SIMULATING SURFACE FLOW AND SEDIMENT TRANSPORT IN VEGETATED WATERSHED FOR CURRENT

SIMULATING SURFACE FLOW AND SEDIMENT TRANSPORT IN VEGETATED WATERSHED FOR CURRENT
SIMULATING SURFACE FLOW AND SEDIMENT
TRANSPORT IN VEGETATED WATERSHED FOR CURRENT
AND FUTURE CLIMATE CONDITION
By
Yang Bai
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF CIVIL ENGINEERING AND ENGINEERING MECHANICS
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
WITH A MAJOR IN CIVIL ENGINEERING
In the Graduate College
THE UNIVERSITY OF ARIZONA
2014
2
UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation
prepared by YANG BAI
entitled SIMULATING SURFACE FLOW AND SEDIMENT TRANSPORT IN VEGETATED
WATERSHED FOR CURRENT AND FUTURE CLIMATE CONDITION
and recommend that it be accepted as fulfilling the dissertation requirement for the
Degree of DOCTOR OF PHILOSOPHY
_____________________________________________________________Date: April 15, 2014
Dr. Jennifer G. Duan
_____________________________________________________________Date: April 15, 2014
Dr. Juan B Valdes
_____________________________________________________________Date: April 15, 2014
Dr. Kevin Lansey
_____________________________________________________________Date: April 15, 2014
Dr. Thomas Mexiner
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and
recommend that it be accepted as fulfilling the dissertation requirement.
_____________________________________________________________Date: April 15, 2014
Dissertation Director: Dr. Jennifer G. Duan
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an
advanced degree at the University of Arizona and is deposited in the University Library to be
made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission, provided
that accurate acknowledgment of source is made. Requests for permission for extended quotation
from or reproduction of this manuscript in whole or in part may be granted by the author.
SIGNED: Yang Bai
4
ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to my advisor, Dr Jennifer Duan, for
her valuable guidance and consistent encouragement throughout this study. Being a
meticulous and creative researcher, she always points me the correct directions and
teaches me how to achieve my target bit by bit. I will always be grateful for her patience
and sincerity.
I am equally grateful to my committee members, Dr. Juan B Valdes, Dr. Kevin
Lansey and Dr. Thomas Mexiner, for their encouragement and insightful comment to my
study.
I am heartily thankful to my colleagues Chuanshui Yu, Jaeho Shim, Khalid A
Abdalrazaak al Asadi, Shiyan Zhang, Ari Posner, Anu Acharya, Jianyin Zhou, Lei liu and
alumnus Erick Rivera-Fernandea, Tongchao Nan and Eleonora Maria Demaria for all the
help they offered in my study. It is a pleasant experience to work with all of them. I also
thank my friends Xiaobin Ding, Rui Chen, Qiuli Shen, Fei Du, Di Wang, Xin Ren, Run
Huang, Xinda Hu, Zhifeng Yan and many others. They have supported me for my life in
a foreign country.
In the last but not the least, I would like to express my thanks to my parents and
my brother. Thanks for your love and support in every step.
5
The funding support from US National Science Foundation, Water and Climate
Sustainability Program, Grant 1038938, to the University of Arizona is highly
appreciated.
6
TABLE OF CONTENTS
TABLE OF CONTENTS .................................................................................................... 6
LIST OF FIGURES ............................................................................................................ 9
LIST OF TABLES ............................................................................................................ 12
ABSTRACT ...................................................................................................................... 13
CHAPTER 1 INTRODUCTION ...................................................................................... 16
1.1 Statement of Problem .............................................................................................. 16
1.2 Literature Review .................................................................................................... 20
1.2.1 Numerical Modeling of Unsteady Flow and Sediment Transport for Channel
Networks .................................................................................................................... 20
1.2.2 Research on Effects of Vegetation on Flow and Sediment Transport .............. 22
1.2.3 Two-dimensional Surface Routing Model ....................................................... 26
1.2.4 Prediction of Flood Frequency Curve Incorporating Climate Change ............. 27
1.3 Objective ................................................................................................................. 30
CHAPTER 2 GOVERNING EQUATIONS AND NUMERICAL METHODS .............. 32
2.1 Governing Equations for One-dimensional Flow and Sediment Model ................. 32
2.2 Governing Equations for Two-dimensional Hydrological Model .......................... 34
2.3 Numerical Methods ................................................................................................. 36
CHAPTER 3 SIMULATING UNSTEADY FLOW AND SEDIMENT TRANSPORT IN
VEGETATED CHANNEL NETWORK .......................................................................... 40
3.1 Introduction ............................................................................................................. 40
3.2 Mathematical Formulation ...................................................................................... 43
3.2.1 Governing Equations ........................................................................................ 43
3.2.2 Empirical Formulae .......................................................................................... 43
3.2.3 Impacts of Vegetation on Flow Field ............................................................... 45
3.2.4 Vegetation Influence on Sediment Transport ................................................... 47
3.2.5 Infiltration Model ............................................................................................. 50
7
TABLE OF CONTENTS - continued
3.3 Numerical Solution Method .................................................................................... 51
3.3.1 Numerical Schemes .......................................................................................... 51
3.3.2 Boundary Conditions ........................................................................................ 51
3.4 Case Study ............................................................................................................... 53
3.4.1 Case 1: Steady Flow in Flume with Vegetation ............................................... 53
3.4.2 Case 2: 2006 Flash Flood in Santa Cruz River, Tucson, AZ............................ 58
3.5. Discussion .............................................................................................................. 66
3.5.1 Influence of Vegetation Coverage .................................................................... 67
3.5.2 Significance of Non-Equilibrium Model .......................................................... 68
3.6 Conclusions ............................................................................................................. 70
CHAPTER 4 APPLICATION OF TWO-DIMENSIONAL SURFACE ROUTING
MODEL FOR SANTA CRUZ WATERSHED BY CONSIDERING VEGETATION
COVERAGE INLUENCE ................................................................................................ 72
4.1 Introduction ............................................................................................................. 72
4.2 Modifications to CHRE2D Model .......................................................................... 73
4.2.1 Quantification of Vegetation Coverage ............................................................ 73
4.2.2 Incorporation of Vegetation Influence ............................................................. 73
4.2.3 Other Hydrological Processes .......................................................................... 76
4.3 Study Site and Data Source ..................................................................................... 77
4.3.1 The Study Site .................................................................................................. 77
4.3.3 Precipitation Data Source ................................................................................. 78
4.4 Model Application and Calibration ......................................................................... 79
4.5 Conclusions ............................................................................................................. 81
CHAPTER 5 IMPACTS OF CLIMATE CHANGE ON MAGNITUDE AND
FREQUENCY OF FLOOD IN LOWER SANTA CRUZ RIVER ................................... 82
5.1. Introduction ............................................................................................................ 82
5.2 Watershed Description and Data Source ................................................................. 85
8
TABLE OF CONTENTS - continued
5.2.1 The Study Site .................................................................................................. 85
5.2.2 Climate Data Source ......................................................................................... 86
5.2.3 Bias Correction of Precipitation Data ............................................................... 86
5.2.4 Watershed Model .............................................................................................. 95
5.3 Results ..................................................................................................................... 95
5.3.1 Comparison of Observed and Simulated Results for Historical Period ........... 95
5.3.2 Flood Magnitude and Frequency Analysis Using Annual Maximum Data ..... 99
5.3.3 Flood Frequency Analysis Using Daily Maximum Data ............................... 102
5.4 Conclusions and Discussion .................................................................................. 104
CHAPTER 6 SUMMARY, CONCLUSIONS AND FUTURE RESEARCH................ 106
6.1 Summary and Conclusions .................................................................................... 106
6.2 Future Research ..................................................................................................... 109
REFERENCES ............................................................................................................... 111
9
LIST OF FIGURES
Figure 1.1 Concept of watershed model and channel network model………….………..17
Figure 3.1 Four zones in the vertical profile for horizontal velocity…………….……....46
Figure 3.2 Sketch map for confluence point…………………………………………......52
Figure 3.3 Flow chart of the program……………………………….…………….…..…53
Figure 3.4 Flume set-up and vegetation arrangement for the experiment case………….55
Figure 3.5 Measured (t = 0h and t =13h) and calculated (t = 13h) bed levels for Run 2..56
Figure 3.6 Measured (t = 0h and t =30h) and calculated (t = 30h) bed levels for Run 2..57
Figure 3.7 Measured (t = 0h and t =14.5h) and calculated (t = 14.5h) bed levels for Run
3....…………………………………………………………………………………….….58
Figure 3.8 Measured (t = 0h and t =32h) and calculated (t = 32h) bed levels for Run 3..58
Figure 3.9 Simulation area and USGS gauge location.…………………...……………..60
Figure 3.10 Flow hydrograph in different gauges…………………………………….…61
Figure 3.11 Simulation and measured flow rate result at La Cholla, Rillito River….…..64
Figure 3.12 Simulation and measured flow rate result at Cortaro gauge, Santa Cruz
River…...............................................................................................................................64
Figure 3.13 Bed elevation change along Rillito River…………………………...…..…..65
Figure 3.14 Bed elevation change along Santa Cruz River………...………………....…66
Figure 3.15 Sensitivity analysis of bed elevation change along Rillito River on vegetation
density…………………………………………………………………………….….......67
Figure 3.16 Sensitivity analysis of bed elevation change along Santa Cruz River on
vegetation density………………………………………………………….…………….68
Figure 3.17 Comparison of results from non-equilibrium and equilibrium model using 7
nodes and 10 nodes at t = 30 hours, respectively…………………...……………..…….70
Figure 4.1 Satellite photos coverage area………………………………………….……74
10
LIST OF FIGURES - continued
Figure 4.2 Extracted vegetation layers for representative photos…………..……………75
Figure 4.3 Map of the Santa Cruz River Watershed………………………………..……78
Figure 4.4 Rainfall depth (mm) distribution at different time (t = 5, 11, 21 hours) for July
15th, 1999 flood event……………………………………………………………….…...79
Figure 4.5 Measured and simulated hydrograph for Cortaro gage of July 15th, 1999…...80
Figure 4.6 Water depth (m) at different time (t = 6, 12, 22 hours) for July 15th, 1999 flood
event…………………………………………………….………………………………..81
Figure 5.1 Monthly precipitation raw data and bias-corrected data for a selected cell….89
Figure 5.2 WRF model raw and bias-corrected monthly P and monthly median
precipitation P data for 1990 -2000 for a selected cell ..……………..………….………90
Figure 5.3 WRF model raw and bias-corrected monthly P and monthly median
precipitation P data for 2031- 2040 for a selected cell…………………………………..90
Figure 5.4 WRF model raw and bias-corrected monthly P and monthly median
precipitation P data for 2071-2079for a selected cell……………………………………91
Figure 5.5 Monthly median precipitation P raw data and bias-corrected data
for different periods with spatial average……………………………………………….92
Figure 5.6 Daily precipitation (P) data for a selected cell for period 1990-2000………..93
Figure 5.7 Hourly precipitation (P) raw data and bias-corrected data (B-C) for
Period 1990-2000………………………………………………………………………...94
Figure 5.8 Percent of yearly peak discharge exceeded certain discharge of observed and
simulated data for 1990-2000 periods ……………………………………….…………..98
Figure 5.9 Current Log-Person III Flood frequency curves for Santa Cruz River at
Cortaro using USGS 17B ………………………………………………………………..98
Figure 5.10 Percent of daily peak discharge exceeded certain discharge for observed and
simulated 1990-2000 period …………………………………………………………….99
Figure 5.11 Percent of yearly peak discharge exceeded certain discharge for observed
historical period and simulated future periods ……………...……………….…………101
11
LIST OF FIGURES - continued
Figure 5.12 Current and future Log-Person III flood frequency curves for Santa Cruz
River at Cortaro using USGS 17B …………………………………………..…………101
Figure 5.13 Percent of daily peak discharge exceeded certain discharge for observed
historical period and simulated future periods ……………………...………………….104
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LIST OF TABLES
Table 1.1 List of some equations to calculate roughness coefficient……………………24
Table 3.1 Parameters used in the cases…………………………………………………..55
Table 3.2 Results related to bed form for Case 1……………………………………......57
Table 3.3 d50 and d90 of sediment samples in different locations of Santa Cruz River and
Rillito River……………………………………………………………...……….……...62
Table 3.4 Errors analysis of flow hydrograph and bed elevation change………………..66
Table 4.1 Vegetation coverage for different locations…………….…………….……....75
Table 5.1 Root-mean-square-error of WRF model bias-corrected daily precipitation and
observed daily precipitation for all cells…..…………………………………………......93
Table 5.2 Statistical analysis results of yearly peak flow for different periods………...102
Table 5.3 Average times of daily peak flow greater than certain discharge for one year of
each period………………………………………………………………..………….....104
13
ABSTRACT
The complex interaction between flow, vegetation and sediment drives the never
settled changes of riverine system. Vegetation intercepts rainfall, adds resistance to
surface flow, and facilitates infiltration. The magnitude and timing of flood flow are
closely related to the watershed vegetation coverage. In the meantime, flood flow can
transport a large amount of sediment resulting in bank erosion, channel degradation, and
channel pattern change. As climate changes, future flood frequency will change with
more intense rainfalls. However, the quantitative simulation of flood flow in vegetated
channel and the influence of climate change on flood frequency, especially for the arid
and semi-arid Southwest, remain challenges to engineers and scientists. Therefore, this
research consists of two main parts: simulate unsteady flow and sediment transport in
vegetated channel network, and quantify the impacts of climate change on flood
frequency.
A one-dimensional model for simulating flood routing and sediment transport
over mobile alluvium in a vegetated channel network was developed. The modified St.
Venant equations together with the governing equations for suspended sediment and bed
load transport were solved simultaneously to obtain flow properties and sediment
transport rate. The Godunov-type finite volume method is employed to discretize the
governing equations. Then, the Exner equation was solved for bed elevation change.
Since sediment transport is non-equilibrium when bed is degrading or aggrading, a
14
recovery coefficient for suspended sediment and an adaptation length for bed load
transport were used to quantify the differences between equilibrium and non-equilibrium
sediment transport rate. The influence of vegetation on floodplain and main channel was
accounted for by adjusting resistance terms in the momentum equations for flow field. A
procedure to separate the grain resistance from the total resistance was proposed and
implemented to calculate sediment transport rate. The model was tested by a flume
experiment case and an unprecedented flood event occurred in the Santa Cruz River,
Tucson, Arizona, in July 2006. Simulated results of flow discharge and bed elevation
changes showed satisfactory agreements with the measurements. The impacts of
vegetation density on sediment transport and significance of non-equilibrium sediment
transport model were accounted for by the model..
The two-dimensional surface flow model, called CHRE2D, was improved by
considering the vegetation influence and then applied to Santa Cruz River Watershed
(SCRW) in the Southern Arizona. The parameters in the CHRE2D model were calibrated
by using the rainfall event in July 15th, 1999.
Hourly precipitation data from a Regional Climate Model (RCM) called Weather
Research and Forecasting model (WRF), for three periods, 1990-2000, 2031-2040 and
2071-2079, were used to quantify the impact of climate change on the magnitude and
frequency of flood for the Santa Cruz River Watershed (SCRW) in the Southern Arizona.
Precipitation outputs from RCM-WRF model were bias-corrected using observed gridded
precipitation data for three periods before directly used in the watershed model. The
15
watershed model was calibrated using the rainfall event in July 15th, 1999. The calibrated
watershed model was applied to SCRW to simulate surface flow routing for the selected
three periods. Simulated annual and daily maximum discharges are analyzed to obtain
future flood frequency curves. Results indicate that flood discharges for different return
periods are increased: the discharges of 100-year and 200-year return period are increased
by 3,000 and 5,000 cfs, respectively.
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CHAPTER 1
INTRODUCTION
1.1 Statement of Problem
Up to date, the interactions between flow, vegetation, and sediment transport are
complicated and not well understood. In a river network (Figure 1.1), a large flood can
cause severe bank erosion, channel degradation and channel pattern change, which can
undermine instream structures. The vegetation-induced drag reduces flow velocity and
increases flood attenuation and sediment deposition. Sediment aggradation or degradation
will change river geometry, and in turns change channel conveyance. In a watershed,
watershed runoff, the main source of flood, is influenced by the resistance of vegetation.
On the other hand, the increased frequencies of heavy precipitation events are very likely
to occur in the 21st Century as a result of higher global mean temperature (IPCC, 2007).
The tendency of increased precipitation events will change the magnitude and frequency
of flood and consequently influence sediment aggradation and degradation. Therefore,
the study of interactions among flow, sediment transport, vegetation and impacts of
climate changes on magnitude and frequency of flood are important engineering research
topics.
17
Precipitation P
Overland Flow
Channel Flow
Infiltrate I
Channel network
Sediment
Figure 1.1 Concept of watershed model and channel network model
Sediment transport is an extremely complex physical process. The movements of
sediment particles are controlled by the resultant of gravity, drag force, lift force, and
extra friction forces caused by collision between solid particles. In natural rivers,
sediment transport is often associated with erosion/deposition; the process of erosion and
deposition changes channel geometry, and can cause severe engineering and
18
environmental problems. Severe erosion can occur during the construction of roads and
bridges when protective vegetation is removed and steep cut-and-fill slopes are left
unprotected; such erosions can cause local scour along with serious sedimentation
downstream (Julien, 2010).
Different forms of vegetation are ubiquitous features in river main channels and
floodplains; such vegetation can significantly influence stream flow processes. Riparian
and in-stream vegetation can alter the hydraulics of main channel and floodplain flows;
cause flow deceleration and deflection as well as local sediment deposition; affect river
morphology; and provide aquatic habitat characterized by reduced or highly variable
velocity, fine sediment deposition, and complex structural cover (Wu, 2005). For these
reasons, the influence of vegetation must be considered when modeling flow and
sediment transport in alluvial rivers.
On the other hand, flood frequency plays an important role in the design of waterrelated structures. It is well known that the climate change will result in changed flood
intensity and/or frequency. The frequent occurrences of extreme floods in recent decades
have received much interest. For example, the Mississippi River Flood in 2010, and the
nearly 500 year flood occurred in Rillito River, AZ in 2006. Many researches attributed
those abnormalities to climate change due to carbon emissions. Studies of climate change
concluded a global surface temperature warming for the next century (Griggs and Noguer,
2002; Klein Tank et al., 2002; Parker et al., 1992). It is expected that the warming climate
will lead to increase in the magnitude of extreme precipitation (Arnbjerg-Nielsen, 2006;
19
Hennessy et al., 1997; Schmidli and Frei, 2005; Zhai et al., 2005) and other climate
properties (e.g., snow melt) contributing to flooding across the globe.
Flood frequency analysis has been a standard tool for designing flood protection
structures. The most common approach is to combine basin-scale hydrologic models with
climate change scenarios derived from climate model. Nowadays, flood flow generated
from the rainfall-runoff models are commonly used for flood analysis in addition to
historical records.
Numerical modeling is the most cost effective tool for evaluating river changes
under engineering modifications (e.g. restoration, construction). With the availability of
high-capacity computers, numerical models have been widely used in one-dimensional,
two-dimensional and three-dimensional hydrodynamic and sediment transport models.
Although three-dimensional model is the best approximation to the real flows, it is too
complicated and time consuming especially when sediment transport is simulated. Two
dimensional models are often used for surface flow routing, dam break flow and sediment
transport modeling. When simulating the fluvial processes in a long river reach for a long
time, one-dimensional model is the most cost-effective tool.
In this study, a one
dimensional model will be developed to simulate flow and sediment transport in
vegetated channel network, and a two dimensional surface routing model is employed to
route projected precipitation throughout the study watershed for flood frequency analysis.
20
1.2 Literature Review
This chapter provides a brief state-of-the-art review of related research on (a) onedimensional numerical modeling of unsteady flow and sediment transport, (b) effects of
vegetation on flow and sediment transport, (c) numerical modeling of channel networks,
(d) two-dimensional watershed model, and (e) prediction of flood frequency curve
considering the impact of climate change.
1.2.1 Numerical Modeling of Unsteady Flow and Sediment Transport for Channel
Networks
The channel network is the focus for the interacting processes which carry water
and sediment out of drainage basin, channels provide the network of routes along which
water and sediment are carried out of a drainage basin. Because of the complex network
topology, a one-dimensional network model may be the best choice for river network
systems, particularly to simulate fluvial processes of long rivers for a long time. (Chen et
al., 2011; Gunduz and Aral, 2005; Islam et al., 2005; Nguyen and Kawano, 1995;
Shabayek et al., 2002; Zhu et al., 2010). The majority of one-dimensional sediment
transport numerical model for channel networks uses weighted four-point finite
difference scheme to discretize the governing equations (Chau and Lee, 1991; Wu et al.,
2004); however, the stability of this scheme is strongly related to the weighting
coefficients and friction terms (Lyn and Goodwin, 1987; Venutelli, 2002). An important
concern for channel network modeling is the method to deal with flow and sediment
exchange in confluence point. Flow condition and sediment transport are very
complicated at confluence point; flow velocity will decrease in this point and resulting in
21
backwater; sediment will be trapped in confluence point. For explicit FVM method, mass
and momentum conservation equations of confluence point will be imported to solve the
governing equations.
One-dimensional (1-D) modeling of sediment transport in streams has been
extensively developed over the past decades and widely used in engineering applications
due to its robustness and simpler structures when compared to multi-dimensional models.
Steady and quasi-steady 1-D models, such as HEC-RAS (HEC 2010), HEC-6 (HEC1991), SRH-1D (BR 2010), IALLUVIAL2 (IIHR 2009) has been widely tested and
applied to flow and sediment transport studies. For one-dimensional numerical models,
Saint Venant equations are commonly used as the governing equations. The original
Saint Venant Equations can be simplified by neglecting the high order differential terms
to dynamic model for unsteady flow simulation (Toro, 2001). Moreover, the effect of the
sediment transport to flow needs to be considered. Convection-diffusion equations can be
used as the governing equations for sediment transport. The Saint Venant equations
together with the sediment transport equations are generally accepted as the governing
equations in most sediment models (Duan and Nanda, 2006; Wu, 2004; Zhang, 2011;
Zhang and Duan, 2011).
Most sediment transport formulae are developed for predicting equilibrium
sediment transport rate in steady uniform flow, which assumes sediment transport rate
equals to the transport capacity. However, when channel bed is degrading or aggrading,
sediment transport rate can be great or less than the transport capacity, called non-
22
equilibrium transport (Zhang et al., 2013). To simulate the process that sediment
transport rate gradually develops into the transport capacity, the adaptation length (Duc
and Rodi, 2008; El kadi Abderrezzak and Paquier, 2009; Wu and Wang, 2007; Zhang et
al., 2013) is often used to calibrate bed load transport; and the recovering coefficient is
used for suspended sediment transport. The adaptation length is defined as the distance
required for the bed load transport rate to reach equilibrium at a given flow condition
(Cao et al., 2004; Wu and Wang, 2007). And the suspended load recovering coefficient is
defined as the ratio of near-bed suspended sediment concentration to the depth-averaged
concentration at equilibrium state (Zhang et al., 2013). The non-equilibrium sediment
transport algorithm is adopted here to simulate sediment transport in unsteady flow in
vegetated channel network.
1.2.2 Research on Effects of Vegetation on Flow and Sediment Transport
Vegetation in rivers increases flow resistance and bank stability, improves water
quality, and promotes habitat diversity, but vegetation also reduces channel conveyance.
The existence of vegetation changes both flow field and sediment transport processes.
The vegetation-induced drag reduces flow discharge in channels and helps in flood
attenuation and sediment deposition. Open channel flows are not only resisted by
boundary shear but also by the drag induced by stems and foliage. Even though the
subject has been studied for decades, the simulation of flow and sediment transport in
vegetated alluvial channels is still challenging because of the complex interaction among
flow, vegetation, and mobile bed.
23
Many research efforts have been devoted to conducting laboratory experiments
and field measurements to quantify the effects of vegetation on flow resistance and
turbulence. Most of them derived empirical relations to calculate Manning’s, Darcy’s, or
Chezy’s coefficients in vegetated channels with rigid beds. Early researchers (Chow,
1959; Cowan, 1956; Kouwen, 1969) found that the Manning’s coefficient varies with
vegetation type, density, and distribution. Relationships between Manning’s coefficient
and vegetation properties are formulated for limited vegetation types and experimental
conditions. Wu (1999) investigated the variation of vegetative roughness coefficients in
non-submerged and submerged flow conditions and developed a model for the drag
coefficient of vegetal elements, and then he converted it into Manning’s n-value. Klopstra
(2002) established an analytical model to calculate the Chezy’s coefficient for submerged
vegetation as a function of vegetation height and density, stem diameter, drag coefficient,
and the characteristic length of large scale turbulence. Kouwen and Unny (1973) found
the drag force of flexible vegetation is smaller than that for rigid upright vegetation
because of a change in vegetation morphology. Similar research results can be found in
other literatures (Carollo et al., 2005; Darby, 1999b; Duan et al., 2006; James et al., 2004;
Katul et al., 2002; Kim et al., 2012; Neary et al., 2012; Stephan and Gutknecht, 2002).
Some of the formulas are compiled in Table 1.1.
24
Table 1.1 List of some equations to calculate roughness coefficient
Researchers
Formulas
nr  n0  0.0239(
Fisher (1992)
Bv
)
uR
nr   / h
Reed et al. (1995)
1
Stone and Shen (2002)
f
 a   log
R
d
For submerged vegetation:
Baptist (2007)
Cr 
(h  hv )3/2 ( g /  ) ln((h  hv ) / ez0 )
1

