Wet weather water quality monitoring and urban flood analysis
in Hue Citadel area
Hiroaki Furumai Professor
Research Center for Water Environment Technology Department of Urban Engineering
The University of Tokyo
2
Canal-pond network and Huong river • The Hue Citadel area is located at
12km upstream from the Huong river mouth.
Area: 5.21km2 Population: 63,638 • The inner canal is linked with the
outer canal which is connected with Huong river.
• In rainy season, inundation occurs several times a year.
1km
N
Citadel area
GEOSS/AWCI: May 27, 2014
Inundation situation on Nov. 16th 2013
Le Thanh Ton Street
1. To investigate the characteristics of water pollution focusing on fecal contamination in the canals and ponds during dry and wet weather periods
2. To assess the influence of river water inflow and wastewater discharge on water flow and water pollution in canals by continuous monitoring with water depth and EC sensor
3. To develop urban inundation model considering river water level change
3 GEOSS/AWCI: May 27, 2014
Research Objectives
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Water sampling and Continuous monitoring
*JICA(2006), **Lieu et al(Hue University) B1
B2
B3
P1
P2
P3
P4 P5
P7
C1
C2 C3
S1
S2
S3
S4
1km
N
Water sampling (16 points) was conducted during dry and wet weather in 2012. Water quality parameters : are E.coli, Total coliform, COD, NH4-N, EC etc.
:Canal (B,C) :Pond (P) :Street(S)
Continuous monitoring (13 points) was conducted by water depth and EC sensors during a rainy season, Sep. to Dec. in 2012.
Water depth sensor
EC sensor
Vinyl pipe
* Measured water depth
GEOSS/AWCI: May 27, 2014 5
• E.coli and Total coliform (TC) concentrations of most samples exceeded the standard values (23 and 24 out of 27 samples (85 and 89%), respectively)
• E.coli and TC concentrations were higher in many samples than the regulation values (5 and 8 out of 8 samples, respectively).
• Inundated water samples at streets also showed high E.coli concentration.
Fecal contamination in canals, ponds, and inundated water
10
100
1000
10000
100000
Canal Pond Street
104
103
102
E.co
li(C
FU/1
00m
L)
N.D.
105
QCVN:B2
{9, 10, 0}{4, 4, 4}n=
Dry weatherWet weather
100
1000
10000
100000
1000000
10000000
Canal Pond StreetTota
lCol
iform
(CFU
/100
mL)
N.D.
106
105
104
103
107
QCVN:B2
{9, 10, 0}{4, 4, 4}n=
P7 (Sep.)
C2 (Sep.)
(Dry weather sample) (Wet weather sample)
DL 102
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Use of EC as pollution indicator and EC monitoring in canal
96
142
187
264 450 427
396
352
163
158
69
72
129
241
234
190
152
135
58
135
56
0 100
200 300 400
EC (μS/cm)
1km
N
y = 0.529x + 9.2894R² = 0.9976
0
50
100
150
200
250
300
0 200 400 600EC (μS/cm)
TDS
(mg/
L)
EC vs TDS
y = 0.0026x + 0.04R² = 0.8096
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0 200 400 600EC (μS/cm)
NH 4
-N (m
g/L)
EC vs NH4-N EC vs Total coliform
y = 0.002x + 3.997R² = 0.4735
3
3.5
4
4.5
5
5.5
0 200 400 600EC (μS/cm)
Tota
lcol
iform
(log(
CFU
/100
mL)
)
QCVN:B2
Inundation simulation in Hue City Model simulation has been conducted in the Hue Citadel area to explain the inundation situation. We plan to conduct model simulation under climate change and discuss on possible effective countermeasures for river flood and urban inundation control.
