厚真川流域の水循環に地震に伴う土地利用・被覆変化が及ぼした影響の評価
メタデータ

言語: English
出版者:
公開日: 2022-12-19
キーワード (Ja):
キーワード (En):作成者: チェン, ユエチャオ
メールアドレス:所属:

URL

https://doi.org/10.15118/00010870

Muroran IT
Muroran Institute of Technology

DISSERTATION
Assessment of impact of land use/cover change caused by
the earthquake on hydrological processes in the Atsuma
River basin

Chen Yuechao

ASSESSMENT OF IMPACT OF LAND USE/COVER CHANGE
CAUSED BY THE EARTHQUAKE ON HYDROLOGICALPROCESSES IN THE ATSUMA RIVER BASIN

by

Chen Yuechao
A dissertation submitted in partial fulfillment of the requirements for the degreeof Doctor of Engineering

Examination Committee:

Prof. Makoto Nakatsugawa
Prof. Katsutoshi Kimura
Prof. Shima Kawamura

Muroran Institute of Technology
Division of Sustainable and Environmental EngineeringJapan
September 2022

Acknowledgements
Time flies like an arrow. Before I knew it, my doctoral career had reached thethird year, also the last year of my student days. Looking back on it, I am filledwith emotion, and my heart is filled with much gratitude.
First of all, I would like to thank my supervisor, Professor Makoto
Nakatsugawa. Thank him for his excellent guidance, warm care, strict
requirements, and providing a good research environment for me over the past
three years. Without the help of Prof. Makoto Nakatsugawa, I could not havecome to Muroran Institute of Technology to pursue my Ph.D. Without his kindand earnest guidance, I would not have been able to complete my research.
I want to express my respect and gratitude to Professor Katsutoshi Kimura
and Professor Shima Kawamura. As examination board members, their objective,
unbiased comments and sound, impartial advice were critical to improving thequality of my research.
I want to express heartfelt thanks to the members of our lab who have helped
me a lot in my research and life. Special thanks to Ms. Kudo Yuki, Mr. Kota
Shimizu, Mr. Tanaka Masato, Mr. Atsuya Takeda, Mr. Tomohiro Sando, and Mr.
Jyunya Seki for their strong support for my research and for taking care of mylife during my study in Japan.
I want to express my sincere thanks to the Muroran Institute of Technology
for providing me with the scholarship and the job opportunity as a research
assistant. Special thanks to the teachers at the Center of International Relations in
the Muroran Institute of Technology for their strong support and help.
I would also like to thank Mr. Hiroki Ohashi and Mr. Satomi Kawamura forproviding me with essential research data, without which I would not be able toconduct and complete this research.
Thanks to my friends Mr. Yanbo Liu, Mr. Yue Zhang, Mr. Jiayi Han, Mr.
Zhenchao Ding, Mr. Song Gao, Mr. Hao Li, Ms. Xue Song, Ms. Dandan Zheng,
and Ms. Bingyu Xi. For three years, whenever I encountered difficulties in
research and life, they all gave me the necessary care and help. We accompany
each other and spend our time studying in Japan together. In addition, I would
like to thank Dr. Qing Zhang and Dr. Yaoyao Han, Mr. Jiaming Zheng, and Mr.
Weilong Wang. Although they are far away in China, when my researchencounters difficulties, they are always willing to discuss with me, find ways toisolve the problem and share the joy and pressure with me.
Finally, I want to thank my family especially. Without the love and support ofmy parents, I would not be who I am today. Without the passion and dedicationof my wife, Jiajia Gao, my life would not be so beautiful and happy. Because offamily's support, tolerance, companionship, and care, I can be neither arrogant
nor impetuous in good times and work hard. I can face and persevere bravely
without abandoning or giving up on adversity. I will always love you.
Thanks again to everyone who gave me help, support, and concern! My deep
thank like the stars in the universe, deep and bright, with eternal brilliance.

Muroran, 2022
Chen YuechaoiiAbstract
In recent decades, land use/cover change has been a significant global change issue.
The impact of land use/cover change on watershed hydrological processes is a research
hotspot in hydrology. The Hokkaido Eastern Iburi earthquake occurred on 6 September
2018, with a maximum intensity of 7, was the largest in Hokkaido ever recorded. Afterthe earthquake, about 4,500 landslides occurred in the Atsuma River basin, which are
the main factors of land use/cover change in the basin in recent years.
This study takes the Atsuma River basin, Hokkaido as the research object. We focus
on the quantification and analysis of land use/cover change in the basin before and after
the earthquake and its impact on runoff and sediment transport processes. Firstly, bycomparing and analyzing the land use maps in 2015 and 2020. We found that the land
use/cover change mainly occurred in the mountainous areas in the upper and middle ofthe basin. The main manifestation is that the earthquake and landslides transformed
about 47.899 km2 forest area into 21.906 km2 bare land and 23.738 km2 grassland.
Secondly, we used the soil and water assessment tool (SWAT) model to accurately
simulate the hydrological processes in the Atsuma River basin.
Regarding the runoff process, the simulation results show that the 2015-2020
daily/monthly runoff simulation results estimated using 2015 and 2020 land use data aresimilar, and the simulation curves are very close, indicating that the land use/coverchange has little impact on the watershed runoff. After careful observation, we foundthat the 2015-2020 daily/monthly runoff simulation results estimated using the 2020
land use data are slightly higher than those estimated using the 2015 land use/cover data
in most flow processes, especially at the peak. This indicates that the reduction in forest
area and the increase in the bare land slightly increased the runoff after the earthquake
in the basin.
Regarding the sediment transport process, the simulation results show that themonthly sediment transport after the earthquake is much higher than before. The
amount of sediment transported per unit runoff after the earthquake was about 5.5 times
before the earthquake, and the change was dramatic. It shows that with the reduction offorest area, the increase of bare land area, and the influence of sediments accumulatedaround the river channel and foot of the mountain, the amount of sediment transport perunit runoff in the basin has increased significantly.
Above research results will be of use to estimate how changes in the hydrologic
process and sediment runoff will affect the environment of rivers and seas when a large
impact such as a big earthquake occurs in the basin.
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概要
ここ数十年で，土地利用/被覆の変化は地球規模の重大な問題になっている．そして，土地利用/被覆の変化は流域の流出過程に影響するため，水文学の研究においても重要な問題となっている．2018 年 9 月 6 日に発生した北海道胆振東部地震は最大強度 7 で，北海道史上最大の地震であった．震災後，厚真川流域では約 4,500 件の地すべりが発生し，これが近年の厚真川流域の土地利用/被覆変化の主な要因となっている．
本研究では，北海道厚真川流域を対象とし，地震前後の土地利用/被覆変化の定量化と分析，および流域流出と土砂流送過程への影響に焦点を当てた．まず，2015 年と 2020 年の厚真川流域の土地利用図を比較分析したところ，主に厚真川流域の上流・中流部の山岳地帯において土地利用・被覆変化が起きていることがわかった．また，地震と地滑りによる
土地利用変化を分析すると，約 47.899km2 の森林面積が 21.906km2 の裸地と 23.738km2 の草地に変化したことが分かった．
次に，厚真川流域の流出過程を正確にシミュレートするために，土壌および水質評価ツールである SWAT モデルを使用した．流出過程に関する結果として，2015 年と 2020 年の土地利用データを使用して推定された 2015-2020 年の日次/月次流出シミュレーション結果が類似しており，土地利用/被覆の変化がほとんど影響を与えないことを示している．流域の流出について，2020 年の土地利用データを使用して推定された 2015-2020 年の日次/月
次流出シミュレーション結果は，ピーク時の流出過程が 2015 年の土地利用/被覆データを使用して推定された結果よりもわずかに高いことがわかった．これは，厚真川流域の地震後，森林面積の減少と裸地面積の増加により流出量がわずかに増加したことを示している．
最後に土砂流出過程に関する結果より，地震後の月間土砂流送量は以前よりもはるかに多いことがわかった．特に震災後の単位流出あたりの土砂輸送量は，震災前の約 5.5 倍に増加した．これは，森林面積の減少，裸地面積の増加，河道や山麓周辺に堆積した土砂の影響により，流域の単位流出量あたりの土砂流送量が大幅に増加していることが考えられる．
以上のような研究成果は，大きな地震のようなインパクトが流域に起きた場合，水循環や土砂流出の変化によって河川や海域の環境にどのような影響が及ぶのかを推定するうえで役立つと考える．

iv

Contents
Title
Acknowledgements……………………………………………………………..
Abstract…………………………………………………………………………
Abstract (Japanese)……………………………………………………………..
Contents………………………………………………………………………...
List of Figures…………………………………………………………………..
List of Tables……………………………………………………………………
List of Abbreviations……………………………………………………………
CHAPTER 1
Introduction……………………………………………………………………
1.1. Research background and significance…………………….……………..
1.2. Research status and development trend…………………………………..
1.2.1. Research progress of the 2018 Hokkaido Eastern Iburi earthquake…..
1.2.2. The impact of land use/cover change on runoff and sediment transport
processes………………………………….…………………………………..
1.2.3. Research progress of the SWAT model….……………………………..
1.3. Current problems………………………………………………………….
1.4. Research content and technical route……………………………………..
1.4.1. Research content……………………………………………………....
1.4.2. Research technology roadmap………………………………………...
1.5. Thesis outline…………………………….……………………………….
1.6. References………………………………………………………………...

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CHAPTER 2
Overview of the study area and 2018 Hokkaido Eastern Iburi earthquake
2.1. Atsuma River basin………………………………………………………...
2.2. 2018 Hokkaido Eastern Iburi earthquake…………………………………..
2.3. Human activities…………………………………………………………...
2.4. References………………………………………………………………….

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CHAPTER 3
Land use/cover change and its driving force analysis……………………….
3.1. Introduction………………………………………………………………...

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3.2. Materials and Methods……………………………………………………..
3.2.1. Landsat 8 satellite data ……………………………………………….
3.2.2. ENVI software………………………………………………………...
3.2.3. Processing of land use/cover data……………………………………..
3.3. Results and Discussions……………………………………………………
3.3.1. Land use/cover change before and after the earthquake………………
3.3.2. Structural change characteristics of land use/cover…………………...
3.3.3. Driving force analysis of land use/cover change ……………………...
3.4. Conclusions………………………………………………………………...
3.5. References………………………………………………………………….
CHAPTER 4
Runoff simulation based on SWAT model …………………………………..
4.1. Introduction………………………………………………………………...
4.2. Materials and Methods……………………………………………………..
4.2.1. Dataset…………………………………………………………………
4.2.2. SWAT model and runoff simulation part……………………………...
4.2.3. Overview of the SWAT-CUP software ………………………………..
4.2.4. Error analysis………………………………………………………….
4.3. Results and Discussions……………………………………………………
4.3.1. Sensitivity analysis and calibration of runoff parameters……………..
4.3.2. Simulation and analysis of the runoff process before and after theearthquake…………………………………………………………………….
4.4. Conclusions………………………………………………………………...
4.5. References………………………………………………………………….
CHAPTER 5
Sediment transport simulation based on SWAT model……………………..
5.1. Introduction………………………………………………………………...
5.2. Materials and Methods……………………………………………………..
5.2.1. Dataset…………………………………………………………………
5.2.2. Sediment simulation part of SWAT model…………………………….
5.3. Results and Discussions……………………………………………………
5.3.1. Sensitivity analysis and calibration of sediment parameters………….
5.3.2. Simulation of sediment transport process before the earthquake……...
5.3.3. Simulation of sediment transport process after the earthquake………..

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5.3.4. Comparison and analysis of sediment transport processes before andafter the earthquake…………………………………………………………..
5.4. Conclusions………………………………………………………………...
5.5. References………………………………………………………………….

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CHAPTER 6
Conclusions and Outlook……………………………………………………...
6.1. Introduction………………………………………………………………...
6.2. Summarize of the study findings…………………………………………..
6.3. Deficiencies ………………………………………………………………..
6.4. Research outlook …………………………………………………………..

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Appendix……………………………………………………………………….
Published works………………………………………………………………...

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List of Figures
List
Figure 1.1 World earthquake distribution map…………………………………...
Figure 1.2 Number of earthquakes per annum with a magnitude of 5.0 andhigher recorded since 1964……………………………………………………....
Figure 1.3 Distribution map of earthquakes in Japan from 1960 to 2011………
Figure 1.4 2018 Hokkaido Eastern Iburi earthquake hazard map………………

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Figure 1.5 A satellite based record of global TC SV and BG cover from 1982 to
2016………………………………………………………………………….…...
Figure 1.6 Land use/cover change map in China from 1985 to 2019…………....
Figure 1.7 2018 Hokkaido Eastern Iburi earthquake map…………………….....
Figure 1.8 SWAT model diagram………………………………………………...
Figure 1.9 Overview of the swat model input/output parameters………………..
Figure 1.10 Sketch map of an HRU in SWAT with a terraced area……………...

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Figure 1.11 Research technology roadmap……………………………………....

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Figure 1.12 Research outline………………………………………………….....

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Figure 2.1 Atsuma River basin map……………………………………………...

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Figure 2.2 Seismic intensity distribution of the 2018 Hokkaido Eastern Iburi
earthquake………………………………………………………………………..
Figure 2.3 Map of the landslide area of the 2018 Hokkaido Eastern Iburi
earthquake………………………………………………………………………

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Figure 2.4 Map of the Apporo and Atsuma dams………………………………...

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Figure 2.5 Sand control map in the Atsuma River basin………………………....

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Figure 3.1 The relationship between land use and land cover…………………...

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Figure 3.2 The relation figure of land use/cover factors………………………...

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Figure 3.3 Landsat 8 satellite image……………………………………………...
Figure 3.4 Landsat 8 satellite data download interface…………………………..

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Figure 3.5 ENVI software diagram……………………………………………....

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Figure 3.6 Processing process diagram…………………………………………..

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Figure 3.7 Comparison of land use in the Atsuma River basin in 2015 and2020………………………………………………………………………………
Figure 3.8 Statistical map of land use types in 2015 and 2020…………………..

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Figure 3.9 Comparison of the slope collapse and sediment accumulation area
after the earthquake with the bare land and grassland in the Atsuma River53

basin………………………………………………………………………………
Figure 3.10 Restoration construction figure of the Atsuma River basin…………

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Figure 3.11 Land use/cover change from 2015 to 2020………………………….

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Figure 3.12 The variation figure of the land use cover acreage………………….

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Figure 3.13 The variation figure of the land use cover structure………………...

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Figure 4.1 Radar station map…………………………………………………….

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Figure 4.2 Soil and elevation map………………………………………………..

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Figure 4.3 Map of the water cycle in the SWAT model………………………….

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Figure 4.4 Schematic of SWAT model components……………………………...

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Figure 4.5 A conceptual illustration of the relationship between parameter
uncertainty and prediction uncertainty……………………………………………
Figure 4.6 SWAT model building diagram………………………………………..
Figure 4.7 Daily simulated runoff calculated from the 2015 land use/coverdata……………………………………………………………………………….
Figure 4.8 Monthly simulated runoff calculated from the 2015 land use/coverdata……………………………………………………………………………….
Figure 4.9 Comparison of simulated daily runoff based on the 2015 and 2020
land use/cover data……………………………………………………………..
Figure 4.10 Comparison of simulated monthly runoff based on the 2015 and2020 land use/cover data……………………………………………………….
Figure 4.11. Ratio of runoff to precipitation
Figure 5.1 Sediment transport simulation based on the 2015 land use/coverdata..………………………………………………………………………………
Figure 5.2 Sediment transport simulation based on the 2020 land use/coverdata………………………………………………………………………………

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Figure 5.3 Ratio of suspended soild to runoff.…………………………………..

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Figure 5.4 Accumulation of transported sediment, adjusted according to runoff..

92xList of Tables
List

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Table 3.1 Landsat 8 satellite data information……………………………………
Table 3.2 Land use/cover distribution in the Atsuma River basin in 2015 and2020 (km2)………………………………………………………………………..

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Table 3.3 The land use/cover change acreage and structure……………………...

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Table 4.1 Data information……………………………………………………….

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Table 4.2 Runoff parameters……………………………………………………...

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Table 4.3 Error analysis results…………………………………………………...

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Table 4.4 Upstream water budget of the Atsuma Bridge station from 2015 to2020(mm/y)……………………………………………………………………….
Figure 5.1 Sediment parameters………………………………………………….
Figure 5.2 Error analysis of the SWAT model for the sediment transport
simulation…………………………………………………………………………

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List of Abbreviations
IHDP

International Human Dimensions Programme on Global Environmental
ChangeIGBPInternational Geosphere and Biosphere Programme
OBIA
Object-Based Image Analysis
SHE
System Hydrologic Europen
ANSWERS Agriculture Nonpoint Source Pollution Model
RHESS
Regional Hydroecological Simulation System
IFAS
Integrated Flood Analysis System
SWAT
Soil and Water Assessment Tool
GIS
Geographic Information System
RS
Remote Sensing
HRU
Hydrological Response Unit
ENVI
Environment for Visualizing Image
SWAT-CUP Soil and Water Assessment Tool-Calibration and Uncertainty Procedure
GFGP
Grain for Green Project
LDCM
Landsat Data Continuity Mission
OLI
Operational Land Imager
TIRS
Thermal Infrared Sensor
NASA
National Aeronautics and Space Administration
GPS
Global Positioning System
ROI
Regions Of Interest
DEM
Digital elevation model
USDA
United States Department of Agriculture
SUFI-2
Sequential Uncertainty Fitting Program Version 2
NSE
Nash–Sutcliffe Efficiency
PBIAS
Percent BIAS
PPMCC
Pearson Product-Moment Correlation Coefficient
QGIS
Quantum Geographic Information System
USLE
Universal Soil Loss Equation
E3SM
Energy Exascale Earth System Model
MUSCLE
Modified Universal Soil Loss Equation
d4PDF
Database for Policy Decision making for Future Climate ChangexiiChapter 1
Introduction

Chapter 1
1.1 Research background and significance
Global change has caused natural disasters to occur frequently [1,2,3]. Humanbeings need to adapt and respond to disasters [4]. In recent years, earthquakes haveoccurred frequently, and earthquakes and secondary disasters have plagued humans fora long time [5,6,7]. Earthquakes are one of the most severe and unpredictable natural
disasters that often result in tremendous casualties and significant economic losses [8].
Over 11,400 massive earthquakes with an Mw (Moment magnitude scale) 6 or higher
intensity have occurred globally between 1 January 1900 and 31 December 2020 [9].
From 2017 to 2020, there were 422 Mw 6 or greater earthquakes worldwide, including
35 earthquakes of Mw 7 or greater and 2 earthquakes of Mw 8 or greater [5]. From 2000
to 2020, 721,514 people died of earthquakes worldwide, and 118,344,432 people wereaffected by earthquakes [10]. Figure 1.1 shows the earthquake distribution in the world.
Figure 1.2 shows the number of earthquakes per annum with a magnitude of 5.0 andhigher recorded since 1964.

