The 8th International Symposium
on Water Environment Systems---with Perspective of Global Safety(November 13th – 14th, 2020)⚫
Department of Civil and Environmental Engineering
Graduate School of Engineering
Tohoku University

Sponsors
International Joint Graduate Program in
Resilience and Safety Studies of Tohoku University
http://gp-rss.tohoku.ac.jp/

WISE Program for Sustainability in the Dynamic
Earth of Tohoku University
https://syde.tohoku.ac.jp/
Department of Civil and Environmental
Engineering, Graduate School of Engineering,Tohoku University
http://www.eng.tohoku.ac.jp/

ORGANIZERSDr. So KAZAMA(Professor, Tohoku University)

Dr. Yu-You LI(Professor, Tohoku University)

Dr. Daisuke KOMORI(Associate Professor, Tohoku University)

Dr. Daisuke SANO

(Associate Professor, Tohoku University)

Dr. Kengo KUBOTA(Associate Professor, Tohoku University)

SECRETARIATS
Assistant Professor, Dr. Yoshiya TOUGE
Assistant Professor, Dr. Yu QIN

yoshiya.touge.a6[at]tohoku.ac.jp
yu.qin.e8[at]tohoku.ac.jp

DC Student, Ying SONG

song.ying.q2[at]dc.tohoku.ac.jp

DC Student, Chao RONG

rong.chao.p7[at]dc.tohoku.ac.jp

DC Student, Shuka KAGEMASA

shuka.kagemasa.t2[at]dc.tohoku.ac.jp*please change the mark [at] to "@"PARTICIPANTSTOHOKU University

JAPAN

Jiayuan JI

P.D.

Qing CHANG

P.D.

Ying SONG

DC student

Ziang HEDC student

Yunzhi QIANDC student

Shuka KAGEMASA

DC student

Kampachiro URASAKIDC student

Hiroyuki OHNO

DC student

Vempi Satriya Adi HENDRAWAN

DC student

Chao RONG

DC student

Yifan ZHU

DC student

Danila PODOBED

DC student

Ke SHIDC student

Tianjie WANG

MC student

Shitong ZHOU

MC student

Zibin LUO

MC student

Amalia Nafisah Rahmani IRAWAN

MC student

Hajiem YANAGISAWA

MC student

Hayata YANAGIHARA

MC student

Chenling SUN

MC student

Qin HUANG

MC student

Runda DU

MC student

Guangze GUO

MC student

Chai YIKAIMC student

Kyoto University

JAPAN

Tomoyuki HINO

Professor

Temur KHUJANAZAROVPhDSanjar SADYROVDC student

Princeton University

United States of America

Z. Jason REN

Professor

Xi'an University of Architecture and Technology

China

Rong CHEN

Professor

Jinfan ZHANG

DC student

Yaqian LIU

MC student

China Agricultural University
Mengmeng JIANG

China
DC student

Shanghai University
Jinghuan LUO

China
PhD

Kasetsart University
Thapthai CHAITHONG

Pati Regency Government Indonesia
Erwan Wahyu WIBOWO

ThailandProfessor

Indonesia

Schedule (GMT+9)
13 Nov (FRI)
8:50~8:55

Opening ceremony

8:55~9:00

Group photo

9:00~9:40

Plenary lecture I
Electroactive Membranes for Resource Recovery from Wastewater,
Prof. Z. Jason REN, Princeton University

9:40~10:20

Plenary lecture II
Mediating methane fermentation and retarding membrane fouling in
AnMBRs by carbon-based materials: efficiency and mechanism
Prof. Rong CHEN, Xi’an University of Architecture and Technology

10:20~10:30

Coffee break

10:30~12:00

Poster exhibition (Room A&B)
Topic 1: Anaerobic membrane bioreactor
Performance of an AnMBR treating real sewage at low temperature,
Jiayuan JI, TOHOKU University
Efficient treatment of municipal wastewater and biogas production by apilot-scale submerged anaerobic membrane bioreactor,
Tianjie WANG, TOHOKU University
High solid mono-digestion and co-digestion performance of food waste
and sewage sludge by a thermophilic anaerobic membrane bioreactor,
Shitong ZHOU, TOHOKU University
Methane fermentation of rich lipid food waste by a hollow fiber
anaerobic membrane bioreactor (HF-AnMBR),
Ziang HE, TOHOKU University

Topic 2: Anammox
Startup of a pilot-scale anammox reactor for municipal wastewater
treatment and biofilm formation,
Zibin LUO, TOHOKU University
Biofilm characterization and operation performance in a single stage
partial nitritation/anammox process with a function carrier,Yunzhi QIAN, TOHOKU University

P1

Topic 3: Environmental microorganism
Microbial diversity of small bacteria in activated sludge,
Shuka KAGEMASA, TOHOKU University
Application of hemin for the detection of environmental
microorganisms, Kampachiro URASAKI, TOHOKU University
Effects of altering process parameters for controlling nutrient
concentration in treated water on microbial community structure andamoA gene in activated sludge,
Hiroyuki OHNO, TOHOKU University

10:30~12:00

Poster exhibition (Room C&D)
Topic : Hydrological ecology
A cross comparison of hydrological similarity and geological similarity
for the sub-catchments within Natori river basin,
Qing CHANG, Tohoku University
Crop yield sensitivity to drought events: a global-scale analysis of major
crops,
Vempi Satriya Adi HENDRAWAN, Tohoku University
Snow cover analysis for dam inflow prediction in Thailand,
Tomoyuki HINO, Kyoto University
The Selection of Temporal Scale for Drought Analysis using SatelliteBased Precipitation Data,
Amalia Nafisah Rahmani IRAWAN, Tohoku University
Relationship between local and spatial probabilities of precipitation in
the Yoneshiro River catchment,
Hajiem YANAGISAWA, Tohoku University
Estimation of the risk of inland flood based on distribution of extreme
precipitation in Japan,
Hayata YANAGIHARA, Tohoku University
Evaluating the effect of dryness on wildfire in Tohoku region using
KBDI and PDSI,
Chenling SUN, Tohoku University
Evaluate the effect of fuel moisture content on the heat required for
ignition in the Tohoku Region of Japan,
Qin HUANG, Tohoku UniversityP2

12:00~13:00

Lunch break

13:00~15:30

Oral session I
Topic 1: Advanced wastewater treatment

13:00~13:15

High loading capacity of EGSB reactor with anammox-HAP sludge at
extremely low temperature of 7°C,
Ying SONG, TOHOKU University

13:15~13:30

Development of an energy saving type municipal wastewater treatment
system by combining AnMBR and Anammox processes: pilot-scale
plant study and system evaluation,
Chao RONG, TOHOKU University

13:30~13:45

Treatment of municipal wastewater by anaerobic membrane bio-reactor:
process performance and mass balance,
Runda DU, TOHOKU University

13:45~13:50

Coffee break

Topic 2: Membrane technology
13:50~14:05

Virus removal by membrane bioreactors: mechanisms and modeling
efforts,
Yifan ZHU, TOHOKU University

14:05~14:20

Mechanisms of MS2 bacteriophage removal in an anaerobic membranebioreactor,
Jinfan ZHANG, Xi'an University of Architecture and Technology

14:20~14:35

High rate anaerobic digestion of food wastewater in an anaerobic
membrane bioreactor,
Mengmeng JIANG, China Agricultural University

14:35~14:40

Coffee break

Topic 3: Bioenergy production
14:40~14:55

Using an expended granular sludge bed reactor for advanced anaerobic
digestion of food waste pretreated with enzyme,
Jinghuan LUO, Shanghai University

14:55~15:10

Biochar sustained high-efficient anaerobic co-digestion by enhancing
direct interspecies electron transfer and alleviating thermodynamic
restriction,
Yaqian LIU, Xi'an University of Architecture and Technology

15:10~15:25

Effect of thermal pretreatment on anaerobic digestion of sewage sludge
by anaerobic membrane bioreactor,
Guangze GUO, TOHOKU UniversityP3

15:25~15:30

Coffee break

15:30~18:00

Oral session II
Topic: Hydrological ecology

15:30~15:55

Assessment of the secondary salinization impact to the water resourcesin the Uzbekistan,
Temur KHUJANAZAROV, Kyoto University

15:55~16:20

Simulating soil water recession coefficients using satellite-based data
for antecedent precipitation index，Thapthai HAITHONG，Kasetsart University

16:20~16:40

Understanding Seasonality and Evapotranspiration of Soil Water under
Tree and Grass Cover Using Natural Isotopes，Danila PODOBED, Tohoku University

16:40~16:50

Coffee break

16:50~17:10

Spatiotemporal analysis of drought indicated by scPDSI over Japan,
Ke SHI, Tohoku University

17:10~17:30

The effect of wealth level and community-based environmental
activities participation on environmental awareness,
Erwan Wahyu WIBOWO, Pati Regency Government Indonesia

17:30~17:50

Hydrological assessment of precipitation products over high mountainregions: case study of Issyk-Kul Lake,
Sanjar SADYROV, Kyoto University

17:50~18:10

Application of simple paddy field dam model for typhoon event at basin
scale,
Chai YIKAI, Tohoku University

18:10~18:20

Closing speech

14 Nov (Sat)10:00~12:00

Online fieldwork
Topic: AnMBR&Anammox pilot-scale plant

P4

Online fieldwork- AnMBR&Anammox pilot-scale plant・New concept of organic wastewater treatment

・Pilot plant of AnMBR and One-stage Anammox in Senen sewage treatment plant

AnMBR and One-stage Anammox is a new technology to develop an
energy-positive innovative sewage treatment system integrating ananaerobic membrane bioreactor (AnMBR) and anaerobic ammonium
oxidation (Anammox) process. The pilot plant in Senen sewage treatment
plant has a working volumes of 5 m3 for the AnMBR and 1.67 m3 for Onestage Anammox reactor and completed with a capacity of 20 m3/d after a
500-day operation.

P5

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

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Updated by 10th Nov, 2020

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8:45~18:30 Main RoomFor more technical questions, please kindly find the “General Flow” in the programor send mails to our technical staff.

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Electroactive Membranes for Resource Recovery from Wastewater○ Z. Jason Ren*, Xi Chen, Xiaobo Zhu, Joshua Jack1

Department of Civil and Environmental Engineering, Princeton University, Princeton, USA.
*
E-mail: zjren@princeton.edu

Abstract

In the past decade energy and environmental innovators have made significant advancements inrecovering energy from waste materials, yet the scalability and economic benefits of suchapplications still face challenges, partially because of the dramatic decrease in renewable electricity
price. With renewable electricity costing 2 cents per kwh to even negative in some places duringsome periods, how to use cheap renewable energy to maximize waste valorization can become aninteresting direction. In addition, waste treatment can carry mutual benefits to renewable energy
industry, as water is a common electrolyte and energy storage medium.
In this talk, I will discuss some recent progress in identifying the synergy between microbial
electrochemistry and membrane processes that led to the development of new materials and systems.
I will report some development on electroactive membrane electrodes that take advantage of the
low-cost renewables and enable specific resource recoveries from wastewater and CO2. While wehave been focusing on energy-neutral waste treatment, maybe we can start to think broadly on
carbon-negative and dollar-positive waste treatment beyond energy production.

1

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Mediating methane fermentation and retarding membrane fouling in
AnMBRs by carbon-based materials: efficiency and mechanism○ Rong Chen1*, Qian Li1 & Gaojun Wang1
Xi’an University of Architecture and Technology, No.13 Yanta Road, Xi’an, 710055, PR China .
Abstract
This study investigated the efficiency and mechanism of carbon-based materials on promoting methane
fermentation and retarding membrane fouling in anaerobic membrane reactors. The results indicate promoting
syntrophic electron exchange between bacteria and archaea via direct interspecific electron transfer (DIET) is themain approach that biochar (BC) and activated carbon (AC) promoting methane fermentation. Mechanisms of BC
and AC mediating DIET are different, BC acts as geobatteries while AC tends to acts as geoconductors. Membrane
fouling performance indicates AC can alleviate membrane fouling in long-term operation, in which suppressing
cake layer formation via impacting enrichment of microbes on membrane surface played a key role.
Keywords: carbon-based materials; biochar; activated carbon; methane fermentation; membrane fouling.
1

3 Results and discussion

Anaerobic digestion (AD) is one of the most promisingfor bio-waste management to attain sustainable conversion of
biomass to renewable energy. However, an excessively highorganic loading rate (OLR) could lead to severe volatile fatty
acids (VFAs) accumulation and pH decrease of AD system,
which largely hinders the methanogenesis process [1]. As anadvanced AD technology, although anaerobic membranereactor has many advantages over conventional AD
technology, membrane fouling of this system still a big
challenge that hinders the widespread of this technology.
To cover the VFAs accumulation and membrane foulingsimultaneously, the addition of carbon-based materials isenvironment friendly and low-cost solution. In the hybrid
system, biochar (BC) or activated carbon (AC) has thepotential to act as the redox-active mediator to achieve directinterspecific electron transfer (DIET), which was beneficial
for promoting CH4 production and increased OLR via
accelerating VFAs degradation. Moreover, the addition ofcarbon-based materials also can enhance the scouring
intensity of biogas on membrane surface by resulting in more
intensive hydraulic disturbance in AnMBRs, which isconsidered the most effective strategy for retarding
membrane fouling.
In this study, the carbon-based materials (both BC andAC) were utilized to explore the efficiency and mechanismof these materials mediating methane fermentation andretarding membrane fouling in AD and AnMBRs systems.

3.1 Efficiency of BC promoting methane fermentation
The presence of BC dramatically shortened the lag timeof methane production and increased the methane production
rate with increased organic loading (Fig.1). The electron
exchange in syntrophic oxidation of butyrate and acetate asintermediate products was significantly facilitated by BC(Fig. 2). CH4 production and VFAs concentrations suggest
biochar addition promoted syntrophic degradation of
butyrate to acetate occurred under high H2 partial pressure(Fig. 3), this is the main way that BC promotes methane
fermentation [4].
Accumulative CH4 production (mL)

1 Introduction

250
200
0.25 FS
0.25 FS+BC
0.75 FS
0.75 FS+BC
1.5 FS
1.5 FS+BC
2.25 FS
2.25 FS+BC
3 FS
3 FS+BC

150
100
50
0

0

10

20

30

40

50

Time (d)Fig. 1. Accumulative methane production atfeedstock/seed sludge (F/S) ratio of 0.25, 0.75, 1.5, 2.25,
and 3 in the control group (FS) and biochar-amended
0.25
0.75
1.5
2.25
group (FS + BC).
4000

6.5

3000

6.0
2000
5.5
1000
5.00
00

Biochar prepared at different pyrolysis temperatures(300, 500, 700), then the promoting efficiency of this BC on
AD was tested by batch experiment to determine the optimal
preparing condition. After the batch test, BC and AC wereadded to sequencing batch reactors and AnMBRs toinvestigate their promotion on AD and membrane foulingcontrol. During long-term operation of the reactors, themechanisms of BC and AC mediating methane fermentation
and retarding membrane fouling were explored by sludge
characteristics, intermediate substances content, andmicrobial succession in each system. Specific information
about the reactors’ operation and analytical methods can befound in our previous studies [2–4].

(b)5000 (b) FS+BC

FS (3)

7.0

VFAs (mg-COD/L)
pH

VFAs (mg-COD/L)pH(a) 5000 (a) FS

2 Materials and methods

5

10
10

15

20
20

Time (d)
Time
(d)

25

30
30

35

6.5

3000

6.0
2000
5.5
1000
5.00
00

40
40

FS+BC (3)

7.0

4000

5

105

15

20 10 25

30 1535

Time (d)
(d)
Time

40

45
20

Fig. 2 Variation of VFAs at F/M of 3.0 in control (a) andBC groups (b)
1000

40

CH4 production (mL)(b)

H2 addition

VFAs concentration (mg/L)

(a)

35
30
25

20
15
10

BC

5

CT
1

2

3

4
5
Time (day)

6

7

8

BC-butyrate
CT-butyrate
BC-acetate
CT-acetate

800
600
400
200
0

0
0

9

0

1

2

3

4

5

6

7

8

Time (day)Fig.3 Effect of biochar on syntrophic oxidation underhigh H2 partial pressure. (a) CH4 production; (b) VFAs.

2

3

7.5

7.5

9

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety
3.2 Promoting mechanism of BC and AC on methane
fermentation
Compared with AC, which showed higher conductivity
but no redox activity, BC exhibited higher electron exchange
capacity (Fig. 4). This higher electron exchange capacity
stemmed from the diverse redox-active moieties, which
resulted in a more efficient DIET. The formation of wire-likeappendages which linked the enriched DIET partners (suchas Syntrophorhabdus and Methanosaeta) on biochar probably
further enhanced the electron transfer. Briefly, BC acted as
geobatteries while AC acted as geoconductors, this is thedifference that BC and AC promote methane fermentation(Fig. 5) [3].

Fig. 7 Impact of AC on gelatinous (a) and soluble (b)biopolymers on membrane surface layer(a)C-AnMBRAll microbes

Proteobacteria

Bacteroidetes

Firmicutes

Synergistetes

Chloroflexi

P-AnMBR

Mixed liquor

Layer on membrane surface

All microbes

Proteobacteria

Bacteroidetes

Firmicutes(b)Fig. 4 (a) Reductive and oxidative current responses tothe increasing amount of biochar (black curve) andGAC (orange curve); (b) electrical conductivity analysis
for the sludge samples

Fig. 8 Effect of AC on the microbial community. (a)bacteria; (b) archaea.

4 Conclusions

Fig. 5 mechanisms of BC and AC intensify methane
fermentation

Both BC and AC significantly promoted methane
fermentation during anaerobic treatment of diversity
biowastes via triggering a more efficient DIET. AC exhibitedhigh conductivity while BC showed a higher electron
exchange capacity because of the diverse redox-active
moieties, which enable BC acted as geobatteries while AC
and geoconductors in the promotion of methane fermentation.
AC alleviates membrane fouling by suppressing cake layer
formation, in which, enrichment of microbes on membranesurface played a key role.

3.3 Efficiency of carbon-based materials alleviate membrane
fouling in AnMBRs
Membrane performance indicates AC remained
efficient for membrane fouling control after a long-term
operation, and it reduced the fouling rate at a hydraulicretention time of 8 h from 3.12 to 0.89 kPa/d (Fig. 6). PAC
mainly mitigated the membrane fouling by restraining theformation of a cake layer while generating a gel layer on themembrane surface (Fig. 7). Microbial community analysis
indicated the phylum Proteobacteria (Pseudomonas) andMethanothrix (79.21%) were the predominant bacteria andarchaea, respectively, in the gel layer (Fig. 8), this result iscompletely different from the presence of a high abundanceof Levilinea (7.1%), Aminivibrio (4.9%) and Methanothrix(90.04%) in the cake layer on the membrane surface without
PAC. The significant difference in the predominant microbes
in the membrane surface layer was attributed to the reduced
enrichment of Levilinea and Methanothrix with PAC
addition [2].
HRT24 HRT16 HRT12

HRT8

HRT 8

HRT 24 HRT 16 HRT 12

HRT6

4

y=
0.2

Membrane changed-0.032y=0.038x

.370
47x-3
y=0.0

0
0

16
4x-13.
y=0.0920 40 60 80 100 120 140 144146148150

Duration (d)160

x+0.077
y=0.030

180

0

14
6.4
x27
0.1
y=
-2.580038x
y=0.

y=4.70x-809.9

y=
2.5
4x355
.9

16
12

y=5.371x-911.4

Membrane changed

20

17
x12
.49

TMP (KPa)

24

8

HRT 6

(b) (b)(a)28y=12.4x-185
4.7(a) 32

References

.2
20
x-1
85
0.8
y=

2
-13.03090x
y=0.

20 40 60 80 100 120 140 144146148150
Duration (d)160 170 180

Fig.6 Membrane fouling performance. (a) control; (b) AC

3[1] Müller N, Worm P, Schink B et al. Syntrophic
butyrate and propionate oxidation processes: From genomes
to reaction mechanisms. Environmental microbiology
reports 2010,2(4):489–99.
[2] Lei Z, Yang S, Li X et al. Revisiting the effects ofpowdered activated carbon on membrane fouling mitigationin an anaerobic membrane bioreactor by evaluating longterm impacts on the surface layer. Water Research
2019,167:115137.
[3] Li Q, Gao X, Liu Y et al. Biochar and GAC intensify
anaerobic phenol degradation via distinctive adsorption andconductive properties. Journal of Hazardous Materials
2020:124183.
[4] Wang G, Li Q, Gao X et al. Synergetic promotion ofsyntrophic methane production from anaerobic digestion ofcomplex organic wastes by biochar: Performance andassociated mechanisms. Bioresource Technology
2018,250:812–20.

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Performance of an AnMBR treating real sewage at low temperature○ Jiayuan JI1*, Runda DU1, Yujie CHEN1 & Yu-You LI1,2
1

Department of Civil and Environmental Engineering, Tohoku University, Sendai 980-8579, Japan.
2
Graduate School of Environmental Studies, Tohoku University, Sendai 980-8579, Japan.
*
E-mail: kikaen@tohoku.ac.jp

Abstract
A 20 L hollow fiber type submerged anaerobic membrane bioreactor (AnMBR) was used to treat real sewage at
15 °C low temperature under varied hydraulic retention times (HRTs) from 6 to 24 hours. The treatment
performance was evaluated by organic removal efficiency, biogas production, sludge yield, and filtration
behaviors of membranes. For HRTs ranging between 16 h and 24 h, the AnMBR showed good organic removal
efficiency with chemical oxygen demand (COD) and biochemical oxygen demand (BOD) removal efficiencies ofabout 90% and 91%, respectively. The biogas production rate was achieved 0.1 L-gas/ L-water, withapproximately 81% methane content. According to the COD balance analysis, the low biogas yield and highsludge yield at HRT of 6 h and 12 h were the result of the insufficient biodegradation under the low operation
temperature.
Keywords: anaerobic membrane bioreactor; real municipal wastewater; low temperature; biogas; anaerobic
digestion.
community while lack of the biogas production
performance. Furthermore, it is also unknown that it
could be able to obtain the same effect when dealing
with the real sewage wastewater.
Based on the results of successfully applied
AnMBRs to the real sewage treatment in room
temperature reported in previous studies, the AnMBR
was used to dealing with the real sewage by operated
temperatures at 15 °C (HRT 6 to 24 hours) in thisstudy. The performance of sewages treated by
AnMBR in different HRTs at low temperature
condition was investigated in aspects on pollutant
removal performance, gas yield, sludge yield, COD
balance as well as the filtration performance.
2 Materials and methods
2.1 Consist of AnMBR and operation conditions

1 Introduction
Recently, researches on wastewaters treatment
solutions tend to achieve recovery or recycling
purpose of energy and resources during the process forpollutants removal. Technologies have already beenused for treating some kinds of industrial wastewaters
or waste sludge and achieved the recovery or reuse ofenergy and resources have been reported while it isstill a bit backward for applying in sewage treatment.
The anaerobic membrane bio-reactor (AnMBR)
integrates anaerobic digestion and membrane
technology creating a new process which provided
with the potential of energy recovery through themethane fermentation process in anaerobic digestion
as well as the high efficiency of sludge-water
separation due to the filtration by membrane. It also
has been successfully applied in dealing withindustrial wastewater and is considered to be atrustworthy process. If the process could be possibly
applied for treating sewages as well, the wastewater
treatment plants could also become net suppliers ofenergy, renewable resources and reclaimed water
which would make a big contribution for theconstruction of sustainable social development.
Some studies that related to the low organic strength
wastewater or sewage based on man-made synthetic
wastewater reported have confirmed that it is possible
to be used for sewage treatment with a high efficiency
of organic removal with less waste sludge produced aswell as energy recovery from the produced biogas. A
previous research based on synthetic sewage
wastewater investigated the response of AnMBR to
the temperature decreased from 25 to 10 on organic
removal, membrane fouling and the microbial

A 20L AnMBR with hollow fiber type membranes set
inside was installed in Sen-En wastewater treatment plant(S-WWTP) located in Tagajo city, Miyagi prefecture ofJapan. The influent of AnMBR was taken from the WWTP
by a peristaltic pump (FP-100-1515, AS ONE, Japan).
Solid-liquid separation was permeated using a
micro-filtration module (MITSUBISHI CHEMICALS,Japan) by a peristaltic pump (FP-100-1515, AS ONE,Japan). Membrane module used in this research was ahollow fiber type polyvinylidene difluoride (PVDF)
membrane with the pore size of 0.4μm.
2.2 Samples collection and analysis methods
Influent, effluent, and mixed liquor samples were regularly
taken in order to analysis water quality index and the sludge
traits. The analysis of COD, BOD, SS, mixed liquor
suspended solid (MLSS) and mixed liquor volatilesuspended solid (MLVSS) were in according with standard
methods. The proportion of CH4, CO2, and N2 in biogas
produced was measured using a gas chromatograph(Shimadzu, GC-8A, Japan) equipped with a thermal

4The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety
conductivity detector. Dissolved methane in the effluent
was determined using a headspace technique which has
been described by former researchers. All the methane
measurements of methane gas were normalized to thestandard temperature and pressure (STP: 0 degree, 1atm).
The details was described in the previous research [1].
The COD balance was calculated for influent, permeate
effluent, biogas produced (methane gas discharged), H 2S inthe biogas produced, methane gas dissolved in the effluent
and sludge growth, all was converted into COD value.

Fig. 1. Average COD, BOD, SS performance indifferent HRTs.

3 Results and discussion
Figure 1 shows the average COD and BOD in effluent aswell as the average removal efficiency for COD, BOD and
SS at different HRTs at low temperature conditions.
According to the result, the effluent COD and BOD was
deceased after the HRT was extended to 12 hours and the
COD was finally decreased to under 40mg/L and theeffluent BOD was around 10mg/L in HRT 16 and 24 hours.
Therefore, the removal efficiency of COD and BOD wasrecovered to above 90% in HRT 16 and 24 hours. The
above analysis shows that at low temperature conditions,organic removal efficiency was increased and recovered byextending HRT condition.
COD balance was calculated and shown in Figure 2.
Compared with the HRT conditions of 6, 12 and 16 hours, it
was found that COD amount in biogas produced wasincreased from the decrease of those COD amount of sludge
growth and remained in effluent permeate. It was thenobtained a further increase of COD amount as produced
biogas due to the well biodegraded of solid organic matters
as shown minus value of sludge yield in HRT 24 hours.
Energy recovery potential could be generated well in HRT
16 and 24 hours as the COD amount was presented as
57.7% and 65.9%, respectively.
The TMP and flux recording data shown in Figure 3
presents the membrane performance during the long-term
operation at 15°C low temperature. The CFV was declined
to 116 m/h after HRT was extended to 12 hours or longer
and TMP was stabled at an extremely low value in thoseconditions due to the smaller flux in long HRTs. This result
shown that membrane filtration was performed very well in
the conditions of HRT 12 hours to 24 hours even in the lowtemperature of 15°C.
As a result, HRT 6h at 15°C presented the weakness
compared with the other conditions on organic removal,sludge yield, biogas production rate and methane content,
while with strength on flux and sewage treatment capacity,with the additional data shown in Table 1. Among the HRTs
implemented at 15°C low temperature, HRT 24h presented
strengths on organic removal, sludge yield, biogas
production rate and methane content, while the sewage
wastewater treatment capacity during the HRT 24 hours was
obtained only 20L per day which also resulted anunderutilization operation for the AnMBR system during
treatment process. Because of organic removal efficiency
was not reach the standards in HRT conditions of 6 hours
and 12 hours, those conditions cannot be applied in the
sewage treatment in this situation.
Consequently, compared the HRTs implemented at low
temperature of 15°C in this study, the suitable HRT was
considered to be 16 hours in this case.

Fig. 2. COD balance in different HRTs (%).

Fig. 3. TMP and flux during the long-term operation.
Table 1 Additional comparison for different HRTs.
6
12
16
24
HRT (h)Sludge yield(g-VSS/g-CODrem)
Biogas production rate
(L-gas/L-water)
Methane content
Treatment capacity (L/d)

0.35

0.21

0.16

-0.05

0.03

0.06

0.09

0.10

66.85
77.8

74.72
39.8

80.82
29.9

80.99
20.0

4 Conclusions
As operated at a low temperature of 15°C in different
HRTs, the below conclusions can be obtained:(1) Applying AnMBR directly into the real sewage
treatment at low temperatures, well organic pollutant
removal performance with a low sludge yield could beachieved by extending the HRT condition.
(2) A high energy recovery potential could be achieved by
the generated biogas during HRT 16 and 24 hours at
15°C low temperature.

Reference[1] J. Ji, S. Sakuma, J. Ni, Y. Chen, Y. Hu, A. Ohtsu, R.
Chen, H. Cheng, Y. Qin, T. Hojo, K. Kubota, Y.Y. Li,
Application of two anaerobic membrane bioreactors withdifferent pore size membranes for municipal wastewater
treatment, Sci. Total Environ. 745 (2020) 140903.
https://doi.org/10.1016/j.scitotenv.2020.140903.

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(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Efficient treatment of municipal wastewater and biogas production by apilot-scale submerged anaerobic membrane bioreactor○ Tianjie WANG1*, Zhe KONG1, Jiang WU1, Yu QIN1 & Yu-You LI1
1

Laboratory of Environmental Protection Engineering, Department of Civil and Environmental
Engineering, Graduate School of Engineering, Tohoku University, 6-6-06 Aza-Aoba, Aramaki, Aoba
Ward,Sendai, Miyagi 980-8579, Japan.

Abstract
The operation performance of a pilot-scale submerged anaerobic membrane bioreactor (AnMBR) treating realmunicipal wastewater with an effective volume of 5.0 m3 was investigated at a temperature of 25℃. This
demonstration plant realized a staged shortening of hydraulic retention time (HRT) from 48 h to 6 h, obtaining
excellent effluent quality with the COD removal efficiency over 90% and BOD5 removal over 95%. Biogas wasrecovered efficiently from municipal wastewater with a biogas production of 0.08-0.10 L L-1 raw wastewater, andthe methane content was 75%-80% in the biogas. A low sludge yield was also obtained in this study.
Keywords: anaerobic digestion; AnMBR; municipal wastewater; anaerobic wastewater treatment.
hypochlorite (NaClO) solution and citrate solution. Biogas
production was measured by a wet gas meter. The actual gas
temperature (℃) and atmospheric pressure (hPa) were
measured by a thermometer located outside the AnMBR.
Biogas volume was standardized and calculated under theconditions of 273.15 K and 1013.25 hPa. The componentsof biogas including N2, CH4 and CO2 were measured by gas
chromatograph (GC-8A, Shimadzu, Japan).
The pH of influent, sludge, and effluent was measured insitu using a pH meter (D-71, HORIBA, Japan). AnMBR
tank internal pressure (kPa), filtration flow rate (L min-1),
and influent and sludge temperature were detected by anautomatic control system. An online transmembranepressure (TMP) sensor displayed real-time TMP
fluctuations and saved the data in the automatic control
system. The Temperature, atmospheric pressure, biogas
production, biogas components, and TMP were recorded
daily.
Liquid samples containing influent, sludge, and effluent
were collected three times a week in a 250 mL plastic
sampling bottles and measured for alkalinity, CODCr(hereinafter referred to as COD), BOD5, andammonia-nitrogen (N-NH4+). Measurements of sludge
concentration represented by mixed liquor suspended solids(MLSS) and mixed volatile suspended solids (MLVSS)
were measured once a week during the experimental period.

1 Introduction
The conventional activated sludge (CAS) process and its
derivatives are the most common treatment of municipal
wastewater and have been widely implemented all over theworld. Despite the reliable effluent quality, the CAS process
not only consumes a great quantity of energy for supplying
oxygen, but also generates a large amount of excessive
sludge. This research aims to develop an energy-saving andenergy-creating wastewater treatment system by utilizing ananaerobic membrane bioreactor (AnMBR), combined by theanaerobic digestion treatment process and the membrane
technology, which are rapidly undergoing technologicaldevelopment.
A demonstration plant using a pilot-scale AnMBR was
conducted to investigate the anaerobic wastewater treatment
performance of real municipal wastewater. As a result of
long-term continuous experiments with the hydraulicretention time (HRT) shortened from 48 h to 6 h at 25℃,
the COD and BOD5 removal efficiency reached 90% and95%, respectively. Biogas was recovered efficiently frommunicipal wastewater with a biogas production of
0.08-0.10L L-1 raw wastewater, and the methane content
was 75%-80% in the biogas.

2 Materials and methods
The Schematic diagram of the AnMBR system is shown in
Fig. 1. The influent of the AnMBR was pumped from thegrit chamber effluent of a full-scale municipal treatment
plant to the raw wastewater tank. The raw wastewater wastreated by a drum screen to remove impurities. The effective
volume of AnMBR was 5 m3, the liquid level wasmaintained at a height of 1910 mm, and both the raw
wastewater tank and AnMBR were kept at 25℃ by aconstant temperature water bath system. The membrane
module consisted of a total of 12 hollow fiber membraneelements, with a total membrane area of 72 m2 (6 m2 foreach element). The hollow-fiber membrane is made ofpolyvinylidene fluoride (PVDF) and has a pore diameter of
0.4 μm and an outer diameter of 2.8 mm. The membrane
filtration operation was 5 minutes per cycle (4 minutes forfiltration, 1 minute for relaxation). Membrane maintenancemethod was the online back-wash applying sodium

Influent

Fig. 1. Schematic of Pilot AnMBR.

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Tohoku University----- with Perspective of Global Safety
3 Results and discussion
As shown in Fig. 3, the temperature of the raw wastewaterand AnMBR was maintained at 25℃ by the constanttemperature water bath system. AnMBR was operated fromthe condition of HRT 48 h, and the COD removal efficiency
gradually increased from 51.0% to 82.9%. This period wasconsidered the start-up period. After nearly a month, HRT
was gradually shortened to 24 h, 12 h, 8 h, and 6 h. The
HRT was set to 8 h eventually because the TMP exceeded
the reasonable limitation (30 kpa) under the 6 h HRT
operating conditions.
AnMBR has achieved a COD removal efficiency of over
90% and a BOD5 removal rate of over 95% during stable
operation. The effluent COD concentration was basicallylower than 50 mg L-1, and the BOD5 concentration was 10
mg L-1. These result shows the excellent municipal
wastewater treatment performance of the pilot-scale
AnMBR at 25℃.
The biogas production was relatively low while the HRT
was 48 h and 24 h, but it increased dramatically when the
HRT was shortened to the stable operation period of 12 h, 8
h. The methane content of biogas exceeded 75%, reaching amaximum of 81%. The biogas production rate from the raw
influent was about 0.11 L L-1 raw water, and the gas
component was about 75% - 81% for methane, 5% - 9% forcarbon dioxide, and 12% - 17% for nitrogen gas.
The membrane filtration operation mode was 4 minutes forfiltration and 1 minute for relaxation, and the membranesurface was continuously washed at a biogas sparging rateof 0.9 m3 min-1. The membrane filtration performance
during the experimental period is shown in Table 1. ΔTMP
was relatively stable During the 48 h, 24 h, and 12 h HRT
periods, which were less than approximately 5 kPa. While
ΔTMP gradually increased and exceeded 10 kPa, it ispossible to perform online membrane backwash chemicalcleaning of 500 ppm NaClO to decrease ΔTMP. When the
HRT was 6 h, the maximum real-time flux was 0.33 m d-1,
meanwhile the membrane fouling occurred during thisperiod progressed very quickly, and the recovery effect ofthe membrane filtration performance by chemical cleaning
was limited.
As a method of maintaining the membrane filtration
performance, the MLSS sludge concentration wasmaintained at 8000-10000 mg L-1, otherwise biogas
sparging and online chemical cleaning were performed
simultaneously, but proper flux and operation mode are also
crucial.

Fig. 3. Long-term performance of Pilot AnMBR.
Table 1 Membrane performance of different HRT.
Period
HRT
Flux
ΔTMP
(day)
(h)
(m d-1)
(kPa)
1-28
48
0.057
0.90-2.60
29-91
24
0.085
2.20-10.7
92-119120.205
2.30-3.10
120-1478
0.289
3.80-12.7
148-175
60.329
6.00-23.5
176-1968
0.259
10.2-22.1

4 Conclusions
1) The pilot-scale AnMBR with an effective volume of 5
m3 realized effectively treating actual municipal wastewaterat 25℃ for the HRT of 6 h. The COD removal efficiency
was over 90%, and the BOD5 removal efficiency was over
95%. The biogas production rate from the influent was
about 0.11 L L-1 raw wastewater, and the gas component
was about 75%-81% for methane, 5%-9% for carbon
dioxide, and 12%-17% for nitrogen gas.
2) Stable membrane filtration was realized with a flux of
0.06-0.29 m d-1 and a ΔTMP of 0.90-12.7 kPa under theconditions of HRT 24 h, 12 h and 8 h. However, at the
maximum flux of 0.33 m d-1, ΔTMP increased rapidly, andrapid membrane fouling was observed. The stable operation
working flux realized in this study was about 0.30 m d -1.

Reference[1] Kong, Z., Wu, J., Rong, C., Wang, T., Li, L., Luo, Z., Ji,
J., Hanaoka, T., Sakemi, S., Ito, M., Kobayashi, S.,
Kobayashi, M., Qin, Y., Li, Y.-Y., 2021. Large pilot-scale
submerged anaerobic membrane bioreactor for the treatmentof municipal wastewater and biogas production at 25 °C.
Bioresour. Technol. 319, 124123.
https://doi.org/10.1016/j.biortech.2020.124123
Fig. 2. Biogas production and content.

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The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

High solid mono-digestion and co-digestion performance of food waste
and sewage sludge by a thermophilic anaerobic membrane bioreactor○ Shitong ZHOU1, Yemei LI2, Hui CHENG2, Guangze GUO1, Yu-You LI1,2
Department of Frontier Science for Advanced Environment, Graduate School of Environment Studies,
Tohoku University,6-6-06 Aoba, Aramaki-Aza, Sendai, Miyagi 980-8579, Japan.
2
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku
University,6-6-06 Aoba, Aramaki-Aza, Sendai, Miyagi 980-8579, Japan.
*
E-mail: zhou.shitong.q2@dc.tohoku.ac.jp.

1

Abstract
The performance of co-digestion of food waste (FW) and sewage sludge (sludge) by a thermophilic anaerobic
membrane bioreactor (ThAnMBR) was firstly investigated. The long-term stable operation showed the feasibilityof the utilization of ThAnMBR for mono- and co-digestion of FW and sludge at a high solid condition. Good
permeate quality was obtained at all sludge ratios while the addition of sludge restricted the methane generation.
For a sludge substitution with 25% TS-based substrate, the biogas yield of 0.812 L/g-VSfed was at 91% and 158%
that of the mono-digestion of FW and sludge respectively. Membrane performance indicated that the ThAnMBR
operated stable at a high flux of 5 LMH under the high solid (∼27 g/L) condition. Furthermore, membrane
filtration with a 0.1 μm pore size of hollow fiber not only completely removed suspended solids but also rejected
about 70% of soluble COD, 80% of soluble carbohydrates and 17% of soluble proteins.
Keywords: AnMBR; Biogas; Sewage Sludge; Food waste; Co-digestion.
provide many economic and environmental advantages,
including smaller footprint, less sludge production,higher biomass retention, higher organic matterbiodegradation efficiency and more excellent effluent
quality. In the thermophilic anaerobic membranebioreactor (ThAnMBR), membrane bioreactors areplaced under thermophilic condition, typically within atemperature range of 50-60 ℃, which guarantee a rapid
biodegradation rate. The main purpose of this study is toinvestigate the feasibility of co-digestion of FW andsludge by a ThAnMBR based on long-term performance
analysis.

1 Introduction
Since the annual waste biomass production isconsiderable, it is an advisable option to utilize thebioenergy to cut emissions and save resources.
Anaerobic digestion (AD) plays an important role inbioenergy utilization from waste biomass in the circularsociety. As a multifunctional waste treatment tool, it iscurrently one of the most promising processes used forthe treatment of various organic waste-streams, andoffers significant benefits, such as greenhouse gases
reduction, energy production in the form of methane-richbiogas and water purification with low energy demand.
As two of most common organic fraction of municipal
wastes, sewage sludge and food waste (FD) have hugebioenergy potential. However, the AD process of each
substrate both has drawbacks respectively, such as low
methane yield of sewage sludge due to low C/N andpossibility of volatile fatty acid accumulation andunstable operating condition of food waste because of
relatively lack of nitrogen. And co-digestion is anefficient method to deal with these problems andimprove biodegradation performance. A mono-digestion
system involves only one substrate in the AD process,whereas a co-digestion system involves the instantaneousdigestion of two or more types of feedback. The
co-digestion system has been shown to have better
process efficiency than the mono-digestion system withregard to process stabilization, nutrient balance, toxicity
dilution,robustmicrobiome
andecologicalsustainability.
One of the biggest challenges of AD process is thepoor retention of slow-growing anaerobic biomass. In
contrast, anaerobic membrane bioreactor (AnMBR) can

2 Materials and methods
2.1 Substrate: FD and sludge
According to previous researches, the FW in this study
was prepared based on the composition of FW in Japanesedaily life. The sludge used in the experiment was consistedof primary sludge and excess sludge collected from amunicipal wastewater treatment plant in Sendai, Japan. The
sludge composition is based on total solid (TS) content,with 45% TS coming from primary sludge and the rest TS
content coming from excess sludge, which was the same asa typical composition of sludge in Japan. As for substratecomposition, FW was gradually replaced by sludge at TS
ratios of 0%, 25%, 50% and 100% in different experimental
phases.

2.2 Configuration of ThAnMBR and systemopetation
The ThAnMBR used in this study was an external
AnMBR, and the schematic diagram is shown in Fig. 1. The
total effective volume of ThAnMBR was 15 L, consisted ofa continuously stirred tank reactor (CSTR) of 13 L and amembrane unit of 2 L. A hollow fiber membrane with pore

8

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety
size of 0.1 μm and membrane area of 0.1 m2 was inserted into the membrane unit. The AnMBR was maintained at athermophilic condition of 55 ±1 ℃.

3.2 Membrane rejection and long-term operation
The rejection efficiency shows the ability of themembrane module to intercept substances. An obvious
rejection of organic substances by the membrane under
different sludge ratios is presented in Fig. 2(a). And a
comparison between the concentration of S-COD,
S-proteins and S-carbohydrates in digested sludge andpermeate were conducted. The linear regression results
showed that about 70% of S-COD in the digester was
rejected by the membrane filtration and 17% and 80% for
S-proteins and S-carbohydrates, respectively. (Fig.4(b), (c)and (d)).

Fig. 1. The schematic diagram of the ThAnMBR
Four different kinds of substrate containing 0%, 25%,
50% and 100% of sludge were implemented in four phases
respectively, during the long-term experiment. The average
TS loading rate was kept at 3.20 ± 0.30 kg-TS/m3/d.
Membrane filtration was operated under a mode of filtration
cycle included 3 min of filtration and 3 min of relaxation
continuously.

3 Results and discussion
3.1 Biogas production and organic removal
The ThAnMBR system was successfully operated forover 180 days, and the ratio of 25% was considered to be
the best addition ratio of sludge to the co-digestion ofsludge and FW. During all the phases, the CH 4 and CO2
content of biogas was very stable. However, the biogas
production rate decreased from 2.84 to 1.31 L/L-reactor/dand biogas yield decreased from 0.893 to 0.428 L/g-VSfed as
the sludge ratio increased from 0 to 100%. The change of
biogas production with different sludge ratios may berelated to the organic composition of the substrates,
especially the amount of carbohydrates, which have higher
degradation rate. The rich carbohydrates, especially thesoluble part, in FW made up for the low carbohydrates insludge in the 25% sludge co-digestion system and further
resulted in an obviously enhanced methane production
compared with that of 100% sludge phase.
The quality of the permeate also can be used to assess thestability of the system. The stable concentration of TVFA at
a low lever reflected steady digestion performance. The
removal efficiency of the ThAnMBR system was excellent
for each sludge ratio. The average removal efficiency of
COD, VS, TS, carbohydrates and proteins were 97.8 ± 0.9%,
97.3 ± 1.0%, 93.1 ± 1.0%, 99.3 ± 0.4% and 95.7 ± 2.5%
respectively. The results of biogas production and organic
removal efficiency are shown in Table 1.
Table 1 Effect on sludge ratio on biogas production andorganic removal efficiency.

Fig. 2. (a) Rejection efficiency of COD, carbohydrates
proteins by the membrane and SS/TS ratio of digested
sludge at different sludge ratio; The retention of (b) S-COD,(c) S-proteins and (d)-carbohydrates by the membrane.

Membrane fouling is an unavoidable problem in theutilization of the membrane reactor. And solid in thedigested sludge is an important cause of membrane fouling.
In this study, the solid concentration of the ThAnMBR kept
at a high level of 27.29 ± 3.34 g-TS/L on average with aratio of SS to TS over 57%. The long-term experimentresults reveal that the ThAnMBR can be operated under ahigh flux mode (∼5 LMH) at a high solid concentration (∼27g/L) and solid concentration showed a significant influenceon the membrane performance. Except for the influence ofsolid concentration, the generation of white crystal in thepermeate pipeline in 50% and 100% sludge period had amarkedly deleterious effect on the membrane performance.

4 Conclusions
The mono- and co-digestion of FW and sludge wereoperated successfully by a ThAnMBR system at a high fluxof 5 LMH under a high solid condition of 27 g/L. Biogas
production changed greatly, from 0.893 to 0.514 L/g-VSfed,
with the sludge ratio increased from 0% to 100%. The
membrane filtration rejected about 70% of soluble COD and
had a higher rejection of soluble carbohydrates than soluble
proteins.

Reference[1] Li, Y.M. et al, High solid mono-digestion andco-digestion performance of food waste and sewage sludge
by a thermophilic anaerobic membrane bioreactor,
Bioresour. Technol, 310 (2020) 123433.

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GP-RSS(Nov. 13th , 2020)

Tohoku University

Methane Fermentation of Lipid-Rich Food Waste by a Hollow Fiber
Type Anaerobic Membrane Bioreactor○ Ziang HE1*, Yuanyuan REN2 ,Chen WANG2&Yu-You Li2
1

Department of Frontier Science for Advanced Environment, Graduate School of Environmental Sciences,
Tohoku University, 6-6-20 Aoba, Aramaki-Aza, Aoba-Ku,Sendai, Miyagi 980-8579,
2
Department of Civil and Environmental Engineering, Tohoku University, 6-6-06 Aramaki Aza Aoba, Aobaku, Sendai, Miyagi, 9808579, Japan*
E-mail: he.zi.ang.p8@dc.tohoku.ac.jp
Abstract
In this study, the performance of a mesophilic (37℃) HF-AnMBR (hollow fiber type anaerobic membranebioreactor) in treating lipid rich food waste for 180 days is studied and its effectiveness is evaluated. The lipid richfood waste used in this study was made according to the pervious study. The organic loading rates (OLR) of HFAnMBR was gradually increased from 1.63g-COD/L/d to 13.54 g-COD/L/d by increasing the lipid content from
10%, 30% and 50%. Respectively，the COD conversion efficiency was as high as 98%, 97% and 98% at OLRs of
3.27, 10.69 and 13.45 g-COD/L/d. Meanwhile, membrane fouling is the serious problem for MBR, which affects
the performance of reactor, so the relationship between the reversible fouling resistance and irreversible fouling
resistance was evaluated by this kind of substrate under flux of 3 L/m2/h. The long-term stable performance of
AnMBR indicates that lipid rich food waste was suitable to decompose under condition of mesophilic .
Abstract: Food waste, lipid, co-digestion, AnMBR

1 Introduction
Nowadays, wastewater treatment plants have to face criticalissue with strict regulation of water discharge quality. The
food industry generates a plenty of milk product waste instock breeding. According to the key facts on food loss andfood waste by the Food and Agriculture Organization of theUnited Nations (FAO), 1/3 of food is lost or wasted during
production process and human consumption globally [1]. The
increasing production of food waste like milk product waste
worldwide and new international regulations call for thedevelopment of new technologies to treat this biowaste. Due
to high-organic content (81.9%) of food waste, many
researchers have investigated the use of the anaerobic
digestion in treatment of food waste, which are able to treat
organic wastes efficiently, producing at the same timedifferent value-added compounds, such as methane.
However, in anaerobic digestion system, HRT and sludge
retention time (SRT) is the same parameter compare toconventional activated sludge process (CAS). Meanwhile,
due to the low growing rate of archeae. The anaerobic
digestion system needs a large reactor volume to maintaincapability of treatment [2]. In recent years, anaerobic
membrane bioreactor (AnMBR) was invented, which is able
to separate HRT and SRT by retaining solids within reactor
and discharging permeate through membrane filtration. This
combination of MBR with anaerobic digestion brings a highbiomass of archeae, which contribute to high organic loading
rate (OLR). Meanwhile, applying co-digestion could be oneof methods to improve the biogas yield and stability of theanaerobic digester by providing some ways i.e. adjustment ofmoisture content; supplying trace metal elements;
maintaining an optimal pH, enhance capacity of buffer.

2 Materials and methods
2.1 Materials
The composition of the food waste used in this study isbased on the earlier research, including fruit, vegetables,1•••••••• 1 1
10meat, fish, eggs and staple foods. Which is 30%, 36%, 14%
and 20%.
Table 1 Characteristics of inoculum sludge
Parameters （mg/L）

Inoculum sludgepH

7.28-7.50

Total COD （g/L）

24.6±0.27

Total Carbohydrates

1862±99

Total Protein

13617±34TS（g/L）

26.75

VS（g/L）

21.92

TSS（g/L）

22.62

VSS（g/L）

19.21+

1316.38

NH4 -N

Table 2 The composition of the food waste used

The 8th International Symposium on Water Environment Systems----- with Perspective of Global Safety

Fig. 1. diagram of HF-AnMBR used in the test
2.2 Analysis methods
Daily biogas production is measured by wet gas meter, thecomposition of (CH4, CO2 and N2) were measured by a
ShimadzuGC-8A gas chromatograph, and COD,
carbohydrate, protein, ammonium nitrogen, alkalinity, TS,
TSS, VS and VSS were measured according to Japan
Standard Testing Method. Sludge samples were taken fromthe digesters and the substrate tank three times a week. The
total effective volume was 15L.The membrane unit was madeof polytetrafluoroethylene, with a mean pore size of 0.2 μm
and an effective filtration area of 0.1m2.

(Nov. 13th , 2020)

GP-RSS
Tohoku University

is 15d, After a successful start-up (phase I), at lipid content
of 10, 30 and 50%, the biogas production rates were 1.79±
0.3, 5.79± 0.3,7.09± 0.57 L/L/d, respectively. The
corresponding methane yields were 0.31±0.1, 0.67±
0.2,0.74±0.2, and 0.79±0.2 L/g-VS, respectively. While thegradual increase of the methane yield was mainly attributed
to the increasing of lipid content. pH in the reactor remained
around 7.2. CH4 and CO2 content of biogas were stable, in
the range of 58.1-61.9% and 38-41.3%, respectively. At
phase Ⅳ, the OLR was 13.45g COD/L/d with 50% of lipid
content, without any inhibition phenomenon like decrease of
biogas production or drop of pH in reactor, this phenomenon
was disagreement with the results from a previous study: it
was reported that methanogens were severely inhibited when
OLR was over 14.58 g COD/L/d, actually, there is no
accumulate of VFA at all phase, which means a great
potential ability of the HF-AnMBR to lipid-rich food wastewith over 50% of lipid content. This can be attributed to thehigh concentration of digestion sludge in the reactor, the TSof digestion sludge was over 23.3, 23.9, 3.6, and 4.2 g/L ateach phase. The high concentration of digestion sludge
owning to rejection of membrane, preventing the washout ofslow-growing methane-forming anaerobic microorganisms.

