&EPA
United States
Environmental Protection
Agency
Floodplain Modeling in the
Kansas River Basin Using
Hydrologic Engineering
Center (HEC) Models
Impacts of Urbanization and
Wetlands for Mitigation
RESEARCH AND DEVELOPMENT
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EPA/600/R-11/116
October 2011
www.epa.gov
Floodplain Modeling in the
Kansas River Basin Using
Hydrologic Engineering
Center (HEC) Models
Impacts of Urbanization and
Wetlands for Mitigation
Yongping Yuan
Kamal Qaiser
U.S. Environmental Protection Agency
National Exposure Research Laboratory
Environmental Sciences Division
Landscape Ecology Branch
Las Vegas, NV89119
Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect
official Agency policy. Mention of trade names and commercial products does not constitute
endorsement or recommendation for use.
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Table of Contents
1. INTRODUCTION AND BACKGROUND INFORMATION 1
1.1. OVERVIEW 1
1.2. WATERSHED DESCRIPTION 2
1.3. MONITORING & OTHER AVAILABLE INFORMATION 4
2. HEC-MODELS-BRIEF OVERVIEW 5
2.1. HEC-HMS 5
2.7.7. Tool Description 5
2.7.2. Why the model was selected 6
2.2. HEC-RAS 6
2.2.7. Tool Description 6
2.2.2. Why the model was selected 7
3. METHOD AND PROCEDURES 8
3.1. HYDROLOGIC MODEL BUILDING 8
3.7.7. HMS Parameters 11
3.7.2. Soil Data Analysis 13
3.7.3. HEC-HMS Calibration Approach 13
3.2. HYDRAULIC MODEL BUILDING 14
3.2.7. Hydraulic Geometry Considerations 14
3.2.2. HEC-GeoRAS Processing 15
3.2.3. HEC-RAS Calibration Approach 16
4. FUTURE SCENARIOS 18
4.1. LAND USE SCENARIOS 18
4.2. CLIMATE SCENARIOS 19
4.3. FUTURE WETLANDS SCENARIOS 20
5. MODEL SIMULATION RESULTS AND DISCUSSION 21
5.1. HEC-HMS CALIBRATION/VALIDATION 21
5.2. HEC-RAS CALIBRATION/VALIDATION 23
5.3. FUTURE SCENARIOS 25
5.3.7. Kansas River Stream/low for Different Landuse Scenarios 25
5.3.2. Impact of wetlands on Kansas River Streamflow 25
5.3.3. Kansas River Water Elevations for Different Landuse Scenarios 26
5.3.4. Impact of Wetlands on Kansas River Water Elevations 27
5.3.5. Kansas River Inundation Extents for Different Land Use Scenarios 28
5.3.6. Impact of Wetlands on Kansas River Inundation Areas 29
6. CONCLUSIONS 30
7. REFERENCES 31
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List of Figures
FIGURE 1. LOCATION OF THE STUDY AREA IN KANSAS STATE, USA 3
FIGURE 2. LOCATION OF THE KANSAS RIVER WITHIN KANSAS RIVER BASIN. ARROW
DENOTES THE DIRECTION OF FLOW 3
FIGURE 3. CURVE NUMBER GRID DEVELOPED IN HEC-GEoHMS FOR THE KANSAS
RIVER BASIN 10
FIGURE 4. KANSAS RIVER REPRESENTATION IN HMS MODEL 13
FIGURE 5 (A)DIGITAL ELEVATION MODEL EXAMPLE FROM KANSAS RIVER BASIN AT
1OM RESOLUTION, (fi) DIGITAL ELEVATION MODEL EXAMPLE FROM KANSAS
RIVER BASIN AT 3 OM RESOLUTION 14
FIGURE 6. A SNAPSHOT OF RIVER, BANKS, FLOW PATH CENTERLINES AND CROSS-
SECTION LAYERS FOR KANSAS RIVER IN HEC-GEORAS 16
FIGURE 7. LOCATION OF USGS GAGES USED FOR CALIBRATION ON THE KANSAS RIVER 167
FIGURE 8. GEOGRAPHIC BOUNDARIES FOR SOIL CONSERVATION SERVICE (SCS)
RAINFALL DISTRIBUTIONS 19
FIGURE 9. 100 YEAR RETURN PERIOD, 24 HOUR DURATION PRECIPITATION MAP FOR
KANSAS, INCHES. THE RED RECTANGLE HIGHLIGHTS KANSAS STATE WITH
PRECIPITATION CONTOUR LINES 20
FIGURE 10A. HMS OPTIMIZATION SUMMARY 22
FIGURE 10B. HMS OPTIMIZED PARAMETERS 22
FIGURE lOc HMS HYDROGRAPH COMPARISON 23
FIGURE 11. GEoRAS INUNDATION AREA FOR COMPARING THE DIFFERENT STORM EVENTS
150,175 AND 200 MM (6, 7 AND 8 INCH) FOR THE 2040 SCENARIO 29
in
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List of Tables
TABLE 1. DATASETS AND THEIR SOURCES USED FOR BUILDING THE MODELS 4
TABLE 2. PHYSICAL CHARACTERISTICS GENERATED BY HEC-GEoHMS 8
TABLE 3. CURVE NUMBERS FOR NLCD LAND USE CLASSES AND SSURGO
HYDROLOGIC SOIL GROUPS 11
TABLE 4. PERCENTAGE OF HYDROLOGIC SOIL GROUPS AT THE KANSAS RIVER BASIN 13
TABLE 5. PERCENTAGE AREA OF DIFFERENT NLCD LAND USE CLASSES FOR THE
SCENARIOS 22
TABLE 6. HMS CALIBRATION PARAMETERS CURVE NUMBER SCALE FACTOR ALL
SUBBASINS, ROUTING PARAMETERS (K,X) FOR RIVER REACHES 23
TABLE?. FLOW, TIME OF PEAK AND VOLUME FOR HMS VALIDATION EVENT 23
TABLES. HEC-RAS MODEL CALIBRATION AND VALIDATION 24
TABLE 9. PEAK RUNOFF ESTIMATES FOR DIFFERENT LAND USE SCENARIOS FOR THE 150,
175,200 MM (6, 7 AND 8 INCH) STORMS 25
TABLE 10. PEAK RUNOFF ESTIMATES WITH INCORPORATED WETLANDS FOR DIFFERENT
LAND USE SCENARIOS FOR 150 MM (6 INCH) STORM 26
TABLE 11. COMPARISON BETWEEN RUNOFF AND VOLUME ESTIMATES FOR ORIGINAL AND
WETLANDS SCENARIOS FOR 150 MM (6 INCH) 31
TABLE 12. KANSAS RIVER WATER ELEVATION ESTIMATES FOR THE DIFFERENT LAND
USE AND SCS STORM SCENARIOS AT USGS GAGE LOCATIONS 27
TABLE 13. COMPARISON BETWEEN WATER ELEVATIONS ESTIMATES FOR ORIGINAL AND
WETLANDS SCENARIOS FOR 150 MM(6 INCH) STORM 28
TABLE 14. INUNDATION AREA (KM2) FOR FUTURE LAND USE AND STORM SCENARIOS
FROMGEORAS 28
TABLE 15. COMPARISON BETWEEN INUNDATION AREAS ESTIMATES FOR ORIGINAL AND
WETLANDS SCENARIOS FOR 150 MM(6 INCH) STORM 29
IV
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Acknowledgement
We are grateful for the valuable inputs and suggestions provided by Dr. Maliha Nash and
Dr. Gerardo Gambirazzio, which improved the comprehensiveness and clarity of this report.
Although this work was reviewed by USEPA and approved for publication, it may not
necessarily reflect official Agency policy. Mention of trade names or commercial products does
not constitute endorsement or recommendation for use.