Figure 1.1. World earthquake distribution map [11]1Chapter 1

Figure 1.2. Number of earthquakes per annum with a magnitude of 5.0 and higher
recorded since 1964 [12]The earthquake in the Sumatra-Andaman Islands in December 2004 had thehighest magnitude and the most significant number of deaths in the 21st century. The
magnitude was as high as 9.3, causing more than 283,100 deaths [13,14,15]. The Great
East Japan Earthquake, which occurred in the Pacific Ocean off the northeastern part ofJapan in March 2011, caused the highest economic loss due to an earthquake in history,
with a magnitude of 9.0 and an economic loss of more than 200 billion dollars [16]. As
the global population rapidly increases, the threats posed by earthquakes and secondary
disasters on human lives, property, and infrastructure are expected to increase [17].
Over 10,000 earthquakes occur each year in Japan, given that the Japanese archipelago
is located at the junction of the Pacific plate and the Asia–Europe plate, where
earthquakes occur frequently. Figure 1.3 shows the earthquakes distributed in Japan
from 1960 to 2011.

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Chapter 1

Figure 1.3. Distribution map of earthquakes in Japan from 1960 to 2011 [11]Landslides caused by severe earthquakes often destroy large amounts of landscapevegetation, resulting in significant changes in runoff potential and sediment transport[18]. It is demonstrated that earthquake disturbance, as a sudden natural disaster withsignificant physical disturbance, can directly or indirectly aggravate the changes of theunderlying surface of a basin, profoundly influencing the basin hydrological situation
and water cycle process [19]. The process and mechanism of gravity erosion in river
basins after an earthquake are complicated. Gravity erosion usually occurs randomly
and combines with hydraulic erosion. They significantly affect runoff, sedimentproduction, and sediment transport. Moreover, processes and mechanisms are also
hotspots in the research fields of debris flow, soil erosion, and river sediment transport[20]. Considerable geomorphic instability is often present, along with extensive erosion,
deposition, and bank line shifting of the river basin after a strong earthquake.
Furthermore, earthquakes cause massive configuration changes to the landmass while
the river's course shifts [21]. Ebel and Mirus [22] took the impact of earthquake
3

Chapter 1
disturbance on the hydrological processes into the category of "DisturbanceHydrology," focusing on how the resulting abrupt changes can alter the basic
hydrological processes. It is vital to assess how disturbances and the resulting long-term
trends affect water resources, hydrological systems' stability, and different runoff
compositions. It is significant for accurately assessing water resources at an
earthquake-affected hotspot after a strong earthquake. In surface processes, numerous
landslides could be triggered by strong mountain earthquakes, such as the Wenchuan
earthquake on 12 May 2008, triggering nearly 2.0×105 landslides with vegetation
destruction up to ~1.3×103 km2 [16]. As important landscape patches, theseearthquake-induced landslides could destroy vegetation cover, surface soil structure andchemical characteristics, and fragment rock mass structures. It directly altered theinfiltration characteristics of the underlying surface and the hydrodynamic field of theslope surface at the patch scale, dramatically influencing the runoff potential andrainstorm-driven flood hydrograph at the basin scale [16]. On 6 September 2018, an
earthquake occurred near Atsuma, Hokkaido, Japan. This was the first time for anearthquake with an intensity of 7 or more to be recorded in Hokkaido. The earthquake
and its landslides threaten the safety and stability of the Atsuma River basin. Figure 1.4
shows the disaster situation of 2018 Hokkaido Eastern Iburi earthquake. Therefore, it isof great significance to study the impact of the earthquake and its secondary disasters on
the hydrological processes in the basin.

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Chapter 1

Figure 1.4. 2018 Hokkaido Eastern Iburi earthquake hazard map [23,24]
Since the 1990s, the land use/cover change, as an important part and main cause of
global change, has attracted increasing attention from countries and internationalorganizations worldwide and has become a frontier and hot topic in global change

5Chapter 1
research (Figure 1.5). In 1995, the "International Human Dimensions Programme on
Global Environmental Change (IHDP)" and the "International Geosphere and Biosphere
Programme (IGBP)" jointly proposed a land use/cover change research plan. Analyzing
the dynamic mechanism of land use/cover change tries to determine the connection
between land use/cover, climate, and hydrological change. It provides an important
decision basis for solving regional water resources, ecology, and water environment
problems.

Figure 1.5. A satellite-based record of global TC, SV and BG cover from 1982 to 2016
(TC, mean tree canopy cover. SV, Mean short vegetation cover. BG, mean bare groundcover. a, mean annual estimates. b, mean Long-term change estimates. Both mean andchange estimates are expressed as per cent of pixel area at 0.05° × 0.05° spatial
resolution. Pixels showing a statistically significant trend (n = 35, two-sided
Mann–Kendall test, P < 0.05) in either TC, SV or BG are depicted on the change map.
Circled numbers in the colour legend denote dominant change directions: 1, TC gain
with SV loss; 2, BG gain with SV loss; 3, TC gain with BG loss; 4, BG gain with TC
6

Chapter 1
loss; 5, SV gain with BG loss; and 6, SV gain with TC loss.) [25]Land use/cover change is one of the most important drivers of hydrologicalprocesses, as it influences all available water resources and flows regimes in river basinsworldwide [26]. The impacts of land use/cover change on hydrological processes within
the watershed have been the subject of many types of research worldwide in the past
decades. It is demonstrated that land use/cover change may have immediate andlong-lasting impacts on terrestrial hydrology, altering the balance between rainfall andevapotranspiration and the resultant runoff. In the short-term, destructive land-use
change may disrupt the hydrological cycle either by increasing the water yield or
diminishing or eliminating the low flow in some circumstances. In the long term, thereductions in evapotranspiration and water recycling arising from land-use changes may
initiate a feedback mechanism that reduces rainfall [27]. At present, research on thehydrological effects of land use/cover change mainly focuses on runoff, such as annual
runoff, runoff in the dry season, and flood process change. Land use/cover change
affects the river flow and the other components of the hydrological processes, such as
sediment transport [28]. Land use/cover change is the major causal factor driving soil
loss in basins. Accelerated land-use alteration due to intensive agronomic practices,
population pressure, poverty, and the absence of comprehensive land-use policies andland tenure systems are intensifying soil erosion rates. The soil loss is also activated byan amalgamation of topographic factors such as slope length and steepness and climatechange, along with land cover patterns and the intrinsic properties of the soil. Many
studies also show that land use/land cover change results in soil erosion andsedimentation processes that disrupt the hydrological balance of watersheds. For
example, degradation of the Chaleleka wetland in the Central Rift Valley region of
Ethiopia has resulted in the rise of the level of Hawassa lake caused by increasing
sedimentation of the lake bed as a result of erosion in the surrounding watersheds. On
the other hand, research findings suggested that only 10% of total soil erosion isdelivered to an outlet that reaches a natural water body. This exported soil offers thedownstream influences of sediment deposition affecting the ecosystem, siltation
reservoirs, and hydroelectric dams [29]. Land use/land cover change is primarily
7

Chapter 1
attributed to human activities [30] and natural disasters [31]. In terms of human activity,
urbanization is one of the key aspects leading to great land use and land cover change.
With experienced accelerated urbanization at a striking pace in China, the urban
build-up areas rose from 20791.30 km2 in 1997 to 56225.38 km2 in 2017. Urbanization
is an irreversible trend. The large-scale expansion of urban construction land caused thelarge-scale encroachment of cultivated land, forest land, and massive loss of ecologicalland [32]. Figure 1.6 shows Land use/cover change in China from 1985 to 2019. In
some areas, land use/cover changes are constantly changing, resulting in fragmented
ecosystems. For example, Côte d'Ivoire's forest cover declined from about 7.9 million
hectares in 1990 to 3.4 million hectares in 2015. This rate of deforestation is one of thehighest rates in the world. The increase in population density and other reasons cause an
unprecedented loss of biodiversity and ecosystem services, land degradation, and pest
and disease outbreaks in this area [33]. In terms of natural disasters, earthquakes,
landslides, floods, mudslides, and so on have a significant impact on land use/cover.
Assessing the impacts of land use/cover change in the hydrological domain is criticalfor watershed management and ecological restoration. In recent years, hydrologists havebeen interested in quantifying the impact of land use/cover change on hydrodynamics inwatersheds. However, most research focuses on land use/cover change due to rapid
urbanization, deforestation, and agriculture. Few studies have focused on the impact ofearthquakes and other natural disasters on land use/cover. It is worth noting that, unlike
human activities, natural disasters such as earthquakes tend to change land use/cover ina short time, and the degree of change is often huge. Therefore, it is of greatsignificance to study the impact of earthquakes and their secondary disasters on land
use/cover change.

8Chapter 1

Figure 1.6. Land use/cover change map in China from 1985 to 2019 [34]
6 September 2018, at 3:7:59.3, an earthquake with a magnitude of Mj (Japanese
Meteorological Agency Magnitude Scale) 6.7 occurred in the eastern Iburi, Hokkaido.
The epicenter was at 42.7°N, 142°E, with a depth of 37 km. It was the first time anearthquake of this magnitude had been recorded in Hokkaido, Japan. The Japan
Meteorological Agency noted that the quake was a backlash-type earthquake, in which
tremors were felt significantly throughout Hokkaido and most of the Tohoku region.
The earthquake killed at least 41 people in the quake zone, injured 692, and caused thecomplete collapse of 462 residential buildings and the partial collapse of 1,570. This
earthquake caused various types of geo-disasters. The earthquake caused a large numberof shallow landslides and several large-scale deep landslides, slope failures occurred
widely in mountainous areas composed of pyroclastic fall deposits in Atsuma and Abira
9

Chapter 1(Hayakita district) near the epicenter, and liquefaction was observed in some residential
areas in Sapporo, 50 km from the epicenter. In addition, the earthquake resulted insignificant damage to residential areas, farmlands, infrastructure (road, port, river, etc.),
and the lifeline (electricity, water networks) over a wide area in Hokkaido. In particular,
many residential buildings in Kiyota-Ward, a suburb of Sapporo, unevenly subsided andwere tilted by the large-scale ground flow failure induced by the liquefaction of a
volcanic soil embankment with significant damage incurred to these buildings. Also,
electric power was lost in Hokkaido following the earthquake partly because of the
shutdown of the Tomato-Atsuma Thermal Electric Power Plant in Tomakomai, locatednear the epicenter. This led to the loss of electricity to 2.95 million households all over
Hokkaido for several days. This widespread blackout had a serious effect on all of theindustries in Hokkaido, including transportation and logistics, agriculture, forestry,
fisheries, manufacturing industry, commerce, and tourism [35]. After the 2018
Hokkaido Eastern Ibur earthquake, scholars have conducted many studies from various
fields and multiple aspects, which are in-depth and comprehensive. However, few
researchers quantified the land use/cover change caused by earthquakes and humanactivities and studied its effects on runoff and sediment transport processes in the
Atsuam river basin. Therefore, it is essential to quantify and study the land use/coverchange in the Atsuam river basin caused by the 2018 Hokkaido Eastern Ibur earthquake
and its landslide and the impact of land use/cover change on runoff and sedimenttransport processes.
1.2. Research status and development trend
1.2.1. Research progress of the 2018 Hokkaido Eastern Iburi earthquake
After the 2018 Hokkaido Eastern Iburi earthquake (Figure 1.7), the riverenvironment, biological environment, ecosystem, and even the ocean fisheryenvironment in the Atsuma River basin and its surrounding areas have been
immeasurably affected. The impact of earthquakes on human society and the naturalenvironment is multifaceted.

10Chapter 1

Figure 1.7. 2018 Hokkaido Eastern Iburi earthquake map [11]After the 2018 Hokkaido Eastern Iburi earthquake, scholars have conducted manystudies. Susukida et al. [36] investigated the tectonic stress field in and around theaftershock area of the earthquake. They found that the reverse fault-type stress field was
dominant in the aftershock area. Naoyuki Yoshihara et al. [37] studied the effect ofearthquake-induced shallow landslides on river water chemistry. The results showedthat the changes in river water quality driven by shallow landslides could be linearly
approximated as the proportion of landslide area at the basin scale. Zhou et al. [38]analyzed the mechanism of regional landslides and the stability and permanent
displacement of slopes based on the effects of continuous heavy rainfall and seismic
motion. Cheng et al. [39] proposed a near-real-time prediction method forearthquake-induced landslides based on recorded ground motions and the Newmark
sliding block method. They applied it to the 2018 Hokkaido Eastern Iburi earthquake.
Shibata et al. [40] reported changes in groundwater levels based on the groundwater
11

Chapter 1
level responses to the M2 tidal constituent before and after the earthquake. Kubo et al.
[41] investigated the source rupture process of an earthquake in Japan and reproduced
the overall ground motion characteristics of the sedimentary layers of the Ishikari
Lowland. Li et al. [42] investigated the controlling role of the Ta-d pumice, whichsignificantly influences the coseismal landslides triggered by an earthquake. Ohtani et al.
[43] assessed the seismic potential of earthquakes in the future. Results showed
increasing seismic risk. Gou et al. [44] investigated the seismic structures and complex
seismic azimuthal anisotropy in the source area of an earthquake. Fujiwara et al. [45]identified and analyzed the surface displacements associated with earthquakes.
Nakamura et al. [46] investigated the S-wave attenuation (Qs) structure in and around
Hokkaido, Japan, considering the source area of the earthquake and its aftershocks.
Fukuda et al. [47] analyzed the sleep pattern data of junior high school students on thenight of a blackout after an earthquake, comparing their sleep to a normal night. Omid
Ghorbanzadeh et al. [48] investigated the application of ResU-net and object-based
image analysis (OBIA) for landslide detection from multi-temporal sentinel-2 images,
the landslide detection of the proposed integration of the ResU-Net with a rule-basedclassification of OBIA is compared with that of the ResU-Net alone. This approach
improves the mean intersection-over-union of the resulting map from the ResU-Net bymore than 22%. They provided a new approach to automatic landslide inspection.
1.2.2. The impact of land use/cover change on runoff and sediment transport
processes
Land use/cover change is one of the prominent signs and main drivers of regionaland global change. Land use/cover change has profound and significant impacts onnatural environment systems, including hydrological and ecological processes. Landuse/cover change changes factors such as surface roughness, vegetation interception,soil moisture infiltration capacity, and surface evaporation, affecting the watershed's
Hydrological regime and runoff mechanism. At the same time, land use/cover change
has changed the original surface vegetation types and their coverage andmicro-topography, thus affecting the dynamics and anti-erosion resistance system of soil
erosion in the basin, becoming the inducing and intensifying factors of soil erosion in
the basin. The reduction in vegetation cover has a magnifying effect on soil erosion.
12

Chapter 1
The different land use/cover types changes have different impacts on the runoff andsediment in the watershed. Regarding forests, Hibbert et al. [49] believed that thedecrease of the forest, the increase of runoff, and the increase of forest, the decrease of
runoff. Bosch and Hewlett affirmed Hibbert's conclusion. Based on recognizing the
opposite trend of change in forest land and runoff, they supplemented the effect ofchanges in the composition of different tree species on runoff. Moreover, it further
pointed out that when the change of forest land was less than 20%, it is difficult toobserve changes in watershed runoff. The forest canopy, understory plant, dry branches,
fallen leaves, and root layers all reduce soil erosion and sand production. It is mainlyreflected that the forest canopy layer can reduce the energy source of erosion and sandproduction, the understory plant layer can buffer the energy of precipitation anddissipate the erosive force of runoff, the fallen leaves layer can carry out the second
interception of precipitation and the absorption of soil water, as well as increase theroughness of the ground. Thereby reducing runoff velocity and ultimately soil loss, andthe root layer can act as sand fixation. Regarding farmland, Alice et al. [50] believedthat the impact of farmland on runoff is generally reflected in the impact on base flow.
Charlier et al. [51] found that large overland flow in cultivated land can reduce theresupply of base flow, thereby reducing base flow and correspondingly increasing
surface runoff. Overcultivated reclamation and unreasonable production activities canlead to soil erosion, thereby changing sediment transport's hydrological process andcapacity [52]. Regarding building land, the current research on the hydrological effects
of urbanization mainly focuses on the impact of changes in the impervious surface onhydrological elements [53]. Some scholars believe that urbanization reduces base flowby reducing infiltration [54], while others believe that the impact of urbanization onbase flow is uncertain. There are seasonal differences, or urbanization in certain regions
has no significant impact on base flow [55]. It is generally believed that building land
will weaken the basin's water storage capacity and infiltration capacity, shorten the timeof surface confluence, increase the peak flow of floods, and increase the risk of flooding.
Aronica and Cannarozzo [56] also pointed out that building land has an amplifying
effect on floods and revealed that the influence of building land on floods is affected by
the spatial distribution of rainfall, the type of rainstorm, the soil moisture environment,
13

Chapter 1
and other factors.
1.2.3. Research progress of the SWAT model
Since ancient times, human beings have never stopped exploring the water cycle
and its evolution rule [57]. In the 19th century, the emergence of Darcy's law and othertheories provided a theoretical basis for the quantitative study of cyclic processes [58].
After the 1930s, Horton put forward the surface theory [59], McCarthy put forward theMuskingum Method [60], Nash and others put forward a series of flood calculation
prediction theories [61], all of these provided a reliable theoretical basis for thegeneration and development of hydrological models. A watershed hydrological model
should be established first to understand the impact of land use/cover change caused byearthquakes and their landslides on watershed runoff and sediment transport processes.
The hydrological model is an essential tool for humans to study the changing processesof water resources. In essence, the hydrological model is a corresponding logicalrelationship established directly between the mathematical expression and thehydrological processes and is the mathematicalization of the hydrological process. Since
the 1950s, the development of hydrological models has gone through empirical models,
lumped hydrological models, and distributed hydrological models. Hydrological models
have become powerful tools for watershed research and management with thedevelopment of computer science and human understanding of various natural processes
in watersheds. The physically-based, spatially distributed hydrological models canaccount for the spatial variability of hydrological processes and help to simulate statevariables and internal fluxes, which can better reflect various hydrological processes in
the watershed and become an important tool in hydrological simulation research in river
basins. At present, it is the most effective way for humans to study the changing
processes of water resources by combining various data such as elevation, soil
utilization, soil, and meteorology to construct a watershed distributed hydrologicalmodel. At present, the representative distributed hydrological models in the world
include: the SHE (System Hydrologic Europen) model, ANSWERS (Areal Nonpoint
Source Watershed Environmental Response Simulation) model, AGNPS (Agriculture
Nonpoint Source Pollution Model) model, RHESS (Regional Hydroecological
Simulation System) model, FLATWOODS model, IFAS (Integrated Flood Analysis
14

Chapter 1
System) model, RUNOFF model, LISEM model, ACRU model, SWAT (Soil and Water
Assessment Tool) model and so on. Among them, the SWAT model, as one of the mostrepresentative models, is the most widely used. At the end of the last century, with therapid development of computer technology, geographic information systems (GIS),
remote sensing (RS), and other high technologies, the development of distributedhydrological models have a solid, real-time technical platform and the Soil and Water
Assessment Tool (SWAT) model comes into being. The SWAT model has been validated
in regions of different spatial scales in the world and is used to solve water resourcesmanagement, rainfall-runoff analysis, watershed sediment transport process research,and other research fields. Figure 1.8 shows the diagram of SWAT model.