1/ Cb2  (Cd mDhv ) / 2 g
h3/2
For non-submerged vegetation: k=h
Yang and Choi (2010)
 2 gh
C ghs
h
h
n
 u
ln( )  v

hv
h
  Cd hv
Cr 
Cheng (2011)
1
ghs 
2/3
 h
 
h 1   1/16 hs 3/2
 g (1   )3 d hv 3/2
( )  4.54 g ( s
) ( )
2CD
 hv h
D 
h
For non-submerged vegetation: k=h
1/3
nr , n0 = representative, bed manning’s roughness respectively( s  m ); B = volumetric blockage factor;
v
u = mean velocity (m/s) ; R = hydraulic radius(m);  = resistance factor; h = water depth(m); f = DarcyWeisbach friction factor; a = cross section shape factor; d = roughness height of vegetation;  = Von
Karman constant; Cr = representative Chezy coefficient; Cb = Chezy coefficient of the bed(m1/2/s); m =
number of cylinders per m2 horizontal area(m-2); D = Cylinder diameter(m); g = gravity
hv = vegetation height(m); Z0 =roughness height in the logarithmic velocity profile
for a fully rough bed(m); hs = water depth below vegetation for submerged condition (m); Cu = velocity
acceleration( m / s 2 );
calculation coefficient;
When developing numerical models for open channel flows with vegetation
influence, some researchers (Baptist, 2005) modified the source terms of momentum
equations by importing roughness calculation equations; other researchers (Wu and
Marsooli, 2012) added vegetation drag forces as additional source terms to momentum
equations. Both methods accounted for the influence of vegetation by modifying source
terms in the momentum equations. Actually, roughness calculation equations are based
25
on force balance, which includes vegetation properties. Therefore, these two methods are
based on the some concept but expressed differently; either method can be chosen.
A few laboratory and field experiments were conducted to study the effects of
vegetation on sediment transport and channel morphodynamic changes. Prosser’s (1995)
experiments showed upland erosion and channel initiation can be prevented by strong soil
cohesion from a dense root mat. López and García (1998) derived an empirical equation
from field data to estimate suspended sediment transport, and their results showed the
suspended sediment transport capacities in vegetated waterways are smaller than that in
non-vegetated channels. Zong and Nepf (2011) found that for a patch of vegetation
located at a channel side, sediment deposition was the highest near the streamwise patch
edge and decreased into the patch. Only a few numerical models (Wu and Shields, 2005)
were capable of simulating both flow and sediment transport in vegetated channels. Wu
and Shields (2005) developed a two-dimensional depth-averaged model to simulate flow
and sediment in channels with riparian vegetation. However, there model can only be
used in small scale curved channels.
Shear stress in vegetated channels is composed of bed shear stress, wall shear
stress and vegetation shear stress. Bed shear stress is the one works on sediment and
causes the motion of sediment. When flow resistance induced by vegetation is considered,
the velocity of flow decrease, resulting in the decrease of bed shear stress. If the threshold
of motion of sediment is defined in terms of a critical shear stress, sediment starts to
move when bed shear stress is larger than critical shear stress. So it is very important to
express bed shear stress correctly when vegetation influence is considered. Although
26
some research results have been reported on vegetation influence on sediment transport
as listed above, the relationship between bed shear stress and vegetation influence still
needs to be investigated, and a formula for the bed shear stress in vegetated channel is not
yet available.
1.2.3 Two-dimensional Surface Routing Model
The response of magnitude and frequency of flood change of natural watershed to
climate depends on the mechanisms of runoff generation and their spatial and temporal
distribution (Vivoni, et al., 2007). Many distributed surface flow routing models have
been developed to represent the runoff response to varying watershed properties. The
distributed surface flow routing includes two components: overland flow routing and
channel flow routing. Two kinds of procedures are used to simulate surface flow routing.
Some researchers used two models for overland and channel flow (Julien, et al., 1995; Qu
and Duffy, 2007) separately. The channel network with reach cross section information
and confluence point information are obtained, and then coupled with the overland model.
The disadvantage of this kind of model is the difficulties of data processing. Other
researches use one model to simulate both overland and channel flow (Cea, et al., 2010;
Kim, et al., 2012). Nearly all the rainfall-runoff models for predicting surface flows are
simply based on the solutions of simplified shallow water equations such as kinematic
wave equation (Vivoni et al., 2007; Yang et al., 1998) or diffusion wave equation(Qu and
Duffy, 2007). Current research(Yu and Duan, 2014) showed that the accuracy of
simplified models are not enough because the neglected terms in kinematic or diffusion
wave equation are significant in predicting flood peak discharge. An advanced 2D
27
hydrodynamic and sediment model, CHRE2D, has been developed in our research group
by Yu and Duan (2013), Yu and Duan (2014). CHRE2D solved the surface flow using
fully two-dimensional shallow water equations. The CHRE2D model is a stable model by
dividing the computational domain into the shallow water zone and the kinematic wave
zone. An approximate solution based on the kinematic wave equation is applied to the
kinematic wave zone. The CHRE2D model was used in this study.
1.2.4 Prediction of Flood Frequency Curve Incorporating Climate Change
In the last decade, the interest in the impacts of climate change on river flows is
increasing. Interagency Panel on Climate Change (IPCC) (2007) found that climate
change will drive changes in magnitude and frequency of flood. The effect of climate
change on river flows has been studied on different spatial locations for various future
time periods using time series of climate model projected data. The most common
procedure to model the effect of climate change on river discharge is achieved by routing
the precipitation generated from a climate model using a basin-scale hydrologic model.
For examples, researches for river basins in the USA include Christensen et al., (2004),
Murphy and Ellis (2014), in Belgium (Goderniaux et al., 2009), in Australia (Leonard et
al., 2008), in Netherlands (Leander et al., 2008), in Denmark (Madsen et al., 2009), in
United Kingdom (Fowler and Kilsby, 2007; Kay et al., 2006b), in China (Cong et al.,
2009) and et al. .
The first requirement for accounting for the impact of climate change on flow
discharge is a reliable estimation of climate change for a given future period. Changes in
28
extreme precipitations directly impact the hydrological processes, consequently the
frequency and magnitude of flood. Although precipitations from the global climate
models (GCMs) were used in several studies (Cameron et al., 1999; Merritt et al., 2006),
GCMs generally do not realistically represent precipitation because of the coarse spatial
resolution and physical parameterizations, especially for complex terrain. Downscaling
has to be performed for regional assessment. The dynamically downscaled method, the
regional climate model (RCM) nested inside the GCM, increases the spatial resolution
and gives a better representation of precipitation. RCMs can capture mean and extreme
precipitation at the regional scale (Dominguez et al., 2012). To date, several researches
have applied the daily or monthly precipitation extremes from the RCM model to
estimate the change of flood magnitude and frequency (Bell et al., 2007; Fowler and
Kilsby, 2007; Hailegeorgis et al., 2013; Hay et al., 2002; Wang et al., 2013). However,
Hanel and Buishand (2010) found the increase in the large quantiles of daily maxima was
much smaller than that in the quantiles of the hourly maxima at the end of the 21st
century based on the transient RCM simulations. Because of this, the hourly precipitation
data from RCM is used in this study for predicting the future flood magnitude and
frequency.
The dynamic downscaling leads to significant advantage to the data from GCM,
which provides precipitation projections at a scale matching the requirement of
catchment scale simulation. However, even with the fine spatial resolution, biases in the
precipitation output need to be corrected (Johnson and Sharma, 2011). Bias correction
methods are designed to address known biases associated with the climate outputs for the
29
region being modeled. Several bias correction methods in the literature were applied to
the daily or monthly averaged data series (Hagemann et al.,2011; Piani et al., 2010; Yang
et al., 2005; Wood et al. 2004). For example, Wood et al. (2004) performed a bias
correction technique based on termed quantile mapping at monthly scale and
disaggregated to daily scale. This study adapted the same quantile mapping method for
monthly averaged precipitation, and then applied a further disaggregation step to hourly
precipitation.
The second requirement for predicting the impact of climate change on flow
discharge is an accurate and robust hydrological model. A physically based approach that
routes rainfall over realistic watershed is superior to a lumped parameter hydrologic
model. One example of lumped parameter model is to use the intensity–duration–
frequency (IDF) curves to derive flood frequency curve (Bloschl and Sivapalan, 1997;
Goel etal., 2000). Another example involves the generation of synthetic meteorological
time series as input to a rainfall–runoff model (Wilks, 1998; Lamb, 1999) to derive
discharge series. Those analytical methods are based on assumptions of uniform terrain
slopes, time of concentration. On the other hand, a hydrodynamic model can route
projected precipitation over watershed at fine grid cells. This will allow the accurate
determination of flood duration and peak flow than an analytical model. Therefore, a
physical based distributed surface flow routing model for irregular terrains, CHRE2D,
was selected in this study. Details regarding CHRE2D will be introduced in the following
sections.
30
1.3 Objective
The main objective of this research is to simulate unsteady flow and sediment
transport in a vegetated channel network and predict future flood magnitude and
frequency considering the change of climate. In order to achieve this objective, the
following specific tasks are completed:

Simulate unsteady flow and sediment transport in a channel network. 1D unsteady
flow and sediment transport model developed by Zhang et al. (2013) was
extended for channel network applications. This feature is essential for simulating
flood routing in the arid and semi-arid Southwest US because tributary flows can
dominant the main flow. The improved model is applied to the lower Santa Cruz
River.

Incorporate the effect of vegetation on sediment transport into the developed
channel network model. A lot of research results of vegetation influence for flow
dynamics and erosion/sedimentation can be found in literatures. The influence of
vegetation in flow field and sediment field need to be investigated. In this
research, vegetation influence need to be injected into the unsteady flow and
sediment transport model by modifying source term in momentum equation; the
relationship between vegetation features and shear stress need to be investigated;
vegetation involved shear stress is required to be expressed in the model.

Improve the CHRE2D model by considering vegetation impacts and apply the
model to SCRW. Vegetation coverage analysis for SCRW is based on satellite
photos. Friction terms in the momentum equations of CHRE2D are modified by
31
addressing the vegetation coverage. The improved model was calibrated using an
observed flood event.

Analyze the possible flood magnitude and frequency changes caused by climate
change in the SCRW. The precipitation data generated from the WRF (Weather
Research and Forecasting)-RCM was routed through the watershed by using the
CHRE2D model. Three periods were simulated: the historical period of 19902000, the future periods of 2031-2040 and 2071-2079. In particular, we applied
quantile-based mapping bias correction method to all three periods, compared the
simulated stream flow for the historical (1990-2000) period with observations,
and developed new flood frequency curves based on climate projected
precipitations.
32
CHAPTER 2
GOVERNING EQUATIONS AND NUMERICAL METHODS
The shallow water equations are derived from depth-integrating the Navier-Stokes
equations, in the case where the horizontal length scale is much greater than the vertical
length scale. Situations where the horizontal length scale is much larger than the vertical
length scale are common, so the shallow water equations are widely used in open channel
modeling, surface routing modeling, atmospheric and oceanic modeling. The shallow
water equations can be further simplified to the commonly used one-dimensional Saint
Venant equation which is widely applied for dam break simulation, open channel
modeling, as well as storm runoff in overland flow. The shallow water equations are used
as governing equations for the two-dimensional hydrological model and the onedimensional Saint Venant equation is used for one-dimensional unsteady flow and
sediment transport model. The Godunov-type finite volume method is used to discretize
the governing equations.
2.1 Governing Equations for One-dimensional Flow and Sediment
Model
The governing equations are the St. Venant equations modified by treating the
density of sediment laden flow as a variable, as well as mass conservation equations for
suspended load and bed load (Zhang and Duan, 2011) written as:
33
A Q   b Ab 


0
t
x
t
(2.1)
n 2Q Q
zs 1

  Q2 

 gAh
 g
0
 Q   
   gA
4
t
x  A 
x 2
x
AR 3
(2.2)

 AC    QC   BE  D 
t
x
  Qb

 t  u b
 Qb 1
 
 Q*b  Qb 
L
 x
(2.3)
(2.4)
where t=time; x = longitudinal coordinate; A = flow area; Q = flow discharge; Ab =
mobile bed area; g = gravitational acceleration; z s = water surface elevation; n =
Manning’s roughness; R = hydraulic radius; C = concentration of suspended load; B =
width of the cross section; E = entrainment rate at the interface between bed load and
suspended load; D = deposition rate at the interface between bed load and suspended load;
L = non-equilibrium adaptation length; Q*b = bed load transport capacity under
equilibrium state; Qb = actual bed load transport rate;  = density of the water-sediment
mixture, where    w 1  Ct    s Ct , where  w and  s are the density of the water and
sediment, respectively, and C t the volumetric concentration of total load sediment,
calculated as Ct  C 
Qb
;
Q
 b = density of mobile bed layer, calculated as
 b   w pm   s 1  pm  , with p m being the porosity of bed load sediment; h = averaged
flow depth in a cross section, equivalent to the local flow depth in a rectangular flume. Eq.
(2.1) and (2.2) are the St. Venant equations for sediment laden flow over alluvial bed, and
34
Eq. (2.3) and (2.4) are mass conservation equations for bed load and suspended load,
respectively.
Bed elevation change is calculated by solving the Exner equation for bed surface
layer, which is written as
1  pm 
Ab
1
 BD  E   Qb  Qb* 
t
L
(2.5)
where Ab = mobile bed area; Q*b = bed load transport capacity under equilibrium state;
Qb = actual bed load transport rate, p m = the porosity of bed load sediment.
2.2 Governing Equations for Two-dimensional Hydrological Model
The governing equations used in CHRE2D are the two-dimensional depthaveraged shallow water equations (Fraccarollo and Toro, 1995; Garcia-Navarro et al.,
1995; Zhao et al., 1994), which can be written in the differential conservative form as:
Q F G


 Ss  Sf  Sr
t x y
(2.6)
where Q is the vector of conservative variables; F and G are the vectors of advective
fluxes in
x
and y directions, respectively; S s is the vector of bed slope source term; S f
is the vector of bed friction source term; S r is the vector of rainfall excess source term.
The vector of primitive variables, U , and the vector of conservative variables, Q , in Eq.
(2.6) are defined as:
35
h
h
 
 
U   u  , Q   hu 
v
 hv 
 
 
where h is flow depth;
u
and
v
(2.7)
are the depth-averaged flow velocities in
x
and y
directions, respectively. The vectors of advective fluxes, F and G , are given as:
hu




hv
 2



2
F   hu  gh / 2  , G  
hvu

 hv 2  gh 2 / 2 


huv




(2.8)
where g is the gravity acceleration. The source terms, including bed slope, bed friction,
and rainfall excess, are calculated by:
 0 
 0 
 i0 




 
C
u
u
Ss   ghS0 x  , Sf   f
 , Sr   0 
0 
C u v
 ghS0 y 
 


 f

where S0 x  
b
b
and S0 y  
are the bed slopes in
x
y
x
(2.9)
and y directions, receptively;
b is bed elevation; C f  gn2 h1/3 is drag coefficient;
n
is Manning’s roughness
coefficient; u  u 2  v 2 ; and i0 is the rainfall excess rate.
The kinematic wave approximation (KWA) is used in CHRE2D model when
water depths are thin. For flow condition with thin water depth, the local acceleration and
advective terms in the momentum equations are insignificant compared to the friction and
the bed slope source terms and the momentum equations of Eq. (2.6) are simplified to:
36
Ss  Sf
(2.10)
From Eq. (2.8), the depth-averaged flow velocities are calculated as:

h 4/3
u
u

S
0
x


n2

4/3
v u  S h
0y

n2

(2.11)
2.3 Numerical Methods
The Godunov-type finite volume method is applied to discretize the governing
equations for both one-dimensional unsteady flow and sediment model and twodimensional hydrological model.
For one-dimensional model, the density of sediment laden flow can be expressed
as    w 1  Ct    s Ct for suspended sediment layer and  b   w 1  pm    s pm for
bed load layer. If only the suspended load is being simulated, or the bed load transport
velocity is close to the flow velocity, Eq.(2.1) to(2. 4) can be reformulated as:
A Q
1


t x 1  p m
Q   Q 2
 
t x  A
1

 Qb*  Qb   BD  E 
 L0

QQ

z
1

   gA s  gAh p
 gn 2
4
x 2
x

AR 3
Ct  1
Q s  w 
1 
  Qb*  Qb   BD  E 

A  w  1  p m  L


 AC    QC   BE  D
t
x
(2.12)
(2.13)
(2.14)
37
  Qb

t  u b
 Qb
1
 
Qb*  Qb 

L0
 x
(2.15)
Eq. (2.12) to (2.15) can be re-written (Zhang and Duan, 2011)in the vector form
as follows:
  F  

 S  

x
 t
(2.16)
where
Q
A

 2
Q

 , F    Q

QC
 AC 



Qb
Qb U b 
 1 1


 Qb*  Qb   BD  E 

1  p m  L0

Q Q Q s  w
z s 1

2


4
S ()   gA x  2 gAh p x  gn
AR 3 A  w

 B( E  D)
1
 (Q*b  Qb )
L


A ,








Ct  1
1 
  Qb*  Qb   BD  E  

 1  p m  L





(2.17)
(2.18)
where S   are the source terms for Eq. (2.1) – (2.4), respectively. The Godunov-type
finite volume method (Toro, 2001) evaluates the value of  at time n+1 using the value
at time n:
 in1   in 

t  n
 F 1  F n 1   t  S

i 
x  i  2
2 
where subscript i represents the node, subscript i 
(2.19)
1
represents the east or the west cell
2
face, superscript n and n+1 present the current and the future time level, respectively; t
= time step; x  distance between two cross sections; F = intercell numerical flux at the
38
cell face; S = source terms. The upwind numerical scheme is implemented, and written in
a general form as seen in (Zhang and Duan, 2011) and (Ying et al., 2004):
F
i
1
2
 Qin k

 n 2 n 
A 
Q
  ni  k n i  k 
Qi  k C i  k 
Q n

 b ik

(2.20)
Where k = 0 if Qi  0 and Qi 1  0 ; k=1 if Qi  0 and Qi 1  0 ; k=1/2 otherwise.
The shallow water equations are also discretized by the Godunov-type finite
volume method (LeVeque, 2002; Toro, 2009) over a regular Cartesian mesh. Using the
explicit Euler scheme for the temporal derivative term, the discretized form of the
governing equations can be written as:
Q( n 1)  Q( n ) 
t 4
Fj (Q(Ln ) , Q(Rn ) )  n j L j  t (S(0n )  S(fn )  S(rn ) )

A j 1
(2.21)
where superscript ( n ) and (n  1) refer to the variables at current and next time step; t
is time step; A is the cell area; j is the index of a cell edge; Fj (Q L , Q R ) is the vector of
Riemann flux calculated by solving a local Riemann problem normal to the edge j ; Q L
and Q R are the vectors of conservative variables on the left and right side of the edge j ;
n j is the normal vector to the edge j ; L j is the length of the edge j . The local Riemann
problem is solved by the Harten-Lax-van Leer (HLL) approximate Riemann solver
(Harten, 1984; Toro, 2001).
The flux across the cell interface is calculated by:
39
F(Q L ),
if sL  0
 *
F(Q L , Q R )  F (Q L , Q R ), if sL  0  sR
F(Q ),
if sR  0
R

(2.22)
*
where the flux vector at the star region F (QL , QR ) is determined by:
F* (QL , QR ) 
sR F(QL )  sLF(QR )  sL sR (QR  QL )
sR  sL
(2.23)
The left wave speed, sL , and, the right wave speed, sR , are estimated by:
 sL  min(uLn  ghL , us  cs )

n
 sR  max(uR  ghR , us  cs )
(2.24)
n
n
where uL and uR are normal velocities at the left and the right side of the cell interface;
us and cs are defined as:
us  (uLn  uRn ) / 2  ( ghL  ghR )

n
n
cs  (uL  uR ) / 4  ( ghL  ghR ) / 2
(2.25)
For the dry bed situation, the estimated wave speeds are replaced by the exact dry
front speed (Causon, et al., 2000; Toro, 2001) as:
 sL  uLn  ghL
right dry bed: 
n
 sR  uR  2 ghL
left dry bed:
 sL  uRn  2 ghR