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1. INTRODUCTION AND BACKGROUND INFORMATION
1.1. OVERVIEW
Flooding is a major natural hazard which every year impacts different regions across the
world. Between 2000 and 2008, various types of natural hazards, mainly floods have affected
the largest number of people worldwide, averaging 99 million people per year (WDR, 2010). In
the United States from 1972 to 2006, the value of property losses due to a catastrophic flood
events average about $80 million (Changnon, 2008). In terms of human life, these catastrophic
floods on an average kill about 140 people each year in the US (USGS, 2006). Climate change is
expected to enhance the risk of extreme storm events (Milly et al., 2002). In addition, the
frequency of flash floods and large-area floods in many regions is very likely to increase (Parry
et al., 2007 & Alley et al., 2003). These dangers are exacerbated by rapid urbanization occurring
across the world (UN, 2010). In the United States, there was a 34% increase in the amount of
land devoted to urban and built-up uses between 1982 and 1997 (USDA, 2001). Urbanization
generally increases the size and frequency of floods and may expose communities to increasing
flood hazards (Parker, 2000; USGS, 2003). These developments have placed a renewed
emphasis on the prediction of flood levels and damages, mainly for the purpose of disaster
management and urban and regional planning (Milly et al., 2008).
Studies show that the state of Kansas ranks high among the US states with highest losses
due to floods (Changnon, 2008). With increasing population growth and urban development, the
likelihood of exposure to flood damage is rising. One of the most effective ways of assessing the
flood risk to people and property is through the production of flood models, which show areas
prone to flooding events of known return periods. The objectives of this study are: 1) to
evaluate the impacts of future land use change in the backdrop of 100 year design storms
(considered to be the most extreme storm event), on the peak runoff and flood inundation extents
for the Kansas River, and 2) to evaluate the potential role of wetlands in flood attenuation. To
mitigate flood risk, reservoirs and levees are used. In recent years, wetlands have been studied
for flood attenuation and water quality improvement (Mitsch et al., 2001; Crumpton et al., 2007).
Wetlands have the capability of short term surface water storage, and can reduce downstream
flood peaks. In addition, wetlands have biological, wildlife habitat and water quality benefits.
(Lewis, 1995, Hey and Philippi, 1995, Wamsley et al., 2010).
In this study, Hydrologic Engineering Center (HEC) tools are used to accomplish the
research objectives. Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)
is used to build the hydrologic model while Hydrologic Engineering Center-River Analysis
System (HEC-RAS) is used to build the hydraulic model. These tools are commonly used and
have been employed for conducting various types of studies including building flood forecasting
and flood inundation models (Knebl et al., 2005; Whiteaker et al., 2006), analyzing different
flood control alternatives (Benavides et al., 2001), addressing social impacts of small dam
removals (Wyrick et al., 2009), and developing a flood early warning system (Matkan et al.,
2009). This study uses these tools to highlight the flooding potential for the Kansas River region
as a result of urbanization and extreme rainfall events, and evaluates the potential of using
wetlands as a mitigation option.
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The Kansas River has been prone to flooding over the last century. It has faced
significant flood events in 1951 and 1993. The damages from the 1951 flood were
extraordinary. Nineteen people were killed and about 1100 injured with total damage estimates
as high as $2.5 billion. During the height of the flood, on July 13, 1951, nearly 90 percent of the
flow in the Missouri River at Kansas City came from the Kansas River, a tributary comprising
only 12% of the Missouri's drainage basin (USGS, 2001). The historic 1993 floods affected nine
states and resulted in $15 billion worth of flood damages. From July 22 to July 24, 50 to 330 mm
(2 to 13 inches) of rain fell in parts of Kansas and Nebraska, contributing large inflows to already
full reservoirs in the Kansas basin. Eighteen of the 163 USGS stream gages in operation in
Kansas during 1993 measured record maximum peak daily flows and 69 stations measured the
highest mean annual streamflow during their period of record for Water Year 1993 (USGS,
2003).
The remainder of the report is organized as follows. In the next parts of Section 1, the
study area and the data sources are discussed. In Section 2, a description of the HEC tools is
given, and an outline of the methods and procedures for model building using HEC tools is given
in Section 3. In Section 4, scenarios for the future period are discussed, and a description of the
results of simulations for those future scenarios is given in Section 5. The conclusions of the
study are given in Section 6.
1.2. WATERSHED DESCRIPTION
The Kansas River is located in northeastern corner of the state of Kansas. It is formed by
the union of the Republican River and the Smoky Hill River, just east of Junction City, Kansas.
From there, the river flows 273 km (170 miles) to Kansas City where it discharges into the
Missouri River. The River Valley is 222 km (138 miles) long. The surplus length is due the
river meandering across the Valley. The river drops about 98 m (320 ft) from its starting point
and has a slope of less than 0.6 m per km (2 feet per mile). The river valley averages 4.2 km (2.6
miles) in width and its widest stretch is between Wamego and Rossville, where it is up to 6.4 km
(4 miles) wide. Below Eudora, the valley narrows to less than half the maximum width, 2.4 km
(1.5 miles) and in parts of this reach the valley is 1.6 km (1 mile) or less in width (KGS, 1998).
Land use throughout the study area is primarily agricultural (cropland and grassland) with some
urban areas including Junction City, Manhattan, Topeka, Lawrence, and Kansas City, Kansas.
Counties adjacent to the Kansas River support a growing population of nearly 800,000 people
(USGS, 2005).
The river is composed of four 8-digit Hydrologic Unit Code (HUC) cataloging units.
These include HUC 10270101 Upper Kansas watershed, HUC 10270102 Middle Kansas
watershed, HUC 10270103 Delaware River watershed, and HUC 10270104 Lower Kansas
watershed. The location of the study area within the USA is shown in Figure 1. Figure 2 shows
the location of the Kansas River within the basin, with river flowing from left to right direction.
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FIGURE 1. Location of the study area in Kansas state, USA.
River
Elevation (m)
High : 439
FIGURE 2. Location of the Kansas River within Kansas River basin. Arrow denotes the direction of flow.
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1.3. MONITORING & OTHER AVAILABLE INFORMATION
Different datasets were used to build the model. These include the elevation, streamflow,
precipitation, soil classification and land use data. The different datasets and their sources are
given in Table 1.
TABLE 1. Datasets and their sources used for building the models
Datasets
Elevation
Streamflow
Precipitation
Soil Classification
Land use
Source
United States Geological Survey (USGS)
United States Geological Survey (USGS)
National Climatic Data Center (NCDC)
Soil Survey Geographic Database (SSURGO)
National Land Cover Databse (NLCD)
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2. HEC-MODELS-BRIEF OVERVIEW
2.1. HEC-HMS
2.1.1. Tool Description
The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS)
simulates the precipitation-runoff processes of watershed systems. HMS uses
deterministic mathematical modeling to compute various components of the hydrologic
cycle. These are evapotranspiration, precipitation, infiltration, and runoff.
Evapotranspiration is the sum of evaporation and plant transpiration from the Earth's
surface to the atmosphere: precipitation is the water being released from the clouds as
rain: infiltration is the portion of precipitation which after hitting the Earth's surface,
seeps through the soil layers: and runoff is precipitation that reaches the Earth's surface
but does not infiltrate the soil. HMS is applicable in a wide range of geographic areas for
solving the widest possible range of problems including large river basin water supply
and flood hydrology, and small urban or natural watershed runoff. Hydrographs
produced by HMS are used directly or in conjunction with other software for studies of
water availability, urban drainage, flow forecasting, future urbanization impact, reservoir
spillway design, flood damage reduction, floodplain regulation, and systems operation.
HEC-HMS is a generalized modeling system capable of representing many
different watersheds. A model of the watershed is constructed by separating the
hydrologic cycle into manageable pieces and constructing boundaries around the
watershed of interest. Any part of the cycle can then be represented with a mathematical
model. In most cases, several model choices are available for representing each part. Six
choices are available for representing infiltration. Seven methods are included for
transforming excess precipitation into runoff. Six routing methods are included for
simulating flow in open channels. Each mathematical component model included in the
tool is suitable in different environments and under different conditions. Making the
correct choice requires knowledge of the watershed, the goals of the hydrologic study,
and engineering judgment. The program features a completely integrated work
environment including a database, data entry utilities, computation engine, and results
reporting tools (HEC, 2010a).