Simulation results
Past inundation record
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GEOSS/AWCI: May 27, 2014 8
・Sewer system 1) Drainage system data ・River, channel and pond
・Ground elevation 2) Ground elevation and surface data
・Land use
・Rainfall 3) Meteorological data
・Water level of river, channel or pond 4) Hydrological data
・Inundation depth and area ・Water quality of inundated water
5) Data for model calibration
Required data for model simulation
GEOSS/AWCI: May 27, 2014 9
Data collection Model construction
Inundation simulation
Model calibration
For model construction - Drainage system - Land use - Ground elevation
Data collection For parameters of calculation - Rainfall data - Water level and quality of river - Water level and quality of
channels and ponds in urban area - Flow and quality of wastewater
Scenario analysis Data collection For calibration
- Record of inundation area, depth - Water quality of inundated water
Data processing by GIS software
Checking the model performance
Flowchart of urban inundation model development and Scenario analysis
Predicted future rainfall data and river flow data under climate change
Possible measures for flood and pollution control
Inundation simulation with water level rise
10
0.5
0.1
Inundation depth(m)
0.2
Water level 2.0m : inundation area = 232ha
Water level 1.0m : inundation area = 172ha
Freefall :inundation area = 167ha
Inundation area expands with increasing the river water level. Therefore, it is very important to consider river water level to evaluate urban inundation situation.
Simulation condition: Return period 2 years -2 days rainfall Maximum rainfall intensity: 61.1mm/hr Total rainfall: 164mm 0
1020304050607080
1 5 9 13 17 21 25 29 33 37 41 45
Duration (hours)
Rain
fall
inte
nsity
(mm
/hr)
2year-2day rainfall (interval 60 minutes)
Total: 164mm61.1mm/hr
GEOSS/AWCI: May 27, 2014
Increase of heavy rainfall event in Hue - GCM model prediction on rainfall -
GEOSS/AWCI: May 27, 2014 11
0
2
4
6
8
10
0
2
4
6
8
10
> 50mm/d > 100mm/d > 200mm/d > 50mm/d > 100mm/d > 200mm/d
1981-2000 2046-2065 [days] [days]
2.40
7.66
0.76 2.28
0.54
6.83
- 5 GCM models : GFDL, MIROC_H, MIROC_M, MIUB, GISS - Model calculation (1981-2000) and prediction (2046-2065) - Average frequency of 5 models during rainy season (Sep-Dec)
GEOSS/AWCI: May 27, 2014 12
• Fecal pollution: Most of the canals and ponds are fecally contaminated during both dry and wet weather periods.
• EC usage as an indicator of water flow: EC value indirectly indicates pathogenic pollution level. EC sensor is very effective tool to know continuous change of water flow as well as water pollution in urban canal which is affected by wastewater and river water inflow.
• Urban inundation simulation: The developed sewerage model linked with river water level is useful to estimate the detailed inundation characteristics in the Citadel area.
In the next step, it is necessary to conduct model estimation of pathogenic pollution during rainy season and to propose effective measures for flood and health risk management under climate change.
Conclusions and future task
13 GEOSS/AWCI: May 27, 2014
Thank you for your attention Hiroaki FURUMAI
Professor, Research Center for Water Environment Technology, Graduate School of Engineering, University of Tokyo [email protected]
River flood Inland flood (Inundation)
Key Points of Inter-linked Research in Hue
• 3 Risks: flood risk, inundation risk, health risk Hydrological model --- Watershed scale Inundation & runoff quality model --- Drainage scale Health risk model --- Community/Human scale • 3 M: Modeling, Monitoring, and Management Data collection for Modeling Model calibration and validation Sampling and Monitoring work Scenario development and analysis for Management
Multi-scale analysis
Inter-linked research GEOSS/AWCI: May 27, 2014 14
Adaptation to Climate Change => Understanding and assessing climate change-related risk
Inter-linked Cooperative Research in Huong River Basin
Research Steps and Final Goal
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How to manage river flood, urban inundation, and flood-related health risk under climate change?
WEB-DHM model --- Watershed scale (River flood risk)
WQ Monitoring ---- Drainage scale (Water quality risk)
Urban inundation model --- Drainage scale (Inundation risk)
Dose-response relationship --- Community/human scale (Health risk)
1) Evaluation of river flow/floods at the Hue city using the watershed hydrological model.
2) Evaluation of inundation characteristics using the urban inundation model considering the output of 1) .
