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Flood Frequency Analysis Using Generalized Extreme Value Distribution and

Floodplain Mapping for Hurricane Harvey in Buffalo Bayou

Conference Paper · May 2018

DOI: 10.1061/9780784481400.034

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Flood Frequency Analysis Using Generalized Extreme Value Distribution and
Floodplain Mapping for Hurricane Harvey in Buffalo Bayou
Narayan Nyaupane1, Swastik Bhandari1, Mofuzur Rahaman1, Kyle Wagner1, Ajay Kalra1, Sajjad
Ahmad2 and Ritu Gupta3
1
Department of Civil and Environmental Engineering, Southern Illinois University Carbondale,
1230 Lincoln Drive, Carbondale, IL 62901-6603. E-mail: kalraa@siu.edu
2
Department of Civil and Environmental Engineering and Construction, University of Nevada
Las Vegas, 4505 S. Maryland Parkway, Las Vegas, NV 89154-4015.
3
Jacobs, One Financial Plaza, 501 North Broadway, Saint Louis, MO63102.

Abstract
Hurricane-induced flooding is a recurring phenomenon causing severe damages to lives and
properties in southern coastal states. The magnitude of rainfall observed during Harvey
Hurricane in August 2017 was the all-time maximum for some of the regions in Houston, TX.
This research focuses on modeling different recurrence frequency flows in a selected river reach
along with the Harvey flooding condition. The statistical part involves the flood frequency
analysis using Generalized Extreme Value distribution. The floodplain analysis is performed by
developing a hydraulic model using Hydraulic Engineering Center’s River Analysis System and
Esri ArcMap software. Historical flow data and terrain geometric data are extracted from United
States Geological Survey. A river reach of Buffalo Bayou is selected and analyzed for several
flow scenarios, and flood inundation map of the affected region is developed. The model is
calibrated and validated for river stage by adjusting Manning’s roughness coefficient. The
evaluation of each flood stage and comparison with the Harvey flooding stage is expected to
provide valuable information for floodplain management, riverside development, and for future
flood assessment plans in the adjoining areas.
Keywords: Hurricane Harvey, HEC-RAS, Floodplain mapping, GEV, Rainfall

Introduction
Recent scientific evidence suggests that climate change has been leading to induce extreme
hydrological events such as flooding and droughts all over the world (Christensen and
Christensen 2003, Kalra et al. 2013, 2017, Stocker 2014). The intensity and frequency of such
weather phenomena are being altered with the advent of global climate change (Jiang et al. 2016,
Carrier et al. 2016; Thakali et al. 2016; Wada et al. 2017). The magnitudes of extreme
precipitations are also showing increasing trend in recent years (Ahmad et al. 2010; Easterling et
al. 2000, Peng et al. 2013; Sagarika et al. 2014, Nyaupane et al. 2017; Pathak et al. 2016, 2017a
& b;). Among the extreme hydrological events, flooding has become a major concern as it has
greater socio-economic implication which can affect thousands of people and bring about
negative impacts on the ecosystem (Brouwer et al. 2007, Gautam et al. 2013, Maheshwari et al.
2016, Paz et al. 2013; Messner and Meyer 2006). Rapid urbanization and other anthropogenic
activities are also the major reasons for increasing the peak flood discharge, which in turn, can
cause severe damage in short span of time (Suriya and Mudgal 2012; Tamaddun 2017, 2018).
The U.S. has sustained more than 200 weather and climate disasters that exceeded $1 billion
since 1980 and total cost of damage was nearly $1.2 trillion (Smith and Katz 2013).

