1. Department of Civil/Environmental and Chemical Engineering
Youngstown State University
Master’s Thesis Defense
April 25, 2016
Niraj Lamichhane
Committee:
Dr. Suresh Sharma
Dr. Tony Vercellino, and
Dr. Bradley A. Shellito
Prediction of Travel Time and Development of Flood
Inundation Maps for Flood Warning System Including Ice
Jam Scenario : A Case Study of the Grand River, Ohio

2. Thesis Overview
 Background/Need of study
 Objectives
 Materials
 Methodology
 Results/Discussions
 Conclusion
 Acknowledgements
2April 25, 2016

3. Background/Need of study
Flood/Flood Warning System:
• Most common form of natural disaster
• ~ 520 millions people affected every year worldwide
• Property loss ~ 50 - 60 billions USD
• In US: loss of 140 people lives/year & property loss ~ 8 billions USD
• Ice jam flooding - common event in Northern region of the US that
result the loss of lives and millions’ dollar worth properties
• Flood warning system - warn people sufficient time ahead
3April 25, 2016

4. Background
General Terms
No Flood
2006 Flood
Location: ECKART America Corporation, 830 E Erie St., Painesville OH
Source : USGS Report 2006 Flood
Flood travel time :
Time taken by flood to travel
from one place to another
Inundation area :
The areas covered by flood during
peak flooding time
cont.
4April 25, 2016

5. I. To quantify the effects of resolution of elevation datasets and
Manning’s roughness in travel time and inundation area prediction
II. To develop an approach for flood warning system and generate flood
inundation maps for the Grand River
III. To assess the potential impacts in river stage and hydraulic structures
due to winter ice cover/ice jam
Flood maps could be uploaded online.
Objectives
5April 25, 2016

6. Theoretical Description
HEC-RAS
(Hydraulic Engineering Center - River Analysis System)
Widely accepted hydraulic tool, developed by United States Army Corps
of Engineers (USACE)
Two types of simulation in HEC-RAS
• Steady Flow
• Unsteady Flow
Chapter 2
6April 25, 2016

7. Theoretical Description
Unsteady flow
• Flow velocity changes with
time
Continuity Equation
Momentum Equation
Steady flow
• Flow velocity do not change with
time
Energy Equation
cont. Chapter 2
7April 25, 2016

8. • Located in Northeastern region of Ohio
• Watershed Area : 705 mi2
• Length of Grand River : 102.7 mi
Materials
Study Area:
Map of USA Grand River Watershed
Grand River Outlet
City of Painesville
Harpersfield
Ohio
Chapter 2
8April 25, 2016

9. • Harpersfield to mouth at Fairport Harbor: 32.2 mi. in length
• Three major tributaries: Mill, Paine, and Big Creek
Materials
Study Area:
cont.
Source: Google Map
Harpersfield
City of
Painesville
Fairport
Harbor
04211820
04212100
Chapter 2
9April 25, 2016

10. Methodology
Collection of input data
Preparation of geospatial
data in HEC-GeoRAS
Simulation in HEC-RAS
Flood maps generation in
HEC-GeoRAS
1. Pre -processing
2. Processing
3. Post -processing
Overall Methodology
Chapter 2Objective I
10April 25, 2016

11. Methodology cont.
1. Pre-processing: Collection of input data
• Elevation datasets (LiDAR, 10m DEM, 30m DEM)
• Detail topographic survey of the river
• Land cover
• Discharge/Stage
• Aerial photographs
• Detail drawing of bridges
Collection of input data
Preparation of geo-spatial
data in HEC-GeoRAS
Simulation in
HEC-RAS
Flood maps generation in
HEC-GeoRAS
Chapter 2Objective I
11April 25, 2016
LiDAR – Light Detection and Ranging
DEM – Digital Elevation Model
Process for obtaining LiDAR data