3) Evaluation of pathogenic pollution during flooding based on the output of 2) and water quality monitoring.
4) Estimation of human health risk during flooding.
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River flood
GEOSS/AWCI: May 27, 2014 17
River flooding in Hue city at Nov. 8th, 2013. http://talkvietnam.com/2013/11/hue-city-hydropower-dams-open-districts-submerged/)
Inland flood (inundation) in Hue city at Sep. 4th, 2009. http://tropical.way-nifty.com/blog/2009/09/rainy-season-ha.html)
Inland flood
18 GEOSS/AWCI: May 27, 2014
Research Background - flood and water-born diseases -
• Frequent urban flooding and its damage during rainy season in Southeast Asia
Flooded period
Epidemic period
90 % percentile
Mea
n nu
mbe
r of
case
s per
wee
k
Weeks
Flood starts
Schwartz et al., 2006
• High occurrence rate of water-born diseases during and after urban flood
Inland flood (inundation) in Hue on Sep. 4th, 2009.
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- Rainy season is Sep. to Dec. - Serious inundations occurred in 1999 and 2004. Inundation depth exceeded 2m.
978mm
682mm
0200400600800
1000
1月 2月 3月 4月 5月 6月 7月 8月 9月 10月 11月 12月1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Rain
fall(
mm
/yea
r)
1999 2004
Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Inundation depth in 1999 Nov.
Inundation Depth
Inundation situation
Monthly rainfall from 1995-2005
Rainy season Dry season
• Limited capacity of urban drainage with pollution In heavy rainfall events, wastewater and runoff water have chance to overflow to streets in the downtowns, because the drainage ca
• pacity was designed for the less-developed condition. In addition, lakes and rivers have been polluted both by wastewater discharge from sewerage system and non-point pollution sources such as wash-off from surface land.
• Importance of pathogenic pollution monitoring It is meaningful to investigate wet weather pollution in urban runoff, overflow to lakes from sewer system and inundated water, focusing on pathogenic indicators.
20 GEOSS/AWCI: May 27, 2014
Research Background (3) - Limited drainage capacity and monitoring data -
C1
C2C3
1km
N
GEOSS/AWCI: May 27, 2014 21
EC change during dry weather
C1 and C3 are affected by the river water inflow from river water. At C2, canal water might be stagnant and less diluted by river water.
Dry weather period
Introduction Methodology Results Summary
C1 C3
0
100
200
300
400
500
00.20.40.60.8
11.2
9/17 9/17 9/18
Wat
er d
epth
*(m
)EC (μS/cm
)
0:00 12:00 0:00
0
100
200
300
400
500
00.20.40.60.8
11.2
9/17 9/17 9/18
Wat
er d
epth
*(m
)
EC (μS/cm)
0:00 12:00 0:00
C2
0
100
200
300
400
500
00.20.40.60.8
11.2
9/17 9/17 9/18
Wat
er d
epth
*(m
)
EC (μS/cm)
0:00 12:00 0:00
Water depth
EC
EC ranges; River water: 40 to 70 μS/cm (*2)
Wastewater: 660 μS/cm (*1)
(*1): Feb. 2012 sampling (*2): Report from HueWACO (water supply company)
C1
C2C3
1km
N
GEOSS/AWCI: May 27, 2014 22
EC change during wet weather Wet weather period
At C1 and C2, canal water was diluted by the river water inflow. C3 might be affected by both the polluted inner canal water and the cleaner river water.
Introduction Methodology Results Summary
0
100
200
300
400
500
00.20.40.60.8
11.2
10/6 10/7 10/7
Wat
er d
epth
*(m
)
EC (μS/cm)Water depth
EC
12:00 0:00 12:00
C1
C2
C3
0
100
200
300
400
500
00.20.40.60.8
11.2
10/6 10/7 10/7
Wat
er d
epth
*(m
)
EC (μS/cm)
12:00 0:00 12:00
0
100
200
300
400
500
00.20.40.60.8
11.2
10/6 10/7 10/7
Wat
er d
epth
*(m
)EC (μS/cm
)
12:00 0:00 12:00
Research objectives
- To develop a urban inundation model by collecting data of drainage system, land use and ground elevation etc.