Figure 1.8. SWAT model diagram [62]The SWAT model is a distributed hydrological model developed by the Agricultural
Service Center of the United States Department of Agriculture in the 1990s for large andmedium watersheds, long-term scales, and based on a physical basis. SWAT model
simulates surface and groundwater quality and quantity and predicts the impact of land
management practices on hydrology, sediment, and agrochemical yields in large-scale,complex watersheds with different soil types, land uses, and management conditions
15

Chapter 1(Figure 1.9). As a rapidly developing and influential distributed hydrological model, theSWAT model has been applied to many regional or national agricultural production
process simulation and environmental impact assessment projects in the United States.
It is also widely used in the European Union, Japan, Australia, Canada, India, China,
and other countries. From the perspective of model structure, the main functional
modules of SWAT are the meteorological module, land surface hydrological cycle
module, management module, and water body process module. It divides the basin into
sub-basins according to the river network. It then divides sub-basins into differenthydrological response units (HRU) (Figure 1.10) according to soil and land use types.
Each HRU calculates the runoff independently and then calculates the river confluence,and finally, can obtain the total outlet runoff. The model can use GIS and RS technology
to simulate the rainfall-runoff process in watersheds under different soil types, land use,
and management and can be applied in areas lacking observation sites and information
data.

Figure 1.9. Overview of the swat model input/output parameters [63]16Chapter 1

Figure 1.10. Sketch map of an HRU in SWAT with a terraced area [64]Previously, the SWAT model has been used to calculate rainfall-runoff andsnowmelt-runoff and in research on sediment, nutrients, pollution, and microbial
transport processes, as well as regional water resource management and other research
fields. About the use of the SWAT model to study the impact of land use/cover change
on watershed hydrological processes. Babur et al. [65] investigated the effects ofclimate and land use/cover change on sediment yield at the Mangla Dam. Perazzoli et al.
[66] analyzed the effects of changes in streamflow and sediment yield under different
land use/cover scenarios in the Concordia River basin. Sadeghi et al. [67] investigated
the runoff response to climate variables and land use/cover change in the Tajan Riverbasin. They found that land use/cover significantly impacted runoff more than climatic
variables. Anand et al. [68] assessed the hydrological regimes of the Ganga River basinthrough land use/cover change. These studies have yielded promising results,
demonstrating the suitability of the SWAT model to evaluate the impact of land
use/cover change on watershed hydrological processes.

17Chapter 1
1.3. Current problems
The impact of the 2018 Hokkaido Eastern Iburi earthquake on the environment andthe impact of land use/cover change on the hydrological processes of the basin are
essential research contents. The main problems in the current research on these two
aspects are:
Many scholars have researched the 2018 Hokkaido Eastern Iburi earthquake.
However, most of the research focuses on the mechanism, characteristics, andprediction of earthquakes and landslides, the application of new technologies andmethods to the study of earthquakes and landslides, and the impact of earthquakes onhuman activities and the social economy. Few studies have paid attention to the impactof earthquakes on the Atsuma River basin, especially those that have not quantified theland use/cover change in the basin caused by earthquakes and landslides and analyzed
the impact of land use/cover change on the hydrological processes of the basin. Thechanges in the hydrological processes of the basin will have a significant impact on theecological environment, the utilization of water resources, the safety of the reservoir, thedevelopment of agriculture, and the marine fishery of the basin.
Previous studies on land use/cover change mainly focused on the impact of humanactivities, especially urbanization, returning farmland to forests, and land cultivation onland use/cover, and less focused on the impact of natural disasters, including
earthquakes and landslides. It should also be noted that land use/cover change caused bynatural disasters and human activities are often different. Natural disasters change land
use/cover, often rapid and dramatic. Furthermore, after a disaster, there will often be
self-healing repair activities of human and natural systems. The process is more
complicated.
In previous studies on the impact of land use/cover change on the hydrologicalprocesses of the basin, due to the temporal and spatial differences in the geographicalenvironment of the research basin, differences in research methods, and differences inhuman activities after land use/cover changes, existing research conclusions vary.
Therefore, a detailed assessment is still urgently needed in the specific area (Atsuma
River basin).

18

Chapter 1
1.4. Research content and technical route
1.4.1. Research content
In response to the need for research on the impact of the 2018 Hokkaido Eastern
Iburi earthquake on the Atsuma River basin, this study took the Atsuma River basin as
the research object, made land use maps before and after the earthquake, and quantified
and analyzed the land use/cover change before and after the earthquake. Based on
various data on the watershed, a distributed SWAT hydrological model of the watershed
was constructed to simulate the runoff and sediment transport processes in the Atsuma
River basin before and after the earthquake and to analyze the impact of the land
use/cover change on runoff and sediment transport processes after the earthquake. This
study mainly includes the following three aspects:
Land use/cover change in the watershed.
Using Environment for Visualizing Images (ENVI) software, based on the Landsat

8 satellite data in 2015 and 2020, we interpreted and obtained the land use maps of theAtsuma River basin before and after the earthquake. By Comparing and analyzing theland use map before and after the earthquake in the Atsuma River basin and the area ofthe slope collapse and sediment accumulation in the Atsuma River basin after theearthquake, we quantified the land use/cover change. We carried out the driving force
analysis of land use/cover change.
Distributed hydrological simulation based on SWAT model.
Based on the 2015 and 2020 land use data, elevation data, soil data, andmeteorological data of the Atsuma River basin, we built the SWAT distributedhydrological model of the Atsuma River basin. We used Soil and Water Assessment
Tool-Calibration and Uncertainty Procedure (SWAT-CUP) software to performsensitivity analysis of runoff and sediment parameters to achieve calibration andvalidation of model parameters, thus achieving high accuracy simulation ofdaily/monthly runoff and monthly sediment transport in the Atsuma River basin from2015 to 2020.


Identify changes in runoff and sediment.
By analyzing the runoff simulation results before and after the earthquake

combined with the basin's water budget, we can judge the change in the runoff before
19Chapter 1
and after the earthquake. By analyzing the simulation results of sediment transport in
the basin before and after the earthquake, we can quantitatively judge the variation of
sediment transport in the basin before and after the earthquake by taking the sedimenttransport volume per unit runoff as the standard.
1.4.2. Research technology roadmap
The overall technical route of this study is as follows: First, review relevantmaterials and papers to understand the basic situation of the Atsushi River Basin, thedisaster situation caused by the 2018 Hokkaido Eastern Iburi earthquake, and the impactof land use/cover change on the hydrological processes of the watershed. Second, learn
the use of the SWAT model, conduct field research in the Atsuma River basin, andcollect and organize relevant primary data (elevation, soil, land use, meteorology, runoff,
sediment transport, etc.). Thirdly, make the land use maps before and after theearthquake in the Atsuma River basin, and build up the SWAT model of the basin. Then,
carry out the calibration of the SWAT model to simulate the daily/monthly runoff
process and the monthly sediment transport process in the Atsuma River basin from2015 to 2020 with high precision. Finally, quantify and analyze the land use/coverchange, and analyze the changes in hydrological processes in the Atsuma River basin
before and after the earthquake. The research technical roadmap is shown in 1.11.

Figure 1.11. Research technology roadmap
20Chapter 1

1.5. Thesis outline
This study consists of six chapters. The research content, processes, and results will
be presented in detail in the following chapters. The main contents of each chapter arepresented as follows:

Chapter 1: Introduce
This chapter reviews the current research results and research status of the impactof land use/cover change on watershed runoff and sediment transport processes, the2018 Hokkaido Eastern Iburi earthquake, and distributed hydrological models in detail.
This study explains the research background and significance, research content, andtechnical route.
Chapter 2: Overview of the study area and 2018 Hokkaido Eastern Iburi
earthquake
This chapter introduces the geography, climate, social economy, and other aspectsof the study area in detail, the basic situation of the 2018 Hokkaido Eastern Iburi
earthquake and its landslides, and the human activities related to runoff and sedimenttransport in the Atsuma River basin.


Chapter 3: Land use/cover change and its driving force analysis
This chapter introduces the sources of land use/cover data used in this study, theprocessing software, and processing procedures, shows the land use/cover situation
before and after the earthquake in the Atsuma River basin, quantifies land use/coverchange, and performs land use/cover analysis of the drivers of change.
Chapter 4: Runoff simulation based on SWAT modelThis chapter introduces the database, model, and parameter calibration software forthe runoff simulation in the Atsuma River basin before and after the earthquake in thisstudy, the processes of runoff simulation, and the sensitivity analysis and calibration
process of runoff parameters. Analyze the runoff simulation results and discusses the21Chapter 1
impact of land use/cover change on the runoff process in the Atsuma River basin.


Chapter 5: Sediment transport simulation based on SWAT modelThis chapter introduces the database and model for the simulation of sedimenttransport in the Atsuma River basin before and after the earthquake, the sedimenttransport simulation process, and the sensitivity analysis and calibration of sedimentparameters. Analyzes the sediment transport simulation results and discuss the impact ofland use/cover change on the sediment transport process in the Atsuma River basin.


Chapter 6: Conclusions and outlook
This chapter summarizes the main results achieved in this study, discusses theresearch goals that have been achieved, and the study's limitations. And the suggestions
for future research.

Figure 1.12. Research outline
22

Chapter 1

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29

Chapter 1

Chapter 2
Overview of the study areaand 2018 Hokkaido Eastern
Iburi earthquake

Chapter 2
2.1. Atsuma River basin
The Atsuma River basin is located in Atsuma town, Yufutsu district, southern
Hokkaido (42°34'N to 42°53'N, 142°7'E to 141°59'E). It originates from the hilly area inthe southern part of Yubari Mountain (1,668 m), with a drainage area of about 382.9
km2, the main channel of 52.3 km, and an altitude of 0–640 meters. The Atsuma Riverbasin has a coastline climate, and the rainfall is mainly concentrated during typhoons. In
the past two decades, the average annual precipitation of the Atsuma River basin is1235.9 mm, the average wind speed is 2.5 m/s, the average solar radiation is 8 MJ/m2,
the average relative humidity is 78%, the minimum temperature is -25.3 °C, themaximum temperature is 32.3 ℃, and the annual average temperature is 6.6 ℃. The
geology of the Atsuma River basin is mainly composed of sandstone, mudstoneinterbeds, mudstone, and volcanic ash in the Tertiary layer. The unconsolidated depositsof the Quaternary are accumulated on the surface. There are two reservoirs in the
Atsuma River basin which are the Atsuma dam (started operation in 1970, watershedarea 52 km2, effective water storage capacity 9.5×106 m3) and Apporo dam (started
operation in 2017, watershed area 105.3 km2, effective water storage 4.3×107 m3). The
watershed area of the Apporo dam occupies about one-third of the entire Atsuma Riverbasin. Hence, it can be inferred that it important influences the runoff and sedimenttransport in the lower reaches of the Atsuma River basin. In the area of Atsuma-Cho, thefour strata of the early-earth layer, the Atsuma layer, the Hongo layer, and the volcanic
eruptions after the Quaternary Dilulic are overlapping distribution. Most of the Atsuma
River basin is a mountainous area covered by broad-leaved forests. There is vast
farmland in the midstream and downstream, of which about 66% are paddy fields, about
20% are paddy fields, and about 14% are pastures and tree gardens. In addition to rice
crops, there are melons, spinach, potatoes, etc.
The upstream area of the Atsuma River basin above the Atsuma dam is mainly
mountainous, where living mammals such as brown bears and antelopes and various
insects and birds. The river is in the shape of a stream, winding in the mountains, andthe woods along the river are mainly willows. The area around the Atsuma dam isdesignated as a "Natural Environment Protection Area" by the "Hokkaido Nature
Conservation Regulations". It is also designated a "Bird and Beast Protection Area," so30

Chapter 2
it has an excellent natural environment. There is a broad valley terrain in the midstream
area from the Atsuma dam to the Atsuma urban area, with secondary forests and larch inthe mountainous part. In addition to deer, scarabs, and raccoons, mammals such as
kaftans, birds, and reptiles. The river meanders relatively slowly through the fields, withmany gravel-covered bottoms. The downstream area from the Atsuma urban area to theestuary is a vast paddy field area presenting a rural landscape. The river channel isalmost straight in farmland and scattered urban areas, and the water flow is slow.
Although a riverside forest dominated by willow trees can be seen on the riverbank, thewidth of the forest belt is narrower and discontinuous than that of the upper and middle
streams. The sub-basins of the Atsushi River Basin mainly include the Shoshiushi river,
Onikeshipegawa river, Hidakahoronai river, Ukuru river, Chiketsuhe river, Tomauchi
river, and Keimai rive. The location of the Atsuma River basin is shown in Figure 2-1.

Figure 2.1. Atsuma River basin map
2.2. 2018 Hokkaido Eastern Iburi earthquake
The Hokkaido Eastern Iburi earthquake occurred on 6 September 2018. The
epicenter was at 42.7° N and 142° E, about 70 km away from Sapporo city and 790 km
31Chapter 2away from Tokyo city, with a depth of 37 km. The seismic intensity (Japan
Meteorological Agency) observed at Atsuma was 7. This was the first time for anearthquake with an intensity of 7 or more to be recorded in Hokkaido. It killed at least
41 people and injured 692 in the earthquake zone. 459 earthquakes occurred between 6
September 2018, 03:00, and 7 September 2018, 23:59. The aftershock area extended 25
km horizontally, approximately in the N–S direction, and 95% of the hypocenters were
distributed at depths from 20 to 40 km. Figure 2.2 shows the seismic intensity
distribution of the 2018 Hokkaido Eastern Iburi earthquake.

Figure 2.2. Seismic intensity distribution of the 2018 Hokkaido Eastern Iburi earthquake[1]
The 2018 Hokkaido Eastern Iburi earthquake caused several large-scale
deep-seated landslides and multiple shallow landslides in approximately 400 km2 ofhilly areas with elevations of 200–400 m (Figure 2.3). Large-scale sliding of rock slopes
comprising mudstone occurred in the Hidaka-Horonai River basin in the Horonai
district of Atsuma. As a result, its debris flowed into the river, causing blockage of theriver channel. Post the aftershock, the total accumulation area was 43.8 km2, including
33.1 km2 in the Atsuma River basin; the total accumulation area is 11.8 km2 which
includes 9.6 km2 in the Atsuma River basin, and the total volume of landslides was 30
million m3. Five main precipitation events occurred in the Atsuma River basin from
April 2018 to August 2018. The nearest precipitation event before the earthquake
32

Chapter 2
occurred on 13-18 August 2018, 18 days before the earthquake. However, the day
before the earthquake, the powerful typhoon Jebi struck Hokkaido, resulting inapproximately 10–20 mm rainfall in the epicentral area. Some studies have shown that
continuous rainfall and strong motion were the main contributors to the failure of theregional slopes [2]. The basement complex in the affected area (consisting of
sedimentary rocks) was covered with thick pyroclastic rocks. The strong seismic shocks
triggered shallow landslides, which moved along valley-type topography instead of theplanar slope topography and traveled greater distances. Some shallow landslides
occurred on relatively gentle slopes (<30°). Therefore, it can be inferred that the main
source of sediment in the Atsuma River basin after the earthquake was erosion andlandslide accumulation.

Figure 2.3. Map of the landslide area of the 2018 Hokkaido Eastern Iburi earthquake [3]33Chapter 2

The impact of the earthquake on Hokkaido society is enormous. In terms of building
facilities, the earthquake caused damage to at least 2,508 buildings in Hokkaido,
including 51 buildings in Sapporo city, 44 buildings in Atsuma town, 12 buildings inAbira town, 17 buildings in Kitahiroshima city, and 6 buildings in Mukawa town. There
were 266 semi-damaged houses, 1,846 partially damaged houses, and 270
non-residential buildings. Soil liquefaction occurred in Kiyoda district and Kita districtin Sapporo city and Tomakomai port, resulting in damage to houses and roads and thesinking of the sea wall in the port area. After the earthquake, a total of 349 hospitals in
Hokkaido were interrupted by the power supply, water supply in 20 hospitals wasinterrupted, medical gas pipeline systems in 2 hospitals were damaged, 60 elderly careinstitutions were damaged, 42 rehabilitation support facilities for the disabled wereaffected, and 1,800 schools of all levels and types temporarily suspend classes,
including 30 public schools and 2 national universities have their school buildings
damaged to varying degrees. In terms of transportation, the earthquake caused thecancellation of 198 flights across Hokkaido from 7 September to 8, and 6 flights
between Hakodate and New Chitose, Narita International, and Chubu International
airport were suspended. The power supply at Obihiro airport, Sapporo Qiuju airport,
and Asahikawa airport were all interrupted after the earthquake, and Memanbetsu
airport announced its closure due to insufficient power. The earthquake also caused
damage and closure of many roads and large-scale suspension of railway transportation.
The postal collection and distribution business in Hokkaido was seriously affected. In
terms of energy communication, the earthquake caused damage to the equipment ofmany power stations in Hokkaido. It caused them to become inoperable, resulting inwidespread power supply interruption. A total of 2.95 million households in Hokkaido
were powered off. The power outage also caused the broadcast to stop, the tap water insome areas has become turbid, and it is impossible to drink normally. Businesses suchas telephone answering in many places have been affected. In terms of agriculture,
forestry, and fishery, Hokkaido was seriously affected by power outages, resulting insignificant economic losses. The power supply of 22 dairy factories in the wholeprovince, including Meiji Dairy and Morinaga Dairy, was interrupted, and they could
34

Chapter 2
not process raw milk. Many refrigerated dairy products showed signs of corruption andneeded to be destroyed, resulting in substantial economic losses. After the earthquake,
due to the shutdown of power outages, cold storage, logistics interruption, damage to
agricultural facilities, breakwaters, and banks of fishing ports caused by earthquake
landslides, Hokkaido's agricultural losses totaled approximately 32.2 billion yen, andthe total forestry losses reached about 33.8 billion yen. The economic loss of the fisheryindustry is nearly 920 million yen. In terms of industrial production, in the special steel
workshop of Nippon Steel and Sumitomo Metal Muroran Steel Works, the surrounding
equipment caught fire due to the heat radiation of steel. Toyota Motor Hokkaido's
factory that produces auto parts such as transmissions, SUMCO's Chitose fab, and someoil refineries, as well as transshipment oil depots, factories such as Sapporo Beer andMaruha, and large-scale convenience store companies that produce bento meals and riceballs, production has been suspended.
After the earthquake, the rescue response of Japanese governments, institutions, andorganizations was swift. As of 10:00 on 8 September, the Japanese government had
opened 447 shelters in Hokkaido, accommodating 11,900 refugees. As of 24 September,
the Japanese government has delivered 30,500 drinking water, 51,800 other beverages,
179,000 food, 18,000 batteries, 1,400 cardboard beds, 2,000 portable toilets, and 29,500
daily necessities to the disaster-stricken areas in Hokkaido. As of 26 September, theSelf-Defense Forces had supplied a total of 1,161.9 tons of water to the disaster-stricken
areas. The Air Force of the Self-Defense Forces had delivered 45,000 food and 30,000
beverages to the disaster-stricken areas in Hokkaido. In addition, the Ministry of Land,
Infrastructure, Transport, and Tourism dispatched 593 members of the Emergency
Disaster Response Team to investigate the disaster situation in each local government in
the earthquake area. A total of 3,580 police officers from various police disasterdispatch teams across Japan entered the disaster-stricken area of Hokkaido. 2,119
firefighters from 544 National Fire Service Teams for disaster response and 339 staff
from 46 Air Rescue Teams were dispatched to participate in rescue activities acrossJapan. 4,000 (20,000 will be dispatched later) self-defense team members went to
Hokkaido to participate in the rescue.