INPUTS
The main inputs to the model include
Watershed stream network and size,
Infiltration loss method i.e. Initial and Constant, Deficit and Constant, Exponential,
Green-Ampt, Smith Parlange, Soil Moisture Accounting, SCS curve Number,
Transform method for transforming excess precipitation into runoff i.e. SCS, Clark
or Snyder unit hydrographs, Kinematic wave, ModClark, User specified unit
hydrograph,
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Routing methods i.e. Muskingum, Kinematic Wave, Lag, Modified Puls,
Muskingum-Cunge, and Straddle Stagger,
Meteorologic data i.e. precipitation, and
The time span of the simulation.
OUTPUTS
The outputs from the model include
Hydrographs
Flow Volume
2.1.2. Why the model was selected
The advantages of using HEC-HMS are that it draws on more than 30 years of
experience in hydrologic simulation. It is freely available for download from the HEC
website and is supported by the US Army Corps of Engineers. It provides a graphical
user interface making it easier to use the software and the program is widely used and
accepted for many official purposes, such as floodway determination for the Federal
Emergency Management Agency (FEMA).
2.2. HEC-RAS
2.2.1. Tool Description
Hydrologic Engineering Centers River Analysis System (HEC-RAS) is a one-
dimensional model, intended for hydraulic analysis of river channels. The model is
comprised of a graphical user interface, separate hydraulic analysis components, data
storage and management capabilities, graphics and reporting facilities. The HEC-RAS
system includes four river analysis components. They include the steady flow water
surface profile computations, unsteady flow simulation, sediment transport computations
and water quality analysis. In addition to these components, the model contains several
hydraulic design features that can be invoked once the basic water surface profiles are
computed. HEC-RAS applications include floodplain management studies, bridge and
culvert analysis and design, and channel modification studies (HEC, 201 Ob).
INPUTS
The main inputs to the model are
River geometric data: width, elevation, shape, location, length,
River floodplain data: length, elevation,
The distance between successive river cross-sections,
Manning 'n' value for the landuse type covering the river and the floodplain area,
Boundary conditions e.g. slope, critical depth,
Stream discharge values.
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OUTPUTS
The outputs from the model include
Water surface elevations
Rating curves
Hydraulic properties i.e. energy grade line slope and elevation, flow area, velocity
Visualization of stream flow, which shows the extent of flooding
2.2.2. Why the model was selected
HEC-RAS has been present in the public realm for more than 15 years and has
been peer reviewed (HEC, 2010c). It is freely available for download from the HEC
website and is supported by the US Army Corps of Engineers. It is also widely used by
many government agencies and private firms. For these reasons, HEC-RAS was selected
for this study.
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3. METHOD AND PROCEDURES
Two models were generated, a hydrologic model using HEC-HMS and a hydraulic model
using HEC-RAS. Then using USGS stream gage and NCDC weather station data, the models
were calibrated and validated for different historic storm events. Future land use scenarios are
developed in GIS for the years 2020, 2030 and 2040 using the ICLUS tool (ICLUS, 2010) with
increasing levels of urban land use. To evaluate the impact of land use changes on runoff, the
hydrologic model was used to generate runoff for the SCS 100 year 24-hour design storms for
different land use scenarios. The hydrologic model runoff estimates were used in the hydraulic
model, to generate the flooding extents for the different land use scenarios. Finally, the potential
of wetlands for flood mitigation were evaluated. Wetlands were simulated using the hydrologic
model.
3.1. HYDROLOGIC MODEL BUILDING
There are multiple ways of creating a watershed stream network and specifying its
properties in HEC-HMS. The user can either develop the network manually in HEC-HMS or
develop it in GIS or use a combination of the two in which the watershed network is developed
in HMS, while its properties are derived in GIS. For developing the watershed properties,
topographic data is needed which maybe obtained through a physical site survey or through
geospatial datasets such as the Digital Elevation Models (DEM). A DEM is continuous spatial
representation of the earth's surface. Traditionally watershed characteristics were obtained from
maps or field surveys which are time consuming and expensive. Currently OEMs are widely
used for developing the watershed characteristics (Garbrecht and Martz, 1999). For this project,
a DEM was used to develop elevation related characteristics for the study site with the help of a
GIS based tool called Geospatial Hydrologic Modeling Extension (HEC-GeoHMS).
HEC-GeoHMS is a geospatial hydrology toolkit designed to prepare data for HEC-HMS
simulations. The program allows users to visualize spatial information, document watershed
characteristics, perform spatial analysis, delineate subbasins and streams, construct inputs to
hydrologic models and assist with report preparations. HEC-GeoHMS creates input files which
can be used for HEC-HMS simulations. To assist with estimating hydrologic parameters, HEC-
GeoHMS can generate tables containing physical characteristics of streams and watersheds
which are shown in Table 2 (HEC, 2010d).
TABLE 2. Physical characteristics generated by HEC-GeoHMS
Characteristics
Stream
Watershed
Length, Slope
Area, Slope,
, Downstream
Curve Number
Connection
Lag Time
HEC-GeoHMS Processing:
For the DEM to be used in GeoHMS, it must first be processed to create certain required
layers. These include the flow direction, flow accumulation, stream, stream segments, slope
grid, catchment grid delineation, catchment polygon, drainage line and adjoint catchment layers.
These layers can be created using either the ArcHydro Tools or the Spatial Analyst extension in
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GIS. The layers are used for further GeoHMS processing, for which the procedure is as follows
(HEC, 2009): the procedure is helpful to assist in replicating the study.
Starting a Project
In GeoHMS, select the Data Management function and confirm or define the layers
created using the ArcHydro Tools or the Spatial Analyst extension.
Creating a new project in HEC-GeoHMS by using the Start New Project function
which will create two new feature classes, ProjectArea and ProjectPoint.
Select the Add Project Point tool, define the outlet for the watershed.
Select the Generate Project function to create a project for the study area.
Deriving Watershed Characteristics
Use the Batch Subwatershed Delineation command to delineate subbasins at USGS
gage points or at any similar points of interest. Then use the Merge Basins command
to merge basins upstream of the gages or points of interest, in order to have one
subbasin for each gage. The subbasins are stored in the Subbasin layer which is
derived from the Catchment Grid layer created earlier.
Running the River Length function to compute the length of river segments
Running the River Slope function to compute slope of river segments.
Running the Basin Slope function to compute the average slope for the subbasins
using a slope grid and subbasins polygons.
Running the Longest Flow Path function to create a polyline layer which stores the
longest flowpath for each subbasin.
Running the Basin Centroid function to create point layer to store the centroid of each
subbasin. Choose the center of gravity method from among the options. Check if the
centroid locations are reasonable, if not edit them.
Running the Basin Centroid Elevation function to compute the elevation of each
centroid using the underlying DEM.
Running the Centroidal Flow Path function to create a new polyline layer showing the
flowpath of each centroid point along the longest flowpath. This finishes the part of
deriving the basin's properties.
HMS Parameters In GeoHMS
HMS parameters can also be specified in GeoHMS through the Hydrologic
Parameters tab. They can be changed later on in HMS if the need arises.
Select the HMS Processes function, and choose the appropriate loss method, the
transform method, the baseflow type and the routing method. The SCS method is
selected as the both the loss method, and the transform method because of available
inputs and GeoHMS's ability to compute them, baseflow was computed outside HMS
using the constant discharge method and Muskingum is selected as the routing
method.
Running the River Auto Name and the Basin Auto Name functions to assign names to
river segments and sub-basins for HMS.
Select Subbasin Parameters from the menu of parameters. Choose the Basin Curve
Number and its associated Curve Number Grid file. After the computations are
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complete, the attribute table for the sub-basin layer will show a field named BasinCN
which is populated with the average curve number for each sub-basin. The curve
number grid file for the watershed has to be prepared beforehand. The production of
the curve number grid file requires the land use, collected from the NLCD database,
and soil classification data, from the SSURGO database. Figure 3 shows a curve
number grid for the study region. The curve number calculation is a part of the loss
method.