4 Membrane resistance distribution

3 Results and discussion

Fig. 3. Membrane fouling
The most of membrane resistance is cake layer and gel gal
layer. which was contribution 83.8% to the total resistance.
Next is organic matter in pore, which was contribution 13.4%
to total resistance, rest of them is inorganic matter in pore andintrinsic membrane resistance, which was contribution 0.16%
and 1.81% to the total resistance. With a combination ofphysical and chemical cleaning, helped membrane
permeability recover as high as 99.3%These results are clear
evidences of the great necessity of chemical cleaning in highstrength organic wastewater treatment by AnMBR in order torecover the initial membrane permeability.

5 Conclusions

Fig. 2. Reactor performance.
The long-term operation of HF-AnMBR is shown in Fig 2.
the HF-AnMBR was operated at 4 phases with different
OLRs about 1.63, 3.27,10.69, and 13.45 g-COD/L/d, when
the feeding lipid-rich food waste consisted of 10%, 30%, 50%of lipid, respectively. HRT of Phase I is 30d, the rest of phase•••••••• 12
11

In this study, we applied the HF-AnMBR to digest theartificial lipid-rich food waste with different contents of lipid.
Operating the HF-AnMBR treating lipid-rich food wastewith around 10%-50% of lipid resulted in stable long-term
performance. There is no inhibition phenomenon shows up
during each phase duo to a high concentration of
biomass.nThe largest contribution to membrane fouling was
made by cake layer and gel layer, followed by the organic
fouling and inorganic fouling, accounting for 83.8%, 13.4%,
and 0.16% of the total hydraulic resistance, respectively.

The 8th International Symposium on Water Environment Systems

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Startup of a pilot-scale anammox reactor for municipal wastewater
treatment and biofilm formation○ Zibin LUO1*, Jiang WU1,2, Yu-You LI1,3
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980 -8579, Japan
Center for Material Cycles and Waste Management Research, National Institute for Environmental
Studies, Tsukuba, 305-8506, Japan
3
Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8579, Japan*
E-mail: luo.zibin.q1@dc.tohoku.ac.jp.

2

Abstract
As conventional biological nitrogen removal process showing the shortage of high energy consumption and large
floor area in wastewater treatment, anammox process, a brand new technology treating ammonium-rich wastewater
efficiently, had attracted special interest recently. This study presents a successful start-up strategy of a partial
nitritation-anammox (PNA) process to achieve a high nitrogen removal in the treatment of municipal wastewater.
A pilot-scale anaerobic membrane bioreactor (AnMBR) was set up by coupling with an PNA reactor for testifying
the feasibility for municipal wastewater treatment. After 240-days operation, the results showed that nitrogen
removal efficiency of higher than 70% had been achieved at 3-hour hydraulic retention time when the ammonium
concentration of the influent was about 35 mg-NH4+-N/L. In the later period, biofilm formation had been observed
from the scanning electron microscope.
Keywords: Partial nitrification; Anammox; Biofilm; Municipal wastewater.
1 Introduction
Anaerobic ammonium oxidation (anammox), is a costeffective and sustainable approach for the removal ofammonium from wastewater or waste gas. In this efficient
process, ammonium could be combined with nitrite to
generate nitrogen gas by anammox bacteria. Compared withthe conventional nitrification/denitrification process, anammox process could be carried on only in a single reactor
which is compact with a small footprint, achieving low
nitrogen concentration in effluent and high nitrogen removal
efficiency. As a brilliant shortcut in the natural nitrogen cycle,
the anammox conversion is destined to be an energy-saving
process, in which 60% of operational costs or energy
consumption could be saved, and CO 2 emission is reducedalso.
For the reason that nitrite, the necessary substrate inanammox process, is rarely found in the common wastewater,
it is vital to generate nitrite to promote anammox process toremove the ammonium. However, partial nitrification andanammox (PNA) remained the most usual way to treat
ammonium-rich wastewater with wide range of applications.
According to the recent studies, PNA had been applied atfull scale for treating high-strength ammonium wastewater(the concentration is over 100 mg/L) with high nitrogen
removal efficiency, which is part of the state of the art now.
But when it is referred to the low-strength ammonium
wastewater, fewer studies had been done to prove its
feasibility. However, a latest lab-scale works achieved highnitrogen removal efficiency of 75 ± 10%, using a moving bed
biofilm reactor at a short HRT of 2 h when the influent
ammonium concentration was 50 mg/L.[1] By being attachedto the carriers and forming the biofilm, anammox bacteria
and AOB could avoid being washed out at such a short HRT
and therefore, be retained in the PNA reactor.
In this study, a pilot-scale PNA reactor was started up withan anaerobic membrane bioreactor (AnMBR) which could

12

provide low COD/BOD concentration and low-strengthammonium wastewater. The aim of the study was to focus on(1) the treatment performance of PNA under various
operation patterns or stages; (2) the biofilm formation toreveal the feasibility for practical application.

2 Materials and methods
2.1 Reactor Setup and Operation
The moving bed biofilm reactor (MBBR) possesses aworking volume of 1.67 m3, equipped with a stirrer, a pH
sensor and a NH4+-N sensor. Hollow cylinder carriers, which
made of hydrophobic polypropylene resin, were added into
the reactor until the filling rate of 15%. Air would be pumped
into the bottom of the reactor to supply the oxygen, which
could be regulated by a rotameter attached. Artificial influent
was transferred by a peristaltic pump while the effluent of
AnMBR was supplied by an impeller pump. Mixed liquidfrom PNA reactor would flow into a 1.92 m3 radial-flow
sedimentation tank and the collected sludge in the bottom
was refluxed into the PNA reactor through an impeller pump.
The sludge containing AOB were inoculated in the reactor
and supplied with synthetic wastewater for proliferation.
Anammox seed sludge were inoculated into the reactor in
Day 91 and fed with synthetic wastewater as well. DO
concentration and pH in the effluent were regularly measured
using a DO meter (HORIBA HC-200NH, Japan) and a portal
pH meter (HORIBA D-71, Japan), respectively.
The operation condition is shown in Table 1.
2.2 Sampling and analytical methods
All of the samples of influent and effluent were analyzed
after being filtrated through Millipore filter units with 0.45
μm pore size. The NH4+-N, NO2−-N, and NO3−-N concentration was measured by capillary electrophoresis (Agilent
7100, Agilent Technologies, USA).

The 8th International Symposium on Water Environment Systems

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Table 1 Operation Conditions
Stage

Ⅰ

Duration (day) 0-90
Influent type

Ⅱ

Ⅲ

Ⅳ

Ⅴ

91-166 167-187 188-205 206-240
SW

SW&MW

MW

Influent
ammonia
250 400-850
concentration+(mg-NH4 -N/L)

150

120

35

HRT (Days)

1

8.35

0.47

0.24

0.12

NLR
(g-N/L/d)

0.24

0.050.10

0.32

0.5

0.3

SW: synthetic wastewater; MW: municipal wastewater;
SW&MW: the mixture of synthetic and real municipal
wastewater.

3 Results and discussion
3.1 Reactor Performance
The experiment, which had lasted for 240 days, contained 5
stages: (Ⅰ) the AOB cultivation period; (Ⅱ) the anammox
bacteria cultivation period; (Ⅲ) the SW&MW adaption
period; (Ⅳ) the decreasing HRT period; (Ⅴ) the stable
operation period.
As is shown in Fig.1, during the stage Ⅰ, in which aiming at
AOB proliferation and NOB inhibition, success had beenachieved due to the high FA/FNA concentration. After being
inoculated and fed with synthetic wastewater, Anammox
bacteria showed slowly growing activity with increasing
NLR and nitrogen removal efficiency of up to 79% had beenobtained in the end of stage Ⅱ. Even though the ammonium
concentration of influent had been dropped to 150 mg/L byusing the mixture of synthetic and real municipal wastewaterin the stage Ⅲ, or in the case that NLR was increased to 0.5
mg-N/L/d in stage Ⅳ, AOR and ANR showed obvious
increasing trend to 0.3 g-N/L/d and 0.4 g-N/L/d respectively,
while NOR kept low. Finally, 80%-nitrogen removal
efficiency had been achieved when low-strength ammonium
wastewater with concentration of about 35 mg/L was treatedin the stable operation period (stage Ⅴ).

Fig. 1. Reactor PerformanceAB

3.2 Biofilm formation
According to the Fig.2, the surface of carriers had been
covered with pink and light red bacteria, which were possibly
anammox bacteria. However, biofilm had been formed
initially, which remained to be observed in the further study.
Fig. 2. Cross section of (A) new carrier; (B) carrier in

4 Conclusions
Efficient treatment for low-strength ammonium wastewater was achieved by using a one-stage PNA pilot plantwith the nitrogen removal efficiency of 80% in the case of 3h
HRT. The biofilm was under formation, which deserves forfurther study and observation.

13stage V

Reference[1] Rong Chen, Using Partial Nitrification and Anammox
To Remove Nitrogen from Low-Strength Wastewater by Coimmobilizing Biofilm inside a Moving Bed Bioreactor, ACS
Sustainable Chemistry & Engineering (2019) 1353-1361.

The 8th International Symposium on Water Environment Systems

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(Nov. 13th ~ 14th , 2020)

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Biofilm characterization and operation performance in a single stage
partial nitritation/anammox process with a function carrier○Yunzhi QIAN1*, Yan GUO2, Junhao SHEN1& Yu-You LI1,2
1

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8579, Japan.
Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan*
E-mail: qian.yunzhi.q8@dc.tohoku.ac.jp

2

Abstract
For single-stage partial nitritation/anammox (SPNA) process in cost-effective nitrogen treatment, the addition offunctional carriers can avoid the loss of biomass and strengthen the functional bacteria activity. In this study, thebiofilm characterization and operation performance were investigated at 25℃ for more than one year using function
carriers. In the biofilm maturation stage, a NRR of 0.78 gN/L/d was obtained with the SAA of 0.34 gN/gMLVSS/d.
The accumulation of inorganic components in biofilm aging stage caused the biomass concentration in the biofilm
to decrease from 4.50 gMLVSS/L to 2.50 gMLVSS/L, resulting in a decrease in the nitrogen removal rate (NRR)from 0.74 gN/L/d to 0.36 gN/L/d. Besides, the cavity created by the accumulation of nitrogen gas also reduces theeffective biomass of anammox bacteria in the biofilm. After mechanical stirring, NRR can recover from 0.36 gN/L/d
to 0.75 gN/L/d within 3 days. The regeneration of biofilm promotes the substrate exchange capacity of
microorganism, which is the key to the restoration of SPNA nitrogen removal capacity.
Keywords: Partial nitritation; Anammox; Carrier; Biofilm
1 Introduction
Partial nitritation-anammox (PNA), as the emerging andlow-cost biotechnological nitrogen removal process, isespecially suitable for high-strength ammonium wastewater,such as anaerobic digestion wastewater. High concentrationof NH4+-N is beneficial to inhibit the activity of nitrite
oxidizing bacteria (NOB) and promote the growth ofanaerobic ammonium-oxidizing bacteria (AnAOB). The
partial nitritation (PN) process and anammox process arecarried out in a single reactor called a single-stage PNA
system (SPNA), and is a space saving process. However, it’sa critical bottleneck to achieve high-efficiency synergistic
between ammonia oxidizing bacteria (AOB) and AnAOB in
the SPNA system. NOB usually coexist in the SPNA systemand compete for substrate (O2 and NO2--N) with AOB andAnAOB[1].
To solve the above challenges, biofilm growth systems was
widely used in the SPNA process. Carrier could provide
surfaces for the attachment of the slow-growing AnAOB inorder to avoid the wash out of the bacteria and attain the
better biomass retention ability. AnAOB can form a biofilmon the anaerobic surface of the biofilm carrier, while AOB
forms a biofilm in the outer biofilm carriers in an aerobic
environment. By controlling the dissolved oxygen (DO)below 0.5mg/L, the activity of NOB can be effectively
inhibited. The low DO concentration is also beneficial to theformation of the double biofilm structure and the stable
operation of the SPNA system.
However, most of the previous studies focused on the effectof different carrier materials on the nitrogen removal
efficiency of SPNA system such as biochar, zeolites, sponges,
non-woven membrane etc. Or investigate the nitrogen
removal performance of the SPNA system under different
conditions under short-term operation, such as different
influent C/N ratios, toxic substances and different
temperatures. There are few studies on the formation

14

characterization of biofilms and the operation performance
under long-term operation.
In this study, a 5L air lift reactor (ALR) was operation for
more than 300 days to study the biofilm characterization andoperation performance at 25℃ using function carriers. The
features of the ALR SPNA process were also verified on
perspectives of the nitrogen removal efficiency and thespecific Anammox activity (SAA).

2 Materials and methods
2.1 Carrier
The carriers used in this study was made of Polypropylene
resin (PP), which has an H×D=10mm×10mm, the specificsurface area of 800m2/m3, and the specific gravity is 0.98.

Fig. 1. Photo of carriers.
2.2 Reactor setup and operation
The SPNA system was equipped with an effective volumeof 5L ALR reactor, a substrate tank, an influent pump, aheater and an air pump.

Fig. 2. Reactor configuration.

The 8th International Symposium on Water Environment Systems

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As shown in Table 1, the whole experiment was divided into
8 phases according to the nitrogen load rate (NLR).
Table 1 The experimental conditions.
Time
HRT NH4+-N
NLR
(d)
(h)
(mg/L)(gN/L/d)
1-145
8
250
0.75
146-19716600
0.9
198-20116
800
1.2
202-23016600
0.9
231-23427
1000
0.9
235-30016
350-600
0.53-0.9
301-35016300
0.45
351-38016600
0.9

Fig. 3. Photo of biofilm: day84 (A); day214 (B);day310 (C); day360 (D).

2.3 Influent and seed sludge
The reactor was fed with a synthetic influent, containing
ammonium as the single nitrogen source, mineral salts, andno organic carbon. NH4+-N was supplemented in the form of
NH4HCO3. The ratio of HCO3- to NH4+ of influent was fixed
at 2.0 by adding approximately equal amounts of NaHCO 3 to
NH4HCO3.
The seed sludge was obtained from a SPNA reactor. The
initial mixed liquor suspended solid (MLSS) concentration of
the airlift reactor was approximately 17.0 g/L.
2.4 Analysis Methods
Influent and effluent samples were measured every 2 days.
The samples were filtered through 0.45μm filter prior tomeasurements. Ammonia, nitrite and nitrate were analyzed
according to standard methods.

The formation of biofilm can be divided into four stages: thebiofilm formation period, the mature period, the aging period
and the reactivation period. The cavity formed by nitrogenproduced by anammox bacterial is one of the reasons for thepoor nitrogen performance.
14

600
400

A

NRE (%)

80
60

300
200
100

NRE
ANR
NOR

AOR

B

0.8
0.4
0.2
0.0

1.00
△NO3--N/△Nr

01.0
0.6

40
20

C

11%
△NO3--N/△Nr

0.8

6

2)

0.2

D

DO
AR

2.5
2.0

0.3

1.5

0.2

1.0

0.1

0.5

0.0

2.25
2.00

4

0.25
Left: Biofilm sludge

Right: Suspended sludge
Biofilm reactivation

0.0

0.00

Left: Biofilm sludge

Right: Suspended sludge

Biofilm maturation Biofilm aging

Biofilm reactivation

The inorganic matter accumulated in the biofilm is themain factor causing the reduction of the nitrogen
removal capacity of the SPNA system. MLSS
concentration increased from 7.64g/L in biofilm
maturation period to 11.51g/L in biofilm aging period,
while the SAA decreased from 0.25 gN/gMLVSS/d to0.21 gN/gMLVSS/d at the same time. After mechanicalstirring, MLSS concentration decreased to 5.86 g/L
while SAA increased to 0.28 gN/gMLVSS/d.

1)

0.4

0.4

0.4

2.50

4 Conclusions

0.6

0.0
0.5
DO(mg/L)

500
400

200
1000

2.75
0.6

8

Fig. 3. Sludge concentration in different stages:
MLSS, MLVSS and MLVSS/MLSS (A); SAA and
SAOA.

Ⅶ Ⅷ

Ⅵ

3)
AR(L/min)N in influent (mg/L)

800

Ⅲ ⅣⅤ

B

SAA
SAOA

3.00

10

Biofilm maturation Biofilm aging

AOR、NOR、ANR (gN/L/d) N in effluent (mg/L)

ⅠⅠ

NH4+-Neff
NO3--Neff
NO3--Neff

3.25

0.2

From phase Ⅰ to phase Ⅳ, the NLR was increased from
0.75gN/L/d to 0.9 gN/L/d, with a high nitrogen removal
effectively (NRE) of 82%. After high ammonium (1000mg/L)
concentration inhibition, the NLR was decreased to 0.45
gN/L/d. Then, the NLR was adjusted back to 0.9 gN/L/d after
mechanical stirring within 3 days.
NH4+-Ninf

1.0

2

3 Results and discussion

1000

A

0.8

0

Ⅰ

MLSS
MLVSS

12

MLVSS/MLSS
SAA、SAOA

Ⅰ
Ⅱ
Ⅲ
Ⅳ
Ⅴ
Ⅵ
Ⅶ
Ⅷ

MLSS、MLVSS (g/L)

Phase

The SPN/A process was successfully operated under
NLR of 0.9 gN/L/d and NRE of 84%.
The cavities generated by nitrogen gas and theaccumulation of inorganic substances are the main
reasons for the decrease in NRE.
Mechanical stirring can maintain the high activity of thebiofilm.

Reference

0.0

50

100

150

200

250

300

[1] Guo,Y., Chen, Y.J., Webeck, E., Li,Y.Y. 2019. Towards
more efficient nitrogen removal and phosphorus recovery
from digestion effluent: Latest developments in the
anammox-based process from the application perspective.
Bioresour Technol, 299(2019) 122560.

350

Time (days)

Fig. 2. The operation performance of SPNA system:
N in influent and effluent (A); NRE, AOR, NOR andANR (B); △NO3--N/△Nr (C); AR and DO (D).

15

The 8th International Symposium on Water Environment Systems(Nov. 13th ~ 14th , 2020)----- with Perspective of Global Safety

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Microbial diversity of small bacteria in activated sludge○ Shuka KAGEMASA1*, Yu-You LI1, Kengo KUBOTA1
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: shuka.kagemasa.t2@dc.tohoku.ac.jp

Abstract
Biological treatment is responsible for a central role in sewage treatment. Clarification of physiologicalcharacteristics and dynamics of microorganisms is of importance to understand the process. Microorganisms
belonging to the uncultured lineages (candidate division) present in activated sludge are one of the factors thatmake it difficult to understand the sewage treatment mechanism. The previous studies showed thatmicroorganisms belonging to the candidate division are present in a high proportion in the fractionated sample. It
was assumed that microorganisms belonging to the candidate division can be detected at a high percentage by
fractionating the activated sludge. In this study, we investigated microbial community of fractionated activatedsludge. In the fractioned samples, the most dominant microorganism was Saccharibacteria regardless of theprocesses and season. In addition to Saccharibacteria, some microorganisms belonging to candidate divisionwere detected at a high percentage in the fractioned samples. Microorganisms belonging to the candidate
division detected in the fractionated activated sludge were CPR (Candidate Phyla Radiation) bacteria. It was
shown that the fractionated activated sludge is characterized by the high abundance of CPR bacteria.
Keywords: Saccharibacteria (TM7); Ultra-small microorganisms; Candidate Phyla radiation bacteria;
Filtration fractionation; Activated sludge.
1 Introduction
The minimum size of microorganisms is 0.2 to 0.3 μm, and
it is generally said that this size contains a sufficient amountof biomolecules necessary for maintaining vital activity.
Using the cell size characteristics of microorganisms, filter
filtration is used for sterilization in the field of biology. On
the other hand, it has been revealed that there were
ultra-small microorganisms that pass through the filter.
Ultra-small microorganisms are often defined as "cellvolume less than 0.1 μm³". From the report by Miyoshi et al.
[1], it was shown that many of the ultra-small
microorganisms that passed through the filtration filter wereclassifid into the uncultured lineages (candidate division).
Groundwater from the Tono uranium mine was filteredthrough a filter with a pore size of 0.2 μm, and this filtrate
was further filtered through a filter with a pore size of 0.1
μm. The microorganisms captured by the 0.1 µm pore size
filter belonged to candidate divisions Parcubacteria (OD1)and Microgenomates (OP11), those accounted about 44% in
16S rRNA gene clones. Another study also reported thatmicroorganisms belonged to the candidate divisions OD1,
OP11 and WEE3 were enriched in an aquifer water sample
passed through the 0.2 µm pore size filter [2]. The candidate
division such as OD1 and OP11 detected in the filtrate were
CPR (Candidate Phyla Radiation) bacteria. CPR bacteria are
a diverse group of uncultured bacterial lineages with poorly
understood metabolic functions. CPR bacteria are suggested
to comprise at least 15% of bacteria domain phyla. CPR
bacteria are characterized by their extremely small cell size,
genome size (< 1 Mbp) and their parasitism and symbiosis
with other microorganisms [3]. Therefore, CPR bacteria
likely are to pass through the filtration filter, and CPR
bacteria can be abundantly detected by size-fractionating thesample.
Since various microorganisms are present in activatedsludge in sewage treatment plant, various types ofultra-small microorganisms or CPR bacteria may be

16

detected at a high percentage by size-fractionating theactivated sludge. We analyzed the microbial community of
the fractionated activated sludge and grasped its
characteristics.

2 Materials and methods
2.1 Sampling and sample pretreatment
Activated sludge was sampled from a reaction tank of the
MGA sewage treatment plant (conventional activatedsludge process). Activated sludge was centrifuged (7,000 ×
g, 5 minutes), and the supernatant of activated sludge after
centrifugation was fractionated using a filter (Stericup, 0.45
µm, 0.22 µm, 0.1 µm, Merck Millipore). In addition, sincedissolved DNA (microbial DNA released extracellularly bypassive and active factors) may be contained in the filtrate, apart of the filtrate was treated with DNase(Deoxyribonuclease I, NIPPON GENE).
2.2 Microbial community analysis
Microbial community analysis was performed on untreatedactivated sludge samples (Indicated as “Sludge”), filter
samples of each pore size (Indicated as “0.45 / 0.22 / 0.1µm Filter”), filtrate samples that passed through eachfiltration filter (Indicated as “0.45 / 0.22 / 0.1 µm Filtrate”),
and samples in which the filtrate was DNase-treated(Indicated as “0.45 / 0.22 / 0.1 µm Filtrate_DNase”).
These samples were subjected to a polymerase chain
reaction using the primer set (341 Forward and 806
Reverse) targeting the V3-V4 region of the 16S rRNA gene.
Then, the sequencing was performed with the Miseq System(Illumina). Sequence data analysis was performed using
QIIME (database: silva).

3 Results and discussion
Figure 1 showed the result of microbial community
analysis of the fractionated activated sludge. Proteobacteria
and Bacteroidia accounted for 70-80% of the total bacteria
of “Sludge” regardless of the treatment plant, treatment

The 8th International Symposium on Water Environment Systems(Nov. 13th ~ 14th , 2020)----- with Perspective of Global Safety
series, and season. The microbial community of thefractionatedsample(“Filter”,“Filtrate”
and“Filtrate_DNase”) was different from that of “Sludge”. In
the fractionated sample, the proportion of “None” becamevery high as the filter pore size became smaller. “None”
means that it could not be identified by sequence data
analysis using QIIME (database: silva). The detected “None”
was reanalyzed by NCBI BLAST, but its identity could not
be specified.
30-40% of the total bacteria of “0.45 µm Filtrate”, “0.45µm Filtrate_DNase” and “0.22 µm filter” were thecandidate division Saccharimonadia (TM7). The
Saccharimonadia was the most dominant microorganism inthe “0.45 µm Filtrate”, “0.45 µm Filtrate_DNase” and“0.22 µm filter”. The percentage of Saccharimonadia in“Sludge” was 3%. This tendency was confirmed regardlessof the difference in treatment plant, treatment series, andseason. “Filtrate_DNase” is a sample from which
dissolved DNA was removed to make it with only microbial
cells. Since Saccharimonadia was detected in“Filtrate_DNase” at the high percentage same as“Filtrate”, Saccharimonadia detected in the fractionatedactivated sludge is considered to be microbial cells. In
addition, when the OTU (operation classification unit) of
the detected Saccharimonadia were confirmed, there were
OTUs showing a high percentage only in the fractionatedsamples. This means that Saccharimonadia, which has verysmall cell size, may be present in activated sludge.
Saccharimonadia present in the oral cavity (TM7x) is
spherical with a diameter of 0.2 to 0.3 μm, and it has beenreported that it survives by parasitizing othermicroorganisms [4]. However, regarding Saccharimonadia
present in activated sludge, it has not been reported that
there is a very small cell size or a parasitism situation.
Therefore, the Saccharimonadia detected in the fractionatedactivated sludge may be a Saccharimonadia with a verysmall cell size that has not been reported so far, and its
ecological characteristics need to be verified.
Furthermore, in addition to Saccharimonadia, some CPR

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bacteria with a higher relative abundance than “Sludge”
were confirmed, especially with the “0.22 µm Filter”.
From the “0.22 µm Filter”, Parcubacteria was detected at
14.2% (“Sludge”:0.3%), Gracilibacteria (GN02) was
detected at 8.94% (“Sludge”: 1.8%), and Microgenomates
was detected at 2.8% (“Sludge”: 0.04%). By size
fractionating the activated sludge, CPR bacteria wereabundantly detected.

4 Conclusions
This study revealed that the fractionated activated sludge
is a treasure trove of CPR bacteria. In particular,
Saccharimonadia in activated sludge has not been reported
on its small cell size or parasitism, so it is necessary toverify its ecological characteristics and isolation culture in
the future study. In particular, Saccharimonadia in activatedsludge has not been reported on its small cell size orparasitism. We plan to verify ecological characteristics andto isolate cultures of Saccharimonadia in future study.
Other CPR bacteria will be analyzed in the same way as
Saccharimonadia. The results obtained from this study areimportant knowledges for clarifying the ecologicalcharacteristics of CPR bacteria and their role in wastewater
treatment.

Reference[1] Miyoshi, T. et al., Phylogenetic characterization of 16SrRNA gene clones from deep-groundwater microorganismsthat pass through 0.2-micrometer-pore-size filters, Appl.
Environ. Microbiol, 71 (2005), 1084–1088.
[2] Luef, B. et al., Diverse uncultivated ultra-small bacterial
cells in groundwater, Nature Communications, 6 (2015),
6372.
[3] Christopher T. Brown et al., Unusual biology across agroup comprising more than 15% of domain Bacteria,
Nature, 523 (2015), 208–211.
[4] Hea, X. et al., Cultivation of a human-associated TM7phylotype reveals a reduced genome and epibiotic parasitic
lifestyle, PNAS, 112 (2015), 244-249.

Fig. 1 Microbial community analysis result of the fractionated activated sludge
Indicated a microorganism with a relative abundance of 0.5% or more. Microorganisms with a relative abundance
lower than 0.5% were grouped in “Other”. However, the microorganisms belonging to the Superphylum
Patescibacteria were shown without omission even if the relative abundance was lower than 0.5%. Superphylum
Patescibacteria is a collection of some of the CPR bacteria.

17

The 8th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 13th ~ 14th , 2020)

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Application of hemin for the detection of environmental microorganisms○ Kampachiro URASAKI1*, Yu-You LI 1 & Kengo KUBOTA1
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: kampachiro.urasaki.p1@dc.tohoku.ac.jp

Abstract
Cell staining is widely used as it can detect microorganisms at single cell resolution. Normally, cell is stained by
non-specific methods such as nucleotide staining and/or specific methods such as FISH (fluorescence in-situ
hybridization). TSA (tyramide signal amplification) is a useful reaction because it can enhance the signal and canlabel cell with desired elements. TSA reaction is caused by peroxidase including hemin. In this study, we attempt
to develop two novel cell staining methods by using hemin designated as the CARD-hemin-staining method andG-quadruplex-hemin-FISH. The CARD-hemin-staining method is a non-specific cell staining method that wasapplicable to environmental sample. G-quadruplex-hemin-FISH is a method employing hemin and G-quadruplex(guanine quadruplex) that can enhance hemin’s peroxidase activity. The G-quadruplex-hemin-FISH was applied
to pure cultures however any signals were not obtained so far.
Keywords: Fluorescence in situ hybridization; non-specific staining; hemin; DNA enzyme; TSA reaction.
1 Introduction
Cell staining is being widely used in a lot of fields as it can
detect microorganisms without cultivation at single-cellresolution. Cell staining can be divided into two types,
which are non-specific nucleotide staining using e.g. DAPI
and specific staining such as a fluorescence in-situ
hybridization (FISH). Nucleotide staining is just fluorescent
stain thus it cannot label desired elements. FISH is an
attractive tool for understanding microbial community
structure because it can detect target microorganisms
phylogenetically. Catalyzed reporter deposition-FISH(CARD-FISH) is a sensitive FISH that utilizes tyramide
signal amplification (TSA) reaction. TSA reaction allows
cell labelling with desired elements therefore CARD-FISH
has been applied as a high sensitive FISH method.
CARD-FISH employs horseradish peroxidase (HRP) as
catalyzer, which needs cell wall permeabilization because
HRP is too big to penetrate cell wall. However, there is nouniversal permeabilization method so that optimization foreach target microorganisms is necessary [1].
Hemin (Fe (III)-proto-porphyrin IX, C34H32ClFeN4O4) has
peroxidase activity as the same as HRP. Hemin’s peroxidase
activity can be enhanced by guanine quadruplex(G-quadruplex). G-quadruplex is one of DNA enzymes
which is formed by guanine-rich sequence. Both of heminand guanine-rich sequence have low number molecularweight compared with HRP.
In this study, a novel non-specific staining method using
hemin and TSA reaction named as CARD-hemin-staining,
and a novel CARD-FISH method named as
G-quadruplex-hemin-FISH were developed.

2 Materials and methods
2.1 Sample preparation
The strains used in this study were Comamonas
testosteroni and Escherichia coli. Both strains were
cultivated in LB medium at 37˚C with agitation. During
exponentially growth phase, these cells were fixed in a 4%paraformaldehyde solution in phosphate buffered saline(PBS; 150 mM NaCl, 20 mM PO43- [pH7.4]) for 12 h at 4oC,
and stored in ethanol/PBS solution at -20˚C. Granularsludge was fixed as soon as sampling and was stored.

18

2.2 CARD-hemin-staining
CARD-hemin-staining was carried out as shown in Fig.1(A). Hemin binds non-specifically with cell tissue andcatalyzes TSA reaction. Ten µl of 10 µM hemin in working
buffer 1 (100 mM Tris-HCl [pH 8.0], 400 mM NaCl, 40
mM KCl and 0.0016 % Triton X-100) was mounted on eachwell. Then glass slides were placed in humidified-chamber
for 0.5 h at room temperature, subsequently immersed in
PBS containing 0.05% [v/v] Triton X-100 (0.05 % PBSX)
for 15 min at room temperature. For TSA reaction, 10 µl of
the working buffer 2 (10% [w/v] dextran sulfate, 0.0015 %[v/v] H2O2 and 0.1% [w/v] blocking reagent in PBS)
containing 20 µg/ml of fluorephore-labelled tyramide wasapplied to each well. Glass slides were placed into ahumidified-chamber, and TSA reaction was carried out for
15 min at room temperature. Then, glass slides were
immersed in 0.05% PBSX for 15 min at room temperature.
2.3 G-quadruplex-hemin-FISH
For phylogenetic cell detection, non-specific fluorescencesignal must be avoided. Therefore, Triton X-100 (anon-ionic surfactant) was tested to eliminate non-specificbinding of hemin with cell tissue. The working buffer 1
containing 0%, 0.01%, 0.05%, 0.1 %, 0.5% and 1 % [v/v]
Triton X-100 were used for hemin binding. For

The 8th International Symposium on Water Environment Systems----- with Perspective of Global Safety(Nov. 13th ~ 14th , 2020)

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equilibration, the same concentration of PBSX as heminbinding was used.
The scheme of G-quadruplex-hemin-FISH is shown in
Fig.1(B). In-situ hybridization with BET42a-PS2.M(5’-GTG GGT AGG GCG GGT TGG Gtt ttt ttt ttG CCT
TCC CAC TTC GTT T-3’) was carried out in accordancewith previous study with some modifications [2]. Afterin-situ hybridization, 10 µl of working buffer 1 was
mounted on each well in order to form G-quadruplex. Glassslides were placed into a humidified-chamber for 1 h atroom temperature. Then, 10 µl of 20 µM hemin in working
buffer 1 with 1 % [v/v] Triton X-100 was mounted on eachwell and incubated for 0.5 h at room temperature. The glass
slide was subsequently immersed in 1% PBSX for 15 min atroom temperature. TSA reaction was carried out using as
beforementioned.

3 Results and discussion
3.1 CARD-hemin-staining
CARD-hemin-staining was applied to pure cultures ofE.coli and C.testosteroni (Fig.2A, 2B). Cell-specific andclear signal was obtained from both E.coli andC.testosteroni. When the CARD-hemin-staining wasapplied to granular sludge (Fig.2C), cell-specific and clear
signals were also obtained from all cells. Fluorescencesignal evenly distributed within cell, and peripheral
halo-shaped signals and spotty signals weren’t observed. It
suggests that hemin penetrated cell wall, and then evenly
bound with entire of cell tissue. In short, the
CARD-hemin-staining was applicable for environmental
samples.
Tyramide labelled with desired element can easily be
synthesized which means desired elements with tyramide
can be labelled by TSA reaction. In summary, the
CARD-hemin-staining method can stain specific elements
such as nanogold particle.
3.2 G-quadruplex-hemin-FISH
Non-specific hemin binding gave the benefit for us.
However, it hinders to develop a novel in-situ hybridization
method. In order to eliminate non-specific fluorescencesignal, Triton X-100 was tested. Non-specific signal was
obtained with 0% [v/v] to 0.5%[v/v] Triton X-100(Fig.3A-E), suggests that hemin non-specifically binds withcell tissue below 0.5% Triton X-100. When Triton X-100
concentration was 1%, fluorescence signal was able to beeliminated (Fig.3F). It indicates that Triton X-100 inhibited
hemin binding so that >1 % Triton X-100 can eliminate
non-specific fluorescence intensity.
G-quadruplex is one of apoenzymes of hemin, andG-quadruplex-hemin complex has a significantly highperoxidase activity compared with hemin alone [3]. When
G-quadruplex-hemin-FISH was applied to C.testosteroni inthe condition that triton X-100 concentration was 1% [v/v],
we
did
not
get
any
signals(Fig.4).

19

G-quadrupex-hemin-FISH targets ribosomal RNA (rRNA).
Since ribosome is made up of large number of rRNA andprotein, it seemed that the ribosome high complexity may
inhibit G-quadruplex complex formation.

4 Conclusions“CARD-hemin-staining”,
developed
asnovel
non-specific staining method, was successfully applied toE.coli, C.testosteroni and granular sludge. We attempted todevelop G-quadruplex-hemin FISH. However, any signals
weren’tobtained,thus
developmentofG-quadruplex-hemin-FISH is in progress.

Reference[1] Kubota K, Imachi H, Kawakami S, Nakamura K,
Harada H, Ohashi A. Evaluation of enzymatic cell
treatments for application of CARD-FISH to methanogens.
J Microbiol Methods. 72(1) (2008):54-9.
[2] Annelie P, Jakob P and Rudolf A. Fluorescence in situ
hybridization and catalyzed reporter deposition for theidentification of marine bacteria. Appl. Environ. Microbiol.
68 (6) (2002): 3094-3101.
[3] Paola T, Paul K. W, A. Grant M, and Dipankar S. The
Peroxidase Activity of a Hemin − DNA Oligonucleotide
Complex: Free Radical Damage to Specific Guanine Basesof the DNA. J. Am. Chem. Soc. 123 (7) (2003): 1337-1348.

The 8th International Symposium on Water Environment Systems

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Effects of Altering Process Parameters for Controlling Nutrient
Concentration in Treated Water on Microbial Community Structure andamoA Gene in Activated Sludge○ Hiroyuki OHNO1*, Mikiko SATO1, Yu-You LI1 & Kengo KUBOTA1
1

School of Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: hiroyuki.ohno.t3@dc.tohoku.ac.jp.

Abstract
Currently, the main microbial community of activated sludge is the black box. In this study, we are trying toestimate the main groups of activated sludge and their functions and dynamics by applying molecular biologicalmethods to activated sludge in wastewater treatment facilities in various parts of Japan. We are focusing on seasonal
operation management implemented in areas such as the Seto Inland Sea coast. Seasonal operation management
achieves and maintains water quality environmental standards for the purpose of realizing and regenerating affluent
seas. However, it is a method to actively control the nutrient concentration in the treated sewage water. As a resultof investigating and analyzing the effect of seasonal operation management on the microorganisms in active sludge,
it was suggested that the microbial community structure changes due to seasonal operation management. In
particular, it was found that the relative abundance of Nitrospira and Nitrosomonas increased. In addition, aquantitative evaluation of the amoA gene revealed that AOB amoA increased during the period of seasonal operation
management.
Keywords: Activated sludge; Microbial community; Ammonium oxidization; qPCR.
2.1 Sample Collection

1 Introduction
The activated sludge process is a wastewater treatment
technology that is widely used mainly in developed countries,
but it has an aspect of being operated empirically. In addition,
there are not many reports on the major microbialcommunities present in activated sludge and their functions.
Therefore, the current situation is that the dynamics andfunctions of microorganisms in activated sludge are regarded
as a black box.
In recent years, in order to increase the concentration ofnutrients in the sea area in winter, the seasonal operation
management that actively release nitrogen and phosphorus
has been implemented in areas such as the coast of the Seto
Inland Sea. Seasonal operation management is a method ofswitching to an operation method that suits the purpose
depending on the season. In particular, the concentration ofnutrients released is important. In summer, it is released in
low concentrations to preserve water quality. In winter, it isreleased in high concentrations for the regeneration of marine
resources. However, although there are reports on changes inwastewater quality and nutrient concentration in the sea area
due to seasonal operation management, there are few reportson the structure of the microbial community of activatedsludge in the reaction tank.
In this study, we analyzed microbial community structureusing next-generation sequencing and real-time PCR on
activated sludge from two wastewater treatment plants that isundergoing seasonal operation management. In this way, weinvestigated the temporal changes in the microbialcommunity structure associated with seasonal operation
management and carried out a quantitative evaluation ofgenes related to ammonia oxidation.

2 Materials and methods

20

From April 2018 to March 2019, activated sludge in thereaction tank was collected from two sewage treatment plants(WWTP-1 and WWTP-2) that are undergoing seasonal
operation management.

2.2 Analysis of microbial community structure
DNA was extracted from the collected activated sludge
sample using ISOIL for Beads Beating (NIPPON GENE),
and PCR was performed using the 341F-806R primer set
targeting the V3-V4 region of the 16S rRNA gene. After that,sequencing was performed by Miseq, and the structure of the
microbial community in activated sludge was analyzed using
QIIME.

2.3 Real-time PCR
The abundances of AOB and AOA amoA gene were
quantified with the primer pairs using real-time quantitative
PCR (qPCR) technology in LightCycler (Roche). AOB andAOA amoA gene abundances were quantified using primers
amoA 1F/2R (Rotthauwe et al., 1997) and arch-amoA F/R(Francis et al., 2005), respectively. The amplification was
conducted in 20 μL reaction mixture consisted of 10 μL
SYBR TB Green Premix Ex Ta II (Takara Bio), 0.5 μL
forward and reverse primers and 2.0 μL template DNA and7.0 μL Milli-Q water. The real-time PCR protocol targeting
AOB amoA gene involved enzyme activation and initialdenaturation at 95 °C for 30 sec, followed by 45 cycles ofdenaturing at 95 °C for 5 s, annealing at 54 °C for 20 s andextension at 72 °C for 40 s. When targeting the AOA amoA
gene, annealing at 54 °C for 30 sec and extension at 72 °C for
1 min. The specificity of the PCR products was checked bydissociation curve and visualized with 1.2% agarose gelelectrophoresis. To obtain the standard curve, a template wasprepared by the PCR products amplified using the primer set
M13F and M13R from cloned DNA fragments obtained in
the clone analysis, which were affiliated with AOB amoA
gene and AOA amoA gene. All the PCR assays for each gene

The 8th International Symposium on Water Environment Systems

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Tohoku University----- with Perspective of Global Safety
and sample were performed at least three independent
replicates. The target gene copies in the unit volume template
were calculated through the standard curve.

3 Results and discussion
3.1 Analysis of microbial community structure
Figure 1 shows the temporal changes in the structure of
microbial communities in activated sludge at wastewater
treatment plants that are undergoing seasonal operation
management. It is generally said that the Proteobacteria is
the main constituent bacteria in activated sludge of
wastewater treatment plants. In the activated sludge of thewastewater treatment plant targeted in this study, theProteobacteria and Bacteroidetes were predominant
throughout the year. In addition, as a result of β-diversity
analysis, the microbial community structure in the activatedsludge of the wastewater treatment plant where seasonal
operation management is carried out formed a cohesive plot
for each sewage treatment plant regardless of the period ofseasonal operation management (data not shown). Thissuggests that the effect of seasonal operation management on
the microbial community structure in activated sludge issmall compared to the effect of the treatment process and thenature of the inflow water.
At wastewater treatment plants that carry out seasonal
operation management, an increase in the relative abundanceof the nitrite-oxidizing bacteria Nitrospira and the ammoniaoxidizing bacteria Nitrosomonas was confirmed over the
winter (Figure. 1). At the WWTP-2, a denitrification
suppression operation is carried out to suppress the supply oforganic components required for the denitrification reaction
by stopping the step inflow. Therefore, in the WWTP-2 inwinter, it is considered that the activity of denitrifying
bacteria is reduced by suppressing the supply of organic
matter, and an environment is formed in which themicroorganisms involved in nitrification can easily grow. It
was also suggested that the structure of the microbialcommunity in activated sludge is affected by changes in
simple treatment processes such as stopping step inflow.

3.2 Quantification of amoA gene
The abundance of AOB amoA and AOA amoA genes in
activated sludge samples from two wastewater treatment
plants were quantified by Real-time PCR. Figure 2 shows thetransition of the amount of AOB amoA in each wastewater
treatment plant. From Figure. 2, it was found that the copy
number of AOB amoA per DNA concentration increased

Figure. 2. Temporal change in the amount of AOB amoA
gene in activated sludge associated with seasonal
operation managementfrom summer to winter in WWTP-1 and WWTP-2. Thisconfirms that the number of ammonia-oxidizing bacteria
increases from summer to winter from the results of
microbial structural analysis, and seasonal operation
management affects the microorganisms involved in thenitrogen cycle in activated sludge. In addition, although realtime PCR was performed on AOA amoA, it was not possible
to quantify AOA amoA from activated sludge of WWTP-1
and WWTP-2 throughout the year (data not shown). These
facts have quantitatively revealed that the abundance of AOBchanges with seasonally controlled operations. In addition,
since the detailed timing and amount of change in the amountof amoA differ between WWTP-1 and WWTP-2, we willproceed with the analysis using the analysis results of the
microbial community structure and other data such as water
quality and climate.

4 Conclusions
This study clarified the structure of microbial communities
in activated sludge of wastewater treatment plants, which are
undergoing seasonal operation management, and thetemporal changes in the amount of AOB / AOA amoA genes.
In the future, we plan to carry out quantitative analysis of theamoA gene derived from Comammox, a microorganism
involved in the nitrogen cycle, which has been attracting
attention in recent years, and investigate whether seasonal
operation management affects Comammox.

Reference[1] Francis CA, Roberts KJ, Beman JM, Santoro AE,
Oakley BB (2005). Ubiquity and diversity of ammoniaoxidizing archaea in water columns and sediments of theocean. Proc Natl Acad Sci USA 102:14683–14688.
[2] Rotthauwe, J. H., Witzel, K. P., & Liesack, W. (1997).
The ammonia monooxygenase structural gene amoA as afunctional marker: molecular fine-scale analysis of naturalammonia-oxidizing populations. Applied and environmental
microbiology, 63(12), 4704-4712.
[3] Sewerage Department, Water and Disaster Management
Bureau, Ministry of Land, Infrastructure, Transport andTourism (2015). Procedure manual (draft) concerning theoperation method for active management of nutrient salts
contained in sewage effluent.

Figure. 1. Temporal changes in microbial community
structure associated with seasonal operation management

21

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

A cross comparison of hydrological similarity and geological similarity
for the sub-catchments within Natori river basin○ Qing CHANG1*, Daisuke KOMORI1 & So KAZAMA1
1

Department of Civil Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.

Abstract
In the science of hydrology, similarity is an important concept which has a lot of application scenarios like flood
estimation, model parametrization or regionalization. The increasing diversity and resolution of spatially distributed
data on terrestrial systems greatly enhance the potential of hydrological modeling. Optimal and reasonable use of
these data sources requires that we better understand which system characteristics exert primary controls onhydrological dynamics. In this study we develop and test an approach to explore how the geo-characteristic canaffect the hydro-characteristic by comparing the geo and hydro similarities. Spatially distributed topographic
information was used as input data and a calibrated hydrological model was used to calculate hydrological
performance. Different indices were used and compared and the sub-catchments were classified into different
similarities groups. The results showed that the geo-similarity can be reflected by hydro-similarity in certain degree
while there is still room for improvement of accuracy.
Keywords: topographic similarity; hydrological similarity; watersheds classification; similarity matrix.
1 Introduction
The key role that surface topography plays in hydrology has
long been recognized (e.g., Horton, 1945). Topography
provides information about the interplay between uplift,
weathering and erosion, and hence about the pastmorphological development of a landscape. Further, it
provides a strong constraint for future hydrological andgeomorphic changes and, importantly for hydrology, is the
key driver and control associated with runoff generation andseveral other hydrological processes. This insight about thepast, present and future roles played by topography is surely
one reason why almost all key landscape entities inhydrology, such as watershed boundaries, hillslopes andchannel networks, are derived from properties of the landsurface topography. In support of this, digital elevation
models (DEMs) are available at fairly high resolutions acrossthe globe, helping to fuel the growing popularity of spatially
explicit hydrologic models.
It is therefore no surprise that hydrology does not sufferfrom a lack of models or indices linking geomorphic
properties of a landscape with its hydrological functioning.
The most popular approach is arguably the topographic
wetness index (TWI). As a function of the local slope withthe upslope contributing area per contour length, the TWI
was originally developed to classify areas of similarfunctioning within a catchment and has been applied andtested in numerous studies.
However, other indices have also been proposed for linking
land-surface topography with its runoff response. Hjerdt et al.
(2004) developed the “downslope topographic wetness index”(also called the tanβ index) that reflects the local hydraulicgradient in the case that flow is exclusively driven by gravity
and under the assumptions of a fixed drop in elevation. They
claimed that this index represents groundwater level
gradients in a manner that is superior to the classical TWI
approach and showed it to be less sensitive to the quality of
the DEM. An approach that has recently gained considerable

22

attention is the “height above the nearest drainage” index(HAND). This approach assumes that water follows thesteepest descent along the surface topography, and, based onthese drainage paths, the corresponding elevation of eachraster cell above the nearest corresponding river cell isestimated. HAND has been successfully applied and tested innumerous studies in a wide range of different landscapes.
The above-mentioned studies highlight the large potentialof the topographic index and its relevance for hydrologicalresearch. From a theoretical point of view, topographic index
like DEM reflects the gravitational potential energy of agiven unit weight of water with the reference level set to theelevation of the nearest corresponding river. In this study, inorder to represent this information in more detailed way, we
will use 2 major histogram indexes: the histogram of slopes
and the histogram of the distances to rivers, as the major
topographic indexes. A distributed hydrological model wasused to calculate the outflow and 3 hydrological indexes wereused to measure the similarity. The similarity matrix of both
geo-features and hydro features will be made to analyze how
geo-feature can affect hydrological performance.

2 Materials and methods
2.1 study site description
The study area is Natori River basin which is located incentral Miyagi prefecture, in the Tohoku region of Japan. The
river's basin is 939 km2 and has two primary reservoirs. The
river's length is 55 km, and its tributaries are the Hirose,
Masuda and Goishi Rivers. The river's flow is the greatest
during the snow melt season from March to April, the rainyseason from June to July and during the typhoon season from
September to October. The entire basin was divided into
totally 83 sub-catchments. Those sub-catchments form thebasic unit of similarity analysis in this sduty. Fig.1 hadshowed the river basin and its sub-catchemnts.