n
 sR  uR  ghR
(2.26)
40
CHAPTER 3
SIMULATING UNSTEADY FLOW AND SEDIMENT TRANSPORT IN
VEGETATED CHANNEL NETWORK
3.1 Introduction
Vegetation in natural rivers increases flow resistance and bank stability, improves
water quality, and promotes habitat diversity, but reduces flood conveyance.
The
presence of vegetation changes both flow field and sediment transport processes.
Although the impact of vegetation on flow and sediment transport has been studied for
decades (Cowan, 1956; Duan et al., 2006; Fisher, 1992; Luhar and Nepf, 2013; Nepf,
2012; Tsujimoto, 1999), the simulation of flow and sediment transport in vegetated
alluvial channels is still challenging because of the complex interactions among flow,
vegetation, and mobile bed sediment.
Many empirical relations have been proposed to quantify the resistance of
vegetated flow over a rigid bed through adjusting Manning’s, Darcy’s, or Chezy’s
coefficients. Early researchers (Chow, 1959; Cowan, 1956; Kouwen, 1969) found that the
Manning’s coefficient varies with vegetation type, density, and distribution.
Relationships between Manning’s coefficient and vegetation properties are formulated
for limited vegetation types and experimental conditions. Klopstra (2002) established an
analytical model to calculate the Chezy’s coefficient for submerged vegetation as a
function of vegetation height and density, stem diameter, drag coefficient, and the
characteristic length of large scale turbulence. Kouwen and Unny (1973) found the drag
41
force of flexible vegetation is smaller than that for rigid upright vegetation because of a
change in vegetation morphology.
Similar research results can be found in other
literatures (Carollo et al., 2005; Darby, 1999b; Duan et al., 2006; James et al., 2004;
Katul et al., 2002; Kim et al., 2012; Neary et al., 2012; Stephan and Gutknecht, 2002).
To simulate vegetated open channel flow using numerical models, some researchers
(Baptist, 2005) modified source terms in the momentum equations by incorporating an
equation for calculating the roughness; other researchers (Stone and Shen, 2002; Wu and
Marsooli, 2012) added vegetation drag forces to the momentum equations as additional
source terms. Both methods accounted for the influence of vegetation by modifying
source terms in the momentum equations.
A few laboratory and field experiments were conducted to study the effects of
vegetation on sediment transport and channel morphodynamic changes. Prosser’s (1995)
experiments showed upland erosion and channel initiation can be prevented by strong soil
cohesion from a dense root mat. López and García (1998) derived an empirical equation
from field data to estimate suspended sediment transport, and their results showed the
suspended sediment transport capacities in vegetated waterways are smaller than that in
non-vegetated channels. Zong and Nepf (2011) found that for a patch of vegetation
located at a channel side, sediment deposition was the highest near the streamwise patch
edge and decreased into the patch. Only a few numerical models (Wu and Shields, 2005)
were capable of simulating both flow and sediment transport in vegetated channels. Wu
and Shields (2005) developed a two-dimensional depth-averaged model to simulate flow
42
and sediment in channels with riparian vegetation. However, there model can only be
used in small scale curved channels.
Most sediment transport formulae are developed for predicting equilibrium
sediment transport rate in steady uniform flow, which assumes sediment transport rate is
equal to the transport capacity. However, when channel bed is degrading or aggrading,
sediment transport rate can be greater or less than the transport capacity, called nonequilibrium transport (Zhang et al., 2013).
To simulate the process that sediment
transport rate gradually develops into the transport capacity, the adaptation length (Duc
and Rodi, 2008; El kadi Abderrezzak and Paquier, 2009; Wu and Wang, 2007; Zhang et
al., 2013) is often used to calibrate bed load transport, and the recovering coefficient is
used for suspended sediment transport. The adaptation length is defined as the distance
required for the bed load transport rate to reach equilibrium at a given flow condition.
And the suspended load recovering coefficient is defined as the ratio of near-bed
suspended sediment concentration to the depth-averaged concentration at equilibrium
state (Zhang et al., 2013). The non-equilibrium sediment transport algorithm is adopted
here to simulate sediment transport in vegetated channel network.
Although 2D and 3D models have been developed to simulate flow and sediment
transport in vegetated channels (Wu, 2005), 1D channel network model is the most costeffective for engineering practices when simulating fluvial processes of a long river reach
for a long time. In this chapter, a one-dimensional model for simulating unsteady flow
and sediment transport in a vegetated channel network will be presented. The model
43
accounts for the effect of vegetation on flow resistance and sediment transport by using
the modified roughness coefficient and shear velocity. A laboratory experimental case
and an unsteady flow event occurred in the Santa Cruz River, Tucson, AZ were used to
verify the model. The governing equations, numerical methods and application of the
developed model are presented in the following sections.
3.2 Mathematical Formulation
3.2.1 Governing Equations
The one-dimensional St. Venant equations were described in Chapter 2.
3.2.2 Empirical Formulae
To close the model, additional empirical sediment transport formulae are required.
Sediment deposition (D) and entrainment rates (E) at the interface between bed-load and
suspended-load layers are defined as follows:
D  s Ca
(3.1)
E  s Ca*
(3.2)
in which ws = settling velocity of sediment particles;
Ca *
= the concentration at the
interface at equilibrium; Ca is the actual suspended sediment concentration at the
interface, it is related to the depth averaged concentration of suspended load as Ca   C ,
in which  = the ratio of the near-bed concentration to the mean concentration of the
suspended sediment at a cross section, also defined as the non-equilibrium suspended
44
load recovery coefficient. The suspended load recovery coefficient,  , was calculated
from the modified Rouse profile as (Zhang et al., 2013):

h
(3.3)
h  z a 
a  z h  a  dz
R0
h

u*'
(1
/

)
[1

(
E
/
D

1)]
/
[
 ( E / D  1)]

s
R0  
1 / 

if s  u*'
if s  u*'
(3.4)
in which z = elevation; a = reference bed level; u*' = grain shear velocity; R0 = Rouse
number;  = correction factor, approximately equal to 1.0;
 = Von Karman constant,
0.41. The adaptation length for non-equilibrium bed load transport, L , is a function of the
dominant bed forms and channel geometry , which is equivalent to the length of sand
dunes, approximately 5-10 times the flow depth (Wu and Wang, 2007). The empirical
formulae proposed by Van Rijn (1984a,b) were used to determine the bed-load transport
capacity, Qb* , and the suspended-load near-bed concentration, Ca* , at the equilibrium
state presented as Eq. 3.5 and Eq. 3.6.
Qb*  0.053
T 2.1
0.5 1.5
( s  1) g  d50
2.1 
D*
Ca*  0.015
d50 T 1.5
a D*0.3
where T = transport stage parameter; D* = particle parameter; s = specific density.
(3.5)
(3.6)
45
3.2.3 Impacts of Vegetation on Flow Field
The increase of flow resistance due to vegetation can be quantified by several
hydraulic roughness coefficients. The most commonly used are the Manning roughness
coefficient (n), the Chezy resistance factor (CR), and the Darcy-Weisbach friction factor
(f) (Kim et al., 2012). Because of the increase of flow resistance, the vertical distribution
of time-averaged velocity doesn’t satisfy the logarithmic law in vegetated channels. A
typical velocity profile for submerged vegetation is shown in Figure 3.1 (Baptist, 2007),
in which velocity in vegetated zone is nearly a constant. Several laboratory and field
experimental studies have correlated vegetation characteristics with roughness
coefficients. Darby (1999a) derived a Darcy-Weisbach friction factor for both flexible
and nonflexible vegetation. Baptist (2007) suggested an analytical equation to calculate
the Chezy coefficient for un-submerged vegetation. He also derived an effective flow
depth method for submerged vegetation. Yang and Choi (2010) developed a two-layer
model for submerged vegetation, additional drag force terms are added to the momentum
equation for each layer. Nepf (2012) proposed an equation for calculating vegetated
channel resistance coefficient from the analytical solution of momentum equation.
46
h
Z (m)
4
hv
3
dz
2
1
u (m/s)
Figure 3.1 Four zones in the vertical profile for horizontal velocity, u(z), through and
over vegetation, where h = water depth, k = vegetation height, d = zero-plane
displacement. (Baptist, 2007)
Recently, Cheng (2011) derived resistance coefficients for both rigid and flexible
vegetation by employing the concept of hydraulic radius.
Cheng (2011) found his
formulae can improve the roughness predictions for both rigid and flexible vegetation
comparing to several existing formulas. Therefore, Cheng’s (2011) method was used to
calculate Manning’s coefficient in this paper. The vegetation-related hydraulic radius is
defined as:
Rv 
 1 
d
4 
(3.7)
where Rv = vegetation-related hydraulic radius;  = vegetation density (defined as the
fraction of the bed area occupied by vegetation); d = stem diameter. The Chezy
coefficient for submerged vegetation is given as:
47
CR 
 g (1   )3 d  hv 
2CD  hv  h 
3/2
 h 1  
 4.54 g  s

d  
1/16
 hs 
h
 
3/2
(3.8)
where
CD 
130
 0.8
Rv0.85
*

  Rv*  
1  exp  400  



(3.9)
with CD = drag coefficient; Rv*  ( g s /  2 )1/3 rv ; h = flow depth; hv = vegetation height;
hs = flow depth over vegetation layer (upper layer), hs  h  hv ;  = kinematic viscosity
of fluid; s = energy slope. For emerged vegetation, hs =0 and hv  h , then Eq.(3.8) is
simplified as:
CR 
 g (1   )3 d
2CD  h
(3.10)
1/6
The Manning coefficient can be calculated as n  h / CR .
3.2.4 Vegetation Influence on Sediment Transport
The presence of vegetation reduces the grain resistance that transports sediment.
No equation in literature is valid for separating the grain resistance for sediment transport
from the total bed shear stress in vegetated channels. In the following sections, we
derived an analytical equation for calculating the grain resistance from the total resistance.
Considering a control volume of unit length, the resistance forces in the streamwise
direction are wall shear stress, bed shear stress including grain resistance and form
resistance, and vegetation drag. The sum of the three resistance forces are the total
resistance force,
48
Ft  Fw  Fb  FD
(3.11)
where Ft is the total resistance force; Fw , Fb and FD are the wall friction force, bed
resistance force and vegetation drag force, respectively. For steady uniform flow, the
total resistance force is equal to the downslope component of gravity force, and can be
expressed as  gsRt Pt ; the wall shear can be expressed as  gsRw Pw , and bed shear as
 gsRb Pb , in which Pt   Pw  Pb  Pv  is the total wetted perimeter; Rt   bh(1   ) / Pt  is the
total hydraulic radius. Pw ( 2h) and Rw are wall-related wetted perimeter and hydraulic
radius, respectively; Pb [ (1   ) B] and Rb are bed-related wetted perimeter and hydraulic
radius, respectively; Pv   Bh / ( d / 4) and Rv are vegetation-related wetted perimeter
and hydraulic radius, respectively. The vegetation drag force in the streamwise direction
for emerged vegetation is (Kothyari et al., 2009):
FD  CD  hd
uv 2
2
(3.12)
where ρ is fluid density; uv is average velocity of the flow among vegetation stems and
can be expressed as (Cheng, 2011):
uv 
2 gRv s
CD
(3.13)
For submerged vegetation, h will be replaced by vegetation height as hv ; and uv will be
the mean velocity of vegetation layer which can be also calculated by Eq.(3.13).
Substituting equation (3.12) into equation (3.11), it yields:
 gsPR
t t   gsPw Rw   gsPb Rb  CD  hd
uv 2
2
(3.14)
49
With Pw , Pb and Pv given above, the total hydraulic radius can be determined as Cheng
and Nguyen (2010):
1
1
1
Rt   
 
 h 0.5B(1   ) Rv 
1
(3.15)
in which Rv   d (1   ) / (4 ) .
The Darcy’s roughness coefficient for side wall can be calculated as
f w  8gRw s / uv 2 , and Rw  f wuv 2 / 8gs , in which f w is wall-related Darcy-Weisbach friction
factor. Applying the explicit form of the revised Colebrook equation (Cheng 2010),
f w  f ws  f wr
in which
f ws
  Re  
 31 ln 1.3

f 
 
2.7
(3.16)


; f wr  11.7 ln  7.6 4 Rt  
 
fksw  
2.5
;   2  4 Rt


fk
 sw 
0.1
; f ws is smooth-
sidewall friction factor; f wr is rough-sidewall friction factor; f  8gRt s / uv 2  is total
friction factor; Re is Reynolds number; k sw is the equivalent wall-roughness height.
Substituting the expressions of Pw , Pb , Rt , Rw to Eq. (3.14), yields
Rb 
PR
C hduv 2
t t  Pw Rw
 D
Pb
2 gspb
(3.17)
If vegetation is submerged, water depth needs to be replaced by vegetation height
in Eq. (3.17). The effective grain-shear velocity equation derived by Van Rijn (1984a),
which removes bed form resistance from bed resistance, was used to calculate bed-load
transport capacity. The shear velocity due to grain resistance can be expressed as:
50
u*' 
u
(3.18)
12Rb
5.75 log
3d 90
'
where u* = shear velocity due to grain resistance, u =mean velocity, d90 = size of
sediment for which 90% of sediment is finer. Once u*' is determined, bed-load transport
capacity Qb* can be calculated through Van Rijn (1984a).
3.2.5 Infiltration Model
Abundant vegetation on the floodplain of natural waterways increases flow
resistance and reduces flow velocity, and consequently increases the opportunity time for
infiltration. Because of this, the infiltration need to be considered in the model, and the
Green and Ampt model was used for calculating infiltration (Bras, 1990):
 H f Md
d f  K s 1 