Running the CN Lag Method function. The BasinLag field in the subbasin feature
class will be populated with numbers that represent basin lag time in hours. The lag
time parameter is a part of the transform method.
Running Map to HMS units to convert units and prepare them for use in HMS.
Use the Check Data function to verify that all the input datasets are in order. A log
file is created and the errors if any can be pinpointed and removed.
CN Gi ic!2O2O
High : 1 OO
FIGURE 3. Curve Number Grid developed in HEC-GeoHMS for the Kansas River basin.
GeoHMS Data Export to HMS
Select the HMS Schematic to create a GIS representation of the hydrologic system
using a schematic network with basin elements and their connectivity. Two new
feature classes HMSLink and HMSNode are added to the map document.
After the schematic is created, you can see how this model will look like in HEC-
HMS by toggling between regular and HMS legend. Select Toggle HMS Legend.
Select the Add Coordinates function. This tool attaches geographic coordinates
(latitude, longitude) to features in HMSLink and HMSNode feature classes.
Select Prepare Data for Model Export to allow the preparation of subbasin and river
features for export.
Create the Background Map File and the Basin Map File.
Select HMS Project Setup. This function copies all the project specific files that were
created to a specified directory, and creates a .hms file containing information on
other files, for input to HMS.
Open HEC-HMS and locate the .hms file created. The basin file will be added to the
program. There is a possibility that the basin file created does not properly represent
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the watershed network and has some errors. In that case, create the watershed
network manually in HEC-HMS through its toolbar and use the data generated in
GeoHMS for the basin, to populate the watershed characteristics in HMS.
3.1.1. HMS Parameters
An HMS model requires three main input process parameters. Among them is the
precipitation loss method for overland flow, which accounts for the infiltration losses.
There are multiple methods available in HMS including Initial and Constant, Deficit and
Constant, Exponential, SCS Curve Number, Green-Ampt, Smith Parlange and Soil
Moisture Accounting. As mentioned in the GeoHMS section, the SCS curve number
method is selected for this process, whose values are computed from the curve number
grid. The curve number values for the different land use classes were taken from KDOT,
2003 and are shown in Table 3.
TABLE 3. Curve Numbers for NLCD land use classes and SSURGO hydrologic soil groups
Land Use Description
Open Water
Developed Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land, Rock, Sand, Clay
Deciduous Forest
Mixed Forest
Scrub/Shrub
Grasslands, Herbaceous
Pasture, Hay
Cultivated Crops
Woody Wetlands
Palustrine Forested Wetlands
Emergent Herbaceous Wetlands
NLCD Class
11
21
22
23
24
31
41
43
52
71
81
82
90
91
95
A
100
39
57
77
98
63
36
36
35
39
49
67
36
49
49
B
100
61
72
85
98
77
60
60
56
61
69
78
60
69
69
C
100
74
81
90
98
85
73
73
70
74
79
85
73
79
79
D
100
80
86
92
98
88
79
79
77
80
84
89
79
84
84
After the precipitation losses are accounted, a transform method must be specified
for transforming overland flow into surface runoff. Different methods are available for
the transform method in HMS including SCS, Clark or Snyder unit hydrographs,
Kinematic wave, ModClark and User specified unit hydrograph. As mentioned in the
GeoHMS section, SCS unit hydrograph method was selected. The Clark and Snyder
methods both require two parameters, while SCS method just requires one parameter, as
it assumes the shape of the unit hydrograph. The Clark or Snyder methods permit more
flexibility in determining the shape of the hydrograph, but the parameters are more
difficult to estimate. In the SCS method, 37.5% of the runoff volume occurs before the
peak flow, and the lag time can be approximated by taking 60% of the time of
concentration. The lag time is the length of time between the centroid of rainfall excess
and the peak flow of the resulting hydrograph, whose values were computed using the
CN Lag Time function in GeoHMS (HEC, 2010e).
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Once excess precipitation has been transformed into overland runoff and routed to
the outlet of a subwatershed, it enters the stream at that point and is added to streamflow
routed from upstream. There are several methods available in HMS for streamflow
routing including Kinematic Wave, Lag, Modified Puls, Muskingum, Muskingum-
Cunge, and Straddle Stagger. As mentioned in the GeoHMS section, the Muskingum
method was selected for this process.
The Muskingum method has different parameters K, X and number of subreaches
(n) which need to be specified. Muskingum K is essentially travel time through the
reach. Muskingum X is the weighting between inflow and outflow influence, it ranges
from 0 to 0.5. The number of subreaches affects attenuation where one subreach gives
more attenuation and increasing the number of sub reaches decreases the attenuation. K
(hrs) is approximately equal to L/3600v, where L = length of river (m), v = reach velocity
(m/s). The minimum number of reaches is estimated by n = int(2x(L/60v)/At) and the
maximum number of subreaches is estimated by n = int((L/60v)/ At), where At = time of
simulation (Oliviera, 1999).
The rest of the steps necessary for completing an HMS model are described below:
Create a meteorologic model file in HMS from the Meteorologic Model Manager
in the Components tab and specify the rainfall. Select the Specified Hyetograph option
and then use the Time Series Data Manager in the Components tab to create a
Precipitation Gage for the watershed. Specify the rainfall event in the Precipitation
Gage. Multiple Precipitation Gages can be created and assigned to the different
subbasins in the watershed.
Create the Control specifications from the Control Specifications Manager in the
Components tab in order to set the time period of the simulation run.
HMS has an optimization feature which can be used to calibrate the model. To
use this feature, a Discharge Gage containing actual flow values is created from the Time
Series Data Manager. The simulated flow can be compared against the Discharge Gage
values and the optimization function will automatically calibrate various parameters to
match the observed values. The parameters that can be calibrated include Loss functions
like initial abstraction and curve cumbers, transform functions like SCS lag, routing
functions like Muskingum routing parameters. Otherwise calibration can also be done by
changing parameters manually and comparing simulated results with field observations.
Validate the model for other events based on the calibrated parameters.
A simplified representation of the HMS model for Kansas River is shown in
Figure 4. The figure shows that the model has 12 subbasins and 4 river reaches. A
junction represents the confluence of streams from different subbasins.
12
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Junctlon-4
FIGURE 4. Kansas River representation in HMS model.
3.1.2. Soil Data Analysis
As the soil groups in the curve number move from A to D, the difference in curve
number values between completely different land use classes decreases. For example,
from Table 1, 22 which is developed low intensity and 81 which is pasture/hay, have a
curve number difference of 8 for group A but this curve number difference decreases to 2
for group D. So, if the area has a higher number of soil groups C and D, the difference in
runoff between the present and future scenarios will be less. Table 4 shows the
percentage share of the soil groups for the Kansas River basin.
TABLE 4. Percentage of hydrologic soil
groups at the Kansas River Basin
Soil Group
A
B
C
D
C/D
% Area Cover
0.278
23.81
35.933
39.39
0.587
3.1.3. HEC-HMS Calibration Approach
It is difficult to calibrate a hydrologic model, especially for a large watershed.
Complete knowledge of antecedent conditions such as soil moisture content and the level
of water in wetlands, for instance, are not available. Calibrating on a major flood event
does not guarantee that the model will accurately simulate another flood event even when
they are of the same magnitude (Bengtson and Padmanabhan, 1999). Keeping this
difficulty in mind, multiple peak flow events were selected for calibration. The USGS
gage on the downstream most side of the river, Gage 06892350 Kansas River at Desoto,
KS, was selected for calibrating the model. Hourly Runoff data was collected from the
USGS Instantaneous Data Archive, which had stream flow records available from 1991
onwards. Hourly Precipitation data was collected from the NCDC weather stations.
13
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3.2. HYDRAULIC MODEL BUILDING
There are two main ways for specifying the inputs to build a model in HEC-RAS. One is
to do a physical survey of the study site, and collect data manually regarding the river geometry.
