Doctoral Thesis

Evaluation of Human Impacts on
Groundwater Resources in Mountainous
Catchments, Western JapanSHARON BIH KIMBI

Graduate School of Integrated Arts and Sciences
Hiroshima University

September 2022

Abstract
The long-term cumulative effect of increasing urbanization, population and climatechange on watershed hydrology has altered the spatial availability of groundwater resources inmany regions. In recent years, demographic trends have led to concerns on how population
growth will affect water resources. In many Asian and Western countries, population densities
are declining despite global population growth with a significant increase in the proportion of
the elderly. With an increasing number of citizens over 65 and many cities experiencing
depopulation, Japan has experienced this demographic change faster than most Western andAsian countries. Despite these social changes, it still faces serious environmental problems
related to groundwater quality and quantity. Population growth and increasing economic
condition have led to the continuous expansion and transformation of many Japanese’s
mountainous rural catchments with associated groundwater resource problems. Despite thesignificant impact of urbanization (population growth) on groundwater resources, most studies
have focused on qualitative, regional impact, with little attention focused on the combine
effects of social and environmental issues on groundwater resources in mountainous ruralcatchments. Therefore, it is necessary to understand how environmental and social aspects
affects groundwater resources. This study provides an insight into groundwater resources
degradation resulting from an aging and depopulation society as well as urbanization andclimate change effects.
The effects of population aging and depopulation on groundwater nitrate contaminationin an island with intensive citrus cultivation, was examined by comparing two neighbouring
villages and watersheds with little different in their social aspects. The northern one (Kubi) has
a little higher farmer average age of 76 years, and the decreasing rate of farmland for 10 years
from 2005 to 2015 of 46%, as compared with the eastern (Ocho) of 73 years and 37%,i

respectively. The population in Ocho was 830, and twice of that in Kubi. Higher mean
concentrations of NO3−−N were recorded in Kubi village (6.55 mg/L) than in the Ocho area
(4.75 mg/L). NO3−−N contamination mainly was estimated to originate from chemical fertilizer
in Kubi and Organic fertilizer in Ocho, using stable nitrogen isotopes and a Bayesian isotope
mixing model. These source distributions were closely associated with a greater extent land
use and social aspects such as aging and depopulation. A substantial quantity of chemicalfertilizer applied by the more aged farmers in more depopulated Kubi was suggested to lead toinefficient nitrogen uptake by plants, which results in higher leaching that pollutes its
groundwater more than those of Ocho.
Likewise, the hydrochemistry and dual isotope approach was used to evaluate
groundwater contamination status in Saijo watershed. Results showed that, calcium (Ca2+) andbicarbonate (HCO3−) were the most abundant cation and anion respectively and the major watertype in the study area was of Ca–Mg–HCO3 type. Inferring from the contamination sources in
the watershed using δ15N-NO3− and δ18O-NO3⁻ measurements indicated that two majorcontamination sources with values characteristic for nitrate derived from nitrification of soilorganic nitrogen (+0.2 ‰ to +7.3 ‰ for δ15N) and nitrate derived from animal wastes or humansewage, e.g., via septic systems (+8.9 ‰ to + 12.3 ‰ for δ15N) in two distinct zones. Despite
agricultural activities in the study area, we found no evidence of nitrate originating fromsynthetic fertilizers in the groundwater.
Furthermore, the spatial variation in groundwater recharge under the effects ofurbanization and climate change, was investigated using SWAT over a period of three decades.
Results suggest, the water balance in the last 30yrs is greatly influenced by land-use changewith urbanization. The most significant change was observed in the decrease in the meanannual groundwater recharge by approximately 25% (i.e., from 184.5 mm in the 1980s to 120.2
mm in the 2000s). Rice paddy areas are observed to be the major recharge zones in theiicatchment, with the highest recharge compared to forest and residential areas. Higher rainfall
interception by forest canopy were responsible for the lower recharge values observed atforested sites. Furthermore, the predicted scenario highlighted that groundwater recharge will
be significantly affected by future climate change which is projected to decrease groundwater
recharge by 29% in the next 30years. Land and water resource management can be improved
if the findings from the study are used by decision-makers, planners, and managers the region.
planners, and managers the region.

iii

Contents
AbstractiContents

iv

List of figuresvii

List of Tablesvii

Acknowledgements

xiii

Chapter 1

1Introduction

1

1.1 Background of Study

1

1.2 Problem Statement5

1.3 Objective

6

1.4 Structure of Thesis

7

1.5 References

9

Chapter 2

13

Study Sites and Analytical Methods

13

2.1 Location and Description
2.1.1 Kurose River catchment and Saijo watershed
2.1.2. Osakishimojima Island

13
13
15

2.2 Research Methods
2.2.1 Hydrochemical approach
2.2.1.1 Pre-field, field, and Post field work
2.2.1.2 Data Analysis and graphical methods
2.2.1.3 Multivariate statistical method
2.2.2 The Soil and Water Assessment Tool
2.2.2.1 Input data and model setup
2.2.2.2 Model calibration
2.2.2.3 Model Evaluation and Statistical methods

17
18
18
20
21
21
25
28
30

2.3 References

31

Chapter 3

35Social Impacts on Groundwater Nitrate Contamination Status and its Sources on an
Island with Intensive Citrus Cultivation
35
3.1. Introduction

35

3.2 Location and description of the study area38

iv

3.3 Materials and Method
3.3.1 Sample collection
3.3.2 Source apportionment calculation
3.3.3 Statistical Method

41
41
43
44

3.4 Results and Discussion
3.4.1 General hydrochemistry
3.4.2 Spatio-temporal variations in the NO3−−N concentration
3.4.3 Relationship between NO3−−N and water chemical parameters
3.4.4 Principal component analysis results
3.4.5 Distribution characteristics of stable isotopes

45
45
49
51
54
56

3.5 Source apportionment of NO3⁻60

3.6 Summary

64

3.7 References

65

Chapter 4

72

Simulation of daily streamflow with SWAT illuminates the impact of land-use changewith urbanization and climate change on groundwater recharge over the last three
decades72
4.1 Introduction

72

4.2 Study catchment

76

4.3 Materials and Methods
4.3.1 Soil and Water Assessment Tool (SWAT)
4.3.2 Model evaluation, statistical methods, and scenario setup

81
81
82

4.4 Results and Discussion
4.4.1 Evaluation of the main parameters and the simulation accuracy
4.4.2 Variation in water balance from the 1980s to 2000s
4.4.2.1 Water balance in a catchment scale
4.4.2.2 Water balance in land use unit
4.4.2.3 Average monthly groundwater recharge
4.4.2.4 Groundwater recharge ratios in three decades
4.4.3 Spatial Variation of groundwater recharge
4.4.4 Relative urbanization effect (spatial variation in forest ET: 1980s − 2000s)
4.4.5 Land use change and climate change scenarios

83
83
85
85
87
90
92
94
97
98

4.5 Summary

100

4.6 References

100

Chapter 5

100

Groundwater Contamination Status in Saijo Basin. Insights From Hydrochemistry andDual Isotope
108
5.1 Introduction

108v

5.2 Materials and Methods

109

5.3 Results and Discussion
5.3.1 Characterization of major ions in different water sources
5.3.2 Gibbs plot to determine the geochemical process prevailing

110
110
114

5.4 Potential Sources of Nitrate-Nitrogen in Groundwater Systems

116

5.5 Nitrate isotopes for the identification of sources and fate117

5.6 Summary

120

5.7 References

120

Chapter 6

123

Discussion

123

6.1 Anthropogenic influence on groundwater resources
123
6.1.1 Groundwater recharge assessment from hydrological model
123
6.1.2 Influence of social aspects on groundwater nitrate nitrogen contamination: Casestudy; Saijo and Osakishimojima
125
6.2 References

127

Chapter 7

127Conclusion and Recommendation

128

7.1 Conclusion

128

7.2 Recommendation

129

Appendix160

viList of figures
Chapter 1
Figure 1.1: Ratio of population aged ≥ 65 to total population, 1950–2050 (Source:United
Nations, Department of Economic and Social Affairs, Population Division, 2015.

2

Figure 1.2: Structure of thesis.

7

Chapter 2
Figure 2.1: Location of study sites in Higashihiroshima city (A) Kurose River Catchment
located in center Higashihiroshima city, (B) Saijo Watershed located in eastern Kurose River
Catchment, and (C) Osakishimojima Island.

13

Figure 2.2: Population trend in Higashihiroshima from 1974 in four of its major towns.

14

Figure 2.3: Land use change from 1987 to 2009 in the Kurose River Catchment.

15

Figure 2.4: Outcrop geological map of Osakishimojima Island.

16

Figure 2.5: Land use of Osakishimojima Island.

17

Figure 2.6: Piper diagram; cations and anions are expressed as percentages of total in
21

meq/L.

Figure 2.7: Schematic diagram of the hydrological cycle and SWAT simulation processes
22(Neitsch et al. 2005.; Nasiri et al. 2020).

Figure 2.8: Input dataset in to ArcSWAT showing Digital Elevation Model (DEM), subbasins
after watershed delineation, slope classes, land use classes and soil classes used in SWAT
model for HRU definition.

25

Figure 2.9: Model setup diagram for SWAT model in ArcGIS and ArcSWAT.

27

Chapter 3
Figure 3.1: (a) Location map of Osakishimojima Island in the Seto Inland Sea showing thesampling points in (b) Kubi village and (c) Ocho village.

vii

39

Figure 3.2: Land use of Osakishimojima, showing proportions of citrus orchards andresidential areas in (a) Kubi and Ocho lowland areas, (b) Kubi sampling area, and (c) Ocho
40

sampling area.
Figure 3.3: Piper diagram of surface water and groundwater in (a) Kubi and (b) Ocho

49

(Adopted after Piper, 1944).

Figure 3.4: (a) Osakishimojima Island. Spatial variation of NO3−−N and δ15NO3−−N
concentrations in groundwater of (b) Kubi and (c) Ocho villages.

50

Figure 3.5: NO3−−N (mg/L) concentration vs. well depth in shallow wells in Kubi and Ocho
51

villages.

Figure 3.6: Relationship between NO3−−N and (a) DOC, (b) Cl⁻, (c) SO42⁻, (d) Na⁺, (e) Mg2⁺,and (f) Ca2⁺.

53

Figure 3.7: Nitrate isotopic signatures for sampling points in Kubi and Ocho with typicalnitrate endmembers (adapted from Kendal et al., 2008).

58

Figure 3.8: Spatial proportional contributions of NO3⁻ sources estimated using SIAR in (a)Kubi and (b) Ocho villages. Boxplots illustrate the 50th, 75th and 95th percentiles from dark to
61

light.

Chapter 4
Figure 4.1: (a) Location of Higashihiroshima in Japan, (b) location of Kurose in
Higashihiroshima, (c) location of Kurose River catchment in Higashihiroshima city showing
meteorological station and river guaging station.

76

Figure 4.2: Land use map and areal extents of land use classes (Km2) for the years 1987
representing the 1980s period and 2009 representing the 2000s period in the Kurose River
77

catchment.

Figure 4.3: (a) Average annual air temperature and (b) average annual precipitation from78

1979−2010 in the Kurose River catchment.

Figure 4.4: (a) Average monthly precipitation and (b) average temperature in the 1980s and2000s in Kurose River catchment.

79

Figure 4.5: An outcrop geological map of Kurose River catchment.

80

viii

Figure 4.6: Comparison of the observed and simulated daily streamflow in the Kurose River
catchment area. Straight lines represent the border of the year for calibration and validation for84

the 1980s and 2000s period.

Figure 4.7: Average annual water balance ratio in the 1980s and 2000s in (a) catchment scaleand (b) per land use unit in the Kurose River catchment.

86

Figure 4.8: Monthly variation in groundwater recharge in (a) basin scale and (b) per land use
90-91

unit in the Kurose River catchment.

Figure 4.9: Scattered plot showing relation between annual groundwater recharge and annual
precipitation in (a) residential area, (b) forest area, and (c) rice paddy for the 1980s and 2000s.
92
Figure 4.10: Average annual distribution of groundwater recharge in the (a) 1980s, (b) 2000s;(c) relative change between 2000s and 1980s with different land use patterns.

94

Figure 4.11: Spatial distribution of groundwater level in the year (a) 1974, (b) 2000, and (c)groundwater level change from the 1974 and 2000 in the Kurose River catchment.

96

Figure 4.12: Change in forest Evapotranspiration in the (a) 1980s, (b) 2000s, and (c) relativechange in forest ET from 1980s to 2000s in the Kurose River catchment.

97

Chapter 5
Figure 5.1: Location map of study area showing sample collection points.

110

Figure 5.2a: Average relative concentrations (mg/l) of cations in the investigated water types.
112
Figure 5.2b: Average relative concentrations (mg/l) of cations and silica in investigated water112

types.
Figure 5.3: Piper trilinear diagram showing major groundwater types and geochemical
evolution in the Saijo basin.

113

Figure 5.4: Gibbs diagram representing controlling factors of groundwater quality.

114

Figure 5.5: Identification of dominant mineral weathering in groundwater.

115

ix

Figure 5.6: Relationship of NO3−−N vs SO42⁻, Cl⁻, EC, and DO in groundwater
116-117

samples.
Figure 5.7: Schematic diagram of the isotopic composition of typical nitrate sources

including data for groundwater, spring and wells in the vicinity of the groundwater source.
Typical ranges of δ15N-NO3⁻ and δ18O-NO3⁻ values for different nitrate sources were takenfrom Kendall (2001).

118

Figure 5.8: Relationship between NO3−−N and δ15N-NO3⁻.

119

Chapter 6
Figure 6.1: Actual evapotranspiration, runoff, leakages, and groundwater recharge for thetwo years of investigation (1980s and 2000s) for the Kurose River catchment.

x123

List of Tables
Chapter 2
Table 2.1: Land use classes used in ArcSWAT, area coverage, and comparison of land use
26change in 1987 and 2009.

Table 2.2: List of sensitive parameters and optimization ranges included in the final
29

calibration.
Chapter 3

Table 3.1: Statistical characteristics of the hydrochemical compositions of the groundwaterand surface water sources in Kubi and Ocho village.
46
Table 3.2: Correlation matrix between measured parameters in Kubi and Ocho villages.

52

Table 3.3: Principal component (PC) loadings of 11 chemical variables in the groundwater
25

samples of Kubi and Ocho.

Table 3.4: Isotopic data (‰) of sampled groundwater in Kubi and Ocho areas, October 2021.
56
Table 3.5: The δ15N and δ18O values of various nitrate sources.

60

Table 3.6: A summary of social aspects and groundwater NO3−−N contamination status in
63

Kubi and Ocho villages.
Chapter 4

Table 4.1: Daily calibration and validation statistics of the SWAT model in the Kurose River
catchment.
85
Table 4.2: Estimated groundwater recharge at average precipitation in the catchment.

93

Table 4.3: Mean annual Evapotranspiration (ET), surface runoff (SurfQ), and groundwater
recharge (RCHG) in land use and climate change scenario simulation.

xi

99Chapter 5
Table 5.1: Descriptive statistics of the different physicochemical properties of the collected
groundwater samples.

111

Table 5.2: δ15N-NO3⁻. and δ18O-NO3⁻ in groundwater water and surface water in Saijo.

117

xii

Acknowledgements
This PhD thesis would not have been achieved without the support and help of many
institutions and individuals that I met while conducting research for the PhD project. And forthis reason, I wish to acknowledge their contributionsFirst and foremost, I thank the Almighty God for giving me good health, courage,
endurance, and determination to successfully go through this academic process despite thenumerous challenges encountered during my studies.
I am highly indebted and thoroughly grateful to the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), Japan for funding the last three years of my PhD
research project.
It is with great appreciation that I acknowledge my supervisor, Professor Shin-ichi
Onodera, whose academic mentoring, constructive criticism and immense support haveenabled me to successfully complete this project, which is of great interest to me and society
as a whole. Furthermore, thank you for hosting me in your team and helping me to achieve alevel of research that I could not have imagined. You have triggered and nurtured myintellectual maturity. Being your student was an honour, Sensei.
I would like to thank Prof. Toshihisa Asano, Associate Professor Hisashi Ozawa,
Associate Professor Tadashi Yokoyama, and Prof. Itaru Nagasaka for their guidance andsupervisory roles during my PhD research project.
Special acknowledgement goes to Prof. Ichirow Kaihostu who provided the major input
and data set for my modelling programme as well as provided constructive criticisms andsuggestions throughout my research. I also owe special thanks to Associate Prof. Mitsuyo Saito
and Assistant Prof. Takuya Ishida from Hiroshima University who invested their time, appeal

xiii

knowledge and energy both during the fieldwork, analysis processing, and editing ofmanuscripts drafted for this PhD work.
My sincerest thanks go out to Dr. Yuta Shimizu from NAFRO, Japan, as well as Dr
Kunyang Wang from the Biogeochemical Laboratory at Hiroshima University for guiding,encouraging and supervising my PhD thesis modelling work. As a result of the numerous
workshops and one-on-one discussions I had with them, I gained a deeper understanding of thefactors governing groundwater occurrence and movement. Thanks to their constructive
criticism and valuable advice, I developed my research skills in modelling.
My profound gratitude to the Mamena Association, Osakishimojima, for providing
accommodation, data provision and their continued support when I was conducting fieldwork.
I owe special thanks to Dr. Fantong Wilson Yetoh for maintaining an open line ofcommunication despite his busy schedule, stimulating discussions, constructive criticism, andsuggestions throughout this PhD research project. In addition, I would like to give special
recognition to Dr Fantong Emilia, who provided moral support and advice throughout thewhole process.
I am also highly indebted and thoroughly grateful to my beloved family for supportingme throughout my research. Their prayers, words of encouragement, and moral support goesbeyond words can express. Many thanks also go to colleagues and friends at Hiroshima
University for their stimulating discussions, constructive criticisms and suggestions during our
PhD Research Seminars and their support in any respect during the PhD research project. Many
thanks also to Yusuke Tomozawa, Shingo Nozaki, and Tamura Masayuki for their kindassistance during fieldwork and laboratory analysis.
Throughout this project, I am deeply indebted to Dr. Anna Fadliah Rusydi of theIndonesian Institute of Sciences, Indonesia, and Dr. Naohiro Imamura of the Forestry andxivForest Products Research Institute, Japan, for their kind assistance and collaboration; foralways motivating me and becoming an inspiration and role model of a hard-working person
to emulate. Dr Anna Fadliah Rusydi, thank you for teaching me how to operate the SWAAT
analysis tool, which is a fundamental part of my results. I also appreciate your unwavering
support, comforting words, and motherly advice, which always encourages me when I feel
downcast. I look forward to having many more opportunities to learn from you in the future.
Lastly, this acknowledgement would not be complete if I failed to thank all those who directly
or indirectly contributed to this thesis and whose names have been forgotten.

xv

Chapter 1
Introduction
1.1 Background of Study
According to Rockstrom et al. (2009), planetary boundaries represent the limits at
which environmental changes take place and Earth's regulatory capacity maintains conditions
for human survival and development. As a result of the uncontrolled development ofanthropogenic activities after the industrial revolution in the eighteenth century, human’s
activities have reached threshold which is gradually harming the earths systems. This could
have dramatic and irreversible effects on the environment with severe impact on water
resources which have become increasingly more vulnerable (Al-Sudani, 2019).
Globally, about 54% of the population resides in cities, and that number is projected toincrease from 60% to 92% by the year 2050 (Ritchie and Roster, 2018). In recent years, halfof the top 20 cities in terms of average annual population growth rate (percent) are developing
cities in East, Southeast, and South Asia (http://www.citymayors.com). There has been asignificant change in the structure and size of the population as a result of changing
demographic factors (Reynaud and Miccoli, 2018). These demographic factors have raised
concerns with respect to the effect of population growth on water resources quantity and quality.
Despite rapid global population growth, population densities are declining in some regions andthe proportion of aged people in most developed countries rising (Figure 1.1). In most countries,
the number of elderly people (greater than 60) has increased dramatically in recent years, andthis trend is expected to accelerate in the coming decades with a faster aging pace in thedeveloped countries of Asian and Pacific region accompanied by significant social

1transformations (United Nations, 2015). In spite of this global trends, Japan has experienced
this demographic transition faster than most Western and Asian countries, with an increasing
number of citizens over the age of 65 (Hayashi et al., 2021).

Figure 1.1: Ratio of population aged ≥ 65 to total population, 1950–2050 (Source:United
Nations, Department of Economic and Social Affairs, Population Division, 2015).

However, despite these changes in its demographics, it still faces serious environmental
problems related to groundwater quantity and quality as documented by (Tase, 1992; Onodera
et al., 2011; Haque et al., 2013). Also, between the 1950s and 1970s, Japanese megacities suchas Tokyo, Osaka, and Nagoya experienced severe groundwater drawdowns and significant
groundwater-related problems (Onodera and Saito, 2005). Due to severe air and water pollution
caused by rapid economic growth in Japan in the 1950s and 1960, significant pollution control
policies and technological measures were enacted since the 1970s to curb this problem (Barrett
and Therivel, 1991). Hence understanding the effects of changing population demographics on2

the groundwater environment and its related environmental and social implication are of
guiding importance for future water resources management especially with increasing
urbanization and population aging.
Rational management of water resource quality and quantity necessitates a basic
understanding of the natural processes that affect water resources as well as human impacts onthese processes (Hem, 1989; Deutsch, 1997; Appelo and Postma, 2005; IAEA, 2006). The
scientific and technological advances of the 20st century led to a rapid global increase inurbanization and industrialization (Hayashi et al., 2009) with an increasing pressure on the
freshwater resources. In response to urbanization, land-use practices in many regions havechanged with the reduction of natural land area and increase in impervious cover, affecting
water balance, particularly in the catchment, and leading to a decline in groundwater
availability (Lerner, 2002; Weng and Yang, 2004). In addition, as cities face more water-related
environmental problems, this could cause water shortages and groundwater overuse, as well as
an overall impact on the quantity and quality of freshwater resources (Onodera, 2011; Steffen
et al., 2015; Doveri et al., 2015; Liu et al., 2020). Land use is a key determinant of water balance,
which in turn affects water resource availability (Calder, 1993; Wang et al., 2021; Ridwansyah
et al., 2020) and has been recognised as a key issue threatening watershed management(Yigzaw and Hossain, 2016). Considering the changes in the hydrological cycle due tourbanization, it is important to study the impact of urbanization on local water resources(Wakode et al., 2014).
The sustainability of water supply is not only affected by anthropogenic changes, butalso by climate change (Leta et al. 2016; Zareian et al., 2016). Extreme events such as droughts
and floods are becoming more frequent and intense as a result of global warming (PEACE,
2007). The combined changes in rainfall patterns and temperature increases have significant
implications for the availability of water sources (Gibson et al., 2005). It is therefore important3

to assess the impacts of climate change and anthropogenic activities on changes in the waterbalance to plan and manage water resources for future sustainable use (Wang et al., 2018).
Numerous studies have demonstrated the impact of land use and climate change on waterbalance and hydrological processes at the global and watershed scales (Leta et al., 2016; Kundu
et al., 2017; Bajracharya et al., 2018; Ridwansyah et al., 2020). These impacts can be evaluatedbased on hydrological models contained in references such as (Meenu et al., 2013; Ou et al.,2017; Kundu et al., 2017; Saefulloh et al., 2018). These models when calibrated can be used toassess the temporal and spatial effects of land-use change and climate change as well as predictpossible future impact through scenario analysis.
Amongst these models, the Soil Assessment Tool (Arnold et al., 2012; Grassman et al.,2007), is a comprehensive, semi-distributed, continuous-time, processed-based model which
has been widely used to correlate the relationship between surface water and groundwater aswell as predict the effects of potential changes in hydrologic components at the catchment scale(Ridwansyah et al., 2012). However as hydrologic components tend to show spatial-temporalvariability owing to different climatic conditions, land use, and hydrogeological settings,
previous studies have integrated semi-distributed catchment scale model (SWAT with fully
distributed MODFLOW groundwater models such as SWAT−MODFLOW (Chung et al.,2007; Kim et al., 2008; Dowlatabadi and Zomorodian, 2016). Hence, the use of a watershed
model to understand how land use change with urbanization and climate change affects thehydrological cycle is critical with major implications for future sustainable water use and land
use planning (Baker and Miller, 2013). The ever-growing demand for freshwater, coupled withthe rate at which much of the earth's fresh waters are being adversely affected by humanactivities, portends a developing crisis if appropriate management procedures are not
implemented (IAHS, 1997). Hence, there is a need to identify approaches that would minimize
human impacts on the urban groundwater environments with results applied to areas currently

4experiences ground water related problems associated to either, urbanization, climate change,
depopulation, and population aging.

