabove
an extension
the organizations
results of a study
on play
Ulaanbaatar
thetransition
Tuul Riversuchwasconditions,
29 riverof
basin
shoulda role ininthisbyBasin (Nakayamaal., 2021a).
The urban
populations
were estimatedusing
implementing
effectiveetwater
management
basedandon rural
integrated
water resource
management
statisticaleach soum every
year (National
Statistics
OfficeBasinof Mongolia,(IWRM).
Thepopulation
first river databasinfororganization
was establishedin theTuul River
in 20122019).2012),
Although
information
on waterdevelopment
not(MEGD,
wheredefinitive
the river regionalflows throughUlaanbaatar
and supply
groundwateris drawnwere
heavilyobtained,
exceptUlaanbaatar,
the author
assumedthatthe water
supplythe
area
would
be
for its
municipal
and inindustrial
water supply(Jamsran
et al.,
2018).
In contrast,
Galba
River
soum
as the same urban population
urbanization
ratetheas southern
the aimagGobi
center.
Basedandon hastheBasin(Galba–Uush–Doloodiing
Gobi andRiver
Basin) is in
Desertfield survey
and literature
data, this assumption
conjectured
to Tavanbe acceptable
after
experienced
extensivemining activities,
such as Oyuwas
Tolgoi(OT) and
Tolgoi (Oyu
confirmingthat theThecalculated
total water
use in theinentire
country
was relatively
similar to
Tolgoi
LLC, 2013).
river basinorganizations
these
two regions
are particularly
the results
existing
studies. Further,looking atofthecountryin thepresent
chapter,challenged
to of
meet
the projectedwater demand
theoverall
rapidly
growingurban
population,
domestic
wateractivity,
use was,and
exceptfor Ulaanbaatar,
spatially
compared to2018).
other Other
water
expandingminingbecoming
sustainable(ADB,localized
2014; Ulaanbaatar,
typesorganizations(Fig. 4.1). The
and domesticwater
use perthese
personwereperformed
14.4–77.6sufficientand 2.8–
riverusebasin
areurbanrelatively
young andwhetherhave
5.6 m3against
functionsrapid and broad socioeconomic development is difficult to say. One reason/yr, respectively.
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilization of water resources as the research scope.
In this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 61
64 - 65 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

Abstract
In Mongolia, groundwater overuse and degradation are serious issues. In previous chapters,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, and NICE-INVERSE has been used to urban and mining hubs to quantify
spatiotemporal variations in water availability and estimate the parameters of anthropogenic
water use. In this chapter, NICE was further scaled up to 29 river basins in the entire country at
0.1° × 0.1° resolution. The model simulated the effects of past climate changes and humanactivity on a broad scale of water resources during 1980–2018. The model reasonably
reproduced the observed river discharge with a maximum value during the summer rainy season.
The simulation also revealed the heterogeneous distributions of hydrological budget and its
responses to climatic and anthropogenic disturbances. In addition, hotspots of groundwater
degradation caused by anthropogenic activity were detected on a national scale. Analyzing therelative contribution of environmental factors further clarified the characteristics in these areas
and quantified the spatiotemporal trends in groundwater levels due to the effects of changes inprecipitation and various water uses. Generally, the results show that changes in precipitation
substantially affected changes in groundwater levels until 2000. In contrast, the model clarified
that recent human activities have significantly impacted the changes in groundwater level. This
trend was particularly conspicuous in river basins with urbanization and mining development,such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. Thismethodology is powerful for resolving future competition for water resources in areas with lessinventory data, which could potentially trigger conflicts among urban, mining, industry, herders,
and local communities.
Keywords:
eco-hydrology
groundwater
degradation;national
relative
Figure4.1 Estimatedwater usemodel;
at 0.1°×0.1°
resolution inentire Mongolia
at 1980,scale;
2000, and2018
contribution of environmental factors; river basins

4.2.3 Boundary Conditions and Simulation Run
At the upstream boundaries, the conditions at the hydraulic head assumed no inflow fromthe mountains in the opposite direction, as described in previous chapters (Nakayama and
Watanabe, 2004). For the hillslope hydrology, the flow depth and discharge at the uppermost
boundaries of the basins were set to zero throughout the simulation. Inflows or outflows fromthe riverbeds were simulated at each time step depending on the difference in the groundwaterand river hydraulic heads. Details have been described in a previous paper (Nakayama et al.,2021a).
The simulation area was scaled up from Tuul and Garba River Basins in previous studies(Nakayama et al., 2021a,b) to all the 29 river basins of entire Mongolia, with 35.0° width by
13.0° longitude in longitude–latitude coordinates (Fig. 1.1). The simulation area has 2214 river
channels, of which 1049 are in the Mongolian Basin. NICE simulation in the original forward
mode was performed at 0.1°×0.1° (approximately 8–12 km) resolution horizontally (350×130
meshes) and 20 layers with 1.1 weighting factor (layer thicknesses being graduated from thinner
in the upper layers to thicker in the deeper layers) vertically. The upper land layer was set at 0–
2 m above the ground surface, and the 20th land layer was defined as a depth of 200 m below
sea level. Simulations were performed with a time step of t = 6 h for 1980–2018 after a 1-year
warm-up period to allow a time-varying equilibrium. The simulated results were calibrated andvalidated using groundwater level and river discharge observations in the Tuul and Garba River- 60
65 - 66 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introduction
Basinsin the previous studies (Nakayama et al., 2021a) (Tables 4.2 and 4.3). In this study, themodel was verified using river discharge throughout Mongolia (NUM, 2018) (Table 4.3).
To separate
As
introduced
theineffects
the previousof climate
chapters,changeMongolia
and humanhasactivities
undergoneon spatiotemporal
rapid urban expansion,changesminingin groundwater
development,
levels,andtheagricultural
relative land
contribution
developmentof environmental
that promoted public
factorssettlement
was analyzed
due tothe(Nakayamarapid socioeconomic
et al., 2021a,changes2023). that
Theaccompanied
model simulated
the introductionchanges in of
precipitation
the market andeconomyvariousin
1990.
water uses
The country(livestock,hasirrigation,achieved amining,high economic
industrial,
growthand domestic)
rate and social
separately,
development
keeping in
only
recentone
years,of theseaseffects
have constant.
emergingThecountries
referenceworldwide.
simulation was
However,
repeated
it tocausesuse 1980
various
forcing
ecosystem
data 39degradations
times during 1980–2018.
such as water
Finally,
resource
the groundwater
depletion, waterlevel inpollution,
the reference
thawingsimulation
permafrost,wasdesertification,
subtracted fromand
that
pasturein thedegradation,
alternative insimulations
addition totothequantify
impact oftheclimate
temporalchangetrends(Banerjeeof theetgroundwateral., 2014) (Picturelevel caused4.1). Particularly,
by various factors.
a comprehensive
Particularly,
basin
themanagement
spatial distributionof waterofresources
relativeshouldcontribution
be immediately
in all 29 riverimplemented
basins was evaluated,because where>80% theof gridwater
withresources
the maximum
are groundwaterwater intake
dependent,
was chosen andas thetherepresentative
excess use andmeshdepletionfor eachof basin.
groundwater has become a serious problem inrecent years. The Ministry of Environment and Green Development (MEGD) directly manages
29 river basins in the country (MNET, 2013) (Fig. 1.1). Areas at serious water risk areTable
4.2 List ofurban
NIESandgroundwater
stations formodel
validationGobi
in Tuulandpredominantly
economic levelhubs observationsuch as Ulaanbaatar
andthe Southern
MiningGalba River
shown
Fig. 1.1 the degradation of water availability due to
Hub (Banerjee
et al.,Basins2014).
To inevaluate
anthropogenic activity and climate change in these two contrasting hubs, the groundwater level
No. Basin Point Name
Lat. (°) Lon. (°) Elev. (m) Areal Class Time Interval Data Sourcehasbeen monitored Typefrom the summerof 2018 (Nakayama et al., 2021a). Then, the process1
Galba Khanbogd-1
Groundwater level 43.25
107.07 1071.0
Mining
30 minNakayama et al. (2021a)
based National Integrated Catchment-based
Eco-hydrology(NICE)
model (Nakayama,
2014,
2
Galba Khanbogd-2
Groundwater level 43.29
107.24 1016.0
Mining
30 minNakayama et al. (2021a)
2015) was applied to two contrasting river basins including the above two hubs,
the Tuul and3
Galba Khanbogd-3
level 43.29
107.24 1017.0
30 minNakayama et al. (2021a)
Galba
River Basins. Groundwater
The model
successfully
simulatedMining
the effectsof past climatechange and4
Galba Khanbogd-4
Groundwater level 43.04
106.97 1171.0
Miningmin
Nakayama et al. (2021a)
human-induced
disturbances,such as urbanization,mining,and30livestock
herding,
on water
5
Galba Khanbogd-5
Groundwater level 43.18
106.79 1192.0
Mining
30 minNakayama et al. (2021a)
resources during the last four decades (Nakayama et al., 2021a,b).
6
Galba Khanbogd-6
Groundwater level 43.08
106.77 1211.0
Mining
30 minNakayama et al. (2021a)
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weak
7
Galba Khanbogd-7
Groundwater level 43.10
107.02 1186.0
Mining
30 minNakayama et al. (2021a)
rural economy to a fast-developing mixed economy characterized by rapid growth in8
Galba Manlai-1
Groundwater level 43.74
106.80 1268.0
Non-mining
30 minNakayama et al. (2021a)
urbanization,industrialization,and mining
for sustainable
development(ADB,2020). Under
9
Galba Manlai-2
Groundwater level 43.92
106.87 1300.0
Non-mining
30 minNakayama et al. (2021a)
suchconditions,
the
above
29
river
basin
organizations
should
play
a
role
in
this
transition by
10 TuulUlaanbaatar-1
Groundwater level 47.81
106.64 1234.0
Urban
30 minNakayama et al. (2021a)
implementing
effective
water management
based on Urban
integrated 30water
resource
management
11 TuulUlaanbaatar-2
Groundwater level 47.81
106.64 1234.0
min
Nakayama et al. (2021a)(IWRM).
The
first
river
basin
organizationwasestablished
in
the
Tuul
River
Basin
in 201212 TuulUlaanbaatar-3
Groundwater level 47.99
106.96 1399.0
Urban
30 minNakayama et al. (2021a)(MEGD,
2012), where
the river flows throughUlaanbaatar
and groundwateris drawn heavily
13 TuulUlaanbaatar-4
Groundwater level 47.94
106.93 1315.0
Urban
30 minNakayama et al. (2021a)forits
municipal
andindustrial
water
supply(Jamsranetal.,2018).
In
contrast,
the GalbaRiver
14 TuulNalaikh-1
Groundwater level 47.66
107.43 1644.0
Non-urban
30 minNakayama
et al. (2021a)
Basinin the southern
Gobi
Deserthas
15 Tuul (Galba–Uush–Doloodiing
Lun-1
Groundwater level Gobi
47.88 River
105.32 Basin)
1022.0 isNon-urban
30 minNakayama
et al.and(2021a)experienced
extensiveminingactivities,
such
as
Oyu
Tolgoi
(OT)
and
Tavan
Tolgoi(Oyu
16 TuulLun-2
Groundwater level 47.87
105.26 1001.0
Non-urban
30 minNakayama et al. (2021a)
TolgoiThe riverorganizationsin these twoare particularly
17 Tuul LLC,
Hustai-12013).Groundwaterlevel basin
47.59
105.85 1216.0
Non-urban
30 min regionsNakayama
et al. (2021a)
challenged
to meet Groundwater
the projectedwater105.82
demandrapidly30 min
growing urban
18 Tuul
Hustai-2
level 47.59
1254.0of the
Non-urban
Nakayamapopulation,
et al. (2021a)
expandingminingactivity,
andbecoming
sustainable
(ADB,
2014;
Ulaanbaatar,
2018).
Other
19 TuulNalaikh-2
Groundwater level 47.69
107.44 1534.0
Non-urban
30 minNakayama et al. (2021a)
river
basin
organizations
arelevelrelatively
young
and *whether
these
have performed
20 TuulUlaanbaatar-5
Groundwater
47.93
106.68 1322.0
Urban
30 minNakayama etsufficiental. (2021a)functions against rapid and broad socioeconomic development is difficult to say. One reason
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilization of water resources as the research scope.
In this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 61
66 - 67 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