- To simulate the inundation situation by the urban model considering Huong river water levels
- To discus on pathogenic pollution during flooding/inundation period
- To estimate the future inundation characteristics under climate change
- To propose effective measures by scenario analysis
23 GEOSS/AWCI: May 27, 2014
GEOSS/AWCI: May 27, 2014 24
1D water flow analysis during dry weather Appendix 2.1
Wat
er le
vel
Time
B3 B1
C3 C2
C1
C3 C2
C1
1D analysis Condition: Water levels are same in high tide
Low tide High tide High tide Low tide
C1
C2C3
1km
N
C1
C2C3
1km
N
050100150200250300350400450500
-10
-5
0
5
10
15
20
10/6 10/7 10/7
EC
Water flow
12:00 0:00 12:00
Wat
er fl
ow (m
3/s)
EC (μS/cm)
GEOSS/AWCI: May 27, 2014 25
1D water flow analysis during wet weather Appendix 2.2
1D analysis was applied to wet weather period. Flow direction might explain why EC fluctuated at C3.
C1
C2 C3
1km
N
GEOSS/AWCI: May 27, 2014 26
Wastewater stagnation during dry weather
• C1 and C3 are affected by the river water inflow from river water. • At C2, canal water might be stagnant and less diluted by river water.
Introduction Methodology Results Summary
0100200300400500600
-0.15-0.1
-0.050
0.050.1
0.15
17-Sep 17-Sep 17-Sep
Wat
er le
vel c
hang
e (m
)
EC (μS/cm)
6:30 12:30 18:30
Water level change at C1
EC C2C1
C3
EC ranges; River water: 40 to 70 μS/cm (*2)
Wastewater: 660 μS/cm (*1)
(*1): Feb. 2012 sampling (*2): Report from HueWACO (water supply company)
Continuous monitoring was conducted from Sep. 2012 in the canals.
C1
C2 C3
1km
N
GEOSS/AWCI: May 27, 2014 27
Dynamic EC change during wet weather
• At C1 and C2, canal water was diluted by the river water. • C3 might be affected by both the inner canal water and the river
water.
Introduction Methodology Results Summary
0
100
200
300
400
500
00.10.20.30.40.50.60.7
6-Oct 7-Oct 7-Oct
Wat
er le
vel c
hang
e (m
)
EC (μS/cm)
12:00 0:00 12:00
Water level change at C1
ECC2
C1C3
Required data for inundation simulation
GEOSS/AWCI: May 27, 2014 28
1) Drainage system data Drainage system data was made by JICA in 2006. Size and location of ponds were given by Lieu. (Hue Univ.)
Conduits Ponds
Manholes
Canals
Ground Ground Elevation
Spill crest
Invert elevation
Bottom elevation
Diameter
Length
Shape
Manhole
Conduit
GEOSS/AWCI: May 27, 2014 29
2) Ground elevation and surface data
Hue Citadel area Ground slope data (TIN; Triangular Irregular Network) was made from ground elevations of 576 nodes. (495/576 were manholes)
Additional canals and ponds
Radar elevation survey device on a car (TOPCON)
Elevation survey using radar was done on August 2012.
Required data for inundation simulation
GEOSS/AWCI: May 27, 2014 30
Required data for inundation simulation
2) Ground elevation and surface data
Hue Citadel area Road land use data was made by JICA.
Vegetation layer (NDVI + NVEI)
IKONO satellite image Multi spectrum image- red/blue/green/NIR
Land use can be evaluated by satellite image data.
GEOSS/AWCI: May 27, 2014 31
・Rainfall
Required data for inundation simulation
3) Meteorological data
1 hour interval data measured by Hue Meteorology and Hydrology Center.
0
20
40
60
9/1 10/1 10/31 11/30 12/30 1/29
Rain
fall
(mm
/hr)
Year of 2011
・Water level of river at Kim Long station 4) Hydrological data
1 hour interval data measured by Hue Meteorology and Hydrology Center.