1.2 Problem Statement
Population growth and increasing economic condition have led to the continuous
expansion and transformation of rural catchments with subsequent related groundwater
problems. Despite the numerous studies on the impacts of anthropogenic activities ongroundwater resources in Japan both qualitative and quantitatively, little attention has been
paid on the combine effects of social and environmental issues on groundwater resources inmountainous rural catchments. Most of these catchments are either rapidly been transformed
into urban catchments or experiencing rapid depopulation such as in Osakishimojima island.
Aging populations is a major issue for Asian countries, and Japan stands as a model for dealing
with environmental issues under these changes. Despite the decrease in contaminant loads
originating from human activities as population declines rural catchments, these catchments
may equally phase an increase in contaminant loads in the groundwater sources originatingfrom non-point sources (e.g., agricultural fields and sewage systems) which becomes neglected
and poorly managed. Thus, a need to evaluate the effects of social challenges on groundwater
resource availability.
On the other hand, compared to continental catchments, most of Japan's water
catchments are located in mountainous areas, which are known to have higher groundwater
potential flows, supporting substantial groundwater resources. In spite of these benefits to thegroundwater resource environment, environmental problems arising from urbanization
processes, land use changes and climatic changes can result in a significant decrease in
groundwater resources, such as in Saijo and Kurose. As a result, we seek to confirm not only
the environmental impacts (urbanization and climate change) on the groundwater resource5environment but also the social impacts (depopulation and population aging) so that we canpropose sustainable water resource management schemes under such changing social andenvironmental trends.
Additionally, this study will provide the public with a glimpse into the environmental
and social challenges posed by aging and depopulating societies, since many cities in Japanand third-world countries are either experiencing or are rapidly approaching similar
demographic challenges.

1.3 Objective
The general objective of this study is to provide a comprehensive evaluation of humanimpacts on groundwater resources in mountainous catchments, western Japan. Furthermore,
the specific objectives are focused on three sub-objectives which provide an insight intochanges brought about by anthropogenic activities on groundwater resource quality andquantity using environmental isotopes and watershed hydrological model.
1. Investigate the social impacts (depopulation and population aging) on groundwater
nitrate-nitrogen contamination and potential sources in an intensive citrus cultivation
island.
2. Estimate the spatial variation in groundwater recharge under the influence ofurbanization and climate change in the last three decades.
3. Evaluate the groundwater chemistry and contamination status in Saijo basin.

6

1.4 Structure of Thesis
This thesis is divided in seven chapters (Figure 1.2). Chapter 1 describes general introduction,
background, and objectives. Chapter 2 provides knowledge related to selected study sites,
samples collection, and study literature pertaining to specific objectives. Furthermore,summary of data analysis and interpretation are described within three chapters: Chapter 3,
Chapter 4 and Chapter 5. Chapter 6 stresses on the total discussion of the human impact onwater resources associated to the three previous chapters. Finally, the conclusion is deliveredin Chapter 7.

Figure 1.2: Structure of thesis.

Chapter 3 (Paper 1) entitled “Nitrate Contamination in Groundwater: Evaluating theEffects of Demographic Aging and Depopulation in an Island with Intensive Citrus Cultivation”
deals with groundwater nitrate contamination and their potential sources in two neighbouring
villages and watersheds with little differences in their social aspect (population aging and7

depopulation) using hydrochemistry data, dual isotope approach as a source tracer and a
Bayesian mixing model to determine the source contribution ratios.
Chapter 4 (Paper 1) entitled “Simulation of daily streamflow with SWAT illuminates
the impact of land-use change with urbanization and climate change on groundwater rechargeover the last three decades”. Based on the SWAT model approach, this chapter examines thespatial variation in groundwater recharge under the effects of urbanization and climate change,
based on water balance components over a period of three decades (1980s-2000s), in westernJapan. Because SWAT can output both direct and relative urbanization impact, these results
will provide a better understanding of groundwater resource availability and implication for
future management at the regional scale especially as the study area is currently undergoing
rapid transformation from rural to urban environment.
Chapter 5 (Paper 3) entitled “Groundwater contamination status in saijo basin: insightsfrom hydrochemistry and dual isotope” addresses groundwater quality using hydrochemical
data. Also, the potential sources of groundwater contamination are addressed using
environmental tracer approach. This chapter examines more closely the relationship between
groundwater and surface water dissolved inorganic nitrogen (DIN)sources.
Chapter 6: represents a general discussion on the anthropogenic impacts on
groundwater resources from the model approach. Further, Social impacts on ground water
nitrate-nitrogen is discussed and compare using three sites as case study.
Chapter 7 presents the general conclusions and summary of the major results of thestudy outlined in chapters 3 to 5

8

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Water. Sci. 2019, 9, 72.
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Bajracharya, A.R., Bajracharya, S.R., Shrestha, A.B., Maharjan, S.B. Climate change impact
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Baker, T.J., Miller, S.N. Using the Soil and Water Assessment Tool (SWAT) to assess land
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Calder, I.R. Hydrologic effects of land-use change. In: Maidment, D.R. 1st ed., Handbook ofHydrology. McGraw-Hill, New York, 1993, 13.1–13.50.
Chung, I.-M., Kim, N.-W., Lee, J., Sophocleous, M. Assessing distributed groundwater
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Gibson, C.A., Meyer, J.L., Poff, N.L., Hay, L.E, Georgakakos, A. Flow regime alterations
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Haque, S.J., Onodera, S., Shimizu, Y. An overview of the effects of urbanization on thequantity and quality of groundwater in South Asian megacities. Limnology. 2013, 14,
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Hayashi, K., Shibata, H., Oita, A., Nishina, K., Ito, A., Katagiri, K., Shindo, J., Winiwarter, W.
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117559.
Hayashi, T., Tokunaga, T., Aichi, M. S., Jun, T. M. Effects of human activities and urbanization
on groundwater environments: An example from the aquifer system of Tokyo and thesurrounding area. Sci. Total. Environ. 2009, 407, 3165-3172.
Hem, J. Study and Interpretation of the Chemical Characteristics of Natural Water. U.S
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Kim, N.W., Chung, M., Won, Y.S., Arnold, J.G. Development and application of theintegrated SWAT–MODFLOW model. J. Hydrol. 2008, 356.
Kundu, S., Khare, D., Mondal, A. (2017). Individual and combined impacts of future climate
and land use changes on the water balance. Ecol. Eng. 2017, 105, 42–57.
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Meenu, R., Rehana, S., Mujumdar, P. Assessment of hydrologic impacts of climate change in
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The impact of land use and climate change on surface runoff and groundwater inCimanuk watershed, Indonesia. Limnology. 2020, 21, 487–498.
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Lenton,T.M., Scheffer, M., Fole, C., Schellnhuber, H.J., et al. A safe operating spacefor humanity. Nature. 2009, 461, 472–475.
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11Wang, K., Onodera, S.I., Saito, M., Shimizu, Y. Long-term variations in water balance byincrease in percent imperviousness of urban regions. J. Hydrol. 2021, 602, 126767.
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12Chapter 2
Study Sites and Analytical Methods
2.1 Location and Description
This study was conducted in three Japanese mountainous catchments (Kubi and Ocho
watershed in Osakishimojima island, Kurose River catchment and Saijo watershed, and) andare briefly discussed in this chapter alongside their Methodology (Figure 2.1).
2.1.1 Kurose River catchment and Saijo watershed
The Kurose River catchment (Figure 1A), with a geographic area of approximately 39.5
km2, is situated between latitude 34°25´30’’N and longitude 132°44´33’’.

Figure 2.1: Location of study sites in Higashihiroshima city (A) Kurose River Catchment
located in center Higashihiroshima city, (B) Saijo Watershed located in eastern Kurose River
Catchment, and (C) Osakishimojima Island.
13

The Saijo watershed or subbasin is located within the Kurose River catchment, hence
their climatic conditions and geology are similar. The total population in the former
Higashihiroshima city (Figure 2.2) which includes the saijo watershed and Kurose River
catchment has doubled over the past three decades, growing from 74,235 persons in the 1980s
to approximately 130 thousand persons in late the 2000s at an urbanization rate of 0.25% per
decade (Higashihiroshima city report, 2021). Note: urbanization here refers to the gradualincrease in the proportion of people living in urban areas and has been calculated as a relative
percentage change of the total population from 1980s to 2000s.

Figure 2.2: Population trend in Higashihiroshima from 1974 in four of its major towns.

14Kurose catchment area has undergone land use changes since the 1980s owing toanthropogenic activities (Figure 2.3) with a greater portion of paddy fields converted toresidential areas. Also, the Kurose River catchment is characterized by a warm humid climate
with mean annual daily temperature varying from 12 °C to 15 °C and mean annual precipitation
is approximately 1469 mm. Further detail on the Kurose River Catchment is expatiated on in
Chapter 3.

Figure 2.3: Land use change from 1987 to 2009 in the Kurose River Catchment.
2.1.2. Osakishimojima Island
Osakishimojima is a small island located along the stretches of the Seto inland sea in
Hiroshima prefecture, western Japan (Figure 1.1C) about 10km off the coast of Kure city. Its
surface area is about 43 km2 with geographic coordinates 34º10'24"N and 132°50'3"E. This15

modest-sized island is large enough to be lined with steep mountains and narrow valleys yet
small enough to be tucked away amid a cluster of other islands. Osakishimojima experiences awarm temperate climate with an annual average temperature of 15.6 ◦C and with an averageannual precipitation of approximately 1000mm. The four seasons are distinct with cold weather
during the winter and hot weather during summer months.

Figure 2.4: Outcrop geological map of Osakishimojima Island.
The geological map (Figure 2.4) of the study area shows that Osakishimojima basin
consists of Quaternary deposits that lie on top of a basement made up of igneous rocks. The
Quaternary cover consists of Late Pleistocene to Holocene marine and non-marine formations
that occupy 8.8% of the land and Holocene reclaimed land that occupies 0.1% of the basin.
Among the underlying units, a mélange matrix representing a Middle Jurassic accretionary
complex covers 30.7% of the surface whereas 19.7% of the land are covered by Late
Cretaceous felsic plutonic rocks and 40.9% are occupied by Late Cretaceous non-alkaline felsic

16

volcanic rocks. Only 0.1% of water is found on the island surface. Osakishimojima isdominated by forest and agricultural land (Figure 2.5) made up of citrus fields. The island has
well-drained soil which makes it perfect to grow citrus rendering it famous for its richcultivation of Mikan (Japanese mandarin) and lemon. Because of its sheltered location andgentle climate, Osakishimojima is Japan’s citrus island taking advantage of the calm air, strong
sunlight, light rainfall, and good mountain drainage, which is so important for citrus orchards
cultivation.

Figure 2.5: Land use of Osakishimojima Island.

2.2 Research Methods
The methods followed in the research process are based on the objectives formulated in Chapter
1 section 1.3. The methodology designed for the research work consists of two major
approaches.
17

1. The hydrochemical approach which consist of pre-fieldwork, fieldwork, and postfieldwork. This approach has been applied to chapters 3 and 5 to determine groundwatercontamination status and proportional contribution rates using nitrogen 15 isotopes andBayesian micing model
2. The Soil and Water Assessment Tool (SWAT) used in chapter 4 on the Kurose River
catchment to estimate the groundwater recharge under the influence of climate change
and urbanization will be introduced in this chapter
2.2.1 Hydrochemical approach
2.2.1.1 Pre-field, field, and Post field work
The major task of this phase in the study has been devoted to the review of previous
works on the area and literature related to the hydrochemical and environmental tracer methods
in hydrological investigations. Apart from the literature review, an archive search SRTM ofthe study area was also part of the pre-fieldwork. Delimitation and selection of the sample
points, acquisition of equipment and preparation of data requirement list and data collection
form were the activities conducted before the field trip.
In the present study, a total of 52 water samples were collected from Kubi and Ocho villages
in Osakishimojima island in the month of October 2021 while 17 water samples were collectedfrom the Saijo area. These samples are classified as follows:➢ Kubi village: One borehole, two rivers and seventeen shallow wells➢ Ocho village: two springs and thirty shallow wells➢ Saijo town: eleven boreholes, three springs, three shallow wells, and one river
Geographical parameters (latitude, longitude, and altitude) of each sample site wererecorded on the field using a hand-held Garmin GPS. The water samples intended for major
ions analysis were collected in acid washed into one 250 mL, three 10 mL, and one 100 mL
18

high-density polyethylene sampling after rinsing three times with the sample and preserved
airtight to avoid evaporation. Water samples were filtered through a 0.2μm membrane andstored at −4°C for further hydrochemical and isotopic analyses. Physicochemical parameters
including groundwater pH, temperature, electrical conductivity (EC), dissolved oxygen (DO),and oxidation-reduction potential (ORP) were measured on-site using a portable
multiparameter water quality meter (pH/COND Meter D-54, HACH LD0101, ORP Meter RM20P). The values of these parameters were recorded after they were stably displayed. Surface
water samples were obtained by submerging the sampling bottles into the water at areas where
there is an active, but not turbulent flow. Water was drawn from the open wells using drawing
buckets tied with ropes. Static water levels in open wells and boreholes were measured.
Data collected during the pre-fieldwork period and fieldwork are processed andanalyzed. The analysis of the samples for major ions, nutrients and Nitrogen 15 isotope has
been carried out at the biogeochemistry lab, Hiroshima University and Institute of Geosciences,
Yamanachi University. The concentrations of the major cations (Na+, K+, Mg2+, and Ca2+) andanions (Cl⁻, NO3⁻, and SO42⁻) were analyzed by ion chromatography with conductivity
detection on ICS-2100 (Dionex integrion, Thermo Fisher Scientific, Waltham, MA, USA).
Alkalinity was analyzed within 12 h of sample collection by acid titration. A volume of 0.02 N

sulfuric acid (H2SO4) was added to the sample to reach the end point titration (pH 4.5). To
evaluate the data quality, the accuracy of the water analysis was checked with the anion-cation
balance. The principle of the anion–cation balance is that the sum of the positive ionic (cationic)
charges (expressed in meq/L) in a solution should equal the negative ionic (anionic) charges;
this because the solution must be electrically neutral. The electroneutrality of the water samples
is commonly calculated with the following equation of Hounslow, 1995:∑(Cations )−∑(Anions )𝐸𝑙𝑒𝑐𝑡𝑟𝑜𝑛𝑒𝑢𝑡𝑟𝑎𝑙𝑖𝑡𝑦 (%) = ∑(Cations )+∑(Anions ) 𝑥100

19

(2.1)

According to Appelo and Postma (1999), solutes present in concentrations above 100 mg/l canbe determined with an accuracy of better than ± 5 %. For solutes concentrations below 1 mg/l
the accuracy is generally not better than ± 10 % and can be poorer. When concentrations arenear the detection limit of the method used, and in all determinations of constituents that arenear or below the mg/l level, both accuracy and precision are even more strongly affected by
the experience and skill of the analyst (Deutsch, 1997).
2.2.1.2 Data Analysis and graphical methods
To meet the objectives fixed for this study, the analysis and interpretation of standardhydrochemical and environmental tracers’ data are required in order to determine the
distribution, transit time and movement of dissolved species throughout the studied areas, andto investigate hydrological links between groundwater and surface. This was achieved using
conventional graphical plots and multivariate statistical techniques which are described below.
Various graphical methods have been developed for the visual inspection of theHydrogeochemical data in order to look for discernible patterns and trends. Piper’s trilinear
diagram, Stiff diagram, Gibbs diagrams and Wilcox were used to determine water type, degreeof mineralization, spatial variation in the chemistry of the water samples and to identify
dominant processes and detect grouping of water samples as used by (Fantong et al.,2009;
Kamtchueng et al., 2016).
For this study the Gibbs and Piper diagrams were used. The Piper’s diagram is agraphical representation of the chemistry of a water sample or samples were cations and anions
are shown by separate ternary plots (Piper, 1994). The apexes of the cation plot are calcium,magnesium and sodium plus potassium cations. The apexes of the anion plot are sulphate,chloride and carbonate plus hydrogen carbonate anions. The two ternary plots are thenprojected onto a diamond (Figure 2.6). The diamond is a matrix transformation of a graph of
the anions (sulphate + chloride/ total anions) and cations (sodium + potassium/total cations).
20

Figure 2.6: Piper diagram; cations and anions are expressed as percentages of total in meq/L.
2.2.1.3 Multivariate statistical method
Multivariate statistical analysis (MSA) consists of a collection of methods that can beused when several measurements are made on each individual or object in one or more samples(Rencher, 2002). Amongst the numerous MSA available, coefficient of determination (R2). In
addition to correlation analysis, principal component analysis (PCA) was used to analyze
groups of ions based on similarities or differences in concentrations. SPSS® ver. 23.2
(Statistical Package for Social and Management Science) was used to computephysicochemical parameters in order to produce Statistical tables, correlation analysis and bar
graphs.
2.2.2 The Soil and Water Assessment Tool
The SWAT (soil and water assessment tool) model, developed by the USDA
Agricultural Research Service, simulates the land phase of the hydrologic cycle in daily time
steps. Routines are also included for simulating the detachment of sediments from the21

watersheds and their transport through the drainage systems. The SWAT model is designed toroute water and sediments from individual watersheds, through the river systems. The model
can be used for the assessment of existing and anticipated water uses and water shortages. The
model provides a complete accounting of the quantity of water that is supplied to the land by
precipitation; enters the streams as surface run-off; is used and returned to the atmosphere by
natural vegetation, agricultural crops and evaporation, and that percolates through the root zone
and a part returns as groundwater contribution.
SWAT simulates the hydrological cycle of a watershed in two phases, the land phaseand the routing phase (Figure 2.7).

Figure 2.7: Schematic diagram of the hydrological cycle and SWAT simulation processes(Neitsch et al., 20011; Nasiri et al., 2020).

The land phase of the hydrologic cycle controls the movement of water, sediment,
nutrient and pesticide loadings to the main channel in each sub-basin. The routing phase of thehydrologic cycle simulates the movement of water, sediments, etc. through the channel network22

of the watershed to the outlet. The following sections describe both phases (i.e., the land androuting phases) and their components as simulated by SWAT. However, the description is
focused on the relevant components and processes to this current work, so that the parts related
to chemical modelling are not described. A full description of all processes in SWAT is given
in Neitsch et al. (2011). The main hydrological components simulated in the land phase of thehydrologic process include canopy interception, infiltration, evapotranspiration, lateralsubsurface flow, surface runoff, groundwater recharge and streamflow in a watershed. This
study focused on the land-phase water balance, where land-use change has its main impact.
The water balance governing the land phase of the SWAT hydrological cycle is given by Eq.
(2.2), Neitsch et al. (2011):
SWt = SWo + Ʃti=1 (R day − Qsurf − Ea − Wseep − Qgw )(2.2)

where, SWt is the final soil water content on day i (mm); SWo is the initial water content (mm)on day i; t is the time (days); Rday is the amount of precipitation on day i (mm); Qsurf is theamount of surface runoff on day i (mm); Ea is the amount of evapotranspiration on day i (mm);Wseep is the amount of water entering the vadose zone from the soil profile on day i (mm); andQgw is the total amount of return flow on day i (mm). SWAT offers two methods for estimating
surface runoff: the Soil Conservation Service Curve Number (SCS-CN) method and Green and
Ampt infiltration method (Green and Ampt, 1911). The SWAT calculates the surface runoff
using the SCS curve number method, as shown in Eqs. (2.3) − (2.4) (Soil Conservation Service,1972).
(Rday −0.2S)2

Qsurf = (R(2.3)

day +0.8S)

1000

(2.4)

S = 25.4 ( CN − 10)23where, CN is the curve number for the day and S is the retention parameter. SCS defines CN2
as a moisture condition II curve number, which is referred to as the average moisture condition.
CN2 is primarily used as the curved number in SWAT, with daily CN2 adjusted from themoisture condition (Soil Conservation Service Engineering Division, 1986).
The SWAT model calculates evapotranspiration from soil and plants separately andprovides three alternative methods for predicting potential evapotranspiration based on
available information: (Hargreaves and Samani, 1985; Priestly− Taylor, 1972; Monteith, 1965).
The information required for the Penman−Monteith method includes solar radiation, airtemperature, wind speed and relative humidity. In the Hargreaves method, only air temperature
information is required, and in the Priestley−Taylor method, radiation information, airtemperature, and relative humidity are required. For this study, the Penman−Monteith method
was used, which is given by Eq. (2.5):
ρa Cp (es − ea )rar −r
HV [δ + γ [ a r c ]]aδ(ho + S) +
Eo =

(2.5)

Where, Eo is evaporation (g m-2 s-1), HV is the latent heat of vaporization (J g-1), ho is the net
radiation (J m-2 s-1), δ is the slope of saturation vapor density function (g m-3 C-1), S is the soil
heat flux (J m-2 s-1), γ is the psychometric constant (g m-3 C-1), ρa is the air density (g m-3), Cp
is the specific heat of air (J g-1 C-1), es is the saturation vapor density (g m-3), ea is the air vapour
density (g m-3), ra is the aerodynamic resistance for heat and vapor transfer (s m -1), and rc isthe canopy resistance for vapor transfer (s m-1). Further details of the water balance equation
can be found in the SWAT theoretical document (Neitsch et al., 2011).

24

2.2.2.1 Input data and model setup
SWAT model needs Digital Elevation Model (DEM) data as input for delineating thecatchment boundary, sub-catchment, and stream network (Figure 2.8).

Figure 2.8: Input dataset in to ArcSWAT showing Digital Elevation Model (DEM), subbasins
after watershed delineation, slope classes, land use classes and soil classes used in SWAT
model for HRU definition.
The DEM, land use data, soil data and climatic data are required as input files for the
SWAT. The DEM was used to delineate the watershed into sub-basins, drainage surfaces,
stream networks and longest reaches. The topographic data were obtained from the Shuttle
Radar Topography Mission of the United States Geological Survey, which has a spatial
resolution of 30 m (https://eart hexplorer.usgs.gov/). Land use and soil data were obtained from
the Ministry of Land, Infrastructure, Transport, and Tourism of Japan (MLIT), with a spatial
resolution of 100 m (https://nlftp.mlit.go.jp/). To assess the effect of land-use change on
25

groundwater resources, it is necessary to possess data from at least two time periods forcomparison. In this study, the 1987 and 2009 land use maps (Figure 2.8), were used for thesimulation period of two-time steps, the 1980s (1983−1993) and the 2000s (2004−2007)
respectively. The coverage areas of several land use categories and input database codes in the
Arc SWAT are presented in Table 2.1.
Table 2.1: Land use classes used in ArcSWAT, area coverage, and comparison of land usechange in 1987 and 2009.

Land
type

use

SWAT
Name

1987

2009

Area ofchange(km2)

Area ofchange
(%)

Area
(km2)

% AreaArea(km2)% Area19872009

19872009

Rice

RICE

13.6

34.7

7.8

19.9

-5.9

-14.8

Agricultural

AGRL

1.0

2.6

0.3

0.9

-0.7

-1.8

Forest

FRSD

18.6

47.3

16.8

42.7

-1.9

-4.7

Residential

URML

5.0

12.8

13.6

34.5

8.6

21.8

Water

WATR

1.0

2.6

0.8

2.0

-0.3

-0.7

A total of five land-use classes (Figure 2.8) were classified from the downloaded Landuse files: rice paddy, agricultural land, residential land, forests, water, and other (shrubs andagriculture). Significant land-use changes between the two land use maps were observed in thestudied watershed, with three dominant land use categories: forest, rice paddy, and residential
land. Four major soil groups were found, either as a single type or in association with other soil
groups. The contribution of Grey low land soils (40.3%) in association with brown Regosol(34.1%) were discovered to have dominated the distribution of soil types in the study area
(Figure 2.8).
The spatial datasets were converted to SWAT input datasets using ArcSWAT 10.6
(Geographical Information System interface) and the SWAT version 2012 was used for map

26

production and model set up. Figure 2.9 gives an overview of SWAT model components andphases in the GIS interphase. With respect to residential areas, this study assumed that theresidential area is not completely impervious, providing some pervious spaces between thehouses that are often used for house yards.