Table 4.3 List of river discharge observation
stations for model validation in Mongolian river
Abstractbasins (NUM, 2018).

In Mongolia,
groundwater
overuse anddegradation are serious issues. In previous chapters,
No. Basin
Point NameType
Lat. (°) Lon. (°) Elev. (m)* River Number Time Interval Data Source
National
Catchment-baseda process-based
eco-hydrology
21 Integrated
Selenge
Ikh-UulRiver discharge Eco-hydrology
49.38
101.58 1045.0(NICE),
236
daily
NUM
(2018)
22
SelengeZuunburenRiverdischarge
50.10
105.81
635.0
758
daily
NUM
model, and NICE-INVERSE has been used to urban and mining hubs (2018)
to quantify
23 Selenge
KhyalgantRiver discharge 49.68
758
daily
(2018)
spatiotemporalvariations
in wateravailability104.38
and1324.0
estimate
the parameters
ofNUManthropogenic
24 Eg
KhantaiRiver discharge 49.55
103.27 865.0
788
daily
NUM (2018)
water use.
In this chapter,
NICE
was further scaled
up to 29754river basinsin the NUM
entirecountry at
25 UurTsgaan uur
River discharge 50.54
101.56 1098.0
daily(2018)
0.1° × 26
0.1°Beltes
resolution. The model
simulated
theofchangesand human
Bayanzurkh
River discharge
50.17
98.97 effects
1622.0767 past climate
daily
NUM (2018)
Delgermurun
Murendischarge resources
49.58
100.16 during
1297.0796
dailyThe model
NUM (2018)activity27 on
a broadscale ofRiverwater
1980–2018.
reasonably
28
Tuul
Bosgo
River
discharge
48.03
107.731535.02
daily
NUM(2018)
reproduced the observed river discharge with a maximum value during the summer rainy season.
29 TuulRiver discharge 47.89
106.95 1290.021
daily
NUM (2018)
The simulation
alsoUlaanbaatar
revealed Riverthedischarge
heterogeneous
distributionsof hydrologicalbudget and its
30 TuulAltanbulag
47.68
106.28 1187.0
27
daily
NUM (2018)
responses
to climatic
hotspots NUMof (2018)
groundwater
31 TuulLun and anthropogenic
River discharge 47.85disturbances.
105.18 1012.0 In16 addition,daily
degradation
by anthropogenic
were 1562.0
detectedscale.
Analyzingthe32 Tereljcaused TereljRiver dischargeactivity
47.97
107.47
3 on a national
daily
NUM
(2018)
33
Selbe
DambadarjaaRiverdischarge
47.98
106.931358.021
daily
NUM(2018)
relative contribution of environmental factors further clarified the characteristics in these areas
34 Orkhon
Kharkhorin
River discharge 47.20
102.80 1447.0
847
NUM (2018)
and quantified
the spatiotemporal
trends in groundwaterlevelsdue todailythe effects
of changes in
35 Orkhon
Sukhbaatar
River discharge 50.22
106.22 602.0
179
daily
NUM (2018)
precipitation
andvariouswater
uses.
Generally,theresultsshow
thatchangesinprecipitation
36 Kharaa
Baruunkharaa
River discharge 48.91
106.08 826.0
780
daily
NUM (2018)
substantiallychanges River
in groundwaterlevels662.0
until 2000.
In contrast,
theNUMmodel
37 Kharaaaffected
Darkhandischarge 49.53
105.90780
daily(2018) clarified
38 Eroo
Dulaankhaan haveRiver significantly
discharge 49.88
106.25633.0 the771changes indailygroundwater
NUM (2018)that recent
human activities
impactedlevel. This
39 Eroo
ErooRiver discharge 49.63
106.83 705.0
771
daily
NUM (2018)
trend was particularly conspicuous in river basins with urbanization and mining development,
40 Ider
IderRiver discharge 48.20
97.38
1971.0831
daily
NUM (2018)such as41theChuluut
Orkhon,Chuluut
Kharaa, Tuul,
Galba, Ongi,100.25
AltainUvur385Govi, andTaats River
Basins. This
River discharge 47.54
2015.0daily
NUM (2018)
methodology
is powerfulfor resolving
competition
for
in
areas
42 Suman gol
TariatRiver discharge future
48.16
99.88
2056.0339 water resources
daily
NUM
(2018) with less
43 Khoid-Tamir
Ikh tamirRiver discharge trigger
47.66
101.271717.0 among406
NUM (2018)
inventorydata, which
could potentiallyconflicts
urban,dailymining, industry,herders,
44
Urd-Tamir
TsetserlegRiverdischarge
47.45
101.501647.0
424
daily
NUM
(2018)and local communities.
45