The 8th International Symposium on Water Environment Systems

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Tohoku University‐‐‐‐‐ with Perspective of Global Safety

3 Results and discussion
Figure 2(a) displays the similarity matrix of the histogramof distance to river for the 83 sub-catchments examined inthis study. The fact that these distributions are relatively
similar is also indicated by the JSD and the values of which
are all rather small, indicating low divergence between the
distributions.
No

Fig. 1. The study area and the divided 83 subcatchments.
2.2 analysis methods
Topographic data used in this study is DEM data set andriver maps of the study area with 40m resolution. These data
were processed and generated 2 kinds of histogram data thatcan describe the features of each sub-catchments: thehistogram of grid slopes and histogram of grid distances torivers. These 2 histograms will be used to compare the geosimilarity between sub-catchments and the dimension ofthese 2 histograms are 255. The hydrographs of each subcatchments were calculated using a distributed model. The
rainfall events used in this study include 2 largest rainfall in
the past 50 years of Sendai area: the 2019-10-11 rainfall(402mm) and the 1986-08-03 rainfall (401mm).
The geo-similarity between sub-catchments were comparedusing the Jensen–Shannon divergence (JSD) method. Thismethod can estimate how similar catchments are with respect
to their features distributions. JSD is based on the wellknown Kullback–Leibler divergence (KLD; sometimes
referred as relative entropy), defined as:
𝐷

𝑋||𝑌

𝑥 𝑙𝑜𝑔
∈

𝑝 𝑥
𝑝 𝑦

Accordingly, the JSD represents the average divergence of
N probability distribution from their midpoint distribution, A
high JSD value indicates a high divergence between the
distributions, with a maximum of 1. It is defined as
1
𝑁

𝐷

Fig. 2. JSD values for the 83 research catchments
between the histogram of distance to rivers. Green: <0.1,
yellow: 0.1~0.2, red:>0.2.
No

12 17 18 19 21 26 23 20 22 24 25 27 28 29 30 31 32 33 34 35 36 37 43 38 39 40 41 42 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
12
1 0.87 0.791 0.544 0.831 0.422 0.244 0.972 0.793 0.826 0.93 0.337 0.86 0.917 0.544 0.854 0.882 0.903 0.98 0.949 0.961 0.811 0.937 0.947 0.95 0.907 0.968 0.861 0.93 0.833 0.965 0.934 0.963 0.866 0.933 0.972 0.93 0.979 0.995 0.632 0.966 0.717 0.997 0.768 0.966 0.966 0.948 0.966 0.793 0.943 0.847 0.985 0.837 0.989 0.993 0.961 0.949 0.967 0.977 0.977 0.991 0.955 0.916 0.973 0.879 0.998 0.983 0.996
17 0.87
1 0.986 0.816 0.951 0.739 0.53 0.95 0.951 0.971 0.985 0.644 0.934 0.992 0.852 0.941 0.933 0.951 0.94 0.975 0.966 0.963 0.97 0.972 0.975 0.991 0.954 0.999 0.983 0.986 0.96 0.979 0.961 0.998 0.982 0.949 0.987 0.939 0.898 0.914 0.96 0.959 0.894 0.977 0.956 0.956 0.97 0.957 0.987 0.978 0.996 0.928 0.995 0.918 0.902 0.961 0.97 0.96 0.941 0.947 0.91 0.971 0.99 0.95 0.998 0.87 0.932 0.895
18 0.791 0.986
1 0.887 0.946 0.833 0.64 0.891 0.955 0.966 0.946 0.749 0.914 0.962 0.925 0.924 0.906 0.919 0.877 0.928 0.914 0.96 0.928 0.926 0.928 0.962 0.897 0.988 0.944 0.982 0.905 0.935 0.904 0.981 0.941 0.89 0.948 0.875 0.822 0.96 0.905 0.984 0.818 0.986 0.899 0.899 0.92 0.9 0.991 0.932 0.986 0.86 0.986 0.848 0.828 0.908 0.92 0.904 0.878 0.886 0.837 0.921 0.955 0.89 0.978 0.789 0.867 0.819
19 0.544 0.816 0.887
1 0.803 0.967 0.892 0.656 0.821 0.81 0.732 0.937 0.741 0.772 0.958 0.758 0.718 0.721 0.638 0.704 0.686 0.811 0.713 0.706 0.707 0.766 0.662 0.82 0.733 0.827 0.673 0.715 0.671 0.802 0.728 0.652 0.735 0.635 0.575 0.899 0.673 0.884 0.569 0.851 0.665 0.665 0.693 0.668 0.848 0.709 0.819 0.617 0.82 0.602 0.581 0.678 0.694 0.673 0.638 0.649 0.59 0.698 0.749 0.654 0.796 0.538 0.626 0.573
21 0.831 0.951 0.946 0.803
1 0.728 0.548 0.912 0.981 0.967 0.944 0.658 0.993 0.953 0.818 0.995 0.985 0.981 0.901 0.934 0.925 0.964 0.96 0.948 0.933 0.962 0.916 0.954 0.952 0.961 0.922 0.935 0.915 0.944 0.947 0.908 0.938 0.897 0.857 0.84 0.918 0.885 0.855 0.906 0.914 0.914 0.929 0.913 0.926 0.936 0.951 0.886 0.94 0.878 0.862 0.925 0.929 0.916 0.899 0.906 0.868 0.928 0.944 0.908 0.947 0.83 0.893 0.856
26 0.422 0.739 0.833 0.967 0.728
1 0.945 0.544 0.756 0.736 0.634 0.985 0.657 0.678 0.973 0.676 0.63 0.629 0.523 0.601 0.579 0.739 0.613 0.603 0.602 0.674 0.551 0.744 0.634 0.756 0.563 0.613 0.561 0.723 0.628 0.54 0.638 0.52 0.454 0.897 0.564 0.852 0.449 0.802 0.554 0.554 0.588 0.556 0.791 0.607 0.745 0.5 0.749 0.484 0.46 0.569 0.588 0.562 0.523 0.536 0.471 0.59 0.654 0.54 0.713 0.416 0.509 0.452
23 0.244 0.53 0.64 0.892 0.548 0.945
1 0.343 0.572 0.539 0.425 0.98 0.475 0.472 0.848 0.494 0.445 0.437 0.326 0.394 0.375 0.546 0.412 0.399 0.396 0.467 0.35 0.536 0.427 0.554 0.36 0.404 0.356 0.511 0.42 0.34 0.427 0.322 0.268 0.716 0.361 0.656 0.264 0.595 0.351 0.352 0.381 0.354 0.584 0.399 0.536 0.306 0.538 0.292 0.273 0.367 0.382 0.359 0.325 0.337 0.281 0.385 0.443 0.34 0.502 0.238 0.314 0.267
20 0.972 0.95 0.891 0.656 0.912 0.544 0.343
1 0.885 0.915 0.988 0.45 0.926 0.98 0.671 0.923 0.942 0.961 0.999 0.995 0.998 0.903 0.987 0.993 0.995 0.976
1 0.946 0.988 0.923 0.999 0.99 0.998 0.949 0.989 0.999 0.986 0.999 0.987 0.755 0.999 0.83 0.985 0.873 0.999 0.999 0.996 0.999 0.895 0.993 0.936 0.996 0.928 0.994 0.989 0.999 0.996 0.998 0.999 0.999 0.991 0.996 0.979 0.999 0.956 0.974 0.998 0.986
22 0.793 0.951 0.955 0.821 0.981 0.756 0.572 0.885
1 0.992 0.93 0.682 0.963 0.939 0.847 0.974 0.948 0.951 0.872 0.916 0.903 0.993 0.937 0.926 0.915 0.953 0.89 0.954 0.936 0.982 0.899 0.92 0.894 0.951 0.932 0.881 0.926 0.869 0.822 0.876 0.896 0.912 0.819 0.93 0.89 0.891 0.909 0.89 0.945 0.918 0.958 0.856 0.95 0.846 0.828 0.902 0.91 0.895 0.871 0.879 0.835 0.91 0.936 0.883 0.949 0.793 0.863 0.821
24 0.826 0.971 0.966 0.81 0.967 0.736 0.539 0.915 0.992
1 0.956 0.65 0.95 0.961 0.84 0.96 0.94 0.951 0.903 0.943 0.931 0.999 0.952 0.947 0.943 0.972 0.92 0.974 0.958 0.996 0.927 0.949 0.925 0.975 0.955 0.912 0.954 0.9 0.855 0.888 0.926 0.93 0.852 0.951 0.921 0.921 0.939 0.921 0.964 0.946 0.977 0.889 0.971 0.878 0.861 0.931 0.939 0.925 0.904 0.909 0.869 0.938 0.962 0.914 0.971 0.826 0.895 0.853
25 0.93 0.985 0.946 0.732 0.944 0.634 0.425 0.988 0.93 0.956
1 0.538 0.945 0.997 0.76 0.946 0.952 0.971 0.981 0.998 0.995 0.945 0.992 0.996 0.998 0.997 0.99 0.984 0.999 0.965 0.992 0.999 0.992 0.985 0.998 0.987 0.999 0.981 0.953 0.837 0.992 0.9 0.95 0.933 0.991 0.991 0.997 0.991 0.95 0.999 0.978 0.974 0.973 0.968 0.956 0.993 0.997 0.991 0.982 0.985 0.962 0.996 0.998 0.987 0.988 0.931 0.977 0.951
27 0.337 0.644 0.749 0.937 0.658 0.985 0.98 0.45 0.682 0.65 0.538
1 0.586 0.585 0.925 0.605 0.555 0.549 0.431 0.505 0.484 0.656 0.524 0.51 0.507 0.581 0.457 0.651 0.54 0.666 0.469 0.516 0.465 0.625 0.533 0.446 0.54 0.427 0.365 0.816 0.469 0.763 0.361 0.707 0.459 0.459 0.492 0.461 0.697 0.511 0.651 0.408 0.653 0.393 0.371 0.475 0.492 0.467 0.43 0.443 0.38 0.495 0.557 0.447 0.616 0.331 0.417 0.364
28 0.86 0.934 0.914 0.741 0.993 0.657 0.475 0.926 0.963 0.95 0.945 0.586
1 0.947 0.755 0.998 0.997 0.992 0.918 0.94 0.934 0.947 0.969 0.956 0.938 0.958 0.929 0.935 0.954 0.94 0.933 0.937 0.925 0.926 0.949 0.923 0.937 0.915 0.882 0.786 0.929 0.84 0.881 0.87 0.927 0.927 0.936 0.925 0.895 0.94 0.93 0.906 0.918 0.9 0.886 0.936 0.936 0.926 0.915 0.921 0.891 0.934 0.939 0.922 0.932 0.86 0.912 0.882
29 0.917 0.992 0.962 0.772 0.953 0.678 0.472 0.98 0.939 0.961 0.997 0.585 0.947
1 0.795 0.95 0.953 0.97 0.973 0.993 0.989 0.951 0.989 0.992 0.993 0.997 0.982 0.99 0.996 0.972 0.985 0.993 0.984 0.988 0.995 0.979 0.997 0.971 0.94 0.861 0.985 0.918 0.937 0.944 0.983 0.983 0.99 0.983 0.96 0.994 0.983 0.964 0.978 0.957 0.944 0.987 0.99 0.984 0.973 0.977 0.95 0.991 0.996 0.979 0.991 0.918 0.967 0.939
30 0.544 0.852 0.925 0.958 0.818 0.973 0.848 0.671 0.847 0.84 0.76 0.925 0.755 0.795
1 0.773 0.733 0.739 0.651 0.728 0.707 0.84 0.734 0.727 0.729 0.794 0.679 0.857 0.758 0.862 0.691 0.741 0.69 0.841 0.752 0.669 0.764 0.648 0.579 0.972 0.691 0.945 0.574 0.909 0.682 0.682 0.716 0.684 0.899 0.735 0.859 0.628 0.864 0.611 0.587 0.696 0.716 0.69 0.652 0.664 0.598 0.718 0.779 0.668 0.832 0.54 0.636 0.576
31 0.854 0.941 0.924 0.758 0.995 0.676 0.494 0.923 0.974 0.96 0.946 0.605 0.998 0.95 0.773
1 0.992 0.99 0.915 0.94 0.933 0.958 0.969 0.955 0.939 0.962 0.926 0.942 0.956 0.951 0.931 0.938 0.924 0.934 0.951 0.919 0.939 0.911 0.876 0.803 0.928 0.856 0.875 0.885 0.925 0.925 0.935 0.923 0.908 0.94 0.939 0.902 0.927 0.895 0.881 0.935 0.936 0.926 0.912 0.918 0.886 0.934 0.943 0.92 0.939 0.853 0.908 0.876
32 0.882 0.933 0.906 0.718 0.985 0.63 0.445 0.942 0.948 0.94 0.952 0.555 0.997 0.953 0.733 0.992
1 0.996 0.935 0.95 0.947 0.935 0.977 0.966 0.949 0.961 0.944 0.934 0.961 0.932 0.946 0.947 0.938 0.925 0.957 0.939 0.945 0.932 0.903 0.771 0.943 0.829 0.902 0.861 0.942 0.942 0.948 0.938 0.886 0.95 0.926 0.925 0.913 0.919 0.907 0.95 0.948 0.939 0.933 0.937 0.911 0.945 0.944 0.937 0.931 0.883 0.929 0.903
33 0.903 0.951 0.919 0.721 0.981 0.629 0.437 0.961 0.951 0.951 0.971 0.549 0.992 0.97 0.739 0.99 0.996
1 0.955 0.969 0.966 0.946 0.991 0.982 0.968 0.978 0.964 0.951 0.978 0.947 0.965 0.966 0.96 0.945 0.976 0.96 0.965 0.953 0.925 0.787 0.963 0.848 0.923 0.882 0.962 0.962 0.967 0.96 0.906 0.97 0.943 0.946 0.932 0.941 0.929 0.969 0.967 0.96 0.954 0.958 0.933 0.965 0.963 0.958 0.951 0.904 0.95 0.924
34 0.98 0.94 0.877 0.638 0.901 0.523 0.326 0.999 0.872 0.903 0.981 0.431 0.918 0.973 0.651 0.915 0.935 0.955
1 0.991 0.996 0.89 0.982 0.989 0.991 0.968 0.998 0.935 0.981 0.91 0.997 0.984 0.996 0.938 0.983 0.999 0.981 0.999 0.992 0.736 0.997 0.814 0.991 0.858 0.997 0.998 0.991 0.997 0.88 0.989 0.924 0.999 0.916 0.998 0.994 0.996 0.991 0.997 0.999 0.999 0.996 0.993 0.972 0.999 0.946 0.982 0.999 0.992
35 0.949 0.975 0.928 0.704 0.934 0.601 0.394 0.995 0.916 0.943 0.998 0.505 0.94 0.993 0.728 0.94 0.95 0.969 0.991
1 0.999 0.932 0.993 0.998
1 0.991 0.996 0.972 0.997 0.951 0.997 0.998 0.997 0.974 0.997 0.994 0.997 0.99 0.968 0.808 0.998 0.876 0.966 0.913 0.997 0.997 0.999 0.996 0.932
1 0.965 0.985 0.959 0.981 0.971 0.998 0.999 0.997 0.991 0.993 0.976 0.999 0.994 0.995 0.979 0.95 0.988 0.967
36 0.961 0.966 0.914 0.686 0.925 0.579 0.375 0.998 0.903 0.931 0.995 0.484 0.934 0.989 0.707 0.933 0.947 0.966 0.996 0.999
1 0.919 0.991 0.997 0.999 0.986 0.998 0.963 0.994 0.94 0.999 0.995 0.999 0.965 0.995 0.997 0.994 0.995 0.978 0.788 0.999 0.86 0.976 0.898 0.999 0.999 0.998 0.998 0.918 0.998 0.954 0.991 0.948 0.988 0.98 0.999 0.998 0.999 0.996 0.997 0.984 0.999 0.989 0.998 0.971 0.963 0.993 0.977
37 0.811 0.963 0.96 0.811 0.964 0.739 0.546 0.903 0.993 0.999 0.945 0.656 0.947 0.951 0.84 0.958 0.935 0.946 0.89 0.932 0.919
1 0.944 0.937 0.932 0.964 0.908 0.966 0.949 0.993 0.915 0.938 0.913 0.967 0.945 0.9 0.943 0.887 0.841 0.885 0.914 0.924 0.838 0.944 0.909 0.909 0.927 0.909 0.958 0.935 0.969 0.875 0.964 0.865 0.847 0.919 0.927 0.912 0.891 0.896 0.855 0.927 0.952 0.902 0.963 0.811 0.881 0.839
43 0.937 0.97 0.928 0.713 0.96 0.613 0.412 0.987 0.937 0.952 0.992 0.524 0.969 0.989 0.734 0.969 0.977 0.991 0.982 0.993 0.991 0.944
1 0.998 0.992 0.992 0.989 0.968 0.996 0.955 0.99 0.99 0.988 0.966 0.995 0.987 0.989 0.982 0.957 0.801 0.99 0.867 0.955 0.904 0.989 0.989 0.991 0.987 0.925 0.993 0.96 0.976 0.952 0.971 0.961 0.992 0.991 0.988 0.982 0.985 0.965 0.99 0.986 0.985 0.972 0.938 0.978 0.956
38 0.947 0.972 0.926 0.706 0.948 0.603 0.399 0.993 0.926 0.947 0.996 0.51 0.956 0.992 0.727 0.955 0.966 0.982 0.989 0.998 0.997 0.937 0.998
1 0.997 0.992 0.995 0.97 0.998 0.953 0.996 0.995 0.995 0.97 0.997 0.993 0.995 0.989 0.967 0.802 0.996 0.87 0.964 0.907 0.995 0.995 0.997 0.994 0.927 0.997 0.962 0.984 0.955 0.979 0.97 0.997 0.997 0.994 0.99 0.992 0.974 0.997 0.991 0.992 0.976 0.949 0.986 0.965
39 0.95 0.975 0.928 0.707 0.933 0.602 0.396 0.995 0.915 0.943 0.998 0.507 0.938 0.993 0.729 0.939 0.949 0.968 0.991
1 0.999 0.932 0.992 0.997
1 0.992 0.996 0.973 0.997 0.952 0.997 0.998 0.998 0.975 0.998 0.994 0.998 0.99 0.969 0.809 0.998 0.878 0.966 0.915 0.997 0.997 0.999 0.997 0.933
1 0.966 0.985 0.96 0.98 0.971 0.998 0.999 0.997 0.991 0.993 0.976
1 0.994 0.995 0.98 0.951 0.988 0.967
40 0.907 0.991 0.962 0.766 0.962 0.674 0.467 0.976 0.953 0.972 0.997 0.581 0.958 0.997 0.794 0.962 0.961 0.978 0.968 0.991 0.986 0.964 0.992 0.992 0.992
1 0.979 0.991 0.998 0.979 0.982 0.994 0.982 0.99 0.997 0.975 0.996 0.967 0.933 0.861 0.982 0.918 0.929 0.947 0.98 0.98 0.99 0.98 0.963 0.993 0.986 0.958 0.981 0.95 0.937 0.984 0.989 0.981 0.969 0.972 0.943 0.989 0.997 0.975 0.993 0.908 0.962 0.931
41 0.968 0.954 0.897 0.662 0.916 0.551 0.35
1 0.89 0.92 0.99 0.457 0.929 0.982 0.679 0.926 0.944 0.964 0.998 0.996 0.998 0.908 0.989 0.995 0.996 0.979
1 0.951 0.99 0.927 0.999 0.992 0.999 0.953 0.991 0.999 0.989 0.998 0.983 0.763 0.999 0.838 0.982 0.879
1
1 0.997 0.999 0.901 0.995 0.941 0.995 0.933 0.992 0.986 0.999 0.997 0.998 0.998 0.998 0.989 0.997 0.982 0.999 0.96 0.97 0.997 0.982
42 0.861 0.999 0.988 0.82 0.954 0.744 0.536 0.946 0.954 0.974 0.984 0.651 0.935 0.99 0.857 0.942 0.934 0.951 0.935 0.972 0.963 0.966 0.968 0.97 0.973 0.991 0.951
1 0.982 0.987 0.956 0.978 0.957 0.998 0.98 0.945 0.984 0.933 0.89 0.917 0.956 0.961 0.886 0.979 0.952 0.952 0.968 0.953 0.988 0.975 0.998 0.922 0.996 0.912 0.895 0.958 0.968 0.956 0.936 0.941 0.903 0.968 0.989 0.945 0.998 0.861 0.927 0.887
44 0.93 0.983 0.944 0.733 0.952 0.634 0.427 0.988 0.936 0.958 0.999 0.54 0.954 0.996 0.758 0.956 0.961 0.978 0.981 0.997 0.994 0.949 0.996 0.998 0.997 0.998 0.99 0.982
1 0.966 0.992 0.998 0.992 0.982
1 0.987 0.998 0.981 0.952 0.831 0.992 0.895 0.949 0.929 0.991 0.991 0.996 0.99 0.947 0.998 0.976 0.974 0.971 0.968 0.956 0.993 0.996 0.991 0.982 0.985 0.962 0.996 0.997 0.987 0.987 0.931 0.977 0.951
45 0.833 0.986 0.982 0.827 0.961 0.756 0.554 0.923 0.982 0.996 0.965 0.666 0.94 0.972 0.862 0.951 0.932 0.947 0.91 0.951 0.94 0.993 0.955 0.953 0.952 0.979 0.927 0.987 0.966
1 0.935 0.958 0.935 0.989 0.963 0.921 0.965 0.909 0.863 0.915 0.935 0.955 0.859 0.972 0.93 0.929 0.947 0.93 0.982 0.955 0.989 0.896 0.986 0.886 0.868 0.938 0.947 0.934 0.912 0.918 0.876 0.948 0.973 0.922 0.985 0.832 0.902 0.86
46 0.965 0.96 0.905 0.673 0.922 0.563 0.36 0.999 0.899 0.927 0.992 0.469 0.933 0.985 0.691 0.931 0.946 0.965 0.997 0.997 0.999 0.915 0.99 0.996 0.997 0.982 0.999 0.956 0.992 0.935
1 0.993 0.999 0.959 0.993 0.998 0.992 0.997 0.982 0.774
1 0.848 0.979 0.889 0.999 0.999 0.997 0.999 0.91 0.996 0.948 0.994 0.941 0.991 0.984 0.999 0.997 0.999 0.997 0.998 0.987 0.998 0.986 0.999 0.966 0.967 0.995 0.98
47 0.934 0.979 0.935 0.715 0.935 0.613 0.404 0.99 0.92 0.949 0.999 0.516 0.937 0.993 0.741 0.938 0.947 0.966 0.984 0.998 0.995 0.938 0.99 0.995 0.998 0.994 0.992 0.978 0.998 0.958 0.993
1 0.995 0.981 0.998 0.989 0.998 0.983 0.957 0.823 0.994 0.889 0.953 0.925 0.993 0.993 0.999 0.993 0.943 0.999 0.973 0.977 0.968 0.971 0.96 0.995 0.998 0.993 0.985 0.987 0.966 0.997 0.997 0.99 0.984 0.936 0.98 0.955
48 0.963 0.961 0.904 0.671 0.915 0.561 0.356 0.998 0.894 0.925 0.992 0.465 0.925 0.984 0.69 0.924 0.938 0.96 0.996 0.997 0.999 0.913 0.988 0.995 0.998 0.982 0.999 0.957 0.992 0.935 0.999 0.995
1 0.961 0.993 0.998 0.992 0.996 0.98 0.777 0.999 0.851 0.978 0.892 0.999 0.999 0.998
1 0.913 0.997 0.949 0.993 0.943 0.99 0.983 0.999 0.998 0.999 0.997 0.997 0.986 0.999 0.987 0.999 0.967 0.965 0.994 0.979
49 0.866 0.998 0.981 0.802 0.944 0.723 0.511 0.949 0.951 0.975 0.985 0.625 0.926 0.988 0.841 0.934 0.925 0.945 0.938 0.974 0.965 0.967 0.966 0.97 0.975 0.99 0.953 0.998 0.982 0.989 0.959 0.981 0.961
1 0.981 0.948 0.987 0.937 0.895 0.911 0.959 0.958 0.891 0.98 0.956 0.956 0.97 0.957 0.989 0.977 0.998 0.926 0.997 0.916 0.9 0.96 0.97 0.96 0.94 0.945 0.908 0.971 0.991 0.949 0.999 0.866 0.931 0.892
50 0.933 0.982 0.941 0.728 0.947 0.628 0.42 0.989 0.932 0.955 0.998 0.533 0.949 0.995 0.752 0.951 0.957 0.976 0.983 0.997 0.995 0.945 0.995 0.997 0.998 0.997 0.991 0.98
1 0.963 0.993 0.998 0.993 0.981
1 0.988 0.998 0.983 0.956 0.828 0.993 0.892 0.953 0.927 0.992 0.992 0.997 0.992 0.945 0.998 0.974 0.976 0.968 0.97 0.959 0.994 0.997 0.992 0.984 0.986 0.965 0.996 0.996 0.989 0.985 0.935 0.979 0.954
51 0.972 0.949 0.89 0.652 0.908 0.54 0.34 0.999 0.881 0.912 0.987 0.446 0.923 0.979 0.669 0.919 0.939 0.96 0.999 0.994 0.997 0.9 0.987 0.993 0.994 0.975 0.999 0.945 0.987 0.921 0.998 0.989 0.998 0.948 0.988
1 0.986 0.999 0.987 0.754 0.999 0.83 0.985 0.872 0.999 0.999 0.995 0.999 0.894 0.993 0.934 0.997 0.927 0.995 0.989 0.998 0.995 0.998 0.999 0.999 0.992 0.995 0.978 0.999 0.955 0.974 0.998 0.986
52 0.93 0.987 0.948 0.735 0.938 0.638 0.427 0.986 0.926 0.954 0.999 0.54 0.937 0.997 0.764 0.939 0.945 0.965 0.981 0.997 0.994 0.943 0.989 0.995 0.998 0.996 0.989 0.984 0.998 0.965 0.992 0.998 0.992 0.987 0.998 0.986
1 0.98 0.953 0.843 0.992 0.906 0.949 0.938 0.99 0.99 0.996 0.991 0.954 0.998 0.979 0.974 0.976 0.967 0.956 0.992 0.996 0.991 0.982 0.985 0.962 0.996 0.998 0.987 0.99 0.931 0.976 0.95
53 0.979 0.939 0.875 0.635 0.897 0.52 0.322 0.999 0.869 0.9 0.981 0.427 0.915 0.971 0.648 0.911 0.932 0.953 0.999 0.99 0.995 0.887 0.982 0.989 0.99 0.967 0.998 0.933 0.981 0.909 0.997 0.983 0.996 0.937 0.983 0.999 0.98
1 0.992 0.734 0.997 0.812 0.99 0.857 0.998 0.998 0.991 0.997 0.879 0.988 0.922 0.999 0.915 0.998 0.994 0.996 0.991 0.997
1
1 0.996 0.992 0.971 0.998 0.945 0.982 0.999 0.991
54 0.995 0.898 0.822 0.575 0.857 0.454 0.268 0.987 0.822 0.855 0.953 0.365 0.882 0.94 0.579 0.876 0.903 0.925 0.992 0.968 0.978 0.841 0.957 0.967 0.969 0.933 0.983 0.89 0.952 0.863 0.982 0.957 0.98 0.895 0.956 0.987 0.953 0.992
1 0.668 0.982 0.753 0.999 0.802 0.983 0.983 0.969 0.983 0.827 0.964 0.877 0.996 0.868 0.998
1 0.979 0.969 0.983 0.991 0.99 0.999 0.973 0.94 0.988 0.906 0.998 0.994
1
55 0.632 0.914 0.96 0.899 0.84 0.897 0.716 0.755 0.876 0.888 0.837 0.816 0.786 0.861 0.972 0.803 0.771 0.787 0.736 0.808 0.788 0.885 0.801 0.802 0.809 0.861 0.763 0.917 0.831 0.915 0.774 0.823 0.777 0.911 0.828 0.754 0.843 0.734 0.668
1 0.775 0.989 0.662 0.969 0.767 0.767 0.798 0.769 0.957 0.815 0.921 0.715 0.929 0.699 0.675 0.777 0.798 0.775 0.738 0.749 0.687 0.799 0.856 0.754 0.901 0.628 0.722 0.663
56 0.966 0.96 0.905 0.673 0.918 0.564 0.361 0.999 0.896 0.926 0.992 0.469 0.929 0.985 0.691 0.928 0.943 0.963 0.997 0.998 0.999 0.914 0.99 0.996 0.998 0.982 0.999 0.956 0.992 0.935
1 0.994 0.999 0.959 0.993 0.999 0.992 0.997 0.982 0.775
1 0.849 0.98 0.889
1
1 0.997 0.999 0.91 0.996 0.947 0.994 0.941 0.991 0.985 0.999 0.998 0.999 0.998 0.999 0.988 0.998 0.986 0.999 0.966 0.968 0.995 0.981
1 0.747 0.994 0.842 0.841 0.868 0.844 0.988 0.883 0.965 0.795 0.971 0.781 0.759 0.85 0.868 0.849 0.816 0.825 0.77 0.87 0.917 0.83 0.952 0.715 0.802 0.748
57 0.717 0.959 0.984 0.884 0.885 0.852 0.656 0.83 0.912 0.93 0.9 0.763 0.84 0.918 0.945 0.856 0.829 0.848 0.814 0.876 0.86 0.924 0.867 0.87 0.878 0.918 0.838 0.961 0.895 0.955 0.848 0.889 0.851 0.958 0.892 0.83 0.906 0.812 0.753 0.989 0.849
58 0.997 0.894 0.818 0.569 0.855 0.449 0.264 0.985 0.819 0.852 0.95 0.361 0.881 0.937 0.574 0.875 0.902 0.923 0.991 0.966 0.976 0.838 0.955 0.964 0.966 0.929 0.982 0.886 0.949 0.859 0.979 0.953 0.978 0.891 0.953 0.985 0.949 0.99 0.999 0.662 0.98 0.747
1 0.797 0.98 0.98 0.966 0.98 0.821 0.961 0.873 0.995 0.863 0.997 0.999 0.977 0.967 0.981 0.989 0.988 0.998 0.971 0.936 0.986 0.902 0.998 0.993
159 0.768 0.977 0.986 0.851 0.906 0.802 0.595 0.873 0.93 0.951 0.933 0.707 0.87 0.944 0.909 0.885 0.861 0.882 0.858 0.913 0.898 0.944 0.904 0.907 0.915 0.947 0.879 0.979 0.929 0.972 0.889 0.925 0.892 0.98 0.927 0.872 0.938 0.857 0.802 0.969 0.889 0.994 0.797
1 0.883 0.883 0.907 0.886 0.998 0.919 0.984 0.842 0.989 0.828 0.808 0.89 0.907 0.89 0.86 0.868 0.818 0.908 0.948 0.874 0.975 0.767 0.847 0.797
60 0.966 0.956 0.899 0.665 0.914 0.554 0.351 0.999 0.89 0.921 0.991 0.459 0.927 0.983 0.682 0.925 0.942 0.962 0.997 0.997 0.999 0.909 0.989 0.995 0.997 0.98
1 0.952 0.991 0.93 0.999 0.993 0.999 0.956 0.992 0.999 0.99 0.998 0.983 0.767
1 0.842 0.98 0.883
1
1 0.997
1 0.904 0.996 0.943 0.995 0.936 0.992 0.985 0.999 0.998 0.999 0.998 0.998 0.989 0.998 0.984 0.999 0.962 0.968 0.996 0.981
61 0.966 0.956 0.899 0.665 0.914 0.554 0.352 0.999 0.891 0.921 0.991 0.459 0.927 0.983 0.682 0.925 0.942 0.962 0.998 0.997 0.999 0.909 0.989 0.995 0.997 0.98
1 0.952 0.991 0.929 0.999 0.993 0.999 0.956 0.992 0.999 0.99 0.998 0.983 0.767
1 0.841 0.98 0.883
1
1 0.997
1 0.904 0.996 0.943 0.995 0.936 0.992 0.985 0.999 0.998 0.999 0.998 0.998 0.989 0.998 0.984 0.999 0.962 0.968 0.996 0.982
62 0.948 0.97 0.92 0.693 0.929 0.588 0.381 0.996 0.909 0.939 0.997 0.492 0.936 0.99 0.716 0.935 0.948 0.967 0.991 0.999 0.998 0.927 0.991 0.997 0.999 0.99 0.997 0.968 0.996 0.947 0.997 0.999 0.998 0.97 0.997 0.995 0.996 0.991 0.969 0.798 0.997 0.868 0.966 0.907 0.997 0.997
1 0.997 0.926 0.999 0.961 0.986 0.955 0.981 0.972 0.998
1 0.997 0.992 0.993 0.976 0.999 0.992 0.995 0.976 0.95 0.989 0.967
63 0.966 0.957 0.9 0.668 0.913 0.556 0.354 0.999 0.89 0.921 0.991 0.461 0.925 0.983 0.684 0.923 0.938 0.96 0.997 0.996 0.998 0.909 0.987 0.994 0.997 0.98 0.999 0.953 0.99 0.93 0.999 0.993
1 0.957 0.992 0.999 0.991 0.997 0.983 0.769 0.999 0.844 0.98 0.886
1
1 0.997
1 0.906 0.995 0.944 0.995 0.938 0.992 0.986 0.999 0.997
1 0.998 0.998 0.989 0.998 0.985 0.999 0.964 0.969 0.996 0.982
64 0.793 0.987 0.991 0.848 0.926 0.791 0.584 0.895 0.945 0.964 0.95 0.697 0.895 0.96 0.899 0.908 0.886 0.906 0.88 0.932 0.918 0.958 0.925 0.927 0.933 0.963 0.901 0.988 0.947 0.982 0.91 0.943 0.913 0.989 0.945 0.894 0.954 0.879 0.827 0.957 0.91 0.988 0.821 0.998 0.904 0.904 0.926 0.906
1 0.937 0.993 0.865 0.996 0.852 0.832 0.911 0.926 0.91 0.883 0.89 0.842 0.927 0.964 0.895 0.985 0.792 0.87 0.822
65 0.943 0.978 0.932 0.709 0.936 0.607 0.399 0.993 0.918 0.946 0.999 0.511 0.94 0.994 0.735 0.94 0.95 0.97 0.989
1 0.998 0.935 0.993 0.997
1 0.993 0.995 0.975 0.998 0.955 0.996 0.999 0.997 0.977 0.998 0.993 0.998 0.988 0.964 0.815 0.996 0.883 0.961 0.919 0.996 0.996 0.999 0.995 0.937
1 0.969 0.982 0.963 0.977 0.967 0.997 0.999 0.996 0.989 0.991 0.972 0.999 0.996 0.993 0.982 0.945 0.985 0.962
66 0.847 0.996 0.986 0.819 0.951 0.745 0.536 0.936 0.958 0.977 0.978 0.651 0.93 0.983 0.859 0.939 0.926 0.943 0.924 0.965 0.954 0.969 0.96 0.962 0.966 0.986 0.941 0.998 0.976 0.989 0.948 0.973 0.949 0.998 0.974 0.934 0.979 0.922 0.877 0.921 0.947 0.965 0.873 0.984 0.943 0.943 0.961 0.944 0.993 0.969
1 0.91 0.999 0.899 0.882 0.949 0.961 0.948 0.925 0.931 0.891 0.961 0.987 0.936 0.997 0.847 0.916 0.874
67 0.985 0.928 0.86 0.617 0.886 0.5 0.306 0.996 0.856 0.889 0.974 0.408 0.906 0.964 0.628 0.902 0.925 0.946 0.999 0.985 0.991 0.875 0.976 0.984 0.985 0.958 0.995 0.922 0.974 0.896 0.994 0.977 0.993 0.926 0.976 0.997 0.974 0.999 0.996 0.715 0.994 0.795 0.995 0.842 0.995 0.995 0.986 0.995 0.865 0.982 0.91
1 0.902
1 0.998 0.992 0.986 0.994 0.999 0.998 0.999 0.988 0.963 0.997 0.935 0.988 0.999 0.995
68 0.837 0.995 0.986 0.82 0.94 0.749 0.538 0.928 0.95 0.971 0.973 0.653 0.918 0.978 0.864 0.927 0.913 0.932 0.916 0.959 0.948 0.964 0.952 0.955 0.96 0.981 0.933 0.996 0.971 0.986 0.941 0.968 0.943 0.997 0.968 0.927 0.976 0.915 0.868 0.929 0.941 0.971 0.863 0.989 0.936 0.936 0.955 0.938 0.996 0.963 0.999 0.902
1 0.891 0.874 0.942 0.955 0.941 0.918 0.924 0.882 0.955 0.983 0.929 0.996 0.837 0.907 0.864
69 0.989 0.918 0.848 0.602 0.878 0.484 0.292 0.994 0.846 0.878 0.968 0.393 0.9 0.957 0.611 0.895 0.919 0.941 0.998 0.981 0.988 0.865 0.971 0.979 0.98 0.95 0.992 0.912 0.968 0.886 0.991 0.971 0.99 0.916 0.97 0.995 0.967 0.998 0.998 0.699 0.991 0.781 0.997 0.828 0.992 0.992 0.981 0.992 0.852 0.977 0.899
1 0.891
1 0.999 0.989 0.982 0.991 0.997 0.997
1 0.984 0.956 0.994 0.926 0.992 0.999 0.997
70 0.993 0.902 0.828 0.581 0.862 0.46 0.273 0.989 0.828 0.861 0.956 0.371 0.886 0.944 0.587 0.881 0.907 0.929 0.994 0.971 0.98 0.847 0.961 0.97 0.971 0.937 0.986 0.895 0.956 0.868 0.984 0.96 0.983 0.9 0.959 0.989 0.956 0.994
1 0.675 0.985 0.759 0.999 0.808 0.985 0.985 0.972 0.986 0.832 0.967 0.882 0.998 0.874 0.999
1 0.982 0.972 0.985 0.993 0.992
1 0.976 0.944 0.989 0.911 0.996 0.996 0.999
71 0.961 0.961 0.908 0.678 0.925 0.569 0.367 0.999 0.902 0.931 0.993 0.475 0.936 0.987 0.696 0.935 0.95 0.969 0.996 0.998 0.999 0.919 0.992 0.997 0.998 0.984 0.999 0.958 0.993 0.938 0.999 0.995 0.999 0.96 0.994 0.998 0.992 0.996 0.979 0.777 0.999 0.85 0.977 0.89 0.999 0.999 0.998 0.999 0.911 0.997 0.949 0.992 0.942 0.989 0.982
1 0.998 0.999 0.997 0.997 0.985 0.998 0.986 0.998 0.966 0.963 0.994 0.978
72 0.949 0.97 0.92 0.694 0.929 0.588 0.382 0.996 0.91 0.939 0.997 0.492 0.936 0.99 0.716 0.936 0.948 0.967 0.991 0.999 0.998 0.927 0.991 0.997 0.999 0.989 0.997 0.968 0.996 0.947 0.997 0.998 0.998 0.97 0.997 0.995 0.996 0.991 0.969 0.798 0.998 0.868 0.967 0.907 0.998 0.998
1 0.997 0.926 0.999 0.961 0.986 0.955 0.982 0.972 0.998
1 0.997 0.992 0.993 0.977 0.999 0.993 0.996 0.976 0.951 0.989 0.968
73 0.967 0.96 0.904 0.673 0.916 0.562 0.359 0.998 0.895 0.925 0.991 0.467 0.926 0.984 0.69 0.926 0.939 0.96 0.997 0.997 0.999 0.912 0.988 0.994 0.997 0.981 0.998 0.956 0.991 0.934 0.999 0.993 0.999 0.96 0.992 0.998 0.991 0.997 0.983 0.775 0.999 0.849 0.981 0.89 0.999 0.999 0.997
1 0.91 0.996 0.948 0.994 0.941 0.991 0.985 0.999 0.997
1 0.997 0.998 0.988 0.999 0.986 0.999 0.966 0.969 0.996 0.981
74 0.977 0.941 0.878 0.638 0.899 0.523 0.325 0.999 0.871 0.904 0.982 0.43 0.915 0.973 0.652 0.912 0.933 0.954 0.999 0.991 0.996 0.891 0.982 0.99 0.991 0.969 0.998 0.936 0.982 0.912 0.997 0.985 0.997 0.94 0.984 0.999 0.982
1 0.991 0.738 0.998 0.816 0.989 0.86 0.998 0.998 0.992 0.998 0.883 0.989 0.925 0.999 0.918 0.997 0.993 0.997 0.992 0.997
1
1 0.996 0.993 0.973 0.999 0.948 0.98 0.999 0.99
75 0.977 0.947 0.886 0.649 0.906 0.536 0.337 0.999 0.879 0.909 0.985 0.443 0.921 0.977 0.664 0.918 0.937 0.958 0.999 0.993 0.997 0.896 0.985 0.992 0.993 0.972 0.998 0.941 0.985 0.918 0.998 0.987 0.997 0.945 0.986 0.999 0.985
1 0.99 0.749 0.999 0.825 0.988 0.868 0.998 0.998 0.993 0.998 0.89 0.991 0.931 0.998 0.924 0.997 0.992 0.997 0.993 0.998
1
1 0.994 0.995 0.976 0.999 0.952 0.979 0.999 0.989
76 0.991 0.91 0.837 0.59 0.868 0.471 0.281 0.991 0.835 0.869 0.962 0.38 0.891 0.95 0.598 0.886 0.911 0.933 0.996 0.976 0.984 0.855 0.965 0.974 0.976 0.943 0.989 0.903 0.962 0.876 0.987 0.966 0.986 0.908 0.965 0.992 0.962 0.996 0.999 0.687 0.988 0.77 0.998 0.818 0.989 0.989 0.976 0.989 0.842 0.972 0.891 0.999 0.882
1
1 0.985 0.977 0.988 0.996 0.994
1 0.98 0.95 0.992 0.918 0.994 0.997 0.998
77 0.955 0.971 0.921 0.698 0.928 0.59 0.385 0.996 0.91 0.938 0.996 0.495 0.934 0.991 0.718 0.934 0.945 0.965 0.993 0.999 0.999 0.927 0.99 0.997
1 0.989 0.997 0.968 0.996 0.948 0.998 0.997 0.999 0.971 0.996 0.995 0.996 0.992 0.973 0.799 0.998 0.87 0.971 0.908 0.998 0.998 0.999 0.998 0.927 0.999 0.961 0.988 0.955 0.984 0.976 0.998 0.999 0.999 0.993 0.995 0.98
1 0.993 0.997 0.977 0.956 0.99 0.972
78 0.916 0.99 0.955 0.749 0.944 0.654 0.443 0.979 0.936 0.962 0.998 0.557 0.939 0.996 0.779 0.943 0.944 0.963 0.972 0.994 0.989 0.952 0.986 0.991 0.994 0.997 0.982 0.989 0.997 0.973 0.986 0.997 0.987 0.991 0.996 0.978 0.998 0.971 0.94 0.856 0.986 0.917 0.936 0.948 0.984 0.984 0.992 0.985 0.964 0.996 0.987 0.963 0.983 0.956 0.944 0.986 0.993 0.986 0.973 0.976 0.95 0.993
1 0.98 0.995 0.916 0.967 0.937
79 0.973 0.95 0.89 0.654 0.908 0.54 0.34 0.999 0.883 0.914 0.987 0.447 0.922 0.979 0.668 0.92 0.937 0.958 0.999 0.995 0.998 0.902 0.985 0.992 0.995 0.975 0.999 0.945 0.987 0.922 0.999 0.99 0.999 0.949 0.989 0.999 0.987 0.998 0.988 0.754 0.999 0.83 0.986 0.874 0.999 0.999 0.995 0.999 0.895 0.993 0.936 0.997 0.929 0.994 0.989 0.998 0.996 0.999 0.999 0.999 0.992 0.997 0.98
1 0.957 0.975 0.998 0.987
80 0.879 0.998 0.978 0.796 0.947 0.713 0.502 0.956 0.949 0.971 0.988 0.616 0.932 0.991 0.832 0.939 0.931 0.951 0.946 0.979 0.971 0.963 0.972 0.976 0.98 0.993 0.96 0.998 0.987 0.985 0.966 0.984 0.967 0.999 0.985 0.955 0.99 0.945 0.906 0.901 0.966 0.952 0.902 0.975 0.962 0.962 0.976 0.964 0.985 0.982 0.997 0.935 0.996 0.926 0.911 0.966 0.976 0.966 0.948 0.952 0.918 0.977 0.995 0.957
1 0.879 0.939 0.903
81 0.998 0.87 0.789 0.538 0.83 0.416 0.238 0.974 0.793 0.826 0.931 0.331 0.86 0.918 0.54 0.853 0.883 0.904 0.982 0.95 0.963 0.811 0.938 0.949 0.951 0.908 0.97 0.861 0.931 0.832 0.967 0.936 0.965 0.866 0.935 0.974 0.931 0.982 0.998 0.628 0.968 0.715 0.998 0.767 0.968 0.968 0.95 0.969 0.792 0.945 0.847 0.988 0.837 0.992 0.996 0.963 0.951 0.969 0.98 0.979 0.994 0.956 0.916 0.975 0.879
1 0.986 0.998
82 0.983 0.932 0.867 0.626 0.893 0.509 0.314 0.998 0.863 0.895 0.977 0.417 0.912 0.967 0.636 0.908 0.929 0.95 0.999 0.988 0.993 0.881 0.978 0.986 0.988 0.962 0.997 0.927 0.977 0.902 0.995 0.98 0.994 0.931 0.979 0.998 0.976 0.999 0.994 0.722 0.995 0.802 0.993 0.847 0.996 0.996 0.989 0.996 0.87 0.985 0.916 0.999 0.907 0.999 0.996 0.994 0.989 0.996 0.999 0.999 0.997 0.99 0.967 0.998 0.939 0.986
1 0.994
83 0.996 0.895 0.819 0.573 0.856 0.452 0.267 0.986 0.821 0.853 0.951 0.364 0.882 0.939 0.576 0.876 0.903 0.924 0.992 0.967 0.977 0.839 0.956 0.965 0.967 0.931 0.982 0.887 0.951 0.86 0.98 0.955 0.979 0.892 0.954 0.986 0.95 0.991
1 0.663 0.981 0.748
1 0.797 0.981 0.982 0.967 0.982 0.822 0.962 0.874 0.995 0.864 0.997 0.999 0.978 0.968 0.981 0.99 0.989 0.998 0.972 0.937 0.987 0.903 0.998 0.994
1Fig. 3. R2 values for the 83 research catchments between
the hydrographs.

where p(xi) and p(yi) are the probabilities that X and Y are
respectively in the states xi and yi. In brief, KLD quantifies
the information loss when the probability density function of
Y is used in place of X. From this, the JSD is developed bycomparing each distribution to the “midpoint” distribution M,
defined as
1
𝑋 𝑌
𝑀
𝑁