The other way is to use geospatial datasets like Digital Elevation Models (DEM) and develop the
geometric data in GIS. OEMs are freely available for download online and can be processed
relatively easily to extract the geometric data. Accordingly, a DEM was used to prepare
geometric data for the study site. This was done using a GIS based tool called HEC-GeoRAS.
HEC-GeoRAS is a set of GIS based utilities for processing geospatial data in ArcGIS
using a graphical user interface. The interface aids the preparation of geometric data for import
into HEC-RAS and processes simulation results exported from HEC-RAS. The user creates a
series of line themes or layers pertinent to developing geometric data for HEC-RAS. The
required themes are the Stream Centerline, Flow Path Centerlines, Main Channel Banks, and
Cross Section Cut Lines referred to as the RAS Themes. Additional RAS Themes may be
created to extract additional geometric data for import in HEC-RAS. These themes include Land
Use, Levee Alignment, Ineffective Flow Areas, and Storage Areas (HEC, 201 Of).
3.2.1. Hydraulic Geometry Considerations
The hydraulic geometry of the river is essential for accurate model simulations
and is basically dependent on the DEM. OEMs are available mainly in two resolutions;
10m and 30 m. Ten metre profile OEMs capture more geomorphologic detail and have
better elevation accuracy than the 30 m DEM (Blackwell & Wells, 2010) as shown in 5.
Consequently a 10 m DEM was used for the study.
a b
FIGURE 5 (a) Digital Elevation Model example from Kansas River Basin at 10m resolution, (b) Digital Elevation Model example
from Kansas River Basin at 30m resolution.
14
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3.2.2. HEC-GeoRAS Processing
To create the geometric data file for HEC-RAS, HEC-GeoRAS requires a digital
terrain model of the river system in the Triangulated Irregular Network (TIN) format. The
DEM file was converted to the TIN format using the 3D Analyst toolbox in ArcGIS.
After conversion, the procedure for extracting the geometric data in HEC-GeoRAS is as
follows: the procedure is useful if someone would want to conduct a similar study,
River Digitization
Create empty GIS layers for the Stream Centerline, Bank Lines, Flow Path Centerlines and
XS Cut Lines
In the editor mode, use the Sketch tool to digitize the centerline of the river from upstream to
the downstream direction. Aerial photographs and topographical datasets are helpful guides
to pinpoint the path of a stream, but usually its path is noticeable on the DEM due to its lower
elevation.
Complete the centerline attributes commands in the RAS Geometry Tab of HEC-GeoRAS to
populate the missing fields of the new layer. A river may also have more than one reach.
Similarly in the editor mode, digitize the River Banks, Flow Path Centerlines and XS Cut
Lines using the Sketch tool.
Complete their attributes commands in the RAS Geometry Tab of HEC-GeoRAS to populate
the missing fields of the new layers. Figure 6 shows a sample of the different layers in HEC-
GeoRAS.
Bank lines are used to distinguish the main channel from the overbank floodplain areas. The
flow path lines are used to determine the downstream reach lengths between cross-sections in
the main channel and over bank areas. Cross-section cutlines are used to extract the
elevation data from the terrain to create a ground profile across channel flow. The
intersection of cutlines with other RAS layers such as centerline and flow path lines are used
to compute HEC-RAS attributes such as bank stations (locations that separate main channel
from the floodplain), downstream reach lengths (distance between cross-sections) and
Mannings n.
GeoRAS Data Export to RAS
Assign Mannings n value to cross-sections. Land use data file along with Mannings value
for those land use types are needed to assign Mannings value to cross-sections. This is not a
compulsory step and can also be performed in HEC-RAS, but it is better to do it GeoRAS as
it automatically picks the value from the data.
Creating the GIS import file for HEC-RAS so that it can read the GIS data and create the
river geometry. The Layer Setup is first checked to see if the correct layers are selected, then
the Export GIS Data function is used to create an export file for use in HEC-RAS.
This file is then imported in HEC-RAS for building a floodplain model.
In HEC-RAS, the river geometry is represented by a sequence of cross-sections called river
stations. The numbering of the river stations increases from downstream to the upstream
side. The distance between adjacent cross-sections is termed the reach length. Each cross-
section is defined by a series of lateral and elevation coordinates, which are typically
obtained from land surveys or geospatial datasets. The numbering of the lateral coordinates
15
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begins at the left end of the cross-section, (looking downstream) and increases until reaching
the right end. (Tate, 1999)
Calibrate the model for a real peak flow event. The parameters that can be adjusted are the
Mannings n value and the boundary conditions like normal or critical depth. The observed
water elevations are compared to the simulated ones.
Validate the model for other events based on the calibrated parameters.
FIGURE 6. A snapshot of River, Banks, Flow Path Centerlines and Cross-section layers for Kansas River in HEC-GeoRAS.
3.2.3. HEC-RAS Calibration Approach
The goal of this project is to create a floodplain model for the Kansas River. This
was done in HEC-RAS by setting up a hydraulic model which is to able to simulate
historic peak flow water elevations with reasonable accuracy. Once this is completed, the
model can be used for floodplain modeling purposes.
USGS flow gages for the Kansas River are used to aid the calibration process.
Five gages located on the Kansas River and selected for this process are,
06887500 Kansas River at Wamego, KS
06888350 Kansas River near Belvue, KS
06889000 Kansas River at Topeka, KS
06891000 Kansas River at Lecompton, KS
06892350 Kansas River at Desoto, KS
The data for a flow gage includes the daily mean flow, the annual peak flow and
its associated water surface elevation. Figure 7 shows the location of these gages along
the river.
16
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FIGURE 7. Location of USGS gages used for calibration on the Kansas River.
The peak flow data is used for calibrating the model. The main parameter that is changed to
calibrate the model is the roughness coefficient, Mannings 'n' but boundary conditions like slope
can also be changed to calibrate the model. Mannings n values are generalized to a single value
for the channel and the over land area, as HEC-RAS has a limitation of 20 points where n values
can be specified per cross section and in almost all the instances in this study the cross-sections
had more than 20 points. Also, using a Left Bank, Channel, Right Bank simplification makes the
calibration process easier and quicker to conduct.
17
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4. FUTURE SCENARIOS
4.1. LAND USE SCENARIOS
To create future land use scenarios, a GIS based tool, Integrated Climate and Land Use
Scenarios (ICLUS): ArcGIS Tools and Datasets for Modeling US Housing Density Growth was
used. The output from ICLUS was modified to create scenarios representing increasing levels of
urban growth and density for the periods 2020, 2030 and 2040. ICLUS was developed by the
EPA-ORD-Global Change Research Program at the National Center for Environmental
Assessment (ICLUS, 2010). It has multiple scenarios for housing density and population, from
which the one giving the highest population was selected. ICLUS output consists of four land use
classes, rural, urban, suburban and exurban, while the existing NLCD land use file has 15 land use
classes. The ICLUS land use classes exurban, suburban and urban were assumed equal to NLCD
urban land use classes 22 (developed low intensity), 23 (developed medium intensity) and 24
(developed high intensity). The ICLUS future output was mosaiced onto the present land use file
to create future land use scenarios for simulation.
Four land use scenarios were created for simulation. Baseline scenario used the NLCD
2001 land use file to run the simulation. Scenario 1, used the output from ICLUS for the year
2020, Scenario 2 interchanged the area for the urban land use classes 22 (developed, low intensity)
and 23 (developed, medium intensity) for the ICLUS output in 2030. Scenario 3 interchanged the
area for the urban land use classes 22 (developed, low intensity) and 24 (developed, high intensity)
for the ICLUS output in 2040. When creating the scenarios the focus was on creating scenarios
that reflect rising urbanization both in terms of area and density, so that urban land use impacts on
surface runoff are discernible. ICLUS outputs for 2030 and 2040 show minimal change in urban
land use, which has a negligible impact on future curve numbers, leading to the need for creating
more robust urbanization scenarios as represented by Scenarios 2 and 3. Table 5 represents the
percentage area share of the different land use classes for the future land use scenarios.