KhanuiErdenemandal

River discharge

48.53

101.391501.0351

daily

NUM (2018)46

Shishgid

RenchinlkhumbeRiver discharge

51.10

99.49

1550.0749

daily

NUM (2018)49

Kherlen

ChoibalsanRiver discharge

48.06

114.53

749.0

791

daily

NUM (2018)50

Khalkhgol

SumberRiver discharge

47.64

118.62

681.0

805

daily

NUM (2018)

51

Ulz

Ereentsav

River discharge

49.88

115.70

613.0

142

daily

NUM (2018)52

KhurkhKhenteiRiver discharge

48.08

109.831223.0310

daily

NUM (2018)
NUM (2018)

Keywords:
model;
degradation;national NUM
scale;
47 Onon eco-hydrology
BinderRiver dischargegroundwater
48.63
110.61 1028.0
809
daily
(2018) relative
contribution
factors;
river 110.67
basins1042.0 366
48 Kherlenof environmental
UndurkhaanRiverdischarge 47.31
daily
NUM (2018)

53

Khovd

Ulgii

River discharge

48.98

89.95

1722.0

237

daily

54

Khovd

MyangadRiver discharge

48.23

91.90

1186.0307

daily

NUM (2018)55

Sagsai

BuyantRiver discharge

48.58

89.53

2006.0289

daily

NUM (2018)

56

Sogoo

Khokh khotol

River discharge

49.24

88.90

2154.0232

daily

NUM (2018)

57

Zavkhan

Taishir

River discharge

46.71

96.55

1650.0456

daily

NUM (2018)

58

Bogd

UliastaiRiver discharge

47.73

96.86

1766.0368

daily

NUM (2018)

59

Harhiraa

TarialanRiver discharge

49.78

91.86

1466.0170

daily

NUM (2018)60

Baidrag

Bayanburd

River discharge

46.18

99.26

1561.0577

daily

NUM (2018)

61

Tui

Bayankhongor

River discharge

46.15

100.731837.0519

daily

NUM (2018)

62

Taaz

Nariin teelRiver discharge

45.96

101.471807.0612

daily

NUM (2018)

63

Ongi

UyangaRiver discharge

46.46

102.301964.0527

daily

NUM (2018)

64

Ongi

Siakhan-Ovoo

River discharge

45.45

103.901302.0604

daily

NUM (2018)

65

Hungui

UrgamalRiver discharge

48.52

94.30

1259.0303

daily

NUM (2018)

66

Buyant

Khovd

River discharge

48.02

91.62

1377.0345

daily

NUM (2018)67

BulganKhovd BulganRiver discharge

46.90

91.12

1928.0423

daily

NUM (2018)

68

BodonchBodonchRiver discharge

46.07

92.52

1675.0508

daily

NUM (2018)

69

Tes

BayanuulRiver discharge

49.74

96.44

1407.0201

daily

NUM (2018)70

Dund sharyn am Sharga

River discharge

46.55

95.70

1816.0416

daily

NUM (2018)

71

Buir

Zagasny uildver

River discharge

47.49

117.53

657.0

309

daily

NUM (2018)72

Khuvsgul

KhatgalRiver discharge

50.29

100.101611.0762

daily

NUM (2018)73

Khyargas

TermesRiver discharge

49.20

93.05

1026.0223

daily

NUM (2018)74

Khar-Us

DurgenRiver discharge

48.23

92.21

1171.0307

daily

NUM (2018)* Elevation was determined by the global digital elevation model (DEM; GTOPO30) (U.S. Geological Survey, 1996a)- 60
67 - 68 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.3 Results
4.1
Introduction
and Discussion
4.3.1As
Evaluation
Discharge
in Entire
Mongolia
the Last
Two
Decades
introducedofinRiverthe previous
chapters,
Mongolia
hasduringundergone
rapid
urban
expansion,mining development, and agricultural land development that promoted public settlement due to
The effectsof past climaticchangesand human-induced
disturbances,
as urbanization
the rapid
socioeconomicchangesthat accompanied
the introductionof thesuch
marketeconomy inand mining,on waterthe Tuuland Garba
Riverof the29 river basins
have
1990.
The country
hasresources
achieved ina higheconomic
growthrateBasins
and social
development
in recentbeen quantified
previous studies(Fig.worldwide.
1.1, TablesHowever,
4.2, and 4.3)
et al.,ecosystem
2021a,b).
years,as have inemerging
countries
it (Nakayama
causes various
This chapter reports
comparingthe observed
and simulated
discharge
at 54 points
otherdegradations
such aswater resource
depletion,water riverpollution,
thawing
permafrost,than these two rivers
across theentire country(Fig. 4.2
(NUM, 2018).
model
desertification,and pasturedegradation,in addition
toand
the Table
impact4.3)
of climatechangeThe(Banerjee
reasonably
theParticularly,
values observed
during the basin
last two
decades, ofandwaterthe discharge
et al., 2014)reproduced(Picture 4.1).
a comprehensive
management
resources
generallya maximum
during thesummerrainfallseason.resources
Particularly,
comparison
shouldbereached
immediately
implemented
because>80%
of waterare agroundwaterbetween
theanddaily
monthlyaggregatedvalidations
showedhas
that
the root-mean-squareerror
dependent,
theandexcessuse anddepletionof groundwater
becomea serious problem
in(RMSE)
in thedischarge wasthat in the(MEGD)
daily meandischarge,
asrecentyears.
Themonthly
Ministrymean
of Environment
andlower
GreenthanDevelopment
directly
managesreported(Nakayamaal., 2021a).
The(Fig.
simulated
results atforserious
the forest
arearisk
at sites
29
river previously
basins in the
country et(MNET,
2013)1.1). Areas
water
are
22,
24, and 35 (downstreamof the Selenge,
Eg,asand
Orkhon Rivers)in goodagreement
predominantly
urban and economic
hubs such
Ulaanbaatar
and thewereSouthern
Gobi
Miningwithobservedet values(Figs 4.2b–d).
In contrast,
the simulated
were overestimated
Hub the(Banerjeeal., 2014).
To evaluate
the degradationof values
water availability
due tocompared
withactivity
the observed
valuesin thein grasslandand steppe hubs,
regionssites 49 and54
anthropogenic
and climatechangethese two contrasting
theatgroundwaterlevel(downstreamof the Kherlen
KhovdofRivers)(Figs 4.2e,f)et(Nakayamaet Then,al., 2023).
has been monitoredfrom theandsummer2018 (Nakayamaal., 2021a).
the processbased National Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014,2015) was applied to two contrasting river basins including the above two hubs, the Tuul andGalba River Basins. The model successfully simulated the effects of past climate change andhuman-induced disturbances, such as urbanization, mining, and livestock herding, on water
resources during the last four decades (Nakayama et al., 2021a,b).
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weakrural economy to a fast-developing mixed economy characterized by rapid growth inurbanization, industrialization, and mining for sustainable development (ADB, 2020). Under
such conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
(MEGD, 2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
for its municipal and industrial water supply (Jamsran et al., 2018). In contrast, the Galba River
Basin (Galba–Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and has
experienced extensive mining activities, such as Oyu Tolgoi (OT) and Tavan Tolgoi (Oyu
Tolgoi LLC, 2013). The river basin organizations in these two regions are particularly
challenged to meet the projected water demand of the rapidly growing urban population,
expanding mining activity, and becoming sustainable (ADB, 2014; Ulaanbaatar, 2018). Other
river basin organizations are relatively young and whether these have performed sufficient
functions against rapid and broad socioeconomic development is difficult to say. One reason
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilizationwater resources
the research scope.
Figure4.2 ofComparisonof (a) as
annual-averaged
discharge in 29 river basins (Fig. 1.1 and TableIn this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
4.3) and (b)–(f) time-series of monthly mean discharge of representative rivers
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 61
68 - 69 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.3.2 Detecting Degradation Hotspots by Abstract
Anthropogenic Activity in Extension to Entire
Mongolia
In Mongolia, groundwater overuse and degradation are serious issues. In previous chapters,
The model
simulated
the annual averagehydrologicalbudget29 river basinsin Mongolia
National
Integrated
Catchment-based
Eco-hydrology(NICE),
a inprocess-based
eco-hydrology
(Fig.
4.3).
The
resultsfor
eachsubcatchment
inthesebasinsshowaheterogeneous
distribution
model, and NICE-INVERSE has been used to urban and mining hubs toquantifyof the hydrologicalbudget,reflecting
the distributionof climatic
zones inofthecountry. In
spatiotemporalvariations
in wateravailability
and estimate
the parameters
anthropogenic
addition,show aNICE
strongcorrelation
between
distributionsof precipitation
water
use.theIn values
this chapter,wasfurther scaled
up tothe29spatial
river basinsin the entire
country atandevaporation.
The
effective
precipitation(difference
betweenprecipitation
andevaporation)
0.1° × 0.1° resolution. The model simulated the effects of past climate changes and human(Fig. 4.3c)
water yield
net amountof waterthat 1980–2018.
leaves the sub-basinand contributes
activityonand
a broadscale (theof waterresources
during
The model
reasonably
tostreamflow
inthereach:
surfacerunoff+lateralflow+
groundwatertransmission
loss)
(Fig.
reproduced the observed river discharge with a maximum value during the summer rainyseason.
4.3d)
reducesinthemiddleandsouthern
regionswithlowgrasslandanddesert.
Particularly,
The simulation also revealed the heterogeneous distributions of hydrological budget and its
these values
locallyin the steppe
regions transitioning
forest of
to desert,
where
responses
towereclimatic
andreduced
anthropogenic
disturbances.
In addition,from
hotspotsgroundwater
Ulaanbaatar,
which
uses
heavy
groundwater,islocated.
Thus,
thecompetition
for
water
degradation caused by anthropogenic activity were detected on a national scale. Analyzing use
the
among
varioussectors
(Fig.
4.1)
will
becomemore
intenseinthefutureunlessappropriate
relative contribution of environmental factors further clarified the characteristics in these areas
measuresare taken(Nakayama et trendsal., 2023).
andquantified
the spatiotemporalin groundwater levels due to the effects of changes inprecipitation and various water uses. Generally, the results show that changes in precipitation
substantially affected changes in groundwater levels until 2000. In contrast, the model clarified
that recent human activities have significantly impacted the changes in groundwater level. This
trend was particularly conspicuous in river basins with urbanization and mining development,such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. Thismethodology is powerful for resolving future competition for water resources in areas with lessinventory data, which could potentially trigger conflicts among urban, mining, industry, herders,
and local communities.
Keywords: eco-hydrology model; groundwater degradation; national scale; relative
contribution of environmental factors; river basins