35

Chapter 2
2.3. Human activities
As of 2017, Atsuma town's area is 404.56 km², with a population of about 4,659 anda density of 12 people per square kilometer. In Atsuma town, there are vast forests in thenorthern and central, farmland is mainly located in the southern and western, and thearea of farmland is relatively fixed. The agriculture in Atsuma town is mainly ricecultivation and field crops such as wheat and soybeans, vegetables and flowers such asbroccoli and carnations. Raising beef cattle, dairy cows, pigs, and broilers dominate
animal husbandry. Coastal fisheries dominate the aquaculture industry.
The Atsuma River basin area includes the Apporo and Atsuma dams (Figure 2.4).
The body of the Atsuma dam was damaged during the Hokkaido Eastern Iburi
earthquake. The drainage channel was blocked due to dirt, sand, trees, and other debris,making the dam unusable. However, debris removal and restoration projects have begun,and the Atsuma dam should be restored for use in 2023. While the body of the Apporo
dam was not damaged, large-scale landslides occurred in the mountains and streams
around the dam. As a result, large amounts of sediment and driftwood accumulated in
the dam and the surrounding slopes, which severely impacted the water storage functionof the dam. This function of the dam will be restored by removing the accumulated
sediment and driftwood, cleaning it up, and reinforcing the unstable slopes. Furthermore,
after the earthquake, large-scale mountain collapses occurred in the Hidaka-horonai,
Chichepe, Chicaep, and Towa rivers, which blocked the river channel. Therefore, theslope will be reinforced, and sand control dams will be built in these areas. Figure 2.5
shows the sand control map of the Atsuma River basin.

36

Chapter 2abFigure 2.4. Map of the Atsuma dam (a) and Apporo dam (b) [4,5]Figure 2.5. Sand control map in the Atsuma River basin [6]2.4. References
1. Ishikawa, T.; Yoshimi, M.; Isobe, K.; Yokohama, S. Reconnaissance report on
geotechnical damage caused by 2018 Hokkaido Eastern Iburi earthquake with JMA
37

Chapter 2

2.

3.

4.

5.

seismic intensity 7. Soils and Foundations. 2021, 61(4), 1151-1171. Doi:
10.1016/j.sandf.2021.06.006.
Zhou, H.; Che, A.; Wang, L.; Wang, L. Investigation and mechanism analysis ofdisasters under Hokkaido Eastern Iburi earthquake. Geomatics. Nat. Hazards Risk.
2021, 12(1), 1–28. Doi: 10.1080/19475705.2020.1856201.
Kasai, M.; Yamada, T. Topographic effects on frequency-size distribution oflandslides triggered by the Hokkaido Eastern Iburi Earthquake in 2018. Earth,Planets and Space. 2019, 71(1), 1-12.
厚 幌 ダ ム の ペ ー ジ - 胆 振 総 合 振 興 局 室 蘭 建 設 管 理 部
https://www.iburi.pref.hokkaido.lg.jp/kk/mkk/mur-dogen/kanriDam/apporodam.html|
厚 真 ダ ム に つ い て
厚 真 町 土 地 改 良 区
https://www.atsuma-kairyoku.jp/%E5%8E%9A%E7%9C%9F%E3%83%80%E3%8
3%A0%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6-2/

6. 厚
真
川
水
系
砂
防
事
https://www.hkd.mlit.go.jp/mr/kouhou/c5b1ee000000f4qa.html

業

所

38

Chapter 2

Chapter 3
Land use/cover change and its driving
force analysis

Chapter 3
3.1. Introduction
Land use/cover includes two parts, in which land use refers to the long-term
management activities of human beings on the land according to specific economic andsocial purposes, such as agriculture, forestry, animal husbandry, urban development, andother use of natural resources related to land by human beings. Land cover refers to thecomplex of surface elements naturally formed or covered by artificial buildings, such as
cultivated land, forest land, grassland, roads, buildings, soil, ice, snow, and water. Somescholars have also pointed out that land cover is the original material and artificial
structures cover the surface. It contains various elements that have been intervened andnot intervened, and the connotation of land use is roughly the same. Land use/coverchange plays a vital role in global environmental change and sustainable development.
The dynamic change of land use/cover directly manifests the evolution of natural, social,and ecological composite systems and is the product of the coupling effect ofhuman-earth systems. In general, land use/cover change refers to changes in land covercaused by human changes in land use and management practices. It thus refers tosimultaneous changes in land use and land cover, such as converting forests to farmland.
In addition to human activities, natural disasters and other factors are also essential
factors causing land use/cover change. The Figure 3.1 shows the relationship between
land use and land cover.

Figure 3.1. The relationship between land use and land cover [1]39Chapter 3
Many factors affect land use/cover type, mainly biophysical, economic andtechnological, demographic, and social. Scholars have conducted many land use/coverchange studies as an important research hotspot. Figure 3.2 shows the relation of land
use/cover factors.

Figure 3.2. The relation figure of land use/cover factors [1]Moma Shiferaw Regasa et al. [2] observed that the rapid land use/cover change overthe past few decades in Ethiopia, Africa, was mainly caused by population pressure,resettlement programs, climate change, and another human- and nature-induced driving
forces. Among them, anthropogenic activities are the most significant factors adverselychanging the natural status of the landscape and resources, which exerts unfavorable
and adverse impacts on the environment and livelihood. Nuissl Henning et al. [3]believed that urbanization is one of the major driving forces behind the formation of
today's land-use systems. An assessment by Karina Winkler et al. [4] found that
land-use change has affected almost a third (32%) of the global land area in just six
decades (1960-2019) and, thus, is around four times greater in extent than previously
estimated from long-term land change assessments. Qingzheng Wang et al. [5] studied
the northern region of the Qilian Mountains, which is situated in the eastern area of theHexi region of northwestern China. They found that this region's land use/cover change
40

Chapter 3
was the most severe between 1987 and 1997. With the Grain for Green Project (GFGP)
implementation in 1999, the ecological environment began to develop towards a
positive trend. However, the built-up land area has also been overgrown. The economy
was the main factor influencing the land use/cover change. Furthermore, it is concluded
that the land use/cover would change dramatically over the next 30 years (likely due tourban expansion). Xiaoyan Chang et al. [6] explored the simulation of land use/coverchange under three scenarios—namely, natural development scenario, ecologicalprotection scenario, and farmland protection scenario—were investigated in the 11
townships with severe ground subsidence located in Weishan County, China. The results
showed that under the three scenarios, the area of other agricultural land and water body
showed an increasing trend; while the area of farmland, urban and rural construction
land, subsided land with water accumulation, and tidal wetland showed a decreasing
trend, and the area of urban and rural construction land and tidal wetland decreased thefastest. This study's results will provide useful information for decision-makers andplanners to take appropriate land management measures in the mining area. Somajita
Paul et al. [7] traced the land use/cover change in peri-urban Delhi, India, over the
1973–2017 period. Finding the urban area expanded and agricultural area diminished atannual rates of 38.6% and 2.1%, respectively, during the 1973–2017 period. Urban
expansion occurred more in scrub and sparse vegetation areas than cultivated lands or
ponds. Loss of cultivated land mainly happened due to the abandonment of cropping
and tree planting in farmhouses developed by the urban elites. Improvement in the stateof forests in terms of their expansion and densification offsets their loss due tourbanization, encroachment, and logging. The increment in the green cover was due tostrict enforcement of compensatory afforestation/forest conservation law, growing
demand for ecotourism, the emergence of tree-clad farmhouses, and increased
environmental awareness and surveillance. Nabaz R. Khwarahm et al. [8] used
multitemporal satellite data for spatial modeling of land use and land cover change in
Sulaimani, Iraq. The change analysis demonstrated that between 1988 and 2017, thebuilt-up areas and agricultural fallow land increased by 419% and 226%, respectively.
In the future predictions for 2037, built-up areas and agricultural fallow land showed
increasing trends of 5.5% and 26.5%, respectively. In contrast, agricultural land,
41

Chapter 3
plantation land, and sparse vegetation areas were predicted to decrease by 29.4%, 65.8%,
and 36.9%, respectively. Zhenhua Chen et al. [9] investigated the impact of China's
high-speed rail on change in land use structure in urban China. The research findings
reveal that HSR plays a significant role in promoting China's urban land use structurechange. The influence of HSR is much more effective than that of other types oftransportation infrastructure. In particular, the impact of HSR on change in urban land
use structure was found significant in Tier 2–3 cities. PriyankJain et al. [10] investigated
the relationship between urbanization and Land-use change in the hill town of Nainital.
The results show that during the last fourteen years, there has been a significant
decrease in Open space, Vegetation, Agricultural Land, about 19% (i.e., 0.03 km2), 22%(i.e., 1.83 km2), and 18% (i.e.,0.03 km2) respectively and the built-up area of theNainital town has been raised for approx 68% (i.e., 1.89 km2). This demonstrates thecontinuous development and drastic land utilization/ land cover changes in the town's
last 14 years, resulting in urbanization, and decreasing open space, vegetation, andagricultural land. Carolina Berget et al. [11] evaluated and reconstructed the recent
history (1976–2019) of land-use change and the associated land-use types that haveemerged over time in two neighboring rural villages in Southern Mexico. The findings
indicated that several interacting historical social-ecological drivers (e.g., colonization
program, soil quality, land conflicts with indigenous people, land-tenure, availability ofsurrounding land where to expand, Guatemala's civil war, several agriculturaldevelopment and conservation programs, regional wildfire, Zapatista uprising, andhighway construction) had influenced each village's own unique land-use change
history and landscape composition.
In this study, the main cause of land use/cover change in the Atsuma River basin inrecent years was the 2018 Hokkaido Eastern Iburi earthquake and its landslide. Humanconstruction and restoration activities also had a particular impact on the land use/coverof the basin. This chapter will introduce in detail the data sources, processing software,and processing procedures for making land use maps before and after the Atsuma Riverbasin and will conduct quantification and driving force analysis of land use/coverchange in the Atsuma River basin.

42

Chapter 3
3.2. Materials and methods
3.2.1. Landsat 8 satellite data
In this study, the remote-sensing data used is Landsat 8 (Figure 3.3), downloaded
from the United States Geological Survey (Figure 3.4). Landsat 8, the eighth satellite of
the U.S. Landsat program (Landsat), was successfully launched on an Atlas-V rocket
from Vandenberg Air Force Base in California on 11 February 2013. Initially called the"Landsat Data Continuity Mission" (Landsat Data Continuity Mission, LDCM). Landsat
8 carries the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS).
These two sensors provide seasonal coverage of the global landmass at a spatial
resolution of 30 meters (visible, NIR, SWIR), 100 meters (thermal), and 15 meters(panchromatic).

Figure 3.3. Landsat 8 satellite image [12]43Chapter 3

Figure 3.4. Landsat 8 satellite data download interface [13]Landsat 8 instruments represent an evolutionary advance in technology. TIRS
includes 2 separate thermal infrared bands with a resolution of 100 m. TIRS collects
data for two more narrow spectral bands in the thermal region formerly covered by one
wide spectral band on Landsat 4–7. The 100 m TIRS data is registered to the OLI data
to create radiometrically, geometrically, and terrain-corrected 12-bit data products.
Landsat 8 requires to return 400 scenes per day to the USGS data archive (150 morethan Landsat 7 is required to capture). Landsat 8 regularly acquires 725 scenes per day(and Landsat 7 is catching 438 scenes per day). This increases the probability ofcapturing cloud-free scenes for the global landmass. The Landsat 8 scene size is
185-km-cross-track-by-180-km-along-track. The nominal spacecraft altitude is 705 km.
Cartographic accuracy of 12 m or better (including compensation for terrain effects) is
required for Landsat 8 data products. The OLI Land Imager includes 9 bands with aspatial resolution of 30 m. OLI improves on past Landsat sensors using a technicalapproach demonstrated by a sensor flown on National Aeronautics and Space
Administration (NASA) 's experimental EO-1 satellite. OLI is a push-broom sensor
with a four-mirror telescope and 12-bit quantization, which collects data for visible,
near-infrared, and short-wave infrared spectral bands and a panchromatic band. OLI
provides two new spectral bands, one for detecting cirrus clouds and the other for
44

Chapter 3
coastal zone observations. Table 3.1 shows the information of Landsat 8 satellite data.
Table 3.1. Landsat 8 satellite data information
Sensortype

Operational
Land
Imager(OLI)Spectralband

Actualwavelengths(µm)

Spatialresolution

Main application

Band 1(Coastal)

0.433-0.453

30

Mainly used for coastal
observation

Band 2(Blue)

0.450-0.515

30

For water penetration,
distinguish soil
vegetation

Band 3(Green)0.525-0.600

30Mainly used for
distinguishingvegetation

Band 4(Red)

0.630-0.680

30

In the chlorophyll
absorption area, used toobserve roads, bare soil,
vegetation types, etc.

Band 5(NIR)

0.845-0.88530

Mainly used for
estimating biomass,
distinguishing wet soils

Band 6(SWIR 1)1.560-1.660

30

Used for distinguishroads, bare soil, andwater. It can also havegood contrast between
different vegetation andhas better atmospheric,cloud, and fog
distinguishing ability.

Band 7(SWIR 2)2.100-2.300

30

Used to distinguishrocks and minerals, andcan also be used to
distinguish vegetation
cover and moist soilBand 8(Pan)0.500-0.680

1515m resolution black
and white image for45

Chapter 3
enhanced resolution

ThermalInfrared
Sensor(TIRS)

Band 9(Cirrus)1.360-1.390

30

Band 10(TIRS 1)10.60-11.19

100

Band 11(TIRS 2)11.50-12.51

100

Contains water vapor
enhanced absorption
features for clouddetection
Targets for Induced
Thermal Radiation
Targets for Induced
Thermal Radiation

Landsat remote sensing image data can be used in many research fields such as
natural resource protection, energy exploration, environmental management, and natural
disaster monitoring. Specifically can be used for classifying land use status andextracting changes, investigating underground mineral deposits, marine resources, andgroundwater resources, monitoring and assisting the management of the rational use inagriculture, forestry, animal husbandry, and water conservancy resources, forecasting
the harvest of crops, studying the growth and landforms of natural plants, investigating
and forecasting various natural severe disasters (such as earthquakes) and environmental
pollution, and mapping various thematic maps (such as geological maps,
geomorphological maps, hydrographic maps), etc. The series of Landsat satellites
provide a large amount of earth resource observation data. Providing users andresearchers in various disciplines and fields with the remote sensing image information
they need is of great value and significance to the entire earth science.
Landsat 8 has been widely used in land use/cover research. Ashish Guruji et al. [14]used Landsat 8 to understand land use/cover change in India and found a 21% increasein agricultural area from 1997 to 2018. The dense forest keeps decreasing, and thesparse forest increases steadily. Mohammad Reza Ansari et al. [15] used Landsat 8
satellite data to study land surface temperature trends relative to land-use change in dust
sources of South East Ahwaz, which works well. Latifa Bou-imajjane et al. [16] used
Landsat 8 satellite data to study soil loss assessment in the Beni Mohand basin of thewestern high atlas of Morocco. These examples illustrate the suitability of Landsat 8
satellite data for land use/cover change studies.
46

Chapter 3

3.2.2. ENVI software
In this study, we used Environment for Visualizing Images (ENVI) software (Figure3.5) to process Landsat 8 satellite data. ENVI is a remote sensing image processing
platform developed by Exelis Visual Information Solutions in the United States. The
software processing technology in the collection covers image data input/output, image
scaling, image enhancement, correction, and correction, radio correction, mosaic, data
fusion, and various transformations, information extraction, image classification,knowledge-based decision tree classification, integration with GIS, DEM, and terrain
information extraction, radar data processing, three-dimensional display analysis. ENVI
has been widely used in scientific research, environmental protection, meteorology,
petroleum and mineral exploration, agriculture, forestry, medicine, national defense andsecurity, earth science, public facility management, remote sensing engineering, water
conservancy, oceanography, surveying and mapping, and urban and regional planning.