𝐽𝑆𝐷

12 17 18 19 21 26 23 20 22 24 25 27 28 29 30 31 32 33 34 35 36 37 43 38 39 40 41 42 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
12
0 0.203 0.127 0.171 0.219 0.229 0.197 0.108 0.211 0.219 0.341 0.269 0.236 0.202 0.291 0.21 0.261 0.268 0.093 0.337 0.442 0.225 0.291 0.291 0.082 0.26 0.175 0.198 0.29 0.278 0.185 0.177 0.167 0.362 0.32 0.325 0.454 0.543 0.504 0.094 0.2 0.078 0.147 0.128 0.262 0.293 0.167 0.41 0.419 0.301 0.414 0.431 0.498 0.537 0.433 0.251 0.166 0.307 0.333 0.164 0.328 0.562 0.484 0.385 0.578 0.477 0.405 0.309
17 0.203
0 0.152 0.144 0.143 0.11 0.109 0.129 0.161 0.177 0.109 0.131 0.168 0.086 0.134 0.199 0.164 0.166 0.174 0.135 0.168 0.211 0.176 0.176 0.179 0.238 0.229 0.223 0.256 0.295 0.112 0.242 0.147 0.346 0.294 0.262 0.332 0.41 0.362 0.216 0.143 0.191 0.146 0.171 0.351 0.352 0.256 0.404 0.388 0.186 0.349 0.37 0.4 0.352 0.298 0.222 0.271 0.243 0.373 0.163 0.295 0.423 0.356 0.297 0.422 0.362 0.303 0.286
18 0.127 0.152
0 0.102 0.147 0.081 0.071 0.109 0.16 0.172 0.144 0.126 0.177 0.103 0.118 0.175 0.188 0.194 0.124 0.143 0.257 0.2 0.211 0.211 0.115 0.228 0.139 0.183 0.252 0.256 0.121 0.193 0.136 0.32 0.287 0.233 0.378 0.48 0.383 0.14 0.136 0.138 0.096 0.183 0.292 0.308 0.222 0.379 0.36 0.194 0.341 0.348 0.442 0.445 0.34 0.279 0.257 0.219 0.334 0.121 0.332 0.426 0.423 0.311 0.463 0.314 0.327 0.248
19 0.171 0.144 0.102
0 0.172 0.074 0.058 0.143 0.186 0.198 0.132 0.117 0.204 0.11 0.115 0.207 0.208 0.212 0.167 0.118 0.316 0.229 0.228 0.227 0.137 0.261 0.081 0.238 0.279 0.28 0.129 0.232 0.158 0.34 0.311 0.243 0.406 0.478 0.337 0.168 0.164 0.138 0.068 0.228 0.323 0.336 0.257 0.394 0.385 0.219 0.38 0.397 0.435 0.439 0.375 0.269 0.314 0.175 0.368 0.147 0.313 0.394 0.454 0.383 0.42 0.367 0.269 0.192
21 0.219 0.143 0.147 0.172
0 0.219 0.206 0.121 0.011 0.014 0.24 0.155 0.004 0.115 0.211 0.02 0.009 0.011 0.197 0.229 0.249 0.025 0.016 0.016 0.128 0.037 0.2 0.057 0.048 0.077 0.026 0.054 0.144 0.114 0.073 0.048 0.116 0.218 0.231 0.142 0.015 0.157 0.168 0.124 0.112 0.112 0.127 0.171 0.144 0.06 0.144 0.139 0.201 0.19 0.133 0.12 0.184 0.139 0.127 0.041 0.151 0.284 0.196 0.119 0.282 0.189 0.263 0.222
26 0.229 0.11 0.081 0.074 0.219
0 0.014 0.129 0.248 0.271 0.054 0.037 0.272 0.081 0.016 0.29 0.273 0.278 0.222 0.052 0.212 0.324 0.296 0.296 0.186 0.364 0.162 0.322 0.392 0.418 0.154 0.332 0.185 0.493 0.442 0.36 0.53 0.645 0.559 0.244 0.202 0.209 0.089 0.31 0.485 0.505 0.35 0.567 0.548 0.262 0.489 0.563 0.629 0.574 0.532 0.423 0.398 0.424 0.535 0.196 0.458 0.571 0.597 0.495 0.632 0.535 0.504 0.453
23 0.197 0.109 0.071 0.058 0.206 0.014
0 0.108 0.233 0.254 0.071 0.032 0.254 0.08 0.055 0.265 0.259 0.264 0.181 0.07 0.221 0.302 0.283 0.283 0.158 0.335 0.129 0.29 0.365 0.393 0.143 0.299 0.161 0.471 0.413 0.337 0.51 0.617 0.538 0.209 0.189 0.169 0.065 0.271 0.444 0.467 0.317 0.546 0.526 0.246 0.467 0.534 0.605 0.542 0.498 0.377 0.352 0.398 0.499 0.183 0.412 0.581 0.578 0.49 0.613 0.504 0.487 0.449
20 0.108 0.129 0.109 0.143 0.121 0.129 0.108
0 0.123 0.132 0.214 0.16 0.137 0.095 0.179 0.124 0.155 0.16 0.103 0.214 0.298 0.147 0.175 0.175 0.107 0.159 0.177 0.114 0.184 0.199 0.108 0.136 0.127 0.256 0.209 0.198 0.315 0.369 0.349 0.106 0.117 0.111 0.111 0.139 0.223 0.243 0.143 0.308 0.302 0.179 0.244 0.305 0.362 0.298 0.293 0.226 0.165 0.177 0.265 0.106 0.243 0.388 0.329 0.258 0.416 0.323 0.225 0.22
22 0.211 0.161 0.16 0.186 0.011 0.248 0.233 0.123
0 0.001 0.266 0.185 0.012 0.115 0.247 0.021 0.021 0.022 0.196 0.247 0.27 0.009 0.028 0.028 0.129 0.034 0.208 0.058 0.043 0.052 0.045 0.044 0.151 0.089 0.066 0.054 0.12 0.21 0.229 0.132 0.031 0.152 0.185 0.108 0.096 0.096 0.118 0.144 0.121 0.081 0.146 0.141 0.192 0.191 0.137 0.107 0.179 0.127 0.113 0.049 0.151 0.268 0.175 0.108 0.269 0.203 0.232 0.201
24 0.219 0.177 0.172 0.198 0.014 0.271 0.254 0.132 0.001
0 0.287 0.199 0.011 0.134 0.268 0.017 0.021 0.023 0.202 0.266 0.284 0.004 0.028 0.028 0.136 0.026 0.216 0.054 0.034 0.041 0.054 0.039 0.164 0.074 0.054 0.047 0.107 0.192 0.216 0.138 0.034 0.159 0.198 0.11 0.082 0.083 0.115 0.126 0.103 0.082 0.132 0.126 0.173 0.176 0.123 0.101 0.177 0.116 0.098 0.053 0.142 0.253 0.159 0.096 0.252 0.19 0.223 0.193
25 0.341 0.109 0.144 0.132 0.24 0.054 0.071 0.214 0.266 0.287
0 0.072 0.286 0.108 0.056 0.325 0.274 0.276 0.292 0.008 0.137 0.346 0.286 0.286 0.273 0.387 0.229 0.374 0.405 0.425 0.191 0.397 0.214 0.475 0.453 0.353 0.483 0.594 0.472 0.332 0.237 0.294 0.156 0.343 0.534 0.542 0.434 0.536 0.521 0.282 0.476 0.521 0.576 0.492 0.465 0.425 0.477 0.336 0.564 0.244 0.439 0.455 0.54 0.468 0.513 0.479 0.389 0.356
27 0.269 0.131 0.126 0.117 0.155 0.037 0.032 0.16 0.185 0.199 0.072
0 0.189 0.11 0.064 0.209 0.187 0.189 0.239 0.074 0.191 0.239 0.198 0.198 0.21 0.253 0.199 0.229 0.274 0.292 0.125 0.268 0.195 0.33 0.306 0.225 0.353 0.398 0.315 0.253 0.144 0.236 0.114 0.27 0.365 0.37 0.307 0.392 0.36 0.172 0.28 0.35 0.4 0.284 0.292 0.294 0.342 0.163 0.382 0.145 0.287 0.329 0.342 0.303 0.364 0.272 0.219 0.212
28 0.236 0.168 0.177 0.204 0.004 0.272 0.254 0.137 0.012 0.011 0.286 0.189
0 0.138 0.26 0.016 0.006 0.006 0.21 0.273 0.271 0.016 0.01 0.01 0.141 0.024 0.222 0.052 0.031 0.059 0.041 0.047 0.162 0.09 0.053 0.037 0.086 0.182 0.206 0.156 0.022 0.168 0.2 0.125 0.088 0.086 0.124 0.143 0.115 0.062 0.12 0.106 0.166 0.159 0.103 0.103 0.176 0.122 0.098 0.052 0.131 0.264 0.163 0.092 0.253 0.161 0.247 0.209
29 0.202 0.086 0.103 0.11 0.115 0.081 0.08 0.095 0.115 0.134 0.108 0.11 0.138
0 0.105 0.168 0.134 0.137 0.183 0.122 0.198 0.164 0.147 0.147 0.122 0.208 0.166 0.198 0.225 0.231 0.087 0.21 0.1 0.275 0.267 0.209 0.3 0.377 0.296 0.187 0.122 0.144 0.117 0.172 0.309 0.283 0.262 0.327 0.311 0.167 0.331 0.314 0.365 0.302 0.286 0.231 0.272 0.156 0.302 0.137 0.25 0.317 0.287 0.233 0.369 0.292 0.193 0.136
30 0.291 0.134 0.118 0.115 0.211 0.016 0.055 0.179 0.247 0.268 0.056 0.064 0.26 0.105
0 0.281 0.258 0.261 0.283 0.053 0.206 0.32 0.275 0.274 0.235 0.345 0.196 0.324 0.365 0.406 0.163 0.342 0.22 0.463 0.408 0.32 0.482 0.557 0.466 0.288 0.201 0.274 0.134 0.338 0.477 0.486 0.373 0.537 0.506 0.24 0.429 0.5 0.571 0.468 0.472 0.417 0.426 0.346 0.506 0.205 0.434 0.49 0.539 0.423 0.556 0.442 0.443 0.394
31 0.21 0.199 0.175 0.207 0.02 0.29 0.265 0.124 0.021 0.017 0.325 0.209 0.016 0.168 0.281
0 0.039 0.041 0.192 0.308 0.322 0.018 0.048 0.048 0.136 0.008 0.213 0.03 0.017 0.052 0.07 0.027 0.178 0.088 0.032 0.048 0.126 0.177 0.221 0.128 0.04 0.156 0.21 0.105 0.065 0.071 0.085 0.144 0.115 0.081 0.101 0.123 0.178 0.165 0.119 0.096 0.145 0.134 0.086 0.053 0.133 0.293 0.178 0.102 0.281 0.179 0.268 0.226
32 0.261 0.164 0.188 0.208 0.009 0.273 0.259 0.155 0.021 0.021 0.274 0.187 0.006 0.134 0.258 0.039
0 2E-04 0.232 0.262 0.251 0.028 0.002 0.002 0.152 0.046 0.232 0.075 0.053 0.073 0.039 0.071 0.167 0.099 0.076 0.041 0.074 0.192 0.201 0.183 0.026 0.182 0.205 0.145 0.111 0.102 0.157 0.149 0.123 0.063 0.14 0.101 0.163 0.162 0.099 0.113 0.203 0.119 0.112 0.061 0.135 0.249 0.158 0.092 0.239 0.153 0.236 0.202
33 0.268 0.166 0.194 0.212 0.011 0.278 0.264 0.16 0.022 0.023 0.276 0.189 0.006 0.137 0.261 0.041 2E-04
0 0.24 0.264 0.25 0.03 0.001 0.001 0.157 0.047 0.236 0.078 0.054 0.074 0.041 0.074 0.171 0.098 0.076 0.04 0.071 0.189 0.198 0.189 0.027 0.186 0.21 0.149 0.112 0.102 0.163 0.147 0.121 0.064 0.14 0.098 0.159 0.158 0.097 0.114 0.208 0.117 0.111 0.064 0.134 0.244 0.155 0.09 0.234 0.15 0.234 0.199
34 0.093 0.174 0.124 0.167 0.197 0.222 0.181 0.103 0.196 0.202 0.292 0.239 0.21 0.183 0.283 0.192 0.232 0.24
0 0.291 0.311 0.208 0.259 0.259 0.109 0.227 0.141 0.169 0.253 0.264 0.178 0.165 0.134 0.332 0.281 0.285 0.395 0.462 0.408 0.118 0.193 0.117 0.139 0.133 0.24 0.266 0.14 0.383 0.372 0.267 0.336 0.363 0.432 0.463 0.345 0.257 0.153 0.28 0.293 0.164 0.31 0.507 0.422 0.339 0.506 0.334 0.304 0.289
35 0.337 0.135 0.143 0.118 0.229 0.052 0.07 0.214 0.247 0.266 0.008 0.074 0.273 0.122 0.053 0.308 0.262 0.264 0.291
0 0.188 0.321 0.273 0.273 0.28 0.368 0.207 0.358 0.384 0.375 0.189 0.382 0.23 0.413 0.426 0.323 0.454 0.543 0.39 0.331 0.227 0.304 0.154 0.353 0.503 0.505 0.429 0.462 0.458 0.285 0.463 0.489 0.503 0.462 0.436 0.433 0.487 0.248 0.521 0.219 0.425 0.354 0.478 0.427 0.412 0.441 0.275 0.238
36 0.442 0.168 0.257 0.316 0.249 0.212 0.221 0.298 0.27 0.284 0.137 0.191 0.271 0.198 0.206 0.322 0.251 0.25 0.311 0.188
0 0.325 0.247 0.247 0.293 0.344 0.429 0.357 0.347 0.394 0.24 0.381 0.28 0.403 0.378 0.319 0.33 0.39 0.198 0.395 0.258 0.338 0.299 0.26 0.474 0.467 0.411 0.434 0.409 0.252 0.236 0.342 0.407 0.21 0.247 0.288 0.372 0.137 0.463 0.298 0.182 0.178 0.279 0.236 0.26 0.211 0.146 0.123
37 0.225 0.211 0.2 0.229 0.025 0.324 0.302 0.147 0.009 0.004 0.346 0.239 0.016 0.164 0.32 0.018 0.028 0.03 0.208 0.321 0.325
0 0.034 0.034 0.144 0.019 0.234 0.05 0.025 0.027 0.076 0.031 0.184 0.056 0.041 0.045 0.093 0.166 0.203 0.143 0.048 0.163 0.228 0.108 0.058 0.057 0.108 0.104 0.083 0.094 0.115 0.104 0.149 0.158 0.106 0.085 0.165 0.106 0.071 0.066 0.128 0.242 0.136 0.076 0.236 0.171 0.211 0.185
43 0.291 0.176 0.211 0.228 0.016 0.296 0.283 0.175 0.028 0.028 0.286 0.198 0.01 0.147 0.275 0.048 0.002 0.001 0.259 0.273 0.247 0.034
0 7E-06 0.175 0.05 0.25 0.087 0.055 0.075 0.051 0.084 0.185 0.094 0.076 0.039 0.06 0.176 0.185 0.21 0.035 0.204 0.229 0.164 0.116 0.103 0.176 0.141 0.115 0.067 0.135 0.089 0.147 0.144 0.087 0.117 0.221 0.112 0.109 0.074 0.131 0.231 0.143 0.082 0.219 0.139 0.225 0.193
38 0.291 0.176 0.211 0.227 0.016 0.296 0.283 0.175 0.028 0.028 0.286 0.198 0.01 0.147 0.274 0.048 0.002 0.001 0.259 0.273 0.247 0.034 7E-06
0 0.175 0.05 0.25 0.087 0.055 0.075 0.051 0.084 0.185 0.094 0.076 0.039 0.06 0.177 0.185 0.21 0.035 0.204 0.229 0.164 0.116 0.103 0.176 0.141 0.115 0.067 0.135 0.089 0.147 0.144 0.087 0.117 0.221 0.112 0.109 0.074 0.131 0.231 0.143 0.082 0.219 0.139 0.225 0.193
39 0.082 0.179 0.115 0.137 0.128 0.186 0.158 0.107 0.129 0.136 0.273 0.21 0.141 0.122 0.235 0.136 0.152 0.157 0.109 0.28 0.293 0.144 0.175 0.175
0 0.174 0.177 0.137 0.194 0.186 0.1 0.118 0.086 0.254 0.224 0.209 0.311 0.386 0.371 0.063 0.12 0.07 0.107 0.092 0.186 0.203 0.131 0.299 0.296 0.182 0.297 0.314 0.357 0.383 0.313 0.172 0.159 0.167 0.235 0.106 0.261 0.398 0.354 0.269 0.408 0.321 0.245 0.198
40 0.26 0.238 0.228 0.261 0.037 0.364 0.335 0.159 0.034 0.026 0.387 0.253 0.024 0.208 0.345 0.008 0.046 0.047 0.227 0.368 0.344 0.019 0.05 0.05 0.174
0 0.265 0.037 0.004 0.042 0.103 0.034 0.218 0.064 0.012 0.046 0.092 0.126 0.185 0.169 0.061 0.197 0.27 0.127 0.049 0.05 0.098 0.113 0.083 0.091 0.072 0.089 0.133 0.121 0.086 0.089 0.155 0.115 0.059 0.081 0.116 0.258 0.134 0.071 0.24 0.142 0.245 0.21
41 0.175 0.229 0.139 0.081 0.2 0.162 0.129 0.177 0.208 0.216 0.229 0.199 0.222 0.166 0.196 0.213 0.232 0.236 0.141 0.207 0.429 0.234 0.25 0.25 0.177 0.265
0 0.235 0.282 0.261 0.171 0.239 0.179 0.309 0.31 0.25 0.395 0.435 0.25 0.193 0.2 0.166 0.117 0.246 0.314 0.327 0.264 0.357 0.342 0.265 0.407 0.381 0.403 0.416 0.388 0.323 0.296 0.133 0.345 0.176 0.327 0.388 0.39 0.362 0.349 0.365 0.227 0.124
42 0.198 0.223 0.183 0.238 0.057 0.322 0.29 0.114 0.058 0.054 0.374 0.229 0.052 0.198 0.324 0.03 0.075 0.078 0.169 0.358 0.357 0.05 0.087 0.087 0.137 0.037 0.235
0 0.059 0.086 0.097 0.027 0.204 0.125 0.07 0.078 0.162 0.23 0.297 0.128 0.061 0.155 0.232 0.103 0.059 0.071 0.065 0.182 0.152 0.091 0.112 0.136 0.203 0.209 0.134 0.087 0.118 0.16 0.089 0.07 0.133 0.361 0.212 0.117 0.338 0.178 0.329 0.277
44 0.29 0.256 0.252 0.279 0.048 0.392 0.365 0.184 0.043 0.034 0.405 0.274 0.031 0.225 0.365 0.017 0.053 0.054 0.253 0.384 0.347 0.025 0.055 0.055 0.194 0.004 0.282 0.059
0 0.041 0.121 0.049 0.233 0.056 0.006 0.049 0.082 0.101 0.159 0.193 0.077 0.217 0.294 0.144 0.056 0.053 0.118 0.1 0.071 0.103 0.068 0.083 0.118 0.101 0.08 0.095 0.173 0.106 0.059 0.099 0.114 0.232 0.117 0.065 0.216 0.134 0.224 0.196
45 0.278 0.295 0.256 0.28 0.077 0.418 0.393 0.199 0.052 0.041 0.425 0.292 0.059 0.231 0.406 0.052 0.073 0.074 0.264 0.375 0.394 0.027 0.075 0.075 0.186 0.042 0.261 0.086 0.041
0 0.151 0.075 0.269 0.012 0.042 0.053 0.095 0.119 0.154 0.196 0.103 0.191 0.291 0.155 0.06 0.055 0.168 0.042 0.033 0.148 0.122 0.089 0.1 0.126 0.102 0.104 0.228 0.066 0.063 0.107 0.151 0.16 0.084 0.064 0.153 0.157 0.149 0.126
46 0.185 0.112 0.121 0.129 0.026 0.154 0.143 0.108 0.045 0.054 0.191 0.125 0.041 0.087 0.163 0.07 0.039 0.041 0.178 0.189 0.24 0.076 0.051 0.051 0.1 0.103 0.171 0.097 0.121 0.151
0 0.095 0.108 0.205 0.159 0.112 0.198 0.332 0.318 0.128 0.015 0.134 0.108 0.123 0.184 0.188 0.151 0.268 0.245 0.071 0.222 0.226 0.306 0.292 0.217 0.152 0.195 0.203 0.211 0.039 0.204 0.377 0.284 0.202 0.377 0.254 0.329 0.282
47 0.177 0.242 0.193 0.232 0.054 0.332 0.299 0.136 0.044 0.039 0.397 0.268 0.047 0.21 0.342 0.027 0.071 0.074 0.165 0.382 0.381 0.031 0.084 0.084 0.118 0.034 0.239 0.027 0.049 0.075 0.095
0 0.189 0.121 0.062 0.079 0.157 0.227 0.291 0.102 0.063 0.145 0.224 0.078 0.036 0.05 0.038 0.18 0.15 0.099 0.109 0.134 0.209 0.221 0.133 0.073 0.088 0.169 0.071 0.074 0.118 0.357 0.211 0.106 0.346 0.189 0.306 0.277
48 0.167 0.147 0.136 0.158 0.144 0.185 0.161 0.127 0.151 0.164 0.214 0.195 0.162 0.1 0.22 0.178 0.167 0.171 0.134 0.23 0.28 0.184 0.185 0.185 0.086 0.218 0.179 0.204 0.233 0.269 0.108 0.189
0 0.339 0.288 0.267 0.334 0.457 0.422 0.141 0.146 0.099 0.14 0.115 0.296 0.289 0.208 0.387 0.382 0.201 0.344 0.359 0.426 0.417 0.336 0.206 0.209 0.314 0.314 0.165 0.256 0.459 0.4 0.318 0.511 0.385 0.357 0.292
49 0.362 0.346 0.32 0.34 0.114 0.493 0.471 0.256 0.089 0.074 0.475 0.33 0.09 0.275 0.463 0.088 0.099 0.098 0.332 0.413 0.403 0.056 0.094 0.094 0.254 0.064 0.309 0.125 0.056 0.012 0.205 0.121 0.339
0 0.048 0.064 0.074 0.08 0.112 0.289 0.146 0.271 0.364 0.232 0.081 0.068 0.227 0.019 0.01 0.176 0.119 0.072 0.063 0.09 0.088 0.138 0.288 0.052 0.065 0.156 0.161 0.107 0.048 0.051 0.095 0.127 0.12 0.102
50 0.32 0.294 0.287 0.311 0.073 0.442 0.413 0.209 0.066 0.054 0.453 0.306 0.053 0.267 0.408 0.032 0.076 0.076 0.281 0.426 0.378 0.041 0.076 0.076 0.224 0.012 0.31 0.07 0.006 0.042 0.159 0.062 0.288 0.048
0 0.054 0.083 0.077 0.141 0.225 0.103 0.247 0.335 0.169 0.05 0.047 0.128 0.086 0.06 0.123 0.055 0.071 0.101 0.082 0.07 0.099 0.183 0.098 0.049 0.121 0.111 0.213 0.1 0.054 0.195 0.118 0.212 0.187
51 0.325 0.262 0.233 0.243 0.048 0.36 0.337 0.198 0.054 0.047 0.353 0.225 0.037 0.209 0.32 0.048 0.041 0.04 0.285 0.323 0.319 0.045 0.039 0.039 0.209 0.046 0.25 0.078 0.049 0.053 0.112 0.079 0.267 0.064 0.054
0 0.082 0.139 0.164 0.226 0.07 0.223 0.275 0.198 0.083 0.076 0.169 0.107 0.078 0.091 0.115 0.073 0.122 0.121 0.082 0.123 0.236 0.087 0.081 0.091 0.132 0.211 0.131 0.072 0.186 0.117 0.214 0.188
52 0.454 0.332 0.378 0.406 0.116 0.53 0.51 0.315 0.12 0.107 0.483 0.353 0.086 0.3 0.482 0.126 0.074 0.071 0.395 0.454 0.33 0.093 0.06 0.06 0.311 0.092 0.395 0.162 0.082 0.095 0.198 0.157 0.334 0.074 0.083 0.082
0 0.091 0.117 0.367 0.151 0.343 0.42 0.281 0.124 0.096 0.266 0.096 0.068 0.15 0.106 0.029 0.057 0.071 0.034 0.153 0.299 0.072 0.082 0.207 0.123 0.148 0.069 0.04 0.127 0.071 0.173 0.157
53 0.543 0.41 0.48 0.478 0.218 0.645 0.617 0.369 0.21 0.192 0.594 0.398 0.182 0.377 0.557 0.177 0.192 0.189 0.462 0.543 0.39 0.166 0.176 0.177 0.386 0.126 0.435 0.23 0.101 0.119 0.332 0.227 0.457 0.08 0.077 0.139 0.091
0 0.038 0.444 0.261 0.411 0.534 0.358 0.138 0.114 0.315 0.083 0.063 0.257 0.108 0.083 0.058 0.025 0.096 0.206 0.35 0.065 0.094 0.286 0.164 0.117 0.059 0.07 0.095 0.075 0.139 0.136
54 0.504 0.362 0.383 0.337 0.231 0.559 0.538 0.349 0.229 0.216 0.472 0.315 0.206 0.296 0.466 0.221 0.201 0.198 0.408 0.39 0.198 0.203 0.185 0.185 0.371 0.185 0.25 0.297 0.159 0.154 0.318 0.291 0.422 0.112 0.141 0.164 0.117 0.038
0 0.466 0.276 0.37 0.421 0.405 0.214 0.189 0.4 0.105 0.101 0.294 0.204 0.139 0.097 0.093 0.16 0.264 0.412 0.087 0.166 0.286 0.221 0.082 0.101 0.133 0.08 0.107 0.098 0.094
55 0.094 0.216 0.14 0.168 0.142 0.244 0.209 0.106 0.132 0.138 0.332 0.253 0.156 0.187 0.288 0.128 0.183 0.189 0.118 0.331 0.395 0.143 0.21 0.21 0.063 0.169 0.193 0.128 0.193 0.196 0.128 0.102 0.141 0.289 0.225 0.226 0.367 0.444 0.466
0 0.136 0.031 0.137 0.028 0.189 0.219 0.105 0.353 0.343 0.199 0.302 0.346 0.436 0.433 0.34 0.155 0.137 0.279 0.266 0.128 0.276 0.541 0.419 0.319 0.559 0.412 0.412 0.368
56 0.2 0.143 0.136 0.164 0.015 0.202 0.189 0.117 0.031 0.034 0.237 0.144 0.022 0.122 0.201 0.04 0.026 0.027 0.193 0.227 0.258 0.048 0.035 0.035 0.12 0.061 0.2 0.061 0.077 0.103 0.015 0.063 0.146 0.146 0.103 0.07 0.151 0.261 0.276 0.136
0 0.155 0.154 0.126 0.127 0.134 0.125 0.205 0.18 0.049 0.163 0.166 0.237 0.233 0.161 0.124 0.175 0.162 0.151 0.022 0.162 0.326 0.226 0.148 0.322 0.198 0.297 0.252
57 0.078 0.191 0.138 0.138 0.157 0.209 0.169 0.111 0.152 0.159 0.294 0.236 0.168 0.144 0.274 0.156 0.182 0.186 0.117 0.304 0.338 0.163 0.204 0.204 0.07 0.197 0.166 0.155 0.217 0.191 0.134 0.145 0.099 0.271 0.247 0.223 0.343 0.411 0.37 0.031 0.155
0 0.115 0.081 0.208 0.229 0.159 0.305 0.328 0.212 0.32 0.325 0.381 0.404 0.319 0.164 0.177 0.142 0.274 0.143 0.292 0.421 0.4 0.305 0.462 0.397 0.227 0.158
58 0.147 0.146 0.096 0.068 0.168 0.089 0.065 0.111 0.185 0.198 0.156 0.114 0.2 0.117 0.134 0.21 0.205 0.21 0.139 0.154 0.299 0.228 0.229 0.229 0.107 0.27 0.117 0.232 0.294 0.291 0.108 0.224 0.14 0.364 0.335 0.275 0.42 0.534 0.421 0.137 0.154 0.115
0 0.187 0.342 0.361 0.247 0.424 0.419 0.221 0.404 0.438 0.499 0.487 0.428 0.304 0.287 0.275 0.396 0.143 0.383 0.489 0.471 0.393 0.512 0.455 0.343 0.288
59 0.128 0.171 0.183 0.228 0.124 0.31 0.271 0.139 0.108 0.11 0.343 0.27 0.125 0.172 0.338 0.105 0.145 0.149 0.133 0.353 0.26 0.108 0.164 0.164 0.092 0.127 0.246 0.103 0.144 0.155 0.123 0.078 0.115 0.232 0.169 0.198 0.281 0.358 0.405 0.028 0.126 0.081 0.187
0 0.132 0.155 0.087 0.284 0.277 0.167 0.237 0.283 0.357 0.362 0.279 0.108 0.127 0.239 0.196 0.13 0.235 0.495 0.346 0.247 0.487 0.332 0.357 0.341
60 0.262 0.351 0.292 0.323 0.112 0.485 0.444 0.223 0.096 0.082 0.534 0.365 0.088 0.309 0.477 0.065 0.111 0.112 0.24 0.503 0.474 0.058 0.116 0.116 0.186 0.049 0.314 0.059 0.056 0.06 0.184 0.036 0.296 0.081 0.05 0.083 0.124 0.138 0.214 0.189 0.127 0.208 0.342 0.132
0 0.003 0.092 0.116 0.097 0.148 0.072 0.074 0.118 0.141 0.079 0.058 0.125 0.113 0.014 0.137 0.083 0.265 0.128 0.041 0.246 0.124 0.245 0.219
61 0.293 0.352 0.308 0.336 0.112 0.505 0.467 0.243 0.096 0.083 0.542 0.37 0.086 0.283 0.486 0.071 0.102 0.102 0.266 0.505 0.467 0.057 0.103 0.103 0.203 0.05 0.327 0.071 0.053 0.055 0.188 0.05 0.289 0.068 0.047 0.076 0.096 0.114 0.189 0.219 0.134 0.229 0.361 0.155 0.003
0 0.117 0.097 0.079 0.153 0.069 0.06 0.093 0.119 0.067 0.063 0.16 0.096 0.006 0.148 0.082 0.234 0.107 0.026 0.213 0.106 0.226 0.203
62 0.167 0.256 0.222 0.257 0.127 0.35 0.317 0.143 0.118 0.115 0.434 0.307 0.124 0.262 0.373 0.085 0.157 0.163 0.14 0.429 0.411 0.108 0.176 0.176 0.131 0.098 0.264 0.065 0.118 0.168 0.151 0.038 0.208 0.227 0.128 0.169 0.266 0.315 0.4 0.105 0.125 0.159 0.247 0.087 0.092 0.117
0 0.293 0.256 0.149 0.141 0.225 0.318 0.304 0.218 0.118 0.064 0.28 0.143 0.133 0.169 0.492 0.314 0.197 0.484 0.266 0.396 0.377
63 0.41 0.404 0.379 0.394 0.171 0.567 0.546 0.308 0.144 0.126 0.536 0.392 0.143 0.327 0.537 0.144 0.149 0.147 0.383 0.462 0.434 0.104 0.141 0.141 0.299 0.113 0.357 0.182 0.1 0.042 0.268 0.18 0.387 0.019 0.086 0.107 0.096 0.083 0.105 0.353 0.205 0.305 0.424 0.284 0.116 0.097 0.293
0 0.033 0.239 0.153 0.095 0.061 0.101 0.117 0.177 0.341 0.046 0.089 0.208 0.186 0.067 0.057 0.071 0.083 0.134 0.082 0.069
64 0.419 0.388 0.36 0.385 0.144 0.548 0.526 0.302 0.121 0.103 0.521 0.36 0.115 0.311 0.506 0.115 0.123 0.121 0.372 0.458 0.409 0.083 0.115 0.115 0.296 0.083 0.342 0.152 0.071 0.033 0.245 0.15 0.382 0.01 0.06 0.078 0.068 0.063 0.101 0.343 0.18 0.328 0.419 0.277 0.097 0.079 0.256 0.033
0 0.197 0.115 0.065 0.052 0.071 0.079 0.158 0.318 0.05 0.069 0.198 0.159 0.102 0.03 0.049 0.074 0.11 0.125 0.111
65 0.301 0.186 0.194 0.219 0.06 0.262 0.246 0.179 0.081 0.082 0.282 0.172 0.062 0.167 0.24 0.081 0.063 0.064 0.267 0.285 0.252 0.094 0.067 0.067 0.182 0.091 0.265 0.091 0.103 0.148 0.071 0.099 0.201 0.176 0.123 0.091 0.15 0.257 0.294 0.199 0.049 0.212 0.221 0.167 0.148 0.153 0.149 0.239 0.197
0 0.142 0.164 0.238 0.21 0.148 0.136 0.194 0.183 0.162 0.116 0.152 0.336 0.228 0.149 0.313 0.174 0.325 0.3
66 0.414 0.349 0.341 0.38 0.144 0.489 0.467 0.244 0.146 0.132 0.476 0.28 0.12 0.331 0.429 0.101 0.14 0.14 0.336 0.463 0.236 0.115 0.135 0.135 0.297 0.072 0.407 0.112 0.068 0.122 0.222 0.109 0.344 0.119 0.055 0.115 0.106 0.108 0.204 0.302 0.163 0.32 0.404 0.237 0.072 0.069 0.141 0.153 0.115 0.142
0 0.083 0.125 0.108 0.083 0.128 0.188 0.153 0.063 0.199 0.122 0.285 0.125 0.072 0.248 0.103 0.275 0.271
67 0.431 0.37 0.348 0.397 0.139 0.563 0.534 0.305 0.141 0.126 0.521 0.35 0.106 0.314 0.5 0.123 0.101 0.098 0.363 0.489 0.342 0.104 0.089 0.089 0.314 0.089 0.381 0.136 0.083 0.089 0.226 0.134 0.359 0.072 0.071 0.073 0.029 0.083 0.139 0.346 0.166 0.325 0.438 0.283 0.074 0.06 0.225 0.095 0.065 0.164 0.083
0 0.061 0.083 0.012 0.148 0.272 0.078 0.05 0.2 0.117 0.183 0.089 0.041 0.152 0.054 0.221 0.193
68 0.498 0.4 0.442 0.435 0.201 0.629 0.605 0.362 0.192 0.173 0.576 0.4 0.166 0.365 0.571 0.178 0.163 0.159 0.432 0.503 0.407 0.149 0.147 0.147 0.357 0.133 0.403 0.203 0.118 0.1 0.306 0.209 0.426 0.063 0.101 0.122 0.057 0.058 0.097 0.436 0.237 0.381 0.499 0.357 0.118 0.093 0.318 0.061 0.052 0.238 0.125 0.061
0 0.069 0.082 0.2 0.363 0.056 0.073 0.26 0.158 0.106 0.062 0.053 0.088 0.073 0.14 0.129
69 0.537 0.352 0.445 0.439 0.19 0.574 0.542 0.298 0.191 0.176 0.492 0.284 0.159 0.302 0.468 0.165 0.162 0.158 0.463 0.462 0.21 0.158 0.144 0.144 0.383 0.121 0.416 0.209 0.101 0.126 0.292 0.221 0.417 0.09 0.082 0.121 0.071 0.025 0.093 0.433 0.233 0.404 0.487 0.362 0.141 0.119 0.304 0.101 0.071 0.21 0.108 0.083 0.069
0 0.094 0.203 0.349 0.072 0.098 0.271 0.16 0.151 0.071 0.081 0.117 0.082 0.166 0.164
70 0.433 0.298 0.34 0.375 0.133 0.532 0.498 0.293 0.137 0.123 0.465 0.292 0.103 0.286 0.472 0.119 0.099 0.097 0.345 0.436 0.247 0.106 0.087 0.087 0.313 0.086 0.388 0.134 0.08 0.102 0.217 0.133 0.336 0.088 0.07 0.082 0.034 0.096 0.16 0.34 0.161 0.319 0.428 0.279 0.079 0.067 0.218 0.117 0.079 0.148 0.083 0.012 0.082 0.094
0 0.152 0.256 0.1 0.059 0.2 0.109 0.21 0.112 0.055 0.18 0.067 0.247 0.224
71 0.251 0.222 0.279 0.269 0.12 0.423 0.377 0.226 0.107 0.101 0.425 0.294 0.103 0.231 0.417 0.096 0.113 0.114 0.257 0.433 0.288 0.085 0.117 0.117 0.172 0.089 0.323 0.087 0.095 0.104 0.152 0.073 0.206 0.138 0.099 0.123 0.153 0.206 0.264 0.155 0.124 0.164 0.304 0.108 0.058 0.063 0.118 0.177 0.158 0.136 0.128 0.148 0.2 0.203 0.152
0 0.156 0.15 0.094 0.145 0.12 0.334 0.194 0.121 0.309 0.186 0.268 0.254
72 0.166 0.271 0.257 0.314 0.184 0.398 0.352 0.165 0.179 0.177 0.477 0.342 0.176 0.272 0.426 0.145 0.203 0.208 0.153 0.487 0.372 0.165 0.221 0.221 0.159 0.155 0.296 0.118 0.173 0.228 0.195 0.088 0.209 0.288 0.183 0.236 0.299 0.35 0.412 0.137 0.175 0.177 0.287 0.127 0.125 0.16 0.064 0.341 0.318 0.194 0.188 0.272 0.363 0.349 0.256 0.156
0 0.314 0.187 0.189 0.187 0.518 0.359 0.243 0.522 0.302 0.392 0.369
73 0.307 0.243 0.219 0.175 0.139 0.424 0.398 0.177 0.127 0.116 0.336 0.163 0.122 0.156 0.346 0.134 0.119 0.117 0.28 0.248 0.137 0.106 0.112 0.112 0.167 0.115 0.133 0.16 0.106 0.066 0.203 0.169 0.314 0.052 0.098 0.087 0.072 0.065 0.087 0.279 0.162 0.142 0.275 0.239 0.113 0.096 0.28 0.046 0.05 0.183 0.153 0.078 0.056 0.072 0.1 0.15 0.314
0 0.089 0.167 0.171 0.087 0.055 0.074 0.064 0.092 0.106 0.098
74 0.333 0.373 0.334 0.368 0.127 0.535 0.499 0.265 0.113 0.098 0.564 0.382 0.098 0.302 0.506 0.086 0.112 0.111 0.293 0.521 0.463 0.071 0.109 0.109 0.235 0.059 0.345 0.089 0.059 0.063 0.211 0.071 0.314 0.065 0.049 0.081 0.082 0.094 0.166 0.266 0.151 0.274 0.396 0.196 0.014 0.006 0.143 0.089 0.069 0.162 0.063 0.05 0.073 0.098 0.059 0.094 0.187 0.089
0 0.17 0.081 0.208 0.089 0.014 0.186 0.088 0.213 0.192
75 0.164 0.163 0.121 0.147 0.041 0.196 0.183 0.106 0.049 0.053 0.244 0.145 0.052 0.137 0.205 0.053 0.061 0.064 0.164 0.219 0.298 0.066 0.074 0.074 0.106 0.081 0.176 0.07 0.099 0.107 0.039 0.074 0.165 0.156 0.121 0.091 0.207 0.286 0.286 0.128 0.022 0.143 0.143 0.13 0.137 0.148 0.133 0.208 0.198 0.116 0.199 0.2 0.26 0.271 0.2 0.145 0.189 0.167 0.17
0 0.194 0.336 0.255 0.176 0.344 0.233 0.289 0.238
76 0.328 0.295 0.332 0.313 0.151 0.458 0.412 0.243 0.151 0.142 0.439 0.287 0.131 0.25 0.434 0.133 0.135 0.134 0.31 0.425 0.182 0.128 0.131 0.131 0.261 0.116 0.327 0.133 0.114 0.151 0.204 0.118 0.256 0.161 0.111 0.132 0.123 0.164 0.221 0.276 0.162 0.292 0.383 0.235 0.083 0.082 0.169 0.186 0.159 0.152 0.122 0.117 0.158 0.16 0.109 0.12 0.187 0.171 0.081 0.194
0 0.291 0.198 0.123 0.277 0.159 0.251 0.25
77 0.562 0.423 0.426 0.394 0.284 0.571 0.581 0.388 0.268 0.253 0.455 0.329 0.264 0.317 0.49 0.293 0.249 0.244 0.507 0.354 0.178 0.242 0.231 0.231 0.398 0.258 0.388 0.361 0.232 0.16 0.377 0.357 0.459 0.107 0.213 0.211 0.148 0.117 0.082 0.541 0.326 0.421 0.489 0.495 0.265 0.234 0.492 0.067 0.102 0.336 0.285 0.183 0.106 0.151 0.21 0.334 0.518 0.087 0.208 0.336 0.291
0 0.1 0.163 0.049 0.18 0.032 0.028
78 0.484 0.356 0.423 0.454 0.196 0.597 0.578 0.329 0.175 0.159 0.54 0.342 0.163 0.287 0.539 0.178 0.158 0.155 0.422 0.478 0.279 0.136 0.143 0.143 0.354 0.134 0.39 0.212 0.117 0.084 0.284 0.211 0.4 0.048 0.1 0.131 0.069 0.059 0.101 0.419 0.226 0.4 0.471 0.346 0.128 0.107 0.314 0.057 0.03 0.228 0.125 0.089 0.062 0.071 0.112 0.194 0.359 0.055 0.089 0.255 0.198 0.1
0 0.059 0.076 0.101 0.119 0.12
79 0.385 0.297 0.311 0.383 0.119 0.495 0.49 0.258 0.108 0.096 0.468 0.303 0.092 0.233 0.423 0.102 0.092 0.09 0.339 0.427 0.236 0.076 0.082 0.082 0.269 0.071 0.362 0.117 0.065 0.064 0.202 0.106 0.318 0.051 0.054 0.072 0.04 0.07 0.133 0.319 0.148 0.305 0.393 0.247 0.041 0.026 0.197 0.071 0.049 0.149 0.072 0.041 0.053 0.081 0.055 0.121 0.243 0.074 0.014 0.176 0.123 0.163 0.059
0 0.143 0.076 0.176 0.165
80 0.578 0.422 0.463 0.42 0.282 0.632 0.613 0.416 0.269 0.252 0.513 0.364 0.253 0.369 0.556 0.281 0.239 0.234 0.506 0.412 0.26 0.236 0.219 0.219 0.408 0.24 0.349 0.338 0.216 0.153 0.377 0.346 0.511 0.095 0.195 0.186 0.127 0.095 0.08 0.559 0.322 0.462 0.512 0.487 0.246 0.213 0.484 0.083 0.074 0.313 0.248 0.152 0.088 0.117 0.18 0.309 0.522 0.064 0.186 0.344 0.277 0.049 0.076 0.143
0 0.143 0.088 0.066
81 0.477 0.362 0.314 0.367 0.189 0.535 0.504 0.323 0.203 0.19 0.479 0.272 0.161 0.292 0.442 0.179 0.153 0.15 0.334 0.441 0.211 0.171 0.139 0.139 0.321 0.142 0.365 0.178 0.134 0.157 0.254 0.189 0.385 0.127 0.118 0.117 0.071 0.075 0.107 0.412 0.198 0.397 0.455 0.332 0.124 0.106 0.266 0.134 0.11 0.174 0.103 0.054 0.073 0.082 0.067 0.186 0.302 0.092 0.088 0.233 0.159 0.18 0.101 0.076 0.143
0 0.202 0.189
82 0.405 0.303 0.327 0.269 0.263 0.504 0.487 0.225 0.232 0.223 0.389 0.219 0.247 0.193 0.443 0.268 0.236 0.234 0.304 0.275 0.146 0.211 0.225 0.225 0.245 0.245 0.227 0.329 0.224 0.149 0.329 0.306 0.357 0.12 0.212 0.214 0.173 0.139 0.098 0.412 0.297 0.227 0.343 0.357 0.245 0.226 0.396 0.082 0.125 0.325 0.275 0.221 0.14 0.166 0.247 0.268 0.392 0.106 0.213 0.289 0.251 0.032 0.119 0.176 0.088 0.202
0 0.036
83 0.309 0.286 0.248 0.192 0.222 0.453 0.449 0.22 0.201 0.193 0.356 0.212 0.209 0.136 0.394 0.226 0.202 0.199 0.289 0.238 0.123 0.185 0.193 0.193 0.198 0.21 0.124 0.277 0.196 0.126 0.282 0.277 0.292 0.102 0.187 0.188 0.157 0.136 0.094 0.368 0.252 0.158 0.288 0.341 0.219 0.203 0.377 0.069 0.111 0.3 0.271 0.193 0.129 0.164 0.224 0.254 0.369 0.098 0.192 0.238 0.25 0.028 0.12 0.165 0.066 0.189 0.036
0

𝑋 ||𝑀

The hydrological similarity between sub-catchments werecompared using the r2 index which is very widely used inhydrology field:

23

4 Conclusions
From the evaluation results, it is possible to estimate asimilar water discharge prediction using the similarity of
topography features. Although the accuracy is not so high,Results showing that the hydrological similarity is reflectedusing the geo-similarity. Even though the possibility ofobtaining the relationships have been proved, it is expectedthat accuracy should be improved by future studies.

Reference[1]Hjerdt, K. N., McDonnell, J. J., Seibert, J., and Rodhe,A.: A new topographic index to quantify downslope
controls on local drainage, Water Resour. Res., 40, 1–6,
[2] Horton, R.: Erosional Development of Streams andtheir Drainage Basins “Hydro-Physical Approach to
Quantitative Morphology”, Bull. Geol. Soc. Am., 56, 275–
370, 1945

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Crop yield sensitivity to drought events:
a global-scale analysis of major crops○ Vempi Satriya Adi HENDRAWAN1,* , Daisuke KOMORI1
1

Graduate School of Environmental Studies, Tohoku University, Sendai Miyagi 980-8579, Japan*
E-mail: vempi@dc.tohoku.ac.jp

Abstract
Drought has contributed to some of the world's most severe famines. Therefore, the understanding of drought andits potential impact are key points towards mitigation, especially in the field of agriculture. There are some gaps
remaining regarding drought impact analysis on crop yield such as lack of understanding in a fine resolution (e.g.
grid) as previous works were mostly done in aggregated scale (country or regional) and factors influencing
vulnerability are mostly based on multi-index in local scale. The analysis of historical crop yield sensitivity anddrought events in grid scale is important for understanding crop-drought vulnerability variation within countries.
This study investigates the response of yield of global major crops (wheat, maize, rice and soybeans) to drought
events during 1983-2014 in 0.5-degree grid resolution. We used standardized precipitation evapotranspiration index(SPEI) and crop yield data to build a risk model. Results show that respectively 11%, 4%, 10%, and 11% of thetotal cropland for maize, rice, soybean, show yield loss (with Pearson correlation of > 0.5). The strong correlation
between drought and yield anomaly and high drought vulnerability shown by high slope of linear regression areprominent in North America (US), South America (Argentina), some countries in Eastern and Southern Europe andSouthern Africa (e.g. Zambia). This study highlights a linear response of yield loss risk to the drought events thatcan be found significantly in some parts of the world where crops are cultivated. These results are important for thefurther understanding of crop-drought vulnerability and signifies that mitigation efforts should be more targeted in
the most vulnerable areas.
Keywords: Agriculture, Drought, SPEI, Vulnerability, Risk
2.

1 Introduction
As reported by IPCC (2014), global warming will result inclimate extreme inducing more frequent and severe natural
disasters. Drought has contributed to some of the world's
most severe famines (e.g. a severe drought in India affecting
300 million people in 2002 and death of 20,000 Somalis due
to drought in 2010). Agriculture is the most affected sector
by drought in developing countries, absorbing about 80% of
all direct impacts (FAO, 2018). Developing countries are
particularly vulnerable to drought because of their geography
and strong dependence on subsistence agriculture (McKAY,
1988) as well as due to physical, socio-economic, knowledge
and skills differences (Miyan et al., 2015). Due to its multiple
effects on food security and livelihood threatening global
sustainability, the diagnosis of drought vulnerability isimportant for future mitigation. The understanding ofdrought, its impact and the vulnerability are key points
towards rising incidence of weather extremes with its
negative impacts on agriculture.
Related previous studies have been conducted both in global
and local scale. For local studies, there were mostly analysis
of vulnerability based on AHP weighting scheme, GIS andremote sensing (Haque et al., 2019; Jain et al., 2015; Wu etal., 2011; Murthy et al., 2014). While in global scale there
were mostly studies about physical and social vulnerability
based on drought index, crop growth and hydrological model(Kamali et al., 2018). However, some gaps are still remaining
as follows:
1. Previous studies were solely focusing on drought
impact based on crop yield modeling,24

Crop-drought impact analyses were mostly in doneaggregated scale (country or regional),
3. Factors influencing vulnerability are defined mostly in
local scale based on indexing.
The analysis of historical crop yield sensitivity and drought
events in grid scale is limited. Therefore, this study
investigates the response of yield of global major crops(wheat, maize, rice and soybeans) to drought events during
1983-2014 in 0.5-degree grid resolution. Furthermore, theobjectives of this study are:
1. To understand crop yield sensitivity by means of crop
yield response and its relationship with drought events,using statistical approach that employs historical yielddataset and global drought dataset instead of crop yieldmodeling,
2. To determine the crop-drought vulnerability using thedisaster risk framework (see IPCC (2012) for further
details).

2 Materials and methods
2.1 Materials
Datasets used in this study are shown in Table 1. All of thedata are set in 0.5-degree spatial resolution and subset along
period of 1983-2014 (32 years).
Table 1 Dataset used in this study
Name
Data
Use
References
Drought(VicenteSPEI base Monthly
aggregation
hazardSerrano etStandardized
modelal., 2010)
Precipitation

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Cropcalendar
The global
dataset of
historicalyield(GDHY)

Evapotranspiration(SPEI)
Planting andExposure
harvesting datemodel
(Day of Year)
Crop yield (t/ha)
Risk
model

(Sacks etal., 2010)(Iizumi etal., 2020)

Figure 2 Regression analysis between drought event andcrop yield loss (source: analysis)

2.2 Methods
As can be seen in Figure 1, first, the risk component for
major crops is assessed by using crop yield anomaly (𝜃 )obtained from crop yield dataset (Eq. 1).
(1)
𝑌𝑡 − 𝑌̅𝑡𝜃𝑡 =× 100%
𝑌̅𝑡2(2)
1
𝑌̅𝑡 = ∑ 𝑌𝑡+15𝑖=−2

Maize and wheat suffer the most drought impact (Figure 3).
Results show that respectively 11%, 4%, 10%, and 11% of
the total cropland for maize, rice, soybean, show yield loss
(with Pearson correlation of > 0.5) during 1983-2014. The
strong correlation and high drought vulnerability areprominent in North America (US), South America(Argentina), some countries in Eastern and Southern Europe
and Southern Africa (e.g. Zambia).

where 𝑌𝑡 is crop yield (tons per hectare, t /ha) and 𝑌̅𝑡 is 5year centered moving average (t /ha).
The hazard component is assessed by using SPEI. Droughtmagnitude at year t (𝐻𝑡 ) is computed as Eq.3.
𝑘(3)
𝐻𝑡 = − ∑ 𝑆𝐶−𝑗, 𝑡
𝑗=0where 𝑆𝑗,𝑡 = k-month aggregated SPEI (drought) in month jand year t and 𝐶 is harvest-related month in 2000 (Sacks etal., 2010; Kim et al., 2019). It should be noted that only SPEI
< 0 indicating drought events are used in this analysis.
While, the exposure is represented by the explicit spatial
and temporal coincidence between the hazard and risk
component using a statistical correlation. The simple
correlation between risk and hazard is analyzed using
Pearson correlation. Then, a model of Risk Disaster using theformula shown in Figure 1 where V, R, H, E is vulnerability,
Risk, Hazard, and Exposure, is used to assess thevulnerability component.

Figure 3 Correlation analysis between drought eventand crop yield loss (source: analysis)

Conclusion
This study is mainly aimed to investigate the response ofyield of global major crop to droughts events, and to
determine the crop-drought vulnerability using disaster risk
framework. This study highlights a linear response of yield
loss risk to the drought events. It can be found significantly
in some part of the world where crops are cultivated. This
results are important for the further understanding of cropdrought vulnerability and signifies that mitigation efforts
should be more targeted in the most vulnerable areas.

Selected references
Figure 1 Study framework

3 Results and discussion
Both positive and negative correlation between drought andyield loss were shown and varied among pixels in global
scale. The negative relationship, where drought affects yield
loss indicates the high vulnerability areas (Figure 2).

25[1] IPCC, 2014: Climate Change 2014: Synthesis Report. Contributionof Working Groups I, II and III to the Fifth Assessment Report of theIntergovernmental Panel on Climate Change.
[2] UNISDR, 2015: The Human Cost of Weather-Related Disasters
1995-2015[3] IPCC, 2012: Managing the Risks of Extreme Events and Disasters
to Advance Climate Change Adaptation.
[4] FAO, 2018: 2017-The impact of disasters and crises on agriculture
and food security.
[5] CRED & UNISDR, 2018: Economic Losses, Poverty &DISASTERS 1998-2017.

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Snow cover analysis for dam inflow prediction in Thailand○ Tomoyuki Hino1*, Kenji Kenji1 , Shigenobu Tanaka2
1

2

Graduate School of Engineering, Kyoto University, Kyoto,615-8510, Japan.
Disaster Prevention Research Institute, Kyoto University, Kyoto,611-0011 Japan.
hino.tomoyuki.77e@st.kyoto-u.ac.jp

Abstract
The purpose of this study is to develop the optimum operation method for large-scale dams in Thailand. The 2011
flood in Thailand severely damaged the Chao Phraya River basin in Thailand. After the disaster, the government is
considering new management methods. In this study, we analyzed the relationship between the snow cover
distribution of the Eurasian continent and the Asian monsoon in order to predict the inflow of dams. Using the
inflow data of Bhumibol dam and Sirikit dam, and the snow water equivalent calculated by the land surface model
SiBUC, we analyzed the relationship between snow cover on the Eurasian continent and dam inflow in Thailand
during the period from 1980 to 2016. The results suggest that anomaly in snow cover on the Tibetan Plateau in
February has negative correlation with dam inflows in May and September.
Keywords: Bhumibol dam; Sirikit dam; reservoir operation; Chao Phraya River
1 Introduction
In 2011, a large-scale flood occurred in the Chao Phraya
basin in Thailand. The largest flood damage in history inThailand has had a great impact not only on Thailand but also
on the world economy. After the disaster, the Thaigovernment has been working on hydraulic control measuresin the Chao Phraya River basin, and efficient operation oflarge-scale dam reservoirs is being considered. In this study,
so far, global precipitation and SST have been analyzed to
predict dam inflow in Thailand. In this time, we analyzed
whether the snow cover on the Tibetan Plateau affects the
inflow of dams in Thailand. According to Yasunari.T(1989)[1], it is pointed out that snow cover on the Tibetan
Plateau may affect the summer Indian monsoon. Considering
this theory, it is possible that if the snowfall on the Tibetan
Plateau in winter is higher than normal, the monsoon will
weaken and the precipitation in Thailand will decrease. Using
this relationship, we can predict the inflow of dams in
Thailand in the summer from the snowfall on the Tibetan
Plateau in the previous winter. In this study, in order to clarify
the above-mentioned relationship, the relationship between
the inflow of two large-scale dams in Thailand and the snow
water equivalent in the Tibetan Plateau was analyzed from1980 to 2016.

Table 1 Specifications of two dams
Name
Bhumibol dam
Catchment area(𝑘𝑚 )26,386
Annual Inflow(10 ㎥)
5,256
Maximum Strage(10 ㎥)
13,462Sirikit dam
13,1305,600
9,508

Fig. 2 Monthly storage of two dams

2.2 Model and Climate Forcing
Describe the experimental settings of the land surface model
SiBUC used to calculate the Snow Water Equivalent (SWE).
SiBUC uses precipitation, downward shortwave radiation,
downward longwave radiation, temperature, wind velocity,
atmospheric pressure, and specific humidity as
meteorological forcing data, and calculates the vertical heat,water, and radiation balance. These meteorological forcing
data are created from JRA55 with 60km resolution. The
analysis period was from 1980 to 2016 (37 years) according
to the obtained dam inflow data. The advantage of using
reanalyzed data like JRA55 is its high update frequency.
Thus, before a rainy season, we can use model simulation
data to predict dam inflow during the following rainy season
in advance.

2 Materials and methods
2.1 Study area
The targets of this study are
the Bhumibol dam and the
Sirikit dam located in northern
Thailand. The Bhumibol dam
has a total reservoir capacityof 13.4 billion m3 and the
Sirikit dam has a totalreservoir capacity of 9.5
billion m3, and its main
purpose is to supply water toirrigation sites. These dams
store water from July to

December and discharge it as irrigation water from January
to June.(Fig.2)

Fig. 1 Study Area and Dams

26

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

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Tohoku University‐‐‐‐‐ with Perspective of Global Safety

2.3 Methods and Results
The relationships of snow water equivalent (SWE),
Monsoon wind velocity and dam inflow in Thailand wereanalyzed by correlation analysis.
The used SWE data are annual maximum value of SWE(usually in February) on Tibetan Plateau. The used windvelocity data are average monthly wind velocity of the Bayof Bengal on each month. The used dam inflow data are theaverage monthly dam inflow on each month.