Figure 2.9: Model setup diagram for SWAT model in ArcGIS and ArcSWAT.
Thus, the class Urban Residential Medium Density (URMD) in the SWAT model wasused to assign parameters in the residential area (Table 2.1). URMD assumes an average of
38% impervious area in the settlement area (Neitsch et al., 2011), which is like the settlement
conditions in the study catchment. To simulate hydrological processes in a watershed, the27

SWAT model divides a catchment into sub-catchments and then further divides each subcatchment into Hydrological Response Units (HRUs) based on a relatively homogenous
combination of land use, soil types and slope, for which a land-phase water balance iscalculated (Arnold et al., 1998). Seven subbasins (Figure 2.8) were generated for both periods,
while 223 and 279 HRUs were created for the years 1980s and 2000s correspondingly.
It should be noted that the irrigation function in SWAT was not activated in thiscatchment study because of the unavailability of observed data for irrigation and crop yield.
Observed meteorological data (daily precipitation, maximum temperature, minimum
temperature, wind speed, solar radiation, and relative humidity) were obtained from the
National Agriculture and Food Research Organization, Japan (https://www.naro.go.jp/) andwere derived from the study catchment. The weather station is located approximately 1 km
southwest of the study catchment and is surrounded by paddy fields.
2.2.2.2 Model calibration
Based on the procedure described by Abbaspour (Arnold et al., 2012), parameters to be
used for SWAT calibration were identified. Calibration estimates model parameters that cannot
be measured directly. This process can be manual or automated, or a combination of bothprocesses while Validation is a comparison between simulation and the observed data on time
step with the use of several indexes, and mainly in Nash and Sutcliffe coefficient. The
calibration period for the 1980s was selected from 1983 to 1989 and validated from 1990 to
1993. For the 2000s, the calibration period was from 2000 to 2005 and was validated from2006 to 2007. For each model run, the first three years of simulations were considered model
warm-up periods, which were not included in the above periods. Based on the observed flow
data collected from the streamflow gauge in the Kurose River, sensitivity analysis, calibration,and validation of the model were performed on a daily time step using Sequential Uncertainty
Fitting 2 (SUFI-2) of the SWAT-CUP (Abbaspour et al., 2007). Parameterization was tested to28

reduce uncertainty by adjusting some relevant parameters affecting the simulation results. The
first parameter ranges were determined based on the minimum and maximum values deduced
from the SWAT user manual (Neitsch et al., 2011) and varied iteratively within the allowable
ranges until satisfactory agreements between measured and simulated streamflow wereobtained for each period. The SUFI-2 algorithm accounts for different sources of parameter,
conceptual model, and input data uncertainties (Gupta et al., 2006). Several iterations were
performed, where each iteration consisted of 1,000 simulations with narrowed parameter
ranges in the subsequent calibration rounds. In addition, daily calibration was performed bymanually adjusting the input parameters to attain satisfactory agreement between the simulated
and observed discharge data for both periods. Calibration finished only when a group ofparameter ranges returned the same fitted parameter values. Depending on the situation, if
calibration results are poor, the parameter range may need to be reselected (Table 2.2).
Table 2.2: List of sensitive parameters and optimization ranges included in the final calibration.
1983-1993

2000-2007

Parameter/file name

Model description

Min

Max

Min

Max

r_CN2.mgt_URML, URLDSCS runoff curve number

-0.43

-0.38

-0.1

0.34

r_CN2.mgt_RICE, ARGLSCS runoff curve number

-0.38

-0.35

-0.33

-0.25

r_CN2.mgt_FRSD

SCS runoff curve number

-0.3

-0.27

-0.2

-0.18

r_CN2.mgt_RNGB, WATRSCS runoff curve number

-0.4

-0.31

-0.05

0.2

v_ESCO.hru, URML, URLD

Soil
evaporationcompensation factor

0.05

0.08

0.7

0.9

v_ESCO.hru_RICE, AGRL

Soil
evaporationcompensation factor

0

0.08

0.7

0.9

v_ESCO.hru_FRSD

Soil
evaporationcompensation factor

0

0.08

0.4

0.7

v_ESCO.hru_RNGB, WATR

Soil
evaporationcompensation factor

0

0.08

0.7

0.9

v_ALPHA_BF.gw

Baseflow alpha factor (days)

0.32

0.56

0.32

0.56

v_GW_DELAY.gw

Groundwater delay (days)

0

220

0

220

29

v_GWQMN.gw

Treshold depth of water in theshallow aquifer required for
return flow to occur (mm)

174.5

209.6

174.5

209.62

r__SOL_K.sol

Saturatedconductivity

hydraulic

-0.28

0.1

-0.29

0.45

r_SOL_AWC.sol

Available water capacity ofthe soil layer

0.23

0.46

0

0.21

r_SOL_BD. Sol

Moist bulk density

0.25

0.5

0.52

0.77

r_HRU_SLP.hru

Average slope steepness

-0.92

-0.91

-0.96

-0.95

v_CANMX.hru

Maximum canopy storage

25.36

32.09

24.36

25.09

V_RCHRG_DP.gw

Deep aquifer
fraction

0.24

0.29

0.28

0.3

V_REVAPMN.gw

Threshold depth of water inthe shallow aquifer for"revap" to occur (mm)

30

80

30

80

R_SLSUBBSN.hru

Average slope length

0.7

0.9

0.5

0.7

percolation

The simulation accuracy was verified using a validation process. In this process, themodel was operated with input parameters set during the calibration process without any further
changes, and the results were analyzed using the remaining observation data. In the studied
catchment, climate and geological conditions did not change significantly. To avoid differencesin results due to parameter changes in this study, a group of parameters that can be used during
both time-step was selected. However, some parameters still responded quite differently duringcalibration in both periods. SWAT output files for (. hru and .sub files) were extracted from
SWATOutput.mdb after the final calibration and validation process.
2.2.2.3 Model Evaluation and Statistical methods
To determine the reliability of the simulation results, it is necessary to evaluate theiraccuracy. In this study, simulations for model calibration were assessed daily, and thefollowing statistical criteria were used to verify the reliability of the results: the coefficient ofdetermination (R2) and the Nash-Sutcliffe Efficiency (NSE) as shown in Eqs. (2.6) − (2.7)(Nash and Sutcliffe, 1970; Fadil et al., 2011; Singh et al., 2004; Moriasi et al., 2007).
30

R2 is the square of the correlation coefficient, which indicates the strength relationshipvalue between the observed and simulated data and can range from 0 to 1 (Santhi et al., 2001).
An R2 value of 1 indicates a perfect alignment between the simulated and observed values
whereas an R2 value of 0 indicates no alignment between the simulated and observed values.
However, the weakness of using only the R2 statistic lies in the fact that the model predictions
can be consistently wrong and still result in an R2 value close to 1 due to systematic over- or
under-prediction; thus, the need to use an additional evaluation statistics such as the NSE toaccurately determine the model results (Krause et al., 2005). The NSE value is a measure of
the predictive power of the model and can range from − ∞ to 1. The values of NSE = 1 are theoptimal values, and the model with NSE > 0.5 is considered satisfactory (Moriasi et al., 2007).
̅)(Yi − Y̅) ][∑ni=1(Xi − X
R = n∑i=1[(Xi − ̅X) ∑ni=1(Yi − ̅Y)2 ]2

NSE = 1 −

(2.6)

t )2
ƩTt=1 (Xot − YmƩTt=1 (Xot − Xo−t )2(2.7)

where, NSE is the Nash−Sutcliffe model efficiency coefficient; Qo represents the observation
discharge, Qm is the model discharge, Ǭo indicates the average of the observation discharge,
and Qt is the discharge at time t.

2.3 References
Abbaspour, K.C., Vejdani, M., Haghighat, S. SWAT-CUP calibration and uncertainty
programs for SWAT. Modsim 2007: International Congress on Modelling and
Simulation: Land, Water and Environmental Management: Integrated Systems for
Sustainability, Christchurch, New Zealand. 2007.
Appelo, C.A.J., Postma, D. Chemical Analysis of Groundwater, Geochemistry, Groundwater& Pollution. Balkema, Rotterdam. 1999.

31

Arnold, J.G., Srinivasan, R., Muttiah, R.S., William, J.R. Large area hydrologic modeling andassessment part i: model development. J. Am. Water. Resour. Assoc.1998, 34, 73–89.
Deutsch, W.J. (1997). Groundwater geochemistry: fundamentals and application tocontamination. Boca Raton (Fla.): Lewis Publishers. 1997
Drever, J.I. The geochemistry of natural waters, 2nd ed. Englewood Cliffs, N.J.: Prentice Hall.
1988, 402.
Fadil, A.H.R., Abdelhadi, K., Youness, K., Bachir, O.A. (2011). Hydrologic modeling of thebouregreg watershed (morocco) using GIS and SWAT model. J. Geogr Inf. Syst. 2011
3, 279–289.
Fantong, W.Y., Satake, H., Ayonghe, S.N., Aka, F.T. and Asai, K. Hydrogeochemical controls
and usability of Groundwater in semi-arid Mayo Tsanaga River Basin, Far North
Region, Cameroon. Environ. Geol. 2009, 58, 1281−1294.
Green, W.H., Ampt, G.A. (1911). Studies on soil physics, 1. The flow of air and water throughsoils. J. Agric. Sci. 1911, 4, 11−24.
Gupta, H.V., Beven, K.J, Wagener, T. Model calibration and uncertainty estimation. In:
Encyclopedia of hydrological sciences. 2006, 11–131.
Hargreaves, G.L., Hargreaves, G.H., Riley, J.P. (1985). Agricultural Benefits for Senegal River
Basin. J. Irrig. Drain. Engr. 1985, 111:113−124.
Higashihiroshimacity

report.

Accessed

12,

November2021.

https://www.city.higashihiroshima.lg.jp.
Hounslow, A.W. Water Quality Data: Analysis and Interpretation (1st ed.). CRC Press. 1995.
Kamtchueng, B. T., Fantong, W.Y., Wirmvem, M. J., Tiodjio, R.E., Takounjou, A.F.,
Ngoupayou, J. R. N., Kusakabe, M., Zhang J., Ohba T., Tanyileke, G., Joseph V. H.
and Akira U. Hydrogeochemistry and quality of surface water and groundwater in thevicinity of Lake Monoun, West Cameroon: approach from multivariate statisticalanalysis and stable isotopic characterization. Environ. Monit. Assess. 2016, 188, 524.
Krause, P., Boyle, D.P., Bäse, F. Comparison of different efficiency criteria for hydrologicalmodel assessment. Adv. Geosci. 2005, 5, 89–97.

32

Ministry of Land, Infrastructure, Transport, and Tourism of Japan, (2020). Available from:https://nlftp.mlit.go.jp/ (accessed on 10 April 2020).
Monteith, J.L. Evaporation and environment. Symp. Soc. Exp. Biol. 1965, 19, 205– 234.
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L. Modelevaluation guidelines for systematic quantification of accuracy in watershedsimulations. Trans. ASABE. 2007, 50, 885–900.
Nash, J.E., Sutcliffe, J.V. River flow forecasting through conceptual models: part I. A
discussion of principles. J. Hydrol. 1970,10, 282–290.
Nasiri, S., Ansari, H. & Ziaei, A.N. Simulation of water balance equation components using
SWAT model in Samalqan Watershed (Iran). Arab. J. Geosci. 2020, 13, 421.
National Agriculture and Food Research Organization, Japan. 2020 (https://www.naro.go.jp/)(Accessed 9 April 2020).
Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R. Soil and water assessment tool
theoretical documentation version 2009. Texas Water Resources Institute 647. 2011.
Piper, A. M. A. Graphic procedure in the geochemical interpretation of water analyses. Trans.
Am. Geophy. Union. 1944, 25, 914–928.
Priestly, C.H.B., Taylor, R.J. “On the Assessment of Surface Heat Flux and Evaporation Using
Large Scale Parameters,” Monthly Weather Review. 1972, 100, 81−92.
Rencher-Arvin, C. Methods of Multivariate Analysis Brigham, Young University 2nd Ed. New
York: John Wiley & Sons, Inc. Publication. 2002.
Santhi, C., Arnold, J.G., Williams, J.R., Dugas, W.A., Srinivasan, R., Hauck, L.M. Validationof the SWAT Model on a Large River Basin with Point and Nonpoint Sources. J. Am.
Water. Resour. Assoc. 2001, 37, 1169–1188.
Shuttle Radar Topography Mission of the United States Geological Survey, 2020. Available
from: https://eart hexplorer.usgs.gov/ (accessed on 10 April 2020).
Singh, J., Knapp, H.V., Demissie, M. Hydrologic modeling of the Iroquois river watershedusing HSPF and SWAT. J. Am. Water. Resour. Assoc. 2004, 41, 343–360.

33

Soil Conservation Service (SCS). National Engineering Handbook, Section 4: Hydrology.
Department of Agriculture, Washington DC. 1972,762.

34

Chapter 3
Social Impacts on Groundwater Nitrate Contamination Status and
its Sources on an Island with Intensive Citrus Cultivation

This chapter is an edited version of the article:
Kimbi, S.B.; Onodera, S.‐I.;Ishida, T.; Saito, M.; Tamura, M.;Tomozawa, Y.; Nagasaka, I.
Nitrate Contamination in Groundwater: Evaluating the Effects of Demographic Aging and
Depopulation in an Island with Intensive Citrus Cultivation. Water 2022, 14, 2277.

3.1. Introduction
In recent years, demographic trends have raised concerns with respect to the effects of
population growth on water resources. Despite rapid global population growth, population
densities are declining in some regions and the proportion of aged people in most developed
countries is rising. However, Japan has experienced this demographic transition faster thanmost Western and Asian countries, with an increasing number of citizens over the age of 65(Hayashi et al., 2021). However, despite these changes in its demographics, it still faces serious
environmental problems related to groundwater pollution, with nitrates being one of the more
serious groundwater contaminants (Tase, 1992; Onodera et al., 2011; Haque et al., 2013).
Agricultural-related nitrate contamination of groundwater is a widespread problem in many
countries, with numerous studies demonstrating a relationship between agriculture and highnitrate concentrations in groundwater. Groundwater contamination is likely to occur when
Nitrate (NO3⁻) input into the soil exceeds the consumption capacity of plants, which leads tonitrogen loss to the environment (Agrawal et al., 1999; Noland et al., 2000; Sacchi et al.,2013;35

Mcclain., 1994). In Japan, nitrate pollution in groundwater has been associated with intense
agricultural production since the 1960s, as chemical fertilizers have increasingly been utilized(Kumazawa, 2002). As a result, nitrogen fertilizer effectiveness has diminished, and thenitrogen remaining in the soil after crop absorption has leached into the groundwater as nitrate.
Due to increasing demographic aging as a major socioeconomic problem in many regions inJapan (Hiyashi et al., 2021), the use of chemical fertilizers to maximize production is increasing.
Prior studies in Japanese agricultural island were found to have high nitrate levels (Hayashi etal., 2021; Saito et al., 2015; Kimbi et al., 2021), which was associated with intensive cultivation.
The widespread use of fertilizers by farmers was believed to be the primary source ofgroundwater contamination; however, agricultural farmland has varied sources ofcontamination input. An effective measure for controlling and managing nitrate pollution is to
identify the sources of nitrate (Zhang et al., 2014), however NO3⁻ pollution comes from diverse
sources even in agricultural fields which can be classified into four groups: chemical fertilizers,
soil nitrogen, atmospheric deposition, and manure and sewage (Kendall et al., 1998; BedardHaughn., 2003; Knapp et al., 2005; Chen et al., 2007., Umezawa et al., 2008; Peralta et al.,2020; Rusydi et al., 2021; Wang et al., 2022). Thus, understanding the different sources ofNO3⁻ in groundwater is crucial, especially for stakeholders intending to implement
groundwater conservation measures (Xue et al., 2008).
In the past, conventional methods for determining groundwater NO3⁻ sources relied on
the association between land-use types and the variation in groundwater chemical composition(Kou et al., 2021). Based on this method, most nitrate sources were inaccurately identified witha high level of uncertainty. Therefore, a technique was proposed for identifying NO3⁻ sources
using N and O isotopes (Kohl et al., 1971; Amberger and Schmidt 1987; Kendall et al., 2008).
NO3⁻ sources are characterized by different isotope signatures that allow them to efficiently
trace point and non-point contamination sources in both ground and surface waters. However,36

a concern arose in relation to overlapping ranges, which may cause inaccurate nitrate sources
to be identified and compromise the usefulness of isotopes alone. As a result, it was determinedthat δ18ONO3− could provide additional information for distinguishing nitrate from chemicalfertilizers and atmospheric deposition because of its distinctive values compared to those of
the overlapping N isotopes (Kendall., 2008). The isotopic compositions of N and O differ
among nitrate sources, and their combination with NO3⁻ is an effective indicator for tracing thesource of groundwater nitrate (Knapp et al., 2005). Several studies have successfully used the
double isotope technique to identify the sources of NO3⁻ pollution in surface and groundwater
after the typical range of δ15N-NO3⁻ and δ18O-NO3⁻ for various sources of NO3⁻ pollution wasreported (Kendall et al., 1998, 2008; Aravena and 1993; Wu et al., 2006; Chen et al., 2009; Liuet al., 2013). Relatively accurate results can be obtained by using these dual isotopes alongwith conventional tracing methods (Maghdadi et al., 2018). Therefore, the isotopic
composition of nitrate can help to distinguish different sources of nitrates in groundwater, suchas synthetic or organic fertilizers, human waste/septic system effluents, and soil organic matter.
The Bayesian isotope mixing model has been used to calculate the proportionate
contributions of the main nitrate sources in groundwater (Chen et al., 2007; Ji et al., 2017;
Parnell et al., 2010; Fu et al., 2019; Yu et al., 2020). Previous studies have successfully usedthe δ15NNO3⁻ and δ18ONO3⁻ and Bayesian isotopic mixing models to determine the proportionate
contribution of different nitrate sources (Zhang et al., 2014; Chen et al., 2007; Torres-Martinez
et al., 2020). The results revealed that by using a Bayesian "fingerprint" for pollution activities,
the SIAR model could predict the probability distribution of the proportional NO3⁻ contribution
source. Nitrogen and oxygen isotopes can be used in conjunction with traditional methods toidentify nitrate sources, and stable isotope analysis in R software (SIAR) can quantify thecontributions of various sources of nitrate groundwater contamination to protect theenvironment of intensive agricultural areas (Kimbi et al., 2021; Yu et al., 2020). Nitrate37contamination of groundwater and surface water in Osakishimojima Island is a cause forconcern, as a previous study (Kimbi et al., 2021) has linked the elevated nitrate concentrations
in water sources to the nitrogenous fertilizers used in citrus farms. Sometimes, the nitrate levels
have been observed to exceed the World Health Organization’s (WHO, 2017) recommended
guideline for maximum NO3⁻ (50 mg/L) in drinking water or Japan’s recommended guideline
for maximum NO3−−N concentration (10 mg/L) in water supplies. Hence, exploring the spatial
variation of NO3⁻ and its sources in two study sites with different extents of areas undercultivation could provide insights into the sources contributing to NO3⁻ pollution on this island,
which would help in the effective management of groundwater sources, particularly
considering that the island has a declining and aging population.
The aims of this investigation were: (1) to demonstrate how the dual isotope approach(δ15N-NO3⁻ and δ18O-NO3⁻) can be used to determine the dominant sources of nitrates in theshallow groundwater of a small island and (2) to evaluate the influence of social aspects suchas population aging and depopulation on NO3⁻ concentrations in groundwater.

3.2 Location and description of the study area
This study was conducted in Osakishimojima (34°10'24" N, 132°50'3" E), a small
island located in the Seto Inland Sea in Hiroshima Prefecture, western Japan (Figure 3.1), about
20km WSW from Kure City. Two neighboring villages and watersheds, Ocho in the eastern of
the island and Kubi in the northern were compared. The population of Kubi and Ocho isapproximately 413 and 830 in 2021, respectively (Kure city report, 2021) and the average ageof farmers is 76 and 73, respectively (Japan Agricultural Cooperative, 2022). Osakishimojima
has a warm temperate climate with all four seasons, including cold winters and hot summers.
The annual average air temperature is 15.6 ℃, and the average annual precipitation isapproximately 1100 mm. The land use of Osakishimojima Island includes citrus orchards, pine38forests, residential areas, paddy weeds, and water bodies, with area proportions of 56.69 %,36.46 %, 5.56 %, 0.82 %, and 0.4 9%, respectively (Kimbi et al., 2021). Land use data wereobtained from another study (Ministry of Land, Infrastructure, Transport, and Tourism of Japan,2021), with a spatial resolution of 100 m and detailed classification carried out using google
earth to suit the purpose of this research.

Figure 3.1: (a) Location map of Osakishimojima Island in the Seto Inland Sea showing thesampling points in (b) Kubi village and (c) Ocho village.
Agricultural land in Ocho and Kubi is approximately 1.6 km2 and 0.6 km2, respectively,
and has decreased by 37% and 46% over the last 10 years (Ministry of Land, Infrastructure,
Transport, and Tourism of Japan, 2021). In comparison, citrus orchards, and residential areasin the lowlands of Ocho and Kubi are 35.2% and 56.7%, and 45.4% and 43.3%, respectively(Figure 3.2). The residential areas in the region mainly consist of scattered villages with low

39urbanization levels and substandard sewage treatment facilities. In addition, the village of Ocho
has a higher building density and greater surface area than Kubi village (low housing density
which are randomly distributed). General information on farmer choice of fertilizer, andinformation pertaining to farmer agricultural practices in each village were determined basedon oral discussion with some farmers and from the areas agricultural company, however noformal interviews were conducted in the study sites during the sample survey.

Figure 3.2: Land use of Osakishimojima, showing proportions of citrus orchards andresidential areas in (a) Kubi and Ocho lowland areas, (b) Kubi sampling area, and (c) Ocho
sampling area.
In addition, hydrogeological data for the study sites were unavailable; thus, only thegeological outcrops were examined. The main geological features of Osakishimojima Island
are Late Cretaceous non-alkaline felsic volcanic rocks and rhyolites (Figure 2.4). They are40

found on mountain slopes, and Late Pleistocene to Holocene marine and non-marine
formations cover the basement rocks in the lowland.