Figure 4.3 Simulated results of annual-averaged hydrologic budget in Mongolia during 1998–
2018: (a) precipitation, (b) evaporation, (c) effective precipitation, and (d) water yield- 60
69 - 70 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introduction
4.3.3
Relative Contribution of Various Sources to Groundwater Degradation in National
Scale
As introduced in the previous chapters, Mongolia has undergone rapid urban expansion,miningTo separate
development,
the effects
and agriculturalof human activities
land development
and climatethatchange,promoted
the relative
public settlement
contributions
due oftothevariousrapidwatersocioeconomic
uses to groundwaterchanges thatlevelaccompaniedchanges in thetheentire
introduction
country wereof thecalculated
market economy(Fig. 4.4).
in1990.
While Thethe present
countryresults
has achievedin the Tuula highandeconomic
Galba River
growthBasinsrate and(Figssocial
4.4b,e)development
are similarintorecent
thoseyears,
reportedasinhave
previous
emerging
studiescountries(Nakayama
worldwide.
et al., 2021a,b),
However, theit contributions
causes variousof ecosystemdomestic,
degradations
irrigation, and suchindustrial
as waterwater use
resource
can bedepletion,
distinguishedwater
in detailpollution,in Tuulthawing
River Basinpermafrost,in thisdesertification,
chapter. The simulationand pasturealsodegradation,
showed thatinthe
additioncontributions
to the impactof mining,of climate
livestock,changeand (Banerjee
industrialetwateral., 2014)
use in (Picture
the Galba
4.1).
River
Particularly,
Basin havea comprehensivechanged in a complex
basin management
manner sinceof2010.
waterGenerally,
resources
shouldthe contributions
be immediatelyof industrial
implemented
and domestic
becausewater>80%
useofwerewater
relatively
resources
large
areingroundwaterthe northern
dependent,
basins (Figsand
4.4b,c),the excess
whereasusetheandcontribution
depletion ofofgroundwatermining waterhas
usebecomein the Gobi
a serious
Desertproblem
increasedinrecentsuddenly
years.
afterThe
2010
Ministry(Fig. 4.4e).
of Environment
The results andclearlyGreen
show
Development
how each sector(MEGD)
has contributed
directly manages
to the29changesriver inbasinsgroundwaterin the country
level in each
(MNET,
basin2013)of the (Fig.
entire 1.1).
country.
Areas at serious water risk are
predominantly
NICE also simulated
urban andthe
economic
spatial distribution
hubs such as
of Ulaanbaatar
relative contribution
and the Southern
averaged during
Gobi Mining
1997–
Hub
2000 (Figs(Banerjee
4.5a–c)et andal.,2015–20182014). To(Figsevaluate
4.5d–f)thecompareddegradation
to thatofduring
water1980
availability
in all Mongolian
due toanthropogenic
river basins. Overall,
activityit andis interestingclimate changethat there
in theseare spatial
two contrasting
variationshubs,
in thethedominant
groundwater
waterlevel
uses
hasand been
their monitored
ratios (Figs
from
4.5a,b,the summer
and 4.5d,e),of 2018
and(Nakayama
that they greatly
et al., 2021a).
affect theThen,changes
the processin the
basedgroundwater
National
level
Integrated(Fig. 4.5c,f).
Catchment-based
While changes
Eco-hydrology
in precipitation(NICE)
substantiallymodel (Nakayama,
impacted changes
2014,
2015)
in groundwater
was appliedlevelsto twountil
contrasting
2000 (Fig.
river
4.4),
basinsthe including
model clarified
the abovethat two
human
hubs,activities
the Tuulhave
and
Galba
recentlyRiver
impactedBasins.
groundwater
The model levelsuccessfullychangessimulated
in addition
thetoeffects
precipitationof past climate(Figs 4.5d–f).
changeThis
andhuman-induced
trend is particularly
disturbances,
conspicuous
such inas riverurbanization,basins withmining,significant
and livestockurbanization
herding,andonminingwater
resourcesdevelopment,
duringsuchtheaslast
thefour
Orkhon,decades
Kharaa,(Nakayama
Tuul, etGalba,
al., 2021a,b).
Ongi, Altain Uvur Govi, and TaatsRiver
Mongolia
Basins (Nakayama
is now seeking
et al.,a 2023).
smooth transition in its socioeconomic structure from a weakrural economy to a fast-developing mixed economy characterized by rapid growth inurbanization, industrialization, and mining for sustainable development (ADB, 2020). Under
such conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
(MEGD, 2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
for its municipal and industrial water supply (Jamsran et al., 2018). In contrast, the Galba River
Basin (Galba–Uush–Doloodiing Gobi River Basin) is in the southern Gobi Desert and has
experienced extensive mining activities, such as Oyu Tolgoi (OT) and Tavan Tolgoi (Oyu
Tolgoi LLC, 2013). The river basin organizations in these two regions are particularly
challenged to meet the projected water demand of the rapidly growing urban population,
expanding mining activity, and becoming sustainable (ADB, 2014; Ulaanbaatar, 2018). Other
river basin organizations are relatively young and whether these have performed sufficient
functions against rapid and broad socioeconomic development is difficult to say. One reason
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilization of water resources as the research scope.
In this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 61
70 - 71 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