12. Methodology cont.
1. Pre-processing: Geo-spatial data preparation
Collection of input data
Preparation of geo-spatial
data in HEC-GeoRAS
Simulation in
HEC-RAS
Flood maps generation in
HEC-GeoRAS
• Create geo-spatial data (river, cross sections, bank
stations, flow path) for all datasets (LiDAR DEM, 10m
DEM, and 30m DEM).
• Export to HEC-RAS
Chapter 2Objective I
Geo-spatial data preparation in HEC-GeoRAS
12April 25, 2016
Surveyed points
Points where cross section
elevation are compared for
various elevation datasets

13. Methodology cont.
2. Processing: Simulation in HEC-RAS
• Import datasets from HEC-GeoRAS
Collection of input data
Preparation of geo-spatial
data in HEC-GeoRAS
Simulation in
HEC-RAS
Flood maps generation in
HEC-GeoRAS
 LiDAR DEM
 10m DEM
 30m DEM
 LiDAR with survey
 10m DEM with survey
 30m DEM with survey
• Surveyed in 77
cross sections
• Integration of survey data in all
datasets.
• Elevation datasets for modeling
Chapter 2Objective I
13April 25, 2016

14. Methodology cont.
2. Processing: Simulation in HEC-RAS
Collection of input data
Preparation of geo-spatial
data in HEC-GeoRAS
Simulation in
HEC-RAS
Flood maps generation in
HEC-GeoRAS
• Apply boundary condition (discharge, stage data, etc.)
• Run simulation for 10, 50, 100, 500 return floods and
2006 floods using6 datasets
• Export data to HEC-GeoRAS
Chapter 2Objective I
HEC-RAS Model
D/S streamgage station
U/S streamgage station
04212100
04211820
14April 25, 2016

15. Collection of input data
Preparation of geo-spatial
data in HEC-GeoRAS
Simulation in
HEC-RAS
Flood maps generation in
HEC-GeoRAS
Methodology cont.
2. Processing: Simulation in HEC-RAS
Calibration/Validation
• Manning’s roughness - only variable in HEC-RAS
• Calibrated/validated for 8 events between 1996-1998 for
stage and discharge
• Model evaluation - Using 4 statistical criteria
• Calibrated roughness value : 0.035 - channel, 0.15 - flood plain
Chapter 2Objective I
15April 25, 2016
R2=0.99 R2=0.99

16. Methodology cont.
Collection of input data
Preparation of geo-spatial
data in HEC-GeoRAS
Simulation in
HEC-RAS
Flood maps generation in
HEC-GeoRAS
3. Post-processing: Flood maps generation
• Hydraulic computations imported from HEC-RAS
• Flood maps generation using HEC-GeoRAS
Chapter 2Objective I
Comparison of predicted flood inundation maps for 2006 Flood when different elevation datasets were used
Area in red color
covers all the area
of other colors.
16April 25, 2016

17. Results & DiscussionsObjective I
Effect of topography (Resolution of elevation datasets) :
• Cross section greatly vary for different resolution datasets
• 10m DEM - better in channel than LiDAR
Chapter 2
• Topographic survey performed.
• LiDAR with integration of survey
data was taken for comparison and
generating maps
17April 25, 2016

18. Results & DiscussionsObjective I
Effect of topography: Flood travel time (to City of Painesville)
• Travel time – calculated for 5 different floods using 6 datasets
Travel time difference:
Chapter 2
Note: Comparison was done with the result obtained from LiDAR with survey
• % difference in travel
time was highest for
coarse elevation
dataset.
18April 25, 2016
11.03%-15.01%1.19%-3.35% 10.24%-11.75%
3.67%-4.87%8.73%-10.52%

19. Results & DiscussionsObjective I
Effect of Topography: Inundation area
• Inundation maps – generated for 5 different floods using 6 datasets
Inundation area difference
Chapter 2
Note: Comparison was done with the result obtained from LiDAR with survey data
• % difference in
inundation area is
highest for coarse
elevation dataset
19April 25, 2016
32.56%-44.52%3.55%-7.80% 8.08%-15.48%
17.36%-18.71%10.85%-18.71%