3.3 Materials and Method
3.3.1 Sample collection
Fifty-two water samples were collected in the study area in October 2021, which
included 32 sites comprising of 2 springs and 30 shallow wells in Ocho area and 20 sites of 1
observation and production borehole, 2 rivers, and 17 shallow wells in Kubi, respectively(Figure 3.1). The sampling location, depth, and utilization information for each sampling sitewere determined from GPS coordinates. All wells chosen for groundwater sampling werecommonly used for domestic and/or agricultural purposes; these wells had a mean depth of
10.8 m and 4.84 m in the Kubi and Ocho villages, respectively. The groundwater pH,
temperature, electrical conductivity (EC), dissolved oxygen (DO), and oxidation- reduction
potential (ORP) were measured on-site using a portable multiparameter water quality meter(pH/COND Meter D-54, HACH LD0101, ORP Meter RM-20P). All samples were collectedfrom a 0.5–1 m depth below the water level in the wells using a high-density polyethylene
sampler and filtered through a 0.2 μm membrane into one 250 mL, three 10 mL, and one 100mL high-density polyethylene sampling bottles. After filtration, the samples were stored at 4 °C
and subsequently transported to the laboratory, where they were frozen at −4 °C until furtherhydrochemical and isotopic analyses. The contents of the main cations, Na+, K+, Mg2+, andCa2+, were analyzed by ion chromatography with conductivity detection on an ICS-2100(Dionex integrion, Thermo Fisher Scientific, Waltham, MA, USA). Analytical precisions forcations were set at <0.2 % for Na+ and < 0.1% for Ca2+; Mg2+ and K+ while for anions, <0.1 %for NO3⁻, Cl⁻ and <0.2 % for SO42⁻. HCO3⁻ was determined within 12 hours of sample

41

collection by acid titration from a volume of 0.02 N H2SO4 added to the sample to reach its
titration endpoint, marked by a pH of 4.5. Dissolved organic carbon (DOC) was determinedusing a total organic carbon analyzer (Total Organic Carbon analyzer, SHIMADZU, Hiroshima
University, Japan). The analyses of major ions and DOC were conducted at the
Biogeochemistry Laboratory, Hiroshima University, Japan.
Isotope analyses were performed at the Institute of Geosciences, Yamanachi University,using a stable isotopic ratio mass spectrometer (MAT 253, Thermo Fisher Scientific, Waltham,
MA, USA). Chemical reduction (cadmium and azide reduction) was used to quantify δ15NNO3
and δ18ONO3, respectively (Mcllvin and Altabet, 2005; Tu et al., 2016). Briefly, to eliminate
nitrite, the samples were treated with a 2.5 mmol/L sulfamic acid solution. The NO3⁻ in thesamples was then quantitatively converted to gaseous nitrous oxide (N2O) using a two-step
reduction process. First, NO3⁻ was reduced to nitrite with spongy cadmium, then nitrite wasconverted to nitrous oxide with an azide/acetic acid buffer, and finally, the δ15N and δ18O of
N2O were determined. The stable isotope ratios of δ15N and δ18O were given in delta value (δ)
and per mil (‰) notation in comparison to international standards as follows (Kendall et al.,1998):
𝛿𝑠𝑎𝑚𝑝𝑙𝑒 (‰) = [(𝑅𝑠𝑎𝑚𝑝𝑙𝑒 ⁄𝑅𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 ) − 1] × 1000

(3.1)

where Rsample and Rstandard are the 15N/14N and 18O/16O ratios of the samples andstandards for δ15N and δ18O, respectively. The δ15N values were reported relative to N2 in
atmospheric air (AIR), and the δ18O values relative to Vienna Standard Mean Ocean Water(VSMOW). The precision values for δ15N and δ18O were ±0.3 ‰ and ±0.4 ‰, respectively.
Two representative samples of organic and chemical fertilizer were collected from local
farmers in Kubi village in January 2022 for analysis as end-member samples. The solid samples
were transported to the laboratory for air-drying and then fully ground for chemical andisotopic analyses. The solid samples were converted to N2 by high-temperature redox reactions,42and δ15N-N2 was measured using an isotopic ratio mass spectrometer. The precision for δ15N
was ±0.3 ‰.
3.3.2 Source apportionment calculation
The Stable Isotope Analysis in R (SIAR) software program was used to develop the
mixing model. The system model used in SIAR can be expressed by defining a set of N mixture
measurements on J isotopes with K source contributors, the mixing model can be expressed asfollows (Kendall et al., 2008):𝑘

𝑋𝑖𝑗 = ∑ 𝑃𝑘 (𝑃𝑗𝑘 + 𝐶𝑗𝑘 ) + 𝜀𝑗𝑘
𝑘=1

(3.2)

2
𝑆𝑗𝑘 ~ 𝑁 (𝜇𝑗𝑘 , 𝜔𝑗𝑘)

2
𝐶𝑗𝑘 ~ 𝑁 (𝜆𝑗𝑘 , 𝜏𝑗𝑘)

𝜀𝑗𝑘 ~ 𝑁 (𝑂, 𝜕𝑗2 )where Xij is the isotope value j of mixture i, in which i=1, 2, 3, …, I and j=1, 2, 3, …, J; Sjk isthe source value k of isotope j (k=1,2,3, …, K), and is normally distributed with a mean value
μjk and a standard deviation ωjk; Pk is the proportion of source k, which is estimated using the
SIAR model; Cjk is the fractionation factor for isotope j of source k, and is normally distributedwith a mean value λjk and a standard deviation τjk; and εij is the residual error representing theadditional unquantified variation between individual mixtures, and is normally distributed witha mean value of 0 and a standard deviation σj. Details of this model were described in previous
studies (Mcclain et al., 1994; Jackson et al., 2009).
A Bayesian model was applied to determine the potential NO3⁻ contribution from eachof four sources for Kubi and Ocho villages: δ15N-NO3⁻ in manure and sewage (M&S), chemicalfertilizer (CF), and organic fertilizer (OF); and soil nitrogen (SN) x (k=4). The end-member

43isotopic compositions for the three sources were used (j=3) in the model setup. SN and M&S
samples were not analyzed in the study area, so the SN values from (Kendall et al., 2008;
Fadhullah et al., 2020; Fu et al., 2019; Yu et al., 2020) were compared with the CF samples,and since they showed similar ranges; they were identified as one source CF_SN in this study.
Meanwhile, the M&S values were obtained from the literature and used in the model as anadditional source. We considered M&S as a suspension of water and solid waste from humans
and animals transported by sewers, and OF exclusively as a mixture of chicken litter andsawdust. Although discrete samples of individual δ15N-NO3⁻ sources in the study area were
measured, the limited number obtained may induce a biased model outcome. Alternatively, themean values of δ15N-NO3⁻ isotopes (μjk) and associated SDs (ωjk) of different M&S wereacquired by consulting previously published data (Kendall et al., 2008; Fu et al., 2019;
Fadhullah et al., 2020; Yu et al., 2020), whereas CF and OF were obtained from the Kubi area.
3.3.3 Statistical Method
Statistical analysis was performed to determine the NO3⁻−N relationship with DOC andthe major ions using scattered plot, which was demonstrated by the coefficient of determination(R2). In addition to correlation analysis, principal component analysis (PCA) has been used toanalyze groups of ions based on similarity or difference in concentration. SPSS® ver. 23.2
(SPSS Inc., Chicago, USA) was used to analyze the groundwater chemical data in this study. A
PCA can explain most of the variability in a data set with fewer variables than the original
dataset, so parameters with similar loadings in the same PC group may have similar
contribution sources or geochemical behavior (Huang et al., 2018a; Zhang et al., 2019). PCA
was used to identify the primary groundwater contamination indicators using Kaiser's varimax
rotation method, adopting only PCs with eigenvalues greater than one. An inverse distanceweighting method (IDW) was also performed using ArcMap 10.6 to show the spatial
distribution of NO3−−N and δ15N-NO3⁻ in Kubi and Ocho groundwaters44

3.4 Results and Discussion
3.4.1 General hydrochemistry
The hydrochemical compositions of Kubi and Ocho are summarized in Table 3.1. The
results showed that the pH of groundwater was near neutral in Kubi (6.4 to 7.5, mean pH 6.8).
The pH values of the river water sample were neutral to alkaline (7.9 to 8.6) and higher than
the groundwater pH values, which may be due to the strong evaporation of the surface water.
In Ocho village, groundwater was slightly acidic to alkaline with pH values ranging from 6.2
to 8.5 with a mean of 6.7. All samples had moderate EC values varying from 106.3 to 472
μS/cm, (mean 306.7 μS/cm) in Kubi and 164.9 to 479 μS/cm (mean 308.5 μS/cm) in Ocho.
Total dissolved solids (TDS) were approximated from EC following the equation (Evangelou,
1998):𝑚𝑔
𝜇𝑆) = 0.64 𝑥 𝐸𝐶 ( )
𝐿
𝑐𝑚

𝑇𝐷𝑆 (

(3.3)

The DOC levels of groundwater in Kubi ranged from 2.5 to 13.33 mg/L (mean 5.1 mg/L),
while those in Ocho ranged from 1.6 to 7.0 mg/L (mean 3.5 mg/L). The DO ranged from 1.1
mg/L to 8.6 mg/L with a mean of 4.9 in the wells in Kubi, while those in Ocho ranged from
0.8 mg/L to 9.1 mg/L with a mean value of 3.4 mg/L. All but one sample from Kubi had DO
values above 2 mg/L, whereas in Ocho, nine samples had values less than 2 mg/L. Also,
observed DO levels were higher in naturally occurring springs (mean 9.2 mg/L in Kubi and 8.6
mg/L in Ocho). Hence, groundwater samples in Ocho had the potential to undergo
denitrification processes as opposed to those in Kubi. Ca2+ was the most dominant cation inboth study areas and ranged from 7.8 – 38.1 mg/L in groundwater samples and from 9.8 mg/L
− 35.5 mg/L in surface water samples in Kubi while in Ocho area, concentrations ranged from
0.5 mg/L – 4.6 mg/L in surface water and from 8.8 mg/L − 50.8 mg/L in groundwater samples.

45

Table 3.1: Statistical characteristics of the hydrochemical compositions of the groundwater and surface water sources in Kubi and Ocho village.
Kubi village
Spring (n = 1)

River (n = 2)

Ocho village

Wells (n = 17)

Borehole

Spring(n = 1)(n =2)

Wells (n = 30)

Parameters

Min/Max

Min.

Max.

Ave.

Min.

Max.

Ave.

Min/Max

Min.

Max.

Ave.

Min.

Max.

Ave.

pH

7.3

7.9

8.6

8.3

NM

7.4

6.8

7.5

7.5

7.5

7.5

6.2

8.5

6.8

Temp

19.1

23.7

24.7

24.2

18.1

26.7

21.2

19

17.6

23.6

20.6

19.2

28.3

22.3

EC

185.7

206

274

240

107

465

314.8

472

133

217

175

165

479

308.5

TDS

118.9

131.9

175.4

153.6

68.5

297.6

201.5

302.1

85.2

138.9

112

105.6

306.6

105.6

DO

9.2

8.3

9

8.7

1.1

8.7

5

6.1

7.7

9.5

8.6

0.8

9.1

3.5

DOC

3.6

3.8

5.3

4.6

2.5

13.3

5.1

4.1

NM

3.5

3.5

NM

7

3.3

Cl⁻

14.7

9.4

11.1

10.3

7.6

34

11.9

13.4

4.6

10.9

7.8

4.1

37.5

11.9

SO42⁻

22.9

30.5

50.4

40.5

22.1

75.3

44.4

80.7

4.5

22.9

13.7

−

145.1

37.7

NO3⁻

7.1

12.4

34.2

23.3

14.9

74.5

41.5

70.3

2.7

13.7

8.2

3.5

66.4

21.7

NO3−−N

1.7

2.9

7.8

5.4

3.4

16.9

9.5

15.9

0.7

3.1

1.9

0.8

15.1

5

HCO3

7.2

50.6

61.2

55.9

−

121.6

48.6

39.1

4

9.3

6.7

4.5

150.2

64.2

Na⁺

21

16.3

18.3

17.3

9.4

32.5

15.7

31.4

−

5.5

2.8

9.4

57.6

18.6

K⁺

2.2

3.2

3.6

3.4

0.7

6.9

4.1

1

3.2

8.4

5.8

0.3

10.5

4.1

46

Mg2⁺

9.8

8.2

10.7

9.5

7

17.8

11.5

17.8

1.4

25.8

13.6

4.9

22.2

10

Ca2⁺

24.6

22.3

30

26.2

20.4

48.4

31.7

44.8

−

4.6

4.6

−

50.8

28.8

Note: n = number of samples; SD = standard deviation; − = below detection limit; NM = not measured, ionic concentrations, DOC and DO =mg/L; EC = μS/cm; Temp = O/C

47

Magnesium concentrations ranged from 1.6 mg/L − 15.3 mg/L in groundwater samples
and 4.5 – 14.4 mg/L in surface water in Kubi whereas in Ocho village, these concentrations
where much lower, varying from 4.9 mg/L – 22.2 mg/L in groundwater samples (Table 3.1).
Ca2+ and Mg2+ can contaminate groundwater through ion exchange when minerals dissolve inrocks, or when fertilizers run off and leach into the groundwater. According to (Earle, 2019),
soils containing CaCO3 and CaSO4 may increase Ca2+ levels in groundwater by leaching,whereas (Keesari et al., 2016) suggests agricultural runoff and sewage disposal may lead tohigh Mg2+ levels. The most abundant anion in the water samples in both villages is HCO3⁻.
In in Kubi, HCO3⁻ in river samples ranged from 50.6 mg/L – 61.2 mg/l with a meanvalue of 55.9 mg/L while concentrations in wells ranged from 14.2 mg/L – 121.6 mg/L with amean of 45.3 mg/L (Table 3.1). HCO3⁻ concentrations in Ocho ranged of 4.5 mg/L – 64.2 mg/l
in wells and from 4 mg/L to 9.25 mg/L in spring samples. Cl⁻ values varied from 4.2 to 14.7
mg/L with a mean value of 9.41 mg/L in Kubi while values varied from 4.1 to 37.5 mg/L witha mean value of 11.59 mg/l in Ocho village. The average concentration of dissolved species inthe samples in both villages was in the sequence of Ca2⁺ > Na+ > Mg2+ > K+ for the cations andHCO3⁻ > SO42⁻ > NO3⁻ > Cl⁻, for the anions.
The chemical compositions of water samples of both Kubi and Ocho village wereidentified and classified using the trilinear plot (Figure 3.3). The major groundwater facies in
Kubi area were mainly Ca−HCO3 and Ca−SO4 water type constituting 55 % and 45 % of thewater samples, respectively. Meanwhile, the Trilinear analysis for Ocho village suggest that
59% of water samples are composed of Ca−HCO3 water type while 42% of water samples
plotted in the zones of Ca+Mg−SO4 or CaMg−Cl water types. Groundwater samples from bothareas showed freshwater signatures with higher percentages in the Ca−HCO3 zone, however48

they show susceptibility to anthropogenic contamination as they evolve between Ca-HCO3 andCa+Mg−SO4 or CaMg−Cl types.

Figure 3.3: Piper diagram of surface water and groundwater in (a) Kubi and (b) Ocho (Adopted
after Piper, 1944).
3.4.2 Spatio-temporal variations in the NO3−−N concentration
Nitrate concentration can characterize the degree of groundwater nitrate contamination.
When the nitrate nitrogen (NO3−−N) concentration exceeds 3 mg/L, it is deemed to be affectedby human factors (Amber and Schmidt, 1987). A mean value of 6.7 mg/L and a median valueof 6.5 mg/L were observed in Kubi, with NO3−−N concentrations ranging from 0.5 mg/L to 13
mg/L. Approximately 33.4 % of groundwater samples had NO3−−N concentrations below 5
mg/L, 47.7% of samples ranged between 5 mg/L − 10 mg/L and 19.1% of samples haveconcentrations above 10 mg/ L. Meanwhile, NO3−−N concentrations in Ocho varied from 0.7
mg/L −15.1 mg/L with a mean and median value of 4.8 mg/L and 4.5 mg/L respectively. These
concentrations were much lower than those of Kubi. In Ocho village, over 62.5% of thegroundwater samples had NO3−−N concentrations less than 5 mg/L 31.3% of the samples had

49concentrations between 5 and 10 mg/L, and 6.2% of the samples had NO3−−N concentrations
above 10 mg/L.

Figure 3.4: (a) Osakishimojima Island. Spatial variation of NO3−−N and δ15NO3−−N
concentrations in groundwater of (b) Kubi and (c) Ocho villages.

The observed higher values of NO3−−N concentrations in Kubi village was likely
attributed to differences in land use pattern and contribution from difference in fertilizer
application by farmers which coincides with the spatial distribution of agricultural areas in the
study sites. From Figure 3.4b, most of the higher concentrations of NO3−−N are locatedupstream of the lowland area which is directly below the upland citrus cultivation zone.
Leaching of chemical fertilizers into the groundwater, which are subsequently
transported downstream, could be responsible for the elevated NO3−−N concentrations in Kubi.
In contrast, Ocho village had lower NO3−−N concentrations, with some sampling points
showing relatively high NO3−−N concentrations, including upstream, midstream, anddownstream sampling points (Figure 3.4c). Groundwater contamination was likely caused by50

organic fertilizer inputs or leakages from the septic systems. The NO3−−N concentration wasobserved to be between 3 and 8 mg/L at well depth between 4 and 6 m (Figure 3.5).

Figure 3.5: NO3−−N (mg/L) concentration vs. well depth in shallow wells in Kubi and Ocho
villages.
A pattern can be observed in Kubi with groundwater NO3−−N concentrations gradually
increasing from deeper depths to shallower depths but for some few samples showing higher
concentrations even at deeper depths. This may reflect variations in NO3−−N residence time in
the groundwater system. The topography and hydrogeological properties of the aquifer might
equally be influencing the distribution of groundwater contaminants (Lasagna et al., 2019),
however, this relationship could not be confirmed in this study because of unavailability ofdata. In Ocho, NO3−−N concentrations didn’t change much with depths with most samplings
plotting at 5 m depth.
3.4.3 Relationship between NO3−−N and water chemical parameters
A correlation coefficients between measured parameters for all groundwater samples
in Kubi and Ocho are presented in Table 3.2. According to the statistical significance levels,

51

most of these correlations are valid and reasonably accurate. In Ocho village, NO3−−N was
positively correlated with SO42⁻ (r = 0.69, p < 0.01), Na+ (r = 0.72, p < 0.01), Mg2+(r = 0.6, p< 0.01), Ca2+ (r = 0.48, p < 0.05) and EC (r =0.6, p < 0.01) meanwhile in Kubi village NO3−−N
was positively correlated with SO42⁻ (r = 0.88, p < 0.01), Na+ (r = 0.68, p < 0.01), Mg2+(r = 84,
p < 0.01), Ca2+ (r = 0.8, p < 0.05) and EC (r =0.6, p < 0.01).
Table 3.2: Correlation matrix between measured parameters in Kubi and Ocho villages.
Kubi village (n =19)
NO3−−N
Cl⁻
HCO3⁻
SO42⁻
Na⁺
K⁺
Mg2⁺
Ca2⁺
EC
DOC

NO3−−N

Cl⁻

HCO3⁻

SO42⁻

Na⁺

K⁺

0.17
-0.2

-0.37

.88**
.68**
-0.18
.84**

0.15
.67**
-0.14
0.38

-0.15
-0.21
-0.21
-0.13

.66**
-0.45
.93**

.77**

-0.12

.80**
.63**
0.37

0.35
0.11
-0.28

0.06
0.32
-0.36

.85**
.66**
0.29

.74**
.60**
-0.02

-0.02
-0.07
-0.04

Mg2⁺

Ca2⁺

EC

.94**
.73**
0.21

.71**
0.1

0.16

.85**
.72**
-0.26

.72**
-0.25

-0.16

DOC

Ocho village (n=23)
NO3−−N
Cl⁻
HCO3⁻
SO42⁻
Na⁺
K⁺
Mg2⁺
Ca2⁺
EC
DOC

0.38
0.08
.69**
.72**
-0.08
.60**
.48*
.60**
-0.03

.59**
0.33
.59**
0.00
.56**
.54**
.67**
-0.23

0.00
0.28
0.05
0.25
0.21
0.34
0.15

* Correlations significant at p = 0.05

.82**
-0.16
.82**
.74**
.67**
-0.12

-0.12
.80**
.62**
.66**
-0.12

-0.18
0.12
-0.27
0.32

** Correlations significant at p = 0.01
The relatively high concentration of NO3−−N in the Kubi groundwater was consistent
with the significantly elevated concentrations of major cations and anions Mg2⁺ and Ca2⁺ andSO42⁻ (Figure 3.6).
52

Figure 3.6: Relationship between NO3−−N and (a) DOC, (b) Cl⁻, (c) SO42⁻, (d) Na⁺, (e) Mg2⁺,and (f) Ca2⁺.
There was a weak positive association among NO3−−N, DOC, and Cl⁻ in both Kubi andOcho villages. In the absence of a geological chloride source, nitrate and chloride are frequently
utilized as indicators of anthropogenic groundwater contamination because of their positive
correlations. While chloride is a conservative tracer that does not normally undergo biologicaltransformation in groundwater, the denitrification process can reduce the nitrate concentrationin groundwater (Rivett et al., 2008; Korom,1992). Thus, the presence of this process in the
groundwater samples from Kubi and Ocho might have reduced the degree of correlation
between the parameters shown in Figure 3.6.
Furthermore, the positive relationship between NO3−−N and SO42⁻ could be due to theuse of chemical fertilizers such as (NH4)2SO42⁻ (Babiker et al., 2004). A positive relationshiphas been suggested among NO3−, Mg2⁺, and Ca2⁺ (Fu et al., 2019), indicating that they are53derived from chemical fertilizers, such as CaCO3 and MgCO3. In Kubi, concentrations of
NO3−−N showed strong significant positive correlations with SO42⁻ (R2 = 0.76, p < 0.01) as
opposed to Ocho, which presented a weak significant positive relationship (R2 = 0.45, p < 0.01).
This agrees with earlier studies by (Cuoco et al., 2015; Rufini et al., 2019) affirming, there exist
a correlation between SO42⁻ and NO3− concentrations in groundwater, indicating that locations
with high SO42⁻ concentrations also have high NO3− concentrations. More chemical fertilizers
may be used in Kubi since the chemical fertilizer derived component was higher in Kubi thanOcho. The observed higher values of NO3−−N concentrations in Kubi village were likely
attributed to differences in the type of fertilizer used by farmers and the proportion applied tothe area under cultivation, which coincides with the spatial distribution of agricultural areas in
the study sites. Other researchers (Nakagawa et al., 2016) found that the application of CaCO3
fertilizer to their research site generated a positive association between NO3− and Ca2+.
Similarly, in Kubi village (R2 = 0.63, p < 0.01), there was a considerable positive correlation
between NO3−−N and Ca2+ compared to that in Ocho (R2 = 0.22, p < 0.01), confirming inputs
from chemical fertilizers. K⁺ showed no correlation with NO3−−N in both study sites.
3.4.4 Principal component analysis results
A three-factor model controlled the groundwater chemistry of in Kubi, and thecumulative variance by the three PCs was 73.2% (Table 3.3). In Kubi, PC1 explained 40.9 %of the total variation, with positive loadings of 6 variables in the order Mg2⁺ > SO42⁻ > NO3−−N> Na⁺> TDS > Ca2⁺ (Table 3.3). A strong positive loading of Mg2⁺, and SO42⁻ and a moderate
positive loading of Ca2⁺ and TDS suggest increased mineralization of groundwater water fromnatural sources, whereas a strong positive loading of NO3−−N indicates anthropogenic pollution
from agricultural activities such as fertilizer usage, manures, and land use (Egbi et al., 2019).
Several studies (Earle, 2009; Yadav et al., 2002; Huang et al., 2018a) have linked domestic
sewage and agricultural fertilizers to the occurrence of Na+, Mg2⁺, K+, SO42⁻, NO3− and Cl⁻.
54

Thus, PC1 was denoted by infiltration of agricultural fertilizer and domestic sewage. PC2,
accounting for 19.5% of the total variance, showed a moderate positive loading for DO and amoderate negative loading for K⁺.
PC3 explained 13% of the total variance with moderate loadings of pH. The
groundwater chemistry of Ocho was also controlled by a three-factors model, and thecumulative variance explained by the three PCs was 67.8% (Table 3.3). PC1, accounting for38.2% of the total variance, showed strong loadings of SO42⁻ and NO3−−N, and moderateloadings for Ca2⁺, Mg2⁺, and Na⁺. Although these variables can be interpreted as anthropogenic
contamination from agricultural fields, their loadings were lower in comparison to PC1 in the
Kubi area, possibly due to difference in rate of leaching and residence time of contaminants in
the groundwater system.
Table 3.3: Principal component (PC) loadings of 11 chemical variables in the groundwater
samples of Kubi and Ocho.
PCs Kubi village (21 samples)

PCs Ocho village (32 samples)

Variables

PC1

PC2

PC3

Variables

PC1

PC2

PC3

pH
TDS
DO
NO3−−N
Cl⁻
SO42⁻
HCO3⁻
Na⁺
K⁺
Mg2⁺
Ca2⁺
Eigen value
EVCV-0.0980.783-0.209
0.903
0.407
0.914
-0.1170.823-0.167
0.917
0.673
4.5
40.9
40.9

0.396
-0.379
0.766
-0.05
0.398
-0.018-0.6030.394-0.747-0.099-0.051
2.2
19.5
60.3

0.663
0.309
0.105
-0.119-0.528-0.0010.574
0.117-0.375
-0.13
0.29
1.5
13
73.2

pH
TDS
DO
NO3−−N
Cl⁻
SO42⁻
HCO3⁻
Na⁺
K⁺
Mg2⁺
Ca2⁺

-0.088
0.542
0.035
0.815
0.376
0.922
-0.131
0.683-0.109
0.719
0.754
4.3
38.2
38.2

-0.2420.686-0.726
0.058
0.723
0.041
0.798
0.517
-0.1440.124-0.023
2
18
56.2

0.768
0.141
0.382
-0.0330.067-0.067-0.002-0.047-0.787
0.055
0.102
1.3
11.7
67.8

EV: Explained variance (%); CV: Cumulative % of variance; Bolding and Italics numbers: PC
loadings that are significant at (> 0.65)

55

PC2 explained 18% of the total variance with moderate positive loadings on 3 variables(HCO3⁻, Cl and TDS) and a negative loading on DO. We can consider that, the dissolution ofcarbonate rocks by water-rock interaction was responsible for the loading of HCO3⁻ while Cl⁻contributing source was from domestic sewage. The third PC, which accounts for 11.7% of thetotal variance in Ocho village, had moderate positive and negative loadings for pH and K,
respectively. The results from PC indicate that natural processes involving mineral components
and anthropogenic contamination from fertilizers and domestic sewage are the main processes
affecting groundwater chemistry in Kubi and Ocho.
3.4.5 Distribution characteristics of stable isotopes
Ten samples from Kubi (1 river, and 9 well samples) and nine well samples from Ocho
were analyzed δ15N-NO3− and δ18O-NO3⁻ (Table 3.4). Samples were selected primarily on thebases of nitrate levels and spatial distribution to represent each study site. The δ15N-NO3−
values of Osakishimojima Island showed high variability in the Kubi and Ocho villages (Figure3.3). In Kubi village, the δ15N-NO3− values of groundwater samples ranged from 3.7 ‰ to13.3 ‰, with a mean value of 6.7 ‰, and the δ18O-NO3⁻ values ranged from -1.0 ‰ to 2.8 ‰,with a mean value of 1.5 ‰. Similarly, in Ocho village, the δ15N-NO3− values ranged from
4.9 ‰ to 26.0 ‰, with a mean value of 11.4 ‰, and the δ15N-NO3− values ranged from 0.1 ‰to 14.9 ‰, with a mean value of 5.3 ‰ (Table 3.4).
Table 3.4: Isotopic data (‰) of sampled groundwater in Kubi and Ocho areas, October 2021.
Kubi village
SampleType

Ocho village

δ15N-NO3−

δ18O-NO3⁻

SampleType

δ15N-NO3−

δ18O-NO3⁻

OKR1

River

7.2

2.1

OG4

Well

9.9

2.6

OK BH

Borehole

6.5

1.2

OG6

Well

26

14.9

OKU2

Well

3.7

2.4

OG7

Well

6.6

1

OKU3

Well

5.1

0.8

OG11

Well

8.6

2.6

56

OKU5

Well

13.3

2.2

OG14

Well

15.2

3.3

OKU6

Well

6.8

1.4

OG17

Well

5.2

0.7

OKU11

Well

5.3

-1

OG21

Well

14.5

14.7

OKD2

Well

6.8

2.8

OG24

Well

12

7.7

OKD4

Well

6.5

1.4

OG26

Well

4.9

0.1

OKD5

Well

6.1

1.6

Min

4.9

0.1

Min

3.7

-1

Max

26

14.9

Max

13.3

2.8

Mean

11.4

5.3

Mean

6.7

1.5

STDev

6.7

5.9

STDEV

2.6

1.1

A relatively high value of δ15N-NO3− (26 ‰) in sample OG6 may suggest othercontrolling processes in the catchment area, such as denitrification, however since the sample
size was small, it was excluded from further analysis to avoid bias estimation, especially in the
SIAR model. The mean values of δ15N-NO3− and δ18O-NO3− in Kubi village were significantly
lower than those in Ocho village. This was contrary to the spatial distribution of NO3−−N
concentrations (Figure 3.3). The reason is that more chemical fertilizers with lower isotopic
values and faster leaching rate were used for citrus cultivation in Kubi than in Ocho. In general,the leaching rate is determined by the degree of water solubility of a fertilizer in combination
with the amount of soil water at any time, soil type, porosity, and compaction (Soil Health,2013). Kubi and Ocho have the same soil type and soil properties, but due to the differences intheir choice of fertilizer, variations in NO3−−N concentrations may arise. Highly soluble
fertilizers dissolve quite rapidly into the soil and since plants can only absorb a certain amountof nutrition at a time, a greater percentage of the fertilizer simply leaches through the soil into
the groundwater. The nitrate sources were identified from the cross-plot of δ15N-NO3− andδ18O-NO3− (Figure 3.7).

