Investigating the role of surface roughness representation in
generating flood inundation extents using hydraulic models
Presenter: Zhu Liu
Institution: Purdue University
Advisor: Dr. Venkatesh Merwade
July 2016, Sacramento
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Contents
 Objective
 Models
 Study Area
 Data
 Method
 Result
 Conclusion
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Flood disasters
Flooding Alone accounted for 47% of all weather related disaster
and affect 2.3 billion people in the world from 1995-2015 (UNISDR).
May 26, 2015
flood in Texas
Source:
http://www.wired.com/2015/05/
texas-floods-big-ended-statesdrought/#slide-1
Dem 31, 2015
Flood in Missouri
Source: UNISDR
http://www.unisdr.org/2015/docs/climatechange/COP21_WeatherDisastersReport_2015_FINAL.pdf
Source:
http://www.techinsider.io/10worst-us-natural-disasters-ofthe-year-2016-1
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Objective
 The impact of model structures on model simulated flood extents given the same input data
and boundary conditions.
The influence of channel surface roughness and floodplain surface roughness on model
performances in simulating inundation extents.
 Compare distributed floodplain surface roughness with unique floodplain surface roughness in
model calibration process.
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Hydraulic models
USA
Europe
① HEC-RAS 1D model (H1 model)
• 1D
• Water surface elevation and velocity computed at
discrete cross section.
• Solving full Saint-Venant equation using finite
difference method.
③ LISFLOOD-FP Diffusive model (LD model)
• 1D/2D coupled raster based model
• Solve continuity and momentum equations for
channel and floodplain respectively.
• Only consider mass transfer between channel and
floodplain while neglecting momentum transfer as
well as secondary circulation on mass transfer.
② HEC-RAS 2D model (H2 model)
• 2D or 1D/2D coupled
• Floodplain is represented by storage areas, which
are divided into meshes for calculation.
• Solve either full version of 2D Saint-Venant
equations or 2D diffusive wave equations.
④ LISFLOOD-FP Subgrid model (LS model)
• 1D/2D coupled, raster based model
• Incorporate subgrid scale representation of
channelized flow
• Continuity and momentum equations have their
specific form considering flow from channel and
floodplain respectively.
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Study reaches
Four study reaches included in this
research:
River
Arkansas
Length
(m)
Sinuosity
Floodplain
White River
56700
High
Wide
Black River
25200
High
Narrow
East River
11200
Moderate
Moderate
Flatrock River
3900
Low
Moderate
Indiana
Study Reaches
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Data ---Flood events and reference flood
maps
Study
Area
Start date
End date
Image
date
Reference
data type
White
River
22-Dem2013
6-Jan2014
1-Jan2014
LandSat
Black
River
4-Mar2015
27-Apr2015
25-Mar2015
LandSat
East
River
5-Jun2008
17-June2008
9-Jun2008
Ground
survey
based
Flatrock
River
4-Jun2008
9-Jun2008
9-Jun2008
Ground
survey
based
Flow and gage height data for upstream USGS stations during
historical flood events at (a) White River; (b)Black River; (c) East River
and (d) Flatrock River
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Reference flood map
All the reference
inundation maps are
treated as truth in
this research
Reference flood inundation maps at (a) White River; (b) Black River; (c)
East River and (d) Flatrock River
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Kalyanapu et al., 2010; McCuen, 1989)
Method
Land Cover
Description
Manning’s n
21
Developed, open
space
0.0404
22
Developed, low
intensity
0.0678
23
Developed, medium
intensity
0.0678
24
Developed, high
intensity
0.0678
24
Developed, high
intensity
0.0404
31
Barren land
0.0113
41
Deciduous forest
0.36
43
Mixed forest
0.4
52
Shrub/scrub
0.4
71
Grassland/herbaceous
0.368
81
Pasture/Hay
0.325
82
Cultivated crops
0.037
90
Woody wetlands
0.086
95
Emergent herbaceous
wetlands
0.1825
Assigning distributed floodplain surface roughness based on the NLCD land use map
(a) White River; (b)Black River; (c) East River and (d) Flatrock River
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Method
Two modeling scenarios
(1)Evaluate the effect of channel surface roughness for H1, LD, LS, H2 models.