Figure 3.5. ENVI software diagram
In terms of image processing, ENVI can read almost any type and format ofimagery and support imagery from various types of aerospace and aerospace sensors,
including panchromatic, multispectral, hyperspectral, radar, thermal infrared, terrain
data, Global Positioning System (GPS) data, laser radar, etc. ENVI provides automatic
47

Chapter 3
preprocessing tools to quickly and easily preprocess images for viewing or otheranalysis. Through ENVI, we can perform geometric correction/orthorectification ofimages, image (automatic) registration, image calibration, atmospheric correction,
creation of vector overlays, determination of regions of interest (ROI), creation ofdigital elevation model (DEM), image fusion, masking, and tessellation, resizing,rotation, or data type conversion, etc. In terms of analyzing images, ENVI includes acomprehensive set of data analysis tools that can analyze images quickly, efficiently,
and accurately. The functions include creating image statistics such as autocorrelation
coefficient and covariance, calculating image statistics such as mean, minimum/Maximum value, standard deviation, extracting linear features, synthesizing radar
images, principal component calculation, change detection, spatial feature measurement,terrain modeling, and feature extraction, applying generic or custom absorbers,
performing custom bands and spectra mathematical functions, etc. In terms of spectral
analysis, ENVI has the most advanced and easy-to-use spectral analysis tools, which
can efficiently perform scientific image analysis. The functions include supervised andunsupervised methods for image classification, use of powerful spectral libraries to
identify spectral features, detecting and identifying objects, identifying features ofinterest, analyzing and mapping substances of interest, performing pixel- and sub-pixel
analysis, refining classification results using post-classification tools, calculation offorest health using vegetation analysis tools, and more.
3.2.3. Processing of land use/cover data
The downloaded Landsat 8 data need to be processed to obtain land use data that
the SWAT model can use. We used ENVI software to process Landsat 8 data. The
specific process is as follows: (1) Cutting. Using the pre-made watershed boundary of
the Atsuma river (this watershed boundary is also used to cut the elevation data and soil
data used in this study) to cut the multispectral data set (including seven bands) in thedownloaded Landsat 8 data package. (2) Radiation calibration. Select the cut
multispectral data for radiometric calibration. Radiometric calibration converts animage's quantized digital value into a physical quantity such as radiance value,
reflectance, or surface temperature. (3) Atmospheric correction. The solar radiation isincident on the object's surface through the atmosphere in a certain way and then48

Chapter 3
reflected back to the sensor. Due to the images of atmospheric aerosols, terrain, andnearby objects, the original image contains information about the object's surface, theatmosphere, and the sun. To understand the spectral properties of an object's surface, wemust separate its reflectance information from atmospheric and solar data, which
requires atmospheric correction. Select the Fast Atmospheric Correction function in
ENVI software to perform atmospheric correction on radiometrically calibrated
multispectral data. (4) Band fusion. Select three different bands from the seven bands,
and the bands were fused according to different combination sequences. After many
attempts, the result was finally able to clearly highlight the ground objects' informationthat we wanted to see. (5) Select training samples for supervised classification. The
land-use types in this study are divided into six classes, including farmland, forest land,grassland, water, bare land, and building land. For each land-use type, corresponding
training samples need to be selected. To improve the accuracy of the final land use data
produced, we need to select multiple sets of training samples for each land-use type. For
example, the image characteristics of forest land in different watershed areas aredifferent. In this case, it is necessary to select the forest land samples of each image
feature in the training samples. After the selection is completed, it is needed to verify
the separability of each type of sample to judge the quality of the training samples. (6)Supervised classification. Using ENVI's supervised classification function, supervisedclassification was performed with selected training samples of six land-use types. Aftercompletion, the same land-use types are merged, and finally, the watershed land-use
data, including six land-use types, are obtained. (7) Post-classification processing.
Including the steps of merging the maximum and minimum values, removing small
patches, etc., modifying the small details of the land use data, and improving the qualityof the land use data. The quality of the produced land use/cover data directly affects theaccuracy of quantifying land use/cover change before and after earthquakes and directly
affects the accuracy of simulating watershed runoff and sediment transport processes
using the SWAT model. Therefore, after the production is completed, it needs to becompared with images such as Google Maps, and small-scale editing and modificationof the deviated areas are required to further improve the quality of the land use/cover
data in the basin before and after the earthquake. Figure 3.6 shows the processing
49

Chapter 3
process diagram.

Figure 3.6. Processing process diagram
3.3. Results and discussion
Land use/cover change mainly refers to changes in the type, quantity, and space ofland use/cover. In this study, we quantified and analyzed the changes in the amount andstructure of land use/cover in the Atsuma River basin between 2015 and 2020 andstudied the driving forces of the changes.
3.3.1. Land use/cover change before and after the earthquake
According to the land use map of the Atsuma River basin in 2015 and 2020, we canobtain the area and proportion of various land use/cover types and the change process ofvarious land use/cover types, as shown in Table 3.2 and shown in Figure 3.7 and Figure
3.8.

50

Chapter 3

Figure 3.7. Comparison of land use in the Atsuma River basin in 2015 and 2020
Table 3.2. Land use/cover distribution in the Atsuma River basin in 2015 and 2020
(km2)
2015

2020

1

Farmland

66.87

70.31

2

Forest242.39194.49

3

Grass

57.94

81.68

4

Water

0.92

1.96

5

Bare land

4.68

26.59

6

Building land

10.10

7.87

51

Chapter 3

Figure 3.8. Statistical map of land use types in 2015 and 2020
Comparing the land use map of the Atsuma River basin in 2015 and 2020, we canfind that from 2015 to 2020, the forest area has changed from 242.39 to 194.49 km2, adecrease of 47.899 km2. The reduction area accounted for about 20% of the forest area
before the earthquake, with a more significant reduction. The building area changed
from 10.1 to 7.87 km2, decreasing by 2.223 km2. The bare land area changed from 4.68
to 26.59 km2, increasing to 21.906 km2. After the increase, the bare land area was about
5.7 times that before the earthquake, and the increase was noticeable. The grassland areachanged from 57.94 to 81.68 km2, increasing to 23.738 km2, which was apparent. The
water area changed from 0.92 to 1.96 km2, increasing to 1.039 km2, and the farmland
area changed from 66.87 to 70.31 km2, an increase of 3.439 km2. The farmland landarea remained stable, and there was no significant change. By analyzing the land
use/cover change area before and after the earthquake, we find that most of the reduced
forest area has become bare land and grassland. The 2018 Hokkaido Eastern Iburi
earthquake and its landslides are the main reasons for converting forest area to bare land
and grassland. The completion of the Apporo dam in 2017 is why the Apporo dam areatransformed from a building land area into water. After the earthquake, the dam body of
the Atsuma dam was damaged and could not be used, which is why the area of theAtsuma dam transformed from water to bare land. Figure 3.9 compares the slope
collapse and sediment accumulation area after the earthquake with the bare land and thegrassland area in the 2020 land use/cover map of the Atsuma River basin.

52

Chapter 3

Figure 3.9. Comparison of the slope collapse and sediment accumulation area after theearthquake with the bare land and grassland in the Atsuma River basin
The Atsuma dam is expected to be restored for use in 2023, which means the
Atsuma dam area will transform from bare land into the water. Furthermore, the basin's
construction of the reinforced slopes and sand control dams of the Hidaka-horonai,
Chichepe, Chicaep, and Towa Rivers has been completed. Among these, theHidaka-horonai River area is 63.17 km2, the Chichepe River area is 43.2 km2, theChicaep River area is 12.74 km2, and the Towa River area is 5.1 km2. The sand control
dams mainly arrest river sediment, regulate sediment transportation, prevent erosion,
control the flow center, and restrain soil and rock flow. Although the construction ofsand control dams does not affect the land use/cover condition, they significantly affectthe amount of sediment transport in the basin. By reviewing documents and reports, it
can find that there is no construction plan for large-scale vegetation restoration or slope
reinforcement on new bare land currently. Therefore, in the short term, the basin's land
use/cover condition is not expected to change drastically, and the runoff in the basin will
not change significantly. The restoration function of the natural system of the watershed
53

Chapter 3
is an essential factor that deserves attention in the future land use/cover change.

Figure 3.10. Restoration construction figure of the Atsuma River basin
3.3.2. Structural change characteristics of land use/cover
54

Chapter 3
Compare the land use maps of the Atsuma River basin in 2015 and 2020, andanalyze the area and proportion of various land use/cover types. The specific situation isshown in Table 3.3 and Figure 3.11, Figure 3.12 and Figure 3.13.
From the perspective of land use/cover change rate, forest land area > grasslandarea> bare land area > water area > farmland area > building land area. Due to thesubstantial reduction of forest land and the substantial increase of bare land, processes
such as runoff and confluence of watersheds, erosion, and sediment production will alsochange accordingly. The ecological environment is threatened to a certain extent.
Table 3.3. The land use/cover change acreage and structure
From 2015 to 2020

Area (km2)

Percentage

1

Farmland

3.439

0.008981

2

Forest-47.8990.1251

3

Grass

23.738

0.062

4

Water

1.039

0.002714

5

Bare land

21.906

0.05721

6

Building land

-2.2230.005806

Figure 3.11. Land use cover change from 2015 to 2020

55

Chapter 3

Figure 3.12. The variation figure of the land use cover acreage

Figure 3.13. The variation figure of the land use cover structure
3.3.3. Driving force analysis of land use/cover change
The dynamic change of land use/cover directly manifests the evolution of natural,socio-economic, and ecological complex systems. It is also the product of the coupling
effect of the human-land system. The driving factors of its changes mainly include
natural factors and human factors. Most current research shows that the key drivers ofsignificant change in land use/cover outcomes over short periods (years or decades) are
56

Chapter 3
human factors. The Atsuma River basin in this study is a special case. In recent years,
the change in land use/cover caused by economic activities in the Atsuma River basin
has been relatively small. The farmland and urban areas have remained stable without
significant changes. The 2018 Hokkaido Eastern Iburi earthquake and its landslides
created a large amount of new bare land, which is the main reason for land use/coverchange in the Atsuma River basin. The significant impacts of human activities on land
use/cover in the Atsuma River basin are as follows: the completion of the Apporo damin 2017 is the reason why the Apporo dam area transformed from building land area towater. After the 2018 Hokkaido Eastern Iburi earthquake, the dam body of the Atsuma
dam was damaged and could not use. The Atsuma dam is expected to resume use in2023, when the Atsuma dam area will transform from bare land to water.
3.4. Conclusions
In this chapter, based on the downloaded Landsat 8 satellite data, we used ENVI
software to make land-use maps before and after the earthquake in the Atsuma Riverbasin. We quantified and analyzed the land use/cover change before and after theearthquake and analyzed the driving force of land use/cover change. The main
conclusions are as follows:

After the earthquake, in the Atsuma River basin, the building land area decreasedby 2.223 km2, with minor changes. The forest area decreased by 47.899 km2,
accounting for about 20% of the forest area before the earthquake, showing a
relatively significant reduction. The farmland area increased by 3.439 km2, thewater area increased by 1.039 km2, the bare land area increased by 21.906 km2, andthe bare land area after the earthquake was about 5.7 times that before theearthquake, a significant increase. The grassland area increased by 23.738 km2, a
noticeable increase. Through the analysis, it can be found that most of the reduced
forest area has become bare land and grassland.



The 2018 Hokkaido Ibiletsu East earthquake and its landslide created a large
amount of new bare land, which is the main reason for the land use/cover change in
the Atsuma River basin in recent years. The completion of the Apporo dam in 2017is why the Apporo dam area transformed from building land area into water. After
57

Chapter 3
the earthquake, the Atsuma dam body was damaged and could not be used, which is
why the area of the Atsuma dam transformed from water to bare land. In the future,human restoration activities will not cause land use/cover drastic changes in the
Atsuma River basin, and the restoration function of the natural system in the basin
is an essential factor that deserves attention in future land use/cover change.
3.5. References
1. Ying Di. Researches on the effects of land cover changes of typical basin in
Sanjiang plain on water resources (三江平原典型流域土地覆被变化对水资源的影响研究) [D]. Northeast Forestry University, June 2015.
2. Regasa, M. S.; Nones, M.; Adeba, D. A review on land use and land cover change in
Ethiopian basins. Land. 2021, 10, 585. Doi: 10.3390/land10060585.
3. Nuissl, H.; Siedentop, S. Urbanisation and land use change. Sustainable Land
Management in a European Context. Springer, Cham. 2021, 75-99.
4. Winkler, K.; Fuchs, R.; Rounsevell, M.; Herold, M. Global land use changes are
four times greater than previously estimated. Nature communications. 2021, 12,
1-10. Doi: 10.1038/s41467-021-22702-2.
5. Wang, Q.Z.; Guan, Q.; Lin, J.K.; Luo, H.P.; Tan, Z.; Ma, Y.R. Simulating land
use/land cover change in an arid region with the coupling models. Ecological
Indicators. 2021, 122, 107231. Doi: 10.1016/j.ecolind.2020.107231.
6. Chang, X.Y.; Zhang, F.; Cong, K.l.; Liu, X.J. Scenario simulation of land use andland cover change in mining area. Scientific Reports. 2021, 11, 1-12. Doi:
10.1038/s41598-021-92299-5.
7. Paul, S.; Saxena, K. G.; Nagendra, H.; Lele, N. Tracing land use and land coverchange in peri-urban Delhi, India, over 1973–2017 period. Environmental
Monitoring and Assessment. 2021, 193, 1-12. Doi: 10.1007/s10661-020-08841-x.
8. Khwarahm, N. R. Spatial modeling of land use and land cover change in Sulaimani,Iraq, using multitemporal satellite data. Environmental Monitoring and Assessment.
2021, 193, 1-18. Doi: 10.1007/s10661-021-08959-6.
58

Chapter 3
9. Chen, Z.; Zhou, Y.; Haynes, K. E. Change in land use structure in urban China:
Does the development of high-speed rail make a difference. Land Use Policy. 2021,
111, 104962. Doi: 10.1016/j.landusepol.2020.104962.
10. Jain, P.; Singh, I.; Jain, M. Urbanization and Land-Use change in the hill town ofNainital. Materials Today: Proceedings. 2021. Doi: 10.1016/j.matpr.2021.01.914.
11. Berget, C.; Verschoor, G.; García-Frapolli, E.; Mondragón-Vázquez, E.; Bongers, F.
Landscapes on the move: Land-use change history in a mexican agroforest frontier.
Land. 2021, 10, 1066. Doi:10.3390/land10101066.
12. NASA https://www.nasa.gov/
13. United States Geological Survey https://earthexplorer.usgs.gov/
14. Guruji, A.; Agnihotri, P. Land Use Land Cover Analysis of Khapri Watershed in
Dang District Using Remote Sensing (RS) and Geographical Information System(GIS). International Conference on Intelligent Computing & Optimization. Springer,
Cham. 2018, 448-454. Doi: 10.1007/978-3-030-00979-3_47.
15. Ansari, M.; Norouzi, A. Investigation of Land Surface Temperature Trends Relativeto Land Use Changes in Dust Sources of South East Ahwaz Using Landsat 8
Satellite Data. Iranian Journal of Soil and Water Research. 2021, 52, 1825-1840.
16. Bou-Imajjane, L.; Belfoul, M. A. Soil loss assessment in Western High Atlas of
Morocco: Beni Mohand watershed study case. Applied and Environmental Soil
Science. 2020, 2020. Doi: 10.1155/2020/6384176.

59Chapter 4
Runoff simulation based on SWAT modelChapter 4
4.1.

Introduction
Runoff is the essential link of the water cycle and the link connecting varioushydrological elements in water balance, and it is the most active in the naturalenvironment. Flood is an essential part of the research field of extreme hydrologicalevents, and it is a hot issue of current research. Scholars have researched the flood,
mainly focusing on flood deduction, flood forecasting, checking flood characteristics,
analyzing flood causes, and analyzing risks and disasters. The amount and frequencychange of watershed runoff is affected by many factors, including topography, soil types
and attributes, land use types and patterns, and environmental factors. These factors cangroup into natural environment factors and land use/cover factors. Among them, naturalenvironment factors include meteorological factors and natural geographical factors.
Meteorological factors mainly include temperature, precipitation, wind speed,
evapotranspiration, and solar radiation. The natural geographical factors mainly include
geological conditions, landform types, basin area, shape, perimeter, average elevation,
the slope of the river basin, river network density, lake density, the length of the main
channel, etc. The land use/cover factors mainly include land use type and land use
pattern.
In the impact of land use/cover change on runoff producing processes. Landuse/cover change alters surface evapotranspiration, surface vegetation interception, andsoil moisture status, which affects the watershed's water balance. Many previous studies
have pointed out that land use leads to land cover changes. The changed land cover isclosely related to hydrological processes such as near-surface evapotranspiration,interception, depression filling, and infiltration. Bronstert summarized land use/coverchange and associated hydrological cycle elements that may affect surface andnear-surface hydrological processes, of which the most significant land use/coverchange affecting hydrological processes are vegetation change (such as crop harvesting,deforestation), crop cultivation, and management practices, urban sewers, and sewage
systems, etc. The long-term effects of forest land, grassland, and farmland on surface
runoff, groundwater recharge, and river runoff are significant in land-use types [1,2].
In terms of the impact of land use/cover change on runoff amount. At present, theinternational debate is fierce, and the focus of the discussion is whether the forests
60

Chapter 4
increase or decrease the watershed runoff amount. Furthermore, among them include
the impact of forests on annual runoff, runoff in the dry season, and reduction of flood
peaks. For the annual runoff, there are currently three main viewpoints. Namely, theforests increase the annual runoff, the forests decrease the annual runoff, and the forests
do not affect the annual runoff. Scholars from the former Soviet Union and China
mainly support the forests increase in annual runoff, scholars from the United States andJapan mainly support the forest decrease in the annual runoff, and the research results ofsome scholars support that the forests have little effect on annual runoff. Afterconsulting many papers, we believe that the influence of forests on annual runoff isaffected by factors such as the type of forest, the watershed's climate zone, the size of
the watershed, and other land use/cover conditions in the watershed. For runoff in thedry season, it needs to be clarified that the runoff in the dry season refers to the runoff
when surface runoff recharge decreases and is mainly recharged by groundwater. Somescholars believe that forests can increase runoff in dry seasons because the soil layer in
the aeration zone of forest land is looser than that of non-forest land. Rainfall is easy toinfiltrate into the soil, promoting the conversion of surface runoff into soil water,intermediate soil flow, and underground runoff, thus playing the role of water storage.
In the dry season, precipitation decreases, and river runoff is mainly supplemented bywater storage in the river basin. The moisture stored in the forest soil can increase thedry water runoff in the river basin and keep the river runoff uniform and stable.
Different scholars hold opposing views. The runoff in the dry season depends on the
combined effect of forest evapotranspiration and soil infiltration rate, and the influenceof factors such as forest species, forest type, and forest area management may be moresignificant. Most views agree that the forests can reduce flood amount, peak flow anddelay and prolong flood collection for reducing flood peaks. Some scholars believe thatthe role of forests in retaining floods is limited and sometimes even counterproductive.
When the precipitation intensity is small, the forest has a more significant impact on theflood. If it is a long-term heavy rain, the forest's impact on the flood gradually weakens,even close to zero. In fact, the effect of vegetation on major floods is minimal. For large
watersheds, the time-lag effect of flood peaks in different tributaries often weakens theimpact of land cover changes. Especially after the first flood peak, the forest soil content
61Chapter 4
is high, followed by the second flood peak, which intensifies the possibility of disasters.
Therefore, it is necessary to recognize that the role of forests in retaining floods is
conditional. Such as the water content of the soil in the early stage, the saturation degreeof the fallen leaves layer after previous rainfall, the intensity and duration of therainstorm, the landform where the forest is distributed, the thickness of the soil layer,
the water permeability of the rock and the size of the watershed will play a role tovarying degrees. Under certain unfavorable combinations, the forest will not play therole of reducing flood peaks. In this study, the 2018 Hokkaido Eastern Iburi earthquake
and its landslides transformed a large amount of forest land into bare land in the basin, aquantitative case to study the impact of forest area changes on runoff.
In addition, in terms of the impact of land use/cover change caused by urbanization
on runoff. Sabita Shrestha et al. [3] studied the effect of land-use change due tourbanization on surface runoff in Xiamen City, China. The impact of urbanization canbe realized from the amount of runoff contributed by built-up land use type in the study
area, which increased from 14.2% to 27.9% with the rise of urban expansion from 1980
to 2015. In this case, the relationship between land use and surface runoff showed thatthe rapid increase in constructed land has significantly influenced the surface runoff ofthe area. Jing Zhang et al. [4] took the Puhe River, which is a middle-sized river withthe largest drainage area and lengthiest in Shenyang, the old industrial base of Northeast
China, as the research object, evaluated the impact of urbanization on land-use change
and the effect of land-use change on watershed runoff. The results show that with thedevelopment of urbanization, a large amount of cultivated land was replaced by urban
construction land. The change in land use type substantially influences runoff andincreases the runoff coefficient. By studying the impact of urbanization on flash floods
in five different watersheds in India, Kopal Kuma et al. [5] found that the growth in a
built-up area and impervious surfaces have enhanced suburban capacity basins togenerate more runoff. Shammout, Maisa'a W, et al. [6] investigated the impact of land
use/cover changes on the flow of the Zarqa River in Jordan over twenty-eight years.
From 1989 to 2017, the urban areas increased from 4.87% to 16.14%, and theagricultural regions increased from 21.69% to 31.66% in the Zarqa River. The
rangelands and bare soil areas decreased from 34.91% to 22.57%, and bare rocks
62