57

Figure 3.7: Nitrate isotopic signatures for sampling points in Kubi and Ocho with typicalnitrate endmembers (adapted from Kendal et al.,2008).
Most of the data points were plotted in the range of synthetic fertilizer and soil nitrogenin Kubi village, while in Ocho village, points were plotted mainly in the manure and sewage
domain. This suggests that the major inputs of NO3− are from synthetic fertilizer in Kubi,
whereas in Ocho, they are mainly from organic fertilizer. The groundwater chemistry in Figure
3.6 and Table 3.3 suggests that more chemical fertilizer is used in Kubi than in Ocho, which
explains the significant differences in the isotopic values between the sites. Additionally,
higher δ15N-NO3− concentrations may have been derived from sewage systems, whose endmember samples were not measured in this study.
Ocho has a higher housing density than Kubi (Figure 3.2), and since the area is a ruraldepopulating and aging community, the sewage system may be poorly managed with possible

58contamination from leakages through worn out pipes into the groundwater system. According
to (Kure City Water Works and Sewerage Bureau, 2021). Kubi has a local waste treatment
plant while Ocho depend solely on septic treatment tanks. Given the density in ocho, sewer
discharge into the groundwater is more likely. The difference in the age structure andpopulation size influences farming practices, such as the method of fertilizer and pesticide
application and the application schedule, which are important processes for quality crop yields.
Chemical fertilizers are water soluble which makes them readily available for plant uptake thanorganic fertilizers thus, aged farmers prefer to use chemical fertilizers that are more costeffective, less time consuming, and more convenient for maximum crop yield. However,
because the nutrient requirements for plant growth, productivity, and fruit quality fluctuateseasonally, the split fertilization method is commonly utilized in citrus farming (Morgan et al.,2020).
While this strategy is acceptable and reduces inorganic N leaching while boosting N
use efficiency (Moe et al., 2014), it necessitates the utilization of an additional labour force,
which is unsuitable for Kubi and Ocho as the population is both declining and aging (Kure city
report, 2021; Japan Agricultural Cooperatives, 2022) with a limited crop-production labour
force. In studies conducted in China by (Moe et al., 2014; Zhou et al., 2017), the association
between fertilization application methods, population ageing, and restricted labour force has
been observed. However, given that Kubi farmers tend to use more fertilizer may possibly bebecause of their lower population size when compared to Ocho (Kure city report, 2021). Since
the decreasing population has a reduced labor force which drives farmers towards the choiceof using chemical fertilizers to increase crop yields even with the low work force to perform
proper farming methods.
Also, the proportion of area under cultivation may influence the quantity of fertilizer
applied, subsequently contributing to elevated NO3−−N. As observed in Figure 3.2, Kubi has

59approximately 40.2 % of its total area occupied by orchard fields, which is twice the coveragearea than that of Ocho village even thou at the catchment scale Ocho has almost thrice
agricultural land than Kubi (Ministry of Land, Infrastructure, Transport, and Tourism of Japan,2021), the pathways of nitrate in the two study sites may be different. Nevertheless, theoverlapping δ15N-NO3− and δ18O-NO3− values of the different sources hinder the quantitative
estimation of their contributions. Some samples showed trends towards denitrification in Ocho;
however, we could not confirm the occurrence of this process because the used sample size inthis study was relatively small (n = 9). Furthermore, a previous study reported related
investigations and concluded that the link among δ15N, δ18O, and NO3− is a complex web in
which all environmental settings should be considered to accurately distinguish between
different biogeochemical processes (Chen et al., 2007). Hence, biological, and geochemical
processes may be occurring simultaneously in Ocho village which results in the shift in δ15Nand δ18O levels.

3.5 Source apportionment of NO3−
The proportional contributions of NO3− sources in groundwater were identified using
the SIAR model. Two isotopes (J = 2) (δ15N-NO3⁻ and δ18O-NO3⁻) and three potential sources(K = 3) (OF, CF_SN, and M&S) were used to estimate the contributions of NO3− sources in
Kubi and Ocho village. The δ15N-NO3⁻ and δ18O-NO3⁻ of groundwater samples (Table 3.4) andsources (both mean and SD in Table 3.5) were input into the SIAR model
Table 3.5: The δ15N and δ18O values of various nitrate sources.
Sourceδ15N (‰)

δ18O (‰)

Mean

SD

Mean

SD

bSN

4.4

0.7

2.85

0.2

aCF

4

0.3

2.85

0.4

a,bCF_SN

4.2

0.5

2.9

0.3

60

aOF

10

0.3

2.85

0.4

M&S

19

0.3

-12.510.4baTheoretical values analyzed, and bdata sources (Kendall et al., 2008; Fahullah et al., 2020; Fu

et al., 2019; Yu et al., 2020).
Sewage was not measured in the study area; however, considering the residential area
in one of the study sites, we applied the literature value for the model, while atmosphericdeposition was not considered. Furthermore, the fractionation factor Cjk was set to zero because
we did not investigate to determine its value. The results of source apportionment are shownin Figure 3.8.

Figure 3.8: Spatial proportional contributions of NO3− sources estimated using SIAR in (a)Kubi and (b) Ocho villages. Boxplots illustrate the 50th, 75th and 95th percentiles from dark tolight.
The source contributions showed significant spatial variation under similar cropping
systems with different land-use proportions. The dominant NO3− pollution source in Kubi
village was CF_SN, which contributed 60.1 %, followed by OF, which contributed 30.1 %,and M&S, which contributed 9.8% (Figure 3.8). An increase in the fertilizer source

61contribution in the research area could also be due to the irrigation system. According toanother study conducted by (Lui et al., 2013), higher yields from irrigated crops are generally
associated with higher nitrogen fertilizer applications, increasing the risk of N loss to theenvironment. In Ocho village, the proportional contribution decreased in the following order:
M&S (10.9 %) < CF_SN (38.5 %) < OF (50.7 %).
The results of NO3− source apportionment with SIAR in Ocho corresponded with ourfield investigation and the land use distribution shown in (Figure 3.2). Residential areas in
Ocho are more concentrated than those in Kubi and have a low urbanization level due todepopulation. Contamination from old and poorly maintained drainage systems is highly
probable. According to (Kure City Water Works and Sewerage Bureau, 2021), 70% of theresident' wastewater for Kubi is treated by a local wastewater treatment plant while theremaining 30% is treated by septic tank whereas in Ocho all waste is treated by septic tanks.
The difference between the waste management system may explain the differences in NO3−−N
concentrations in each area. Hence, chemical fertilizer is mainly used in Kubi and organic
fertilizer in Ocho (Figure 3.7). However, due to differences in wastewater treatment systems,
Ocho has higher values of sewage and manure contributions than Kubi (Kure City Water
Works and Sewerage Bureau, 2021), but this impact is not so high when compared to organic
fertilizer input, so we conclude that the main source of nitrate pollution is organic fertilizer.
Additionally, Kubi's proportional input of manure, sewage, and chemical fertilizer suggests ahigh rate of leaching that may differ from Ocho village and result in a higher concentration of
nitrates in the ground-water system.
In summary, chemical fertilizers are mainly used in Kubi and organic fertilizers areused in Ocho (Figure 7). Due to differences in the wastewater treatment systems, Ocho has
higher values of sewage and manure contribution than Kubi (Kure City Water Works andSewerage Bureau, 2021) but its impact is not as high as that of organic fertilizer input; therefore,62we concluded that the main source of nitrate pollution is organic fertilizers. Additionally, large
quantities of manure, sewage, and chemical fertilizers are used in Kubi, suggesting a high rateof leaching, resulting in a higher concentration of nitrates in the groundwater system. Table 3.6
demonstrates the relationship between social factors and groundwater nitrate–nitrogencontamination in Kubi and Ocho villages, with Kubi having a smaller population andagricultural area and a higher concentration of NO3−−N levels, primarily due to chemicalfertilizers. Compared to Kubi village, Ocho village had lower NO3−−N levels despite having alarger population, and the organic fertilizers used in agricultural areas were the primary sourceof contamination.
Table 3.6: A summary of social aspects and groundwater NO3−−N contamination status in
Kubi and Ocho villages.
Social Information

Units

Population

Kubi

Ocho

Village

Village

413

830

Key References
Kure city population
Statistics, 2021

Average farmer age

Years

76

73

JA Yukata, 2022

Agricultural land

km2

0.6

1.6

MLIT, 2021
Kure City Water Works

Sewage treatment coverage

%

70

0

and Sewerage Bureau,
2021

Summary of NO3−−N concentration

Figure 3.4

Average NO3−−N concentration

mg/L

6.6

4.8

<5

%

33.3

62.5

5–10

%

47.7

31.3

>10

%

19

6.2

Main NO3−−N contribution ratio

Figure 3.8

Chemical fertilizer

%

60.1

38.4

Organic fertilizer

%

30.1

50.7

Manure and sewage

%

9.8

10.9

63

3.6 Summary
Japan's society has undergone major social change, including increased population
aging and depopulation, but it still faces serious environmental problems due to groundwaterpollution. Intensification of fertilizer use in agricultural activity constitute one of the major
factors contributing to elevated nitrate levels in Japanese groundwater resources. However,other factors may also contribute to pollution, such as land use, the proportion of cultivationand farming methods. We investigated the impact of population aging and depopulation on
groundwater nitrate contamination on a western Japanese island that has extensive citrus
farming. In comparison to the eastern Ocho village, where the average age of farmers is 73
years, the northern Kubi farmers are slightly older on average, at 76 years, and agricultural landdecreased by 46% over the past ten years, from 2005 to 2015. Ocho had 830 residents, which
was twice as many as Kubi. In comparison to Ocho, which had an average NO3−−N
concentration of 4.8 mg/L (37.5% of water samples > 5 mg/L), Kubi village had a higher
average NO3−−N concentration of 66.7 mg/L (66.7% of water samples > 5 mg/L). The
proportional contribution for each NO3−−N source using SIAR (endmember method) showedthat chemical fertilizer (60.1%) was the main contributing NO3−−N source in Kubi, while inOcho, Organic fertilizer (50.7 %) was the major contributors, with minor contributions
observed from sewage. These source distributions were associated to social aspects such as
depopulation and population aging and to a greater extent land use type. It was suspected thata considerable quantity of chemical fertilizers applied by the older farmers in Kubi leads toinefficient nitrogen uptake by the plants, resulting in more leaching, which contaminates thegroundwater more in Kubi than in Ocho.

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71

Chapter 4
Simulation of daily streamflow with SWAT illuminates the impactof land-use change with urbanization and climate change on
groundwater recharge over the last three decades

This chapter is an edited version of the article:
Kimbi, S.B., Wang, K., Onodera, Shin-I., Kaihotsu, I., Shimizu, Y. Simulation of daily
streamflow with SWAT illuminates the impact of land-use change with urbanization andclimate change on groundwater recharge over the last three decades (To be submitted) Journalof Environmental science and Technology

4.1 Introduction
Urbanization is a major social change that refers to the gradual increase in the
proportion of people living in urban areas and how each society manages this change，and
globally, 54% of the population resides in cities, and that number is projected to increase from
60% to 92% by the year 2050 (Ritchie and Rose, 2018). Urbanization is a major social changethat refers to the gradual increase in the proportion of people living in urban areas and howeach society manages this change (NLM, 2014). In decades, the increase in urbanization has
resulted in major challenges in freshwater distribution, with a noticeable impact on the quality
and quantity of water sources (Nasiri et al., 2020; Onodera 2011; Doveri et al., 2015; Wakode
et al., 2014). Urbanization processes results in significant topographic and vegetationalchanges, ultimately affecting groundwater resources and the environment (Hamel et al., 2013;72

Gatwaza et al., 2016). These changes have been recognized as significant issues affecting
catchment water balances both globally and at watershed scales (Haase, 2009; Yigzaw andHossain, 2016; Kundu et al., 2017; Terskii et al., 2017; Oudin et al., 2018). In addition toanthropogenic impact on watershed hydrology, climate change has been identified as one of
the major stressors on water resource sustainability, and to a larger extent, the combination ofthese stressors has major implications on the water cycle (Zareian et al., 2016; Zhang et al.,2018; Chen and Bofu, 2013). Precipitation and temperature cycles, which have become
increasingly unpredictable due to climate change, impact water resources both spatially andtemporally (Petersen-Perlman et al., 2017). In fact, according to a report by (Climate Service
Center, 2018), the average temperatures have increased by 0.04°C over the last decade while
the total annual rainfall has increased by 12% over the three decades. These, combined changes
will increase the occurrence and intensity of rainfall events with corresponding adverse effects
such as increase floods and droughts events (Gibson et al., 2005). This will certainly have ahuge impact on the environment, as changes in urbanization, climate, and land cover will have
undesirable effect on the water balance of a given region, subsequently affecting the quantityand quality of freshwater entering the ground. Understanding the impact of environmental
factors on hydrological processes calls for the application of hydrological models that cansimulate changes in the water cycle over time and space and quantify the impacts of thesechanges on renewable freshwater availability for multiple sectoral water use (Chung et al.,2010; Flörke et al., 2018). Compared to other modelling techniques, hydrological models are
more effective for planning and developing integrated approaches to water resourcesmanagement, as they are capable of directly estimating water balance components andanalyzing human influences with more accurate and useful results (Naef et al., 2002). In
addition, these models can be used to assess past as well as possible future impact scenarios at
the catchment scale.

73

Water balance provides an overview of the availability of water in the watershed andto appropriately manage these water resources, it is important to quantify the components ofhydrologic processes. The assessment of water balance using hydrological models is a common
method used to analyze the effects of land-use changes, climate change, and urbanization
processes on watershed hydrology. Several models have been used to study hydrologicalaspects in watersheds such as the Hydrologic Engineering Centres–River Analysis System(Thakur et al., 2017), Tank model (Ou et al., 2017) and Soil and Water Assessment Tool
(Arnold et al., 2012). Among these models, Soil and Water Assessment Tool (SWAT) is acontinuous semi-distributed hydrological model that has been used widely for assessing theimpacts of land-use change on hydrological processes and simulates agricultural watershedmanagement practices (Moriasi et al., 2007; Francesconi et al., 2016), helps to evaluate andquantify surface and groundwater flow in various watersheds (Vazquez-Amabile and Engel.,2008), assesses the impact of climate and land-use changes under different management
practices (Singh and Gosain, 2011; Kundu et al., 2017; Ridwansyah et al., 2020) and is used invarious climatic areas of the worldwide ranging from a semi-arid climate (Baker and Miller,
2013; Shivhare et al., 2014; Sellami et al., 2016 Suryavanshi et al., 2014) in humid and tropicalregions (Faramarzi et al., 2013; Nguyen and Kappas et al., 2015). Fiseha et al. (2014) applied
SWAT to understand the hydrological responses to climate change in the Upper Tiber Riverbasin daily and the SWAT has also been used to understand the water balance in watershedunder various climatic and socioeconomic conditions (Neitsch et al., 2011; Wang and Xia.,
2010; Nasiri et al., 2020). Considering the changes brought about by urbanization and climate
on the water cycle, it is important to study these impact on local water resources, especially on
available groundwater sources in the surrounding area (Wakode et al., 2018). Several
continental and regional scale studies have shown the net effect of urbanization processes on74

water quality (Onodera et al., 2011; Morris et al., 2003). However, quantifying this resource isdifficult because a variety of data is needed, which is often lacking or limited.
Historically, groundwater has been a reliable source of water in Japan. As the country's
industrialization and urbanization rates increased, however, regional as well as local water
demands grew. Several studies have illustrated a decrease in groundwater recharge because ofurbanization because surface sealing prevents infiltration and increases runoff (Rose and Peter,
2001), while other studies indicate an increase in groundwater recharge due to reduced
evapotranspiration (Hooker et al., 1999; Barron et al., 2013). In general, studies on groundwater
recharge have shown that it is difficult to estimate the recharge process because each region
has a different climate and environment. Higashihiroshima has experienced rapid urbanization
with significant changes on its land use pattern since the 1980s. Although the impact ofurbanization and climate change on groundwater resources has been extensively explored,
studies on its spatial variability for long-term groundwater recharge remains poorly understoodat the regional level. As a result, for long-term groundwater water resource management, athorough understanding of the changes in hydrological processes occurring due to socioeconomic changes (urbanization) and climate change at the catchment scale is required. To
identify approaches that would minimize the impacts of these changes on the available
groundwater environment, estimating groundwater recharge is essential for gaining an insight
into changes in climatic conditions and land use over time on the spatiotemporal distributionof groundwater recharge (Zomlot et al., 2015).
Hence, we assessed the spatial variation in groundwater recharge under the effects ofurbanization and climate change, based on water balance components using SWAT over thelast three decades, in western Japan. Observed daily streamflow simulation were conductedbased on two representative time steps 1980, and 2000s to demonstrate the impact ofanthropogenic activities on hydrological processes at the catchment scale. Because SWAT can

75

output both direct and relative impact of urbanization effect, these results will provide a better
understanding of groundwater resource availability and implication for future management at
the regional scale.

4.2 Study catchment
The Kurose River catchment (Figure 4.1), with a geographic area of approximately 39.5
km2, is situated between latitude 34°25´30’’N and longitude 132°44´33’’E in Higashihiroshima
City, Hiroshima prefecture and with an elevation between 204 m to 642 m above sea level.

Figure 4.1: (a) Location of Higashihiroshima in Japan, (b) location of Kurose in
Higashihiroshima, (c) location of Kurose River catchment in Higashihiroshima city showing
meteorological station and river guaging station.
The catchment area has undergone significant anthropogenic changes since the 1980s
evident by increase population, changes in land use pattern and increased climatic variations
within the catchment. Land-use change is obvious within the catchment, whereby areas76

dominated by paddy fields in the 1980s have been gradually converted from natural
wetlands/rice paddies to medium and low-density residential areas over the last three decades(Figure 4.2).

Figure 4.2: Land use map and areal extents of land use classes (Km2) for the years 1987
representing the 1980s period and 2009 representing the 2000s period in the Kurose River
catchment.
For instance, in the 1980s, the area’s forest covered over 18.6 km2, accounting for
approximately 47.3% of the catchment area; paddy fields covered 13.6 km2, which accounted
for 34.7%. Rural residential areas covered only 5 km2 representing 12.8% of the total catchment
area (Figure 4.2). By the 2000s, residential areas had increased to 13.6 km2, accounting for34.5% of the total catchment area with an urbanization rate of approximately 7.4% per decade(Figure 4.2). However, paddy fields and forest areas decreased by 14.8% and 4.7%,
respectively (Table 4.1). From the 1970s to the 2010s, the catchment’s total population
increased from 72,741 to 128,744, constituting an increase of approximately 76.9%. It should
be noted that the relocation of Hiroshima University in the late 1970s to an area southeast of77

the study region may be a major contributing factor to the rapid urbanization rate within the
study area. The Kurose River, with a length of approximately 43 km, is the mainstream in thecatchment, flowing from the northwest to the southeast through the urban and agricultural areasof Higashihiroshima City into the Seto Inland Sea. The Kurose River catchment ischaracterized by a warm humid climate with mean annual daily temperature varying from
12 °C to 15 °C (Japan Meteorological Agency 2021). The mean air temperatures (1979 − 2010)
increased at a rate of 0.4°C per decade (Figure 4.3a) across the study area, suggesting an
increasing trend in evaporative potential during the past three decades.

Figure 4.3: (a) Average annual air temperature and (b) average annual precipitation from1979−2010 in the Kurose River catchment.
The mean annual precipitation over the last three decades (1979 − 2010) wasapproximately 1469 mm and there exists a clear difference in rainfall pattern between the four
seasons in the watershed (Figure 4.4). A considerable amount of rainfall (about 80%) and mostof the annual rainfall is received during one rainy season from June to August, and low
precipitation from December to February (Figure 4.4). Precipitation within the study area
showed a decreasing trend of 6.9 mm per decade (Figure 4.3b). The observed decline in rainfall
78

patterns coupled with rising temperatures due to urbanization may have a dramatic effect on
the water resources in the catchment. Therefore, it is of utmost importance to understand thehydrological responses in the catchment under the aforementioned factors to ensure effective
and sustainable management of water resources.

Figure 4.4: (a) Average monthly precipitation and (b) average temperature in the 1980s and 2000s in
Kurose River catchment.

The depth to static water level measurements from boreholes within the research
catchment was used to generate groundwater depth map. In total, 30 static water levelmeasurements were used for the interpolation of the groundwater depth grid map also known
as groundwater flow net, with most of them centred in the northeast and central areas for theyears 1974 and 2000. The Inverse Distance Weighted (IDM) interpolation method was
employed in this study because it provides consistent results with known values.

79An outcrop geological map of the study area (Figure 4.5) reveals that the Kurose
catchment is largely composed of Quaternary sediments, such as alluvial fan, terrace, andpaleolake deposits. These fluvial deposits consisting of Middle Pleistocene to Holocene marine
and non-marine sediments possibly represent the Saijo Formation, which lies unconformably
on top of a granitic basement.

Figure 4.5: An outcrop geological map of Kurose River catchment.

The Hiroshima Granite that intruded in the Late Cretaceous period and which is
distributed in southwestern Japan in the Chugoku and northern Shikoku areas (Kagami etal.,1988).

80

4.3 Materials and Methods
4.3.1 Soil and Water Assessment Tool (SWAT)
SWAT is a hydrological modelling program based on the watershed scope developedby the Agricultural Research Service of the United States Department of Agriculture (USDAARS) for predicting the impact of land management practices on water, sediment, andagricultural chemical yields in large and complex watersheds with varying soil, land use andmanagement conditions over long periods (Arnold et al., 2012; Wallen et al., 2015). SWAT
has been employed to model watersheds at various scales for different purposes. For example,
Singh et al. (2013) applied SWAT to the Tungabhadra catchment in India for the measuring
stream flow and reported a result in which the observed and simulated data had excellent
correlation during the monthly calibration time step.
Similarly, Chekol et al. (2007) used the SWAT model for assessing the spatial
distribution of water resources and analyzed the impact of different land management practices
on the hydrologic response and soil erosion in the Upper Awash River Basin watershed,
Ethiopia. Although SWAT has mainly been documented in regional studies, some studies haveequally highlighted its successful applicability to various small-sized watersheds. Tudose et al.
(2021) applied SWAT to a small mountainous forested watershed of 184 km2 in central
Romania and obtained satisfactory performance and results with a lower uncertainty of model
results in replicating river discharge compared with observed discharge; Shrivastava et al.
(2004) tested the SWAT model on a small watershed of 17.31 km2 on both daily and monthly
basis for estimating surface runoff and sediment yield and obtained satisfactory results. Marcon
et al. (2011) evaluated a 4.5 km2 watershed area of the Atlantic Forest biome in southern Brazil
and obtained a good fit at the monthly scale but unsatisfactory results for daily calibration.