Fig. 3 Monthly Wind velocity in
Bengal Bay(band is standard deviation ±σ)

SWE in Tibetan Plateau. it is suggested that there is arelatively strong negative correlation between SWE on
Tibetan Plateau and Bhumibol dam inflow in September.
Regarding Bhumibol dam, it is suggested that, over rainyseason(from May to October), there is a negative correlation
between dam inflow in Thailand on Tibetan Plateau andmonsoon wind velocity. On the other hand, regarding Sirikit
dam, it is suggested that there is a week negative correlation
between dam inflow in Thailand on Tibetan Plateau andmonsoon wind velocity from June to October.
In 2011, there was a weak negative anomaly of SWE in the
Tibetan Plateau area. On the other hand, in 2006, when theannual inflow of Bhumibol dam was the second largest in 37
years, there was a negative deviation of SWE over a wide
area of the Tibetan Plateau. (Fig.9)

Fig. 4 Monthly SWE in Tibetan
Plateau(band is standard deviation ±σ)

The correlation coefficient was calculated from the
maximum SWE value on each grid and the monthly averagewind velocity in the Bay of Bengal on each month of the rainyseason. It is suggested that there is a relatively strong
negative correlation between SWE on Tibetan Plateau andmonsoon wind velocity in May and September. (Fig.5, Fig.6)

Fig. 8 Correlation Coefficient Fig. 9 Anomaly of SWE (maximum
for a year) in Tibetan Plateau in
Map between SWE and2006
Bhumibol dam inflow(September)

3 Discussion
Inflows due to typhoon precipitation are considered to havelittle relation to the effects of monsoons. Therefore, it isconsidered that the inflow due to the typhoon is a factor thatlowers the correlation coefficient. A relatively strong
correlation appears at the beginning and end of the rainyseason, but since there is a weak correlation from June toAugust, it is a challenge to investigate the relationshipbetween snow cover and monsoon strength from June toAugustFig. 5 Correlation Coefficient Fig. 6 Correlation Coefficient
Map between SWE and Wind Map between SWE and Wind
velocity (May)velocity (September)

4 Conclusions
The correlation coefficient is calculated from averagemonthly dam inflow on each month and monthly averagewind velocity in the Bay of Bengal on each month of the rainyseason. It is suggested that there is a negative correlation
between dam inflow in Thailand and monsoon wind velocity
in May of the rainy season.
Regarding Bhumibol dam, relatively strong positive
correlation is analyzed in September as well. (Fig.7)

The purpose of this study is to efficiently operate a largescale dam in Thailand, and in order to predict the inflow inadvance, we analyzed whether the snowfall on the Tibetan
Plateau affects the inflow of the dam. As a result, it issuggested that the snow cover on the Tibetan Plateau affects
the strength of the Southeast Asian monsoon.
Regarding Bhumibol dam, there is a negative correlation
between dam inflow during the rainy season and SWE on the
Tibetan Plateau. it is founded that the SWE condition in early
spring is an important factor in predicting dam inflow during
rainy season.

Reference

Fig.7 Correlation Coefficient between Monthly Wind
velocity and Monthly each dam inflow on each month

The correlation coefficient was calculated from averagemonthly dam inflow on each month and maximum value of27[1]安成 哲三, 1989 :ユーラシア大陸の積雪と ENSO 雪氷・大気・海洋結合系の提唱-. 地学雑誌, 98-5, 613-622.
[2]Saw.O.2009, Inflow Prediction for long-term reservoir
operation through lag correlation analysis of global
meteorological information. Doctoral Dissertation, Graduate
School of Engineering, Kyoto University[3]Tanaka.K.2004. Development of the New Land Surface
Scheme SiBUC Commonly Applicable to Basin Water
Management and Numerical Weather Prediction Model.
Doctoral Dissertation, Graduate School of Engineering,
Kyoto University: Kyoto;289

The 8​th​ International Symposium on Water Environment Systems----- with Perspective of Global Safety ​(Nov. 13​th​ ~​ 14​th​ , 2020)

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The selection of temporal scale for drought analysis using
satellite-based precipitation data

○Amalia Nafisah Rahmani IRAWAN​1​, Daisuke KOMORI​2​ & Vempi Satriya Adi HENDRAWAN​1
1​

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8577, Japan.
Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8577, Japan.
*​
E-mail: amalia.nafisah.rahmani.irawan.q3@dc.tohoku.ac.jp.

2​

Abstract
The availability of precipitation data is an important aspect for several purposes, one of them is drought analysis.
However, the limitations in the availability of measured precipitation data (MPD) makes satellite-based
precipitation data become one of the alternative sources that can be utilized. This study aims to examine theagreement between local measured (MPD) and satellite-based precipitation data (GSMaP) in West Java, Indonesia
and determine an appropriate temporal scale to be used for drought analysis. Interpolation of MPD is also
conducted using Inverse Distance Weighted (IDW) method to obtain the spatial precipitation distribution. Fromthe correlation analysis results of point dataset and area dataset, it can be concluded that the appropriate
temporal-scale that can be used for drought analysis is a 90-days period. In addition, the area dataset analysis haslarger mean error and root mean square error values compared to the point dataset.
Keywords:​ ​Precipitation Data; Satellite-based Data; IDW; Drought Analysis; Temporal Scale.
1 Introduction

Many studies have been carried out in various fields thatutilize precipitation data for various purposes. Research on
drought will also rely heavily on the precipitation data,
especially when drought can be understood as a condition
when an area experiences a water deficit from its normal
condition. However, nowadays there are many limitations in
the availability of precipitation data, either it does not haveenough data spatiotemporally or data not available publicly.
The common precipitation data that can be used aremeasured precipitation data that have an advantage of highaccuracy but only available on the point scale area. To
assess drought, the spatial precipitation data is also needed.
In the area without rainfall-gauge, satellite-based
precipitation data can be used because it has a higher spatial
resolution but also has many uncertainties that can cause
low accuracy when predicting the precipitation value. So,
the utilization of satellite-based precipitation data fordrought assessment, must be preceded by assessing theagreement of precipitation data between local measurements
and the satellite-based dataset.
Previous studies have shown the use of satellite-based
weather data for different purposes, including drought
analysis by comparing it first with the local data. For
example, Mourtzinis et al., (2017) studied about theapplicability of satellite-based precipitation data for
agricultural application across the US Corn Belt. ​In thisresearch, the objectives are i) to examine the agreement
between local measured precipitation data (MPD) andsatellite-based precipitation data (GSMaP) for point data
analysis and interpolated-MPD and GSMaP for area data
analysis, ii) determine the appropriate temporal scale thatcan be used for drought analysis, and iii) assessing the
spatial distribution of the rainfall distribution.
West Java is one of the provinces in Indonesia with therainy season from October - March and the dry season from
April - September and dominated by agricultural land which
is very vulnerable to climate related hazards, such as
drought and flood. The results of this study will be helpful
to get a better understanding of utilizing the satellite-based

28

precipitation data especially in the area with a limited
measured precipitation data for drought analysis.

2 Materials and methods

2.1 Precipitation Data
The daily measured precipitation data (MPD) are obtained
from Meteorology, Climatology, and Geophysical Agency
in Indonesia from January 1981 - March 2013 with missing
data for some period in some stations. There are a total of
52 stations across 16 regencies in West Java with averagecoverage reaching 680.38 km​2​ per station.

Fig. 1. The Location of 52 Rain Gauge Stations in WestJava (represented by red dots)
Meanwhile, the satellite-based precipitation data collectedfrom Japan Aerospace Exploration Agency (JAXA) which
provide the near real-time rainfall data on their productcalled Global Satellite Mapping of Precipitation (GSMaP).
The daily precipitation data are available ​from March 2000 present date and were retrieved across West Java ​with 0.1​° x
0.1​°​ resolution.
2.2 Analysis Methods
The total precipitation for different timescale (daily,
weekly, biweekly, 30-days, 60-days, 90-days, 120-days,
150-days, 180-days, 210-days, 240-days, 270-days,
300-days, 330-days, and 360-days) were calculated for theperiod between April 2000 - March 2013 both for MPD and
GSMaP dataset.
The bias and agreement was assessed by calculating themean error (ME) and root mean square error (RMSE). Then,

The 8​th​ International Symposium on Water Environment Systems----- with Perspective of Global Safety ​(Nov. 13​th​ ~​ 14​th​ , 2020)
to examine the agreement between MPD and GSMaP data,linear regression was performed and the coefficient ofdetermination or R-square (R​2​) was calculated to decide theappropriate timescale for drought. Those analysis appliedboth for point data analysis (52 locations of local rainfall
gauge stations (MPD) and the grid from GSMaP thatcoincided with those stations) and area data analysis​. For
the area data analysis, the interpolation of MPD dataset was
conducted using Inverse Distance Weighted (IDW) methodwith 0.1​° x ​0.1​° resolution (the same with GSMaP
resolution).

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period.

3 Results and discussion

3.1 Point Data Analysis
Figure 2 summarizes the agreement between MPD and
GSMaP where horizontal axis indicates the various
timescale and vertical axis indicates the value of R​2​. It canbe seen that the agreement performed a parabolic pattern forthe daily until 360-days scale with the turning point during
the 150-days period and an anomaly during the 180-days
period where the sudden decrease of R​2 values happened.
This pattern and anomaly was predicted to be caused by thecharacteristic of the rainy and the dry season of the country
which happened every 6 months. Also, the result will
depend on the characteristics of the season for each region.

Fig. 2. Resume of agreement between MPD and GSMaPdata for various timescale
Based on this result, it can be concluded that theacceptable timescale that can be considered to be used for
drought assessment is a 90-days (3-months) period when thevalue of R​2 reaches the peak. For additional information, theblue line on the horizontal axis indicates the planting periodof paddy occurred within 120 days and the red line
indicated the harvesting period which usually occurred inthe 30 days after the planting period ended. This resultshows a good agreement of the two datasets occurred during
the paddy’s planting and harvesting period, so the timescale
can be used for monitoring agricultural drought that might
affect the paddy during crop season.
3.2 Area Data Analysis
Basically, the analysis to examine the area dataset is thesame with point data analysis, the major difference lies in
the interpolation method of MPD. The resume of R-square
which examines the agreement between interpolated-MPD
and GSMaP data can be seen in Figure 3. Generally, thepattern is the same with point dataset analysis (Figure 2),
but unlike the point dataset result, during 150-days period or
during harvesting period, the agreement shows more stable
R-square value and starts decreasing during 180-days

29

Fig. 3. Resume of agreement between interpolated-MPD
and GSMaP data for various timescale
To see the difference in spatial distribution of
precipitation between interpolated-MPD and GSMaP
dataset, Figure 4 shows the map of West Java withinformation about 90-days R-square values on each gridfrom various timescale. The greenish color indicated higher
R-square values which meen better agreement between two
dataset, meanwhile the reddish color indicated lower
R-square values or lower agreement between the dataset.
The good agreement is an important aspect to beconsidered, because in this context, it can be interpreted that
there is a strong relationship between the GSMaP dataset
and the interpolated-MPD dataset or the difference between
two precipitation dataset was much lower.

Fig. 4. Map of distribution R-square value for 90-days
timescale

Conclusions

For the location with limited measured precipitation data,
the satellite-based data can be utilized by assessing theagreement between the two datasets first. In West Java, it issuggested that the drought analysis can be conducted byusing GSMaP precipitation data with 3-months period
timescale, both for point dataset or area dataset. Further
research needs to be done to validate the result before
GSMaP dataset can be used for drought analysis, especiallyfor agricultural applications.

Reference[1] ​Mourtzinis, S. et al, From grid to field: Assessing
quality of gridded weather data for agricultural applications,
European Journal of Agronomy, 82 (2017), 163-172.
[2]
World
MeteorologicalOrganization,
2012:Standardized Precipitation Index User Guide (M.
Svoboda, M. Hayes and D. Wood). (WMO-No. 1090),
Geneva.

G‐Safety

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Tohoku University

Relationship between local and spatial probabilities of precipitation in theYoneshiro River catchment○Hajime YANAGISAWA1, So KAZAMA1, Ryo INOUE2
1

2

Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan*E-mail : hajime.yanagisawa.q7@dc.tohoku.ac.jp

Abstract
Probability of precipitation were calculated using Radar-AMeDAS rainfall and compared with the theoretical andanalytical values of spatial probabilities in the Yoneshiro River catchment. Since the spatial probability of extreme
precipitation is smaller in the analysis than in the theory, we focused on the independence and dependence of extreme
precipitation occurrence. This study revealed that extreme precipitation event with a 100-year return period occurs once
every 2.6 years in the Yoneshiro River catchment, and extreme precipitation actually occurs over an area that istheoretically thought to have little or no occurrence, and that there is a limit to the area of extreme precipitation occurrence.
Keywords: return period; extreme precipitation; radar-AMeDAS rainfall.
1. Introduction
Climate change associated with global warming will change
the rainfall pattern in Japan, and short duration rainfall islikely to increase across the country in the future1). Extreme
precipitation event is sometimes represented as "once in 50
years" in terms of its return period at a location. Such extreme
precipitation often causes widespread damage2). Therefore, it
is important to know the probability of extreme precipitation
over a large area in order to reduce the damage of futuredisasters. The purpose of this study is to evaluate therelationship between the point-occurrence probability and thespatial-occurrence probability in extreme precipitation.
Fig. 1. Spatial-occurrence probability

2. Methods and Data
2.1 Study area
The target area is the Yoneshiro River catchment located in
the north part of Akita Prefecture. It is the fifth largest riverin the Tohoku region with a total length of 136 km and adrainage area of 4100 km2.
2.2 Data sources
Radar-AMeDAS rainfall data were used to determine theprobability of extreme precipitation events. The period
covered is 29 years, from 1988 to 2016. The target area isdivided into 18 meshes from east to west and 15 meshes from
north to south, for a total of 270 meshes. 1 mesh is about 5
km square. The data size is unified to a coarse data size by
averaging fine resolution data.
2.3 Spatial probability of precipitation
The annual maximum hourly precipitation was extracted
from the Radar-AMeDAS rainfall data. The probability
density functions of extreme precipitation were generatedusing the annual maximum hourly precipitation for eachmesh, the GEV distribution and the PWM method, and thespatial distribution of extreme precipitation was generated.
By comparing extreme precipitation and annual maximum
hourly precipitation in each mesh, the number of years
extreme precipitation event has been occurring in 29 years is
calculated.

30

The area for calculating the probability of spatial occurrenceis called a compartment. Compartment should be square andnot overlap in the target area. The spatial-occurrnce
probability is defined as "the probability of extreme
precipitation greater than or equal to extreme precipitation in
one or more meshes in a compartment". Figure 1 shows thespatial-occurrence probability of extreme precipitation in the
50-year return period for each compartment area. Extreme
precipitation with a return period of 50 years occurs once
every 1.38 years in the Yoneshiro River catchment.
2.4 Independence and Dependence of Precipitation
Next, in order to compare the spatial-occurrence probability
obtained from the analysis with the theoretical probability,
we calculate the theoretical value of the spatial-occurrence
probability using the equation (1). The theoretical value ofthe spatial-occurrence probability is shown in Figure 1.

𝑃

1
𝑛

𝑛

1

・ ⬚𝐶

𝑛

1

・ ⬚𝐶

P：space probability of precipitation
k：term number（area：mesh）
n：return period (year)

・・・ ＋ 𝑛

1

・ ⬚𝐶

…(1)

G‐Safety

The 8th International Symposium on Water Environment Systems(Nov. 13th~14th, 2020)‐‐‐‐‐ with Perspective of Global Safety

35
Annual Extreme
Precipitation Area (mesh)weight coefficient"a"

2500
2000
1500
1000
500
0
0

5

10
15
20
Precipitation area (mesh)

25

weight coefficient"a"

5
4
3
2
1
0
0

10
20
Precipitation area (mesh)30

Fig. 3. Change in weight coefficient（enlarged version）

Figure 1 shows that the analytical value of the spatialoccurrence probability is lower than the theoretical value ofthe spatial-occurrence probability regardless of the return
period. The reason for the difference between the theoreticaland analytical values of the spatial-occurrence probability isrelated to whether or not extreme precipitation occurs
independently between each mesh. We introduce a weight
coefficient “a” as an indicator of independence. The weight
coefficient is the ratio of the analytical value to the theoreticalvalue of the occurrence probability for each precipitation area.
The theoretical value of the spatial-occurrence probability
including the weight coefficient can be calculated by
Equation (2).
1
𝑎 ・ 𝑛
𝑛

1

・ ⬚𝐶

𝑎 ・ 𝑛

・・・ ＋𝑎 ・ 𝑛

𝑎 𝑖

・ ⬚𝐶

1
1

・ ⬚𝐶

降水発生限界extremeprecipitation
limit area

30

域

25

RA式
RA
relation formula

20
15
10
5
0

60
90
120
Return period (yaer)
Fig. 4. RA-ralation formura and extreme precipitation limit area

30

Fig. 2. Change in weight coefficient𝑃

Tohoku University…(2)

1, 𝑘 : weight coefficient

3. Results
As an example, the relationship between the weight
coefficient and the precipitation area is shown in Figure 3 and
4 for a 10-year return period and 64-mesh compartment area.
A weight coefficient of less than 1 indicates that the extreme
precipitation occurs with high level of independence. From
Figure. 3 and 4, it can be seen that up to about 11 meshes ofprecipitation area, the extreme precipitation occurs relatively
independently at each mesh, which is in accordance with thetheoretical spatial-occurrence probability. On the other hand,when the precipitation area is between 12 and 18 meshes, thevalue of the weight coefficient increases. This means that theextreme precipitation occurs over a wider area than is

31

0

30

theoretically possible. No precipitation was observed above
19 meshes. The results of the analysis for multiple
compartment area and return period show that the area of
independent extreme precipitation is a constant percentage ofthe compartment area, while the area of dependent extreme
precipitation is constant regardless of the compartment area.
For extreme precipitation with a return period longer than 60
years, independent extreme precipitation occurs over aconstant area regardless of the compartment area, anddependent extreme precipitation rarely occurs.
Since there is a limit to the annual extreme precipitation area
regardless of the compartment area, the extreme precipitation
limit area was determined for each return period. These
results are compared with the RA relationship (therelationship between return period and extreme precipitationarea) reported by Sugawara et al.3) and are shown in Figure 4.
As a result, it is suggested that extreme precipitation with areturn period of more than 30 years occurs once a year in theYoneshiro River catchment, and that extreme precipitation
with a return period of less than 30 years may occur morethan once a year.

4. Conclusions
The following are the conclusions obtained.
1) The spatial-occurrence probability of extreme
precipitation is smaller in the analytical value than in thetheoretical value. This trend is not changed by the return
period.
2) Comparison with the RA relation suggests that extreme
precipitation with a return period of less than 30 years may
occur more than once a year in the Yoneshiro River
catchment.
5. Acknowledgements
We wish to thank Tohoku Regional Development
Association and Mitsui Consultants Co., Ltd for theirgenerous financial assistance. This research was supportedby the Social Implementation Program on Climate Change
Adaptation Technology (SI-CAT) of MEXT, Japan

6. Reference[1]. Global warming prediction information, Vol.8, 2013[2]. K. Ikeyama, T,Yoshida, Y. Minakawa, T,Kubota,
Analysis of long-term changes in heavy rainfall interms of rainfall and temporal concentration, Japan
Society of Hydrology and Water Resources, p.128-,2019[3]. Y. Sugawara, S. Kazama, Y. Touge：Analysis ofspatial distribution characteristic of heavy rainfall
using Rader-AMeDAS composite precipitation,
journal of JSCE Vol.74, No.4, I_343-I348, 2018

The 8th International Symposium on Water Environment Systems

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(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Estimation of the risk of inland flood based on distribution of extreme
precipitation in Japan○ Hayata YANAGIHARA1*, Tao YAMAMOTO1 & So KAZAMA1
1

Department of Civil Engineering, Graduate School of Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: yanagihara.hayata.r1@dc.tohoku.ac.jp

Abstract
This study estimated the damage cost caused by inland flood in Japan based on the distribution of extreme
precipitation and identified insufficient areas of flood protection for high risk. The inundation depths and the damage
costs were calculated using the two-dimensional unsteady flow model and the Manual for Economic Evaluation of
Flood Control Investment (Draft) published by the Ministry of Land, Infrastructure, Transport, and Tourism. In
flood simulation, we set up two inundation scenarios (natural drainage scenario and poor drainage scenario) with orwithout drainage by gravity to rivers. The natural drainage scenario corresponds to inland floods caused by theelevation, while the poor drainage scenario corresponds to inland floods caused by drainage to rivers. As a result,the expected damage cost was estimated to be 618 billion JPY/year in the natural drainage scenario. The damage
cost was the largest in Kanagawa prefecture, followed by Aichi prefecture, Osaka prefecture, Okayama prefecture,and Hokkaido prefecture. On the other hand, the expected damage cost was estimated to be 9,422.4 billion JPY/yearin the poor drainage scenario. The damage cost was the largest in Tokyo Metropolis, followed by Osaka prefecture,
Aichi prefecture, Hokkaido prefecture, and Saitama prefecture.
Keywords: urban flood disaster; flood simulation; economic loss; drainage by gravity to rivers.
1 Introduction
In urban areas, there are concerns that inland flood damage
will increase due to climate change. It is necessary toquantitatively evaluate the spatial risk of inland flood toconsider the direction and priority of adaptation measures. As
a previous study on evaluation of the risk of inland flood,
Karamouz et al. (2017)[1] analyzed inland flood consideringstorm surge in Lower Manhattan, New York City. Kawagoe
et al. (2011)[2] estimated the inland flood risk in Fukushima
prefecture. However, these studies analyzed inland flood in alimited area. In this study, we estimated the damage costcaused by inland flood in Japan based on extreme
precipitation data of Kawagoe et al. (2008)[3] and identified
insufficient areas of flood protection for high risk.

2 Datasets
2.1 Distribution of extreme precipitation data
We used the distribution of extreme precipitation data witha 1km spatial resolution created by Kawagoe et al. (2008)[3]in order to estimate the risk of inland flood considering
regional climatic characteristics. The distributions of extreme
precipitation data for return periods of 5, 10, 30, 50, 100, and
200 years were used.

2.2 Elevation data and land-use data
The elevation data of the fifth-order mesh used in flood
simulation was obtained from the Digital National Land
Information download services[4]. The land-use data of thefifth-order mesh created by Tanaka et al. (2019)[5] was usedin this study. The land-use data was used for calculating
inundation depths and the damage costs.

2.3 River identification data
The river identification data used in flood simulation is acombination of the following: (1) meshes classified as rivers
in the land-use data, (2) meshes in the river mesh data of the32

Table 1 Inundation scenario

Scenario nameNatural drainage
scenario

Poor drainage
scenarioContent1) Precipitation with a return periodof 5 years is drained away.
2) Precipitation is always drained
to rivers by gravity.
1) Precipitation with a return periodof 5 years is drained away.
2) All precipitation stays on inland.

fifth-order mesh where the section type is designated as either
a Class A direct-controlled sections, Class A designatedsections, or Class B river sections. The river mesh data of thefifth-order mesh was created by rasterizing the river line dataof the Digital National Land Information download
services[4].

3 Method
3.1 Inundation scenario and flood simulation
In this study, we set up two inundation scenarios with orwithout drainage by gravity to rivers as shown in Table 1.
The natural drainage scenario corresponds to inland floods
caused by the elevation, while the poor drainage scenario
corresponds to inland floods caused by drainage to rivers. In
flood simulation, the two-dimensional unsteady flow modelin the Cartesian coordinate system (Tezuka et al. 2014[6]) wasapplied without separating the river and the floodplain. The
grid size of flood simulation is the fifth-order mesh(approximately 250m×250m). The parameters used in flood
simulation were based on Tanaka et al. (2019)[5]. In thenatural drainage scenario, the water depth of rivers was set to
0 to express drainage by gravity to rivers. On the other hand,
the inflow to rivers was set to 0 in the poor drainage scenario
to express poor drainage to rivers.

The 8th International Symposium on Water Environment Systems

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(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety
The current sewage system is planned to be able to drain
precipitation with a return period of 5 years in many areas.
Considering the increase in inland flood damage due toclimate change, it is necessary to raise the level of inland
flood protection. It is also necessary to consider the direction
and priority of inland flood protection to reduce inland flood
damage with a limited budget. Therefore, in inundation
scenarios shown in Table 1, we assume that it is possible for
precipitation with a return period of 5 years to drain properly.
Based on the results of the analysis, we discuss the direction
and priority of inland flood protection for each inundation
scenario.

3.2 Estimation of the damage cost
The damage costs were calculated using the Manual for
Economic Evaluation of Flood Control Investment (Draft)[7]published by the Ministry of Land, Infrastructure, Transport,
and Tourism. This study targeted only direct damage togeneral assets and agricultural products. The damage cost
was calculated by multiplying asset values by damage rates
defined by inundation depth. The expected damage cost wascalculated by aggregating the annual average damage cost by
the scale of precipitation which was obtained by multiplying
the damage cost by occurrence probabilities of precipitation
events.

4 Results and discussion
Fig.1 shows the expected damage cost by prefecture. The
expected damage cost was estimated to be 618 billion
JPY/year in the natural drainage scenario. The damage cost
was the largest in Kanagawa prefecture, followed by Aichi
prefecture, Osaka prefecture, Okayama prefecture, andHokkaido prefecture. On the other hand, the expected
damage cost was estimated to be 9,422.4 billion JPY/year in
the poor drainage scenario. The damage cost was the largest
in Tokyo Metropolis, followed by Osaka prefecture, Aichi
prefecture, Hokkaido prefecture, and Saitama prefecture. In
areas where the damage cost is high in the natural drainage
scenario, precipitation tends to stay because the drainage by
gravity to rivers is poor due to the terrain. In such areas, thedevelopment of pump stations is necessary to reduce theinland flood risk. In areas where the damage cost is high inthe poor drainage scenario, it is necessary to improve theoperability of sluicing outlets and to promote cooperation
between rivers and sewers. This study is expected to provide
useful information to consider the direction and priority ofinland flood protection.

5 Conclusions
This study estimated the damage cost caused by inland floodin Japan based on the distribution of extreme precipitation
data. The damage cost was the largest in Kanagawa
prefecture, followed by Aichi prefecture, Osaka prefecture,
Okayama prefecture, and Hokkaido prefecture in the naturaldrainage scenario which corresponds to inland floods causedby the elevation. On the other hand, in the poor drainage
scenario which corresponds to inland floods caused bydrainage to rivers, the damage cost was the largest in Tokyo
Metropolis, followed by Osaka prefecture, Aichi prefecture,
Hokkaido prefecture, and Saitama prefecture.

33

Fig. 1. The expected damage cost by prefecture(From the top to bottom: natural drainage scenario,
poor drainage scenario)

Reference[1] Karamouz, M. et al., Integration of inland and coastal
storms for flood hazard assessment using a distributedhydrologic model, Environmental Earth Sciences, Vol.76,
No.395 (2017).
[2] Kawagoe, S. et al., Projection of inland flood risk due tohourly heavy rainfall in administrative district, Journal ofJapan Society of Civil Engineers, Ser.G (Environment),
Vol.67, No.5 (2011), pp.I_47-I_54 (in Japanese).
[3] Kawagoe, S. et al., A probability model of sedimenthazard based on numerical geographic information andextreme precipitation data, Journal of Japan Society for
Natural Disaster Science, Vol.27, No.1 (2008), pp.69-83 (inJapanese).
[4] Ministry of Land, Infrastructure, Transport and Tourism,
Digital National Land Information download services,
https://nlftp.mlit.go.jp/ksj/.
[5] Tanaka, Y. et al., Assessment of the risk of flood andstorm surge with flood control facilities, Journal of Japan
Society of Civil Engineers, Ser.B1 (Hydraulic Engineering),
Vol.75, No.2 (2019), pp.I_109-I_114 (in Japanese).
[6] Tezuka, S. et al., Estimation of the effects of climatechange on flood-triggered economic losses in Japan,
International Journal of Disaster Risk Reduction, Vol.9(2014), pp.58-67.
[7] Ministry of Land, Infrastructure, Transport and Tourism,
Manual for Economic Evaluation of Flood Control
Investment (Draft), River Bureau (2005).

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Evaluating the effect of dryness on wildfire in Tohoku region using
KBDI and PDSI
○ Chenling Sun1*, Yoshiya Touge1, So Kazama1
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: sun.chenling.s5@dc.tohoku.ac.jp.

Abstract
There is a significant link between dryness and wildfire because dryness on a time scale of climate and weather
can significantly affect the moisture content of available fuel. Many dryness indices have been developed toeffectively evaluate dryness and thus be used to assess wildfire risk. Tohoku region of Japan is prone to wildfires,
especially from March to May. In this study, we utilized KBDI and PDSI to evaluate the effect of dryness on wildfirein Tohoku region of Japan by comparing with wildfire statistics. Results show that KBDI is not suitable dryness
index for wildfire research in the Tohoku region of Japan. On the other hand, compared the result of PDSI andwildfire statistics, it indicated that dryness will increase the probability of wildfire and severe dryness is likely tocause widespread burning. However, these two dryness indexes cannot take into account the impact of snowmelt
on dryness. We need to select another index that takes into account the effects of snowmelt to analyze the impactof dryness on wildfires in the future.
Keywords: KBDI; PDSI; Wildfire; Dryness.
1 Introduction
In the coming decades, climate change will bring higher
temperatures and changes in rainfall patterns. These climatechanges will increase the likelihood of dryness in many partsof the world, thus creating favorable conditions for wildfires.
There is a significant link between dryness and wildfire
because dryness on a time scale of climate and weather cansignificantly affect the moisture content of available fuel.
Many dryness indices have been developed to effectively
evaluate dryness and thus be used to assess wildfire risk. The
Keetch/Byram dryness index (KBDI) is typical index forassessing dryness and widely used in wildfire monitoring thathas been applied in a variety of areas, including the UnitedStates, south eastern Australia and Malaysia. Furthermore,statistical analysis shows that KBDI is the most appropriate
empirical dryness index in the Mediterranean region. Palmer(1965) developed the Palmer Dryness Severity Index (PDSI)
using temperature data and physical water balance models
that take into account precipitation, soil moisture, runoff, andpotential evapotranspiration. The dryness index involves
clear physical mechanisms, so it can be used to monitor the
long-term evolution of dryness and capture the basic impact
of global warming on dryness through changes in potential
evapotranspiration.
Each dryness index has its own advantages anddisadvantages. Neither of these two dryness indexes canconsider the impact of snowmelt on dryness, so there is alimit to the evaluation effect of dryness in areas with longsnowfalls. In Japan, Tohoku region is prone to wildfire.
Kamaishi and Kurihara wildfires occurred successively on
May 8, 2017. For Kamaishi wildfire, it was a big-scale fire
and its burned area was 413ha, which was greater than the
total of burned area for the whole of Japan in 2016. The
purpose of this study is to use KBDI and PDSI to evaluate
dryness in Tohoku of Japan, and compare the results withwildfire statistics, so as to evaluate the impact of dryness onwildfire in Tohoku region of Japan.

2 Materials and methods
2.1 Keetch/Byram dryness index

34

KBDI is a number representing the net effect of
evapotranspiration and precipitation in producing cumulative
moisture deficiency in deep duff and upper soil layers [1].
This index requires only few meteorological data, maximum
daily temperature, total daily precipitation and the meanannual precipitation. The KBDI is calculated from thefollowing equation in metric system:
𝐾𝐵𝐷𝐼
𝐷𝐹
𝑅𝐹
(1)
𝐾𝐵𝐷𝐼
Where 𝐷𝐹𝑡 is dry factor in one day, 𝐷𝐹𝑡 is rainfall factor.
.

.

.

.

(2)
𝑅𝐹
.
.
𝑡
𝑅𝐹 is depended on daily precipitation. 𝑇𝑚 represents daily
maximum temperature, 𝑅0 is the mean annual rainfall. Whenthe daily precipitation is more than 5mm (0.2in), the daily
precipitation will decrease KBDI. The value of KBDI ranges
from 0 to 203mm, the larger the KBD value indicates thedrier condition.
2.2 Palmer Dryness Severity Index
The calculation of PDSI requires precipitation and potential
evapotranspiration, as well as the available water capacity(AWC) of the soil. Firstly, the difference between the actual
precipitation (P) and climatically appropriate for existing
conditions precipitation (𝑃) can be calculated based on thewater balance. It is an indicator of water deficiency or surplus.
𝑑 𝑃 𝑃
(3)
The same water deficiency (d) value may reflect different
humidity conditions given specific region and specific month.
To correct for this aspect, the climatic characteristic K is used
to weight the water deficiency.
Then, d and climatic characteristic K were used for climate
correction to obtain water deficit index Z, which indicates thedeviation of actual wetness/dryness conditions from the longterm annual average water availability in a given region
during a given month. Considering the influence of earlywater shortage on dryness conditions, the dryness duration
and Z value of each dryness event were calculated to analyze
the influence of early water shortage on dryness intensity(Equation 4). The PDSI is a standardized measure, ranging
from about −10 (dry) to +10 (wet).

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety
𝑃𝐷𝑆𝐼

0.897𝑃𝐷𝑆𝐼

𝑍(4)
Daily precipitation data covering a period of 1995–2012
were obtained from the Asian Precipitation Highly-Resolved
Observational Data Integration Towards Evaluation(APHRODITE) of Water Resources project and wereanalyzed in this study. We used wind speed, air temperatureand radiation data obtained from dynamical regional
downscaling of the JRA-55 reanalysis data set (DSJRA-55).

3 Results and discussion
Not only did more than 100 hectares of wildfire occur in
Tohoku between 1995 and 2012, but there were also many
wildfires burning less than one hectare. In order to makebetter use of the dryness index to analyze the impact ofdryness on wildfire, we divided the discussion into three
categories according to the burned area: less than 1 hectare,110 hectare and more than 10 hectares. In this study, we
selected the average KBDI value and the average PDSI value
in the city where wildfire occurred and compared them withand wildfire statistics (Fig 1 and Fig 2).

stands for mild dryness. The total frequency of PDSI values
less than -0.5 was approximately 67% when the burned area
was more than 10 hectares, 66% when the burned area was
1-10 hectares, and 56% when the burned area was less than 1
hectare. Regardless of the size of the burned area, more thanhalf of wildfires in 1995-2012 occurred during periods of
dryness. The results show that dryness increases theprobability of wildfire. When the PDSI is less than -3.0, it
indicates that the area is in severe dryness and extreme
dryness, and the frequency of wildfires with burned areas
greater than 10 hectares is much greater than that of small
burned areas. It turned out that the degree of dryness affects
the severity of wildfires and severe dryness is likely to cause
widespread burning. But PDSI results also show that nearly
40 percent of wildfires, even those burned more than 10
hectares, occurred during periods that are not dry. The PDSI
is based on a physical water-balance model, uses morevariables as input and takes the precedent condition into
account. Nevertheless, snowfall, snow cover, and frozen
ground are not included in the index. All precipitation istreated as rain, so that the PDSI values may be inaccurate in
the winter and spring months in regions where snow occurs.
Statistics displayed that most wildfires in Tohoku region
occurred between March and May, therefore, PDSI has
limited applicability in the Tohoku region of Japan, which iscovered by snow in winter and spring.

4 Conclusions

Fig. 1. Frequency of 10 intervals of KBDI in 3 categories.

Fig. 2. Frequency of 11 intervals of PDSI in 3
categories.
The frequency in these figures was defined by following
equation:
𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦

The KBDI value indicated that leaf litter begins to dry whenthe value is more than 50. However, most KBDI values areconcentrated in the 0-40 mm and when KBDI is greater than
40 mm, the total frequency is less than 15%. KBDI results
are similar for the three categories. Compared with wildfire
statistics, it cannot be shown that the correlation between
KBDI and wildfire occurrence and burned area in Tohoku
region of Japan. Although KBDI is widely used, it has been
indicated that there is still a need to improve the structure orapplication of the KBDI model, especially for climate, soil,
and hydrology in areas that differ from Florida, such as
tropical wetland ecosystems in Southeast Asia. These
revealed that KBDI is not applicable to the study of wildfirein Tohoku region of Japan. For PDSI, -0.5 is an indicator forthe beginning of dry spell. The maximum frequency occurred
when the PDSI value is at the interval of -1.0 to -1.99, which

35

In this study, KBDI and PDSI were used to analyze theeffects of dryness on Tohoku region of Japan. The
application of KBDI in other climates may still be
problematic without any adjustment to the model structure,as the drying rate of the index depends on the mean annual
precipitation in Florida state [2]. It was shown that KBDI isnot suitable dryness index for wildfire research in the Tohoku
region of Japan. On the other hand, more than half ofwildfires in 1995-2012 occurred during periods of dryness(PDSI<-0.5), which indicates that dryness increases theprobability of wildfire. In addition, the degree of dryness
affects the severity of wildfires and severe dryness is likely
to cause widespread burning. Although KBDI and PDSI havebeen widely used for dryness monitoring and wildfire risk
monitoring, but they still have some limitations. In Tohoku
region of Japan, the area is covered with snow for a long timein winter, and the melting of snow in spring causes more
dryness in the area. These two dryness indexes all cannot take
into account the impact of snowmelt on dryness. Therefore,
we need to use other dryness index to consider the effect ofsnowmelt on dryness in the future.

Acknowledgements
This research was supported by the Ministry of Education,
Science, Sports and Culture, Grant-in Aid for Scientific
Research(B), 2020-2023(20H02248, Yoshiya Touge).

Reference[1] Keetch, J.J., Byram, O.M., 1968. A dryness index forforest fire control. USDA Forest Service Research
Southeastern Forest Experiment Station, Asheville (2004).
pp. SE-38.
[2] Taufik et al., 2015 M. Taufik, B.I. Setiawan, H.A.J. van
Lanen Modification of a fire dryness index for tropical
wetland ecosystems by including water table depth Agric.
For. Meteorol., 203 (2015), pp. 1-10.

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Evaluate the effect of fuel moisture content on the heat requiredfor ignition in the Tohoku Region of Japan○ Qin HUANG1*, Yoshiya TOUGE1
1

Department of Environmental Engineering, Environmental University, Miyagi 980-8579, Japan.
*
E-mail: huang.qin.p5@dc.tohoku.ac.jp.

Abstract
The research of litter layers in forests is of great significance to the control and prevention of wildfires. The duff
moisture content is an important characteristic of the fuel. In this study, through on-site sampling experiments, thechanges in the water content of deciduous leaves of four tree species in Tohoku Region of Japan were observed andanalyzed. And taking into account the general combustion process of forest combustibles, uses formulas to calculate
the heat required for combustibles to be heated to ignition temperature. Evaluate and demonstrate the effect ofmoisture content in the sample on heat required for ignition and fire process. The results show that the vast majorityof the heat required for ignition is used for the evaporation of water in combustibles, almost 80% to 90% of the total.
Keywords: heat required; duff moisture; ignition; combustibility; evaporation.
1 Introduction
Ignition is the initial stage of combustion and is one of themost important processes of fire disaster. As the main fuelsource for surface fires, to a certain extent, the in-depth studyof the duff layers is the key to the simulation of thesubsequent fire development and spreading process, also forthe subsequent reduction of the fire risk. And the control offire danger provides a certain theoretical basis.
The duff litter on the woodland is the main kindling. Whenthe fire source falls on the ground, it first comes into contact
with these potential combustibles, causing combustion. If the
combustibles are continuous, the combustion moves from theprimary reaction to the secondary reaction, and the
combustion process alternates. The so-called primary
reaction means that after the combustibles are heated, theyevaporate the moisture content and undergo thermal
decomposition to produce combustible gas and startspontaneous combustion. According to the combustion
process, Rothermel proposed the equation of the heat energy
required for pre-ignition [1]. Based on this equation, Zhang
after further analysis and modification, and a special
discussion on the energy required for the escape of water [2].
A heat energy balance equation including four factors ofignition point, calorific value, moisture content, and water
evaporation rate in combustibles was proposed to reveal the
combustion characteristics of different plant species.
The fuel moisture affects the combustibility of combustibles
and is a major factor of the occurrence and development of
wildfires, which in turn affects the fire probability, spread
speed, and fire behavior [3].
In this research, considering the general process of forest
combustible combustion, calculate the amount of heat energy
required when the combustible is heated to the ignition point
so that the heat required for ignition of different combustibles
can be distinguished more accurately, to determine the
combustibility of combustibles. Evaluate and demonstrate
the influence of sample moisture content on the ignition heatand ignition process.

2 Materials and methods
2.1 Field sites and sampling

36

Aoba Mori, located in Sendai City, Miyagi Prefecture, wasselected as the observation field. The observation period isfrom March 18 to April 4 and August 20 to August 28 in 2020.
Selected four fuel species. Respectively cedar, broadleaf,
cypress and pine zone. Three collection points were selected
at each site, and the surrounding terrain was taken into
consideration. Samples from litter layers were collected daily
on no-rain days, and randomly collect the duff sample at eachpoint. In March, mixed sampling of the upper and lower
layers of the garbage layer was carried out, and in August,only the upper layer of the garbage layer was collected.
Measure soil moisture at the same place. Dry in thelaboratory and weigh wet and dry mass.
2.2 Heat required for ignition
Rothermel put forward in the mathematical model of forest
fire spread, the calculation equation of heat required for
ignition of unit mass combustibles is;𝑄𝐶 ∙ 𝑇𝑀 𝐶 ∙ 𝑇
𝑉
(1)
Where: 𝐶 is the specific heat of wood (kJꞏkg-1ꞏ℃-1); 𝑇
is the temperature range to ignition (℃); 𝑀 is the fuelmoisture content (%); 𝐶𝑝𝑤 is the specific heat of water(kJꞏkg-1ꞏ℃-1); 𝑇𝑏 is the temperature range to boiling (℃);
𝑉𝑏 is the latent heat of vaporization (kJꞏkg-1).
Rothermel takes the ignition temperature to be 320℃ andthe boiling temperature of water to be 100℃, then the heatrequired for ignition can be expressed by an equation
containing only 𝑀 :𝑄
250 1116𝑀(2)
However, at the same moisture content level, due to thedifferent physical and chemical properties and size of the
combustibles, especially the difference in specific surface
area, the moisture escape speed is different, and thedifference in the amount of heat required for ignition iscaused. For this reason, Zhang proposed the following
equations of heat energy requirement for pre-ignition
considering moisture content, ignition point, and moistureevaporation rate:𝐶 ∙ 𝑇 1 𝑀
𝑀 𝐶 ∙ 𝑇𝑘∙𝑉
(3)
𝑄
Where k is the vaporization coefficient of water vaporization
in wood combustible equivalent to pure water, dimensionless.
Through the water evaporation measurement experiment on

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safetypure water and water in combustibles, the 𝑘 value can becalculated. Due to the different calculation methods of watercontent, the dry matter content turns to 1 𝑀 .

By analyzing Zhang's experimental results, the results areshown in the fig. 2.

3 Results and discussion
For northeastern Japan, the traditional peak wildfires period
is during the dry season, which is generally throughout
March and April, it makes sense to sample and experimentwith forest combustibles during this period. The end of
August is the hottest time of the year, but fires are rare during
this period, experiments with forest combustibles during thisperiod show how the duff moisture content of litter layers isdifferent from that of spring at the highest temperatures. The
observation results are shown in Fig.1.

Fig. 2. The values of the three components of heatrequired for 15 species of samples.
It can be seen from the above figure that the third item of
the heat required equation requires several dozens of times
the energy required by the first and second items, almost 8090% of the total. So that the most of the heat required for theignition of forest combustibles is used for the evaporation ofwater. Combustibles with high moisture content consume alarge amount of heat energy for water evaporation before
being ignited. Although the moisture of all samples is not
uniform, the range is between 45-60%, but due to the large
difference in the 𝑘, which is 7-45%, it has a greater impact
on the required heat. It can be explained that the water
vaporization coefficient has a greater impact on heat required.
Among them, P. tabulaeformis, its branches and leaves were
tested separately, and the energy required by the branches
was 3.6 times that of the leaves. This conclusion reasonably
explains why fine combustibles can burn earlier than thickcombustibles under the same moisture content.

4 Conclusions

Fig. 1. The duff moisture contents and soil moisture
contents of four sites in March and August
As can be seen from the figure, soil moisture at sites 1, 3
and 4 at the end of August changes between 20% and 40%,
while site 2 content changes between 10% and 30%. The duff
moisture basically maintained between 20% and 40%. As aresult of the rain on the morning of August 24th, we can see
the 24th day of the moisture content increased significantly,
in the absence of rain days to maintain a decline. In general,
there were similar trends in duff moisture content changes in
the four sites. Compared with August, the trend of duff
moisture change in March is wide and not obvious, generally
between 30% and 60%, but the situation larger than 60% is
also very common, because 22nd and 29th had rainfall,moisture content had a small increase. Soil moisture in site 3changes in 30% to 50%, while changes in other sites range
from about 20% to 40%. Although the comparison between
the two periods is somewhat different from what was
expected, I think it may be due to a change in sampling
methods and will continue to be observed in the next spring.
According to equation (3), we can divide the required heat
into three parts, the first of which is the heat to increase theamount of dry matter contained in a unit of combustible fromroom temperature to the point of ignition; The second item isthe heat to increase the moisture in a unit of f combustible
from room temperature to 100℃; The third item is the heatrequired for the complete vaporization of water under 100℃.

37

By calculating the heat required for ignition, analyzing the
combustibility of fuels, and judging the difficulty of
combustion. As a result, among the components of the heatrequired, the part used to evaporate the water in the accounts
for most of the total. This shows that the moisture content
affects the required heat, i.e. the effect on the combustibilityof the fuel. Meanwhile, the moisture escape intensity is also
a significant factor. We plan to observe the moisture content
of the litter layers at the four selected observation sites, anduse it to calculate the heat required for ignition in this place,then evaluate the combustibility. Due to results from theobtained, sampling experiments will continue in the next
spring.

5 Acknowledgements
This research was supported by the Ministry of Education,
Science, Sports and Culture, Grant-in Aid for Scientific
Research(B), 2020-2023 (20H02248, Yoshiya Touge).

Reference[1] Rothermel, Richard C. A mathematical model for
predicting fire spread in wildland fuels. Vol. 115.
Intermountain Forest & Range Experiment Station, Forest
Service, US Department of Agriculture, 1972.
[2] Zhang, J. Q., et al. Heat Balance Analysis on Burning
Process of Woody Plants, Journal of Northeast Forestry
University (1998), 26 (5). 34-38.
[3] Fang, M. X., et al. Kinetic study on pyrolysis andcombustion of wood under different oxygen concentrations
by using TG-FTIR analysis, Journal of analytical and applied
pyrolysis 77.1 (2006): 22-27.

The 8th International Symposium on Water Environment Systems

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(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

High loading capacity of EGSB reactor with anammox-HAP sludge at
extremely low temperature of 7°C○ Ying SONG1*, Lan LIN1 & Yu-You LI2
1

Department of Environmental Engineering, Environmental University, Miyagi 980-8579, Japan.
2
Department of Water Environment, Engineering University, Miyagi 980-8579, Japan.
*
E-mail: song.ying.q2@dc.tohoku.ac.jp

Abstract
Anammox process as an autotrophic nitrogen removal appears to be a cost-effective and high-efficient technology
in wastewater treatment field. The main challenges to overcome of this new frontier have been widely thought aslower temperature, together with a requirement of high and stable nitrogen removal efficiency. In this study, an
EGSB reactor with anammox-HAP granules was operated more than 200 days at constant 7°C to explore thenitrogen capacity and activity of the anammox bacteria. The nitrogen concentration of influent was kept at 250mgN L-1 and the nitrogen loading rate was improved from 1.0 g-N L-1 d-1 to 3.5 g-N L-1 d-1 by shortening the hydraulicretention time from 6 h to 1.7 h. The high settling velocity of anammox-HAP granules guaranteed no sludge washout
under lower HRT conditions. The yield of biomass and microbial community were also investigated. AnammoxHAP granular sludge adapted to 7°C was used for batch experiments at different temperatures, representing therecoverability of anammox activity when turning to higher temperature.
Keywords: Anammox; Hydroxyapatite(HAP); Low temperature
1 Introduction
Anaerobic ammonium oxidation(anammox), as one of themost attractive and promising nitrogen removal processes,
has been gradually advanced to engineering applications inrecent years. No demand of oxygen and organic carbon
addition are the advantages of anammox process in which
nitrite and ammonium could be converted to nitrogen gas byautotrophic deep-branching Planctomycetes.
High temperature dependency(20 to 30°C), notoriously
long doubling time(10 to 25 days) and stability of operation
have been hindering the implementation of mainstream
anammox. To further broaden its applications for the regionswith low temperature, several attempts have been made to
explore the impact of lower temperature on the anammox
activity and biomass growth (e.g. long-term, short-term cold
shock and ambient temperature operation). Both in the longterm exposure and batch tests of low temperature, sludge
activity was considerably inhibited with heterotrophic
denitrification bacteria proliferation. For the one-stage
anammox process, the response of anammox bacteria to lowtemperature was more intense than ammonia-oxidizing
bacteria(AOB), resulting in imbalance the nitritation andanammox process, further disturbing and deteriorating theperformance of the reactor. As reported, anammox activity
was dramatically decreased with a sharp drop of temperaturefrom 15°C to 11°C and was barely undetectable after
operated at 11°C for a month(Laureni et al., 2016b). However,
sufficient biomass retention time could compensate for the
lose in anammox activity caused by low temperature, whichindicated lower temperatures was not necessarily a problem
for anammox process(Hendrickx et al., 2012).
A new concept combining anammox and phosphorus
recovery by forming hydroxyapatite(HAP) was proposed(Maet al., 2020). The distinct double-layer granules of the
anammox biofilm attached to the HAP-core were observed.
It is proven that the settling velocity and retention
performance were remarkably improved by anammox-HAP

38

granules. The feasibility of anammox-HAP granules has yet
to be proven, as granule disintegration may occur due tolower temperature, as was previously observed for anaerobic
granules.
This study was conducted to a explore the nitrogen
removal capacity, activity and metabolism of the anammox
bacteria using an Expanded Granular Sludge Bed(EGSB)
reactor with anammox-HAP granules under extremely low
temperature of 7°C which is by far the lowest reported in theliterature.
2 Materials and methods

Effluent

Water seal

Gas meter

Anammox-HAP granules

CoolingsystemPump

7°C
AnammoxbiofilmPump

HAP
Ca10(OH)2(PO4)6N
Substrate tank

Ca P

Influent

Fig.1 Schematic diagram of the EGSB reactor withanammox-HAP sludge
The schematic diagram of the expanded reactor set-up wasshown in the Fig.1. The nitrogen species of liquid samples
were tested by Agilent 7100 capillary electrophoresis (CE)system. Specific anammox activity and stoichiometry wascalculated according to previous research(Zhang et al., 2018).
VSS (volatile suspended solid) was measured based on theThe 8th International Symposium on Water Environment Systems

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Tohoku University----- with Perspective of Global Safety
APHA methods. The temperature in the reactor wascontrolled at around 7°C by a water jacket.

indicating anammox species was a little sensitive to thetemperature change.
3.3 Yield of biomass and stoichiometry under 7°C

3 Results and discussion

40

3.1 Nitrogen removal performance
VSS (g)

NLR, NRR(g-N/L/d)

NLR
NRR

20
15
10

1

5
0

0

300

0
influent NO₂⁻-N
influent NH₄⁺-N

250
200

400

600

800

1000

Fig.4 Yield of biomass under 7°C

150

At the NLR of 2.5 g-N/L/d, the biomass was drew from the
top of the reactor periodically. The yield pf biomass was
0.038VSS/g-total nitrogen, assuming the increase of the VSS
was caused by anammox growth. The value of 0.038VSS/gtotal nitrogen was quite lower than 0.046 and 0.050 VSS/gtotal nitrogen reported by (Hendrickx et al., 2012) and(Strous et al., 1999). Since nitrite is partly oxidized to nitrate
to provide electrons for carbon fixation, produced nitrate isresulted from the growth of anammox bacteria. In this study,
the ratio of produced nitrate and consumed ammonium wasonly 0.212, which was also lower than the universallyacknowledged 0.26 at mesophilic temperatures, indicating
the less biomass yield under low temperature. As reported,
the yield of biomass production on NH4+ uptake wascalculated to be 0.071 C-mol/NH4+-mol(C-mol=22.1 gVS/Cmol),in correspondence with 0.051 mgVSS/mgN. Thisdifference might be contributed to sludge washout or
microbial population since the yield of biomass of 0.051
mgVSS/mgN was obtained using membrane bioreactor(MBR) and the dominant species was Brocadia. Besides,
most bacteria cannot adapt to 7 °C. Another possible reason
was considered as the cavitation formation and floatation
behavior of anammox-HAP granular.