Tropical storms and landfalling hurricanes are the primary hydrological events causing
disastrous flooding in southern coastal regions of the United States. A recent category 4 Harvey
Hurricane caused historic extreme precipitation of over 50-inch magnitude in a 7-day period.
National Oceanic and Atmospheric Administration have estimated that the cost of damage was
more than $160 billion and displaced over 30 thousand of people. Estimation of flood magnitude,
intensity, and occurrence frequency is a primary task for hydrologists and meteorologist for
future assessment of floods. Flood risk analysis and management planning require flood
frequency analysis and floodplain mapping of vulnerable riverside areas (Di Baldassarre et al.
2010; Jobe et al. 2017; Sagarika et al. 2015, 2016). The floodplain maps illustrate hazard,
vulnerability, and risks associated with a flood in the region. Development of a flood mapping
includes the hydrological analysis to estimate the peak flow with the aid of flood frequency
analysis, hydraulic analysis to find the water levels in flooding, and terrain modeling to see the
flooded regions (Robayo et al. 2004). Flood frequency analysis uses the probability distribution
to calculate the flood peak discharge for a given year. One of the commonly used tools for
modeling extreme weather events such as flooding, wind, precipitation is Generalized Extreme
Value (GEV) distribution (Martins and Stedinger 2000). GEV distribution shows the best fit
distribution for flood duration and observed flood duration (Salleh et al. 2016, Thakali et al.
2017).

Several techniques are available to model the flooding scenarios and find flood levels along the
river reach of interest (Islam and Sado 2000, Knebl et al. 2005, Tingsanchali and Gautam 2000,
Wada et al. 2017, Zhao et al. 2017). Various hydraulic models are used to precisely simulate the
flooding events to enhance the decision-making process regarding the prediction and prevention
of floods. Various studies have shown the capability of a commercially available version of 2D
numerical simulation (Quiroga et al. 2013, Seyoum et al. 2011). The traditional one-dimensional
(1D) hydraulic model could not perform the hydrodynamic as well as physical conditions of river
channels. Thus, many 1D hydraulic models are being replaced by 2D and 3D hydraulic models
(Merwade et al. 2008). Though there is huge uncertainty about the characteristics of a flood
event, two-dimensional numerical analysis has been proved as an important tool to characterize
the flood. HEC-RAS hydraulic model has been widely used in conjunction with Environmental
System Research Institute (ESRI) ArcGIS software and HEC-GeoRAS for 1D analysis and
mapping of floods (Thakur et al. 2017a & b). HEC-RAS has released added 2D capabilities after
its 2014 beta version. The latest version, HEC-RAS 5.0.1 is introduced not only with the stand-
alone capability to perform 2D hydraulic routing but is also enhanced by capabilities of detailed
animation and mapping of flood within the RAS mapper in HEC-RAS itself (Bhandari et al.
2017;). This ability allows hydraulic engineers to analyze model results through the geospatial
visualization with geometric data to more readily identify hydraulic model deficiencies and make
model improvements (Brunner 2002; Chen et al. 2018). This will ultimately support the
concerned authorities to make precise and accurate decisions regarding water resources
management.

Study area and Data

The study area is a river reach of Buffalo Bayou flowing through Fort Bend and Harris County
of Texas. The watershed area of Buffalo Bayou is nearly 260 square kilometers. This region has
been susceptible to flooding and recently, Hurricane Harvey caused submergence of the area in
between August 17 and September 3, 2017. For the hydraulic modeling, a river reach in between
United States Geological Survey (USGS) gaging stations number 08072300 and 08072350 is
selected. Figure 1 shows the location of the study area and the watershed of Buffalo Bayou.

Figure 1: Reach length of Buffalo Bayou in Fort Bend County, TX.

The primary data used for the analysis are streamflow and stage data of the river, Digital
Elevation Model (DEM) data of the terrain, and land cover data. The annual maximum flow and
stage data were extracted from USGS website ranging from1977 to 2017. The DEM data of one-
third arc second resolution was downloaded from USGS website for terrain modeling. For the
roughness coefficient calculation, land cover data for the river reach was extracted from National
Land Cover Database (NLCD 2011) website (http://www.mrlc.gov/nlcd2011.php). Figure 2
shows the yearly peak runoff value from 1977 to 2017.