81

According to Gassman et al. (2007) and Daloglu et al. (2014), the main advantage of
SWAT is its capability to run simulations for watersheds without extensive monitoring data,and it can predict changes in hydrological parameters under different management practices
and physical environmental factors. This makes it suitable for evaluating the water balance
allocation and climatic changes in watersheds worldwide. A detailed Model setup procedure
has been discussed in chapter 2 section 2.2.2.
4.3.2 Model evaluation, statistical methods, and scenario setup
Model evaluation and statistical methods has been detailed out in chapter 2.2.2.3 HRU
files for specific land-use types were extracted and necessary statistical calculations were
performed on the chosen hydrological components to determine the influence of land use andclimate change on water balance in the Kurose River catchment. Further visualization of thedatasets was performed using ArcGIS software version 10.6.1. The relationship between
precipitation and simulated groundwater recharge in all HRUs were expressed through linear
regression and by applying a linear equation (Eq 4.1) to the observed dataset, as follows:y = b + kx

(4.1)

where, y is the simulated groundwater recharge in the HRU (mm); x is the observed
precipitation (mm); k is the slope of the line signifying the groundwater ratio, and b is theintercept.
In addition, we developed three future scenarios utilizing climate and land use data fromthe 2000s as baseline data to determine the influence of either land use change or climatechange on the water balance in the current watershed. To test the influence of land use in
scenario 1, we used a land use scenario in which 15% of paddy land was converted to residential
land while climate data remained constant. To exclude the influence of land use change inscenario 2, we chose a climatic scenario with a 13 % decrease in precipitation and a 0.7°C

82

increase in temperature. The allocated values for land use change and climate change are
average values between the 1980s and 2000s time step.

4.4 Results and Discussion
4.4.1 Evaluation of the main parameters and the simulation accuracy
Nineteen parameters from the SWAT theoretical document affecting streamflow wereidentified through sensitivity analysis (Table 2.2). The most sensitive hydrological parameters
were used for the model calibration and validation processes. Surface runoff parameters suchas CN2.mgt, SOL_AWC.sol, SOL_K.sol and CH_K2.rte were more effective parameters tochange in streamflow. Baseflow parameters such as ALPHA_BF.gw, GWQMN.gw,
REVAPMN.gw, RCHRG_DP.gw, GW_delay.gw, and GW_REVAP.gw were found to be themost sensitive parameters for all subbasins. To correct the delay in daily peak values,
r_HRU_SLP.hru and r_SLSUBBSN.hru, which affect lateral flow, were considered in thecalibration process. In this study, the SCS runoff curve number (CN2. mgt) was found to be
the most sensitive parameter for estimating surface runoff based on discharge simulation results.
Studies have demonstrated the sensitivity of CN2.mgt in estimating surface runoff incalibration (Can et al., 2015). The curve number parameter CN2 controls how much water
infiltrates into the soil and how much runoff is generated by overland flow, a high CN2 value
results in less infiltration and more runoff. Land use classes affected the curve number
significantly during model calibration. As for ESCO.hru, it was sensitive to actual
evapotranspiration. The performance of SWAT model was evaluated by comparing theobserved and simulated discharge data (Figure 4.6). The simulated and observed streamflowhydrograph indicated that the model captured the patterns of hydrologic variability during bothat calibration and validation periods, which proved the SWAT model to be capable ofsimulating the hydrologic processes in the Kurose basin. A comparison of the observed andsimulated flow discharge during the calibration and validation periods for the 1980s and the83

2000s can be seen with the recommended statistical performance ratings adapted from Moriasi
et al. (2007). The results suggest that the model can, be used to predict average annual river
flow values. The statistical evaluators showed a good correlation between the annuallyobserved and simulated river discharges as follows: According to R2 and NSE, the model
results for both calibration and validation were good. The statistical evaluation of the model ispresented in Table 4.1.

Figure 4.6: Comparison of the observed and simulated daily streamflow in the Kurose River
catchment area. Straight lines represent the border of the year for calibration and validation for
the 1980s and 2000s period.

84

Table 4.1: Daily calibration and validation statistics of the SWAT model in the Kurose River
catchment.
PeriodPeriod range

R2

NSE

1980s (calibration)

1980-1989

0.79

0.71

1980s (validation)

1990-1993

0.83

0.78

2000s (calibration)

2000-2005

0.74

0.65

2000s (validation)

2006-2007

0.85

0.74

Good model performance was obtained for the 1980s, with NS and R2 values of 0.71
and 0.79 for the calibration period. Meanwhile for the 2000s, a satisfactory to very good
performance rating was obtained with NS and R2 values of 0.65 and 0.74, respectively. The
same parameters also require validation in the simulation to confirm their viability. During thevalidation period, the R2 and NSE values were much higher. Overall, based on a visualinspection of the time series graphs (Figure 4.6), the SWAT model can be considered an
appropriate tool for simulating the hydrological variability of watershed management in the
Kurose Basin.
4.4.2 Variation in water balance from the 1980s to 2000s
4.4.2.1 Water balance in a catchment scale
In the Kurose River catchment, the simulated water balance components included
surface runoff (SurfQ), lateral flow (LatQ), groundwater discharge (GW_Q), groundwater
recharge to the deep aquifer (DA_RCHG), and actual evapotranspiration (ET) are presented asmean annual values (Figure 4.7a) and calculated as relative percentages of average annual
rainfall. The mean annual precipitation in the catchment area was 1573 mm/yr and 1374.9 mm
in the 1980s and 2000s, respectively. The estimated DA_RCHG decreased considerably from
184.5 mm in the 1980s to 120.2 mm in the 2000s, which correspond to approximately 43.8%
and 30.2% of the annual mean precipitation. The mean annual SurfQ values for the 1980s and85

2000s were given by 91.7 mm and 212.6 mm which correspond to approximately 5.9% and15.5% of the mean annual rainfall, respectively (Figure 4.7a). The recent 2000s yielded higher
SurfQ and lower GW_RCHG compared to the earlier years due to urbanization driven
processes within the catchment, such as land cover alteration. CN2 has been identified as oneof the most influential parameters that affect surface runoff (Arnold et al., 2012) and is
influenced by land use and soil type. The 1980s had a lower average curve number of 47.4,
indicating a reduction in surface runoff, as the land-use and soil type combination is lessresistant to infiltration and favours recharge by precipitation.

Figure 4.7: Average annual water balance ratio in the 1980s and 2000s in (a) catchment scaleand (b) per land use unit in the Kurose River catchment.
By the 2000s, the average curve number had increased to 65.6 as a result of an increasein impervious surfaces as the land-use and soil type combination became more resistant toinfiltration and favoured higher overland. Many researchers have documented the increase of
surface runoff resulting from urbanization (Phuong, Thong, Ngoc, Chuong, 2014; Tao et al.,2015). Approximately 699.1 mm and 663.7 mm of water was lost through ET from the basin
86

in the 1980s and 2000s, respectively. This accounted for approximately 44% and 48% of thecatchment’s annual precipitation, which can be attributed to a decline in precipitation andevaporative surfaces under increasing urbanization processes (Figure 4.7a). The study area has
experienced substantial urbanization in the last two decades, which has resulted in thetransformation of forests, paddy fields, and agriculture areas into residential, commercial,industrial, and transportation land. The water balance under changing land use (Figure 4.2)
changed significantly from the 1980s to the 2000s, with the most noticeable change being an
increase in surface runoff and a decrease in groundwater recharge (Figure 4.7a). Based on theseresults and those of Gatwaza et al. (2016), Marhaento et al. (2017), and Wang et al. (2021),
surface runoff is most sensitive to land-use change, and increasing urban areas result in agreater ratio between surface runoff and streamflow with a diminishing ratio of groundwater
flow. In addition, the lateral flow also showed few changes, less than 1%, whereas thegroundwater contribution to streamflow decreased by 10% from 503.7 mm in the 1980s to295.1 mm in the 2000s.
4.4.2.2 Water balance in land use unit
The results of the model simulation clearly show the strong impact of land use changeon the water balance in the Kurose watershed at the basinal scale. Hence, we compared thevariation in the mean annual water balance components for three dominant land uses (ricepaddy fields, forest areas, and residential areas) for each period (the 1980s and 2000s) in thecatchment area. The estimated mean groundwater recharge for the three land use types showeda difference in magnitude, as shown in (Figure 4.7b). In the forest area, the mean annual
recharge amounted to 167 mm (10.7% of mean annual precipitation) in the 1980s and 9 mm
(122% of mean annual precipitation) in the 2000s, whereas the mean annual recharge in theresidential area was higher in the 1980s than in forest area with a value of 199 mm (12.8% of
precipitation), but with a lower value of 88 mm (6.5% precipitation) in the 2000s. Much greater87mean annual groundwater recharge occurred in the paddy field, where total recharge amounted
to 212.2 mm (13.5% of precipitation) and 180 mm (13.1% of precipitation) for the 1980s and2000s, respectively (Figure 4.7b). The striking decrease in groundwater recharge in theresidential area as compared to the other land uses between the two periods can be explainedby the significant increase in residential area in the catchment area from 12.8% in the 1980s to
34.5% in the 2000s, with over 45% of paddy field area being converted to residential areas(Figure 4.2) in response to urbanization and expansion of rural settlements. Infiltration is
reduced in urban areas due to the substitution of natural vegetation by impervious surfaces(Miguez and Magalhães, 2010). According to Takie et al. (2016), natural ecosystems havehigher water infiltration capabilities than urban soils, which is largely due to their high level ofsurface imperviousness. The result is that urban soils do not infiltrate water into the subsoil,
thereby reducing recharge rates.
Rice paddy areas had the highest groundwater recharge among the three land use types,
with little variation in both periods. The fact that rice paddy fields are naturally flooded by
precipitation as well as artificially irrigated makes them good contributors to groundwater
recharge. Several studies have found that wetlands increase groundwater recharge compared
to forests and other land-use types (Owuor et al., 2016; Takehide et al., 2020). Furthermore,
the groundwater recharge in the residential area was much higher in the 1980s than the forest
recharge in both periods. In this study, residential areas were not recognized as completely
impervious land, but rather as having a fraction of vegetation cover. Furthermore, in the 1980s
the residential area had a smaller surface area, with a larger portion of bare soil and vegetation(Figure 4.2), allowing it to capture more infiltrating rainfall for groundwater recharge than theforested area, which is limited by forest canopy interception (Fan et al., 2014). As a result,recharge values in forest areas were lower than those in rice paddy fields. Meanwhile incomparison to residential and rice paddy areas, much more soil water is discharged by lateral88flow in the soil profile. Accordingly, surface runoff in residential areas increased considerably
in the 2000s as compared to the 1980s, i.e. from 212.1 mm to 482.4 mm (Figure 4.7b); whereaschanges in rice paddy and forest areas were insignificant. Based on these results, several otherstudies have characterized urban areas as impervious areas that promote surface runoff andinhibit infiltration (Jat et al., 2009; Miguez and Magalhães, 2010). Soils in naturalenvironments, such as rice paddies and forests, allow water to slowly infiltrate the soil throughthe process of stemflow when precipitation is not intercepted and evaporated. This water canbe used by vegetation to increase groundwater saturation, reduce runoff, and recharge thegroundwater system, whereas in residential areas, sealed and compacted soils are unable toabsorb and retain moisture, and the capacity for groundwater percolation is limited (Coutu andVega, 2007; Tashie et al., 2016). Therefore, in the case of storm events in the catchment,
surface runoff will dramatically increase with a higher risk of flooding due to increased sealingof ground surfaces with concrete (Brath et al., 2006) and groundwater contamination due toleakage from drainage pipes and industrial effluent (Baier Azzam and Strohschon, 2014).
The average annual evapotranspiration results indicate that the ET values were higher
in the 1980s than in the 2000s across the three land-use types, with forest ET values being
much higher than those of paddy areas and residential areas. In the forest area, ET values varied
from 715.7 mm in the 1980s to 705.6 mm in the 2000s, amounting to approximately 46% and
52% of mean annual rainfall. In the paddy area, the mean annual ET amounted to 698.3 mm(45%) for the 1980s and 672.3 mm (49%) for the 2000s. Much lower ET values were recordedin the residential area, where mean annual ET amounted to 603 mm (39% of mean annual
rainfall) and 5887 mm (43% of mean annual rainfall) for the 1980s and 2000s, respectively.
Generally, in the 2000s, the ET ratios were higher than those in the 1980s, which reflects thechange in climatic conditions in the catchment due to natural or anthropogenic causes.
According to Lipczynska-Kochany. (2018), climate change has the potential to alter both the89quantity and quality of groundwater by altering ET, precipitation patterns, and air temperature.
As such, an increase in precipitation intensity and frequency would contribute to runoff and anincrease in ET rates owing to global warming, which might be the case in the Kurose catchment
area.
Additionally, there has been a noticeable influence of climate change effect on the
groundwater recharge and ET rate, accompanied by an 11.8% decrease in annual precipitation
values and a 0.8% increase in mean temperatures over the last two decades (Figure 4.7a and b).
In addition, the observed lower ET values in the residential area compared to the forest andpaddy land use are due to lower soil moisture availability as evaporative surfaces decrease withsurface sealing (Li et al., 2004).
4.4.2.3 Average monthly groundwater recharge
At both the average monthly catchment scale and per land use unit, recharge in the
2000s was significantly lower than in the 1980s. According to the simulated monthly recharge,
there was a clear seasonal trend, with major recharge occurring during the wet summer andautumn (Figure 4.8).

90Figure 4.8: Monthly variation in groundwater recharge in (a) basin scale and (b) per land use
unit in the Kurose River catchment.

The peaks of groundwater recharge coincide well with those of precipitation, in which
precipitation replenishes soil moisture and creates groundwater recharge. Although the annual
recharge average from the three land uses amounted to 184.5 mm and 120.2 mm in the 1980s
and 2000s, respectively, the monthly average recharge distribution showed remarkablevariations for the two periods having minimum and maximum values in January and July bothin the 1980s and 2000s (Figure 4.8). The monthly average recharge estimates for the 1980s
ranged from 23.7 mm to 270 mm, representing approximately 19% − 48% of the monthly
average net precipitation; whereas for the 2000s, mean monthly recharge values varied from36.4 mm to 191 mm, representing 12% − 67% of the net monthly rainfall. In both periods, thehigher recharge value can be associated with the transition from winter to spring, which ischaracterized by higher temperatures above freezing and precipitation. In the 1980s, significant
groundwater recharge occurred in the summer months of June and July, and in the early autumn
months of September due to frequent summer rainfall events and typhoons, resulting insubstantial replenishment of soil moisture by rainwater (Figure 4.3b and Figure 4.8a). In the
2000s, most recharge occurs in the late spring month of May and the mid-summer month ofJuly, which coincide with the rainfall patterns shown in Figure 4.3b.
These observed changes in precipitation and recharge patterns could be due to thechanging climatic conditions in the catchment, with the general increase in temperature andevapotranspiration leading to more intense precipitation events that promote more surface
runoff than recharge. Such occurrences have been observed in the study catchment in recent
years. The temporal recharge patterns for the three land uses were similar because of similar
rainfall patterns, but the magnitude of recharge was different (Figure 4.8b). Rice fields had thehighest groundwater recharge in the 1980s and 2000s (147.1 mm and 130.9 mm, respectively),91

followed closely by forest land use (130.3 mm in the 1980s and 110 mm in the 2000s) in thesummer month of July, while residential groundwater recharge was much lower 123 mm and43.9 mm in the 1980s and 2000s respectively, due to the above environmental conditions.
4.4.2.4 Groundwater recharge ratios in three decades
The annual precipitation and groundwater recharge per land use were compared
between the two periods (Figure 4.9). Groundwater recharge increased considerably with
precipitation in all land uses during both time steps with R2 values >0.95, in the forest and rice
paddy land use; while in the residential area, R2 was 0.99 and 0.87 for the 1980s and 2000s
respectively (Figure 4.9). Direct recharge occurs from precipitation reaching the land surface.

Figure 4.9: Scattered plot showing relation between annual groundwater recharge and annual
precipitation in (a) residential area, (b) forest area, and (c) rice paddy for the 1980s and2000s.
In rice paddy and forest areas, a similar trend in gradient was observed with little change
in slope value (k) for both the 1980s and 2000s, which may reflect their similar recharge
characteristics such as high infiltration capacity due to more permeable surfaces. Whereas, inthe residential area, the obvious disparity between the gradient in the 1980s and 2000s is anindication of the influence of different contributing factors to groundwater recharge other than
precipitation.
92

The recharge ratio (0.66) and gradient in the residential area were similar to those in
the forest (0.68) and rice paddy areas (0.7), as expected. During this period, the residential area
was smaller, with more vegetative surfaces and water permeable pavements which had a highinfiltration capacity and increased groundwater recharge by reducing surface runoff. By the
2000s, the residential area had doubled, alongside paved surfaces and rooftops, which diverted
precipitation, leading to reduced infiltration with higher surface runoff. Hence, theintensification of urbanization processes in the 2000s resulted in a substantial decrease in therecharge ratio from 0.66 in the 1980s to 0.23 in the 2000s. This data illustrates the direct impactof urbanization on groundwater recharge in the catchment and its temporal distribution. The
annual average precipitation and groundwater recharge over a period of 20 years and theirpercentages based on precipitation varied among the three land uses (Table 4.2).
Table 4.2: Estimated groundwater recharge at average precipitation in the catchment.

Land use
Precipitation
1980s
2000s

Mean
Annual(mm)
1400
639.03228.33% Based on
rainfallResidential
45.7
16.4

Mean
Annual(mm)
1400
614.91535.51% Based on
rainfallForest
44
38.3

Mean
% Based on
Annualrainfall(mm)
Rice paddy
1400
655.9746.9
620.73
44.4

Approximately 46.9 and 44.4% of the total precipitation was recharged in the paddy
areas in the 1980s and 2000s, respectively. In the forest area, 44% and 38.3% of the total
precipitation was infiltrated as groundwater recharge, whereas in the residential area, therecharge rate was greatly reduced with given values of 45.7% and 16.7% of the total
precipitation. The average annual groundwater recharge was highest in the paddy areas, withvalues ranging from 656 mm in the 1980s to 620.8 mm in the 2000s, amounting to a decrease
rate of 2.5%. The high recharge values are due to uncovered land and permeable soils, that are
mostly waterlogged throughout the year. In the Kurose River catchment, paddy areas are the93highest recharge zones; therefore, conservation of these areas is of utmost importance for futuresustainability of groundwater recharge.

4.4.3 Spatial Variation of groundwater recharge
The outcomes of the SWAT model simulation revealed that changes in land use had an
impact on groundwater recharge (Figure 4.10) over the last 30 years. The mean annual recharge
was estimated at 689 mm (50% of annual mean rainfall) and 416 mm (40% of annual mean
rainfall) in the 1980s and 2000s, respectively.

Figure 4.10: Average annual distribution of groundwater recharge in the (a) 1980s, (b) 2000s;(c) relative change between 2000s and 1980s with different land use patterns.

In the 1980s, over 80% of the catchment area (the center and southern parts) had
recharge values greater than 700 mm which was dominated by paddy fields, as compared to94the northern areas, which were dominated by forest vegetation and showed relatively lower
values. By the 2000s, 44.4% of total paddy field area had been converted to residential areas,accounting for a drastic loss in recharge. This drastic decrease in groundwater recharge in the
Kurose catchment over the past two decades shows a response to anthropogenic disturbancesand closely depicts the changes in land use in the catchment area (Figure 4.2). In the Kurose
catchment, it can be stated that groundwater recharge is reduced by the urbanization processes.
In agreement with this study, Hardison et al. (2009) also indicated that recharge decreases inurbanized areas as surface sealing increases, which prevents infiltration while increasing
surface runoff as compared to the natural environment. Contrary to these results, a study by
Minnig et al. (2018) showed that highly urbanized watersheds exhibited higher groundwater
recharge than predominantly rural watersheds due to a reduction in evapotranspiration and anincrease in water main and sewer leakage. The considerable variation in the spatial pattern ofgroundwater recharge rates in the Kurose catchment appeared to be primarily dominated byland use with minor contributions from geology, topography, and soils. The southern andcentral part of the catchment is dominated by permeable alluvial sediments (Figure 4.5) which
can be considered as the primary source of recharge to the aquifer and due to their fertile soils,
they are used for the rice paddy farming. In addition, the central and southern parts of thecatchment showed relatively higher recharge because of the presence of perennial streams fromthe western and eastern escarpment, which, in addition to the faults, can facilitate recharge tothe aquifer. The major sources of groundwater recharge for the unconsidered sediment aquifer
may be subsurface seepage from fractures openings or seepage from runoff generated fromprecipitation in the highlands and infiltration from rainfall that occurs in the alluvial plains.
The spatial analysis of groundwater level maps (Figure 4.11) depicts that the
groundwater level is shallower in the southern region as compared to the northern part of thestudy area and the maximum depth to groundwater level is observed in the north-western part95

of the study area in both the 1970s and 2000s. This agrees with the groundwater recharge map
in (Figure 4.9) which recorded higher recharge values in the southern and central parts of thestudy area. This is because, in areas where the groundwater table is high, the soil is alreadysaturated and any infiltrating water seeps out as lateral flow to streams and rivers. The
difference in groundwater level between the year 2000 and 1974 shows that over 76% of thecatchment area had a decline in groundwater depth between 0.1m to 2m. The dwindling
precipitation from the 1970s to recent years (Figure 4.2b) equally has a decreasing effect on
the groundwater recharge in the study area which leads to comparative decline in groundwaterlevel in the 2000s.

Figure 4.11: Spatial distribution of groundwater level in the year (a) 1974, (b) 2000, and (c)groundwater level change from the 1974 and 2000 in the Kurose River catchment.

In addition, from the groundwater level maps its indicative that areas with high waterlevels act as discharge zones because they lose water to nearby rivers and streams compared toareas with low water levels which may constitute recharge zones at higher elevations.

96

4.4.4 Relative urbanization effect (spatial variation in forest ET: 1980s − 2000s)
By increasing infiltration rates and reducing surface runoff, forests can increase
groundwater recharge (Lv et al., 2019; Wang et al., 2022). However, forests can also reduce
recharge through an increase in ET, which increases with tree age and temperature. Also, alinear relationship between forest age and ET has been reported by), whereby an increase intree age subsequently increased forest ET. The spatial distribution of forest ET presented in
Figure 4.12. The mean annual ET as a relative percentage of average precipitation for the study
area was 46% and 52% for the 1980s and 2000s periods respectively. Spatially, the northernpart of the catchment, which is mostly vegetative, showed higher ET values of 720 mm – 730
mm in the 1980s while in the southern part the ET values were less than 720 mm.

Figure 4.12: Change in forest Evapotranspiration in the (a) 1980s, (b) 2000s, and (c) relativechange in forest ET from 1980s to 2000s in the Kurose River catchment.

97However, by the 2000s, the ET values had increased and decreased in some parts of thesouthern and northern sections of the catchment area (Figure 3.12). This might suggest forest
growth with elevated ET as older trees tend to have higher transpiration rates than younger
trees. Also, the decline in ET can be explained by forest degradation in the observed sections,as residential areas encroach into forest land. In addition, more than 80% of the forest area
showed a decreasing trend in ET values, thus, the decline in forest vegetation influenced the
reduction in ET and increased surface runoff. Consequently, infiltration tends to decrease
whereas surface runoff rates increased as soil water storage capacity is inhibited, subsequently
reducing groundwater recharge (Brown et al., 2005). However, because evapotranspiration was
not calibrated in this study and considering the use of only one weather station, ET values
might have been underestimated. Coupled with this, variations in ET may be influenced byseveral factors, such as soil, precipitation, and atmospheric changes.
In addition, more than 80% of the forest area showed a decreasing trend in ET values,
thus, the decline in forest vegetation influenced the reduction in ET and increased surface
runoff. Consequently, infiltration tends to decrease whereas surface runoff rates increased assoil water storage capacity is inhibited, subsequently reducing groundwater recharge (Brown
et al., 2005). Similarly, forest growth decreased groundwater recharge due to increases intranspiration due to increases in temperature. However, because evapotranspiration was notcalibrated in this study and considering the use of only one weather station, ET values might
have been underestimated. Coupled with this, variations in ET may be influenced by several
factors, such as soil, precipitation, and atmospheric changes.
4.4.5 Land use change and climate change scenarios
The response of groundwater recharge, surface runoff and evapotranspiration todifferent scenarios based on the last 30yrs is depicted in Table 4.3. The response of98

groundwater recharge and evapotranspiration to different scenarios based on the baseline
scenario (2000s) is depicted in Table 4.3. In scenario 1, groundwater decreased only by 9 %meanwhile under scenario 2, groundwater recharge will be further reduced by 23% comparedto the baseline scenario. In scenario 3, the combined effect of land use change and climatechange will have a greater impact on the groundwater recharge with a total decline in theavailable groundwater by 29%. This will be primary due to encroachment of paddy areas which
are the major recharge zones by industrial and infrastructural development in the catchment.
The effect on groundwater environment will be more severe when combined with climatic
variability in the catchment. In addition, evapotranspiration will equally reduce significantly
under scenario 2 by 1%. Vegetation cover lowers surface and air temperatures throughevapotranspiration which helps to improve and regulate the urban temperature as well as
mitigate heat island effect (Kurn et al., 1994), but with the loss in vegetation cover in the study
area, this will further increase the urban thermal environment and possible heat island
conditions.
Table 4.3: Mean annual Evapotranspiration (ET), surface runoff (SurfQ), and groundwater
recharge (RCHG) in land use and climate change scenario simulation.