100
50
100

120
100
80
60
40
20
0

80
TN removal efficiency
effluent NO₂⁻-N
effluent NH₄⁺-N

60

effluent NO₃⁻-N

40
20
0

20

40

60

80

100
120
140
Time(day)

0.8

160

180

200

220

0
240

4 Conclusions
Nitrogen loading rate of 3.5 gN/L/d was achieved at 7°C
in long-term operation indicating anammox could be stably
processed even in cold weather. Biomass yield at 7 °C was
0.035 g VSS/g-total nitrogen, lower to that at higher
temperatures. Average biomass specific activity was 30.5 mg
N/g VSS /d at 7 °C.

0lnSAA
Arrhenius fitting curve

(a)

SAA
Arrhenius fitting curve

lnSAA (gN/gVSS/d)

0.6

0.4

0.2

0.0
0

200

Total nitrogen (g)Fig.2 Nitrogen Loading Rate (NLR), Nitrogen Removal
Rate (NRR), influent NH4+-N, NO2--N concentration,effluent NH4+-N, NO2--N, NO3--N concentration andTotal Nitrogen(TN) removal efficiency during thecontinuous experiment.
The long-term performance of the EGSB reactor isshown in Fig. 2. The reactor was operated in three phases
over an operational period of 230 days. In phase I, the total
nitrogen(TN) concentration was kept at 250mg/L and thenitrogen loading rate(NLR) was increased from 1.0 g-N/L/d
to 3.5 g-N/L/d by shortening hydraulic retention time from 6
h to 1.7 h. The nitrogen removal efficiency was slightly
decreased by residual nitrite of the effluent at the beginningof increased NLR, then stable nitrogen removal efficiency of
83.9%-91.9 was obtained. The concentration of TN was
doubled and HRT was prolonged to 3.5 h to maintain thesame NLR in the reactor. However, the performance of thereactor was steadily worsening in a week. Therefore, theoperation condition in the following phase was reverted tothe same as phase I and the performance was recovered soon.
3.2 Specific anammox activity(SAA)
SAA(gN/gVSS/d)25

2

TN removal efficiency (%)

Nitrogen Conc. (mg-N/L) Nitrogen Conc. (mg-N/L)

30
Phase III

Phase I
4
3

y=0.038+3.42x
R2=0.95

35
Phase II

10

20

30

40

Temperature(°C)

50

60

(b)

-1

-2

-3

-4
3.2

Reference
Ea=78.37kJ/mol
y = -9426.5x + 30.36
R²= 0.9782
3.3

3.4

3.5

3.6

3.7

1/Temperature(K)*10-3

Fig.3 (a): SAA measured at different temperatures andArrenius fitting curve; (b): Arrhenius fitting curve andactivation energy (Ea) calculation.
SAA tests were performed at different temperatures after
230th of the operation day. The results were simulated by themodified Gompertz equation and Ea was calculated by
Arrhenius equation. The optimal temperature for Anammox
bacteria(Ca. Kuenenia) is 35 °C and only 10% and 5%
activity remains when the temperature is reduced to 10 °C
7 °C. The activation energy was calculated to be 78.37kJ/mol
which is consistent as reported 63–70 kJ/mol determined for
anammox biomass enriched at temperatures 20 °C previously,

39[1] Hendrickx et.al, Autotrophic nitrogen removal from low
strength waste water at low temperature. Water Res.(2012)Vol.46, 2187–2193.
[2] Marc Strous et.al, Key Physiology of Anaerobic
Ammonium Oxidation, Appl Environ Microbiol. 1999 Jul;
65(7): 3248–3250.
[3] Haiyuan Ma et.al, Simultaneous nitrogen removal andphosphorus recovery using an anammox expanded reactor
operated at 25 °C, Water Res.(2020)Vol.172, 115510[4]Yanlong Zhang et.al, Stoichiometric variation andloading capacity of a high-loading anammox attached film
expanded bed (AAEEB) reactor, Bioresour. Technol. 253(2018) 130–140[5] Laureni, M. et.al, Mainstream partial nitritation andanammox: Long-term process stability and effluent quality at
low temperatures. Water Res.(2016) 101, 628–639.

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Development of an energy saving type municipal wastewater treatment
system by combining AnMBR and Anammox processes: pilot-scale
plant study and system evaluation○ Chao RONG1*, Tianjie WANG2, Zhe KONG2 & Yu-You LI1, 2
1

Department of Frontier Science for Advanced Environment, Graduate School of Environmental Studies,
Tohoku University, 6-6-20 Aoba, Aramaki-Aza, Sendai, Miyagi 980-8579, Japan.
2
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku
University, 6-6-06 Aoba, Aramaki-Aza, Sendai, Miyagi 980-8579, Japan.
*
E-mail: rong.chao.p7@dc.tohoku.ac.jp.

Abstract
An innovative municipal wastewater treatment process which combined AnMBR and Anammox wasinvestigated by a pilot-scale plant under 25 oC for the first time. The working volume of submerged AnMBR and
one-stage Anammox reactor were 5 m3 and 1.67 m3, respectively, with a mximum treatment capacity 20 m3/d.
During the long-term operation period, excellent performance has been achieved both in the pollutants removal
and bioenergy recovery. In AnMBR unit, more than 90% COD and 95% BOD 5 in the raw wastewater were
removed, and accompanied by 0.27 L/gCOD removal biogas recovery with a methane content at a high 75-81%. In the
Anammox part, almost all of the NH4+-N was removed, resulting in a TN removal efficiency >80% and remained
TN in effluent less than 10 mg/L. Serious membarane fouling was encountered after one year’s operation, bothon-line chemical enhanced backwash and off-line manual cleaning are showed limited effectiveness in membrane
permeablilty recovery. The SEM result suggested that the membrane aging should be blamed to the membrane
performance degradation.
Keywords: Municipal wastewater treatment; AnMBR; Anammox; membrane fouling; pilot-scale plant.
1 Introduction
The conventional activated sludge (CAS) process has been
applicated in municipal wastewater treatment for almost 100
years, which have made great contribution to theenvironmental protection. However, due to the high energy
consumption for aeration and tremendous amount excessive
sludge production, which mke the CAS no longer meet thehigher wastewater treatment standerd in the future, in where
the wastewater should be accepted as a potential nexus
resource (e.g. nutrients, energy and water reuse) rather thanwaste.
As one of the most poential alternatives to CAS,AnMBR-Anammox was considered to have promising
future in wastewater treatment and has attracted increasing
attention over the world. In the AnMBR part, due to the
combination of conventional anaerobic technology withmodern membrane system, the sludge retention time (SRT)and hydraulic retention time (HRT) can be totally separated,which have compensated the disadvantage of the slow
growth speed of anaerobic microorganism and obtain thehigh concentration of biomass in the reactor to achieve
excellent waste removal efficiency and bioenergy
production. After most of organics in the raw wastewater
were removed and different forms of nitrogen wereconverted into ammonia nitrogen (NH4+-N), the effluent of
AnMBR which charactered by low carbon ratio (C:N≈1:1)
is most suitable to be further treated by Anammox. In the
Anammox part, the major functional microorganisms,
ammonia-oxidizing bacteria (AOB) and anammox bacteria,
can achieve high nitrogen removal efficiency by partial
nitritation/anammox (PNA), and the aeration demand is
only 30% of the traditional denitrification process.

40

Currently, there are plenty of researches on individual unit
application, AnMBR or Anammox, but few focuse on theapplication of the entire system. And this happens to be themost important for the future application of this process inlarge-scale.
In this study, a pilot-scale AnMBR-Anammox plant wasconstructed and applied in real municipal wastewater
treatment. The process performance inclcuding pollutants
removal, bioenergy recovery and membrane permability
were systematically studied during the long-term operation.
It is expected that the results from this research can provide
useful reference to promot the AnMBR-Anammox process
widely application in the future.

2 Materials and methods
The flow chart and real photograph of the entire
AnMBR-Anammox system are illustrated in Fig. 1.

Fig. 1. The flow chart and real photograph of pilot-scale
AnMBR-Anammox plant
Due to the auto-control system and thermostatic system,
this plant can realize automatic operation at 25 oC. A total
aera of 72 m2 hollow fiber membrane module was installed

The 8th International Symposium on Water Environment Systems

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in the submerged AnMBR, and a novel carrier was added to
the Anammox reactor to increase the contact area between
microorganism and water.
Biogas, temperature, pH and TMP were daily recorded
in-site, while other parameters and indicators like COD,
BOD et al. were measured in the laboratory three times a
week. The detailed procedures were reported in ourprevious research (Kong et al., 2020).

3 Results and discussion
About 100 days’ operation data was provided in Fig. 2,
which include three differernt HRTs and two membrane
module, the old one and the new one.

Fig. 2. The long-term performace of entire process.
A good orgaincs removal efficiency has been achieved
during the entire stable operation period. The total COD
removal efficiency for different HRTs is over 90% andBOD5 removal efficiency is over 95%. It should be noted
that, Anammox reactor also contributes to the removal oforganics, which lead to a great effluent with residual COD
and BOD5 less than 40 mg/L and 10 mg/L, respectively.
The biogas is smoothly produced in the AnMBR with aconsistently methane content over 75% and reaching a highof 81% under stable operation period. By the calculation,
the biogas production was 0.25-0.27 L/g removed COD and
0.09-0.10 L/L raw wastewater, in which the methane
production rate was around 0.18-0.23 L/g COD removed
and 0.07-0.08 L/L raw wastewater.

41

For the nitrogen removal in the whole system (shown in
Table 1), the NH4+-N in the raw wastewater represents the
majority of TN inf, accounting for nearly 65%. After AnMBR,
the organic-N (soluble protein) and particle-N (influent SS)
were all transformed into NH4+-N, and about 7% nitrogen
was transfered to the sludge in AMBR. Anammox showed
an excellent performance in nitrogen removal, about 80%
TN was removed and the remained TN in the effluent < 10
mg/L.
Table 1 Nitrogen removal in the entire process
Raw
AnMBR AnMBR
Anammoxwastewater influent
effluent
effluent
NH4+-N23.1±3.3 23.7±3.3 33.6±2.4
1.2±1.3
NO2--N000
1.6±0.7
NO3--N0002.5±1.7TN35.9±2.8
37±2.233±2.524.1±2.2
The membrane was run at a mode of 4 min for filtrationand 1 min for relaxation (F:R=4:1). Fixed biogas sparging at
rate of 0.9 m3/min and online chemical enhanced backwash
with 500 ppm NaClO were employed to maintain themembrane permeability. After achieve a good performance(∆TMP was relatively stable and remaining lower than 5kPa) for more than 300 days, serious membrane fouling was
occurred and cannot be mitigated by the CEB. The offline
manual cleaning with 3000 ppm NaClO and 2% citric acidwas also conducted to try recover the membrane
performance, but the effect seems temporary and limited(serious membrane fouling reappeared within 15 days). So a
new membrane module was installed to replace the old one
on day 450.

Fig. 3. SEM picture of virgin membrane and usedmembrane after manual cleaning.
According to the SEM picture of virgin membrane and theused membrane afer cleaning in fig. 3, the relative roughsurface and pore caused by the membrane aging tend to be
one of the main reasons for the poor performance of the oldmembrane.

4 Conclusions
A pilot-scale AnMBR-Aammox plant has performed
successfully in real municipal wastewater treatment. The
COD and BOD5 removal efficiency for the entire process
were >90% and >95%, respectively. The nitrogen removal
efficiency in Anammox reactor is about 80%. The
membrane ageing maybe one of the major reasons lead tothe membrane performace degradation.

Reference[1] Kong Z, et al., 2020. Large pilot-scale submerged
anaerobic membrane bioreactor for the treatment ofmunicipal wastewater and biogas production at 25 °C.
Bioresour Technol. DOI: 10.1016/j.biortech.2020.124123.

The 8th International Symposium on Water Environment Systems

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(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Treatment of Municipal Wastewater by Anaerobic Membrane
Bio-reactor: Process Performance and Mass Balance○ Runda Du1*, Yisong Hu1,2, Jiayuan Ji1, Yu-You Li1
1

Department of Civil Engineering and Architecture, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba-ku, Miyagi
980-8579, Japan.
2
Key Lab of Environmental Engineering, Shanxi Province, Xi’an University of Architecture and Technology, No.
13 Yanta Road, Xi’an 710055, P. R. China*E-mail: du.runda.t3@dc.tohoku.ac.jp.
Abstract
The performance of real municipal wastewater treatment by an anaerobic membrane bioreactor (AnMBR) was
studied through a lab-scale bioreactor. In detail, membrane performance was evaluated by flux, TMP andmembrane fouling substances. Mass balance was also conducted to analysis the material flow in this process.
Keywords: AnMBR; municipal wastewater; membrane fouling; mass balance
contributed to the low influent SS concentration and high
VSS proportion, most of the which were decomposed into
biogas by microbes.

(c)

(d)

3 Results and discussion(1) Removal of organic matters & biogas production
Fig. 1 (b) shows that COD of the municipal sewage
basically varies between 200-400 mg/L, lower than that ofthe pilot AnMBR in the same plant. It was deduced that thefine influent pipe could filter out some sand and large fibers
in sewage, resulting in a low SS concentration (120-180
mg/L, Fig. 1 (a)). In addition, frequent rain also reduced
influent COD. Through the retention of membrane andanaerobic digestion, COD removal rate maintained at 85%,and effluent COD was less than 50 mg/L.
For the undecomposed SS accumulated in the seed sludge,
MLSS showed a negative growth trend during the start-up
phase at HRT 12 h in Fig. 1 (c). Then HRT was reduced to
6 h quickly and MLSS was reached 8 g/L by sludge
supplement. During the operation at HRT 6 h, except for thesludge required for sampling, the reactor produced almost
no excess sludge and the sludge yield was 0.081
gVSS/gCODrem, keeping MLSS at about 10 g/L, which was

42

(e)

Sludge (g/L)

A 20 L lab-scale AnMBR was installed at a local
wastewater treatment plant (WWTP) to treat municipal
wastewater. The membrane was submerged in the AnMBR
filled with anaerobic sludge as seed sludge from apilot-scale AnMBR at the same WWTP. During the start-up
phase, HRT was adjusted from 12 h to 8 h and 6 h finally(about 30 days after beginning). Then the AnMBR was
operated at HRT 6 h.

HRT6hInfluent VSS

VSS/SS

80

300

60

200

40

100

20
Influent T-COD

Influent S-COD

Effluent COD

COD Removal

0
100

800

80

600

60

400

40

200

20
MLSS

MLVSS

0
100

VSS/SS

16

80

12

60

8

40

4

20

0
150

BiogasMethane0

Dissolved Methane

100
50
0
100

CH₄

N₂

CO₂

80
60
40
20
0

0

10

20

30
40
Operation Time (day)COD Removal (%)

01000

VSS/SS (%)

100

400

0
20

2 Materials and methods

HRT8h

Influent SS

500

50

60

70

Fig. 1. Performance of sewage treatment.
As shown in Fig. 1 (d) and (e), during the start-up process,as the microbes were in the adaptation stage, the methane
production was always below 50 mL/L-water. In the stable
operation at HRT 6 h, the methane production gradually
increased to more than 75 mL/L-water. Meanwhile, thebiogas production showed a good correlation with the
influent COD. The methane content in biogas gradually
increased and then stabilized at about 80%.

VSS/SS (%)(b)

COD (mg/L)(a)Suspended Solid (mg/L)

HRT12hBiogas (mL/L-water)

The activated sludge process has evolved into a mature
method for wastewater treatment for over 100 years.
However, there are still some issues such as high energy
consumption and high sludge yield to be solved. Recent
decades, anaerobic membrane bioreactor (AnMBR) process
has been attached to great attention because of its high COD
removal, low energy demand, low sludge generation andbioenergy recovery (Rong Chen et al., 2017). Although thistechnology has been successfully applied in the field ofindustrial wastewater treatment, it has not yet been applied
to the treatment of municipal wastewater, which hascomplex compositions, and low organic concentrations(Zhen Lei et al., 2018). In this study, a lab-scale AnMBR
was used for real municipal sewage treatment to achieve
stable operation and to investigate the treatment
performance and various material flows.

Biogas Content (%)

1 Introduction

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University

4 Conclusions
The AnMBR showed excellent COD removal and biogas
production for municipal wastewater treatment with low
excess sludge generation. The transformation paths of C, N,
S, P in the system are investigated, which will be helpful forthe research of element removal and resource recovery.

43

5

1

0
5000

0

Permealbility

4000
3000
2000
1000
0

0

10

20

30
40
Operation Time (day)

50

60

70

Fig. 2. Performance of membrane filtration.
120

(a)

100

4.64
5.86

80

20.3

60
100
57.1

40

Influent-COD
Effluent-COD
Biogas-CH₄
Dissolved-CH₄
MLVSS-convert
S²⁻

20
0
120

Input

13.2

Output

(b)
14.1

100

4.39
33.6

80
60

90.5

40
66.4

20
0
120

Influent-NH₃-N
Influent-Other-N
MLSSconvert-N
Effluent-NH₃-N
Effluent-Other-N

Input

Output

(c)

100
80

39.5

60

76.5
22.5

40
20

38.1

0

Phosphorus balance (%)

120

Influent-SO₄²¯
Influent-S²¯
Influent-Other-S
H₂S
MLSSconvert-S
Effluent-SO₄²¯
Effluent-S²¯
Effluent-Other-S

Input

3.84
4.37
6.34

Output

(d)

100

9.24
16.5

80
52.1

60
40
20
0

77.5
47.9

Influent-PO₄³¯
Influent-Other-P
MLSSconvert-P
Effluent-PO₄³¯
Effluent-Other-P

Input

Fig. 3. Mass balance.

Output2.24∆TMP (kPa)FLUX (LMH)3
2

COD balance (%)

Permealbility (LMH/bar)

∆TMP

10

Reference[1] Rong Chen, Yulun Nie, Jiayuan Ji, Tetsuya Utashiro,
Qian Li, Daisuke Komori and Yu-You Li, (2017),
Submerged anaerobic membrane bioreactor (SAnMBR)
performance on sewage treatment: removal efficiencies,
biogas production and membrane fouling, Wat. Sci. andTechnol., 76(6): 1308-1317.
[2] Zhen Lei, Shuming Yang, Yu-you Li, Wen Wen,
Xiaochang C. Wang and Rong Chen, (2018), Application ofanaerobic membrane bioreactors to municipal wastewater
treatment at ambient temperature: A review of achievements,
challenges, and perspectives. Bioresour. Technol., 267:
756-768.

FLUX

15

Nitrogen balance (%)(2) Membrane performanceFig. 2 shows that the membrane flux is 6, 9, 12 LMH at
HRT 12 h, 8 h and 6 h, respectively. Due to the short
operation time for just 70 days, ∆TMP (membrane pressure
between filtration and relaxation) did not change
significantly and remained around 1 kPa. The membrane
permeability (flux per unit pressure) was also basicallymaintained at a constant level.
(3) Mass balancea. COD balance. As shown in Fig. 3 (a), COD balance wascalculated by measuring the equivalent COD counterparts
both in the influent and the effluent. About 77% of theinfluent COD was converted to methane, including 20% of
the dissolved methane. In addition to the 13% COD in theeffluent, about 4%~5% of COD was consumed by sulfate
reduction, and about 6% of COD was converted into
MLVSS by microbial growth. It can be seen that AnMBR
was characterized by low excess sludge production.
b. Nitrogen balance. From Fig. 3 (b), NH3-N accounts for
60~70% of TNinf, leaving 60~70% in the SS and solubleorganic-N (such as protein). NH3-N in effluent was about
80~90%, and the concentration was higher than that ofinfluent, which indicated that part of SS-N and organic-N
were decomposed into NH3-N in AnMBR. In addition,
4~5% of TNinf entered sludge by microbial proliferation andnitrogen was not removed in other forms.
c. Sulfur balance. In Fig. 3 (c), sulfate and sulfide
accounted for about 80% of TSinf, leaving 20% in SS andsoluble organic-S. The sulfate in the influent was reduced tosulfide by sulfate-reducing bacteria in AnMBR, some ofwhich was hydrolyzed to form hydrogen sulfide into biogas.
Most of organic-S in the influent was also decomposed into
inorganic-S, and about 4% of TSinf was assimilated by
microbes.
d. Phosphorus balance. Similar to nitrogen, phosphorus in
influent existed in the form of phosphate and organic
phosphorus, each of which accounted for about 50%. The
ratio of phosphate in effluent was 70~80%, which was
higher than that in influent, indicating that organic
phosphorus in influent was partly decomposed into
phosphate, and about 16% of TPinf was used for sludge
proliferation. In addition, phosphorus was also not removed
in the AnMBR.

Sulfur balance (%)----- with Perspective of Global Safety

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Virus removal by membrane bioreactors:
mechanisms and modeling efforts○ Yifan ZHU1*, Rong CHEN2, Yu-You LI3 & Daisuke SANO1,3
1Department of Frontier Sciences for Advanced Environment, Graduate School of Environmental Studies,

Tohoku University, Miyagi 980-8579, Japan.
Key Laboratory of Northwest Water Resource, Ecology and Environment, Ministry of Education, Shaanxi Key
Laboratory of Environmental Engineering, Xi’an University of Architecture and Technology, Xi’an
710055, China.
3
Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University,
Miyagi 980-8579, Japan*
E-mail: zhu.yifan.t1@dc.tohoku.ac.jp & yifanz35@uw.edu
Abstract
The increasing pressure on the global water supply has led to the promptly developing treatment technologies andrisk management strategies, and microbial safety is becoming a crucial aspect in the interest of public health.
Backed up by the development of membrane technology, membrane bioreactors (MBR) have received substantial
attention for their superiority over conventional treatment methods in many ways and are considered promising in
the water reclamation realm. This review study provides an overview of the efforts made to manage and control
the potential waterborne viral disease risks raised by the use of effluent from MBR treatment processes, including
the mechanisms involved in the virus removal process and the attempts to model the dynamics of the removal
process. Generalized and integrated virus removal models that provide insight into real-time monitoring areurgently needed for advanced real-time control purposes. Modeling methods that can well handle the inherent
uncertainty and nonlinearity of the complex removal process are crucial to the development and promotion ofrelated technologies.
Keywords: Membrane bioreactor, Virus removal modeling, HACCP, Soft-sensor approach, Process-driven
modeling, Data-driven modeling.
2

1 Introduction

2.1 MBR virus removal performance

Access to sufficient and safe water resources plays a central
role in the prosperity and development of human society.
Despite the rich history of water reclamation, the latent healthrisks and the importance of proper sanitation strategy have
only become a serious concern in the eyes of the public andauthorities in recent decades. In particular, waterborne
enteric viruses present in reclaimed wastewater with fecaloral transmission routes are considered a primary healthconcern globally since they can cause several acute illnesses
including gastroenteritis and hepatitis. Several factors,
including the high shedding rate from infected individuals,
resistance to environmental factors, and the low infectiousdose, have made these viruses a particular threat to publichealth.
Powered by the rapid development of materials science andengineering, the use of membrane in the realm of water
treatment has become increasingly popular with one
successful example being the membrane bioreactors (MBR).
With the utilization of a membrane, MBR can serve as aneffective barrier against waterborne pathogens. This study,
therefore, focuses on the efforts dedicated to establishing amathematical model for the virus removal process in MBR
systems. Firstly, the virus removal performance of MBR
systems and involved mechanisms are briefly discussed.
Secondly, we reviewed the historical and recent attempts toconnect easy-to-measure variables with virus removal
efficiency via either conventional process-driven approachbased on the physicochemical and biological relationship andequations or novel data-driven modeling techniques.

Previous studies have reported that depending on themembrane installed, virus strain, and operating conditions,the log reduction value (LRV) can vary from less than 1 tolarger than 7, but overall showing a decent and stableperformance. Although only a limited number of studies havefocused on AnMBR while the virus removal property of
AeMBR has been investigated under various settings, as thecapability of AnMBR systems is gaining recognition, we
expect to see more thorough studies in the near future.

2 Performance and contributing mechanisms

44

2.2 Mechanisms involved
2.2.1 The role of membrane
When the pore size of membrane is comparable to the sizeof virus particles (e.g. 0.04 μm), direct membrane rejection isenough to provide decent removal performance, but tomaintain throughput and keep operating cost low, many
plants prefer membrane with larger pores, when that is thecase, mechanical sieving, virus aggregation, membraneadsorption, and electrostatic repulsion come in as the main
factors, which form a complex matrix under the influence of
membrane material, virus surface characteristics, andenvironmental conditions such as pH.
2.2.2 Effect of biomass adsorption
As the biomass in the reactor grows, both the suspended
solids and the gel/cake layer attached to the membranesurface contribute to virus retention. Generally speaking,higher biomass content provides more adsorption spots andblocks membrane pores, hence resulting in better virus

The 8th International Symposium on Water Environment Systems

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Tohoku University----- with Perspective of Global Safety
retention capacity, but a balance must be found between virus
removal performance and throughput.

connection between input and output variables purely using
existing dataset and algorithms, albeit at the sacrifice of theunderstanding of the underlying mechanisms. So far, datadriven modeling methods have found successful applicationsin the water-related research realm, most commonly serving
as a tool of water quality or plant status prediction. Especially,
artificial neural network (ANN), adaptive neuro-fuzzy
inference system (ANFIS), principal component analysis(PCA), and partial least squares (PLS) are more frequently
employed among others. The former two can be categorized
as machine learning methods that specialize in establishing
nonlinear input-output relationships without prior knowledge,
and the latter two belong to the family of multivariate
statistics which comes in handy when trying to find thevariables that are most correlated with the desired output.

Fig. 1. A demonstration of the mechanisms involved in
the virus removal process in MBR. ➀, size exclusion,
electrostatic repulsion, and sorption onto themembrane; ➁, pore blocking and adsorption onto thebiomass layer attached on membrane surface; ➂,adsorption and predation of suspended biomass; ➃,spontaneous decay and inactivation.
2.2.3 Decay and inactivation
Virus particles present in the reactor are subject to naturaldegradation and the predation of microorganisms. Due to thehigh bacteria concentration inside the reactor, the recorded
decay rate is higher than the expected washout rate, however,considering the relatively short hydraulic retention time, theoverall contribution is likely not to be as pronounced as othermechanisms.

3 Modelling efforts
3.1 Process-driven models
Process-driven models are generally based on the physicalinterpretation of the underlying mechanisms, althoughsometimes statistical methods also help select and calibrate
the coefficients. Considering the involvement of membrane,
many studies have focused on the membrane filtration
process, more specifically, how the membrane rejection andfouling translate to the virus particle retention. Commonlyused physical models include Darcy’s law and mechanicalsieving equation, but the growth of biomass has yet to bemathematically connected to membrane fouling and theincrease in virus removal performance, which may sacrificethe model’s responsiveness to the reactor dynamics. Also, as
previously mentioned, the adsorption onto suspended
biomass plays a critical role in the virus adsorption, which isoften described by first-order kinetic model. However, fromthe perspective of preventive water safety management,
existing physical models may find trouble being applied as areal-time control measure as they tend to be overwhelmingly
complex with tens of parameters and coefficients.
3.2 Data-driven models
Since the complexity of the virus removal process happened
in MBR is still a huge hindrance, some studies advocate using
data-driven models to bypass the actual process and build the45

Fig. 2. The structure of a 2-input, 1 output artificial
neural network with one hidden layer and two nodes.
3.3 Hybrid model
Driven by the intention of overcoming the respective
inherent shortcomings of both types of models, thediscussion about combining them to develop a hybrid model
has also been raised. Conceptually, the overwhelming
superiority of data-driven approaches in perceiving nonlinear patterns can greatly reduce the workload, while
physical model would give better interpretation and elasticity
to the model. As promising as it sounds, despite someattempts related to water quality and membrane permeability
have been reported, the true potential of hybrid models,
especially in predicting virus removal performance, is yet tobe fully explored and verified.

4 Conclusions
With the employment of membrane that efficiently retains
large particles, MBR systems have the potential to deliver
decent and stable virus removal performance that can be put
under real-time monitoring, paving its way for its acceptancein a field where advanced risk management is gaining
importance. Although the intricate nature of the process
makes the modeling an arduous task, as related researchcarries on, new methods and models will continue to thrive.

Reference[1] Zhu Y. et al, Virus removal by membrane bioreactors:
A review of mechanism investigation and modeling efforts,
Water Res., 188 (2021), 116522.

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

Mechanisms of MS2 bacteriophage removal in an anaerobic membranebioreactor○ Jinfan Zhang1, Zhendong Liu1 & Rong Chen1,2*
Key Lab of Environment Engineering, Shanxi Province, Xi’an University of Architecture and Technology, No.
13 Yanta Road, Xi’an 710055, PR China
2International S＆T Cooperation Center for Urban Alternative Water Resources Development, Key Laboratory ofNorthwest Water Resource, Environment and Ecology, MOE, Xi’an University of Architecture and Technology,
No. 13 Yanta Road, Xi’an 710055, PR China*
E-mail: chenrong@xauat.edu.cn
Abstract
To investigated MS2 removal performance for municipal wastewater using an anaerobic membrane bioreactor(AnMBR) and attachment and inactivation of MS2 in the mixed liquor. The results revealed that the removal ofMS2 by AnMBR mainly depends on the attachment and inactivation of MS2 in mixed liquor before the formationof serious membrane fouling. In addition, the anaerobic intermediated acetic acid (CH3COOH) in mixed liquor
inactivated MS2 mainly by destroying the proteins involved in MS2 replication.
Keywords: Anaerobic membrane bioreactor; Mixed liquor; Attachment; Inactivation; Acetic acid1

1 Introduction
Viral from wastewater have a potential harm to theenvironment and human health. Therefore, control of virus
has been a major concern in wastewater treatment and water
reclamation and reuse. AnMBR combine the benefits ofanaerobic treatment: bioenergy recovery, low sludge
production, with the benefits of MBR: solid-liquid
separation, safe and clean, reduce pathogens. AnMBR
technology has the ability to meet the increasingly stringent
regulations for recycled water, including virus removal.
Bacteriophage MS2 was used as a conservative virus
surrogate in this study because of its smaller size comparedwith human enteric virus. The objectives of this study tobetter understand AnMBR behavior in virus removal formunicipal wastewater and the roles of mixed liquor inremoval.

2 Materials and methods
2.1 AnMBR and operation condition
Fig.1 shows a diagram of the AnMBR. AnMBR was
operated with a working liquid volume of 2.0 L, hydraulicretention time of 8 h. The SS of seed sludge inoculated into
the reactor was 8.0 g/L. The membrane module, with asurface area of 0.036 m2 and nominal pore size of 0.2 μm,
was immersed in AnMBR.

Solid and liquid fractions of mixed liquor samples were
quantified separately to determine the quantity of MS2 inmixed liquor.
2.3 Virus functionality assay
RNase protection assay. Inactivation-treated MS2
samples and untreated MS2 controls were incubated with
RNase, followed by heat-inactivation of enzyme. RNA ofMS2 was extracted and quantified with qRT-PCR.
Binding Assay. MS2 samples were incubated with E.colifor 90 min at 4℃ which allowed the MS2 to attach to thebacterial host without injecting or replication the genomes,
and the RNA of MS2 was extracted and enumerated by
RT-qPCR [1].
Injection Assay. MS2 samples were bound to host cells at
4℃ for 30 min. 0.2 mM chloramphenicol was then added tothe bacterial-viral suspensions and the samples wereincubated at 4℃ for 60 min. the RNA of MS2 was extracted
and enumerated by RT-qPCR [1].
Replication Assay. Replication was calculated based on
the rate constants for infectivity, binding, injection loss:
Kreplication =Kinfectivity-Kbinding-Kinjection-KRNA damage

2.4 MS2 attachment to different size particles
The mixed liquor was filtered step by step throughmembranes of 10 μm，5 μm，1 μm and 0.45 μm to determine
the quantity MS2 attachment by biomass flocs,
micro-particles, sub-micro particles, insoluble colloids,
respectively. Mg2+ was added into the filtrate after 0.45 μm,
and then filtered with 0.22 μm membrane to obtain the MS2
in the liquid phase. MS2 was determined by RT-qPCR.

2.5 MS2 inactivation in mixed liquor

Fig.1. Schematic of anaerobic membrane bioreactor
2.2 Samples
The influent and effluent of AnMBR were determined bydouble agar layer (DAL) according to EPA Method 1602.

46

Regular sampling from mixed liquor to study MS2
inactivation in mixed liquor. 2 mL of mixed liquor samples
was separated into solid and liquid components bycentrifugation at 10 000g for 1 h. The supernatants were
harvested for infectivity assay. The solid fraction was
suspended in 2 mL of 10% tris-glycine beef extract buffer.
Elution and then centrifugation at 10 000g for 30 min. thesupernatants were harvested for infectivity assay. MS2 insolids was calculated based on MS2 recovery rate. MS2 inmixed liquor is the sum of MS2 in the liquid and solid.

3 Results and discussion

The 8th International Symposium on Water Environment Systems

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(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety
3.1 MS2 removal by AnMBR
Fig. 2 is the log removal value (LRV) in the short-term
AnMBR treatment of municipal wastewater. During the
AnMBR operation, the LRV was between 0.2~1.2 log10.
With the slow increase of transmembrane pressure (TMP),
the efficiency of MS2 removal in the AnMBR increased
gradually. This is because the nominal pore size ofmembrane is larger than the diameter of MS2. Before
serious membrane fouling, the interception of MS2 bymembrane is weak.
2.0
1.8

6
5

1.4
1.2

4

1.0

3

0.8
0.6

2

0.4

1

0.2
0.0

0

TMP(KPa)LRV (Log 10)

1.6

3.3 Mechanisms of CH3COOH inactivation regarding
virus function
Figure 5 (A) shows that the concentration of CH3COOH
was 0, 500, 1500, 3000 and 5000 mg/L, the first-order
inactivation rate constants were 1.68, 2.27, 1.14, 2.47, 2.09,
respectively. The results showed that CH3COOH has aneffect on the inactivation of virus. There was no significant
difference in the RNA signal (Fig. 5(B)). Therefore,
structural protein and RNA damage was negligible
following CH3COOH treatment. Fig. 5(C) and (D) showsthat CH3COOH concentration of 500 mg/L causes to theinjection protein and the replicated protein of MS2, while
the CH3COOH of 1500 mg/L only affects the replicatedprotein.

3

6

0

9 12 15 18 21 24 27 30
Time (day)Fig.2. MS2 removal by AnMBR
3.2 Attachment/inactivation of MS2 in mixed liquor
Fig. 3 (A) shows that attachment of MS2 by biomass flocs,
micro-particles, sub-micro particles, insoluble colloids,
respectively. The results showed that attachment of MS2
mainly depended on the biomass flocs. However, sub-micro
particles had higher attachment capacity. Fig. 4 shows thatkinetics of MS2 inactivation in liquid, sludge and mixed
liquid, respectively. The first-order inactivation rate
constants of MS2 in liquid, sludge and mixed liquor were1.38 day-1, 0.99 day-1, 0.90 day-1, respectively. The
inactivated rate of MS2 in sludge is lower than that of MS2
in liquid.

Fig.5. Pseudo-first-order rate inactivation constant (K)of MS2 inactivation in different CH3COOH
concentration (A), RNase protection assay (B), The
first-order rate constant of binding loss and injection
loss (C), Relative contribution of RNA damage, binding,
injection and replication loss to CH3COOH (D)

4 Conclusions
Removal of MS2 by AnMBR mainly depends on theattachment and inactivation in mixed liquor before theformation of serious membrane fouling. Anaerobic
intermediated acetic acid in mixed liquor inactivated MS2
mainly by destroying the proteins involved in MS2 injection
and replication.
Fig.3. Contribution of different sizes to MS2
attachment (A), MS2 adsorption capacity of different
size particles (B)Fig. 4. Kinetics of MS2 inactivation in liquid, sludge
and mixed liquor, respectively

47

Reference[1] Krista R. W., Brian M. P., Therese S., Franziska B.,
Tamar K., Virus inactivation mechanisms: Impact ofdisinfectants on virus function and structural integrity,
Environmental Science & Technology, (2012) 69-78

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety

High rate anaerobic digestion of food wastewater in an anaerobic
membrane bioreactor○ Mengmeng JIANG1, Renjie DONG1, Yu-You LI2, Wei QIAO1*
1College of Engineering, China Agricultural University, Beijing 100083, China
2 Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku*

University, Japan
E-mail: qiaowei@cau.edu.cn

Abstract
Food wastewater was treated by two anaerobic membrane bioreactor systems (AnMBR) consist of a completely
stirred tank reactor (CSTR) and a separated membrane chamber under thermophilic condition (50±1) °C for 75 days.
Both of the two systems were started with a hydraulic retention time (HRT) of 20 days, and operated at HRT 15
days. About 3 L biofilm carriers were immersed at the bottom of CSTR unit in one AnMBR as biofilm membranebioreactor (Bf-AMBR) at HRT 15 days. The results showed that both the AnMBR and Bf-AMBR showed stable
methane production of 0.32 L/kgCODin and a comparable COD removal efficiency of 86%. The maximum TMP of
AnMBR risen significantly from 4.2 to 25 kPa after 55 days, the flux dropped from 8.2 to 6.5 LMH. In comparison,
the maximum TMP of Bf-AMBR rises slowly from 4.2 to 19.6 kPa after 75 days, and the flux can be maintained at
a high state around 8 LMH at HRT 20 days and 7.7 LMH at HRT 15 days. After biofilm carrier introduced into thesystem, the TS and VS concentration was 22% and 14% lower in Bf-AMBR than AnMBR, showed an adsorptionof biofilm carriers on microorganisms. The biofilm carriers were effective in alleviating membrane pollution.
Keywords: Bf-AMBR; AnMBR; biofilm carrier; membrane fouling.
1 Introduction

Table1 Characteristics of food wastewater and inoculum

In China, food waste was produced at 100 million tons in
2018. Food waste treatment consists of sorting, crushing andhigh temperature degreasing, etc. Commonly used anaerobic
reactors such as UASB and IC were not suitable for those
wastewater due to the high suspended solids concentration.
The anaerobic membrane bioreactor (AnMBR) has
advantages of enhanced biogas production and improved
effluent quality over conventional reactors and wasdeveloped to treat a variety of high strength wastewater.
However, the membrane fouling was a long-term challenge
to the application of AnMBR. Measures must be taken toprevent membrane fouling and thus reduce operational cost.
It was found that adding inorganic particulates in AnMBR
can effectively alleviate membrane fouling (Akram &Stuckey, 2008). From this point, particulates which canprovide bacterial attach growth would be a promisingsolution considering the reduced membrane fouling factors
such as suspended biomass concentration, liquid viscosity
and soluble organics.
In this study, polyurethane biofilm carriers were immersedin the AnMBR system to establish a biofilm enhanced
AnMBR (Bf-AMBR). The fermentation and membrane
performance were investigated through long-term
continuously operated experiment.

Parameters

unit

TS
VS
SS
VSS
TCOD
SCOD
NH4+-N

g/L
g/L
g/L
g/L
g/L
g/L
mg/L

Water vapor
condensation(n=2)

62.7±0.0
43.1±0.056.5±0.4
22.9±0.1
49.4±1.3
10.4±1.31034±11

TMP sensor
P

P

Permeate

Biogas circulation

Gas bagFeed inM

Digestatecirculation48

Inoculum

The hollow fiber membranes used in this study were madeof polytetrafluoroethylene (PTFE) with a mean pore size of
0.1~0.2 μm and an effective filtration area of 0.1m2(Sumitomo Electric, Japan). The filtration mode (off/on cycle)of the two AnMBR were 4 min on and 1 min off. The
polyurethane biofilm carrier cube has a side length of 1 cm
and porosity of 98%. The filling volume of the biofilm carrierin the reactor was around 3 L.

2 Materials and methods
Food wastewater was taken from a full-scale industrial plant
located in Jiangsu province. The wastewater was produced
after sorting, crushing, pulping and high temperature
degreasing treatment. Trace elements were added with aconcentration of Fe: 100 mg/L, Co: 1 mg/L and Ni: 1 mg/L
before feeding. The inoculum was taken from a mesophilic
sewage sludge digester. As an acclimation process, thetemperature was increased in steps of 5 ºС everyday until itreached to 50 ºС, and then maintained in 50 ºС for 3 days
activation before starting experiment.

Food
wastewater(n=15)83.1±3.471.1±3.539.4±7.1
37.1±6.9
114.3±9.3
57.3±6.3
557±67P
H
Heating water

Membrane
unit

CSTR

Biofilm carriersFig. 1 Flow chart of the Bf-AMBR

The AnMBR system consists of a CSTR and a separated
membrane chamber. The biogas recirculation was
implemented with a diaphragm pump and a sparging rate of
7 L/min. Two systems were operated during this study:
parallel operated during the startup period which was HRT

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University

𝑱

Y= P

(1)

Where Y is the permeability of the membrane, LMH/kPa; J
is the flux of membrane, LMH; P is the TMP, kPa.

3 Results and discussion
AnMBR and Bf-AMBR performance: The long-term
operation performance was given in Fig. 2 and was organizedin Table 3. The OLR was increased from 4.2 to 5.4
kgCOD/(m3·d) at HRT 20 days, and improved to 7.1 at HRT
15 d (Fig. 2(1)). The methane production performance of thetwo reactors was similar while VFA and sludge concentration
were much different after biofilm carriers introduced into BfAMBR showed in Fig. 2(2, 3). The specific methane yield at
HRT 20 days was around 0.27 m3/kgCOD in both AnMBR
and Bf-AMBR, while 0.32 m3/kgCOD in AnMBR at HRT 15
days. A low VFA concentration were found in both reactors.
But the relative lower VFA was observed in Bf-AMBR. The
acetate in Bf-AMBR was 18% and 32% lower than AnMBR
at HRT 20 days and 15 days, respectively. At HRT 20 days
stage, the TS and VS concentration of the bulk sludge weresimilar in AnMBR and Bf-AMBR which has a value of
38.3g/L and 21.3 g/L at HRT 20 days showed in Fig. 2(4),while differ parentally at HRT 15 days which was 30.5 g/Lof TS and 26.3 g/L of VS in Bf-AMBR, almost 22% and 14%
higher than the TS and VS concentration in AnMBR,
respectively. This may due to the attached growth of
microorganism on biofilm carrier which also causing adecrease of bulk sludge concentration.

Membrane filtration performance: The membranes
filtration operation was start-up with a flux of around 8.1±1.0
LMH and initial negative TMP was around 4.2~4.3 kPa ofboth the two systems showed in Fig. 3A. While the AnMBR
and Bf-AMBR behaved difference as the experiment
progresses. The maximum TMP of both the two membranes
risen significantly with a decrease of permeability (Fig. 3A).
The maximum TMP of AnMBR reached to 25 kPa after 55
days while raised slowly and reached to 19.6 kPa in day 72
in Bf-AMBR (Fig. 3(A1)). The flux of AnMBR dropped to6.45 LMH and needs to be adjusted frequently but had
difficulty adjusting to initial value, while in Bf-AMBR, theflux can be maintained at around 7.7 LMH (Fig. 3(A2)). In
addition, periodic TMP comparison within 1 off/on cycle wasanalysis showed in Fig. 3(B). At HRT 20 days, the tworeactors showed similar TMP variation curve (Fig. 3B1). At
HRT 15 days, the TMP of both membranes began to increase,but the increase of AnMBR was relatively obvious (Fig.
3(B2)); after 20 days operation, the max TMP of AnMBR
jumped to 19 kPa, while the Bf-AMBR was below 10 kPa(Fig. 3B3). After 68 days running, there was a high TMP for
about 200s in one on/off cycle in AnMBR which was around
25kPa (Fig. 3(B3)). The periodic TMP comparison showed
an outstanding release of membrane fouling of Bf-AMBR.
HRT 20d30

HRT 15d

HRT 20 d
10

A1

25

1.5

20

AnMBR-maxTMP

15

Bf-AMBR-maxTMP

1

AnMBR permeability

10

0.5

Bf-AMBR permeability

5
0

0
1

6

11

21

31
41
51
Duration days

61

Permeability (LMH/kPa)
Flux (LMH)

20 days (SRT 100 days); biofilm carrier was added in one
CSTR bottom unit as the HRT was shorten to 15 days (SRT
50 days). The setup of Bf-AMBR was given in Fig.1.
The calculation of permeability was given below:

Max TMP (kPa)----- with Perspective of Global Safety

HRT15d

A2

8

6

AnMBR
Bf-AMBR

4
2

71

0

15

30
45
Duration days

60

75

600
900
Duration secondss

1200

1500

10

B1: Day 28

B2: Day 39
85

6
HRT 20 d,
Parallel operation

HRT 20 d, Parallel
operation

HRT 15 d, with biofilm
carrier adding in Bf-AMBR

(1)
6
4
2

0
0

15

30
45
Duration days

60

3

AnMBR
Bf-AMBR

2.0

1.5
1.0
0.5

200

15

30
45
Duration days

60

75

100
50

B4: Day 68
25

20

20

50

10

10

5

TS
VS

(4)

150

0
30

B3: Day 50

150

Sludge concentration (g/L)

250

2

2
25

15

0.0

60

acetatepropionate
butyrate
TVFA(3)
VFA concentration (mg/L)

(2)

2.5

75

300

4

3.0

TMP(kPa)

Methane produciton rate (L/L/d)

OLR(kgCOD/m3/d)

8

4

HRT 15 d, with biofilm
carrier adding in Bf-AMBR

5
0

0
0

40

300

600
900
Duration seconds

30

1200

1500

AnMBR

0

300

Bf-AMBR20

Fig. 3 Membrane filtration performance

10
0

0

AnMBR

Bf-AMBR

HRT 20d

AnMBR

Bf-AMBR

AnMBR

Bf-AMBRAnMBR

HRT 20 d

HRT 15d

Bf-AMBR

4 Conclusions

HRT 15 dFig. 2 The reactor operation performance
Table 3 Performance summary of the two systems
Parameters
Duration (d)CH4(%)
Methaneproduction rate
(L/L/d)Methane yield(m3/kgCODin)
pH in reactor
NH4+-N(mg/L)
Permeate SCOD

HRT20d(AnMBR/AnMBR)
1~3060.7±2.759.6±37.6

HRT15d(AnMBR/Bf-AMBR)
31~75
60.2±0.759.7±0.9

1.18±0.08

1.18±1.11

2.23±0.18

2.26±0.13

0.27±0.03

0.27±0.02

0.32±0.03

0.33±0.02

7.7±0.21366±158
1.08±0.36

7.7±0.21326±188
1.09±0.27

7.7±0.11618±1403.58±1.85

7.7±0.11636±173
3.77±2.11

49

Thermophilic AnMBR achieved a high degree digestion offood wastewater with low VFA and stable biogas production.
Embedded biofilm carriers significantly reduced membrane
fouling and thus prolong membrane service time.

5 Acknowledgement
This work was partially supported by National Science
Foundation of China (51778616) and International Training
and Promotion Project of China Agricultural University(00119521).

Reference[1] Akram A, Stuckey D C. Flux and performance improvement in asubmerged anaerobic membrane bioreactor (SAMBR) using powdered
activated carbon (PAC). Process Biochemistry, 2008, 43(1):93-102.

The 8th International Symposium on Water Environment Systems

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Tohoku University----- with Perspective of Global Safety

Using an expanded granular sludge bed reactor for advanced anaerobic
digestion of food waste pretreated with enzyme
Jinghuan LUO*, Sitong ZHANG & Jianyong LIU
School of Environmental and Chemical Engineering, Shanghai University, 333 Nanchen Road, Shanghai
200444, China.
*
E-mail: jhuanluo@shu.edu.cn.

Abstract
The high content of solid organics in food waste (FW) results in a low and unstable anaerobic digestion (AD)efficiency. Improving methane production rate and process stability is attracting much attention towards advanced
AD of FW. The feasibility of advanced AD of FW pretreated with enzyme was investigated by batch experiments
and 164 days running of an expanded granular sludge bed (EGSB) reactor. Simulation study based on the resultsof batch experiments indicates it is possible to treat enzymatically pretreated FW using an EGSB reactor. During
the running of an EGSB reactor, the organic loading rate went up to 20 g chemical oxygen demand (COD)/L.d,and the total COD removal rate reached 88%. The significance of this study is to achieve an advanced AD of
enzymatically pretreated FW with a stable and efficient methane production with biogas residue being reduced
greatly.
Keywords: Food waste; Advanced anaerobic digestion; Suspended solid; Expanded granular sludge bed;
Enzymatic pretreatment.
1 Introduction
Food waste (FW) contains a large amount of organic
matters and has a good potential for methane production via
anaerobic digestion (AD). However, the poor systemstability and the low methane production efficiency are thetwo main problems [1]. In order to get an advanced AD(AAD) to improve the efficiency of methane production
from FW, many pretreatment methods were performed to
improve the hydrolysis rate. Among them, enzymatic
pretreatment performs well with the best hydrolysis effect[2]. It can convert a mass of suspended solids (SS) to
soluble chemical oxygen demand (SCOD) [3], and also
results in an obvious reduction of solid content of FW toimprove methane production with a higher organic loading
rate (OLR) of the AD reactor.
Because of the high SS concentration of FW as afeedstock, continuous stirred tank reactor (CSTR) is the
only used reactor to treat FW in practice, with a low OLR of
2~4.5 g VS/L.d and a long hydraulic retention time (HRT)of 15~30 d [2]. With the solid organic matters in FW
hydrolyzed enough by enzymatic pretreatment, it could be
expected to treat FW by using an efficient anaerobic
digestion reactor (such as an expanded granular sludge bed(EGSB) reactor). Furthermore, a higher biodegradation of
FW pretreated with enzyme will result in a higher
ammoniacal nitrogen and alkalinity production in the AAD
system, which can help much to address the acidification
problem [4, 5].
The objectives of this study were: 1) to investigate thehydrolysis and degradation performance of enzymatically
pretreated FW via batch AD experiments; 2) to predict the
SS accumulation rule in an EGSB reactor by mathematicalsimulation, and to study the feasibility of AAD of FW using
an EGSB reactor; and 3) to investigate the methane
production and reactor performance of an EGSB reactor
treating FW pretreated with enzyme.