Abstract
In Mongolia, groundwater overuse and degradation are serious issues. In previous chapters,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, and NICE-INVERSE has been used to urban and mining hubs to quantify
spatiotemporal variations in water availability and estimate the parameters of anthropogenic
water use. In this chapter, NICE was further scaled up to 29 river basins in the entire country at
0.1° × 0.1° resolution. The model simulated the effects of past climate changes and humanactivity on a broad scale of water resources during 1980–2018. The model reasonably
reproduced the observed river discharge with a maximum value during the summer rainy season.
The simulation also revealed the heterogeneous distributions of hydrological budget and its
responses to climatic and anthropogenic disturbances. In addition, hotspots of groundwater
degradation caused by anthropogenic activity were detected on a national scale. Analyzing therelative contribution of environmental factors further clarified the characteristics in these areas
and quantified the spatiotemporal trends in groundwater levels due to the effects of changes inprecipitation and various water uses. Generally, the results show that changes in precipitation
substantially affected changes in groundwater levels until 2000. In contrast, the model clarified
that recent human activities have significantly impacted the changes in groundwater level. This
trend was particularly conspicuous in river basins with urbanization and mining development,such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. Thismethodology is powerful for resolving future competition for water resources in areas with lessinventory data, which could potentially trigger conflicts among urban, mining, industry, herders,
and local communities.
Keywords: eco-hydrology model; groundwater degradation; national scale; relative

Figure4.4 Relative
contributionfactors;of various
wateruses to changes in groundwater level duringcontributionof environmentalriver
basins1980–2018 compared to the reference simulation in the Mongolian river basins- 60
71 - 72 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

4.1 Introduction
As introduced in the previous chapters, Mongolia has undergone rapid urban expansion,mining development, and agricultural land development that promoted public settlement due tothe rapid socioeconomic changes that accompanied the introduction of the market economy in1990. The country has achieved a high economic growth rate and social development in recent
years, as have emerging countries worldwide. However, it causes various ecosystemdegradations such as water resource depletion, water pollution, thawing permafrost,desertification, and pasture degradation, in addition to the impact of climate change (Banerjee
et al., 2014) (Picture 4.1). Particularly, a comprehensive basin management of water resources
should be immediately implemented because >80% of water resources are groundwaterdependent, and the excess use and depletion of groundwater has become a serious problem inrecent years. The Ministry of Environment and Green Development (MEGD) directly manages
29 river basins in the country (MNET, 2013) (Fig. 1.1). Areas at serious water risk are
predominantly urban and economic hubs such as Ulaanbaatar and the Southern Gobi Mining
Hub (Banerjee et al., 2014). To evaluate the degradation of water availability due toanthropogenic activity and climate change in these two contrasting hubs, the groundwater levelhas been monitored from the summer of 2018 (Nakayama et al., 2021a). Then, the processbased National Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2014,2015) was applied to two contrasting river basins including the above two hubs, the Tuul andGalba River Basins. The model successfully simulated the effects of past climate change andhuman-induced disturbances, such as urbanization, mining, and livestock herding, on water
resources during the last four decades (Nakayama et al., 2021a,b).
Mongolia is now seeking a smooth transition in its socioeconomic structure from a weak
Figure4.5 Spatialof relative
contribution
during by(left)
1997–2000
rural economy
to distributiona fast-developing
mixed
economyaveraged
characterized
rapid
growthandin(right)2015–2018
comparedtothatduring1980
intheMongolianriver
basinsurbanization, industrialization, and mining for sustainable development (ADB, 2020). Under
such conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
4.4
Conclusion(MEGD,
2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
for its municipal and industrial water supply (Jamsran et al., 2018). In contrast, the Galba RiverThis
chapter reports the applicationof NICE,a process-based
eco-hydrology
model,
29
Basin(Galba–Uush–Doloodiing
Gobi River
Basin)
is in the southern
Gobi Desert
andtohasriverbasins inextensive
Mongoliamining
as an extension
the workreportedprevious
The model
experienced
activities,ofsuch
as OyuTolgoiin(OT)
and chapters.
Tavan Tolgoi(Oyu
simulates
the effects
past climatechangeand human activity
waterregions
resources
1980–
Tolgoi LLC,
2013).ofTheriver basinorganizationsin theseontwo
areduringparticularly
2018
in eachtobasin.
reasonably
reproduced
values ofurbanriver discharge
atchallenged
meetThethe model
projected
water demandof the observed
rapidly growing
population,
54
points during
last twoand
decades,and sustainable
the discharge
generallya maximum
expandingminingthe
activity,
becoming(ADB,
2014;reached
Ulaanbaatar,
2018).during
Other
the
rainfall season.areThe
results also
revealin thehave
spatialdistributionof theriversummer
basin organizations
relatively
young
anddifferences
whether these
performed
sufficienthydrologicalbudgetandandits responses
to climatic andanthropogenic
disturbances.
addition,functions against
rapid
broad socioeconomic
development
is difficult
to say. InOnereasonanalyzingtherelative
contributionofenvironmental
factors
helpedus
toclarifythefor this is that the mechanism of the hydrological cycle in all basins and its changes due towater
past
degradation
hotspotsquantify
interannualor temporal
trends inandgroundwater
human activities
and andclimatechangehave not variation
been quantitatively
evaluatedshould belevelsdue beyond
to the changes
precipitationand various
sectors ofofwatertheimprovedpreviousinstudies.
The successful
application
NICEuse.
in Particularly,
existing studies
model
clarifiedin addition
human
substantially
impacted(Nakayama
et al.,that
2021a,b)enablestoitsprecipitation,
expansion fromthese activities
two hubs tothe entire country.
Achangesgroundwaterlevelofafter
2000.anthropogenic
This trend hasdisturbances
been conspicuous
inavailability
river basinsinwith
model toin
evaluate
the impact
variouson waterthesignificant
urbanization
development
years.developed
Thus, thisfor
methodology
isentire Mongolian
Basinand(29mining
river basins)shouldinberecent
urgently
the effective
usefulfor evaluating
spatiotemporalvariations
in water availability for the effective utilization
utilizationof water resources
as the researchscope.
of waterin regionswith lessinventorydata. toIt 29
is alsopredicting(Fig.
andIn thisresources
chapter, theprocess-based
NICE
was appliedriverusefulbasinsforin Mongolia
resolvingcompetition
for water
resourcesthat could
trigger
between1.1) as anfuture
extensionof recentstudies(Nakayama
et al.,potentially2023). Basedonconflicts
the backgroundurban
areas,
miningcompanies,
herders,andlocalcommunities
acrosstheentirecountry.
described above, three basic scientific questions were addressed: (i) How has past climate- 61
72 - 73 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin
CGER-I167-2023, CGER/NIES

Abstract
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4.1 Introduction
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such conditions, the above 29 river basin organizations should play a role in this transition byimplementing effective water management based on integrated water resource management(IWRM). The first river basin organization was established in the Tuul River Basin in 2012
This article was published in Ecohydrology and Hydrobiology, 23(4), Nakayama, T., Okadera, T., Wang, Q.,(MEGD, 2012), where the river flows through Ulaanbaatar and groundwater is drawn heavily
Impactvarious anthropogenic
disturbances
on wateravailability
entire Mongolianbasinsfor
its ofmunicipal
and industrial
water supply(Jamsran
et inal.,the2018).
In contrast,
the towards
Galba River
Basin
Gobi River
Basin)
is in the southern Gobi Desert and has
effective(Galba–Uush–Doloodiing
utilization of water resources, Copyright
Elsevier(2023).
experienced extensive mining activities, such as Oyu Tolgoi (OT) and Tavan Tolgoi (Oyu
Tolgoi LLC, 2013). The river basin organizations in these two regions are particularly
challenged to meet the projected water demand of the rapidly growing urban population,
expanding mining activity, and becoming sustainable (ADB, 2014; Ulaanbaatar, 2018). Other
river basin organizations are relatively young and whether these have performed sufficient
functions against rapid and broad socioeconomic development is difficult to say. One reason
for this is that the mechanism of the hydrological cycle in all basins and its changes due to pasthuman activities and climate change have not been quantitatively evaluated and should be
improved beyond previous studies. The successful application of NICE in existing studies(Nakayama et al., 2021a,b) enables its expansion from these two hubs to the entire country. A
model to evaluate the impact of various anthropogenic disturbances on water availability in theentire Mongolian Basin (29 river basins) should be urgently developed for the effective
utilization of water resources as the research scope.
In this chapter, the process-based NICE was applied to 29 river basins in Mongolia (Fig.
1.1) as an extension of recent studies (Nakayama et al., 2023). Based on the backgrounddescribed above, three basic scientific questions were addressed: (i) How has past climate- 61
74 - 75 -Chapter 4 Impact of Anthropogenic Activity and Climate Change on Water Resources in Entire Mongolian Basin