Scenarios
Baseline
Scenario
1
Scenario
2
Scenario3

Rainfallmm
1374.9

ET
mm
663.5

ET rate
%
48

SurfQ
mm
212.7

SurfQ
rate
%
36

RCHG
mm
415.21

RCHG
rate
%
30.2

Decreasedrecharge%
01374.9

657.1

48

253.6

42

381.4927.8

8.1

1196.9

656.9

55

156.7

35

323.2227.1

231196.9

650.3

54

191.6

42

295.4

24.7

29

In addition, it is reported that the thermal environment may worsen due to increased
heat discharge from industries, vehicles, and buildings, with urban temperatures being
relatively higher than the surrounding countryside (Guo-yu et al., 2013), which is likely to99

occur in the study area. Therefore, groundwater recharge will be reduced over time due to theincreased urban population, which will increase the pressure on water supply and may impair
the quality as well as quantity of the available water resources. Thus, the impact of both land
use change and climate change deserves more attention for future urban planning and water
resource management.

4.5 Summary
The SWAT model is a promising tool for assessing spatial variation in water balancecomponents and for evaluating scenario analyses for urbanization and climate change. to
identify approaches that would minimize the impact of these changes on the available
groundwater environment. Based on the statistical index for the daily time steps of streamflow
predictions, the model SWAT showed good criteria for hydrological simulations,demonstrating its ability to reasonably replicate daily streamflow. Due to the significant
magnitude of change in vegetation cover, land use is a major factor affecting the componentsof the water balance in the Kurose River basin, as shown by the substantial increase in surface
runoff and decrease in groundwater recharge. In addition, the predicted scenario showed that
relative groundwater recharge will be reduced due to future increase in urbanization (i.e. 10%
increase in residential areas and decrease in rice paddy areas) and the increase in global
warming, characterized by a 1°C temperature increase by 2030 to 2050. Therefore, thecombined impacts of land use change and climate change deserve more attention for future
urban planning and water resource management in the study area.

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Chapter 5
Groundwater Contamination Status in Saijo Basin. Insights From
Hydrochemistry and Dual Isotope

5.1 Introduction
Groundwater is an important resource for human development. It is a dependable sourceof water supply in many areas of the world (Zakaria et al., 2021). The rapid increase in the
population, industrialization and human activities have led to the degradation in water quality,and globally, water resources overexploitation has been reported by many researchers globally(Maurya et al., 2019). The quality of groundwater is mostly determined by natural andhydrogeological factors as well as the anthropogenic activities prevailing in an area (Cao et al.,2019). It is important to understand the hydrochemistry and quality of groundwater for efficientmanagement and resource conservation. Groundwater hydrochemistry has been used to
identify and interpret groundwater quality, the impact of anthropogenic activities ongroundwater quality and to determine the water chemistry (Kortatsi et al., 2008; Gibrilla et al.,2011; Zakaria et al., 2012). Generally, the quality of groundwater depends on physiochemical
properties of aquifer and its composition. This state is controlled by the properties of soil androcks of aquifer media (Foster et al., 2002). Chemical components of industrial, agricultural,
urban sewage, mining extracts are the main causes of high pollutant contamination in
groundwater (Amirabdollahian and Datta 2013). Many previous studies have been conductedon the assessment of groundwater pollution in different parts of the world and findings revealed
significant deterioration in water quality for both drinking and irrigation purposes (Raymohan
et al. 2008; Bhunia et al., 2018).

108

Saijo is an agricultural and industrial town in the heart of higashihiroshima which has
experienced rapid urbanization since the 1970s. Due to increasing population, rapid socioeconomic development, intensification of industrial and irrigation activities in the watershed,
the pressure on groundwater demand has significantly increased in the last 50yrs. Deep
borehole construction in the area for groundwater extraction commenced in the 1970s withover twelve sake brewery located within 1km span from east to west of the watershed.
Considering the above-mentioned possible threats to groundwater quality, there a need toassess the groundwater quality for proper management of water resources. On this note, thephysiochemical properties of water sources were explored with an insight into determining thesource of contaminants using Isotopic indicators.

5.2 Materials and Methods
The study area is situated in the eastern part of the Kurose River catchment in
Higashihiroshima with an estimated area of 17km2 (Figure 5.1). The catchment is characterized
by a warm humid climate with mean annual daily temperature varying from 12 °C to 15 °C.
The mean annual precipitation is approximately 1469 mm and there exists a clear difference inrainfall pattern between the four seasons in the watershed. Sixteen groundwater and surface
water (eight borehole (BH), two hand-dug wells (HDW), three rivers and three spring samples)
were collected in the month of October 2021 from the study area for hydrochemical analyses
and Isotope 15 of nitrogen. Groundwater and surface water samples were collected into
sterilized 250ml and 100ml polyethylene bottles. Samples in 250ml bottles were filtered in thelaboratory after conducting acid-base titration on the water samples. Also, ten groundwater
samples were collected in 100ml bottles after filtering through a 0.2μm filter membrane forδ15N-NO3⁻ and δ18O-NO3⁻ analyses. The temperature, pH, electrical conductivity (EC), oxygen
redox potential (ORP) and dissolved oxygen (DO) were measured on the field; pH and109

temperature were tested with the HM-200 pH meter and the electrical conductivity and DO
were measured with the WTW Multi 3320 multi-parameter instrument.

Figure 5.1: Location map of study area showing sample collection points.
Samples were then transported to the laboratory and stored at 4 °C until analysis. The
cations and anions were analysed with the HPLC Shimadzu LC-20AD Ion Chromatograph.
Alkalinity was determined by titration using 0.05 HCl. Sample duplicates and standards wereroutinely analysed to ensure quality control of analytical data. The electroneutrality of the data
was assessed by calculating the charge-balance error (E) using equation (2.1).

5.3 Results and Discussion
5.3.1 Characterization of major ions in different water sources
The physico-chemical parameters of collected water samples in the study are presentedin (Table 5.1) which are analysed individually and systematically for understanding the generalwater quality.
110Table 5.1: Descriptive statistics of the different physicochemical properties of the collected
groundwater samples.
Sample

pH

EC

DO

SO42-

Cl-

NO3⁻

HCO3⁻

Na+

K⁺

Mg2⁺

Ca2⁺

Ryou Spring

6.6

63.3

1.0

5.0

3.8

2.9

19.5

8.8

0.2

1.1

4.4

Dam left

6.5

139.0

7.1

0.5

2.9

BDL

73.9

6.2

1.1

2.2

14.2

Dam Middle

6.8

68.4

7.9

3.7

3.5

2.3

29.4

7.0

0.8

1.6

5.5

Dam River

6.9

63.3

8.6

4.1

3.5

2.6

22.1

6.9

0.7

1.4

4.5

Saijo River

7.0

115.9

7.9

5.5

8.5

4.7

33

9.8

2.0

2.0

9.6

Kamotsuru

6.2

222.0

4.0

18.1

17.2

8.2

65.1

20.7

3.9

4.7

19.4

Hakuboran

6.2

210.0

5.7

25.9

14.6

9.7

46.3

18.0

13.4

5.7

12.3

Hamotsuru

6.48

264

7.25

17.7

15.1

16.0

80.6

BDL

7.4

24.5

BDL

Fujiwara

6.7

187.0

7.0

7.3

2.1

8.4

86.9

5.1

6.0

2.6

26.9

Manoki

6.2

217.0

6.5

22.8

13.4

17.2

43.3

17.8

5.4

4.1

17.1

Fukubijin

6.2

222.0

5.3

24.1

14.5

13.0

93.8

18.3

4.7

6.5

31.1

Jiroma

6.4

345.0

4.6

38.1

18.8

16.9

95.2

27.0

6.0

8.6

31.2

Yamamoto

5.4

89.1

5.9

26.2

13.9

18.9

12.3

14.6

2.1

7.1

10.9

Tsumura

7.3

224.0

3.0

3.9

3.8

BDL

123.3

5.8

8.8

3.0

33.9

Upstream Well

6.1

89.1

5.9

1.0

8.3

11.8

17.3

10.3

1.4

1.1

5.3

BDL: Below detectable limit. Anions, cations and DO are given in mg/L, EC: μS/cm
The pH value ranged from ranged from 5.43 to 7.29 in boreholes and wells, 6.8 to 6.5499 in rivers and springs which shows that the waters were alkaline in nature but for one well
sample with relatively high redox potential (403mv). Electrical conductivity in μS/cm shows arange of 89.1 μS/cm to 345 μS/cm in boreholes and wells, 63.3 μS/cm to 139 μS/cm in rivers
and springs. Major ion hydrogeochemistry is characterized by the prevalence of calcium andbicarbonates as the dominant cation and anion, respectively (Figure 5.2).

111

Figure 5.2a: Average relative concentrations (mg/l) of cations in the investigated water types.

Figure 5.2b: Average relative concentrations (mg/l) of cations and silica in investigated watertypes.

The average concentration (mg/l) of dissolved species in the samples was in the orderof Ca2+ > Na+ > Mg2+ > K+ for the cations and HCO3⁻ > SO42- > NO3- > Cl- for the anions.
Figure 4.2b shows the average relative concentrations of major anions, cations, and silica in all
112

the water types in which dissolved ions increase progressively from a minimum in rivers,
springs, and maximum in the groundwater. Such an evolution indicates that the groundwater isa chemically enriched equivalent of more diluted surface waters. The Piper trilinear diagram(Piper, 1944) has been used for the purpose of characterizing the water types present in the
study area. Trilinear analysis suggested that all but one the samples belong to a mixed Ca-MgHCO3 water type as seen in Figure 5.3.

Figure 5.3: Piper trilinear diagram showing major groundwater types and geochemical
evolution in the Saijo basin.
The high concentration of HCO3⁻ have two possible sources. The main one is relatedwith dissolution of rocks during chemical weathering processes taking place in this aquifer.
whilst secondarily their concentration might be enriched by the decomposition of organic
matter and root respiration in the soil zone which creates under the precipitation effect initiallyweak carbonic acid (H2CO3) and finally bicarbonates (HCO3⁻) through the sequence offollowing reactions:113

CO2 + H2O → H2CO3
H2CO3 → H + HCO3
5.3.2 Gibbs plot to determine the geochemical process prevailing
Gibbs diagram (Gibbs, 1970) helps in understanding the relationship possesses between
the aquifer lithology and the water composition. It consists of three different distinct fields that
are; evaporation dominance, rock-water or rock dominance and the precipitation dominance.
For cations TDS and (Na⁺ + K⁺)/(Na⁺ + K⁺ + Ca) are plotted whereas for anions TDS and (Cl⁻/Cl⁻ + HCO3) are plotted to determine the prevailing mechanisms leading to the variable
concentrations of the ion in the groundwater and its impact on the surrounding country rock
(Figure 5.4).

Figure 5.4: Gibbs diagram representing controlling factors of groundwater quality.

114

The results illustrate that all the samples point towards precipitation domain. Thus,
suggesting that incongruent dissolution of minerals through rainwater controls the major ion
chemistry of groundwater. Information about the weathering processes (carbonate vs silicate)
can be explained by a plot of ratios of (Ca + Mg) against (HCO3 + SO4) in meq/L (Figure 5.5).

Figure 5.5: Identification of dominant mineral weathering in groundwater
According to Abdul-Wahab et al., (2016) if Ca, Mg, SO4 and HCO3 are derived from a
simple dissolution of calcite, dolomite, and gypsum, a 1:1 stoichiometry of (Ca + Mg) to(SO4 + HCO3) should exist (McLean et al., 2000). Sample points that lie in the right of 1:1
equiline suggest, apart from potential ion-exchange, a significant contribution of silicate
weathering in groundwater chemistry. Indeed, most of the samples fell around or slightly below
the equiline which may suggest weathering of the aluminosilicate minerals of metamorphic
rocks and quaternary deposits, as a major impact in groundwater chemistry. However, somesamples plotted around or slightly above the equiline and suggest the contribution fromcarbonate weathering.

115

5.4 Potential Sources of Nitrate-Nitrogen in Groundwater Systems
According to Table 5.1, All groundwater samples had relatively low DO concentrations
while elevated values were recorded in rivers samples; (Mean DO = 8.2 mg/l river, 5.8 mg/L
borehole, 4.5 mg/L wells, and 4.1 spring). This suggest that the denitrification process was
likely to occur in the groundwater samples. The mean concentrations of NO3−−N were in theorder 3.5 mg/l wells, 2.9 mg/L in boreholes, 0.7 mg/L in rivers, and 0.5 to 5 mg/L in springs.
Mean SO42⁻ and Cl⁻ showed similar trend decreasing from Boreholes (19.7 mg/L and 12.4mg/L) to wells (13.6 mg/L and 11.1 mg/L) to rivers (4.4 and 5.2 mg/l) and finally the lowest
concentrations in spring (2.8 mg/L and 3.4 mg/L) respectively. The compositional groups wereidentified by plotting NO3−−N against SO42⁻, Cl⁻, EC and DO (Figure 5.6) for all samples
obtained from the Saijo groundwater source.

116

Figure 5.6: Relationship of NO3−−N vs SO42⁻, Cl⁻, EC, and DO in groundwater samples.
The bi-plot of NO3−−N against Cl⁻ concentrations and SO42⁻ concentrations highlight a
positive moderate degree of correlation of 0.54 and 0.56 respectively. Nitrate and chloride areoften used as indicators of contamination in groundwater. However, their behaviour tends tobe different in groundwater depending on various factors: While chloride is a conservative
tracer that does not typically undergo biological transformations or attach to soil, nitrate ingroundwater can be reduced by denitrification processes in the presence of appropriate bacteria
and redox conditions (Rivett et al., 2008; Kohn et al., 2016). Thus, the presence of this process
in Saijo’s groundwater could have reduced the degree of correlation between the parameters inthe biplot of Figure 5.6. Also, the biplot NO3−−N against EC highlights low degree ofcorrelation while no correlation was observed between NO3−−N and DO.

5.5 Nitrate isotopes for the identification of sources and fate
The δ15N-NO3⁻. of nitrate ranged from +6.7 ‰ to +11.9 ‰ in Boreholes and +9.5 ‰ to
+ 12.3 ‰ in wells while δ18O-NO3⁻ values of nitrate varied between -0.9 ‰ to 4.2 ‰ inboreholes and 0.0 ‰ to 2.2 ‰ in wells (Table 4.4). The spring sample had δ15N-NO3⁻. andδ18O-NO3⁻ values of 0.11 ‰ and 4.51‰. In order to define the main sources of nitrate ingroundwater, isotope data of nitrate were plotted in Figure 5.7.
Table 5.2: δ15N-NO3⁻. and δ18O-NO3⁻ in groundwater water and surface water in Saijo.
Sample NameType

NO3−−N (mg/L)

δ15N-NO3⁻.

δ18O-NO3⁻

Ryou Spring

Spring0.65540.118644.515304

Saijo RiverRiver

1.06227.265371-2.417

Maneki

Borehole

3.8872

11.13735

4.134532

JiromaBorehole

3.819411.21773

1.085061

Hamotsuru

Borehole

3.616

8.988091

0.693675

Fukubijin

Borehole

2.938

11.97817

4.175538

Hakuboran

Borehole

2.19227.32371

3.16281

Fujiwara

Borehole

1.89846.670061-0.93167

117

YamamotoWell

4.271412.24176

2.123642

Upstream

Well

2.66689.483232

0.002013

The application of the dual isotope method of measuring the δ15N-NO3⁻. and δ18O-NO3⁻
values of dissolved NO3−−N in this study identified two major contamination sources (Aravena
et al., 1993): nitrate derived from soil organic nitrogen (0.1‰ to 7.3 ‰, Figure 5.7) and nitrate
resulting from animal waste and human sewage, e.g via septic systems (+9.8‰ to +11.9‰).
The δ15N values of nitrate derived from manure are often in the range of +10 to +20‰ or higher(Karr et al., 2001).

Figure 5.7: Schematic diagram of the isotopic composition of typical nitrate sources including
data for groundwater, spring and wells in the vicinity of the groundwater source. Typical rangesof δ15N-NO3⁻ and δ18O-NO3⁻ values for different nitrate sources were taken from Kendall(2001).
The results of δ15N-NO3⁻ revealed an absence of nitrate originating from mineral
fertilizers in the investigated groundwaters due to either little use of mineral fertilizers or theproper use of fertilizers according to crop needs. In addition to nitrate sources identification,
118

the correlation analysis δ15N-NO3⁻ and δ18O-NO3⁻ was also used to evaluate the denitrificationand nitrification processes. According to Choi et al. (2003), nitrification produces a positive
correlation between the nitrate concentrations and δ18O-NO3⁻ due to isotopic fractionation.
Indeed, since 14N reacts faster than 15N during nitrification, incomplete nitrification results in
15

N-depleted nitrate (Mariotti et al. 1981).
Therefore, preferential inflows into groundwater of nitrate produced throughincomplete nitrification results in low nitrate concentrations and δ15NNO3. With the proceedingof nitrification to completion, the concentration of nitrate and δ15N-NO3⁻ increase gradually.
The slope of regression line would be different depending on the initial δ15N-NO3⁻ of original
source-N (Choi et al., 2003). A significant denitrification, highlighted by a progressiveenrichment in δ15N-NO3⁻, which is distinguished in the groundwater samples (Figure 5.8).

Figure 5.8: Relationship between NO3−−N and δ15N-NO3⁻.
119This is consistent with the results of Figure 5.7, in which all but one sample shows isotopic
composition corresponding to synthetic sources, partially nitrified Similar results have equally
been highlighted in previous studies (Debernardi et al., 2008).

5.6 Summary
Groundwater and surface water samples were analysed for their physico-chemical
properties. The groundwater samples are fresh with TDS values less 1000 mg/L. The Calcium(Ca2+) and bicarbonate (HCO3−) were the most abundant cation and anion respectively. The
major water types in the study area are of Ca–Mg–HCO3 and the mixed Ca–Mg–Cl type. Gibbs
diagrams showed that the geochemical evolution of groundwater in the present study site whichis controlled primary by precipitation The application of the dual isotope method of both δ15NNO3⁻. and δ18O-NO3⁻ measurements of dissolved nitrates at the groundwater source identified
two major contamination sources with values characteristic for nitrate derived from
nitrification of soil organic nitrogen (+0.1 ‰ to +7.3 ‰ for δ15N) and nitrate derived fromanimal wastes or human sewage, e.g., via septic systems (+8.9 ‰ to + 12.3 ‰ for δ15N) in two
distinct zones. In spite of agricultural activities in the study area, we found no evidence of
nitrate originating from synthetic fertilizers in the groundwater. Elevated δ15N values ofdissolved nitrate were associated with higher nitrate-nitrogen concentrations.

5.7 References
Abdul-Wahab, D. Hydrogeochemical and isotopic studies of ground and surface waters in theLower Anayari catchment area, Upper East region of Ghana (Doctoral dissertation,
University of Ghana). 2016.
Amirabdollahian, M., Datta, B. Identification of contaminant source characteristics andmonitoring network design in groundwater aquifers: an overview. J Environ Prot, 2013,
4, 26−41.
120

Aravena, R., Evans, M.L., Cherry, J.A. Stable isotopes of oxygen, and nitrogen source
identification of nitrate from septic systems. Ground Water.1993, 31, 180–186.
Bhunia, G. S., Shit, P. K., Maiti, R. Comparison of GIS-based interpolation methods for spatial
distribution of soil organic carbon (SOC). J. Saudi Soc. Agric. Sci. 2018, 17, 114−126.
Cao X., Lu Y., Wang C., Zhang M., Yuan J., Zhang A., Song S., Baninla Y., Khan K., Wang
Y. Hydrogeochemistry and quality of surface water and groundwater in the drinking
water source area of an urbanizing region Ecotoxicol. Environ. Saf. 2019, 186, 109628.
Choi, W.J., Lee, S.M., Ro, H.M. Evaluation of contamination sources of groundwater NO3
using nitrogen isotope data: a review. Geosci J. 2003, 7, 81–87.
Debernardi, L., De Luca, D.A., Lasagna, M. Correlation between nitrate concentration in
groundwater and parameter affecting aquifer intrinsic vulnerability. Environ Geol.
2008, 55, 539–558.
Foster, S. S. D., Hirata, R., Gomes, D., D’Elia, M. and Paris, M. Groundwater quality
protection: a guide for water utilities, municipal authorities and environment agencies.
World Bank, Washington DC. 2002.
Gibrilla, A., Bam, E.K.P., Adomako, D., Ganyaglo, S., Osae, S., Akiti, T.T. (2011).
Application of water quality index (WQI) and multivariate analysis for groundwater
quality assessment of the birimian and cape coast granitoid complex: Densu River
Basin of Ghana. Water Qual Expo Health. 2011, 3, 63.
Karr, J. D., Showers, W. J., Gilliam, J. W., Andres, A. S. Tracing nitrate transport andenvironmental impact from intensive swine farming using delta nitrogen‐15. J. Environ.
Qual. 2001, 30,1163-1175.
Kendall, C., Coplen, T.B. Distribution of oxygen-18 and deuterium in river waters across theUnited States. Hydrol. Process. 2001, 15, 1363–1393.
Kohn, J., Soto, D. X., Iwanyshyn, M., Olson, B., Kalischuk, A., Lorenz, K., Hendry, M.J.
(2016). Groundwater nitrate and chloride trends in an agriculture-intensive area insouthern Alberta, Canada. Water Qual. Res. J. 2016; 51, 47–59.
Kortatsi, B.K., Tay, C.K., Anornu, G., Hayford, E., Dartey, G.A. Hydrogeochemical evaluationof groundwater in the lower Offin basin, Ghana. Environ. Geol. 2008, 53, 1651–1662.
Kumar, M., Ramanathan, A.L., Rao, M.S., and Kumar, B. Identification and evaluation ofhydrogeochemical processes in the groundwater environment of Delhi, India: J.
Environ. Geol, 2006, 50, 1025–1039.
Mariotti, A., Germon, J. C., Hubert, P., Kaiser, P., Letolle, R., Tardieux, A., & Tardieux, P.
Experimental determination of nitrogen kinetic isotope fractionation: some principles;121

illustration for the denitrification and nitrification processes. Plant and soil. 1981, 62,
413-430.
Maurya, P., Kumari, R., Mukherjee, S. Hydrochemistry in integration with stable isotopes(δ18O and δD) to assess seawater intrusion in coastal aquifers of Kachchh district,
Gujarat, India. J. Geochem. Explor. 2019, 196, 42-56.
McLean, W., Jankowski, J., Lavitt, N. Groundwater quality and sustainability in an alluvial
aquifer, Australia. In: Groundwater, past achievements and future challenges (Sililo etal.,Eds): Balkema, Rotterdam. 2000, 567–573.
Piper, A.M. A graphic procedure in the geochemical interpretation of water-ana- lyses. Eos,Trans. Am. Geophys. Union 25, 1944, 914–928.
Rajmohan, N., Al‐Futaisi, A., Jamrah, A. Evaluation of long‐term groundwater level data inregular monitoring wells, Barka, Sultanate of Oman. Hydrological Processes: An
International Journal. 2007, 21, 3367−3379.
Rivett, M.O., Buss, S.R., Morgan, P., Smith, J.W.N, Bemment, C.D. Nitrate attenuation in
groundwater: a review of biogeochemical controlling processes. Water Res. 2008. 42,4215–4232.
Zakaria, N., Akiti, T.T., Osae, S., Dickson, A., Ganyaglo, S.Y., Hanson, J.E.K., Ayanu, G.
Hydrogeochemistry of groundwater in parts of the Ayensu Basin of Ghana. Proc. Int.
Acad. Ecol. Environ. Sci. 2012, 2, 128–135.
Zakaria, N., Anornu, G., Adomako, D., Owusu-Nimo, F., Gibrilla, A. Evolution ofgroundwater hydrogeochemistry and assessment of groundwater quality in the Anayari
catchment. Groundwater for Sustainable Development. 2021, 12, 100489.

122Chapter 6
Discussion

6.1 Anthropogenic influence on groundwater resources
6.1.1 Groundwater recharge assessment from hydrological modelLand use is a key factor affecting water balance, which in turn affects the water resource
availability. Increasing urbanization has altered water balances in river catchments and theeffects of urbanization processes have been poorly understood at the catchment and sub
catchment scale. The contribution from groundwater is vitally important for water supply
especially for developing rural catchments whereby groundwater is largely used for foodproduction and drinking water supply. The results of the modelling simulation have clearlyshown the strong impact of land use change on the water balance in the Kurose River catchmentfrom the 1980s to 2000s with a significant decline in groundwater recharge (Figure 6.1).