10000 Yearly Peak Streamflow

8000
Streamflow (cfs)

6000

4000

2000

0
1975 1980 1985 1990 1995 Year 2000 2005 2010 2015 2020

Figure 2: Yearly peak streamflow of Buffalo Bayou near Katy, TX.

Methodology
(a) Statistical Evaluation (b) HEC-RAS Modeling (c)Calibration and Validation
Gauge & streamflow Geometric data = 1/3 arc sec
Upstream
data collection DEM from national map
unsteady
hydrograp
Yearly peak Manning’s roughness =
streamflow NDCL2011 land cover Modeling

Model plan = unsteady Rearrange

Statistical evaluating flow for Harvey Period the model
peak streamflow of
(1977-2016) U/S boundary condition = Model No
Hydrograph WSE =
Probability of Harvey D/S boundary condition = Gage ht
periods streamflow Normal depth

Run time = Harvey period Yes

Return period Calibrated &
in years validated model
Output Gage
Figure 3: Schematic of the modeling approach

The 1/3 arcsecond DEM model from the National map (USGS) was used as the terrain model
and based on which was used to define the hydraulic and geometric properties. The roughness of
the bed channel and overbanks were taken based upon land cover map provided on National
Land Cover Database 2011 (NLCD 2011) map. The range of Manning’s n value used for each
land cover definition and value of the NLCD 2011 map are listed in Table 1.
Table 1: Manning’s roughness for each land cover definition of NLCD.

NLCD Value Land Cover Definition Range of Manning's n Value

11 Open Water 0.025-0.05
21 Developed, Open Space 0.03-0.05
22 Developed, Low Intensity 0.08-0.12
23 Developed, Medium Intensity 0.06-0.14
24 Developed, High Intensity 0.12-0.2
31 Barren Land (Rock/Sand/Clay) 0.023-0.03
41 Deciduous Forest 0.1-0.16
42 Evergreen Forest 0.1-0.16
43 Mixed Forest 0.1-0.16
52 Shrub/Scrub 0.07-0.16
71 Grassland/Herbaceous 0.07-0.16
81 Pasture/Hay 0.025-0.05
82 Cultivated Crops 0.025-0.05
90 Woody Wetlands 0.045-0.15
95 Emergent Herbaceous Wetlands 0.05-0.085
Upstream hydrograph downstream normal depth was considered as the upstream and
downstream boundary condition for the flow analysis. The upstream hydrograph from USGS
08072300 Buffalo Bayou near Katy, TX was used for flow routing. The time period of the
hydrograph was selected similar with the time of hurricane Harvey period over the area so that
the result could be checked against the available data. The downstream slope was calculated
using terrain map and applied to the downstream boundary condition for normal depth condition.
Model simulation interval was gradually decreased during the model as run time increases with
decrease in simulation interval. Finally, 5 min interval was considered after a number of coarser
interval of run. The gage height was considered for the calibration of the model. The model was
calibrated against the 2D flow area, simulation interval and manning’s roughness. Figure 4
provides the terrain map of the project area after conversion to the RAS Mapper.
Figure 4: represents the terrain map developed from available DEM using RAS Mapper of HEC-
RAS.

Result and Discussion

For the analysis 5 km reach length of Buffalo Bayou from gage station USGS 08072300 to
USGS 08072350 is considered. The annual peak discharge data from the upstream gage site were
evaluated using GEV as probability distribution method. The return period of the peak discharge
occurred during the Harvey was 917yr return period with the probability value of 0.109%. Shape
parameter of 0.15 is considered for the analysis.
During the Harvey period wide area of the reach length was flooded, thus at least 400m of width
on either side was considered for the 2D flow area. Total 481578 number of cells were created
for 2D flow area with a mesh size of 1.5m X 1.5m. Cropped NLCD 2011 map by 2D flow area
was overlaid on terrain map and Manning's roughness coefficient was extracted.
 a) b)