2 Materials and methods
2.1. Batch anaerobic digestion of FW
To enzymatically pretreat FW, fungal α-amylase andglucoamylase were added with the dosage of 32 U/g FW,
acid protease was added with the dosage of 50 U/g FW.
Then, pretreatment was performed in a water bath at 55 °C
for 8 h. The batch AD experiments were set up in serum
bottles, including three groups: only sludge, control with
unpretreated FW, and enzymatically pretreated FW. The
F/M ratio was set at 0.6, and the pH was adjusted to an
initial value of 7.5. An air bath oscillator was used tomaintain the temperature at 35 ±1 °C for 34 days.

2.2. EGSB reactor operation
Methane conversion of enzymatically pretreated FW was
carried out using an EGSB reactor (with an effective
volume of 6 L) as shown in Fig. 1. The experiment was
divided into two phases. During phase I (day 0 to day 27),
the OLR was increased from 1 to 20 g chemical oxygen
demand (COD)/L.d by increasing the COD concentration of
the influent using the supernatant of the enzymatically
pretreated FW. While during phase II (day 28 to day 164),
the enzymatically pretreated FW was fed directly. The
temperature of the reactor was maintained at 35 ±1 °C.

Fig. 1. The flow chart of the EGSB reactor.

50

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University----- with Perspective of Global Safety
3 Results and discussion
3.1. Batch anaerobic digestion of enzymatically
pretreated FW to predict the feasibility of FW AAD
using an EGSB reactor
The characteristics of the control and pretreated FW for
batch experiments are shown in Table 1.
Table 1 Characteristics of the control and enzymatically
pretreated FW.

4 Conclusions
The feasibility and performance of AAD of enzymatically
pretreated FW in an EGSB reactor was investigated usingbatch experiments and long-term reactor operation.
Simulation study based on results of batch experiments
indicated that the enzymatically pretreated FW could betreated using an EGSB reactor. The performance of thebioreactor was demonstrated with different OLRs. At the
OLR of 20 g COD/L.d, the methane yield and COD
removal rate was 0.27 m3 CH4/kg CODre and 88%,
respectively. The significance of this study is to make itpossible to achieve AAD of enzymatically pretreated FW in
an EGSB reactor.

Reference

Enzymatic pretreatment is an efficient way to increase themethane production. On day 34, the methane production of
the control and the enzymatically pretreated FW were 4065
mL and 5212 mL, respectively. The methane production of
the enzymatically pretreated FW was 28.2% higher than thatof the control.
Due to the high hydrolysis of enzymatic pretreatment, theSS of the enzymatically pretreated FW on day 0 was ~49%of the control. The key point of using an EGSB reactor for
FW AAD is to ensure its stable operation with a suitable SS
accumulation. According to a mathematical simulation
based on the SS reduction results of the batch AD
experiments, the total accumulated SS were 25.1 g/L and13.2 g/L, respectively for the control and the pretreatedgroup. In addition, there would be a balance between thedaily discharge and the daily growth of microorganisms forthe enzymatically pretreated FW, i.e., both 1.6 g VSS/L.d.
This indicates that the enzymatically pretreated FW might
be beneficial to an EGSB reactor.

3.2. The performance of an EGSB reactor treating FW
pretreated by enzyme
The OLR of the reactor was achieved to be 20 g COD/L.d(~15.4 g VS/L.d) with a COD removal rate around 90%.
The biogas production rate and the methane yield was 7.8 ±0.1 L/L.d and 0.27 m3 CH4/kg CODre, respectively. In
addition, the proportion of methane was mostly in the rangeof 60–64%. The VFA maintained at 380.6 mg/L at 20 g
COD/L.d of OLR. The balance between VFA and NH4+-N
kept the pH in the range of 7.6–7.8. The EGSB reactor canthus achieve a well buffering performance and a long-term
stable operation for AD of enzymatically pretreated FW.
The concentrations of MLSS and MLVSS increased from36.6 and 32.5 g/L to 59.6 and 53.6 g/L, respectively.
Therefore, the daily SS growth rate was less than 0.3 g/L.d.
Based on COD balance calculation, 81.1% of the total
COD was converted into methane, meaning that an AAD of
FW was obtained. The biomass COD was 1.8%, indicating
most of the solid organics were degraded with a very low
SS accumulation. About 6.8% of the total COD cannot be
accounted due to sampling and test errors.

51[1] M. Ye, J. Liu, C. Ma, Y.-Y. Li, L. Zou, G. Qian and Z.P.
Xu, Improving the stability and efficiency of anaerobic
digestion of food waste using additives: A critical review, J.
Cleaner Prod., 192 (2018), pp. 316-326.
[2] C.M. Braguglia, A. Gallipoli, A. Gianico and P.
Pagliaccia, Anaerobic bioconversion of food waste into
energy: A critical review, Bioresour. Technol., 248 (2018),
pp. 37-56.
[3] E.U. Kiran, A.P. Trzcinski and Y. Liu, Enhancing thehydrolysis and methane production potential of mixed foodwaste by an effective enzymatic pretreatment, Bioresour.
Technol., 183 (2015), pp. 47-52.
[4] N. Gu, J. Liu, J. Ye, N. Chang and Y.Y. Li, Bioenergy,
ammonia and humic substances recovery from municipal
solid waste leachate: A review and process integration,
Bioresour. Technol., 293 (2019), pp. 122159.
[5] L. Zhao, C. Su, S. Chen, Z. Ye, X. Wei, T. Yao, G. Li
and P. Wang, Expanded granular sludge blanket reactor
treatment of food waste at ambient temperature: Analysis ofnitrogen compositions and microbial community structure,
Bioresour. Technol., 294 (2019), pp. 122134.

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th~ 14th, 2020)

Tohoku University----- with Perspective of Global Safety

Biochar sustained high-efficient anaerobic co-digestion by enhancing
direct interspecies electron transfer and alleviating thermodynamic
restriction
Yaqian Liu1, Qian Li1,2*, Rong Chen1,2
1

Key Lab of Environmental Engineering, Shaanxi Province, Xi'an University of Architecture andTechnology, No.13 Yanta Road, Xi'an 710055, PR China.
2International S&T Cooperation Center for Urban Alternative Water Resources Development, Key
Laboratory of Northwest Water Resource, Environment and Ecology, MOE, Xi'an University ofArchitecture and Technology, No.13 Yanta Road, Xi'an 710055, PR China.
*
E-mail: Qian.LI@xauat.edu.cn (Qian Li)

Abstract
To investigate the kinetic and thermodynamic effects on the stimulating effect of biochar in thermophilic
co-digestion process of food waste (FW) and waste activated sludge (WAS), a series of batch experiments werecarried out. This study confirms that compared with control group, the sustained and high-efficiency promotion ofbiochar is mainly due to the effect on syntrophic methane production. Further, biochar alleviated the restriction ofhigh concentration hydrogen to the main pathway of interspecies hydrogen transfer (IHT), significantly expanded
the thermodynamic reaction window of the syntrophic oxidation process. And in terms of electron transfer
efficiency, based on the redox capacity of biochar, the equivalent current of the maximum direct electron transfer(DIET) flux obtained is 1.1×10-4A, which is 108 and 104 times than that of IHT and conductive electron transfer
process respectively. In addition, for the suspended sludge system, the hydrogenotrophic methanogens wereenriched as the main microorganism, while on the surface of biochar, main functional microorganism
Methanosarcina is likely to be a participant of DIET, which indicates the synergistic effect of biochar surface
through DIET alleviated the direct adverse impact of on the IHT path, thereby improving the stability andefficiency of the system.
Keywords: Anaerobic co-digestion, Organic wastes, Thermodynamics, Biochar, Direct interspecies
electron transfer.
1 Introduction
Thermophilic anaerobic digestion (AD) process (55-60 ℃)
to be considered a highly-efficiency strategy, but for
high-loading easily degradable organic matter, the hightemperature aggravated the rate imbalance between thehydrolysis and syntrophic oxidation methanogenesis stage,
resulting in the large accumulation of volatile fatty acids(VFAs) accompanied by the high concentration of hydrogen,which inhibits the interspecies hydrogen transfer (IHT)
process, it is further to result in the system collapse.
Biochar (BC) added into AD systems for energy recovery
enhancement has been widely confirmed, in recent years,
researchers revealed that the redox-active organic functionalgroups to make BC act as electron transfer mediators, andBC can selectively enrich functional microbes located on
the surface and protected them from high-loaded substrate.
However, researchers more often than not paid more
attention to the impact of BC on the degradation process ofdifferent substances, but ignored the promotion of the
mitigation of thermodynamic. In fact, studies have shownthat the maximum reaction rate of the syntrophic oxidation
bacteria of acetate is much faster than that of acetoclastic
methanogen, thus, the syntrophic oxidation process willshow a faster reaction rate when the thermodynamic
constraints are alleviated.
Therefore, in the long-term experiment, whether the
microorganisms enriched on the surface of BC can
influence the thermodynamic window is a valuable problem

52

for revealing the action mechanism of biochar, and on thisbasis, the coupling effect of different paths on electrontransfer efficiency and microbial community is also worthyof further investigation.

2 Materials and methods
All series of batch experiments were conducted using
120mL serum bottles on the thermophilic condition. The
organic wastes used in the co-digestion test were a mixtureof food waste (FW) and dewatered activated sludge (DAS)with the ratio of 1.5:1. BC was made of sawdust, which
pyrolyzed in a 500℃ muffle furnace. The concentration ofindividual VFA (acetate, propionate) was 2500mg-COD/L.
Related parameters and electrochemical characteristics of
BC were measured, and by high-throughput DNA
sequencing techniques to analyze the microbiology
community structure.

3 Results and discussion
Compared with the control group, the addition of BC
obviously promoted the degradation of the three-period
co-digestion process. What's more important is the pureeffect of BC has different promoting effects in multi-period
after removing the effects of microbial domestication(Fig.1). This means it can relief the rate imbalance ofhydrolysis and syntrophic oxidation and become the main
reason for the long-term application of BC in complex
co-digestion systems.

The 8th International Symposium on Water Environment Systems

GP-RSS & SyDe

(Nov. 13th~ 14th, 2020)

Tohoku University----- with Perspective of Global Safety

Foodwaste and waste activated sludge(Car bohydr ates, proteins, lipids, ect)

Per iod

1→2

2→3①Hydrolysis:

17%

5%

②Acidogenesis: 33%

12%③Acetogenesis: 13%38%

More notably, in the archaeal community of the biofilm on
BC, the microbial population distribution was unique,especially for Methanosarcina, it became the most abundant
genus (49.6%), which was 6.9 times than that in thesuspended sludge.

Soluble or ganic molecular(Sugar s, amino acids, ect)(a) Bacter ia

Volatile fatty acid
(Propionate, butyr ate, ect)100%(b) Archaea
100%

Defluviitoga

Methanobacterium④Methanogenesis: 15% 29%
CH 4Fig. 1. Promotion effect of biochar on conversion rateof the four stages in three periods.
Fig. 2 shows the degradation process of acetate andpropionate. In the first period, when BC was added, thedegradation rate of acetate and propionate was observed toincrease significantly. In the second period, the environment
was under a condition of high hydrogen concentration, sothat IHT was restrained completely. Methane production in
the control group was almost fully suppressed, but when BC
AcetateAcetateAcetatewas present, VFAsAcetate
completed the conversion to methane,
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rate of acetate
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oxidizers and methanogens via
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VFAs
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3000
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3000
0
3000
Period-2
500
3000
Period-1
Period-2
3000
3000
3000
0
0
Period-2
500
3000
3000
Period-1
500
00 00 Period-1
Period-2
Period-2500
Period-1
30000 0 Period-1
Period-1
Period-2
Period-2
Period-1
500
Period-2
Period-2
Period-1
Period-1
Period-2
Period-2
Period-2
Period-1
Period-1
Period-1
Period-2
Period-2
Period-2
Period-1
Period-2
Period-2
Period-2
Period-2
Period-1
Period-1
Period-2
Period-1
Period-1
Period-1
Period-1
1015205Period-1
1015
Period-1
Period-1
Period-1
Period-1
Period-2
5Period-2
10152020 25003000
5Period-1
15 00000 555Period-2
Period-2
Period-2
Period-2
Period-2
Period-2
Period-1
Period-1
Period-1
Period-2
Period-2
Period-1
Period-1
Period-1
Period-1
1520
101515 15
2500
5
10
15
0
5
10
Period-1
Period-2
Period-2
10
15 20
20 20
1010
2500
Period-2
Period-2
Period-2
Period-1
Period-1
Period-1
Period-1
10
15
20
0
5
10
15
Period-1
2500
510
10
15
10
15
20
0
5
10
15
Period-2
Period-2
2500
2500
Period-1
0000 005555 Period-2
150000 0 55Period-1
10
2500
0
5
10 15 20 2500
5
2500
2500
3000
2500
10
15
20
Acetate55Period-1
10
2500
3000
2500
2500
2500
2500
2500
2500
2500
510
10
15
20
55Period-1
101515
15
3000
3000
2500
2500
3000
3000
2500
2500
2500
0000000
2500
2500
3000
2500
3000
3000
3000
3000
2500
3000
2500
2500
2500
3000
2500
2500
3000
3000
0000 00 0
Time
(d)
3000
3000
3000
Time
(d)
Biochar
added
gr
oup
3000
3000
3000
2500
2500
3000
Contr
ol
gr
oup
Biochar
added
gr
oup
3000
3000
ol
gr
oup
3000
Time
(d)
3000
Biochar
added
gr
oup
3000
3000
Biochar
added
gr
oup
3000
3000
Contr
ol
gr
oup
Biochar
added
gr
oup
Time
(d)
Contr
ol
gr
oup
Contr
ol
gr
oup
3000
Time
(d)
Biochar
added
gr
oup
3000
3000
Contr
ol
gr
oup
Biochar
added
gr
oup
Biochar
added
gr
oup
Acetate
ol
oup
Contr
ol
gr
oup
0ol
5oup
10
15
20
5Period-1
10
15
Time
(d)
AcetateBiocharadded
oup
Period-2
5Period-2
10152020 2000
5Period-1
153000
Biochar
gr
oup
Contr
ol
gr
oup
Biocharadded
oup
AcetatePeriod-2
Contr
ol
oup
2000
Contr
gr
AcetateAcetatePeriod-2
Period-200added
5gr
10
15
20
000 0Period-1
5Period-1
1015
Period-1
0gr
5Period-2
10 15
1520
10
Period-2
2000
Period-1
Biochargroup
Period-1
0added
5gr
10
15 20
20 20
5Period-1
1015
Period-1
AcetatePeriod-2
Contr
gr
Pr
opionate
0ol
5oup
10
000000 Period-1
5Period-1
10
15
Acetate2000
AcetatePeriod-2
Period-2
Period-2Propionate
Period-2000gr
5Period-2
10152055Period-1
15
2000
Period-1
0
5
10
15
5
10
15
Period-1
AcetatePeriod-2
2000
Period-2
2000
AcetateAcetatePeriod-2
Period-1
Period-2
2000
0
5
10
15
2000
0
5
10
15
Period-2
5
10
15
20
5
10
opionate
Period-2
2000
Period-1
2000
Period-1
5
10
15
20
5
10
15
Period-1
2500
Period-1
Period-2Propionate
2000
2500
2000
2000
AcetatePeriod-2
2000
2000
Period-2Propionate
2000
2000
Period-2
Period-1
Period-1
0
5
10
15
20
0
5
10
15
Period-1
2000
Period-1
2500
2000
2000
3000
2500
2500
2000
2500
2000
2000
3000
Period-2
opionate
2000
2000
2000
2500
2500
Period-1 Time
2000
2500
2500
3000
2500
2000
2000
2000
2500
3000
2500
2000
2000
3000
3000
2500
2500
3000
2500
2500
2500
2500
2500
3000
3000
(d)
2000
2000
3000
3000
2500
2500
2500
3000
Time
(d)(d)
2500
2500
3000
2500
3000
2500
3000
3000
3000
2500
3000
2500
3000
Time
(d)
2500
2500
2500
3000
Time
(d)
3000
3000
3000
2500
2500
3000
3000
Time
(d)
3000
Period-1
Period-2
3000
Period-2
Period-1
Period-2
3000
2500
3000
Period-1
Time
Period-2
3000
Period-1
Period-1
Period-2
Period-2
3000
Period-2
Period-1
1500
Period-1
Period-1
Period-2
Period-2
Period-1
1500
Period-2
Period-1
Period-1
Period-1
Period-2
Period-1Pr
Period-2
Period-2
Period-1
Period-2
Period-2
Period-2
Period-1
Period-1
Period-2
Period-1
Propionate
1500
1500
Period-1
Period-2
Period-1
Period-1
Propionate
Period-2
Period-1
Period-2
1500
Period-1
Period-2
1500
Period-2
1500
Period-1
Period-1
Period-2
1500
1500
Period-1
Period-1
1500
Period-2
Period-1
1500
Propionate
1500
Period-2
1500
Period-2
Period-1
Period-2
1500
Period-1
opionate
1500
1500
Period-1
1500
Period-1
Period-1
Propionate
1500
1500
1500
2000
1500
2000
2500
Period-2
1500
2000
2500
1500
1500
1500
Period-1
Propionate
1500
2000
1500
1500
1500
2000
1500
1500
2000
2000
2500
2500
2000
2500
2000
2500
2000
2500
2000
2000
2500
2000
2000
1500
1500
2500
2000
2000
2500
2500
2000
2500
2000
2000
2000
2500
2000
3000
2000
2500
2000
2500
3000
2500
2000
2500
3000
2500
2000
3000
2500
2000
2000
2500
2500
2000
2500
2000
2500
3000
2500
3000
2500
3000
2500
3000
2000
2500
3000
2500
2500
3000
3000
2500
1000
3000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1500
1500
2000
1000
1500
1500
2000
1000
1000
1000
1000
1000
1000
1000
1000
1500
1500
2000
2000
1500
1500
1500
1500
1500
1500
1500
2000
2000
2000
1500
1500
2000
1000
1000
1500
2000
1500
2000
2000
1500
1500
1500
1500
1500
2000
2000
2000
1500
1500
1500
1500
2000
2500
2500
2000
1500
1500
2000
2000
2000
1500
2000
2500
2000
2000
1500
1500
1500
2500
2000
2000
2500
2500
2500
2000
2000
1500
2000
2000
2500
2500
2000
2500
2500
2000
500
2500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
1000
500
1000
1000
1500
1000
1500
500
500
500
500
500
500
500
500
1500
1000
1000
1000
1000
1000
1500
1500
1000
1000
1000
1000
1500
1500
1500
1000
1000
500
500
1000
1000
1500
1500
1500
1500
1000
1000
1500
1000
1000
1000
1500
1500
1000
1000
1000
1000
2000
2000
1000
1000
1500
1000
1500
1500
1500
1500
1500
1000
1500
1500
1500
1000
1000
2000
2000
2000
2000
2000
1000
1500
1500
1500
1500
1500
2000
2000
2000
2000
1500
2000
000500000000000000
00000000
00500
500
500
500
1000
0
1000
500
500
1000
500
500
1000
500
500
500
1000
1000
1000
500
0
1000
500
500
500
500
500
1000
1000
1000
500
1000
500
1000
1000
500
500
500
500
500
1000
500
500
1500
500
500
1500
1000
1000
1000
500
10
15
1000
10
15
1000
500
1000
1000
1000
1000
10
15
20
10
15
20
1500
500
500
10
15
1500
10
10
15
1500
500
00000000000000 555555555555555 10
1500
1500
1000
10
15
20
15
20
15
1000
1000
000000000000000000 5555555555555510
15
20
10
20
10
15
10
10
15
015
15
20
15
1500
1000
10
15
20
10
15
1500
10
20
10
1500
10
10
15
10
15
1500
15
20
10
15
15
20
10
15
20
10
15
15
1010
1515
2020
10
20
10
1515
1015
15
15
20
015
15
20
15
20
00000000000555555555510
15
15
1000
10
20
20
10
15
1500
510
10
15
20
10
15
00000 00050555555 510
15
20
10
15
20
10
15
10
15
10
15
20
15
20
10
15
10
15
10
15
510
10
15
20
10
15
20 1000
510
10
10
15
10
20
10
10
15
510
10
15
20
0
5 10
10
15
20
10
00 555 10
10
15
Time
(d)
Time
(d)
Time
(d)
00500
Time
(d)
Time
(d)
500
500
Time
Time
(d)
0500
(d)
500
Time
(d)
Time
(d)
500
00500
00500
Time
(d)
Time
(d)
500
500
500
Time
(d)
Time
500
Time
(d)
Time
Time
(d)
Time
(d)
0500
0000000000
000000
0
Time
(d)
500
500
0
500
Time
(d)
Time
(d)
Time
(d)
Time
(d)
Time
0
0
0
500
500
500
1000
1000
Time
(d)
Time
(d)
Time
(d)
Time
(d)
500
500
500
1000
1000
Time
(d)
Time
(d)
500
500
500
1000
1000
1000
5000000 20
500
500
1000
1000
1000
500
1000
500
Pr
opionatePr
opionate
1000
520
10
15
20
5520
10
15
20
520
10
15
520
10
15
40
60
0opionate
2040
6000000000 520
PrPr
opionate
20
40
60
Pr
opionate
204015
6000opionate
40
60
40
60
520
10
15
20
5520
15
20
5520
10
15
5 10
10
15
15
20
0
520
40
60
0opionate
40
60
40
60
40
60
Pr
opionate
Pr
40
60
40
60
Pr
10
15
20
520
10
15
20
520
15
520
10
15
520
10
15
20
520
15
20
520
10
15
520
10
15
10
20
10
15
40
60
20
40
Pr
Pr
60
40
60
10
15
2020
0000 000520
15
20
10
15
10
15
20
10
15
0000000000 520
15
40
60
40
60
40
60
40
60
40
60
015
40
60
40
60
20
40
60
10
20
5 10
10
15
20
520
10
15
10
15
40
60
0opionate
20
40
Pr
60
55 520
10
15
20
060
5520
15
20
55520
10
15
5520
10
15
40
60
40
60
40
60
015
40
60
40
60
40
60
40
60
00opionate
20
40
60
520
10
15
20
10
15
10
15
20
5
10
15
10
15
10
15
0520
510
10
15
20
510
10
40
40
60
20
40
60
20
40
60
0 0000000000 20
3000
3000
510
10
15
20
510
10
40
60
40
60
20
40
60
00(d)
20
40
60
0500
3000
3000
3000
3000
3000
03000
Time
(d)
Time
(d)
00000000000000 520
3000
3000
3000
3000
Time
(d)
Time
3000
3000
3000
3000
3000
00000000
3000
3000
500
3000
3000
3000
Time
(d)
500
3000
500
Time
(d)
Time
Time
(d)
Time
(d)
Time
(d)
000500
3000
3000
Time
(d)
Time
(d)
3000
Time
(d)
Time
(d)
Time
Time
(d)
Time
(d)
500
0
500
500
3000
3000
500
500
500
Time
(d)
Time
(d)
0
Time
(d)
Time
(d)
Time
(d)
Time
(d)
0
500
5520
10
15
20
5520
10
15
000opionate
520
10
15
20
555552010
1500opionate
152000000000000 520
15
20
10
15
00000000 520
10
15
20
10
15
5555 1020555 10
10
15
5520
20
10
15
555 510101520
5 55510
10
15
015
15
20
0 0 520
55520
10
15
10
20
10
15
0015
15
20
15
10
15
20 20
10
15
520
10
15
20
10
15
10
20
10
15
Pr
Pr
opionate
10
15
20
10
15
4015
60
40
60
40
60
10
15
20
10
15
40
60
0000000 0020
15
20
10
15
Pr
opionate
10
15
20
10
15
Pr
40
60
Pr
opionate
40
60
40
60
40
60
510
10
15
10
Pr
Pr
opionate
15
10
15
05520
540
10
15
20
10
Pr
opionate
2500
0 20
20
40
60
2500
20 10
401515
60
0520
510
10
15
20
10
40
60
4010
60
Pr
opionate
40
60
40
60
Pr
opionate
20
40
60
2500
00ate
40
60
2055204060
40600000000000000020
60
015
60
2500
Pr
opionate
2500
2500
Butyr
ate
Butyr
ate
Butyr
ate
2500
Butyr
2500
20540
40
60
0(d)
20
40
60 20
2500
2500
2500
2500
2500
2500
40
60
0(d)
20
40
60
2500
2500
2500
Butyr
ate20
2500
2500
0000000
Butyr
ate
2500
2500
Time
3000
Time
(d)
3000
2500
2500
3000
Time
(d)
2500
2500
3000
Time
(d)
3000
Time
2500
2500
3000
Time
(d)
3000
3000
Time
(d)
3000
0000 00 0
3000
3000
Time
(d)
3000
3000
3000
3000
Time
(d)
Time
(d)
3000
Time
(d)
3000
3000
Time
(d)
3000
Time
(d)
Time
(d)
3000
Time
(d)
3000
3000
Period-1
Period-2
3000
Period-1
Period-2
3000
Period-1
Period-2
Period-1
Period-2
Period-1
Period-2
3000
Period-1
Period-2
Period-1
Period-2
3000
3000
Time
(d)
Period-2Time(d)Period-1
Period-2
Period-1
3000
Period-2
Period-1
Period-1
Period-2
Period-2
Period-1
Period-2
Period-1
Period-2
Period-1
Period-2
Period-1
Period-2
Period-1
Period-1
Period-2
Period-1
Period-2Pr
Period-2
Period-1
Period-1
Propionate
Period-1
Period-2
Period-1
Period-1
opionate
Period-1
Period-2Propionate
Period-2
Period-2
20
40 Pr
60
Period-1
Period-2Propionate
20
40
60
Period-2204060
Period-2
0opionate
20
40
60
20
40
60
Period-1
Period-2Propionate
0000 202040
60000000 20
4060
Period-2
0opionate
40
Period-2
Period-2
Period-1
Period-1
Period-1
Period-1
2000
Propionate
2000
Period-1
Propionate
2000
Period-1
2000
Period-2Propionate
2000
20
40
60
Period-2
20
40 60
60
20
4040
600060
20
40
60 60
Pr
2000
20
40
60
20
40
60
20
40
60
20
40
60
20
40
60
00 002020
40
60
Period-2
20000000 00 20
40
60
Period-2
Period-1
2000
Period-1
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2040
4060
60
20
40
60
2000
2000
20
20
40
2000
2000
Butyr
ate
2000
Butyr
ate
Butyr
ate
Butyr
ate
2500
2500
2000
2000
2500
2000
2000
2500
3000
2500
3000
2500
3000
2000
3000
2000
2500
3000
2500
3000
2500
2500
3000
3000
Butyr
ate
3000
Butyr
ate
2500
3000
2500
3000
2500
2500
3000
2500
3000
2500
2500
2500
2500
3000
2500
3000
2500
2500
2500
3000
2500
3000
Time
(d)(d)
Time
(d)
3000
3000
Time
(d)
Time
(d)
3000
3000
2500
2500
2500
2500
3000
3000
3000
3000
Time
(d)
Time
3000
3000
3000
3000
3000
3000
3000
3000
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
2000
2000
2000
2000
1500
1500
2000
Butyr
ate
2000
1500
1500
2500
Butyr
ate
2500
2000
Butyr
ate
2500
1500
1500
2500
Butyr
ate
1500
1500
2500
2000
2500
2000
2500
2000
2000
2500
2500
2500
2500
2000
2500
2000
2500
2000
2000
2000
2000
2000
2000
2500
2500
2000
Butyr
ate
2000
2000
2000
2500
Butyr
ate
2500
2000
2500
2000
2500
2500
2500
2000
2000
2500
2500
2000
2000
2500
2500
2500
2500
2500
3000
2500
3000
2500
3000
3000
3000
2500
3000
3000
2500
3000
2500
3000
2500
3000
3000
1000
3000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1500
1500
1500
1500
1500
1500
1000
1000
2000
2000
2000
1500
1000
1000
2000
1000
1000
2000
2000
1500
1000
1000
1500
2000
1500
1500
2000
2000
2000
1500
1500
1500
2000
2000
2000
1500
1500
1500
2000
1500
1500
1500
1500
2000
2000
1500
1500
2000
1500
2000
2000
1500
2000
2000
2000
1500
1500
2000
2000
2000
1500
1500
2000
2000
2000
2500
2000
2500
2000
2500
2500
2500
2500
2000
2500
2500
2000
2000
2500
2000
2500
2500
500
2500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
1000
500
500
500
1000
500
1000
500
1000
1000
1500
1000
1500
1000
1500
500
500
1000
1500
1500
500
500
1000
1500
1500
500
500
1000
1500
1500
500
500
1000
1500
1000
1000
1500
1000
1000
1000
1500
1500
1000
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AcetatePeriod-1
150△G 0' (KJ /mol)

0-50Period-2

-100

50
0
Biochar added-50

Control-100

4H2+HCO3-+H+=CH4+3H2O

4H2+HCO3-+H+=CH4+3H2O

-150

-150

-4

-2

0

logP H 2

2

4

-4

-2

0

2

unclassified

Clostridium III
40%

Caloramator
Syntrophomonas
Hydrogenispora

20%

Methanothermobacter
Methanospirillum
Methanolinea

Methanosphaerula

40%

Methanomethylovorans
Methanomassiliicoccus

20%

unclassified

Tepidanaerobacter

o fi

lm

on

othersSyntrophothermus

0%Bi

Methanothrix

60%

c
b io
Su

h

sp

ar

en

de

d

d
s lu

ge
Co

ntr

o

ro
lg

0%

up
Bi

ofi

lm

o

io
nb

Su

a
ch

sp

r

en

de

lu
ds

e
dg
Co

nt

g
rol

p
rou

Fig. 4. Comparison of bacteria (a) and archaea (b)relative abundance differences with control group,suspended sludge, and biofilm on biochar.
The abundances of functional enzymes involved in the
propionate metabolism, carbon reduction and acetate
decarbonxylation pathways of three systems were analyzed(Fig.5). It should be noted that acetate kinase (EC: 2.7.2.1)
and phosphotrans acetylase (EC: 2.3.1.8) showed higher
relative abundance in the sample of biofilm on biochar
whereas acetyl-CoA synthase (EC: 6.2.1.1) was main
enzymes in control group. Also, informylmethanofuran
dehydrogenase (EC: 1.2.7.12) and coenzyme F420
hydrogenase (EC: 1.12.98.2) were enriched on the surfaceof BC, of which coenzyme F420 hydrogenase is involved in
the last step of the conversion of CO2 to CH4.

6.2.1.17>60

1.2.7.12

6.4.1.1

50-55
45-50

CH3CH2COO- +3H2O=
CH3COO- +HCO3- +H++3H2

100

50

others60%

Methanosarcina

55-60

150

CH3COO- +4H2O=
2HCO3- +4H2+H+

100

Fervidobacterium80%

2.3.1.101

Propionate

Period-1

Period-2

Anaerobaculum

6.2.1.1

Fig. 2. Variation of individual VFA when biochar waspresent or absent.
From Fig.3, the addition of BC significantly expands thethermodynamic windows both for acetate and propionate.
Specifically, whether or not to add BC is very different forTime(d)(d)
Time
(d)
(d)
Time
(d) energy in theTimethe changes of H2 and the actual GibbsTime
freesyntrophic oxidation process. For propionate, BC may
perform as a buffer to make the hydrogen content was 0.08%
lower than control group, which made the overall actual
Gibbs free energy was favorable (-51~-84KJ/mol to-64~-87KJ/mol) and the reaction rate faster. In addition,with some assumptions, we got that the maximum carrier
flux transferred through the redox reaction was 108 and 104
times than that of IHT and the conductivity mechanism
respectively. Therefore, in the case that high hydrogenpartial pressure inhibits IHT, BC as an efficient electron
carrier through redox ability, expand the thermodynamic
window and promote the degradation of VFAs.
△G 0' (KJ /mol)

VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)

VFAs(mg-COD/L)(mg-COD/L)
VFAs
VFAs
VFAs
(mg-COD/L)(mg-COD/L)
VFAsVFAs
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAs(mg-COD/L)
VFAsVFAs
VFAs(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAsVFAs
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs

VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)

VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAs
VFAs
(mg-COD/L)
VFAs(mg-COD/L)
VFAs(mg-COD/L)(mg-COD/L)
VFAs
VFAs
(mg-COD/L)

3000
3000
3000
3000
3000
3000
3000
3000
3000
Period-2
Period-1
3000
Period-2
Period-1
Period-2
Period-1
3000
3000
Period-2
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2500 Period-1
2500
2500
2500
2500
2500
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2500
2000
2000
2000
2000
2000
2000
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2000
2000
2000
2000
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
500
500
500
500
500
500
500
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500
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50050000
0000000000
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0 0 5 5 10
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3000
3000
3000
3000
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3000
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Period-2
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Period-1
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Period-1
Period-2
Period-1
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500
500
500
500
500
500
500
500
500
500
500
1000
500
1000
500
500
500
500
1000
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500
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000000
0500
0500
0500
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0500
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500
0500
500
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10
15
20
10
15
10
15
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10
15
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10
15
20
15
10
15
20
10
15
10
15
10
15
10
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0000000000000000 55555555555555510
2000000000000000000055555555555555555510
10
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1015
1520
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10
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10
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15
20
10
15
10
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10
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000000000000
Pr
opionatePr
opionate
opionatePr
opionate
20
40
60
20
40
60
Pr
0opionate
20
40
60
Pr
000 2020
40
60
opionate
Pr
20
40
60
20
40
60
Pr
opionate
20
40
0opionate
20
40
60
Pr
opionate
2040
6000opionate
40
60
Pr
Pr
20
40
60
Pr
2040
40Pr
60
0opionate
20
40
60
20 40
40 60
60
000000000000 20
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0opionate
60
Pr
Pr
20
40
60
20
40
60
20
40
60
20
40
60
Pr
opionatePrPr
opionate
20
40
60
0
40
60
Pr
opionate
20
40
60
20
40
60
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
2500
2500
Butyr
ate
Butyr
ate
2500
2500
Butyr
ate
2500
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2500
2500
2500
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ate
2500
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80%

The relative abundance

AcetateThe relative abundance

Coprothermobacter
H 2+CO 2

4

logP H 2

Fig. 3. Changes of thermodynamic windows whenbiochar was present or absent.

53

6.4.1.3

1.2.7.1

40-45

3.5.4.27

35-40
30-35

6.2.1.1

2.3.1.8

25-30

2.1.1.86

5.1.99.1

20-25

1.12.98.2

1.5.99.9

3.1.2.1

10-15

2.8.4.1
5.4.99.2

6.2.1.1

15-20

<10

2.7.2.1

Relative
abundance(%)

1.1.1.37
6.2.1.1

6.2.1.1

1.3.5.1
3.1.2.1
4.2.1.2

2.7.2.1

2.3.1.8
Biofilm on
biochar

SuspendedsludgeControl group

Fig. 5. Comparison of functional enzymes involved in
the propionate metabolism, carbon reduction andacetate decarbonxylation pathways.

4 Conclusions
The sustained and effective stimulation of BC is mainly
manifested in the process of syntrophic methane production,
while the reason for this may be due to that the surface ofBC triggers a electron transfer pathway like DIET, which
accelerates electron transfer, and more importantly, theenriched Methanosarcina may through synergistic action
alleviate the inhibition effect of high hydrogen partialpressure on IHT and hydrogenotrophic pathway, to makethe thermodynamic window expanded and promote the AD
process more efficiently.

The 8th International Symposium on Water Environment Systems(Nov. 13th ~ 14th,2020)----- with Perspective of Global Safety

GP-RSS&SyDe
Tohoku University

Effect of Thermal Pretreatment on Digestion of Sewage Sludge in An
Anaerobic Membrane Bioreactor○ Guangze GUO1*, Yemei LI2, Hui CHENG2 & Yu-You LI 1,2
1

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-8579, Japan.

2Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*

E-mail: guo.guangze.t1@dc.tohoku.ac.jp

Abstract
The treatment of sewage sludge has become an important environmental issue all over the world. Anaerobic
digestion is a simple method that converts chemical energy from sewage sludge into biogas like methane as a
substitute for fossil fuels in contemporary society while eliminating pathogens and eliminating odors. According
to the previous study, hydrolysis becomes a rate-limiting step in the anaerobic digestion process due to thecomplex floc structure of the sewage sludge (eg, extracellular polymer) and hard cell walls. In order to accelerate
the hydrolysis process and increase the efficiency of anaerobic digestion, a thermal pretreatment method is applied
to carry out the solubilization reaction to decompose the organic matter in the sludge. This study explored theeffects of thermal treatment on sludge properties, methane production efficiency, and membrane reactor
performance. Current results showed that thermal pretreatment had a more obvious influence on the property ofexcess sludge than on that of primary sludge.
Keywords: Thermal pretreatment; Sewage sludge digestion; AnMBR.
1 Introduction
With the increase in sewerage penetration rate, the amountof sludge generated is increasing to a large extent (about 75
million tons per year). There is scientific evidence thatthe application of converting sewage sludge to fuels
provides a series of agronomic benefits, in particular the
reducing of greenhouse gas emissions such as carbon
dioxide, and construction of a resource recycling society.
The conventional sewage sludge treatment method has
high energy consumption and treatment cost with a large
amount of excess sludge production. Therefore, it isnecessary to develop and disseminate technologies which
are able to reduce greenhouse gases and produce energy
from sewage sludge. In recent years, the AnMBR
technology (Anaerobic Membrane Bio-reactor) combines
anaerobic digestion and membrane separation method toutilize biomass as a substrate, which can reduce organic
pollutants and recover energy at the same time.
AnMBR technology provides improved effluent quality,
reliability, high loading rate and removal rate of organic
substances. In order to contribute to the long-term
environmental profitability for society by utilizing AnMBR,
stratgies for promoting decomposition of sewage sludge andmembrane mitigation fouling are expected for research anddevelopment. Moreover, since the excess sludge iscomposed of microbial flocs and bacterial cells, hydrolysis
step is considered to the rate-determining step.
In this study, in order to clarify the characteristics ofhigh-concentration (TS=5%) anaerobic digestion through
AnMBR and the effect of thermal pretreatment, batch
experiment and long term continuous experiment at
mesophilic temperature (35℃) were conducted.

2 Materials and methods
2.1 Biochemical methane potential (BMP) assay
Biochemical methane potential (BMP) assay was carriedout at mesophilic temperature conditions to measure themethane yield of the thermal pretreated sludge samples. 55•••••••• 1 1
54mL of seed sludge and 15 mL of substrate sludge weretransferred into serum bottles with total volume of 120 mL
according to the VS ratio of seed sludge and substratesludge. A blank assay, only filled with 70 mL of inoculum,
was conducted in order to determine the biogas production
from endogenous inspiration of the inoculum. Once thereactors were loaded the headspace was purged withhigh-purity nitrogen gas at the flow rate of 0.5 L/min for 1–
2 min to remove oxygen. After sealed with rubber stoppers
secured by aluminum crimp, the bottles were placed in awater bath (35 ± 1°C) (BT100, Yamato, Japan) undercontinuous shaking (110 ± 1 rpm). Seed sludge was takenfrom AnMBR reactor which continuous operating for long
period. Analysis of the composition of biogas was
performed through a TCD gas chromatograph. The amountof biogas production of various heat-treated samples wasmeasured everyday. The digestion period was 30 days bywhich stage the methane production nearly ceased.
2.2 Configuration and long-term operation of AnMBR
Figure 1. shows a schematic diagram of the hollow fiber
anaerobic membrane bioreactor and equipment used in thisstudy. The AnMBR consists of a substrate tank, CSTR anda membrane unit. The total effective capacity is 15 L. The
membrane material is a tetrafluorinated ethylene resin(PTFE) hollow membrane. The pore size is 0.1 μm and theeffective filtration area is 0.1m2.
In this study, in order to clarify the effect of thermal
pretreatment, operating stages of the reactor were divided
into Stag1 and Stag2. The substrate sewage sludge used in
Stag1 was a mixture of primary sludge and excess sludge(primary sludge: excess sludge = 45%:55%). In Stag2,
Excess sludge was thermal pretreated in a water bath at
70℃ and then mixed with the primary sludge (primarysludge: excess sludge=45%:55%). The substrate was stored
at 4℃ in a mechanically stirred tank and charged into the
CSTR reaction tank 10 times everyday (100 ml each time)by a timer-controlled peristaltic pump. TS concentration of
the substrate was adjusted to 5%, HRT was fixed on 15 days,

The 8th International Symposium on Water Environment Systems(Nov. 13th ~ 14th,2020)----- with Perspective of Global Safety

GP-RSS&SyDe
Tohoku University

and a long-term continuous operation experiment was
conducted at a temperature of 35℃. The effects of reactor
operation stability, biogas production status, organic matterdecomposition and mass balance due to the introduction ofthermal pretreatment were evaluated. The continuous
operation management factors of AnMBR are summarizedin figure 3.

Fig. 1. Configuration of AnMBR
3 Results and discussion

模型
方程
绘图

Methane Production(ml/g-VS)

f*(1-exp(-k*x))
空白f83.24777 ± 2.73862

k0.06667 ± 0.00399

Reduced Chi-Sqr
R平方(COD)

500

NewFunction4 (User)

调整后R平方3.40443
0.99366
0.99334

模型

NewFunction4 (User)

方程f*(1-exp(-k*x))

绘图蒸汽Fig. 3. Variations in pH (a), Alkalinity(b), NH4+-N(c), COD (d) and biogas production (e) during thewhole operation.

400f423.22117 ± 11.22064

k0.30352 ± 0.028

Reduced Chi-Sqr
R平方(COD)

调整后R平方300

模型

957.4972
0.94436
0.94158

NewFunction4 (User)

It was 7.9% higher than the untreated sewage sludge
operation stage. At each stage of AnMBR long-term
continuous operation, the pH is in the weak alkaline rangeof 7.0-8.0, and there is no VFA accumulation. The alkalinityof 4.8 is about 3088 and 3850 mg-CaCO3/L, respectively.
TAN was 1128 and 1145 mg/L. The methane content of thebiogas was stable at about 60%, and it can be said that good
operation was performed.
方程
绘图

200

100

0
0

5

10

15

20

25

60℃

70℃

80℃

Control

70f455.84527 ± 5.27503

k0.31332 ± 0.0114

Reduced Chi-Sqr

160.5496

R平方(COD)

0.99371

调整后R平方

0.99326

4 Conclusions

30

Time(days)
50℃

f*(1-exp(-k*x))

Blank

Fig. 2. Net cumulative methane production fordifferent pretreated sludge samples.
The results of the excess sludge gas generation potential of
the thermal pretreatment sludge at 50℃, 60℃, 70℃, 80℃
were 432.62, 439.75, 455.85 and 478.65 mL/g-VS,
respectively. Gas production potential improved 2.4%, 4.1%,
8.0% and 13.35% comparing with the untreated sample. It
was clarified that the thermal treatment promoted anaerobic
digestion of excess sludge and increased the amount ofmethane gas generation. The best thermal pretreatment
condition is 80°C and the biogas production potential was
478 mL/g-VS, which was an increase of about 13%
compared to the untreated sample. The effect of thermal
treatment on excess sludge is more remarkable as thetemperature higher.
Fig. 3 shows changes in pH, biogas production rate,methane content, TAN concentration, alkalinity and COD
removal rate in the AnMBR reactor during the Stag1 andStag2. The COD concentration of membrane filtration
permeate were 767.5 mg/L and 719.0 mg/L, and the COD
removal rates were 99.14% and 98.35%, respectively. With
the introduction of thermal pretreatment sludge, the gas
generation rate increased significantly to 1.36 L/L/d.

•••••••• 1 2
55(1) Excess sludge showed a remarkable solubilizing effectand methane production potential by thermal pretreatment.
The best thermal pretreatment condition is 80℃ whichsoluble COD concentration and the soluble protein
improved 29.98%, 252.0% and biogas production improved
13.4% comparing with untreated excess sludge.
(2) After tranferring the thermal pretreated excess sludge
into the AnMBR, methane production rate increased 7.9%.
The COD removal rate in the two operation stages is 98%.
(3) During the stage2, the membrane fouling rate increased
to a large extent. The main substance which lead tomembrane contamination is inorganic substances.

Reference[1] Zhen G, Lu X, Kobayashi T, et al. Mesophilicanaerobic co-digestion of waste activated sludge andEgeria densa: performance assessment and kinetic
analysis[J]. Applied energy, 2015, 148: 78-86.
[2] Chen R, Nie Y, Ji J, et al. Submerged anaerobic
membrane bioreactor (SAnMBR) performance on
sewage treatment: removal efficiencies, biogas
production and membrane fouling[J]. Water Science andTechnology, 2017, 76(6): 1308-1317.

The 8th International Symposium on Water Environment Systems

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Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Assessment of the secondary salinization impact to the water resourcesin the Uzbekistan○ Temur KHUJANAZAROV1*, Kristina TODERICH2, Yoshiya TOUGE3, Kenji TANAKA1
1

Disaster Prevention Research Institute, Water Resources Research Center, Kyoto University, Kyoto 6110011, Japan2
Department of Civil and Environmental Engineering, Tohoku University, Tohoku, 980-8579, Japan
3
International Platform for Dryland Research and Education, Tottori University, Tottori, 680-0001, Japan*
E-mail: khujanazarov.temur.7n@kyoto-u.ac.jp

Abstract
Demand for water resources has been increasing over last decades, growing population, anthropogenic andclimate change impacts have increased pressure on natural resources. However, current practises that are
generally applied in Uzbekistan induce mineralization of both water and soils. Agricultural water availability
and its quality has direct impact to the crop yield and are especially important here. Water mismanagement
and overuse exaggerates secondary salinization, the condition when irrigation waters salinize soils andincreases mineralization of itself. This activity further increases pollution of all surface flows and deficit of thedrinking waters to local communities. Defining such regions and reducing these areas impacts to the wholebasin is a subject of this paper discussion. In this research field measurement of both soil conditions and water
quality during irrigation season is presented. Water quality sampling to understand extend of the mineralizationand its distribution in the basin are compared with soil salinity during irrigation of the agricultural fields,
especially in the downstream flow of the Amu Darya and Zeravshan Rivers.
Keywords: salinization; Uzbekistan; water quality;
1 Introduction
Water resources mismanagement has long being one of themain issues for Central Asian agriculture production.
Historically, this region has developed a comprehensive
irrigation system at the same time increasing pressure towater and land resources. Converting river flow of the Amu
Darya River and the Syr Darya River to create wide
distributed irrigation canals has resulted in significantly
reduced flows to the Aral Sea [1], which had reduced surface
area of the Aral Sea significantly [2]. Dried seabed createddesert with sands of concentrated salt remained after water
evaporation that is currently spread with winds over thewhole region, increasing soil salinization.
Nevertheless, main impact to salinization of soils and lands
comes from irrigation and the secondary salinization.
Although, facing severe consequences of decisions made in
the past, current irrigation practices and water management
have not changed significantly. In Central Asia there aremainly three irrigation practices: furrow (more than 60%),
strip and basin irrigation. Furrow irrigation is dominant here
and most of the channels are unlined. Unlined canals greatly
reduce amount of flow water that ultimately reaches
agricultural fields, as some of it is lost to the groundwater.
Furrow irrigation applied over most of the irrigation areas
combined with high temperatures and evaporation rates,
creates favorable conditions of soil salinization,waterlogging and water quality deterioration. Accumulated
salts and minerals are then washed to the river flow,
increasing water salinity in the river, and consequentiallydownstream flow. The drainage channels are also logged andabandoned for long period of time.

56

Fig. 1. Study area, research sites are shown in frames
The predominant reasons for these developments are poor
irrigation water management, inadequate drainage, risinggroundwater tables and associated mobilization of primarysalts within the soil profile. As a result, huge amount of
productive irrigated lands are turning into degraded marginallands, which are then abandoned by farmers. According to
the ADB Final Report, 2008 estimation approximately
600,000 ha of irrigated lands in Central Asia has beenstressed over the last decade due to water logging andsalinization. Farmers’ awareness on soil/water conservationand management of marginal lands is also very low.
Measurement of water amount applied to crops and an
irrigation scheduling for reclamation of salt prone marginallands are still based on conventional approaches. Touge, [2]had showed that application of the adequate water saving
techniques, could had had decreased impact to the Aral Sea,
while reducing waterlogging and salinization due toapplication of different irrigation technique. It is further

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aggravated by the fact that there is no available data on basic
requirements of water management for crops, soil moistureextraction pattern, water requirements of different crops,
impacts of water deficits at different crop growth periods andothers.
Several development projects have been realized toreconstruct irrigation canals and drainage in the recent
decade. While demand for the water resources has been
increasing over last decades here, distribution of the water
usage had varied.
Agriculture water withdrawal has been slowly decreasing,while municipal water withdrawal has increased at the sametime fertilizer application has been increasing (fig. 2), which
impacts for the further soil salinization [3]. Growing
population, anthropogenic and climate change impacts canincrease pressure on the water and land resources. In case ofCentral Asian countries, this has not only food availability
complications but also huge economic impact, as it threatenssecurity of water resources availability and agriculture itself.

river channel. Mainly this are chlorine, calcium concentrated
waters that are applied to the irrigation field, thus increasing
salinity in the downstream.

Fig. 3. Water quality analysis of the collected samples(up), mineralization correlation (bellow) in the
Zeravshan River Basin

Fig. 2. Distribution of the fertilizers application in
Uzbekistan (Source: modified from FAO, 2020).
A current water resources salinization issues in Central Asia
has been analyzed. Overall assessment includes analysis ofchanges happen in last decade, characteristics of different soil
and water salinization issues facing Central Asia issummarized and assessed.

Soil electro-conductivity measurements have confirmedthat application of the water for irrigation instantly increases
salinization, which then gradually reduces over time. Thesesalts later accumulate on the surface of the soils and therefore
required to be removed at the end of irrigation season. Bothsites have shown higher salt concentration in the water;
however, results in Karakalpakstan are much worth. It is due
to a bigger accumulation basin, and high groundwater levels
that induces salinization in the upper layers of the soil.

4 Conclusions

2 Materials and methods
To assess water resources mismanagement and rate impactsof the water salinization to the downstream of the river flowa series of the field sampling of the water quality andmeasurements of the soil salinity during irrigation wereconducted. Two locations were selected: the Zeravshan Riverbasin, including one location in the downstream for the soil
electro-conductivity measurements, and the Amu Darya
downstream area in Karakalpakstan.
Observation sites were on the agricultural farm to confirm
irrigation practices and their impact to the salinization
process. Water quality sampling was held for the three years
along the Zeravshan river flow to analyze how salinization
distributes from the upstream to the downstream. Samples
were collected on main channels and along the main flow,
including drainage and secondary irrigation network canals.
Soil electro-conductivity were measured continuously forthree location using stationary installed monitoring station.

3 Results and discussion
Results show that salinity concentration increases over the
distance from the upstream and reaches the highest values inthe downstream (fig. 3). Mineralization concentration
increases with drainage water return flows back to the main

57

Increase in fertilizer application as well as overflow of theirrigation water application is continuously reducing
productivity of the soils. This research conducted in two
locations in Uzbekistan widens previous research to address
water quality deterioration and focuses on understanding of
the pollution sources and their distribution during irrigation
practices.