Abstract
In Mongolia, groundwater overuse and degradation are serious issues. In previous chapters,
National Integrated Catchment-based Eco-hydrology (NICE), a process-based eco-hydrology
model, and NICE-INVERSE has been used to urban and mining hubs to quantify
spatiotemporal variations in water availability and estimate the parameters of anthropogenic
water use. In this chapter, NICE was further scaled up to 29 river basins in the entire country at
0.1° × 0.1° resolution. The model simulated the effects of past climate changes and humanactivity on a broad scale of water resources during 1980–2018. The model reasonably
reproduced the observed river discharge with a maximum value during the summer rainy season.
The simulation also revealed the heterogeneous distributions of hydrological budget and its
responses to climatic and anthropogenic disturbances. In addition, hotspots of groundwater
degradation caused by anthropogenic activity were detected on a national scale. Analyzing therelative contribution of environmental factors further clarified the characteristics in these areas
and quantified the spatiotemporal trends in groundwater levels due to the effects of changes inprecipitation and various water uses. Generally, the results show that changes in precipitation
substantially affected changes in groundwater levels until 2000. In contrast, the model clarified
that recent human activities have significantly impacted the changes in groundwater level. This
trend was particularly conspicuous in river basins with urbanization and mining development,such as the Orkhon, Kharaa, Tuul, Galba, Ongi, Altain Uvur Govi, and Taats River Basins. Thismethodology is powerful for resolving future competition for water resources in areas with lessinventory data, which could potentially trigger conflicts among urban, mining, industry, herders,
and local communities.
Keywords: eco-hydrology model; groundwater degradation; national scale; relative
contribution of environmental factors; river basins- 60 - 76 -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29
CGER-I167-2023, CGER/NIES

Chapter 5
Final Conclusions and Future Work

- 75 - 77 -

Chapter 5

Final Conclusions and Future Work

- 76 - 78 -

CGER-I167-2023, CGER/NIES

5.1 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, 2023;
Nakayama and Fujita, 2010; Nakayama and Hashimoto, 2011; Nakayama and Maksyutov,
2018; Nakayama and Osako, 2023a,b; Nakayama and Pelletier, 2018; Nakayama andShankman, 2013a,b; Nakayama and Watanabe, 2004, 2005, 2006a,b, 2008a–c; Nakayama etal., 2006, 2007, 2010, 2012, 2021a,b, 2023) (Fig. 1.3). NICE has been applied to various
basins/catchments on local and regional scales, such as the Changjiang and Yellow Rivers in
China, Ob River in West Siberia, Mekong River in Southeast Asia, Tokyo Metropolitan area inJapan, Kushiro Wetland (the largest wetland in Japan), and Lake Kasumigaura catchment (ahighly eutrophic lake in Japan) in the previous monograph series (Parts I–IV) as well as global
and continental scales (Part V) (Fig. 1.4). In this monograph (Part VI), NICE was further
extended to couple with the inverse method for sensitivity analysis and parameter estimation ofanthropogenic water use (NICE-INVERSE) as a case study in the arid and semi-arid regions ofMongolia (Fig. 1.5).
Chapter 2 reports the building of a high-resolution data grid representing water use forlivestock, urban populations, and mining in the entire basin by combining a global dataset,statistical data, GIS data, observational data, and field surveys to improve the accuracy of themodel results. The NICE simulated the effects of past climate change and human-induced
disturbances in urban and mining hubs on water resources during 1980–2018 (Nakayama et al.,2021a).
Chapter 3 reports the further extension of NICE to couple it with the inverse method forsensitivity analysis and parameter estimation of anthropogenic water use (NICE-INVERSE).
The results clarified that the substantial increase in water stress from groundwater degradation
was mainly caused by urbanization and local mining, in addition to climate change (Nakayama
et al., 2021b).
Chapter 4 reports the scaling up of NICE to 29 river basins throughout Mongolia. Hotspotsof groundwater degradation caused by anthropogenic activity were detected on a national scale.
Analyzing the relative contribution of environmental factors further clarified the characteristics
in these areas and quantified spatiotemporal trends in groundwater levels due to the changes inprecipitation and various water uses (Nakayama et al., 2023).
The model simulated the effects of past climate change and human activity on water
resources during 1980–2018 using a gridded dataset representing various water uses in
Mongolian river basins. Frequent increased temperatures, extreme flooding (Picture 5.1), andheavy winter precipitation in recent years have decreased water supply reliability (UNEP, 2009;
FAO, 2012). Although NICE is a useful tool for improving the accuracy of hydrologic budgets
in basins, more spatiotemporal input data on water use in various sectors are required (Fig. 4.1).
The use of more ground data, GPS tracking, image analysis of drones, and aerial photographs(Payne et al., 2020), and the application of a learning algorithm, such as random forest, toimprove census data downscaling (Nicolas et al., 2016) are necessary to improve the accuracyof the heterogeneous distribution of water uses and their seasonal variations on a national scaleby extending the results of a previous study (Nakayama et al., 2021b).
Analyzing the relative contributions also supported the simulated results, showing adecrease in the groundwater level in some basins and quantified the large impact of water use,
particularly due to urbanization, mining, and industry, on groundwater degradation around the- 77 - 79 -

Chapter 5

Final Conclusions and Future Workwater sources (Fig. 4.5). Since >80% water resources in Mongolia are groundwater-dependent,
and deep aquifers have been continuously degraded by urban and mining overuse during thelast few decades (IFC, 2017), the results of this study imply the need to make proper use ofgroundwater resources based on scientific results at the basin scale. The future water demandin Ulaanbaatar and large new mines in Southern Gobi are expected to exceed the maximum
available resources in the near future (Banerjee et al., 2014). Thus, a method for the effective
utilization of groundwater resources with a low environmental load on domestic water use in
Ulaanbaatar (Fig. 4.4b), industrial water use in northern industrial cities (Fig. 4.4c), and mining
water use in Southern Gobi (Fig. 4.4e) should urgently be developed. NICE is a useful tool for
predicting and resolving future competition for water resources, which could potentially trigger
conflicts between mining companies, herders, and local communities (Banerjee et al., 2014).
The model clarified that in addition to precipitation, human activities substantially impact
the changes in groundwater level after 2000. This trend has been conspicuous in river basins
with significant urbanization and mining development in recent years. This methodology isuseful for evaluating spatiotemporal variations in water availability for effective utilization ofwater resources in regions with limited inventory data. The future effects of extensive
groundwater extraction have not been clarified yet. Therefore, it is also a useful tool to predict
and resolve future competition for water resources, which could potentially trigger conflicts
between urban areas, mining companies, herders, and local communities.