TABLES. Percentage area of different NLCD land use classes for the scenarios
Land Use
Class
11
21
22
23
24
31
41
43
42
71
81
82
90
91
95
Description
Open Water
Developed Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land, Rock, Sand, Clay
Deciduous Forest
Mixed Forest
Scrub/Shrub
Grasslands, Herbaceous
Pasture, Hay
Cultivated Crops
Woody Wetlands
Palustrine Forested Wetlands
Emergent Herbaceous Wetlands
Scenario (%)
Baseline
1.692
4.961
2.957
0.884
0.368
0.153
2.502
4.273
0.799
26.754
25.093
23.041
0.207
6.183
0.134
1 (2020)
1.500
2.872
24.742
2.497
0.748
0.136
1.445
2.512
0.653
22.831
17.671
17.624
0.185
4.463
0.122
2 (2030)
1.496
2.852
2.588
24.936
0.783
0.134
1.434
2.495
0.653
22.801
17.560
17.520
0.185
4.441
0.122
3 (2040)
1.494
2.845
0.879
2.658
24.947
0.134
1.430
2.488
0.652
22.790
17.526
17.470
0.185
4.433
0.122
18
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4.2. CLIMATE SCENARIOS
For future precipitation, the SCS design storm method was selected. The SCS storm
method implements the design storm developed by the Natural Resources Conservation Service
(NRCS) (formerly SCS). The SCS hypothetical storm method implements four synthetic rainfall
distributions from observed precipitation events. Each distribution is 24 hours long and contains
rainfall intensities arranged to maximize the peak runoff for a given total storm depth. The four
distributions correspond to different geographical regions. Storms with return periods ranging
from 1 to 100 years, and with durations ranging from 30 minutes to 24 hrs are available. The
100 year 24 hour storm is considered to have the severest intensity, and was selected for
simulation. Figure 8 shows the approximate geographic boundaries for the rainfall distributions
which clearly show that the storm type for Kansas has type 2 distribution. Figure 9 shows the
different rainfall depth projections crossing Kansas. The figure shows that the 150, 175 and 200
mm (6, 7 and 8 inch) lines cross the state, and all of them were selected for analysis.
RiipfiI I
Distribution
FIGURE 8. Geographic boundaries for Soil Conservation Service (SCS) rainfall distributions
19
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FIGURE 9. 100 Year Return Period, 24 hour duration Precipitation map for Kansas, inches. The red
rectangle highlights Kansas state with precipitation contour lines.
4.3. FUTURE WETLANDS SCENARIOS
From ICLUS simulations over the three periods 2020, 2030 and 2040, the average
percentage area of wetlands is about 5%, as shown in Table 5, which is obtained by adding the
three wetland classes; 90, 91 and 95. Three scenarios were created for the future periods for
modeling the effect of wetlands on flooding. The wetland area was increased gradually to 6 % in
2020, 8 % in 2030 and 10 % in 2040. These three scenarios were simulated for the 150 mm (6
inch) storm only and a uniform depth of 0.30 m (1 ft) was assumed for all wetlands for
calculating flow volume.
20
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There is no specific module or component for representing wetlands in HMS. They can
be represented as a reservoir at the end of a subwatershed, but this would require assuming that
all flows in a subwatershed will be caught by the reservoir. In our case, wetlands at different
locations in the basin were combined together to simplify their representation in the model.
Also, modeling a reservoir would require making some assumptions as the appropriate data for
creating a storage-discharge relationship and other factors required for adequately modeling a
reservoir in HMS are not available. Wetlands were subsequently modeled using the diversion
component in HMS (Bengtson and Padmanabhan, 1999).
A diversion allows a user specified portion of the flow to be diverted and taken out of the
system. Diversions permit considerable flexibility in setting the rate of diverted flow to
incoming flow and various rates can be modeled. Fixing diversions as a rate of incoming flow
can mirror the subwatershed area that contributes to the wetlands. The timing of diversions can
be controlled by not allowing water to be diverted until a specific flow is reached. There are two
options for diverting water, either flow can start being diverted as soon as runoff is generated, or
it can be diverted after a flow magnitude is exceeded. The second case is hypothetical and it
would mean assuming that the flows to the wetlands are controlled through some structural
adjustments in the watershed. The volume diverted in both the cases would be the same but
changing the timing of the diversion would have a different impact on the flood hydrograph.
Since our interest is in gauging the effect of wetlands on flood attenuation, the second option was
chosen (Juliano and Simonovic, 1999). The total amount of diversion can be set equal to the
storage capacity of the wetlands. Once the wetland storage capacity was satisfied, all remaining
flow was routed downstream. For modeling diversions, 25 % of the subwatershed peak runoff
was set as the diversion rate. Since wetlands cover a maximum of 10 % of the watershed area in
the scenarios, this assumption is sufficient for filling the volume of the wetlands. The flow
hydrographs from the earlier runs were used as a guide for developing inflow diversion
relationships that are required for diversions, with the timing being adjusted in such a manner
that the peak flow is impacted.
5. MODEL SIMULATION RESULTS AND DISCUSSION
5.1. HEC-HMS CALIBRATION/VALIDATION
HEC-HMS has an optimization feature which can be used to match the simulated flow
with observed flow. The optimization feature was used to carry out the calibration process. It
allows for multiple parameter calibration at the same time. Three parameters were selected for
calibration. They were the curve number, Muskingum K and Muskingum X parameters. A
sample of an optimization run is shown in figure 10 a, b and c. Five different events were
calibrated using the data, and the average of those calibrated parameters was taken to develop the
final calibrated parameter values.
21
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Objective Function Results for Trial "Trial 3"
Project: 2nd4HUCManual Optimization Trial: Trial 3
Start of Trial: 26Mayl996J 00:00
End of Trial: 30May 1996, 00:00
Compute Time: 18Feb2011, 16:06:28
Objective Function at Basin Element "Junction-4"
Start of Function : 26Mayl996j 00:00
End of Function : 3QMayl996., 00:00
Basin Model: Basin KDoT
Meteorologic Model: Met 1
Control Specifications: Control 1
Type : Percent Error in Peak Flow
Value : 0.0
Volume Units: © MM Q 1000 M3
Measure
Volume (MM)
Peak Flow (M3/S)
Time of Peak
Time of Center of Mass
Simulated
13.78
1230.8
28Mayl996, 14:00
2BMayl996, 08:47
Observed
13.75
1230.8
28Mayl996, 05:00
2SMayl996, 07:45
Difference
0.03
-0.0
Percent
Difference
0.20
-0.0
FIGURE lOa. HMS Optimization Summary
m Optimized Parameter Results for Trial "Trial 3" | _ j[n][x]
Project: 2nd4HUCManual Optimization
Start of Trial: 26Mayl996, 00:00 Basin Mode
End of Trial: 30Mayl996, 00:OO Meteorolog
Compute Time: 18Feb2011, 16:06:28 Control Spe
Element
All Subbasins
Reach-O
Reach- 1
Reach-2
Reach-a
Reach-O
Reach-1
Reach-2
Reach-a
Parameter
Curve Number Scale . . .
Muskingum K
Muskingum K
Muskjngum K
Muskingum K
Muskjngum X
Muskingum X
Muskjngum X
Muskjngum X
Units
HR
HR
HR
HR
Initial
Value
0.75
15
11
14
9
0.47
0.45
0.2
0.4
Trial: Trial 3
: Basin KDoT
c Model: Met 1
cifications: Control 1
Optimized
Value
0.73656
15.165
11.174
14.134
8.9636
0.48472
0.47454
0.21453
0.43870
Objective Function
Sensitivity
O.72
-O.20
O.59
6.13
-1.26
O.22
O.13
O.01
-0.11
FIGURE lOb. HMS Optimized Parameters
22
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^3 Optimization Trial "Trial 3" ( _ jf l~~l
Hydro graph Comparison
1.000-
in BOO
ii 600
200-
00
Legend (C
-------
calibration and validation process are close in values with percent difference ranging from -6.73
% to 8.43 % (Table 8).