Chapter 4
decreased from 35.98% to 27.57%. The increase in urban and agricultural regions
resulted in runoff coefficient improvement from 1.89% in 1989/1990 to 2.72% in
2016/2017. Priyamitra Munoth et al. [7] discussed the impacts of land use/cover change
on surface runoff of the Upper Tapi River Sub-basin, India. It is found that from 1975 to
2016, the agricultural area increased by 18%, whereas forest land and rangelanddecreased by 7% and 10%, respectively, in the basin. The results showed that land
use/cover change have resulted in a corresponding increase in surface runoff and water
yield. The surface runoff has increased about 36%, and water yield has increased about
22%.
To study the impact of land use/cover change on runoff in the Atsuma River basin,
after quantifying the watershed's land use/cover change, it is necessary to simulate therunoff process before and after the land use/cover change in the watershed. Under thecondition of considering precipitation change, analyze the change of runoff.
4.2. Materials and Methods
4.2.1. Dataset
In this study, we used kinds of basin data, including meteorology, hydrology, landuse, soil, and topography (Table 4.1).
Table 4.1. Data information
Data Type Description

Resolution

Digital0.4” ×0.4”
Topography
elevation (About 10 m
map
model (DEM)square)Land
Land use and 30 m (OLI)
use/cover land cover
100 m
map
classifications(TIRS)

Soils map

Soil types

1/200,000

SourceDownload Path

Acquisition
date

https://fgd.gsi.go.j
Geographicalp/download/mapG
information
3 November
is.php?tab=dem
Authority of
2019(accessed on 20
Japan
November 2021)
https://earthexplor
United Stateser.usgs.gov/
15 March
Geological2021(accessed on 20
SurveyNovember 2021)
Ministry of https://nlftp.mlit.g
Land,o.jp/kokjo/inspect/
Infrastructure, landclassification/l 3 November
2019
Transport and and/l_national_ma
Tourism of
p_20-1.html
Japan(accessed on 20
63

Chapter 4
November 2021)
Japan
http://www.jmbsc.
Radar
Daily
Meteorological or.jp/jp/ (accessed
precipitation(450 stations)
Businesson 20 November
data
Support Center
2021)
Minimum andDaily
maximum(Atsuma
Meteorologi temperature
station)https://www.data.j 9 May 2021
cal data
Wind speedJapanma.go.jp/gmd/risk/
Daily
Meteorological obsdl/index.php(acRelative(Tomakomai
Agencycessed on 20
humidity
station)November 2021)
Daily
Solar radiation (Sapporo
station)The meteorological data (precipitation, temperature, average wind speed, relative
humidity, and solar radiation) for 2009–2020 was taken from the Japan Meteorological
Agency and the Japan Meteorological Business Support Center. The precipitation data isradar precipitation data, with the density of one observation site per square kilometer,using the observation data of 450 observation sites in and around the basin (Figure 4.1).

64Chapter 4

Figure 4.1. Radar station map
The hydrological data (suspended solids, runoff, and inflow and outflow data ofdams) for 2015–2020, taken from the Hokkaido Government Iburi General
Sub-Prefectural

Bureau,MuroranConstruction

Management

Department(Supplementary Materials). Observation stations include the Atsuam dam, Apporo dam,Atsuam bridge station, and the upstream construction section. However, the data is
missing for some periods.
The geographic data includes 2015 and 2020 land-use data deciphered fromLandsat 8 satellite data, 10m grid digital elevation model from Japan Geographic
Information Agency, and 1/200,000 Land Classification Basic Survey data from the
Ministry of Land, Infrastructure, Transport, and Tourism. Figure 4.2 shows the soil and65

Chapter 4elevation map in the Atsuma River basin. Data preprocessing is necessary before using
geographic data, including cropping, projection, etc.

Figure 4.2. Soil and elevation map
4.2.2. SWAT model and runoff simulation part
Early studies on the impact of land use/cover change on runoff mostly used themethod of experimental watersheds. This method is beneficial for revealing themechanism of vegetation-soil-atmosphere interactions. Still, it is difficult toquantitatively assess the hydrological effects of land use/cover change on a larger scale.
Since the 1960s, with the development of computer technology and remote sensing
technology, watershed hydrological models have gradually become an important choice
for analyzing the hydrological effects of land use/cover change on the entire watershedscale. The distributed hydrological model can weaken the influence of spatialdifferences in the watershed, divide the watershed into many cells and sub-basins in thehorizontal direction, stratify the soil vertically, and use some physical, differential
equations of hydraulics solve the spatiotemporal variation of runoff.
The Soil and Water Assessment Tool (SWAT) is a physics-based distributedhydrological model developed by the United States Department of Agriculture (USDA).
66

Chapter 4
The original purpose of model development was to predict the long-term impacts ofland management measures on runoff and sediment production and agrochemical
pollutants in large-scale and complex watersheds characterized by different soil types,
land use, and management conditions. The model has the following characteristics: (1)The SWAT model requires collecting information and data on meteorology, soil,
landform, vegetation, watershed soil management, etc. The model has a physical basis.
The SWAT model can simulate physical processes such as water movement, sediment
movement, crop growth, and nutrient cycling in the watershed by using collected data.
(2) The SWAT model adopts the input data that is easier to obtain. Most of the data usedin the SWAT model can be obtained from government departments. (3) The SWAT
model is computationally efficient. (4) The SWAT model can study the impact oflong-term changes in watersheds at different scales.
The SWAT model contains 701 equations and 1013 intermediate variables used tosimulate various hydrological and physical processes in the basin. The SWAT model has
eight modules of meteorology, hydrology, nutrients/pesticides, soil erosion, land cover,management measures, main channel processes, and water bodies. The watershedhydrological process includes runoff, slope confluence, and channel confluence. The
former controls the land surface water cycle process in each sub-basin, including the
input of water, sand, nutrients, and chemicals in the main channel. The latter determines
the transport of water and sediment to the outlet of the basin. The SWAT model cansimulate all aspects, from precipitation to runoff, as shown in Figure 4.3. Figure 4.4
shows the schematic structure of the SWAT model.

67

Chapter 4

Figure 4.3. Map of the water cycle in the SWAT model [8]Figure 4.4. Schematic structure of the SWAT model [9]Among them, the SWAT model water balance formula (1) is as follows:
68

Chapter 4
��� = ��� + �

�=1���� − ����� − �� − ����� − ���（1）where SWt stands for soil water content on day t (mm), SWo stands for initial soil
water content on the day t (mm), t represents the time (days), Rday represents
precipitation on the day t (mm), Qsurf represents surface runoff on the day t (mm), Ea
represents evapotranspiration on the day t (mm), Wseep represents the amount of water
seeping into the soil profile (mm), and Qgw represents the amount of return flow on the
day t (mm).
In calculating total surface runoff, the SWAT model takes the hydrological response
unit (HRU) as the basic unit. The runoff value of each HRU is accumulated to the
sub-basin where it is located to calculate the total runoff at the outlet of the basin. The
model has two methods to calculate the total runoff, the Green-Ampt infiltration method
and the SCE curve method. The Green-Ampt method requires precipitation data of lessthan one day as the basis for the infiltration calculation, and the data requirements are
relatively high. Therefore, we selected the SCS curve method in this study. The SCS
curve value shows a nonlinear change with soil moisture, and the surface runoff
generated by daily precipitation is predicted according to the daily condition of soil
moisture. The curve decreases continuously as the soil moisture content approaches thewithering point. The curve number approaches 100 when the soil water content tends tobe saturated. The model has three assumptions which are the existence of the maximum
soil water storage capacity S, the proportion of the actual water storage capacity F to the
maximum water storage capacity S is equal to the ratio of the difference between therunoff and the precipitation, and the initial loss Ia and the linear relationship between Ia
and S. Its precipitation-runoff relationship is expressed as:
�����
�
=
� � ���� − ��

（2）Where Rday is the total amount of once precipitation (mm), Qsurf is the surface runoff(mm), Ia is the precipitation loss before the surface runoff (mm), F is the precipitation
loss after the surface runoff (mm), and S is the maximum possible retention in thewatershed (mm), the upper limit of F. Among them:
Ia = aS（3）

69

Chapter 4
a is a constant. Usually, the value in the SCS model is 0.2.
According to the water balance, we can get:Among them,

F = Rday − Ia − Qsurf
Qsurf =S=

Rday −Ia

2

（4）

（5）

Rday−Ia +S

25400
CN − 254（6）

Therefore, the runoff calculation formula after introducing the CN value becomes:
Rday − 0.2S
Qsurf =
Rday + 0.8S

2（7）

It can be seen from the formula that runoff will occur only when Rday is greater thanIa.
4.2.3. Overview of SWAT-CUP software
The Soil and Water Assessment Tool-Calibration and Uncertainty Procedure(SWAT-CUP) is a procedure for calibrating SWAT models and can be used to perform
calibration, validation, sensitivity analyses (primary and global) and uncertainty
analyses. The uncertainty analysis method used in this study is the Sequential
Uncertainty Fitting Program Version 2 (SUFI-2) in the SWAT-CUP platform.
SUFI-2, based on a Bayesian framework, operates within the uncertainty domains(prior and posterior) associated with each parameter. The parameters' uncertainties
mainly include three aspects: the input datasets, the model structure, and the measured
data. Further, SUFI-2 performs combined optimization and uncertainty analysis using a
global search procedure and deals with several parameters through Latin hypercube
sampling.
The algorithm of SUFI-2 uncertainty analysis can be shown in Figure 4.5.

70

Chapter 4

Figure 4.5. A conceptual illustration of the relationship between parameter uncertainty
and prediction uncertainty [10]A is the single factor (represented by a dot), and figure a is the response it evokes. A
resulting uncertainty (represented by the line) is derived, thus leading to the linkage of
85PPU represented by the shaded part in figure b reaction. As parameter uncertainty
increases, so does output uncertainty (not necessarily a linear relationship). Therefore,
SUFI-2 starts by assuming the uncertainty of many parameters that have physicalmeaning, those measured parameters that fall within the range of 95 PPU from the
beginning and control the P-factor and R-factor to reduce the uncertainty gradually.
Each step updates the range of parameters by calculating the sensitivity analysis(equivalent to using the Jacobian determinant), which is equivalent to calculating the71Chapter 4
Hessian matrix, and then calculating the 95% confidence interval covariance matrix andcorrelation coefficient matrix. Parameters are updated in this way. Each time the newrange is smaller than the old range or closer to the optimal range value. The best
simulation level is achieved according to the above two methods. In theory, the P factor
is in the range of 0-100%, and the R factor is in the range of 0 to infinity, and thedifference between these values can judge the level of our parameter adjustment.
Therefore, the two must have a balance value directly. When the ideal values of the P
factor and R factor are obtained, the uncertainty range of the parameters is also obtained.
Then, the best fitting effect between the observed and simulated values can be obtained,
represented by indicators such as R2 and NS correlation coefficient.
4.2.4. Error analysis
Simulation effect evaluation is an essential part of the research. In this study, thesimulation results were evaluated using the coefficient of determination (R2), theNash–Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and the Pearson
product-moment correlation coefficient (PPMCC). The calculation process formulas of R2,
NSE, PBIAS, and PPMCC are shown in Equations (8)– (11):
�2 =

�
�=1

�
�=1

�� − � �� − �

�� − � 2

��� = 1 −����� =
����� =

�
�=1

�
�=1
�
�=1

�
�=1

�
�=1

�
�=1

（8）

�� − � 2

�� − �� 2
�� − � 2

�� − ��
�
�
�=1 �

（10）

�� − � �� − �

�� − � 2

�
�=1

（9）

（11）

�� − � 2

where i is the calculation time (days/months); Oi is the observation runoff at the time i;
Si is the calculation runoff at the time i; � is the average observation runoff; � is theaverage calculation runoff.

Based on previous experience and the actual situation of this study. In this study, themodel's performance is considered good when NSE>0.65, R2>0.75, PBIAS=±15%, and
PPMCC>0.8.
72Chapter 4
4.3. Results and discussions
4.3.1. Sensitivity analysis and calibration of runoff parameters
The construction of the database lays the data foundation for running the SWAT
model. Import DEM, land use, soil, meteorology, and other data into the SWAT model
to realize the initial operation, as figure 4.6 shown. Next, we performed the sensitivity
analysis and calibration of the model parameters.

Figure 4.6. SWAT model building diagram
Sensitivity analysis of model parameters is to analyze and judge which factors'
values are more critical to the accuracy and to understand the model behavior andimprove the usability of the model. The SWAT model itself has the function ofgenerating default parameters based on the input geographic data of the study area.
SWAT model simulates using default parameters often cannot model runoff accurately.
In this study, we performed the tuning of the SWAT model parameters by SWAT-CUP.
First, using the daily/monthly runoff data from the Atsuma bridge station in the Atsuma
River basin from 2015 to 2017, the most sensitive parameters for reproducing runoff
were determined by the global sensitivity analysis part of SWAT-CUP. Second, using thesequential uncertainty fitting (SUFI-2) algorithm to obtain the corrected runoff
73Chapter 4
parameters (Table 4.2) so that the simulated runoff is as consistent as possible with themeasured runoff. Then, the daily/monthly runoff data of the Atsuma bridge Station in
the Atsuma River basin from 2018 to 2020 are used for verification. This study sets themodel warm-up period from 2009 to 2014, the calibration period from 2015 to 2017,
and the validation period from 2018 to 2020. According to the above-determinedparameters, we can obtain the runoff of the watershed in the calibration period
2015-2017 and the verification period 2018-2020 through simulation.
Table 4.2. Runoff parameters
Daily
No

Name

1

TIMP.bsn

2

CN2.mgt

3

SMFMN.bsn

4

SMFMX.bsn

5

TLAPS.sub

6

SOL_AWC.sol

7

SMTMP.bsn

Description

Snow pack temperature lag
factorInitial SCS runoff curve number
for moisture condition II
Melt factor for snow on
December 21 (mm H2O/°C-day)Melt factor for snow on June 21(mm H2O/°C-day)
Temperature lapse rate (°C/km)
Available water capacity of thesoil layer (mm H2O/mm soil)
Snow melts base temperature(°C)

Monthly

2015 2020 2015 2020 2015 2020 2015 2020
Default

Corrected

Default

Corrected

valuesvaluesvaluesvalues110.161241

79

87

1

1

1

79

87

4.5

4.5

1.359352

4.5

4.5

4.916194

4.5

4.5

2.048258

4.5

4.5

1.63829800-2.39200-1.221248

0.1

0.1

0.025737

0.1

0.1

0.09

0.5

0.5

0.108525

0.5

0.5

3.543045

00 52.697681

0016.700487

0

0

0

0

0.503748

63.18 69.58
9432 8362

46.11 50.789
9094 382

Effective hydraulic conductivity
8

CH_K2.rtein tributary channel alluvium(mm/hrh)9

CH_K1.sub

Effective hydraulic conductivity

12.198375

74

Chapter 4
in tributary channel alluvium(mm/h)
Minimum snow water content
10 SNOCOVMX.bsn

that corresponds to 100%1

10.639671

1

10.375468

snowcover, SNO100 (mm H2O)
Manning’s “n” value for the11

CH_N2.rte

12

CH_N1.sub

13

ESCO.hru

14

PLAPS.sub

15

SFTMP.bsn

Snowfall temperature (°C)

16

ALPHA_BF.gw

Baseflow alpha factor (days)

17

SURLAG.bsn

Surface runoff lag coefficient

4

4

4

4

4

0.932302

18 GW_DELAY.gw Groundwater delay time (days)

31

31

62.26182931

31

84.258835

0.5

0.5

0.743382

0.5

0.5

0.771502

1000 1000 0.362763 1000 1000

2.610365

main channel
Manning’s “n” value for themain channel
Soil evaporation compensation
factorPrecipitation lapses rate (mmH2O/km)0.014 0.014 0.304582 0.014 0.014

0.0321190.014 0.014 0.230863 0.014 0.014

0.513274

0.95 0.95

0.76840.95

0.95

0.6114700173.700012

00-109.683922

1

1-0.908274

11-1.100187

0.048 0.048 0.972874 0.048 0.048

0.004363

Fraction of snow volume
19 SNO50COV.bsn

represented by SNOCOVMX
that corresponds to 50% snow
cover
Threshold depth of water in the20

GWQMN.gw

shallow aquifer required for
return flow to occur (mm H2O)

4.3.2. simulation and analysis of the runoff process before and after the earthquake
To study the impact of land use/cover change on the runoff process, we firstsimulated the daily/monthly runoff process in the Atsuma River basin based on the 2015
land use/cover data. Next, the calibrated SWAT model and the 2020 land use/cover data
were used to simulate the daily/monthly runoff process in the Atsuma River basin
without changing the soil, elevation, and meteorological data. Figures 4.7–4.10 show75

Chapter 4
the runoff simulation results. Table 4.3 shows the error analysis results. For the 2015
land use/cover data, the daily runoff simulation results in the calibration period had an
R2 of 0.876, NSE of 0.707, PBIAS of 10.52%, and PPMCC of 0.876. Similarly, for themonthly runoff simulation results, R2 was 0.910, NSE was 0.691, PBIAS was 16.15%,
and PPMCC was 0.911. For the daily runoff simulation results in the verification period,
R2 was 0.874, NSE was 0.620, PBIAS was −6.85%, and PPMCC was 0.874. For themonthly runoff simulation results, R2 was 0.861, NSE was 0.729, PBIAS was −6.028%,
and PPMCC was 0.610. As a result, the runoff simulation error was small, and thesimulation effect was good, indicating that the SWAT model can accurately simulate therunoff process in the Atsuma River basin.