Picture 5.1 Effect of extreme flood; water tank before (May 2018) and after (September 2018) theflood

5.2 Future Work
This monograph (Part VI) indicates that the model clarified that in addition to climatechange, human activities have recently substantially impacted hydrological changes in the arid
and semi-arid regions of the Mongolian river basins. This trend was particularly conspicuousin river basins with urbanization and mining development, such as the Orkhon, Kharaa, Tuul,
Galba, Ongi, Altain Uvur Govi, and Taats River Basins. This methodology is also useful for- 78 - 80 -

CGER-I167-2023, CGER/NIES

resolving future competition for water resources in areas with limited inventory data
(Nakayama et al., 2021a,b, 2023).
However, there are areas of improvement in water use in various sectors. NICE-INVERSE
is coupled with precise input data for water use, such as those for industries, energy,
construction, transport, and utility services (ADB, 2014; Ulaanbaatar, 2018). The future effectsof these extensive groundwater extractions should be clarified using process-based models.
Reservoirs are also being planned in the Tuul River Basin to meet the increasing water demandof Ulaanbaatar. In contrast, the Orkhon–Gobi water transfer project has been planned to supply
water from the Orkhon River in northern Mongolia to several mines in the South Gobi via a>700 km long pipeline to meet the high water demand of mines. Thus, the effects of thesehydraulic structures on hydrological changes and biogeochemical cycles should be estimatedon a national scale under future climate projections in the future (Nakayama and Shankman,
2013; Nakayama and Pelletier, 2018). In addition to water shortage, organic matter
contamination, particularly from toilets, is another problem in Ulaanbaatar, whereas heavy
metal pollution is a serious problem in South Gobi. In future, the NICE should evaluate water
quality in addition to hydrological cycle imbalances.
Plastic pollution is also another serious problem in recent years in Mongolia, as well asother developing economies. This is mainly caused by inappropriate waste management owing
to a lack of understanding of the behavior of plastic waste (Battulga et al., 2019). To solve thisplastic problem, now NICE is being extended to couple with plastic debris model on bothregional and global scales with respect to the following (Nakayama and Osako, 2023a,b):(i) A new model coupled with NICE and a plastic debris model should include advection,
dispersion, diffusion, settling, dissolution, and deterioration due to light and temperature (Kooiet al., 2017) (Fig. 5.1). The model will help identify plastic pollution hotspots and sinks andresolve the source–flux–sink nexus within river basins (Windsor et al., 2019). The results also
help quantify plastic waste impacts on terrestrial and aquatic ecosystems and may be useful fordevising solutions and measures to reduce plastic input to the ocean on a regional scale.
(ii) The model should be expanded to the global scale in the same manner as in the previous
monograph series (Part V). Previous studies have clarified the dominant contribution of thesummer monsoon in Asia and that more than 74% waste is delivered between May and Octoberfrom few large rivers in Asia (Lebreton et al., 2017; Best, 2018). Uncertainty and sensitivity
analyses using Monte Carlo model simulations are useful for clarifying most plastic fluxes
originating from few global rivers, particularly in Asia (Fig. 5.2).
(iii) The interaction with suspended matter (heteroaggregation), 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. 5.3). In the future, NICE-BGC
should be expanded to include heteroaggregation, resuspension, biofouling, and wind effects toimprove the accuracy of the plastic cycle.
(iv) The model should include the effects of waste plastic trade and disposal between exportingand importing countries (Brooks et al., 2018). Therefore, the fate and transport models should
be combined with social system methods such as material flow analysis to assess emissions into
the environment under climate change conditions (OECD, 2022).
Plastic pollution is considered a serious environmental problem, and pollutants from land
to rivers and finally to oceans severely affect human health and the environment (Siegfried etal., 2017). Previous studies on the origin and fate of plastic waste in freshwater systems- 79 - 81 -Chapter 5

Final Conclusions and Future Worksuggested that land-derived plastics are the dominant source of marine plastic pollution(Thompson et al., 2004). From this viewpoint, developing a framework to reverse thedegradation of global water resources, particularly plastic pollution, is crucial 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 Conference of the Parties (COP) as a part of global warming
countermeasures. Quantitative assessment of plastic fate and transport will be necessary forrealizing of this vision because countries around the world 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 a monograph (Part VII) in the
future.

Figure 5.1 Flow diagram of macro and micro plastic transport in land and inland water byextending NICE

- 80 - 82 -

CGER-I167-2023, CGER/NIES

Figure 5.2 Seasonal variations of (a) river discharge and (b) total, (c) macro, and (d) micro plastic
flux in each continent (Asia, Europe, Oceania, Africa, North America, and SouthAmerica)- 81 - 83 -Chapter 5

Final Conclusions and Future Work

Figure 5.3 Flow diagram of the original NICE regarding water resources, and recent developmentof the eco-hydrological and biogeochemical coupling model (NICE-BGC)

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Chapter 5

Final Conclusions and Future Work

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
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- 84 - 88 -

CGER’S SUPERCOMPUTER MONOGRAPH REPORT Vol.29
CGER-I167-2023, CGER/NIES

Appendix
Publications and Presentations- 87 - 89 -Appendix Publications and Presentations

Publications and Presentations
Original Papers and Reviews Related to This Monograph:
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(3), 490-500, doi:10.1016/j.ecohyd.2021.07.006
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

Books Related to This Monograph:
Wang, Q., Nakayama, T., Okadera, T. (2022) Evaluation of the pasture carrying capacity and its vulnerability
based on water resources in arid and semi-arid regions. NIES Research Project Report, No.139,https://www.nies.go.jp/kanko/tokubetu/setsumei/sr-139-2021b.html
Wang, Q., Okadera, T., Nakayama, T., Deni, E. (2022) Towards symbiosis with grasslands -Vulnerability
assessmentofpasturesin
Mongolia-.
NIES
Research
Booklet,No.83,
https://www.nies.go.jp/kanko/kankyogi/83/02-03.html

Conference Reports Related to This Monograph:
Nakayama, T., Wang, Q., Okadera, T., Deni, E. (2019) Evaluation of spatio-temporal variations of water
availability by using process-based eco-hydrology model in arid and semi-arid regions of Mongolia. session
number O15.03, ISEM 2019, Salzburg, Austria, 1 – 5 October (Website)
Nakayama, T., Wang, Q., Okadera, T., Batkhishig, O., Bayarsaikhan, U. (2020) Application of process-based ecohydrology model to urban and mining hubs in Mongolia. session number 3, ICOMP 2020, Ulaanbaatar,
Mongolia (Virtual Conference), 25 September (Website)
Nakayama, T., Wang, Q., Okadera, T. (2021) Quantifying impact of anthropogenic disturbances on water
availability and water stress in Mongolian urban and mining hubs by using process-based eco-hydrology
model. paper number H21H-06, AGU Fall Meeting 2021, New Orleans, USA (Virtual Conference), 13– 17
December (Website), doi:10.1002/essoar.10508192.1
Nakayama, T. (2022) Impact of anthropogenic disturbances on water availability in Mongolian urban and mining
hubs towards effective utilization of water resources. session number 4, 5th International Symposium of
Healthy Rivers and Sustainable Water Resources Management, Warsaw, Poland (Virtual Conference), 8 –
10 June (Website)
Nakayama, T. (2023) Impacts of anthropogenic activity and climate change on water resources for the whole ofMongolia by using process-based eco-hydrology model. paper number EGU23-10173, EGU General
Assembly 2023, Vienna, Austria (Virtual Conference), 23– 28 April (Website), doi:10.5194/egusphereegu23-10173

NICE Series in CGER's Supercomputer Monograph Report (Part I - V):
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- 88 - 90 -Appendix Publications and Presentations

CGER-I167-2023, CGER/NIES

Nakayama,
T. (2012)
Development of process-based NICE model and simulation of ecosystem dynamics in the
Publications
and Presentations
catchment 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 theOriginal
Papers and Reviews Related to This Monograph:catchment 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.,
Wang, Development
Q., Okadera, T.
Evaluationof modelspatio-temporalvariations
in water availability
using
Nakayama,
T. (2019)
of(2021a)
process-based
NICE
and simulationof ecosystem
dynamics intheacatchment
process-based
eco-hydrology
model
in
arid
and
semi-arid
regionsofMongolia.
EcologicalModelling,440,
of East Asia (Part V). CGER's Supercomputer Monograph Report, 26, NIES, 122p.,
109404, doi:10.1016/j.ecolmodel.2020.109404
http://www.cger.nies.go.jp/publications/report/i148/en/
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(3), 490-500, doi:10.1016/j.ecohyd.2021.07.006
Nakayama, T., Okadera, T., Wang, Q. (2023) Impact of various anthropogenic disturbances on water availability
Contactin thePerson
entire Mongolian basins towards effective utilization of water resources. Ecohydrology andHydrobiology, 23(4), doi:10.1016/j.ecohyd.2023.04.006