Df




(3.19)
in which d f = infiltration rate; Ks = saturated hydraulic conductivity; Hf = capillary
pressure head at the wetting front; Md = soil moisture deficit, defined as Md = (e  i ) ,
 e = effective porosity,  i = soil initial moisture content; D f = accumulated infiltrated
depth.
51
3.3 Numerical Solution Method
3.3.1 Numerical Schemes
The Godunov-type finite volume method is used to discretize the governing
equations as shown in Chapter 2.
A simplified time-explicit solution to the Green-Ampt equation was proposed by
Li et al. (1976) as follows:
d f i n 1 
1
 P1i  ( P1i2  8P2i t )1/2 
2t
(3.20)
in which subscript i represents the node; P1 and P2 are variables which can be written as
P1  K s t  2D f n and P2  Ks D f n  Ks H f M d ; D f (  d f t ) is the accumulated depth of
infiltration, defined as the volume of water infiltrated per unit area. Water depth is
updated every time step after the infiltration depth is calculated. Then, an updated cross
section area is used for the calculation in the next time step.
3.3.2 Boundary Conditions
The upstream boundary condition is flow hydrograph, and the downstream
boundary condition is the discharge stage rating curve. At the river confluences as shown
in Figure 3.2, three additional boundary conditions are needed to close the solutions.
Assuming cross section 1, 2 and 3 are the confluence cross sections for each reach, the
mass conservation equation, Eq. (3.21), needs to be satisfied, and the water surface
elevations should satisfy Eq.(3.22) as follows:
Q1  Q2  Q 3
(3.21)
52
Z s1  Z s 2  Z s 3
(3.22)
in which Q1 , Q2 , Q3 , Z s1 , Z s 2 , and Z s 3 are flow discharges and water surface elevations at
Section 1, 2, and 3, respectively. To solve Eq. (3.21) and (3.22), water surface elevation
at the confluence was assumed, and then used as the downstream boundary condition for
Reach 1 and 2, respectively. Flow discharges for these two reaches were solved from Eqs.
(2.1) to (2.4). For Reach 3, flow discharge can be obtained from Eq.(3.21), and water
surface elevation at the upstream section was solved from Eq. (2.1) to (2.4). If the solved
water surface elevation at Reach 3 doesn’t satisfy Eq. (3.22), a correction to the guessed
water surface elevation will be made until the solved water surface elevation at Reach 3
satisfies Eq. (3.22).
Confluence cross
section
Reach 1
CS 1
CS 3
Reach 3
CS 2
Reach 2
Figure 3.2 Sketch map for confluence point
The program language is FORTRAN 90, Figure 3.3 is the program flow chart.
The model runs on a dual-core personal computer.
53
Start
Input(Initialization and
boundary condition)
Calculate C’
Calculate τb*’
Calculate A, Q, C, Qb
Zs1=Zs2=Zs3
Yes
No
Zs1'=Zs2'=1/3*(Zs1+Zs2+Zs3)
Update Zb
t<tmax
Yes
No
t=t+dt
End
Figure 3.3 Flow chart of the program
3.4 Case Study
3.4.1 Case 1: Steady Flow in Flume with Vegetation
The developed model is applied to simulate a laboratory experiment in an open
channel experimental flume (Baptist, 2005). The experimental flume is 35 m long and 80
cm wide; it is a straight, horizontal open channel with a concrete bottom and glass walls
(Figure 3.4). A longitudinal section of artificial, flexible vegetation with a density of 400
m-2 was installed over a length of 15.85 m. The length of plants was 27 cm. Vegetation
54
stems were mounted onto 18mm wood-cement boards by sticking the plant into 2.0 mm
diameter drilled holes. The alignment of vegetation is rectangular with a spacing of 5 by
5 cm. A 9-cm thick layer of sand was distributed evenly between the submerged plants.
The quartz sand has a D50 of 321 μm and a D90 of 450 μm. Three runs were carried out
with plants mounted to the flume floor, and another three on bare sediment. Detailed bed
elevation profiles were measured in Run 2 and 3, which were selected to test the model
performance.
Flow discharges for Run 2 and 3 were 0.129 m3/s and 0.106 m3/s,
respectively; and the durations of Run 2 and 3 were 30 hours and 32 hours.
The
experimental vegetation is artificial plants with 2mm thick stems and leaves; each leaf
was 15mm long and 4mm wide at its widest point; and the leaves were placed along the
stem in alternating groups with 5 leaves for each group. The vegetation coverage was
calculated as 12% from the plant size and density. Since the leaves were not rigid, the
vegetation coverage of 12% was overestimated. It turned out that 10% yielded better
results than using 12%. Therefore, the vegetation density in both runs is chosen as 10%.
Since the channel bottom is impermeable concrete, the infiltration depth is set as zero.
The non-equilibrium recovery coefficient is calculated by using Eq. (3.3) and Table 3.1
shows the recovery coefficient ranges from 5.33 to 6.15. The adaptation length is set as
1.52m, which is approximately 5 times of the flow depth. There are 30 nodes in the
domain, the grid spacing ranges from 0.2 to 0.82 m, and the simulation time step is 0.02 s.
55
Figure 3.4 Flume set-up and vegetation arrangement for the experiment of Baptist(2005)
Table 3.1 Parameters used in the cases
Symbol
Parameter
Case 1
Case 2
Δt
Time space (s)
0.02
0.03
Δx
Cross section interval (m)
0.2~0.82
91.4-182.9
λ
Vegetation density
10%
15%
hv
Vegetation height (m)
0.27
1.52
d50
Sediment size (mm)
0.321
see table 2
d90
Sediment size (mm)
0.45
see table 2
L
Adaptation length
1.64
121.9
α
Recovery coefficient (from Eq.(8))
5.33-6.15
3.2-20.2
Ks
Saturated hydraulic conductivity(cm/hr)
1.09
Hf
Capillary pressure head at the wetting front(cm)
11.01
Md
Soil moisture deficit
0.29
Figures 3.5 and 3.6 are the simulated and measured bed profiles starting at x= 2m
for Run 2 in the vegetation zone along the channel at t=13 and t=30 hours. Bed surface
56
from x=0 to 2 m is erodible, but was not shown because no measurement was made for
this reach. Almost all cross sections are eroded as shown in both figures. Bed forms were
present although not discussed in Baptist (2008). According to Van Rijn (1984c), the type
of bed form is ripple, approximately 7 cm high as shown in Table 3.2. The erosion
reached 4 cm at the last cross section when the experiment had run for 30 hours. More
erosion can be seen downstream of the vegetation zone because flow condition changes
rapidly from the vegetation to the bare sediment zone. The calculated results matches
well with the measured at t=13 hours in Figure 3.5. At t=30 hours (Figure 3.5), bed
erosion increases comparing to that at t=13 hours. A sand dune was formed at the
beginning of the vegetation section due to local scour (Baptist 2005), which is not
captured by the simulation. This attributes to the limitation of one dimensional model’s
Bed level(m)
capability of simulating local scour induced by the flow turbulence.
0.05
0.04
0.03
0.02
0.01
0.00
measured at t=0 h
-0.01
measured at t= 13 h
-0.02
calculated at t=13 h
-0.03
2
4
6
8
10
12
14
x(m)
16
Figure 3.5 Measured (t = 0h and t =13h) and calculated (t = 13h) bed levels for Run 2
Bed level(m)
57
0.05
0.04
0.03
0.02
0.01
0.00
measured at t=0 h
-0.01
calculated at t=30 h
-0.02
measured at t= 30 h
-0.03
2
4
6
8
10
12
x(m) 16
14
Figure 3.6 Measured (t = 0h and t =30h) and calculated (t = 30h) bed levels for Run 2
Table 3.2 Results related to bed form for Case 1
Test
2
Test
3
Measured
(t=30hrs)
Calculated
(t=30hrs)
Measured
(t=32hrs)
Calculated
(t=32hrs)
d*
T
Δ(m)
Bed
form
type
0.31
8.12
_
_
_
0.489
0.291
8.12
2.21~2.93
0.07~0.073
Ripples
0.287
0.46
0.27
8.12
_
_
0.29
0.454
0.268
8.12
1.44~2.27
0.053~0.062
At x =10 m
Q
(m3/s)
h (m)
u (m/s)
Fr
0.129
0.305
0.53
0.129
0.329
0.106
0.106
Ripples
d* = dimensionless particle diameter; T = transport parameter, Δ = height of dunes
Figure 3.7 and 3.8 show the results for Run 3 at t=14.5 hours and t= 32 hours.
Based on flow and sediment data, bed form is ripple with an approximate height of
5.3~6.2cm according to Van Rijn (1984c)’s Diagram. The calculated bed surface profiles
fit well with the measured. The erosion pattern is similar to Run 2 although less erosion
was observed in Run 3 because the discharge for this run is much smaller.
58
Bed level (m)
0.05
0.04
0.03
0.02
0.01
measured at t=0 h
0.00
measured at t=14.5 h
-0.01
calculated at t=14.5 h
-0.02
-0.03
2
4
6
8
10
12
14
x(m)
16
Bed level (m)
Figure 3.7 Measured (t = 0h and t =14.5h) and calculated (t = 14.5h) bed levels for Run 3
0.05
0.04
0.03
0.02
0.01
0.00
measured at t=0 h
-0.01
measured at t=32
-0.02
calculated at t=32
-0.03
2
4
6
8
10
12
14
x(m)
16
Figure 3.8 Measured (t = 0h and t =32h) and calculated (t = 32h) bed levels for Run 3
3.4.2 Case 2: 2006 Flash Flood in Santa Cruz River, Tucson, AZ
3.4.2.1 Study Area
The Santa Cruz River is an ephemeral river passing through the City of Tucson in
Arizona. The Santa Cruz River has two major tributaries, Rillito River and Canyon Del
Oro (CDO) River. Two USGS stream gauges, Tucson and Cortaro, are available on the
59
Santa Cruz River, while one (Ina Gauge) on the CDO River, and two (Dodge and Lo
Cholla Gauge) on the Rillito River (See Figure 3.9). The simulation reach from Tucson
Gauge to Cortaro Gauge on the Santa Cruz River is 20.92 km, including two tributaries:
15.77 km of Rillito River and 3.22 km of CDO River. The topographic data are a
1.52 1.52 m DEM obtained from airborne LIDAR survey in 2005. This DEM was used
to cut cross sections along the rivers as geometric input data. There are 134 cross
sections for the Rillito River, 163 for the Santa Cruz River, and 25 for the CDO River.
The distance range between two cross sections is 91.44-182.88 m. Each cross section has
about 80-200 surveyed elevation data. The upstream boundary conditions are stage
hydrographs at the Tucson Gauge on the Santa Cruz River, the Dodge Gauge on the
Rillito River, and the Ina Gauge on CDO River.
60
Cortaro
CDO
Ina
La Cholla
Santa Cruz
Dodge
Rillito
0
2
4 mi
Tucson
Figure 3.9 Simulation area and USGS gauge location. Red dot (rectangular): simulation
boundary. Blue dot (ellipse): gauge location
3.4.2.2 Flow, Sediment and Vegetation Data
The storm duration is 96 hours from July 29th, 2006 to August 1st. During this
event, considerable amounts of sediment were deposited in the river and many vegetation
were uprooted and removed. The maximum flow rate reached 1073.21 m3/s at the Dodge
Gauge in the Rillito River exceeding the 100-year discharge. Observed flow hydrographs
in Tucson Gauge, Dodge Gauge, and Ina Gauge are shown in Figure 3.10.
flow rate(m3/s)
61
1200
1000
Dodge gage, Rillito River
Tucson gage, Santa Cruz River
800
Ina gage, CDO River
600
400
200
0
0
10
20
30
40
50
time(hrs)
60
70
80
90
100
Figure 3.10 Flow hydrograph in different gauges
Surface material was sampled at several locations on the Santa Cruz River and
Rillito River. These samples were dried and weighted at the soil laboratory at Civil
Engineering Department at the University of Arizona. The d50 and d90 of surface material
samples are tabulated in Table 3.3. According to sediment size and flow conditions, Van
Rijn’s relations were used for calculating the equilibrium bed load transport rate because
Van Rijn’s relations are valid for 0.09mm < d50 < 1.5mm, flow depth within 0.1-17 m,
and flow velocity from 0.36-2.4 m/s (Van Rijn,1984a,b). Since no sediment inflow data
is available, the sediment boundary conditions were set to be equilibrium load calculated
by van Rijn’s (1984a, b) formula for both suspended load and bed load. The suspended
load recovery coefficient was estimated by using equation (3.3) and ranged from 3.2 to
20.2; the bed load adaptation length was 121.9 m, which is approximately the distance
between two cross sections based on Zhang and Duan’s (2011) work.
62
Table 3.3 d50 and d90 of sediment samples in different locations of Santa Cruz River and
Rillito River
Santa Cruz River
Rauthrauff
Cortaro
Sanders
Sunset
Tangerine
Ina
Twin peak
Avra Vally
Trico
d50 (mm)
1.50
1.50
1.09
0.42
1.85
1.70
0.47
1.45
0.96
d90 (mm)
25.40
27.20
16.00
0.67
7.51
12.70
25.70
3.82
2.86
Rillito River
Dodge
Country
club
Campbell
Mountain
First
Stone
Oracle
La
Canada
La
Cholla
I-10
d50 (mm)
1.66
2.29
1.72
0.73
1.96
1.07
1.34
0.62
1.46
0.81
d90 (mm)
8.77
13.54
4.46
1.94
7.89
5.67
8.52
1.94
4.47
4.48
Vegetation on Santa Cruz River floodplain is mostly herbaceous(Stromberg and
Tellman, 2009). The average height is 1.52 m, and the coverage percentage is about 15%.
Since there has not been a large flood for several years prior to this event, vegetation has
very well established in the main channel and floodplains.
To evaluate whether
vegetation impact is essential to sediment transport, two scenarios were selected: one
assumes vegetation impacts is negligible, and the other accounts for the effect of
vegetation. Time step was set as 0.03 seconds for both scenarios. It took about 3 hours to
run each scenario.
3.4.2.3 Simulation Results
The simulated results of flow discharge and bed elevation change in the Santa
Cruz and Rillito Rivers are compared with the measurements. There is no measurement
in the CDO River for comparison. Figure 3.11 and 3.12 showed the simulated flow
discharges at the La Cholla and the Cortaro Gauges.
Results from both scenarios
matched measurements well for both high and low flows. At the La Cholla Gauge, the
simulated discharge is 93.73 m3/s less than the 1st observed peak flow. This may attribute
63
to the flow from one tributary about 2.3 miles upstream of the gauge. No flow data is
available from this channel. Other peak flows match well with the measured data. At the
Cortaro Gauge, the maximum flow reaches 1158.16 m3/s. Both simulated results slightly
over-estimated flow discharge at the receding limb although the differences are small.
Figure 3.12 shows that the simulated results accounting for the vegetation effects are
slightly better in the Santa Cruz River. The Nash-Sutcliffe efficiency factor is a common
parameter to quantify the accuracy of hydrologic models. The mathematical definition is
written as:
n
2
 (Oi  Ci )
Nash - Sutcliffe efficiency  1  i n1
2
 (Oi  O)
(3.23)
i 1
where Oi and Ci are the ith observed and calculated data, respectively, and n is the total
number of observed or calculated data. The Nash-Sutcliffe efficiency ranges from -∞ to
1. An efficiency of 1 corresponds to a perfect match of modeling results to the observed
data. An efficiency of 0 indicates that the model predictions are as accurate as the mean
of the observed data, whereas a less than zero efficiency occurs when the observed mean
is a better predictor than the model. The closer the model efficiency is to 1, the more
accurate the model is (Nash and Sutcliffe, 1970). The Nash-Sutcliffe efficiencies of flow
discharges for all the simulations are between 0.8 and 1 as shown in Table 3.4, which
indicates the simulated flow discharge is accurate. Since the Nash-Sutcliffe efficiencies
are similar for scenarios with and without vegetation, this indicates the vegetation
induced resistance is insignificant comparing to other resistance in simulating this large
flood event.
64
flow rate(m3/s)
1400
1200
measured data
not considering vegetation effect
1000
considering vegetation effect
800
600
400
200
0
0
10
20
30
40
50
time(hrs)
60
70
80
90
100
Figure 3.11 Simulation and measured flow rate result at La Cholla, Rillito River
1400
measured data
1200
not considering vegetation effects
considering vegetation effects
flow rate(m3/s)
1000
800
600
400
200
0
0
10
20
30
40
50
time(hrs)
60
70
80
90
100
Figure 3.12 Simulation and measured flow rate result at Cortaro gauge, Santa Cruz River
65
As to bed elevation changes, Figure 3.13 and Figure 3.14 showed the simulated bed
elevation changes along the Rillito and the Santa Cruz River.
The results without
considering the vegetation impacts deviate significantly from the measurements.
Considering the impact of vegetation, the results have been improved significantly;
especially for the maximum bed elevation change. The changes of bed elevation are
smaller at both the Rillito and the Santa Cruz River comparing to the results without
vegetation effect, because vegetation decreases shear stress at bed surface so that
sediment transport rate is reduced. From Table 3.5 we can see that the Root Mean Square
Error (RMSE) of modeling error decreases after incorporating vegetation effect.
Although the vegetation model doesn’t considerably affect flow hydrograph, it
bed elevation change(m)
significantly alters sediment transport regime and consequently bed elevation changes.
4.0
2.0
0.0
-2.0
measured data
not considering vegetation effect
considering vegetation effect
-4.0
0
2000
4000
6000
8000
10000
distance from upstream(m)
12000
Figure 3.13 Bed elevation change along Rillito River
14000
16000
66
bed elevation change(m)
8
measured data
6
not cosidering vegetation effect
considering vegetation effect
4
2
0
-2
0
5000
10000
distance from upstream(m)
15000
20000
Figure 3.14 Bed elevation change along Santa Cruz River
Table 3.4 Errors analysis of flow hydrograph and bed elevation change
Flow discharge
NASH
RMSE(ft)
Santa Cruz River
Rillito River
Ignore
Consider
Ignore
Consider
vegetation
vegetation
vegetation
vegetation
0.8863
0.8819
0.9591
0.9697
Bed elevation change
Santa Cruz River
Rillito River
Ignore
Consider
Ignore
Consider
vegetation
vegetation
vegetation
vegetation
1.8542
1.1147
1.9484
1.0206
3.5. Discussion
Flow hydraulics and sediment transport in vegetated channel network are
simulated using a non-equilibrium sediment transport model with consideration of
vegetation induced drag force. The significance of introducing vegetation impacts are
67
illustrated in Section 3.4 (Figures 3.13 and 3.14). Other issues regarding how vegetation
coverage influences the modeling results and non-equilibrium vs equilibrium sediment
transport model are discussed in the following.
3.5.1 Influence of Vegetation Coverage
Figure 3.15 and 3.16 showed a sensitivity analysis of bed elevation change in the
Rillito River and Santa Cruz River if changing vegetation coverage to 10%, 15%, and
30%, respectively. The results in Figure 3.15 and Figure 3.16 showed that smaller
vegetation coverage resulted in more erosion and sedimentation. At larger vegetation
coverage, bed elevation change becomes smaller. This result confirms that vegetation
increases flow resistance and decreases flow velocity and bed shear stress in vegetated
area, so high density vegetation coverage result in less erosion.
elevation change(m)
3.0
measured data
λ=0.1
2.0
λ=0.15
λ=0.3
1.0
0.0
-1.0
-2.0
-3.0
0
2000
4000
6000
8000
10000
distance from upstream(m)
12000
14000
16000
Figure 3.15 Sensitivity analysis of bed elevation change along Rillito River on vegetation
density
bed elevation change(m)
68
6.0
measured data
λ=0.10
λ=0.15
4.0
λ=0.3
2.0
0.0
-2.0
0
5000
10000
distance from upstream(m)
15000
20000
Figure 3.16 Sensitivity analysis of bed elevation change along Santa Cruz River on
vegetation density
3.5.2 Significance of Non-Equilibrium Model
In our non-equilibrium sediment transport model, the bed elevation change was
calculated by solving the Exner equation for bed surface layer as expressed in Eq.(2.5).
Eq.(2.5) can be discretized with the explicit scheme adopted in this study as:
zb n 1  zb n 
t
1