Figure 4.7. Daily simulated runoff calculated from the 2015 land use/cover data76

Chapter 4

Figure 4.8. Monthly simulated runoff calculated from the 2015 land use/cover data

Figure 4.9. Comparison of simulated daily runoff based on the 2015 and 2020 land
use/cover data77

Chapter 4

Figure 4.10. Comparison of simulated monthly runoff based on the 2015 and 2020
land use/cover data
Table 4.3. Error analysis results
Atsuma Bridge station
Daily
MonthlyR2

NSE PBIAS PPMCC

Calibration period (2015–2017)0.876 0.707 10.52%

0.876

Verification period (2018–2020)

0.874 0.620 −6.85%

0.874

Calibration period (2015–2017)

0.910 0.691 16.15%

0.911

Verification period (2018–2020)

0.861 0.729 −6.028%

0.610

By comparing the runoff simulation results obtained using the 2015 and 2020 land
use/cover data, it was seen that the simulation results were very similar, and the curve
was consistent, indicating that the land use/cover change in the Atsuma River basinfrom 2015 to 2020 has had little impact on the runoff process. However, after carefulobservation, it was found that the peak of the daily/monthly runoff simulation results
obtained for the 2020 data was slightly larger than that of 2015. This indicates that thedecrease in forest land area and the increase in the bare land area slightly increased the
proportion of precipitation transformed into the runoff in the Atsuma River basin. By
analyzing the water budget of the Atsuma River basin from 2015 to 2020 (Table 4.4)
78

Chapter 4
and the ratio of runoff to precipitation (Figure 4.11), it was found that the proportion of
precipitation that is transformed into runoff was higher from 2018 to 2020 compared to2015 to 2017, thereby showing an increasing trend, which accompanied the land
use/cover change from 2015 to 2020.

Figure 4.11. Ratio of runoff to precipitation
Table 4.4. Upstream water budget of the Atsuma Bridge station from 2015 to 2020
(mm/y)
Year

2015 2016 2017 2018 2019 2020

Average

Precipitation

1274 1540 1171 1451 1067 985

1248

Rainfall994 1299 1068 1253 999

915

1088

Snowfall

280

241

103

198

68

70

160

Runoff

585

835

490

918

615

518

660

0.46 0.54 0.42 0.63 0.58 0.53

0.53

Runoff/Precipitation
4.4. Conclusions

Based on the meteorological data from 2009 to 2020 and the hydrological data from2015 to 2020 in the Atsuma River basin, we used Quantum Geographic Information
System (QGIS) to establish a spatial and attribute database of watershed meteorology
79

Chapter 4
and soil and land use. Next, we used the SWAT distributed hydrological model tosimulate and analyze the watershed runoff process. We used the SUFI-2 method of
SWAT-CUP software to perform sensitivity analysis and calibration of model
parameters. The main conclusions are as follows:

During the calibration period and the verification period, the R2 of thedaily/monthly runoff simulation results are above 0.874, the NSE are all above 0.62,
the PBIAS are all below 16.149%, and the PPMCC are all above 0.61. It shows thatthe model has a better effect on the daily/monthly runoff model of the Atsuma Riverbasin, and the model accuracy is high.

Comparing the runoff simulation results obtained using the land use data in 2015
and 2020, it can be found that the daily/monthly runoff simulation results obtained
based on the two land-use data are very close. The daily/monthly runoff simulation
results obtained using the 2020 land use data are slightly larger than the simulation
results obtained using the land use data in 2015 at the peak and some periods,
indicating that the change in land use/cover in the Atsuma River basin from 2015 to2020 have little impact on runoff. However, the forest area decreased, and the bareland area increased, causing the proportion of precipitation converted into runoff toincrease slightly. The basin runoff increased after the earthquake.

4.5. References
1. Eckhardt, K.; Haverkamp, S.; Fohrer, N.; Frede, H. G. SWAT-G, a version of
SWAT99. 2 modified for application to low mountain range catchments. Physics andChemistryoftheEarth,Parts

A/B/C.

2002,

27(9-10),641-644.

Doi:

10.1016/S1474-7065(02)00048-7.
2. Eckhardt, K.; Breuer, L.; Frede, H. G. Parameter uncertainty and the significance of
simulated land use change effects. Journal of hydrology. 2003, 273(1-4), 164-176. Doi:
10.1016/S0022-1694(02)00395-5.
3. Shrestha, S.; Cui, S.; Xu, L.; Wang, L.; Manandhar, B.; Ding, S.

Impact of Land

Use Change Due to Urbanisation on Surface Runoff Using GIS-Based SCS–CN Method:
A

Case

Study

of

XiamenCity,

China.

Land.

2021,

10(8),

839.

Doi:

10.3390/land10080839.
80

Chapter 4
4. Zhang, J.; Yu X. Analysis of land use change and its influence on runoff in the Puhe
River Basin. Environmental Science and Pollution Research. 2021, 28(30),40116-40125. Doi: 10.1007/s11356-020-09798-7.
5. Kumar, K.; Dhorde, A. Impact of land use land cover change on storm runoff
generation: a case study of suburban catchments of Pune, Maharashtra, India.
Environment, Development and Sustainability. 2021, 23(3), 4559-4572.

Doi:

10.1007/s10668-020-00787-x.
6. Shammout, M. A. W.; Shatanawi, K.; Al-Bakri, J.; Abualhaija, M. M. Impact ofLand Use/Cover Changes on the Flow of the Zarqa River in Jordan. Journal of
Ecological Engineering. 2021, 22(10). Doi: 10.12911/22998993/142184.
7. Munoth, P.; Goyal, R. Impacts of land use land cover change on runoff and sedimentyield of Upper Tapi River Sub-Basin, India. International Journal of River BasinManagement. 2020, 18(2), 177-189. Doi: 10.1080/15715124.2019.1613413.
8. Castelli, G.; Foderi, C.; Guzman, B. H.; Ossoli, L.; Kempff, Y.; Bresci, E.; Salbitano,
F.

Planting waterscapes: Green infrastructures, landscape and hydrological modelingfor the future of Santa Cruz de la Sierra, Bolivia. Forests. 2017, 8(11), 437. Doi:
10.3390/f8110437.
9. Lee, S.; Sadeghi, A. M.; Yeo, I. Y.; McCarty, G. W.; Hively, W. D. Assessing theimpacts of future climate conditions on the effectiveness of winter cover crops in
reducing nitrate loads into the Chesapeake Bay watersheds using the SWAT model.
Transactions of the ASABE. 2017, 60(6), 1939-1955. Doi: 10.13031/trans.12390.
10. Ali, W. R. M.; Chen, N.; Umar, W. R. M.; Sundas, A.; Mahfuzur, R. ( Assessmentof runoff, sediment yields and nutrient loss using the swat model in upper Indus Basinof Pakistan. Journal of Geoscience and Environment Protection. 2020, 8(9), 62-81. Doi:
10.4236/gep.2020.89004.

81

Chapter 5
Sediment transport simulation
based on SWAT modelChapter 5
5.1. Introduction
Land use/cover change changes the original surface vegetation type, coverage, andmicro-topography, thus affecting the dynamics and anti-erosion resistance system of soil
erosion and becoming the inducing and strengthening factor of soil erosion in the basin.
After the 2018 Hokkaido Eastern Iburi earthquake, the mainly manifested land ofuse/cover change in the Atsuma River basin was about 20% of the forest landtransformed into bare land and grassland. As a common land use/cover type, the forest
has potent water and soil conservation and soil erosion control functions. This function
is closely related to the structural characteristics of the forest. The main manifestations
are the forest canopy and litter layer intercept part of the precipitation, which reduces
the net rain in the forest land. In particular, the litter layer has a substantial protective
effect on the soil, preventing the surface soil from splash erosion, hindering theconsumption of the kinetic energy of rainfall, and reducing the disturbance of the kinetic
energy to the surface runoff and the ability to carry sand. Blocking the surface runoff
velocity increases the runoff infiltration time and minimizes the runoff volume. The
improvement of the soil surface structure increases the water storage capacity of theforest land and reduces the surface runoff; the change of the surface layer thickness
delays and changes the collection and confluence of the runoff and reduces the scour ofthe stream. The water flow pattern of the litter layer has changed, which has a specificfiltering effect on the water flow carrying sand and reduces the sediment content in therunoff. The forest root layer has several soil-fixing functions such as network, seriesconnection, bonding, and fixation, which significantly improves the soil's anti-scour andcorrosion resistance. A series of effects of forests have reduced soil erosion considerably.
The research on the impact of land-use change on sediment production in the watershed
shows that the conclusions are the same. The sediment production in the watersheddecreases with the increase of the vegetation coverage in the watershed. Therefore, land
use/cover changes, especially changes in forest area, have a significant impact on
sediment production and transport in the watershed.
In 1877, the German soil scientist Ewald Wallny started quantitative research on
soil erosion. In 1947, the American scholar Musgrave first proposed the vegetation
action coefficient and introduced it into the soil loss equation in the central and eastern82

Chapter 5
United States. In 1954, Wischmeier and Smith proposed the soil loss equation Universal
Soil Loss Equation (USLE), which comprehensively considers the natural factors that
affect soil erosion. Soil erosion and sediment yield were quantitatively calculated by theequation of rainfall-runoff factors, topographic factors, vegetation cover factors, andsoil and water conservation measures. There have been many studies on the impact ofland use/cover change on sediment production and transport in river basins in recent
years. Mohammadi et al. [1] studied the impact of Land Use changes on soil erosion
intensity and sediment yield in the Talar Watershed of Iran. From 1991 to 2014, theforest area in the study area decreased by 12,478.04 ha (6%), while the other land uses,
including rainfed agriculture, rangeland, irrigated agriculture, and residential area,
increased by 7248.25, 4481.05, 476.00, and 273.95 ha, respectively. Which caused theamount of production of erosion material (gross erosion) in the watershed to increase
from 1,918,186 to 2,183,558 m3/yr, and the actual soil losses per year (sediment yield)of the watershed increased from 440,482.4 to 501,421.3 m3/yr. Taking the Južna Morava
river basin as the research area, Manojlović et al. [2] expounded on the dynamic
relationships among agricultural land-use change, rural population migration, andsediment transport and obtained the decrease in rural population, agricultural land, andarable land were directly related to the decline of suspended sediment. Taking the
Songkhram river basin, the second largest river basin in Northeast Thailand, as theresearch area, Shrestha et al. [3] used the Soil and Water Assessment Tool (SWAT) to
quantify the combined effects of climate and land-use change on sediment production.
For land use/cover change, considering economics and conservation, research results
show that land use cover change will increase future sediment production in this area.
Liu et al. [4] showed that the land use statistics showed a decreasing trend of cropland(231.91 km2) from 1980 to 2018. The cropland conversion resulted in an annual
sediment reduction of 757×104 t in the 1990s and 1,408×104 t in 2001–2018 comparedwith 1980–1989. Daramola et al. [5] studied the Kaduna Watershed (Nigeria), Western
Africa. Findings suggest that the extinction of evergreen forests and a significant change
in land use from range grasses and forests to agriculture generic and residential types
between 1975 and 2013 resulted in surface runoff, sediment yield, and flow alteration.
Evapotranspiration increased by 22.40% between 1975 and 2013. These changes have83

Chapter 5
negatively impacted the watershed runoff by 56.00% and model sediment yield by
68.00% at the end of 2013. Liu et al. [6] took the Xunwu river catchment located insouthern China's red soil hilly region as the research object. They used the SWAT model
to evaluate land use/land cover on watershed runoff and sediment yield. The results
showed that in the case of orchard expansion in the past 26 years, the runoff andsediment yield in this area did not change much and did not show serious soil erosion.
Taking the Upper Chao Phraya River basin (UCPRB), a typical tropical monsoon basin
is an essential area of water resources in Thailand as the research object, Zhao et al. [7]studied the impact of continuous deforestation on hydrological processes. They foundthat one percent reduction of the forest cover can increase the annual suspended
sediment load by 8.7%. Tan et al. [8] explicitly parameterize cropland management
actions (i.e., irrigation, conserved agriculture, and crop residue management) andgeological factors (i.e., lithology and glacier) in the Energy Exascale Earth System
Model (E3SM) soil erosion module. This improvement is mainly due to a better
representation of the topographic effect on soil erosion. The findings show thatconservation agriculture practices effectively reduce soil erosion on farmland by morethan 25 % in the United States and Argentina. In contrast, irrigation increases soil
erosion in many Asian countries.
To sum up, land use/cover change will significantly impact sediment production
and transport in basins. Most studies believe that the increase of forest land andgrassland area or vegetation coverage has a noticeable effect on sand reduction. In
contrast, increased cultivated land (especially sloping cultivated land) and construction
land have a sand increase effect. At the same time, the impact of land use/cover change
on sediment production and transport in the basin has obvious spatial and temporal scalevariability, so it cannot be generalized. In the Atsuma River basin, exposed soil allowed
more significant runoff and erosion and increased the potential connectivity of runoff
and sediments within the watershed compared to pre-earthquake conditions. To study
the impact of land use/cover change on sediment transport in the Atsuma River basin,
after quantifying the land use/cover change in the basin, it is necessary to simulate thesediment transport process before and after the land use/cover change in the basin.
Under the condition of considering the change of runoff, analyze sediment transport84Chapter 5change.
5.2. Materials and methods
5.2.1. Dataset
The data used in this section are essentially the same as those used for runoff
simulation, including data such as watershed meteorology, hydrology, land use, soil, andtopography. For data details, please refer to section 4.2.1 of the paper.
5.2.2. Sediment simulation part of SWAT model
The SWAT model uses the Modified Universal Soil Loss Equation (MUSLE)
empirical formula to simulate watershed erosion and sediment production and to
calculate the average amount of erosion in each catchment. MUSLE is a modified
general soil loss equation. It replaces the traditional precipitation kinetic energy with arainfall factor, which improves the accuracy of sediment production prediction. It canalso be used for a single precipitation and sediment production event. The formula is asfollows:
��� = 11.8 × ����� × ����� × ����ℎ��

0.56

× � × � × � × �� × ����（1）

Where Sed stands for the sediment yield (tons/day) of an HRU, Qsurf stands for thevolume of surface runoff (mm/103·m2), qsurf stands for the peak runoff rate (m3/s),
areahru stands for the area of the HRU (103·m2), K represents the Universal Soil Loss
Equation (USLE) soil erodibility factor (dimensionless), C the USLE cover andmanagement factor (dimensionless), P represents the USLE support practice factor(dimensionless), LS represents the USLE topographic factor (dimensionless), and CFRG
represents the coarse fragment factor (dimensionless).
Soil erosion factor K
Soil erosion factor K value is a necessary parameter for soil erosion prediction

models, which is used to evaluate the difficulty degree of soil separation, erosion, andtransport by rainfall. Standard plot runoff experiments can obtain the K value. It can becalculated according to a general equation proposed by Wischmeier et al.:�=

0.00021 ∙ � ∙ 1.14 ∙ 12 − �� + 3.25 ∙ �������� − 2 + 2.5 ∙ ����� − 3
100

（2）

The applicable conditions of this equation are: when the composition of silt soil and85

Chapter 5
ultra-fine sand soil is less than 70%. M is the soil particle composition parameter. OM
stands for soil organic matter content. Csoilatr is the soil structure grading code. Cperm isthe soil profile permeability level.
Among them, M=(msilt+mvfs), where msilt is the content of silt soil in % (soilparticle size is 0.002-0.05mm). mvfs is the content of very fine sandy soil (soil particle
size 0.05-0.1mm). Mc is the clay content in % (soil particle size is less than 0.002mm).
OM=1.72orgC, orgC is each layer's soil organic carbon (%). Csoilstr includes 4 grades,
which are very fine particles, fine particles, medium or coarse particles, and bulk orlarge particles. Cperm is divided into 6 grades based on soil saturated hydraulicconductivity: 1. High permeability (greater than 150mm/hr). 2. Higher permeability(50-150mm/hr). 3. Moderate permeability (15-50mm/hr). 4. Lower permeability(5-15mm/hr). 5. Low permeability (1-5mm/hr).


Vegetation cover and crop management factors C
C is the ratio of soil loss between vegetation coverage and continuous fallow or bareland, representing the benefits of plant coverage and crop cultivation measures for soil
erosion control, with a maximum value of 1.0. The C formula of the SWAT model is:� = ��� ��0.8 − �� � ∙ ��� −0.00115 ∙ ������� + �� ���（3）

The formula rsdsurf represents the residual amount of surface vegetation (kg/ha) in
the formula. C is the minimum vegetation cover and management factor. Among them,C,mn=1.463 In (C,aa)+0.1034, C,aa is the annual average C value of different
vegetation coverage.


Soil and water conservation factor P
The soil and water conservation factor are the ratio of surface soil loss with theimplementation of soil and water conservation measures to that without any measures.


Terrain factor LS
The calculation formula of the terrain factor LS is:�� =�ℎ��� �∙ 65.41 ∙ sin2 αhill + 4.56 ∙ sinαhill + 0.065
22.1

（4）

Where Lhill is the slope length. m is the slope length index. αhill is the slope.
� = 0.6 ∙ 1 − ��� −35.835 ∙ ���

（5）

86

Chapter 5
Where SLP is the hydrological unit's slope (m/m). SLP=tanαhill.


CFRG factor
The formula for calculating the CFRG factor is:（6）���� = ��� ∙ −0.053 ∙ ����In the formula, rock represents the gravel content (%) in the first layer of soil.
5.3. Results and discussion
5.3.1. Sensitivity analysis and calibration of sediment parameters
Keeping the runoff parameters the same, the sediment-sensitive parameters were
determined using SWAT-CUP, and the corrected sediment parameters were obtainedusing the SUFI-2 algorithm (Table 5.1). Make the simulated runoff value as consistent
as possible with the measured value.
Table 5.1. Sediment parameters
Before earthquake
No

Name

Description

Default

After earthquake

Corrected Default Correctedvaluesvaluesvaluesvalues1

USLE_P.mgt

USLE equation support practice factor

1

0.090052

1

0.060287

2

SOL_BD().sol

Moist bulk density (Mg/m3 or g/cm3)

0.3

1.370370.3

1.954686

750

186.166534

0.001

0.0008159 0.001 0.00102348

0.00010.006331 0.0001 0.002783

Threshold depth of water in the shallow
3

REVAPMN.gw

aquifer for“revap” or percolation to the750 401.453583

deep aquifer to occur (mmH2O)
Minimum value of USLE C factor for
4 USLE_C{2}.plant.dat

water erosion applicable to the land
cover/plant (Forest)
Linear parameter for calculating the5

SPCON.bsn

maximum amount of sediment that canbe reentrained during channel sedimentrouting87

Chapter 5
Minimum value of USLE C factor for
6 USLE_C{3}.plant.dat

water erosion applicable to the land

0.003

0.00083717 0.003 0.00069073

cover/plant (Grass)
CH_ERODMO is set to a value
between 0.0 and 1.0. A value of 0.0
7

CH_ERODMO.rte

indicates a non-erosive channel while a03.74480608.263742

value of 1.0 indicates no resistance toerosion.
8

HRU_SLP.hru

Average slope steepness (m/m)

0.05155

0.0708287 0.6687

0.06280.3016999 0.0628 0.042489

USLE equation soil erodibility (K)9USLE_K.sol

factor (units: 0.013 (metric ton m2
hr)/(m3-metric ton cm))10

EPCO.hru

11

SOL_K.sol

12

OV_N.hru

1310.490527

10.63

6.69041

10.63 13.267877

Manning's "n" value for overland flow

0.1

0.0937598

0.14

GW_REVAP.gw

Groundwater "revap" coefficient

0.02

0.02342478 0.02

14

CH_COV1.rte

Channel cover factor

025.236423

15

CH_L2.rte

Length of main channel (km)16

CH_S2.rte

17

CH_COV2.rte

18

USLE_C {1}.
plant.dat

Plant uptake compensation factor
Saturated hydraulic conductivity(mm/hr)Average slope of tributary channels(m/m)
Channel cover factor

1

0.992019

0.249963
0.02

011.92763

10.9016 10.4795119 10.9016 12.2942685
0.011634 0.0117971
022.798399

0.01163400.01718588
21.584068

Minimum value of USLE C factor forwater erosion applicable to the land

0.001

0.0013788 0.001 0.00027133

cover/plant (Farmland)
Exponent parameter for calculating

19

SPEXP.bsn

sediment reentrained in channel

11.321381

1

1.157127

sediment routing
20

SLSUBBSN.hru

Average slope length (m)60.97561

60.97561 9.14634 7.10126411

5.3.2. Simulation of sediment transport process before the earthquake
88Chapter 5This study uses the 2015 land use data to simulate the sediment transport process
before the earthquake (2017.1-2018.8). The sediment transport process simulation
results are shown in Figure 5.1, and the error analysis results are shown in Table 5.2. It
can be observed that the R2 of the simulation results of the sediment migration process
before the earthquake is 0.834, the NSE is 0.684, the PBIAS is 7.565%, and the PPMCC
is 0.834. The simulation error of the sediment transport process before the earthquake issmall, and the simulation effect is good.