20. Results & DiscussionsObjective I
Effect of Manning’s roughness “n” : Flood travel time (Table 2-6)
Chapter 2
In channel sections
• 0.035, 0.030, 0.025, and 0.020
• Varied in channel & constant in
flood plains
• Travel time: highest for higher
“n” (0.035)
• Decrement in travel time:
7.48% - 22.35%
In flood plains
• 0.15, 0.10, 0.09, and 0.7
• Varied in flood plains & constant
in channel
• Travel time: lowest for higher
“n” (0.15)
• Increment in travel time:
0.60% - 3.45%
20April 25, 2016

21. Results & DiscussionsObjective I
Effect of Manning’s roughness: Inundation area
Chapter 2
In channel section In flood plain
Note: Comparison was done with the result obtained when roughness value is
0.035 in channel & 0.15 in flood plain
• The sensitivity of Manning’s roughness - found to be more in channel
than in floodplain
21April 25, 2016

22. Results & DiscussionsObjective I
Effect of Manning’s roughness: Inundation area
Chapter 2
Difference in predicted inundation area for various sets of Manning’s roughness values
0.020_0.15
0.025_0.15
0.030_0.15
0.035_0.07
0.035_0.09
0.035_0.10
0.035_0.15
World Imagery
Ü
22April 25, 2016

23. To develop an approach for flood warning system and generate inundation
maps for various flood stages in the Grand River
Objective II
23April 25, 2016

24. MethodologyObjective II Chapter 3
Overall Flood Warning Approach
Development of
hydraulic model
Calibrate steady model
and run the simulation
Preparation of digital
flood inundation maps
Installation of siren
system
Evacuation time
Recommendation for better
warning system
24April 25, 2016

25. Results & DiscussionsObjective II Chapter 3
Rating curve
for streamgage at City of Painesville
Validation of rating curve
Steady flow : Calibrated using high-water marks of 2006 flood
calibration
Rating curve : Developed using 75% exceedance flow of 1988-2005
Q=166.67H1.79
Validation : 2006-2015
R2=0.93
25April 25, 2016

26. Results & DiscussionsObjective II Chapter 3
Calculation of travel time & development of flood-inundation maps
• Flood travel time for 12 different flood stages
• Developed digital inundation maps can be uploaded online.
( E.g. http://water.weather.gov/ahps/inundation.php )
Travel time and inundation area for various flood stages
26April 25, 2016

27. Results & DiscussionsObjective II Chapter 3
Flood damages along the Grand River
• Major affected places: Hidden-Valley Park near S. Madison Rd, Helen
Hazen Wyman Park, Mill Stone Drive, Steel Ave. and Grand River Ave,
Kiwanis Recreation Park, Western Reserve & Fairport Harbor Yacht
Clubs, Ram Island, Hidden Harbor Drive area, Fairport Harbor
• Affected bridges: Bridges at Vrooman Rd., Lakeland Freeway &
Fairport Rd. Vrooman Bridge - more critical (water level increased>3 ft
above road level) from the analysis as well as from the historical data
• More than 100 houses, many roads and parks are susceptible to 500 year
return period or greater floods
27April 25, 2016

28. Results & DiscussionsObjective II Chapter 3
Flood inundation map for 19.35 ft stage (2006 flood) near City of Painesville
S.MadisonRd
Huntington Road
Western Reserve &
Fairport Yacht Club
28April 25, 2016

29. Results & DiscussionsObjective II Chapter 3
1D and 2D animation of 2006 flood in the Grand River
Flood Animation in HEC-RAS
29April 25, 2016

30. Results & DiscussionsObjective II Chapter 3
Water level at various bridges for 19.35 ft flood stage (2006 Flood)
30April 25, 2016

31. Results & DiscussionsObjective II Chapter 3
Recommendation for more effective automated flood warning system
• Reestablish discontinued streamgage (04211820) at Harpersfield
• Establish new streamgages for major creeks like Mill, Paine and Big
Creeks
• Install automated warning system containing a rain gauge, a
Geostationary Operational Environmental Satellite (GOES) transmitter,
a Radio Frequency transmitter using Automated Local Evaluation in
Real Time (ALERT) protocol and a voice modem
31April 25, 2016