GEOSS/AWCI: May 27, 2014 32
At 16 points, including 3 points on street, water level and EC sensors are installed.
165mm
32mm
EC logger
150mm
25mm
Water level logger
B1
B2
B3
P1
P2
P3
P4P5
S1
S2
P7
S3
P6
C1
C2 C3
Measured parameters by sensorsWater levelWater level・EC (and Turbidity at C3)
P:Pond C:Canal B:Boundary between Canal and Huong river S:Street(ForInundated water)
Required data for inundation simulation
Hue Citadel area
Survey for setting the sensors (At S2, frequently inundated point)
5) Data for model calibration ・Water level and water quality of inundated water Sensors are useful to get WQ data under different inundation conditions.
GEOSS/AWCI: May 27, 2014 33
1km
0.5
Depth (m)
0.2
No consideration of river water level
Flooded area: 82.6ha Max depth: 0.95m
Flooded area: 100.2ha Max depth: 0.96m
Given with river water level: 2.0m
1D2D analysis by XPSWMM
Simulation condition: Return period 2 years -2 days rainfall Maximum rainfall intensity: 61.1mm/hr Total rainfall: 164mm
Urban inundation simulation
01020304050607080
1 5 9 13 17 21 25 29 33 37 41 45
Duration (hours)
Rain
fall
inte
nsity
(mm
/hr)
2year-2day rainfall (interval 60 minutes)
Total: 164mm61.1mm/hr
Collaboration with other GRENE groups • in the simulation of future flooding situations, need
prediction data of rainfall and fluctuation of water level. – If rainfall predictions are different in the upstream and
downstream of Huong river, must consider flow rate Q (m3/sec) of whole Huong river basen.
• Corroborate with calculate result of other GRENE groups!
34
Thua Thien Hue Province (Phong Tran et al. 2007.)
Hue city →
(H-Q curve of Huong River observed in Kim Long Station, 2005.)
↑ Kim Long Station
H = 1.2438xQ0.6908 R2 = 0.8267
050
100150200250300350
0 1000 2000 3000 4000
H (C
m)
Q (m3/s)
Relationship Q=F(H) - Kim Long
GEOSS/AWCI: May 27, 2014
List of required data to simulate with the distributed model
1. Drainage system data – Sewer system – River, canal and pond
2. Ground surface data – Ground elevation – Landuse
3. Meteorological data – Rainfall
4. Hydrological data – Water level of river, canal and pond
5. Calibration data – Water level of canal or pond – Inundation depth and area
GEOSS/AWCI: May 27, 2014 35
2. Methodology
Num. of Nodes : 495 Num. of Links : 677 from JICA in 2006.
Ground Ground Elevation
Spill crest
Invert elevation
Bottom elevation
Diameter
Length
Shape
node
links
List of required data to simulate with the distributed model
1. Drainage system data – Sewer system – River, canal and pond
2. Ground surface data – Ground elevation – Landuse
3. Meteorological data – Rainfall
4. Hydrological data – Water level of river, canal and pond
5. Calibration data – Water level of canal or pond – Inundation depth and area
GEOSS/AWCI: May 27, 2014 36
2. Methodology
List of required data to simulate with the distributed model
1. Drainage system data – Sewer system – River, canal and pond
2. Ground surface data – Ground elevation – Landuse
3. Meteorological data – Rainfall
4. Hydrological data – Water level of river, canal and pond
5. Calibration data – Water level of canal or pond – Inundation depth and area
GEOSS/AWCI: May 27, 2014 37
2. Methodology
Rainfall (mm/hour)
Water level (m)
List of required data to simulate with the distributed model
1. Drainage system data – Sewer system – River, canal and pond
2. Ground surface data – Ground elevation – Landuse
3. Meteorological data – Rainfall
4. Hydrological data – Water level of river, canal and pond
5. Calibration data – Water level of canal or pond – Inundation depth and area
GEOSS/AWCI: May 27, 2014 38
2. Methodology
Past inundation record
Simulated area
GEOSS/AWCI: May 27, 2014 39