(c) (d)
Figure 5: a) Applied hydrograph as upstream boundary condition b) hydraulic properties created
c) flow mesh created on terrain for the analysis with upstream and downstream boundary
conditions. d) Land cover map overlaid on terrain with stream condition at 25Aug2017.
Unsteady flow data of Harvey period starting from 15Aug2017 to
17Sep2017 were applied as the upstream boundary condition as shown in
a) b)
. The peak of the hydrograph is 257 m3/s occurred on 29 Aug 2017. Downstream boundary
condition was applied as normal depth for friction slope of 0.001.
For the flow analysis, the same period of applied hydrograph is considered. The computation
interval was set for 5 minutes while mapping, hydrograph and detail output interval was set for
15 minutes. From the multiple simulations using different time interval, it is noticed that the
shorter the computation interval better would be the result while the computation time increases
rapidly.
Calibration of the model was carried out by comparing the water surface elevation (WSE) from
HEC-RAS with observed gage height at gauging station USGS 08072300 for the period of
15Aug2017 to 17Sep2017 as given in Figure 6 (a) and (b) respectively. The difference in
elevation between the modeled and observed gage height is due to the difference in the datum of
the observed gage height from terrain level. Figure 6 (c) represents the plot between modeled
WSE and observed gage height. A trendline was fitted to get the linear correlation between these
two values. A correlation coefficient (r) of 0.998 was obtained with a coefficient of
determination (R2) 0.996. This validates the HEC-RAS 2D model for the area.

120 Gage height vs time plot

115
Gage Height (ft)

110

105

100

95

Date

a) b)

Relation betwen modeled vs observed gage height

80
Modeled wate surface elevation (ft)

70 R² = 0.99

60

50

40

30

20

10

0
96 98 100 102 104 106 108
Observed gage height (ft)

(c)
Figure 6: Comparison of observed gage height vs HEC-RAS water surface elevation (a) gage
height at the field (b) gage height from the model (c) correlation plot between plot height from
HEC-RAS model and existing gage height.
Velocity distribution plot from the model is obtained for the gage station as shown in Figure 7
(a). The maximum velocity obtained is 2.2 m/s for the gage station occurred on 27Aug2017.
Similarly, maximum depth occurred on the same day with value 11.3m. A flood map for the
Harvey period was prepared. The prepared map shows the flooding all over the considered 2D
mesh area.

Figure 7: Model results (a) depth at the gage station (b) peak velocity near the gage station (c)
Flood map representing the condition of the 29Aug2017.
A flood map was prepared for the Harvey peak flow as shown in Figure 7 (c). The map
represents the whole of the region flooded due to the peak flow occurred on 29Aug2017 at
Houston.

Conclusion
The study aims to evaluate the peak flood of the Buffalo Bayou and conduct 2D unsteady flow
simulation using HEC-RAS 2D with the latest available version of 5.03. A 5km reach length of
the river is considered for the simulation with an almost 400m wide area on both sides of the
river as the floodplain. Flooding occurred on Aug 2017 during the Harvey peak period was
represented on the model. The model was run from 15Aug2017 to 17Sep2017. The result of 2D
unsteady flow can represent the flood simulation real-time scenario. Following conclusion can be
drawn from this study.
  The peak flood of 257 m3/s was found to be nearly 1000-year return period i.e.
probability value of 0.109% based on historical data using GEV method with shape
parameter 0.15.
 For the plain area, 2D flow simulation gives better result over 1D due to the lateral flow
simulation capability.
 HEC-RAS 2D is strong enough for 2D flow simulation with good representation on RAS
Mapper.
 With the increase in computation better result will be obtained.
 The whole area was flooded during the peak Harvey period.
Similar study would help for better understanding and preparation of peak flood events. The
evaluation and simulation of such extreme flood events like Harvey flooding will provide
valuable information for floodplain management, riverside development, and for future flood
assessment plans in the adjoining areas.

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