Figure 6.1: Actual evapotranspiration, runoff, leakages, and groundwater recharge for thetwo years of investigation (1980s and 2000s) for the Kurose River catchment.

123Several studies have highlighted the fact that watersheds that are urbanized exhibit
significantly lower groundwater recharge than watersheds that remain in their natural condition
as surface sealing prevents infiltration and increases surface water runoff compared to naturallandscapes (Grischek et al., 1996; Hardison et al., 2009). Other studies indicate an increase in
groundwater recharge rates due to the reduction in evapotranspiration linked with surface
sealing (Barron et al., 2013; Garcia-Fresca, 2007). Results from this study align with thosementioned above. During the period 1980s to 2000s, residential areas which occupied only
12% of the catchments total area increased to approximately 34.6% resulting to a substantial
decrease in rice paddy fields which have been recognised as good recharge zones. This led tosignificant decrease in the catchment’s groundwater rates (Figure 6.1). Furthermore, land usechange and anthropogenic activities in the catchment such as surface sealing due to streets andbuildings modified the infiltration and movement of water which led to a significant increasein the catchment’s runoff. Inferring from (Figure 4.4), the recent historic years are proven to
be more susceptible to flooding and in case of heavy rainfall events. The immediate
repercussions of urbanization the hydrological cycle has been documented by various
researchers which has been summarized below (Jha, Singh, and Vatsa, 2008; Strohschon et al.,2013).
▪

Flooding (e.g because of increased soil sealing)▪

water shortage (e.g. due to rising consumption)▪

changes in the river and groundwater regimes▪

Groundwater pollution (Urban areas generate both nonpoint and point sources ofcontaminants are due to leakage from drainage and, industrial wastage and effluent

124

The impact of urbanization on groundwater sources is not only quantitative, but also
qualitative. For qualitative assessment of urbanization processes on groundwater sources in the
study area, a sub-basin in the Kurose River basin was studied
6.1.2 Influence of social aspects on groundwater nitrate nitrogen contamination: Casestudy; Saijo and Osakishimojima
Groundwater plays a significant role in the ecosystem, and it is a vital local water source
for industrial, agricultural, and domestic purposes. When comparing to surface water, it isusually higher in quality with better protection from direct pollution as well as less subject toevapotranspiration. As much as urbanization processes lead to groundwater decline, it equallyleads to groundwater contamination. Groundwater contamination sources are usually variedand difficult to detect thus using tracer-methods can help distinguish potential contamination
source. Both Saijo and Osakishimojima are agricultural catchments with different croppingtypes, while rice paddy dominates in Saijo, citrus orchard is the main agricultural crop in
Osakishimojima. However, the level of urbanization is significantly different in the two study
areas, Saijo is a rapid developing rural catchment and is still undergoing development while
Osakishimojima is a rural island with an aging population. However, from our studies, results
show higher NO3−−N contamination levels in Osakishimojima as compared to Saijo. In Saijo
NO3-N concentration ranged from 0.4 mg/L to 4.3 mg/L with a mean value of 2.1 mg/l in allinvestigated water types. Mean NO3−−N values increased from 0.5mg/L in springs to 0.7 mg/L
in rivers to 2.9 mg/L in boreholes and 3.5 mg/L in wells. This concentrations are relatively low
when compare with those from the investigated study sites in Osakishimojima. For instance, inKubi, NO3−−N concentration ranged from 0.5 mg/L to 13m/L with a mean value of 6.54 mg/L
while in Ocho NO3−−N values ranged from 0.7 mg/L to 15.1 mg/L with a mean value of 4.75
mg/L. Mean groundwater (wells ad boreholes) NO3−−N concentration were higher in Kubi (7.2mg/L) than Ocho (4.94 mg/L), and Saijo (1.9 mg/L). Almost twice the concentration in Ocho
125

and more than thrice in Saijo. This results are quite interesting especially seeing as all study
sites are agriculture oriented, however several factors may affect the variation in NO3−−Nlevels in this areas. For instance,
1. Proportion of land use under cultivation which will affect fertilizer ratios during
application: In Kubi approximately 40.19% is orchard cultivation and 24.62% Ocho
while in Saijo, (20.91%) is cultivated by rice paddy. The proportional difference
between Ocho and Kubi may be the reason for the elevated concentrations ingroundwater, whereas for Saijo, this may not be the case; reasons being that most of
the groundwater samples were collected in the residential area with little or no
agricultural activity in its vicinity.
2. Type of fertilizer used and method of application: From field investigation. The
difference in fertilizer usage in both villages maybe due to the difference in farmer age
group (76> in Kubi and 73 > in Ocho). Fertilizers provide plants with the nutrients theyneed to grow immediately, thus aged farmers will prefer to use chemical fertilizers
which are more cost effective, less time consuming and more convenient for maximum
crop yield. However further investigations need to be carried to confirm this assumption.
This factor is ruled out in Saijo due to the above-mentioned reason.
3. Housing density: The residential area in Kubi and Ocho varies significantly as seenfrom (Figure. 3.2). Ocho has a higher ratio of residential area (75.4 %) as compared to
Kubi (59.8%). When compared with Saijo, Saijo has a much lower percentage (34.4%).
However, the low NO3−−N concentrations in Saijo may be due to the more advanced
development status in Saijo especially when taking into consideration sewage system
which maybe a contributing factor to the higher NO3−−N in Ocho and Kubi.

126

Generally, the variation of NO3−−N concentration in groundwater from the three study sites
may be related to several contributing factors however this results may be limited by the sample
number and frequency of collection. Also, geology plays an important role in contamination
status through mineral dissolution.

6.2 References
Barron, O. V., Barr, A. D., Donn, M. J. Effect of urbanization on the water balance of acatchment with shallow groundwater, J. Hydrol. 2013, 485, 162–176,
Garcia-Fresca, B. Urban-enhanced groundwater recharge: review and case study of Austin,
Texas, USA, Urban Groundwater, Meeting the Challenge: IAH Selected Papers on
Hydrogeology 8. 2007.
Grischek, T., Nestler, W., Piechniczek, D., Fischer, T. Urban groundwater in Dresden,
Germany. Hydrogeol. J. 1996, 4, 48–63.
Hardison, E.C., O’Driscoll, M.A., DeLoatch, J.P., Howard, R.J., Brinson, M.M. (2009). Urban
land use, channel incision, and water table decline along coastal plain streams, North
Carolina. Am. Water Resour. Assoc. 2009, 45, 1032–1046.
Jha, R., Singh, V. P. and Vatsa, V. (2008). Analysis of urban development of Haridwar, India,
using entropy approach. KSCE Journal of Civil Engineering, 12, 281−288.
Strohschön, R., Wiethoff, K., Baier, K., Lu, L., Bercht, A., Wehrhahn, R. and Azzam, R. Landuse and Water Quality in Guangzhou, China: A survey of ecological and Social
Vulnerability in Four Urban Units of the Rapidly Developing Megacity. Int J Environ
Res. 2013, 7, 343−358.

127Chapter 7
Conclusion and Recommendation

7.1 Conclusion
In order to confirm the social effect (aging and depopulation) and environmental effect(land use change, urbanization, and climate change) on the groundwater resources, we madeuse of hydrochemical and isotopic tracer methods as well as a watershed hydrology model inthree mountainous rural catchments. As many cities in Japan and third-world countries areeither experiencing or are rapidly approaching similar demographic challenges, this study will
also provide the public with an insight into the environmental and social challenges associatedwith aging and depopulating societies on groundwater resources. The general conclusions are
as follows:
Employing hydrochemical and stable nitrogen 15 isotopes, the villages of Ocho andKubi, where found to have different NO3⁻-N contamination status (Ocho: 4.8 mg/Land Kubi:
6.7 mg/L) partly due to the slight difference in their social aspects (population size and farmerage) which tends to influence their choice of fertilizer. The proportional source contributionsof NO3⁻ in each village was determined using an end‒member method which showed that Kubi
had a greater contribution from chemical fertilizer while Ocho village was from Organic
fertilizer. However, both villages had minor contributions from sewage. It was believed thatthe older farmers in Kubi employed a significant quantity of chemical fertilizers due to theirsmaller population size (reduced work force) and older farmer group (reduced work efficiency),
which caused inefficient nitrogen uptake by plants, leading to increased leaching, and moregroundwater contamination than in Ocho.

128

Similarly, the same approach was conducted in a developing rural catchment; Saijo andcontrary to Kubi and Ocho, 99% of the groundwater samples plotted in the manure and sewage
zone. Despite the agricultural activities in the area, we found no evidence of nitrate originatingfrom synthetic fertilizers in the groundwater. This could either be as a result of proper
agricultural practices or limited dataset, however future investigations will employ long termtime series data for proper investigation.
Furthermore, based on the result of study about SWAT and GIS application to address
impact of urbanization and climate change on groundwater resources in the Kurose River
catchment area, the general conclusions have been formulated. The population growth andeconomic development in the Kurose River catchment area in the last three decades had an
impact on land use change and also on groundwater availability.
During the 1980s ‒ 2000s most of the land use change was caused by the conversion ofrice paddy fields to residential areas resulting in increased impervious surfaces with negative
implication on the catchment hydrology. From SWAT simulation, results suggest that the waterbalance in the 1980s and the 2000s was greatly influenced by land use change, urbanization,and potential contributions from climate change on the catchments hydrology characteristics.
The most significant change was observed in the decrease in the mean annual groundwater
recharge by approximately 25% over the past three decades. Additionally, the predicted land
use and climate scenario highlighted that groundwater recharge will be significantly affectedby the combined effect of future land use and climate change which is projected to decrease
groundwater recharge by 29% by 2040.

7.2 Recommendation
In order to achieve better results, input data and process need to be improved, especiallyby using better resolution data set (spatial and temporal) and including irrigation function to
fully estimate recharge in paddy fields. Additionally, to obtain better results on the temporal129

changes in groundwater recharge with changing land use and climate, SWAT can also be
integrated with fully distributed MODFLOW groundwater models (SWAT-MODFLOW).

130

Appendix
Appendix 1: Hydrochemical compositions of the groundwater and surface water sources in Kubi and Ocho village for the 2021 sampling campaign.
Note: BDL = below detection limit; NM = not measured, ionic concentrations, TDS and DO = mg/L; EC = μS/cm; Temp = °C

OKBH
OKR1
OKR2
OKU1
OKU2
OKU3
OKU4
OKU5
OKU6
OKU7
OKU8
OKU9
OKU10
OKU11
OKD1
OKD2
OKD3
OKD4
OKD5

Typology
Borehole
River
River
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well

Well depth
30

5.11
3.12
3.78
7.59
12.5
70
4.68
4.46
5.71
5.65
1.2
NM
NM
3.72
4.35
6.76

pH
7.5
7.9
8.59
6.46
6.45
6.51
6.44
6.94
6.89
6.49
6.41
6.74
6.5
6.68
7.32
7.09
7.16
6.4
NM

Temp
19
24
24.7
20.7
20.6
18.3
21.2
20.9
18.3
24.3
21.7
24.9
20.6
26.7
20.4
20.6
22
20.4
20.7

EC
472
274
206
333
326
321
265
238
465
407
255
106.3
226
354
283
418
378
261
248

TDS
302.1
175.4
131.9
213.2
208.7
205.5
169.6
152.4
297.6
260.5
163.2
68.1
144.7
226.6
181.2
267.6
242
167.1
158.8

DO
6.1
9
8.3
3.9
8.2
7.5
6.5
5.5
4.7
3.8
4.9
8.6
3
3.9
1.1
2.8
3.8
4.6
6

DOC
4.1
3.8
5.3
11.6
13.3
6
3.3
NM
4.8
4.1
3
2.5
3.6
3.7
4.9
4.9
2.8
4.6
3.5

Cl⁻
10.1
7.4
6.4
7
8.1
7.6
9.9
11.7
10.7
6.5
9.5
9
4.2
14.3
9.4
10
11.1
8.7
10.8

SO42⁻
67.6
36.8
16.8
37.7
50.8
74.6
36.2
26.3
64.1
34.3
37.6
5.1
13.6
51.5
36.3
41.2
38.2
32.6
41.6

NO3⁻
56.7
20.9
5.5
32.1
49.2
40.8
27.9
22
57.2
13.7
28.6
2.1
10.4
54.9
26.1
42.8
33.5
28.5
32

NO3-N
12.9
4.8
1.3
7.3
11.2
9.3
6.4
5
13
3.1
6.5
0.5
2.4
12.5
5.9
9.7
7.6
6.5
7.3

HCO3⁻
39.1
61.2
50.6
30.2
14.2
28.4
43.5
43.5
62.1
121.6
47.1
55.9
57.7
0
37.3
70.1
47.1
24.9
37.3

Na⁺
28.6
14.4
10.1
13.1
15.5
14.8
15.5
17.5
27.9
9.8
13.4
9.6
6
14.4
16.3
19.3
16.4
11.7
15.9

K⁺
0.6
2.4
1.6
3.3
4.1
1.4
4.5
1.7
0.8
6.6
4.1
1
4.9
5.4
4.9
0.8
7.1
4.7
5.2

Mg2⁺
14.4
7.8
4.5
8.2
11.4
13.9
8.2
5.4
14.4
9.7
8.9
1.6
4.2
15.3
9.5
11.2
10.8
8.5
9.4

Ca2⁺
35.5
23.2
9.8
22.1
25.4
29.3
22
18.5
38.1
30.1
23.7
7.8
13.6
BDL
24
27.3
27.9
21
24.8

131

OKD6
O-KUS
OOG1
OOG2
OOG3
OOG4
OOG5
OOG6
OOG7
OOG8
OOG9
OOG10
OOG11
OOG12
OOG13
OOG14
OOG15
OOG16
OOG17
OOG18
OOG19
OOG20
OOG21
OOG22
OOG23
OOG24
OOG25
OOG26
OOG27
OOG28

Well
Spring
Well
Well
Well
Well
Well
Well
Well
Tap
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
Well
SpringSpring
Well
Well
Well
Well
Well

6.64
2
3
9
5
5
NM
4
4
5
7
6
4
4
5
2
6
NM
5
5

5
10
4
5
6

6.89
7.21
6.72
6.72
6.64
6.4
6.53
6.74
6.52
7.16
6.34
6.27
6.19
6.22
6.44
6.44
6.57
6.94
7.08
6.34
6.27
6.89
6.5
7.45
7.45
6.83
6.95
6.74
6.58
8.44

18.3
19.1
23.6
28.3
22.6
22.5
20.4
24.6
19.2
22.8
22.4
21
19.6
20.9
23.1
21.4
22.6
21.3
20.6
21.3
21.6
23.5
22.7
23.6
17.6
22.2
22.7
21.6
23.3
22.8

465
185.7
266
346
239
191.7
229
479
373
164.9
237
301
453
354
378
285
269
317
340
236
323
318
410
217
133
331
333
210
324
291

297.6
118.9
170.3
221.5
153
122.7
146.6
306.6
238.8
105.6
151.7
192.7
290
226.6
242
182.4
172.2
202.9
217.6
151.1
206.8
203.6
262.4
138.9
85.2
211.9
213.2
134.4
207.4
186.3

4.7
9.2
0.8
4.5
3.1
5.3
2.3
2.9
4.9
9.1
3.4
1.1
2.8
2.3
1.7
3.2
3.2
1.6
8.2
3.3
1.1
1.7
1.7
7.7
9.5
3.6
2.4
7.5
3.4
4.1

4.1
3.6
6.7
3.5
3.4
2.2
2.2
1.7
3.2
3.2
7
2.9
4.4
3
4.8
5
4
3.1
4.1
5.9
3.3
4.9
2.5
0
3.5
2.8
2.8
-3.7
2.5
3.6

10.5
14.7
8.4
15.4
13.6
8
11.8
37.5
13
5.3
9.7
8.4
17.9
18.3
14.6
13.8
7.9
12
5.1
9.2
21.3
10.9
12
10.9
4.6
13.7
4.1
5.9
11
11.3

31.1
22.9
11.3
49
22.8
18.1
39.5
34.1
47
19.8
14.2
19.2
145.1
58.4
84.3
22.2
14.6
10.1
46.4
21.5
BDL
26.4
27
22.9
4.5
71.4
45
35.2
74.2
24.3

11.2
7.1
3.5
21.3
11.6
19.7
13.5
26.5
48.8
13.3
22.1
12.3
66.4
31.9
19.2
33.4
16.1
7.4
28.5
23.5
19.6
11.2
28
13.7
2.7
41.9
18.9
24.3
12.5
24.3

2.6
1.7
0.8
4.9
2.7
4.5
3.1
6
11.1
3.1
5
2.8
15.1
7.3
4.4
7.6
3.7
1.7
6.5
5.4
4.5
2.6
6.4
3.1
0.7
9.5
4.3
5.5
2.9
5.5

55.9
7.2
89.8
141.2
98.7
55.6
55.2
150.2
75.7
34
51.3
107
54.1
70.4
82.3
69
105.1
143.6
78.9
70.7
89.7
133.2
83.4
9.3
4
8.9
9.1
4.5
12.8
11.4

17.4
21
12.2
27.1
20.6
12.9
20.1
28.1
18.6
9.4
9.5
21.2
57.6
22.6
15.8
16.8
11.1
22.2
15.3
14.7
21.5
19.3
12.4
0
5.5
21.5
10.2
12
22
16.6

2.7
2.2
2
1.9
8
5.4
3.6
1.9
0.7
1
4.8
8.8
2.2
2.7
7.2
10.5
5.5
3.2
0.7
10.2
0.3
6.1
1.4
8.4
3.2
4.7
0.8
1.9
4.8
4.3

8.4
9.8
5
19.1
8.9
5.7
9.1
15.3
13.9
4.9
5.1
8
22.2
14.3
11
7.4
5.4
7.3
11.4
7.5
13.6
7.8
6.5
25.8
1.4
16.8
6.5
8.5
22.1
7.6

17.1
24.6
13.9
43.3
29.7
20.2
21.3
39.4
29.1
13.5
14.2
BDL
42.8
32.4
48.1
29.6
24.5
BDL
27
26.4
8.8
BDL
BDL
BDL
4.6
45
28.9
23.6
50.8
35.1

132

OOG29
OOG30
OOG31
OOG32

Well
Well
Well
Well

NM
4
3
3

6.41
6.76
6.81
7.1

22.9
21.8
22.1
21.9

266
347
368
275

170.3
222.1
235.6
176

1.8
2.1
1.6
8.1

2.4
3.6
1.8
2.2

9.7
6.6
12.8
6.4

28.8
21.7
36.7
22.9

14.2
10.1
3.7
21

3.3
2.3
0.9
4.8

7
10.5
14
8.5

14.6
12.4
27.1
11

6.3
2.7
5.1
4.3

7.3
5.7
9.9
5.9

BDL
18.7
BDL
23.3

133

Appendix 2: Groundwater level data collected in various observation wells in the 1974 and 2000 for the Kurose River catchment. Note: Allmeasurements are in meters (m)
Well

Long

Lat

107
112
113
116
117
123
127
128
129
132
137
138
139
140
141
143
145
146
203
206
207
208
211
212

132.7525
132.7506
132.7581
132.7436
132.7453
132.7519
132.7572
132.7501132.7581
132.7503
132.735132.7461
132.7453
132.7458
132.7594
132.7756
132.7619
132.7506
132.7356
132.7481
132.7486132.77132.7622
132.7519

34.42417
34.4119434.40167
34.41278
34.41028
34.4019434.40722
34.40944
34.41028
34.40361
34.40972
34.4
34.40722
34.40028
34.4275
34.42167
34.43139
34.4425
34.43694
34.43667
34.41917
34.44139
34.44
34.41833

Wellheight
0.35
0.38
0
0.49
0.35
0.46
0.33
0.52
0.36
0.47
0.61
0.53
0.72
0.34
0.3
0.47
0.65
-9999
0.51
0.46
0.45
0.37
0.4
0.4

1974
Water
depth
1.44
0.92
1
3.04
2.63
0.4
1.69
1.02
1.31
0.62
0.66
1
2.14
3.64
1.67
1.07
4.14
-9999
0.69
1.71
1.31
2.57
5.6
0.71

Bottom
depth
3.11
3.14
2.58
5.09
4.39
8.35
4.45
4.08
3.28
4.54
2.02
2.36
3.19
5.38
5.67
2.71
6.75
-9999
2.84
3.5
9.28
5.04
6.23
4.03

Surface
elev.
212.5
208.7
210.1
225.4
217.5
225
216
215.2
210
219
210.5
208
214.5
214.5
208
212.5
234
250
212
223
237.5
235
236
225

Groundwate
r elev.
211.41
208.16209.1
222.85215.22225.06214.64214.7
209.05218.85210.45207.53213.08211.2
206.63211.9
230.51-9999
211.82221.75236.64232.8
230.8
224.69Wellheight
0.26
0.5
0.62
0.19
0.33
0.29
0.09
0.28
0.46
0.47
0.44
0.59
0.14
0.34
0.55
0.51
0.58
0.41
0.155
0.56
0.36
0.49
0.24
0.54

Water
depth
2.66
2.52
2.61
3.17
3.71
6.27
1.99
2.55
2.69
1.11
1.89
2.86
5.22
4.44
1.9
5.25
1.62
1.12
1.9
2.14
5.6
1.15
1.32
1.94

20000
Bottom
depth
3.29
3.57
3.45
5.39
4.36
8.55
4.18
3.65
3.95
2.1
2.37
3.1
5.75
5.69
3.45
6.61
3.61
2.8
6.53
3.62
6.19
4.3
2.46
2.93

Surface
elev.
212.5
208.7
210.1
225.4
217.5
225
215.2
210
219
210.5
208
214.5
214.5
208
212.5
234
250
212
223
235
236
225
215
214

Groundwate
r elev.
210.1
206.68208.11222.42214.12219.02213.3
207.73216.77209.86206.55212.23209.42203.9
211.15229.26248.96211.29221.255
233.42230.76224.34213.92
212.6

134

228
229
231
238
243
305
309
312
319
320
329
330
331
402
404
414
425
430
431
432
434
435
438
509
516
524

132.7514
132.7439
132.7608
132.7394
132.7297
132.7214132.7189
132.7364
132.7381
132.7347
132.7428
132.735132.7086
132.7156
132.7136
132.7297
132.7017
132.7006
132.7197
132.7208132.7047
132.7253
132.7039
132.6858
132.6858
132.7114

34.41889
34.43556
34.43556
34.43861
34.44333
34.42972
34.42167
34.43361
34.43333
34.43528
34.43917
34.43278
34.41056
34.42639
34.4275
34.42167
34.42583
34.40972
34.40083
34.41
34.46028
34.45472
34.45639
34.45222
34.45222
34.435

0.27
0.7
0.45
0.35
0.4
0.23
0.48
0.46
0.48
0.67
0.59
0.03
0.08
0.72
0.21
0.59
0.57
0.36
0.18
0.44
0.63
0.6
0.78
0.49
0.3
0.35

0.88
1.31
4.08
10.29
1.45
4.85
2.59
0.5
0.52
1.34
1.32
1.4
1.67
1.66
0.47
1.67
0.99
5
2.77
1.37
0.67
0.57
1.61
3.08
0.81
0.61

2.5
3.15
6.36
11.55
6.03
6.86
5.54
3.89
2.44
4.04
4
5.55
3.94
4.48
2.78
4.09
3.69
5.95
4.1
5.2
8.43
5.49
3.65
7.6
2.3
2.1

215
214
221
229
225
240
222.9
214.2
240
229
226
221.5
217
218
220
228
217
225
225
220
221.5
221.5
227.5
252.5
245
246

214.39213.39217.37219.06223.95235.38220.79214.16239.96228.33225.27220.13215.41217.06219.74226.92216.58220.36222.41
219.07221.46221.53226.67249.91
244.49245.74

0.23
0.39
0.265
0.355
0.61
0.424
0.37
0.1
0.45
0.58
0.59
0.03
0
0.635
0.185
0.445
0.56
0.445
0.425
0.54
0.44
0.48
0.735
0.135
0.055
0.74

3.72
2.74
9.51
1.76
5.33
2.7
2.65
0.88
1.2
2.01
1.86
1.15
1.1
2.08
0.89
2.64
1.5
4.03
2.91
2.21
1.3
1.59
1.92
1.08
4.89
2.18

6.3
4.68
10.95
6.02
5.85
3.27
5.23
4.17
2.51
3.09
4.1
5.16
3.65
4.54
2.85
4.09
3.72
6.05
6.16
5.58
7.75
3.1
3.69
1.64
6.15
6.2

221
221
229
225
240
235
222.9
214.2
240
229
226
221.5
217
218
220
228
217
225
225
220
221.5
221.5
227.5
252.5
245
246

217.51218.65219.755
223.595
235.28232.724220.62213.42239.25227.57224.73220.38215.9
216.555
219.295
225.805
216.06221.415222.515218.33220.64220.39226.315251.555
240.165244.56135

136