Bs (Cain  Ca*in )   Qbi n  Qb*i n  

B 1  pm  
L

(3.24)
where zb = bed elevation.
In equilibrium sediment transport models, the geomorphic change of the stream
bed can be calculated by solving the sediment continuity equation (Exner equation) given
by (pp. 426, Sturm 2001):
B1  p m 
z b Qt

0
t
x
(3.25)
69
where Qt = total sediment load including bed load and suspended load. It is assumed that
the sediment transport rate is equal to the sediment transport capacity everywhere in the
channel, i.e.,
Qt  Qt*
(3.26)
Therefore, the bed deformation rate is calculated as:
z b
Qt*
1

t
B1  p m  x
(3.27)
Eq. (3.25) can be discretized with the explicit scheme adopted in this study:
zbin 1  zbin 
t
1
Qt*i n  Qt*i 1n 
x B 1  pm 
(3.28)
When comparing Eq. (3.24) and Eq. (3.28), it can be easily seen that the bed
elevation calculated by the equilibrium model is a function of the grid size, and therefore
is grid-dependent, an undesirable property of a model. We incorporate the equilibrium
model into our numerical model and simulate the Run 2 in Baptist (2008), by using 7
nodes and 10 nodes in the domain. To simplify the process, suspended load is neglected.
The measured and simulated bed profiles at the end of the simulation (t = 30 hours) are
plotted in Figure 3.17. The measured and simulated profiles from the non-equilibrium
model are also plotted for comparison. Results from non-equilibrium model are better in
both figures. When comparing the results by using 7 nodes and 10 nodes, it is clear that
the number of nodes used in the equilibrium simulation significantly affected the
simulated bed elevation change. However, the simulated bed profiles did not show much
difference when applying non-equilibrium model. In the equilibrium model test, when the
number of grid cells is set larger than 10, the solution becomes unstable. As the grid
70
becomes finer, the bed elevation change in one time step becomes larger. The fast
changing bed elevation produces an unrealistic, but significant bed surface gradient term
Bed level(m)
in the momentum equation and causes the instability of flow solutions.
0.04
0.02
0.00
measured at t=0 h
-0.02
measured at t= 30 h
eq.model, 10 nodes
non-eq. model, 10 nodes
-0.04
eq. model, 7 nodes
non-eq. model, 7 nodes
-0.06
2
4
6
8
10
12
14
x(m)
16
Figure 3.17 Comparison of results from non-equilibrium and equilibrium model using 7
nodes and 10 nodes at t = 30 hours, respectively
3.6 Conclusions
A one-dimensional flow and sediment transport model for vegetated channel
network was developed.
Both submerged and non-submerged vegetation were
considered in the model to account for the vegetation influence. Vegetation induced
roughness coefficient was used in the momentum equation; grain resistance is separated
from the sidewall, vegetation, and bed form resistance to calculate sediment transport rate.
The grain resistance is a function of vegetation density, and therefore the dense the
71
vegetation, the less erosion.
The sediment transport model solved both bed load and
suspended load transport equation. The non-equilibrium sediment transport algorithm is
adopted for bed elevation changes.
The model has been tested by simulating two cases: one is a steady flow experiment
in a vegetated channel, and the other is a large flash flood event in the Santa Cruz and
Rillito River basin. The simulated results were compared with the observed data. In the
experimental case, bed profiles from simulated results agreed well with the experimental
results, although the dune occurred in Run 3 cannot be simulated because this 1D model
cannot capture flow turbulence. When simulating 2006 flash food in the Santa Cruz and
Rillito river network, the simulated flow hydrograph and bed profiles showed reasonable
agreements with measured data. We also found the model accounting for the impacts of
vegetation gave better results of flow and sediment transport, especially bed elevation
changes.
The influence of vegetation on sediment transport and channel
mrophodynamics has been successfully simulated by the model.
72
CHAPTER 4
APPLICATION OF TWO-DIMENSIONAL SURFACE ROUTING
MODEL FOR SANTA CRUZ WATERSHED BY CONSIDERING
VEGETATION COVERAGE INLUENCE
4.1 Introduction
Surface flow routing, including overland and channel flow routing, refers to the
process that a precipitation generated surface runoff moves over land surface from source
areas to an outlet (Figure 1.1). The simulation of surface flow routing over a watershed
are accomplished by using either two models for overland and channel flow (Julien, et al.,
1995; Qu and Duffy, 2007), separately, or one model to simulate both overland and
channel flow (Cea, et al., 2010; Kim, et al., 2012; ). The disadvantage of the first kind of
model is the difficulties of data processing, and the second method is commonly adopted.
Nearly all the rainfall-runoff models for predicting surface flows are simply based on the
solutions of kinematic wave equations (Vivoni et al., 2007; Yang et al., 1998) or
diffusion wave equations (Qu and Duffy, 2007). An advanced 2-D hydrodynamic and
sediment model, CHRE2D, has been developed in our research group by Yu and Duan
(2013), Yu and Duan (2014). CHRE2D solved the surface flow by fully two-dimensional
shallow water equations. In order to increase the stability of the model, the KWA was
used for thin water depth in CHRE2D.
In this study, to improve the capability of CHRE2D to account for vegetation
impacts, friction terms in the momentum equations are modified by addressing the
73
vegetation coverage. The improved CHRE2D model was verified by an observed storm
event.
4.2 Modifications to CHRE2D Model
4.2.1 Quantification of Vegetation Coverage
70 Satellite photos were used to analyze vegetation coverage, the satellite photo
coverage area are shown in Figure 4.1. Each photo was portioned into 6 layers; the
vegetation layer was extracted and used to calculate vegetation coverage. The extracted
vegetation layers for representative photos are shown in Figure 4.2. And the vegetation
coverage for different locations are listed in Table 4.1.
4.2.2 Incorporation of Vegetation Influence
The modified roughness coefficient is calculated by equations derived by Cheng
(2011) as introduced in Chapter 3. For the Santa Cruz River Watershed, the vegetation
are emerged for the most of time, then the representative Chezy coefficient is presented
as
CR 
 g (1   )3 d
2CD
 h
(4.1)
where  = vegetation density; h = flow depth; CD = drag coefficient in Eq.3.9. The
Manning’s coefficient in Eq. (momentum) was calculated as n  h1/6 / CR . The updated
74
roughness coefficient taking account of the vegetation density was imbedded into the
CHRE2D model to modify Eq.2.9.
Figure 4.1 Satellite photos coverage area
75
Figure 4.2 Extracted vegetation layers for representative photos
Table 4.1 Vegetation coverage for different locations
Site
NO.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Coverage
%
4.168
16.231
6.441
21.914
12.235
11.491
14.047
8.668
20.541
17.693
15.825
2.051
33.465
18.792
17.574
10.336
18.428
5.6564
Site
NO.
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Coverage
%
16.671
18.952
16.337
11.483
9.1662
18.542
11.801
9.7738
17.471
12.847
15.975
13.85
10.602
15.736
13.036
11.199
10.193
15.78
Site
NO.
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
Coverage
%
11.099
3.8193
13.136
14.227
7.7058
13.069
9.2967
12.036
17.643
2.9747
13.664
17.852
8.9102
17.575
12.415
17.807
4.5685
12.138
Site
NO.
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
Coverage
%
11.095
16.259
11.977
9.4191
10.838
24.765
4.2486
18.971
15.785
11.33
19.603
20.269
21.254
12.931
8.2697
16.05
76
4.2.3 Other Hydrological Processes
Other hydrological processes including precipitation, interception and infiltration
used in CHRE2D are summarized here. The precipitation intensity is interpolated by the
inverse distance squared approximations:
NRG
im ( jrg , krg )
d2
i ( j , k )  m 1 NRG m
1

2
m 1 d m

(4.2)
where i( j, k ) is the rainfall intensity in cell ( j, k ) ; im ( jrg , krg ) is the measured rainfall
intensity by the mth rainfall gauge at cell ( jrg , krg ) ; d m is the distance from cell (i, j ) to
mth rainfall gauge at cell ( jrg , krg ) ; NRG is the total number of rainfall gauges.
The interception loss is modeled by the concept of interception depth. When the
intercepted water does not reach land surface, the rainfall excess rate is equal to zero in
each time step. As soon as the interception depth is satisfied, the rainfall excess is
generated and accounted in mass conservation equation. The Green-Ampt model is used
for calculating the infiltration loss. By neglecting the ponding on the surface, the GreenAmpt model can be written as:
H f Md 