Reference[1] Micklin, P., 2007. The Aral Sea Disaster. Annual Rev.
Earth Planet. Science, 35, pp.47-72.
[2] TOUGE, Yoshiya. (2018). Development of terrestrial
water circulation model in the Aral Sea Basin considering
human impacts and climate change. Samarkand, October
2018.
[3] Kulmatov R., Opp C., Groll M., Kulmatova D., (2013):
Assessment of Water Quality of the Trans-Boundary
Zarafshan River in the Territory of Uzbekistan. Journal of
Water Resource and Protection, 2013, 5, 17-26
doi:10.4236/jwarp.2013.51003

The 8th International Symposium on Water Environment Systems

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Simulating soil water recession coefficients using satellite-based data
for antecedent precipitation index○ Thapthai Chaithong1* & Daisuke Komori 2
1Department of Geography,
2Department of Civil

Faculty of Social Sciences, Kasetsart University, Bangkok 10900, Thailand.
and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*E-mail: thapthai.c@gmail.com

Abstract
The aim of the study is to simulate the soil water recession coefficients (k value) for antecedent precipitation index(API) using the satellite-based data. The soil water recession coefficients (k value) are the main parameter tocalculate the API. The k value depends on the maximum soil moisture available and evapotranspiration. Generally,
the evapotranspiration is calculated by using the equation such as Penman-Monteith equation, or Priestley-Taylor
equation. However, the evapotranspiration value is complicated to calculate by the evapotranspiration equations
because they are require the many hydro-meteorological parameters to input the equations. Moreover, the hydrometeorological parameters are measure at meteorological stations which they can represent only small area aroundthe stations. Hence, the study uses the GLEAM (Global Land surface Evaporation) which is the global actual
evapotranspiration dataset. Regarding to the calculation, the satellite-based data can apply to estimate the k value
for API, but the resolution of GLEAM is course for region scale. Therefore, the downscaling method is significant
importance.
Keywords: antecedent precipitation index; soil water recession; evapotranspiration; GLEAM.
1 Introduction
Antecedent precipitation index (API) is the indicator torepresent the moisture condition in soil [1]. The API is basedon rainfall that has fallen over the previous day. The API isapplied to a regional landslide and flashflood early warning
systems. The API can be expressed as follows [2]:(1)
A P i I k Ai P- 1I
tP

based on the Priestley-Taylor equation. Figure 1 presents thefour modules for the GLEAM.

where APIi is the API on the “i” the day (mm), Pt is theamount of rain on the “i” the day (mm), APIi-1 is the API on
the previous day (i-1) and k is the soil water recession
coefficient.
The soil water recession coefficients (k value) can becalculated using the equation which can be expressed asfollows [3]:k  E X PEt W/m (2)where Et is the evapotranspiration (mm), and Wm is the
maximum soil moisture available. Considering to the k value
equation, there are two main parameters to control the k value
which are the evapotranspiration and soil properties. The
maximum soil moisture available is amount of moisture insoil for evaporation process. It is depends on the volumetr ic
water content at field capacity and permanent wilting pointof soil and bulk density of soil. The satellite-based
evapotranspiration can be applied to estimate the k value. The
Global Land Evaporation Amsterdam Model (GLEAM) is
the global evaporation model which is driven by remotesensing observations [4]. The GLEAM considers only threesources of evaporation: (1) bare soil, (2) Short vegetable, and(3) vegetable with a tall canopy [4]. Based on thefundamental concept of the GLEAM, four modules was
departed in the GLEAM. First module focuses on theevaporation of intercepted rainfall from the forest canopies.
Second module describes the soil-water budgets. Third
module is the stress module. Last module is the evaporation
form each of three surface components which is computed

58

Fig. 1. Four modules for GLEAM.

2 Materials and methods
The study area of the research is the South of Thailand
where is a part of Malay Peninsula because there are manylandslides occurred in this region such as Krabi landslide on
2011 or Nakhon Si Thammarat landslide on 1988 and 2011
In the study, there are two required parameters. First, thestudy used the 21 years of GLEAM data from 1989 to 2009
for the evapotranspiration dataset. Second, the bulk densityof soil is provided by the ISRIC world soil information. The
ISRIC world soil information provided the open-accessdataset about the texture and chemistry of soils.

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3 Results and discussion
Figure 2 presents the soil water recession coefficients (kvalue) for 12 month for the South of Thailand. According tocalculation, we found that the k value is in range between
0.84 and 0.96. Considering to the spatial detail, the k value
vary depending on the evapotranspiration and soil texture.

Fig. 2. Soil water recession coefficients (continues).

4 Conclusions
The satellite-based evapotranspiration dataset can beapplied to simulate the soil water recession coefficient. The
key advantage of satellite-based dataset is the spatial detail of
evapotranspiration which can cover the entire region.
However, the horizontal resolution is the important limita tio nof satellite-based evapotranspiration. The original resolutio nof GLEAM is 0.25 degree, hence the downscaling method issignificantly important for regional scale.

Reference[1] Zhao B, Dai Q, Han D, Mao J, Zhuo L, and Rong G,
Estimation of soil moisture using modified antecedent
precipitation index with application in landslide predictions,Landslides, 16(2019), 2381-2393.
[2] Choudhury B J, and Blanchard B J, Simulating soil
water recession coefficients for agricultural watershed,
WATER RESOURCES BULLETIN, 19:2(1983), 241-247.
[3] Teng W L, Wang J R, and Doraiswamy P C,
Relationship between satellite microwave radiometric data,antecedent precipitation index, and regional soil moisture,
INT. J. REMOTE SENSING, 14:13(1993), 2483-2500.
[4] Miralles D G, Holmes T R H, De Jeu R A M, Gash J H,
Meesters A G C A, and Dolman A J, Global land-surface
evaporation estimated from satellite-based observations,Hydrol. Earth Syst. Sci., 15(2011), 453-469.

Fig. 2. Soil water recession coefficients.

59

The 8th International Symposium on Water Environment Systems

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Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Understanding Seasonality and Evapotranspiration of Soil Water under
Tree and Grass Cover Using Natural Isotopes○ Danila PODOBED1*, Daisuke KOMORI2, Kei YOSHIMURA3, Masahiro TANOUE4
1

Graduate School of Environmental Studies, Tohoku University, Miyagi 980-0845, Japan.
2
School of Engineering, Tohoku University, Miyagi 980-0845, Japan.
3
Institute of Industrial Science, University of Tokyo, Chiba 277-0882, Japan.
4
Satellite Observation Center, National Institute for Environmental Studies, Ibaraki 305-8506, Japan.
*
E-mail: ilmarinen87@yandex.ru.

Abstract
This study employed naturally-occurring isotopes 18O and 2H as tracers of water origin and evapotranspiration
dynamics to understand the soil water environment in the unsaturated zone under tree and grass cover in northeasternJapan. Over the course of one year, 18O and 2H concentrations were observed in soil water profiles under tree andgrass cover. The observed soil water isotope concentrations were then compared with precipitation isotope data
simulated by the IsoRSM model. 18O was used as the primary indicator of evaporative processes and d-excess wasused to assess seasonality. The changes in relative and absolute concentrations of 18O and 2H with depth wereanalyzed to understand water transport mechanisms. The results indicated that summer rainfall was excluded fromthe soil profile below ~2m, and likely played no role in groundwater recharge. Additionally, the results suggested
preferential transport of heavy, isotopically-depleted precipitation under tree cover, as well as differences in the way
snowmelt and summer precipitation are affected by different land cover regimes. Additional work is needed toverify the interpretation of observed data by numerical simulation.
Keywords: water isotopes; unsaturated zone; land cover; seasonality; preferential flow; snowmelt
1 Introduction
One of the main issues involved in land use changes are theeffects of such transformations on local water resources.
While there is a general consensus among researchers thatafforestation in arid and semi-arid regions produce an overall
net reduction in water availability and groundwater levels inparticular relative to bare soil or grasslands, such differences
are small or negligible in wetter regions or during periods ofheavy rainfall. The primary aim of the current study was toachieve an understanding of the snowmelt infiltration andseasonal contributions to deep drainage are affected bydifferent land cover regimes (forest and grass) in a temperate,water-abundant region like Japan. The method selected forinvestigation in the current study was the so-called “natural
isotope tracer” method, whereby soil water concentrations of18
O and 2H are compared to those in local precipitation. Thismethod has been widely used to study soil moistureseasonality and permeation, as well as to understand theprevalence of preferential vs. piston flow under different
ground cover regimes (ex., Allison and Hughes, 1983). The
current study attempted to fill a gap in the use of natural
isotope tracers by analyzing the relationship between 18O/2H
concentrations found at various depths of the soil profile.

2 Materials and methods
2.1 Field sites and sampling
Sample collection was conducted on the undeveloped
section of the Aobayama Campus of Tohoku University,
Sendai, Japan. Survey sites under forest and grass cover wereselected at 8 locations. Soil moisture sampling was carriedout in two stages; in the first stage, boreholes were excavatedbetween April-October of 2017, spaced several months apart.
A second, intensive period of sampling at 6 of the original 860

locations took place in April 2018, to better represent the
infiltration of the same isotopic input. Excavation of soil
samples was conducted by means of a hand auger and a
motorized percussion sampler to a depth of 5 meters. The soil
moisture content was measured by evaporating sub-samples
in an industrial oven and comparing the mass of dried andwet soil. Additional sub-samples were then centrifuged to
extract soil water for isotope analysis; this water was filteredand isotope concentrations measured using a Picarro L2120i CRDS water isotope analyzer.
2.2 Analysis Methods
In order to identify to seasonal patterns in soil moistureisotope profiles, the study considered simulated precipitation
isotope data for the years 2016-2017 produced by a regional
climate model (IsoRSM). Simulation of 2018 precipitation
isotopes was not possible due to lack of model input data for2018 at the time the model was run; subsequent work will be
carried out to correct this omission. Observed precipitation
isotope data for Sendai, collected by the Isotope Mapping
Working Group of the Japanese Association of Hydrological
Sciences in 2013, was used for model calibration (per Tanoue
et al., 2016). Additionally, monthly precipitation data for
Sendai, acquired from the Japan Meteorological Agency, wasused to achieve an understanding of the relationship between
isotope concentrations and precipitation volume. Due to thehigh correlation of δ18O and δ2H, δ18O was used as theprimary tracer in this study; δ2H was used in a supplementary
fashion to evaluate evaporative processes based on the δ18O /δ2H relationship and also to employ d-excess as a secondorder tracer. D-excess is defined by the following equation:d-excess = δ2H – 8 × δ18O
As discussed in Waseda and Nakai (1983), d-excess can beused to identify the seasonal origin of precipitation.

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3 Results and discussion
The δ18O/δ2H relationship of the soil moisture samples andsimulated 2016-2017 precipitation was considered. The
shallower slope of the δ2H = 5.25 *δ18O - 9.38 trend for grasscover boreholes, as compared to the δ2H = 6.67 *δ18O +2.41
for tree cover, suggested an elevated impact of evaporationon the surface of grass cover plots. An analysis of theδ18O/δ2H relationship dynamics by depth also attested to theincreased impact of evaporation on grass-cover profiles, as
the slope of the grass-cover δ18O/δ2H trend line became
progressively shallower with depth, while that for the treecover profiles remained relatively steady (Fig. 1).
Additionally, observed isotopic delta-values under tree cover
became progressively more depleted with depth, but were
evenly distributed regardless of depth for grass-cover (Fig. 1).
This observation indirectly suggested the presence of flowmechanisms under tree cover that preferentially distributed
depleted (heavier) rainfall to deeper soil layers.

a simulated d-excess of 5-10‰, was not present in the soil
moisture below 2 m. This was likely the result of
exceptionally high evapotranspiration during the summer
months consuming most of the recent precipitation, as
described in a number of studies (for example, Allison andHughes, 1983).

Fig. 2. Selected δ18O profiles during the 2017-2018
sampling period (green indicates tree cover, yellow
indicates grass cover).

4 Conclusions
Fig. 1. Depth distribution of δ18O/δ2H values and trendlines in the 2017-2018 soil moisture profiles.
Two sets of soil moisture profiles taken at the same time atadjacent tree-cover and grass cover plots - two excavated on
September 2017 (E-F17, Fig. 2), and two in April 2018 –were compared directly (E-F18, Fig. 2). The April 2018 treecover profile exhibited a depletion bulge at 40-50 cm depth,with a δ18O of -11‰ corresponding to simulated
winter/spring precipitation; this bulge was entirely absent in2018 grass-cover profiles. During Sept. 2017, such disparity
between grass and tree cover profiles was not observed. Thissuggested that while ground-level evaporation on grass-coverplots and evaporation of moisture intercepted by the tree
canopy on tree-cover plots was nearly equal during summer
months, evaporation of snowmelt was more limited undertree cover. The author’s hypothesis is that evaporation ofsnowmelt was delayed until after the snow had alreadypassed through the tree canopy, and was thus reduced, and no
significant canopy-level evaporation of snowmelt occurred.
The d-excess values across all boreholes were similar,
averaging around ~ 12‰ below 1.5-2.0 m depth. Following
Waseda and Nakai (1983), averages for the months of Dec. –Feb. (winter) and Jun. – Aug. (summer) were used torepresent the seasonal extremes in d-excess, with values of
14.06‰ and 7.97‰, respectively. D-excess of soil moisturecannot be higher than the precipitation from which this soil
moisture originated; it can only decrease by evaporation. It
was therefore considered probable that summer rainfall, with

61

The most significant finding of the current study was theapparent exclusion of summer precipitation from deeper soil
layers. The exact mechanism causing this phenomenon is
uncertain but is suspected to involve very highevapotranspiration during the summer months. This would
suggest groundwater recharge by summer precipitation is
negligible. Additionally, preferential transport of heavy
rainfall is likely the cause of depleted isotopic concentrations
below tree cover. Finally, the study provides evidence ofdifferential evaporative effects on snow deposited in thewinter and spring months, suggesting it is less affected bycanopy-level evaporation than precipitation during otherseasons.
Upcoming work will include simulation of soil watertransport and evaporation by a land-surface model (TBD) toverify the mechanisms responsible for the observed results.

Reference[1] Allison, G. B., & Hughes, M. W. 1983. The use ofnatural tracers as indicators of soil-water movement in atemperate semi-arid region. Journal of Hydrology, 60(1–4),
157–173.
[2] Tanoue, M., Ichiyanagi, K., & Yoshimura, K. 2016.
Verification of the isotopic composition of precipitation
simulated by a regional isotope circulation model overJapan*. Isotopes in Environmental and Health Studies, 52(4–5), 329–342.
[3] Waseda, A., & Nakai, N. 1983. Isotopic compositions ofmeteoric and surface waters in Central and Northeast Japan(in Japanese). Chikyukagaku, 17, 83–91.

The 8th International Symposium on Water Environment Systems

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Spatiotemporal analysis of drought indicated by scPDSI over Japan○ Ke SHI1*, Yoshiya TOUGE1
1

Department of Civil and Environmental Engineering, Tohoku University, Miyagi 980-8579, Japan.
*
E-mail: shi.ke.s5@dc.tohoku.ac.jp
Abstract
Drought disasters, such as water scarcity and wildfires, are severe natural disasters in Japan that are also affectedby climate change. However, as drought generally has widespread impacts and the duration of drought can varyconsiderably, it is difficult to assess the spatiotemporal characteristics of drought comprehensively. Therefore, toidentify the drought homogeneous regions and further understand regional drought over Japan, this study provides
a spatiotemporal analysis for historical droughts patterns over Japan. The drought characterised by the
Self-calibrating Palmer Drought Severity Index (scPDSI) was investigated for the period 1960~2018.
Subsequently, patterns of drought were identified through distinct empirical orthogonal function (DEOF). Results
indicate that two major subregions of drought variability—the western Japan (W region) and most of thenorthernmost Japan near the Pacific (N region) were identified. Further, whether in W region or N region,
wildfires with large burned area were more likely to occur when the scPDSI was less than -1, which meant that
excess dried-out vegetation provided ample fuel for the wildfires. This study is the first to identify homogeneous
regions with distinct drought characteristics over Japan. Also, the results are beneficial for regional droughtmanagement over Japan.
Keywords: Drought; scPDSI; DEOF; Wildfire.

1 Introduction
Under climate change conditions, especially direct andclear global warming, drought has shown increasing trends
in some regions of the world. Considering the duration,
intensity, geographical extent, and broad effects of droughts,
it is challenging to determine drought occurrences and theireffects [1]. Additionally, drought is considered to be themost complex and impenetrable extreme climate event,affecting more people than any other natural hazard. In
particular, the deficit in precipitation combined with highevapotranspiration losses during a drought would increase
the risk of other disasters such as wildfires. The risk ofwater scarcity would be exacerbated by long-term drought.
The research on droughts over Japan has been receiving
increasing attention. More than 70% of Japan ismountainous, causing rain to flow quickly to the ocean after
falling. Historically, most areas of Japan have experienced
droughts with varying duration, frequency, severity, andintensity. Particularly, in 1994, almost the whole of Japan
experienced a long-lasting drought and the drought caused
an economic loss of approximately 1.3 billion dollars as aresult of the decline in agricultural production [2]. The
number of areas affected by drought in Japan is decreasing
but still exists every year.
Overall, to further improve the capacity of early warningand risk assessment of drought at the regional level, it isnecessary to identify the homogeneous regions with distinct
drought characteristics over Japan. However, there is still alack of understanding of the specific consequences ofdroughts in Japan. For this purpose, this study attempts toprovide an analysis of historical droughts over Japan to fillthese gaps. The drought events were characterised by
Self-calibrating Palmer Drought Severity Index (scPDSI)
using the most up-to-date data. Major patterns of long-term
trends in drought and periodicity were then identified using
the distinct empirical orthogonal function (DEOF) andcontinuous wavelet transform, respectively. Additionally,62

the relationship between drought index and wildfire wasanalysed to identify the effects of the drought.

2 Materials and methods
2.1 Data
The 0.5° high-resolution gridded datasets of precipitation,near-surface temperature, potential evapotranspiration, and
scPDSI were obtained from the Climatic Research Unit at
the University of East Anglia. In this paper, the selectedperiod ranges from 1960 to 2018. The scPDSI class isshown in Table 1. This scPDSI was presented by Wells et al[3], which was a variant of the original PDSI [4] used tomake the results from different climatic regimes morecomparable.
2.2 Distinct Empirical Orthogonal Function
The empirical orthogonal function (EOF), which deals
with temporal and spatial functions, is used to extract the
spatiotemporal modes based on the data variance
representations. The EOF analysis method can decompose
the time-varying variable fields into the space function part
(EOFs) that does not change with time and the time function
part (principal components, PCs) that depends only on time.
The distinct EOF (DEOF) analysis was subsequently
introduced to overcome problems in the EOF analysis [5].
In the DEOF, a continuous spectrum of spatial patterns
resulting from a stochastic process can be represented by
EOF modes, where some spatial structures will be more
dominant than others. Based on the isotropic diffusion null
hypothesis, the EOF modes (DEOFs) can be found byrotating the leading EOF modes, corresponding to the
distinguished principal components (DPCs). These DPCs
take up a large part of the total variance in all the variablesin the original field, which is equivalent to the main
information of the original field concentrated on a few main
components. The higher the eigenvalues, the more typicalthe corresponding modes, and the more significant thecontribution to the total variance.

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The DEOF calculation used the scPDSI time series of eachgrid point on the monthly scale. Fig. 1 (a) and (b) displays
the spatial partitioning results of the first two DEOFs. The
explained variances in the first two DEOFs were 46.85%
and 20.55% respectively, which meant that the DEOFs
could explain approximately 67.40% of the total spatial
wet/dry characteristics of Japan from 1960 to 2018. The
first two had sufficiently explained variances to represent
most of the wet/dry conditions in Japan.
The spatial distribution of DEOF1 illustrated that a highpositive loading occurred in the western region at
approximately 35°N (W region). This finding meant that the
W region had similar drought characteristics from 1960 to2018. Similarly, the spatial distribution of DEOF2
illustrated the common positive spatial behaviour of drought
in most of the northernmost region near the Pacific (Nregion). However, the central region, western region, andmost parts of the northwestern region showed common
negative spatial behaviour, indicating that these regions
showed the opposite drought characteristics as the N region.
Notably, the two DEOFs were unable to represent all
drought characteristics across the whole of Japan. For thecorresponding drought temporal characteristics, the DPC
scores are displayed in Fig. 1 (c) and (d). The DPC1 scoresshowed a decreasing trend, which meant that the W region
became drier. However, the N region was getting wetter.

In a wet spring, when the scPDSI was positive, the burned
area of western Japan was less than 100 ha. The threesprings with severe wildfires, when the burned area waslarger than 300 ha, were accompanied by drought events inwhich the scPDSI was less than -1. Although there werefewer wildfire occurrences in the N region than in the W
region, these two regions followed a similar pattern. A totalof six wildfires with burned areas of over 60 ha occurred in
the N region. The scPDSI values corresponding to these six
wildfires were all negative, and four of them experienced
drought (scPDSI ≤ -1). When the scPDSI was more than 1,
there were only six wildfire occurrences in the N region,and the burned area was less than 60 ha.
(a) DPC1 VS Burned Area in W region
700

Burned Area In Srping (ha)3 Results and discussion

600
500
400
300
200
100
0
-4

-3

-2

-1

scPDSI In Spring

0

1

2

(b) DPC2 VS Burned Area in N region
200

Burned Area In Srping (ha)‐‐‐‐‐ with Perspective of Global Safety

180
160
140
120
100
80
60
40
20
0
-4

-3

-2

-1

0

1

2

scPDSI In Srping

Fig. 2. Comparison of DPCs for annual burned forest
area in spring (a) DPC1. (b) DPC2.

4 Conclusions

44(c) DPC1(d) DPC2

2

scPDSIscPDSI2

The main conclusions obtained from this paper are
summarised as follows: (1) DEOF was used to identify two
major subregions of drought variability—the western region(W region) and most of the northernmost region near the
Pacific (N region). The corresponding scores of DPC1 andDPC2 showed a trend of decreasing (increasing in drought)
and increasing (decreasing in drought), respectively. (2)When scPDSI was less than -1, wildfires with larger burned
areas were more likely to occur.

0

0

-2

-2

-4
1960

-4

1970

1980

1990
Year

2000

2010 20181960

Reference
1970

1980

1990
Year

2000

2010 2018

Fig. 1. First two DEOFs and DPCs for scPDSI. (a)DEOF1. (b) DEOF2 (c) DPC1 (d) DPC2.
Considering the ability of scPDSI to represent the waterbalance adequately, the analysis of combining drought withwildfires would further enhance the understanding ofdrought-induced natural disasters. Soil drought occurred
most commonly in spring, which was also the season when
wildfires frequently happen in Japan. Therefore, thecomparison between the annual burned forest area in spring(March to May) with DPCs was extracted, as shown in Fig.
2. The burned area data come from fire reports provided by
the Fire and Disaster Management Agency, Government ofJapan. The burned area data of the W region included the
Ishikawa prefecture, Fukui prefecture, Gifu prefecture,
Aichi prefecture, Shiga prefecture, Kyoto prefecture, Osaka
prefecture, Hyogo prefecture, Nara prefecture, Wakayama
prefecture, Tottori prefecture, Okayama prefecture, and Mie
prefecture. The burned area data of the N region included
the Hokkaido prefecture.

63[1] ASONG, Z. E., et al. Historical drought patterns overCanada and their teleconnections with large-scale climate
signals. Hydrology & Earth System Sciences 22.6 (2018).
[2] LEE, S., et al. Spatiotemporal characteristics ofdrought occurrences over Japan. Journal of applied
meteorology and climatology 51.6 (2012): 1087-1098.
[3] WELLS, N., et al. A self-calibrating Palmer drought
severity index. Journal of Climate 17.12 (2004): 2335-2351.
[4] PALMER, W.C., Meteorological Drought, Office of
Climatology U.S. Weather Bureau, Washington, D.C.
(1965).
[6] DOMMENGET, D. Evaluating EOF modes against a
stochastic null hypothesis. Climate dynamics 28.5 (2007):
517-531.

Acknowledgements
The authors declare no conflict of interest.
This study was jointly supported by the Ministry of
Education, Science, Sports and Culture, Grant-in-Aid for
Exploratory Research, 2019-2021 (19K21982, So Kazama)
and Grant-in Aid for Scientific Research (B), 2020-2023(20H02248, Yoshiya Touge).

The 8th International Symposium on Water Environment Systems----- with Perspective of Global Safety (Nov. 13th ~ 14th, 2020)

GP-RSS & SyDe
Tohoku University

The Effect of Wealth Level and Community-Based Environmental
Activities Participation on Environmental Awareness○ Erwan Wahyu WIBOWO1*, & Daisuke KOMORI2
1 Pati Regency Government, Central Java Province, Indonesia.
2Department of Civil Engineering, Tohoku University, Sendai, Miyagi, 980-8579 Japan.
*E-mail: r_one7@icloud.com.

Abstract
Pollution degrades the environment and threatens life. In this study, a system dynamics model was proposed forenvironmental awareness based on Environmental Kuznets Curve (EKC) coupled with the community-based
recycling activities. The system dynamics model describes how environmental performance (pollution level),wealth level, community-based recycling participation, population density, and environmental awareness areinterrelated and evolve gradually. Moreover, pollution levels decrease dynamically due to increasing environmental
awareness. Using quantitative analysis to understand the interrelation of EKC and community-based recycling
activity by validating the model. By using the model's quantitative analysis, the community-based recycling activity
has potential as a trigger to increase environmental awareness against EKC in Phase 1 to accelerate the achievementof turning points. In addition, it was also confirmed that the community-based recycling activity has no role withinincreasing environmental awareness in the rural area.
Keywords: Environmental awareness, Environmental Kuznets Curve (EKC), Engel coefficient (ECF), Communitybased waste recycling activities, System dynamics modelthrough field surveys conducted on 21 April - 4 May 2020 in
1 Introduction
Pollution having an irreversible impact on the environment,
empirical evidence shows that pollution in some countries
already has dangerous consequences. Based on Jambeck et al.
(2015), there are 275 million tons of plastic waste produced
worldwide. Around 4.8-12.7 million tons are polluting thesea. The Environmental Kuznets Curve (EKC) shows that thelevel of environmental pollution would increase and would
later experience a decline or a turning point, in line with theincrease in economic growth (Todaro et al., 2006). The EKC
phase is divided into two (Kaika and Zervas, 2016). Phase I
of EKC is the economic growth phase of the transition fromagriculture to industry or changes in economic structure fromrural to urban. Phase II is the transition from the post-industry
stage to a service-based economic system. The challenge isto accelerate the achievement of turning points of EKC
before environmental degradation results in fatalities.
To address environmental problem and create new turning
point Environmental awareness is important (Chen et al.,2019). Most study about environmental awareness only focus
on factors that influence and study based on the current
situation (Culiberg and Rojšek, 2008; Gadenne et al., 2008).
Lack of study discusses environmental awareness shift(increase/decrease) by comparing case in two or moresites/situations and its correlated factors. An approach toincrease environmental awareness such as community-based
waste recycling empowers the community to manage waste
wisely by sorting, and 3R practices.
From background of study mentioned before, the objectiveof study is using quantitative analysis to understandinterrelation of EKC and community-based recycling
activities.

Sukoharjo and Wonosekar Village, Pati Regency, Central
Java Province, Indonesia. The primary data represents
information from individuals', and individuals' motivation.
2.2. Parameter of Variables
This study using Engel coefficient (ECF) to determine thewealth level. The poorer family is, the larger the budget shareit spends on food consumption (Engel, 1857 in Chai andMoneta, 2010). ECF implies that Low ECF means wealthy,
high ECF mean Poor.
The questionnaire is used to identify respondents'
perceptions of environmental awareness trough their current
practices of pro-environmental actions, refer to European
Commission (2008), Eurobarometer: Attitudes of European
Citizens Towards the Environment such as household wastesorting, reduction of plastic waste, and recycling activities.
Community-based recycling participation is determined byparticipation in the waste bank program. This study usedparticipants, non-participants, and ex-participants groupings.
2.3. Analysis Method
The quantitative analysis applies to the average sample set
arranged by respondents’ category (participants, nonparticipants, and ex-participants) and area distributions(urban and rural) to determine the interrelation between thevariables. As for qualitative analysis, the method of data
analysis used is an interactive model analysis by data
reduction, data testing or data analysis after data collection to
draw conclusions and verification.

3 Results and discussion

2 Methods
2.1. Study Area and Data SourceThis study was conducted in communities in two different
areas (urban and rural) with differences in socio-economicand environmental characteristics. The data were collected

64

Fig. 1. The system dynamics model combines EKC andcommunity-based recycling activities to describe
environmental awareness shifting.

The 8th International Symposium on Water Environment Systems----- with Perspective of Global Safety (Nov. 13th ~ 14th, 2020)
This study designed a system dynamics model forenvironmental performance based on EKC and thecommunity-based recycling activity.
Based on previous research, population density increases
pollution levels (A), with excessive consumption patterns,
the environment becomes more polluted as the wastes areproduced (Jambeck et al., 2015). The relationship between
economic growth and population density (B) depicts thatindustrialization's massive growth focuses on advancing the
economy and absorbing labour. This growth drives
urbanization.
An increase in pollution levels causes an increase inenvironmental awareness among the public (C) (Wang et al.,2016), when consequence of fatalities occur, people getaware and government set a regulation to control pollution.
Increasing in environmental awareness has an impact on
increasing environmental performance (D) (Chen et al.,2019). Many research of the EKC shows that environmental
pollution increases in the early stages of development during
an economic growth—as we know as Phase I of EKC (E)(Stern, 2004). Previous studies found that the communitybased recycling activities is a form of social engineering at
the community level to reduce waste piles (F).
There are three interrelation to be investigate in this study;
the interrelation between the wealthy level andenvironmental awareness (G), the relation between thewealthy level and participation of community-based
recycling (H) and the effect of participation of communitybased recycling toward environmental awareness (I).
Responses from each groups of respondents in urban andrural areas used to examine the interrelation between
variables.
Table 1. Comparison of variables on communities intarget areas*(Source: Statistics Agency of Pati Regency, 2019)
On the community scale, it can be seen that an increase in
wealth level from 40% to 38% ECF causes the decrease ofenvironmental awareness, from 4.2 to 3.9. The interrelationof wealth level and environmental awareness is negative
The interrelation between ECF and environmental
awareness among participants and ex-participants versus
non-participants in rural areas shows an increase inenvironmental awareness (from 2.8 to 4.3) in spite of ECF
was slightly decreased or steady. This finding can explain theimportance of community-based recycling activity tokeep/increase environmental awareness against EKC in
urban area. In rural areas is negative (from 3.8 to 4.6). Even
environmental awareness, both high but interrelation towards
ECF, is negative. This might explain that there is no effect of
the community-based recycling activity in the initial Phase 1of EKC because those who live in rural areas do not faceenvironmental problems.
In the urban area, people who reluctant to join the wastebank activities are people with a moderate level of wealth(Fig. 2). Based on this distribution of ECF in non-participantsgroup, it cannot conclude the interrelation of wealth level andcommunity-based recycling activities.

65

GP-RSS & SyDe
Tohoku University

Fig. 2. The wealth level of non-participants in urban area

Fig. 3. The system dynamics model of the target area. Red+/- were confirmed in the case of this study. Red
dash line still unconfirmed
Quantification of the relationship between the wealth levelof individuals – environmental awareness in the target area
as pure EKC in Phase 1 (G), is negative, suggests that withincreasing wealth levels, environmental awareness decreases,that increasing wealth levels from rural to urban, resulted ina decrease in environmental awareness (Table 1).
The interrelation between the community-based recycling
activity (waste bank) and environmental awareness (I) is
positive. Moreover, community-based recycling activities
has the potency to increase environmental awareness against
pure EKC in Phase 1 (G). Community members who
participate in the waste bank program have higher
environmental awareness comparing with those who notparticipate, resulting in a comparison of environmental
awareness between non-participants and participants group(Table 1). Waste banks help foster collective awareness ofthe community to start sorting, recycling and utilizing waste.
On the other hands, the interrelation of the wealth level andparticipation in the target area (H) is still not confirm. Using
both cases in urban and rural areas cannot be concluded that
wealth level implies participation increase or decreases.

4 Conclusions
Based on quantitative analysis, system dynamics model can
explain the interrelationship of variables in target areas. In
addition, community-based recycling activity has potential
as a trigger to increase environmental awareness against EKC
in Phase 1 to accelerate the achievement of turning points.

Reference[1] Chai, A., & Moneta A., (2010), Retrospectives: Engel Curves. Journal of Economic
Perspectives, 24(1), pp. 225-40.
[2] Chen, X., Huang, B., & Lin, C. T., (2019), Environmental Awareness & Environmental
Kuznets Curve, Economic Modelling, 77, pp. 2-11
[3] Culiberg, B., and Rojšek, I., (2008), Understanding Environmental Consciousness: A
Multidimensional Perspective. In Insights for measuring environmental awareness, Ham M.,
Mrčela D., Horvat M., (2016), Ekon. Vjesn., 29, pp. 159–176[4] Gadenne, D.L., Kennedy, J., and McKeiver, C., (2008), An Empirical Study ofEnvironmental Awareness and Practices in SMEs. J Bus Ethics 84, pp. 45–63[5] Jambeck, J. R., Geyer, R., Wilcox, C., Siegler, T. R., Perryman, M., Andrady, A., Narayan
R., & Law, K. L., (2015), Plastic Waste Inputs from Land into The Ocean, Science, 347(6223),
pp. 768–771[6] Kaika, D., and Zervas, E., (2013), The Environmental Kuznets Curve (EKC) theory—Part
A: Concept, causes and the CO2 emissions case. Energy Policy, 62, pp. 1392–1402[7] Kuznets, S., (1955), Economic Growth and Income Inequality, The American Economic
Review 45(1), pp. 1-28.
[8] Stern, D. I., (2004), The Rise & Fall of the Environmental Kuznets Curve, World
Development, 32(8), pp. 1419-1439.
[9] Todaro, Michael P., and S.C. Smith, (2006), Economic Development, 9th edition. Pearson
Education Limited. Essex.
[10] Wang, Y., Sun, M., Yang, X., & Yuan, X., (2016), Public awareness and willingness topay for tackling smog pollution in China: a case study. Journal of Cleaner Production, 112, pp.
1627–1634

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Hydrological assessment of precipitation products over high mountainregions: case study of Issyk-Kul Lake.
○ Sanjar SADYROV1*, Kenji TANAKA2, Shigenobu TANAKA2, Temur KHUJANAZAROV2
1

2

Graduate School of Engineering, Kyoto University, Kyoto 615-8530, Japan.
Disaster Prevention Research Institute, Kyoto University, Kyoto 611-0011, Japan.
*
E-mail: sadyrov.kalinurovich.82m@st.kyoto-u.ac.jp.

Abstract
The evaluation of reanalysis data is essential in improving the accuracy of forcing data for hydrological and land
surface models (LSMs). It is especially important for regions with complex terrain and limited in-situ data, suchas high mountainous regions of Central Asia. This study aims to assess the accuracy and limits of the spatial
distribution of various precipitation products (JRA55, GSMaPv6, ERA5, GPCC, APHRODITE) over Issyk-Kul
Lake Basin located in the Northern Tian-Shan Mountains compared to daily observation data in 5 meteorologicalstations located around Issyk-Kul Lake. The GPCC includes observed data from some stations and thus
outperforms other products in monthly values with RMSE of 15.8 (mm). Additionally, modified APHRODITE
precipitation data incorporating in-situ gauge data and altitude gradient for precipitation was included in theevaluation. The Land Surface Simple Biosphere including Urban Canopy Model (SiBUC) and Rainfall-Runoff
Inundation Model (RRI) integration was used to estimate lake’s water level. Results of Jason-2 Satellite Altimetry
Data of Issyk-Kul Lake water level were used to compare outputs between several precipitation products. The
spatial pattern of precipitation is crucial for the water balance of the basin. However, results have shown thatneither product were able to successfully represent precipitation distribution in the basin, and only combination ofseveral reanalysis data and integration of the observed dataset have given positive outcome.
Keywords: Precipitation; Hydrological modeling; Water balance; reanalysis datasets; Issyk-Kul.
1 Introduction
The water resources system in Central Asia is under stressdue to multiple interrelated drivers. The water supply fromseasonal snow and glacial melt is already impacted byclimate change, and water demands continue to increase
with population growth and land-use change.
Issyk-Kul Lake is the tenth largest lake in the world by
volume (1783 km3). It is endorheic, with relatively
consistent low irrigation rate and water outflow from thecatchment is mainly driven by evapotranspiration. Locatedat 1607 meters above sea level, the catchment represents the
typical High Mountainous Watershed of Tian-Shan or
so-called Northern Himalaya. Modeling and validation of
the lake surface fluctuations provide an understanding ofhydrological processes in the catchment, including snow
and glacier melt with a minimum of uncertainty; it willfurther help assess climate change impact on the basin.
However, due to the scarcity of available observed data andcomplex terrain, it is challenging to assess model
performance at its best. Evaluation of reanalysis andgauge-based precipitation products is thus essential in
improving the accuracy of forcing data for hydrological andland surface models (LSMs), especially important for theregions with complex terrain and limited in-situ data.
Satellite-based reanalysis data, especially in themountainous areas, need bias correction that requires acontinuous long period observed data. Kyrgyzstan, theformer part of the Soviet Union, had been conducting local
observations since the 1950s, but after collapse of the Soviet
Union, only one-third of stations continued to operate, so
there is a lack of data since the 1990s. Nowadays, more andmore meteorological stations start their operation, but it is
not enough for continuous analysis.

66

Fig. 1. Location of Issyk-Kul Lake Basin and spatial
distribution of glaciers from Randolph Glaciers
Inventory (RGI 6.0).

2 Materials and methods
2.1 Statistical analysis and performance
Precipitation data of five meteorological stations located in
Balykchy, Cholpon-Ata, Kyzyl-Suu, Karakol, andChon-Ashuu were compared with global precipitation
products: JRA-55, ERA-5, APHRODITE, GSMaP, and
GPCC. The performance of the products was evaluated by
Pearson correlation factor and value difference using RMSE.
No precipitation product showed a significant correlation
with observed data. ERA-5 has shown a better value, but
only for the single event values. It is possibly due to thelimitation of the station equipment’s measurements, as
precipitations with less than 1mm are not detected but areabundant in reanalysis products. Resolution of precipitation
products is important for regional scale modeling.

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

scheme was further expanded to higher locations in theaccumulation zone from 2013.
From these measurements, it is possible to assess runoff
from the glacier. We assume that every glacier in the basin
is melting the same way as Karabatkak glacier with the areaof ~2.5km² while the area of all glaciers is 650.5km². We
also assume that area of all glaciers is not changing overtime period (2008-2016). It means such approach is not able
to consider the dynamical shrinkage of the glaciers in theregion.

Table 1. Analysis of Pearson Correlation and root meansquared error (RMSE) among 5 stations:
KZLS – Kyzyl-Suu; KRKL – Karakol; BLKC –Balykchy; CHAS – Chon-Ashuu; CHAT – Cholpon-Ata
DatasetKZLS KRKL BLKC CHAS CHAT
ERA5
0.53
0.59
0.55
0.63
0.44
(RMSE)
52.8
37.0
46.2
31.7
39.0
JRA55
0.44
0.45
0.41
0.49
0.35
(RMSE)
36.4
34.9
48.6
26.4
37.3
GSMaPv6
0.48
0.49
0.33
0.48
0.37
(RMSE)
19.4
23.2
22.9
31.1
28.9
APHRO
0.43
0.46
0.33
0.54
0.3
(RMSE)
23.3
25.8
15.0
38.3
22.9
GPCC
0.43
0.43
0.43
0.51
0.37
(RMSE)
7.5
17.9
19.4
16.9
18.1

3 Results and discussion

Analysis of the Pearson correlation and RMSE of different
reanalysis products is shown in table 1. Some of theseproducts usually include in-situ observation data; that is
why GPCC, APHRODITE, and GSMaPv6 can outperform
other datasets. GPCC shows the best results but is limitedwith the resolution of (1ox1o) for daily precipitation. This
resolution doesn’t provide enough accuracy for themountainous catchment with complex terrain.
2.2 Modification of precipitation data
Orography impact on precipitation distribution is crucial inthis study, though reanalysis products are inaccurate inregional scale precipitation estimation for mountainous
regions. The altitude difference in the basin is 1607m and4772m, with considerable variations in precipitation. For
example, the mean annual precipitation at Kyzyl-Suu station(1750m) is 400mm, while at high elevations (>3500m),
mean annual precipitation can reach 1000mm.
Precipitation distribution research in the Chon-Kyzyl-Suu
River Basin, a part of Issyk-Kul Lake Catchment was
conducted during 1956-1967. 30 precipitation gauges were
installed at different altitudes and different points for aperiod of 1956-1967. Results showed that mean annual
precipitation increases as altitude increases. Altitude
coefficients were developed using same concept of thealtitude and precipitation correlation.
APHRODITE precipitation product has shown good
applicability in water related studies [1], and its algorithm
considers orography in the estimation of precipitation fromin-situ stations. This approach can be useful for
mountainous regions and particularly for this study.
Precipitation distribution based on 6 observation stations
with a resolution of 5km was created for Issyk-Kul Lake
Basin and compared to other global products.
2.3 Hydrological modelingState-of-the-art Land Surface Models are used as effective,
physically based models to describe land surface processes.
In this study SiBUC model is used to evaluate hydrologicalcomponents of the water cycle. Water level fluctuation of
the lake is estimated using Rainfall-Runoff Inundation
Model, a kinematic model for runoff and evapotranspiration
outputs from SiBUC.
2.3.1 Glacier component
Tian-Shan High Mountain Research Centre’s observations
data on Karabatkak Glacier since 2007 is also used. The
methodology includes measurement of daily ablation stakes
during a summer ice melting period. The observation

67

Fig. 2. Issyk-Kul Lake water level variations observed
and modeled
Figure 2 shows JRA55 precipitation product overestimates
overall precipitation for the whole catchment, and
GSMaPv6 underestimates. APHRO-3 precipitation dataset
includes in-situ meteorological station data using
APHRODITE algorithms for interpolation. *gradient is analtitude coefficient for precipitation in high elevation.
The model run using JRA55 forcing data (temperature,humidity, air pressure, wind speed, downward radiation)shows a slight underestimation in overall water level
fluctuation. It can be affected by the output component of
the water balance, which is evapotranspiration. Results are
improved with the dataset from ERA5 forcing data. The
best result is found using an effective combination ofvarious reanalysis products and incorporated simple glaciercomponents.

4 Conclusions
Hydrological modeling of high mountainous areas is very
challenging in terms of data availability and considerationof various water balance components. This study show thatthe efficiency of global products on the regional scale,especially remote ungauged areas, may not give precise
results and proper downscale approaches should be
implemented in order to improve accuracy of the input data.

Reference[1] Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada,N. Yasutomi, and A. Kitoh, APHRODITE: Constructing aLong-Term Daily Gridded Precipitation Dataset for Asia
Based on a Dense Network of Rain Gauges. Bull. Amer.
Meteor.Soc.,2012, 93,1401–1415, doi 10.1175/BAMS-D-11-00122.1.

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety

Application of simple paddy field dam model for typhoon eventat basin scale○ Yikai CHAI1*, Yoshiya TOUGE1, So KAZAMA1
1

Department of Civil Engineering, Tohoku University, Miyagi 980-0845, Japan.
*
E-mail: chai.yikai.r1@dc.tohoku.ac.jp

Abstract
In recent years, to mitigate the flood disaster caused by the increasing intensity and frequency of torrential rain
events, some places have adjusted the retention of the paddy field by installing the runoff control device called
paddy field dam. In comparison with other flood control facilities, the installation cost is much lower mainly fordrainage control devices at the drain outlet of the paddy field, while the expected effect is significant since paddy
fields are widely distributed in Japan. Therefore, this research aims to evaluate the potential flood mitigation
effect of the paddy field dam for typhoon 19th in October 2019 in the Naruse River basin using theRainfall-Runoff-Inundation model. To consider the potential effect of all paddy fields inside the basin, a simple
one-dimensional paddy field dam model was developed, which can be applied for the whole basin. Moreover, thescenario analysis was conducted for different heights of free-drain and proportion of application. In conclusion,
the paddy field could store about 90 million m3 water in whole paddy fields in the basin using 15 cm height offree-drain, and it significantly mitigated flood in the downstream of the Naruse River basin.
Keywords: paddy field dam, RRI model, Naruse River basin, flood control.
1 Introduction

2 Materials and methods

In 2019 from Oct. 12th to 13th, Typhoon No.19 attacked the
Kanto and Tohoku regions of Japan. Naruse River basin
was also suffered severe damage, especially in the Yoshida
River basin, where the total rainfall during the event was
343.0 mm, 26% of the annual precipitation. Around 5,700
ha of land along the Yoshida River was covered with water,
and the maximum inundation depth was about 4.5 m.
Paddy field dam is one of the flood control facilities that isgetting more consideration recently in Japan. It takes
advantage of the paddy fields’ temporary storage capacity
by installing drainage control devices at drain outlet. Since
the drainage control devices reduce the cross-sectional areaof the drain outlet, rainwater is temporarily stored in the
paddy field, and the peak discharge downstream from theriver will therefore reduce. In 2014, there wereapproximately 1,580,000 ha of paddy fields in Japan.
Consequently, it can be expected that paddy field dams
installed in this wide area have a significant capacity tostore rainwater. Due to existing models are developed for
application in small areas, they are detailed and required
many structural data of paddy fields and channels. To
consider the paddy field dam for an entire basin water
management, evaluation of the paddy field dam’s effect on
flood control in the whole basin is required.
Therefore, the objective of this research is firstly todevelop a simple paddy field dam model that can be applied
for the whole basin, and secondly to evaluate the potential
flood mitigation effect of the paddy field dam. The target
rainfall event and basin is typhoon No.19 in 2019 in the
Naruse River basin, where 21% of the area consists of
paddy fields. The flood inundation area was analyzed using
the Rainfall-Runoff-Inundation (RRI) model and a newly
developed simple one-dimension paddy field dam model.
Moreover, several scenarios were analyzed for different
heights of free-drain, and the proportion of the paddy fields
applied for dam operation.

2.1 RRI Model
RRI Model is a two-dimensional model which cansimulate rainfall-runoff and inundation simultaneously1).
The model assumes that both river and slope are located at
the same grid cell where the river channel is located. Thismodel considers slopes and river channels independently.
The flow on the slope grid cell is calculated by thetwo-dimensional diffusion wave model, and the channel
flow is calculated by the one-dimensional diffusion wave
model simultaneously.

68

2.2 Paddy field dam model
The developed paddy field dam model is a simple
one-dimensional model to be applied for the whole basin forassessing the potential effect of water outflow through the
paddy field dam to confirm the effect of water storage
capacity. The structure of the paddy field dam includes twoparts: a cone with an orifice (α) and a cylindrical free-drainpipe (β), as shown in Fig.1. According to the water depth ofthe paddy field, the factor of outflow from the paddy fielddam will be changed from the cylindrical weir to the orifice.
The outflow rates of the two were calculated sequentiallyand selected the smaller one as the outflow from thefree-drain.

Fig.1 Runoff control device for free-drain system.

Fig.2 The location of Naruse River basin.

The 8th International Symposium on Water Environment Systems

GP‐RSS & SyDe

(Nov. 13th ~ 14th , 2020)

Tohoku University‐‐‐‐‐ with Perspective of Global Safety
2.3 Study area and model application
The Naruse River, located in the northern part of Miyagi
prefecture with a total length of 89 km. The catchment areaof the Naruse River basin is approximately 1290 km2.
The topographic data used in this study were obtained
from Merit DEM with 3 arc-second after adjusted. The flow
accumulation data and flow direction data were computed
by ArcGIS. The hourly rainfall distribution obtained fromtwelve rainfall gauge stations were spatially interpolated
over the Naruse River basin using the Inverse Distance
Weighting. For the river cross-sections, the following
equations were used to estimate a simple river cross-section:
width (m)= 7S0.42, depth (m)= 1.5S0.18 where S (km2) was
the flow accumulation area. The other model parameters
were determined after calibration, as shown in Table.1.
In addition, the paddy field dam was designed to bearranged in all paddy fields mesh in the entire basin, only
one paddy field dam was installed for one mesh with paddy
field, and the resolution of mesh was 100 m × 100 m. To
estimate the potential of the paddy field in whole basin
approximately, two scenarios were formulated. The firstscenario was to change the height of free-drain. There were
six scenarios: 5 cm, 10 cm, 15 cm, 20 cm, 25 cm, 30 cm
height. And the second scenario was to change theproportion of paddy fields applied for paddy field dam in
the case of 15 cm height of the free-drain. There were five
scenarios: 20%, 40%, 60%, 80%, 100% of paddy field meshhave been installed paddy field dam.
Table 1 Model parameter setting.
Parameter
Mountains
Plainsn(m3/s)
0.3
0.3
da
(m)
2
(m/s)
0.4
ka
(m/s)
1.67×10-7
kv
φ
0.475
(m)
0.3163Sf(m3/s)
0.03
nr

3 Results and discussion
RRI model was applied to the Naruse River basin, and thecomparisons of the observed and simulated discharge at
gauging stations are showed in Fig.3. These four gauging
stations showed high corresponding.
(a)Sanbongibashi station (b)Nodabashi station(c)Ochiai station(d)Hataya station

the difference between the 0 cm and 5 cm scenario wassignificant. In the second scenario, as shown in Fig.5, themore paddy field dam installed in the paddy field, the effectof flood mitigation was more significant.

Fig.4 Comparison of discharge using the different
height of free-drain with paddy field dam applied in the
100% of paddy field. (Left)
Fig.5 Comparison of discharge using the different
proportion of paddy field dam in the paddy field with 15
cm height of free-drain. (Right)
To evaluate the flood mitigation effect of paddy field dam,
total stored water in the whole basin and maximum
inundated volume was calculated for each scenario.

Fig.6 Water storage volume in different height offree-drain. (Left)
Fig.7 Water storage volume in different proportion of
paddy field dam. (Right)
In Fig.6, when all of the paddy fields were applied for the
paddy field dam, 15 cm of free-drain could store 90 million
m3, while assumed water storage under the outlet offree-drain was more than 40 million m3, and it had mitigatedabout 50% of inundated water. Total stored water increasedby higher height of free-drain, but the difference between
assumed water storage and storage volume was getting
close because outflow from the paddy field was mainly
regulated by free-drain. In Fig.7, when the installation rateincreased, its effect of flood mitigation was getting higher.
Changing trends were linear even for inundation volume.

4 Conclusions
Overall, to evaluate the potential effect of the paddy fielddam distributed in the whole basin, a simple one-dimension
paddy field dam model has been developed and applied for
Typhoon 19th event in 2019 in Naruse River basin using the
RRI model. The peak discharge in the river and inundation
volume decreased as the height and proportion of paddy
field dam increasing. Clearly, the results showed that the
paddy field dam had a significant effect on flood control.

Acknowledgment
This research was supported by the Environment Research
and Technology Development Fund (S-18) of theEnvironmental Restoration and Conservation Agency ofJapan.

Reference
Fig.3 Comparison of simulation and observation
discharge at four gauging observation stations.
In the first scenario, as shown in Fig.4, the peak discharge
was reduced by increasing the height of free-drain. However,69[1] Sayama, T., Ozawa, G., Kawakami, T., Nabesaka, S.
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