32. Objective III
To assess the potential impacts in river stage and hydraulic structures due
to winter ice cover/ice jam
32April 25, 2016

33. Objective III
Near Fairport Harbor
Near Fairport Rd. bridge
33April 25, 2016
Objective III Chapter 4
Historical
Source: CRREL Ice Jam Database, USACE (2015)

34. 34
MethodologyObjective III Chapter 4
Overall Approach
April 25, 2016
Historical analysis and
estimate ice thickness
Prepare input data and run the
simulation for various
scenarios
Compare and analyze results
of those scenarios
Generate flood inundation
maps
Maximum estimated ice thickness= 10 inches for
1977/1978 period
Where
ti = thickness of ice
𝛼 = coefficient for wind and snow cover
AFDD = Accumulated Freezing Degree Days
Ta = Temperature of air
Historical analysis and ice thickness estimation
𝑡𝑖=𝛼√𝐴𝐹𝐷𝐷
𝐴𝐹𝐷𝐷 = (32−𝑇 𝑎)
Modified Stefan’s equation

35. MethodologyObjective III Chapter 4
Ice jam simulation was run for 3 different scenarios using 5 winter discharge
a) without ice cover & jam with bridges
b) with ice cover and ice jam with bridge
c) with ice cover & ice jam but no bridges
35April 25, 2016
cont.
5 different winter discharge values
a) 25 Percentile b) 50 Percentile
c) 75 Percentile d) 90 Percentile
e) 100 Percentile

36. Results & DiscussionsObjective III Chapter 4
Relationship discharge, AFDD & precipitation
• Increase in river discharge due to ice melting not due to precipitation.
Increase in discharge
1976/1977 1984/1985
36April 25, 2016

37. Results & DiscussionsObjective III Chapter 4
Comparison of water level
• Overall increase in river stage (~2 ft.) due to ice cover/jam
• Increase in stage was higher mostly at the upstream part of bridge (Table 4-6)
Longitudinal profile of bridge at South Madison road Longitudinal profile of bridge at Blair Road
Effect of bridge:
Increase in river stage by 4.16 ft Increase in river stage by 1.21 ft
37April 25, 2016
4.16 ft
1.21 ft

38. Results & DiscussionsObjective III Chapter 4
Vrooman bridge - more crucial - water level crossed deck level
Affected several times - 2007, 2010, 2011, and 2014
Large volume of ice blocks in jam location - can result flooding when it
melts
38April 25, 2016

39. Results & DiscussionsObjective III Chapter 4
Flood inundation maps - Generated for 5 different winter discharge
Increase in inundation area for all return period floods
22 % for 100 percentile & 52% for 25 percentile
Flood plain map for highest winter flow (100 percentile flow)
39April 25, 2016

40. Conclusions/Recommendations Chapter 5
• Travel time and inundation area: high for coarse elevation datasets
• Channel is better represented by 10m DEM than by LiDAR data
• Manning’s roughness – more sensitive in channel than in flood plains
• > 100 houses, many roads, parks and bridges affected by 500 years return
period flood or the higher ones
• Significant effects of ice cover/jam observed in river stage and flood
• Installation of siren system in suitable location to warn people
40April 25, 2016

41. Conclusions/Recommendations Chapter 5
• Results can be uploaded in National/Regional Portal System
• Further calibration and validation with detail recording of high-water
marks and ice jam recordings might help develop more reliable model
41April 25, 2016

42. Acknowledgements
Advisor: Dr. Suresh Sharma
Committee Members: Dr. Tony Vercellino and Dr. Bradley A. Shellito
• Dr. Anwarul Islam, Department Chair
• Ohio Sea Grant – for providing research grant
• Christopher R. Goodel
• My parents and families
• Friends and well wishers: Binod, Kamal, Shobha, Aashish, Janga, Bhishan, Sabin,
Sanjay, Abhijeet and many others
• Department of Civil/Environmental and Chemical Engineering
April 25, 2016

43. Thank you !
April 25, 2016