f  K s 1 

F 

(4.3)
where f is the infiltration rate; K s is the saturated hydraulic conductivity; H f is the
capillary pressure head at the wetting front; M d is the soil moisture deficit; F is the total
77
infiltrated depth. At each time step, the infiltrated loss is subtracted from the interpolated
rainfall intensity. The resultant rainfall excess is adopted in Eq. (2,9) as the source term.
4.3 Study Site and Data Source
4.3.1 The Study Site
The Santa Cruz River Watershed (SCRW), located in the south central part of the
Arizona (Figure 4.3), is a trans-boundary watershed with an elevation ranges from 2192
to 9337 feet. The Santa Cruz River (SCR) is an ephemeral tributary that drains into the
Gila River, a tributary to the Colorado River (Shamir et al., 2007). The Santa Cruz River
flows to the South and makes a 25-mile loop through Mexico before re-entering the
United States 5 miles of Nogales, Arizona. Afterwards, the river flows northward to its
confluence with the Gila River. The study area for the current work is the watershed
encompassing the reach of the Santa Cruz River within the US, which is approximately
8,000 square miles, representing about 10% area of the State of Arizona. The resolution
of digital elevation model (DEM) used in this study is 100 m extracted from the Lidar
survey in 2005 by the Pima County Flood Control District.
78
Figure 4.3 Map of the Santa Cruz River Watershed (Norman et al., 2010)
4.3.3 Precipitation Data Source
A 24 hours rainfall event of July 15th, 1999 was selected to calibrate the rainfallrunoff model. The hourly gridded precipitation data which has a resolution of 32 km is
obtained from
NLDAS (http://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing.php).
rainfall distribution at t=5hr, t=11hr and t=21hr were shown in Figure 4.4.
The
79
Figure 4.4 Rainfall depth (mm) distribution at different time (t = 5, 11, 21 hours) for July
15th, 1999 flood event.
4.4 Model Application and Calibration
The CHRE2D model has already been verified by many experimental and field
cases (Yu and Duan, 2013; Yu and Duan, in press). However, the modeling parameters,
such as infiltration rate, need to be calibrated for different cases. A 24 hours rainfall event
of July 15th, 1999 was selected to calibrate the rainfall-runoff model. The observed flow
discharges at the Cortaro gauge, located near the downstream outlet of the study reach,
were used to compare with calculated results. The maximum flow rate of this event was
13,700 cfs at Cortaro Gauge, which was the largest flood in 1999. The observed flow
hydrograph of Cortaro gauge was used for model calibration. The soil type of SCRW is
gravel sandy loam, the saturated hydraulic conductivity (Ks) is set as 1.09 cm/hr, and the
calibrated value is 1.5cm/hr. The capillary pressure head at the wetting front Hf is set as
11.01cm, and the calibrated one is 12cm. The soil moisture deficit Md is 0.29 in the model
80
(Rawls et al., 1983). The interception loss is calibrated as 0.5mm. The Manning’s
roughness coefficient is 0.03. The grid size is 100 m by 100 m. The time step for the
simulation ranges from 1.0 s to 10 s; it takes about 1.5 hours to run a 24-hour storm event.
The model runs on a 12-core server.
The measured and simulated hydrograph at the Cortaro gauge were shown in
Figure 4.5. The simulated hydrograph matched the peak arriving times and the peak
discharges well. Though the measured low flow at the first several hours was not
captured by the model, the results from the watershed model accurately estimated flood
peak discharge, which is essential for flood magnitude and frequency analysis. Flow
depth at different times, t=6hr, t=12hr, and t=22hr, were shown in Figure 4.6. The
calibrated model is used to simulate surface flow routing for the historical and future
periods in next chapter.
16000
Measured
14000
Simulated
Flow rate(cfs)
12000
10000
8000
6000
4000
2000
0
0
5
10
Time (hrs)
15
20
25
Figure 4.5 Measured and simulated hydrograph for Cortaro gage of July 15th, 1999
81
Figure 4.6 Water depth (m) at different time (t = 6, 12, 22 hours) for July 15th, 1999 flood
event
4.5 Conclusions
A two-dimensional surface flow routing model, CHRE2D, was modified by
considering the vegetation influence on Manning’s roughness factor. Then, the improved
model requires the calculation of the Manning’s roughness coefficient based on the
vegetation coverage. The watershed model is then calibrated by simulating an observed
flood event in the SCRW. The calibrated model is used for further simulations of
projected surface runoff event in the next Chapter.
82
CHAPTER 5
IMPACTS OF CLIMATE CHANGE ON MAGNITUDE AND
FREQUENCY OF FLOOD IN LOWER SANTA CRUZ RIVER
5.1. Introduction
Flood magnitude and frequency play an important role in the design of hydraulic
structures. It is well known that the climate change will result in the change of flood
magnitude and/or frequency. The frequent occurrences of extreme floods in recent
decades have received much interest. For example, the Mississippi River Flood in 2010,
and the nearly 500 year flood occurred in the Rillito River, AZ in 2006. Many researches
attributed those abnormalities to climate change due to carbon emissions. Studies of
climate change concluded a global surface temperature warming for the next century
(Griggs and Noguer, 2002; Klein Tank et al., 2002; Parker et al., 1992). It is expected that
the warming climate will lead to increase in the magnitude of extreme precipitation
(Arnbjerg-Nielsen, 2006; Hennessy et al., 1997; Schmidli and Frei, 2005; Zhai et al.,
2005) and other climate properties (e.g., snow melt) contributing to flooding across the
globe. A number of studies have re-calibrated the flood magnitude and frequency curves
by incorporating the impact of climate change. Most of those studies used a simplified
rainfall-runoff model to route precipitations predicted from the climate model (e.g. RCM)
at a given watershed. Kay et al. (2006b) used the simulated data from RCM to identify
possible flood frequency changes by using a conceptual rainfall-runoff model between
1970s and 2080s for 15 catchments across the Great Britain. His results showed that the
83
annual averaged rainfall decreased for all the catchments except for one, and the flood
magnitudes for most return periods are increased. Li et al. (2013) applied two conceptual
rainfall-runoff models to simulate runoff using the historical and projected future
precipitation in Tibet. They found the mean annual runoff increased 13%, and the
average temperature increased 1oC in the study area. Similar researches (Booij, 2005;
Chen and Grasby, 2014; Hay et al., 2002; Leonard et al., 2008; Mailhot et al., 2007;
Mirza et al., 2003; Raff et al., 2009) for different regions all over the world showed that
the change of flood magnitude and frequency varied regionally depending on the location
and elevation because of the complex watershed characteristics. In this dissertation, flood
magnitude and frequency curve will be developed using both historical and climate
model projected daily and annual flow data. A physical based two dimensional
hydrodynamic rainfall-runoff model (Yu and Duan 2012; Yu and Duan 2014) is used to
route the hourly precipitation data for the historical (1990-2000) and future periods
(2031-2040, 2071-2079) for the Santa Cruz River Watershed in Tucson, AZ.
The precipitation output from the global climate models (GCMs) has commonly
been used to investigate the impact of climate change on flood risk (Cameron et al., 1999;
Merritt et al., 2006). GCMs typically have a resolution of approximately 200-400 km
which is too coarse for hydrological model (Bell et al., 2007). Because of the coarse
spatial resolution of GCMs and the uncertainty of their precipitation output at a fine
temporal resolution, the results from GCMs are not suitable for direct flood modelling
(Kay et al., 2006b). The high resolution RCM embedded within a GCM can provide a
suitable scale for reliable use to assess the impact of climate change on stream flow.
84
Therefore, the hourly precipitations from RCM model were used in this study. The hourly
precipitations were bias corrected and then used as input to a rainfall-runoff model to
simulate flood flows in rivers.
Flood frequency curve is required for designing in-stream engineering structures,
such as bridges. One of the key assumptions of flood frequency analysis is that the return
period of a flood peak of given magnitude is stationary with time. This assumption has
always been questioned, and appeared to become even more questionable in the future
because of climate change. The analysis that solely relies on historical records can no
longer be suitable for making reliable inferences on the future behavior of the hydroclimate conditions. Nowadays, the flood flows generated from projected climate data
together with historical records are widely used for flood risk analysis.
Nearly all the rainfall-runoff models for predicting flood flows are based on the
solutions of kinematic wave (Vivoni et al., 2007; Yang et al., 1998) or diffusion wave
(Qu and Duffy, 2007) approximations. Although the conceptual model is usually
computationally efficient and easy to handle, the disadvantage is the accuracies and
inability to route surface flow over complex terrains. The common input precipitation
data for conceptual models are daily or monthly averaged precipitation, which resulted in
the daily or monthly averaged flow in the river. However, water related hydraulic
structures require the peak discharge as the design flow, such as 100-year flood flow for
bridge design. The daily averaged flow can be much less that the peak flow for the
Southwest US because of the short duration and high intensity precipitation event. Hanel
85
and Buishand (2010) found the increase in large quantiles of the daily maxima was much
smaller than that in the quantiles of the hourly maxima at the end of the 21st century.
Therefore, the two-dimensional physical based hydrodynamic watershed model,
CHRE2D, developed by Yu and Duan (2013) was used in this paper to route surface flow
over the Santa Cuz River watershed to evaluate the potential changing of flood intensity
and frequency.
The objective of this chapter is to analyze the possible flood magnitude and
frequency change caused by climate change in the Santa Cruz River Watershed. Instead
of historical records, the precipitation data generated from the WRF (Weather Research
and Forecasting) Regional Climate Model was routed through the watershed using
CHRE2D watershed model. Three periods were simulated: the historical period of 19902000, the future periods of 2031-2040 and 2071-2079. In particular, we applied quantilebased mapping bias correction method to all three periods, calibrated the CHRE2D model
using the rainfall event of July 15th, 1999, compared the simulated stream flow for the
historical (1990-2000) period with observations, and developed new flood magnitude and
frequency curves based on climate projected precipitations.
5.2 Watershed Description and Data Source
5.2.1 The Study Site
For the SCRW, the mean monthly precipitation in the summer is greater than that
in the winter (Wood et al., 1999). The summer rainy season starts from July to September,
86
while the winter rainy season is from December to March. The spring and fall months in
Arizona usually are dry season, and winter and summer seasons are the only sources of
precipitation in the study area. The mean annual precipitation at the Nogales gauge from
1914 to 2000 was 422 mm, consisting of an average 59% in the summer and 29% in the
winter (Shamir et al., 2007).
5.2.2 Climate Data Source
The climate data for the study area was extracted from the output of Weather
Research and Forecasting (WRF) regional climate model (Dominguez et al., 2012). The
regional climate model WRF driven by the HadCM3 GCM was executed continuously
for 111 years (1967-2079) with a spatial resolution of 35 km and a temporal resolution of
6 hours. Due to the limitation of currently computation power, it is unrealistic to run the
111-year continuous simulation at a high resolution. Three periods were selected to
represent the typical current and future climate. Current climate conditions are
represented by the time period of 1990-2000; changed climate conditions are represented
by the time period of 2031-2040 and 2071-2079. A further downscaled simulation was
performed to refine the precipitation data at 10 km spatial resolution and hourly temporal
resolution for the selected three periods. The gridded precipitation data downscaled from
the WRF model was used as input into the hydrological model.
5.2.3 Bias Correction of Precipitation Data
It is well known that, unless the output from the climate model is corrected for
biases, the projected precipitations can be unrealistic and thus of limited use (Sharma et
87
al., 2007). Bias correction was traditionally used for correcting daily or monthly
distributions (Fowler and Kilsby, 2007; Piani et al., 2010; Yang et al., 2005). Bias
correction of precipitation data at hourly scale is needed to satisfy the input requirement
for the hydrological model. A two-step quantile-based mapping method derived by Wood
et al. (2002) was modified and applied to reduce the errors between the daily observed
and the WRF simulated precipitations.
Monthly precipitation outputs from the climate model within the study area were
bias-corrected on a cell-by-cell basis. The downscaled WRF hourly precipitation data for
1990-2000 with a resolution of 10 km was aggregated to monthly precipitation and then
re-gridded to 1/8 degree cell in the first step. The observed monthly precipitation was
obtained from Maurer et al. (2002) on 1/8 degree grid for the same period. The simulated
and observed monthly precipitation is assigned a non-exceedance percentile for a given
month for the entire eleven year period. For example, there are eleven monthly
precipitation data for January for the entire simulation period. The eleven monthly
precipitation data are sorted in a descending order, and each is assigned a non-exceedence
percentile. The same procedures are applied for the observed monthly precipitations in
the same period. The bias correction are expressed as multiplicative anomalies as shown
in Eq. (5.1).
M ihist
,q 
Pi ,obs
q
Pi ,wrf
q
(5.1)
88
th
wrf
where Pi ,obs
quantile of observed and simulated montly
q and Pi , q represent the q
precipitation from WRF model for the ith month during the period of 1990-2000,
respectively; M ihist
is the scaling factor, representing the qth quantile multiplicative
,q
anomalies for the ith month for the current period. The bias-corrected monthly
precipitations for the current period are calculated using Eq. (5.2). The scaling factor was
then applied to the future two periods: 2031-2040 and 2071-2079 as shown in Eq. (5.3).
The above procedure was repeated for each month from January to December. The bias
correction was applied atr each cell for a total of 45 cells in the study area.
hist
Pi wrf  Pi ,wrf
q M i ,q
(5.2)
hist
Pi fut  Pi ,fut
q M i ,q
(5.3)
th
where Pi ,fut
quantile of WRF simulated precipitation for the ith month in
q represents the q
the future period; Pi wrf and Pi wrf represent the bias-corrected precipitation for the ith
month in the current and future period, respectively.
Figure 5.1 shows the bias correction results for for a selected cell during three
periods. For each cell, the bias-corrected monthly median precipitation is the same as in
the observed data for period 1990-2000. It shows that the raw data from WRF model
overestimated the precipitation, especially in the wet seasons. The ratios of observed
monthly precipitation and the WRF simulated monthly precipitation were applied to
future periods cell by cell, which reduced the bias of simulated precipitations of future
periods.
89
Figure 5.2, 5.3 and 5.4 showed the bias-corrected and raw monthly precipitation
data for each year of three periods, respectively.
100
observed 1990-2000
WRF B-C 1990-2000
WRF raw 1990-2000
WRF B-C 2031-2040
WRF raw 2031-2040
WRF B-C 2071-2079
WRF raw 2071-2079
90
80
Precipitation (mm)
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
calendar month
9
10
11
12
Figure 5.1 Monthly precipitation (P) raw data and bias-corrected data (B-C) for a selected
cell (cell number marked as 33). Observed monthly median P for 1990-2000 and WRF
model raw and B-C monthly median P data for different periods
90
200
B-C
raw
B-C median
raw median
180
160
Precipitation (mm)
140
120
100
80
60
40
20
0
1
2
3
4
5
6
7
8
calendar month
9
10
11
12
Figure 5.2 WRF model raw and bias-corrected (B-C) monthly P and monthly median
precipitation P data for 1990 -2000 for a selected cell (cell number marked as 33).
300
B-C
raw
B-C median
raw median
Precipitation (mm)
250
200
150
100
50
0
1
2
3
4
5
6
7
8
calendar month
9
10
11
12
Figure 5.3 WRF model raw and bias-corrected (B-C) monthly P and monthly median
precipitation P data for 2031- 2040 for a selected cell (cell number marked as 33).
91
400
B-C
raw
B-C median
raw median
350
Precipitation (mm)
300
250
200
150
100
50
0
1
2
3
4
5
6
7
8
calendar month
9
10
11
12
Figure 5.4 WRF model raw and bias-corrected (B-C) monthly P and monthly median
precipitation P data for 2071-2079for a selected cell (cell number marked as 33).
The spatial averaged monthly median precipitation data are presented in Figure
5.5. The observed spatial averaged monthly median precipitation does not exactly match
with the spatial averaged WRF bias-corrected data in Figure 5.5 because the bias
correction was applied individually for each cell. The monthly precipitation medians in
two future periods are larger than that for the period 1990-2000. The WRF model
overestimated the average precipitation in the summer season and slightly underestimated
it in the winter season.
92
120
observed 1990-2000
WRF B-C 1990-2000
WRF raw 1990-2000
WRF B-C 2031-2040
WRF raw 2031-2040
WRF B-C 2071-2079
WRF raw 2071-2079
Precipitation (mm)
100
80
60
40
20
0
1
2
3
4
5
6
7
8
calendar month
9
10
11
12
Figure 5.5 Monthly median precipitation (P) raw data and bias-corrected data (B-C) for
different periods with spatial average
Followed by the bias correction step, an additional step was taken to disaggregate
the monthly data into a daily time series. For each cell, the daily precipitation values for a
given year and month were scaled to match the monthly total precipitation for the same
month. Figure 5.6 shows the disaggregate result of daily precipitation for a selected cell.
The comparisons of the observed daily precipitation with the bias-corrected WRF
modeling results and the WRF model’s raw results are shown in Figure 5.6 (a) and (b),
respectively. It can be seen that the bias-corrected result matched well with the
observation. The Root Mean Square Errors (RMSEs) between the biased corrected and
the observed precipitations for each cell are summarized in Table 5.1. The calculated
RMSE ranges from 4.21 to 6.92 (mm), the normalized root-mean-square deviation which
indicate the residual variance ranges from 6.94% to 13.75% with most of cells less than
93
10%. Since no hourly observed precipitation data is available for period 1990-2000, we
will not compare the hourly bias-corrected precipitation with observations.
(a)
80
Observed daily P
WRF B-C daily P
Precipitation (mm)
70
60
50
40
30
20
10
0
500
1000
1500
(b)
2000
2500
Continus day number (days)
3000
3500
4000
80
Precipitation(mm)
Observed daily P
WRF raw daily P
60
40
20
0
500
1000
1500
2000
2500
Continus day number (days)
3000
3500
4000
Figure 5.6 Daily precipitation (P) data for a selected cell (cell number marked as 33) for
period 1990-2000. (a) Comparison of observed daily P and WRF model bias-corrected
daily P, (b) Comparison of observed daily P and WRF raw daily P.
Table 5.1 Root-mean-square-error of WRF model bias-corrected daily precipitation and
observed daily precipitation for all cells
Cell
#
1
2
3
4
5
6
7
8
9
RMSE(mm) Cell # RMSE(mm) Cell # RMSE(mm) Cell # RMSE(mm) Cell # RMSE(mm)
6.9235
10
6.4961
19
6.9113
28
4.4060
34
4.2334
6.6674
11
5.6143
20
6.1986
29
4.5335
35
4.4713
6.3720
12
5.0630
21
6.3358
30
4.8463
36
4.3497
5.8454
13
6.5137
22
5.2490
31
4.8276
37
4.3270
6.0385
14
6.7290
23
4.8468
32
4.3710
38
4.2133
6.7084
15
6.0207
24
5.4447
33
4.2668
39
4.5897
6.8397
16
5.6664
25
5.8356
34
4.4569
40
4.8166
5.8534
17
4.6299
26
4.8862
35
5.0865
41
6.2054
6.2025
18
5.1663
27
4.6287
36
5.2144
42
5.1340
94
20
WRF B-C hourly P
WRF raw hourly P
18
16
Precipitation (mm)
14
12
10
8
6
4
2
0
500
1000
1500
2000 2500 3000
hour numbers
3500
4000
4500
5000
Figure 5.7 Hourly precipitation (P) raw data and bias-corrected data (B-C) for period
1990-2000
In this study, hourly precipitations are used as input data for hydrological model.
A further step of disaggregating daily time series into hourly distribution is taken for each
cell. The strategy is similar as disaggregating monthly data to daily data by scale daily
precipitation to match the daily total precipitation. The hourly precipitation data of RCMWRF raw data and bias-corrected data for period 1990-2000 were shown in Figure 5.7.