Scenarios
①
②
Goal
Modeling
Evaluate the effect of channel
surface roughness on model
performance
Channel
roughness
Floodplain
roughness
Range:0.01-0.05
Step:0.005
Distributed
roughness
S1: 144
Evaluate the effect of
floodplain surface roughness
on model performance
Channel
roughness
Floodplain
roughness
S2:80
Fixed: 0.03
Range:0.03-0.15
Step:0.03
Total: 224
Simulations
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Method
Comparison of flood inundation maps
F index:
F=100*
C index:
C=100*
𝐴𝑜𝑜
𝐴𝐴+𝐴𝐴−𝐴𝐴𝐴
Ao
Aom
Am
𝐴𝐴𝐴
𝐴𝐴
Ao---observed area of inundation
Am---model simulated area of inundation
Aom---both observed and simulated as area of inundation
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Results
nch=0.03
nch=0.01
Scenario 1
npl is distributed
(a) White River; (b)Black River; (c) East River and (d) Flatrock River (a) White River; (b)Black River; (c) East River and (d) Flatrock River
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Model performance with respect to
channel surface roughness
 H1, LD, H2 model performances are
more sensitive to channel surface
roughness.
 Sensitivity of model performance for
LS model depends on the study area.
(a) White River; (b)Black River; (c) East River and (d) Flatrock River
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Change of inundation area respect to
channel surface roughness
Generally speaking, inundation
area will increase as channel
surface roughness becoming
higher for all models.
(a) White River; (b)Black River; (c) East River and (d) Flatrock River
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npl=0.12
npl=0.06
Scenario 2
nch=0.03
(a) White River; (b)Black River; (c) East River and (d) Flatrock River (a) White River; (b)Black River; (c) East River and (d) Flatrock River
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Change of inundation area respect to
floodplain surface roughness
Inundation extents increase as
floodplain surface roughness
increases for H1 and LS model.
While area increase is not
significant for LD model and H2
model.
(a) White River; (b)Black River; (c) East River and (d) Flatrock River
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Comparison two roughness
calibration strategy
One special
profile
nch=0.03
Strategy 1: By taking one single nch and
npl in the parameter space each time run
to find the best model performance
Strategy 2: By taking one single nch in its range
with distributed npl each time to run the
model to find the best model performance
Comparison best model Performance (F index)
from strategy 1 and strategy 2
(a) White River; (b)Black River; (c) East River and (d) Flatrock River
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Conclusion
 LD model overestimates the flood extent for most study reaches. The reason is that LD model only
considers mass transfer between channel and floodplain but neglects channel-floodplain momentum
transfer, and the effects of advection as well as secondary circulation on mass transfer. While LS model
performs well for Black River, White River and left floodplain of East River. This might because LS
model considers both mass and momentum transfer between channel and floodplain.
H1 model and H2 model solve the full Saint-Venant equations, but in one and two dimensional
format respectively. H1 model performs better when the channel surface roughness is low. On the
contrary, when the channel surface roughness becomes higher H2 model behaves better than H1
model.
H1 model, LD model and H2 model are more sensitive to channel surface roughness compared with
LS model. However, the sensitivity of LS model to channel surface roughness depending on the study
area. H1 model and LS model are more sensitive to floodplain Manning’s n. While the other two
model including LD model and H2 model are insensitive to floodplain friction, which describes that
floodplain processes containing a momentum effect are insignificant in these two models.
Flood inundation area will increase for all models as the channel surface roughness increases.
Inundation extents increase as floodplain surface roughness increases for H1 and LS model. While area
increase is insignificant for LD model and H2 model.
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Conclusion
 Applying distributed floodplain surface roughness could not improve the model performances
for calibration purpose. This indicates that adopting spatially varied floodplain surface roughness
value will reduce the variability of model performances. In addition, combination of unique
channel and floodplain Manning’s n value should still be used to calibrate the hydraulic models
regarding inundation extent in the future.
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