TABLES. HEC-RAS Model Calibration and Validation Calibration
Event 2005-06-05
Gage 06887500
Gage 06888350
Gage 06889000
Gage 06891 000
Gage 06892350
Flow
(m3/s)
1064
1596
1913
2072
2001
Water Elevation
Observed (m)
4.55
5.37
7.38
5.18
5.99
Simulated
(m)
4.53
5.37
7.34
5.20
6.02
% Difference
0.34
0.06
0.58
-0.35
-0.41
Mannings n
Main Channel
0.056
0.1
0.07
0.033
0.02
Overbanks
0.14
0.08
The calibration results have negligible difference between the observed and the simulated
values. The validation results are also satisfactory with the difference in water surface elevations
being small. The overall validation process R2 and Nash-Sutcliffe model efficiency co-efficient
are 0.971 and 0.96 respectively, so the model is performing reasonably well, and can be used to
assess the changes in land use on flooding.
VALIDATION
Gage 06887500
Event 2001 -06-21
Event 2009-04-27
Event 2008-06-20
Event 2007-05-07
Gage 06888350
Event 2001 -06-21
Event 2009-04-27
Event 2008-06-20
Event 2007-05-07
Gage 06889000
Event 2001 -06-21
Event 2009-04-27
Event 2008-06-20
Event 2007-05-07
Gage 06891000
Event 2001 -06-21
Event 2009-04-27
Event 2008-06-20
Event 2007-05-07
Gage 06892350
Event 2001 -06-21
Event 2009-04-27
Event 2008-06-20
Event 2007-05-07
Flow (m3/s)
880
690
908
1049
Flow (m3/s)
934
843
962
1760
Flow (m3/s)
1268
1661
1035
2592
Flow (m3/s)
1534
2216
1194
3481
Flow (m3/s)
2287
2157
1106
3396
Observed (m)
3.99
3.63
4.16
4.39
Observed (m)
4.45
4.41
4.63
5.47
Observed (m)
6.02
7.00
5.51
8.75
Observed (m)
4.46
5.36
3.93
6.67
Observed (m)
6.40
6.10
4.58
7.92
Simulated (m)
4.09
3.61
4.23
4.69
Simulated (m)
4.45
4.29
4.49
5.57
Simulated (m)
6.35
7.01
5.85
8.01
Simulated (m)
4.60
5.33
4.11
6.24
Simulated (m)
6.33
6.19
4.84
7.40
% Difference
-2.44
0.42
-1.54
-6.73
% Difference
0.00
2.70
2.96
-1.78
% Difference
-5.52
-0.13
-6.08
8.43
% Difference
-3.01
0.51
-4.66
6.44
% Difference
1.05
-1.50
-5.73
6.62
24
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5.3. FUTURE SCENARIOS
For the new land use scenarios, the corresponding new curve numbers were generated to
be used along with the future storms, and wetland scenarios in the HMS model to generate runoff
estimates. Those runoff estimates were used in the RAS model to generate water elevations and
flood inundation extents. The analysis of these simulations is presented in the next sections.
5.3.1. Kansas River Streamflow for Different Landuse Scenarios
The HMS model was run for the future scenarios to generate runoff estimates.
The modeling results for the different landuse scenarios, the Baseline scenario, Scenario
1 (ICLUS output for 2020), Scenario 2 (interchanging low and medium, developed
intensity classes for 2030) and Scenario 3 (interchanging low and high, developed
intensity classes) are shown in Table 9.
TABLE 9. Peak Runoff estimates for different land use scenarios for the 150, 175, 200 mm (6, 7 and 8
inch) storms
Storm
(in)
6
7
8
(mm)
152.4
177.8
203.2
Runoff (m3/s)
Baseline
5162.1
6675.9
8252.9
Scenario 1, 2020
5208.7
6723.6
8300.1
Scenario 2, 2030
5551 .4
7022.1
8532.9
Scenario 3, 2040
6144.2
7681 .2
9250.2
The table shows that the runoff values increase with time. The 200 mm (8 inch)
storm as expected generates more runoff compared to the 175 and 150 mm (7 and 6 inch)
storms. The increase is small in 2020, and grows more pronounced in 2030 and 2040.
The runoff values show a marked increase with the shift in time from a low intensity
development to a higher intensity development. The probable reason for the subtle
increase between the present and the 2020 scenarios is the small difference between the
curve numbers for low intensity development and other land use classes like cultivated
crops, pasture, forest etc. and the majority of soil cover for the area being of groups C or
D as mentioned in Table 2.
5.3.2. Impact of wetlands on Kansas River Streamflow
The model was simulated again for the future scenarios (Scenarios 1, 2 and 3),
after incorporating the wetlands into it. Wetlands are 6 %, 8 % and 10 % of the total
basin area in 2020, 2030 and 2040, respectively. Wetlands can intercept runoff and
reduce the flow volume; in addition to that, they have been modeled in such a manner
that they also impact the peak flow. For simulations, only the 150 mm storm was selected
and the results are shown in Table 10. Comparison for the original and wetland scenarios
is given in Table 11.
25
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TABLE 10. Peak Runoff estimates with incorporated wetlands for different land use
scenarios for 150 mm (6 inch) storm
Storm
(in)
6
(mm)
152.4
Runoff (m3/s)
Scenario 1, 2020
4606
Scenario 2, 2030
4744.2
Scenario 3, 2040
5060.6
Table 11 shows the impacts of increasing the area of wetlands on flood
attenuation. The peak flow and the volume of the storm event are reduced by varying
degrees over the different scenarios. For the runoff, a high of 17.64 % reduction is
obtained while for volume a high of 36.83 % reduction is obtained. As the area of
wetlands increases, the volume of water that can be retained increases. The peak runoff
increases over the future due to urbanization, and it is noted that the higher the runoff the
higher the flow reduction due to increase in area of wetlands. Incorporating wetlands
result in an average 14 % reduction in peak flow and an average 31 % reduction in flow
volume for the Kansas River basin.
TABLE 11. Comparison between runoff and volume estimates for original and wetlands scenarios for 150 mm
(6 inch)
Scenario
Scenario 1 (6%)
Scenario 2 (8%)
Scenarios (10%)
Scenario
Scenario 1 (6%)
Scenario 2 (8%)
Scenario 3 (10%)
Original Runoff (m3/s)
5208.7
5551 .4
6144.2
Original Volume (mm)
72.71
76.93
81.52
Runoff with Wetlands (m3/s)
4606
4744.2
5060.6
Volume with Wetlands (mm)
54.69
52.91
51.5
% Diff. (m3/s)
-1 1 .57
-14.54
-17.64
% Diff. (mm)
-24.78
-31 .22
-36.83
5.3.3. Kansas River Water Elevations for Different Landuse Scenarios
The estimates of the peak runoff from the HMS model for the future scenarios are
used in the hydraulic model built in HEC-RAS. Flow values at the location of the five
calibration gages were selected. The RAS model was then simulated to obtain water
elevation levels and flood inundation extent estimates. The water elevation results for the
different landuse and storm scenarios, at the calibration gage locations are shown in
Table 12.
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TABLE 12. Kansas River Water Elevation Estimates for the different land use and SCS
storm scenarios at USGS gage locations
SCS Storm
150 mm
6887500
6888350
6889000
6891 000
6892350
Water elevation (m)
Baseline
4.68
5.56
8.74
7.01
8.82
Scenario 1
4.75
5.62
8.79
7.04
8.85
Scenario 2
4.88
5.75
8.9
7.11
9.08
Scenario 3
5.03
5.89
9.03
7.18
9.44
175 mm
6887500
6888350
6889000
6891 000
6892350
Baseline
5.21
6.09
9.36
7.59
9.75
Scenario 1
5.27
6.15
9.41
7.62
9.78
Scenario 2
5.39
6.26
9.51
7.67
9.94
Scenario 3
5.51
6.38
9.63
7.73
10.28
200 mm
6887500
6888350
6889000
6891 000
6892350
Baseline
5.62
6.57
9.86
8.05
10.54
Scenario 1
5.66
6.57
9.91
8.07
10.56
Scenario 2
5.77
6.68
10.04
8.12
10.67
Scenario 3
5.88
6.81
10.13
8.2
10.98
The results depict a gradual increase in water elevation levels in the future. The
increase is small between the present and 2020 scenarios, but grows more pronounced
when the present and 2040 scenarios are compared. The highest water elevations are
obtained from the 200 mm (8 inch) which is understandable since it has the highest
runoff. The largest increase in elevations is observed for the last calibration gage where
the difference between the present and 2040 scenarios is 0.62 m for the 150 mm storm,
0.53 m for the 175 mm storm and 0.44 m for the 200 mm storm. This decreasing
difference trend illustrates the effect of the topography of the river basin, which is usually
funnel shaped, meaning with increase in elevation there is an increase in basin area.