The bias-corrected hourly precipitation data are then interpolated back to a 10 km grid
cells and used as the input for the hydrological model.
95
5.2.4 Watershed Model
The impact of climate change on flood frequency is assessed using a physical
based SWE-KWA watershed model, CHRE2D, developed by Yu and Duan (2013). The
model has already been introduced and calibrated in Chapter 4. The model was
successfully applied for Santa Cruz River Watershed, and the calibrated parameters were
directly used in the simulations in this Chapter.
5.3 Results
The CHRE2D model was used to simulate flow hydrograph for all three period.
Because of the limitation on computational capability, it is not necessary to run each
period continually. The summer and winter rainy seasons are the mainly source of
precipitation for the SCRW, and during most days of Spring and Fall seasons, there is no
precipitation. Therefore, only rainfall events in each period were simulated. Days without
precipitation were deleted from the input precipitation data. In total, 96 rainfall events
were simulated for the historical period of 1990-2000, 80 rainfall events for 2031-2040,
and 70 rainfall events for 2071-2079. The Cortaro gauge has observed flows for the
historical period 1990-2000, so the simulated results of flow discharge at the Cortaro
gauge were used to verify the accuracy of simulated discharges.
5.3.1 Comparison of Observed and Simulated Results for Historical Period
The accuracy of the calibrated watershed model was evaluated by comparing the
simulated flow results at the Cortaro Gauge with the observations. Figure 5.8 compares
96
the observed and simulated percent of yearly peak flow exceeding a given discharge for
the period of 1990-2000. The largest yearly peak discharge during this period is about
27,000 cfs for both simulated and observed data. The second largest simulated yearly
peak during this period is about 9,000 cfs larger than observed data. The rest of simulated
yearly peaks match well with the observed data. 60% of the yearly peak discharges are
greater than 5,000 cfs for both the simulated and observed data. Statistics of annual
observed and simulated peak discharges from 1990 to 2000 are shown in the first two
rows in Table 5.2. The simulated mean annual peak flows is nearly the same as the
observed one. Nevertheless, the standard deviation and skewness of annual peak flows
are different. The weighted skewness and frequency factors are used to estimate flow
rates of different return periods. As can be seen in the table, there is no obvious
difference of skewness and frequency factors for observed and simulated data during this
period.
Figure 5.9 shows the current Log-Person III type flood frequency curves for the
Santa Cruz River at Cortaro Gauge. The flood magnitude and frequency curves are
estimated according to USGS 17B report. The solid line was calculated using the
historical data from 1940 to 1998, 2001 to 2013, and the simulated data from 1999 to
2000. The dashed line was calculated using only the historical data from 1940 to 2013.
The flood frequency curve calculated from the observed and simulated data matches well
with the curve derived from only the observed data. For the 100-year return period flood,
both curves predict the same flow discharge, which is 40,000 cfs. For the 200-year flow,
there is 2,000 cfs difference between the results from two curves.
97
The percent of daily peak flows exceeding a given discharge for the observed and
simulated 1990-2000 period was shown in Figure 5.10. Only 0.25% percent of daily peak
discharges are larger than 5,000cfs for both observed and simulated data. There are 0.83%
percent of daily peak flows greater than 1,000cfs for observed data and 0.76% percent for
simulated data. Only four observed daily peak discharges are greater than 10,000cfs. For
daily peak flows greater than 8,000cfs, the simulated data are over-estimated comparing
with observed data. For flows less than 8,000cfs, the simulated data match with the
observations. Daily peak flows are summarized in Table 5.3. Table 5.3 shows the
frequency of daily peak flow greater than a given discharge for every year of each period.
Results in this table show the frequency of daily peak flow exceeding a given discharge
are similar for observed and simulated data during 1990-2000 period. The simulated
results show the frequency of daily peak flow greater than 20,000 cfs is twice as much as
that of observed data.
One can see from results Figure 5.8 to Figure 5.10, most of the flood events
during 1990-2000 are very small, only four events have discharges larger than 10,000 cfs.
The watershed model results performed well for low to mid flows, but a bit overestimated
the large floods. Flood magnitude and frequency curves obtained from observed and
simulated data sets are similar with 2,000 cfs difference for the 200-year flow.
98
30000
25000
Discharge (cfs)
20000
15000
10000
Observed 1990-2000 yearly
peak
5000
0
1
10
Percent of years exceeded (%)
100
Figure 5.8 Percent of yearly peak discharge exceeded certain discharge of observed and
simulated data for 1990-2000 periods
50000
Discharge (cfs)
40000
30000
20000
10000
17B - Observed and simulated historical
17B - Observed hitorical
0
1
10
Return Period (years)
100
1000
Figure 5.9 Current Log-Person III Flood frequency curves for Santa Cruz River at
Cortaro using USGS 17B (Solid line: using observed historical data from 1940-1998,
2001-2013 and simulated data from 1999-2000; dashed line: using observed historical
data from 1940-2013)
99
30000
25000
Discharge (cfs)
20000
15000
10000
observed 1990-2000
5000
Simulated 1990-2000
0
0.01
0.1
1
Percent of days exceeded (%)
10
100
Figure 5.10 Percent of daily peak discharge exceeded certain discharge for observed and
simulated 1990-2000 period
5.3.2 Flood Magnitude and Frequency Analysis Using Annual Maximum Data
The percent of yearly peak discharges exceeding a given discharges for three
periods are shown in Figure 5.11. Observed yearly peak flow rates for period 1990-2000
are used to represent historical condition. For historical period, 45% yearly peak flows
exceeds 10,000cfs and 9% exceeds 20,000cfs. For period 2031-2040, 70% of yearly peak
discharges are greater than 10,000cfs and 20% of yearly peak discharges are larger than
20,000cfs. The frequencies of floods exceed 10,000cfs and 20,000cfs for period 20312040 are twice as much as that for historical period. The curve for period 2031-2040
locates on the right of historical period curve, which indicates the magnitude and
intensity of yearly peak discharges for future period 2031-2040 are increased compared
with historical period. For period 2071-2079, there are also 70% of data greater than
100
10,000cfs and only 11% of data greater than 20,000cfs. When comparing data of period
2071-2079 with historical observed data, it is noticed that the magnitude and intensity of
flood increased for all flow rate grades except for discharges between 10,000cfs and
15,000cfs. The statistical analysis results of above three periods can be found in Table 5.2.
The mean yearly peak discharges for two future periods are greater than historical period,
with the standard deviation smaller than historical period.
Figure 5.12 shows the current and future Log- Person III flood frequency curves for
Santa Cruz River at Cortaro Gauge. Current flood frequency curve was calculated using
the historical observed data from 1940 to 2013. The future flood frequency curve was
calculated using the historical data from 1940 to 2013 and the simulated data of 20312040 and 2071-2079. Comparing the current and future flood frequency curves, one can
conclude the discharge of 100-year flood is increased by 3,000 cfs when including the
climate generated flow data. It is about 7.5% of the current 100-year flood discharge The
200-year flood discharge is increased by 5,000 cfs, which is about 11.1% of the current
200-year flood discharge. The correlations of these two data sets are listed in Table 5.2.
Although all the statistical parameters did not show a big difference between these two
data sets, the Log-Person III flood frequency curves showed flood discharges for
different return periods has been moderately increased when including the climate model
generated future data.
.
101
40000
35000
30000
Discharge (cfs)
25000
20000
15000
observed 19902000
10000
simulated 20312040
5000
0
1
10
Percent of years exceeded (%)
100
Figure 5.11 Percent of yearly peak discharge exceeded certain discharge for observed
historical and simulated future periods
70000
60000
Discharge (cfs)
50000
40000
30000
17B - Future
20000
17B - Current
Observed historical
10000
2031-2040 simulated
2071-2079 simulated
0
1
10
Return Period (years)
100
Figure 5.12 Current and future Log-Person III flood frequency curves for Santa Cruz
River at Cortaro using USGS 17B (Solid line: current flood frequency curve using
historical data from 1940-2013; dash line: future flood frequency curve using historical
data from 1940-2013 and simulated data of 2031-2040 and 2071-2079)
102
Table 5.2 Statistical analysis results of yearly peak flow for different periods
Xave
Standard
deviation
of
logarithms
Sdev
3.732
Mean
logarithm
Observed
period 19902000
Simulated
period 19902000
Simulated
period 20312040
simulated
period 20712079
Observed
historical period
1940-2013
observed
historical
1940-1989,
2001-2013 and
simulated
1990-2000
observed
historical and all
simulated
Skewness Skewness
of
of
logarithms logarithms
Frequency factors for different
recurrence interval years
Cs
Cw
2
10
50
100
0.520
-0.629
-0.302
0.050 1.245
1.889 2.102 2.292
3.664
0.682
-0.874
-0.387
0.064 1.233
1.841 2.039 2.213
3.946
0.463
-0.728
-0.327
0.054 1.241
1.875 2.084 2.269
3.993
0.325
-0.172
-0.126
0.021 1.267
1.986 2.233 2.457
3.891
0.358
-0.623
-0.475
0.079 1.220
1.791 1.974 2.131
3.880
0.403
-1.191
-0.747
0.124 1.175
1.636 1.772 1.884
3.908
0.365
-0.589
-0.471
0.078 1.220
1.793 1.976 2.135
5.3.3 Flood Frequency Analysis Using Daily Maximum Data
In order to compare daily peak flow discharges of future periods and current
periods, the percentages of daily peak discharge exceeding a certain discharge for three
periods are shown in Figure 5.13. The observed daily peak flow discharges for period
200
103
1990-2000 are used to represent historical conditions. 0.25% of daily peak discharges are
greater than 5,000 cfs for the period 1990-2000, 0.5% of data for the period, 2031-2040,
and 0.65% of data for the period, 2071-2079. Comparing data for the period 1990-2000
with those for 2031-2040, the flood frequency curve moves to the right. This indicates
that the flood magnitude and frequency increase from the historical to the future
condition. The curve for period 2071-2080 also shifts to the right of period 1990-2000,
which indicates the flood magnitude and frequency are larger than historical condition.
Comparing two curves of future periods, the flood magnitude and frequency for period
2071-2079 is greater than 2031-2040 for flood discharge less than 12,000 cfs. The largest
flood discharge is 36,851 cfs for period 2071-2079 and 28,756 cfs for period 2031-2040.
The daily peak flow summary for these three different periods is listed in Table 5.3.
Comparing the observed data for the period 1990-2000 with the simulated data for the
period 2031-2040, there are 3.2 days with daily peak discharge greater than 1,500 cfs of
every year during 2031-2040 and 2.36 days during 1990-2000. The frequency of daily
peak discharge greater than 5,000 cfs during period 2031-2040 is twice as much as that
for the period 1990-2000. When the daily peak discharge is greater than 20,000 cfs, the
frequency during the period 2031-2040 is more than three times as much as that for the
period 1990-2000. For both periods: 1990-2000 and 2071-2079, the frequencies for all
discharge are greater than those in the period 1990-2000. For period 1990-2000 and
period 2071-2079, the frequencies for all discharge grades are greater than period 19902000. When daily peak discharge is greater than 2,500cfs, the frequencies for period
2071-2079 are much larger than that for historical period.
104
40000
35000
Discharge (cfs)
30000
25000
20000
observed 1990-2000
15000
simulated 2031-2040
Simulated 2071-2079
10000
5000
0
0.01
0.1
1
Percent of days exceeded (%)
10
100
Figure 5.13 Percent of daily peak discharge exceeded certain discharge for observed and
simulated periods
Table 5.3 Average times of daily peak flow greater than certain discharge for one year of
each period
Time period
>800
1990-2000 observed (days) 3.55
1990-2000 simulated
(days)
3.27
2031-2040 simulated
(days)
3.70
2071-2079 simulated
(days)
5.11
Discharge (cfs)
>5000 >10000
0.82
0.36
>1500
2.36
>2500
1.36
>20000
0.09
>30000
0.00
2.09
1.55
0.91
0.45
0.18
0.00
3.20
2.20
1.90
0.80
0.30
0.00
4.00
3.67
2.33
1.11
0.11
0.11
5.4 Conclusions and Discussion
This research used a physical based rainfall-runoff model to route bias-corrected
RCM climate data to identify possible changes in flood magnitude and frequency for the
105
Santa Cruz River basin. Through the use of precipitation data derived from the historical
(1990-2000) and future (2031-2040, 2071-2079) RCM data; the annual maximum
discharges, daily maximum discharges, and hourly discharges were obtained from the
watershed model for the listed three periods. These data were then compared with the
observed data for the historical period and put into the context of flood magnitude and
frequency analysis.
Flood discharges for different return periods increased when considering climate
change for the future, the discharge of 100-year return period flood increases by 3,000cfs,
and the discharge of 200-year return period flood increases by 5,000cfs. Annual
maximum discharge results indicate both flood magnitude and frequency increase for
period 2031-2040 and flood magnitude and frequency of middle and low flow (flow less
than 12,000cfs) increase for period 2071-2079 period 2071-2079. Daily maximum
discharge results show the increase of magnitude and frequency for period 2031-2040
and period 2071-2079 compared with period 1990-2000.
Since the study area is located in the dryland, large flood events are seldom
occurred. When estimating the flood frequency using annual peak flow data, the lack of
large event can lead to uncertainty in the large flood estimation. A continuous long term
run would be ideal to reduce sample error. At present, this possibility is limited by the
computer power, but could be more feasible with future computing improvements.
106
CHAPTER 6
SUMMARY, CONCLUSIONS AND FUTURE RESEARCH
6.1 Summary and Conclusions
The objective of this study is to simulate unsteady flow and sediment transport in
vegetated channel network and apply a two-dimensional surface flow routing model to
study the impact of climate change on flood magnitude and frequency.
Chapter 1 is the literature review of related studies and the objective of this
dissertation. In Chapter 2, the governing equations for one-dimensional and twodimensional hydrodynamic and sediment transport models are presented. The numerical
schemes to solve the governing equations, the Godunov-type finite volume method, were
briefly described here. Chapter 3 is the detailed description of the one-dimensional
unsteady flow and sediment transport model for vegetated channel networks. The
modified St. Venant equations together with the governing equations for suspended
sediment and bed load transport were solved simultaneously to obtain flow properties and
sediment transport rate. The Godunov-type finite volume method was employed to
discretize the governing equations. Then, the Exner equation is solved for bed elevation
change. Since sediment transport is non-equilibrium when bed is degrading or aggrading,
a recovery coefficient for suspended sediment and an adaptation length for bed load
transport are used to quantify the differences between equilibrium and non-equilibrium
sediment transport rate. The influence of vegetation on floodplain and main channel was
107
accounted for by adjusting resistance terms in the momentum equations for flow field. A
procedure to separate the grain resistance from the total resistance was proposed and to
calculate sediment transport rate. The model was tested by a flume experiment case and
an unprecedented flood event occurred in the Santa Cruz River, Tucson, Arizona, in July
2006. Simulated results of flow discharge and bed elevation changes showed satisfactory
agreements with the measurements. This part of 1-D modeling study concludes:
1. In vegetated channel, flow resistance includes grain resistance, bed form
resistance, vegetation resistance, and side wall resistance. As vegetation density
increases, vegetation resistance increases, so flow resistance increases.
2. The increase of vegetation resistance will reduce the grain resistance that
transports bed load sediment. The denser the vegetation, the less the sediment
transport.
3. The grain resistance rather than the total resistance should be used in sediment
transport model for calculating sediment transport rate. A new method to separate
grain resistance from the total resistance is presented in this study.
The 2nd part of this research is to apply the two-dimensional surface routing
model, CHRE2D, to SCRW, which is detailed in Chapter 4. The vegetation influence was
considered by using the modified Manning’s coefficient that takes account of the
vegetation coverage. The vegetation coverage was extracted from satellite photos,
108
although the accuracy remains to be verified. The CHRE2D model was calibrated using
the observed data at the Cortaro Gauge on the Santa Cruz River.
In Chapter 5, flood magnitude and frequency change were estimated by using the
RCM climate outputs. Hourly precipitation data derived from the RCM-for three periods
(1990-2000, 2031-2040 and 2071-2079) were used as input data for the calibrated
CHRE2D model for predicting the impact of climate change on magnitude and flood
frequency. The Santa Cruz River Watershed located in the Southern Arizona was chosen
as the study area. Precipitation output in the study area from RCM-WRF was biascorrected using the observed gridded precipitation data for 1990-2000. The scaling
factors obtained from historical periods were applied for two future periods to correct the
bias of hourly precipitation data. The watershed model was calibrated and then used to
simulate flood routing for three periods. Simulated annual and daily maximum discharges
and hourly discharges are obtained and analyzed for the above three periods.
Conclusions regarding the impacts of climate change on flood frequency curve are:
1. Flood discharges for different return periods increased by considering future
climate change, the discharge of 100-year return period flood increases by
3,000cfs, and the discharge of 200-year return period flood increases by 5,000cfs.
2. Annual maximum discharge results indicate both flood magnitude and frequency
increases for period 2031-2040, while the magnitude of small flood increases and
the frequency of large flood decreases for period 2071-2079.
109
3. Daily maximum discharge results show the increase of both magnitude and
frequency for period 2031-2040 and 2071-2079 compared with period 1990-2000.
6.2 Future Research
Results from this study showed vegetation has significant influence on flow and
sediment transport, and this influence can be quantified by using 1D and 2D models. An
accurate and reliable 2D surface flow model is capable of supplementing additional data
for flood frequency analysis. Future research will be focused on the experiment and
numerical study of interactions among vegetation, flow and sediment transport; twodimensional surface flow routing with sediment transport.
Vegetation plays an important role for flow routing and sediment transport in both
floodplain and riparian area. The resistance to flow through vegetated area increases and
the mean velocities reduces comparing to the non-vegetated area. However, sediment
transport may be increased as the vegetation coverage is sparse due to the perturbation
and turbulence vortex induced by vegetation stems, branches and leaves. Intensive
researches have been conducted on the flow resistance force caused by vegetation; but
few studies have reported the interaction of vegetation and sediment transport. The
mechanism of sediment transport in vegetated channel needs further investigation.
Another potential research discussed here is the numerical model of surface flow
routing with sediment transport. The very thin overland flow is often supercritical. Under
this condition, sediment transport rate can be very high. However, this kind of sediment
110
transport rate cannot be calculated directly using existing sediment transport formulas
applicable only to steady uniform channel flow. A new formula needs to be developed for
sediment transport in supercritical flow in order to better simulate the physical process of
sedimentation processes in watershed.
111
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