Figure 5.1. Sediment transport simulation based on the 2015 land use/cover data
Table 5.2. Error analysis of the SWAT model for the sediment transport simulation
Upstream siteof Kobushi bashi Bridge

R2

NSE

PBIAS

PPMCC

January 2017–August 2018

0.802 0.621 10.205%

0.802

October 2018–December 2020

0.823 0.638 15.755%

0.823

5.3.3. Simulation of sediment transport process after the earthquake
This study uses the 2020 land use data to simulate the sediment transport process
after the earthquake (2018.10-2020.12). The sediment transport process simulation
results are shown in Figure 5.2, and the error analysis results are shown in Table 5.2. It
can be observed that the R2 of the simulation results of the sediment migration process89Chapter 5
after the earthquake is 0.862, the NSE is 0.697, the PBIAS is 14.9275%, and the
PPMCC is 0.862. The simulation error of the sediment transport process after theearthquake is also small; the simulation effect is better and slightly better than thesediment transport process before the earthquake simulated using the 2015 land use
data.

Figure 5.2. Sediment transport simulation based on the 2020 land use/cover data
5.3.4. Comparison and analysis of sediment transport processes before and after theearthquake
By comparing the simulation results in Figure 5.1 and Figure 5.2, it can be seen thatthe sediment transport after the earthquake increased by order of magnitude comparedto that before the earthquake. This indicates that the sediment transport in the Atsuma
River basin increased significantly after the earthquake. Figure 5.3 shows the ratio of
suspended soild to runoff.

90

Chapter 5

Figure 5.3. shows the ratio of suspended soild to runoff
Figure 5.4 shows the accumulation of transported sediment, adjusted according to
the runoff before and after the earthquake. First, the rate of sediment transportation
increased significantly after the earthquake, becoming 5.5 times that before theearthquake. Second, by analyzing the cumulative curve before the earthquake, it was
found that the sediment transport rate was relatively stable, indicating that soil erosion
and sediment transport capacity in the Atsuma River basin was relatively stable before
the rapid land use/cover change that was caused by the earthquake and landslide.
Moreover, by analyzing the cumulative curve after the earthquake, it was found that the
sediment transport rate increased significantly and became unstable after the earthquake.
The cumulative curve after the earthquake can be roughly divided into six stages: 6
September–October

2018,

November–December

2018,

January–July2019,

August–December 2019, January–June 2020, and July–December 2020.

91

Chapter 5

Figure 5.4. Accumulation of transported sediment, adjusted according to runoff
From 6 September 2018 to October 2018, the rate of sediment transportation did
not immediately increase due to the damage caused to the Apporo dam by theearthquake. This decreased the runoff and the capacity to transport sediment. Therefore,
the rate of sediment transportation increased minimally. From November to December
2018, the runoff increased with the release of water following construction. The flowtransported the sediment accumulated in and around the river channel to thedownstream of the river. Therefore, the sediment transport rate increased significantly.
From January 2019 to July 2019, first, because the precipitation was in the form ofsnowfall from January to March 2019 in the Atsuma River basin and the temperature
was low, the snowfall mainly existed in the form of snow cover and did not transform
into the runoff. Second, from April 2019 to July 2019, the runoff was small due to the
Apporo dam's construction, and the capacity to transport sediment was weak, resulting
in a lower sediment transport rate. From August 2019 to December 2019, when the
Apporo dam was functional, the water levels rose in the Atsuma River, and the runoff
transported the sediment to the downstream of the river. Furthermore, the sedimentproduced under the erosion of flow was transported by runoff to the downstream of theriver. Therefore, the rate of sediment transport increased significantly at this stage. From
January 2020 to June 2020, also because the precipitation was mainly in the form ofsnow, snowfall mainly existed in the form of snow cover. Moreover, there is very little
rain in spring, so the runoff was mainly due to the reservoir's seasonal outflow, which is92

Chapter 5part of the river's continual flow. At this time, the basin erosion was mainly caused byriver channel erosion. Therefore, the change in the rate of sediment transportation wasless in this stage. It was basically stable. From July 2020 to December 2020, with theadvent of the rainy season, the erosion process of the basin was mainly due to theerosion of soil and river channels that occurred as a result of precipitation, confluence,
runoff, and other processes. Due to the significant increase in bare land in the basin after
the earthquake, the erosion effect strengthened. More sediment was generated in theriver channel and transported downstream by the runoff. Therefore, the rate of sedimenttransport increased significantly at this stage. In general, the amount of sedimenttransported increased significantly after the earthquake.
5.4. Conclusion
For future studies, we will evaluate the impacts of climate change on severe floods
considering some aspects below to obtain reliable prediction results in the target basin.
This chapter uses the meteorological data from 2009 to 2020 and the hydrological
data from 2015 to 2020 in the Atsuma River basin and uses QGIS as a platform toestablish a spatial and attribute database of watershed meteorology, soil, and land use.
Moreover, we used the SWAT distributed hydrological model to simulate and analyze
the sediment transport process in the basin. Sensitivity analysis and calibration of model
parameters were performed using the SUFI-2 method of SWAT-CUP software. The
main conclusions are as follows:

The coefficient of determination (R2) of the simulation results of the sedimentmigration process before the earthquake is 0.834, the Nash–Sutcliffe efficiency
coefficient (NSE) is 0.684, the percent bias (PBIAS) is 7.565%, and the Pearson
product-moment correlation coefficient (PPMCC) is 0.834. The R2 of thesimulation results of the sediment transport process after the earthquake is 0.862,
the NSE is 0.697, the PBIAS is 14.9275%, and the PPMCC is 0.862. It can befound that the simulation error of the sediment transport process before and after
the earthquake is small, and the simulation effect is good, which proves that the
SWAT model can estimate the sediment transport process in the Atsuma River basinwith high accuracy.
93

Chapter 5Comparing the simulation results of sediment transport before and after theearthquake, we can find that the amount of sediment transport after the earthquake
significantly increased than before the earthquake. By analyzing the accumulationof sediment transport volume per unit runoff before and after the earthquake, we
can clearly observe that the sediment transport volume per unit runoff after theearthquake has increased significantly, and the sediment transport volume per unit
runoff after the earthquake is 5.5 times of that before the earthquake. Secondly, by
analyzing the cumulative curve before the earthquake, we can find that the amountof sediment transport per unit of runoff before the earthquake was relatively stable,
indicating that before the earthquake and the landslides caused rapid changes inland use in the Atsuma River basin. The ability of soil erosion and river channel totransport sediment is relatively stable. By analyzing the cumulative curve after theearthquake, we can find that the amount of sediment transport per unit runoff after
the earthquake is greatly increased and unstable.

5.5. References
1. Mohammadi, M.; Khaledi Darvishan, A. K.; Spalevic, V.; Dudic, B.; Billi, P.
Analysis of the impact of land use changes on soil erosion intensity and sediment yieldusing the intErO model in the talar watershed of iran. Water. 2021, 13(6), 881. Doi:
10.3390/w13060881.
2. Manojlović, S.; Sibinović, M.; Srejić, T.; Hadud, A.; Sabri, I. Agriculture land usechange and demographic change in response to decline suspended sediment in JužnaMorava River basin (Serbia). Sustainability. 2021, 13(6), 3130. Doi:
10.3390/su13063130.
3. Shrestha, S.; Bhatta, B.; Talchabhadel, R.; Virdis, S.G.P. Integrated assessment of
the landuse change and climate change impacts on the sediment yield in the SongkhramRiver
Basin,
Thailand.
CATENA.
2022,
209,
105859.
Doi:
10.1016/j.catena.2021.105859.
4. Liu, W.; Shi, C.; Ma, Y.; Wang, Y. Evaluating sediment connectivity and its effects
on sediment reduction in a catchment on the Loess Plateau, China. Geoderma. 2022,
408, 115566. Doi: 0.1016/j.geoderma.2021.115566.
5. Daramola, J.; Adepehin, E. J.; Ekhwan, T. M.; Choy, L. K.; Mokhtar, J.; Tabiti, T. S.
Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed
94

Chapter 5
Sediment Yield: Implications from the Kaduna Watershed. Water. 2022, 14(3), 325.
Doi: 10.3390/w14030325.
6. Liu, G.; Schmalz, B.; Zhang, Q.; Qi, S.; Zhang, L.; Liu, S. Assessing effects of land
use and land cover changes on hydrological processes and sediment yield in the Xunwu
River watershed, Jiangxi Province, China. Frontiers of Earth Science. 2022, 1-15. Doi:
10.1007/s11707-021-0959-9.
7. Zhao, B.; Lei, H.; Yang, D.; Yang, S.; Santisirisomboon, J. Runoff and sediment
response to deforestation in a large Southeast Asian monsoon watershed. Journal of
Hydrology. 2022, 127432. Doi: 10.1016/j.jhydrol.2022.127432.
8. Tan, Z.; Leung, L. R.; Li, H. Y.; Cohen, S. Representing global soil erosion andsediment flux in Earth System Models. Journal of Advances in Modeling Earth Systems.
2022, 14(1). Doi:10.1029/2021MS002756.

95

Chapter 6
Conclusions and outlook

Chapter 6
6.1. Introduction
This chapter introduces the conclusions of this study, the current shortcomings, andprospects for further research.
6.2. Summarize of the study findings
The impact of land use/cover change on the hydrological cycle processes in thebasin has become a scientific issue of widespread concern. Studying the impact of land
use/cover change in the Atsuma River basin on the runoff and sediment transport
processes after the 2018 Hokkaido Eastern Iburi earthquake has great significance tounderstand the impact of the earthquake on the watershed, the hydrological laws of thewatershed under changing conditions and practicing rational water management.
This research mainly studies the hydrological cycle response of the watershedunder land use/cover change. Taking the Atsuma River basin as the research object, weused ENVI software to interpret the land use map before and after the earthquake. We
quantified the land use/cover change in the Atsuma River basin. The temporal andspatial changes and driving forces of land use/cover in the Atsuma River basin wereanalyzed before and after the earthquake. After that, we used the SWAT model toconstruct a distributed hydrological model to evaluate how land use/cover change
impacts the hydrological cycle in the Atsuma River basin. Conclusions as below:
Comparing the land use maps of the Atsuma River basin in 2015 and 2020, we canfind that from 2015 to 2020, the farmland area increased by 3.439 km2, and thefarmland area remained stable without significant changes. The forest area
decreased by 47.899 km2, accounting for about 20% of the forest area before theearthquake, showing a relatively significant reduction. The bare land area
increased by 21.906 km2, and the bare land area after the increase was about 5.7
times that before the earthquake, a significant increase. The grassland area
increased by 23.738 km2, an obvious increase. The water area increased by 1.039
km2, and the building area decreased by 2.223 km2. We can find that most reduced
forest areas have become new bare land and grassland through analysis. The 2018
Hokkaido Eastern Iburi earthquake and its landslides are the main reasons forconverting forest areas into bare land and grassland. The completion of the Apporo
96

Chapter 6
dam in 2017 is why the Apporo dam area transformed from a construction area
into water.
Comparing the runoff simulation results obtained using the 2015 and 2020 landuse data, we find that the daily/monthly runoff simulation results obtained basedon the two land-use data are very close. The curves of the simulation results fit
well, indicating that the land use/cover change in the Atsuma River basin has less
impact on runoff. A closer look reveals that the daily/monthly runoff simulation
results using the 2020 land use data are slightly more prominent at the peak andpart-time than the simulation results using the 2015 land use data. It shows that the
reduction of the forest land area and the increase of the bare land area in the
Atsuma River basin resulted in a slight increase in the proportion of precipitation
converted to runoff. By analyzing the water balance of the Atsuma River basinfrom 2015 to 2020, we also can find that the proportion of precipitation converted
to runoff from 2018 to 2020 is higher than that from 2015 to 2017, and the
proportion of precipitation converted to runoff is increasing, which once again
proves that from 2015 to 2020 change in land use/cover have increased the
proportion of precipitation converted to runoff in the Atsuma River basin.

Comparing the simulation results of sediment transport before and after theearthquake, we find that the amount of sediment transport after the earthquake isalmost an order of magnitude larger than before the earthquake. The sedimenttransport in the Atsuma River basin after the earthquake can be determined to haveincreased substantially. By analyzing the accumulation of sediment transport perunit runoff before and after the earthquake, we can observe that the sedimenttransport per unit runoff after the earthquake has increased significantly, and thesediment transport per unit runoff after the earthquake became about 5.5 times that
before the earthquake. Secondly, by analyzing the cumulative curve before theearthquake, we can find that the amount of sediment transport per unit of runoff
was relatively stable, indicating that before the earthquake and the landslides
caused rapid land use/cover changes in the Atsuma River basin. The ability of soil
erosion and river channel to transport sediment is relatively stable. By analyzing
the cumulative curve after the earthquake, we can find that the amount of sediment97

Chapter 6transport per unit runoff is greatly increased and unstable. We think it is related tothe massive new bare land and the accumulation of sediment and wood chips
around the river channel and foothills.
6.3. Deficiencies
This study has achieved some valuable conclusions, but due to the limitations oftime and data, there are still some insufficient and shortcomings in this study's researchmethod and research depth, which need to be further improved and perfect in futurework.
The simulation effect of the model on sediment transport is not comprehensive
enough. First, the SWAT model does not consider the impact of agricultural farming
adjustment and restoration activities on the simulation effect. It is necessary to
clarify the limiting factors of the model further and improve the model. Second, it isneeded to clarify further and accurately quantify the impact of human activities,
especially the impact of restoration activities on sediment production and transport
in the basin, and improve the simulation accuracy of sediment transport.

Limit the available data in the research. Some meteorological and hydrological data
have improved room. The radar rainfall data used in this study are of high quality interms of meteorological data. The watershed area is small, and the solar radiation
within the watershed is not much different. However, few stations have the highest
and lowest temperature data, average wind speed, and relative humidity. In the
future, if the measured data of more stations can be obtained, it will be helpful toimprove the simulation accuracy. In terms of hydrological data, this study uses themonthly sediment transport data in the middle and upper reaches of the basin.
Suppose the downstream and daily sediment transport data can be obtained. In thatcase, we can better understand the sediment transport change in the whole basin
before and after the earthquake and analyze the impact of land use/cover change on
the sediment transport process.

6.4. Research outlook
In future research, we will consider adding content to assess the climate change in98

Chapter 6
the Atsuma River basin and its impact on the hydrological cycle process in the basin andthe laws of the hydrological cycle process in the Atsuma River basin under future land
use/cover and climatic conditions. For more accurate and comprehensive results.


The global climate is undergoing significant changes characterized by warming.
Global warming and the resulting problems of hydrology and water resources are
becoming more and more prominent and are also attracting more and more
attention from the international community. According to previous researchconclusions, hydrological processes are often affected by the combined effects ofmultiple factors (precipitation, temperature, vegetation, topography, soil, etc.).
Changes in precipitation are often amplified into the runoff, and higher
temperatures lead to increased potential evapotranspiration, which reduces runoff
and soil moisture. Rivers fed by snowmelt are more vulnerable to a warming
climate, which affects the process and amount of snowmelt due to increased
temperatures. Overall, climate change will profoundly affect terrestrial water cycle
processes and change watershed runoff and sediment transport processes. Therefore,
future research plans to consider the climate change in the Atsuma River basin andthe impact of climate change on the hydrological cycle process in the basin. The
impact of climate change and land use/cover change on runoff and sedimenttransport changes in the Atsuma River basin was quantitatively assessed using
double cumulative curves and regression analysis.

Applying IDRISI software, based on the 2015 land use map of land use in the
Atsuma River basin, use the CA-Markov module to obtain the land use typetransfer area matrix in the basin from 2000 to 2015, and use it as the macro quantity
transformation specification of cellular automata. Then, based on the 2020 land use
map, select factors such as nature and human activities as the input layer of the
MLP-Ann model, and the land-use conversion suitability atlas is generated as the
micro-spatial transformation law of the cell. The land-use conversion in 2040 iscompleted. Use pattern prediction. Based on the rainfall under future climatechange scenarios obtained from the Database for Policy Decision making for Future
Climate Change (d4PDF) dataset, assess the hydrological cycle process in the
Atsuma River basin under future climate and land use/cover conditions.
99

Chapter 6

Appendix

Appendix
Journals[1] Chen, Y.C.; Nakatsugawa, M.; Ohashi, H. Research of Impacts of the 2018
Hokkaido Eastern Iburi Earthquake on Sediment Transport in the Atsuma River
Basin Using the SWAT Model. Water. 2021, 13(3), 356. DOI:
10.3390/w13030356[2] Chen, Y.C.; Nakatsugawa, M. Analysis of Changes in Land Use/Land Cover andHydrological Processes Caused by Earthquakes in the Atsuma River Basin in
Japan. Sustainability. 2021, 13(23), 13041. DOI: 10.3390/su132313041

Proceedings and Conferences[1] Chen, Y.C.; Nakatsugawa, M.; Ohashi, H. Research on sediment transport in the
Atsuma River basin using QSWAT model. Joint Seminar on Environmental
Science and Disaster Mitigation Research 2020, Muroran, Japan.
[2] Chen, Y.C.; Nakatsugawa, M. Impact Research of Land-use/Land-cover Change
on Runoff and sediment transport in the Atsuma River basin Caused by 2018
Hokkaido Eastern Iburi Earthquake. AGU fall meeting 2021, New Orleans,
America.

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