Among the calibration validation data, the highest water elevation level observed was
8.75 m (Table 8). Compared to that, the results show a maximum increase of 2.23 m in
2040.
5.3.4. Impact of Wetlands on Kansas River Water Elevations
The effect of wetland on the water elevation levels was evaluated using the
wetland incorporated runoff estimates generated from the HMS model for the 150 mm
storm. Table 13 shows a comparison between the original and wetland scenarios.
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TABLE 13. Comparison between water elevations estimates for original and wetlands scenarios for 150 mm (6 inch)
storm
SCS 150 mm
6887500
6888350
6889000
6891000
6892350
Scenario 1 , 6% (m)
Original
Elevation
4.75
5.62
8.79
7.04
8.85
Wetland
Elevation
4.71
5.4
7.78
6.24
8.43
% Diff.
0.84
3.91
11.49
11.36
4.75
Scenario 2, 8% (m)
Original
Elevation
4.88
5.75
8.9
7.11
9.08
Wetland
Elevation
4.84
5.46
7.6
6.22
8.53
% Diff.
0.82
5.04
14.61
12.52
6.06
Scenarios, 10% (m)
Original
Elevation
5.03
5.89
9.03
7.18
9.44
Wetland
Elevation
4.99
5.57
7.63
6.14
8.75
% Diff.
0.80
5.43
15.50
14.48
7.31
The table shows a decrease in water elevations when wetlands are simulated. The
difference varies at the different locations on the river but it is considerable with a
maximum decrease of 15.50 % and 1.4 m being observed.
5.3.5. Kansas River Inundation Extents for Different Land Use Scenarios
The area that gets inundated in the river basin can be modeled in HEC-GeoRAS
by using the RAS model results as inputs. The extent of the inundation can be calculated
and the vulnerable segments of the river basin where flooding is severe, can be identified
in GeoRAS. The inundation area results for the different future land use and storm
scenarios are shown in Table 14.
TABLE 14. Inundation Area (km2) for future land use and storm scenarios from GeoRAS
SCS Storm
(in)
6
7
8
(mm)
152.4
177.8
203.2
Area (km")
Baseline
350.83
409.16
458.69
Scenario 1
354.82
413.59
461 .61
Scenario 2
366.35
424.02
473.11
Scenario 3
379.91
436.27
484.49
The table shows that with increasing storm depth and urbanization over the future,
the flood inundation area also increases. The difference in inundation area between any
two consecutive storms for any scenario is not the same. The difference is higher
between 150 mm and 175 mm storms than compared to 175 mm and 200 mm storms. An
average decrease of 58 km2 is simulated between 150 mm and 175 mm storms and an
average decrease of 48 km2 is observed between the 175 mm and 200 mm storms, for all
the scenarios. This is due to the funnel shaped topography of a watershed. Figure 11
shows the flooding visualization in GeoRAS and how it can allow for comparing the
effects of different storm events and tracing which location along the river is more
susceptible to flooding.
28
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Banks
150mm
175mm
200mm
FIGURE 11. GeoRAS inundation area for comparing the
different storm events 150,175 and 200 mm (6,
7 and 8 inch) for the 2040 scenario.
A snapshot of the simulated flooding along the Kansas River for the different
storm scenarios in 2040 is shown in figure 11. The locations at which the 175 and 200
mm start having an impact can be easily observed.
5.3.6. Impact of Wetlands on Kansas River Inundation Areas
The effect of wetlands on flood inundation for the 150 mm storm and the land use
scenarios is shown in Table 15.
TABLE 15. Comparison between inundation areas estimates for original and
wetlands scenarios for 150 mm (6 inch) storm
SCS 150 mm
Scenario 1 (6%)
Scenario 2 (8%)
Scenario 3 (10%)
Original Area (km2)
354.82
366.35
379.91
Area with Wetland (km2)
300.13
301.33
306.9
% Diff.
15.41
17.75
19.22
The table shows that inundation areas decrease when runoff with wetlands is
simulated. The presence of wetlands reduces the extent of flooding, on average by about
17.5 %.
29
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6. CONCLUSIONS
The Kansas River basin was modeled using HEC-HMS and HEC-RAS to determine the effects of
increasing urbanization on peak flood runoff over future periods. A GIS tool ICLUS's projections of
urban land use densities for 2020 were modified to represent increasing densification of urban areas, by
converting low intensity development to medium intensity in 2030 and to high intensity developments in
2040. Design storms of 100 year return periods for the region were simulated. The watershed was also
modeled to evaluate the impact of wetlands on flood attenuation. Wetlands were assumed to have a
storage depth of 0.3 m (1 ft) and their percentage area share of the watershed was 6 % in 2020, 8 % in
2030 and 10 % in 2040. Wetlands were also modeled in such a manner that the peak flow was affected,
for this an assumption was made that there would be some control structure for regulating flows in and
out of wetlands, which is typical of many restored wetland designs.
The HEC-HMS and HEC-RAS models were both calibrated and validated for historic flow
events. The HMS model was then used to generate estimates of peak flows for the design storms for
different land use scenarios. The output from HMS was then used to run the RAS model. The RAS
model then generated estimates of water elevations and flood inundation extents for those design storms
and land use scenarios. The wetlands were modeled as diversions in HMS. The amount of flow diverted
to wetlands was set in such a manner that the volume of storage available in the wetlands was filled.
Comparisons were made for gauging the impacts of wetlands, between the original runs without wetlands
for the 150 mm (6 inch) storm for all the land use scenarios, then running the models after incorporating
wetlands.
The results show an appreciable increase in peak runoff and flood inundation extents for the
various scenarios. There is about a 1000 cms increase in peak runoff for the different storms from the
baseline scenario to the 2040 one, as a consequence of increasing urbanization and densification. This is
about an average 15% increase in runoff for all the land use and design storm scenarios. The maximum
increase in water elevations between the present and 2040 scenarios is about 0.62 m for 150 mm storm,
0.53 m for the 7 inch storm and 0.44 m for the 8 inch storm. This is about an average maximum increase
of 5 % in the water elevations for all scenarios. The flood inundation extents for the watershed also
increase correspondingly. There is an average inundation area increase of 6.85 % between the present to
the 2040 scenario for the various design storms.
The presence of wetlands reduced peak flows and inundation extents. For the different scenarios,
an average decline of 14 % was achieved in the peak runoff, while for the flood volumes; an average
decline of 31 % was noted. Modeling the different wetlands scenarios resulted in an average decrease of
8.7 % in the water elevations with a maximum decrease of 15.5 % at one location. An average decrease
of 17.5 % was obtained in the inundation area extents.
The models created can be used to test the impacts of land use changes, rainfall predictions, and
channel modifications in the Kansas River basin. One limitation of the model is that it is built on a macro
scale, and if the results are applied to a small segment on the watershed, they might not be accurate. Also,
an economic analysis would be needed to determine whether the savings in damages obtained from flood
reductions as a result of increasing wetland volumes justify the cost of constructing and maintaining those
wetlands. Environmental managers and practitioners in USEPA's regional offices are evaluating this
approach to prioritize and inform decisions about ecological restoration in specific watersheds and for the
implementation and assessment of Best Management Practices within the floodplains of the Kansas River
and other rivers of the Midwest.
30
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