Tadanobu Nakayama
Books
Relatedto ThisConservation
Monograph:Division
Regional
Environment
National
Institute for Environmental Studies (NIES)
Wang, Q., Nakayama, T., Okadera, T. (2022) Evaluation of the pasture carrying capacity and its vulnerability
16-2based
Onogawa,
Tsukuba,
Ibaraki
305-8506,
Japanregions. NIES Research Project Report, No.139,
on waterresources
in aridand semi-arid
Phone:+81-29-850-2564
https://www.nies.go.jp/kanko/tokubetu/setsumei/sr-139-2021b.html
Wang, Q.,nakat@nies.go.jp
Okadera, T., Nakayama, T., Deni, E. (2022) Towards symbiosis with grasslands -Vulnerability
E-mail:assessmentofpasturesin
Mongolia-.
https://www.nies.go.jp/kanko/kankyogi/83/02-03.html

NIES

Research

Booklet,No.83,

Profile: https://www.nies.go.jp/researchers-e/100190.html
ORCID: https://orcid.org/0000-0002-8233-034X
Webof Science:https://www.webofscience.com/wos/author/record/938567
Conference
Reports
Related to This Monograph:
ResearchGate: https://www.researchgate.net/profile/Tadanobu_Nakayama

Nakayama, T., Wang, Q., Okadera, T., Deni, E. (2019) Evaluation of spatio-temporal variations of water
availability by using process-based eco-hydrology model in arid and semi-arid regions of Mongolia. session
number O15.03, ISEM 2019, Salzburg, Austria, 1 – 5 October (Website)
Nakayama, T., Wang, Q., Okadera, T., Batkhishig, O., Bayarsaikhan, U. (2020) Application of process-based ecohydrology model to urban and mining hubs in Mongolia. session number 3, ICOMP 2020, Ulaanbaatar,
Mongolia (Virtual Conference), 25 September (Website)
Nakayama, T., Wang, Q., Okadera, T. (2021) Quantifying impact of anthropogenic disturbances on water
availability and water stress in Mongolian urban and mining hubs by using process-based eco-hydrology
model. paper number H21H-06, AGU Fall Meeting 2021, New Orleans, USA (Virtual Conference), 13– 17
December (Website), doi:10.1002/essoar.10508192.1
Nakayama, T. (2022) Impact of anthropogenic disturbances on water availability in Mongolian urban and mining
hubs towards effective utilization of water resources. session number 4, 5th International Symposium of
Healthy Rivers and Sustainable Water Resources Management, Warsaw, Poland (Virtual Conference), 8 –
10 June (Website)
Nakayama, T. (2023) Impacts of anthropogenic activity and climate change on water resources for the whole ofMongolia by using process-based eco-hydrology model. paper number EGU23-10173, EGU General
Assembly 2023, Vienna, Austria (Virtual Conference), 23– 28 April (Website), doi:10.5194/egusphereegu23-10173

NICE Series in CGER's Supercomputer Monograph Report (Part I - V):
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- 89
88 - 91 -Appendix Publications and Presentations

CGER-I167-2023, CGER/NIES

Nakayama,
T. (2012)
Development of process-based NICE model and simulation of ecosystem dynamics in the
Publications
and Presentations
catchment 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 theOriginal
Papers and Reviews Related to This Monograph:catchment 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.,
Wang, Development
Q., Okadera, T.
Evaluationof modelspatio-temporalvariations
in water availability
using
Nakayama,
T. (2019)
of(2021a)
process-based
NICE
and simulationof ecosystem
dynamics intheacatchment
process-based
eco-hydrology
model
in
arid
and
semi-arid
regionsofMongolia.
EcologicalModelling,440,
of East Asia (Part V). CGER's Supercomputer Monograph Report, 26, NIES, 122p.,
109404, doi:10.1016/j.ecolmodel.2020.109404
http://www.cger.nies.go.jp/publications/report/i148/en/
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(3), 490-500, doi:10.1016/j.ecohyd.2021.07.006
Nakayama, T., Okadera, T., Wang, Q. (2023) Impact of various anthropogenic disturbances on water availability
Contactin thePerson
entire Mongolian basins towards effective utilization of water resources. Ecohydrology andHydrobiology, 23(4), doi:10.1016/j.ecohyd.2023.04.006

Tadanobu Nakayama
Books
Relatedto ThisConservation
Monograph:Division
Regional
Environment
National
Institute for Environmental Studies (NIES)
Wang, Q., Nakayama, T., Okadera, T. (2022) Evaluation of the pasture carrying capacity and its vulnerability
16-2based
Onogawa,
Tsukuba,
Ibaraki
305-8506,
Japanregions. NIES Research Project Report, No.139,
on waterresources
in aridand semi-arid
Phone:+81-29-850-2564
https://www.nies.go.jp/kanko/tokubetu/setsumei/sr-139-2021b.html
Wang, Q.,nakat@nies.go.jp
Okadera, T., Nakayama, T., Deni, E. (2022) Towards symbiosis with grasslands -Vulnerability
E-mail:assessmentofpasturesin
Mongolia-.
https://www.nies.go.jp/kanko/kankyogi/83/02-03.html

NIES

Research

Booklet,No.83,

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
Conference
Reports
Related to This Monograph:
ResearchGate: https://www.researchgate.net/profile/Tadanobu_Nakayama

Nakayama, T., Wang, Q., Okadera, T., Deni, E. (2019) Evaluation of spatio-temporal variations of water
availability by using process-based eco-hydrology model in arid and semi-arid regions of Mongolia. session
number O15.03, ISEM 2019, Salzburg, Austria, 1 – 5 October (Website)
Nakayama, T., Wang, Q., Okadera, T., Batkhishig, O., Bayarsaikhan, U. (2020) Application of process-based ecohydrology model to urban and mining hubs in Mongolia. session number 3, ICOMP 2020, Ulaanbaatar,
Mongolia (Virtual Conference), 25 September (Website)
Nakayama, T., Wang, Q., Okadera, T. (2021) Quantifying impact of anthropogenic disturbances on water
availability and water stress in Mongolian urban and mining hubs by using process-based eco-hydrology
model. paper number H21H-06, AGU Fall Meeting 2021, New Orleans, USA (Virtual Conference), 13– 17
December (Website), doi:10.1002/essoar.10508192.1
Nakayama, T. (2022) Impact of anthropogenic disturbances on water availability in Mongolian urban and mining
hubs towards effective utilization of water resources. session number 4, 5th International Symposium of
Healthy Rivers and Sustainable Water Resources Management, Warsaw, Poland (Virtual Conference), 8 –
10 June (Website)
Nakayama, T. (2023) Impacts of anthropogenic activity and climate change on water resources for the whole ofMongolia by using process-based eco-hydrology model. paper number EGU23-10173, EGU General
Assembly 2023, Vienna, Austria (Virtual Conference), 23– 28 April (Website), doi:10.5194/egusphereegu23-10173

NICE Series in CGER's Supercomputer Monograph Report (Part I - V):
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- 88
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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)

Vol. 19

CGER-I108-2013 (Out of stock)
KOMORI S.: Numerical Simulations of Turbulence Structure and Scalar Transfer across the Air-Water
Interfaces

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Vol. 20

CGER-I114-2014 (Out of stock)
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

Vol. 22CGER-I127-2016
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

CGER-I148-2019
NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem Dynamics
in the Catchment of East Asia (Part V)

Vol. 27

CGER-I153-2021
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
configurations

Vol. 28

CGER-I158-2022
YOKOHATA T.: Development of an integrated land surface model with ecosystems, human water
management, crop growth, and land-use change: MIROC-INTEG-LAND

Vol. 29

CGER-I167-2023
NAKAYAMA T.: Development of Process-based NICE Model and Simulation of Ecosystem
Dynamics in the Catchment of East Asia (Part VI)

レポートの多くは、地球環境研究センターのウェブサイトから PDF 形式で閲覧可能です。
https://www.cger.nies.go.jp/ja/activities/supporting/publications/report/
Many of the reports are also available as PDF files.
See: https://www.cger.nies.go.jp/ja/activities/supporting/publications/report/

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